From 5538177993640fbd096ef763bbabbad30bacdcd0 Mon Sep 17 00:00:00 2001 From: chengfeng27 Date: Mon, 8 Apr 2024 11:00:15 +0800 Subject: [PATCH] ndk build gn repeat with interface repository --- .gitignore | 2 - include/c_api/context_c.h | 50 +- include/c_api/data_type_c.h | 44 +- include/c_api/format_c.h | 38 +- include/c_api/model_c.h | 70 +- include/c_api/status_c.h | 68 +- include/c_api/tensor_c.h | 34 +- include/c_api/types_c.h | 28 +- include/sdk_api/context.h | 180 + include/sdk_api/data_type.h | 52 + include/sdk_api/format.h | 46 + include/sdk_api/model.h | 138 + include/sdk_api/status.h | 80 + include/sdk_api/tensor.h | 146 + include/sdk_api/types.h | 50 + .../plugin/device/cpu/kernel/nnacl/BUILD.gn | 885 + .../nnacl/fp16_grad/activation_grad_fp16.c | 2 +- .../include/nnacl/activation_fp32_simd.h | 36 + .../include/nnacl/activation_grad_simd.h | 36 + .../nnacl/include/nnacl/adam_fp32_simd.h | 36 + .../nnacl/include/nnacl/add_fp32_simd.h | 36 + .../nnacl/apply_proximal_adagrad_fp32_simd.h | 36 + ...pply_proximal_gradient_descent_fp32_simd.h | 36 + .../include/nnacl/arithmetic_fp32_simd.h | 36 + .../include/nnacl/arithmetic_self_fp32_simd.h | 36 + .../include/nnacl/avx/activation_fp32_avx.h | 285 + .../include/nnacl/avx/activation_grad_avx.h | 57 + .../nnacl/include/nnacl/avx/adam_fp32_avx.h | 210 + .../nnacl/include/nnacl/avx/add_fp32_avx.h | 124 + .../avx/apply_proximal_adagrad_fp32_avx.h | 75 + ...apply_proximal_gradient_descent_fp32_avx.h | 71 + .../include/nnacl/avx/arithmetic_fp32_avx.h | 294 + .../nnacl/avx/arithmetic_self_fp32_avx.h | 148 + .../include/nnacl/avx/batchnorm_fp32_avx.h | 67 + .../nnacl/avx/bce_with_logits_loss_fp32_avx.h | 69 + .../nnacl/include/nnacl/avx/bias_add_avx.h | 64 + .../nnacl/include/nnacl/avx/cast_base_avx.h | 56 + .../nnacl/avx/cast_gather_reduce_fp32_avx.h | 72 + .../nnacl/include/nnacl/avx/cdist_fp32_avx.h | 70 + .../nnacl/include/nnacl/avx/cumsum_fp32_avx.h | 121 + .../nnacl/include/nnacl/avx/div_fp32_avx.h | 167 + .../include/nnacl/avx/dropout_fp32_avx.h | 46 + .../nnacl/include/nnacl/avx/exp_fp32_avx.h | 63 + .../nnacl/include/nnacl/avx/fill_base_avx.h | 53 + .../include/nnacl/avx/group_norm_fp32_avx.h | 77 + .../include/nnacl/avx/layer_norm_fp32_avx.h | 68 + .../nnacl/include/nnacl/avx/matmul_fp32_avx.h | 155 + .../nnacl/include/nnacl/avx/mul_fp32_avx.h | 218 + .../include/nnacl/avx/pooling_fp32_avx.h | 84 + .../nnacl/include/nnacl/avx/power_fp32_avx.h | 101 + .../nnacl/avx/reduce_concat_fp32_avx.h | 122 + .../nnacl/include/nnacl/avx/reduce_fp32_avx.h | 227 + .../include/nnacl/avx/scatter_nd_binary_avx.h | 52 + .../include/nnacl/avx/softmax_fp32_avx.h | 87 + .../nnacl/avx/softmax_grad_fusion_fp32_avx.h | 62 + .../nnacl/include/nnacl/avx/sub_fp32_avx.h | 167 + .../nnacl/avx512/activation_fp32_avx512.h | 285 + .../nnacl/avx512/activation_grad_avx512.h | 57 + .../include/nnacl/avx512/adam_fp32_avx512.h | 210 + .../include/nnacl/avx512/add_fp32_avx512.h | 124 + .../apply_proximal_adagrad_fp32_avx512.h | 75 + ...ly_proximal_gradient_descent_fp32_avx512.h | 71 + .../nnacl/avx512/arithmetic_fp32_avx512.h | 294 + .../avx512/arithmetic_self_fp32_avx512.h | 148 + .../nnacl/avx512/batchnorm_fp32_avx512.h | 67 + .../avx512/bce_with_logits_loss_fp32_avx512.h | 69 + .../include/nnacl/avx512/bias_add_avx512.h | 64 + .../include/nnacl/avx512/cast_base_avx512.h | 56 + .../avx512/cast_gather_reduce_fp32_avx512.h | 72 + .../include/nnacl/avx512/cdist_fp32_avx512.h | 70 + .../include/nnacl/avx512/cumsum_fp32_avx512.h | 121 + .../include/nnacl/avx512/div_fp32_avx512.h | 167 + .../nnacl/avx512/dropout_fp32_avx512.h | 46 + .../include/nnacl/avx512/exp_fp32_avx512.h | 63 + .../include/nnacl/avx512/fill_base_avx512.h | 53 + .../nnacl/avx512/group_norm_fp32_avx512.h | 77 + .../nnacl/avx512/layer_norm_fp32_avx512.h | 68 + .../include/nnacl/avx512/matmul_fp32_avx512.h | 155 + .../include/nnacl/avx512/mul_fp32_avx512.h | 218 + .../nnacl/avx512/pooling_fp32_avx512.h | 84 + .../include/nnacl/avx512/power_fp32_avx512.h | 101 + .../nnacl/avx512/reduce_concat_fp32_avx512.h | 122 + .../include/nnacl/avx512/reduce_fp32_avx512.h | 227 + .../nnacl/avx512/scatter_nd_binary_avx512.h | 52 + .../nnacl/avx512/softmax_fp32_avx512.h | 87 + .../avx512/softmax_grad_fusion_fp32_avx512.h | 62 + .../include/nnacl/avx512/sub_fp32_avx512.h | 167 + .../nnacl/include/nnacl/batchnorm_fp32_simd.h | 36 + .../nnacl/bce_with_logits_loss_fp32_simd.h | 36 + .../nnacl/include/nnacl/bias_add_simd.h | 36 + .../nnacl/include/nnacl/cast_base_simd.h | 36 + .../nnacl/cast_gather_reduce_fp32_simd.h | 36 + .../nnacl/include/nnacl/cdist_fp32_simd.h | 36 + .../nnacl/include/nnacl/cumsum_fp32_simd.h | 36 + .../nnacl/include/nnacl/div_fp32_simd.h | 36 + .../nnacl/include/nnacl/dropout_fp32_simd.h | 36 + .../nnacl/include/nnacl/exp_fp32_simd.h | 36 + .../nnacl/include/nnacl/fill_base_simd.h | 36 + .../include/nnacl/group_norm_fp32_simd.h | 36 + .../include/nnacl/layer_norm_fp32_simd.h | 36 + .../nnacl/include/nnacl/matmul_fp32_simd.h | 36 + .../nnacl/include/nnacl/mul_fp32_simd.h | 36 + .../include/nnacl/neon/activation_fp32_neon.h | 284 + .../include/nnacl/neon/activation_grad_neon.h | 56 + .../nnacl/include/nnacl/neon/adam_fp32_neon.h | 209 + .../nnacl/include/nnacl/neon/add_fp32_neon.h | 123 + .../neon/apply_proximal_adagrad_fp32_neon.h | 74 + ...pply_proximal_gradient_descent_fp32_neon.h | 70 + .../include/nnacl/neon/arithmetic_fp32_neon.h | 293 + .../nnacl/neon/arithmetic_self_fp32_neon.h | 147 + .../include/nnacl/neon/batchnorm_fp32_neon.h | 66 + .../neon/bce_with_logits_loss_fp32_neon.h | 68 + .../nnacl/include/nnacl/neon/bias_add_neon.h | 63 + .../nnacl/include/nnacl/neon/cast_base_neon.h | 55 + .../nnacl/neon/cast_gather_reduce_fp32_neon.h | 71 + .../include/nnacl/neon/cdist_fp32_neon.h | 69 + .../include/nnacl/neon/cumsum_fp32_neon.h | 120 + .../nnacl/include/nnacl/neon/div_fp32_neon.h | 166 + .../include/nnacl/neon/dropout_fp32_neon.h | 45 + .../nnacl/include/nnacl/neon/exp_fp32_neon.h | 62 + .../nnacl/include/nnacl/neon/fill_base_neon.h | 52 + .../include/nnacl/neon/group_norm_fp32_neon.h | 76 + .../include/nnacl/neon/layer_norm_fp32_neon.h | 67 + .../include/nnacl/neon/matmul_fp32_neon.h | 154 + .../nnacl/include/nnacl/neon/mul_fp32_neon.h | 217 + .../include/nnacl/neon/pooling_fp32_neon.h | 83 + .../include/nnacl/neon/power_fp32_neon.h | 100 + .../nnacl/neon/reduce_concat_fp32_neon.h | 121 + .../include/nnacl/neon/reduce_fp32_neon.h | 226 + .../nnacl/neon/scatter_nd_binary_neon.h | 51 + .../include/nnacl/neon/softmax_fp32_neon.h | 86 + .../neon/softmax_grad_fusion_fp32_neon.h | 61 + .../nnacl/include/nnacl/neon/sub_fp32_neon.h | 166 + .../nnacl/include/nnacl/pooling_fp32_simd.h | 36 + .../nnacl/include/nnacl/power_fp32_simd.h | 36 + .../include/nnacl/reduce_concat_fp32_simd.h | 36 + .../nnacl/include/nnacl/reduce_fp32_simd.h | 36 + .../include/nnacl/scatter_nd_binary_simd.h | 36 + .../nnacl/include/nnacl/softmax_fp32_simd.h | 36 + .../nnacl/softmax_grad_fusion_fp32_simd.h | 36 + .../include/nnacl/sse/activation_fp32_sse.h | 285 + .../include/nnacl/sse/activation_grad_sse.h | 57 + .../nnacl/include/nnacl/sse/adam_fp32_sse.h | 210 + .../nnacl/include/nnacl/sse/add_fp32_sse.h | 124 + .../sse/apply_proximal_adagrad_fp32_sse.h | 75 + ...apply_proximal_gradient_descent_fp32_sse.h | 71 + .../include/nnacl/sse/arithmetic_fp32_sse.h | 294 + .../nnacl/sse/arithmetic_self_fp32_sse.h | 148 + .../include/nnacl/sse/batchnorm_fp32_sse.h | 67 + .../nnacl/sse/bce_with_logits_loss_fp32_sse.h | 69 + .../nnacl/include/nnacl/sse/bias_add_sse.h | 64 + .../nnacl/include/nnacl/sse/cast_base_sse.h | 56 + .../nnacl/sse/cast_gather_reduce_fp32_sse.h | 72 + .../nnacl/include/nnacl/sse/cdist_fp32_sse.h | 70 + .../nnacl/include/nnacl/sse/cumsum_fp32_sse.h | 121 + .../nnacl/include/nnacl/sse/div_fp32_sse.h | 167 + .../include/nnacl/sse/dropout_fp32_sse.h | 46 + .../nnacl/include/nnacl/sse/exp_fp32_sse.h | 63 + .../nnacl/include/nnacl/sse/fill_base_sse.h | 53 + .../include/nnacl/sse/group_norm_fp32_sse.h | 77 + .../include/nnacl/sse/layer_norm_fp32_sse.h | 68 + .../nnacl/include/nnacl/sse/matmul_fp32_sse.h | 155 + .../nnacl/include/nnacl/sse/mul_fp32_sse.h | 218 + .../include/nnacl/sse/pooling_fp32_sse.h | 84 + .../nnacl/include/nnacl/sse/power_fp32_sse.h | 101 + .../nnacl/sse/reduce_concat_fp32_sse.h | 122 + .../nnacl/include/nnacl/sse/reduce_fp32_sse.h | 227 + .../include/nnacl/sse/scatter_nd_binary_sse.h | 52 + .../include/nnacl/sse/softmax_fp32_sse.h | 87 + .../nnacl/sse/softmax_grad_fusion_fp32_sse.h | 62 + .../nnacl/include/nnacl/sse/sub_fp32_sse.h | 167 + .../nnacl/include/nnacl/sub_fp32_simd.h | 36 + .../cpu/kernel/nnacl/infer/infer_register.c | 4 +- mindspore/core/mindrt/BUILD.gn | 49 + mindspore/core/ops/base_operator.h | 4 +- mindspore/lite/BUILD.gn | 484 + mindspore/lite/CMakeLists.txt | 11 + mindspore/lite/mindir/BUILD.gn | 61 + mindspore/lite/mindir/CMakeLists.txt | 31 + mindspore/lite/mindir/include/mindir.h | 447 + .../lite/mindir/include/mindir_lite_graph.h | 57 + .../lite/mindir/include/mindir_primitive.h | 15 + mindspore/lite/mindir/include/mindir_tensor.h | 61 + mindspore/lite/mindir/include/mindir_types.h | 211 + .../lite/mindir/inner_headers/lite_graph.h | 27 + .../inner_headers/mindir_memory_manager.h | 33 + mindspore/lite/mindir/inner_headers/utils.h | 28 + mindspore/lite/mindir/src/mindir.cc | 4339 ++ .../lite/mindir/src/mindir_memory_manager.cc | 129 + .../lite/mindir/src/mindir_nnrt_lite_graph.cc | 87 + .../src/mindir_nnrt_lite_graph_to_model.cc | 1496 + .../mindir_nnrt_lite_graph_to_model_v2_0.cc | 1497 + mindspore/lite/mindir/src/mindir_tensor.cc | 433 + mindspore/lite/mindir/src/utils.cc | 102 + mindspore/lite/mindir/tests/BUILD.gn | 35 + mindspore/lite/mindir/tests/mindir_test.cc | 51 + mindspore/lite/schema/gpu_cache_generated.h | 319 + .../lite/schema/inner/gpu_cache_generated.h | 573 + mindspore/lite/schema/inner/model_generated.h | 3452 ++ mindspore/lite/schema/inner/ops_generated.h | 34917 ++++++++++++++++ .../lite/schema/inner/ops_types_generated.h | 1168 + mindspore/lite/schema/model_generated.h | 2623 ++ mindspore/lite/schema/ops_generated.h | 14900 +++++++ mindspore/lite/schema/ops_types_generated.h | 750 + mindspore/lite/schema/schema_generated.h | 18994 +++++++++ mindspore/lite/src/CMakeLists.txt | 6 + mindspore/lite/src/common/BUILD.gn | 83 + .../lite/src/common/dynamic_library_loader.cc | 3 +- mindspore/lite/src/common/log.cc | 10 +- mindspore/lite/src/common/log.h | 45 +- mindspore/lite/src/common/thread_utils.cc | 2 +- mindspore/lite/src/expression/node.cc | 4 +- mindspore/lite/src/litert/c_api/context_c.cc | 60 +- mindspore/lite/src/litert/c_api/context_c.h | 2 +- mindspore/lite/src/litert/c_api/model_c.cc | 178 +- mindspore/lite/src/litert/c_api/tensor_c.cc | 45 +- mindspore/lite/src/litert/cpu_info.cc | 2 +- .../lite/src/litert/cxx_api/converters.cc | 6 +- .../src/litert/delegate/nnrt/CMakeLists.txt | 30 + .../delegate/nnrt/checker/primitive_check.cc | 187 + .../delegate/nnrt/checker/primitive_check.h | 12 + .../src/litert/delegate/nnrt/nnrt_delegate.cc | 360 + .../src/litert/delegate/nnrt/nnrt_delegate.h | 51 + .../litert/delegate/nnrt/nnrt_model_kernel.cc | 175 + .../litert/delegate/nnrt/nnrt_model_kernel.h | 57 + mindspore/lite/src/litert/kernel/cpu/BUILD.gn | 307 + mindspore/lite/src/litert/lite_session.cc | 2 +- mindspore/lite/src/litert/scheduler.cc | 4 + mindspore/lite/test/ohos_test/BUILD.gn | 40 + mindspore/lite/test/ohos_test/ohos_test.xml | 23 + .../ohos_test/resource/models/mobilenetv2.ms | Bin 0 -> 13988384 bytes .../lite/test/ohos_test/src/context_test.cc | 76 + .../lite/test/ohos_test/src/model_test.cc | 234 + .../lite/test/ohos_test/src/tensor_test.cc | 98 + mindspore/lite/tools/benchmark/BUILD.gn | 66 + .../lite/tools/benchmark/benchmark_base.cc | 2 - .../lite/tools/benchmark/benchmark_c_api.cc | 142 +- .../lite/tools/benchmark/benchmark_c_api.h | 24 +- .../tools/benchmark/benchmark_unified_api.cc | 6 +- 239 files changed, 106438 insertions(+), 418 deletions(-) create mode 100644 include/sdk_api/context.h create mode 100644 include/sdk_api/data_type.h create mode 100644 include/sdk_api/format.h create mode 100644 include/sdk_api/model.h create mode 100644 include/sdk_api/status.h create mode 100644 include/sdk_api/tensor.h create mode 100644 include/sdk_api/types.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/BUILD.gn create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_grad_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/adam_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/add_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_adagrad_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_gradient_descent_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_self_fp32_simd.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_grad_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/adam_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/add_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_adagrad_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_gradient_descent_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_self_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/batchnorm_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bce_with_logits_loss_fp32_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bias_add_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_base_avx.h create mode 100644 mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_gather_reduce_fp32_avx.h create mode 100644 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create mode 100644 mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.h create mode 100644 mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.cc create mode 100644 mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.h create mode 100644 mindspore/lite/src/litert/kernel/cpu/BUILD.gn create mode 100644 mindspore/lite/test/ohos_test/BUILD.gn create mode 100644 mindspore/lite/test/ohos_test/ohos_test.xml create mode 100644 mindspore/lite/test/ohos_test/resource/models/mobilenetv2.ms create mode 100644 mindspore/lite/test/ohos_test/src/context_test.cc create mode 100644 mindspore/lite/test/ohos_test/src/model_test.cc create mode 100644 mindspore/lite/test/ohos_test/src/tensor_test.cc create mode 100644 mindspore/lite/tools/benchmark/BUILD.gn diff --git a/.gitignore b/.gitignore index 0d0837ca..acfdf600 100644 --- a/.gitignore +++ b/.gitignore @@ -29,8 +29,6 @@ mindspore/lite/tools/converter/parser/onnx/onnx.pb.h mindspore/lite/tools/converter/parser/onnx/onnx.pb.h mindspore/lite/tools/converter/schema/*.h mindspore/lite/tools/converter/schema/inner -mindspore/lite/schema/*.h -mindspore/lite/schema/inner mindspore/lite/src/litert/kernel/opencl/cl/fp16/*.inc mindspore/lite/src/litert/kernel/opencl/cl/fp32/*.inc diff --git a/include/c_api/context_c.h b/include/c_api/context_c.h index 980b55b6..53839e80 100644 --- a/include/c_api/context_c.h +++ b/include/c_api/context_c.h @@ -25,54 +25,54 @@ extern "C" { #endif -typedef void *MSContextHandle; -typedef void *MSDeviceInfoHandle; +typedef void *OH_AI_ContextHandle; +typedef void *OH_AI_DeviceInfoHandle; /// \brief Create a context object. /// /// \return Context object handle. -MS_API MSContextHandle MSContextCreate(); +OH_AI_API OH_AI_ContextHandle OH_AI_ContextCreate(); /// \brief Destroy the context object. /// /// \param[in] context Context object handle address. -MS_API void MSContextDestroy(MSContextHandle *context); +OH_AI_API void OH_AI_ContextDestroy(OH_AI_ContextHandle *context); /// \brief Set the number of threads at runtime. /// /// \param[in] context Context object handle. /// \param[in] thread_num the number of threads at runtime. -MS_API void MSContextSetThreadNum(MSContextHandle context, int32_t thread_num); +OH_AI_API void OH_AI_ContextSetThreadNum(OH_AI_ContextHandle context, int32_t thread_num); /// \brief Obtain the current thread number setting. /// /// \param[in] context Context object handle. /// /// \return The current thread number setting. -MS_API int32_t MSContextGetThreadNum(const MSContextHandle context); +OH_AI_API int32_t OH_AI_ContextGetThreadNum(const OH_AI_ContextHandle context); /// \brief Set the thread affinity to CPU cores. /// /// \param[in] context Context object handle. /// \param[in] mode: 0: no affinities, 1: big cores first, 2: little cores first -MS_API void MSContextSetThreadAffinityMode(MSContextHandle context, int mode); +OH_AI_API void OH_AI_ContextSetThreadAffinityMode(OH_AI_ContextHandle context, int mode); /// \brief Obtain the thread affinity of CPU cores. /// /// \param[in] context Context object handle. /// /// \return Thread affinity to CPU cores. 0: no affinities, 1: big cores first, 2: little cores first -MS_API int MSContextGetThreadAffinityMode(const MSContextHandle context); +OH_AI_API int OH_AI_ContextGetThreadAffinityMode(const OH_AI_ContextHandle context); /// \brief Set the thread lists to CPU cores. /// -/// \note If core_list and mode are set by MSContextSetThreadAffinityMode at the same time, +/// \note If core_list and mode are set by OH_AI_ContextSetThreadAffinityMode at the same time, /// the core_list is effective, but the mode is not effective. /// /// \param[in] context Context object handle. /// \param[in] core_list: a array of thread core lists. /// \param[in] core_num The number of core. -MS_API void MSContextSetThreadAffinityCoreList(MSContextHandle context, const int32_t *core_list, size_t core_num); +OH_AI_API void OH_AI_ContextSetThreadAffinityCoreList(OH_AI_ContextHandle context, const int32_t *core_list, size_t core_num); /// \brief Obtain the thread lists of CPU cores. /// @@ -80,98 +80,98 @@ MS_API void MSContextSetThreadAffinityCoreList(MSContextHandle context, const in /// \param[out] core_num The number of core. /// /// \return a array of thread core lists. -MS_API const int32_t *MSContextGetThreadAffinityCoreList(const MSContextHandle context, size_t *core_num); +OH_AI_API const int32_t *OH_AI_ContextGetThreadAffinityCoreList(const OH_AI_ContextHandle context, size_t *core_num); /// \brief Set the status whether to perform model inference or training in parallel. /// /// \param[in] context Context object handle. /// \param[in] is_parallel: true, parallel; false, not in parallel. -MS_API void MSContextSetEnableParallel(MSContextHandle context, bool is_parallel); +OH_AI_API void OH_AI_ContextSetEnableParallel(OH_AI_ContextHandle context, bool is_parallel); /// \brief Obtain the status whether to perform model inference or training in parallel. /// /// \param[in] context Context object handle. /// /// \return Bool value that indicates whether in parallel. -MS_API bool MSContextGetEnableParallel(const MSContextHandle context); +OH_AI_API bool OH_AI_ContextGetEnableParallel(const OH_AI_ContextHandle context); /// \brief Add device info to context object. /// /// \param[in] context Context object handle. /// \param[in] device_info Device info object handle. -MS_API void MSContextAddDeviceInfo(MSContextHandle context, MSDeviceInfoHandle device_info); +OH_AI_API void OH_AI_ContextAddDeviceInfo(OH_AI_ContextHandle context, OH_AI_DeviceInfoHandle device_info); /// \brief Create a device info object. /// /// \param[in] device_info Device info object handle. /// /// \return Device info object handle. -MS_API MSDeviceInfoHandle MSDeviceInfoCreate(MSDeviceType device_type); +OH_AI_API OH_AI_DeviceInfoHandle OH_AI_DeviceInfoCreate(OH_AI_DeviceType device_type); /// \brief Destroy the device info object. /// /// \param[in] device_info Device info object handle address. -MS_API void MSDeviceInfoDestroy(MSDeviceInfoHandle *device_info); +OH_AI_API void OH_AI_DeviceInfoDestroy(OH_AI_DeviceInfoHandle *device_info); /// \brief Set provider's name. /// /// \param[in] device_info Device info object handle. /// \param[in] provider define the provider's name. -MS_API void MSDeviceInfoSetProvider(MSDeviceInfoHandle device_info, const char *provider); +OH_AI_API void OH_AI_DeviceInfoSetProvider(OH_AI_DeviceInfoHandle device_info, const char *provider); /// \brief Obtain provider's name /// /// \param[in] device_info Device info object handle. /// /// \return provider's name. -MS_API const char *MSDeviceInfoGetProvider(const MSDeviceInfoHandle device_info); +OH_AI_API const char *OH_AI_DeviceInfoGetProvider(const OH_AI_DeviceInfoHandle device_info); /// \brief Set provider's device type. /// /// \param[in] device_info Device info object handle. /// \param[in] device define the provider's device type. EG: CPU. -MS_API void MSDeviceInfoSetProviderDevice(MSDeviceInfoHandle device_info, const char *device); +OH_AI_API void OH_AI_DeviceInfoSetProviderDevice(OH_AI_DeviceInfoHandle device_info, const char *device); /// \brief Obtain provider's device type. /// /// \param[in] device_info Device info object handle. /// /// \return provider's device type. -MS_API const char *MSDeviceInfoGetProviderDevice(const MSDeviceInfoHandle device_info); +OH_AI_API const char *OH_AI_DeviceInfoGetProviderDevice(const OH_AI_DeviceInfoHandle device_info); /// \brief Obtain the device type of the device info. /// /// \param[in] device_info Device info object handle. /// /// \return Device Type of the device info. -MS_API MSDeviceType MSDeviceInfoGetDeviceType(const MSDeviceInfoHandle device_info); +OH_AI_API OH_AI_DeviceType OH_AI_DeviceInfoGetDeviceType(const OH_AI_DeviceInfoHandle device_info); /// \brief Set enables to perform the float16 inference, Only valid for CPU/GPU. /// /// \param[in] device_info Device info object handle. /// \param[in] is_fp16 Enable float16 inference or not. -MS_API void MSDeviceInfoSetEnableFP16(MSDeviceInfoHandle device_info, bool is_fp16); +OH_AI_API void OH_AI_DeviceInfoSetEnableFP16(OH_AI_DeviceInfoHandle device_info, bool is_fp16); /// \brief Obtain enables to perform the float16 inference, Only valid for CPU/GPU. /// /// \param[in] device_info Device info object handle. /// /// \return Whether enable float16 inference. -MS_API bool MSDeviceInfoGetEnableFP16(const MSDeviceInfoHandle device_info); +OH_AI_API bool OH_AI_DeviceInfoGetEnableFP16(const OH_AI_DeviceInfoHandle device_info); /// \brief Set the NPU frequency, Only valid for NPU. /// /// \param[in] device_info Device info object handle. /// \param[in] frequency Can be set to 1 (low power consumption), 2 (balanced), 3 (high performance), 4 (extreme /// performance), default as 3. -MS_API void MSDeviceInfoSetFrequency(MSDeviceInfoHandle device_info, int frequency); +OH_AI_API void OH_AI_DeviceInfoSetFrequency(OH_AI_DeviceInfoHandle device_info, int frequency); /// \brief Obtain the NPU frequency, Only valid for NPU. /// /// \param[in] device_info Device info object handle. /// /// \return NPU frequency -MS_API int MSDeviceInfoGetFrequency(const MSDeviceInfoHandle device_info); +OH_AI_API int OH_AI_DeviceInfoGetFrequency(const OH_AI_DeviceInfoHandle device_info); #ifdef __cplusplus } diff --git a/include/c_api/data_type_c.h b/include/c_api/data_type_c.h index 3b736e0c..124ed17b 100644 --- a/include/c_api/data_type_c.h +++ b/include/c_api/data_type_c.h @@ -22,29 +22,29 @@ extern "C" { #endif -typedef enum MSDataType { - kMSDataTypeUnknown = 0, - kMSDataTypeObjectTypeString = 12, - kMSDataTypeObjectTypeList = 13, - kMSDataTypeObjectTypeTuple = 14, - kMSDataTypeObjectTypeTensor = 17, - kMSDataTypeNumberTypeBegin = 29, - kMSDataTypeNumberTypeBool = 30, - kMSDataTypeNumberTypeInt8 = 32, - kMSDataTypeNumberTypeInt16 = 33, - kMSDataTypeNumberTypeInt32 = 34, - kMSDataTypeNumberTypeInt64 = 35, - kMSDataTypeNumberTypeUInt8 = 37, - kMSDataTypeNumberTypeUInt16 = 38, - kMSDataTypeNumberTypeUInt32 = 39, - kMSDataTypeNumberTypeUInt64 = 40, - kMSDataTypeNumberTypeFloat16 = 42, - kMSDataTypeNumberTypeFloat32 = 43, - kMSDataTypeNumberTypeFloat64 = 44, - kMSDataTypeNumberTypeEnd = 46, +typedef enum OH_AI_DataType { + OH_AI_DATATYPE_UNKNOWN = 0, + OH_AI_DATATYPE_OBJECTTYPE_STRING = 12, + OH_AI_DATATYPE_OBJECTTYPE_LIST = 13, + OH_AI_DATATYPE_OBJECTTYPE_TUPLE = 14, + OH_AI_DATATYPE_OBJECTTYPE_TENSOR = 17, + OH_AI_DATATYPE_NUMBERTYPE_BEGIN = 29, + OH_AI_DATATYPE_NUMBERTYPE_BOOL = 30, + OH_AI_DATATYPE_NUMBERTYPE_INT8 = 32, + OH_AI_DATATYPE_NUMBERTYPE_INT16 = 33, + OH_AI_DATATYPE_NUMBERTYPE_INT32 = 34, + OH_AI_DATATYPE_NUMBERTYPE_INT64 = 35, + OH_AI_DATATYPE_NUMBERTYPE_UINT8 = 37, + OH_AI_DATATYPE_NUMBERTYPE_UINT16 = 38, + OH_AI_DATATYPE_NUMBERTYPE_UINT32 = 39, + OH_AI_DATATYPE_NUMBERTYPE_UINT64 = 40, + OH_AI_DATATYPE_NUMBERTYPE_FLOAT16 = 42, + OH_AI_DATATYPE_NUMBERTYPE_FLOAT32 = 43, + OH_AI_DATATYPE_NUMBERTYPE_FLOAT64 = 44, + OH_AI_DATATYPE_NUMBERTYPE_END = 46, // add new enum here - kMSDataTypeInvalid = INT32_MAX, -} MSDataType; + OH_AI_DataTypeInvalid = INT32_MAX, +} OH_AI_DataType; #ifdef __cplusplus } diff --git a/include/c_api/format_c.h b/include/c_api/format_c.h index 7b73dabf..36b355fe 100644 --- a/include/c_api/format_c.h +++ b/include/c_api/format_c.h @@ -20,25 +20,25 @@ extern "C" { #endif -typedef enum MSFormat { - kMSFormatNCHW = 0, - kMSFormatNHWC = 1, - kMSFormatNHWC4 = 2, - kMSFormatHWKC = 3, - kMSFormatHWCK = 4, - kMSFormatKCHW = 5, - kMSFormatCKHW = 6, - kMSFormatKHWC = 7, - kMSFormatCHWK = 8, - kMSFormatHW = 9, - kMSFormatHW4 = 10, - kMSFormatNC = 11, - kMSFormatNC4 = 12, - kMSFormatNC4HW4 = 13, - kMSFormatNCDHW = 15, - kMSFormatNWC = 16, - kMSFormatNCW = 17 -} MSFormat; +typedef enum OH_AI_Format { + OH_AI_FORMAT_NCHW = 0, + OH_AI_FORMAT_NHWC = 1, + OH_AI_FORMAT_NHWC4 = 2, + OH_AI_FORMAT_HWKC = 3, + OH_AI_FORMAT_HWCK = 4, + OH_AI_FORMAT_KCHW = 5, + OH_AI_FORMAT_CKHW = 6, + OH_AI_FORMAT_KHWC = 7, + OH_AI_FORMAT_CHWK = 8, + OH_AI_FORMAT_HW = 9, + OH_AI_FORMAT_HW4 = 10, + OH_AI_FORMAT_NC = 11, + OH_AI_FORMAT_NC4 = 12, + OH_AI_FORMAT_NC4HW4 = 13, + OH_AI_FORMAT_NCDHW = 15, + OH_AI_FORMAT_NWC = 16, + OH_AI_FORMAT_NCW = 17 +} OH_AI_Format; #ifdef __cplusplus } diff --git a/include/c_api/model_c.h b/include/c_api/model_c.h index dac35b2f..12a46bcd 100644 --- a/include/c_api/model_c.h +++ b/include/c_api/model_c.h @@ -24,48 +24,48 @@ extern "C" { #endif -typedef void *MSModelHandle; +typedef void *OH_AI_ModelHandle; -typedef struct MSTensorHandleArray { +typedef struct OH_AI_TensorHandleArray { size_t handle_num; - MSTensorHandle *handle_list; -} MSTensorHandleArray; + OH_AI_TensorHandle *handle_list; +} OH_AI_TensorHandleArray; -#define MS_MAX_SHAPE_NUM 32 -typedef struct MSShapeInfo { +#define OH_AI_MAX_SHAPE_NUM 32 +typedef struct OH_AI_ShapeInfo { size_t shape_num; - int64_t shape[MS_MAX_SHAPE_NUM]; -} MSShapeInfo; + int64_t shape[OH_AI_MAX_SHAPE_NUM]; +} OH_AI_ShapeInfo; -typedef struct MSCallBackParamC { +typedef struct OH_AI_CallBackParam { char *node_name; char *node_type; -} MSCallBackParamC; +} OH_AI_CallBackParam; -typedef bool (*MSKernelCallBackC)(const MSTensorHandleArray inputs, const MSTensorHandleArray outputs, - const MSCallBackParamC kernel_Info); +typedef bool (*OH_AI_KernelCallBack)(const OH_AI_TensorHandleArray inputs, const OH_AI_TensorHandleArray outputs, + const OH_AI_CallBackParam kernel_Info); /// \brief Create a model object. /// /// \return Model object handle. -MS_API MSModelHandle MSModelCreate(); +OH_AI_API OH_AI_ModelHandle OH_AI_ModelCreate(); /// \brief Destroy the model object. /// /// \param[in] model Model object handle address. -MS_API void MSModelDestroy(MSModelHandle *model); +OH_AI_API void OH_AI_ModelDestroy(OH_AI_ModelHandle *model); /// \brief Set workspace for the model object. Only valid for Iot. /// /// \param[in] model Model object handle. /// \param[in] workspace Define the workspace address. /// \param[in] workspace_size Define the workspace size. -MS_API void MSModelSetWorkspace(MSModelHandle model, void *workspace, size_t workspace_size); +OH_AI_API void OH_AI_ModelSetWorkspace(OH_AI_ModelHandle model, void *workspace, size_t workspace_size); /// \brief Calculate the workspace size required for model inference. Only valid for Iot. /// /// \param[in] model Model object handle. -MS_API size_t MSModelCalcWorkspaceSize(MSModelHandle model); +OH_AI_API size_t OH_AI_ModelCalcWorkspaceSize(OH_AI_ModelHandle model); /// \brief Build the model from model file buffer so that it can run on a device. /// @@ -75,9 +75,9 @@ MS_API size_t MSModelCalcWorkspaceSize(MSModelHandle model); /// \param[in] model_type Define The type of model file. /// \param[in] model_context Define the context used to store options during execution. /// -/// \return MSStatus. -MS_API MSStatus MSModelBuild(MSModelHandle model, const void *model_data, size_t data_size, MSModelType model_type, - const MSContextHandle model_context); +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelBuild(OH_AI_ModelHandle model, const void *model_data, size_t data_size, OH_AI_ModelType model_type, + const OH_AI_ContextHandle model_context); /// \brief Load and build the model from model path so that it can run on a device. /// @@ -86,9 +86,9 @@ MS_API MSStatus MSModelBuild(MSModelHandle model, const void *model_data, size_t /// \param[in] model_type Define The type of model file. /// \param[in] model_context Define the context used to store options during execution. /// -/// \return MSStatus. -MS_API MSStatus MSModelBuildFromFile(MSModelHandle model, const char *model_path, MSModelType model_type, - const MSContextHandle model_context); +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelBuildFromFile(OH_AI_ModelHandle model, const char *model_path, OH_AI_ModelType model_type, + const OH_AI_ContextHandle model_context); /// \brief Resize the shapes of inputs. /// @@ -97,8 +97,8 @@ MS_API MSStatus MSModelBuildFromFile(MSModelHandle model, const char *model_path /// \param[in] shape_infos Defines the new shapes of inputs, should be consistent with inputs. /// \param[in] shape_info_num The num of shape_infos. /// -/// \return MSStatus. -MS_API MSStatus MSModelResize(MSModelHandle model, const MSTensorHandleArray inputs, MSShapeInfo *shape_infos, +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelResize(OH_AI_ModelHandle model, const OH_AI_TensorHandleArray inputs, OH_AI_ShapeInfo *shape_infos, size_t shape_info_num); /// \brief Inference model. @@ -109,9 +109,9 @@ MS_API MSStatus MSModelResize(MSModelHandle model, const MSTensorHandleArray inp /// \param[in] before CallBack before predict. /// \param[in] after CallBack after predict. /// -/// \return MSStatus. -MS_API MSStatus MSModelPredict(MSModelHandle model, const MSTensorHandleArray inputs, MSTensorHandleArray *outputs, - const MSKernelCallBackC before, const MSKernelCallBackC after); +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelPredict(OH_AI_ModelHandle model, const OH_AI_TensorHandleArray inputs, OH_AI_TensorHandleArray *outputs, + const OH_AI_KernelCallBack before, const OH_AI_KernelCallBack after); /// \brief Run model by step. Only valid for Iot. /// @@ -119,8 +119,8 @@ MS_API MSStatus MSModelPredict(MSModelHandle model, const MSTensorHandleArray in /// \param[in] before CallBack before RunStep. /// \param[in] after CallBack after RunStep. /// -/// \return MSStatus. -MS_API MSStatus MSModelRunStep(MSModelHandle model, const MSKernelCallBackC before, const MSKernelCallBackC after); +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelRunStep(OH_AI_ModelHandle model, const OH_AI_KernelCallBack before, const OH_AI_KernelCallBack after); /// \brief Set the model running mode. Only valid for Iot. /// @@ -128,7 +128,7 @@ MS_API MSStatus MSModelRunStep(MSModelHandle model, const MSKernelCallBackC befo /// \param[in] train True means model runs in Train Mode, otherwise Eval Mode. /// /// \return Status of operation. -MS_API MSStatus MSModelSetTrainMode(const MSModelHandle model, bool train); +OH_AI_API OH_AI_Status OH_AI_ModelSetTrainMode(const OH_AI_ModelHandle model, bool train); /// \brief Export the weights of model to the binary file. Only valid for Iot. /// @@ -136,21 +136,21 @@ MS_API MSStatus MSModelSetTrainMode(const MSModelHandle model, bool train); /// \param[in] export_path Define the export weight file path. /// /// \return Status of operation. -MS_API MSStatus MSModelExportWeight(const MSModelHandle model, const char *export_path); +OH_AI_API OH_AI_Status OH_AI_ModelExportWeight(const OH_AI_ModelHandle model, const char *export_path); /// \brief Obtain all input tensor handles of the model. /// /// \param[in] model Model object handle. /// /// \return The array that includes all input tensor handles. -MS_API MSTensorHandleArray MSModelGetInputs(const MSModelHandle model); +OH_AI_API OH_AI_TensorHandleArray OH_AI_ModelGetInputs(const OH_AI_ModelHandle model); /// \brief Obtain all output tensor handles of the model. /// /// \param[in] model Model object handle. /// /// \return The array that includes all output tensor handles. -MS_API MSTensorHandleArray MSModelGetOutputs(const MSModelHandle model); +OH_AI_API OH_AI_TensorHandleArray OH_AI_ModelGetOutputs(const OH_AI_ModelHandle model); /// \brief Obtain the input tensor handle of the model by name. /// @@ -158,7 +158,7 @@ MS_API MSTensorHandleArray MSModelGetOutputs(const MSModelHandle model); /// \param[in] tensor_name The name of tensor. /// /// \return The input tensor handle with the given name, if the name is not found, an NULL is returned. -MS_API MSTensorHandle MSModelGetInputByTensorName(const MSModelHandle model, const char *tensor_name); +OH_AI_API OH_AI_TensorHandle OH_AI_ModelGetInputByTensorName(const OH_AI_ModelHandle model, const char *tensor_name); /// \brief Obtain the output tensor handle of the model by name. /// @@ -166,7 +166,7 @@ MS_API MSTensorHandle MSModelGetInputByTensorName(const MSModelHandle model, con /// \param[in] tensor_name The name of tensor. /// /// \return The output tensor handle with the given name, if the name is not found, an NULL is returned. -MS_API MSTensorHandle MSModelGetOutputByTensorName(const MSModelHandle model, const char *tensor_name); +OH_AI_API OH_AI_TensorHandle OH_AI_ModelGetOutputByTensorName(const OH_AI_ModelHandle model, const char *tensor_name); #ifdef __cplusplus } diff --git a/include/c_api/status_c.h b/include/c_api/status_c.h index 82b4f2d5..ce71d5ba 100644 --- a/include/c_api/status_c.h +++ b/include/c_api/status_c.h @@ -22,57 +22,57 @@ extern "C" { #endif -enum MSCompCode { - kMSCompCodeCore = 0x00000000u, - kMSCompCodeMD = 0x10000000u, - kMSCompCodeME = 0x20000000u, - kMSCompCodeMC = 0x30000000u, - kMSCompCodeLite = 0xF0000000u, +enum OH_AI_CompCode { + OH_AI_COMPCODE_CORE = 0x00000000u, + OH_AI_COMPCODE_MD = 0x10000000u, + OH_AI_COMPCODE_ME = 0x20000000u, + OH_AI_COMPCODE_MC = 0x30000000u, + OH_AI_COMPCODE_LITE = 0xF0000000u, }; -typedef enum MSStatus { - kMSStatusSuccess = 0, +typedef enum OH_AI_Status { + OH_AI_STATUS_SUCCESS = 0, // Core - kMSStatusCoreFailed = kMSCompCodeCore | 0x1, + OH_AI_STATUS_CORE_FAILED = OH_AI_COMPCODE_CORE | 0x1, // Lite // Common error code, range: [-1, -100) - kMSStatusLiteError = kMSCompCodeLite | (0x0FFFFFFF & -1), /**< Common error code. */ - kMSStatusLiteNullptr = kMSCompCodeLite | (0x0FFFFFFF & -2), /**< NULL pointer returned.*/ - kMSStatusLiteParamInvalid = kMSCompCodeLite | (0x0FFFFFFF & -3), /**< Invalid parameter.*/ - kMSStatusLiteNoChange = kMSCompCodeLite | (0x0FFFFFFF & -4), /**< No change. */ - kMSStatusLiteSuccessExit = kMSCompCodeLite | (0x0FFFFFFF & -5), /**< No error but exit. */ - kMSStatusLiteMemoryFailed = kMSCompCodeLite | (0x0FFFFFFF & -6), /**< Fail to create memory. */ - kMSStatusLiteNotSupport = kMSCompCodeLite | (0x0FFFFFFF & -7), /**< Fail to support. */ - kMSStatusLiteThreadPoolError = kMSCompCodeLite | (0x0FFFFFFF & -8), /**< Error occur in thread pool. */ - kMSStatusLiteUninitializedObj = kMSCompCodeLite | (0x0FFFFFFF & -9), /**< Object is not initialized. */ - kMSStatusLiteFileError = kMSCompCodeLite | (0x0FFFFFFF & -10), /**< Invalid file. */ - kMSStatusLiteServiceDeny = kMSCompCodeLite | (0x0FFFFFFF & -11), /**< Denial of service. */ - kMSStatusLiteModelRebuild = kMSCompCodeLite | (0x0FFFFFFF & -12), /**< Model has been built. */ + OH_AI_STATUS_LITE_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -1), /**< Common error code. */ + OH_AI_STATUS_LITE_NULLPTR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -2), /**< NULL pointer returned.*/ + OH_AI_STATUS_LITE_PARAM_INVALID = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -3), /**< Invalid parameter.*/ + OH_AI_STATUS_LITE_NO_CHANGE = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -4), /**< No change. */ + OH_AI_STATUS_LITE_SUCCESS_EXIT = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -5), /**< No error but exit. */ + OH_AI_STATUS_LITE_MEMORY_FAILED = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -6), /**< Fail to create memory. */ + OH_AI_STATUS_LITE_NOT_SUPPORT = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -7), /**< Fail to support. */ + OH_AI_STATUS_LITE_THREADPOOL_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -8), /**< Error occur in thread pool. */ + OH_AI_STATUS_LITE_UNINITIALIZED_OBJ = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -9), /**< Object is not initialized. */ + OH_AI_STATUS_LITE_FILE_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -10), /**< Invalid file. */ + OH_AI_STATUS_LITE_SERVICE_DENY = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -11), /**< Denial of service. */ + OH_AI_STATUS_LITE_MODEL_REBUILD = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -12), /**< Model has been built. */ // Executor error code, range: [-100,-200) - kMSStatusLiteOutOfTensorRange = kMSCompCodeLite | (0x0FFFFFFF & -100), /**< Failed to check range. */ - kMSStatusLiteInputTensorError = kMSCompCodeLite | (0x0FFFFFFF & -101), /**< Failed to check input tensor. */ - kMSStatusLiteReentrantError = kMSCompCodeLite | (0x0FFFFFFF & -102), /**< Exist executor running. */ + OH_AI_STATUS_LITE_OUT_OF_TENSOR_RANGE = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -100), /**< Failed to check range. */ + OH_AI_STATUS_LITE_INPUT_TENSOR_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -101), /**< Failed to check input tensor. */ + OH_AI_STATUS_LITE_REENTRANT_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -102), /**< Exist executor running. */ // Graph error code, range: [-200,-300) - kMSStatusLiteGraphFileError = kMSCompCodeLite | (0x0FFFFFFF & -200), /**< Failed to verify graph file. */ + OH_AI_STATUS_LITE_GRAPH_FILE_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -200), /**< Failed to verify graph file. */ // Node error code, range: [-300,-400) - kMSStatusLiteNotFindOp = kMSCompCodeLite | (0x0FFFFFFF & -300), /**< Failed to find operator. */ - kMSStatusLiteInvalidOpName = kMSCompCodeLite | (0x0FFFFFFF & -301), /**< Invalid operator name. */ - kMSStatusLiteInvalidOpAttr = kMSCompCodeLite | (0x0FFFFFFF & -302), /**< Invalid operator attr. */ - kMSStatusLiteOpExecuteFailure = kMSCompCodeLite | (0x0FFFFFFF & -303), /**< Failed to execution operator. */ + OH_AI_STATUS_LITE_NOT_FIND_OP = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -300), /**< Failed to find operator. */ + OH_AI_STATUS_LITE_INVALID_OP_NAME = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -301), /**< Invalid operator name. */ + OH_AI_STATUS_LITE_INVALID_OP_ATTR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -302), /**< Invalid operator attr. */ + OH_AI_STATUS_LITE_OP_EXECUTE_FAILURE = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -303), /**< Failed to execution operator. */ // Tensor error code, range: [-400,-500) - kMSStatusLiteFormatError = kMSCompCodeLite | (0x0FFFFFFF & -400), /**< Failed to checking tensor format. */ + OH_AI_STATUS_LITE_FORMAT_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -400), /**< Failed to checking tensor format. */ // InferShape error code, range: [-500,-600) - kMSStatusLiteInferError = kMSCompCodeLite | (0x0FFFFFFF & -500), /**< Failed to infer shape. */ - kMSStatusLiteInferInvalid = kMSCompCodeLite | (0x0FFFFFFF & -501), /**< Invalid infer shape before runtime. */ + OH_AI_STATUS_LITE_INFER_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -500), /**< Failed to infer shape. */ + OH_AI_STATUS_LITE_INFER_INVALID = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -501), /**< Invalid infer shape before runtime. */ // User input param error code, range: [-600, 700) - kMSStatusLiteInputParamInvalid = kMSCompCodeLite | (0x0FFFFFFF & -600), /**< Invalid input param by user. */ -} MSStatus; + OH_AI_STATUS_LITE_INPUT_PARAM_INVALID = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -600), /**< Invalid input param by user. */ +} OH_AI_Status; #ifdef __cplusplus } #endif diff --git a/include/c_api/tensor_c.h b/include/c_api/tensor_c.h index 9783bd90..f18ba163 100644 --- a/include/c_api/tensor_c.h +++ b/include/c_api/tensor_c.h @@ -25,7 +25,7 @@ extern "C" { #endif -typedef void *MSTensorHandle; +typedef void *OH_AI_TensorHandle; /// \brief Create a tensor object. /// @@ -37,53 +37,53 @@ typedef void *MSTensorHandle; /// \param[in] data_len The length of the memory, in bytes. /// /// \return Tensor object handle. -MS_API MSTensorHandle MSTensorCreate(const char *name, MSDataType type, const int64_t *shape, size_t shape_num, +OH_AI_API OH_AI_TensorHandle OH_AI_TensorCreate(const char *name, OH_AI_DataType type, const int64_t *shape, size_t shape_num, const void *data, size_t data_len); /// \brief Destroy the tensor object. /// /// \param[in] tensor Tensor object handle address. -MS_API void MSTensorDestroy(MSTensorHandle *tensor); +OH_AI_API void OH_AI_TensorDestroy(OH_AI_TensorHandle *tensor); /// \brief Obtain a deep copy of the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return Tensor object handle. -MS_API MSTensorHandle MSTensorClone(MSTensorHandle tensor); +OH_AI_API OH_AI_TensorHandle OH_AI_TensorClone(OH_AI_TensorHandle tensor); /// \brief Set the name for the tensor. /// /// \param[in] tensor Tensor object handle. /// \param[in] name The name of the tensor. -MS_API void MSTensorSetName(MSTensorHandle tensor, const char *name); +OH_AI_API void OH_AI_TensorSetName(OH_AI_TensorHandle tensor, const char *name); /// \brief Obtain the name of the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return The name of the tensor. -MS_API const char *MSTensorGetName(const MSTensorHandle tensor); +OH_AI_API const char *OH_AI_TensorGetName(const OH_AI_TensorHandle tensor); /// \brief Set the data type for the tensor. /// /// \param[in] tensor Tensor object handle. /// \param[in] type The data type of the tensor. -MS_API void MSTensorSetDataType(MSTensorHandle tensor, MSDataType type); +OH_AI_API void OH_AI_TensorSetDataType(OH_AI_TensorHandle tensor, OH_AI_DataType type); /// \brief Obtain the data type of the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return The date type of the tensor. -MS_API MSDataType MSTensorGetDataType(const MSTensorHandle tensor); +OH_AI_API OH_AI_DataType OH_AI_TensorGetDataType(const OH_AI_TensorHandle tensor); /// \brief Set the shape for the tensor. /// /// \param[in] tensor Tensor object handle. /// \param[in] shape The shape array. /// \param[in] shape_num Dimension of shape. -MS_API void MSTensorSetShape(MSTensorHandle tensor, const int64_t *shape, size_t shape_num); +OH_AI_API void OH_AI_TensorSetShape(OH_AI_TensorHandle tensor, const int64_t *shape, size_t shape_num); /// \brief Obtain the shape of the tensor. /// @@ -91,54 +91,54 @@ MS_API void MSTensorSetShape(MSTensorHandle tensor, const int64_t *shape, size_t /// \param[out] shape_num Dimension of shape. /// /// \return The shape array of the tensor. -MS_API const int64_t *MSTensorGetShape(const MSTensorHandle tensor, size_t *shape_num); +OH_AI_API const int64_t *OH_AI_TensorGetShape(const OH_AI_TensorHandle tensor, size_t *shape_num); /// \brief Set the format for the tensor. /// /// \param[in] tensor Tensor object handle. /// \param[in] format The format of the tensor. -MS_API void MSTensorSetFormat(MSTensorHandle tensor, MSFormat format); +OH_AI_API void OH_AI_TensorSetFormat(OH_AI_TensorHandle tensor, OH_AI_Format format); /// \brief Obtain the format of the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return The format of the tensor. -MS_API MSFormat MSTensorGetFormat(const MSTensorHandle tensor); +OH_AI_API OH_AI_Format OH_AI_TensorGetFormat(const OH_AI_TensorHandle tensor); /// \brief Obtain the data for the tensor. /// /// \param[in] tensor Tensor object handle. /// \param[in] data A pointer to the data of the tensor. -MS_API void MSTensorSetData(MSTensorHandle tensor, void *data); +OH_AI_API void OH_AI_TensorSetData(OH_AI_TensorHandle tensor, void *data); /// \brief Obtain the data pointer of the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return The data pointer of the tensor. -MS_API const void *MSTensorGetData(const MSTensorHandle tensor); +OH_AI_API const void *OH_AI_TensorGetData(const OH_AI_TensorHandle tensor); /// \brief Obtain the mutable data pointer of the tensor. If the internal data is empty, it will allocate memory. /// /// \param[in] tensor Tensor object handle. /// /// \return The data pointer of the tensor. -MS_API void *MSTensorGetMutableData(const MSTensorHandle tensor); +OH_AI_API void *OH_AI_TensorGetMutableData(const OH_AI_TensorHandle tensor); /// \brief Obtain the element number of the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return The element number of the tensor. -MS_API int64_t MSTensorGetElementNum(const MSTensorHandle tensor); +OH_AI_API int64_t OH_AI_TensorGetElementNum(const OH_AI_TensorHandle tensor); /// \brief Obtain the data size fo the tensor. /// /// \param[in] tensor Tensor object handle. /// /// \return The data size of the tensor. -MS_API size_t MSTensorGetDataSize(const MSTensorHandle tensor); +OH_AI_API size_t OH_AI_TensorGetDataSize(const OH_AI_TensorHandle tensor); #ifdef __cplusplus } diff --git a/include/c_api/types_c.h b/include/c_api/types_c.h index 342e24bd..dba54ffa 100644 --- a/include/c_api/types_c.h +++ b/include/c_api/types_c.h @@ -20,27 +20,29 @@ extern "C" { #endif -#ifndef MS_API +#ifndef OH_AI_API #ifdef _WIN32 -#define MS_API __declspec(dllexport) +#define OH_AI_API __declspec(dllexport) #else -#define MS_API __attribute__((visibility("default"))) +#define OH_AI_API __attribute__((visibility("default"))) #endif #endif -typedef enum MSModelType { - kMSModelTypeMindIR = 0, +typedef enum OH_AI_ModelType { + OH_AI_MODELTYPE_MINDIR= 0, // insert new data type here - kMSModelTypeInvalid = 0xFFFFFFFF -} MSModelType; + OH_AI_MODELTYPE_INVALID = 0xFFFFFFFF +} OH_AI_ModelType; -typedef enum MSDeviceType { - kMSDeviceTypeCPU = 0, - kMSDeviceTypeGPU, - kMSDeviceTypeKirinNPU, +typedef enum OH_AI_DeviceType { + OH_AI_DEVICETYPE_CPU = 0, + OH_AI_DEVICETYPE_GPU, + OH_AI_DEVICETYPE_KIRIN_NPU, // add new type here - kMSDeviceTypeInvalid = 100, -} MSDeviceType; + // ohos-only device range: [60, 80) + OH_AI_DEVICETYPE__NNRT = 60, + OH_AI_DEVICETYPE_INVALID = 100, +} OH_AI_DeviceType; #ifdef __cplusplus } diff --git a/include/sdk_api/context.h b/include/sdk_api/context.h new file mode 100644 index 00000000..5bfc9279 --- /dev/null +++ b/include/sdk_api/context.h @@ -0,0 +1,180 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_CONTEXT_C_H +#define MINDSPORE_INCLUDE_C_API_CONTEXT_C_H + +#include +#include +#include +#include "mindspore/types.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *OH_AI_ContextHandle; +typedef void *OH_AI_DeviceInfoHandle; + +/// \brief Create a context object. +/// +/// \return Context object handle. +OH_AI_API OH_AI_ContextHandle OH_AI_ContextCreate(); + +/// \brief Destroy the context object. +/// +/// \param[in] context Context object handle address. +OH_AI_API void OH_AI_ContextDestroy(OH_AI_ContextHandle *context); + +/// \brief Set the number of threads at runtime. +/// +/// \param[in] context Context object handle. +/// \param[in] thread_num the number of threads at runtime. +OH_AI_API void OH_AI_ContextSetThreadNum(OH_AI_ContextHandle context, int32_t thread_num); + +/// \brief Obtain the current thread number setting. +/// +/// \param[in] context Context object handle. +/// +/// \return The current thread number setting. +OH_AI_API int32_t OH_AI_ContextGetThreadNum(const OH_AI_ContextHandle context); + +/// \brief Set the thread affinity to CPU cores. +/// +/// \param[in] context Context object handle. +/// \param[in] mode: 0: no affinities, 1: big cores first, 2: little cores first +OH_AI_API void OH_AI_ContextSetThreadAffinityMode(OH_AI_ContextHandle context, int mode); + +/// \brief Obtain the thread affinity of CPU cores. +/// +/// \param[in] context Context object handle. +/// +/// \return Thread affinity to CPU cores. 0: no affinities, 1: big cores first, 2: little cores first +OH_AI_API int OH_AI_ContextGetThreadAffinityMode(const OH_AI_ContextHandle context); + +/// \brief Set the thread lists to CPU cores. +/// +/// \note If core_list and mode are set by OH_AI_ContextSetThreadAffinityMode at the same time, +/// the core_list is effective, but the mode is not effective. +/// +/// \param[in] context Context object handle. +/// \param[in] core_list: a array of thread core lists. +/// \param[in] core_num The number of core. +OH_AI_API void OH_AI_ContextSetThreadAffinityCoreList(OH_AI_ContextHandle context, const int32_t *core_list, + size_t core_num); + +/// \brief Obtain the thread lists of CPU cores. +/// +/// \param[in] context Context object handle. +/// \param[out] core_num The number of core. +/// +/// \return a array of thread core lists. +OH_AI_API const int32_t *OH_AI_ContextGetThreadAffinityCoreList(const OH_AI_ContextHandle context, size_t *core_num); + +/// \brief Set the status whether to perform model inference or training in parallel. +/// +/// \param[in] context Context object handle. +/// \param[in] is_parallel: true, parallel; false, not in parallel. +OH_AI_API void OH_AI_ContextSetEnableParallel(OH_AI_ContextHandle context, bool is_parallel); + +/// \brief Obtain the status whether to perform model inference or training in parallel. +/// +/// \param[in] context Context object handle. +/// +/// \return Bool value that indicates whether in parallel. +OH_AI_API bool OH_AI_ContextGetEnableParallel(const OH_AI_ContextHandle context); + +/// \brief Add device info to context object. +/// +/// \param[in] context Context object handle. +/// \param[in] device_info Device info object handle. +OH_AI_API void OH_AI_ContextAddDeviceInfo(OH_AI_ContextHandle context, OH_AI_DeviceInfoHandle device_info); + +/// \brief Create a device info object. +/// +/// \param[in] device_info Device info object handle. +/// +/// \return Device info object handle. +OH_AI_API OH_AI_DeviceInfoHandle OH_AI_DeviceInfoCreate(OH_AI_DeviceType device_type); + +/// \brief Destroy the device info object. +/// +/// \param[in] device_info Device info object handle address. +OH_AI_API void OH_AI_DeviceInfoDestroy(OH_AI_DeviceInfoHandle *device_info); + +/// \brief Set provider's name. +/// +/// \param[in] device_info Device info object handle. +/// \param[in] provider define the provider's name. +OH_AI_API void OH_AI_DeviceInfoSetProvider(OH_AI_DeviceInfoHandle device_info, const char *provider); + +/// \brief Obtain provider's name +/// +/// \param[in] device_info Device info object handle. +/// +/// \return provider's name. +OH_AI_API const char *OH_AI_DeviceInfoGetProvider(const OH_AI_DeviceInfoHandle device_info); + +/// \brief Set provider's device type. +/// +/// \param[in] device_info Device info object handle. +/// \param[in] device define the provider's device type. EG: CPU. +OH_AI_API void OH_AI_DeviceInfoSetProviderDevice(OH_AI_DeviceInfoHandle device_info, const char *device); + +/// \brief Obtain provider's device type. +/// +/// \param[in] device_info Device info object handle. +/// +/// \return provider's device type. +OH_AI_API const char *OH_AI_DeviceInfoGetProviderDevice(const OH_AI_DeviceInfoHandle device_info); + +/// \brief Obtain the device type of the device info. +/// +/// \param[in] device_info Device info object handle. +/// +/// \return Device Type of the device info. +OH_AI_API OH_AI_DeviceType OH_AI_DeviceInfoGetDeviceType(const OH_AI_DeviceInfoHandle device_info); + +/// \brief Set enables to perform the float16 inference, Only valid for CPU/GPU. +/// +/// \param[in] device_info Device info object handle. +/// \param[in] is_fp16 Enable float16 inference or not. +OH_AI_API void OH_AI_DeviceInfoSetEnableFP16(OH_AI_DeviceInfoHandle device_info, bool is_fp16); + +/// \brief Obtain enables to perform the float16 inference, Only valid for CPU/GPU. +/// +/// \param[in] device_info Device info object handle. +/// +/// \return Whether enable float16 inference. +OH_AI_API bool OH_AI_DeviceInfoGetEnableFP16(const OH_AI_DeviceInfoHandle device_info); + +/// \brief Set the NPU frequency, Only valid for NPU. +/// +/// \param[in] device_info Device info object handle. +/// \param[in] frequency Can be set to 1 (low power consumption), 2 (balanced), 3 (high performance), 4 (extreme +/// performance), default as 3. +OH_AI_API void OH_AI_DeviceInfoSetFrequency(OH_AI_DeviceInfoHandle device_info, int frequency); + +/// \brief Obtain the NPU frequency, Only valid for NPU. +/// +/// \param[in] device_info Device info object handle. +/// +/// \return NPU frequency +OH_AI_API int OH_AI_DeviceInfoGetFrequency(const OH_AI_DeviceInfoHandle device_info); + +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_CONTEXT_C_H diff --git a/include/sdk_api/data_type.h b/include/sdk_api/data_type.h new file mode 100644 index 00000000..124ed17b --- /dev/null +++ b/include/sdk_api/data_type.h @@ -0,0 +1,52 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_DATA_TYPE_C_H +#define MINDSPORE_INCLUDE_C_API_DATA_TYPE_C_H + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +typedef enum OH_AI_DataType { + OH_AI_DATATYPE_UNKNOWN = 0, + OH_AI_DATATYPE_OBJECTTYPE_STRING = 12, + OH_AI_DATATYPE_OBJECTTYPE_LIST = 13, + OH_AI_DATATYPE_OBJECTTYPE_TUPLE = 14, + OH_AI_DATATYPE_OBJECTTYPE_TENSOR = 17, + OH_AI_DATATYPE_NUMBERTYPE_BEGIN = 29, + OH_AI_DATATYPE_NUMBERTYPE_BOOL = 30, + OH_AI_DATATYPE_NUMBERTYPE_INT8 = 32, + OH_AI_DATATYPE_NUMBERTYPE_INT16 = 33, + OH_AI_DATATYPE_NUMBERTYPE_INT32 = 34, + OH_AI_DATATYPE_NUMBERTYPE_INT64 = 35, + OH_AI_DATATYPE_NUMBERTYPE_UINT8 = 37, + OH_AI_DATATYPE_NUMBERTYPE_UINT16 = 38, + OH_AI_DATATYPE_NUMBERTYPE_UINT32 = 39, + OH_AI_DATATYPE_NUMBERTYPE_UINT64 = 40, + OH_AI_DATATYPE_NUMBERTYPE_FLOAT16 = 42, + OH_AI_DATATYPE_NUMBERTYPE_FLOAT32 = 43, + OH_AI_DATATYPE_NUMBERTYPE_FLOAT64 = 44, + OH_AI_DATATYPE_NUMBERTYPE_END = 46, + // add new enum here + OH_AI_DataTypeInvalid = INT32_MAX, +} OH_AI_DataType; + +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_DATA_TYPE_C_H diff --git a/include/sdk_api/format.h b/include/sdk_api/format.h new file mode 100644 index 00000000..36b355fe --- /dev/null +++ b/include/sdk_api/format.h @@ -0,0 +1,46 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_FORMAT_C_H +#define MINDSPORE_INCLUDE_C_API_FORMAT_C_H + +#ifdef __cplusplus +extern "C" { +#endif + +typedef enum OH_AI_Format { + OH_AI_FORMAT_NCHW = 0, + OH_AI_FORMAT_NHWC = 1, + OH_AI_FORMAT_NHWC4 = 2, + OH_AI_FORMAT_HWKC = 3, + OH_AI_FORMAT_HWCK = 4, + OH_AI_FORMAT_KCHW = 5, + OH_AI_FORMAT_CKHW = 6, + OH_AI_FORMAT_KHWC = 7, + OH_AI_FORMAT_CHWK = 8, + OH_AI_FORMAT_HW = 9, + OH_AI_FORMAT_HW4 = 10, + OH_AI_FORMAT_NC = 11, + OH_AI_FORMAT_NC4 = 12, + OH_AI_FORMAT_NC4HW4 = 13, + OH_AI_FORMAT_NCDHW = 15, + OH_AI_FORMAT_NWC = 16, + OH_AI_FORMAT_NCW = 17 +} OH_AI_Format; + +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_FORMAT_C_H diff --git a/include/sdk_api/model.h b/include/sdk_api/model.h new file mode 100644 index 00000000..4be9b457 --- /dev/null +++ b/include/sdk_api/model.h @@ -0,0 +1,138 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_MODEL_C_H +#define MINDSPORE_INCLUDE_C_API_MODEL_C_H + +#include "mindspore/tensor.h" +#include "mindspore/context.h" +#include "mindspore/status.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *OH_AI_ModelHandle; + +typedef struct OH_AI_TensorHandleArray { + size_t handle_num; + OH_AI_TensorHandle *handle_list; +} OH_AI_TensorHandleArray; + +#define OH_AI_MAX_SHAPE_NUM 32 +typedef struct OH_AI_ShapeInfo { + size_t shape_num; + int64_t shape[OH_AI_MAX_SHAPE_NUM]; +} OH_AI_ShapeInfo; + +typedef struct OH_AI_CallBackParam { + char *node_name; + char *node_type; +} OH_AI_CallBackParam; + +typedef bool (*OH_AI_KernelCallBack)(const OH_AI_TensorHandleArray inputs, const OH_AI_TensorHandleArray outputs, + const OH_AI_CallBackParam kernel_Info); + +/// \brief Create a model object. +/// +/// \return Model object handle. +OH_AI_API OH_AI_ModelHandle OH_AI_ModelCreate(); + +/// \brief Destroy the model object. +/// +/// \param[in] model Model object handle address. +OH_AI_API void OH_AI_ModelDestroy(OH_AI_ModelHandle *model); + +/// \brief Build the model from model file buffer so that it can run on a device. +/// +/// \param[in] model Model object handle. +/// \param[in] model_data Define the buffer read from a model file. +/// \param[in] data_size Define bytes number of model file buffer. +/// \param[in] model_type Define The type of model file. +/// \param[in] model_context Define the context used to store options during execution. +/// +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelBuild(OH_AI_ModelHandle model, const void *model_data, size_t data_size, + OH_AI_ModelType model_type, const OH_AI_ContextHandle model_context); + +/// \brief Load and build the model from model path so that it can run on a device. +/// +/// \param[in] model Model object handle. +/// \param[in] model_path Define the model file path. +/// \param[in] model_type Define The type of model file. +/// \param[in] model_context Define the context used to store options during execution. +/// +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelBuildFromFile(OH_AI_ModelHandle model, const char *model_path, + OH_AI_ModelType model_type, const OH_AI_ContextHandle model_context); + +/// \brief Resizes the shapes of inputs. +/// +/// \param[in] model Model object handle. +/// \param[in] inputs The array that includes all input tensor handles. +/// \param[in] shape_infos Defines the new shapes of inputs, should be consistent with inputs. +/// \param[in] shape_info_num The num of shape_infos. +/// +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelResize(OH_AI_ModelHandle model, const OH_AI_TensorHandleArray inputs, + OH_AI_ShapeInfo *shape_infos, size_t shape_info_num); + +/// \brief Inference model. +/// +/// \param[in] model Model object handle. +/// \param[in] inputs The array that includes all input tensor handles. +/// \param[out] outputs The array that includes all output tensor handles. +/// \param[in] before CallBack before predict. +/// \param[in] after CallBack after predict. +/// +/// \return OH_AI_Status. +OH_AI_API OH_AI_Status OH_AI_ModelPredict(OH_AI_ModelHandle model, const OH_AI_TensorHandleArray inputs, + OH_AI_TensorHandleArray *outputs, const OH_AI_KernelCallBack before, + const OH_AI_KernelCallBack after); + +/// \brief Obtains all input tensor handles of the model. +/// +/// \param[in] model Model object handle. +/// +/// \return The array that includes all input tensor handles. +OH_AI_API OH_AI_TensorHandleArray OH_AI_ModelGetInputs(const OH_AI_ModelHandle model); + +/// \brief Obtains all output tensor handles of the model. +/// +/// \param[in] model Model object handle. +/// +/// \return The array that includes all output tensor handles. +OH_AI_API OH_AI_TensorHandleArray OH_AI_ModelGetOutputs(const OH_AI_ModelHandle model); + +/// \brief Obtains the input tensor handle of the model by name. +/// +/// \param[in] model Model object handle. +/// \param[in] tensor_name The name of tensor. +/// +/// \return The input tensor handle with the given name, if the name is not found, an NULL is returned. +OH_AI_API OH_AI_TensorHandle OH_AI_ModelGetInputByTensorName(const OH_AI_ModelHandle model, const char *tensor_name); + +/// \brief Obtains the output tensor handle of the model by name. +/// +/// \param[in] model Model object handle. +/// \param[in] tensor_name The name of tensor. +/// +/// \return The output tensor handle with the given name, if the name is not found, an NULL is returned. +OH_AI_API OH_AI_TensorHandle OH_AI_ModelGetOutputByTensorName(const OH_AI_ModelHandle model, const char *tensor_name); + +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_MODEL_C_H diff --git a/include/sdk_api/status.h b/include/sdk_api/status.h new file mode 100644 index 00000000..102c7962 --- /dev/null +++ b/include/sdk_api/status.h @@ -0,0 +1,80 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_STATUS_C_H +#define MINDSPORE_INCLUDE_C_API_STATUS_C_H + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +enum OH_AI_CompCode { + OH_AI_COMPCODE_CORE = 0x00000000u, + OH_AI_COMPCODE_MD = 0x10000000u, + OH_AI_COMPCODE_ME = 0x20000000u, + OH_AI_COMPCODE_MC = 0x30000000u, + OH_AI_COMPCODE_LITE = 0xF0000000u, +}; + +typedef enum OH_AI_Status { + OH_AI_STATUS_SUCCESS = 0, + // Core + OH_AI_STATUS_CORE_FAILED = OH_AI_COMPCODE_CORE | 0x1, + + // Lite // Common error code, range: [-1, -100） + OH_AI_STATUS_LITE_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -1), /**< Common error code. */ + OH_AI_STATUS_LITE_NULLPTR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -2), /**< NULL pointer returned.*/ + OH_AI_STATUS_LITE_PARAM_INVALID = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -3), /**< Invalid parameter.*/ + OH_AI_STATUS_LITE_NO_CHANGE = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -4), /**< No change. */ + OH_AI_STATUS_LITE_SUCCESS_EXIT = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -5), /**< No error but exit. */ + OH_AI_STATUS_LITE_MEMORY_FAILED = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -6), /**< Fail to create memory. */ + OH_AI_STATUS_LITE_NOT_SUPPORT = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -7), /**< Fail to support. */ + OH_AI_STATUS_LITE_THREADPOOL_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -8), /**< Error occur in thread pool. */ + OH_AI_STATUS_LITE_UNINITIALIZED_OBJ = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -9), /**< Object is not initialized. */ + + // Executor error code, range: [-100,-200) + OH_AI_STATUS_LITE_OUT_OF_TENSOR_RANGE = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -100), /**< Failed to check range. */ + OH_AI_STATUS_LITE_INPUT_TENSOR_ERROR = + OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -101), /**< Failed to check input tensor. */ + OH_AI_STATUS_LITE_REENTRANT_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -102), /**< Exist executor running. */ + + // Graph error code, range: [-200,-300) + OH_AI_STATUS_LITE_GRAPH_FILE_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -200), /**< Failed to verify graph file. */ + + // Node error code, range: [-300,-400) + OH_AI_STATUS_LITE_NOT_FIND_OP = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -300), /**< Failed to find operator. */ + OH_AI_STATUS_LITE_INVALID_OP_NAME = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -301), /**< Invalid operator name. */ + OH_AI_STATUS_LITE_INVALID_OP_ATTR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -302), /**< Invalid operator attr. */ + OH_AI_STATUS_LITE_OP_EXECUTE_FAILURE = + OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -303), /**< Failed to execution operator. */ + + // Tensor error code, range: [-400,-500) + OH_AI_STATUS_LITE_FORMAT_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -400), /**< Failed to checking tensor format. */ + + // InferShape error code, range: [-500,-600) + OH_AI_STATUS_LITE_INFER_ERROR = OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -500), /**< Failed to infer shape. */ + OH_AI_STATUS_LITE_INFER_INVALID = + OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -501), /**< Invalid infer shape before runtime. */ + + // User input param error code, range: [-600, 700) + OH_AI_STATUS_LITE_INPUT_PARAM_INVALID = + OH_AI_COMPCODE_LITE | (0x0FFFFFFF & -600), /**< Invalid input param by user. */ +} OH_AI_Status; +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_STATUS_C_H diff --git a/include/sdk_api/tensor.h b/include/sdk_api/tensor.h new file mode 100644 index 00000000..f6ba02cd --- /dev/null +++ b/include/sdk_api/tensor.h @@ -0,0 +1,146 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_TENSOE_C_H +#define MINDSPORE_INCLUDE_C_API_TENSOE_C_H + +#include +#include "mindspore/types.h" +#include "mindspore/data_type.h" +#include "mindspore/format.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *OH_AI_TensorHandle; + +/// \brief Create a tensor object. +/// +/// \param[in] name The name of the tensor. +/// \param[in] type The data type of the tensor. +/// \param[in] shape The shape of the tensor. +/// \param[in] shape_num The num of the shape. +/// \param[in] data The data pointer that points to allocated memory. +/// \param[in] data_len The length of the memory, in bytes. +/// +/// \return Tensor object handle. +OH_AI_API OH_AI_TensorHandle OH_AI_TensorCreate(const char *name, OH_AI_DataType type, const int64_t *shape, + size_t shape_num, const void *data, size_t data_len); + +/// \brief Destroy the tensor object. +/// +/// \param[in] tensor Tensor object handle address. +OH_AI_API void OH_AI_TensorDestroy(OH_AI_TensorHandle *tensor); + +/// \brief Obtain a deep copy of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return Tensor object handle. +OH_AI_API OH_AI_TensorHandle OH_AI_TensorClone(OH_AI_TensorHandle tensor); + +/// \brief Set the name for the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// \param[in] name The name of the tensor. +OH_AI_API void OH_AI_TensorSetName(OH_AI_TensorHandle tensor, const char *name); + +/// \brief Obtain the name of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The name of the tensor. +OH_AI_API const char *OH_AI_TensorGetName(const OH_AI_TensorHandle tensor); + +/// \brief Set the data type for the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// \param[in] type The data type of the tensor. +OH_AI_API void OH_AI_TensorSetDataType(OH_AI_TensorHandle tensor, OH_AI_DataType type); + +/// \brief Obtain the data type of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The date type of the tensor. +OH_AI_API OH_AI_DataType OH_AI_TensorGetDataType(const OH_AI_TensorHandle tensor); + +/// \brief Set the shape for the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// \param[in] shape The shape array. +/// \param[in] shape_num Dimension of shape. +OH_AI_API void OH_AI_TensorSetShape(OH_AI_TensorHandle tensor, const int64_t *shape, size_t shape_num); + +/// \brief Obtain the shape of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// \param[out] shape_num Dimension of shape. +/// +/// \return The shape array of the tensor. +OH_AI_API const int64_t *OH_AI_TensorGetShape(const OH_AI_TensorHandle tensor, size_t *shape_num); + +/// \brief Set the format for the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// \param[in] format The format of the tensor. +OH_AI_API void OH_AI_TensorSetFormat(OH_AI_TensorHandle tensor, OH_AI_Format format); + +/// \brief Obtain the format of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The format of the tensor. +OH_AI_API OH_AI_Format OH_AI_TensorGetFormat(const OH_AI_TensorHandle tensor); + +/// \brief Obtain the data for the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// \param[in] data A pointer to the data of the tensor. +OH_AI_API void OH_AI_TensorSetData(OH_AI_TensorHandle tensor, void *data); + +/// \brief Obtain the data pointer of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The data pointer of the tensor. +OH_AI_API const void *OH_AI_TensorGetData(const OH_AI_TensorHandle tensor); + +/// \brief Obtain the mutable data pointer of the tensor. If the internal data is empty, it will allocate memory. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The data pointer of the tensor. +OH_AI_API void *OH_AI_TensorGetMutableData(const OH_AI_TensorHandle tensor); + +/// \brief Obtain the element number of the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The element number of the tensor. +OH_AI_API int64_t OH_AI_TensorGetElementNum(const OH_AI_TensorHandle tensor); + +/// \brief Obtain the data size fo the tensor. +/// +/// \param[in] tensor Tensor object handle. +/// +/// \return The data size of the tensor. +OH_AI_API size_t OH_AI_TensorGetDataSize(const OH_AI_TensorHandle tensor); + +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_TENSOE_C_H diff --git a/include/sdk_api/types.h b/include/sdk_api/types.h new file mode 100644 index 00000000..a39c6daa --- /dev/null +++ b/include/sdk_api/types.h @@ -0,0 +1,50 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_INCLUDE_C_API_TYPES_C_H +#define MINDSPORE_INCLUDE_C_API_TYPES_C_H + +#ifdef __cplusplus +extern "C" { +#endif + +#ifndef OH_AI_API +#ifdef _WIN32 +#define OH_AI_API __declspec(dllexport) +#else +#define OH_AI_API __attribute__((visibility("default"))) +#endif +#endif + +typedef enum OH_AI_ModelType { + OH_AI_MODELTYPE_MINDIR = 0, + // insert new data type here + OH_AI_MODELTYPE_INVALID = 0xFFFFFFFF +} OH_AI_ModelType; + +typedef enum OH_AI_DeviceType { + OH_AI_DEVICETYPE_CPU = 0, + OH_AI_DEVICETYPE_GPU, + OH_AI_DEVICETYPE_KIRIN_NPU, + // add new type here + // ohos-only device range: [60, 80) + OH_AI_DeviceType_NNRT = 60, + OH_AI_DEVICETYPE_INVALID = 100, +} OH_AI_DeviceType; + +#ifdef __cplusplus +} +#endif +#endif // MINDSPORE_INCLUDE_C_API_TYPES_C_H diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/BUILD.gn b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/BUILD.gn new file mode 100644 index 00000000..a1e7908e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/BUILD.gn @@ -0,0 +1,885 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import("//build/ohos.gni") + +config("nnacl_public_config") { + include_dirs = [ + "../", + "include/", + ] + + configs = [ "../../../../../../lite:disable_android" ] + + defines = [ "MS_COMPILE_OHOS" ] + cflags_c = [ + "-fomit-frame-pointer", + "-fstrict-aliasing", + "-Wno-ignored-qualifiers", + "-Wno-unknown-pragmas" + ] + if (target_cpu == "arm" || target_cpu == "arm64") { + defines += [ + "ENABLE_ARM", + "ENABLE_NEON", + ] + cflags_c += [ "-march=armv8.2-a+dotprod+fp16" ] + if (target_cpu == "arm") { + defines += [ "ENABLE_ARM32" ] + } else if (target_cpu == "arm64") { + defines += [ + "ENABLE_ARM64", + "ENABLE_FP16", + ] + cflags_c += [ "-flax-vector-conversions" ] + } + } else if (target_cpu == "x86_64") { + cflags_c += [ + "-mavx512f", + "-mavx", + "-mavx2", + "-mfma", + "-msse4.1", + "-fPIC", + ] + defines += [ + "ENABLE_SSE", + "ENABLE_AVX", + "ENABLE_AVX512", + ] + } +} + +ohos_source_set("nnacl_obj") { + deps = [ + ":nnacl_common", + ":nnacl_no_fast_math", + ] + part_name = "mindspore" + subsystem_name = "thirdparty" +} + +# list of ${NNACL_DIR}/*.c +common_sources = [ + "./common_func.c", + "./kernel.c", + "./nnacl_common.c", + "./nnacl_utils.c", + "./tensor_c_utils.c", + "./tensorlist_c_utils.c", +] + +# list of ${NNACL_DIR}/kernel/*.c +c_kernel_sources = [ + "kernel/activation.c", + "kernel/addn.c", + "kernel/arg_min_max.c", + "kernel/arithmetic.c", + "kernel/arithmetic_compare.c", + "kernel/arithmetic_self.c", + "kernel/biasadd.c", + "kernel/cast.c", + "kernel/clip.c", + "kernel/concat.c", + "kernel/convolution_1x1.c", + "kernel/convolution_base.c", + "kernel/convolution_delegate.c", + "kernel/convolution_depthwise_3x3.c", + "kernel/convolution_depthwise.c", + "kernel/convolution_depthwise_indirect.c", + "kernel/convolution_depthwise_sw_avx.c", + "kernel/convolution_depthwise_sw.c", + "kernel/convolution_im2col_arm32.c", + "kernel/convolution_im2col_arm64.c", + "kernel/convolution_im2col_avx512.c", + "kernel/convolution_im2col_avx.c", + "kernel/convolution_im2col_base.c", + "kernel/convolution_im2col.c", + "kernel/convolution_im2col_sse.c", + "kernel/convolution_slidewindow.c", + "kernel/convolution_sw_1x1.c", + "kernel/convolution_sw_arm64.c", + "kernel/convolution_sw_avx.c", + "kernel/convolution_winograd_arm32.c", + "kernel/convolution_winograd_arm64.c", + "kernel/convolution_winograd_avx.c", + "kernel/convolution_winograd_base.c", + "kernel/convolution_winograd.c", + "kernel/convolution_winograd_sse.c", + "kernel/crop.c", + "kernel/deconvolution.c", + "kernel/deconvolution_depthwise.c", + "kernel/deconvolution_winograd.c", + "kernel/default_kernel_base.c", + "kernel/exp.c", + "kernel/fill.c", + "kernel/fullconnection.c", + "kernel/gather.c", + "kernel/gather_d.c", + "kernel/group_convolution.c", + "kernel/group_norm.c", + "kernel/init_exec_env.c", + "kernel/init_vs_kernels.c", + "kernel/layer_norm.c", + "kernel/local_response_norm.c", + "kernel/log_softmax.c", + "kernel/matmul_arm32.c", + "kernel/matmul_arm64.c", + "kernel/matmul_avx512.c", + "kernel/matmul_avx.c", + "kernel/matmul_base.c", + "kernel/matmul.c", + "kernel/matmul_create.c", + "kernel/matmul_sse.c", + "kernel/nllloss.c", + "kernel/non_zero.c", + "kernel/one_hot.c", + "kernel/ones_like.c", + "kernel/pad.c", + "kernel/pooling.c", + "kernel/prior_box.c", + "kernel/ragged_range.c", + "kernel/range.c", + "kernel/rank.c", + "kernel/reduce.c", + "kernel/reshape.c", + "kernel/scale.c", + "kernel/shape.c", + "kernel/size.c", + "kernel/softmax.c", + "kernel/splice.c", + "kernel/stack.c", + "kernel/strided_slice.c", + "kernel/tile.c", + "kernel/transpose.c", + "kernel/tril.c", + "kernel/triu.c", + "kernel/unique.c", + "kernel/zeros_like.c", +] + +# list of ${NNACL_DIR}/experimental/*.c +experimental_kernel_sources = [ +] + +# list of ${NNACL_DIR}/base/*.c +base_kernel_sources = [ + "base/arithmetic_base.c", + "base/batch_to_space_base.c", + "base/broadcast_to.c", + "base/cast_base.c", + "base/concat_base.c", + "base/conv1x1_base.c", + "base/conv_common_base.c", + "base/crop_base.c", + "base/depth_to_space_base.c", + "base/fill_base.c", + "base/format_transpose.c", + "base/gather_base.c", + "base/gather_d_base.c", + "base/minimal_filtering_generator.c", + "base/scatter_nd_binary.c", + "base/slice_base.c", + "base/space_to_depth_base.c", + "base/split_base.c", + "base/split_with_over_lap_base.c", + "base/stack_base.c", + "base/tile_base.c", + "base/transpose_base.c", + "base/unsorted_segment_sum_base.c", + "base/unstack_base.c", +] + +# list of ${NNACL_DIR}/fp32/*.c +fp32_kernel_sources = [ + "fp32/activation_fp32.c", + "fp32/adam_fp32.c", + "fp32/adder_fp32.c", + "fp32/add_fp32.c", + "fp32/arg_min_max_fp32.c", + "fp32/arithmetic_compare_fp32.c", + "fp32/arithmetic_fp32.c", + "fp32/arithmetic_self_fp32.c", + "fp32/attention_fp32.c", + "fp32/batchnorm_fp32.c", + "fp32/bce_with_loigts_loss_fp32.c", + "fp32/bias_add.c", + "fp32/cdist_fp32.c", + "fp32/common_func_fp32.c", + "fp32/conv_1x1_avx_fp32.c", + "fp32/conv_common_fp32.c", + "fp32/conv_depthwise_avx_fp32.c", + "fp32/conv_depthwise_fp32.c", + "fp32/conv_im2col_avx512_fp32.c", + "fp32/conv_im2col_fp32.c", + "fp32/conv_sw_arm64_fp32.c", + "fp32/conv_sw_avx_fp32.c", + "fp32/conv_winograd_fp32.c", + "fp32/crop_fp32.c", + "fp32/cumsum_fp32.c", + "fp32/custom_gru_fp32.c", + "fp32/deconv_fp32.c", + "fp32/deconv_winograd_fp32.c", + "fp32/detection_post_process_fp32.c", + "fp32/div_fp32.c", + "fp32/dropout_fp32.c", + "fp32/embedding_lookup_fp32.c", + "fp32/exp_fp32.c", + "fp32/gatherNd_fp32.c", + "fp32/group_norm_fp32.c", + "fp32/gru_fp32.c", + "fp32/instance_norm_fp32.c", + "fp32/invert_permutation_fp32.c", + "fp32/l2_norm_fp32.c", + "fp32/layer_norm_fp32.c", + "fp32/local_response_norm_fp32.c", + "fp32/log_softmax_fp32.c", + "fp32/lstm_fp32.c", + "fp32/matmul_avx512_fp32.c", + "fp32/matmul_avx512_mask_fp32.c", + "fp32/matmul_avx_fp32.c", + "fp32/matmul_fp32.c", + "fp32/mul_fp32.c", + "fp32/nllloss_fp32.c", + "fp32/one_hot_fp32.c", + "fp32/pack_fp32.c", + "fp32/pack_fp32_opt.c", + "fp32/pad_fp32.c", + "fp32/pooling_fp32.c", + "fp32/power_fp32.c", + "fp32/prelu_fp32.c", + "fp32/ragged_range_fp32.c", + "fp32/reduce_fp32.c", + "fp32/resize_fp32.c", + "fp32/reverse_fp32.c", + "fp32/reverse_sequence_fp32.c", + "fp32/rmsprop_fp32.c", + "fp32/roi_pooling_fp32.c", + "fp32/scale_fp32.c", + "fp32/softmax_fp32.c", + "fp32/softmax_grad_fusion_fp32.c", + "fp32/space_to_batch_fp32.c", + "fp32/sparse_to_dense_fp32.c", + "fp32/splice_fp32.c", + "fp32/squared_difference.c", + "fp32/strided_slice_fp32.c", + "fp32/sub_fp32.c", + "fp32/topk_fp32.c", + "fp32/transpose_fp32.c", + "fp32/transpose_server_fp32.c", + "fp32/triu_tril_fp32.c", + "fp32/unique_fp32.c", + "fp32/where_fp32.c", + "fp32/winograd_avx.c", + "fp32/winograd_transform.c", + "fp32/winograd_utils.c", + "fp32/online_fusion/cast_gather_reduce_fp32.c", + "fp32/online_fusion/reduce_concat_fp32.c", + "fp32/online_fusion/split_reduce_concat_fp32.c", +] + +no_fast_math_fp32_kernel_sources = [ + "fp32/resize_fp32.c", +] + +avx512_fp32_kernel_sources = [ + "fp32/matmul_avx512_fp32.c", + "fp32/conv_im2col_avx512_fp32.c", + "fp32/matmul_avx512_mask_fp32.c" +] + +avx_fp32_kernel_sources = [ + "fp32/conv_sw_avx_fp32.c", + "fp32/conv_1x1_avx_fp32.c", + "fp32/matmul_avx_fp32.c", + "fp32/conv_depthwise_avx_fp32.c", +] + +arm64_fp32_kernel_sources = [ + "fp32/conv_sw_arm64_fp32.c", +] + +fp32_sparse_kernel_sources = [ + "fp32_sparse/matmul_sparse_x1_fp32.c", +] + +# list of ${NNACL_DIR}/fp32_grad/*.c +fp32_grad_kernel_sources = [ + "fp32_grad/activation_grad_fp32.c", + "fp32_grad/apply_proximal_adagrad_fp32.c", + "fp32_grad/apply_proximal_gradient_descent_fp32.c", + "fp32_grad/arithmetic_grad.c", + "fp32_grad/batch_norm_grad.c", + "fp32_grad/binary_cross_entropy.c", + "fp32_grad/binary_cross_entropy_grad.c", + "fp32_grad/convolution_grad_filter.c", + "fp32_grad/convolution_grad_input.c", + "fp32_grad/dropout_grad.c", + "fp32_grad/gemm.c", + "fp32_grad/layernorm_grad.c", + "fp32_grad/lstm_grad_fp32.c", + "fp32_grad/maxpool_grad_grad.c", + "fp32_grad/nllloss_grad_fp32.c", + "fp32_grad/pack_ext.c", + "fp32_grad/pooling_grad.c", + "fp32_grad/reduce_grad.c", + "fp32_grad/resize_grad.c", + "fp32_grad/softmax_cross_entropy_with_logits.c", + "fp32_grad/softmax_grad.c", + "fp32_grad/softmax_grad_utils.c", + "fp32_grad/strided_slice_grad.c", +] + +fp16_kernel_sources = [ + "fp16/activation_fp16.c", + "fp16/arg_min_max_fp16.c", + "fp16/arithmetic_fp16.c", + "fp16/arithmetic_self_fp16.c", + "fp16/batchnorm_fp16.c", + "fp16/common_func_fp16.c", + "fp16/conv_depthwise_fp16.c", + "fp16/conv_fp16.c", + "fp16/crop_fp16.c", + "fp16/custom_gru_fp16.c", + "fp16/deconv_fp16.c", + "fp16/deconv_winograd_fp16.c", + "fp16/dynamic_quant_fp16.c", + "fp16/exp_fp16.c", + "fp16/fill_fp16.c", + "fp16/gru_fp16.c", + "fp16/instance_norm_fp16.c", + "fp16/layer_norm_fp16.c", + "fp16/log_softmax_fp16.c", + "fp16/lstm_fp16.c", + "fp16/matmul_fp16.c", + "fp16/matrix_fp16.c", + "fp16/one_hot_fp16.c", + "fp16/pack_fp16.c", + "fp16/pad_fp16.c", + "fp16/pooling_fp16.c", + "fp16/power_fp16.c", + "fp16/prelu_fp16.c", + "fp16/quant_dtype_cast_fp16.c", + "fp16/ragged_range_fp16.c", + "fp16/reduce_fp16.c", + "fp16/resize_fp16.c", + "fp16/scale_fp16.c", + "fp16/softmax_fp16.c", + "fp16/sparse_to_dense_fp16.c", + "fp16/splice_fp16.c", + "fp16/topk_fp16.c", + "fp16/transpose_fp16.c", + "fp16/unique_fp16.c", + "fp16/utils_fp16.c", + "fp16/where_fp16.c", + "fp16/winograd_transform_fp16.c", + "fp16/winograd_utils_fp16.c", + "kernel/f16/arithmetic_compare_f16.c", + "kernel/f16/arithmetic_f16.c", + "kernel/f16/concat_f16.c", + "kernel/f16/reduce_f16.c", + "kernel/f16/stack_f16.c", +] + +fp16_grad_sources = [ + "fp16_grad/activation_grad_fp16.c", + "fp16_grad/arithmetic_grad.c", + "fp16_grad/arithmetic_self_grad.c", + "fp16_grad/batch_norm.c", + "fp16_grad/convolution_grad_filter.c", + "fp16_grad/convolution_grad_input.c", + "fp16_grad/dropout_grad.c", + "fp16_grad/gemm_fp16.c", + "fp16_grad/layernorm_grad.c", + "fp16_grad/pack_fp16_ext.c", + "fp16_grad/pooling_grad.c", + "fp16_grad/resize_grad.c", + "fp16_grad/strided_slice_grad.c", + "fp16_grad/unsorted_segment_sum.c", +] + +int8_kernel_sources = [ + "int8/add_int8.c", + "int8/arg_min_max_int8.c", + "int8/arithmetic_int8.c", + "int8/arithmetic_self_int8.c", + "int8/batchnorm_int8.c", + "int8/batch_to_space_int8.c", + "int8/common_func_int8.c", + "int8/concat_int8.c", + "int8/conv1x1_int8.c", + "int8/conv3x3_int8.c", + "int8/conv_depthwise_int8.c", + "int8/conv_int8.c", + "int8/crop_int8.c", + "int8/deconv_int8.c", + "int8/depth_to_space_int8.c", + "int8/div_int8.c", + "int8/dynamic_gather_int8.c", + "int8/dynamic_matmul_int8.c", + "int8/dynamic_quant_int8.c", + "int8/fixed_point.c", + "int8/gather_int8.c", + "int8/gatherNd_int8.c", + "int8/hswish_int8.c", + "int8/l2_norm_int8.c", + "int8/layer_norm_int8.c", + "int8/leaky_relu_int8.c", + "int8/matmul_int8.c", + "int8/mul_int8.c", + "int8/pack_int8.c", + "int8/pad_int8.c", + "int8/pooling_int8.c", + "int8/power_int8.c", + "int8/quant_dtype_cast_int8.c", + "int8/quantize.c", + "int8/reduce_int8.c", + "int8/relux_int8.c", + "int8/reshape_int8.c", + "int8/resize_int8.c", + "int8/scale_int8.c", + "int8/sigmoid_int8.c", + "int8/slice_int8.c", + "int8/softmax_int8.c", + "int8/space_to_batch_int8.c", + "int8/split_int8.c", + "int8/squeeze_int8.c", + "int8/sub_int8.c", + "int8/tanh_int8.c", + "int8/topk_int8.c", + "int8/transpose_int8.c", + "int8/unsqueeze_int8.c", +] + +# list of ${NNACL_DIR}/infer/*.c +infer_shape_sources = [ + "infer/activation_grad_infer.c", + "infer/adam_infer.c", + "infer/adam_weight_decay_infer.c", + "infer/addn_infer.c", + "infer/add_sub_grad_infer.c", + "infer/affine_infer.c", + "infer/all_gather_infer.c", + "infer/apply_momentum_infer.c", + "infer/argmin_max_infer.c", + "infer/arithmetic_compare_infer.c", + "infer/arithmetic_grad_infer.c", + "infer/arithmetic_infer.c", + "infer/assert_op_infer.c", + "infer/assign_add_infer.c", + "infer/assign_infer.c", + "infer/attention_infer.c", + "infer/audio_spectrogram_infer.c", + "infer/batch_to_space_infer.c", + "infer/bias_grad_infer.c", + "infer/binary_cross_entropy_infer.c", + "infer/bn_grad_infer.c", + "infer/broadcast_to_infer.c", + "infer/cast_gather_reduce_infer.c", + "infer/cast_infer.c", + "infer/common_infer.c", + "infer/concat_infer.c", + "infer/constant_of_shape_infer.c", + "infer/conv2d_grad_filter_infer.c", + "infer/conv2d_grad_input_infer.c", + "infer/conv2d_infer.c", + "infer/crop_and_resize_infer.c", + "infer/crop_infer.c", + "infer/cumsum_infer.c", + "infer/custom_gru_infer.c", + "infer/decoder_layer_infer.c", + "infer/deconv2d_infer.c", + "infer/depth_to_space_infer.c", + "infer/depthwise_conv2d_infer.c", + "infer/detection_post_process_infer.c", + "infer/dropout_grad_infer.c", + "infer/dropout_infer.c", + "infer/dynamic_quant_infer.c", + "infer/embedding_lookup_infer.c", + "infer/encoder_layer_infer.c", + "infer/expand_dims_infer.c", + "infer/fft_imag_infer.c", + "infer/fft_real_infer.c", + "infer/fill_infer.c", + "infer/flatten_grad_infer.c", + "infer/flatten_infer.c", + "infer/format_transpose_infer.c", + "infer/fse_decoder_infer.c", + "infer/full_connection_infer.c", + "infer/fused_batchnorm_infer.c", + "infer/gather_d_infer.c", + "infer/gather_infer.c", + "infer/gather_nd_infer.c", + "infer/glu_infer.c", + "infer/group_conv2d_grad_input_infer.c", + "infer/group_norm_infer.c", + "infer/gru_infer.c", + "infer/infer_register.c", + "infer/instance_norm_infer.c", + "infer/invert_permutation_infer.c", + "infer/isfinite_infer.c", + "infer/layer_norm_grad_infer.c", + "infer/layer_norm_infer.c", + "infer/lin_space_infer.c", + "infer/log_softmax_infer.c", + "infer/lstm_grad_data_infer.c", + "infer/lstm_grad_infer.c", + "infer/lstm_grad_weight_infer.c", + "infer/lstm_infer.c", + "infer/matmul_infer.c", + "infer/max_min_grad_infer.c", + "infer/mfcc_infer.c", + "infer/nllloss_grad_infer.c", + "infer/nllloss_infer.c", + "infer/non_max_suppression_infer.c", + "infer/one_hot_infer.c", + "infer/pad_infer.c", + "infer/partial_infer.c", + "infer/pooling_grad_infer.c", + "infer/pooling_infer.c", + "infer/power_infer.c", + "infer/prior_box_infer.c", + "infer/quant_dtype_cast_infer.c", + "infer/ragged_range_infer.c", + "infer/random_normal_infer.c", + "infer/random_standard_normal_infer.c", + "infer/range_infer.c", + "infer/rank_infer.c", + "infer/reduce_concat_infer.c", + "infer/reduce_infer.c", + "infer/reduce_scatter_infer.c", + "infer/reshape_infer.c", + "infer/resize_grad_infer.c", + "infer/resize_infer.c", + "infer/rfft_infer.c", + "infer/roi_pooling_infer.c", + "infer/scatter_nd_infer.c", + "infer/scatter_nd_update_infer.c", + "infer/select_infer.c", + "infer/sgd_infer.c", + "infer/shape_fusion_infer.c", + "infer/shape_infer.c", + "infer/size_infer.c", + "infer/slice_infer.c", + "infer/softmax_cross_entropy_infer.c", + "infer/softmax_infer.c", + "infer/space_to_batch_infer.c", + "infer/space_to_batch_nd_infer.c", + "infer/space_to_depth_infer.c", + "infer/sparse_fill_empty_rows_infer.c", + "infer/sparse_reshape_infer.c", + "infer/sparse_segment_sum_infer.c", + "infer/sparse_softmax_cross_entropy_with_logits_infer.c", + "infer/sparse_to_dense_infer.c", + "infer/splice_infer.c", + "infer/split_infer.c", + "infer/split_reduce_concat_infer.c", + "infer/split_with_over_lap_infer.c", + "infer/squeeze_infer.c", + "infer/stack_infer.c", + "infer/strided_slice_grad_infer.c", + "infer/strided_slice_infer.c", + "infer/tile_infer.c", + "infer/topk_infer.c", + "infer/transpose_infer.c", + "infer/triu_tril_infer.c", + "infer/uniform_real_infer.c", + "infer/unique_infer.c", + "infer/unsorted_segment_sum_infer.c", + "infer/unsqueeze_infer.c", + "infer/unstack_infer.c", + "infer/where_infer.c", +] + +infer_string_sources = [ + "infer/string/custom_extract_features_infer.c", + "infer/string/custom_normalize_infer.c", + "infer/string/custom_predict_infer.c", + "infer/string/hashtable_lookup_infer.c", + "infer/string/lsh_projection_infer.c", + "infer/string/skip_gram_infer.c", +] + +infer_control_sources = [ + "infer/control/tensor_array_infer.c", + "infer/control/tensor_array_read_infer.c", + "infer/control/tensor_array_write_infer.c", + "infer/control/tensorlist_fromtensor_infer.c", + "infer/control/tensorlist_getitem_infer.c", + "infer/control/tensorlist_reserve_infer.c", + "infer/control/tensorlist_setitem_infer.c", + "infer/control/tensorlist_stack_infer.c", +] + +arm64_assembly_sources = [ + "assembly/arm64/AdderFp32.S", + "assembly/arm64/BigMatmulFp32Opt.S", + "assembly/arm64/ConvDw3x3Fp32Corner.S", + "assembly/arm64/ConvDw3x3Fp32Horizontal.S", + "assembly/arm64/ConvDw3x3Fp32Stride1.S", + "assembly/arm64/ConvDw3x3Fp32Stride2.S", + "assembly/arm64/ConvDw3x3Fp32Vertical.S", + "assembly/arm64/ConvDw3x3Int8Corner.S", + "assembly/arm64/ConvDw3x3Int8Horizontal.S", + "assembly/arm64/ConvDw3x3Int8.S", + "assembly/arm64/ConvDw3x3Int8Stride2.S", + "assembly/arm64/ConvDw3x3Int8Vertical.S", + "assembly/arm64/ConvDw3x3Line.S", + "assembly/arm64/ConvDwFp32Border.S", + "assembly/arm64/ConvDwFp32Center.S", + "assembly/arm64/ConvDwFp32Indirect3x3.S", + "assembly/arm64/ConvDwFp32Indirect5x5.S", + "assembly/arm64/ConvDwFp32Row.S", + "assembly/arm64/ConvDwInt8Center.S", + "assembly/arm64/ConvDwInt8PostAlign4PerChannel.S", + "assembly/arm64/ConvDwInt8PostAlign4.S", + "assembly/arm64/ConvDwInt8Row.S", + "assembly/arm64/ConvFp32Center.S", + "assembly/arm64/ConvSW1x16Kernel.S", + "assembly/arm64/ConvSW1x8Kernel.S", + "assembly/arm64/ConvSW2x16Kernel.S", + "assembly/arm64/ConvSW2x8Kernel.S", + "assembly/arm64/ConvSW3x16Kernel.S", + "assembly/arm64/ConvSW3x8Kernel.S", + "assembly/arm64/ConvSW4x16Kernel.S", + "assembly/arm64/ConvSW4x8Kernel.S", + "assembly/arm64/ConvSW5x16Kernel.S", + "assembly/arm64/ConvSW5x8Kernel.S", + "assembly/arm64/DeconvDwFp32Border.S", + "assembly/arm64/DeconvDwFp32Center.S", + "assembly/arm64/DeconvDwInt8Center.S", + "assembly/arm64/DeconvDwInt8Post.S", + "assembly/arm64/DynamicGatherArm64.S", + "assembly/arm64/IndirectGemmInt16to32_8x4.S", + "assembly/arm64/MatmulFp32OptRow12.S", + "assembly/arm64/MatmulFp32OptRow4.S", + "assembly/arm64/MatmulFp32OptRow8.S", + "assembly/arm64/MatmulFp32Opt.S", + "assembly/arm64/MatmulFp32.S", + "assembly/arm64/MatmulInt8Opt.S", + "assembly/arm64/MatmulInt8.S", + "assembly/arm64/MatmulR4Int8.S", + "assembly/arm64/MatmulWinogradFp32.S", + "assembly/arm64/MatVecMulFp32.S", + "assembly/arm64/MatVecMulPackFp32.S", + "assembly/arm64/PostFuncBiasReluC4.S", + "assembly/arm64/PostFuncBiasReluC8.S", + "assembly/arm64/PostFuncInt8C4Neon64.S", + "assembly/arm64/PreSum4x16Int8Peroc.S", + "assembly/arm64/PreSum4x16Int8Pert.S", + "assembly/arm64/SPMM8x8Fp32.S", + "assembly/arm64/TiledC4MatmulFp32.S", + "assembly/arm64/WinogradTransLeft.S", + "assembly/arm64/WinogradTransRight.S", +] + +arm32_assembly_sources = [ + "assembly/arm32/ConvDw3x3Int8BorderPixel.S", + "assembly/arm32/ConvDwFp32Border.S", + "assembly/arm32/ConvDwFp32Center.S", + "assembly/arm32/ConvDwFp32Row.S", + "assembly/arm32/ConvDwInt8Center.S", + "assembly/arm32/ConvDwInt8PostAlign4PerChannel.S", + "assembly/arm32/ConvDwInt8PostAlign4.S", + "assembly/arm32/ConvDwInt8Row.S", + "assembly/arm32/DeconvDwFp32Center.S", + "assembly/arm32/DeconvDwInt8Center.S", + "assembly/arm32/DeconvDwInt8Post.S", + "assembly/arm32/IndirectGemmInt16to32_8x4.S", + "assembly/arm32/IndirectGemmInt8_2x4.S", + "assembly/arm32/MatmulFp32Opt12x4.S", + "assembly/arm32/MatmulFp32Opt.S", + "assembly/arm32/MatmulFp32.S", + "assembly/arm32/MatmulInt8Opt.S", + "assembly/arm32/MatmulInt8.S", + "assembly/arm32/MatmulWinogradFp32.S", + "assembly/arm32/MatVecMulFp32.S", + "assembly/arm32/PostFuncBiasReluC4.S", + "assembly/arm32/PostFuncBiasReluC8.S", + "assembly/arm32/PreSum4x16Int8Peroc.S", + "assembly/arm32/PreSum4x16Int8Pert.S", + "assembly/arm32/TiledC4MatmulFp32.S", + "assembly/arm32/WinogradTransLeft.S", + "assembly/arm32/WinogradTransRight.S", +] + +fp16_assembly_sources = [ + "assembly/fp16/CalculateMinMaxFp16Count8.S", + "assembly/fp16/ConvDwFp16Border.S", + "assembly/fp16/ConvDwFp16Center.S", + "assembly/fp16/ConvDwFp16Row.S", + "assembly/fp16/DeconvDwFp16Border.S", + "assembly/fp16/DeconvDwFp16Center.S", + "assembly/fp16/DynamicGatherArm64ForFp16.S", + "assembly/fp16/Float16ToFloat32.S", + "assembly/fp16/Float32ToFloat16.S", + "assembly/fp16/Matmul12X16Fp16.S", + "assembly/fp16/MatmulBaseFp16Neon.S", + "assembly/fp16/MatmulFp16Opt.S", + "assembly/fp16/MatmulFp16.S", + "assembly/fp16/MatmulWinogradFp16.S", + "assembly/fp16/MatVecMulFp16.S", + "assembly/fp16/PostFuncBiasReluC4Fp16.S", + "assembly/fp16/PostFuncBiasReluC8Fp16.S", + "assembly/fp16/TiledC4MatmulFp16.S", + "assembly/fp16/VecMatmulFp16.S", + "assembly/fp16/WinogradTransLeftFp16.S", + "assembly/fp16/WinogradTransRightFp16.S", +] + +optimizing_assembly_sources = [ + "assembly/opt/DynamicMatmulSdot4x4x16AIWIForFp16.S", + "assembly/opt/DynamicMatmulSdot4x4x16AIWI.S", + "assembly/opt/MatmulDpInt8Opt.S", + "assembly/opt/MatmulDpInt8.S", + "assembly/opt/MatmulOptR4Int8.S", +] + +sse_avx_avx512_sources = [ + "intrinsics/avx/DeconvMatMulAvx.c", + "intrinsics/avx/PostFuncBiasReluC8.c", + "intrinsics/avx/TiledC8MatMulFp32.c", + "intrinsics/avx/WinogradPostFuncBiasReluC8.c", + "intrinsics/avx/WinogradTransAvx.c", + "intrinsics/avx/common_utils.c", + "intrinsics/ms_simd_cpu_info.c", + "intrinsics/sse/ConvDwFp32IndirectRow.c", + "intrinsics/sse/ConvDwFp32Row_sse.c", + "intrinsics/sse/DepthwiseFp32_Sse.c", + "intrinsics/sse/MatMul_Sse.c", + "intrinsics/sse/PostFuncBiasReluC8.c", + "intrinsics/sse/TiledC4MatMulFp32.c", + "intrinsics/sse/WinogradPostFuncBiasReluC4.c", + "intrinsics/sse/WinogradTrans.c", + "assembly/avx/ConvDwFp32Avx3x3.S", + "assembly/avx/ConvDwFp32BorderAvx.S", + "assembly/avx/ConvDwFp32RowAvx.S", + "assembly/avx/ConvDwFp32RowOptAVX.S", + "assembly/avx/MatmulAvx.S", +] + +gemm_avx512_kernel_sources = [ + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_10x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_10x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_11x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_11x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_12x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_12x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_1x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_1x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_1x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_1x64_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_1x80_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_1x96_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_2x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_2x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_2x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_2x64_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_2x80_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_2x96_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_3x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_3x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_3x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_3x64_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_3x80_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_3x96_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_4x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_4x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_4x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_4x64_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_4x80_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_4x96_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_5x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_5x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_5x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_5x64_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_5x80_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_6x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_6x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_6x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_6x64_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_7x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_7x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_7x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_8x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_8x32_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_8x48_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_9x16_kernel_nhwc_fp32.c", + "experimental/HPC-generator/gemm_avx512/nnacl_gemm_avx512_9x32_kernel_nhwc_fp32.c", +] + +fp32_kernel_sources -= no_fast_math_fp32_kernel_sources +fp32_kernel_sources -= avx_fp32_kernel_sources +fp32_kernel_sources -= avx512_fp32_kernel_sources +fp32_kernel_sources -= arm64_fp32_kernel_sources + +# source files on all target +nnacl_sources = common_sources +nnacl_sources += base_kernel_sources +nnacl_sources += c_kernel_sources +nnacl_sources += experimental_kernel_sources +nnacl_sources += fp32_kernel_sources +nnacl_sources += fp32_sparse_kernel_sources +nnacl_sources += fp32_grad_kernel_sources +nnacl_sources += int8_kernel_sources +nnacl_sources += infer_shape_sources +nnacl_sources += infer_string_sources +nnacl_sources += infer_control_sources + +# source files on arm32 +arm_only_sources = arm32_assembly_sources + +# source files on arm64 +arm64_only_sources = fp16_kernel_sources +arm64_only_sources += fp16_grad_sources +arm64_only_sources += fp16_assembly_sources +arm64_only_sources += arm64_assembly_sources +arm64_only_sources += optimizing_assembly_sources +arm64_only_sources += arm64_fp32_kernel_sources + +# sources files on x86_64 +x86_64_only_sources = sse_avx_avx512_sources +x86_64_only_sources += avx_fp32_kernel_sources +x86_64_only_sources += avx512_fp32_kernel_sources +x86_64_only_sources += gemm_avx512_kernel_sources + +if (target_cpu == "arm") { + nnacl_sources += arm_only_sources + not_needed(arm64_only_sources) + not_needed(x86_64_only_sources) +} else if (target_cpu == "arm64") { + nnacl_sources += arm64_only_sources + not_needed(arm_only_sources) + not_needed(x86_64_only_sources) +} else if (target_cpu == "x86_64") { + nnacl_sources += x86_64_only_sources + not_needed(arm_only_sources) + not_needed(arm64_only_sources) +} + +ohos_source_set("nnacl_common") { + configs = [ ":nnacl_public_config" ] + cflags_c = [ "-ffast-math" ] + sources = nnacl_sources + part_name = "mindspore" + subsystem_name = "thirdparty" +} + +ohos_source_set("nnacl_no_fast_math") { + configs = [ ":nnacl_public_config" ] + cflags_c = [ "-fno-fast-math" ] + sources = no_fast_math_fp32_kernel_sources + part_name = "mindspore" + subsystem_name = "thirdparty" +} diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp16_grad/activation_grad_fp16.c b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp16_grad/activation_grad_fp16.c index 11499071..ad32339a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp16_grad/activation_grad_fp16.c +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp16_grad/activation_grad_fp16.c @@ -131,7 +131,7 @@ int EluFp16Grad(const float16_t *src0, const float16_t *src1, int length, float1 uint16x4_t mask_4 = vcgt_f16(src1_4, zero_4); float32x4_t tmp; simd_exp128(vcvt_f32_f16(src1_4), (float *)&tmp); - uint16x4_t expm1_4 = vsub_f16(vcvt_f16_f32(tmp), one_4); + float16x4_t expm1_4 = vsub_f16(vcvt_f16_f32(tmp), one_4); float16x4_t dst_4 = vbsl_f16(mask_4, src0_4, vmul_f16(alpha_4, vmul_f16(expm1_4, src0_4))); vst1_f16(dst + i, dst_4); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_fp32_simd.h new file mode 100644 index 00000000..843733ff --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_ACTIVATION_FP32_SIMD_H_ +#define NNACL_ACTIVATION_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/activation_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/activation_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/activation_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/activation_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_grad_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_grad_simd.h new file mode 100644 index 00000000..c4b9d684 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/activation_grad_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_ACTIVATION_GRAD_SIMD_H_ +#define NNACL_ACTIVATION_GRAD_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/activation_grad_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/activation_grad_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/activation_grad_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/activation_grad_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/adam_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/adam_fp32_simd.h new file mode 100644 index 00000000..1c7f107a --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/adam_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_ADAM_FP32_SIMD_H_ +#define NNACL_ADAM_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/adam_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/adam_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/adam_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/adam_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/add_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/add_fp32_simd.h new file mode 100644 index 00000000..46970d78 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/add_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_ADD_FP32_SIMD_H_ +#define NNACL_ADD_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/add_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/add_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/add_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/add_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_adagrad_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_adagrad_fp32_simd.h new file mode 100644 index 00000000..749c8dc2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_adagrad_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_APPLY_PROXIMAL_ADAGRAD_FP32_SIMD_H_ +#define NNACL_APPLY_PROXIMAL_ADAGRAD_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/apply_proximal_adagrad_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/apply_proximal_adagrad_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/apply_proximal_adagrad_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/apply_proximal_adagrad_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_gradient_descent_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_gradient_descent_fp32_simd.h new file mode 100644 index 00000000..3d3fe4dc --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/apply_proximal_gradient_descent_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_APPLY_PROXIMAL_GRADIENT_DESCENT_FP32_SIMD_H_ +#define NNACL_APPLY_PROXIMAL_GRADIENT_DESCENT_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/apply_proximal_gradient_descent_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/apply_proximal_gradient_descent_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/apply_proximal_gradient_descent_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/apply_proximal_gradient_descent_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_fp32_simd.h new file mode 100644 index 00000000..4d9bcb5b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_ARITHMETIC_FP32_SIMD_H_ +#define NNACL_ARITHMETIC_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/arithmetic_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/arithmetic_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/arithmetic_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/arithmetic_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_self_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_self_fp32_simd.h new file mode 100644 index 00000000..00c2a2e8 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/arithmetic_self_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_ARITHMETIC_SELF_FP32_SIMD_H_ +#define NNACL_ARITHMETIC_SELF_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/arithmetic_self_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/arithmetic_self_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/arithmetic_self_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/arithmetic_self_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_fp32_avx.h new file mode 100644 index 00000000..bd92d0ce --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_fp32_avx.h @@ -0,0 +1,285 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_AVX_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int Fp32ReluAVX(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_F32(SIMD_LD_F32(src + index), zero)); + } + return index; +} + +static inline int Int32ReluAVX(int index, const int32_t *src, int length, int32_t *dst) { + SIMD_EPI32 zero = SIMD_MOV_EPI32(0.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_MAX_EPI32(SIMD_LD_EPI32(src + index), zero)); + } + return index; +} + +static inline int Fp32Relu6AVX(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 six = SIMD_MOV_F32(6.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), zero, six)); + } + return index; +} + +static inline int Fp32ClipAVX(int index, const float *src, int length, float *dst, float min, float max) { + SIMD_F32 min_val = SIMD_MOV_F32(min); + SIMD_F32 max_val = SIMD_MOV_F32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int Int32ClipAVX(int index, const int32_t *src, int length, int32_t *dst, int min, int max) { + SIMD_EPI32 min_val = SIMD_MOV_EPI32(min); + SIMD_EPI32 max_val = SIMD_MOV_EPI32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_CLAMP_EPI32(SIMD_LD_EPI32(src + index), min_val, max_val)); + } + return index; +} + +static inline int LReluAVX(int index, const float *src, int length, float *dst, float alpha) { + SIMD_F32 alpha_data = SIMD_MOV_F32(alpha); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_MASK mask = SIMD_CMPGT_F32(SIMD_SET0_F32, src_tmp); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_F32(src_tmp, alpha_data), mask)); + } + return index; +} + +static inline int SigmoidAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, (SIMD_LD_F32(src + index))), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(SIMD_MOV_F32(1.0f), SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int TanhAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + index); + SIMD_ST_F32(dst + index, SIMD_TANH_F32(input)); + } + return index; +} + +static inline int SwishAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, src_value), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(src_value, SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int HSwishAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(SIMD_MUL_F32(src_value, relu6), 6)); + } + return index; +} + +static inline int HSigmoidAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(relu6, 6)); + } + return index; +} + +static inline int HardTanhNoLimitMinAVX(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MIN_N_F32(SIMD_LD_F32(src + index), max_val)); + } + return index; +} + +static inline int HardTanhNoLimitMaxAVX(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_N_F32(SIMD_LD_F32(src + index), min_val)); + } + return index; +} + +static inline int HardTanhLimitMinMaxAVX(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_N_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int GeluTanhApproximateAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 tmp1 = SIMD_FMADD_F32(SIMD_MUL_N_F32(in, 0.035677408136f), in, SIMD_MOV_F32(0.79788456080287f)); + SIMD_F32 tmp2 = SIMD_MUL_F32(tmp1, in); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_MUL_N_F32(in, 0.5f), SIMD_ADD_N_F32(SIMD_TANH_F32(tmp2), 1.0f))); + } + return index; +} + +static inline int GeluAVX(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_F32(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline SIMD_F32 SIMD_ERFCCHEBAVX(SIMD_F32 src) { + static const int ncof = 7; + const double cof[7] = {-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, -9.561514786808631e-3, + -9.46595344482036e-4, 3.66839497852761e-4, 4.2523324806907e-5}; + SIMD_F32 dst; + SIMD_F32 d = SIMD_SET0_F32; + SIMD_F32 dd = SIMD_SET0_F32; + SIMD_F32 t = SIMD_DIV_F32(SIMD_MOV_F32(2.0f), SIMD_ADD_F32(src, SIMD_MOV_F32(2.0f))); + SIMD_F32 ty = SIMD_SUB_F32(SIMD_MUL_F32(SIMD_MOV_F32(4.0f), t), SIMD_MOV_F32(2.0f)); + + for (int j = ncof - 1; j > 0; j--) { + SIMD_F32 tmp = d; + d = SIMD_SUB_F32(SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[j])), dd); + dd = tmp; + } + + dst = + SIMD_FMADD_F32(src, src, MS_FSMUL_F32(dd, SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[0])), SIMD_MOV_F32(0.5f))); + dst = SIMD_MUL_F32(t, SIMD_EXP_F32(SIMD_MUL_F32(SIMD_MOV_F32(-1.0f), dst))); + return dst; +} + +static inline SIMD_F32 SIMD_ERF_APPROXIMATEAVX(SIMD_F32 src) { + SIMD_F32 abs_src = SIMD_ABS_F32(src); + SIMD_F32 sign = SIMD_GETSIGN_F32(src); + SIMD_F32 dst = SIMD_ERFCCHEBAVX(abs_src); + return SIMD_MUL_F32(sign, SIMD_SUB_F32(SIMD_MOV_F32(1.0f), dst)); +} + +static inline int GeluErfAPPROXIMATEAVX(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_APPROXIMATEAVX(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline int EluAVX(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(src_tmp), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int CeluAVX(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(SIMD_DIV_N_F32(src_tmp, alpha)), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int HardShrinkAVX(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & in */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), src_t); + /* v1 = (in < -lamdb) & in */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), src_t); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftShrinkAVX(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & (in - lamdb) */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), SIMD_SUB_F32(src_t, pos_lamdb_v)); + /* v1 = (in < -lamdb) & (in + lamdb) */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), SIMD_ADD_F32(src_t, pos_lamdb_v)); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftsignFp32OptAVX(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 divisor_tmp = SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_ABS_F32(src_tmp)); + SIMD_ST_F32(dst + index, SIMD_DIV_F32(src_tmp, divisor_tmp)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_grad_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_grad_avx.h new file mode 100644 index 00000000..f3c0b393 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/activation_grad_avx.h @@ -0,0 +1,57 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NNACL_FP32_GRAD_ACTIVATION_GRAD_AVX_H_ +#define NNACL_FP32_GRAD_ACTIVATION_GRAD_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int ShrinkGradAVX(int index, const float *src0, const float *src1, + int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src0_t = SIMD_LD_F32(src0 + index); + SIMD_F32 src1_t = SIMD_LD_F32(src1 + index); + + SIMD_MASK mask0 = SIMD_CMPLE_F32(src1_t, pos_lamdb_v); + SIMD_MASK mask1 = SIMD_CMPLE_F32(neg_lamdb_v, src1_t); + SIMD_MASK mask = SIMD_AND_MASK(mask0, mask1); + + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src0_t, SIMD_MOV_F32(0.0f), mask)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/adam_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/adam_fp32_avx.h new file mode 100644 index 00000000..54743d80 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/adam_fp32_avx.h @@ -0,0 +1,210 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ADAM_FP32_AVX_H_ +#define MINDSPORE_NNACL_FP32_ADAM_FP32_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX +#ifdef MS_SIMD_AVX512 + static inline size_t AdamWeightDecayFp32AVX(size_t index, float *var, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + const float *gradient, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient + index); + + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_F32(var + index, var_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp16AVX(size_t index, float *var, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp32AVX(size_t index, float *var, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp16AVX(size_t index, int16_t *var16, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp32AVX(size_t index, int16_t *var16, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/add_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/add_fp32_avx.h new file mode 100644 index 00000000..f228644a --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/add_fp32_avx.h @@ -0,0 +1,124 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_ADD_AVX_H_ +#define MINDSPORE_NNACL_FP32_ADD_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int ElementOptAddAVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, + int size) { + SIMD_EPI32 vin0_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddReluAVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddRelu6AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddReluAVX(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddRelu6AVX(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_adagrad_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_adagrad_fp32_avx.h new file mode 100644 index 00000000..619e03bd --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_adagrad_fp32_avx.h @@ -0,0 +1,75 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_AVX_H_ +#define NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t ApplyProximalAdagradOptAVX( + int64_t index, float *var, float *accum, float lr, float l1, float l2, float *grad, int64_t size) { + SIMD_F32 lr_vec = SIMD_MOV_F32(lr); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec1 = SIMD_LD_F32(grad + index); + SIMD_F32 accum_vec = SIMD_LD_F32(accum + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + accum_vec = SIMD_FMADD_F32(tmp_vec1, tmp_vec1, accum_vec); + SIMD_F32 learn_rate_vec = SIMD_DIV_F32(lr_vec, SIMD_SQRT_F32(accum_vec)); + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(tmp_vec1, learn_rate_vec)); + SIMD_ST_F32(accum + index, accum_vec); + tmp_vec1 = SIMD_FMADD_F32(l2_vec, learn_rate_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + learn_rate_vec = SIMD_MUL_F32(learn_rate_vec, l1_vec); + learn_rate_vec = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), learn_rate_vec); + learn_rate_vec = SIMD_MAX_F32(learn_rate_vec, SIMD_MOV_F32(0.0f)); + learn_rate_vec = SIMD_DIV_F32(learn_rate_vec, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(learn_rate_vec, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_gradient_descent_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_gradient_descent_fp32_avx.h new file mode 100644 index 00000000..f02a4072 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/apply_proximal_gradient_descent_fp32_avx.h @@ -0,0 +1,71 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_AVX_H_ +#define NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t ApplyProximalGradientDescentOptAVX( + int64_t index, float *var, float alpha, float l1, float l2, float *delta, int64_t size) { + SIMD_F32 alpha_vec = SIMD_MOV_F32(alpha); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 delta_vec = SIMD_LD_F32(delta + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(delta_vec, alpha_vec)); + SIMD_F32 tmp_vec1 = SIMD_FMADD_F32(l2_vec, alpha_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + SIMD_F32 tmp_vec2 = SIMD_MUL_F32(alpha_vec, l1_vec); + tmp_vec2 = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), tmp_vec2); + tmp_vec2 = SIMD_MAX_F32(tmp_vec2, SIMD_MOV_F32(0.0f)); + tmp_vec2 = SIMD_DIV_F32(tmp_vec2, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(tmp_vec2, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_fp32_avx.h new file mode 100644 index 00000000..06b4d650 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_fp32_avx.h @@ -0,0 +1,294 @@ +/** + * Copyright 2022-2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_AVX_H_ +#define MINDSPORE_NNACL_ARITHMETIC_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +#ifndef MS_SIMD_NEON +static inline int ElementFloorModAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementFloorDivAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, floor_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} +#endif + +static inline int ElementFloorDivIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline size_t AssignSubOptAVX(int index, float *in0, const float *in1, size_t size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(in0 + index, out_tmp); + } + return index; +} + +int ElementLogicalAndAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +int ElementOptLogicalAndAVX(int index, const float *in0, const float *in1, float *out, int size, bool first_scalar) { + if (first_scalar) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(*in0); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } else { + SIMD_F32 in1_tmp = SIMD_MOV_F32(*in1); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_self_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_self_fp32_avx.h new file mode 100644 index 00000000..c457e47d --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/arithmetic_self_fp32_avx.h @@ -0,0 +1,148 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_AVX_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +#if defined(MS_SIMD_AVX512) +// only avx512 support abs fp32 instruction +static inline int ElementAbsAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ABS_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementAbsIntAVX(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ABS_EPI32(SIMD_LD_EPI32(input + index))); + } + return index; +} +#endif + +#if !defined(MS_SIMD_NEON) +// not support neon + static inline int ElementCosAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_COS_F32(vin)); + } + return index; + } + + static inline int ElementLogAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_LOG_F32(vin)); + } + return index; + } +#endif + +static inline int ElementSquareAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_MUL_F32(vin, vin)); + } + return index; +} + +static inline int ElementSqrtAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_SQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementRsqrtAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_RSQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +#if defined(MS_SIMD_AVX) || defined(MS_SIMD_SSE) +// avx512 dont support round fp32 instruction +static inline int ElementRoundAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ROUND_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +// neon dont support floor fp32 instruction +static inline int ElementFloorAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_FLOOR_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +static inline int ElementCeilAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_CEIL_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +static inline int ElementNegativeAVX(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_N_F32(SIMD_LD_F32(input + index), -1.0f)); + } + return index; +} + +static inline int ElementNegativeIntAVX(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MUL_N_EPI32(SIMD_LD_EPI32(input + index), -1)); + } + return index; +} + +static inline int ElementReciprocalAVX(int index, const float *input, float *output, const int element_size) { + SIMD_F32 num1 = SIMD_MOV_F32(1.0f); + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_DIV_F32(num1, SIMD_LD_F32(input + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/batchnorm_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/batchnorm_fp32_avx.h new file mode 100644 index 00000000..11a9087b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/batchnorm_fp32_avx.h @@ -0,0 +1,67 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_AVX_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int BatchNormFp32AVX(int index, const float *input, const float *mean, + const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 output_data = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +static inline int FusedBatchNormFp32AVX(int index, const float *input, const float *scale, + const float *offset, const float *mean, const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 scale_ = SIMD_LD_F32(scale + index); + SIMD_F32 offset_ = SIMD_LD_F32(offset + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_F32 output_data = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_), offset_); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bce_with_logits_loss_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bce_with_logits_loss_fp32_avx.h new file mode 100644 index 00000000..b7d8303c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bce_with_logits_loss_fp32_avx.h @@ -0,0 +1,69 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_AVX_H_ +#define MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int BCEWithLogitLossAVX(int index, const float *logits, const float *label, + const float *weight, const float *pos_weight, int length, bool reduction, float *output, + float *reduction_sum) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 ones = SIMD_MOV_F32(1.0f); + SIMD_F32 middle_output = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 logits_tmp = SIMD_LD_F32(logits + index); + SIMD_F32 label_tmp = SIMD_LD_F32(label + index); + SIMD_F32 weight_tmp = SIMD_LD_F32(weight + index); + SIMD_F32 pos_weight_tmp = SIMD_LD_F32(pos_weight + index); + SIMD_F32 neg_logits_tmp = SIMD_SUB_F32(zero, logits_tmp); + SIMD_F32 max_value = neg_logits_tmp; + max_value = SIMD_MAX_F32(max_value, zero); + SIMD_F32 neg_max_value = SIMD_SUB_F32(zero, max_value); + SIMD_F32 log_weight = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(pos_weight_tmp, ones), label_tmp), ones); + SIMD_F32 log_exp_value = + SIMD_LOG_F32(SIMD_ADD_F32(SIMD_HEXP_F32(neg_max_value), SIMD_HEXP_F32(SIMD_SUB_F32(neg_logits_tmp, max_value)))); + SIMD_F32 loss = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(ones, label_tmp), logits_tmp), + SIMD_MUL_F32(log_weight, SIMD_ADD_F32(log_exp_value, max_value))); + if (reduction) { + middle_output = SIMD_FMADD_F32(loss, weight_tmp, middle_output); + } else { + SIMD_ST_F32(output + index, SIMD_MUL_F32(loss, weight_tmp)); + } + } + if (reduction) { + *reduction_sum += SIMD_GET_SUM_F32(middle_output); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bias_add_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bias_add_avx.h new file mode 100644 index 00000000..e54588bb --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/bias_add_avx.h @@ -0,0 +1,64 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_BIAS_ADD_AVX_H_ +#define MINDSPORE_NNACL_FP32_BIAS_ADD_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int BiasAddByInnerCoreAVX(int index, const float *input, const float *bias, float *output, + int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(input + index); + SIMD_F32 vin1 = SIMD_LD_F32(bias + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(output + index, vout); + } + return index; +} + +static inline int BiasAddByBatchCoreAVX(int index, const float *input, const float *bias, float *output1, + float *output2, float *output3, float *output4, int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_LDX4_F32(input_data, input + index, num); + SIMD_F32 bias_data = SIMD_LD_F32(bias + index); + SIMD_ST_F32(output1 + index, SIMD_ADD_F32(input_data1, bias_data)); + SIMD_ST_F32(output2 + index, SIMD_ADD_F32(input_data2, bias_data)); + SIMD_ST_F32(output3 + index, SIMD_ADD_F32(input_data3, bias_data)); + SIMD_ST_F32(output4 + index, SIMD_ADD_F32(input_data4, bias_data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +}; +#endif + +#endif // MINDSPORE_NNACL_FP32_BIAS_ADD_SIMD_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_base_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_base_avx.h new file mode 100644 index 00000000..2420b236 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_base_avx.h @@ -0,0 +1,56 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_CAST_BASE_AVX_H_ +#define NNACL_BASE_CAST_BASE_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int Int32ToFloat32AVX(int index, const int32_t *input, float *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 value = SIMD_LD_EPI32(input + index); + SIMD_ST_F32(output + index, SIMD_EPI32_TO_F32(value)); + } + return index; +} + +#ifndef MS_SIMD_NEON +static inline int Float32ToInt32AVX(int index, const float *input, int32_t *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(input + index); + SIMD_ST_EPI32(output + index, SIMD_F32_TO_EPI32(value)); + } + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_gather_reduce_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_gather_reduce_fp32_avx.h new file mode 100644 index 00000000..69fd032e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cast_gather_reduce_fp32_avx.h @@ -0,0 +1,72 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_AVX_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int Fp32CastGatherReduceInt64FusionAVX(int index, float *output_data, const int64_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int64_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + + +static inline int Fp32CastGatherReduceInt32FusionAVX(int index, float *output_data, const int32_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int32_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cdist_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cdist_fp32_avx.h new file mode 100644 index 00000000..dac9efa9 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cdist_fp32_avx.h @@ -0,0 +1,70 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CDIST_AVX_H_ +#define MINDSPORE_NNACL_FP32_CDIST_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t CdistTwoNormalOptAVX(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + result_vec = SIMD_FMADD_F32(tmp_vec, tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t CdistPNormalOptAVX(int64_t index, const float *a, const float *b, + float *out, int64_t size, float p) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + SIMD_F32 p_vec = SIMD_MOV_F32(p); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + tmp_vec = SIMD_POW_F32(tmp_vec, p_vec); + result_vec = SIMD_ADD_F32(tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cumsum_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cumsum_fp32_avx.h new file mode 100644 index 00000000..a8463bb6 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/cumsum_fp32_avx.h @@ -0,0 +1,121 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CUMSUM_AVX_H_ +#define MINDSPORE_NNACL_FP32_CUMSUM_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumOutputInitWithInputAVX(int64_t index, const float *layer_input, + float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_LD_F32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumOutputInitWithZeroAVX(int64_t index, float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_MOV_F32(0.0f)); + } + return index; +} + +static inline int64_t CumsumAVX(int64_t index, const float *layer_input, float *layer_output, float *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input + index); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output + index); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseAVX(int64_t index, const float *layer_input, float *layer_output, + float *layer_last_output, int inner_dim) { + + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input - index - BLOCK_NUM + 1); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumIntOutputInitWithInputAVX(int64_t index, const int32_t *layer_input, + int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_LD_EPI32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumIntOutputInitWithZeroAVX(int64_t index, int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_MOV_EPI32(0.0f)); + } + return index; +} + +static inline int64_t CumsumIntAVX(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input + index); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output + index); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseIntAVX(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input - index - BLOCK_NUM + 1); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/div_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/div_fp32_avx.h new file mode 100644 index 00000000..b65b1692 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/div_fp32_avx.h @@ -0,0 +1,167 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_AVX_H_ +#define MINDSPORE_NNACL_FP32_DIV_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int ElementOptDivNum0AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivNum1AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum0AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum1AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num0AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num1AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementDivReluAVX(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivRelu6AVX(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/dropout_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/dropout_fp32_avx.h new file mode 100644 index 00000000..cbd4eca5 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/dropout_fp32_avx.h @@ -0,0 +1,46 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_DROPOUTFP32_AVX_H_ +#define MINDSPORE_NNACL_FP32_DROPOUTFP32_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int DropoutFp32AVX(int index, const float *input, float scale, + int length, float *output) { + SIMD_F32 scale_value = SIMD_MOV_F32(scale); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_F32(SIMD_LD_F32(input + index), scale_value)); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/exp_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/exp_fp32_avx.h new file mode 100644 index 00000000..cf7cbd37 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/exp_fp32_avx.h @@ -0,0 +1,63 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_AVX_H_ +#define MINDSPORE_NNACL_FP32_DIV_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t ExpFp32AVX(int64_t index, const float *src, float *dst, int num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithInScaleAVX(int64_t index, const float *src, float *dst, int num, float in_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(in_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_MUL_F32(SIMD_LD_F32(src + index), scale_vec), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithOutScaleAVX(int64_t index, const float *src, float *dst, int num, float out_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(out_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_LD_F32(dst + index), scale_vec)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/fill_base_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/fill_base_avx.h new file mode 100644 index 00000000..e900cd44 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/fill_base_avx.h @@ -0,0 +1,53 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_FILL_BASE_AVX_H_ +#define NNACL_BASE_FILL_BASE_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int FillFp32AVX(int index, float *output, int size, float data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MOV_F32(data)); + } + return index; +} + +static inline int FillInt32AVX(int index, int *output, int size, int data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MOV_EPI32(data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif + diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/group_norm_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/group_norm_fp32_avx.h new file mode 100644 index 00000000..d5076e59 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/group_norm_fp32_avx.h @@ -0,0 +1,77 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_AVX_H_ +#define MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t GroupNormFp32AVX(int64_t index, const float *unit_input, float scale, float offset, float mean, + float var_sqrt, int unit, float *unit_output) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 v_sqrt = SIMD_MOV_F32(var_sqrt); + SIMD_F32 scale_val = SIMD_MOV_F32(scale); + SIMD_F32 offset_val = SIMD_MOV_F32(offset); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(unit_input + index); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input, mean_val), v_sqrt); + SIMD_F32 output = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_val), offset_val); + SIMD_ST_F32(unit_output + index, output); + } + return index; +} + +static inline int64_t GroupNormReduceSumAVX(int64_t index, const float *in, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(in + index)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +static inline int64_t GroupNormReduceVarAVX(int64_t index, const float *in, float mean, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_SUB_F32(SIMD_LD_F32(in + index), mean_val); + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_F32(input, input)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/layer_norm_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/layer_norm_fp32_avx.h new file mode 100644 index 00000000..96fdf185 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/layer_norm_fp32_avx.h @@ -0,0 +1,68 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_AVX_H_ +#define MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int LayerNormMeanAndSquareAVX(int index, const float *src, int num, float *mean, float *square_mean) { + if (num >= 4 * BLOCK_NUM) { + SIMD_F32 sum_val = SIMD_SET0_F32; + SIMD_F32 square_sum_val = SIMD_SET0_F32; + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 square_value = SIMD_MUL_F32(value, value); + sum_val = SIMD_ADD_F32(sum_val, value); + square_sum_val = SIMD_ADD_F32(square_sum_val, square_value); + } + *mean += SIMD_GET_SUM_F32(sum_val); + *square_mean += SIMD_GET_SUM_F32(square_sum_val); + } + return index; +} + +static inline int LayerNormGammaAndBetaAVX(int index, float *dst, const float *src, const float *gamma_data, + const float *beta_data, int num, const float mean, const float deno) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 deno_val = SIMD_MOV_F32(deno); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 out_value = SIMD_SUB_F32(value, mean_val); + out_value = SIMD_MUL_F32(out_value, deno_val); + out_value = SIMD_FMADD_F32(out_value, SIMD_LD_F32(gamma_data + index), SIMD_LD_F32(beta_data + index)); + SIMD_ST_F32(dst + index, out_value); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/matmul_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/matmul_fp32_avx.h new file mode 100644 index 00000000..2787c7a8 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/matmul_fp32_avx.h @@ -0,0 +1,155 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_MATMUL_F32_AVX_H_ +#define MINDSPORE_NNACL_FP32_MATMUL_F32_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t GemmIsNotPackAVX(int64_t index, const float *a, const float *b, float *c, const float *bias, int row, + int deep, int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 b_data16 = SIMD_MOV_F32(b[0]); + SIMD_F32 bias_data16 = SIMD_MOV_F32(bias[0]); + for (int block_max_size = row - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_data = SIMD_LD_F32(a + index); + SIMD_F32 dst = SIMD_FMADD_F32(b_data16, a_data, bias_data16); + if (act_type != 0) { + dst = SIMD_MAX_F32(dst, down_threshold); + if (act_type == 3) { + dst = SIMD_MIN_F32(dst, up_threshold); + } + } + SIMD_ST_F32(c + index, dst); + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1GemmIsNotPackAVX(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1NoBiasGemmIsNotPackAVX(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +#if defined(MS_SIMD_AVX512) || defined(MS_SIMD_AVX) +static inline int64_t GemmIsNotPackOptimizeCoreAVX(int64_t index, const float *a, const float *b, int k, float *dst) { + SIMD_F32 dst1 = SIMD_MOV_F32(0.0f); + for (int block_max_size = k - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 weight = SIMD_LD_F32(b + index); + SIMD_F32 a1 = SIMD_LD_F32(a + index); + dst1 = SIMD_FMADD_F32(weight, a1, dst1); + } + *dst += SIMD_REDUCE_ADD_F32(dst1); + return index; +} +#endif + +static inline int64_t MatVecMulNoPackCoreAVX(int64_t oc_index, const float *a, const float *b, float *c, const float *bias, + int act_type, int64_t depth, int64_t oc, int64_t col, int64_t inc_flag) { + for (int64_t oc_max_size = oc - BLOCK_NUM; oc_index <= oc_max_size; oc_index += BLOCK_NUM) { + SIMD_F32 out = (inc_flag & 0x1) == 0 ? SIMD_LD_F32(c + oc_index) : (bias == NULL ? SIMD_MOV_F32(0.0f) : SIMD_LD_F32(bias + oc_index)); + for (int64_t k = 0; k < depth; ++k) { + SIMD_F32 left = SIMD_MOV_F32(a[k]); + SIMD_F32 right = SIMD_LD_F32(b + oc_index + k * col); + out = SIMD_FMADD_F32(left, right, out); + } + if ((inc_flag & 0x2) != 0 && act_type != 0) { + out = SIMD_MAX_F32(out, SIMD_MOV_F32(0.0f)); + if (act_type == 0x3) { + out = SIMD_MIN_F32(out, SIMD_MOV_F32(6.0f)); + } + } + SIMD_ST_F32(c + oc_index, out); + } + return oc_index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/mul_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/mul_fp32_avx.h new file mode 100644 index 00000000..bbc47ef4 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/mul_fp32_avx.h @@ -0,0 +1,218 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_AVX_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int ElementMulAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6AVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6IntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0_opt_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num0AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num1AVX(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0_opt_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/pooling_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/pooling_fp32_avx.h new file mode 100644 index 00000000..d4bd2305 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/pooling_fp32_avx.h @@ -0,0 +1,84 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POOLING_AVX_H_ +#define MINDSPORE_NNACL_FP32_POOLING_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int AvgPoolingBatchAVX(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_avg = SIMD_SET0_F32; + int real_count = 0; + for (int h = real_win_h_start; h < real_win_h_end; h++) { + for (int w = real_win_w_start; w < real_win_w_end; w++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + h) * in_w + in_w_index + w) * channel; + tmp_avg = SIMD_ADD_F32(tmp_avg, SIMD_LD_F32(src_win_ptr)); + ++real_count; + } + } + tmp_avg = SIMD_DIV_F32(tmp_avg, SIMD_MOV_F32(real_count)); + tmp_avg = SIMD_MAX_F32(tmp_avg, min_val); + tmp_avg = SIMD_MIN_F32(tmp_avg, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_avg); + } + return ci; +} + +static inline int MaxPoolingBatchAVX(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_max = min_val; + for (int kh = real_win_h_start; kh < real_win_h_end; kh++) { + for (int kw = real_win_w_start; kw < real_win_w_end; kw++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + kh) * in_w + in_w_index + kw) * channel; + tmp_max = SIMD_MAX_F32(tmp_max, SIMD_LD_F32(src_win_ptr)); + } + } + tmp_max = SIMD_MIN_F32(tmp_max, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_max); + } + return ci; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/power_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/power_fp32_avx.h new file mode 100644 index 00000000..2ada6cb3 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/power_fp32_avx.h @@ -0,0 +1,101 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POWER_AVX_H_ +#define MINDSPORE_NNACL_FP32_POWER_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int PowerBroadCastIntExponentAVX(int index, const float *input, int exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result = SIMD_MOV_F32(1.0f); + int exp = abs(exponent); + while (exp) { + if (exp % 2) { + result = SIMD_MUL_F32(result, tmp); + } + tmp = SIMD_MUL_SQUARE_F32(tmp); + exp = exp / 2; + } + SIMD_ST_F32(output + index, exponent >= 0 ? result : SIMD_DIV_F32(SIMD_MOV_F32(1), result)); + } + return index; +} + +static inline int PowerBroadCastFloatExponentAVX(int index, const float *input, float exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result; + for (int i = 0; i < BLOCK_NUM; ++i) { + SIMD_F32_GETI(result, i) = powf(SIMD_F32_GETI(tmp, i), exponent); + } + SIMD_ST_F32(output + index, result); + } + return index; +} + +static inline int PowerSingleExponentAVX(int index, const float *input, const float *exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + for (int j = 0; j < BLOCK_NUM; ++j) { + float cur_exponent = exponent[index + j]; + float cur_val = SIMD_F32_GETI(tmp_vec, j); + if (fabsf(cur_exponent - (int)(cur_exponent)) < 0.000001) { + int exp = abs((int)(cur_exponent)); + float result = 1; + while (exp) { + if (exp % 2) { + result *= cur_val; + } + cur_val *= cur_val; + exp = exp / 2; + } + output[index + j] = *exponent >= 0 ? result : 1 / result; + } else { + output[index + j] = powf(cur_val, cur_exponent); + } + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/reduce_concat_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/reduce_concat_fp32_avx.h new file mode 100644 index 00000000..6772f6f1 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/reduce_concat_fp32_avx.h @@ -0,0 +1,122 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_AVX_H_ +#define MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +#ifdef MS_SIMD_AVX512 +static inline int Fp32ReduceSumConcatAxisSize16FusionAVX(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (1 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data)); + } + SIMD_ST_F32(output_data, zmm00); + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize32FusionAVX(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (2 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize64FusionAVX(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (4 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize128FusionAVX(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + SIMD_F32 zmm04 = SIMD_LD_F32(input_data + 4 * BLOCK_NUM); + SIMD_F32 zmm05 = SIMD_LD_F32(input_data + 5 * BLOCK_NUM); + SIMD_F32 zmm06 = SIMD_LD_F32(input_data + 6 * BLOCK_NUM); + SIMD_F32 zmm07 = SIMD_LD_F32(input_data + 7 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (8 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + zmm04 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 4 * BLOCK_NUM)); + zmm05 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 5 * BLOCK_NUM)); + zmm06 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 6 * BLOCK_NUM)); + zmm07 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 7 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + SIMD_ST_F32(output_data + 4 * BLOCK_NUM, zmm04); + SIMD_ST_F32(output_data + 5 * BLOCK_NUM, zmm05); + SIMD_ST_F32(output_data + 6 * BLOCK_NUM, zmm06); + SIMD_ST_F32(output_data + 7 * BLOCK_NUM, zmm07); + + return index; +} + +#endif + + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/reduce_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/reduce_fp32_avx.h new file mode 100644 index 00000000..b9b6513f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/reduce_fp32_avx.h @@ -0,0 +1,227 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_REDUCE_FP32_AVX_H_ +#define MINDSPORE_NNACL_FP32_REDUCE_FP32_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t ReduceSumAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumByLastAxisAVX(int64_t index, const float *src, float* tmp_sum, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_sum += SIMD_GET_SUM_F32(tmp); + return index; +} + +static inline int64_t ReduceMeanAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, SIMD_DIV_N_F32(tmp, axis_size)); + } + return index; +} + +static inline int64_t ReduceMinAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxByLastAxisAVX(int64_t index, const float *src, float* tmp_max, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(*tmp_max); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_max = SIMD_GET_MAX_F32(tmp); + return index; +} + +static inline int64_t ReduceProdAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(1.0f); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MUL_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumSquareAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceL2NormAVX(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, SIMD_SQRT_F32(tmp)); + } + return index; +} + +static inline int64_t IntReduceSumAVX(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMeanAVX(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, SIMD_DIV_N_EPI32(tmp, axis_size)); + } + return index; +} + +static inline int64_t IntReduceMinAVX(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMaxAVX(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumDim2Axis0AVX(int64_t index, size_t col_size, size_t col_len, size_t row_len, const float *src_data, float *dst_data) { + for (int block_max_size = col_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + const float *inner_src = src_data + index; + float *inner_dst = dst_data + index; + for (size_t i = 0; i < row_len; ++i) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * col_len)); + } + SIMD_ST_F32(inner_dst, tmp); + } + return index; +} + +static inline int64_t FloatReduceDeviationAVX(int64_t index, const float *src_data, float mean, size_t size, float *deviation) { + SIMD_F32 fs_deviation = SIMD_MOV_F32(0); + SIMD_F32 fs_mean = SIMD_MOV_F32(mean); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 fs_sub = SIMD_LD_F32(src_data + index); + + fs_sub = SIMD_SUB_F32(fs_sub, fs_mean); + SIMD_F32 fs_pow = SIMD_MUL_F32(fs_sub, fs_sub); + fs_deviation = SIMD_ADD_F32(fs_deviation, fs_pow); + } + *deviation += SIMD_GET_SUM_F32(fs_deviation); + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/scatter_nd_binary_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/scatter_nd_binary_avx.h new file mode 100644 index 00000000..3a2ecb13 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/scatter_nd_binary_avx.h @@ -0,0 +1,52 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_SCATTER_ND_BINARY_AVX_H_ +#define NNACL_BASE_SCATTER_ND_BINARY_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + + static inline int ScatterNDAddFp32AVX(int index, const float *update, int size, float *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ADD_F32(SIMD_LD_F32(output + index), SIMD_LD_F32(update + index))); + } + return index; +} + +static inline int ScatterNDAddInt32AVX(int index, const int *update, int size, int *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ADD_EPI32(SIMD_LD_EPI32(output + index), SIMD_LD_EPI32(update + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif // NNACL_BASE_SCATTER_ND_BINARY_AVX_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/softmax_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/softmax_fp32_avx.h new file mode 100644 index 00000000..8229111d --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/softmax_fp32_avx.h @@ -0,0 +1,87 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_AVX_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t SoftmaxNormGetMaxAVX(int64_t index, const float *src, int cur_batch_offset, + float *max, int channel) { + if (channel >= BLOCK_NUM * BLOCK_NUM) { + SIMD_F32 max_val = SIMD_MOV_F32(*max); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + max_val = SIMD_MAX_F32(max_val, SIMD_LD_F32(src + cur_batch_offset + index)); + } + *max = SIMD_GET_MAX_F32(max_val); + } + return index; +} + +static inline int64_t SoftmaxNormCalcNormAVX(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, int channel) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 output = SIMD_SUB_F32(SIMD_LD_F32(src + cur_batch_offset + index), SIMD_MOV_F32(max)); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +static inline int64_t SoftmaxLastAxisGetExpSumAVX(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, float *exp_sum, int channel) { +#ifndef _WIN32 + SIMD_F32 sum_val = SIMD_SET0_F32; + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_SUB_F32(input, SIMD_MOV_F32(max)); + SIMD_F32 exp_out = SIMD_EXP_F32(output); + sum_val = SIMD_ADD_F32(sum_val, exp_out); + SIMD_ST_F32(dst + cur_batch_offset + index, exp_out); + } + *exp_sum += SIMD_GET_SUM_F32(sum_val); +#endif + return index; +} + +static inline int64_t SoftmaxLastAxisGetResultAVX(int64_t index, const float *src, float *dst, + int cur_batch_offset, float exp_sum, int channel) { + SIMD_F32 exp_sum_val = SIMD_MOV_F32(exp_sum); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_MUL_F32(input, exp_sum_val); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/softmax_grad_fusion_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/softmax_grad_fusion_fp32_avx.h new file mode 100644 index 00000000..6138e4cd --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/softmax_grad_fusion_fp32_avx.h @@ -0,0 +1,62 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_AVX_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int64_t SoftmaxGradFusionOptAVX(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + result_vec = SIMD_FMADD_F32(a_vec, b_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t ElementOptSubMulAVX(int index, const float *in0, const float *in1, float sum, + float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(sum); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_ST_F32(out + index, SIMD_MUL_F32(vin0, SIMD_SUB_F32(vin1, vin1_opt_))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/sub_fp32_avx.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/sub_fp32_avx.h new file mode 100644 index 00000000..bd119175 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx/sub_fp32_avx.h @@ -0,0 +1,167 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SUB_AVX_H_ +#define MINDSPORE_NNACL_FP32_SUB_AVX_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx", "avx2") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX_INSTRUCTION +#define BLOCK_NUM 8 +#define MS_SIMD_AVX + +static inline int ElementOptSubNum0AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubNum1AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum0AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum1AVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum0AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum1AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num0AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num1AVX(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubAVX(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubIntAVX(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementSubReluAVX(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubRelu6AVX(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/activation_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/activation_fp32_avx512.h new file mode 100644 index 00000000..3907cdd7 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/activation_fp32_avx512.h @@ -0,0 +1,285 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_AVX512_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int Fp32ReluAVX512(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_F32(SIMD_LD_F32(src + index), zero)); + } + return index; +} + +static inline int Int32ReluAVX512(int index, const int32_t *src, int length, int32_t *dst) { + SIMD_EPI32 zero = SIMD_MOV_EPI32(0.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_MAX_EPI32(SIMD_LD_EPI32(src + index), zero)); + } + return index; +} + +static inline int Fp32Relu6AVX512(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 six = SIMD_MOV_F32(6.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), zero, six)); + } + return index; +} + +static inline int Fp32ClipAVX512(int index, const float *src, int length, float *dst, float min, float max) { + SIMD_F32 min_val = SIMD_MOV_F32(min); + SIMD_F32 max_val = SIMD_MOV_F32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int Int32ClipAVX512(int index, const int32_t *src, int length, int32_t *dst, int min, int max) { + SIMD_EPI32 min_val = SIMD_MOV_EPI32(min); + SIMD_EPI32 max_val = SIMD_MOV_EPI32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_CLAMP_EPI32(SIMD_LD_EPI32(src + index), min_val, max_val)); + } + return index; +} + +static inline int LReluAVX512(int index, const float *src, int length, float *dst, float alpha) { + SIMD_F32 alpha_data = SIMD_MOV_F32(alpha); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_MASK mask = SIMD_CMPGT_F32(SIMD_SET0_F32, src_tmp); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_F32(src_tmp, alpha_data), mask)); + } + return index; +} + +static inline int SigmoidAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, (SIMD_LD_F32(src + index))), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(SIMD_MOV_F32(1.0f), SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int TanhAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + index); + SIMD_ST_F32(dst + index, SIMD_TANH_F32(input)); + } + return index; +} + +static inline int SwishAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, src_value), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(src_value, SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int HSwishAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(SIMD_MUL_F32(src_value, relu6), 6)); + } + return index; +} + +static inline int HSigmoidAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(relu6, 6)); + } + return index; +} + +static inline int HardTanhNoLimitMinAVX512(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MIN_N_F32(SIMD_LD_F32(src + index), max_val)); + } + return index; +} + +static inline int HardTanhNoLimitMaxAVX512(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_N_F32(SIMD_LD_F32(src + index), min_val)); + } + return index; +} + +static inline int HardTanhLimitMinMaxAVX512(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_N_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int GeluTanhApproximateAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 tmp1 = SIMD_FMADD_F32(SIMD_MUL_N_F32(in, 0.035677408136f), in, SIMD_MOV_F32(0.79788456080287f)); + SIMD_F32 tmp2 = SIMD_MUL_F32(tmp1, in); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_MUL_N_F32(in, 0.5f), SIMD_ADD_N_F32(SIMD_TANH_F32(tmp2), 1.0f))); + } + return index; +} + +static inline int GeluAVX512(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_F32(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline SIMD_F32 SIMD_ERFCCHEBAVX512(SIMD_F32 src) { + static const int ncof = 7; + const double cof[7] = {-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, -9.561514786808631e-3, + -9.46595344482036e-4, 3.66839497852761e-4, 4.2523324806907e-5}; + SIMD_F32 dst; + SIMD_F32 d = SIMD_SET0_F32; + SIMD_F32 dd = SIMD_SET0_F32; + SIMD_F32 t = SIMD_DIV_F32(SIMD_MOV_F32(2.0f), SIMD_ADD_F32(src, SIMD_MOV_F32(2.0f))); + SIMD_F32 ty = SIMD_SUB_F32(SIMD_MUL_F32(SIMD_MOV_F32(4.0f), t), SIMD_MOV_F32(2.0f)); + + for (int j = ncof - 1; j > 0; j--) { + SIMD_F32 tmp = d; + d = SIMD_SUB_F32(SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[j])), dd); + dd = tmp; + } + + dst = + SIMD_FMADD_F32(src, src, MS_FSMUL_F32(dd, SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[0])), SIMD_MOV_F32(0.5f))); + dst = SIMD_MUL_F32(t, SIMD_EXP_F32(SIMD_MUL_F32(SIMD_MOV_F32(-1.0f), dst))); + return dst; +} + +static inline SIMD_F32 SIMD_ERF_APPROXIMATEAVX512(SIMD_F32 src) { + SIMD_F32 abs_src = SIMD_ABS_F32(src); + SIMD_F32 sign = SIMD_GETSIGN_F32(src); + SIMD_F32 dst = SIMD_ERFCCHEBAVX512(abs_src); + return SIMD_MUL_F32(sign, SIMD_SUB_F32(SIMD_MOV_F32(1.0f), dst)); +} + +static inline int GeluErfAPPROXIMATEAVX512(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_APPROXIMATEAVX512(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline int EluAVX512(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(src_tmp), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int CeluAVX512(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(SIMD_DIV_N_F32(src_tmp, alpha)), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int HardShrinkAVX512(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & in */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), src_t); + /* v1 = (in < -lamdb) & in */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), src_t); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftShrinkAVX512(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & (in - lamdb) */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), SIMD_SUB_F32(src_t, pos_lamdb_v)); + /* v1 = (in < -lamdb) & (in + lamdb) */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), SIMD_ADD_F32(src_t, pos_lamdb_v)); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftsignFp32OptAVX512(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 divisor_tmp = SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_ABS_F32(src_tmp)); + SIMD_ST_F32(dst + index, SIMD_DIV_F32(src_tmp, divisor_tmp)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/activation_grad_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/activation_grad_avx512.h new file mode 100644 index 00000000..4011e639 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/activation_grad_avx512.h @@ -0,0 +1,57 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NNACL_FP32_GRAD_ACTIVATION_GRAD_AVX512_H_ +#define NNACL_FP32_GRAD_ACTIVATION_GRAD_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int ShrinkGradAVX512(int index, const float *src0, const float *src1, + int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src0_t = SIMD_LD_F32(src0 + index); + SIMD_F32 src1_t = SIMD_LD_F32(src1 + index); + + SIMD_MASK mask0 = SIMD_CMPLE_F32(src1_t, pos_lamdb_v); + SIMD_MASK mask1 = SIMD_CMPLE_F32(neg_lamdb_v, src1_t); + SIMD_MASK mask = SIMD_AND_MASK(mask0, mask1); + + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src0_t, SIMD_MOV_F32(0.0f), mask)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/adam_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/adam_fp32_avx512.h new file mode 100644 index 00000000..0579d58a --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/adam_fp32_avx512.h @@ -0,0 +1,210 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ADAM_FP32_AVX512_H_ +#define MINDSPORE_NNACL_FP32_ADAM_FP32_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 +#ifdef MS_SIMD_AVX512 + static inline size_t AdamWeightDecayFp32AVX512(size_t index, float *var, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + const float *gradient, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient + index); + + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_F32(var + index, var_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp16AVX512(size_t index, float *var, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp32AVX512(size_t index, float *var, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp16AVX512(size_t index, int16_t *var16, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp32AVX512(size_t index, int16_t *var16, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/add_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/add_fp32_avx512.h new file mode 100644 index 00000000..264cac5f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/add_fp32_avx512.h @@ -0,0 +1,124 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_ADD_AVX512_H_ +#define MINDSPORE_NNACL_FP32_ADD_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int ElementOptAddAVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, + int size) { + SIMD_EPI32 vin0_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddReluAVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddRelu6AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddReluAVX512(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddRelu6AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/apply_proximal_adagrad_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/apply_proximal_adagrad_fp32_avx512.h new file mode 100644 index 00000000..34feb71b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/apply_proximal_adagrad_fp32_avx512.h @@ -0,0 +1,75 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_AVX512_H_ +#define NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t ApplyProximalAdagradOptAVX512( + int64_t index, float *var, float *accum, float lr, float l1, float l2, float *grad, int64_t size) { + SIMD_F32 lr_vec = SIMD_MOV_F32(lr); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec1 = SIMD_LD_F32(grad + index); + SIMD_F32 accum_vec = SIMD_LD_F32(accum + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + accum_vec = SIMD_FMADD_F32(tmp_vec1, tmp_vec1, accum_vec); + SIMD_F32 learn_rate_vec = SIMD_DIV_F32(lr_vec, SIMD_SQRT_F32(accum_vec)); + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(tmp_vec1, learn_rate_vec)); + SIMD_ST_F32(accum + index, accum_vec); + tmp_vec1 = SIMD_FMADD_F32(l2_vec, learn_rate_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + learn_rate_vec = SIMD_MUL_F32(learn_rate_vec, l1_vec); + learn_rate_vec = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), learn_rate_vec); + learn_rate_vec = SIMD_MAX_F32(learn_rate_vec, SIMD_MOV_F32(0.0f)); + learn_rate_vec = SIMD_DIV_F32(learn_rate_vec, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(learn_rate_vec, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/apply_proximal_gradient_descent_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/apply_proximal_gradient_descent_fp32_avx512.h new file mode 100644 index 00000000..bb7e6dc7 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/apply_proximal_gradient_descent_fp32_avx512.h @@ -0,0 +1,71 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_AVX512_H_ +#define NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t ApplyProximalGradientDescentOptAVX512( + int64_t index, float *var, float alpha, float l1, float l2, float *delta, int64_t size) { + SIMD_F32 alpha_vec = SIMD_MOV_F32(alpha); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 delta_vec = SIMD_LD_F32(delta + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(delta_vec, alpha_vec)); + SIMD_F32 tmp_vec1 = SIMD_FMADD_F32(l2_vec, alpha_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + SIMD_F32 tmp_vec2 = SIMD_MUL_F32(alpha_vec, l1_vec); + tmp_vec2 = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), tmp_vec2); + tmp_vec2 = SIMD_MAX_F32(tmp_vec2, SIMD_MOV_F32(0.0f)); + tmp_vec2 = SIMD_DIV_F32(tmp_vec2, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(tmp_vec2, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/arithmetic_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/arithmetic_fp32_avx512.h new file mode 100644 index 00000000..cd3edc85 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/arithmetic_fp32_avx512.h @@ -0,0 +1,294 @@ +/** + * Copyright 2022-2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_AVX512_H_ +#define MINDSPORE_NNACL_ARITHMETIC_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +#ifndef MS_SIMD_NEON +static inline int ElementFloorModAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementFloorDivAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, floor_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} +#endif + +static inline int ElementFloorDivIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline size_t AssignSubOptAVX512(int index, float *in0, const float *in1, size_t size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(in0 + index, out_tmp); + } + return index; +} + +int ElementLogicalAndAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +int ElementOptLogicalAndAVX512(int index, const float *in0, const float *in1, float *out, int size, bool first_scalar) { + if (first_scalar) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(*in0); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } else { + SIMD_F32 in1_tmp = SIMD_MOV_F32(*in1); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/arithmetic_self_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/arithmetic_self_fp32_avx512.h new file mode 100644 index 00000000..2812c15e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/arithmetic_self_fp32_avx512.h @@ -0,0 +1,148 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_AVX512_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +#if defined(MS_SIMD_AVX512) +// only avx512 support abs fp32 instruction +static inline int ElementAbsAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ABS_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementAbsIntAVX512(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ABS_EPI32(SIMD_LD_EPI32(input + index))); + } + return index; +} +#endif + +#if !defined(MS_SIMD_NEON) +// not support neon + static inline int ElementCosAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_COS_F32(vin)); + } + return index; + } + + static inline int ElementLogAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_LOG_F32(vin)); + } + return index; + } +#endif + +static inline int ElementSquareAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_MUL_F32(vin, vin)); + } + return index; +} + +static inline int ElementSqrtAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_SQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementRsqrtAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_RSQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +#if defined(MS_SIMD_AVX) || defined(MS_SIMD_SSE) +// avx512 dont support round fp32 instruction +static inline int ElementRoundAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ROUND_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +// neon dont support floor fp32 instruction +static inline int ElementFloorAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_FLOOR_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +static inline int ElementCeilAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_CEIL_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +static inline int ElementNegativeAVX512(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_N_F32(SIMD_LD_F32(input + index), -1.0f)); + } + return index; +} + +static inline int ElementNegativeIntAVX512(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MUL_N_EPI32(SIMD_LD_EPI32(input + index), -1)); + } + return index; +} + +static inline int ElementReciprocalAVX512(int index, const float *input, float *output, const int element_size) { + SIMD_F32 num1 = SIMD_MOV_F32(1.0f); + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_DIV_F32(num1, SIMD_LD_F32(input + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/batchnorm_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/batchnorm_fp32_avx512.h new file mode 100644 index 00000000..fd945984 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/batchnorm_fp32_avx512.h @@ -0,0 +1,67 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_AVX512_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int BatchNormFp32AVX512(int index, const float *input, const float *mean, + const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 output_data = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +static inline int FusedBatchNormFp32AVX512(int index, const float *input, const float *scale, + const float *offset, const float *mean, const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 scale_ = SIMD_LD_F32(scale + index); + SIMD_F32 offset_ = SIMD_LD_F32(offset + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_F32 output_data = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_), offset_); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/bce_with_logits_loss_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/bce_with_logits_loss_fp32_avx512.h new file mode 100644 index 00000000..12f727be --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/bce_with_logits_loss_fp32_avx512.h @@ -0,0 +1,69 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_AVX512_H_ +#define MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int BCEWithLogitLossAVX512(int index, const float *logits, const float *label, + const float *weight, const float *pos_weight, int length, bool reduction, float *output, + float *reduction_sum) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 ones = SIMD_MOV_F32(1.0f); + SIMD_F32 middle_output = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 logits_tmp = SIMD_LD_F32(logits + index); + SIMD_F32 label_tmp = SIMD_LD_F32(label + index); + SIMD_F32 weight_tmp = SIMD_LD_F32(weight + index); + SIMD_F32 pos_weight_tmp = SIMD_LD_F32(pos_weight + index); + SIMD_F32 neg_logits_tmp = SIMD_SUB_F32(zero, logits_tmp); + SIMD_F32 max_value = neg_logits_tmp; + max_value = SIMD_MAX_F32(max_value, zero); + SIMD_F32 neg_max_value = SIMD_SUB_F32(zero, max_value); + SIMD_F32 log_weight = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(pos_weight_tmp, ones), label_tmp), ones); + SIMD_F32 log_exp_value = + SIMD_LOG_F32(SIMD_ADD_F32(SIMD_HEXP_F32(neg_max_value), SIMD_HEXP_F32(SIMD_SUB_F32(neg_logits_tmp, max_value)))); + SIMD_F32 loss = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(ones, label_tmp), logits_tmp), + SIMD_MUL_F32(log_weight, SIMD_ADD_F32(log_exp_value, max_value))); + if (reduction) { + middle_output = SIMD_FMADD_F32(loss, weight_tmp, middle_output); + } else { + SIMD_ST_F32(output + index, SIMD_MUL_F32(loss, weight_tmp)); + } + } + if (reduction) { + *reduction_sum += SIMD_GET_SUM_F32(middle_output); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/bias_add_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/bias_add_avx512.h new file mode 100644 index 00000000..abdad5ff --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/bias_add_avx512.h @@ -0,0 +1,64 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_BIAS_ADD_AVX512_H_ +#define MINDSPORE_NNACL_FP32_BIAS_ADD_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int BiasAddByInnerCoreAVX512(int index, const float *input, const float *bias, float *output, + int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(input + index); + SIMD_F32 vin1 = SIMD_LD_F32(bias + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(output + index, vout); + } + return index; +} + +static inline int BiasAddByBatchCoreAVX512(int index, const float *input, const float *bias, float *output1, + float *output2, float *output3, float *output4, int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_LDX4_F32(input_data, input + index, num); + SIMD_F32 bias_data = SIMD_LD_F32(bias + index); + SIMD_ST_F32(output1 + index, SIMD_ADD_F32(input_data1, bias_data)); + SIMD_ST_F32(output2 + index, SIMD_ADD_F32(input_data2, bias_data)); + SIMD_ST_F32(output3 + index, SIMD_ADD_F32(input_data3, bias_data)); + SIMD_ST_F32(output4 + index, SIMD_ADD_F32(input_data4, bias_data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +}; +#endif + +#endif // MINDSPORE_NNACL_FP32_BIAS_ADD_SIMD_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cast_base_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cast_base_avx512.h new file mode 100644 index 00000000..bef848c5 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cast_base_avx512.h @@ -0,0 +1,56 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_CAST_BASE_AVX512_H_ +#define NNACL_BASE_CAST_BASE_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int Int32ToFloat32AVX512(int index, const int32_t *input, float *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 value = SIMD_LD_EPI32(input + index); + SIMD_ST_F32(output + index, SIMD_EPI32_TO_F32(value)); + } + return index; +} + +#ifndef MS_SIMD_NEON +static inline int Float32ToInt32AVX512(int index, const float *input, int32_t *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(input + index); + SIMD_ST_EPI32(output + index, SIMD_F32_TO_EPI32(value)); + } + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cast_gather_reduce_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cast_gather_reduce_fp32_avx512.h new file mode 100644 index 00000000..eed7cece --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cast_gather_reduce_fp32_avx512.h @@ -0,0 +1,72 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_AVX512_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int Fp32CastGatherReduceInt64FusionAVX512(int index, float *output_data, const int64_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int64_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + + +static inline int Fp32CastGatherReduceInt32FusionAVX512(int index, float *output_data, const int32_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int32_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cdist_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cdist_fp32_avx512.h new file mode 100644 index 00000000..11a2abcf --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cdist_fp32_avx512.h @@ -0,0 +1,70 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CDIST_AVX512_H_ +#define MINDSPORE_NNACL_FP32_CDIST_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t CdistTwoNormalOptAVX512(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + result_vec = SIMD_FMADD_F32(tmp_vec, tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t CdistPNormalOptAVX512(int64_t index, const float *a, const float *b, + float *out, int64_t size, float p) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + SIMD_F32 p_vec = SIMD_MOV_F32(p); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + tmp_vec = SIMD_POW_F32(tmp_vec, p_vec); + result_vec = SIMD_ADD_F32(tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cumsum_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cumsum_fp32_avx512.h new file mode 100644 index 00000000..6a064375 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/cumsum_fp32_avx512.h @@ -0,0 +1,121 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CUMSUM_AVX512_H_ +#define MINDSPORE_NNACL_FP32_CUMSUM_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumOutputInitWithInputAVX512(int64_t index, const float *layer_input, + float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_LD_F32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumOutputInitWithZeroAVX512(int64_t index, float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_MOV_F32(0.0f)); + } + return index; +} + +static inline int64_t CumsumAVX512(int64_t index, const float *layer_input, float *layer_output, float *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input + index); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output + index); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseAVX512(int64_t index, const float *layer_input, float *layer_output, + float *layer_last_output, int inner_dim) { + + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input - index - BLOCK_NUM + 1); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumIntOutputInitWithInputAVX512(int64_t index, const int32_t *layer_input, + int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_LD_EPI32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumIntOutputInitWithZeroAVX512(int64_t index, int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_MOV_EPI32(0.0f)); + } + return index; +} + +static inline int64_t CumsumIntAVX512(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input + index); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output + index); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseIntAVX512(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input - index - BLOCK_NUM + 1); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/div_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/div_fp32_avx512.h new file mode 100644 index 00000000..286cd239 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/div_fp32_avx512.h @@ -0,0 +1,167 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_AVX512_H_ +#define MINDSPORE_NNACL_FP32_DIV_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int ElementOptDivNum0AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivNum1AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum0AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum1AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num0AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num1AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementDivReluAVX512(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivRelu6AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/dropout_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/dropout_fp32_avx512.h new file mode 100644 index 00000000..eb847c23 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/dropout_fp32_avx512.h @@ -0,0 +1,46 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_DROPOUTFP32_AVX512_H_ +#define MINDSPORE_NNACL_FP32_DROPOUTFP32_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int DropoutFp32AVX512(int index, const float *input, float scale, + int length, float *output) { + SIMD_F32 scale_value = SIMD_MOV_F32(scale); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_F32(SIMD_LD_F32(input + index), scale_value)); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/exp_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/exp_fp32_avx512.h new file mode 100644 index 00000000..14386f5f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/exp_fp32_avx512.h @@ -0,0 +1,63 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_AVX512_H_ +#define MINDSPORE_NNACL_FP32_DIV_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t ExpFp32AVX512(int64_t index, const float *src, float *dst, int num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithInScaleAVX512(int64_t index, const float *src, float *dst, int num, float in_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(in_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_MUL_F32(SIMD_LD_F32(src + index), scale_vec), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithOutScaleAVX512(int64_t index, const float *src, float *dst, int num, float out_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(out_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_LD_F32(dst + index), scale_vec)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/fill_base_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/fill_base_avx512.h new file mode 100644 index 00000000..ad4f7706 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/fill_base_avx512.h @@ -0,0 +1,53 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_FILL_BASE_AVX512_H_ +#define NNACL_BASE_FILL_BASE_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int FillFp32AVX512(int index, float *output, int size, float data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MOV_F32(data)); + } + return index; +} + +static inline int FillInt32AVX512(int index, int *output, int size, int data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MOV_EPI32(data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif + diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/group_norm_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/group_norm_fp32_avx512.h new file mode 100644 index 00000000..f26537d9 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/group_norm_fp32_avx512.h @@ -0,0 +1,77 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_AVX512_H_ +#define MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t GroupNormFp32AVX512(int64_t index, const float *unit_input, float scale, float offset, float mean, + float var_sqrt, int unit, float *unit_output) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 v_sqrt = SIMD_MOV_F32(var_sqrt); + SIMD_F32 scale_val = SIMD_MOV_F32(scale); + SIMD_F32 offset_val = SIMD_MOV_F32(offset); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(unit_input + index); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input, mean_val), v_sqrt); + SIMD_F32 output = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_val), offset_val); + SIMD_ST_F32(unit_output + index, output); + } + return index; +} + +static inline int64_t GroupNormReduceSumAVX512(int64_t index, const float *in, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(in + index)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +static inline int64_t GroupNormReduceVarAVX512(int64_t index, const float *in, float mean, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_SUB_F32(SIMD_LD_F32(in + index), mean_val); + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_F32(input, input)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/layer_norm_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/layer_norm_fp32_avx512.h new file mode 100644 index 00000000..e5fb6d7b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/layer_norm_fp32_avx512.h @@ -0,0 +1,68 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_AVX512_H_ +#define MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int LayerNormMeanAndSquareAVX512(int index, const float *src, int num, float *mean, float *square_mean) { + if (num >= 4 * BLOCK_NUM) { + SIMD_F32 sum_val = SIMD_SET0_F32; + SIMD_F32 square_sum_val = SIMD_SET0_F32; + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 square_value = SIMD_MUL_F32(value, value); + sum_val = SIMD_ADD_F32(sum_val, value); + square_sum_val = SIMD_ADD_F32(square_sum_val, square_value); + } + *mean += SIMD_GET_SUM_F32(sum_val); + *square_mean += SIMD_GET_SUM_F32(square_sum_val); + } + return index; +} + +static inline int LayerNormGammaAndBetaAVX512(int index, float *dst, const float *src, const float *gamma_data, + const float *beta_data, int num, const float mean, const float deno) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 deno_val = SIMD_MOV_F32(deno); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 out_value = SIMD_SUB_F32(value, mean_val); + out_value = SIMD_MUL_F32(out_value, deno_val); + out_value = SIMD_FMADD_F32(out_value, SIMD_LD_F32(gamma_data + index), SIMD_LD_F32(beta_data + index)); + SIMD_ST_F32(dst + index, out_value); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/matmul_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/matmul_fp32_avx512.h new file mode 100644 index 00000000..a2311c8d --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/matmul_fp32_avx512.h @@ -0,0 +1,155 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_MATMUL_F32_AVX512_H_ +#define MINDSPORE_NNACL_FP32_MATMUL_F32_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t GemmIsNotPackAVX512(int64_t index, const float *a, const float *b, float *c, const float *bias, int row, + int deep, int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 b_data16 = SIMD_MOV_F32(b[0]); + SIMD_F32 bias_data16 = SIMD_MOV_F32(bias[0]); + for (int block_max_size = row - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_data = SIMD_LD_F32(a + index); + SIMD_F32 dst = SIMD_FMADD_F32(b_data16, a_data, bias_data16); + if (act_type != 0) { + dst = SIMD_MAX_F32(dst, down_threshold); + if (act_type == 3) { + dst = SIMD_MIN_F32(dst, up_threshold); + } + } + SIMD_ST_F32(c + index, dst); + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1GemmIsNotPackAVX512(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1NoBiasGemmIsNotPackAVX512(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +#if defined(MS_SIMD_AVX512) || defined(MS_SIMD_AVX) +static inline int64_t GemmIsNotPackOptimizeCoreAVX512(int64_t index, const float *a, const float *b, int k, float *dst) { + SIMD_F32 dst1 = SIMD_MOV_F32(0.0f); + for (int block_max_size = k - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 weight = SIMD_LD_F32(b + index); + SIMD_F32 a1 = SIMD_LD_F32(a + index); + dst1 = SIMD_FMADD_F32(weight, a1, dst1); + } + *dst += SIMD_REDUCE_ADD_F32(dst1); + return index; +} +#endif + +static inline int64_t MatVecMulNoPackCoreAVX512(int64_t oc_index, const float *a, const float *b, float *c, const float *bias, + int act_type, int64_t depth, int64_t oc, int64_t col, int64_t inc_flag) { + for (int64_t oc_max_size = oc - BLOCK_NUM; oc_index <= oc_max_size; oc_index += BLOCK_NUM) { + SIMD_F32 out = (inc_flag & 0x1) == 0 ? SIMD_LD_F32(c + oc_index) : (bias == NULL ? SIMD_MOV_F32(0.0f) : SIMD_LD_F32(bias + oc_index)); + for (int64_t k = 0; k < depth; ++k) { + SIMD_F32 left = SIMD_MOV_F32(a[k]); + SIMD_F32 right = SIMD_LD_F32(b + oc_index + k * col); + out = SIMD_FMADD_F32(left, right, out); + } + if ((inc_flag & 0x2) != 0 && act_type != 0) { + out = SIMD_MAX_F32(out, SIMD_MOV_F32(0.0f)); + if (act_type == 0x3) { + out = SIMD_MIN_F32(out, SIMD_MOV_F32(6.0f)); + } + } + SIMD_ST_F32(c + oc_index, out); + } + return oc_index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/mul_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/mul_fp32_avx512.h new file mode 100644 index 00000000..70b7ae23 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/mul_fp32_avx512.h @@ -0,0 +1,218 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_AVX512_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int ElementMulAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6AVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6IntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0_opt_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num0AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num1AVX512(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0_opt_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/pooling_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/pooling_fp32_avx512.h new file mode 100644 index 00000000..d1e001ee --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/pooling_fp32_avx512.h @@ -0,0 +1,84 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POOLING_AVX512_H_ +#define MINDSPORE_NNACL_FP32_POOLING_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int AvgPoolingBatchAVX512(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_avg = SIMD_SET0_F32; + int real_count = 0; + for (int h = real_win_h_start; h < real_win_h_end; h++) { + for (int w = real_win_w_start; w < real_win_w_end; w++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + h) * in_w + in_w_index + w) * channel; + tmp_avg = SIMD_ADD_F32(tmp_avg, SIMD_LD_F32(src_win_ptr)); + ++real_count; + } + } + tmp_avg = SIMD_DIV_F32(tmp_avg, SIMD_MOV_F32(real_count)); + tmp_avg = SIMD_MAX_F32(tmp_avg, min_val); + tmp_avg = SIMD_MIN_F32(tmp_avg, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_avg); + } + return ci; +} + +static inline int MaxPoolingBatchAVX512(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_max = min_val; + for (int kh = real_win_h_start; kh < real_win_h_end; kh++) { + for (int kw = real_win_w_start; kw < real_win_w_end; kw++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + kh) * in_w + in_w_index + kw) * channel; + tmp_max = SIMD_MAX_F32(tmp_max, SIMD_LD_F32(src_win_ptr)); + } + } + tmp_max = SIMD_MIN_F32(tmp_max, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_max); + } + return ci; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/power_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/power_fp32_avx512.h new file mode 100644 index 00000000..a31eaf2f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/power_fp32_avx512.h @@ -0,0 +1,101 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POWER_AVX512_H_ +#define MINDSPORE_NNACL_FP32_POWER_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int PowerBroadCastIntExponentAVX512(int index, const float *input, int exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result = SIMD_MOV_F32(1.0f); + int exp = abs(exponent); + while (exp) { + if (exp % 2) { + result = SIMD_MUL_F32(result, tmp); + } + tmp = SIMD_MUL_SQUARE_F32(tmp); + exp = exp / 2; + } + SIMD_ST_F32(output + index, exponent >= 0 ? result : SIMD_DIV_F32(SIMD_MOV_F32(1), result)); + } + return index; +} + +static inline int PowerBroadCastFloatExponentAVX512(int index, const float *input, float exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result; + for (int i = 0; i < BLOCK_NUM; ++i) { + SIMD_F32_GETI(result, i) = powf(SIMD_F32_GETI(tmp, i), exponent); + } + SIMD_ST_F32(output + index, result); + } + return index; +} + +static inline int PowerSingleExponentAVX512(int index, const float *input, const float *exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + for (int j = 0; j < BLOCK_NUM; ++j) { + float cur_exponent = exponent[index + j]; + float cur_val = SIMD_F32_GETI(tmp_vec, j); + if (fabsf(cur_exponent - (int)(cur_exponent)) < 0.000001) { + int exp = abs((int)(cur_exponent)); + float result = 1; + while (exp) { + if (exp % 2) { + result *= cur_val; + } + cur_val *= cur_val; + exp = exp / 2; + } + output[index + j] = *exponent >= 0 ? result : 1 / result; + } else { + output[index + j] = powf(cur_val, cur_exponent); + } + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/reduce_concat_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/reduce_concat_fp32_avx512.h new file mode 100644 index 00000000..9ae1f4ef --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/reduce_concat_fp32_avx512.h @@ -0,0 +1,122 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_AVX512_H_ +#define MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +#ifdef MS_SIMD_AVX512 +static inline int Fp32ReduceSumConcatAxisSize16FusionAVX512(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (1 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data)); + } + SIMD_ST_F32(output_data, zmm00); + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize32FusionAVX512(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (2 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize64FusionAVX512(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (4 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize128FusionAVX512(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + SIMD_F32 zmm04 = SIMD_LD_F32(input_data + 4 * BLOCK_NUM); + SIMD_F32 zmm05 = SIMD_LD_F32(input_data + 5 * BLOCK_NUM); + SIMD_F32 zmm06 = SIMD_LD_F32(input_data + 6 * BLOCK_NUM); + SIMD_F32 zmm07 = SIMD_LD_F32(input_data + 7 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (8 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + zmm04 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 4 * BLOCK_NUM)); + zmm05 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 5 * BLOCK_NUM)); + zmm06 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 6 * BLOCK_NUM)); + zmm07 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 7 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + SIMD_ST_F32(output_data + 4 * BLOCK_NUM, zmm04); + SIMD_ST_F32(output_data + 5 * BLOCK_NUM, zmm05); + SIMD_ST_F32(output_data + 6 * BLOCK_NUM, zmm06); + SIMD_ST_F32(output_data + 7 * BLOCK_NUM, zmm07); + + return index; +} + +#endif + + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/reduce_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/reduce_fp32_avx512.h new file mode 100644 index 00000000..feb04728 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/reduce_fp32_avx512.h @@ -0,0 +1,227 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_REDUCE_FP32_AVX512_H_ +#define MINDSPORE_NNACL_FP32_REDUCE_FP32_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t ReduceSumAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumByLastAxisAVX512(int64_t index, const float *src, float* tmp_sum, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_sum += SIMD_GET_SUM_F32(tmp); + return index; +} + +static inline int64_t ReduceMeanAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, SIMD_DIV_N_F32(tmp, axis_size)); + } + return index; +} + +static inline int64_t ReduceMinAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxByLastAxisAVX512(int64_t index, const float *src, float* tmp_max, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(*tmp_max); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_max = SIMD_GET_MAX_F32(tmp); + return index; +} + +static inline int64_t ReduceProdAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(1.0f); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MUL_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumSquareAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceL2NormAVX512(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, SIMD_SQRT_F32(tmp)); + } + return index; +} + +static inline int64_t IntReduceSumAVX512(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMeanAVX512(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, SIMD_DIV_N_EPI32(tmp, axis_size)); + } + return index; +} + +static inline int64_t IntReduceMinAVX512(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMaxAVX512(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumDim2Axis0AVX512(int64_t index, size_t col_size, size_t col_len, size_t row_len, const float *src_data, float *dst_data) { + for (int block_max_size = col_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + const float *inner_src = src_data + index; + float *inner_dst = dst_data + index; + for (size_t i = 0; i < row_len; ++i) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * col_len)); + } + SIMD_ST_F32(inner_dst, tmp); + } + return index; +} + +static inline int64_t FloatReduceDeviationAVX512(int64_t index, const float *src_data, float mean, size_t size, float *deviation) { + SIMD_F32 fs_deviation = SIMD_MOV_F32(0); + SIMD_F32 fs_mean = SIMD_MOV_F32(mean); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 fs_sub = SIMD_LD_F32(src_data + index); + + fs_sub = SIMD_SUB_F32(fs_sub, fs_mean); + SIMD_F32 fs_pow = SIMD_MUL_F32(fs_sub, fs_sub); + fs_deviation = SIMD_ADD_F32(fs_deviation, fs_pow); + } + *deviation += SIMD_GET_SUM_F32(fs_deviation); + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/scatter_nd_binary_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/scatter_nd_binary_avx512.h new file mode 100644 index 00000000..2f3f7117 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/scatter_nd_binary_avx512.h @@ -0,0 +1,52 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_SCATTER_ND_BINARY_AVX512_H_ +#define NNACL_BASE_SCATTER_ND_BINARY_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + + static inline int ScatterNDAddFp32AVX512(int index, const float *update, int size, float *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ADD_F32(SIMD_LD_F32(output + index), SIMD_LD_F32(update + index))); + } + return index; +} + +static inline int ScatterNDAddInt32AVX512(int index, const int *update, int size, int *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ADD_EPI32(SIMD_LD_EPI32(output + index), SIMD_LD_EPI32(update + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif // NNACL_BASE_SCATTER_ND_BINARY_AVX512_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/softmax_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/softmax_fp32_avx512.h new file mode 100644 index 00000000..1fa1907e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/softmax_fp32_avx512.h @@ -0,0 +1,87 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_AVX512_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t SoftmaxNormGetMaxAVX512(int64_t index, const float *src, int cur_batch_offset, + float *max, int channel) { + if (channel >= BLOCK_NUM * BLOCK_NUM) { + SIMD_F32 max_val = SIMD_MOV_F32(*max); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + max_val = SIMD_MAX_F32(max_val, SIMD_LD_F32(src + cur_batch_offset + index)); + } + *max = SIMD_GET_MAX_F32(max_val); + } + return index; +} + +static inline int64_t SoftmaxNormCalcNormAVX512(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, int channel) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 output = SIMD_SUB_F32(SIMD_LD_F32(src + cur_batch_offset + index), SIMD_MOV_F32(max)); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +static inline int64_t SoftmaxLastAxisGetExpSumAVX512(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, float *exp_sum, int channel) { +#ifndef _WIN32 + SIMD_F32 sum_val = SIMD_SET0_F32; + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_SUB_F32(input, SIMD_MOV_F32(max)); + SIMD_F32 exp_out = SIMD_EXP_F32(output); + sum_val = SIMD_ADD_F32(sum_val, exp_out); + SIMD_ST_F32(dst + cur_batch_offset + index, exp_out); + } + *exp_sum += SIMD_GET_SUM_F32(sum_val); +#endif + return index; +} + +static inline int64_t SoftmaxLastAxisGetResultAVX512(int64_t index, const float *src, float *dst, + int cur_batch_offset, float exp_sum, int channel) { + SIMD_F32 exp_sum_val = SIMD_MOV_F32(exp_sum); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_MUL_F32(input, exp_sum_val); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/softmax_grad_fusion_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/softmax_grad_fusion_fp32_avx512.h new file mode 100644 index 00000000..a0ed525c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/softmax_grad_fusion_fp32_avx512.h @@ -0,0 +1,62 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_AVX512_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int64_t SoftmaxGradFusionOptAVX512(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + result_vec = SIMD_FMADD_F32(a_vec, b_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t ElementOptSubMulAVX512(int index, const float *in0, const float *in1, float sum, + float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(sum); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_ST_F32(out + index, SIMD_MUL_F32(vin0, SIMD_SUB_F32(vin1, vin1_opt_))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/sub_fp32_avx512.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/sub_fp32_avx512.h new file mode 100644 index 00000000..e7a6b0c0 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/avx512/sub_fp32_avx512.h @@ -0,0 +1,167 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SUB_AVX512_H_ +#define MINDSPORE_NNACL_FP32_SUB_AVX512_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_avx512_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("avx512f") +#define MS_SIMD_INSTRUCTION MS_SIMD_AVX512_INSTRUCTION +#define BLOCK_NUM 16 +#define MS_SIMD_AVX512 + +static inline int ElementOptSubNum0AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubNum1AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum0AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum1AVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum0AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum1AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num0AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num1AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubAVX512(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubIntAVX512(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementSubReluAVX512(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubRelu6AVX512(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_AVX512 +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/batchnorm_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/batchnorm_fp32_simd.h new file mode 100644 index 00000000..b91d419e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/batchnorm_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BATCHNORM_FP32_SIMD_H_ +#define NNACL_BATCHNORM_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/batchnorm_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/batchnorm_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/batchnorm_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/batchnorm_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/bce_with_logits_loss_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/bce_with_logits_loss_fp32_simd.h new file mode 100644 index 00000000..ecd79008 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/bce_with_logits_loss_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BCE_WITH_LOGITS_LOSS_FP32_SIMD_H_ +#define NNACL_BCE_WITH_LOGITS_LOSS_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/bce_with_logits_loss_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/bce_with_logits_loss_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/bce_with_logits_loss_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/bce_with_logits_loss_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/bias_add_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/bias_add_simd.h new file mode 100644 index 00000000..c53d5b01 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/bias_add_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BIAS_ADD_SIMD_H_ +#define NNACL_BIAS_ADD_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/bias_add_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/bias_add_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/bias_add_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/bias_add_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cast_base_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cast_base_simd.h new file mode 100644 index 00000000..f9119a18 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cast_base_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_CAST_BASE_SIMD_H_ +#define NNACL_CAST_BASE_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/cast_base_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/cast_base_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/cast_base_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/cast_base_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cast_gather_reduce_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cast_gather_reduce_fp32_simd.h new file mode 100644 index 00000000..eac633ea --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cast_gather_reduce_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_CAST_GATHER_REDUCE_FP32_SIMD_H_ +#define NNACL_CAST_GATHER_REDUCE_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/cast_gather_reduce_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/cast_gather_reduce_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/cast_gather_reduce_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/cast_gather_reduce_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cdist_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cdist_fp32_simd.h new file mode 100644 index 00000000..dea57b2e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cdist_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_CDIST_FP32_SIMD_H_ +#define NNACL_CDIST_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/cdist_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/cdist_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/cdist_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/cdist_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cumsum_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cumsum_fp32_simd.h new file mode 100644 index 00000000..465a90b4 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/cumsum_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_CUMSUM_FP32_SIMD_H_ +#define NNACL_CUMSUM_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/cumsum_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/cumsum_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/cumsum_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/cumsum_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/div_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/div_fp32_simd.h new file mode 100644 index 00000000..a281aaf9 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/div_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_DIV_FP32_SIMD_H_ +#define NNACL_DIV_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/div_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/div_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/div_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/div_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/dropout_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/dropout_fp32_simd.h new file mode 100644 index 00000000..a307a6e0 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/dropout_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_DROPOUT_FP32_SIMD_H_ +#define NNACL_DROPOUT_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/dropout_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/dropout_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/dropout_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/dropout_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/exp_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/exp_fp32_simd.h new file mode 100644 index 00000000..dc245521 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/exp_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_EXP_FP32_SIMD_H_ +#define NNACL_EXP_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/exp_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/exp_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/exp_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/exp_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/fill_base_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/fill_base_simd.h new file mode 100644 index 00000000..b50c2cfb --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/fill_base_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FILL_BASE_SIMD_H_ +#define NNACL_FILL_BASE_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/fill_base_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/fill_base_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/fill_base_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/fill_base_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/group_norm_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/group_norm_fp32_simd.h new file mode 100644 index 00000000..4dc7212d --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/group_norm_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_GROUP_NORM_FP32_SIMD_H_ +#define NNACL_GROUP_NORM_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/group_norm_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/group_norm_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/group_norm_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/group_norm_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/layer_norm_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/layer_norm_fp32_simd.h new file mode 100644 index 00000000..b607da9e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/layer_norm_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_LAYER_NORM_FP32_SIMD_H_ +#define NNACL_LAYER_NORM_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/layer_norm_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/layer_norm_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/layer_norm_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/layer_norm_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/matmul_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/matmul_fp32_simd.h new file mode 100644 index 00000000..8b3ea331 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/matmul_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_MATMUL_FP32_SIMD_H_ +#define NNACL_MATMUL_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/matmul_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/matmul_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/matmul_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/matmul_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/mul_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/mul_fp32_simd.h new file mode 100644 index 00000000..ae690d08 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/mul_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_MUL_FP32_SIMD_H_ +#define NNACL_MUL_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/mul_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/mul_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/mul_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/mul_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/activation_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/activation_fp32_neon.h new file mode 100644 index 00000000..02425c88 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/activation_fp32_neon.h @@ -0,0 +1,284 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_NEON_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int Fp32ReluNEON(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_F32(SIMD_LD_F32(src + index), zero)); + } + return index; +} + +static inline int Int32ReluNEON(int index, const int32_t *src, int length, int32_t *dst) { + SIMD_EPI32 zero = SIMD_MOV_EPI32(0.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_MAX_EPI32(SIMD_LD_EPI32(src + index), zero)); + } + return index; +} + +static inline int Fp32Relu6NEON(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 six = SIMD_MOV_F32(6.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), zero, six)); + } + return index; +} + +static inline int Fp32ClipNEON(int index, const float *src, int length, float *dst, float min, float max) { + SIMD_F32 min_val = SIMD_MOV_F32(min); + SIMD_F32 max_val = SIMD_MOV_F32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int Int32ClipNEON(int index, const int32_t *src, int length, int32_t *dst, int min, int max) { + SIMD_EPI32 min_val = SIMD_MOV_EPI32(min); + SIMD_EPI32 max_val = SIMD_MOV_EPI32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_CLAMP_EPI32(SIMD_LD_EPI32(src + index), min_val, max_val)); + } + return index; +} + +static inline int LReluNEON(int index, const float *src, int length, float *dst, float alpha) { + SIMD_F32 alpha_data = SIMD_MOV_F32(alpha); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_MASK mask = SIMD_CMPGT_F32(SIMD_SET0_F32, src_tmp); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_F32(src_tmp, alpha_data), mask)); + } + return index; +} + +static inline int SigmoidNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, (SIMD_LD_F32(src + index))), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(SIMD_MOV_F32(1.0f), SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int TanhNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + index); + SIMD_ST_F32(dst + index, SIMD_TANH_F32(input)); + } + return index; +} + +static inline int SwishNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, src_value), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(src_value, SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int HSwishNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(SIMD_MUL_F32(src_value, relu6), 6)); + } + return index; +} + +static inline int HSigmoidNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(relu6, 6)); + } + return index; +} + +static inline int HardTanhNoLimitMinNEON(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MIN_N_F32(SIMD_LD_F32(src + index), max_val)); + } + return index; +} + +static inline int HardTanhNoLimitMaxNEON(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_N_F32(SIMD_LD_F32(src + index), min_val)); + } + return index; +} + +static inline int HardTanhLimitMinMaxNEON(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_N_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int GeluTanhApproximateNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 tmp1 = SIMD_FMADD_F32(SIMD_MUL_N_F32(in, 0.035677408136f), in, SIMD_MOV_F32(0.79788456080287f)); + SIMD_F32 tmp2 = SIMD_MUL_F32(tmp1, in); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_MUL_N_F32(in, 0.5f), SIMD_ADD_N_F32(SIMD_TANH_F32(tmp2), 1.0f))); + } + return index; +} + +static inline int GeluNEON(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_F32(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline SIMD_F32 SIMD_ERFCCHEBNEON(SIMD_F32 src) { + static const int ncof = 7; + const double cof[7] = {-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, -9.561514786808631e-3, + -9.46595344482036e-4, 3.66839497852761e-4, 4.2523324806907e-5}; + SIMD_F32 dst; + SIMD_F32 d = SIMD_SET0_F32; + SIMD_F32 dd = SIMD_SET0_F32; + SIMD_F32 t = SIMD_DIV_F32(SIMD_MOV_F32(2.0f), SIMD_ADD_F32(src, SIMD_MOV_F32(2.0f))); + SIMD_F32 ty = SIMD_SUB_F32(SIMD_MUL_F32(SIMD_MOV_F32(4.0f), t), SIMD_MOV_F32(2.0f)); + + for (int j = ncof - 1; j > 0; j--) { + SIMD_F32 tmp = d; + d = SIMD_SUB_F32(SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[j])), dd); + dd = tmp; + } + + dst = + SIMD_FMADD_F32(src, src, MS_FSMUL_F32(dd, SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[0])), SIMD_MOV_F32(0.5f))); + dst = SIMD_MUL_F32(t, SIMD_EXP_F32(SIMD_MUL_F32(SIMD_MOV_F32(-1.0f), dst))); + return dst; +} + +static inline SIMD_F32 SIMD_ERF_APPROXIMATENEON(SIMD_F32 src) { + SIMD_F32 abs_src = SIMD_ABS_F32(src); + SIMD_F32 sign = SIMD_GETSIGN_F32(src); + SIMD_F32 dst = SIMD_ERFCCHEBNEON(abs_src); + return SIMD_MUL_F32(sign, SIMD_SUB_F32(SIMD_MOV_F32(1.0f), dst)); +} + +static inline int GeluErfAPPROXIMATENEON(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_APPROXIMATENEON(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline int EluNEON(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(src_tmp), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int CeluNEON(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(SIMD_DIV_N_F32(src_tmp, alpha)), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int HardShrinkNEON(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & in */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), src_t); + /* v1 = (in < -lamdb) & in */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), src_t); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftShrinkNEON(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & (in - lamdb) */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), SIMD_SUB_F32(src_t, pos_lamdb_v)); + /* v1 = (in < -lamdb) & (in + lamdb) */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), SIMD_ADD_F32(src_t, pos_lamdb_v)); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftsignFp32OptNEON(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 divisor_tmp = SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_ABS_F32(src_tmp)); + SIMD_ST_F32(dst + index, SIMD_DIV_F32(src_tmp, divisor_tmp)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/activation_grad_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/activation_grad_neon.h new file mode 100644 index 00000000..7973a331 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/activation_grad_neon.h @@ -0,0 +1,56 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NNACL_FP32_GRAD_ACTIVATION_GRAD_NEON_H_ +#define NNACL_FP32_GRAD_ACTIVATION_GRAD_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int ShrinkGradNEON(int index, const float *src0, const float *src1, + int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src0_t = SIMD_LD_F32(src0 + index); + SIMD_F32 src1_t = SIMD_LD_F32(src1 + index); + + SIMD_MASK mask0 = SIMD_CMPLE_F32(src1_t, pos_lamdb_v); + SIMD_MASK mask1 = SIMD_CMPLE_F32(neg_lamdb_v, src1_t); + SIMD_MASK mask = SIMD_AND_MASK(mask0, mask1); + + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src0_t, SIMD_MOV_F32(0.0f), mask)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/adam_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/adam_fp32_neon.h new file mode 100644 index 00000000..fda41ec2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/adam_fp32_neon.h @@ -0,0 +1,209 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ADAM_FP32_NEON_H_ +#define MINDSPORE_NNACL_FP32_ADAM_FP32_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON +#ifdef MS_SIMD_AVX512 + static inline size_t AdamWeightDecayFp32NEON(size_t index, float *var, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + const float *gradient, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient + index); + + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_F32(var + index, var_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp16NEON(size_t index, float *var, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp32NEON(size_t index, float *var, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp16NEON(size_t index, int16_t *var16, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp32NEON(size_t index, int16_t *var16, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/add_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/add_fp32_neon.h new file mode 100644 index 00000000..ea44cd4b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/add_fp32_neon.h @@ -0,0 +1,123 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_ADD_NEON_H_ +#define MINDSPORE_NNACL_FP32_ADD_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int ElementOptAddNEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, + int size) { + SIMD_EPI32 vin0_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddReluNEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddRelu6NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddReluNEON(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddRelu6NEON(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/apply_proximal_adagrad_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/apply_proximal_adagrad_fp32_neon.h new file mode 100644 index 00000000..6498d72c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/apply_proximal_adagrad_fp32_neon.h @@ -0,0 +1,74 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_NEON_H_ +#define NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t ApplyProximalAdagradOptNEON( + int64_t index, float *var, float *accum, float lr, float l1, float l2, float *grad, int64_t size) { + SIMD_F32 lr_vec = SIMD_MOV_F32(lr); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec1 = SIMD_LD_F32(grad + index); + SIMD_F32 accum_vec = SIMD_LD_F32(accum + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + accum_vec = SIMD_FMADD_F32(tmp_vec1, tmp_vec1, accum_vec); + SIMD_F32 learn_rate_vec = SIMD_DIV_F32(lr_vec, SIMD_SQRT_F32(accum_vec)); + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(tmp_vec1, learn_rate_vec)); + SIMD_ST_F32(accum + index, accum_vec); + tmp_vec1 = SIMD_FMADD_F32(l2_vec, learn_rate_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + learn_rate_vec = SIMD_MUL_F32(learn_rate_vec, l1_vec); + learn_rate_vec = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), learn_rate_vec); + learn_rate_vec = SIMD_MAX_F32(learn_rate_vec, SIMD_MOV_F32(0.0f)); + learn_rate_vec = SIMD_DIV_F32(learn_rate_vec, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(learn_rate_vec, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/apply_proximal_gradient_descent_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/apply_proximal_gradient_descent_fp32_neon.h new file mode 100644 index 00000000..893215e2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/apply_proximal_gradient_descent_fp32_neon.h @@ -0,0 +1,70 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_NEON_H_ +#define NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t ApplyProximalGradientDescentOptNEON( + int64_t index, float *var, float alpha, float l1, float l2, float *delta, int64_t size) { + SIMD_F32 alpha_vec = SIMD_MOV_F32(alpha); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 delta_vec = SIMD_LD_F32(delta + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(delta_vec, alpha_vec)); + SIMD_F32 tmp_vec1 = SIMD_FMADD_F32(l2_vec, alpha_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + SIMD_F32 tmp_vec2 = SIMD_MUL_F32(alpha_vec, l1_vec); + tmp_vec2 = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), tmp_vec2); + tmp_vec2 = SIMD_MAX_F32(tmp_vec2, SIMD_MOV_F32(0.0f)); + tmp_vec2 = SIMD_DIV_F32(tmp_vec2, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(tmp_vec2, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/arithmetic_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/arithmetic_fp32_neon.h new file mode 100644 index 00000000..d9dbbf1a --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/arithmetic_fp32_neon.h @@ -0,0 +1,293 @@ +/** + * Copyright 2022-2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_NEON_H_ +#define MINDSPORE_NNACL_ARITHMETIC_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +#ifndef MS_SIMD_NEON +static inline int ElementFloorModNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementFloorDivNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, floor_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} +#endif + +static inline int ElementFloorDivIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline size_t AssignSubOptNEON(int index, float *in0, const float *in1, size_t size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(in0 + index, out_tmp); + } + return index; +} + +int ElementLogicalAndNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +int ElementOptLogicalAndNEON(int index, const float *in0, const float *in1, float *out, int size, bool first_scalar) { + if (first_scalar) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(*in0); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } else { + SIMD_F32 in1_tmp = SIMD_MOV_F32(*in1); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/arithmetic_self_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/arithmetic_self_fp32_neon.h new file mode 100644 index 00000000..e19cd46c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/arithmetic_self_fp32_neon.h @@ -0,0 +1,147 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_NEON_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +#if defined(MS_SIMD_AVX512) +// only avx512 support abs fp32 instruction +static inline int ElementAbsNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ABS_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementAbsIntNEON(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ABS_EPI32(SIMD_LD_EPI32(input + index))); + } + return index; +} +#endif + +#if !defined(MS_SIMD_NEON) +// not support neon + static inline int ElementCosNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_COS_F32(vin)); + } + return index; + } + + static inline int ElementLogNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_LOG_F32(vin)); + } + return index; + } +#endif + +static inline int ElementSquareNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_MUL_F32(vin, vin)); + } + return index; +} + +static inline int ElementSqrtNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_SQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementRsqrtNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_RSQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +#if defined(MS_SIMD_AVX) || defined(MS_SIMD_SSE) +// avx512 dont support round fp32 instruction +static inline int ElementRoundNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ROUND_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +// neon dont support floor fp32 instruction +static inline int ElementFloorNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_FLOOR_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +static inline int ElementCeilNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_CEIL_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +static inline int ElementNegativeNEON(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_N_F32(SIMD_LD_F32(input + index), -1.0f)); + } + return index; +} + +static inline int ElementNegativeIntNEON(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MUL_N_EPI32(SIMD_LD_EPI32(input + index), -1)); + } + return index; +} + +static inline int ElementReciprocalNEON(int index, const float *input, float *output, const int element_size) { + SIMD_F32 num1 = SIMD_MOV_F32(1.0f); + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_DIV_F32(num1, SIMD_LD_F32(input + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/batchnorm_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/batchnorm_fp32_neon.h new file mode 100644 index 00000000..5e169d62 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/batchnorm_fp32_neon.h @@ -0,0 +1,66 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_NEON_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int BatchNormFp32NEON(int index, const float *input, const float *mean, + const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 output_data = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +static inline int FusedBatchNormFp32NEON(int index, const float *input, const float *scale, + const float *offset, const float *mean, const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 scale_ = SIMD_LD_F32(scale + index); + SIMD_F32 offset_ = SIMD_LD_F32(offset + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_F32 output_data = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_), offset_); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/bce_with_logits_loss_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/bce_with_logits_loss_fp32_neon.h new file mode 100644 index 00000000..c12bc0f1 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/bce_with_logits_loss_fp32_neon.h @@ -0,0 +1,68 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_NEON_H_ +#define MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int BCEWithLogitLossNEON(int index, const float *logits, const float *label, + const float *weight, const float *pos_weight, int length, bool reduction, float *output, + float *reduction_sum) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 ones = SIMD_MOV_F32(1.0f); + SIMD_F32 middle_output = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 logits_tmp = SIMD_LD_F32(logits + index); + SIMD_F32 label_tmp = SIMD_LD_F32(label + index); + SIMD_F32 weight_tmp = SIMD_LD_F32(weight + index); + SIMD_F32 pos_weight_tmp = SIMD_LD_F32(pos_weight + index); + SIMD_F32 neg_logits_tmp = SIMD_SUB_F32(zero, logits_tmp); + SIMD_F32 max_value = neg_logits_tmp; + max_value = SIMD_MAX_F32(max_value, zero); + SIMD_F32 neg_max_value = SIMD_SUB_F32(zero, max_value); + SIMD_F32 log_weight = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(pos_weight_tmp, ones), label_tmp), ones); + SIMD_F32 log_exp_value = + SIMD_LOG_F32(SIMD_ADD_F32(SIMD_HEXP_F32(neg_max_value), SIMD_HEXP_F32(SIMD_SUB_F32(neg_logits_tmp, max_value)))); + SIMD_F32 loss = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(ones, label_tmp), logits_tmp), + SIMD_MUL_F32(log_weight, SIMD_ADD_F32(log_exp_value, max_value))); + if (reduction) { + middle_output = SIMD_FMADD_F32(loss, weight_tmp, middle_output); + } else { + SIMD_ST_F32(output + index, SIMD_MUL_F32(loss, weight_tmp)); + } + } + if (reduction) { + *reduction_sum += SIMD_GET_SUM_F32(middle_output); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/bias_add_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/bias_add_neon.h new file mode 100644 index 00000000..afaf0de5 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/bias_add_neon.h @@ -0,0 +1,63 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_BIAS_ADD_NEON_H_ +#define MINDSPORE_NNACL_FP32_BIAS_ADD_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int BiasAddByInnerCoreNEON(int index, const float *input, const float *bias, float *output, + int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(input + index); + SIMD_F32 vin1 = SIMD_LD_F32(bias + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(output + index, vout); + } + return index; +} + +static inline int BiasAddByBatchCoreNEON(int index, const float *input, const float *bias, float *output1, + float *output2, float *output3, float *output4, int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_LDX4_F32(input_data, input + index, num); + SIMD_F32 bias_data = SIMD_LD_F32(bias + index); + SIMD_ST_F32(output1 + index, SIMD_ADD_F32(input_data1, bias_data)); + SIMD_ST_F32(output2 + index, SIMD_ADD_F32(input_data2, bias_data)); + SIMD_ST_F32(output3 + index, SIMD_ADD_F32(input_data3, bias_data)); + SIMD_ST_F32(output4 + index, SIMD_ADD_F32(input_data4, bias_data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +}; +#endif + +#endif // MINDSPORE_NNACL_FP32_BIAS_ADD_SIMD_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cast_base_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cast_base_neon.h new file mode 100644 index 00000000..34e85827 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cast_base_neon.h @@ -0,0 +1,55 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_CAST_BASE_NEON_H_ +#define NNACL_BASE_CAST_BASE_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int Int32ToFloat32NEON(int index, const int32_t *input, float *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 value = SIMD_LD_EPI32(input + index); + SIMD_ST_F32(output + index, SIMD_EPI32_TO_F32(value)); + } + return index; +} + +#ifndef MS_SIMD_NEON +static inline int Float32ToInt32NEON(int index, const float *input, int32_t *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(input + index); + SIMD_ST_EPI32(output + index, SIMD_F32_TO_EPI32(value)); + } + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cast_gather_reduce_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cast_gather_reduce_fp32_neon.h new file mode 100644 index 00000000..9d725d6c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cast_gather_reduce_fp32_neon.h @@ -0,0 +1,71 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_NEON_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int Fp32CastGatherReduceInt64FusionNEON(int index, float *output_data, const int64_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int64_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + + +static inline int Fp32CastGatherReduceInt32FusionNEON(int index, float *output_data, const int32_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int32_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cdist_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cdist_fp32_neon.h new file mode 100644 index 00000000..09f55bbf --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cdist_fp32_neon.h @@ -0,0 +1,69 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CDIST_NEON_H_ +#define MINDSPORE_NNACL_FP32_CDIST_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t CdistTwoNormalOptNEON(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + result_vec = SIMD_FMADD_F32(tmp_vec, tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t CdistPNormalOptNEON(int64_t index, const float *a, const float *b, + float *out, int64_t size, float p) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + SIMD_F32 p_vec = SIMD_MOV_F32(p); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + tmp_vec = SIMD_POW_F32(tmp_vec, p_vec); + result_vec = SIMD_ADD_F32(tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cumsum_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cumsum_fp32_neon.h new file mode 100644 index 00000000..75552de8 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/cumsum_fp32_neon.h @@ -0,0 +1,120 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CUMSUM_NEON_H_ +#define MINDSPORE_NNACL_FP32_CUMSUM_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumOutputInitWithInputNEON(int64_t index, const float *layer_input, + float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_LD_F32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumOutputInitWithZeroNEON(int64_t index, float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_MOV_F32(0.0f)); + } + return index; +} + +static inline int64_t CumsumNEON(int64_t index, const float *layer_input, float *layer_output, float *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input + index); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output + index); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseNEON(int64_t index, const float *layer_input, float *layer_output, + float *layer_last_output, int inner_dim) { + + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input - index - BLOCK_NUM + 1); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumIntOutputInitWithInputNEON(int64_t index, const int32_t *layer_input, + int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_LD_EPI32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumIntOutputInitWithZeroNEON(int64_t index, int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_MOV_EPI32(0.0f)); + } + return index; +} + +static inline int64_t CumsumIntNEON(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input + index); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output + index); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseIntNEON(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input - index - BLOCK_NUM + 1); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/div_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/div_fp32_neon.h new file mode 100644 index 00000000..5a0995ae --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/div_fp32_neon.h @@ -0,0 +1,166 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_NEON_H_ +#define MINDSPORE_NNACL_FP32_DIV_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int ElementOptDivNum0NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivNum1NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum0NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum1NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num0NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num1NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementDivReluNEON(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivRelu6NEON(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/dropout_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/dropout_fp32_neon.h new file mode 100644 index 00000000..b71db336 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/dropout_fp32_neon.h @@ -0,0 +1,45 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_DROPOUTFP32_NEON_H_ +#define MINDSPORE_NNACL_FP32_DROPOUTFP32_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int DropoutFp32NEON(int index, const float *input, float scale, + int length, float *output) { + SIMD_F32 scale_value = SIMD_MOV_F32(scale); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_F32(SIMD_LD_F32(input + index), scale_value)); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/exp_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/exp_fp32_neon.h new file mode 100644 index 00000000..a594abd2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/exp_fp32_neon.h @@ -0,0 +1,62 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_NEON_H_ +#define MINDSPORE_NNACL_FP32_DIV_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t ExpFp32NEON(int64_t index, const float *src, float *dst, int num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithInScaleNEON(int64_t index, const float *src, float *dst, int num, float in_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(in_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_MUL_F32(SIMD_LD_F32(src + index), scale_vec), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithOutScaleNEON(int64_t index, const float *src, float *dst, int num, float out_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(out_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_LD_F32(dst + index), scale_vec)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/fill_base_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/fill_base_neon.h new file mode 100644 index 00000000..f32c6ddf --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/fill_base_neon.h @@ -0,0 +1,52 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_FILL_BASE_NEON_H_ +#define NNACL_BASE_FILL_BASE_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int FillFp32NEON(int index, float *output, int size, float data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MOV_F32(data)); + } + return index; +} + +static inline int FillInt32NEON(int index, int *output, int size, int data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MOV_EPI32(data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif + diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/group_norm_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/group_norm_fp32_neon.h new file mode 100644 index 00000000..0eb6c9d2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/group_norm_fp32_neon.h @@ -0,0 +1,76 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_NEON_H_ +#define MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t GroupNormFp32NEON(int64_t index, const float *unit_input, float scale, float offset, float mean, + float var_sqrt, int unit, float *unit_output) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 v_sqrt = SIMD_MOV_F32(var_sqrt); + SIMD_F32 scale_val = SIMD_MOV_F32(scale); + SIMD_F32 offset_val = SIMD_MOV_F32(offset); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(unit_input + index); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input, mean_val), v_sqrt); + SIMD_F32 output = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_val), offset_val); + SIMD_ST_F32(unit_output + index, output); + } + return index; +} + +static inline int64_t GroupNormReduceSumNEON(int64_t index, const float *in, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(in + index)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +static inline int64_t GroupNormReduceVarNEON(int64_t index, const float *in, float mean, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_SUB_F32(SIMD_LD_F32(in + index), mean_val); + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_F32(input, input)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/layer_norm_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/layer_norm_fp32_neon.h new file mode 100644 index 00000000..0c528616 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/layer_norm_fp32_neon.h @@ -0,0 +1,67 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_NEON_H_ +#define MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int LayerNormMeanAndSquareNEON(int index, const float *src, int num, float *mean, float *square_mean) { + if (num >= 4 * BLOCK_NUM) { + SIMD_F32 sum_val = SIMD_SET0_F32; + SIMD_F32 square_sum_val = SIMD_SET0_F32; + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 square_value = SIMD_MUL_F32(value, value); + sum_val = SIMD_ADD_F32(sum_val, value); + square_sum_val = SIMD_ADD_F32(square_sum_val, square_value); + } + *mean += SIMD_GET_SUM_F32(sum_val); + *square_mean += SIMD_GET_SUM_F32(square_sum_val); + } + return index; +} + +static inline int LayerNormGammaAndBetaNEON(int index, float *dst, const float *src, const float *gamma_data, + const float *beta_data, int num, const float mean, const float deno) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 deno_val = SIMD_MOV_F32(deno); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 out_value = SIMD_SUB_F32(value, mean_val); + out_value = SIMD_MUL_F32(out_value, deno_val); + out_value = SIMD_FMADD_F32(out_value, SIMD_LD_F32(gamma_data + index), SIMD_LD_F32(beta_data + index)); + SIMD_ST_F32(dst + index, out_value); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/matmul_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/matmul_fp32_neon.h new file mode 100644 index 00000000..b49c6d7a --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/matmul_fp32_neon.h @@ -0,0 +1,154 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_MATMUL_F32_NEON_H_ +#define MINDSPORE_NNACL_FP32_MATMUL_F32_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t GemmIsNotPackNEON(int64_t index, const float *a, const float *b, float *c, const float *bias, int row, + int deep, int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 b_data16 = SIMD_MOV_F32(b[0]); + SIMD_F32 bias_data16 = SIMD_MOV_F32(bias[0]); + for (int block_max_size = row - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_data = SIMD_LD_F32(a + index); + SIMD_F32 dst = SIMD_FMADD_F32(b_data16, a_data, bias_data16); + if (act_type != 0) { + dst = SIMD_MAX_F32(dst, down_threshold); + if (act_type == 3) { + dst = SIMD_MIN_F32(dst, up_threshold); + } + } + SIMD_ST_F32(c + index, dst); + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1GemmIsNotPackNEON(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1NoBiasGemmIsNotPackNEON(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +#if defined(MS_SIMD_AVX512) || defined(MS_SIMD_AVX) +static inline int64_t GemmIsNotPackOptimizeCoreNEON(int64_t index, const float *a, const float *b, int k, float *dst) { + SIMD_F32 dst1 = SIMD_MOV_F32(0.0f); + for (int block_max_size = k - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 weight = SIMD_LD_F32(b + index); + SIMD_F32 a1 = SIMD_LD_F32(a + index); + dst1 = SIMD_FMADD_F32(weight, a1, dst1); + } + *dst += SIMD_REDUCE_ADD_F32(dst1); + return index; +} +#endif + +static inline int64_t MatVecMulNoPackCoreNEON(int64_t oc_index, const float *a, const float *b, float *c, const float *bias, + int act_type, int64_t depth, int64_t oc, int64_t col, int64_t inc_flag) { + for (int64_t oc_max_size = oc - BLOCK_NUM; oc_index <= oc_max_size; oc_index += BLOCK_NUM) { + SIMD_F32 out = (inc_flag & 0x1) == 0 ? SIMD_LD_F32(c + oc_index) : (bias == NULL ? SIMD_MOV_F32(0.0f) : SIMD_LD_F32(bias + oc_index)); + for (int64_t k = 0; k < depth; ++k) { + SIMD_F32 left = SIMD_MOV_F32(a[k]); + SIMD_F32 right = SIMD_LD_F32(b + oc_index + k * col); + out = SIMD_FMADD_F32(left, right, out); + } + if ((inc_flag & 0x2) != 0 && act_type != 0) { + out = SIMD_MAX_F32(out, SIMD_MOV_F32(0.0f)); + if (act_type == 0x3) { + out = SIMD_MIN_F32(out, SIMD_MOV_F32(6.0f)); + } + } + SIMD_ST_F32(c + oc_index, out); + } + return oc_index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/mul_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/mul_fp32_neon.h new file mode 100644 index 00000000..f157a774 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/mul_fp32_neon.h @@ -0,0 +1,217 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_NEON_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int ElementMulNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6NEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6IntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0_opt_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num0NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num1NEON(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0_opt_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/pooling_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/pooling_fp32_neon.h new file mode 100644 index 00000000..ea6acf62 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/pooling_fp32_neon.h @@ -0,0 +1,83 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POOLING_NEON_H_ +#define MINDSPORE_NNACL_FP32_POOLING_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int AvgPoolingBatchNEON(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_avg = SIMD_SET0_F32; + int real_count = 0; + for (int h = real_win_h_start; h < real_win_h_end; h++) { + for (int w = real_win_w_start; w < real_win_w_end; w++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + h) * in_w + in_w_index + w) * channel; + tmp_avg = SIMD_ADD_F32(tmp_avg, SIMD_LD_F32(src_win_ptr)); + ++real_count; + } + } + tmp_avg = SIMD_DIV_F32(tmp_avg, SIMD_MOV_F32(real_count)); + tmp_avg = SIMD_MAX_F32(tmp_avg, min_val); + tmp_avg = SIMD_MIN_F32(tmp_avg, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_avg); + } + return ci; +} + +static inline int MaxPoolingBatchNEON(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_max = min_val; + for (int kh = real_win_h_start; kh < real_win_h_end; kh++) { + for (int kw = real_win_w_start; kw < real_win_w_end; kw++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + kh) * in_w + in_w_index + kw) * channel; + tmp_max = SIMD_MAX_F32(tmp_max, SIMD_LD_F32(src_win_ptr)); + } + } + tmp_max = SIMD_MIN_F32(tmp_max, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_max); + } + return ci; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/power_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/power_fp32_neon.h new file mode 100644 index 00000000..fd8699c7 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/power_fp32_neon.h @@ -0,0 +1,100 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POWER_NEON_H_ +#define MINDSPORE_NNACL_FP32_POWER_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int PowerBroadCastIntExponentNEON(int index, const float *input, int exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result = SIMD_MOV_F32(1.0f); + int exp = abs(exponent); + while (exp) { + if (exp % 2) { + result = SIMD_MUL_F32(result, tmp); + } + tmp = SIMD_MUL_SQUARE_F32(tmp); + exp = exp / 2; + } + SIMD_ST_F32(output + index, exponent >= 0 ? result : SIMD_DIV_F32(SIMD_MOV_F32(1), result)); + } + return index; +} + +static inline int PowerBroadCastFloatExponentNEON(int index, const float *input, float exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result; + for (int i = 0; i < BLOCK_NUM; ++i) { + SIMD_F32_GETI(result, i) = powf(SIMD_F32_GETI(tmp, i), exponent); + } + SIMD_ST_F32(output + index, result); + } + return index; +} + +static inline int PowerSingleExponentNEON(int index, const float *input, const float *exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + for (int j = 0; j < BLOCK_NUM; ++j) { + float cur_exponent = exponent[index + j]; + float cur_val = SIMD_F32_GETI(tmp_vec, j); + if (fabsf(cur_exponent - (int)(cur_exponent)) < 0.000001) { + int exp = abs((int)(cur_exponent)); + float result = 1; + while (exp) { + if (exp % 2) { + result *= cur_val; + } + cur_val *= cur_val; + exp = exp / 2; + } + output[index + j] = *exponent >= 0 ? result : 1 / result; + } else { + output[index + j] = powf(cur_val, cur_exponent); + } + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/reduce_concat_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/reduce_concat_fp32_neon.h new file mode 100644 index 00000000..1be9d7ea --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/reduce_concat_fp32_neon.h @@ -0,0 +1,121 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_NEON_H_ +#define MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +#ifdef MS_SIMD_AVX512 +static inline int Fp32ReduceSumConcatAxisSize16FusionNEON(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (1 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data)); + } + SIMD_ST_F32(output_data, zmm00); + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize32FusionNEON(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (2 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize64FusionNEON(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (4 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize128FusionNEON(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + SIMD_F32 zmm04 = SIMD_LD_F32(input_data + 4 * BLOCK_NUM); + SIMD_F32 zmm05 = SIMD_LD_F32(input_data + 5 * BLOCK_NUM); + SIMD_F32 zmm06 = SIMD_LD_F32(input_data + 6 * BLOCK_NUM); + SIMD_F32 zmm07 = SIMD_LD_F32(input_data + 7 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (8 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + zmm04 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 4 * BLOCK_NUM)); + zmm05 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 5 * BLOCK_NUM)); + zmm06 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 6 * BLOCK_NUM)); + zmm07 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 7 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + SIMD_ST_F32(output_data + 4 * BLOCK_NUM, zmm04); + SIMD_ST_F32(output_data + 5 * BLOCK_NUM, zmm05); + SIMD_ST_F32(output_data + 6 * BLOCK_NUM, zmm06); + SIMD_ST_F32(output_data + 7 * BLOCK_NUM, zmm07); + + return index; +} + +#endif + + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/reduce_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/reduce_fp32_neon.h new file mode 100644 index 00000000..c72d510e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/reduce_fp32_neon.h @@ -0,0 +1,226 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_REDUCE_FP32_NEON_H_ +#define MINDSPORE_NNACL_FP32_REDUCE_FP32_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t ReduceSumNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumByLastAxisNEON(int64_t index, const float *src, float* tmp_sum, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_sum += SIMD_GET_SUM_F32(tmp); + return index; +} + +static inline int64_t ReduceMeanNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, SIMD_DIV_N_F32(tmp, axis_size)); + } + return index; +} + +static inline int64_t ReduceMinNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxByLastAxisNEON(int64_t index, const float *src, float* tmp_max, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(*tmp_max); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_max = SIMD_GET_MAX_F32(tmp); + return index; +} + +static inline int64_t ReduceProdNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(1.0f); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MUL_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumSquareNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceL2NormNEON(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, SIMD_SQRT_F32(tmp)); + } + return index; +} + +static inline int64_t IntReduceSumNEON(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMeanNEON(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, SIMD_DIV_N_EPI32(tmp, axis_size)); + } + return index; +} + +static inline int64_t IntReduceMinNEON(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMaxNEON(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumDim2Axis0NEON(int64_t index, size_t col_size, size_t col_len, size_t row_len, const float *src_data, float *dst_data) { + for (int block_max_size = col_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + const float *inner_src = src_data + index; + float *inner_dst = dst_data + index; + for (size_t i = 0; i < row_len; ++i) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * col_len)); + } + SIMD_ST_F32(inner_dst, tmp); + } + return index; +} + +static inline int64_t FloatReduceDeviationNEON(int64_t index, const float *src_data, float mean, size_t size, float *deviation) { + SIMD_F32 fs_deviation = SIMD_MOV_F32(0); + SIMD_F32 fs_mean = SIMD_MOV_F32(mean); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 fs_sub = SIMD_LD_F32(src_data + index); + + fs_sub = SIMD_SUB_F32(fs_sub, fs_mean); + SIMD_F32 fs_pow = SIMD_MUL_F32(fs_sub, fs_sub); + fs_deviation = SIMD_ADD_F32(fs_deviation, fs_pow); + } + *deviation += SIMD_GET_SUM_F32(fs_deviation); + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/scatter_nd_binary_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/scatter_nd_binary_neon.h new file mode 100644 index 00000000..91f00900 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/scatter_nd_binary_neon.h @@ -0,0 +1,51 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_SCATTER_ND_BINARY_NEON_H_ +#define NNACL_BASE_SCATTER_ND_BINARY_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + + static inline int ScatterNDAddFp32NEON(int index, const float *update, int size, float *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ADD_F32(SIMD_LD_F32(output + index), SIMD_LD_F32(update + index))); + } + return index; +} + +static inline int ScatterNDAddInt32NEON(int index, const int *update, int size, int *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ADD_EPI32(SIMD_LD_EPI32(output + index), SIMD_LD_EPI32(update + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif // NNACL_BASE_SCATTER_ND_BINARY_NEON_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/softmax_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/softmax_fp32_neon.h new file mode 100644 index 00000000..f116d92f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/softmax_fp32_neon.h @@ -0,0 +1,86 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_NEON_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t SoftmaxNormGetMaxNEON(int64_t index, const float *src, int cur_batch_offset, + float *max, int channel) { + if (channel >= BLOCK_NUM * BLOCK_NUM) { + SIMD_F32 max_val = SIMD_MOV_F32(*max); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + max_val = SIMD_MAX_F32(max_val, SIMD_LD_F32(src + cur_batch_offset + index)); + } + *max = SIMD_GET_MAX_F32(max_val); + } + return index; +} + +static inline int64_t SoftmaxNormCalcNormNEON(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, int channel) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 output = SIMD_SUB_F32(SIMD_LD_F32(src + cur_batch_offset + index), SIMD_MOV_F32(max)); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +static inline int64_t SoftmaxLastAxisGetExpSumNEON(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, float *exp_sum, int channel) { +#ifndef _WIN32 + SIMD_F32 sum_val = SIMD_SET0_F32; + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_SUB_F32(input, SIMD_MOV_F32(max)); + SIMD_F32 exp_out = SIMD_EXP_F32(output); + sum_val = SIMD_ADD_F32(sum_val, exp_out); + SIMD_ST_F32(dst + cur_batch_offset + index, exp_out); + } + *exp_sum += SIMD_GET_SUM_F32(sum_val); +#endif + return index; +} + +static inline int64_t SoftmaxLastAxisGetResultNEON(int64_t index, const float *src, float *dst, + int cur_batch_offset, float exp_sum, int channel) { + SIMD_F32 exp_sum_val = SIMD_MOV_F32(exp_sum); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_MUL_F32(input, exp_sum_val); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/softmax_grad_fusion_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/softmax_grad_fusion_fp32_neon.h new file mode 100644 index 00000000..c2057835 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/softmax_grad_fusion_fp32_neon.h @@ -0,0 +1,61 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_NEON_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int64_t SoftmaxGradFusionOptNEON(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + result_vec = SIMD_FMADD_F32(a_vec, b_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t ElementOptSubMulNEON(int index, const float *in0, const float *in1, float sum, + float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(sum); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_ST_F32(out + index, SIMD_MUL_F32(vin0, SIMD_SUB_F32(vin1, vin1_opt_))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/sub_fp32_neon.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/sub_fp32_neon.h new file mode 100644 index 00000000..1fae8d24 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/neon/sub_fp32_neon.h @@ -0,0 +1,166 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SUB_NEON_H_ +#define MINDSPORE_NNACL_FP32_SUB_NEON_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_neon_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define MS_SIMD_INSTRUCTION MS_SIMD_NEON_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_NEON + +static inline int ElementOptSubNum0NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubNum1NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum0NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum1NEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum0NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum1NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num0NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num1NEON(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubNEON(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubIntNEON(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementSubReluNEON(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubRelu6NEON(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM + +#undef MS_SIMD_NEON +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/pooling_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/pooling_fp32_simd.h new file mode 100644 index 00000000..90e4ca4f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/pooling_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_POOLING_FP32_SIMD_H_ +#define NNACL_POOLING_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/pooling_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/pooling_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/pooling_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/pooling_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/power_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/power_fp32_simd.h new file mode 100644 index 00000000..acf36877 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/power_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_POWER_FP32_SIMD_H_ +#define NNACL_POWER_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/power_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/power_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/power_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/power_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/reduce_concat_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/reduce_concat_fp32_simd.h new file mode 100644 index 00000000..37f549bb --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/reduce_concat_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_REDUCE_CONCAT_FP32_SIMD_H_ +#define NNACL_REDUCE_CONCAT_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/reduce_concat_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/reduce_concat_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/reduce_concat_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/reduce_concat_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/reduce_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/reduce_fp32_simd.h new file mode 100644 index 00000000..595833e0 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/reduce_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_REDUCE_FP32_SIMD_H_ +#define NNACL_REDUCE_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/reduce_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/reduce_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/reduce_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/reduce_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/scatter_nd_binary_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/scatter_nd_binary_simd.h new file mode 100644 index 00000000..4e384f77 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/scatter_nd_binary_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_SCATTER_ND_BINARY_SIMD_H_ +#define NNACL_SCATTER_ND_BINARY_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/scatter_nd_binary_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/scatter_nd_binary_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/scatter_nd_binary_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/scatter_nd_binary_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/softmax_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/softmax_fp32_simd.h new file mode 100644 index 00000000..48ac7afa --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/softmax_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_SOFTMAX_FP32_SIMD_H_ +#define NNACL_SOFTMAX_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/softmax_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/softmax_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/softmax_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/softmax_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/softmax_grad_fusion_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/softmax_grad_fusion_fp32_simd.h new file mode 100644 index 00000000..8f2d180b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/softmax_grad_fusion_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_SOFTMAX_GRAD_FUSION_FP32_SIMD_H_ +#define NNACL_SOFTMAX_GRAD_FUSION_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/softmax_grad_fusion_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/softmax_grad_fusion_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/softmax_grad_fusion_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/softmax_grad_fusion_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/activation_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/activation_fp32_sse.h new file mode 100644 index 00000000..90cc0dd5 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/activation_fp32_sse.h @@ -0,0 +1,285 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_SSE_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int Fp32ReluSSE(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_F32(SIMD_LD_F32(src + index), zero)); + } + return index; +} + +static inline int Int32ReluSSE(int index, const int32_t *src, int length, int32_t *dst) { + SIMD_EPI32 zero = SIMD_MOV_EPI32(0.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_MAX_EPI32(SIMD_LD_EPI32(src + index), zero)); + } + return index; +} + +static inline int Fp32Relu6SSE(int index, const float *src, int length, float *dst) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 six = SIMD_MOV_F32(6.0f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), zero, six)); + } + return index; +} + +static inline int Fp32ClipSSE(int index, const float *src, int length, float *dst, float min, float max) { + SIMD_F32 min_val = SIMD_MOV_F32(min); + SIMD_F32 max_val = SIMD_MOV_F32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int Int32ClipSSE(int index, const int32_t *src, int length, int32_t *dst, int min, int max) { + SIMD_EPI32 min_val = SIMD_MOV_EPI32(min); + SIMD_EPI32 max_val = SIMD_MOV_EPI32(max); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(dst + index, SIMD_CLAMP_EPI32(SIMD_LD_EPI32(src + index), min_val, max_val)); + } + return index; +} + +static inline int LReluSSE(int index, const float *src, int length, float *dst, float alpha) { + SIMD_F32 alpha_data = SIMD_MOV_F32(alpha); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_MASK mask = SIMD_CMPGT_F32(SIMD_SET0_F32, src_tmp); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_F32(src_tmp, alpha_data), mask)); + } + return index; +} + +static inline int SigmoidSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, (SIMD_LD_F32(src + index))), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(SIMD_MOV_F32(1.0f), SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int TanhSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + index); + SIMD_ST_F32(dst + index, SIMD_TANH_F32(input)); + } + return index; +} + +static inline int SwishSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_EXP_ST_F32(SIMD_SUB_F32(SIMD_SET0_F32, src_value), dst + index); + SIMD_ST_F32(dst + index, + SIMD_DIV_F32(src_value, SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_LD_F32(dst + index)))); + } + return index; +} + +static inline int HSwishSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(SIMD_MUL_F32(src_value, relu6), 6)); + } + return index; +} + +static inline int HSigmoidSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_value = SIMD_LD_F32(src + index); + SIMD_F32 relu6 = SIMD_CLAMP_N_F32(SIMD_ADD_N_F32(src_value, 3), 0, 6); + SIMD_ST_F32(dst + index, SIMD_DIV_N_F32(relu6, 6)); + } + return index; +} + +static inline int HardTanhNoLimitMinSSE(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MIN_N_F32(SIMD_LD_F32(src + index), max_val)); + } + return index; +} + +static inline int HardTanhNoLimitMaxSSE(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_MAX_N_F32(SIMD_LD_F32(src + index), min_val)); + } + return index; +} + +static inline int HardTanhLimitMinMaxSSE(int index, const float *src, int length, float *dst, float min_val, + float max_val) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(dst + index, SIMD_CLAMP_N_F32(SIMD_LD_F32(src + index), min_val, max_val)); + } + return index; +} + +static inline int GeluTanhApproximateSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 tmp1 = SIMD_FMADD_F32(SIMD_MUL_N_F32(in, 0.035677408136f), in, SIMD_MOV_F32(0.79788456080287f)); + SIMD_F32 tmp2 = SIMD_MUL_F32(tmp1, in); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_MUL_N_F32(in, 0.5f), SIMD_ADD_N_F32(SIMD_TANH_F32(tmp2), 1.0f))); + } + return index; +} + +static inline int GeluSSE(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_F32(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline SIMD_F32 SIMD_ERFCCHEBSSE(SIMD_F32 src) { + static const int ncof = 7; + const double cof[7] = {-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, -9.561514786808631e-3, + -9.46595344482036e-4, 3.66839497852761e-4, 4.2523324806907e-5}; + SIMD_F32 dst; + SIMD_F32 d = SIMD_SET0_F32; + SIMD_F32 dd = SIMD_SET0_F32; + SIMD_F32 t = SIMD_DIV_F32(SIMD_MOV_F32(2.0f), SIMD_ADD_F32(src, SIMD_MOV_F32(2.0f))); + SIMD_F32 ty = SIMD_SUB_F32(SIMD_MUL_F32(SIMD_MOV_F32(4.0f), t), SIMD_MOV_F32(2.0f)); + + for (int j = ncof - 1; j > 0; j--) { + SIMD_F32 tmp = d; + d = SIMD_SUB_F32(SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[j])), dd); + dd = tmp; + } + + dst = + SIMD_FMADD_F32(src, src, MS_FSMUL_F32(dd, SIMD_FMADD_F32(ty, d, SIMD_MOV_F32(cof[0])), SIMD_MOV_F32(0.5f))); + dst = SIMD_MUL_F32(t, SIMD_EXP_F32(SIMD_MUL_F32(SIMD_MOV_F32(-1.0f), dst))); + return dst; +} + +static inline SIMD_F32 SIMD_ERF_APPROXIMATESSE(SIMD_F32 src) { + SIMD_F32 abs_src = SIMD_ABS_F32(src); + SIMD_F32 sign = SIMD_GETSIGN_F32(src); + SIMD_F32 dst = SIMD_ERFCCHEBSSE(abs_src); + return SIMD_MUL_F32(sign, SIMD_SUB_F32(SIMD_MOV_F32(1.0f), dst)); +} + +static inline int GeluErfAPPROXIMATESSE(int index, const float *src, int length, float *dst) { + SIMD_F32 para1 = SIMD_MOV_F32(1.4142135623730951f); + SIMD_F32 para2 = SIMD_MOV_F32(1.0f); + SIMD_F32 para3 = SIMD_MOV_F32(0.5f); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in = SIMD_LD_F32(src + index); + SIMD_F32 res = SIMD_MUL_F32(SIMD_MUL_F32(para3, in), SIMD_ADD_F32(para2, SIMD_ERF_APPROXIMATESSE(SIMD_DIV_F32(in, para1)))); + SIMD_ST_F32(dst + index, res); + } + return index; +} + +static inline int EluSSE(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(src_tmp), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int CeluSSE(int index, const float *src, int length, float *dst, float alpha) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 exp_tmp = SIMD_SUB_N_F32(SIMD_EXP_F32(SIMD_DIV_N_F32(src_tmp, alpha)), 1.0f); + SIMD_MASK mask = SIMD_CMPLE_F32(src_tmp, SIMD_SET0_F32); + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src_tmp, SIMD_MUL_N_F32(exp_tmp, alpha), mask)); + } + return index; +} + +static inline int HardShrinkSSE(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & in */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), src_t); + /* v1 = (in < -lamdb) & in */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), src_t); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftShrinkSSE(int index, const float *src, int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_t = SIMD_LD_F32(src + index); + /* v0 = (in > lamdb) & (in - lamdb) */ + SIMD_F32 value0 = SIMD_AND_MASK_F32(SIMD_CMPGT_F32(src_t, pos_lamdb_v), SIMD_SUB_F32(src_t, pos_lamdb_v)); + /* v1 = (in < -lamdb) & (in + lamdb) */ + SIMD_F32 value1 = SIMD_AND_MASK_F32(SIMD_CMPLT_F32(src_t, neg_lamdb_v), SIMD_ADD_F32(src_t, pos_lamdb_v)); + /* out = (v0 | v1) */ + SIMD_ST_F32(dst + index, SIMD_OR_F32(value0, value1)); + } + return index; +} + +static inline int SoftsignFp32OptSSE(int index, const float *src, int length, float *dst) { + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src_tmp = SIMD_LD_F32(src + index); + SIMD_F32 divisor_tmp = SIMD_ADD_F32(SIMD_MOV_F32(1.0f), SIMD_ABS_F32(src_tmp)); + SIMD_ST_F32(dst + index, SIMD_DIV_F32(src_tmp, divisor_tmp)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/activation_grad_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/activation_grad_sse.h new file mode 100644 index 00000000..3d0e052b --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/activation_grad_sse.h @@ -0,0 +1,57 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NNACL_FP32_GRAD_ACTIVATION_GRAD_SSE_H_ +#define NNACL_FP32_GRAD_ACTIVATION_GRAD_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int ShrinkGradSSE(int index, const float *src0, const float *src1, + int length, float *dst, float lambd) { + SIMD_F32 pos_lamdb_v = SIMD_MOV_F32(lambd); + SIMD_F32 neg_lamdb_v = SIMD_MOV_F32(-lambd); + + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 src0_t = SIMD_LD_F32(src0 + index); + SIMD_F32 src1_t = SIMD_LD_F32(src1 + index); + + SIMD_MASK mask0 = SIMD_CMPLE_F32(src1_t, pos_lamdb_v); + SIMD_MASK mask1 = SIMD_CMPLE_F32(neg_lamdb_v, src1_t); + SIMD_MASK mask = SIMD_AND_MASK(mask0, mask1); + + SIMD_ST_F32(dst + index, SIMD_BLEND_F32(src0_t, SIMD_MOV_F32(0.0f), mask)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/adam_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/adam_fp32_sse.h new file mode 100644 index 00000000..1f5291a4 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/adam_fp32_sse.h @@ -0,0 +1,210 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ADAM_FP32_SSE_H_ +#define MINDSPORE_NNACL_FP32_ADAM_FP32_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE +#ifdef MS_SIMD_AVX512 + static inline size_t AdamWeightDecayFp32SSE(size_t index, float *var, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + const float *gradient, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient + index); + + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_F32(var + index, var_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp16SSE(size_t index, float *var, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp32Fp32SSE(size_t index, float *var, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_LD_F32(var + index); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(var + index, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp16SSE(size_t index, int16_t *var16, const int16_t *gradient16, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(gradient16 + index)); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} + +static inline size_t FusedCastAdamFp16Fp32SSE(size_t index, int16_t *var16, const float *gradient32, float *m, float *v, float lr, float beta1, float beta2, float epsilon, float decay, + float global_norm_reciprocal, size_t end) { + SIMD_F32 beta1_r = SIMD_MOV_F32(beta1); + SIMD_F32 beta2_r = SIMD_MOV_F32(beta2); + SIMD_F32 beta1_minus_r = SIMD_MOV_F32(1.0f - beta1); + SIMD_F32 beta2_minus_r = SIMD_MOV_F32(1.0f - beta2); + SIMD_F32 lr_neg_r = SIMD_MOV_F32(-lr); + SIMD_F32 epsilon_r = SIMD_MOV_F32(epsilon); + SIMD_F32 decay_r = SIMD_MOV_F32(decay); + SIMD_F32 global_norm_reciprocal_r = SIMD_MOV_F32(global_norm_reciprocal); + + for (size_t block_max_size = end - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 var_r = SIMD_F16_TO_F32(SIMD_LD_HALF_EPI32(var16)); + SIMD_F32 m_r = SIMD_LD_F32(m + index); + SIMD_F32 v_r = SIMD_LD_F32(v + index); + SIMD_F32 g_r = SIMD_LD_F32(gradient32 + index); + g_r = SIMD_MUL_F32(g_r, global_norm_reciprocal_r); + m_r = SIMD_MUL_F32(m_r, beta1_r); + v_r = SIMD_MUL_F32(v_r, beta2_r); + SIMD_F32 avx_r0 = SIMD_MUL_F32(g_r, g_r); + m_r = SIMD_FMADD_F32(g_r, beta1_minus_r, m_r); + v_r = SIMD_FMADD_F32(avx_r0, beta2_minus_r, v_r); + avx_r0 = SIMD_SQRT_F32(v_r); + avx_r0 = SIMD_DIV_F32(m_r, SIMD_ADD_F32(avx_r0, epsilon_r)); + avx_r0 = SIMD_FMADD_F32(var_r, decay_r, avx_r0); + var_r = SIMD_FMADD_F32(avx_r0, lr_neg_r, var_r); + SIMD_ST_F32(m + index, m_r); + SIMD_ST_F32(v + index, v_r); + SIMD_ST_HALF_EPI32(var16 + index, SIMD_F32_TO_F16(var_r, 0)); + } + + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/add_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/add_fp32_sse.h new file mode 100644 index 00000000..46b63bf2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/add_fp32_sse.h @@ -0,0 +1,124 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_ADD_SSE_H_ +#define MINDSPORE_NNACL_FP32_ADD_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int ElementOptAddSSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, + int size) { + SIMD_EPI32 vin0_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddReluSSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptAddRelu6SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddReluSSE(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddRelu6SSE(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_ADD_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementAddIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_ADD_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/apply_proximal_adagrad_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/apply_proximal_adagrad_fp32_sse.h new file mode 100644 index 00000000..a32bd7c7 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/apply_proximal_adagrad_fp32_sse.h @@ -0,0 +1,75 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_SSE_H_ +#define NNACL_FP32_APPLY_PROXIMAL_ADAGRAD_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t ApplyProximalAdagradOptSSE( + int64_t index, float *var, float *accum, float lr, float l1, float l2, float *grad, int64_t size) { + SIMD_F32 lr_vec = SIMD_MOV_F32(lr); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec1 = SIMD_LD_F32(grad + index); + SIMD_F32 accum_vec = SIMD_LD_F32(accum + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + accum_vec = SIMD_FMADD_F32(tmp_vec1, tmp_vec1, accum_vec); + SIMD_F32 learn_rate_vec = SIMD_DIV_F32(lr_vec, SIMD_SQRT_F32(accum_vec)); + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(tmp_vec1, learn_rate_vec)); + SIMD_ST_F32(accum + index, accum_vec); + tmp_vec1 = SIMD_FMADD_F32(l2_vec, learn_rate_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + learn_rate_vec = SIMD_MUL_F32(learn_rate_vec, l1_vec); + learn_rate_vec = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), learn_rate_vec); + learn_rate_vec = SIMD_MAX_F32(learn_rate_vec, SIMD_MOV_F32(0.0f)); + learn_rate_vec = SIMD_DIV_F32(learn_rate_vec, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(learn_rate_vec, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/apply_proximal_gradient_descent_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/apply_proximal_gradient_descent_fp32_sse.h new file mode 100644 index 00000000..3fdd3dd3 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/apply_proximal_gradient_descent_fp32_sse.h @@ -0,0 +1,71 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_SSE_H_ +#define NNACL_FP32_APPLY_PROXIMAL_GRADIENT_DESCENT_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t ApplyProximalGradientDescentOptSSE( + int64_t index, float *var, float alpha, float l1, float l2, float *delta, int64_t size) { + SIMD_F32 alpha_vec = SIMD_MOV_F32(alpha); + SIMD_F32 l1_vec = SIMD_MOV_F32(l1); + SIMD_F32 l2_vec = SIMD_MOV_F32(l2); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 delta_vec = SIMD_LD_F32(delta + index); + SIMD_F32 prox_v_vec = SIMD_LD_F32(var + index); + + prox_v_vec = SIMD_SUB_F32(prox_v_vec, SIMD_MUL_F32(delta_vec, alpha_vec)); + SIMD_F32 tmp_vec1 = SIMD_FMADD_F32(l2_vec, alpha_vec, SIMD_MOV_F32(1)); + if (l1 > 0) { + SIMD_F32 tmp_vec2 = SIMD_MUL_F32(alpha_vec, l1_vec); + tmp_vec2 = SIMD_SUB_F32(SIMD_ABS_F32(prox_v_vec), tmp_vec2); + tmp_vec2 = SIMD_MAX_F32(tmp_vec2, SIMD_MOV_F32(0.0f)); + tmp_vec2 = SIMD_DIV_F32(tmp_vec2, tmp_vec1); + + SIMD_MASK greater_mask = SIMD_CMPGT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_MASK less_mask = SIMD_CMPLT_F32(SIMD_SET0_F32, prox_v_vec); + SIMD_F32 greater_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, greater_mask); + SIMD_F32 less_v = SIMD_BLEND_F32(SIMD_MOV_F32(1), SIMD_SET0_F32, less_mask); + greater_v = SIMD_SUB_F32(greater_v, less_v); + + prox_v_vec = SIMD_MUL_F32(tmp_vec2, greater_v); + } else { + prox_v_vec = SIMD_DIV_F32(prox_v_vec, tmp_vec1); + } + SIMD_ST_F32(var + index, prox_v_vec); + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/arithmetic_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/arithmetic_fp32_sse.h new file mode 100644 index 00000000..9302ebe7 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/arithmetic_fp32_sse.h @@ -0,0 +1,294 @@ +/** + * Copyright 2022-2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_SSE_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +#ifndef MS_SIMD_NEON +static inline int ElementFloorModSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorModNum1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, SIMD_MUL_F32(floor_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementFloorDivSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 floor_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, floor_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivNum1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_FLOOR_F32(SIMD_DIV_F32(in0_tmp, in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} +#endif + +static inline int ElementFloorDivIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptFloorDivIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_DIV_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumNum1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MAX_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMaximumIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMaximumIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MAX_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in0_tmp = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in1_tmp = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 in1_tmp = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 in0_tmp = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 out_tmp = SIMD_MIN_EPI32(in0_tmp, in1_tmp); + SIMD_ST_EPI32(out + index, out_tmp); + } + return index; +} + +static inline int ElementMinimumSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline int ElementOptMinimumNum1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 in1_tmp = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_MIN_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +static inline size_t AssignSubOptSSE(int index, float *in0, const float *in1, size_t size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_SUB_F32(in0_tmp, in1_tmp); + SIMD_ST_F32(in0 + index, out_tmp); + } + return index; +} + +int ElementLogicalAndSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + return index; +} + +int ElementOptLogicalAndSSE(int index, const float *in0, const float *in1, float *out, int size, bool first_scalar) { + if (first_scalar) { + SIMD_F32 in0_tmp = SIMD_MOV_F32(*in0); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in1_tmp = SIMD_LD_F32(in1 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } else { + SIMD_F32 in1_tmp = SIMD_MOV_F32(*in1); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 in0_tmp = SIMD_LD_F32(in0 + index); + SIMD_F32 out_tmp = SIMD_AND_F32(SIMD_GETSIGN_F32(in0_tmp), SIMD_GETSIGN_F32(in1_tmp)); + SIMD_ST_F32(out + index, out_tmp); + } + } + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/arithmetic_self_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/arithmetic_self_fp32_sse.h new file mode 100644 index 00000000..131a5395 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/arithmetic_self_fp32_sse.h @@ -0,0 +1,148 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_SSE_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +#if defined(MS_SIMD_AVX512) +// only avx512 support abs fp32 instruction +static inline int ElementAbsSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ABS_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementAbsIntSSE(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ABS_EPI32(SIMD_LD_EPI32(input + index))); + } + return index; +} +#endif + +#if !defined(MS_SIMD_NEON) +// not support neon + static inline int ElementCosSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_COS_F32(vin)); + } + return index; + } + + static inline int ElementLogSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_LOG_F32(vin)); + } + return index; + } +#endif + +static inline int ElementSquareSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin = SIMD_LD_F32(input + index); + SIMD_ST_F32(output + index, SIMD_MUL_F32(vin, vin)); + } + return index; +} + +static inline int ElementSqrtSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_SQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +static inline int ElementRsqrtSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_RSQRT_F32(SIMD_LD_F32(input + index))); + } + return index; +} + +#if defined(MS_SIMD_AVX) || defined(MS_SIMD_SSE) +// avx512 dont support round fp32 instruction +static inline int ElementRoundSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ROUND_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +// neon dont support floor fp32 instruction +static inline int ElementFloorSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_FLOOR_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +#ifndef MS_SIMD_NEON +static inline int ElementCeilSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_CEIL_F32(SIMD_LD_F32(input + index))); + } + return index; +} +#endif + +static inline int ElementNegativeSSE(int index, const float *input, float *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_N_F32(SIMD_LD_F32(input + index), -1.0f)); + } + return index; +} + +static inline int ElementNegativeIntSSE(int index, const int32_t *input, int32_t *output, const int element_size) { + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MUL_N_EPI32(SIMD_LD_EPI32(input + index), -1)); + } + return index; +} + +static inline int ElementReciprocalSSE(int index, const float *input, float *output, const int element_size) { + SIMD_F32 num1 = SIMD_MOV_F32(1.0f); + for (int block_max_size = element_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_DIV_F32(num1, SIMD_LD_F32(input + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/batchnorm_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/batchnorm_fp32_sse.h new file mode 100644 index 00000000..f04b4e1f --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/batchnorm_fp32_sse.h @@ -0,0 +1,67 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_SSE_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int BatchNormFp32SSE(int index, const float *input, const float *mean, + const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 output_data = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +static inline int FusedBatchNormFp32SSE(int index, const float *input, const float *scale, + const float *offset, const float *mean, const float *variance, int channel, float epsilon, float *output) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_data = SIMD_LD_F32(input + index); + SIMD_F32 scale_ = SIMD_LD_F32(scale + index); + SIMD_F32 offset_ = SIMD_LD_F32(offset + index); + SIMD_F32 mean_ = SIMD_LD_F32(mean + index); + SIMD_F32 variance_ = SIMD_LD_F32(variance + index); + SIMD_F32 variance_sqrt = SIMD_SQRT_F32(SIMD_ADD_F32(variance_, SIMD_MOV_F32(epsilon))); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input_data, mean_), variance_sqrt); + SIMD_F32 output_data = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_), offset_); + SIMD_ST_F32(output + index, output_data); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/bce_with_logits_loss_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/bce_with_logits_loss_fp32_sse.h new file mode 100644 index 00000000..5106e414 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/bce_with_logits_loss_fp32_sse.h @@ -0,0 +1,69 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_SSE_H_ +#define MINDSPORE_NNACL_FP32_BCE_WITH_LOGITS_LOSS_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int BCEWithLogitLossSSE(int index, const float *logits, const float *label, + const float *weight, const float *pos_weight, int length, bool reduction, float *output, + float *reduction_sum) { + SIMD_F32 zero = SIMD_SET0_F32; + SIMD_F32 ones = SIMD_MOV_F32(1.0f); + SIMD_F32 middle_output = SIMD_SET0_F32; + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 logits_tmp = SIMD_LD_F32(logits + index); + SIMD_F32 label_tmp = SIMD_LD_F32(label + index); + SIMD_F32 weight_tmp = SIMD_LD_F32(weight + index); + SIMD_F32 pos_weight_tmp = SIMD_LD_F32(pos_weight + index); + SIMD_F32 neg_logits_tmp = SIMD_SUB_F32(zero, logits_tmp); + SIMD_F32 max_value = neg_logits_tmp; + max_value = SIMD_MAX_F32(max_value, zero); + SIMD_F32 neg_max_value = SIMD_SUB_F32(zero, max_value); + SIMD_F32 log_weight = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(pos_weight_tmp, ones), label_tmp), ones); + SIMD_F32 log_exp_value = + SIMD_LOG_F32(SIMD_ADD_F32(SIMD_HEXP_F32(neg_max_value), SIMD_HEXP_F32(SIMD_SUB_F32(neg_logits_tmp, max_value)))); + SIMD_F32 loss = SIMD_ADD_F32(SIMD_MUL_F32(SIMD_SUB_F32(ones, label_tmp), logits_tmp), + SIMD_MUL_F32(log_weight, SIMD_ADD_F32(log_exp_value, max_value))); + if (reduction) { + middle_output = SIMD_FMADD_F32(loss, weight_tmp, middle_output); + } else { + SIMD_ST_F32(output + index, SIMD_MUL_F32(loss, weight_tmp)); + } + } + if (reduction) { + *reduction_sum += SIMD_GET_SUM_F32(middle_output); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/bias_add_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/bias_add_sse.h new file mode 100644 index 00000000..0544d239 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/bias_add_sse.h @@ -0,0 +1,64 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_BIAS_ADD_SSE_H_ +#define MINDSPORE_NNACL_FP32_BIAS_ADD_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int BiasAddByInnerCoreSSE(int index, const float *input, const float *bias, float *output, + int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(input + index); + SIMD_F32 vin1 = SIMD_LD_F32(bias + index); + SIMD_F32 vout = SIMD_ADD_F32(vin0, vin1); + SIMD_ST_F32(output + index, vout); + } + return index; +} + +static inline int BiasAddByBatchCoreSSE(int index, const float *input, const float *bias, float *output1, + float *output2, float *output3, float *output4, int64_t num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_LDX4_F32(input_data, input + index, num); + SIMD_F32 bias_data = SIMD_LD_F32(bias + index); + SIMD_ST_F32(output1 + index, SIMD_ADD_F32(input_data1, bias_data)); + SIMD_ST_F32(output2 + index, SIMD_ADD_F32(input_data2, bias_data)); + SIMD_ST_F32(output3 + index, SIMD_ADD_F32(input_data3, bias_data)); + SIMD_ST_F32(output4 + index, SIMD_ADD_F32(input_data4, bias_data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +}; +#endif + +#endif // MINDSPORE_NNACL_FP32_BIAS_ADD_SIMD_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cast_base_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cast_base_sse.h new file mode 100644 index 00000000..c0810f2c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cast_base_sse.h @@ -0,0 +1,56 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_CAST_BASE_SSE_H_ +#define NNACL_BASE_CAST_BASE_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int Int32ToFloat32SSE(int index, const int32_t *input, float *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 value = SIMD_LD_EPI32(input + index); + SIMD_ST_F32(output + index, SIMD_EPI32_TO_F32(value)); + } + return index; +} + +#ifndef MS_SIMD_NEON +static inline int Float32ToInt32SSE(int index, const float *input, int32_t *output, int number) { + for (int block_max_size = number - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(input + index); + SIMD_ST_EPI32(output + index, SIMD_F32_TO_EPI32(value)); + } + return index; +} +#endif + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cast_gather_reduce_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cast_gather_reduce_fp32_sse.h new file mode 100644 index 00000000..ce84a604 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cast_gather_reduce_fp32_sse.h @@ -0,0 +1,72 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_ARITHMETIC_SELF_SSE_H_ +#define MINDSPORE_NNACL_ARITHMETIC_SELF_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int Fp32CastGatherReduceInt64FusionSSE(int index, float *output_data, const int64_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int64_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + + +static inline int Fp32CastGatherReduceInt32FusionSSE(int index, float *output_data, const int32_t *input_indices, const float *input_data, + int32_t inner_size, int32_t input_data_inner_size, int32_t outer_start, + int32_t outer_end) { + for (int block_max_size = input_data_inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + for (int i = outer_start; i < outer_end; i++) { + SIMD_F32 result = SIMD_SET0_F32; + for (int j = 0; j < inner_size; j++) { + int32_t indice = input_indices[i * inner_size + j]; + result = SIMD_ADD_F32(result, SIMD_LD_F32(input_data + indice * input_data_inner_size + index)); + } + SIMD_ST_F32(output_data + i * input_data_inner_size + index, result); + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cdist_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cdist_fp32_sse.h new file mode 100644 index 00000000..3d116113 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cdist_fp32_sse.h @@ -0,0 +1,70 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CDIST_SSE_H_ +#define MINDSPORE_NNACL_FP32_CDIST_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t CdistTwoNormalOptSSE(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + result_vec = SIMD_FMADD_F32(tmp_vec, tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t CdistPNormalOptSSE(int64_t index, const float *a, const float *b, + float *out, int64_t size, float p) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + SIMD_F32 p_vec = SIMD_MOV_F32(p); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + SIMD_F32 tmp_vec = SIMD_SUB_F32(a_vec, b_vec); + tmp_vec = SIMD_ABS_F32(tmp_vec); + tmp_vec = SIMD_POW_F32(tmp_vec, p_vec); + result_vec = SIMD_ADD_F32(tmp_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cumsum_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cumsum_fp32_sse.h new file mode 100644 index 00000000..094962aa --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/cumsum_fp32_sse.h @@ -0,0 +1,121 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_CUMSUM_SSE_H_ +#define MINDSPORE_NNACL_FP32_CUMSUM_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumOutputInitWithInputSSE(int64_t index, const float *layer_input, + float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_LD_F32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumOutputInitWithZeroSSE(int64_t index, float *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(layer_output + index, SIMD_MOV_F32(0.0f)); + } + return index; +} + +static inline int64_t CumsumSSE(int64_t index, const float *layer_input, float *layer_output, float *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input + index); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output + index); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseSSE(int64_t index, const float *layer_input, float *layer_output, + float *layer_last_output, int inner_dim) { + + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input_val = SIMD_LD_F32(layer_input - index - BLOCK_NUM + 1); + SIMD_F32 last_output_val = SIMD_LD_F32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_F32 out_val = SIMD_ADD_F32(input_val, last_output_val); + SIMD_ST_F32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +// (a, b, c) -> (a, a+b, a+b+c) exclusive == false +// (a, b, c) -> (0, a, a+b) exclusive == true +static inline int64_t CumsumIntOutputInitWithInputSSE(int64_t index, const int32_t *layer_input, + int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_LD_EPI32(layer_input + index)); + } + return index; +} + +static inline int64_t CumsumIntOutputInitWithZeroSSE(int64_t index, int32_t *layer_output, int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(layer_output + index, SIMD_MOV_EPI32(0.0f)); + } + return index; +} + +static inline int64_t CumsumIntSSE(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input + index); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output + index); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output + index, out_val); + } + return index; +} + +// (a, b, c) -> (c+b+a, c+b, c) exclusive==false +// (a, b, c) -> (c+b, c, 0) exclusive==true +static inline int64_t CumsumReverseIntSSE(int64_t index, const int32_t *layer_input, int32_t *layer_output, int32_t *layer_last_output, + int inner_dim) { + for (int block_max_size = inner_dim - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 input_val = SIMD_LD_EPI32(layer_input - index - BLOCK_NUM + 1); + SIMD_EPI32 last_output_val = SIMD_LD_EPI32(layer_last_output - index - BLOCK_NUM + 1); + SIMD_EPI32 out_val = SIMD_ADD_EPI32(input_val, last_output_val); + SIMD_ST_EPI32(layer_output - index - BLOCK_NUM + 1, out_val); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/div_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/div_fp32_sse.h new file mode 100644 index 00000000..6441bf5c --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/div_fp32_sse.h @@ -0,0 +1,167 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_SSE_H_ +#define MINDSPORE_NNACL_FP32_DIV_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int ElementOptDivNum0SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivNum1SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum0SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivReluNum1SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num0SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptDivRelu6Num1SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_DIV_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_DIV_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementDivReluSSE(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementDivRelu6SSE(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_DIV_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/dropout_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/dropout_fp32_sse.h new file mode 100644 index 00000000..2429ed38 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/dropout_fp32_sse.h @@ -0,0 +1,46 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_DROPOUTFP32_SSE_H_ +#define MINDSPORE_NNACL_FP32_DROPOUTFP32_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int DropoutFp32SSE(int index, const float *input, float scale, + int length, float *output) { + SIMD_F32 scale_value = SIMD_MOV_F32(scale); + for (int block_max_size = length - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MUL_F32(SIMD_LD_F32(input + index), scale_value)); + } + return index; +} +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/exp_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/exp_fp32_sse.h new file mode 100644 index 00000000..3d802fb3 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/exp_fp32_sse.h @@ -0,0 +1,63 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_DIV_SSE_H_ +#define MINDSPORE_NNACL_FP32_DIV_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t ExpFp32SSE(int64_t index, const float *src, float *dst, int num) { + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithInScaleSSE(int64_t index, const float *src, float *dst, int num, float in_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(in_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_MUL_F32(SIMD_LD_F32(src + index), scale_vec), dst + index); + } + return index; +} + +static inline int64_t ExpFp32WithOutScaleSSE(int64_t index, const float *src, float *dst, int num, float out_scale) { + SIMD_F32 scale_vec = SIMD_MOV_F32(out_scale); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EXP_ST_F32(SIMD_LD_F32(src + index), dst + index); + SIMD_ST_F32(dst + index, SIMD_MUL_F32(SIMD_LD_F32(dst + index), scale_vec)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/fill_base_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/fill_base_sse.h new file mode 100644 index 00000000..29571417 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/fill_base_sse.h @@ -0,0 +1,53 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_FILL_BASE_SSE_H_ +#define NNACL_BASE_FILL_BASE_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int FillFp32SSE(int index, float *output, int size, float data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_MOV_F32(data)); + } + return index; +} + +static inline int FillInt32SSE(int index, int *output, int size, int data) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_MOV_EPI32(data)); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif + diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/group_norm_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/group_norm_fp32_sse.h new file mode 100644 index 00000000..1c1f57da --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/group_norm_fp32_sse.h @@ -0,0 +1,77 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_SSE_H_ +#define MINDSPORE_NNACL_FP32_GROUP_NORM_FP32_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t GroupNormFp32SSE(int64_t index, const float *unit_input, float scale, float offset, float mean, + float var_sqrt, int unit, float *unit_output) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 v_sqrt = SIMD_MOV_F32(var_sqrt); + SIMD_F32 scale_val = SIMD_MOV_F32(scale); + SIMD_F32 offset_val = SIMD_MOV_F32(offset); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(unit_input + index); + SIMD_F32 norm_val = SIMD_DIV_F32(SIMD_SUB_F32(input, mean_val), v_sqrt); + SIMD_F32 output = SIMD_ADD_F32(SIMD_MUL_F32(norm_val, scale_val), offset_val); + SIMD_ST_F32(unit_output + index, output); + } + return index; +} + +static inline int64_t GroupNormReduceSumSSE(int64_t index, const float *in, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(in + index)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +static inline int64_t GroupNormReduceVarSSE(int64_t index, const float *in, float mean, float *sum, int unit) { + if (unit - index >= 4 * BLOCK_NUM) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = unit - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_SUB_F32(SIMD_LD_F32(in + index), mean_val); + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_F32(input, input)); + } + *sum += SIMD_GET_SUM_F32(tmp); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/layer_norm_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/layer_norm_fp32_sse.h new file mode 100644 index 00000000..30af87c3 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/layer_norm_fp32_sse.h @@ -0,0 +1,68 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_SSE_H_ +#define MINDSPORE_NNACL_FP32_LAYER_NORM_FP32_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int LayerNormMeanAndSquareSSE(int index, const float *src, int num, float *mean, float *square_mean) { + if (num >= 4 * BLOCK_NUM) { + SIMD_F32 sum_val = SIMD_SET0_F32; + SIMD_F32 square_sum_val = SIMD_SET0_F32; + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 square_value = SIMD_MUL_F32(value, value); + sum_val = SIMD_ADD_F32(sum_val, value); + square_sum_val = SIMD_ADD_F32(square_sum_val, square_value); + } + *mean += SIMD_GET_SUM_F32(sum_val); + *square_mean += SIMD_GET_SUM_F32(square_sum_val); + } + return index; +} + +static inline int LayerNormGammaAndBetaSSE(int index, float *dst, const float *src, const float *gamma_data, + const float *beta_data, int num, const float mean, const float deno) { + SIMD_F32 mean_val = SIMD_MOV_F32(mean); + SIMD_F32 deno_val = SIMD_MOV_F32(deno); + for (int block_max_size = num - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 value = SIMD_LD_F32(src + index); + SIMD_F32 out_value = SIMD_SUB_F32(value, mean_val); + out_value = SIMD_MUL_F32(out_value, deno_val); + out_value = SIMD_FMADD_F32(out_value, SIMD_LD_F32(gamma_data + index), SIMD_LD_F32(beta_data + index)); + SIMD_ST_F32(dst + index, out_value); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/matmul_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/matmul_fp32_sse.h new file mode 100644 index 00000000..c37c4c08 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/matmul_fp32_sse.h @@ -0,0 +1,155 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_MATMUL_F32_SSE_H_ +#define MINDSPORE_NNACL_FP32_MATMUL_F32_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t GemmIsNotPackSSE(int64_t index, const float *a, const float *b, float *c, const float *bias, int row, + int deep, int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 b_data16 = SIMD_MOV_F32(b[0]); + SIMD_F32 bias_data16 = SIMD_MOV_F32(bias[0]); + for (int block_max_size = row - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_data = SIMD_LD_F32(a + index); + SIMD_F32 dst = SIMD_FMADD_F32(b_data16, a_data, bias_data16); + if (act_type != 0) { + dst = SIMD_MAX_F32(dst, down_threshold); + if (act_type == 3) { + dst = SIMD_MIN_F32(dst, up_threshold); + } + } + SIMD_ST_F32(c + index, dst); + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1GemmIsNotPackSSE(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 vec_bias = SIMD_LD_F32(bias + index); + SIMD_F32 dst = SIMD_FMADD_F32(vec_a, vec_b, vec_bias); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +// act_type must be 0, 1, 2. 0: no_act, 1: relu, 3: relu6. +static inline int64_t Row1Deep1NoBiasGemmIsNotPackSSE(int64_t index, const float *a, const float *b, float *c, const float *bias, int col, + int act_type) { + SIMD_F32 down_threshold = SIMD_MOV_F32(0.0f); + SIMD_F32 up_threshold = SIMD_MOV_F32(6); + SIMD_F32 vec_a = SIMD_MOV_F32(a[0]); + if (act_type == 1) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_MAX_F32(dst, down_threshold)); // relu + } + } else if (act_type == 3) { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, SIMD_CLAMP_F32(dst, down_threshold, up_threshold)); // relue6 + } + } else { + for (int block_max_size = col - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vec_b = SIMD_LD_F32(b + index); + SIMD_F32 dst = SIMD_MUL_F32(vec_a, vec_b); + SIMD_ST_F32(c + index, dst); // no_act + } + } + + return index; +} + +#if defined(MS_SIMD_AVX512) || defined(MS_SIMD_AVX) +static inline int64_t GemmIsNotPackOptimizeCoreSSE(int64_t index, const float *a, const float *b, int k, float *dst) { + SIMD_F32 dst1 = SIMD_MOV_F32(0.0f); + for (int block_max_size = k - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 weight = SIMD_LD_F32(b + index); + SIMD_F32 a1 = SIMD_LD_F32(a + index); + dst1 = SIMD_FMADD_F32(weight, a1, dst1); + } + *dst += SIMD_REDUCE_ADD_F32(dst1); + return index; +} +#endif + +static inline int64_t MatVecMulNoPackCoreSSE(int64_t oc_index, const float *a, const float *b, float *c, const float *bias, + int act_type, int64_t depth, int64_t oc, int64_t col, int64_t inc_flag) { + for (int64_t oc_max_size = oc - BLOCK_NUM; oc_index <= oc_max_size; oc_index += BLOCK_NUM) { + SIMD_F32 out = (inc_flag & 0x1) == 0 ? SIMD_LD_F32(c + oc_index) : (bias == NULL ? SIMD_MOV_F32(0.0f) : SIMD_LD_F32(bias + oc_index)); + for (int64_t k = 0; k < depth; ++k) { + SIMD_F32 left = SIMD_MOV_F32(a[k]); + SIMD_F32 right = SIMD_LD_F32(b + oc_index + k * col); + out = SIMD_FMADD_F32(left, right, out); + } + if ((inc_flag & 0x2) != 0 && act_type != 0) { + out = SIMD_MAX_F32(out, SIMD_MOV_F32(0.0f)); + if (act_type == 0x3) { + out = SIMD_MIN_F32(out, SIMD_MOV_F32(6.0f)); + } + } + SIMD_ST_F32(c + oc_index, out); + } + return oc_index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/mul_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/mul_fp32_sse.h new file mode 100644 index 00000000..52552364 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/mul_fp32_sse.h @@ -0,0 +1,218 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_ACTIVATION_SSE_H_ +#define MINDSPORE_NNACL_FP32_ACTIVATION_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int ElementMulSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6SSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementMulIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulReluIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementMulRelu6IntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0_opt_, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulNum1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MUL_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluNum1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num0SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin0_opt_ = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6Num1SSE(int index, const float *in0, const float *in1, float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_MUL_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0_opt_, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MUL_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulReluIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt_ = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0_opt_, vin1), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptMulRelu6IntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_MIN_N_EPI32(SIMD_MAX_N_EPI32(SIMD_MUL_EPI32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/pooling_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/pooling_fp32_sse.h new file mode 100644 index 00000000..ad9239fd --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/pooling_fp32_sse.h @@ -0,0 +1,84 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POOLING_SSE_H_ +#define MINDSPORE_NNACL_FP32_POOLING_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int AvgPoolingBatchSSE(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_avg = SIMD_SET0_F32; + int real_count = 0; + for (int h = real_win_h_start; h < real_win_h_end; h++) { + for (int w = real_win_w_start; w < real_win_w_end; w++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + h) * in_w + in_w_index + w) * channel; + tmp_avg = SIMD_ADD_F32(tmp_avg, SIMD_LD_F32(src_win_ptr)); + ++real_count; + } + } + tmp_avg = SIMD_DIV_F32(tmp_avg, SIMD_MOV_F32(real_count)); + tmp_avg = SIMD_MAX_F32(tmp_avg, min_val); + tmp_avg = SIMD_MIN_F32(tmp_avg, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_avg); + } + return ci; +} + +static inline int MaxPoolingBatchSSE(int ci, const float *src_plane_ptr, int channel, + float *dst_plane_ptr, int real_win_h_start, int real_win_h_end, int real_win_w_start, int real_win_w_end, + int in_h_index, int in_w, int in_w_index, float minf, float maxf) { + SIMD_F32 min_val = SIMD_MOV_F32(minf); + SIMD_F32 max_val = SIMD_MOV_F32(maxf); + for (int block_max_size = channel - BLOCK_NUM + 1; ci < block_max_size; ci += BLOCK_NUM) { + const float *src_c_ptr = src_plane_ptr + ci; + float *dst_c_ptr = dst_plane_ptr + ci; + SIMD_F32 tmp_max = min_val; + for (int kh = real_win_h_start; kh < real_win_h_end; kh++) { + for (int kw = real_win_w_start; kw < real_win_w_end; kw++) { + const float *src_win_ptr = src_c_ptr + ((in_h_index + kh) * in_w + in_w_index + kw) * channel; + tmp_max = SIMD_MAX_F32(tmp_max, SIMD_LD_F32(src_win_ptr)); + } + } + tmp_max = SIMD_MIN_F32(tmp_max, max_val); + SIMD_ST_F32(dst_c_ptr, tmp_max); + } + return ci; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/power_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/power_fp32_sse.h new file mode 100644 index 00000000..4c46310e --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/power_fp32_sse.h @@ -0,0 +1,101 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_POWER_SSE_H_ +#define MINDSPORE_NNACL_FP32_POWER_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int PowerBroadCastIntExponentSSE(int index, const float *input, int exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result = SIMD_MOV_F32(1.0f); + int exp = abs(exponent); + while (exp) { + if (exp % 2) { + result = SIMD_MUL_F32(result, tmp); + } + tmp = SIMD_MUL_SQUARE_F32(tmp); + exp = exp / 2; + } + SIMD_ST_F32(output + index, exponent >= 0 ? result : SIMD_DIV_F32(SIMD_MOV_F32(1), result)); + } + return index; +} + +static inline int PowerBroadCastFloatExponentSSE(int index, const float *input, float exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + SIMD_F32 result; + for (int i = 0; i < BLOCK_NUM; ++i) { + SIMD_F32_GETI(result, i) = powf(SIMD_F32_GETI(tmp, i), exponent); + } + SIMD_ST_F32(output + index, result); + } + return index; +} + +static inline int PowerSingleExponentSSE(int index, const float *input, const float *exponent, float *output, int len, + float scale, float shift) { + SIMD_F32 scale_vec = SIMD_MOV_F32(scale); + SIMD_F32 shift_vec = SIMD_MOV_F32(shift); + for (int block_max_size = len - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp_vec = SIMD_FMADD_F32(scale_vec, SIMD_LD_F32(input + index), shift_vec); + for (int j = 0; j < BLOCK_NUM; ++j) { + float cur_exponent = exponent[index + j]; + float cur_val = SIMD_F32_GETI(tmp_vec, j); + if (fabsf(cur_exponent - (int)(cur_exponent)) < 0.000001) { + int exp = abs((int)(cur_exponent)); + float result = 1; + while (exp) { + if (exp % 2) { + result *= cur_val; + } + cur_val *= cur_val; + exp = exp / 2; + } + output[index + j] = *exponent >= 0 ? result : 1 / result; + } else { + output[index + j] = powf(cur_val, cur_exponent); + } + } + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/reduce_concat_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/reduce_concat_fp32_sse.h new file mode 100644 index 00000000..cde516eb --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/reduce_concat_fp32_sse.h @@ -0,0 +1,122 @@ +/** + * Copyright 2023 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_SSE_H_ +#define MINDSPORE_NNACL_REDUCE_CONCAT_FP32_SIMD_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +#ifdef MS_SIMD_AVX512 +static inline int Fp32ReduceSumConcatAxisSize16FusionSSE(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (1 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data)); + } + SIMD_ST_F32(output_data, zmm00); + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize32FusionSSE(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (2 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize64FusionSSE(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (4 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + + return index; +} + +static inline int Fp32ReduceSumConcatAxisSize128FusionSSE(int index, float *output_data, const float *input_data, int64_t reduce_axis_size) { + SIMD_F32 zmm00 = SIMD_LD_F32(input_data + 0 * BLOCK_NUM); + SIMD_F32 zmm01 = SIMD_LD_F32(input_data + 1 * BLOCK_NUM); + SIMD_F32 zmm02 = SIMD_LD_F32(input_data + 2 * BLOCK_NUM); + SIMD_F32 zmm03 = SIMD_LD_F32(input_data + 3 * BLOCK_NUM); + SIMD_F32 zmm04 = SIMD_LD_F32(input_data + 4 * BLOCK_NUM); + SIMD_F32 zmm05 = SIMD_LD_F32(input_data + 5 * BLOCK_NUM); + SIMD_F32 zmm06 = SIMD_LD_F32(input_data + 6 * BLOCK_NUM); + SIMD_F32 zmm07 = SIMD_LD_F32(input_data + 7 * BLOCK_NUM); + for (int l = 1; l < reduce_axis_size; l++) { + input_data += (8 * BLOCK_NUM); + zmm00 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 0 * BLOCK_NUM)); + zmm01 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 1 * BLOCK_NUM)); + zmm02 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 2 * BLOCK_NUM)); + zmm03 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 3 * BLOCK_NUM)); + zmm04 = SIMD_ADD_F32(zmm00, SIMD_LD_F32(input_data + 4 * BLOCK_NUM)); + zmm05 = SIMD_ADD_F32(zmm01, SIMD_LD_F32(input_data + 5 * BLOCK_NUM)); + zmm06 = SIMD_ADD_F32(zmm02, SIMD_LD_F32(input_data + 6 * BLOCK_NUM)); + zmm07 = SIMD_ADD_F32(zmm03, SIMD_LD_F32(input_data + 7 * BLOCK_NUM)); + } + + SIMD_ST_F32(output_data + 0 * BLOCK_NUM, zmm00); + SIMD_ST_F32(output_data + 1 * BLOCK_NUM, zmm01); + SIMD_ST_F32(output_data + 2 * BLOCK_NUM, zmm02); + SIMD_ST_F32(output_data + 3 * BLOCK_NUM, zmm03); + SIMD_ST_F32(output_data + 4 * BLOCK_NUM, zmm04); + SIMD_ST_F32(output_data + 5 * BLOCK_NUM, zmm05); + SIMD_ST_F32(output_data + 6 * BLOCK_NUM, zmm06); + SIMD_ST_F32(output_data + 7 * BLOCK_NUM, zmm07); + + return index; +} + +#endif + + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/reduce_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/reduce_fp32_sse.h new file mode 100644 index 00000000..3fc49468 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/reduce_fp32_sse.h @@ -0,0 +1,227 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// clang-format off +#ifndef MINDSPORE_NNACL_FP32_REDUCE_FP32_SSE_H_ +#define MINDSPORE_NNACL_FP32_REDUCE_FP32_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t ReduceSumSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumByLastAxisSSE(int64_t index, const float *src, float* tmp_sum, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_sum += SIMD_GET_SUM_F32(tmp); + return index; +} + +static inline int64_t ReduceMeanSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, SIMD_DIV_N_F32(tmp, axis_size)); + } + return index; +} + +static inline int64_t ReduceMinSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(FLT_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceMaxByLastAxisSSE(int64_t index, const float *src, float* tmp_max, int axis_size) { + SIMD_F32 tmp = SIMD_MOV_F32(*tmp_max); + for (int block_max_size = axis_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + tmp = SIMD_MAX_F32(tmp, SIMD_LD_F32(src + index)); + } + *tmp_max = SIMD_GET_MAX_F32(tmp); + return index; +} + +static inline int64_t ReduceProdSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(1.0f); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MUL_F32(tmp, SIMD_LD_F32(inner_src + i * inner_size)); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumSquareSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceL2NormSSE(int64_t index, const float *outer_src, float *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const float *inner_src = outer_src + index; + SIMD_F32 tmp = SIMD_MOV_F32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_F32(tmp, SIMD_MUL_SQUARE_F32(SIMD_LD_F32(inner_src + i * inner_size))); + } + SIMD_ST_F32(outer_dst + index, SIMD_SQRT_F32(tmp)); + } + return index; +} + +static inline int64_t IntReduceSumSSE(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMeanSSE(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(0); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_ADD_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, SIMD_DIV_N_EPI32(tmp, axis_size)); + } + return index; +} + +static inline int64_t IntReduceMinSSE(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MAX); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MIN_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t IntReduceMaxSSE(int64_t index, const int32_t *outer_src, int32_t *outer_dst, int inner_size, + int axis_size) { + for (int block_max_size = inner_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + const int32_t *inner_src = outer_src + index; + SIMD_EPI32 tmp = SIMD_MOV_EPI32(INT32_MIN); + for (int i = 0; i < axis_size; i++) { + tmp = SIMD_MAX_EPI32(tmp, SIMD_LD_EPI32(inner_src + i * inner_size)); + } + SIMD_ST_EPI32(outer_dst + index, tmp); + } + return index; +} + +static inline int64_t ReduceSumDim2Axis0SSE(int64_t index, size_t col_size, size_t col_len, size_t row_len, const float *src_data, float *dst_data) { + for (int block_max_size = col_size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 tmp = SIMD_MOV_F32(0); + const float *inner_src = src_data + index; + float *inner_dst = dst_data + index; + for (size_t i = 0; i < row_len; ++i) { + tmp = SIMD_ADD_F32(tmp, SIMD_LD_F32(inner_src + i * col_len)); + } + SIMD_ST_F32(inner_dst, tmp); + } + return index; +} + +static inline int64_t FloatReduceDeviationSSE(int64_t index, const float *src_data, float mean, size_t size, float *deviation) { + SIMD_F32 fs_deviation = SIMD_MOV_F32(0); + SIMD_F32 fs_mean = SIMD_MOV_F32(mean); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 fs_sub = SIMD_LD_F32(src_data + index); + + fs_sub = SIMD_SUB_F32(fs_sub, fs_mean); + SIMD_F32 fs_pow = SIMD_MUL_F32(fs_sub, fs_sub); + fs_deviation = SIMD_ADD_F32(fs_deviation, fs_pow); + } + *deviation += SIMD_GET_SUM_F32(fs_deviation); + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/scatter_nd_binary_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/scatter_nd_binary_sse.h new file mode 100644 index 00000000..dff6e1e2 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/scatter_nd_binary_sse.h @@ -0,0 +1,52 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_BASE_SCATTER_ND_BINARY_SSE_H_ +#define NNACL_BASE_SCATTER_ND_BINARY_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + + static inline int ScatterNDAddFp32SSE(int index, const float *update, int size, float *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_F32(output + index, SIMD_ADD_F32(SIMD_LD_F32(output + index), SIMD_LD_F32(update + index))); + } + return index; +} + +static inline int ScatterNDAddInt32SSE(int index, const int *update, int size, int *output) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_ST_EPI32(output + index, SIMD_ADD_EPI32(SIMD_LD_EPI32(output + index), SIMD_LD_EPI32(update + index))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif // NNACL_BASE_SCATTER_ND_BINARY_SSE_H_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/softmax_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/softmax_fp32_sse.h new file mode 100644 index 00000000..71c89ebc --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/softmax_fp32_sse.h @@ -0,0 +1,87 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_SSE_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t SoftmaxNormGetMaxSSE(int64_t index, const float *src, int cur_batch_offset, + float *max, int channel) { + if (channel >= BLOCK_NUM * BLOCK_NUM) { + SIMD_F32 max_val = SIMD_MOV_F32(*max); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + max_val = SIMD_MAX_F32(max_val, SIMD_LD_F32(src + cur_batch_offset + index)); + } + *max = SIMD_GET_MAX_F32(max_val); + } + return index; +} + +static inline int64_t SoftmaxNormCalcNormSSE(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, int channel) { + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 output = SIMD_SUB_F32(SIMD_LD_F32(src + cur_batch_offset + index), SIMD_MOV_F32(max)); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +static inline int64_t SoftmaxLastAxisGetExpSumSSE(int64_t index, const float *src, float *dst, + int cur_batch_offset, float max, float *exp_sum, int channel) { +#ifndef _WIN32 + SIMD_F32 sum_val = SIMD_SET0_F32; + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_SUB_F32(input, SIMD_MOV_F32(max)); + SIMD_F32 exp_out = SIMD_EXP_F32(output); + sum_val = SIMD_ADD_F32(sum_val, exp_out); + SIMD_ST_F32(dst + cur_batch_offset + index, exp_out); + } + *exp_sum += SIMD_GET_SUM_F32(sum_val); +#endif + return index; +} + +static inline int64_t SoftmaxLastAxisGetResultSSE(int64_t index, const float *src, float *dst, + int cur_batch_offset, float exp_sum, int channel) { + SIMD_F32 exp_sum_val = SIMD_MOV_F32(exp_sum); + for (int block_max_size = channel - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 input = SIMD_LD_F32(src + cur_batch_offset + index); + SIMD_F32 output = SIMD_MUL_F32(input, exp_sum_val); + SIMD_ST_F32(dst + cur_batch_offset + index, output); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/softmax_grad_fusion_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/softmax_grad_fusion_fp32_sse.h new file mode 100644 index 00000000..a1bd6fec --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/softmax_grad_fusion_fp32_sse.h @@ -0,0 +1,62 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_SSE_H_ +#define MINDSPORE_NNACL_FP32_SOFTMAX_GRAD_FUSION_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int64_t SoftmaxGradFusionOptSSE(int64_t index, const float *a, const float *b, + float *out, int64_t size) { + SIMD_F32 result_vec = SIMD_MOV_F32(0.0f); + for (int64_t block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 a_vec = SIMD_LD_F32(a + index); + SIMD_F32 b_vec = SIMD_LD_F32(b + index); + result_vec = SIMD_FMADD_F32(a_vec, b_vec, result_vec); + } + *out += SIMD_GET_SUM_F32(result_vec); + + return index; +} + +static inline int64_t ElementOptSubMulSSE(int index, const float *in0, const float *in1, float sum, + float *out, int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(sum); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_ST_F32(out + index, SIMD_MUL_F32(vin0, SIMD_SUB_F32(vin1, vin1_opt_))); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +} +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/sub_fp32_sse.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/sub_fp32_sse.h new file mode 100644 index 00000000..cd8b91eb --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sse/sub_fp32_sse.h @@ -0,0 +1,167 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_NNACL_FP32_SUB_SSE_H_ +#define MINDSPORE_NNACL_FP32_SUB_SSE_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#include "nnacl/intrinsics/ms_simd_sse_instructions.h" + +#ifdef __cplusplus +extern "C" { +#endif +#pragma GCC push_options +#pragma GCC target("sse4.1") +#define MS_SIMD_INSTRUCTION MS_SIMD_SSE_INSTRUCTION +#define BLOCK_NUM 4 +#define MS_SIMD_SSE + +static inline int ElementOptSubNum0SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0_opt, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubNum1SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1_opt_); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum0SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin0_opt = SIMD_MOV_EPI32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0_opt, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubIntNum1SSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + SIMD_EPI32 vin1_opt_ = SIMD_MOV_EPI32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1_opt_); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum0SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubReluNum1SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num0SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin0_opt = SIMD_MOV_F32(in0[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0_opt, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementOptSubRelu6Num1SSE(int index, const float *in0, const float *in1, float *out, + int size) { + SIMD_F32 vin1_opt_ = SIMD_MOV_F32(in1[0]); + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1_opt_), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubSSE(int index, const float *in0, const float *in1, float *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_SUB_F32(vin0, vin1); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubIntSSE(int index, const int32_t *in0, const int32_t *in1, int32_t *out, int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_EPI32 vin0 = SIMD_LD_EPI32(in0 + index); + SIMD_EPI32 vin1 = SIMD_LD_EPI32(in1 + index); + SIMD_EPI32 vout = SIMD_SUB_EPI32(vin0, vin1); + SIMD_ST_EPI32(out + index, vout); + } + return index; +} + +static inline int ElementSubReluSSE(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +static inline int ElementSubRelu6SSE(int index, const float *in0, const float *in1, float *out, + int size) { + for (int block_max_size = size - BLOCK_NUM + 1; index < block_max_size; index += BLOCK_NUM) { + SIMD_F32 vin0 = SIMD_LD_F32(in0 + index); + SIMD_F32 vin1 = SIMD_LD_F32(in1 + index); + SIMD_F32 vout = SIMD_MIN_N_F32(SIMD_MAX_N_F32(SIMD_SUB_F32(vin0, vin1), 0.0f), 6.0f); + SIMD_ST_F32(out + index, vout); + } + return index; +} + +#undef MS_SIMD_INSTRUCTION +#undef BLOCK_NUM +#pragma GCC pop_options +#undef MS_SIMD_SSE +#ifdef __cplusplus +}; +#endif +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sub_fp32_simd.h b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sub_fp32_simd.h new file mode 100644 index 00000000..e79b0dac --- /dev/null +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/include/nnacl/sub_fp32_simd.h @@ -0,0 +1,36 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef NNACL_SUB_FP32_SIMD_H_ +#define NNACL_SUB_FP32_SIMD_H_ + +#include "nnacl/intrinsics/ms_simd_instructions.h" +#ifdef ENABLE_AVX512 +#include "nnacl/avx512/sub_fp32_avx512.h" +#endif + +#ifdef ENABLE_AVX +#include "nnacl/avx/sub_fp32_avx.h" +#endif + +#ifdef ENABLE_SSE +#include "nnacl/sse/sub_fp32_sse.h" +#endif + +#ifdef ENABLE_ARM +#include "nnacl/neon/sub_fp32_neon.h" +#endif + +#endif diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/infer/infer_register.c b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/infer/infer_register.c index 515c0917..3d702d0a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/infer/infer_register.c +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/infer/infer_register.c @@ -417,8 +417,8 @@ void RegAllInferFunc5() { } #else -__attribute__((init_priority(101))) InferShape g_infer_func[PrimType_MAX] = {0}; -__attribute__((init_priority(101))) InferShape g_inner_op_infer_func[PrimType_InnerOpMax - PrimType_InnerOpMin] = {0}; +InferShape g_infer_func[PrimType_MAX] = {0}; +InferShape g_inner_op_infer_func[PrimType_InnerOpMax - PrimType_InnerOpMin] = {0}; #endif // _MSC_VER InferShape GetInferFunc(int prim_type) { diff --git a/mindspore/core/mindrt/BUILD.gn b/mindspore/core/mindrt/BUILD.gn new file mode 100644 index 00000000..b56d5f5c --- /dev/null +++ b/mindspore/core/mindrt/BUILD.gn @@ -0,0 +1,49 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +import("//build/ohos.gni") +ohos_source_set("mindrt_obj") { + sources = [ + "src/actor/actor.cc", + "src/actor/actormgr.cc", + "src/actor/aid.cc", + "src/actor/mailbox.cc", + "src/actor/op_actor.cc", + "src/async/future.cc", + "src/async/uuid_base.cc", + "src/async/uuid_generator.cc", + "src/mindrt.cc", + "src/fork_utils.cc", + "src/thread/actor_threadpool.cc", + "src/thread/core_affinity.cc", + "src/thread/parallel_threadpool.cc", + "src/thread/parallel_thread_pool_manager.cc", + "src/thread/threadpool.cc", + ] + include_dirs = [ + "./", + "include/", + "src/", + "../../lite/", + "../../lite/src/", + "../../core/", + ] + + remove_configs = [ "//build/config/compiler:no_rtti" ] + defines = [ "BUILD_LITE" ] + + part_name = "mindspore" + subsystem_name = "thirdparty" +} diff --git a/mindspore/core/ops/base_operator.h b/mindspore/core/ops/base_operator.h index c59e961c..811a6000 100644 --- a/mindspore/core/ops/base_operator.h +++ b/mindspore/core/ops/base_operator.h @@ -80,8 +80,8 @@ class MIND_API OperatorRegisterHelper { ~OperatorRegisterHelper() = default; - private: - int id_{0}; +// private: +// int id_{0}; }; #define OPERATOR_CREATOR_REG(K_NAME, OP_CLASS) \ diff --git a/mindspore/lite/BUILD.gn b/mindspore/lite/BUILD.gn new file mode 100644 index 00000000..6d83e6f9 --- /dev/null +++ b/mindspore/lite/BUILD.gn @@ -0,0 +1,484 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# Here we port mindespore for OHOS from upstream community, by creating gn scripts +# which translated from the cmake scripts at upstream. +# +# This BUILD.gn is the main build entry of ohos-ms. And it is corresponding to the +# cmake procedure with following options applied: +# +# MSLITE_GPU_BACKEND = "" +# MSLITE_REGISTRY_DEVICE = off +# MSLITE_MICRO_PLATFORM = auto +# MSLITE_ENABLE_NPU = off +# MSLITE_ENABLE_TRAIN = off (train features depends on minddata, not support yet.) +# MSLITE_ENABLE_SSE = off (default off, turn on for x86_64) +# MSLITE_ENABLE_AVX = off (default off, turn on for x86_64) +# MSLITE_ENABLE_AVX512 = off (default off, turn on for x86_64) +# MSLITE_ENABLE_CONVERTER = off (converter tool is not embeded in ohos image) +# MSLITE_ENABLE_TOOLS = off (no lite tool is embeded in ohos image) +# MSLITE_ENABLE_TESTCASES = off +# MSLITE_ENABLE_RUNTIME_PASS = on +# MSLITE_ENABLE_HIGH_PERFORMANCE = off +# MSLITE_ENABLE_STRING_KERNEL = on +# MSLITE_ENABLE_CONTROLFLOW = on +# MSLITE_ENABLE_AUTO_PARALLEL = on +# MSLITE_ENABLE_WEIGHT_DECODE = on +# MSLITE_ENABLE_CUSTOM_KERNEL = on +# MSLITE_ENABLE_MINDRT = on +# MSLITE_ENABLE_DELEGATE = on +# MSLITE_ENABLE_FP16 = off (default off, turn on for arm64) +# MSLITE_ENABLE_INT8 = on +# MSLITE_ENABLE_ACL = off +# MSLITE_ENABLE_ACL_QUANT_PARAM = off +# MSLITE_ENABLE_MODEL_ENCRYPTION = off +# MSLITE_ENABLE_SPARSE_COMPUTE = off +# MSLITE_ENABLE_RUNTIME_CONVERT = off +# MSLITE_ENABLE_RUNTIME_GLOG = off (ohos-ms adapts to os-buildin hilog) +# MSLITE_ENABLE_COVERAGE = off +# MSLITE_ENABLE_SERVER_INFERENCE = off +# MSLITE_ENABLE_DYNAMIC_THREAD_DISTRIBUTE = off +# MSLITE_ENABLE_BFC_MEMORY = off +# MSLITE_ENABLE_PARALLEL_INFERENCE = off +# MSLITE_ENABLE_SHARING_MODEL_WEIGHT = off +# MSLITE_ENABLE_EXPERIMENTAL_KERNEL = on +# MSLITE_ENABLE_GRAPH_KERNEL = off +# MSLITE_ENABLE_CONVERT_PYTORCH_MODEL = off +# MSLITE_ENABLE_KERNEL_EXECUTOR = off +# MSLITE_ENABLE_GITEE_MIRROR = off (ohos-ms should only use the third-parties which provides by other ohos repos) +# MSLITE_ENABLE_CLOUD_FUSION_INFERENCE = off +# MSLITE_ENABLE_CLOUD_INFERENCE = off (alias to MSLITE_ENABLE_CLOUD_FUSION_INFERENCE) +# MSLITE_ENABLE_MODEL_OBF = off (enable model obfuscation) +# MSLITE_ENABLE_MODEL_PRE_INFERENCE = off (enable model do pre-inference when Build interface is called) +# MSLITE_EXPORT_COMPUTE_IR = off (export graph structure dotfile when debug mode) +# MSLITE_ENABLE_PACKAGE_WHEEL = off (enable package python wheel) +# MSLITE_ENABLE_NNRT = off (enable NNRT) +# +# We try to keep the options consist with the default ones at upstream, and provide +# the reason on the behind of non-consist options. + +import("//build/ohos.gni") + +ohos_group("mindspore") { + deps = [ + ":mindspore_lib", + "mindir:mindir_lib", + ] +} + +cxx_api_sources = [ + "src/litert/cxx_api/cell.cc", + "src/litert/cxx_api/context.cc", + "src/litert/cxx_api/converters.cc", + "src/litert/cxx_api/kernel.cc", + "src/litert/cxx_api/serialization.cc", + "src/litert/cxx_api/tensor_utils.cc", + "src/litert/cxx_api/types.cc", + "src/litert/cxx_api/model/model.cc", + "src/litert/cxx_api/model/model_group.cc", + "src/litert/cxx_api/model/model_group_impl.cc", + "src/litert/cxx_api/model/model_impl.cc", + "src/litert/cxx_api/graph/graph.cc", + "src/litert/cxx_api/graph/net_data.cc", + "src/litert/cxx_api/tensor/tensor_impl.cc", +] + +c_api_sources = [ + "src/litert/c_api/context_c.cc", + "src/litert/c_api/model_c.cc", + "src/litert/c_api/tensor_c.cc", +] + +api_source = [ + "../core/utils/status.cc", +] + +control_flow_kernel_sources = [ + "src/control_flow/kernel/entrance_subgraph_kernel.cc", + "src/control_flow/kernel/exit_subgraph_kernel.cc", + "src/control_flow/kernel/identity_kernel.cc", +] + +experimental_sources = [ +] + +string_kernel_source = [ +] + +auto_parallel_source = [ + "src/litert/sub_graph_split.cc" +] + +custom_registry_sources = [ + "src/registry/kernel_interface_registry.cc", + "src/registry/register_kernel.cc", + "src/registry/register_kernel_impl.cc", + "src/registry/register_kernel_interface.cc", +] + +weight_decode_source = [ + "src/litert/weight_decoder.cc" +] + +all_lite_sources = [ + "src/litert/allocator.cc", + "src/litert/inner_allocator.cc", + "src/litert/runtime_allocator.cc", + "src/litert/infer_manager.cc", + "src/litert/runtime_shape_fusion_pass.cc", + "src/litert/runtime_pass.cc", + "src/litert/schema_tensor_wrapper.cc", + "src/litert/huffman_decode.cc", + "src/tensor.cc", + "src/tensorlist.cc", + "src/litert/tensor_category.cc", + "src/litert/executor.cc", + "src/litert/inner_context.cc", + "src/litert/lite_model.cc", + "src/litert/kernel_registry.cc", + "src/litert/lite_kernel.cc", + "src/executor/kernel_exec.cc", + "src/litert/kernel_exec_util.cc", + "src/executor/sub_graph_kernel.cc", + "src/litert/scheduler.cc", + "src/litert/lite_session.cc", + "src/litert/model_manager.cc", + "src/errorcode.cc", + "src/litert/cpu_info.cc", + "src/litert/pack_weight_manager.cc", + "src/control_flow/control_flow_scheduler.cc", + "src/control_flow/control_subgraph_creator.cc", + "tools/converter/quantizer/fse_bit_stream.cc", + "tools/converter/quantizer/fse_decoder.cc", + "src/litert/thread_pool_reuse_manager.cc", + "src/litert/runtime_packed_node_pass.cc", +] + +lite_mindrt_sources = [ + "src/litert/lite_mindrt.cc", + "src/litert/parallel_lite_actor.cc", + "src/litert/mindrt_executor.cc", + "src/control_flow/control_actor_creator.cc", + "src/control_flow/actor/entrance_actor.cc", + "src/control_flow/actor/exit_actor.cc", + "src/control_flow/actor/switch_actor.cc", +] + +all_lite_sources += cxx_api_sources +all_lite_sources += c_api_sources +all_lite_sources += api_source +all_lite_sources += control_flow_kernel_sources +all_lite_sources += experimental_sources +all_lite_sources += string_kernel_source +all_lite_sources += auto_parallel_source +all_lite_sources += custom_registry_sources +all_lite_sources += weight_decode_source +all_lite_sources += lite_mindrt_sources + +ops_base_sources = [ +# "src/common/ops/anf_utils.cc", # disable runtiem convert +# "src/common/ops/ops_def.cc", # disable kernel executor +# "src/common/ops/ops_utils.cc" # disable kernel executor +] + +basic_populate_sources = [ + "src/common/ops/populate/activation_grad_populate.cc", + "src/common/ops/populate/activation_populate.cc", + "src/common/ops/populate/adam_populate.cc", + "src/common/ops/populate/adder_populate.cc", + "src/common/ops/populate/add_populate.cc", + "src/common/ops/populate/affine_populate.cc", + "src/common/ops/populate/all_gather.cc", + "src/common/ops/populate/argmax_populate.cc", + "src/common/ops/populate/argmin_populate.cc", + "src/common/ops/populate/arithmetic_populate.cc", + "src/common/ops/populate/arithmetic_self_populate.cc", + "src/common/ops/populate/assert_populate.cc", + "src/common/ops/populate/assign_add_populate.cc", + "src/common/ops/populate/assign_populate.cc", + "src/common/ops/populate/attention_populate.cc", + "src/common/ops/populate/audio_spectrogram_populate.cc", + "src/common/ops/populate/batch_norm_populate.cc", + "src/common/ops/populate/batch_to_space_populate.cc", + "src/common/ops/populate/bias_add_populate.cc", + "src/common/ops/populate/broadcast_to_populate.cc", + "src/common/ops/populate/call_populate.cc", + "src/common/ops/populate/cast_populate.cc", + "src/common/ops/populate/clip_populate.cc", + "src/common/ops/populate/common_populate.cc", + "src/common/ops/populate/concat_populate.cc", + "src/common/ops/populate/constant_of_shape_populate.cc", + "src/common/ops/populate/conv2d_populate.cc", + "src/common/ops/populate/crop_and_resize_populate.cc", + "src/common/ops/populate/crop_populate.cc", + "src/common/ops/populate/cumsum_populate.cc", + "src/common/ops/populate/custom_populate.cc", + "src/common/ops/populate/deconv2d_populate.cc", + "src/common/ops/populate/default_populate.cc", + "src/common/ops/populate/depth_to_space_populate.cc", + "src/common/ops/populate/detection_post_process_populate.cc", + "src/common/ops/populate/div_populate.cc", + "src/common/ops/populate/dynamic_quant_populate.cc", + "src/common/ops/populate/eltwise_populate.cc", + "src/common/ops/populate/embedding_lookup_populate.cc", + "src/common/ops/populate/erf_populate.cc", + "src/common/ops/populate/expand_dims_populate.cc", + "src/common/ops/populate/exp_populate.cc", + "src/common/ops/populate/fill_populate.cc", + "src/common/ops/populate/flatten_populate.cc", + "src/common/ops/populate/full_connection_populate.cc", + "src/common/ops/populate/fused_batchnorm_populate.cc", + "src/common/ops/populate/gather_d_populate.cc", + "src/common/ops/populate/gather_nd_populate.cc", + "src/common/ops/populate/gather_populate.cc", + "src/common/ops/populate/glu_populate.cc", + "src/common/ops/populate/group_norm_populate.cc", + "src/common/ops/populate/gru_populate.cc", + "src/common/ops/populate/instance_norm_populate.cc", + "src/common/ops/populate/invert_permutation_populate.cc", + "src/common/ops/populate/isfinite_populate.cc", + "src/common/ops/populate/l2_norm_populate.cc", + "src/common/ops/populate/layer_norm_grad_populate.cc", + "src/common/ops/populate/layer_norm_populate.cc", + "src/common/ops/populate/lin_space_populate.cc", + "src/common/ops/populate/local_response_normalization_populate.cc", + "src/common/ops/populate/log_softmax_populate.cc", + "src/common/ops/populate/lstm_populate.cc", + "src/common/ops/populate/matmul_populate.cc", + "src/common/ops/populate/mfcc_populate.cc", + "src/common/ops/populate/mul_populate.cc", + "src/common/ops/populate/nllloss_populate.cc", + "src/common/ops/populate/non_max_suppression_populate.cc", + "src/common/ops/populate/nonzero_populate.cc", + "src/common/ops/populate/one_hot_populate.cc", + "src/common/ops/populate/oneslike_populate.cc", + "src/common/ops/populate/pad_populate.cc", + "src/common/ops/populate/partial_populate.cc", + "src/common/ops/populate/pooling_populate.cc", + "src/common/ops/populate/populate_register.cc", + "src/common/ops/populate/power_populate.cc", + "src/common/ops/populate/p_relu_populate.cc", + "src/common/ops/populate/prior_box_populate.cc", + "src/common/ops/populate/quant_dtype_cast_populate.cc", + "src/common/ops/populate/ragged_range_populate.cc", + "src/common/ops/populate/random_normal_populate.cc", + "src/common/ops/populate/random_standard_normal_populate.cc", + "src/common/ops/populate/range_populate.cc", + "src/common/ops/populate/rank_populate.cc", + "src/common/ops/populate/reduce_populate.cc", + "src/common/ops/populate/reduce_scatter.cc", + "src/common/ops/populate/reshape_populate.cc", + "src/common/ops/populate/resize_populate.cc", + "src/common/ops/populate/reverse_populate.cc", + "src/common/ops/populate/reverse_sequence_populate.cc", + "src/common/ops/populate/roi_pooling_populate.cc", + "src/common/ops/populate/scale_populate.cc", + "src/common/ops/populate/scatter_element_populate.cc", + "src/common/ops/populate/scatter_nd_populate.cc", + "src/common/ops/populate/scatter_nd_update_populate.cc", + "src/common/ops/populate/select_populate.cc", + "src/common/ops/populate/shape_populate.cc", + "src/common/ops/populate/size_populate.cc", + "src/common/ops/populate/slice_populate.cc", + "src/common/ops/populate/softmax_populate.cc", + "src/common/ops/populate/space_to_batch_nd_populate.cc", + "src/common/ops/populate/space_to_batch_populate.cc", + "src/common/ops/populate/space_to_depth_populate.cc", + "src/common/ops/populate/sparse_fill_empty_rows_populate.cc", + "src/common/ops/populate/sparse_reshape_populate.cc", + "src/common/ops/populate/sparse_segment_sum_populate.cc", + "src/common/ops/populate/sparse_softmax_cross_entropy_with_logits_populate.cc", + "src/common/ops/populate/sparse_to_dense_populate.cc", + "src/common/ops/populate/splice_populate.cc", + "src/common/ops/populate/split_populate.cc", + "src/common/ops/populate/split_with_overlap_populate.cc", + "src/common/ops/populate/squeeze_populate.cc", + "src/common/ops/populate/stack_populate.cc", + "src/common/ops/populate/strided_slice_grad_populate.cc", + "src/common/ops/populate/strided_slice_populate.cc", + "src/common/ops/populate/sub_populate.cc", + "src/common/ops/populate/tile_populate.cc", + "src/common/ops/populate/topk_populate.cc", + "src/common/ops/populate/transpose_populate.cc", + "src/common/ops/populate/triu_tril_populate.cc", + "src/common/ops/populate/uniform_real_populate.cc", + "src/common/ops/populate/unique_populate.cc", + "src/common/ops/populate/unsorted_segment_sum_populate.cc", + "src/common/ops/populate/unsqueeze_populate.cc", + "src/common/ops/populate/unstack_populate.cc", + "src/common/ops/populate/where_populate.cc", +] + +string_populate_sources = [ + "src/common/ops/populate/string/custom_extract_features_populate.cc", + "src/common/ops/populate/string/custom_normalize_populate.cc", + "src/common/ops/populate/string/custom_predict_populate.cc", + "src/common/ops/populate/string/hashtable_lookup_populate.cc", + "src/common/ops/populate/string/lsh_projection_populate.cc", + "src/common/ops/populate/string/skip_gram_populate.cc", +] + +control_populate_sources = [ + "src/common/ops/populate/control/switch_populate.cc", + "src/common/ops/populate/control/tensor_array_populate.cc", + "src/common/ops/populate/control/tensorlistfromtensor_populate.cc", + "src/common/ops/populate/control/tensorlistgetitem_populate.cc", + "src/common/ops/populate/control/tensorlistreserve_populate.cc", + "src/common/ops/populate/control/tensorlistsetlitem_populate.cc", + "src/common/ops/populate/control/tensorliststack_populate.cc", +] + +all_ops_sources = ops_base_sources +all_ops_sources += basic_populate_sources +all_ops_sources += string_populate_sources +all_ops_sources += control_populate_sources + +all_sources = all_lite_sources +all_sources += all_ops_sources + +missing_sources = [ + "src/extendrt/mindir_loader/model_loader.cc", +] + +all_sources += missing_sources + +ohos_shared_library("mindspore_lib") { + deps = [ + "../ccsrc/plugin/device/cpu/kernel/nnacl/:nnacl_obj", + "../core/mindrt/:mindrt_obj", + "src/litert/kernel/cpu/:cpu_kernel_obj", + "src/common/:lite_common_mid_obj", + ] + + sources = all_sources + + include_dirs = [ + "//base/hiviewdfx/hilog/interfaces/native/innerkits/include", + "//third_party/flatbuffers/include", + "./", + "../", + "../../", + "../core", + "src", + "src/c_api/", + "../ccsrc/plugin/device/cpu/kernel/", + "../core/mindrt/src/", + "../core/mindrt/include/", + "../../third_party/", + "./schema/", + "../ccsrc/", + "src/litert/kernel/cpu/", + "../core/mindrt/src/", + ] + + defines = [ + "ENABLE_MINDRT", + "MS_COMPILE_OHOS", + "PRIMITIVE_WRITEABLE", + "RUNTIME_PASS_CLIP", + "ENABLE_MULTI_LAYOUT", + "VERSION_STR=\"2.1.0\"", + ] + + if (target_cpu == "arm") { + defines += [ + "ENABLE_ARM", + "ENABLE_ARM32", + "ENABLE_NEON", + ] + } else if (target_cpu == "arm64") { + defines += [ + "ENABLE_ARM", + "ENABLE_ARM64", + "ENABLE_NEON", + "ENABLE_FP16", + "USE_OPENCL_WRAPPER", + "MS_OPENCL_PROFILE=false", + "CL_TARGET_OPENCL_VERSION=200", + "CL_HPP_TARGET_OPENCL_VERSION=120", + "CL_HPP_MINIMUM_OPENCL_VERSION=120", + ] + } + + configs = [ + ":mindspore_api", + ":disable_android", + ":opencl_option", + ":secure_option", + ] + + remove_configs = [ "//build/config/compiler:no_rtti" ] + + external_deps = [ "hilog:libhilog" ] + + output_name = "libmindspore-lite.huawei" + output_extension = "so" + innerapi_tags = [ "platformsdk" ] + SUPPORT_NNRT = true + if (SUPPORT_NNRT) { + sources += [ + # "mindir/src/mindir_nnrt_lite_graph.cc", + "src/litert/delegate/nnrt/checker/primitive_check.cc", + "src/litert/delegate/nnrt/nnrt_delegate.cc", + "src/litert/delegate/nnrt/nnrt_model_kernel.cc", + ] + include_dirs += [ + "//foundation/ai/neural_network_runtime", + "src/delegate/nnrt/include", + "../../mindspore/core/ir", + "mindir/include", + "mindir/inner_headers", + ] + external_deps += [ "neural_network_runtime:nnrt_target" ] + deps += [ "mindir:mindir_lib" ] + defines += [ "SUPPORT_NNRT" ] + defines += [ "MSLITE_ENABLE_EXPERIMENTAL_KERNEL" ] + } + cflags_cc = [ + "-Wno-ignored-qualifiers", + "-Wunused-private-field", + "-Wno-unused-private-field", + "-Wno-inconsistent-missing-override", + "-Wno-macro-redefined", + "-Wno-constant-conversion", + ] + part_name = "mindspore" + subsystem_name = "thirdparty" +} + +config("opencl_option") { + cflags_cc = [ "-Wno-missing-braces" ] +} + +config("mindspore_api") { + include_dirs = [ "../../include/" ] +} + +config("disable_android") { + cflags = [ + "-UANDROID", + "-U__ANDROID__", + "-U__ANDROID_API__", + ] + cflags_cc = [ + "-UANDROID", + "-U__ANDROID__", + "-U__ANDROID_API__", + ] +} + +config("secure_option") { + cflags = [ "-fstack-protector-all" ] +} diff --git a/mindspore/lite/CMakeLists.txt b/mindspore/lite/CMakeLists.txt index 721a9b0f..72337f70 100644 --- a/mindspore/lite/CMakeLists.txt +++ b/mindspore/lite/CMakeLists.txt @@ -57,6 +57,7 @@ option(MSLITE_ENABLE_MODEL_OBF "enable model obfuscation" off) option(MSLITE_ENABLE_MODEL_PRE_INFERENCE "enable model do pre-inference when Build interface is called" off) option(MSLITE_EXPORT_COMPUTE_IR "export graph structure dotfile when debug mode" off) option(MSLITE_ENABLE_PACKAGE_WHEEL "enable package python wheel" off) +option(MSLITE_ENABLE_NNRT "enable NNRT" off) #Option that can be configured through manually option(ENABLE_VERBOSE "" off) @@ -266,6 +267,10 @@ if(MSLITE_ENABLE_ACL AND (MSLITE_ENABLE_CLOUD_FUSION_INFERENCE OR MSLITE_ENABLE_ set(PLATFORM_ARM32 off) endif() +if(DEFINED ENV{MSLITE_ENABLE_NNRT}) + set(MSLITE_ENABLE_NNRT $ENV{MSLITE_ENABLE_NNRT}) +endif() + if(MACHINE_LINUX_ARM64) add_compile_definitions(MACHINE_LINUX_ARM64) add_compile_definitions(LINUX_RUNTIME) @@ -496,6 +501,11 @@ if(TARGET_AOS_ARM) endif() endif() +if(MSLITE_ENABLE_NNRT) + set(SUPPORT_NNRT on) + add_compile_definitions(SUPPORT_NNRT) +endif() + message(STATUS "************MindSpore Lite Build Option:************") message(STATUS "\tMSLITE_GPU_BACKEND = \t${MSLITE_GPU_BACKEND}") message(STATUS "\tMSLITE_REGISTRY_DEVICE = \t${MSLITE_REGISTRY_DEVICE}") @@ -542,6 +552,7 @@ message(STATUS "\tMSLITE_ENABLE_MODEL_OBF = \t${MSLITE_ENABLE message(STATUS "\tMSLITE_ENABLE_MODEL_PRE_INFERENCE = \t${MSLITE_ENABLE_MODEL_PRE_INFERENCE}") message(STATUS "\tMSLITE_EXPORT_COMPUTE_IR = \t${MSLITE_EXPORT_COMPUTE_IR}") message(STATUS "\tMSLITE_ENABLE_PACKAGE_WHEEL = \t${MSLITE_ENABLE_PACKAGE_WHEEL}") +message(STATUS "\tMSLITE_ENABLE_NNRT = \t${MSLITE_ENABLE_NNRT}") if((MSLITE_ENABLE_CONVERTER OR MSLITE_ENABLE_TESTCASES) AND ( NOT MSLITE_ENABLE_MINDRT diff --git a/mindspore/lite/mindir/BUILD.gn b/mindspore/lite/mindir/BUILD.gn new file mode 100644 index 00000000..b1435ef7 --- /dev/null +++ b/mindspore/lite/mindir/BUILD.gn @@ -0,0 +1,61 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import("//build/ohos.gni") + +# ohos_group("mindir_test") { +# deps = [ +# "tests:mindir_test", +# ] +# } +ohos_shared_library("mindir_lib") { + include_dirs = [ + "../", + "../../core", + "include", + "inner_headers", + "//third_party/flatbuffers/include", + "//third_party/bounds_checking_function/include", + "//base/hiviewdfx/hilog/interfaces/native/innerkits/include", + ] + sources = [ + "../src/common/log.cc", + "src/mindir.cc", + "src/mindir_memory_manager.cc", + "src/mindir_nnrt_lite_graph.cc", + "src/mindir_nnrt_lite_graph_to_model.cc", + "src/mindir_nnrt_lite_graph_to_model_v2_0.cc", + "src/mindir_tensor.cc", + "src/utils.cc", + ] + external_deps = [ + "c_utils:utils", + "drivers_interface_nnrt:libnnrt_proxy_1.0", + "drivers_interface_nnrt:libnnrt_proxy_2.0", + "hdf_core:libhdi", + "hilog:libhilog", + "ipc:ipc_core", + "bounds_checking_function:libsec_shared", + "drivers_interface_nnrt:nnrt_idl_headers" + ] + configs = [ + "../:disable_android", + "../:secure_option", + ] + defines = [ "MS_COMPILE_OHOS" ] + output_name = "mindir" + innerapi_tags = [ "platformsdk_indirect" ] + part_name = "mindspore" + subsystem_name = "thirdparty" +} diff --git a/mindspore/lite/mindir/CMakeLists.txt b/mindspore/lite/mindir/CMakeLists.txt new file mode 100644 index 00000000..42b89711 --- /dev/null +++ b/mindspore/lite/mindir/CMakeLists.txt @@ -0,0 +1,31 @@ +#set(CMAKE_TOOLCHAIN_FILE /heaven/wty/devtools/ohos_sdk/native/build/cmake/ohos.toolchain.cmake) +set(OHOS_ARCH arm64-v8a) +set(OHOS_STL c++_static) +set(OHOS_PLATFORM rk3568) +set(CMAKE_CXX_COMPILER /usr/bin/g++) +project(mindir) +cmake_minimum_required(VERSION 3.18) + +file(GLOB source src/*.cc) +file(GLOB convert_source src/converter/*.cpp) +set(mindir_source ../src/common/log.cc) +include_directories(include) +include_directories(inner_headers) +include_directories(../) +include_directories(../../../../../out/rk3568/gen/drivers/interface) +include_directories(../../../../../third_party/flatbuffers/include) +include_directories(../../core) + +include_directories(../../foundation/communication/ipc/interfaces/innerkits/ipc_core/include) +include_directories(../../../../../utils/native/base/include) +add_compile_definitions(MINDIR_INTERFACE) +add_library(mindir SHARED ${source} ${convert_source} ${mindir_source}) +target_link_libraries(mindir ../../../../../out/rk3568/hdf/drivers_interface_nnrt/libnnrt_proxy_1.0.z.so + ../../../../../out/rk3568/commonlibrary/c_utils/libutils.z.so + hilog_ndk.z + ../../../../../out/rk3568/communication/ipc/libipc_core.z.so + ) +file(GLOB test_sources tests/*.cc) + +add_executable(mindir_test ${test_sources}) +target_link_libraries(mindir_test mindir) diff --git a/mindspore/lite/mindir/include/mindir.h b/mindspore/lite/mindir/include/mindir.h new file mode 100644 index 00000000..ca811dce --- /dev/null +++ b/mindspore/lite/mindir/include/mindir.h @@ -0,0 +1,447 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_LITE_MINDIR_H +#define MINDSPORE_LITE_MINDIR_H +#include "mindir_types.h" +#include "mindir_lite_graph.h" +#include "mindir_tensor.h" +#include "mindir_primitive.h" +namespace OHOS { +namespace HDI { +namespace Nnrt { +namespace V1_0 { +struct Model; +} // namespace V1_0 +} // namespace Nnrt +} // namespace HDI +} // namespace OHOS + +namespace OHOS { +namespace HDI { +namespace Nnrt { +namespace V2_0 { +struct Model; +} // namespace V2_0 +} // namespace Nnrt +} // namespace HDI +} // namespace OHOS + +namespace mindspore { +namespace schema { +struct Attribute; +} + +namespace lite { + +// ********** Model ********** +OHOS::HDI::Nnrt::V1_0::Model *MindIR_LiteGraph_To_Model(const LiteGraph *lite_graph, + const OHOS::HDI::Nnrt::V1_0::SharedBuffer &buffer); +void MindIR_Model_Destroy(OHOS::HDI::Nnrt::V1_0::Model **model); + +OHOS::HDI::Nnrt::V2_0::Model *MindIR_LiteGraph_To_Model(const LiteGraph *lite_graph, + const OHOS::HDI::Nnrt::V2_0::SharedBuffer &buffer); +void MindIR_Model_Destroy(OHOS::HDI::Nnrt::V2_0::Model **model); + +// ********** Activation ********** +PrimitivePtr MindIR_Activation_CreatePrimitive(ActivationType activation_type, float alpha, float min_val, + float max_val, bool approximate); +ActivationType MindIR_Activation_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_Activation_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); +float MindIR_Activation_GetAlpha(ConstPrimitivePtr primitive); +void MindIR_Activation_SetAlpha(PrimitivePtr *primitive, float alpha); +float MindIR_Activation_GetMinVal(ConstPrimitivePtr primitive); +void MindIR_Activation_SetMinVal(PrimitivePtr *primitive, float min_val); +float MindIR_Activation_GetMaxVal(ConstPrimitivePtr primitive); +void MindIR_Activation_SetMaxVal(PrimitivePtr *primitive, float max_val); +bool MindIR_Activation_GetApproximate(ConstPrimitivePtr primitive); +void MindIR_Activation_SetApproximate(PrimitivePtr *primitive, bool approximate); + +// ********** AddFusion ********** +PrimitivePtr MindIR_AddFusion_CreatePrimitive(ActivationType activation_type); +ActivationType MindIR_AddFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_AddFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** ArgMaxFusion ********** +PrimitivePtr MindIR_ArgMaxFusion_CreatePrimitive(int64_t axis, int64_t top_k, bool keep_dims, bool out_max_value); +int64_t MindIR_ArgMaxFusion_GetAxis(ConstPrimitivePtr primitive); +void MindIR_ArgMaxFusion_SetAxis(PrimitivePtr *primitive, int64_t axis); +int64_t MindIR_ArgMaxFusion_GetTopK(ConstPrimitivePtr primitive); +void MindIR_ArgMaxFusion_SetTopK(PrimitivePtr *primitive, int64_t top_k); +bool MindIR_ArgMaxFusion_GetKeepDims(ConstPrimitivePtr primitive); +void MindIR_ArgMaxFusion_SetKeepDims(PrimitivePtr *primitive, bool keep_dims); +bool MindIR_ArgMaxFusion_GetOutMaxValue(ConstPrimitivePtr primitive); +void MindIR_ArgMaxFusion_SetOutMaxValue(PrimitivePtr *primitive, bool out_max_value); + +// ********** AvgPoolFusion ********** +PrimitivePtr MindIR_AvgPoolFusion_CreatePrimitive(const std::vector &kernel_size, + const std::vector &strides, const std::vector &pad, + PadMode pad_mode, RoundMode round_mode, Format format, bool global, + ActivationType activation_type); +std::vector MindIR_AvgPoolFusion_GetKernelSize(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size); +std::vector MindIR_AvgPoolFusion_GetStrides(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetStrides(PrimitivePtr *primitive, const std::vector &strides); +std::vector MindIR_AvgPoolFusion_GetPad(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetPad(PrimitivePtr *primitive, const std::vector &pad); +PadMode MindIR_AvgPoolFusion_GetPadMode(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode); +RoundMode MindIR_AvgPoolFusion_GetRoundMode(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetRoundMode(PrimitivePtr *primitive, RoundMode round_mode); +Format MindIR_AvgPoolFusion_GetFormat(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetFormat(PrimitivePtr *primitive, Format format); +bool MindIR_AvgPoolFusion_GetGlobal(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetGlobal(PrimitivePtr *primitive, bool global); +ActivationType MindIR_AvgPoolFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_AvgPoolFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** BatchToSpaceND ********** +PrimitivePtr MindIR_BatchToSpaceND_CreatePrimitive(const std::vector &block_shape, + const std::vector> &crops); +std::vector MindIR_BatchToSpaceND_GetBlockShape(ConstPrimitivePtr primitive); +void MindIR_BatchToSpaceND_SetBlockShape(PrimitivePtr *primitive, const std::vector &block_shape); +std::vector> MindIR_BatchToSpaceND_GetCrops(ConstPrimitivePtr primitive); +void MindIR_BatchToSpaceND_SetCrops(PrimitivePtr *primitive, const std::vector> &crops); + +// ********** BiasAdd ********** +PrimitivePtr MindIR_BiasAdd_CreatePrimitive(); + +// ********** Cast ********** +PrimitivePtr MindIR_Cast_CreatePrimitive(); + +// ********** Concat ********** +PrimitivePtr MindIR_Concat_CreatePrimitive(int64_t axis); +int64_t MindIR_Concat_GetAxis(ConstPrimitivePtr primitive); +void MindIR_Concat_SetAxis(PrimitivePtr *primitive, int64_t axis); + +// ********** Conv2DFusion ********** +PrimitivePtr MindIR_Conv2DFusion_CreatePrimitive(const std::vector &kernel_size, + const std::vector &stride, + const std::vector &dilation, PadMode pad_mode, + const std::vector &pad_list, int64_t group, + int64_t in_channel, int64_t out_channel, + ActivationType activation_type); +std::vector MindIR_Conv2DFusion_GetKernelSize(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size); +std::vector MindIR_Conv2DFusion_GetStride(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetStride(PrimitivePtr *primitive, const std::vector &stride); +std::vector MindIR_Conv2DFusion_GetDilation(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetDilation(PrimitivePtr *primitive, const std::vector &dilation); +PadMode MindIR_Conv2DFusion_GetPadMode(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode); +std::vector MindIR_Conv2DFusion_GetPadList(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetPadList(PrimitivePtr *primitive, const std::vector &pad_list); +int64_t MindIR_Conv2DFusion_GetGroup(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetGroup(PrimitivePtr *primitive, int64_t group); +int64_t MindIR_Conv2DFusion_GetInChannel(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetInChannel(PrimitivePtr *primitive, int64_t in_channel); +int64_t MindIR_Conv2DFusion_GetOutChannel(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetOutChannel(PrimitivePtr *primitive, int64_t out_channel); +ActivationType MindIR_Conv2DFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_Conv2DFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** Conv2dTransposeFusion ********** +PrimitivePtr MindIR_Conv2dTransposeFusion_CreatePrimitive( + const std::vector &kernel_size, const std::vector &stride, const std::vector &dilation, + PadMode pad_mode, const std::vector &pad_list, int64_t group, int64_t in_channel, int64_t out_channel, + ActivationType activation_type, const std::vector &output_paddings); +std::vector MindIR_Conv2dTransposeFusion_GetKernelSize(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size); +std::vector MindIR_Conv2dTransposeFusion_GetStride(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetStride(PrimitivePtr *primitive, const std::vector &stride); +std::vector MindIR_Conv2dTransposeFusion_GetDilation(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetDilation(PrimitivePtr *primitive, const std::vector &dilation); +PadMode MindIR_Conv2dTransposeFusion_GetPadMode(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode); +std::vector MindIR_Conv2dTransposeFusion_GetPadList(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetPadList(PrimitivePtr *primitive, const std::vector &pad_list); +int64_t MindIR_Conv2dTransposeFusion_GetGroup(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetGroup(PrimitivePtr *primitive, int64_t group); +int64_t MindIR_Conv2dTransposeFusion_GetInChannel(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetInChannel(PrimitivePtr *primitive, int64_t in_channel); +int64_t MindIR_Conv2dTransposeFusion_GetOutChannel(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetOutChannel(PrimitivePtr *primitive, int64_t out_channel); +ActivationType MindIR_Conv2dTransposeFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); +std::vector MindIR_Conv2dTransposeFusion_GetOutputPaddings(ConstPrimitivePtr primitive); +void MindIR_Conv2dTransposeFusion_SetOutputPaddings(PrimitivePtr *primitive, + const std::vector &output_paddings); + +// ********** DivFusion ********** +PrimitivePtr MindIR_DivFusion_CreatePrimitive(ActivationType activation_type); +ActivationType MindIR_DivFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_DivFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** Eltwise ********** +PrimitivePtr MindIR_Eltwise_CreatePrimitive(EltwiseMode mode); +EltwiseMode MindIR_Eltwise_GetMode(ConstPrimitivePtr primitive); +void MindIR_Eltwise_SetMode(PrimitivePtr *primitive, EltwiseMode mode); + +// ********** ExpandDims ********** +PrimitivePtr MindIR_ExpandDims_CreatePrimitive(); + +// ********** Fill ********** +PrimitivePtr MindIR_Fill_CreatePrimitive(); + +// ********** FullConnection ********** +PrimitivePtr MindIR_FullConnection_CreatePrimitive(bool has_bias, bool use_axis, int64_t axis, + ActivationType activation_type); +bool MindIR_FullConnection_GetHasBias(ConstPrimitivePtr primitive); +void MindIR_FullConnection_SetHasBias(PrimitivePtr *primitive, bool has_bias); +bool MindIR_FullConnection_GetUseAxis(ConstPrimitivePtr primitive); +void MindIR_FullConnection_SetUseAxis(PrimitivePtr *primitive, bool use_axis); +int64_t MindIR_FullConnection_GetAxis(ConstPrimitivePtr primitive); +void MindIR_FullConnection_SetAxis(PrimitivePtr *primitive, int64_t axis); +ActivationType MindIR_FullConnection_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_FullConnection_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** FusedBatchNorm ********** +PrimitivePtr MindIR_FusedBatchNorm_CreatePrimitive(float epsilon); +float MindIR_FusedBatchNorm_GetEpsilon(ConstPrimitivePtr primitive); +void MindIR_FusedBatchNorm_SetEpsilon(PrimitivePtr *primitive, float epsilon); + +// ********** Gather ********** +PrimitivePtr MindIR_Gather_CreatePrimitive(); + +// ********** LayerNormFusion ********** +PrimitivePtr MindIR_LayerNormFusion_CreatePrimitive(int64_t begin_norm_axis, float epsilon, bool elementwise_affine, + int64_t begin_params_axis); +int64_t MindIR_LayerNormFusion_GetBeginNormAxis(ConstPrimitivePtr primitive); +void MindIR_LayerNormFusion_SetBeginNormAxis(PrimitivePtr *primitive, int64_t begin_norm_axis); +float MindIR_LayerNormFusion_GetEpsilon(ConstPrimitivePtr primitive); +void MindIR_LayerNormFusion_SetEpsilon(PrimitivePtr *primitive, float epsilon); +bool MindIR_LayerNormFusion_GetElementwiseAffine(ConstPrimitivePtr primitive); +void MindIR_LayerNormFusion_SetElementwiseAffine(PrimitivePtr *primitive, bool elementwise_affine); +int64_t MindIR_LayerNormFusion_GetBeginParamsAxis(ConstPrimitivePtr primitive); +void MindIR_LayerNormFusion_SetBeginParamsAxis(PrimitivePtr *primitive, int64_t begin_params_axis); + +// ********** LessEqual ********** +PrimitivePtr MindIR_LessEqual_CreatePrimitive(); + +// ********** MatMulFusion ********** +PrimitivePtr MindIR_MatMulFusion_CreatePrimitive(bool transpose_a, bool transpose_b, ActivationType activation_type); +bool MindIR_MatMulFusion_GetTransposeA(ConstPrimitivePtr primitive); +void MindIR_MatMulFusion_SetTransposeA(PrimitivePtr *primitive, bool transpose_a); +bool MindIR_MatMulFusion_GetTransposeB(ConstPrimitivePtr primitive); +void MindIR_MatMulFusion_SetTransposeB(PrimitivePtr *primitive, bool transpose_b); +ActivationType MindIR_MatMulFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_MatMulFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** Maximum ********** +PrimitivePtr MindIR_Maximum_CreatePrimitive(); + +// ********** MaxPoolFusion ********** +PrimitivePtr MindIR_MaxPoolFusion_CreatePrimitive(const std::vector &kernel_size, + const std::vector &strides, const std::vector &pad, + PadMode pad_mode, Format format, bool global, + ActivationType activation_type); +std::vector MindIR_MaxPoolFusion_GetKernelSize(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size); +std::vector MindIR_MaxPoolFusion_GetStrides(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetStrides(PrimitivePtr *primitive, const std::vector &strides); +std::vector MindIR_MaxPoolFusion_GetPad(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetPad(PrimitivePtr *primitive, const std::vector &pad); +PadMode MindIR_MaxPoolFusion_GetPadMode(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode); +Format MindIR_MaxPoolFusion_GetFormat(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetFormat(PrimitivePtr *primitive, Format format); +bool MindIR_MaxPoolFusion_GetGlobal(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetGlobal(PrimitivePtr *primitive, bool global); +RoundMode MindIR_MaxPoolFusion_GetRoundMode(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetRoundMode(PrimitivePtr *primitive, RoundMode round_mode); +ActivationType MindIR_MaxPoolFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_MaxPoolFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** MulFusion ********** +PrimitivePtr MindIR_MulFusion_CreatePrimitive(ActivationType activation_type); +ActivationType MindIR_MulFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_MulFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** OneHot ********** +PrimitivePtr MindIR_OneHot_CreatePrimitive(int64_t axis); +int64_t MindIR_OneHot_GetAxis(ConstPrimitivePtr primitive); +void MindIR_OneHot_SetAxis(PrimitivePtr *primitive, int64_t axis); + +// ********** PadFusion ********** +PrimitivePtr MindIR_PadFusion_CreatePrimitive(const std::vector> &paddings, + PaddingMode padding_mode, float constant_value); +std::vector> MindIR_PadFusion_GetPaddings(ConstPrimitivePtr primitive); +void MindIR_PadFusion_SetPaddings(PrimitivePtr *primitive, const std::vector> &paddings); +PaddingMode MindIR_PadFusion_GetPaddingMode(ConstPrimitivePtr primitive); +void MindIR_PadFusion_SetPaddingMode(PrimitivePtr *primitive, PaddingMode padding_mode); +float MindIR_PadFusion_GetConstantValue(ConstPrimitivePtr primitive); +void MindIR_PadFusion_SetConstantValue(PrimitivePtr *primitive, float constant_value); + +// ********** PowFusion ********** +PrimitivePtr MindIR_PowFusion_CreatePrimitive(float scale, float shift); +float MindIR_PowFusion_GetScale(ConstPrimitivePtr primitive); +void MindIR_PowFusion_SetScale(PrimitivePtr *primitive, float scale); +float MindIR_PowFusion_GetShift(ConstPrimitivePtr primitive); +void MindIR_PowFusion_SetShift(PrimitivePtr *primitive, float shift); + +// ********** PReLUFusion ********** +PrimitivePtr MindIR_PReLUFusion_CreatePrimitive(bool channel_shared); +bool MindIR_PReLUFusion_GetChannelShared(ConstPrimitivePtr primitive); +void MindIR_PReLUFusion_SetChannelShared(PrimitivePtr *primitive, bool channel_shared); + +// ********** QuantDTypeCast ********** +PrimitivePtr MindIR_QuantDTypeCast_CreatePrimitive(int64_t src_t, int64_t dst_t); +int64_t MindIR_QuantDTypeCast_GetSrcT(ConstPrimitivePtr primitive); +void MindIR_QuantDTypeCast_SetSrcT(PrimitivePtr *primitive, int64_t src_t); +int64_t MindIR_QuantDTypeCast_GetDstT(ConstPrimitivePtr primitive); +void MindIR_QuantDTypeCast_SetDstT(PrimitivePtr *primitive, int64_t dst_t); + +// ********** ReduceFusion ********** +PrimitivePtr MindIR_ReduceFusion_CreatePrimitive(bool keep_dims, ReduceMode mode, bool reduce_to_end, float coeff); +bool MindIR_ReduceFusion_GetKeepDims(ConstPrimitivePtr primitive); +void MindIR_ReduceFusion_SetKeepDims(PrimitivePtr *primitive, bool keep_dims); +ReduceMode MindIR_ReduceFusion_GetMode(ConstPrimitivePtr primitive); +void MindIR_ReduceFusion_SetMode(PrimitivePtr *primitive, ReduceMode mode); +bool MindIR_ReduceFusion_GetReduceToEnd(ConstPrimitivePtr primitive); +void MindIR_ReduceFusion_SetReduceToEnd(PrimitivePtr *primitive, bool reduce_to_end); +float MindIR_ReduceFusion_GetCoeff(ConstPrimitivePtr primitive); +void MindIR_ReduceFusion_SetCoeff(PrimitivePtr *primitive, float coeff); + +// ********** Reshape ********** +PrimitivePtr MindIR_Reshape_CreatePrimitive(); + +// ********** Resize ********** +PrimitivePtr MindIR_Resize_CreatePrimitive(ResizeMethod method, int64_t new_height, int64_t new_width, + bool preserve_aspect_ratio, + CoordinateTransformMode coordinate_transform_mode, float cubic_coeff, + int64_t exclude_outside, float extrapolation_value, + NearestMode nearest_mode); +ResizeMethod MindIR_Resize_GetMethod(ConstPrimitivePtr primitive); +void MindIR_Resize_SetMethod(PrimitivePtr *primitive, ResizeMethod method); +int64_t MindIR_Resize_GetNewHeight(ConstPrimitivePtr primitive); +void MindIR_Resize_SetNewHeight(PrimitivePtr *primitive, int64_t new_height); +int64_t MindIR_Resize_GetNewWidth(ConstPrimitivePtr primitive); +void MindIR_Resize_SetNewWidth(PrimitivePtr *primitive, int64_t new_width); +bool MindIR_Resize_GetPreserveAspectRatio(ConstPrimitivePtr primitive); +void MindIR_Resize_SetPreserveAspectRatio(PrimitivePtr *primitive, bool preserve_aspect_ratio); +CoordinateTransformMode MindIR_Resize_GetCoordinateTransformMode(ConstPrimitivePtr primitive); +void MindIR_Resize_SetCoordinateTransformMode(PrimitivePtr *primitive, + CoordinateTransformMode coordinate_transform_mode); +float MindIR_Resize_GetCubicCoeff(ConstPrimitivePtr primitive); +void MindIR_Resize_SetCubicCoeff(PrimitivePtr *primitive, float cubic_coeff); +int64_t MindIR_Resize_GetExcludeOutside(ConstPrimitivePtr primitive); +void MindIR_Resize_SetExcludeOutside(PrimitivePtr *primitive, int64_t exclude_outside); +float MindIR_Resize_GetExtrapolationValue(ConstPrimitivePtr primitive); +void MindIR_Resize_SetExtrapolationValue(PrimitivePtr *primitive, float extrapolation_value); +NearestMode MindIR_Resize_GetNearestMode(ConstPrimitivePtr primitive); +void MindIR_Resize_SetNearestMode(PrimitivePtr *primitive, NearestMode nearest_mode); + +// ********** Rsqrt ********** +PrimitivePtr MindIR_Rsqrt_CreatePrimitive(); + +// ********** ScaleFusion ********** +PrimitivePtr MindIR_ScaleFusion_CreatePrimitive(int64_t axis, ActivationType activation_type); +int64_t MindIR_ScaleFusion_GetAxis(ConstPrimitivePtr primitive); +void MindIR_ScaleFusion_SetAxis(PrimitivePtr *primitive, int64_t axis); +ActivationType MindIR_ScaleFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_ScaleFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** Shape ********** +PrimitivePtr MindIR_Shape_CreatePrimitive(); + +// ********** SliceFusion ********** +PrimitivePtr MindIR_SliceFusion_CreatePrimitive(const std::vector &axes); +std::vector MindIR_SliceFusion_GetAxes(ConstPrimitivePtr primitive); +void MindIR_SliceFusion_SetAxes(PrimitivePtr *primitive, const std::vector &axes); + +// ********** Softmax ********** +PrimitivePtr MindIR_Softmax_CreatePrimitive(const std::vector &axis); +std::vector MindIR_Softmax_GetAxis(ConstPrimitivePtr primitive); +void MindIR_Softmax_SetAxis(PrimitivePtr *primitive, const std::vector &axis); + +// ********** SpaceToBatchND ********** +PrimitivePtr MindIR_SpaceToBatchND_CreatePrimitive(const std::vector &block_shape, + const std::vector> &paddings); +std::vector MindIR_SpaceToBatchND_GetBlockShape(ConstPrimitivePtr primitive); +void MindIR_SpaceToBatchND_SetBlockShape(PrimitivePtr *primitive, const std::vector &block_shape); +std::vector> MindIR_SpaceToBatchND_GetPaddings(ConstPrimitivePtr primitive); +void MindIR_SpaceToBatchND_SetPaddings(PrimitivePtr *primitive, const std::vector> &paddings); + +// ********** Split ********** +PrimitivePtr MindIR_Split_CreatePrimitive(int64_t output_num, const std::vector &size_splits, int64_t axis); +int64_t MindIR_Split_GetOutputNum(ConstPrimitivePtr primitive); +void MindIR_Split_SetOutputNum(PrimitivePtr *primitive, int64_t output_num); +std::vector MindIR_Split_GetSizeSplits(ConstPrimitivePtr primitive); +void MindIR_Split_SetSizeSplits(PrimitivePtr *primitive, const std::vector &size_splits); +int64_t MindIR_Split_GetAxis(ConstPrimitivePtr primitive); +void MindIR_Split_SetAxis(PrimitivePtr *primitive, int64_t axis); + +// ********** Sqrt ********** +PrimitivePtr MindIR_Sqrt_CreatePrimitive(); + +// ********** SquaredDifference ********** +PrimitivePtr MindIR_SquaredDifference_CreatePrimitive(); + +// ********** Squeeze ********** +PrimitivePtr MindIR_Squeeze_CreatePrimitive(const std::vector &axis); +std::vector MindIR_Squeeze_GetAxis(ConstPrimitivePtr primitive); +void MindIR_Squeeze_SetAxis(PrimitivePtr *primitive, const std::vector &axis); + +// ********** Stack ********** +PrimitivePtr MindIR_Stack_CreatePrimitive(int64_t axis); +int64_t MindIR_Stack_GetAxis(ConstPrimitivePtr primitive); +void MindIR_Stack_SetAxis(PrimitivePtr *primitive, int64_t axis); + +// ********** StridedSlice ********** +PrimitivePtr MindIR_StridedSlice_CreatePrimitive(int64_t begin_mask, int64_t end_mask, int64_t ellipsis_mask, + int64_t new_axis_mask, int64_t shrink_axis_mask); +int64_t MindIR_StridedSlice_GetBeginMask(ConstPrimitivePtr primitive); +void MindIR_StridedSlice_SetBeginMask(PrimitivePtr *primitive, int64_t begin_mask); +int64_t MindIR_StridedSlice_GetEndMask(ConstPrimitivePtr primitive); +void MindIR_StridedSlice_SetEndMask(PrimitivePtr *primitive, int64_t end_mask); +int64_t MindIR_StridedSlice_GetEllipsisMask(ConstPrimitivePtr primitive); +void MindIR_StridedSlice_SetEllipsisMask(PrimitivePtr *primitive, int64_t ellipsis_mask); +int64_t MindIR_StridedSlice_GetNewAxisMask(ConstPrimitivePtr primitive); +void MindIR_StridedSlice_SetNewAxisMask(PrimitivePtr *primitive, int64_t new_axis_mask); +int64_t MindIR_StridedSlice_GetShrinkAxisMask(ConstPrimitivePtr primitive); +void MindIR_StridedSlice_SetShrinkAxisMask(PrimitivePtr *primitive, int64_t shrink_axis_mask); + +// ********** SubFusion ********** +PrimitivePtr MindIR_SubFusion_CreatePrimitive(ActivationType activation_type); +ActivationType MindIR_SubFusion_GetActivationType(ConstPrimitivePtr primitive); +void MindIR_SubFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type); + +// ********** TileFusion ********** +PrimitivePtr MindIR_TileFusion_CreatePrimitive(const std::vector &dims); +std::vector MindIR_TileFusion_GetDims(ConstPrimitivePtr primitive); +void MindIR_TileFusion_SetDims(PrimitivePtr *primitive, const std::vector &dims); + +// ********** TopKFusion ********** +PrimitivePtr MindIR_TopKFusion_CreatePrimitive(bool sorted, int64_t axis); +bool MindIR_TopKFusion_GetSorted(ConstPrimitivePtr primitive); +void MindIR_TopKFusion_SetSorted(PrimitivePtr *primitive, bool sorted); +int64_t MindIR_TopKFusion_GetAxis(ConstPrimitivePtr primitive); +void MindIR_TopKFusion_SetAxis(PrimitivePtr *primitive, int64_t axis); + +// ********** Transpose ********** +PrimitivePtr MindIR_Transpose_CreatePrimitive(); + +// ********** Unsqueeze ********** +PrimitivePtr MindIR_Unsqueeze_CreatePrimitive(const std::vector &axis); +std::vector MindIR_Unsqueeze_GetAxis(ConstPrimitivePtr primitive); +void MindIR_Unsqueeze_SetAxis(PrimitivePtr *primitive, const std::vector &axis); + +// ********** Custom ********** +std::vector MindIR_Custom_GetAttr(ConstPrimitivePtr primitive); +std::string MindIR_Attribute_GetName(const mindspore::schema::Attribute &attr); +std::vector MindIR_Attribute_GetData(const mindspore::schema::Attribute &attr); +} // namespace lite +} // namespace mindspore +#endif diff --git a/mindspore/lite/mindir/include/mindir_lite_graph.h b/mindspore/lite/mindir/include/mindir_lite_graph.h new file mode 100644 index 00000000..24684914 --- /dev/null +++ b/mindspore/lite/mindir/include/mindir_lite_graph.h @@ -0,0 +1,57 @@ +#ifndef LITE_NNRT_NNRT_LITE_GRAPH_H +#define LITE_NNRT_NNRT_LITE_GRAPH_H +#include +#include +#include +namespace mindspore { +namespace lite { + +typedef void *PrimitivePtr; +typedef const void *ConstPrimitivePtr; + +typedef void *TensorPtr; +typedef const void *ConstTensorPtr; + +struct LiteGraph { + struct Node { + std::string name_; + std::string op_type_; // hnn no use + int node_type_; // hnn no use + PrimitivePtr primitive_ = nullptr; + std::shared_ptr base_operator_ = nullptr; // hnn no use + std::vector input_indices_; + std::vector output_indices_; + int quant_type_; + int device_type_ = -1; // hnn no use + }; + + struct SubGraph { + std::string name_; + std::vector input_indices_; + std::vector output_indices_; + std::vector node_indices_; + std::vector tensor_indices_; // hnn no use + }; + + std::string name_; + std::string version_; // hnn no use + std::vector input_indices_; + std::vector output_indices_; + std::vector all_tensors_; + std::vector all_nodes_; + std::vector sub_graphs_; +#ifdef ENABLE_MODEL_OBF + std::vector all_prims_type_; // hnn no use + std::vector all_nodes_stat_; // hnn no use + bool model_obfuscated_ = false; // hnn no use + std::vector deobf_prims_; // hnn no use +#endif +}; + +void MindIR_LiteGraph_Destroy(LiteGraph **lite_graph); +size_t MindIR_LiteGraph_GetConstTensorSize(const LiteGraph *lite_graph); + +} // namespace lite +} // namespace mindspore + +#endif // LITE_NNRT_NNRT_LITE_GRAPH_H diff --git a/mindspore/lite/mindir/include/mindir_primitive.h b/mindspore/lite/mindir/include/mindir_primitive.h new file mode 100644 index 00000000..b67c608a --- /dev/null +++ b/mindspore/lite/mindir/include/mindir_primitive.h @@ -0,0 +1,15 @@ +#ifndef MINDIR_MINDIR_PRIMITIVE_H +#define MINDIR_MINDIR_PRIMITIVE_H +#include "mindir_lite_graph.h" +#include "mindir_types.h" + +namespace mindspore { +namespace lite { + +// ********** PrimitiveBase ********** +NodeType MindIR_Primitive_GetType(PrimitivePtr primitive); +void MindIR_Primitive_Destroy(PrimitivePtr *primitive); + +} // namespace lite +} // namespace mindspore +#endif // MINDIR_MINDIR_PRIMITIVE_H diff --git a/mindspore/lite/mindir/include/mindir_tensor.h b/mindspore/lite/mindir/include/mindir_tensor.h new file mode 100644 index 00000000..836bc8af --- /dev/null +++ b/mindspore/lite/mindir/include/mindir_tensor.h @@ -0,0 +1,61 @@ +#ifndef LITE_TENSOR_H +#define LITE_TENSOR_H +#include "mindir_lite_graph.h" +#include "mindir_types.h" + +namespace OHOS { +namespace HDI { +namespace Nnrt { +namespace V1_0 { +struct SharedBuffer; +} // namespace V1_0 +} // namespace Nnrt +} // namespace HDI +} // namespace OHOS + +namespace OHOS { +namespace HDI { +namespace Nnrt { +namespace V2_0 { +struct SharedBuffer; +} // namespace V2_0 +} // namespace Nnrt +} // namespace HDI +} // namespace OHOS + +namespace mindspore { +namespace lite { + +// ********** Tensor ********** +TensorPtr MindIR_Tensor_Create(); +TensorPtr MindIR_Tensor_Create(const std::string &name, DataType data_type, const std::vector &dims, + Format format, const std::vector &data, + const std::vector &quant_params); +std::string MindIR_Tensor_GetName(ConstTensorPtr tensor); +void MindIR_Tensor_SetName(TensorPtr *tensor, const std::string &name); +DataType MindIR_Tensor_GetDataType(ConstTensorPtr tensor); +void MindIR_Tensor_SetDataType(TensorPtr *tensor, DataType data_type); +std::vector MindIR_Tensor_GetDims(ConstTensorPtr tensor); +void MindIR_Tensor_SetDims(TensorPtr *tensor, const std::vector &dims); +Format MindIR_Tensor_GetFormat(ConstTensorPtr tensor); +void MindIR_Tensor_SetFormat(TensorPtr *tensor, Format format); + +OHOS::HDI::Nnrt::V1_0::SharedBuffer MindIR_Tensor_GetData(ConstTensorPtr tensor, + const OHOS::HDI::Nnrt::V1_0::SharedBuffer &buffer_templete, + uint8_t *mmap_ptr, unsigned int offset); + +OHOS::HDI::Nnrt::V2_0::SharedBuffer MindIR_Tensor_GetData_V2_0(ConstTensorPtr tensor, + const OHOS::HDI::Nnrt::V2_0::SharedBuffer &buffer_templete, + uint8_t *mmap_ptr, unsigned int offset); + +void MindIR_Tensor_SetData(TensorPtr *tensor, const std::vector &data); +std::vector MindIR_Tensor_GetData(ConstTensorPtr tensor); +std::vector MindIR_Tensor_GetQuantParams(ConstTensorPtr tensor); +void MindIR_Tensor_SetQuantParams(TensorPtr *tensor, const std::vector &quant_params); + +void MindIR_Tensor_Destroy(TensorPtr *tensor); + +} // namespace lite +} // namespace mindspore + +#endif // LITE_TENSOR_H diff --git a/mindspore/lite/mindir/include/mindir_types.h b/mindspore/lite/mindir/include/mindir_types.h new file mode 100644 index 00000000..ad272f8e --- /dev/null +++ b/mindspore/lite/mindir/include/mindir_types.h @@ -0,0 +1,211 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_LITE_TYPES_H +#define MINDSPORE_LITE_TYPES_H +#include +namespace mindspore { +namespace lite { + +struct QuantParam { + int32_t zeroPoint; + double scale; + int numBits; +}; + +enum DataType : int8_t { + DATA_TYPE_UNKNOWN = 0, + DATA_TYPE_BOOL = 30, + DATA_TYPE_INT8 = 32, + DATA_TYPE_INT16 = 33, + DATA_TYPE_INT32 = 34, + DATA_TYPE_INT64 = 35, + DATA_TYPE_UINT8 = 37, + DATA_TYPE_UINT16 = 38, + DATA_TYPE_UINT32 = 39, + DATA_TYPE_UINT64 = 40, + DATA_TYPE_FLOAT16 = 42, + DATA_TYPE_FLOAT32 = 43, + DATA_TYPE_FLOAT64 = 44, +}; + +enum Format : int8_t { + FORMAT_NCHW = 0, + FORMAT_NHWC = 1, +}; + +enum QuantType : int8_t { + QUANT_TYPE_NONE, + QUANT_TYPE_ALL, +}; + +enum NodeType : uint32_t { + NODE_TYPE_NONE = 0, + NODE_TYPE_ACTIVATION = 2, + NODE_TYPE_ADD_FUSION = 5, + NODE_TYPE_ARGMAX_FUSION = 11, + NODE_TYPE_AVG_POOL_FUSION = 17, + NODE_TYPE_BATCH_TO_SPACE_ND = 22, + NODE_TYPE_BIAS_ADD = 23, + NODE_TYPE_CAST = 28, + NODE_TYPE_CONCAT = 31, + NODE_TYPE_CONV2D_FUSION = 35, + NODE_TYPE_CONV2D_TRANSPOSE_FUSION = 36, + NODE_TYPE_DIV_FUSION = 47, + NODE_TYPE_ELTWISE = 52, + NODE_TYPE_EXPAND_DIMS = 56, + NODE_TYPE_FILL = 66, + NODE_TYPE_FULL_CONNECTION = 67, + NODE_TYPE_FUSED_BATCH_NORM = 68, + NODE_TYPE_GATHER = 69, + NODE_TYPE_LAYER_NORM_FUSION = 75, + NODE_TYPE_LESS_EQUAL = 78, + NODE_TYPE_MATMUL_FUSION = 89, + NODE_TYPE_MAXIMUM = 90, + NODE_TYPE_MAX_POOL_FUSION = 92, + NODE_TYPE_MUL_FUSION = 99, + NODE_TYPE_ONE_HOT = 105, + NODE_TYPE_PAD_FUSION = 107, + NODE_TYPE_POW_FUSION = 110, + NODE_TYPE_PRELU_FUSION = 112, + NODE_TYPE_QUANT_DTYPE_CAST = 113, + NODE_TYPE_REDUCE_FUSION = 118, + NODE_TYPE_RESHAPE = 119, + NODE_TYPE_RESIZE = 120, + NODE_TYPE_RSQRT = 126, + NODE_TYPE_SCALE_FUSION = 127, + NODE_TYPE_SHAPE = 130, + NODE_TYPE_SLICE_FUSION = 135, + NODE_TYPE_SOFTMAX = 138, + NODE_TYPE_SPACE_TO_BATCH_ND = 141, + NODE_TYPE_SPLIT = 145, + NODE_TYPE_SQRT = 146, + NODE_TYPE_SQUEEZE = 147, + NODE_TYPE_SQUARED_DIFFERENCE = 149, + NODE_TYPE_STACK = 150, + NODE_TYPE_STRIDED_SLICE = 151, + NODE_TYPE_SUB_FUSION = 152, + NODE_TYPE_TILE_FUSION = 160, + NODE_TYPE_TOPK_FUSION = 161, + NODE_TYPE_TRANSPOSE = 162, + NODE_TYPE_UNSQUEEZE = 165, + NODE_TYPE_CUSTOM = 191, +}; + +enum ResizeMethod : int8_t { + RESIZE_METHOD_UNKNOWN = -1, + RESIZE_METHOD_LINEAR = 0, + RESIZE_METHOD_NEAREST = 1, + RESIZE_METHOD_CUBIC = 2, +}; + +enum CoordinateTransformMode : int8_t { + COORDINATE_TRANSFORM_MODE_ASYMMETRIC = 0, + COORDINATE_TRANSFORM_MODE_ALIGN_CORNERS = 1, + COORDINATE_TRANSFORM_MODE_HALF_PIXEL = 2, +}; + +enum NearestMode : int8_t { + NEAREST_MODE_NORMAL = 0, + NEAREST_MODE_ROUND_HALF_DOWN = 1, + NEAREST_MODE_ROUND_HALF_UP = 2, + NEAREST_MODE_FLOOR = 3, + NEAREST_MODE_CEIL = 4, +}; + +enum ActivationType : int8_t { + ACTIVATION_TYPE_NO_ACTIVATION = 0, + ACTIVATION_TYPE_RELU = 1, + ACTIVATION_TYPE_SIGMOID = 2, + ACTIVATION_TYPE_RELU6 = 3, + ACTIVATION_TYPE_ELU = 4, + ACTIVATION_TYPE_LEAKY_RELU = 5, + ACTIVATION_TYPE_ABS = 6, + ACTIVATION_TYPE_RELU1 = 7, + ACTIVATION_TYPE_SOFTSIGN = 8, + ACTIVATION_TYPE_SOFTPLUS = 9, + ACTIVATION_TYPE_TANH = 10, + ACTIVATION_TYPE_SELU = 11, + ACTIVATION_TYPE_HSWISH = 12, + ACTIVATION_TYPE_HSIGMOID = 13, + ACTIVATION_TYPE_THRESHOLDRELU = 14, + ACTIVATION_TYPE_LINEAR = 15, + ACTIVATION_TYPE_HARD_TANH = 16, + ACTIVATION_TYPE_SIGN = 17, + ACTIVATION_TYPE_SWISH = 18, + ACTIVATION_TYPE_GELU = 19, + ACTIVATION_TYPE_UNKNOWN = 20, +}; + +enum ReduceMode : int8_t { + REDUCE_MODE_MEAN = 0, + REDUCE_MODE_MAX = 1, + REDUCE_MODE_MIN = 2, + REDUCE_MODE_PROD = 3, + REDUCE_MODE_SUM = 4, + REDUCE_MODE_SUM_SQUARE = 5, + REDUCE_MODE_ASUM = 6, + REDUCE_MODE_ALL = 7, +}; + +enum PoolMode : int8_t { + POOL_MODE_MAX_POOLING = 0, + POOL_MODE_MEAN_POOLING = 1, +}; + +enum EltwiseMode : int8_t { + ELTWISE_MODE_PROD = 0, + ELTWISE_MODE_SUM = 1, + ELTWISE_MODE_MAXIMUM = 2, + ELTWISE_MODE_UNKNOWN = 3, +}; + +enum PadMode : int8_t { + PAD_MODE_PAD = 0, + PAD_MODE_SAME = 1, + PAD_MODE_VALID = 2, +}; + +enum RoundMode : int8_t { + ROUND_MODE_FLOOR = 0, + ROUND_MODE_CEIL = 1, +}; + +enum PaddingMode : int8_t { + PADDING_MODE_CONSTANT = 0, + PADDING_MODE_REFLECT = 1, + PADDING_MODE_SYMMETRIC = 2, + PADDING_MODE_RESERVED = 3, +}; + +enum LshProjectionType : int8_t { + UNKNOWN = 0, + SPARSE = 1, + DENSE = 2, +}; + +enum Reduction : int8_t { + REDUCTION_SUM = 0, + MEAN = 1, + NONE = 2, +}; + +struct Attribute { + std::string name; + uint32_t data; +}; +} // namespace lite +} // namespace mindspore +#endif // MIDIR_LITE_TYPES_H diff --git a/mindspore/lite/mindir/inner_headers/lite_graph.h b/mindspore/lite/mindir/inner_headers/lite_graph.h new file mode 100644 index 00000000..f2599cc9 --- /dev/null +++ b/mindspore/lite/mindir/inner_headers/lite_graph.h @@ -0,0 +1,27 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MIDIR_LITE_LITE_GRAPH_H +#define MIDIR_LITE_LITE_GRAPH_H +#include + +namespace mindspore { +namespace lite { + +std::vector Convert(NodeType type, PrimitivePtr primitive); + +} // namespace lite +} // namespace mindspore +#endif // MIDIR_LITE_LITE_GRAPH_H diff --git a/mindspore/lite/mindir/inner_headers/mindir_memory_manager.h b/mindspore/lite/mindir/inner_headers/mindir_memory_manager.h new file mode 100644 index 00000000..29ef0b31 --- /dev/null +++ b/mindspore/lite/mindir/inner_headers/mindir_memory_manager.h @@ -0,0 +1,33 @@ +#ifndef LITE_MINDIR_MEMORY_MANAGER_H +#define LITE_MINDIR_MEMORY_MANAGER_H +#include +#include +#include +#include +#include "include/errorcode.h" +#include "schema/model_generated.h" +#include "mindir_lite_graph.h" +// using namespace OHOS::HDI::Nnrt::V1_0; + +namespace mindspore { +namespace lite { +class MindIRMemoryManager { + public: + static MindIRMemoryManager *GetInstance(); + ~MindIRMemoryManager() = default; + void *CreateTensorFromBuilder(flatbuffers::FlatBufferBuilder &fbb_new, schema::Tensor *tensor); + void DeleteTensor(schema::Tensor *tensor); + void *CreatePrimitiveFromBuilder(flatbuffers::FlatBufferBuilder &fbb_new, schema::Primitive *primitive); + void DeletePrimitive(schema::Primitive *primitive); + void ClearAllMemory(); + + private: + MindIRMemoryManager() = default; + static void *CopyFbbToNewMemory(flatbuffers::FlatBufferBuilder &fbb_new); + std::map primitive_map; + std::map tensor_map; + std::mutex mutex; +}; +} // namespace lite +} // namespace mindspore +#endif // LITE_MINDIR_MEMORY_MANAGER_H diff --git a/mindspore/lite/mindir/inner_headers/utils.h b/mindspore/lite/mindir/inner_headers/utils.h new file mode 100644 index 00000000..0e6eb35d --- /dev/null +++ b/mindspore/lite/mindir/inner_headers/utils.h @@ -0,0 +1,28 @@ +#ifndef MIDIR_LITE_UTILS_H +#define MIDIR_LITE_UTILS_H +#include "mindir_types.h" +#include "mindir_lite_graph.h" +#include "schema/model_generated.h" +namespace mindspore { +namespace lite { + +// ********** PrimitiveBase ********** +PrimitivePtr MindIR_CreatePrimitiveFromBuilder(flatbuffers::FlatBufferBuilder &fbb); + +flatbuffers::Offset CreateVec2D(flatbuffers::FlatBufferBuilder &fbb, + const std::vector> &data); +flatbuffers::Offset CreateVec2D(flatbuffers::FlatBufferBuilder &fbb, + const mindspore::schema::Vec2D *data); + +mindspore::schema::PrimitiveType MindIR_GetPrimitiveType(PrimitivePtr prim); + +flatbuffers::Offset>> ConvertQuantParams( + flatbuffers::FlatBufferBuilder &fbb, const std::vector &quant_params); + +flatbuffers::Offset>> ConvertQuantParams( + flatbuffers::FlatBufferBuilder &fbb, + const flatbuffers::Vector> *quant_params); + +} // namespace lite +} // namespace mindspore +#endif // MIDIR_LITE_UTILS_H diff --git a/mindspore/lite/mindir/src/mindir.cc b/mindspore/lite/mindir/src/mindir.cc new file mode 100644 index 00000000..7fc9c00e --- /dev/null +++ b/mindspore/lite/mindir/src/mindir.cc @@ -0,0 +1,4339 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "mindir.h" +#include "utils.h" +#include "schema/model_generated.h" +#include "mindir_memory_manager.h" +//----TODO---write an example to run MindIRMemoryManager +namespace mindspore { +namespace lite { + +// ********** Activation ********** +PrimitivePtr MindIR_Activation_CreatePrimitive(ActivationType activation_type, float alpha, float min_val, + float max_val, bool approximate) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateActivation(fbb, static_cast(activation_type), alpha, min_val, + max_val, approximate); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ACTIVATION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +ActivationType MindIR_Activation_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_Activation_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateActivation(fbb, static_cast(activation_type), value->alpha(), + value->min_val(), value->max_val(), value->approximate()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ACTIVATION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_Activation_GetAlpha(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + return value->alpha(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_Activation_SetAlpha(PrimitivePtr *primitive, float alpha) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateActivation(fbb, static_cast(value->activation_type()), + alpha, value->min_val(), value->max_val(), value->approximate()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ACTIVATION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_Activation_GetMinVal(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + return value->min_val(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_Activation_SetMinVal(PrimitivePtr *primitive, float min_val) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateActivation(fbb, static_cast(value->activation_type()), + value->alpha(), min_val, value->max_val(), value->approximate()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ACTIVATION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_Activation_GetMaxVal(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + return value->max_val(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_Activation_SetMaxVal(PrimitivePtr *primitive, float max_val) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateActivation(fbb, static_cast(value->activation_type()), + value->alpha(), value->min_val(), max_val, value->approximate()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ACTIVATION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_Activation_GetApproximate(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + return value->approximate(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_Activation_SetApproximate(PrimitivePtr *primitive, bool approximate) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Activation(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateActivation(fbb, static_cast(value->activation_type()), + value->alpha(), value->min_val(), value->max_val(), approximate); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ACTIVATION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** AddFusion ********** +PrimitivePtr MindIR_AddFusion_CreatePrimitive(ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAddFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ADD_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +ActivationType MindIR_AddFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AddFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_AddFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AddFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAddFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ADD_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** ArgMaxFusion ********** +PrimitivePtr MindIR_ArgMaxFusion_CreatePrimitive(int64_t axis, int64_t top_k, bool keep_dims, bool out_max_value) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateArgMaxFusion(fbb, axis, top_k, keep_dims, out_max_value); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ARGMAX_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_ArgMaxFusion_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_ArgMaxFusion_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateArgMaxFusion(fbb, axis, value->top_k(), value->keep_dims(), value->out_max_value()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ARGMAX_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_ArgMaxFusion_GetTopK(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + return value->top_k(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_ArgMaxFusion_SetTopK(PrimitivePtr *primitive, int64_t top_k) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateArgMaxFusion(fbb, value->axis(), top_k, value->keep_dims(), value->out_max_value()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ARGMAX_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_ArgMaxFusion_GetKeepDims(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + return value->keep_dims(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_ArgMaxFusion_SetKeepDims(PrimitivePtr *primitive, bool keep_dims) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateArgMaxFusion(fbb, value->axis(), value->top_k(), keep_dims, value->out_max_value()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ARGMAX_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_ArgMaxFusion_GetOutMaxValue(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + return value->out_max_value(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_ArgMaxFusion_SetOutMaxValue(PrimitivePtr *primitive, bool out_max_value) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateArgMaxFusion(fbb, value->axis(), value->top_k(), value->keep_dims(), out_max_value); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ARGMAX_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** AvgPoolFusion ********** +PrimitivePtr MindIR_AvgPoolFusion_CreatePrimitive(const std::vector &kernel_size, + const std::vector &strides, const std::vector &pad, + PadMode pad_mode, RoundMode round_mode, Format format, bool global, + ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(kernel_size.data(), kernel_size.size()), fbb.CreateVector(strides.data(), strides.size()), + fbb.CreateVector(pad.data(), pad.size()), static_cast(pad_mode), + static_cast(round_mode), static_cast(format), global, + static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_AvgPoolFusion_GetKernelSize(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->kernel_size(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_AvgPoolFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(kernel_size.data(), kernel_size.size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + static_cast(value->round_mode()), static_cast(value->format()), + value->global(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_AvgPoolFusion_GetStrides(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->strides(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_AvgPoolFusion_SetStrides(PrimitivePtr *primitive, const std::vector &strides) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(strides.data(), strides.size()), fbb.CreateVector(value->pad()->data(), value->pad()->size()), + static_cast(value->pad_mode()), static_cast(value->round_mode()), + static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_AvgPoolFusion_GetPad(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->pad(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_AvgPoolFusion_SetPad(PrimitivePtr *primitive, const std::vector &pad) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), fbb.CreateVector(pad.data(), pad.size()), + static_cast(value->pad_mode()), static_cast(value->round_mode()), + static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +PadMode MindIR_AvgPoolFusion_GetPadMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->pad_mode()); + } else { + PadMode en = static_cast(0); + return en; + } + } else { + PadMode en = static_cast(0); + return en; + } +} + +void MindIR_AvgPoolFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(pad_mode), + static_cast(value->round_mode()), static_cast(value->format()), + value->global(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +RoundMode MindIR_AvgPoolFusion_GetRoundMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->round_mode()); + } else { + RoundMode en = static_cast(0); + return en; + } + } else { + RoundMode en = static_cast(0); + return en; + } +} + +void MindIR_AvgPoolFusion_SetRoundMode(PrimitivePtr *primitive, RoundMode round_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + static_cast(round_mode), static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +Format MindIR_AvgPoolFusion_GetFormat(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->format()); + } else { + Format en = static_cast(0); + return en; + } + } else { + Format en = static_cast(0); + return en; + } +} + +void MindIR_AvgPoolFusion_SetFormat(PrimitivePtr *primitive, Format format) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + static_cast(value->round_mode()), static_cast(format), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_AvgPoolFusion_GetGlobal(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + return value->global(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_AvgPoolFusion_SetGlobal(PrimitivePtr *primitive, bool global) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + static_cast(value->round_mode()), static_cast(value->format()), global, + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ActivationType MindIR_AvgPoolFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_AvgPoolFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateAvgPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + static_cast(value->round_mode()), static_cast(value->format()), + value->global(), static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_AVG_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** BatchToSpaceND ********** +PrimitivePtr MindIR_BatchToSpaceND_CreatePrimitive(const std::vector &block_shape, + const std::vector> &crops) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateBatchToSpaceND(fbb, fbb.CreateVector(block_shape.data(), block_shape.size()), + CreateVec2D(fbb, crops)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_BATCH_TO_SPACE_ND), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_BatchToSpaceND_GetBlockShape(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_BatchToSpaceND(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->block_shape(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_BatchToSpaceND_SetBlockShape(PrimitivePtr *primitive, const std::vector &block_shape) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_BatchToSpaceND(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateBatchToSpaceND(fbb, fbb.CreateVector(block_shape.data(), block_shape.size()), + CreateVec2D(fbb, value->crops())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_BATCH_TO_SPACE_ND), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector> MindIR_BatchToSpaceND_GetCrops(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_BatchToSpaceND(); + if (prim != nullptr && value != nullptr) { + std::vector> out; + auto src = value->crops(); + for (auto sub_list : *src->data()) { + std::vector result_tmp; + result_tmp.resize(sub_list->data()->size()); + std::transform(sub_list->data()->begin(), sub_list->data()->end(), result_tmp.begin(), + [](int64_t item) { return item; }); + out.emplace_back(result_tmp); + } + return out; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_BatchToSpaceND_SetCrops(PrimitivePtr *primitive, const std::vector> &crops) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_BatchToSpaceND(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateBatchToSpaceND( + fbb, fbb.CreateVector(value->block_shape()->data(), value->block_shape()->size()), CreateVec2D(fbb, crops)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_BATCH_TO_SPACE_ND), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** BiasAdd ********** +PrimitivePtr MindIR_BiasAdd_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateBiasAdd(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_BIAS_ADD), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** Cast ********** +PrimitivePtr MindIR_Cast_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateCast(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CAST), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** Concat ********** +PrimitivePtr MindIR_Concat_CreatePrimitive(int64_t axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConcat(fbb, axis); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONCAT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_Concat_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Concat(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Concat_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Concat(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConcat(fbb, axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONCAT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Conv2DFusion ********** +PrimitivePtr MindIR_Conv2DFusion_CreatePrimitive(const std::vector &kernel_size, + const std::vector &stride, + const std::vector &dilation, PadMode pad_mode, + const std::vector &pad_list, int64_t group, + int64_t in_channel, int64_t out_channel, + ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, fbb.CreateVector(kernel_size.data(), kernel_size.size()), + fbb.CreateVector(stride.data(), stride.size()), fbb.CreateVector(dilation.data(), dilation.size()), + static_cast(pad_mode), fbb.CreateVector(pad_list.data(), pad_list.size()), 0, group, in_channel, + out_channel, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_Conv2DFusion_GetKernelSize(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->kernel_size(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2DFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, fbb.CreateVector(kernel_size.data(), kernel_size.size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2DFusion_GetStride(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->stride(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2DFusion_SetStride(PrimitivePtr *primitive, const std::vector &stride) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(stride.data(), stride.size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2DFusion_GetDilation(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->dilation(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2DFusion_SetDilation(PrimitivePtr *primitive, const std::vector &dilation) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(dilation.data(), dilation.size()), static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +PadMode MindIR_Conv2DFusion_GetPadMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->pad_mode()); + } else { + PadMode en = static_cast(0); + return en; + } + } else { + PadMode en = static_cast(0); + return en; + } +} + +void MindIR_Conv2DFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), static_cast(pad_mode), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2DFusion_GetPadList(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->pad_list(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2DFusion_SetPadList(PrimitivePtr *primitive, const std::vector &pad_list) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), fbb.CreateVector(pad_list.data(), pad_list.size()), 0, + value->group(), value->in_channel(), value->out_channel(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Conv2DFusion_GetGroup(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + return value->group(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Conv2DFusion_SetGroup(PrimitivePtr *primitive, int64_t group) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, group, value->in_channel(), + value->out_channel(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Conv2DFusion_GetInChannel(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + return value->in_channel(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Conv2DFusion_SetInChannel(PrimitivePtr *primitive, int64_t in_channel) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), in_channel, + value->out_channel(), static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Conv2DFusion_GetOutChannel(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + return value->out_channel(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Conv2DFusion_SetOutChannel(PrimitivePtr *primitive, int64_t out_channel) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + out_channel, static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ActivationType MindIR_Conv2DFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_Conv2DFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2DFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2DFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Conv2dTransposeFusion ********** +PrimitivePtr MindIR_Conv2dTransposeFusion_CreatePrimitive( + const std::vector &kernel_size, const std::vector &stride, const std::vector &dilation, + PadMode pad_mode, const std::vector &pad_list, int64_t group, int64_t in_channel, int64_t out_channel, + ActivationType activation_type, const std::vector &output_paddings) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, fbb.CreateVector(kernel_size.data(), kernel_size.size()), + fbb.CreateVector(stride.data(), stride.size()), fbb.CreateVector(dilation.data(), dilation.size()), + static_cast(pad_mode), 0, fbb.CreateVector(pad_list.data(), pad_list.size()), 0, group, in_channel, + out_channel, static_cast(activation_type), + fbb.CreateVector(output_paddings.data(), output_paddings.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_Conv2dTransposeFusion_GetKernelSize(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->kernel_size(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2dTransposeFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, fbb.CreateVector(kernel_size.data(), kernel_size.size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2dTransposeFusion_GetStride(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->stride(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2dTransposeFusion_SetStride(PrimitivePtr *primitive, const std::vector &stride) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(stride.data(), stride.size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2dTransposeFusion_GetDilation(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->dilation(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2dTransposeFusion_SetDilation(PrimitivePtr *primitive, const std::vector &dilation) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(dilation.data(), dilation.size()), static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +PadMode MindIR_Conv2dTransposeFusion_GetPadMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->pad_mode()); + } else { + PadMode en = static_cast(0); + return en; + } + } else { + PadMode en = static_cast(0); + return en; + } +} + +void MindIR_Conv2dTransposeFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), static_cast(pad_mode), + 0, fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), + value->in_channel(), value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2dTransposeFusion_GetPadList(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->pad_list(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2dTransposeFusion_SetPadList(PrimitivePtr *primitive, const std::vector &pad_list) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, fbb.CreateVector(pad_list.data(), pad_list.size()), 0, + value->group(), value->in_channel(), value->out_channel(), + static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Conv2dTransposeFusion_GetGroup(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + return value->group(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Conv2dTransposeFusion_SetGroup(PrimitivePtr *primitive, int64_t group) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, group, value->in_channel(), + value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Conv2dTransposeFusion_GetInChannel(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + return value->in_channel(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Conv2dTransposeFusion_SetInChannel(PrimitivePtr *primitive, int64_t in_channel) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), in_channel, + value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Conv2dTransposeFusion_GetOutChannel(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + return value->out_channel(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Conv2dTransposeFusion_SetOutChannel(PrimitivePtr *primitive, int64_t out_channel) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + out_channel, static_cast(value->activation_type()), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ActivationType MindIR_Conv2dTransposeFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_Conv2dTransposeFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(activation_type), + fbb.CreateVector(value->output_paddings()->data(), value->output_paddings()->size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Conv2dTransposeFusion_GetOutputPaddings(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->output_paddings(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Conv2dTransposeFusion_SetOutputPaddings(PrimitivePtr *primitive, + const std::vector &output_paddings) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateConv2dTransposeFusion( + fbb, mindspore::schema::Format_NCHW, + fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->stride()->data(), value->stride()->size()), + fbb.CreateVector(value->dilation()->data(), value->dilation()->size()), + static_cast(value->pad_mode()), 0, + fbb.CreateVector(value->pad_list()->data(), value->pad_list()->size()), 0, value->group(), value->in_channel(), + value->out_channel(), static_cast(value->activation_type()), + fbb.CreateVector(output_paddings.data(), output_paddings.size())); + auto prim_offset = schema::CreatePrimitive( + fbb, static_cast(NODE_TYPE_CONV2D_TRANSPOSE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** DivFusion ********** +PrimitivePtr MindIR_DivFusion_CreatePrimitive(ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateDivFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_DIV_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +ActivationType MindIR_DivFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_DivFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_DivFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_DivFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateDivFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_DIV_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Eltwise ********** +PrimitivePtr MindIR_Eltwise_CreatePrimitive(EltwiseMode mode) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateEltwise(fbb, static_cast(mode)); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ELTWISE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +EltwiseMode MindIR_Eltwise_GetMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Eltwise(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->mode()); + } else { + EltwiseMode en = static_cast(0); + return en; + } + } else { + EltwiseMode en = static_cast(0); + return en; + } +} + +void MindIR_Eltwise_SetMode(PrimitivePtr *primitive, EltwiseMode mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Eltwise(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateEltwise(fbb, static_cast(mode)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ELTWISE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** ExpandDims ********** +PrimitivePtr MindIR_ExpandDims_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateExpandDims(fbb); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_EXPAND_DIMS), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** Fill ********** +PrimitivePtr MindIR_Fill_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFill(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FILL), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** FullConnection ********** +PrimitivePtr MindIR_FullConnection_CreatePrimitive(bool has_bias, bool use_axis, int64_t axis, + ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateFullConnection(fbb, has_bias, use_axis, axis, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FULL_CONNECTION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +bool MindIR_FullConnection_GetHasBias(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + return value->has_bias(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_FullConnection_SetHasBias(PrimitivePtr *primitive, bool has_bias) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFullConnection(fbb, has_bias, value->use_axis(), value->axis(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FULL_CONNECTION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_FullConnection_GetUseAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + return value->use_axis(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_FullConnection_SetUseAxis(PrimitivePtr *primitive, bool use_axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFullConnection(fbb, value->has_bias(), use_axis, value->axis(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FULL_CONNECTION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_FullConnection_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_FullConnection_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFullConnection(fbb, value->has_bias(), value->use_axis(), axis, + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FULL_CONNECTION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ActivationType MindIR_FullConnection_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_FullConnection_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_FullConnection(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFullConnection(fbb, value->has_bias(), value->use_axis(), value->axis(), + static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FULL_CONNECTION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** FusedBatchNorm ********** +PrimitivePtr MindIR_FusedBatchNorm_CreatePrimitive(float epsilon) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFusedBatchNorm(fbb, 0.9, 0); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FUSED_BATCH_NORM), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +float MindIR_FusedBatchNorm_GetEpsilon(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FusedBatchNorm(); + if (prim != nullptr && value != nullptr) { + return value->epsilon(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_FusedBatchNorm_SetEpsilon(PrimitivePtr *primitive, float epsilon) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_FusedBatchNorm(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateFusedBatchNorm(fbb, epsilon, 0.9, 0); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_FUSED_BATCH_NORM), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Gather ********** +PrimitivePtr MindIR_Gather_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateGather(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_GATHER), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** LayerNormFusion ********** +PrimitivePtr MindIR_LayerNormFusion_CreatePrimitive(int64_t begin_norm_axis, float epsilon, bool elementwise_affine, + int64_t begin_params_axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateLayerNormFusion(fbb, begin_norm_axis, epsilon, elementwise_affine, begin_params_axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_LAYER_NORM_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_LayerNormFusion_GetBeginNormAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + return value->begin_norm_axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_LayerNormFusion_SetBeginNormAxis(PrimitivePtr *primitive, int64_t begin_norm_axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateLayerNormFusion(fbb, begin_norm_axis, value->epsilon(), + value->elementwise_affine(), value->begin_params_axis()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_LAYER_NORM_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_LayerNormFusion_GetEpsilon(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + return value->epsilon(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_LayerNormFusion_SetEpsilon(PrimitivePtr *primitive, float epsilon) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateLayerNormFusion(fbb, value->begin_norm_axis(), epsilon, + value->elementwise_affine(), value->begin_params_axis()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_LAYER_NORM_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_LayerNormFusion_GetElementwiseAffine(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + return value->elementwise_affine(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_LayerNormFusion_SetElementwiseAffine(PrimitivePtr *primitive, bool elementwise_affine) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateLayerNormFusion(fbb, value->begin_norm_axis(), value->epsilon(), + elementwise_affine, value->begin_params_axis()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_LAYER_NORM_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_LayerNormFusion_GetBeginParamsAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + return value->begin_params_axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_LayerNormFusion_SetBeginParamsAxis(PrimitivePtr *primitive, int64_t begin_params_axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_LayerNormFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateLayerNormFusion(fbb, value->begin_norm_axis(), value->epsilon(), + value->elementwise_affine(), begin_params_axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_LAYER_NORM_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** LessEqual ********** +PrimitivePtr MindIR_LessEqual_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateLessEqual(fbb); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_LESS_EQUAL), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** MatMulFusion ********** +PrimitivePtr MindIR_MatMulFusion_CreatePrimitive(bool transpose_a, bool transpose_b, ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateMatMulFusion(fbb, transpose_a, transpose_b, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MATMUL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +bool MindIR_MatMulFusion_GetTransposeA(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MatMulFusion(); + if (prim != nullptr && value != nullptr) { + return value->transpose_a(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_MatMulFusion_SetTransposeA(PrimitivePtr *primitive, bool transpose_a) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MatMulFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMatMulFusion(fbb, transpose_a, value->transpose_b(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MATMUL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_MatMulFusion_GetTransposeB(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MatMulFusion(); + if (prim != nullptr && value != nullptr) { + return value->transpose_b(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_MatMulFusion_SetTransposeB(PrimitivePtr *primitive, bool transpose_b) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MatMulFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMatMulFusion(fbb, value->transpose_a(), transpose_b, + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MATMUL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ActivationType MindIR_MatMulFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MatMulFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_MatMulFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MatMulFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMatMulFusion(fbb, value->transpose_a(), value->transpose_b(), + static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MATMUL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Maximum ********** +PrimitivePtr MindIR_Maximum_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaximum(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAXIMUM), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** MaxPoolFusion ********** +PrimitivePtr MindIR_MaxPoolFusion_CreatePrimitive(const std::vector &kernel_size, + const std::vector &strides, const std::vector &pad, + PadMode pad_mode, Format format, bool global, + ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(kernel_size.data(), kernel_size.size()), fbb.CreateVector(strides.data(), strides.size()), + fbb.CreateVector(pad.data(), pad.size()), static_cast(pad_mode), + mindspore::schema::RoundMode_FLOOR, static_cast(format), global, + static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_MaxPoolFusion_GetKernelSize(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->kernel_size(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_MaxPoolFusion_SetKernelSize(PrimitivePtr *primitive, const std::vector &kernel_size) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(kernel_size.data(), kernel_size.size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + mindspore::schema::RoundMode_FLOOR, static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_MaxPoolFusion_GetStrides(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->strides(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_MaxPoolFusion_SetStrides(PrimitivePtr *primitive, const std::vector &strides) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(strides.data(), strides.size()), fbb.CreateVector(value->pad()->data(), value->pad()->size()), + static_cast(value->pad_mode()), mindspore::schema::RoundMode_FLOOR, + static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_MaxPoolFusion_GetPad(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->pad(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_MaxPoolFusion_SetPad(PrimitivePtr *primitive, const std::vector &pad) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), fbb.CreateVector(pad.data(), pad.size()), + static_cast(value->pad_mode()), mindspore::schema::RoundMode_FLOOR, + static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +PadMode MindIR_MaxPoolFusion_GetPadMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->pad_mode()); + } else { + PadMode en = static_cast(0); + return en; + } + } else { + PadMode en = static_cast(0); + return en; + } +} + +void MindIR_MaxPoolFusion_SetPadMode(PrimitivePtr *primitive, PadMode pad_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(pad_mode), + mindspore::schema::RoundMode_FLOOR, static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +Format MindIR_MaxPoolFusion_GetFormat(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->format()); + } else { + Format en = static_cast(0); + return en; + } + } else { + Format en = static_cast(0); + return en; + } +} + +void MindIR_MaxPoolFusion_SetFormat(PrimitivePtr *primitive, Format format) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + mindspore::schema::RoundMode_FLOOR, static_cast(format), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_MaxPoolFusion_GetGlobal(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + return value->global(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_MaxPoolFusion_SetGlobal(PrimitivePtr *primitive, bool global) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + mindspore::schema::RoundMode_FLOOR, static_cast(value->format()), global, + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +RoundMode MindIR_MaxPoolFusion_GetRoundMode(ConstPrimitivePtr primitive) { + RoundMode round_mode = static_cast(0); // set default value: RoundMode_FLOOR + + if (primitive == nullptr) { + return round_mode; + } + auto prim = static_cast(primitive); + + auto value = prim->value_as_MaxPoolFusion(); + if (value == nullptr) { + return round_mode; + } + round_mode = static_cast(value->round_mode()); + return round_mode; +} + +void MindIR_MaxPoolFusion_SetRoundMode(PrimitivePtr *primitive, RoundMode round_mode) { + if (primitive == nullptr) { + return; + } + auto prim = static_cast(*primitive); + if (prim == nullptr) { + return; + } + auto value = prim->value_as_MaxPoolFusion(); + if (value == nullptr) { + return; + } + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + static_cast(round_mode), static_cast(value->format()), value->global(), + static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto new_prim = flatbuffers::GetMutableRoot(new_addr); + *primitive = new_prim; +} + +ActivationType MindIR_MaxPoolFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_MaxPoolFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMaxPoolFusion( + fbb, fbb.CreateVector(value->kernel_size()->data(), value->kernel_size()->size()), + fbb.CreateVector(value->strides()->data(), value->strides()->size()), + fbb.CreateVector(value->pad()->data(), value->pad()->size()), static_cast(value->pad_mode()), + mindspore::schema::RoundMode_FLOOR, static_cast(value->format()), value->global(), + static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MAX_POOL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** MulFusion ********** +PrimitivePtr MindIR_MulFusion_CreatePrimitive(ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMulFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MUL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +ActivationType MindIR_MulFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MulFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_MulFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_MulFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateMulFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_MUL_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** OneHot ********** +PrimitivePtr MindIR_OneHot_CreatePrimitive(int64_t axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateOneHot(fbb, axis); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ONE_HOT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_OneHot_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_OneHot(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_OneHot_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_OneHot(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateOneHot(fbb, axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_ONE_HOT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** PadFusion ********** +PrimitivePtr MindIR_PadFusion_CreatePrimitive(const std::vector> &paddings, + PaddingMode padding_mode, float constant_value) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreatePadFusion(fbb, CreateVec2D(fbb, paddings), + static_cast(padding_mode), constant_value); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_PAD_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector> MindIR_PadFusion_GetPaddings(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PadFusion(); + if (prim != nullptr && value != nullptr) { + std::vector> out; + auto src = value->paddings(); + for (auto sub_list : *src->data()) { + std::vector result_tmp; + result_tmp.resize(sub_list->data()->size()); + std::transform(sub_list->data()->begin(), sub_list->data()->end(), result_tmp.begin(), + [](int64_t item) { return item; }); + out.emplace_back(result_tmp); + } + return out; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_PadFusion_SetPaddings(PrimitivePtr *primitive, const std::vector> &paddings) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_PadFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreatePadFusion(fbb, CreateVec2D(fbb, paddings), + static_cast(value->padding_mode()), value->constant_value()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_PAD_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +PaddingMode MindIR_PadFusion_GetPaddingMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PadFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->padding_mode()); + } else { + PaddingMode en = static_cast(0); + return en; + } + } else { + PaddingMode en = static_cast(0); + return en; + } +} + +void MindIR_PadFusion_SetPaddingMode(PrimitivePtr *primitive, PaddingMode padding_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_PadFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreatePadFusion(fbb, CreateVec2D(fbb, value->paddings()), + static_cast(padding_mode), value->constant_value()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_PAD_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_PadFusion_GetConstantValue(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PadFusion(); + if (prim != nullptr && value != nullptr) { + return value->constant_value(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_PadFusion_SetConstantValue(PrimitivePtr *primitive, float constant_value) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_PadFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreatePadFusion(fbb, CreateVec2D(fbb, value->paddings()), + static_cast(value->padding_mode()), constant_value); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_PAD_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** PowFusion ********** +PrimitivePtr MindIR_PowFusion_CreatePrimitive(float scale, float shift) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreatePowFusion(fbb, scale, shift); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_POW_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +float MindIR_PowFusion_GetScale(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PowFusion(); + if (prim != nullptr && value != nullptr) { + return value->scale(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_PowFusion_SetScale(PrimitivePtr *primitive, float scale) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_PowFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreatePowFusion(fbb, scale, value->shift()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_POW_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_PowFusion_GetShift(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PowFusion(); + if (prim != nullptr && value != nullptr) { + return value->shift(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_PowFusion_SetShift(PrimitivePtr *primitive, float shift) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_PowFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreatePowFusion(fbb, value->scale(), shift); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_POW_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** PReLUFusion ********** +PrimitivePtr MindIR_PReLUFusion_CreatePrimitive(bool channel_shared) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreatePReLUFusion(fbb, channel_shared); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_PRELU_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +bool MindIR_PReLUFusion_GetChannelShared(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PReLUFusion(); + if (prim != nullptr && value != nullptr) { + return value->channel_shared(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_PReLUFusion_SetChannelShared(PrimitivePtr *primitive, bool channel_shared) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_PReLUFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreatePReLUFusion(fbb, channel_shared); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_PRELU_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** QuantDTypeCast ********** +PrimitivePtr MindIR_QuantDTypeCast_CreatePrimitive(int64_t src_t, int64_t dst_t) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateQuantDTypeCast(fbb, src_t, dst_t); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_QUANT_DTYPE_CAST), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_QuantDTypeCast_GetSrcT(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_QuantDTypeCast(); + if (prim != nullptr && value != nullptr) { + return value->src_t(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_QuantDTypeCast_SetSrcT(PrimitivePtr *primitive, int64_t src_t) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_QuantDTypeCast(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateQuantDTypeCast(fbb, src_t, value->dst_t()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_QUANT_DTYPE_CAST), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_QuantDTypeCast_GetDstT(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_QuantDTypeCast(); + if (prim != nullptr && value != nullptr) { + return value->dst_t(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_QuantDTypeCast_SetDstT(PrimitivePtr *primitive, int64_t dst_t) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_QuantDTypeCast(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateQuantDTypeCast(fbb, value->src_t(), dst_t); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_QUANT_DTYPE_CAST), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** ReduceFusion ********** +PrimitivePtr MindIR_ReduceFusion_CreatePrimitive(bool keep_dims, ReduceMode mode, bool reduce_to_end, float coeff) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateReduceFusion(fbb, keep_dims, static_cast(mode), reduce_to_end, coeff); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_REDUCE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +bool MindIR_ReduceFusion_GetKeepDims(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + return value->keep_dims(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_ReduceFusion_SetKeepDims(PrimitivePtr *primitive, bool keep_dims) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateReduceFusion(fbb, keep_dims, static_cast(value->mode()), + value->reduce_to_end(), value->coeff()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_REDUCE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ReduceMode MindIR_ReduceFusion_GetMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->mode()); + } else { + ReduceMode en = static_cast(0); + return en; + } + } else { + ReduceMode en = static_cast(0); + return en; + } +} + +void MindIR_ReduceFusion_SetMode(PrimitivePtr *primitive, ReduceMode mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateReduceFusion(fbb, value->keep_dims(), static_cast(mode), + value->reduce_to_end(), value->coeff()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_REDUCE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_ReduceFusion_GetReduceToEnd(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + return value->reduce_to_end(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_ReduceFusion_SetReduceToEnd(PrimitivePtr *primitive, bool reduce_to_end) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateReduceFusion( + fbb, value->keep_dims(), static_cast(value->mode()), reduce_to_end, value->coeff()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_REDUCE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_ReduceFusion_GetCoeff(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + return value->coeff(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_ReduceFusion_SetCoeff(PrimitivePtr *primitive, float coeff) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ReduceFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateReduceFusion( + fbb, value->keep_dims(), static_cast(value->mode()), value->reduce_to_end(), coeff); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_REDUCE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Reshape ********** +PrimitivePtr MindIR_Reshape_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateReshape(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESHAPE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** Resize ********** +PrimitivePtr MindIR_Resize_CreatePrimitive(ResizeMethod method, int64_t new_height, int64_t new_width, + bool preserve_aspect_ratio, + CoordinateTransformMode coordinate_transform_mode, float cubic_coeff, + int64_t exclude_outside, float extrapolation_value, + NearestMode nearest_mode) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateResize( + fbb, mindspore::schema::Format_NCHW, static_cast(method), new_height, new_width, + preserve_aspect_ratio, static_cast(coordinate_transform_mode), cubic_coeff, + exclude_outside, extrapolation_value, static_cast(nearest_mode)); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +ResizeMethod MindIR_Resize_GetMethod(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->method()); + } else { + ResizeMethod en = static_cast(0); + return en; + } + } else { + ResizeMethod en = static_cast(0); + return en; + } +} + +void MindIR_Resize_SetMethod(PrimitivePtr *primitive, ResizeMethod method) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateResize(fbb, mindspore::schema::Format_NCHW, static_cast(method), + value->new_height(), value->new_width(), value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), + value->cubic_coeff(), value->exclude_outside(), value->extrapolation_value(), + static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Resize_GetNewHeight(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return value->new_height(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Resize_SetNewHeight(PrimitivePtr *primitive, int64_t new_height) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateResize(fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), + new_height, value->new_width(), value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), + value->cubic_coeff(), value->exclude_outside(), value->extrapolation_value(), + static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Resize_GetNewWidth(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return value->new_width(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Resize_SetNewWidth(PrimitivePtr *primitive, int64_t new_width) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateResize(fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), + value->new_height(), new_width, value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), + value->cubic_coeff(), value->exclude_outside(), value->extrapolation_value(), + static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +bool MindIR_Resize_GetPreserveAspectRatio(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return value->preserve_aspect_ratio(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_Resize_SetPreserveAspectRatio(PrimitivePtr *primitive, bool preserve_aspect_ratio) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateResize(fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), + value->new_height(), value->new_width(), preserve_aspect_ratio, + static_cast(value->coordinate_transform_mode()), + value->cubic_coeff(), value->exclude_outside(), value->extrapolation_value(), + static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +CoordinateTransformMode MindIR_Resize_GetCoordinateTransformMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->coordinate_transform_mode()); + } else { + CoordinateTransformMode en = static_cast(0); + return en; + } + } else { + CoordinateTransformMode en = static_cast(0); + return en; + } +} + +void MindIR_Resize_SetCoordinateTransformMode(PrimitivePtr *primitive, + CoordinateTransformMode coordinate_transform_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateResize(fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), + value->new_height(), value->new_width(), value->preserve_aspect_ratio(), + static_cast(coordinate_transform_mode), + value->cubic_coeff(), value->exclude_outside(), value->extrapolation_value(), + static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_Resize_GetCubicCoeff(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return value->cubic_coeff(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_Resize_SetCubicCoeff(PrimitivePtr *primitive, float cubic_coeff) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateResize(fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), + value->new_height(), value->new_width(), value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), + cubic_coeff, value->exclude_outside(), value->extrapolation_value(), + static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Resize_GetExcludeOutside(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return value->exclude_outside(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Resize_SetExcludeOutside(PrimitivePtr *primitive, int64_t exclude_outside) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateResize( + fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), value->new_height(), + value->new_width(), value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), value->cubic_coeff(), + exclude_outside, value->extrapolation_value(), static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +float MindIR_Resize_GetExtrapolationValue(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return value->extrapolation_value(); + } else { + return .0; + } + } else { + return .0; + } +} + +void MindIR_Resize_SetExtrapolationValue(PrimitivePtr *primitive, float extrapolation_value) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateResize( + fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), value->new_height(), + value->new_width(), value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), value->cubic_coeff(), + value->exclude_outside(), extrapolation_value, static_cast(value->nearest_mode())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +NearestMode MindIR_Resize_GetNearestMode(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->nearest_mode()); + } else { + NearestMode en = static_cast(0); + return en; + } + } else { + NearestMode en = static_cast(0); + return en; + } +} + +void MindIR_Resize_SetNearestMode(PrimitivePtr *primitive, NearestMode nearest_mode) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Resize(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateResize( + fbb, mindspore::schema::Format_NCHW, static_cast(value->method()), value->new_height(), + value->new_width(), value->preserve_aspect_ratio(), + static_cast(value->coordinate_transform_mode()), value->cubic_coeff(), + value->exclude_outside(), value->extrapolation_value(), static_cast(nearest_mode)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RESIZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Rsqrt ********** +PrimitivePtr MindIR_Rsqrt_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateRsqrt(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_RSQRT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** ScaleFusion ********** +PrimitivePtr MindIR_ScaleFusion_CreatePrimitive(int64_t axis, ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateScaleFusion(fbb, axis, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SCALE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_ScaleFusion_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ScaleFusion(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_ScaleFusion_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ScaleFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateScaleFusion(fbb, axis, static_cast(value->activation_type())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SCALE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +ActivationType MindIR_ScaleFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ScaleFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_ScaleFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_ScaleFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateScaleFusion(fbb, value->axis(), static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SCALE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Shape ********** +PrimitivePtr MindIR_Shape_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateShape(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SHAPE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** SliceFusion ********** +PrimitivePtr MindIR_SliceFusion_CreatePrimitive(const std::vector &axes) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSliceFusion(fbb, fbb.CreateVector(axes.data(), axes.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SLICE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_SliceFusion_GetAxes(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SliceFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->axes(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_SliceFusion_SetAxes(PrimitivePtr *primitive, const std::vector &axes) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_SliceFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSliceFusion(fbb, fbb.CreateVector(axes.data(), axes.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SLICE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Softmax ********** +PrimitivePtr MindIR_Softmax_CreatePrimitive(const std::vector &axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSoftmax(fbb, fbb.CreateVector(axis.data(), axis.size())); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SOFTMAX), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_Softmax_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Softmax(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->axis(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Softmax_SetAxis(PrimitivePtr *primitive, const std::vector &axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Softmax(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSoftmax(fbb, fbb.CreateVector(axis.data(), axis.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SOFTMAX), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** SpaceToBatchND ********** +PrimitivePtr MindIR_SpaceToBatchND_CreatePrimitive(const std::vector &block_shape, + const std::vector> &paddings) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSpaceToBatchND(fbb, fbb.CreateVector(block_shape.data(), block_shape.size()), + CreateVec2D(fbb, paddings)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPACE_TO_BATCH_ND), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_SpaceToBatchND_GetBlockShape(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SpaceToBatchND(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->block_shape(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_SpaceToBatchND_SetBlockShape(PrimitivePtr *primitive, const std::vector &block_shape) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_SpaceToBatchND(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSpaceToBatchND(fbb, fbb.CreateVector(block_shape.data(), block_shape.size()), + CreateVec2D(fbb, value->paddings())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPACE_TO_BATCH_ND), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector> MindIR_SpaceToBatchND_GetPaddings(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SpaceToBatchND(); + if (prim != nullptr && value != nullptr) { + std::vector> out; + auto src = value->paddings(); + for (auto sub_list : *src->data()) { + std::vector result_tmp; + result_tmp.resize(sub_list->data()->size()); + std::transform(sub_list->data()->begin(), sub_list->data()->end(), result_tmp.begin(), + [](int64_t item) { return item; }); + out.emplace_back(result_tmp); + } + return out; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_SpaceToBatchND_SetPaddings(PrimitivePtr *primitive, const std::vector> &paddings) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_SpaceToBatchND(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSpaceToBatchND( + fbb, fbb.CreateVector(value->block_shape()->data(), value->block_shape()->size()), CreateVec2D(fbb, paddings)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPACE_TO_BATCH_ND), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Split ********** +PrimitivePtr MindIR_Split_CreatePrimitive(int64_t output_num, const std::vector &size_splits, int64_t axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateSplit(fbb, output_num, fbb.CreateVector(size_splits.data(), size_splits.size()), axis); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPLIT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_Split_GetOutputNum(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Split(); + if (prim != nullptr && value != nullptr) { + return value->output_num(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Split_SetOutputNum(PrimitivePtr *primitive, int64_t output_num) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Split(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSplit( + fbb, output_num, fbb.CreateVector(value->size_splits()->data(), value->size_splits()->size()), value->axis()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPLIT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +std::vector MindIR_Split_GetSizeSplits(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Split(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->size_splits(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Split_SetSizeSplits(PrimitivePtr *primitive, const std::vector &size_splits) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Split(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSplit(fbb, value->output_num(), + fbb.CreateVector(size_splits.data(), size_splits.size()), value->axis()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPLIT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_Split_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Split(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Split_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Split(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSplit( + fbb, value->output_num(), fbb.CreateVector(value->size_splits()->data(), value->size_splits()->size()), axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SPLIT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Sqrt ********** +PrimitivePtr MindIR_Sqrt_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSqrt(fbb); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SQRT), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** SquaredDifference ********** +PrimitivePtr MindIR_SquaredDifference_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSquaredDifference(fbb); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SQUARED_DIFFERENCE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** Squeeze ********** +PrimitivePtr MindIR_Squeeze_CreatePrimitive(const std::vector &axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSqueeze(fbb, fbb.CreateVector(axis.data(), axis.size())); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SQUEEZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_Squeeze_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Squeeze(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->axis(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Squeeze_SetAxis(PrimitivePtr *primitive, const std::vector &axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Squeeze(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSqueeze(fbb, fbb.CreateVector(axis.data(), axis.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SQUEEZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Stack ********** +PrimitivePtr MindIR_Stack_CreatePrimitive(int64_t axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStack(fbb, axis); + auto prim_offset = schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STACK), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_Stack_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Stack(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_Stack_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Stack(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStack(fbb, axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STACK), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** StridedSlice ********** +PrimitivePtr MindIR_StridedSlice_CreatePrimitive(int64_t begin_mask, int64_t end_mask, int64_t ellipsis_mask, + int64_t new_axis_mask, int64_t shrink_axis_mask) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = + schema::CreateStridedSlice(fbb, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STRIDED_SLICE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +int64_t MindIR_StridedSlice_GetBeginMask(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + return value->begin_mask(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_StridedSlice_SetBeginMask(PrimitivePtr *primitive, int64_t begin_mask) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStridedSlice(fbb, begin_mask, value->end_mask(), value->ellipsis_mask(), + value->new_axis_mask(), value->shrink_axis_mask()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STRIDED_SLICE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_StridedSlice_GetEndMask(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + return value->end_mask(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_StridedSlice_SetEndMask(PrimitivePtr *primitive, int64_t end_mask) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStridedSlice(fbb, value->begin_mask(), end_mask, value->ellipsis_mask(), + value->new_axis_mask(), value->shrink_axis_mask()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STRIDED_SLICE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_StridedSlice_GetEllipsisMask(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + return value->ellipsis_mask(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_StridedSlice_SetEllipsisMask(PrimitivePtr *primitive, int64_t ellipsis_mask) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStridedSlice(fbb, value->begin_mask(), value->end_mask(), ellipsis_mask, + value->new_axis_mask(), value->shrink_axis_mask()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STRIDED_SLICE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_StridedSlice_GetNewAxisMask(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + return value->new_axis_mask(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_StridedSlice_SetNewAxisMask(PrimitivePtr *primitive, int64_t new_axis_mask) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStridedSlice(fbb, value->begin_mask(), value->end_mask(), value->ellipsis_mask(), + new_axis_mask, value->shrink_axis_mask()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STRIDED_SLICE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_StridedSlice_GetShrinkAxisMask(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + return value->shrink_axis_mask(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_StridedSlice_SetShrinkAxisMask(PrimitivePtr *primitive, int64_t shrink_axis_mask) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_StridedSlice(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateStridedSlice(fbb, value->begin_mask(), value->end_mask(), value->ellipsis_mask(), + value->new_axis_mask(), shrink_axis_mask); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_STRIDED_SLICE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** SubFusion ********** +PrimitivePtr MindIR_SubFusion_CreatePrimitive(ActivationType activation_type) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSubFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SUB_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +ActivationType MindIR_SubFusion_GetActivationType(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SubFusion(); + if (prim != nullptr && value != nullptr) { + return static_cast(value->activation_type()); + } else { + ActivationType en = static_cast(0); + return en; + } + } else { + ActivationType en = static_cast(0); + return en; + } +} + +void MindIR_SubFusion_SetActivationType(PrimitivePtr *primitive, ActivationType activation_type) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_SubFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateSubFusion(fbb, static_cast(activation_type)); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_SUB_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** TileFusion ********** +PrimitivePtr MindIR_TileFusion_CreatePrimitive(const std::vector &dims) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateTileFusion(fbb, fbb.CreateVector(dims.data(), dims.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_TILE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_TileFusion_GetDims(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TileFusion(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->dims(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_TileFusion_SetDims(PrimitivePtr *primitive, const std::vector &dims) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_TileFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateTileFusion(fbb, fbb.CreateVector(dims.data(), dims.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_TILE_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** TopKFusion ********** +PrimitivePtr MindIR_TopKFusion_CreatePrimitive(bool sorted, int64_t axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateTopKFusion(fbb, sorted, axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_TOPK_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +bool MindIR_TopKFusion_GetSorted(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TopKFusion(); + if (prim != nullptr && value != nullptr) { + return value->sorted(); + } else { + return false; + } + } else { + return false; + } +} + +void MindIR_TopKFusion_SetSorted(PrimitivePtr *primitive, bool sorted) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_TopKFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateTopKFusion(fbb, sorted, value->axis()); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_TOPK_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} +int64_t MindIR_TopKFusion_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TopKFusion(); + if (prim != nullptr && value != nullptr) { + return value->axis(); + } else { + return 0; + } + } else { + return 0; + } +} + +void MindIR_TopKFusion_SetAxis(PrimitivePtr *primitive, int64_t axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_TopKFusion(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateTopKFusion(fbb, value->sorted(), axis); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_TOPK_FUSION), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Transpose ********** +PrimitivePtr MindIR_Transpose_CreatePrimitive() { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateTranspose(fbb); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_TRANSPOSE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +// ********** Unsqueeze ********** +PrimitivePtr MindIR_Unsqueeze_CreatePrimitive(const std::vector &axis) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateUnsqueeze(fbb, fbb.CreateVector(axis.data(), axis.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_UNSQUEEZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} +std::vector MindIR_Unsqueeze_GetAxis(ConstPrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Unsqueeze(); + if (prim != nullptr && value != nullptr) { + std::vector result; + auto src = value->axis(); + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int64_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Unsqueeze_SetAxis(PrimitivePtr *primitive, const std::vector &axis) { + if (primitive != nullptr && *primitive != nullptr) { + auto prim = static_cast(*primitive); + auto value = prim->value_as_Unsqueeze(); + if (prim != nullptr && value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + auto ops_offset = schema::CreateUnsqueeze(fbb, fbb.CreateVector(axis.data(), axis.size())); + auto prim_offset = + schema::CreatePrimitive(fbb, static_cast(NODE_TYPE_UNSQUEEZE), ops_offset.o); + fbb.Finish(prim_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreatePrimitiveFromBuilder(fbb, prim); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *primitive = ret_value; + } + } +} + +// ********** Custom ********** +std::vector MindIR_Custom_GetAttr(ConstPrimitivePtr primitive) { + if (primitive == nullptr) { + return {}; + } + auto prim = static_cast(primitive); + auto value = prim->value_as_Custom(); + if (value == nullptr) { + return {}; + } + std::vector result; + if (value->attr() == nullptr) { + return {}; + } + for (auto attr: *(value->attr())) { + result.push_back(attr); + } + return result; +} + +std::string MindIR_Attribute_GetName(const mindspore::schema::Attribute &attr) { + if (attr.name() == nullptr) { + return ""; + } + return attr.name()->str(); +} + +std::vector MindIR_Attribute_GetData(const mindspore::schema::Attribute &attr) { + if (attr.data() == nullptr) { + return {}; + } + std::vector tmp_data(attr.data()->size()); + std::transform(attr.data()->begin(), attr.data()->end(), tmp_data.begin(), + [](uint8_t item) { return item; }); + return tmp_data; +} +} // namespace lite +} // namespace mindspore diff --git a/mindspore/lite/mindir/src/mindir_memory_manager.cc b/mindspore/lite/mindir/src/mindir_memory_manager.cc new file mode 100644 index 00000000..88c78d25 --- /dev/null +++ b/mindspore/lite/mindir/src/mindir_memory_manager.cc @@ -0,0 +1,129 @@ +#include "mindir_memory_manager.h" +#include +#include "securec.h" +#include "utils.h" +#include "src/common/log.h" + +namespace mindspore { +namespace lite { +namespace { +template +void ClearMap(std::map &map) { + for (auto iter = map.begin(); iter != map.end();) { + if (iter->second != nullptr) { + free(iter->second); + map.erase(iter++); + } else { + iter++; + } + } +} +} // namespace +MindIRMemoryManager *MindIRMemoryManager::GetInstance() { + static MindIRMemoryManager instance; + return &instance; +} + +void *MindIRMemoryManager::CopyFbbToNewMemory(flatbuffers::FlatBufferBuilder &fbb) { + auto buff = reinterpret_cast(malloc(fbb.GetSize())); + if (buff == nullptr) { + MS_LOG(ERROR) << "malloc memory for primitive failed!"; + fbb.Clear(); + return nullptr; + } + auto ret = memcpy_s(buff, fbb.GetSize(), fbb.GetBufferPointer(), fbb.GetSize()); + if (ret != EOK) { + MS_LOG(ERROR) << "memcpy failed, ret:" << ret; + fbb.Clear(); + return nullptr; + } + fbb.Clear(); + return buff; +} +void *MindIRMemoryManager::CreateTensorFromBuilder(flatbuffers::FlatBufferBuilder &fbb_new, schema::Tensor *tensor) { + std::lock_guard lck(mutex); + if (tensor != nullptr) { + // find primitive exist + if (tensor_map.find(tensor) != tensor_map.end()) { + // if find, then delete + void *flatbuffer_ptr = tensor_map[tensor]; + if (flatbuffer_ptr != nullptr) { + free(flatbuffer_ptr); + tensor_map[tensor] = nullptr; + tensor_map.erase(tensor_map.find(tensor)); + } + } + } + // then copy fbb + auto new_memory_ptr = CopyFbbToNewMemory(fbb_new); + auto tensor_root = flatbuffers::GetMutableRoot(new_memory_ptr); + tensor_map[tensor_root] = new_memory_ptr; + return new_memory_ptr; +} + +void *MindIRMemoryManager::CreatePrimitiveFromBuilder(flatbuffers::FlatBufferBuilder &fbb_new, + schema::Primitive *primitive) { + std::lock_guard lck(mutex); + if (primitive != nullptr) { + // find primitive exist + if (primitive_map.find(primitive) != primitive_map.end()) { + // if find, then delete + void *flatbuffer_ptr = primitive_map[primitive]; + if (flatbuffer_ptr != nullptr) { + free(flatbuffer_ptr); + primitive_map[primitive] = nullptr; + primitive_map.erase(primitive_map.find(primitive)); + } + } + } + // then copy fbb + auto new_memory_ptr = CopyFbbToNewMemory(fbb_new); + auto primitive_root = flatbuffers::GetMutableRoot(new_memory_ptr); + primitive_map[primitive_root] = new_memory_ptr; + return new_memory_ptr; +} + +void MindIRMemoryManager::DeletePrimitive(schema::Primitive *primitive) { + std::lock_guard lck(mutex); + if (primitive == nullptr) { + MS_LOG(ERROR) << "primitive is nullptr, no need to delete."; + return; + } + if (primitive_map.find(primitive) != primitive_map.end()) { + // if find, then delete + void *flatbuffer_ptr = primitive_map[primitive]; + if (flatbuffer_ptr != nullptr) { + free(flatbuffer_ptr); + primitive_map[primitive] = nullptr; + primitive_map.erase(primitive_map.find(primitive)); + } + } +} + +void MindIRMemoryManager::DeleteTensor(schema::Tensor *tensor) { + std::lock_guard lck(mutex); + if (tensor == nullptr) { + MS_LOG(ERROR) << "tensor is nullptr, no need to delete."; + return; + } + if (tensor != nullptr) { + // find primitive exist + if (tensor_map.find(tensor) != tensor_map.end()) { + // if find, then delete + void *flatbuffer_ptr = tensor_map[tensor]; + if (flatbuffer_ptr != nullptr) { + free(flatbuffer_ptr); + tensor_map[tensor] = nullptr; + tensor_map.erase(tensor_map.find(tensor)); + } + } + } +} + +void MindIRMemoryManager::ClearAllMemory() { + std::lock_guard lck(mutex); + ClearMap(primitive_map); + ClearMap(tensor_map); +} +} // namespace lite +} // namespace mindspore \ No newline at end of file diff --git a/mindspore/lite/mindir/src/mindir_nnrt_lite_graph.cc b/mindspore/lite/mindir/src/mindir_nnrt_lite_graph.cc new file mode 100644 index 00000000..a914fa6b --- /dev/null +++ b/mindspore/lite/mindir/src/mindir_nnrt_lite_graph.cc @@ -0,0 +1,87 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "mindir_lite_graph.h" +#include "mindir_tensor.h" +#include "mindir_primitive.h" +#include "src/common/log.h" +#include "schema/model_generated.h" +#include "mindir_memory_manager.h" +#include +namespace mindspore { +namespace lite { +void MindIR_LiteGraph_Destroy(LiteGraph **lite_graph) { + if (lite_graph != nullptr && *lite_graph != nullptr) { + MS_LOG(INFO) << "start to destroy LiteGraph."; + auto graph = *lite_graph; + graph->name_.clear(); + graph->input_indices_.clear(); + graph->output_indices_.clear(); + MS_LOG(INFO) << "Destroying nodes."; + // node + for (size_t idx = 0; idx < graph->all_nodes_.size(); idx++) { + if (graph->all_nodes_[idx] != nullptr) { + MindIRMemoryManager::GetInstance()->DeletePrimitive( + static_cast(graph->all_nodes_[idx]->primitive_)); + delete graph->all_nodes_[idx]; + } + } + MS_LOG(INFO) << "Destroying subgraphs."; + // subgraph + for (size_t idx = 0; idx < graph->sub_graphs_.size(); idx++) { + if (graph->sub_graphs_[idx] != nullptr) { + delete graph->sub_graphs_[idx]; + } + } + MS_LOG(INFO) << "Destroying tensors."; + // tensor + for (size_t idx = 0; idx < graph->all_tensors_.size(); idx++) { + if (graph->all_tensors_[idx] != nullptr) { + MindIRMemoryManager::GetInstance()->DeleteTensor(static_cast(graph->all_tensors_[idx])); + } + } + // graph + delete graph; + *lite_graph = nullptr; + } else { + MS_LOG(ERROR) << "nnrt_lite_graph is nullptr, can not delete."; + } +} + +size_t MindIR_LiteGraph_GetConstTensorSize(const LiteGraph *lite_graph) { + if (lite_graph != nullptr) { + size_t size = 0; + for (auto tensor : lite_graph->all_tensors_) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + auto src = value->data(); + if (src == nullptr) { + continue; + } + size += src->size(); + } + } + } + MS_LOG(DEBUG) << "lite_graph has " << lite_graph->all_tensors_.size() << "tensors ,const tensor size = " << size; + return size; + } else { + MS_LOG(ERROR) << "lite_graph is nullptr"; + return 0; + } +} + +} // namespace lite +} // namespace mindspore \ No newline at end of file diff --git a/mindspore/lite/mindir/src/mindir_nnrt_lite_graph_to_model.cc b/mindspore/lite/mindir/src/mindir_nnrt_lite_graph_to_model.cc new file mode 100644 index 00000000..df39e04b --- /dev/null +++ b/mindspore/lite/mindir/src/mindir_nnrt_lite_graph_to_model.cc @@ -0,0 +1,1496 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "mindir.h" +#include +#include +#include +#include "src/common/log.h" +#include "lite_graph.h" +#include "schema/model_generated.h" +#include "mindir_types.h" +#include "message_parcel.h" +#include "nnrt/v1_0/nnrt_types.h" +#include "nnrt/v1_0/node_attr_types.h" +#include "nnrt/v1_0/model_types.h" + +using namespace OHOS::HDI::Nnrt::V1_0; +namespace mindspore { +namespace lite { + +constexpr size_t kNumTwo = 2; +constexpr size_t kNumFour = 4; +constexpr size_t kNumEight = 8; + +inline std::vector MindIR_Tensor_GetQuantParams_OHOS(TensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + + if (value != nullptr) { + std::vector result; + auto src = value->quantParams(); + if (src == nullptr) { + return {}; + } + size_t size = src->size(); + result.reserve(src->size()); + for (size_t i = 0; i < size; i++) { + auto tmp = src->Get(i); + OHOS::HDI::Nnrt::V1_0::QuantParam quantParam{tmp->numBits(), tmp->zeroPoint(), tmp->scale()}; + result.emplace_back(quantParam); + } + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Model_Destroy(OHOS::HDI::Nnrt::V1_0::Model **model) { + if (model != nullptr) { + auto model_data = *model; + if (model_data != nullptr) { + delete (model_data); + *model = nullptr; + } else { + MS_LOG(ERROR) << "*model is nullptr, desrtoy model fail."; + } + } +} + +OHOS::HDI::Nnrt::V1_0::Model *MindIR_LiteGraph_To_Model(const LiteGraph *lite_graph, const OHOS::HDI::Nnrt::V1_0::SharedBuffer &buffer) { + if (lite_graph != nullptr) { + MS_LOG(INFO) << "MindIR_LiteGraph_To_Model begin"; + if (!lite_graph->name_.empty()) { + MS_LOG(INFO) << "Start converting lite graph,name =" << lite_graph->name_; + } else { + MS_LOG(INFO) << "Start converting lite graph, but lite graph has no name."; + } + std::vector inputIndex; + std::vector outputIndex; + std::vector nodes; + std::vector allTensors; + std::vector subGraph; + // nodes + MS_LOG(INFO) << "Start converting nodes, vector size = " << lite_graph->all_nodes_.size(); + nodes.reserve(lite_graph->all_nodes_.size()); + for (auto node : lite_graph->all_nodes_) { + if (node == nullptr) { + MS_LOG(ERROR) << "node is nullptr, convert fail."; + return nullptr; + } + OHOS::HDI::Nnrt::V1_0::Node tmp; + tmp.name = node->name_; + if (node->primitive_ == nullptr) { + MS_LOG(ERROR) << "node primitive is nullptr, convert fail."; + return nullptr; + } + auto prim = static_cast(node->primitive_); + auto value = prim->value_type(); + tmp.nodeType = static_cast(value); + tmp.nodeAttr = Convert(static_cast(value), node->primitive_); + tmp.inputIndex = node->input_indices_; + tmp.outputIndex = node->output_indices_; + tmp.quantType = static_cast(node->quant_type_); + nodes.emplace_back(tmp); + } + + MS_LOG(INFO) << "Start converting Tensor,Tensor size=" << lite_graph->all_tensors_.size(); + // Tensor + allTensors.reserve(lite_graph->all_tensors_.size()); + unsigned int tensor_buffer_offset = 0; + uint8_t *mmap_ptr = nullptr; + if (buffer.fd != -1) { + mmap_ptr = + static_cast(mmap(nullptr, buffer.bufferSize, PROT_READ | PROT_WRITE, MAP_SHARED, buffer.fd, 0)); + if (mmap_ptr == MAP_FAILED) { + MS_LOG(ERROR) << "mmap failed"; + return nullptr; + } + } + MS_LOG(INFO) << "Start parsing tensor, mmap buffer size = " << buffer.bufferSize; + for (auto tensor : lite_graph->all_tensors_) { + OHOS::HDI::Nnrt::V1_0::Tensor tmp; + tmp.name = MindIR_Tensor_GetName(tensor); + tmp.dataType = static_cast(MindIR_Tensor_GetDataType(tensor)); + tmp.dims = MindIR_Tensor_GetDims(tensor); + tmp.format = static_cast(MindIR_Tensor_GetFormat(tensor)); + tmp.data = MindIR_Tensor_GetData(tensor, buffer, mmap_ptr, tensor_buffer_offset); + tmp.quantParams = MindIR_Tensor_GetQuantParams_OHOS(tensor); + allTensors.emplace_back(tmp); + tensor_buffer_offset = tmp.data.offset + tmp.data.dataSize; + } + MS_LOG(INFO) << ("Parsing tensor finish."); + if (buffer.fd != -1) { + auto munmap_res = munmap(mmap_ptr, buffer.bufferSize); + if (munmap_res != 0) { + MS_LOG(ERROR) << "unmap failed."; + return nullptr; + } + } + + MS_LOG(INFO) << "Start converting SubGraph,SubGraph size=" << lite_graph->sub_graphs_.size(); + // SubGraph + subGraph.reserve(lite_graph->sub_graphs_.size()); + for (auto graph : lite_graph->sub_graphs_) { + OHOS::HDI::Nnrt::V1_0::SubGraph tmp; + tmp.name = graph->name_; + tmp.inputIndices = std::vector(graph->input_indices_); + tmp.outputIndices = std::vector(graph->output_indices_); + tmp.nodeIndices = std::vector(graph->node_indices_); + subGraph.emplace_back(tmp); + } + + MS_LOG(INFO) << "Start copying model"; + auto *ret_model = new (std::nothrow) Model(); + if (ret_model == nullptr) { + MS_LOG(ERROR) << "new Model failed."; + return nullptr; + } + ret_model->name = lite_graph->name_; + ret_model->inputIndex = lite_graph->input_indices_; + ret_model->outputIndex = lite_graph->output_indices_; + ret_model->nodes = nodes; + ret_model->allTensors = allTensors; + ret_model->subGraph = subGraph; + MS_LOG(INFO) << "MindIR_LiteGraph_To_Model success"; + return ret_model; + } else { + MS_LOG(ERROR) << "lite graph is nullptr"; + return nullptr; + } +} + +std::vector ConvertActivation(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (value != nullptr) { + Activation activation{}; + activation.activationType = + static_cast(value->activation_type()); + activation.alpha = value->alpha(); + activation.minVal = value->min_val(); + activation.maxVal = value->max_val(); + activation.approximate = value->approximate(); + OHOS::MessageParcel data; + (void)ActivationBlockMarshalling(data, activation); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertAddFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AddFusion(); + if (value != nullptr) { + AddFusion add_fusion{}; + add_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)AddFusionBlockMarshalling(data, add_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertArgMaxFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (value != nullptr) { + ArgMaxFusion arg_max_fusion{}; + arg_max_fusion.axis = value->axis(); + arg_max_fusion.topK = value->top_k(); + arg_max_fusion.keepDims = value->keep_dims(); + arg_max_fusion.outMaxValue = value->out_max_value(); + OHOS::MessageParcel data; + (void)ArgMaxFusionBlockMarshalling(data, arg_max_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertAvgPoolFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (value != nullptr) { + AvgPoolFusion avg_pool_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->strides() == nullptr || value->strides()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->strides()->begin(), value->strides()->end()); + } + std::vector padList; + strides.reserve(kNumTwo); + if (value->pad() == nullptr || value->pad()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad()->begin(), value->pad()->end()); + } + avg_pool_fusion.kernelSize = kernel_size; + avg_pool_fusion.strides = strides; + avg_pool_fusion.pad = padList; + avg_pool_fusion.padMode = static_cast(value->pad_mode()); + avg_pool_fusion.roundMode = static_cast(value->round_mode()); + avg_pool_fusion.format = static_cast(value->format()); + avg_pool_fusion.global = value->global(); + avg_pool_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)AvgPoolFusionBlockMarshalling(data, avg_pool_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertBatchToSpaceND(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_BatchToSpaceND(); + if (value != nullptr) { + BatchToSpaceND batch_to_space_n_d{}; + std::vector blockShape; + blockShape.reserve(kNumTwo); + if (value->block_shape() == nullptr || value->block_shape()->size() < kNumTwo) { + blockShape = {0, 0}; + } else { + blockShape = std::vector(value->block_shape()->begin(), value->block_shape()->end()); + } + batch_to_space_n_d.blockShape = blockShape; + auto crops = value->crops(); + std::vector> crops_vec2d; + if (crops->data() == nullptr) { + MS_LOG(ERROR) << "crops_data is nullptr"; + crops_vec2d = {{}}; + } else { + crops_vec2d.reserve(crops->data()->size()); + for (size_t i = 0; i < crops->data()->size(); i++) { + auto vet = crops->data()->Get(i); + crops_vec2d.emplace_back(std::vector(vet->data()->begin(), vet->data()->end())); + } + } + batch_to_space_n_d.crops = crops_vec2d; + OHOS::MessageParcel data; + (void)BatchToSpaceNDBlockMarshalling(data, batch_to_space_n_d); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertBiasAdd(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_BiasAdd(); + if (value != nullptr) { + BiasAdd bias_add{}; + OHOS::MessageParcel data; + (void)BiasAddBlockMarshalling(data, bias_add); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertCast(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Cast(); + if (value != nullptr) { + Cast cast{}; + OHOS::MessageParcel data; + (void)CastBlockMarshalling(data, cast); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertConcat(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Concat(); + if (value != nullptr) { + Concat concat{}; + concat.axis = value->axis(); + OHOS::MessageParcel data; + (void)ConcatBlockMarshalling(data, concat); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertConv2DFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (value != nullptr) { + Conv2DFusion conv2_d_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->stride() == nullptr || value->stride()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->stride()->begin(), value->stride()->end()); + } + std::vector dilation; + dilation.reserve(kNumTwo); + if (value->dilation() == nullptr || value->dilation()->size() < kNumTwo) { + dilation = {}; + } else { + dilation = std::vector(value->dilation()->begin(), value->dilation()->end()); + } + std::vector padList; + strides.reserve(kNumTwo); + if (value->pad_list() == nullptr || value->pad_list()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad_list()->begin(), value->pad_list()->end()); + } + conv2_d_fusion.kernelSize = kernel_size; + conv2_d_fusion.stride = strides; + conv2_d_fusion.dilation = dilation; + conv2_d_fusion.padMode = static_cast(value->pad_mode()); + conv2_d_fusion.padList = padList; + conv2_d_fusion.group = value->group(); + conv2_d_fusion.inChannel = value->in_channel(); + conv2_d_fusion.outChannel = value->out_channel(); + conv2_d_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)Conv2DFusionBlockMarshalling(data, conv2_d_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertConv2dTransposeFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (value != nullptr) { + Conv2dTransposeFusion conv2d_transpose_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->stride() == nullptr || value->stride()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->stride()->begin(), value->stride()->end()); + } + std::vector dilation; + dilation.reserve(kNumTwo); + if (value->dilation() == nullptr || value->dilation()->size() < kNumTwo) { + dilation = {}; + } else { + dilation = std::vector(value->dilation()->begin(), value->dilation()->end()); + } + std::vector padList; + strides.reserve(kNumTwo); + if (value->pad_list() == nullptr || value->pad_list()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad_list()->begin(), value->pad_list()->end()); + } + std::vector output_paddings; + output_paddings.reserve(kNumTwo); + if (value->output_paddings() == nullptr || value->output_paddings()->size() < kNumTwo) { + output_paddings = {}; + } else { + output_paddings = std::vector(value->output_paddings()->begin(), value->output_paddings()->end()); + } + conv2d_transpose_fusion.kernelSize = kernel_size; + conv2d_transpose_fusion.stride = strides; + conv2d_transpose_fusion.dilation = dilation; + conv2d_transpose_fusion.padMode = static_cast(value->pad_mode()); + conv2d_transpose_fusion.padList = padList; + conv2d_transpose_fusion.group = value->group(); + conv2d_transpose_fusion.inChannel = value->in_channel(); + conv2d_transpose_fusion.outChannel = value->out_channel(); + conv2d_transpose_fusion.activationType = static_cast(value->activation_type()); + conv2d_transpose_fusion.outputPaddings = output_paddings; + OHOS::MessageParcel data; + (void)Conv2dTransposeFusionBlockMarshalling(data, conv2d_transpose_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertDivFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_DivFusion(); + if (value != nullptr) { + DivFusion div_fusion{}; + div_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)DivFusionBlockMarshalling(data, div_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertEltwise(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Eltwise(); + if (value != nullptr) { + Eltwise eltwise{}; + eltwise.mode = static_cast(value->mode()); + OHOS::MessageParcel data; + (void)EltwiseBlockMarshalling(data, eltwise); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertExpandDims(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ExpandDims(); + if (value != nullptr) { + ExpandDims expand_dims{}; + OHOS::MessageParcel data; + (void)ExpandDimsBlockMarshalling(data, expand_dims); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertFill(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Fill(); + if (value != nullptr) { + Fill fill{}; + OHOS::MessageParcel data; + (void)FillBlockMarshalling(data, fill); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertFullConnection(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FullConnection(); + if (value != nullptr) { + FullConnection full_connection{}; + full_connection.hasBias = value->has_bias(); + full_connection.useAxis = value->use_axis(); + full_connection.axis = value->axis(); + full_connection.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)FullConnectionBlockMarshalling(data, full_connection); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertFusedBatchNorm(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FusedBatchNorm(); + if (value != nullptr) { + FusedBatchNorm fused_batch_norm{}; + fused_batch_norm.epsilon = value->epsilon(); + OHOS::MessageParcel data; + (void)FusedBatchNormBlockMarshalling(data, fused_batch_norm); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertGather(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Gather(); + if (value != nullptr) { + Gather gather{}; + OHOS::MessageParcel data; + (void)GatherBlockMarshalling(data, gather); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertLayerNormFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LayerNormFusion(); + if (value != nullptr) { + LayerNormFusion layer_norm_fusion{}; + layer_norm_fusion.beginNormAxis = value->begin_norm_axis(); + layer_norm_fusion.epsilon = value->epsilon(); + layer_norm_fusion.elementwiseAffine = value->elementwise_affine(); + layer_norm_fusion.beginParamsAxis = value->begin_params_axis(); + OHOS::MessageParcel data; + (void)LayerNormFusionBlockMarshalling(data, layer_norm_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertLessEqual(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LessEqual(); + if (value != nullptr) { + LessEqual less_equal{}; + OHOS::MessageParcel data; + (void)LessEqualBlockMarshalling(data, less_equal); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMatMulFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MatMulFusion(); + if (value != nullptr) { + MatMulFusion mat_mul_fusion{}; + mat_mul_fusion.transposeA = value->transpose_a(); + mat_mul_fusion.transposeB = value->transpose_b(); + mat_mul_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)MatMulFusionBlockMarshalling(data, mat_mul_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMaximum(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Maximum(); + if (value != nullptr) { + Maximum maximum{}; + OHOS::MessageParcel data; + (void)MaximumBlockMarshalling(data, maximum); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMaxPoolFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (value != nullptr) { + MaxPoolFusion max_pool_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->strides() == nullptr || value->strides()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->strides()->begin(), value->strides()->end()); + } + std::vector padList; + padList.reserve(kNumFour); + if (value->pad() == nullptr || value->pad()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad()->begin(), value->pad()->end()); + } + max_pool_fusion.kernelSize = kernel_size; + max_pool_fusion.strides = strides; + max_pool_fusion.pad = padList; + max_pool_fusion.padMode = static_cast(value->pad_mode()); + max_pool_fusion.format = static_cast(value->format()); + max_pool_fusion.global = value->global(); + max_pool_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)MaxPoolFusionBlockMarshalling(data, max_pool_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMulFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MulFusion(); + if (value != nullptr) { + MulFusion mul_fusion{}; + mul_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)MulFusionBlockMarshalling(data, mul_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertOneHot(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_OneHot(); + if (value != nullptr) { + OneHot one_hot{}; + one_hot.axis = value->axis(); + OHOS::MessageParcel data; + (void)OneHotBlockMarshalling(data, one_hot); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertPadFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PadFusion(); + if (value != nullptr) { + PadFusion pad_fusion{}; + auto paddings = value->paddings(); + std::vector> paddings_vec2d; + if (paddings == nullptr || paddings->data()->size() < kNumTwo) { + paddings_vec2d = {{0}, {0}, {0}, {0}}; + } else { + paddings_vec2d.reserve(paddings->data()->size()); + for (size_t i = 0; i < paddings->data()->size(); i++) { + auto vet = paddings->data()->Get(i); + paddings_vec2d.emplace_back(std::vector(vet->data()->begin(), vet->data()->end())); + } + } + pad_fusion.paddings = paddings_vec2d; + pad_fusion.paddingMode = static_cast(value->padding_mode()); + pad_fusion.constantValue = value->constant_value(); + OHOS::MessageParcel data; + (void)PadFusionBlockMarshalling(data, pad_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertPowFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PowFusion(); + if (value != nullptr) { + PowFusion pow_fusion{}; + pow_fusion.scale = value->scale(); + pow_fusion.shift = value->shift(); + OHOS::MessageParcel data; + (void)PowFusionBlockMarshalling(data, pow_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertPReLUFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PReLUFusion(); + if (value != nullptr) { + PReLUFusion p_re_l_u_fusion{}; + p_re_l_u_fusion.channelShared = value->channel_shared(); + OHOS::MessageParcel data; + (void)PReLUFusionBlockMarshalling(data, p_re_l_u_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertQuantDTypeCast(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_QuantDTypeCast(); + if (value != nullptr) { + QuantDTypeCast quant_d_type_cast{}; + quant_d_type_cast.srcT = value->src_t(); + quant_d_type_cast.dstT = value->dst_t(); + OHOS::MessageParcel data; + (void)QuantDTypeCastBlockMarshalling(data, quant_d_type_cast); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertReduceFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ReduceFusion(); + if (value != nullptr) { + ReduceFusion reduce_fusion{}; + reduce_fusion.keepDims = value->keep_dims(); + reduce_fusion.mode = static_cast(value->mode()); + reduce_fusion.reduceToEnd = value->reduce_to_end(); + reduce_fusion.coeff = value->coeff(); + OHOS::MessageParcel data; + (void)ReduceFusionBlockMarshalling(data, reduce_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertReshape(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Reshape(); + if (value != nullptr) { + Reshape reshape{}; + OHOS::MessageParcel data; + (void)ReshapeBlockMarshalling(data, reshape); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} + +std::vector ConvertResize(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (value != nullptr) { + Resize resize{}; + resize.method = static_cast(value->method()); + resize.newHeight = value->new_height(); + resize.newWidth = value->new_width(); + resize.preserveAspectRatio = value->preserve_aspect_ratio(); + resize.coordinateTransformMode = + static_cast(value->coordinate_transform_mode()); + resize.cubicCoeff = value->cubic_coeff(); + resize.excludeOutside = value->exclude_outside(); + resize.extrapolationValue = value->extrapolation_value(); + resize.nearestMode = static_cast(value->nearest_mode()); + OHOS::MessageParcel data; + (void)ResizeBlockMarshalling(data, resize); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertRsqrt(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Rsqrt(); + if (value != nullptr) { + Rsqrt rsqrt{}; + OHOS::MessageParcel data; + (void)RsqrtBlockMarshalling(data, rsqrt); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertScaleFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ScaleFusion(); + if (value != nullptr) { + ScaleFusion scale_fusion{}; + scale_fusion.axis = value->axis(); + scale_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)ScaleFusionBlockMarshalling(data, scale_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertShape(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Shape(); + if (value != nullptr) { + Shape shape{}; + OHOS::MessageParcel data; + (void)ShapeBlockMarshalling(data, shape); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSliceFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SliceFusion(); + if (value != nullptr) { + SliceFusion slice_fusion{}; + std::vector axes; + if (value->axes() == nullptr) { + axes = {1, 2, 3, 4, 5, 6, 7}; + } else { + axes = std::vector(value->axes()->begin(), value->axes()->end()); + } + slice_fusion.axes = axes; + OHOS::MessageParcel data; + (void)SliceFusionBlockMarshalling(data, slice_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSoftmax(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Softmax(); + if (value != nullptr) { + Softmax softmax{}; + std::vector axis; + if (value->axis() == nullptr) { + axis = {}; + } else { + axis = std::vector(value->axis()->begin(), value->axis()->end()); + } + softmax.axis = axis; + OHOS::MessageParcel data; + (void)SoftmaxBlockMarshalling(data, softmax); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSpaceToBatchND(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SpaceToBatchND(); + if (value != nullptr) { + SpaceToBatchND space_to_batch_n_d{}; + std::vector blockShape; + blockShape.reserve(kNumTwo); + if (value->block_shape() == nullptr || value->block_shape()->size() < kNumTwo) { + blockShape = {0, 0}; + } else { + blockShape = std::vector(value->block_shape()->begin(), value->block_shape()->end()); + } + space_to_batch_n_d.blockShape = blockShape; + auto paddings = value->paddings(); + std::vector> paddings_vec2d; + if (paddings == nullptr || paddings->data()->size() == 0 || *(paddings->data()->begin()) == nullptr || + (*(paddings->data()->begin()))->data() == nullptr) { + paddings_vec2d = {}; + } else { + paddings_vec2d.reserve(paddings->data()->size()); + for (size_t i = 0; i < paddings->data()->size(); i++) { + auto vet = paddings->data()->Get(i); + paddings_vec2d.emplace_back(std::vector(vet->data()->begin(), vet->data()->end())); + } + } + space_to_batch_n_d.paddings = paddings_vec2d; + OHOS::MessageParcel data; + (void)SpaceToBatchNDBlockMarshalling(data, space_to_batch_n_d); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSplit(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Split(); + if (value != nullptr) { + Split split{}; + split.outputNum = value->output_num(); + std::vector sizeSplits; + sizeSplits.reserve(split.outputNum); + if (value->size_splits() == nullptr || value->size_splits()->size() <= static_cast(split.outputNum)) { + sizeSplits = {}; + } else { + sizeSplits = std::vector(value->size_splits()->begin(), value->size_splits()->end()); + } + split.sizeSplits = sizeSplits; + split.axis = value->axis(); + OHOS::MessageParcel data; + (void)SplitBlockMarshalling(data, split); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSqrt(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Sqrt(); + if (value != nullptr) { + Sqrt sqrt{}; + OHOS::MessageParcel data; + (void)SqrtBlockMarshalling(data, sqrt); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSquaredDifference(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SquaredDifference(); + if (value != nullptr) { + SquaredDifference squared_difference{}; + OHOS::MessageParcel data; + (void)SquaredDifferenceBlockMarshalling(data, squared_difference); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSqueeze(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Squeeze(); + if (value != nullptr) { + Squeeze squeeze{}; + std::vector axis; + if (value->axis() == nullptr) { + axis = {}; + } else { + axis = std::vector(value->axis()->begin(), value->axis()->end()); + } + squeeze.axis = axis; + OHOS::MessageParcel data; + (void)SqueezeBlockMarshalling(data, squeeze); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertStack(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Stack(); + if (value != nullptr) { + Stack stack{}; + stack.axis = value->axis(); + OHOS::MessageParcel data; + (void)StackBlockMarshalling(data, stack); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertStridedSlice(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (value != nullptr) { + StridedSlice strided_slice{}; + strided_slice.beginMask = value->begin_mask(); + strided_slice.endMask = value->end_mask(); + strided_slice.ellipsisMask = value->ellipsis_mask(); + strided_slice.newAxisMask = value->new_axis_mask(); + strided_slice.shrinkAxisMask = value->shrink_axis_mask(); + OHOS::MessageParcel data; + (void)StridedSliceBlockMarshalling(data, strided_slice); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSubFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SubFusion(); + if (value != nullptr) { + SubFusion sub_fusion{}; + sub_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)SubFusionBlockMarshalling(data, sub_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertTileFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TileFusion(); + if (value != nullptr) { + TileFusion tile_fusion{}; + std::vector dims; + dims.reserve(kNumEight); + if (value->dims() == nullptr) { + dims = {0, 0, 0, 0, 0, 0, 0, 0}; + } else { + dims = std::vector(value->dims()->begin(), value->dims()->end()); + } + tile_fusion.dims = dims; + OHOS::MessageParcel data; + (void)TileFusionBlockMarshalling(data, tile_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertTopKFusion(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TopKFusion(); + if (value != nullptr) { + TopKFusion top_k_fusion{}; + top_k_fusion.sorted = value->sorted(); + top_k_fusion.axis = value->axis(); + OHOS::MessageParcel data; + (void)TopKFusionBlockMarshalling(data, top_k_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertTranspose(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Transpose(); + if (value != nullptr) { + Transpose transpose{}; + OHOS::MessageParcel data; + (void)TransposeBlockMarshalling(data, transpose); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertUnsqueeze(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Unsqueeze(); + if (value != nullptr) { + Unsqueeze unsqueeze{}; + std::vector axis; + axis.reserve(kNumEight); + if (value->axis() == nullptr) { + axis = {0, 0, 0, 0}; + } else { + axis = std::vector(value->axis()->begin(), value->axis()->end()); + } + unsqueeze.axis = axis; + OHOS::MessageParcel data; + (void)UnsqueezeBlockMarshalling(data, unsqueeze); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} + +std::vector Convert(NodeType type, PrimitivePtr primitive) { + switch (type) { + case NODE_TYPE_ACTIVATION: + return ConvertActivation(primitive); + break; + case NODE_TYPE_ADD_FUSION: + return ConvertAddFusion(primitive); + break; + case NODE_TYPE_ARGMAX_FUSION: + return ConvertArgMaxFusion(primitive); + break; + case NODE_TYPE_AVG_POOL_FUSION: + return ConvertAvgPoolFusion(primitive); + break; + case NODE_TYPE_BATCH_TO_SPACE_ND: + return ConvertBatchToSpaceND(primitive); + break; + case NODE_TYPE_BIAS_ADD: + return ConvertBiasAdd(primitive); + break; + case NODE_TYPE_CAST: + return ConvertCast(primitive); + break; + case NODE_TYPE_CONCAT: + return ConvertConcat(primitive); + break; + case NODE_TYPE_CONV2D_FUSION: + return ConvertConv2DFusion(primitive); + break; + case NODE_TYPE_CONV2D_TRANSPOSE_FUSION: + return ConvertConv2dTransposeFusion(primitive); + break; + case NODE_TYPE_DIV_FUSION: + return ConvertDivFusion(primitive); + break; + case NODE_TYPE_ELTWISE: + return ConvertEltwise(primitive); + break; + case NODE_TYPE_EXPAND_DIMS: + return ConvertExpandDims(primitive); + break; + case NODE_TYPE_FILL: + return ConvertFill(primitive); + break; + case NODE_TYPE_FULL_CONNECTION: + return ConvertFullConnection(primitive); + break; + case NODE_TYPE_FUSED_BATCH_NORM: + return ConvertFusedBatchNorm(primitive); + break; + case NODE_TYPE_GATHER: + return ConvertGather(primitive); + break; + case NODE_TYPE_LAYER_NORM_FUSION: + return ConvertLayerNormFusion(primitive); + break; + case NODE_TYPE_LESS_EQUAL: + return ConvertLessEqual(primitive); + break; + case NODE_TYPE_MATMUL_FUSION: + return ConvertMatMulFusion(primitive); + break; + case NODE_TYPE_MAXIMUM: + return ConvertMaximum(primitive); + break; + case NODE_TYPE_MAX_POOL_FUSION: + return ConvertMaxPoolFusion(primitive); + break; + case NODE_TYPE_MUL_FUSION: + return ConvertMulFusion(primitive); + break; + case NODE_TYPE_ONE_HOT: + return ConvertOneHot(primitive); + break; + case NODE_TYPE_PAD_FUSION: + return ConvertPadFusion(primitive); + break; + case NODE_TYPE_POW_FUSION: + return ConvertPowFusion(primitive); + break; + case NODE_TYPE_PRELU_FUSION: + return ConvertPReLUFusion(primitive); + break; + case NODE_TYPE_QUANT_DTYPE_CAST: + return ConvertQuantDTypeCast(primitive); + break; + case NODE_TYPE_REDUCE_FUSION: + return ConvertReduceFusion(primitive); + break; + case NODE_TYPE_RESHAPE: + return ConvertReshape(primitive); + break; + case NODE_TYPE_RESIZE: + return ConvertResize(primitive); + break; + case NODE_TYPE_RSQRT: + return ConvertRsqrt(primitive); + break; + case NODE_TYPE_SCALE_FUSION: + return ConvertScaleFusion(primitive); + break; + case NODE_TYPE_SHAPE: + return ConvertShape(primitive); + break; + case NODE_TYPE_SLICE_FUSION: + return ConvertSliceFusion(primitive); + break; + case NODE_TYPE_SOFTMAX: + return ConvertSoftmax(primitive); + break; + case NODE_TYPE_SPACE_TO_BATCH_ND: + return ConvertSpaceToBatchND(primitive); + break; + case NODE_TYPE_SPLIT: + return ConvertSplit(primitive); + break; + case NODE_TYPE_SQRT: + return ConvertSqrt(primitive); + break; + case NODE_TYPE_SQUARED_DIFFERENCE: + return ConvertSquaredDifference(primitive); + break; + case NODE_TYPE_SQUEEZE: + return ConvertSqueeze(primitive); + break; + case NODE_TYPE_STACK: + return ConvertStack(primitive); + break; + case NODE_TYPE_STRIDED_SLICE: + return ConvertStridedSlice(primitive); + break; + case NODE_TYPE_SUB_FUSION: + return ConvertSubFusion(primitive); + break; + case NODE_TYPE_TILE_FUSION: + return ConvertTileFusion(primitive); + break; + case NODE_TYPE_TOPK_FUSION: + return ConvertTopKFusion(primitive); + break; + case NODE_TYPE_TRANSPOSE: + return ConvertTranspose(primitive); + break; + case NODE_TYPE_UNSQUEEZE: + return ConvertUnsqueeze(primitive); + break; + default: + return {}; + } +} + +} // namespace lite +} // namespace mindspore \ No newline at end of file diff --git a/mindspore/lite/mindir/src/mindir_nnrt_lite_graph_to_model_v2_0.cc b/mindspore/lite/mindir/src/mindir_nnrt_lite_graph_to_model_v2_0.cc new file mode 100644 index 00000000..4e109eff --- /dev/null +++ b/mindspore/lite/mindir/src/mindir_nnrt_lite_graph_to_model_v2_0.cc @@ -0,0 +1,1497 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "mindir.h" +#include +#include +#include +#include "src/common/log.h" +#include "lite_graph.h" +#include "schema/model_generated.h" +#include "mindir_types.h" +#include "message_parcel.h" +#include "nnrt/v2_0/nnrt_types.h" +#include "nnrt/v2_0/node_attr_types.h" +#include "nnrt/v2_0/model_types.h" + +using namespace OHOS::HDI::Nnrt::V2_0; +namespace mindspore { +namespace lite { + +constexpr size_t kNumTwo = 2; +constexpr size_t kNumFour = 4; +constexpr size_t kNumEight = 8; + +inline std::vector MindIR_Tensor_GetQuantParams_OHOS_V2_0(TensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + + if (value != nullptr) { + std::vector result; + auto src = value->quantParams(); + if (src == nullptr) { + return {}; + } + size_t size = src->size(); + result.reserve(src->size()); + for (size_t i = 0; i < size; i++) { + auto tmp = src->Get(i); + OHOS::HDI::Nnrt::V2_0::QuantParam quantParam{tmp->numBits(), tmp->zeroPoint(), tmp->scale()}; + result.emplace_back(quantParam); + } + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Model_Destroy(OHOS::HDI::Nnrt::V2_0::Model **model) { + if (model != nullptr) { + auto model_data = *model; + if (model_data != nullptr) { + delete (model_data); + *model = nullptr; + } else { + MS_LOG(ERROR) << "*model is nullptr, desrtoy model fail."; + } + } +} + +std::vector ConvertActivation_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Activation(); + if (value != nullptr) { + Activation activation{}; + activation.activationType = + static_cast(value->activation_type()); + activation.alpha = value->alpha(); + activation.minVal = value->min_val(); + activation.maxVal = value->max_val(); + activation.approximate = value->approximate(); + OHOS::MessageParcel data; + (void)ActivationBlockMarshalling(data, activation); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertAddFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AddFusion(); + if (value != nullptr) { + AddFusion add_fusion{}; + add_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)AddFusionBlockMarshalling(data, add_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertArgMaxFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ArgMaxFusion(); + if (value != nullptr) { + ArgMaxFusion arg_max_fusion{}; + arg_max_fusion.axis = value->axis(); + arg_max_fusion.topK = value->top_k(); + arg_max_fusion.keepDims = value->keep_dims(); + arg_max_fusion.outMaxValue = value->out_max_value(); + OHOS::MessageParcel data; + (void)ArgMaxFusionBlockMarshalling(data, arg_max_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertAvgPoolFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_AvgPoolFusion(); + if (value != nullptr) { + AvgPoolFusion avg_pool_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->strides() == nullptr || value->strides()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->strides()->begin(), value->strides()->end()); + } + std::vector padList; + strides.reserve(kNumTwo); + if (value->pad() == nullptr || value->pad()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad()->begin(), value->pad()->end()); + } + avg_pool_fusion.kernelSize = kernel_size; + avg_pool_fusion.strides = strides; + avg_pool_fusion.pad = padList; + avg_pool_fusion.padMode = static_cast(value->pad_mode()); + avg_pool_fusion.roundMode = static_cast(value->round_mode()); + avg_pool_fusion.format = static_cast(value->format()); + avg_pool_fusion.global = value->global(); + avg_pool_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)AvgPoolFusionBlockMarshalling(data, avg_pool_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertBatchToSpaceND_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_BatchToSpaceND(); + if (value != nullptr) { + BatchToSpaceND batch_to_space_n_d{}; + std::vector blockShape; + blockShape.reserve(kNumTwo); + if (value->block_shape() == nullptr || value->block_shape()->size() < kNumTwo) { + blockShape = {0, 0}; + } else { + blockShape = std::vector(value->block_shape()->begin(), value->block_shape()->end()); + } + batch_to_space_n_d.blockShape = blockShape; + auto crops = value->crops(); + std::vector> crops_vec2d; + if (crops->data() == nullptr) { + MS_LOG(ERROR) << "crops_data is nullptr"; + crops_vec2d = {{}}; + } else { + crops_vec2d.reserve(crops->data()->size()); + for (size_t i = 0; i < crops->data()->size(); i++) { + auto vet = crops->data()->Get(i); + crops_vec2d.emplace_back(std::vector(vet->data()->begin(), vet->data()->end())); + } + } + batch_to_space_n_d.crops = crops_vec2d; + OHOS::MessageParcel data; + (void)BatchToSpaceNDBlockMarshalling(data, batch_to_space_n_d); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertBiasAdd_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_BiasAdd(); + if (value != nullptr) { + BiasAdd bias_add{}; + OHOS::MessageParcel data; + (void)BiasAddBlockMarshalling(data, bias_add); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertCast_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Cast(); + if (value != nullptr) { + Cast cast{}; + OHOS::MessageParcel data; + (void)CastBlockMarshalling(data, cast); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertConcat_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Concat(); + if (value != nullptr) { + Concat concat{}; + concat.axis = value->axis(); + OHOS::MessageParcel data; + (void)ConcatBlockMarshalling(data, concat); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertConv2DFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2DFusion(); + if (value != nullptr) { + Conv2DFusion conv2_d_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->stride() == nullptr || value->stride()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->stride()->begin(), value->stride()->end()); + } + std::vector dilation; + dilation.reserve(kNumTwo); + if (value->dilation() == nullptr || value->dilation()->size() < kNumTwo) { + dilation = {}; + } else { + dilation = std::vector(value->dilation()->begin(), value->dilation()->end()); + } + std::vector padList; + strides.reserve(kNumTwo); + if (value->pad_list() == nullptr || value->pad_list()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad_list()->begin(), value->pad_list()->end()); + } + conv2_d_fusion.kernelSize = kernel_size; + conv2_d_fusion.stride = strides; + conv2_d_fusion.dilation = dilation; + conv2_d_fusion.padMode = static_cast(value->pad_mode()); + conv2_d_fusion.padList = padList; + conv2_d_fusion.group = value->group(); + conv2_d_fusion.inChannel = value->in_channel(); + conv2_d_fusion.outChannel = value->out_channel(); + conv2_d_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)Conv2DFusionBlockMarshalling(data, conv2_d_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertConv2dTransposeFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Conv2dTransposeFusion(); + if (value != nullptr) { + Conv2dTransposeFusion conv2d_transpose_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->stride() == nullptr || value->stride()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->stride()->begin(), value->stride()->end()); + } + std::vector dilation; + dilation.reserve(kNumTwo); + if (value->dilation() == nullptr || value->dilation()->size() < kNumTwo) { + dilation = {}; + } else { + dilation = std::vector(value->dilation()->begin(), value->dilation()->end()); + } + std::vector padList; + strides.reserve(kNumTwo); + if (value->pad_list() == nullptr || value->pad_list()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad_list()->begin(), value->pad_list()->end()); + } + std::vector output_paddings; + output_paddings.reserve(kNumTwo); + if (value->output_paddings() == nullptr || value->output_paddings()->size() < kNumTwo) { + output_paddings = {}; + } else { + output_paddings = std::vector(value->output_paddings()->begin(), value->output_paddings()->end()); + } + conv2d_transpose_fusion.kernelSize = kernel_size; + conv2d_transpose_fusion.stride = strides; + conv2d_transpose_fusion.dilation = dilation; + conv2d_transpose_fusion.padMode = static_cast(value->pad_mode()); + conv2d_transpose_fusion.padList = padList; + conv2d_transpose_fusion.group = value->group(); + conv2d_transpose_fusion.inChannel = value->in_channel(); + conv2d_transpose_fusion.outChannel = value->out_channel(); + conv2d_transpose_fusion.activationType = static_cast(value->activation_type()); + conv2d_transpose_fusion.outputPaddings = output_paddings; + OHOS::MessageParcel data; + (void)Conv2dTransposeFusionBlockMarshalling(data, conv2d_transpose_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertDivFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_DivFusion(); + if (value != nullptr) { + DivFusion div_fusion{}; + div_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)DivFusionBlockMarshalling(data, div_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertEltwise_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Eltwise(); + if (value != nullptr) { + Eltwise eltwise{}; + eltwise.mode = static_cast(value->mode()); + OHOS::MessageParcel data; + (void)EltwiseBlockMarshalling(data, eltwise); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertExpandDims_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ExpandDims(); + if (value != nullptr) { + ExpandDims expand_dims{}; + OHOS::MessageParcel data; + (void)ExpandDimsBlockMarshalling(data, expand_dims); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertFill_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Fill(); + if (value != nullptr) { + Fill fill{}; + OHOS::MessageParcel data; + (void)FillBlockMarshalling(data, fill); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertFullConnection_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FullConnection(); + if (value != nullptr) { + FullConnection full_connection{}; + full_connection.hasBias = value->has_bias(); + full_connection.useAxis = value->use_axis(); + full_connection.axis = value->axis(); + full_connection.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)FullConnectionBlockMarshalling(data, full_connection); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertFusedBatchNorm_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_FusedBatchNorm(); + if (value != nullptr) { + FusedBatchNorm fused_batch_norm{}; + fused_batch_norm.epsilon = value->epsilon(); + OHOS::MessageParcel data; + (void)FusedBatchNormBlockMarshalling(data, fused_batch_norm); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertGather_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Gather(); + if (value != nullptr) { + Gather gather{}; + OHOS::MessageParcel data; + (void)GatherBlockMarshalling(data, gather); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertLayerNormFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LayerNormFusion(); + if (value != nullptr) { + LayerNormFusion layer_norm_fusion{}; + layer_norm_fusion.beginNormAxis = value->begin_norm_axis(); + layer_norm_fusion.epsilon = value->epsilon(); + layer_norm_fusion.elementwiseAffine = value->elementwise_affine(); + layer_norm_fusion.beginParamsAxis = value->begin_params_axis(); + OHOS::MessageParcel data; + (void)LayerNormFusionBlockMarshalling(data, layer_norm_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertLessEqual_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_LessEqual(); + if (value != nullptr) { + LessEqual less_equal{}; + OHOS::MessageParcel data; + (void)LessEqualBlockMarshalling(data, less_equal); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMatMulFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MatMulFusion(); + if (value != nullptr) { + MatMulFusion mat_mul_fusion{}; + mat_mul_fusion.transposeA = value->transpose_a(); + mat_mul_fusion.transposeB = value->transpose_b(); + mat_mul_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)MatMulFusionBlockMarshalling(data, mat_mul_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMaximum_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Maximum(); + if (value != nullptr) { + Maximum maximum{}; + OHOS::MessageParcel data; + (void)MaximumBlockMarshalling(data, maximum); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMaxPoolFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MaxPoolFusion(); + if (value != nullptr) { + MaxPoolFusion max_pool_fusion{}; + std::vector kernel_size; + kernel_size.reserve(kNumTwo); + if (value->kernel_size() == nullptr || value->kernel_size()->size() < kNumTwo) { + kernel_size = {}; + } else { + kernel_size = std::vector(value->kernel_size()->begin(), value->kernel_size()->end()); + } + std::vector strides; + strides.reserve(kNumTwo); + if (value->strides() == nullptr || value->strides()->size() < kNumTwo) { + strides = {}; + } else { + strides = std::vector(value->strides()->begin(), value->strides()->end()); + } + std::vector padList; + padList.reserve(kNumFour); + if (value->pad() == nullptr || value->pad()->size() < kNumFour) { + padList = {}; + } else { + padList = std::vector(value->pad()->begin(), value->pad()->end()); + } + max_pool_fusion.kernelSize = kernel_size; + max_pool_fusion.strides = strides; + max_pool_fusion.pad = padList; + max_pool_fusion.padMode = static_cast(value->pad_mode()); + max_pool_fusion.format = static_cast(value->format()); + max_pool_fusion.global = value->global(); + max_pool_fusion.activationType = static_cast(value->activation_type()); + max_pool_fusion.roundMode = static_cast(value->round_mode()); + OHOS::MessageParcel data; + (void)MaxPoolFusionBlockMarshalling(data, max_pool_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertMulFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_MulFusion(); + if (value != nullptr) { + MulFusion mul_fusion{}; + mul_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)MulFusionBlockMarshalling(data, mul_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertOneHot_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_OneHot(); + if (value != nullptr) { + OneHot one_hot{}; + one_hot.axis = value->axis(); + OHOS::MessageParcel data; + (void)OneHotBlockMarshalling(data, one_hot); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertPadFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PadFusion(); + if (value != nullptr) { + PadFusion pad_fusion{}; + auto paddings = value->paddings(); + std::vector> paddings_vec2d; + if (paddings == nullptr || paddings->data()->size() < kNumTwo) { + paddings_vec2d = {{0}, {0}, {0}, {0}}; + } else { + paddings_vec2d.reserve(paddings->data()->size()); + for (size_t i = 0; i < paddings->data()->size(); i++) { + auto vet = paddings->data()->Get(i); + paddings_vec2d.emplace_back(std::vector(vet->data()->begin(), vet->data()->end())); + } + } + pad_fusion.paddings = paddings_vec2d; + pad_fusion.paddingMode = static_cast(value->padding_mode()); + pad_fusion.constantValue = value->constant_value(); + OHOS::MessageParcel data; + (void)PadFusionBlockMarshalling(data, pad_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertPowFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PowFusion(); + if (value != nullptr) { + PowFusion pow_fusion{}; + pow_fusion.scale = value->scale(); + pow_fusion.shift = value->shift(); + OHOS::MessageParcel data; + (void)PowFusionBlockMarshalling(data, pow_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertPReLUFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_PReLUFusion(); + if (value != nullptr) { + PReLUFusion p_re_l_u_fusion{}; + p_re_l_u_fusion.channelShared = value->channel_shared(); + OHOS::MessageParcel data; + (void)PReLUFusionBlockMarshalling(data, p_re_l_u_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertQuantDTypeCast_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_QuantDTypeCast(); + if (value != nullptr) { + QuantDTypeCast quant_d_type_cast{}; + quant_d_type_cast.srcT = value->src_t(); + quant_d_type_cast.dstT = value->dst_t(); + OHOS::MessageParcel data; + (void)QuantDTypeCastBlockMarshalling(data, quant_d_type_cast); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertReduceFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ReduceFusion(); + if (value != nullptr) { + ReduceFusion reduce_fusion{}; + reduce_fusion.keepDims = value->keep_dims(); + reduce_fusion.mode = static_cast(value->mode()); + reduce_fusion.reduceToEnd = value->reduce_to_end(); + reduce_fusion.coeff = value->coeff(); + OHOS::MessageParcel data; + (void)ReduceFusionBlockMarshalling(data, reduce_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertReshape_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Reshape(); + if (value != nullptr) { + Reshape reshape{}; + OHOS::MessageParcel data; + (void)ReshapeBlockMarshalling(data, reshape); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} + +std::vector ConvertResize_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Resize(); + if (value != nullptr) { + Resize resize{}; + resize.method = static_cast(value->method()); + resize.newHeight = value->new_height(); + resize.newWidth = value->new_width(); + resize.preserveAspectRatio = value->preserve_aspect_ratio(); + resize.coordinateTransformMode = + static_cast(value->coordinate_transform_mode()); + resize.cubicCoeff = value->cubic_coeff(); + resize.excludeOutside = value->exclude_outside(); + resize.extrapolationValue = value->extrapolation_value(); + resize.nearestMode = static_cast(value->nearest_mode()); + OHOS::MessageParcel data; + (void)ResizeBlockMarshalling(data, resize); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertRsqrt_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Rsqrt(); + if (value != nullptr) { + Rsqrt rsqrt{}; + OHOS::MessageParcel data; + (void)RsqrtBlockMarshalling(data, rsqrt); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertScaleFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_ScaleFusion(); + if (value != nullptr) { + ScaleFusion scale_fusion{}; + scale_fusion.axis = value->axis(); + scale_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)ScaleFusionBlockMarshalling(data, scale_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertShape_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Shape(); + if (value != nullptr) { + Shape shape{}; + OHOS::MessageParcel data; + (void)ShapeBlockMarshalling(data, shape); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSliceFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SliceFusion(); + if (value != nullptr) { + SliceFusion slice_fusion{}; + std::vector axes; + if (value->axes() == nullptr) { + axes = {1, 2, 3, 4, 5, 6, 7}; + } else { + axes = std::vector(value->axes()->begin(), value->axes()->end()); + } + slice_fusion.axes = axes; + OHOS::MessageParcel data; + (void)SliceFusionBlockMarshalling(data, slice_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSoftmax_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Softmax(); + if (value != nullptr) { + Softmax softmax{}; + std::vector axis; + if (value->axis() == nullptr) { + axis = {}; + } else { + axis = std::vector(value->axis()->begin(), value->axis()->end()); + } + softmax.axis = axis; + OHOS::MessageParcel data; + (void)SoftmaxBlockMarshalling(data, softmax); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSpaceToBatchND_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SpaceToBatchND(); + if (value != nullptr) { + SpaceToBatchND space_to_batch_n_d{}; + std::vector blockShape; + blockShape.reserve(kNumTwo); + if (value->block_shape() == nullptr || value->block_shape()->size() < kNumTwo) { + blockShape = {0, 0}; + } else { + blockShape = std::vector(value->block_shape()->begin(), value->block_shape()->end()); + } + space_to_batch_n_d.blockShape = blockShape; + auto paddings = value->paddings(); + std::vector> paddings_vec2d; + if (paddings == nullptr || paddings->data()->size() == 0 || *(paddings->data()->begin()) == nullptr || + (*(paddings->data()->begin()))->data() == nullptr) { + paddings_vec2d = {}; + } else { + paddings_vec2d.reserve(paddings->data()->size()); + for (size_t i = 0; i < paddings->data()->size(); i++) { + auto vet = paddings->data()->Get(i); + paddings_vec2d.emplace_back(std::vector(vet->data()->begin(), vet->data()->end())); + } + } + space_to_batch_n_d.paddings = paddings_vec2d; + OHOS::MessageParcel data; + (void)SpaceToBatchNDBlockMarshalling(data, space_to_batch_n_d); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSplit_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Split(); + if (value != nullptr) { + Split split{}; + split.outputNum = value->output_num(); + std::vector sizeSplits; + sizeSplits.reserve(split.outputNum); + if (value->size_splits() == nullptr || value->size_splits()->size() <= static_cast(split.outputNum)) { + sizeSplits = {}; + } else { + sizeSplits = std::vector(value->size_splits()->begin(), value->size_splits()->end()); + } + split.sizeSplits = sizeSplits; + split.axis = value->axis(); + OHOS::MessageParcel data; + (void)SplitBlockMarshalling(data, split); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSqrt_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Sqrt(); + if (value != nullptr) { + Sqrt sqrt{}; + OHOS::MessageParcel data; + (void)SqrtBlockMarshalling(data, sqrt); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSquaredDifference_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SquaredDifference(); + if (value != nullptr) { + SquaredDifference squared_difference{}; + OHOS::MessageParcel data; + (void)SquaredDifferenceBlockMarshalling(data, squared_difference); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSqueeze_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Squeeze(); + if (value != nullptr) { + Squeeze squeeze{}; + std::vector axis; + if (value->axis() == nullptr) { + axis = {}; + } else { + axis = std::vector(value->axis()->begin(), value->axis()->end()); + } + squeeze.axis = axis; + OHOS::MessageParcel data; + (void)SqueezeBlockMarshalling(data, squeeze); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertStack_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Stack(); + if (value != nullptr) { + Stack stack{}; + stack.axis = value->axis(); + OHOS::MessageParcel data; + (void)StackBlockMarshalling(data, stack); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertStridedSlice_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_StridedSlice(); + if (value != nullptr) { + StridedSlice strided_slice{}; + strided_slice.beginMask = value->begin_mask(); + strided_slice.endMask = value->end_mask(); + strided_slice.ellipsisMask = value->ellipsis_mask(); + strided_slice.newAxisMask = value->new_axis_mask(); + strided_slice.shrinkAxisMask = value->shrink_axis_mask(); + OHOS::MessageParcel data; + (void)StridedSliceBlockMarshalling(data, strided_slice); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertSubFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_SubFusion(); + if (value != nullptr) { + SubFusion sub_fusion{}; + sub_fusion.activationType = static_cast(value->activation_type()); + OHOS::MessageParcel data; + (void)SubFusionBlockMarshalling(data, sub_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertTileFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TileFusion(); + if (value != nullptr) { + TileFusion tile_fusion{}; + std::vector dims; + dims.reserve(kNumEight); + if (value->dims() == nullptr) { + dims = {0, 0, 0, 0, 0, 0, 0, 0}; + } else { + dims = std::vector(value->dims()->begin(), value->dims()->end()); + } + tile_fusion.dims = dims; + OHOS::MessageParcel data; + (void)TileFusionBlockMarshalling(data, tile_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertTopKFusion_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_TopKFusion(); + if (value != nullptr) { + TopKFusion top_k_fusion{}; + top_k_fusion.sorted = value->sorted(); + top_k_fusion.axis = value->axis(); + OHOS::MessageParcel data; + (void)TopKFusionBlockMarshalling(data, top_k_fusion); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertTranspose_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Transpose(); + if (value != nullptr) { + Transpose transpose{}; + OHOS::MessageParcel data; + (void)TransposeBlockMarshalling(data, transpose); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} +std::vector ConvertUnsqueeze_V2_0(PrimitivePtr primitive) { + if (primitive != nullptr) { + auto prim = static_cast(primitive); + auto value = prim->value_as_Unsqueeze(); + if (value != nullptr) { + Unsqueeze unsqueeze{}; + std::vector axis; + axis.reserve(kNumEight); + if (value->axis() == nullptr) { + axis = {0, 0, 0, 0}; + } else { + axis = std::vector(value->axis()->begin(), value->axis()->end()); + } + unsqueeze.axis = axis; + OHOS::MessageParcel data; + (void)UnsqueezeBlockMarshalling(data, unsqueeze); + std::vector ret(reinterpret_cast(data.GetData()), + reinterpret_cast(data.GetData()) + data.GetDataSize()); + return ret; + } else { + return {}; + } + } else { + return {}; + } +} + +std::vector Convert_V2_0(NodeType type, PrimitivePtr primitive) { + switch (type) { + case NODE_TYPE_ACTIVATION: + return ConvertActivation_V2_0(primitive); + break; + case NODE_TYPE_ADD_FUSION: + return ConvertAddFusion_V2_0(primitive); + break; + case NODE_TYPE_ARGMAX_FUSION: + return ConvertArgMaxFusion_V2_0(primitive); + break; + case NODE_TYPE_AVG_POOL_FUSION: + return ConvertAvgPoolFusion_V2_0(primitive); + break; + case NODE_TYPE_BATCH_TO_SPACE_ND: + return ConvertBatchToSpaceND_V2_0(primitive); + break; + case NODE_TYPE_BIAS_ADD: + return ConvertBiasAdd_V2_0(primitive); + break; + case NODE_TYPE_CAST: + return ConvertCast_V2_0(primitive); + break; + case NODE_TYPE_CONCAT: + return ConvertConcat_V2_0(primitive); + break; + case NODE_TYPE_CONV2D_FUSION: + return ConvertConv2DFusion_V2_0(primitive); + break; + case NODE_TYPE_CONV2D_TRANSPOSE_FUSION: + return ConvertConv2dTransposeFusion_V2_0(primitive); + break; + case NODE_TYPE_DIV_FUSION: + return ConvertDivFusion_V2_0(primitive); + break; + case NODE_TYPE_ELTWISE: + return ConvertEltwise_V2_0(primitive); + break; + case NODE_TYPE_EXPAND_DIMS: + return ConvertExpandDims_V2_0(primitive); + break; + case NODE_TYPE_FILL: + return ConvertFill_V2_0(primitive); + break; + case NODE_TYPE_FULL_CONNECTION: + return ConvertFullConnection_V2_0(primitive); + break; + case NODE_TYPE_FUSED_BATCH_NORM: + return ConvertFusedBatchNorm_V2_0(primitive); + break; + case NODE_TYPE_GATHER: + return ConvertGather_V2_0(primitive); + break; + case NODE_TYPE_LAYER_NORM_FUSION: + return ConvertLayerNormFusion_V2_0(primitive); + break; + case NODE_TYPE_LESS_EQUAL: + return ConvertLessEqual_V2_0(primitive); + break; + case NODE_TYPE_MATMUL_FUSION: + return ConvertMatMulFusion_V2_0(primitive); + break; + case NODE_TYPE_MAXIMUM: + return ConvertMaximum_V2_0(primitive); + break; + case NODE_TYPE_MAX_POOL_FUSION: + return ConvertMaxPoolFusion_V2_0(primitive); + break; + case NODE_TYPE_MUL_FUSION: + return ConvertMulFusion_V2_0(primitive); + break; + case NODE_TYPE_ONE_HOT: + return ConvertOneHot_V2_0(primitive); + break; + case NODE_TYPE_PAD_FUSION: + return ConvertPadFusion_V2_0(primitive); + break; + case NODE_TYPE_POW_FUSION: + return ConvertPowFusion_V2_0(primitive); + break; + case NODE_TYPE_PRELU_FUSION: + return ConvertPReLUFusion_V2_0(primitive); + break; + case NODE_TYPE_QUANT_DTYPE_CAST: + return ConvertQuantDTypeCast_V2_0(primitive); + break; + case NODE_TYPE_REDUCE_FUSION: + return ConvertReduceFusion_V2_0(primitive); + break; + case NODE_TYPE_RESHAPE: + return ConvertReshape_V2_0(primitive); + break; + case NODE_TYPE_RESIZE: + return ConvertResize_V2_0(primitive); + break; + case NODE_TYPE_RSQRT: + return ConvertRsqrt_V2_0(primitive); + break; + case NODE_TYPE_SCALE_FUSION: + return ConvertScaleFusion_V2_0(primitive); + break; + case NODE_TYPE_SHAPE: + return ConvertShape_V2_0(primitive); + break; + case NODE_TYPE_SLICE_FUSION: + return ConvertSliceFusion_V2_0(primitive); + break; + case NODE_TYPE_SOFTMAX: + return ConvertSoftmax_V2_0(primitive); + break; + case NODE_TYPE_SPACE_TO_BATCH_ND: + return ConvertSpaceToBatchND_V2_0(primitive); + break; + case NODE_TYPE_SPLIT: + return ConvertSplit_V2_0(primitive); + break; + case NODE_TYPE_SQRT: + return ConvertSqrt_V2_0(primitive); + break; + case NODE_TYPE_SQUARED_DIFFERENCE: + return ConvertSquaredDifference_V2_0(primitive); + break; + case NODE_TYPE_SQUEEZE: + return ConvertSqueeze_V2_0(primitive); + break; + case NODE_TYPE_STACK: + return ConvertStack_V2_0(primitive); + break; + case NODE_TYPE_STRIDED_SLICE: + return ConvertStridedSlice_V2_0(primitive); + break; + case NODE_TYPE_SUB_FUSION: + return ConvertSubFusion_V2_0(primitive); + break; + case NODE_TYPE_TILE_FUSION: + return ConvertTileFusion_V2_0(primitive); + break; + case NODE_TYPE_TOPK_FUSION: + return ConvertTopKFusion_V2_0(primitive); + break; + case NODE_TYPE_TRANSPOSE: + return ConvertTranspose_V2_0(primitive); + break; + case NODE_TYPE_UNSQUEEZE: + return ConvertUnsqueeze_V2_0(primitive); + break; + default: + return {}; + } +} + +OHOS::HDI::Nnrt::V2_0::Model *MindIR_LiteGraph_To_Model(const LiteGraph *lite_graph, const OHOS::HDI::Nnrt::V2_0::SharedBuffer &buffer) { + if (lite_graph != nullptr) { + MS_LOG(INFO) << "MindIR_LiteGraph_To_Model begin"; + if (!lite_graph->name_.empty()) { + MS_LOG(INFO) << "Start converting lite graph,name =" << lite_graph->name_; + } else { + MS_LOG(INFO) << "Start converting lite graph, but lite graph has no name."; + } + std::vector inputIndex; + std::vector outputIndex; + std::vector nodes; + std::vector allTensors; + std::vector subGraph; + // nodes + MS_LOG(INFO) << "Start converting nodes, vector size = " << lite_graph->all_nodes_.size(); + nodes.reserve(lite_graph->all_nodes_.size()); + for (auto node : lite_graph->all_nodes_) { + if (node == nullptr) { + MS_LOG(ERROR) << "node is nullptr, convert fail."; + return nullptr; + } + OHOS::HDI::Nnrt::V2_0::Node tmp; + tmp.name = node->name_; + if (node->primitive_ == nullptr) { + MS_LOG(ERROR) << "node primitive is nullptr, convert fail."; + return nullptr; + } + auto prim = static_cast(node->primitive_); + auto value = prim->value_type(); + tmp.nodeType = static_cast(value); + tmp.nodeAttr = Convert_V2_0(static_cast(value), node->primitive_); + tmp.inputIndex = node->input_indices_; + tmp.outputIndex = node->output_indices_; + tmp.quantType = static_cast(node->quant_type_); + nodes.emplace_back(tmp); + } + + MS_LOG(INFO) << "Start converting Tensor,Tensor size=" << lite_graph->all_tensors_.size(); + // Tensor + allTensors.reserve(lite_graph->all_tensors_.size()); + unsigned int tensor_buffer_offset = 0; + uint8_t *mmap_ptr = nullptr; + if (buffer.fd != -1) { + mmap_ptr = + static_cast(mmap(nullptr, buffer.bufferSize, PROT_READ | PROT_WRITE, MAP_SHARED, buffer.fd, 0)); + if (mmap_ptr == MAP_FAILED) { + MS_LOG(ERROR) << "mmap failed"; + return nullptr; + } + } + MS_LOG(INFO) << "Start parsing tensor, mmap buffer size = " << buffer.bufferSize; + for (auto tensor : lite_graph->all_tensors_) { + OHOS::HDI::Nnrt::V2_0::Tensor tmp; + tmp.name = MindIR_Tensor_GetName(tensor); + tmp.dataType = static_cast(MindIR_Tensor_GetDataType(tensor)); + tmp.dims = MindIR_Tensor_GetDims(tensor); + tmp.format = static_cast(MindIR_Tensor_GetFormat(tensor)); + tmp.data = MindIR_Tensor_GetData_V2_0(tensor, buffer, mmap_ptr, tensor_buffer_offset); + tmp.quantParams = MindIR_Tensor_GetQuantParams_OHOS_V2_0(tensor); + allTensors.emplace_back(tmp); + tensor_buffer_offset = tmp.data.offset + tmp.data.dataSize; + } + MS_LOG(INFO) << ("Parsing tensor finish."); + if (buffer.fd != -1) { + auto munmap_res = munmap(mmap_ptr, buffer.bufferSize); + if (munmap_res != 0) { + MS_LOG(ERROR) << "unmap failed."; + return nullptr; + } + } + + MS_LOG(INFO) << "Start converting SubGraph,SubGraph size=" << lite_graph->sub_graphs_.size(); + // SubGraph + subGraph.reserve(lite_graph->sub_graphs_.size()); + for (auto graph : lite_graph->sub_graphs_) { + OHOS::HDI::Nnrt::V2_0::SubGraph tmp; + tmp.name = graph->name_; + tmp.inputIndices = std::vector(graph->input_indices_); + tmp.outputIndices = std::vector(graph->output_indices_); + tmp.nodeIndices = std::vector(graph->node_indices_); + subGraph.emplace_back(tmp); + } + + MS_LOG(INFO) << "Start copying model"; + auto *ret_model = new (std::nothrow) Model(); + if (ret_model == nullptr) { + MS_LOG(ERROR) << "new Model failed."; + return nullptr; + } + ret_model->name = lite_graph->name_; + ret_model->inputIndex = lite_graph->input_indices_; + ret_model->outputIndex = lite_graph->output_indices_; + ret_model->nodes = nodes; + ret_model->allTensors = allTensors; + ret_model->subGraph = subGraph; + MS_LOG(INFO) << "MindIR_LiteGraph_To_Model success"; + return ret_model; + } else { + MS_LOG(ERROR) << "lite graph is nullptr"; + return nullptr; + } +} + +} // namespace lite +} // namespace mindspore \ No newline at end of file diff --git a/mindspore/lite/mindir/src/mindir_tensor.cc b/mindspore/lite/mindir/src/mindir_tensor.cc new file mode 100644 index 00000000..9ec2d0e4 --- /dev/null +++ b/mindspore/lite/mindir/src/mindir_tensor.cc @@ -0,0 +1,433 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "src/common/log.h" +#include "mindir.h" +#include "utils.h" +#include "securec.h" +#include "mindir_memory_manager.h" +#include "nnrt/v1_0/nnrt_types.h" +#include "nnrt/v2_0/nnrt_types.h" + +using namespace OHOS::HDI::Nnrt::V1_0; + +namespace mindspore { +namespace lite { +// ********** Tensor ********** +TensorPtr MindIR_Tensor_Create() { + flatbuffers::FlatBufferBuilder fbb; + std::vector dims(1, 0); + std::vector data(1, 0); + std::vector quant_params(1, {0, 0, 8}); + std::string name = " "; + auto ops_offset = schema::CreateTensor(fbb, 0, DataType::DATA_TYPE_INT32, 0, schema::Format::Format_NCHW, 0, 0, 0, 0, + 0, fbb.CreateString(name.c_str(), name.size())); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +TensorPtr MindIR_Tensor_Create(const std::string &name, DataType data_type, const std::vector &dims, + Format format, const std::vector &data, + const std::vector &quant_params) { + flatbuffers::FlatBufferBuilder fbb; + + auto ops_offset = + schema::CreateTensor(fbb, 0, data_type, fbb.CreateVector(dims.data(), dims.size()), + static_cast(format), 0, 0, fbb.CreateVector(data.data(), data.size()), + ConvertQuantParams(fbb, quant_params), 0, fbb.CreateString(name.c_str(), name.size())); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, nullptr); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + return ret_value; +} + +std::string MindIR_Tensor_GetName(ConstTensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + return value->name()->str(); + } else { + return ""; + } + } else { + return ""; + } +} + +void MindIR_Tensor_SetName(TensorPtr *tensor, const std::string &name) { + if (tensor != nullptr && *tensor != nullptr) { + auto value = static_cast(*tensor); + if (value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + flatbuffers::Offset> dims; + if (value->dims() == nullptr || value->dims()->size() <= 0) { + dims = 0; + } else { + dims = fbb.CreateVector(value->dims()->data(), value->dims()->size()); + } + flatbuffers::Offset> data; + if (value->data() == nullptr || value->data()->size() <= 0) { + data = 0; + } else { + data = fbb.CreateVector(value->data()->data(), value->data()->size()); + } + auto ops_offset = schema::CreateTensor( + fbb, 0, value->dataType(), dims, static_cast(value->format()), 0, 0, data, + ConvertQuantParams(fbb, value->quantParams()), 0, fbb.CreateString(name.c_str(), name.size())); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, value); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *tensor = ret_value; + } + } +} +DataType MindIR_Tensor_GetDataType(ConstTensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + return static_cast(value->dataType()); + } else { + DataType en = DATA_TYPE_INT32; + return en; + } + } else { + DataType en = DATA_TYPE_INT32; + return en; + } +} + +void MindIR_Tensor_SetDataType(TensorPtr *tensor, DataType data_type) { + if (tensor != nullptr && *tensor != nullptr) { + auto value = static_cast(*tensor); + if (value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + flatbuffers::Offset> dims; + if (value->dims() == nullptr || value->dims()->size() <= 0) { + dims = 0; + } else { + dims = fbb.CreateVector(value->dims()->data(), value->dims()->size()); + } + flatbuffers::Offset> data; + if (value->data() == nullptr || value->data()->size() <= 0) { + data = 0; + } else { + data = fbb.CreateVector(value->data()->data(), value->data()->size()); + } + flatbuffers::Offset name; + if (value->name() == nullptr || value->name()->size() <= 0) { + name = 0; + } else { + name = fbb.CreateString(value->name()->c_str(), value->name()->size()); + } + auto ops_offset = + schema::CreateTensor(fbb, 0, value->dataType(), dims, static_cast(value->format()), 0, 0, data, + ConvertQuantParams(fbb, value->quantParams()), 0, name); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, value); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *tensor = ret_value; + } + } +} + +std::vector MindIR_Tensor_GetDims(ConstTensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + std::vector result; + auto src = value->dims(); + if (src == nullptr) { + return {}; + } + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](int32_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Tensor_SetDims(TensorPtr *tensor, const std::vector &dims) { + if (tensor != nullptr && *tensor != nullptr) { + auto value = static_cast(*tensor); + if (value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + flatbuffers::Offset> data; + if (value->data() == nullptr || value->data()->size() <= 0) { + data = 0; + } else { + data = fbb.CreateVector(value->data()->data(), value->data()->size()); + } + flatbuffers::Offset name; + if (value->name() == nullptr || value->name()->size() <= 0) { + name = 0; + } else { + name = fbb.CreateString(value->name()->c_str(), value->name()->size()); + } + auto ops_offset = schema::CreateTensor(fbb, 0, value->dataType(), fbb.CreateVector(dims.data(), dims.size()), + static_cast(value->format()), 0, 0, data, + ConvertQuantParams(fbb, value->quantParams()), 0, name); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, value); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *tensor = ret_value; + } + } +} +Format MindIR_Tensor_GetFormat(ConstTensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + return static_cast(value->format()); + } else { + Format en = FORMAT_NCHW; + return en; + } + } else { + Format en = FORMAT_NCHW; + return en; + } +} + +void MindIR_Tensor_SetFormat(TensorPtr *tensor, Format format) { + if (tensor != nullptr && *tensor != nullptr) { + auto value = static_cast(*tensor); + if (value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + flatbuffers::Offset> dims; + if (value->dims() == nullptr || value->dims()->size() <= 0) { + dims = 0; + } else { + dims = fbb.CreateVector(value->dims()->data(), value->dims()->size()); + } + flatbuffers::Offset> data; + if (value->data() == nullptr || value->data()->size() <= 0) { + data = 0; + } else { + data = fbb.CreateVector(value->data()->data(), value->data()->size()); + } + flatbuffers::Offset name; + if (value->name() == nullptr || value->name()->size() <= 0) { + name = 0; + } else { + name = fbb.CreateString(value->name()->c_str(), value->name()->size()); + } + auto ops_offset = schema::CreateTensor(fbb, 0, value->dataType(), dims, static_cast(format), 0, 0, + data, ConvertQuantParams(fbb, value->quantParams()), 0, name); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, value); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *tensor = ret_value; + } + } +} + +OHOS::HDI::Nnrt::V1_0::SharedBuffer MindIR_Tensor_GetData(ConstTensorPtr tensor, + const OHOS::HDI::Nnrt::V1_0::SharedBuffer &buffer_templete, + uint8_t *mmap_ptr, unsigned int offset) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + OHOS::HDI::Nnrt::V1_0::SharedBuffer result{}; + + if (value->data() == nullptr || value->data()->size() == 0) { + result.fd = -1; + result.bufferSize = buffer_templete.bufferSize; + result.offset = offset; + result.dataSize = 0; + return result; + } + if (mmap_ptr == nullptr) { + MS_LOG(ERROR) << "Tensor GetData failed, mmap pointer should not be nullptr"; + return {-1, 0, offset, 0}; + } + result.fd = buffer_templete.fd; + result.bufferSize = buffer_templete.bufferSize; + auto ret = memcpy_s(mmap_ptr + offset, value->data()->size(), value->data()->data(), value->data()->size()); + if (ret != EOK) { + MS_LOG(ERROR) << "Tensor memcpy failed, ret:" << ret; + return {-1, 0, offset, 0}; + } + result.offset = offset; + result.dataSize = value->data()->size(); + return result; + } else { + MS_LOG(ERROR) << "Tensor GetData failed, mmap pointer should not be nullptr"; + return {-1, 0, offset, 0}; + } + } else { + return {-1, 0, offset, 0}; + } +} + +OHOS::HDI::Nnrt::V2_0::SharedBuffer MindIR_Tensor_GetData_V2_0(ConstTensorPtr tensor, + const OHOS::HDI::Nnrt::V2_0::SharedBuffer &buffer_templete, + uint8_t *mmap_ptr, unsigned int offset) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + OHOS::HDI::Nnrt::V2_0::SharedBuffer result{}; + + if (value->data() == nullptr || value->data()->size() == 0) { + result.fd = -1; + result.bufferSize = buffer_templete.bufferSize; + result.offset = offset; + result.dataSize = 0; + return result; + } + if (mmap_ptr == nullptr) { + MS_LOG(ERROR) << "Tensor GetData failed, mmap pointer should not be nullptr"; + return {-1, 0, offset, 0}; + } + result.fd = buffer_templete.fd; + result.bufferSize = buffer_templete.bufferSize; + auto ret = memcpy_s(mmap_ptr + offset, value->data()->size(), value->data()->data(), value->data()->size()); + if (ret != EOK) { + MS_LOG(ERROR) << "Tensor memcpy failed, ret:" << ret; + return {-1, 0, offset, 0}; + } + result.offset = offset; + result.dataSize = value->data()->size(); + return result; + } else { + MS_LOG(WARNING) << "Tensor GetData failed, mmap pointer should not be nullptr"; + return {-1, 0, offset, 0}; + } + } else { + return {-1, 0, offset, 0}; + } +} + +std::vector MindIR_Tensor_GetData(ConstTensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + std::vector result; + auto src = value->data(); + if (src == nullptr) { + return {}; + } + result.resize(src->size()); + std::transform(src->begin(), src->end(), result.begin(), [](uint8_t item) { return item; }); + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Tensor_SetData(TensorPtr *tensor, const std::vector &data) { + if (tensor != nullptr && *tensor != nullptr) { + auto value = static_cast(*tensor); + if (value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + flatbuffers::Offset> dims; + if (value->dims() == nullptr || value->dims()->size() <= 0) { + dims = 0; + } else { + dims = fbb.CreateVector(value->dims()->data(), value->dims()->size()); + } + flatbuffers::Offset name; + if (value->name() == nullptr || value->name()->size() <= 0) { + name = 0; + } else { + name = fbb.CreateString(value->name()->c_str(), value->name()->size()); + } + auto ops_offset = schema::CreateTensor( + fbb, 0, value->dataType(), dims, static_cast(value->format()), 0, 0, + fbb.CreateVector(data.data(), data.size()), ConvertQuantParams(fbb, value->quantParams()), 0, name); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, value); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *tensor = ret_value; + } + } +} +std::vector MindIR_Tensor_GetQuantParams(ConstTensorPtr tensor) { + if (tensor != nullptr) { + auto value = static_cast(tensor); + if (value != nullptr) { + std::vector result; + auto src = value->quantParams(); + if (src == nullptr) { + return {}; + } + size_t size = src->size(); + result.reserve(src->size()); + for (size_t i = 0; i < size; i++) { + auto tmp = src->Get(i); + QuantParam q{tmp->zeroPoint(), tmp->scale(), tmp->numBits()}; + result.emplace_back(q); + } + return result; + } else { + return {}; + } + } else { + return {}; + } +} + +void MindIR_Tensor_SetQuantParams(TensorPtr *tensor, const std::vector &quant_params) { + if (tensor != nullptr && *tensor != nullptr) { + auto value = static_cast(*tensor); + if (value != nullptr) { + flatbuffers::FlatBufferBuilder fbb; + flatbuffers::Offset> dims; + if (value->dims() == nullptr || value->dims()->size() <= 0) { + dims = 0; + } else { + dims = fbb.CreateVector(value->dims()->data(), value->dims()->size()); + } + flatbuffers::Offset> data; + if (value->data() == nullptr || value->data()->size() <= 0) { + data = 0; + } else { + data = fbb.CreateVector(value->data()->data(), value->data()->size()); + } + flatbuffers::Offset name; + if (value->name() == nullptr || value->name()->size() <= 0) { + name = 0; + } else { + name = fbb.CreateString(value->name()->c_str(), value->name()->size()); + } + auto ops_offset = + schema::CreateTensor(fbb, 0, value->dataType(), dims, static_cast(value->format()), 0, 0, data, + ConvertQuantParams(fbb, quant_params), 0, name); + fbb.Finish(ops_offset); + auto new_addr = MindIRMemoryManager::GetInstance()->CreateTensorFromBuilder(fbb, value); + auto ret_value = flatbuffers::GetMutableRoot(new_addr); + *tensor = ret_value; + } + } +} + +void MindIR_Tensor_Destroy(TensorPtr *tensor) { + if (tensor != nullptr && *tensor != nullptr) { + auto schema = static_cast(*tensor); + MindIRMemoryManager::GetInstance()->DeleteTensor(schema); + *tensor = nullptr; + } + *tensor = nullptr; +} +} // namespace lite +} // namespace mindspore diff --git a/mindspore/lite/mindir/src/utils.cc b/mindspore/lite/mindir/src/utils.cc new file mode 100644 index 00000000..28d66ceb --- /dev/null +++ b/mindspore/lite/mindir/src/utils.cc @@ -0,0 +1,102 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "utils.h" +#include "securec.h" +#include "src/common/log.h" +#include "mindir_memory_manager.h" +namespace mindspore { +namespace lite { + +// ********** PrimitiveBase ********** +NodeType MindIR_Primitive_GetType(PrimitivePtr primitive) { + auto prim = flatbuffers::GetMutableRoot(primitive); + auto type = prim->value_type(); + return static_cast(type); +} + +void MindIR_Primitive_Destroy(PrimitivePtr *primitive) { + if (primitive != nullptr && *primitive != nullptr) { + auto schema = static_cast(*primitive); + MS_LOG(ERROR) << "schema=" << schema->value_type(); + MindIRMemoryManager::GetInstance()->DeletePrimitive(schema); + *primitive = nullptr; + } +} +PrimitivePtr MindIR_CreatePrimitiveFromBuilder(flatbuffers::FlatBufferBuilder &fbb) { + auto buff = reinterpret_cast(malloc(fbb.GetSize())); + if (buff == nullptr) { + MS_LOG(ERROR) << "malloc memory for primitive failed!"; + fbb.Clear(); + return nullptr; + } + auto ret = memcpy_s(buff, fbb.GetSize(), fbb.GetBufferPointer(), fbb.GetSize()); + if (ret != EOK) { + MS_LOG(ERROR) << "memcpy failed, ret:" << ret; + fbb.Clear(); + return nullptr; + } + fbb.Clear(); + return buff; +} +flatbuffers::Offset CreateVec2D(flatbuffers::FlatBufferBuilder &fbb, + const std::vector> &data) { + std::vector> vet2d; + vet2d.reserve(data.size()); + for (const auto &data_one : data) { + vet2d.emplace_back(schema::CreateVec(fbb, fbb.CreateVector(data_one))); + } + flatbuffers::Offset v2d = schema::CreateVec2D(fbb, fbb.CreateVector(vet2d)); + return v2d; +} +flatbuffers::Offset CreateVec2D(flatbuffers::FlatBufferBuilder &fbb, + const mindspore::schema::Vec2D *data) { + auto data_inner = data->data(); + std::vector> vet2d; + vet2d.reserve(data_inner->size()); + for (const auto data_one : *data_inner) { + vet2d.emplace_back(schema::CreateVec(fbb, fbb.CreateVector(data_one->data()->data(), data_one->data()->size()))); + } + flatbuffers::Offset v2d = schema::CreateVec2D(fbb, fbb.CreateVector(vet2d)); + return v2d; +} + +flatbuffers::Offset>> ConvertQuantParams( + flatbuffers::FlatBufferBuilder &fbb, const std::vector &quant_params) { + std::vector> tmp_vec; + tmp_vec.reserve(quant_params.size()); + for (auto q_param : quant_params) { + tmp_vec.emplace_back(schema::CreateQuantParam(fbb, q_param.scale, q_param.zeroPoint, 0, 0, true, q_param.numBits)); + } + flatbuffers::Offset>> ret_quant_param = + fbb.CreateVector(tmp_vec.data(), tmp_vec.size()); + return ret_quant_param; +} + +flatbuffers::Offset>> ConvertQuantParams( + flatbuffers::FlatBufferBuilder &fbb, + const flatbuffers::Vector> *quant_params) { + std::vector> tmp_vec; + tmp_vec.reserve(quant_params->size()); + for (auto q_param : *quant_params) { + tmp_vec.emplace_back( + schema::CreateQuantParam(fbb, q_param->scale(), q_param->zeroPoint(), 0, 0, true, q_param->numBits())); + } + flatbuffers::Offset>> ret_quant_param = + fbb.CreateVector(tmp_vec.data(), tmp_vec.size()); + return ret_quant_param; +} +} // namespace lite +} // namespace mindspore \ No newline at end of file diff --git a/mindspore/lite/mindir/tests/BUILD.gn b/mindspore/lite/mindir/tests/BUILD.gn new file mode 100644 index 00000000..de1902fe --- /dev/null +++ b/mindspore/lite/mindir/tests/BUILD.gn @@ -0,0 +1,35 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +import("//build/ohos.gni") + +ohos_executable("mindir_test") { + sources = [ "mindir_test.cc" ] + + include_dirs = [ + "./", + "../include", + "../include/inner", + "../../", + "//third_party/flatbuffers/include", + ] + remove_configs = [ "//build/config/compiler:no_rtti" ] + + deps = [ "../:mindir" ] + + output_name = "mindir_test" + install_enable = true + part_name = "mindspore" +} diff --git a/mindspore/lite/mindir/tests/mindir_test.cc b/mindspore/lite/mindir/tests/mindir_test.cc new file mode 100644 index 00000000..09ef7090 --- /dev/null +++ b/mindspore/lite/mindir/tests/mindir_test.cc @@ -0,0 +1,51 @@ +#include "mindir.h" +#include "mindir_memory_manager.h" +#include +#include +using namespace mindspore::lite; +int main() { + int loop = 0; + int all = 100; + while (loop < all) { + auto tensor = MindIR_Tensor_Create(); + auto str = MindIR_Tensor_GetName(tensor); + DataType dataType = DATA_TYPE_UINT16; + MindIR_Tensor_SetDataType(&tensor, dataType); + std::cout << "set data type" << std::endl; + DataType d = MindIR_Tensor_GetDataType(tensor); + std::cout << "loop = " << loop << ",data type = " << (int)d << std::endl; + PrimitivePtr ret = nullptr; + ret = MindIR_SquaredDifference_CreatePrimitive(); + std::cout << "MindIR_SquaredDifference_CreatePrimitive 1,PrimitivePtr = " << ret << std::endl; + ret = MindIR_SquaredDifference_CreatePrimitive(); + std::cout << "MindIR_SquaredDifference_CreatePrimitive 1,PrimitivePtr = " << ret << std::endl; + ret = MindIR_SubFusion_CreatePrimitive(ACTIVATION_TYPE_RELU6); + std::cout << "MindIR_SubFusion_CreatePrimitive 2,PrimitivePtr = " << ret << std::endl; + ret = MindIR_Activation_CreatePrimitive(ACTIVATION_TYPE_RELU6, .5, 0, 1, true); + std::cout << "MindIR_Activation_CreatePrimitive 3,PrimitivePtr = " << ret << std::endl; + MindIR_Primitive_Destroy(&ret); + std::cout << "MindIR_Primitive_Destroy,PrimitivePtr = " << ret << std::endl; + TensorPtr t_ret = nullptr; + t_ret = MindIR_Tensor_Create(); + std::cout << "MindIR_Tensor_Create 3,TensorPtr = " << t_ret << std::endl; + MindIR_Tensor_Destroy(&t_ret); + std::cout << "MindIR_Tensor_Destroy,Tensor = " << t_ret << std::endl; + ret = MindIR_SpaceToBatchND_CreatePrimitive({2, 2}, {{0}, {0}, {0}, {0}}); + auto blockshape = MindIR_SpaceToBatchND_GetBlockShape(ret); + std::string bs_(""); + for (int i = 0; i < 2; i++) { + bs_.append(std::to_string(blockshape[i]).c_str()); + } + std::cout << "MindIR_SpaceToBatchND_GetBlockShape,blockshape = " << bs_ << std::endl; + auto paddings = MindIR_SpaceToBatchND_GetPaddings(ret); + std::string pad_(""); + for (auto item : paddings) { + pad_.append(std::to_string(item[0]).c_str()); + } + std::cout << "MindIR_SpaceToBatchND_GetPaddings,Paddings = " << pad_ << std::endl; + loop++; + } + MindIRMemoryManager::GetInstance()->ClearAllMemory(); + std::cout << "MindIRMemoryManager::GetInstance()->ClearAllMemory()" << std::endl; + loop++; +} \ No newline at end of file diff --git a/mindspore/lite/schema/gpu_cache_generated.h b/mindspore/lite/schema/gpu_cache_generated.h new file mode 100644 index 00000000..4a95aa29 --- /dev/null +++ b/mindspore/lite/schema/gpu_cache_generated.h @@ -0,0 +1,319 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_GPUCACHE_MINDSPORE_SCHEMA_H_ +#define FLATBUFFERS_GENERATED_GPUCACHE_MINDSPORE_SCHEMA_H_ + +#include "flatbuffers/flatbuffers.h" + +namespace mindspore { +namespace schema { + +struct TuneParam; +struct TuneParamBuilder; + +struct ProgramBinary; +struct ProgramBinaryBuilder; + +struct GpuCache; +struct GpuCacheBuilder; + +struct TuneParam FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TuneParamBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_LOCAL = 4, + VT_BLOCK = 6, + VT_SHAPE = 8, + VT_OPPARA = 10 + }; + const flatbuffers::Vector *local() const { + return GetPointer *>(VT_LOCAL); + } + const flatbuffers::Vector *block() const { + return GetPointer *>(VT_BLOCK); + } + const flatbuffers::Vector *shape() const { + return GetPointer *>(VT_SHAPE); + } + const flatbuffers::Vector *opPara() const { + return GetPointer *>(VT_OPPARA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_LOCAL) && + verifier.VerifyVector(local()) && + VerifyOffset(verifier, VT_BLOCK) && + verifier.VerifyVector(block()) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.VerifyVector(shape()) && + VerifyOffset(verifier, VT_OPPARA) && + verifier.VerifyVector(opPara()) && + verifier.EndTable(); + } +}; + +struct TuneParamBuilder { + typedef TuneParam Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_local(flatbuffers::Offset> local) { + fbb_.AddOffset(TuneParam::VT_LOCAL, local); + } + void add_block(flatbuffers::Offset> block) { + fbb_.AddOffset(TuneParam::VT_BLOCK, block); + } + void add_shape(flatbuffers::Offset> shape) { + fbb_.AddOffset(TuneParam::VT_SHAPE, shape); + } + void add_opPara(flatbuffers::Offset> opPara) { + fbb_.AddOffset(TuneParam::VT_OPPARA, opPara); + } + explicit TuneParamBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTuneParam( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> local = 0, + flatbuffers::Offset> block = 0, + flatbuffers::Offset> shape = 0, + flatbuffers::Offset> opPara = 0) { + TuneParamBuilder builder_(_fbb); + builder_.add_opPara(opPara); + builder_.add_shape(shape); + builder_.add_block(block); + builder_.add_local(local); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTuneParamDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *local = nullptr, + const std::vector *block = nullptr, + const std::vector *shape = nullptr, + const std::vector *opPara = nullptr) { + auto local__ = local ? _fbb.CreateVector(*local) : 0; + auto block__ = block ? _fbb.CreateVector(*block) : 0; + auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; + auto opPara__ = opPara ? _fbb.CreateVector(*opPara) : 0; + return mindspore::schema::CreateTuneParam( + _fbb, + local__, + block__, + shape__, + opPara__); +} + +struct ProgramBinary FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ProgramBinaryBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PROGRAM_NAME = 4, + VT_BUILD_OPTION = 6, + VT_TUNE = 8, + VT_DATA = 10 + }; + const flatbuffers::String *program_name() const { + return GetPointer(VT_PROGRAM_NAME); + } + const flatbuffers::String *build_option() const { + return GetPointer(VT_BUILD_OPTION); + } + const mindspore::schema::TuneParam *tune() const { + return GetPointer(VT_TUNE); + } + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_PROGRAM_NAME) && + verifier.VerifyString(program_name()) && + VerifyOffset(verifier, VT_BUILD_OPTION) && + verifier.VerifyString(build_option()) && + VerifyOffset(verifier, VT_TUNE) && + verifier.VerifyTable(tune()) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + verifier.EndTable(); + } +}; + +struct ProgramBinaryBuilder { + typedef ProgramBinary Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_program_name(flatbuffers::Offset program_name) { + fbb_.AddOffset(ProgramBinary::VT_PROGRAM_NAME, program_name); + } + void add_build_option(flatbuffers::Offset build_option) { + fbb_.AddOffset(ProgramBinary::VT_BUILD_OPTION, build_option); + } + void add_tune(flatbuffers::Offset tune) { + fbb_.AddOffset(ProgramBinary::VT_TUNE, tune); + } + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(ProgramBinary::VT_DATA, data); + } + explicit ProgramBinaryBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateProgramBinary( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset program_name = 0, + flatbuffers::Offset build_option = 0, + flatbuffers::Offset tune = 0, + flatbuffers::Offset> data = 0) { + ProgramBinaryBuilder builder_(_fbb); + builder_.add_data(data); + builder_.add_tune(tune); + builder_.add_build_option(build_option); + builder_.add_program_name(program_name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateProgramBinaryDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *program_name = nullptr, + const char *build_option = nullptr, + flatbuffers::Offset tune = 0, + const std::vector *data = nullptr) { + auto program_name__ = program_name ? _fbb.CreateString(program_name) : 0; + auto build_option__ = build_option ? _fbb.CreateString(build_option) : 0; + auto data__ = data ? _fbb.CreateVector(*data) : 0; + return mindspore::schema::CreateProgramBinary( + _fbb, + program_name__, + build_option__, + tune, + data__); +} + +struct GpuCache FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GpuCacheBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_VERSION = 6, + VT_ALLBINS = 8 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + const flatbuffers::String *version() const { + return GetPointer(VT_VERSION); + } + const flatbuffers::Vector> *allBins() const { + return GetPointer> *>(VT_ALLBINS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_VERSION) && + verifier.VerifyString(version()) && + VerifyOffset(verifier, VT_ALLBINS) && + verifier.VerifyVector(allBins()) && + verifier.VerifyVectorOfTables(allBins()) && + verifier.EndTable(); + } +}; + +struct GpuCacheBuilder { + typedef GpuCache Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(GpuCache::VT_NAME, name); + } + void add_version(flatbuffers::Offset version) { + fbb_.AddOffset(GpuCache::VT_VERSION, version); + } + void add_allBins(flatbuffers::Offset>> allBins) { + fbb_.AddOffset(GpuCache::VT_ALLBINS, allBins); + } + explicit GpuCacheBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGpuCache( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset version = 0, + flatbuffers::Offset>> allBins = 0) { + GpuCacheBuilder builder_(_fbb); + builder_.add_allBins(allBins); + builder_.add_version(version); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateGpuCacheDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + const char *version = nullptr, + const std::vector> *allBins = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto version__ = version ? _fbb.CreateString(version) : 0; + auto allBins__ = allBins ? _fbb.CreateVector>(*allBins) : 0; + return mindspore::schema::CreateGpuCache( + _fbb, + name__, + version__, + allBins__); +} + +inline const mindspore::schema::GpuCache *GetGpuCache(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const mindspore::schema::GpuCache *GetSizePrefixedGpuCache(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline bool VerifyGpuCacheBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(nullptr); +} + +inline bool VerifySizePrefixedGpuCacheBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(nullptr); +} + +inline void FinishGpuCacheBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root); +} + +inline void FinishSizePrefixedGpuCacheBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root); +} + +} // namespace schema +} // namespace mindspore + +#endif // FLATBUFFERS_GENERATED_GPUCACHE_MINDSPORE_SCHEMA_H_ diff --git a/mindspore/lite/schema/inner/gpu_cache_generated.h b/mindspore/lite/schema/inner/gpu_cache_generated.h new file mode 100644 index 00000000..7641a210 --- /dev/null +++ b/mindspore/lite/schema/inner/gpu_cache_generated.h @@ -0,0 +1,573 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_GPUCACHE_MINDSPORE_SCHEMA_H_ +#define FLATBUFFERS_GENERATED_GPUCACHE_MINDSPORE_SCHEMA_H_ + +#include "flatbuffers/flatbuffers.h" + +namespace mindspore { +namespace schema { + +struct TuneParam; +struct TuneParamBuilder; +struct TuneParamT; + +struct ProgramBinary; +struct ProgramBinaryBuilder; +struct ProgramBinaryT; + +struct GpuCache; +struct GpuCacheBuilder; +struct GpuCacheT; + +inline const flatbuffers::TypeTable *TuneParamTypeTable(); + +inline const flatbuffers::TypeTable *ProgramBinaryTypeTable(); + +inline const flatbuffers::TypeTable *GpuCacheTypeTable(); + +struct TuneParamT : public flatbuffers::NativeTable { + typedef TuneParam TableType; + std::vector local{}; + std::vector block{}; + std::vector shape{}; + std::vector opPara{}; +}; + +struct TuneParam FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TuneParamT NativeTableType; + typedef TuneParamBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TuneParamTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_LOCAL = 4, + VT_BLOCK = 6, + VT_SHAPE = 8, + VT_OPPARA = 10 + }; + const flatbuffers::Vector *local() const { + return GetPointer *>(VT_LOCAL); + } + flatbuffers::Vector *mutable_local() { + return GetPointer *>(VT_LOCAL); + } + const flatbuffers::Vector *block() const { + return GetPointer *>(VT_BLOCK); + } + flatbuffers::Vector *mutable_block() { + return GetPointer *>(VT_BLOCK); + } + const flatbuffers::Vector *shape() const { + return GetPointer *>(VT_SHAPE); + } + flatbuffers::Vector *mutable_shape() { + return GetPointer *>(VT_SHAPE); + } + const flatbuffers::Vector *opPara() const { + return GetPointer *>(VT_OPPARA); + } + flatbuffers::Vector *mutable_opPara() { + return GetPointer *>(VT_OPPARA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_LOCAL) && + verifier.VerifyVector(local()) && + VerifyOffset(verifier, VT_BLOCK) && + verifier.VerifyVector(block()) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.VerifyVector(shape()) && + VerifyOffset(verifier, VT_OPPARA) && + verifier.VerifyVector(opPara()) && + verifier.EndTable(); + } + TuneParamT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TuneParamT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TuneParamT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TuneParamBuilder { + typedef TuneParam Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_local(flatbuffers::Offset> local) { + fbb_.AddOffset(TuneParam::VT_LOCAL, local); + } + void add_block(flatbuffers::Offset> block) { + fbb_.AddOffset(TuneParam::VT_BLOCK, block); + } + void add_shape(flatbuffers::Offset> shape) { + fbb_.AddOffset(TuneParam::VT_SHAPE, shape); + } + void add_opPara(flatbuffers::Offset> opPara) { + fbb_.AddOffset(TuneParam::VT_OPPARA, opPara); + } + explicit TuneParamBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTuneParam( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> local = 0, + flatbuffers::Offset> block = 0, + flatbuffers::Offset> shape = 0, + flatbuffers::Offset> opPara = 0) { + TuneParamBuilder builder_(_fbb); + builder_.add_opPara(opPara); + builder_.add_shape(shape); + builder_.add_block(block); + builder_.add_local(local); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTuneParamDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *local = nullptr, + const std::vector *block = nullptr, + const std::vector *shape = nullptr, + const std::vector *opPara = nullptr) { + auto local__ = local ? _fbb.CreateVector(*local) : 0; + auto block__ = block ? _fbb.CreateVector(*block) : 0; + auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; + auto opPara__ = opPara ? _fbb.CreateVector(*opPara) : 0; + return mindspore::schema::CreateTuneParam( + _fbb, + local__, + block__, + shape__, + opPara__); +} + +flatbuffers::Offset CreateTuneParam(flatbuffers::FlatBufferBuilder &_fbb, const TuneParamT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ProgramBinaryT : public flatbuffers::NativeTable { + typedef ProgramBinary TableType; + std::string program_name{}; + std::string build_option{}; + std::unique_ptr tune{}; + std::vector data{}; +}; + +struct ProgramBinary FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ProgramBinaryT NativeTableType; + typedef ProgramBinaryBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ProgramBinaryTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PROGRAM_NAME = 4, + VT_BUILD_OPTION = 6, + VT_TUNE = 8, + VT_DATA = 10 + }; + const flatbuffers::String *program_name() const { + return GetPointer(VT_PROGRAM_NAME); + } + flatbuffers::String *mutable_program_name() { + return GetPointer(VT_PROGRAM_NAME); + } + const flatbuffers::String *build_option() const { + return GetPointer(VT_BUILD_OPTION); + } + flatbuffers::String *mutable_build_option() { + return GetPointer(VT_BUILD_OPTION); + } + const mindspore::schema::TuneParam *tune() const { + return GetPointer(VT_TUNE); + } + mindspore::schema::TuneParam *mutable_tune() { + return GetPointer(VT_TUNE); + } + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + flatbuffers::Vector *mutable_data() { + return GetPointer *>(VT_DATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_PROGRAM_NAME) && + verifier.VerifyString(program_name()) && + VerifyOffset(verifier, VT_BUILD_OPTION) && + verifier.VerifyString(build_option()) && + VerifyOffset(verifier, VT_TUNE) && + verifier.VerifyTable(tune()) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + verifier.EndTable(); + } + ProgramBinaryT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ProgramBinaryT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ProgramBinaryT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ProgramBinaryBuilder { + typedef ProgramBinary Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_program_name(flatbuffers::Offset program_name) { + fbb_.AddOffset(ProgramBinary::VT_PROGRAM_NAME, program_name); + } + void add_build_option(flatbuffers::Offset build_option) { + fbb_.AddOffset(ProgramBinary::VT_BUILD_OPTION, build_option); + } + void add_tune(flatbuffers::Offset tune) { + fbb_.AddOffset(ProgramBinary::VT_TUNE, tune); + } + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(ProgramBinary::VT_DATA, data); + } + explicit ProgramBinaryBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateProgramBinary( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset program_name = 0, + flatbuffers::Offset build_option = 0, + flatbuffers::Offset tune = 0, + flatbuffers::Offset> data = 0) { + ProgramBinaryBuilder builder_(_fbb); + builder_.add_data(data); + builder_.add_tune(tune); + builder_.add_build_option(build_option); + builder_.add_program_name(program_name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateProgramBinaryDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *program_name = nullptr, + const char *build_option = nullptr, + flatbuffers::Offset tune = 0, + const std::vector *data = nullptr) { + auto program_name__ = program_name ? _fbb.CreateString(program_name) : 0; + auto build_option__ = build_option ? _fbb.CreateString(build_option) : 0; + auto data__ = data ? _fbb.CreateVector(*data) : 0; + return mindspore::schema::CreateProgramBinary( + _fbb, + program_name__, + build_option__, + tune, + data__); +} + +flatbuffers::Offset CreateProgramBinary(flatbuffers::FlatBufferBuilder &_fbb, const ProgramBinaryT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GpuCacheT : public flatbuffers::NativeTable { + typedef GpuCache TableType; + std::string name{}; + std::string version{}; + std::vector> allBins{}; +}; + +struct GpuCache FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GpuCacheT NativeTableType; + typedef GpuCacheBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GpuCacheTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_VERSION = 6, + VT_ALLBINS = 8 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + const flatbuffers::String *version() const { + return GetPointer(VT_VERSION); + } + flatbuffers::String *mutable_version() { + return GetPointer(VT_VERSION); + } + const flatbuffers::Vector> *allBins() const { + return GetPointer> *>(VT_ALLBINS); + } + flatbuffers::Vector> *mutable_allBins() { + return GetPointer> *>(VT_ALLBINS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_VERSION) && + verifier.VerifyString(version()) && + VerifyOffset(verifier, VT_ALLBINS) && + verifier.VerifyVector(allBins()) && + verifier.VerifyVectorOfTables(allBins()) && + verifier.EndTable(); + } + GpuCacheT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GpuCacheT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GpuCacheT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GpuCacheBuilder { + typedef GpuCache Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(GpuCache::VT_NAME, name); + } + void add_version(flatbuffers::Offset version) { + fbb_.AddOffset(GpuCache::VT_VERSION, version); + } + void add_allBins(flatbuffers::Offset>> allBins) { + fbb_.AddOffset(GpuCache::VT_ALLBINS, allBins); + } + explicit GpuCacheBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGpuCache( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset version = 0, + flatbuffers::Offset>> allBins = 0) { + GpuCacheBuilder builder_(_fbb); + builder_.add_allBins(allBins); + builder_.add_version(version); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateGpuCacheDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + const char *version = nullptr, + const std::vector> *allBins = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto version__ = version ? _fbb.CreateString(version) : 0; + auto allBins__ = allBins ? _fbb.CreateVector>(*allBins) : 0; + return mindspore::schema::CreateGpuCache( + _fbb, + name__, + version__, + allBins__); +} + +flatbuffers::Offset CreateGpuCache(flatbuffers::FlatBufferBuilder &_fbb, const GpuCacheT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +inline TuneParamT *TuneParam::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TuneParamT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TuneParam::UnPackTo(TuneParamT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = local(); if (_e) { _o->local.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->local[_i] = _e->Get(_i); } } } + { auto _e = block(); if (_e) { _o->block.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block[_i] = _e->Get(_i); } } } + { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } + { auto _e = opPara(); if (_e) { _o->opPara.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->opPara[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset TuneParam::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TuneParamT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTuneParam(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTuneParam(flatbuffers::FlatBufferBuilder &_fbb, const TuneParamT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TuneParamT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _local = _o->local.size() ? _fbb.CreateVector(_o->local) : 0; + auto _block = _o->block.size() ? _fbb.CreateVector(_o->block) : 0; + auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; + auto _opPara = _o->opPara.size() ? _fbb.CreateVector(_o->opPara) : 0; + return mindspore::schema::CreateTuneParam( + _fbb, + _local, + _block, + _shape, + _opPara); +} + +inline ProgramBinaryT *ProgramBinary::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ProgramBinaryT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ProgramBinary::UnPackTo(ProgramBinaryT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = program_name(); if (_e) _o->program_name = _e->str(); } + { auto _e = build_option(); if (_e) _o->build_option = _e->str(); } + { auto _e = tune(); if (_e) _o->tune = std::unique_ptr(_e->UnPack(_resolver)); } + { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } } +} + +inline flatbuffers::Offset ProgramBinary::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ProgramBinaryT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateProgramBinary(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateProgramBinary(flatbuffers::FlatBufferBuilder &_fbb, const ProgramBinaryT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ProgramBinaryT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _program_name = _o->program_name.empty() ? 0 : _fbb.CreateString(_o->program_name); + auto _build_option = _o->build_option.empty() ? 0 : _fbb.CreateString(_o->build_option); + auto _tune = _o->tune ? CreateTuneParam(_fbb, _o->tune.get(), _rehasher) : 0; + auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; + return mindspore::schema::CreateProgramBinary( + _fbb, + _program_name, + _build_option, + _tune, + _data); +} + +inline GpuCacheT *GpuCache::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GpuCacheT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GpuCache::UnPackTo(GpuCacheT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = version(); if (_e) _o->version = _e->str(); } + { auto _e = allBins(); if (_e) { _o->allBins.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->allBins[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } +} + +inline flatbuffers::Offset GpuCache::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GpuCacheT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGpuCache(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGpuCache(flatbuffers::FlatBufferBuilder &_fbb, const GpuCacheT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GpuCacheT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _version = _o->version.empty() ? 0 : _fbb.CreateString(_o->version); + auto _allBins = _o->allBins.size() ? _fbb.CreateVector> (_o->allBins.size(), [](size_t i, _VectorArgs *__va) { return CreateProgramBinary(*__va->__fbb, __va->__o->allBins[i].get(), __va->__rehasher); }, &_va ) : 0; + return mindspore::schema::CreateGpuCache( + _fbb, + _name, + _version, + _allBins); +} + +inline const flatbuffers::TypeTable *TuneParamTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ProgramBinaryTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_UCHAR, 1, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::TuneParamTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *GpuCacheTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::ProgramBinaryTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const mindspore::schema::GpuCache *GetGpuCache(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const mindspore::schema::GpuCache *GetSizePrefixedGpuCache(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline GpuCache *GetMutableGpuCache(void *buf) { + return flatbuffers::GetMutableRoot(buf); +} + +inline bool VerifyGpuCacheBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(nullptr); +} + +inline bool VerifySizePrefixedGpuCacheBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(nullptr); +} + +inline void FinishGpuCacheBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root); +} + +inline void FinishSizePrefixedGpuCacheBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root); +} + +inline std::unique_ptr UnPackGpuCache( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetGpuCache(buf)->UnPack(res)); +} + +inline std::unique_ptr UnPackSizePrefixedGpuCache( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetSizePrefixedGpuCache(buf)->UnPack(res)); +} + +} // namespace schema +} // namespace mindspore + +#endif // FLATBUFFERS_GENERATED_GPUCACHE_MINDSPORE_SCHEMA_H_ diff --git a/mindspore/lite/schema/inner/model_generated.h b/mindspore/lite/schema/inner/model_generated.h new file mode 100644 index 00000000..c3bc5dbf --- /dev/null +++ b/mindspore/lite/schema/inner/model_generated.h @@ -0,0 +1,3452 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_MODEL_MINDSPORE_SCHEMA_H_ +#define FLATBUFFERS_GENERATED_MODEL_MINDSPORE_SCHEMA_H_ + +#include "flatbuffers/flatbuffers.h" + +#include "ops_types_generated.h" +#include "ops_generated.h" + +namespace mindspore { +namespace schema { + +struct QuantParam; +struct QuantParamBuilder; +struct QuantParamT; + +struct ExternalData; +struct ExternalDataBuilder; +struct ExternalDataT; + +struct Tensor; +struct TensorBuilder; +struct TensorT; + +struct Primitive; +struct PrimitiveBuilder; +struct PrimitiveT; + +struct CNode; +struct CNodeBuilder; +struct CNodeT; + +struct SubGraph; +struct SubGraphBuilder; +struct SubGraphT; + +struct MetaGraph; +struct MetaGraphBuilder; +struct MetaGraphT; + +inline const flatbuffers::TypeTable *QuantParamTypeTable(); + +inline const flatbuffers::TypeTable *ExternalDataTypeTable(); + +inline const flatbuffers::TypeTable *TensorTypeTable(); + +inline const flatbuffers::TypeTable *PrimitiveTypeTable(); + +inline const flatbuffers::TypeTable *CNodeTypeTable(); + +inline const flatbuffers::TypeTable *SubGraphTypeTable(); + +inline const flatbuffers::TypeTable *MetaGraphTypeTable(); + +enum WeightQuantCompressType : int32_t { + WeightQuantCompressType_NONE = 0, + WeightQuantCompressType_INDEXING = 1, + WeightQuantCompressType_SPARSE = 2, + WeightQuantCompressType_FSE = 3, + WeightQuantCompressType_BITPACKING = 4, + WeightQuantCompressType_FSE_INT = 5, + WeightQuantCompressType_FSE_INFER = 6, + WeightQuantCompressType_MIN = WeightQuantCompressType_NONE, + WeightQuantCompressType_MAX = WeightQuantCompressType_FSE_INFER +}; + +inline const WeightQuantCompressType (&EnumValuesWeightQuantCompressType())[7] { + static const WeightQuantCompressType values[] = { + WeightQuantCompressType_NONE, + WeightQuantCompressType_INDEXING, + WeightQuantCompressType_SPARSE, + WeightQuantCompressType_FSE, + WeightQuantCompressType_BITPACKING, + WeightQuantCompressType_FSE_INT, + WeightQuantCompressType_FSE_INFER + }; + return values; +} + +inline const char * const *EnumNamesWeightQuantCompressType() { + static const char * const names[8] = { + "NONE", + "INDEXING", + "SPARSE", + "FSE", + "BITPACKING", + "FSE_INT", + "FSE_INFER", + nullptr + }; + return names; +} + +inline const char *EnumNameWeightQuantCompressType(WeightQuantCompressType e) { + if (flatbuffers::IsOutRange(e, WeightQuantCompressType_NONE, WeightQuantCompressType_FSE_INFER)) return ""; + const size_t index = static_cast(e); + return EnumNamesWeightQuantCompressType()[index]; +} + +enum QuantType : int32_t { + QuantType_QUANT_NONE = 0, + QuantType_AwareTraining = 1, + QuantType_WeightQuant = 2, + QuantType_PostTraining = 3, + QuantType_QUANT_WEIGHT = 4, + QuantType_QUANT_ALL = 5, + QuantType_QUANT_DYNAMIC = 6, + QuantType_MIN = QuantType_QUANT_NONE, + QuantType_MAX = QuantType_QUANT_DYNAMIC +}; + +inline const QuantType (&EnumValuesQuantType())[7] { + static const QuantType values[] = { + QuantType_QUANT_NONE, + QuantType_AwareTraining, + QuantType_WeightQuant, + QuantType_PostTraining, + QuantType_QUANT_WEIGHT, + QuantType_QUANT_ALL, + QuantType_QUANT_DYNAMIC + }; + return values; +} + +inline const char * const *EnumNamesQuantType() { + static const char * const names[8] = { + "QUANT_NONE", + "AwareTraining", + "WeightQuant", + "PostTraining", + "QUANT_WEIGHT", + "QUANT_ALL", + "QUANT_DYNAMIC", + nullptr + }; + return names; +} + +inline const char *EnumNameQuantType(QuantType e) { + if (flatbuffers::IsOutRange(e, QuantType_QUANT_NONE, QuantType_QUANT_DYNAMIC)) return ""; + const size_t index = static_cast(e); + return EnumNamesQuantType()[index]; +} + +struct QuantParamT : public flatbuffers::NativeTable { + typedef QuantParam TableType; + double scale = 1.0; + int32_t zeroPoint = 0; + double min = 0.0; + double max = 0.0; + bool narrowRange = true; + int32_t numBits = 8; + bool inited = false; + float varCorr = 1.0f; + float meanCorr = 0.0f; + int32_t dstDtype = 32; + int32_t roundType = 1; + int32_t multiplier = 1; +}; + +struct QuantParam FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef QuantParamT NativeTableType; + typedef QuantParamBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return QuantParamTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SCALE = 4, + VT_ZEROPOINT = 6, + VT_MIN = 8, + VT_MAX = 10, + VT_NARROWRANGE = 12, + VT_NUMBITS = 14, + VT_INITED = 16, + VT_VARCORR = 18, + VT_MEANCORR = 20, + VT_DSTDTYPE = 22, + VT_ROUNDTYPE = 24, + VT_MULTIPLIER = 26 + }; + double scale() const { + return GetField(VT_SCALE, 1.0); + } + bool mutate_scale(double _scale) { + return SetField(VT_SCALE, _scale, 1.0); + } + int32_t zeroPoint() const { + return GetField(VT_ZEROPOINT, 0); + } + bool mutate_zeroPoint(int32_t _zeroPoint) { + return SetField(VT_ZEROPOINT, _zeroPoint, 0); + } + double min() const { + return GetField(VT_MIN, 0.0); + } + bool mutate_min(double _min) { + return SetField(VT_MIN, _min, 0.0); + } + double max() const { + return GetField(VT_MAX, 0.0); + } + bool mutate_max(double _max) { + return SetField(VT_MAX, _max, 0.0); + } + bool narrowRange() const { + return GetField(VT_NARROWRANGE, 1) != 0; + } + bool mutate_narrowRange(bool _narrowRange) { + return SetField(VT_NARROWRANGE, static_cast(_narrowRange), 1); + } + int32_t numBits() const { + return GetField(VT_NUMBITS, 8); + } + bool mutate_numBits(int32_t _numBits) { + return SetField(VT_NUMBITS, _numBits, 8); + } + bool inited() const { + return GetField(VT_INITED, 0) != 0; + } + bool mutate_inited(bool _inited) { + return SetField(VT_INITED, static_cast(_inited), 0); + } + float varCorr() const { + return GetField(VT_VARCORR, 1.0f); + } + bool mutate_varCorr(float _varCorr) { + return SetField(VT_VARCORR, _varCorr, 1.0f); + } + float meanCorr() const { + return GetField(VT_MEANCORR, 0.0f); + } + bool mutate_meanCorr(float _meanCorr) { + return SetField(VT_MEANCORR, _meanCorr, 0.0f); + } + int32_t dstDtype() const { + return GetField(VT_DSTDTYPE, 32); + } + bool mutate_dstDtype(int32_t _dstDtype) { + return SetField(VT_DSTDTYPE, _dstDtype, 32); + } + int32_t roundType() const { + return GetField(VT_ROUNDTYPE, 1); + } + bool mutate_roundType(int32_t _roundType) { + return SetField(VT_ROUNDTYPE, _roundType, 1); + } + int32_t multiplier() const { + return GetField(VT_MULTIPLIER, 1); + } + bool mutate_multiplier(int32_t _multiplier) { + return SetField(VT_MULTIPLIER, _multiplier, 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SCALE) && + VerifyField(verifier, VT_ZEROPOINT) && + VerifyField(verifier, VT_MIN) && + VerifyField(verifier, VT_MAX) && + VerifyField(verifier, VT_NARROWRANGE) && + VerifyField(verifier, VT_NUMBITS) && + VerifyField(verifier, VT_INITED) && + VerifyField(verifier, VT_VARCORR) && + VerifyField(verifier, VT_MEANCORR) && + VerifyField(verifier, VT_DSTDTYPE) && + VerifyField(verifier, VT_ROUNDTYPE) && + VerifyField(verifier, VT_MULTIPLIER) && + verifier.EndTable(); + } + QuantParamT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantParamT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantParamT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct QuantParamBuilder { + typedef QuantParam Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_scale(double scale) { + fbb_.AddElement(QuantParam::VT_SCALE, scale, 1.0); + } + void add_zeroPoint(int32_t zeroPoint) { + fbb_.AddElement(QuantParam::VT_ZEROPOINT, zeroPoint, 0); + } + void add_min(double min) { + fbb_.AddElement(QuantParam::VT_MIN, min, 0.0); + } + void add_max(double max) { + fbb_.AddElement(QuantParam::VT_MAX, max, 0.0); + } + void add_narrowRange(bool narrowRange) { + fbb_.AddElement(QuantParam::VT_NARROWRANGE, static_cast(narrowRange), 1); + } + void add_numBits(int32_t numBits) { + fbb_.AddElement(QuantParam::VT_NUMBITS, numBits, 8); + } + void add_inited(bool inited) { + fbb_.AddElement(QuantParam::VT_INITED, static_cast(inited), 0); + } + void add_varCorr(float varCorr) { + fbb_.AddElement(QuantParam::VT_VARCORR, varCorr, 1.0f); + } + void add_meanCorr(float meanCorr) { + fbb_.AddElement(QuantParam::VT_MEANCORR, meanCorr, 0.0f); + } + void add_dstDtype(int32_t dstDtype) { + fbb_.AddElement(QuantParam::VT_DSTDTYPE, dstDtype, 32); + } + void add_roundType(int32_t roundType) { + fbb_.AddElement(QuantParam::VT_ROUNDTYPE, roundType, 1); + } + void add_multiplier(int32_t multiplier) { + fbb_.AddElement(QuantParam::VT_MULTIPLIER, multiplier, 1); + } + explicit QuantParamBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateQuantParam( + flatbuffers::FlatBufferBuilder &_fbb, + double scale = 1.0, + int32_t zeroPoint = 0, + double min = 0.0, + double max = 0.0, + bool narrowRange = true, + int32_t numBits = 8, + bool inited = false, + float varCorr = 1.0f, + float meanCorr = 0.0f, + int32_t dstDtype = 32, + int32_t roundType = 1, + int32_t multiplier = 1) { + QuantParamBuilder builder_(_fbb); + builder_.add_max(max); + builder_.add_min(min); + builder_.add_scale(scale); + builder_.add_multiplier(multiplier); + builder_.add_roundType(roundType); + builder_.add_dstDtype(dstDtype); + builder_.add_meanCorr(meanCorr); + builder_.add_varCorr(varCorr); + builder_.add_numBits(numBits); + builder_.add_zeroPoint(zeroPoint); + builder_.add_inited(inited); + builder_.add_narrowRange(narrowRange); + return builder_.Finish(); +} + +flatbuffers::Offset CreateQuantParam(flatbuffers::FlatBufferBuilder &_fbb, const QuantParamT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExternalDataT : public flatbuffers::NativeTable { + typedef ExternalData TableType; + std::string checkSum{}; + std::string location{}; + int64_t offset = 0; + int64_t length = -1LL; +}; + +struct ExternalData FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExternalDataT NativeTableType; + typedef ExternalDataBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ExternalDataTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CHECKSUM = 4, + VT_LOCATION = 6, + VT_OFFSET = 8, + VT_LENGTH = 10 + }; + const flatbuffers::String *checkSum() const { + return GetPointer(VT_CHECKSUM); + } + flatbuffers::String *mutable_checkSum() { + return GetPointer(VT_CHECKSUM); + } + const flatbuffers::String *location() const { + return GetPointer(VT_LOCATION); + } + flatbuffers::String *mutable_location() { + return GetPointer(VT_LOCATION); + } + int64_t offset() const { + return GetField(VT_OFFSET, 0); + } + bool mutate_offset(int64_t _offset) { + return SetField(VT_OFFSET, _offset, 0); + } + int64_t length() const { + return GetField(VT_LENGTH, -1LL); + } + bool mutate_length(int64_t _length) { + return SetField(VT_LENGTH, _length, -1LL); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CHECKSUM) && + verifier.VerifyString(checkSum()) && + VerifyOffset(verifier, VT_LOCATION) && + verifier.VerifyString(location()) && + VerifyField(verifier, VT_OFFSET) && + VerifyField(verifier, VT_LENGTH) && + verifier.EndTable(); + } + ExternalDataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExternalDataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExternalDataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExternalDataBuilder { + typedef ExternalData Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_checkSum(flatbuffers::Offset checkSum) { + fbb_.AddOffset(ExternalData::VT_CHECKSUM, checkSum); + } + void add_location(flatbuffers::Offset location) { + fbb_.AddOffset(ExternalData::VT_LOCATION, location); + } + void add_offset(int64_t offset) { + fbb_.AddElement(ExternalData::VT_OFFSET, offset, 0); + } + void add_length(int64_t length) { + fbb_.AddElement(ExternalData::VT_LENGTH, length, -1LL); + } + explicit ExternalDataBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExternalData( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset checkSum = 0, + flatbuffers::Offset location = 0, + int64_t offset = 0, + int64_t length = -1LL) { + ExternalDataBuilder builder_(_fbb); + builder_.add_length(length); + builder_.add_offset(offset); + builder_.add_location(location); + builder_.add_checkSum(checkSum); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateExternalDataDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *checkSum = nullptr, + const char *location = nullptr, + int64_t offset = 0, + int64_t length = -1LL) { + auto checkSum__ = checkSum ? _fbb.CreateString(checkSum) : 0; + auto location__ = location ? _fbb.CreateString(location) : 0; + return mindspore::schema::CreateExternalData( + _fbb, + checkSum__, + location__, + offset, + length); +} + +flatbuffers::Offset CreateExternalData(flatbuffers::FlatBufferBuilder &_fbb, const ExternalDataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorT : public flatbuffers::NativeTable { + typedef Tensor TableType; + int32_t nodeType = 0; + int32_t dataType = 0; + std::vector dims{}; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + int32_t refCount = 0; + int32_t offset = 0; + std::vector data{}; + std::vector> quantParams{}; + std::vector quantClusters{}; + std::string name{}; + bool enableHuffmanCode = false; + mindspore::schema::WeightQuantCompressType weightQuantCompressType = mindspore::schema::WeightQuantCompressType_NONE; + std::vector> externalData{}; +}; + +struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorT NativeTableType; + typedef TensorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NODETYPE = 4, + VT_DATATYPE = 6, + VT_DIMS = 8, + VT_FORMAT = 10, + VT_REFCOUNT = 12, + VT_OFFSET = 14, + VT_DATA = 16, + VT_QUANTPARAMS = 18, + VT_QUANTCLUSTERS = 20, + VT_NAME = 22, + VT_ENABLEHUFFMANCODE = 24, + VT_WEIGHTQUANTCOMPRESSTYPE = 26, + VT_EXTERNALDATA = 28 + }; + int32_t nodeType() const { + return GetField(VT_NODETYPE, 0); + } + bool mutate_nodeType(int32_t _nodeType) { + return SetField(VT_NODETYPE, _nodeType, 0); + } + int32_t dataType() const { + return GetField(VT_DATATYPE, 0); + } + bool mutate_dataType(int32_t _dataType) { + return SetField(VT_DATATYPE, _dataType, 0); + } + const flatbuffers::Vector *dims() const { + return GetPointer *>(VT_DIMS); + } + flatbuffers::Vector *mutable_dims() { + return GetPointer *>(VT_DIMS); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + int32_t refCount() const { + return GetField(VT_REFCOUNT, 0); + } + bool mutate_refCount(int32_t _refCount) { + return SetField(VT_REFCOUNT, _refCount, 0); + } + int32_t offset() const { + return GetField(VT_OFFSET, 0); + } + bool mutate_offset(int32_t _offset) { + return SetField(VT_OFFSET, _offset, 0); + } + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + flatbuffers::Vector *mutable_data() { + return GetPointer *>(VT_DATA); + } + const flatbuffers::Vector> *quantParams() const { + return GetPointer> *>(VT_QUANTPARAMS); + } + flatbuffers::Vector> *mutable_quantParams() { + return GetPointer> *>(VT_QUANTPARAMS); + } + const flatbuffers::Vector *quantClusters() const { + return GetPointer *>(VT_QUANTCLUSTERS); + } + flatbuffers::Vector *mutable_quantClusters() { + return GetPointer *>(VT_QUANTCLUSTERS); + } + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + bool enableHuffmanCode() const { + return GetField(VT_ENABLEHUFFMANCODE, 0) != 0; + } + bool mutate_enableHuffmanCode(bool _enableHuffmanCode) { + return SetField(VT_ENABLEHUFFMANCODE, static_cast(_enableHuffmanCode), 0); + } + mindspore::schema::WeightQuantCompressType weightQuantCompressType() const { + return static_cast(GetField(VT_WEIGHTQUANTCOMPRESSTYPE, 0)); + } + bool mutate_weightQuantCompressType(mindspore::schema::WeightQuantCompressType _weightQuantCompressType) { + return SetField(VT_WEIGHTQUANTCOMPRESSTYPE, static_cast(_weightQuantCompressType), 0); + } + const flatbuffers::Vector> *externalData() const { + return GetPointer> *>(VT_EXTERNALDATA); + } + flatbuffers::Vector> *mutable_externalData() { + return GetPointer> *>(VT_EXTERNALDATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NODETYPE) && + VerifyField(verifier, VT_DATATYPE) && + VerifyOffset(verifier, VT_DIMS) && + verifier.VerifyVector(dims()) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_REFCOUNT) && + VerifyField(verifier, VT_OFFSET) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + VerifyOffset(verifier, VT_QUANTPARAMS) && + verifier.VerifyVector(quantParams()) && + verifier.VerifyVectorOfTables(quantParams()) && + VerifyOffset(verifier, VT_QUANTCLUSTERS) && + verifier.VerifyVector(quantClusters()) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyField(verifier, VT_ENABLEHUFFMANCODE) && + VerifyField(verifier, VT_WEIGHTQUANTCOMPRESSTYPE) && + VerifyOffset(verifier, VT_EXTERNALDATA) && + verifier.VerifyVector(externalData()) && + verifier.VerifyVectorOfTables(externalData()) && + verifier.EndTable(); + } + TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorBuilder { + typedef Tensor Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_nodeType(int32_t nodeType) { + fbb_.AddElement(Tensor::VT_NODETYPE, nodeType, 0); + } + void add_dataType(int32_t dataType) { + fbb_.AddElement(Tensor::VT_DATATYPE, dataType, 0); + } + void add_dims(flatbuffers::Offset> dims) { + fbb_.AddOffset(Tensor::VT_DIMS, dims); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(Tensor::VT_FORMAT, static_cast(format), 0); + } + void add_refCount(int32_t refCount) { + fbb_.AddElement(Tensor::VT_REFCOUNT, refCount, 0); + } + void add_offset(int32_t offset) { + fbb_.AddElement(Tensor::VT_OFFSET, offset, 0); + } + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(Tensor::VT_DATA, data); + } + void add_quantParams(flatbuffers::Offset>> quantParams) { + fbb_.AddOffset(Tensor::VT_QUANTPARAMS, quantParams); + } + void add_quantClusters(flatbuffers::Offset> quantClusters) { + fbb_.AddOffset(Tensor::VT_QUANTCLUSTERS, quantClusters); + } + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Tensor::VT_NAME, name); + } + void add_enableHuffmanCode(bool enableHuffmanCode) { + fbb_.AddElement(Tensor::VT_ENABLEHUFFMANCODE, static_cast(enableHuffmanCode), 0); + } + void add_weightQuantCompressType(mindspore::schema::WeightQuantCompressType weightQuantCompressType) { + fbb_.AddElement(Tensor::VT_WEIGHTQUANTCOMPRESSTYPE, static_cast(weightQuantCompressType), 0); + } + void add_externalData(flatbuffers::Offset>> externalData) { + fbb_.AddOffset(Tensor::VT_EXTERNALDATA, externalData); + } + explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensor( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t nodeType = 0, + int32_t dataType = 0, + flatbuffers::Offset> dims = 0, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + int32_t refCount = 0, + int32_t offset = 0, + flatbuffers::Offset> data = 0, + flatbuffers::Offset>> quantParams = 0, + flatbuffers::Offset> quantClusters = 0, + flatbuffers::Offset name = 0, + bool enableHuffmanCode = false, + mindspore::schema::WeightQuantCompressType weightQuantCompressType = mindspore::schema::WeightQuantCompressType_NONE, + flatbuffers::Offset>> externalData = 0) { + TensorBuilder builder_(_fbb); + builder_.add_externalData(externalData); + builder_.add_weightQuantCompressType(weightQuantCompressType); + builder_.add_name(name); + builder_.add_quantClusters(quantClusters); + builder_.add_quantParams(quantParams); + builder_.add_data(data); + builder_.add_offset(offset); + builder_.add_refCount(refCount); + builder_.add_format(format); + builder_.add_dims(dims); + builder_.add_dataType(dataType); + builder_.add_nodeType(nodeType); + builder_.add_enableHuffmanCode(enableHuffmanCode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTensorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t nodeType = 0, + int32_t dataType = 0, + const std::vector *dims = nullptr, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + int32_t refCount = 0, + int32_t offset = 0, + const std::vector *data = nullptr, + const std::vector> *quantParams = nullptr, + const std::vector *quantClusters = nullptr, + const char *name = nullptr, + bool enableHuffmanCode = false, + mindspore::schema::WeightQuantCompressType weightQuantCompressType = mindspore::schema::WeightQuantCompressType_NONE, + const std::vector> *externalData = nullptr) { + auto dims__ = dims ? _fbb.CreateVector(*dims) : 0; + auto data__ = data ? _fbb.CreateVector(*data) : 0; + auto quantParams__ = quantParams ? _fbb.CreateVector>(*quantParams) : 0; + auto quantClusters__ = quantClusters ? _fbb.CreateVector(*quantClusters) : 0; + auto name__ = name ? _fbb.CreateString(name) : 0; + auto externalData__ = externalData ? _fbb.CreateVector>(*externalData) : 0; + return mindspore::schema::CreateTensor( + _fbb, + nodeType, + dataType, + dims__, + format, + refCount, + offset, + data__, + quantParams__, + quantClusters__, + name__, + enableHuffmanCode, + weightQuantCompressType, + externalData__); +} + +flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PrimitiveT : public flatbuffers::NativeTable { + typedef Primitive TableType; + mindspore::schema::PrimitiveTypeUnion value{}; +}; + +struct Primitive FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PrimitiveT NativeTableType; + typedef PrimitiveBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PrimitiveTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUE_TYPE = 4, + VT_VALUE = 6 + }; + mindspore::schema::PrimitiveType value_type() const { + return static_cast(GetField(VT_VALUE_TYPE, 0)); + } + const void *value() const { + return GetPointer(VT_VALUE); + } + template const T *value_as() const; + const mindspore::schema::Abs *value_as_Abs() const { + return value_type() == mindspore::schema::PrimitiveType_Abs ? static_cast(value()) : nullptr; + } + const mindspore::schema::Activation *value_as_Activation() const { + return value_type() == mindspore::schema::PrimitiveType_Activation ? static_cast(value()) : nullptr; + } + const mindspore::schema::ActivationGrad *value_as_ActivationGrad() const { + return value_type() == mindspore::schema::PrimitiveType_ActivationGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Adam *value_as_Adam() const { + return value_type() == mindspore::schema::PrimitiveType_Adam ? static_cast(value()) : nullptr; + } + const mindspore::schema::AddFusion *value_as_AddFusion() const { + return value_type() == mindspore::schema::PrimitiveType_AddFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::AdderFusion *value_as_AdderFusion() const { + return value_type() == mindspore::schema::PrimitiveType_AdderFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::AddGrad *value_as_AddGrad() const { + return value_type() == mindspore::schema::PrimitiveType_AddGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::AddN *value_as_AddN() const { + return value_type() == mindspore::schema::PrimitiveType_AddN ? static_cast(value()) : nullptr; + } + const mindspore::schema::All *value_as_All() const { + return value_type() == mindspore::schema::PrimitiveType_All ? static_cast(value()) : nullptr; + } + const mindspore::schema::ApplyMomentum *value_as_ApplyMomentum() const { + return value_type() == mindspore::schema::PrimitiveType_ApplyMomentum ? static_cast(value()) : nullptr; + } + const mindspore::schema::ArgMaxFusion *value_as_ArgMaxFusion() const { + return value_type() == mindspore::schema::PrimitiveType_ArgMaxFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::ArgMinFusion *value_as_ArgMinFusion() const { + return value_type() == mindspore::schema::PrimitiveType_ArgMinFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Assert *value_as_Assert() const { + return value_type() == mindspore::schema::PrimitiveType_Assert ? static_cast(value()) : nullptr; + } + const mindspore::schema::Assign *value_as_Assign() const { + return value_type() == mindspore::schema::PrimitiveType_Assign ? static_cast(value()) : nullptr; + } + const mindspore::schema::AssignAdd *value_as_AssignAdd() const { + return value_type() == mindspore::schema::PrimitiveType_AssignAdd ? static_cast(value()) : nullptr; + } + const mindspore::schema::AudioSpectrogram *value_as_AudioSpectrogram() const { + return value_type() == mindspore::schema::PrimitiveType_AudioSpectrogram ? static_cast(value()) : nullptr; + } + const mindspore::schema::AvgPoolFusion *value_as_AvgPoolFusion() const { + return value_type() == mindspore::schema::PrimitiveType_AvgPoolFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::AvgPoolGrad *value_as_AvgPoolGrad() const { + return value_type() == mindspore::schema::PrimitiveType_AvgPoolGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::BatchNorm *value_as_BatchNorm() const { + return value_type() == mindspore::schema::PrimitiveType_BatchNorm ? static_cast(value()) : nullptr; + } + const mindspore::schema::BatchNormGrad *value_as_BatchNormGrad() const { + return value_type() == mindspore::schema::PrimitiveType_BatchNormGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::BatchToSpace *value_as_BatchToSpace() const { + return value_type() == mindspore::schema::PrimitiveType_BatchToSpace ? static_cast(value()) : nullptr; + } + const mindspore::schema::BatchToSpaceND *value_as_BatchToSpaceND() const { + return value_type() == mindspore::schema::PrimitiveType_BatchToSpaceND ? static_cast(value()) : nullptr; + } + const mindspore::schema::BiasAdd *value_as_BiasAdd() const { + return value_type() == mindspore::schema::PrimitiveType_BiasAdd ? static_cast(value()) : nullptr; + } + const mindspore::schema::BinaryCrossEntropy *value_as_BinaryCrossEntropy() const { + return value_type() == mindspore::schema::PrimitiveType_BinaryCrossEntropy ? static_cast(value()) : nullptr; + } + const mindspore::schema::BinaryCrossEntropyGrad *value_as_BinaryCrossEntropyGrad() const { + return value_type() == mindspore::schema::PrimitiveType_BinaryCrossEntropyGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::BiasAddGrad *value_as_BiasAddGrad() const { + return value_type() == mindspore::schema::PrimitiveType_BiasAddGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::BroadcastTo *value_as_BroadcastTo() const { + return value_type() == mindspore::schema::PrimitiveType_BroadcastTo ? static_cast(value()) : nullptr; + } + const mindspore::schema::Cast *value_as_Cast() const { + return value_type() == mindspore::schema::PrimitiveType_Cast ? static_cast(value()) : nullptr; + } + const mindspore::schema::Ceil *value_as_Ceil() const { + return value_type() == mindspore::schema::PrimitiveType_Ceil ? static_cast(value()) : nullptr; + } + const mindspore::schema::Clip *value_as_Clip() const { + return value_type() == mindspore::schema::PrimitiveType_Clip ? static_cast(value()) : nullptr; + } + const mindspore::schema::Concat *value_as_Concat() const { + return value_type() == mindspore::schema::PrimitiveType_Concat ? static_cast(value()) : nullptr; + } + const mindspore::schema::Attention *value_as_Attention() const { + return value_type() == mindspore::schema::PrimitiveType_Attention ? static_cast(value()) : nullptr; + } + const mindspore::schema::Conv2DBackpropFilterFusion *value_as_Conv2DBackpropFilterFusion() const { + return value_type() == mindspore::schema::PrimitiveType_Conv2DBackpropFilterFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Conv2DBackpropInputFusion *value_as_Conv2DBackpropInputFusion() const { + return value_type() == mindspore::schema::PrimitiveType_Conv2DBackpropInputFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Conv2DFusion *value_as_Conv2DFusion() const { + return value_type() == mindspore::schema::PrimitiveType_Conv2DFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Conv2dTransposeFusion *value_as_Conv2dTransposeFusion() const { + return value_type() == mindspore::schema::PrimitiveType_Conv2dTransposeFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Cos *value_as_Cos() const { + return value_type() == mindspore::schema::PrimitiveType_Cos ? static_cast(value()) : nullptr; + } + const mindspore::schema::ConstantOfShape *value_as_ConstantOfShape() const { + return value_type() == mindspore::schema::PrimitiveType_ConstantOfShape ? static_cast(value()) : nullptr; + } + const mindspore::schema::Crop *value_as_Crop() const { + return value_type() == mindspore::schema::PrimitiveType_Crop ? static_cast(value()) : nullptr; + } + const mindspore::schema::CustomExtractFeatures *value_as_CustomExtractFeatures() const { + return value_type() == mindspore::schema::PrimitiveType_CustomExtractFeatures ? static_cast(value()) : nullptr; + } + const mindspore::schema::CustomNormalize *value_as_CustomNormalize() const { + return value_type() == mindspore::schema::PrimitiveType_CustomNormalize ? static_cast(value()) : nullptr; + } + const mindspore::schema::CustomPredict *value_as_CustomPredict() const { + return value_type() == mindspore::schema::PrimitiveType_CustomPredict ? static_cast(value()) : nullptr; + } + const mindspore::schema::DeConv2DGradFilter *value_as_DeConv2DGradFilter() const { + return value_type() == mindspore::schema::PrimitiveType_DeConv2DGradFilter ? static_cast(value()) : nullptr; + } + const mindspore::schema::Depend *value_as_Depend() const { + return value_type() == mindspore::schema::PrimitiveType_Depend ? static_cast(value()) : nullptr; + } + const mindspore::schema::DepthToSpace *value_as_DepthToSpace() const { + return value_type() == mindspore::schema::PrimitiveType_DepthToSpace ? static_cast(value()) : nullptr; + } + const mindspore::schema::DetectionPostProcess *value_as_DetectionPostProcess() const { + return value_type() == mindspore::schema::PrimitiveType_DetectionPostProcess ? static_cast(value()) : nullptr; + } + const mindspore::schema::DivFusion *value_as_DivFusion() const { + return value_type() == mindspore::schema::PrimitiveType_DivFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::DivGrad *value_as_DivGrad() const { + return value_type() == mindspore::schema::PrimitiveType_DivGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Dropout *value_as_Dropout() const { + return value_type() == mindspore::schema::PrimitiveType_Dropout ? static_cast(value()) : nullptr; + } + const mindspore::schema::DropoutGrad *value_as_DropoutGrad() const { + return value_type() == mindspore::schema::PrimitiveType_DropoutGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Elu *value_as_Elu() const { + return value_type() == mindspore::schema::PrimitiveType_Elu ? static_cast(value()) : nullptr; + } + const mindspore::schema::Eltwise *value_as_Eltwise() const { + return value_type() == mindspore::schema::PrimitiveType_Eltwise ? static_cast(value()) : nullptr; + } + const mindspore::schema::Equal *value_as_Equal() const { + return value_type() == mindspore::schema::PrimitiveType_Equal ? static_cast(value()) : nullptr; + } + const mindspore::schema::EmbeddingLookupFusion *value_as_EmbeddingLookupFusion() const { + return value_type() == mindspore::schema::PrimitiveType_EmbeddingLookupFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::ExpFusion *value_as_ExpFusion() const { + return value_type() == mindspore::schema::PrimitiveType_ExpFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::ExpandDims *value_as_ExpandDims() const { + return value_type() == mindspore::schema::PrimitiveType_ExpandDims ? static_cast(value()) : nullptr; + } + const mindspore::schema::FakeQuantWithMinMaxVars *value_as_FakeQuantWithMinMaxVars() const { + return value_type() == mindspore::schema::PrimitiveType_FakeQuantWithMinMaxVars ? static_cast(value()) : nullptr; + } + const mindspore::schema::FakeQuantWithMinMaxVarsPerChannel *value_as_FakeQuantWithMinMaxVarsPerChannel() const { + return value_type() == mindspore::schema::PrimitiveType_FakeQuantWithMinMaxVarsPerChannel ? static_cast(value()) : nullptr; + } + const mindspore::schema::FftReal *value_as_FftReal() const { + return value_type() == mindspore::schema::PrimitiveType_FftReal ? static_cast(value()) : nullptr; + } + const mindspore::schema::FftImag *value_as_FftImag() const { + return value_type() == mindspore::schema::PrimitiveType_FftImag ? static_cast(value()) : nullptr; + } + const mindspore::schema::Flatten *value_as_Flatten() const { + return value_type() == mindspore::schema::PrimitiveType_Flatten ? static_cast(value()) : nullptr; + } + const mindspore::schema::FlattenGrad *value_as_FlattenGrad() const { + return value_type() == mindspore::schema::PrimitiveType_FlattenGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Floor *value_as_Floor() const { + return value_type() == mindspore::schema::PrimitiveType_Floor ? static_cast(value()) : nullptr; + } + const mindspore::schema::FloorDiv *value_as_FloorDiv() const { + return value_type() == mindspore::schema::PrimitiveType_FloorDiv ? static_cast(value()) : nullptr; + } + const mindspore::schema::FloorMod *value_as_FloorMod() const { + return value_type() == mindspore::schema::PrimitiveType_FloorMod ? static_cast(value()) : nullptr; + } + const mindspore::schema::Fill *value_as_Fill() const { + return value_type() == mindspore::schema::PrimitiveType_Fill ? static_cast(value()) : nullptr; + } + const mindspore::schema::FullConnection *value_as_FullConnection() const { + return value_type() == mindspore::schema::PrimitiveType_FullConnection ? static_cast(value()) : nullptr; + } + const mindspore::schema::FusedBatchNorm *value_as_FusedBatchNorm() const { + return value_type() == mindspore::schema::PrimitiveType_FusedBatchNorm ? static_cast(value()) : nullptr; + } + const mindspore::schema::Gather *value_as_Gather() const { + return value_type() == mindspore::schema::PrimitiveType_Gather ? static_cast(value()) : nullptr; + } + const mindspore::schema::GatherNd *value_as_GatherNd() const { + return value_type() == mindspore::schema::PrimitiveType_GatherNd ? static_cast(value()) : nullptr; + } + const mindspore::schema::Greater *value_as_Greater() const { + return value_type() == mindspore::schema::PrimitiveType_Greater ? static_cast(value()) : nullptr; + } + const mindspore::schema::GreaterEqual *value_as_GreaterEqual() const { + return value_type() == mindspore::schema::PrimitiveType_GreaterEqual ? static_cast(value()) : nullptr; + } + const mindspore::schema::HashtableLookup *value_as_HashtableLookup() const { + return value_type() == mindspore::schema::PrimitiveType_HashtableLookup ? static_cast(value()) : nullptr; + } + const mindspore::schema::InstanceNorm *value_as_InstanceNorm() const { + return value_type() == mindspore::schema::PrimitiveType_InstanceNorm ? static_cast(value()) : nullptr; + } + const mindspore::schema::LayerNormFusion *value_as_LayerNormFusion() const { + return value_type() == mindspore::schema::PrimitiveType_LayerNormFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::LeakyRelu *value_as_LeakyRelu() const { + return value_type() == mindspore::schema::PrimitiveType_LeakyRelu ? static_cast(value()) : nullptr; + } + const mindspore::schema::Less *value_as_Less() const { + return value_type() == mindspore::schema::PrimitiveType_Less ? static_cast(value()) : nullptr; + } + const mindspore::schema::LessEqual *value_as_LessEqual() const { + return value_type() == mindspore::schema::PrimitiveType_LessEqual ? static_cast(value()) : nullptr; + } + const mindspore::schema::Log *value_as_Log() const { + return value_type() == mindspore::schema::PrimitiveType_Log ? static_cast(value()) : nullptr; + } + const mindspore::schema::LogGrad *value_as_LogGrad() const { + return value_type() == mindspore::schema::PrimitiveType_LogGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::LogicalAnd *value_as_LogicalAnd() const { + return value_type() == mindspore::schema::PrimitiveType_LogicalAnd ? static_cast(value()) : nullptr; + } + const mindspore::schema::LogicalNot *value_as_LogicalNot() const { + return value_type() == mindspore::schema::PrimitiveType_LogicalNot ? static_cast(value()) : nullptr; + } + const mindspore::schema::LogicalOr *value_as_LogicalOr() const { + return value_type() == mindspore::schema::PrimitiveType_LogicalOr ? static_cast(value()) : nullptr; + } + const mindspore::schema::LpNormalization *value_as_LpNormalization() const { + return value_type() == mindspore::schema::PrimitiveType_LpNormalization ? static_cast(value()) : nullptr; + } + const mindspore::schema::LRN *value_as_LRN() const { + return value_type() == mindspore::schema::PrimitiveType_LRN ? static_cast(value()) : nullptr; + } + const mindspore::schema::LshProjection *value_as_LshProjection() const { + return value_type() == mindspore::schema::PrimitiveType_LshProjection ? static_cast(value()) : nullptr; + } + const mindspore::schema::LSTM *value_as_LSTM() const { + return value_type() == mindspore::schema::PrimitiveType_LSTM ? static_cast(value()) : nullptr; + } + const mindspore::schema::L2NormalizeFusion *value_as_L2NormalizeFusion() const { + return value_type() == mindspore::schema::PrimitiveType_L2NormalizeFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::MatMulFusion *value_as_MatMulFusion() const { + return value_type() == mindspore::schema::PrimitiveType_MatMulFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Maximum *value_as_Maximum() const { + return value_type() == mindspore::schema::PrimitiveType_Maximum ? static_cast(value()) : nullptr; + } + const mindspore::schema::MaximumGrad *value_as_MaximumGrad() const { + return value_type() == mindspore::schema::PrimitiveType_MaximumGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::MaxPoolFusion *value_as_MaxPoolFusion() const { + return value_type() == mindspore::schema::PrimitiveType_MaxPoolFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::MaxPoolGrad *value_as_MaxPoolGrad() const { + return value_type() == mindspore::schema::PrimitiveType_MaxPoolGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::SwitchLayer *value_as_SwitchLayer() const { + return value_type() == mindspore::schema::PrimitiveType_SwitchLayer ? static_cast(value()) : nullptr; + } + const mindspore::schema::Mfcc *value_as_Mfcc() const { + return value_type() == mindspore::schema::PrimitiveType_Mfcc ? static_cast(value()) : nullptr; + } + const mindspore::schema::Minimum *value_as_Minimum() const { + return value_type() == mindspore::schema::PrimitiveType_Minimum ? static_cast(value()) : nullptr; + } + const mindspore::schema::MinimumGrad *value_as_MinimumGrad() const { + return value_type() == mindspore::schema::PrimitiveType_MinimumGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Mod *value_as_Mod() const { + return value_type() == mindspore::schema::PrimitiveType_Mod ? static_cast(value()) : nullptr; + } + const mindspore::schema::MulFusion *value_as_MulFusion() const { + return value_type() == mindspore::schema::PrimitiveType_MulFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::MulGrad *value_as_MulGrad() const { + return value_type() == mindspore::schema::PrimitiveType_MulGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Neg *value_as_Neg() const { + return value_type() == mindspore::schema::PrimitiveType_Neg ? static_cast(value()) : nullptr; + } + const mindspore::schema::NegGrad *value_as_NegGrad() const { + return value_type() == mindspore::schema::PrimitiveType_NegGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::NotEqual *value_as_NotEqual() const { + return value_type() == mindspore::schema::PrimitiveType_NotEqual ? static_cast(value()) : nullptr; + } + const mindspore::schema::NonMaxSuppression *value_as_NonMaxSuppression() const { + return value_type() == mindspore::schema::PrimitiveType_NonMaxSuppression ? static_cast(value()) : nullptr; + } + const mindspore::schema::OneHot *value_as_OneHot() const { + return value_type() == mindspore::schema::PrimitiveType_OneHot ? static_cast(value()) : nullptr; + } + const mindspore::schema::OnesLike *value_as_OnesLike() const { + return value_type() == mindspore::schema::PrimitiveType_OnesLike ? static_cast(value()) : nullptr; + } + const mindspore::schema::PadFusion *value_as_PadFusion() const { + return value_type() == mindspore::schema::PrimitiveType_PadFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::PartialFusion *value_as_PartialFusion() const { + return value_type() == mindspore::schema::PrimitiveType_PartialFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::PowerGrad *value_as_PowerGrad() const { + return value_type() == mindspore::schema::PrimitiveType_PowerGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::PowFusion *value_as_PowFusion() const { + return value_type() == mindspore::schema::PrimitiveType_PowFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::PriorBox *value_as_PriorBox() const { + return value_type() == mindspore::schema::PrimitiveType_PriorBox ? static_cast(value()) : nullptr; + } + const mindspore::schema::PReLUFusion *value_as_PReLUFusion() const { + return value_type() == mindspore::schema::PrimitiveType_PReLUFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::QuantDTypeCast *value_as_QuantDTypeCast() const { + return value_type() == mindspore::schema::PrimitiveType_QuantDTypeCast ? static_cast(value()) : nullptr; + } + const mindspore::schema::Rank *value_as_Rank() const { + return value_type() == mindspore::schema::PrimitiveType_Rank ? static_cast(value()) : nullptr; + } + const mindspore::schema::Range *value_as_Range() const { + return value_type() == mindspore::schema::PrimitiveType_Range ? static_cast(value()) : nullptr; + } + const mindspore::schema::Reciprocal *value_as_Reciprocal() const { + return value_type() == mindspore::schema::PrimitiveType_Reciprocal ? static_cast(value()) : nullptr; + } + const mindspore::schema::RealDiv *value_as_RealDiv() const { + return value_type() == mindspore::schema::PrimitiveType_RealDiv ? static_cast(value()) : nullptr; + } + const mindspore::schema::ReduceFusion *value_as_ReduceFusion() const { + return value_type() == mindspore::schema::PrimitiveType_ReduceFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Reshape *value_as_Reshape() const { + return value_type() == mindspore::schema::PrimitiveType_Reshape ? static_cast(value()) : nullptr; + } + const mindspore::schema::Resize *value_as_Resize() const { + return value_type() == mindspore::schema::PrimitiveType_Resize ? static_cast(value()) : nullptr; + } + const mindspore::schema::ReverseSequence *value_as_ReverseSequence() const { + return value_type() == mindspore::schema::PrimitiveType_ReverseSequence ? static_cast(value()) : nullptr; + } + const mindspore::schema::ReverseV2 *value_as_ReverseV2() const { + return value_type() == mindspore::schema::PrimitiveType_ReverseV2 ? static_cast(value()) : nullptr; + } + const mindspore::schema::Rfft *value_as_Rfft() const { + return value_type() == mindspore::schema::PrimitiveType_Rfft ? static_cast(value()) : nullptr; + } + const mindspore::schema::ROIPooling *value_as_ROIPooling() const { + return value_type() == mindspore::schema::PrimitiveType_ROIPooling ? static_cast(value()) : nullptr; + } + const mindspore::schema::Round *value_as_Round() const { + return value_type() == mindspore::schema::PrimitiveType_Round ? static_cast(value()) : nullptr; + } + const mindspore::schema::Rsqrt *value_as_Rsqrt() const { + return value_type() == mindspore::schema::PrimitiveType_Rsqrt ? static_cast(value()) : nullptr; + } + const mindspore::schema::ScaleFusion *value_as_ScaleFusion() const { + return value_type() == mindspore::schema::PrimitiveType_ScaleFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::ScatterNd *value_as_ScatterNd() const { + return value_type() == mindspore::schema::PrimitiveType_ScatterNd ? static_cast(value()) : nullptr; + } + const mindspore::schema::SGD *value_as_SGD() const { + return value_type() == mindspore::schema::PrimitiveType_SGD ? static_cast(value()) : nullptr; + } + const mindspore::schema::Shape *value_as_Shape() const { + return value_type() == mindspore::schema::PrimitiveType_Shape ? static_cast(value()) : nullptr; + } + const mindspore::schema::SigmoidCrossEntropyWithLogits *value_as_SigmoidCrossEntropyWithLogits() const { + return value_type() == mindspore::schema::PrimitiveType_SigmoidCrossEntropyWithLogits ? static_cast(value()) : nullptr; + } + const mindspore::schema::SigmoidCrossEntropyWithLogitsGrad *value_as_SigmoidCrossEntropyWithLogitsGrad() const { + return value_type() == mindspore::schema::PrimitiveType_SigmoidCrossEntropyWithLogitsGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Sin *value_as_Sin() const { + return value_type() == mindspore::schema::PrimitiveType_Sin ? static_cast(value()) : nullptr; + } + const mindspore::schema::SkipGram *value_as_SkipGram() const { + return value_type() == mindspore::schema::PrimitiveType_SkipGram ? static_cast(value()) : nullptr; + } + const mindspore::schema::SliceFusion *value_as_SliceFusion() const { + return value_type() == mindspore::schema::PrimitiveType_SliceFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::SmoothL1Loss *value_as_SmoothL1Loss() const { + return value_type() == mindspore::schema::PrimitiveType_SmoothL1Loss ? static_cast(value()) : nullptr; + } + const mindspore::schema::SmoothL1LossGrad *value_as_SmoothL1LossGrad() const { + return value_type() == mindspore::schema::PrimitiveType_SmoothL1LossGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Softmax *value_as_Softmax() const { + return value_type() == mindspore::schema::PrimitiveType_Softmax ? static_cast(value()) : nullptr; + } + const mindspore::schema::SoftmaxCrossEntropyWithLogits *value_as_SoftmaxCrossEntropyWithLogits() const { + return value_type() == mindspore::schema::PrimitiveType_SoftmaxCrossEntropyWithLogits ? static_cast(value()) : nullptr; + } + const mindspore::schema::SpaceToBatch *value_as_SpaceToBatch() const { + return value_type() == mindspore::schema::PrimitiveType_SpaceToBatch ? static_cast(value()) : nullptr; + } + const mindspore::schema::SpaceToBatchND *value_as_SpaceToBatchND() const { + return value_type() == mindspore::schema::PrimitiveType_SpaceToBatchND ? static_cast(value()) : nullptr; + } + const mindspore::schema::SpaceToDepth *value_as_SpaceToDepth() const { + return value_type() == mindspore::schema::PrimitiveType_SpaceToDepth ? static_cast(value()) : nullptr; + } + const mindspore::schema::SparseSoftmaxCrossEntropyWithLogits *value_as_SparseSoftmaxCrossEntropyWithLogits() const { + return value_type() == mindspore::schema::PrimitiveType_SparseSoftmaxCrossEntropyWithLogits ? static_cast(value()) : nullptr; + } + const mindspore::schema::SparseToDense *value_as_SparseToDense() const { + return value_type() == mindspore::schema::PrimitiveType_SparseToDense ? static_cast(value()) : nullptr; + } + const mindspore::schema::Split *value_as_Split() const { + return value_type() == mindspore::schema::PrimitiveType_Split ? static_cast(value()) : nullptr; + } + const mindspore::schema::Sqrt *value_as_Sqrt() const { + return value_type() == mindspore::schema::PrimitiveType_Sqrt ? static_cast(value()) : nullptr; + } + const mindspore::schema::Squeeze *value_as_Squeeze() const { + return value_type() == mindspore::schema::PrimitiveType_Squeeze ? static_cast(value()) : nullptr; + } + const mindspore::schema::Square *value_as_Square() const { + return value_type() == mindspore::schema::PrimitiveType_Square ? static_cast(value()) : nullptr; + } + const mindspore::schema::SquaredDifference *value_as_SquaredDifference() const { + return value_type() == mindspore::schema::PrimitiveType_SquaredDifference ? static_cast(value()) : nullptr; + } + const mindspore::schema::Stack *value_as_Stack() const { + return value_type() == mindspore::schema::PrimitiveType_Stack ? static_cast(value()) : nullptr; + } + const mindspore::schema::StridedSlice *value_as_StridedSlice() const { + return value_type() == mindspore::schema::PrimitiveType_StridedSlice ? static_cast(value()) : nullptr; + } + const mindspore::schema::SubFusion *value_as_SubFusion() const { + return value_type() == mindspore::schema::PrimitiveType_SubFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::SubGrad *value_as_SubGrad() const { + return value_type() == mindspore::schema::PrimitiveType_SubGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Switch *value_as_Switch() const { + return value_type() == mindspore::schema::PrimitiveType_Switch ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorListFromTensor *value_as_TensorListFromTensor() const { + return value_type() == mindspore::schema::PrimitiveType_TensorListFromTensor ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorListGetItem *value_as_TensorListGetItem() const { + return value_type() == mindspore::schema::PrimitiveType_TensorListGetItem ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorListReserve *value_as_TensorListReserve() const { + return value_type() == mindspore::schema::PrimitiveType_TensorListReserve ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorListSetItem *value_as_TensorListSetItem() const { + return value_type() == mindspore::schema::PrimitiveType_TensorListSetItem ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorListStack *value_as_TensorListStack() const { + return value_type() == mindspore::schema::PrimitiveType_TensorListStack ? static_cast(value()) : nullptr; + } + const mindspore::schema::TileFusion *value_as_TileFusion() const { + return value_type() == mindspore::schema::PrimitiveType_TileFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::TopKFusion *value_as_TopKFusion() const { + return value_type() == mindspore::schema::PrimitiveType_TopKFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Transpose *value_as_Transpose() const { + return value_type() == mindspore::schema::PrimitiveType_Transpose ? static_cast(value()) : nullptr; + } + const mindspore::schema::Unique *value_as_Unique() const { + return value_type() == mindspore::schema::PrimitiveType_Unique ? static_cast(value()) : nullptr; + } + const mindspore::schema::UnsortedSegmentSum *value_as_UnsortedSegmentSum() const { + return value_type() == mindspore::schema::PrimitiveType_UnsortedSegmentSum ? static_cast(value()) : nullptr; + } + const mindspore::schema::Unsqueeze *value_as_Unsqueeze() const { + return value_type() == mindspore::schema::PrimitiveType_Unsqueeze ? static_cast(value()) : nullptr; + } + const mindspore::schema::Unstack *value_as_Unstack() const { + return value_type() == mindspore::schema::PrimitiveType_Unstack ? static_cast(value()) : nullptr; + } + const mindspore::schema::LSTMGrad *value_as_LSTMGrad() const { + return value_type() == mindspore::schema::PrimitiveType_LSTMGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Where *value_as_Where() const { + return value_type() == mindspore::schema::PrimitiveType_Where ? static_cast(value()) : nullptr; + } + const mindspore::schema::ZerosLike *value_as_ZerosLike() const { + return value_type() == mindspore::schema::PrimitiveType_ZerosLike ? static_cast(value()) : nullptr; + } + const mindspore::schema::Select *value_as_Select() const { + return value_type() == mindspore::schema::PrimitiveType_Select ? static_cast(value()) : nullptr; + } + const mindspore::schema::ScatterNdUpdate *value_as_ScatterNdUpdate() const { + return value_type() == mindspore::schema::PrimitiveType_ScatterNdUpdate ? static_cast(value()) : nullptr; + } + const mindspore::schema::GRU *value_as_GRU() const { + return value_type() == mindspore::schema::PrimitiveType_GRU ? static_cast(value()) : nullptr; + } + const mindspore::schema::NonZero *value_as_NonZero() const { + return value_type() == mindspore::schema::PrimitiveType_NonZero ? static_cast(value()) : nullptr; + } + const mindspore::schema::InvertPermutation *value_as_InvertPermutation() const { + return value_type() == mindspore::schema::PrimitiveType_InvertPermutation ? static_cast(value()) : nullptr; + } + const mindspore::schema::Size *value_as_Size() const { + return value_type() == mindspore::schema::PrimitiveType_Size ? static_cast(value()) : nullptr; + } + const mindspore::schema::RandomStandardNormal *value_as_RandomStandardNormal() const { + return value_type() == mindspore::schema::PrimitiveType_RandomStandardNormal ? static_cast(value()) : nullptr; + } + const mindspore::schema::CropAndResize *value_as_CropAndResize() const { + return value_type() == mindspore::schema::PrimitiveType_CropAndResize ? static_cast(value()) : nullptr; + } + const mindspore::schema::Erf *value_as_Erf() const { + return value_type() == mindspore::schema::PrimitiveType_Erf ? static_cast(value()) : nullptr; + } + const mindspore::schema::StridedSliceGrad *value_as_StridedSliceGrad() const { + return value_type() == mindspore::schema::PrimitiveType_StridedSliceGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::IsFinite *value_as_IsFinite() const { + return value_type() == mindspore::schema::PrimitiveType_IsFinite ? static_cast(value()) : nullptr; + } + const mindspore::schema::LinSpace *value_as_LinSpace() const { + return value_type() == mindspore::schema::PrimitiveType_LinSpace ? static_cast(value()) : nullptr; + } + const mindspore::schema::UniformReal *value_as_UniformReal() const { + return value_type() == mindspore::schema::PrimitiveType_UniformReal ? static_cast(value()) : nullptr; + } + const mindspore::schema::AbsGrad *value_as_AbsGrad() const { + return value_type() == mindspore::schema::PrimitiveType_AbsGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::RsqrtGrad *value_as_RsqrtGrad() const { + return value_type() == mindspore::schema::PrimitiveType_RsqrtGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::SqrtGrad *value_as_SqrtGrad() const { + return value_type() == mindspore::schema::PrimitiveType_SqrtGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::LayerNormGrad *value_as_LayerNormGrad() const { + return value_type() == mindspore::schema::PrimitiveType_LayerNormGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::ResizeGrad *value_as_ResizeGrad() const { + return value_type() == mindspore::schema::PrimitiveType_ResizeGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::Splice *value_as_Splice() const { + return value_type() == mindspore::schema::PrimitiveType_Splice ? static_cast(value()) : nullptr; + } + const mindspore::schema::LogSoftmax *value_as_LogSoftmax() const { + return value_type() == mindspore::schema::PrimitiveType_LogSoftmax ? static_cast(value()) : nullptr; + } + const mindspore::schema::Call *value_as_Call() const { + return value_type() == mindspore::schema::PrimitiveType_Call ? static_cast(value()) : nullptr; + } + const mindspore::schema::Custom *value_as_Custom() const { + return value_type() == mindspore::schema::PrimitiveType_Custom ? static_cast(value()) : nullptr; + } + const mindspore::schema::CumSum *value_as_CumSum() const { + return value_type() == mindspore::schema::PrimitiveType_CumSum ? static_cast(value()) : nullptr; + } + const mindspore::schema::SplitWithOverlap *value_as_SplitWithOverlap() const { + return value_type() == mindspore::schema::PrimitiveType_SplitWithOverlap ? static_cast(value()) : nullptr; + } + const mindspore::schema::GenOP *value_as_GenOP() const { + return value_type() == mindspore::schema::PrimitiveType_GenOP ? static_cast(value()) : nullptr; + } + const mindspore::schema::RaggedRange *value_as_RaggedRange() const { + return value_type() == mindspore::schema::PrimitiveType_RaggedRange ? static_cast(value()) : nullptr; + } + const mindspore::schema::GLU *value_as_GLU() const { + return value_type() == mindspore::schema::PrimitiveType_GLU ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorArray *value_as_TensorArray() const { + return value_type() == mindspore::schema::PrimitiveType_TensorArray ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorArrayRead *value_as_TensorArrayRead() const { + return value_type() == mindspore::schema::PrimitiveType_TensorArrayRead ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorArrayWrite *value_as_TensorArrayWrite() const { + return value_type() == mindspore::schema::PrimitiveType_TensorArrayWrite ? static_cast(value()) : nullptr; + } + const mindspore::schema::Affine *value_as_Affine() const { + return value_type() == mindspore::schema::PrimitiveType_Affine ? static_cast(value()) : nullptr; + } + const mindspore::schema::AllGather *value_as_AllGather() const { + return value_type() == mindspore::schema::PrimitiveType_AllGather ? static_cast(value()) : nullptr; + } + const mindspore::schema::ReduceScatter *value_as_ReduceScatter() const { + return value_type() == mindspore::schema::PrimitiveType_ReduceScatter ? static_cast(value()) : nullptr; + } + const mindspore::schema::DynamicQuant *value_as_DynamicQuant() const { + return value_type() == mindspore::schema::PrimitiveType_DynamicQuant ? static_cast(value()) : nullptr; + } + const mindspore::schema::LSTMGradData *value_as_LSTMGradData() const { + return value_type() == mindspore::schema::PrimitiveType_LSTMGradData ? static_cast(value()) : nullptr; + } + const mindspore::schema::LSTMGradWeight *value_as_LSTMGradWeight() const { + return value_type() == mindspore::schema::PrimitiveType_LSTMGradWeight ? static_cast(value()) : nullptr; + } + const mindspore::schema::RandomNormal *value_as_RandomNormal() const { + return value_type() == mindspore::schema::PrimitiveType_RandomNormal ? static_cast(value()) : nullptr; + } + const mindspore::schema::NLLLoss *value_as_NLLLoss() const { + return value_type() == mindspore::schema::PrimitiveType_NLLLoss ? static_cast(value()) : nullptr; + } + const mindspore::schema::NLLLossGrad *value_as_NLLLossGrad() const { + return value_type() == mindspore::schema::PrimitiveType_NLLLossGrad ? static_cast(value()) : nullptr; + } + const mindspore::schema::FormatTranspose *value_as_FormatTranspose() const { + return value_type() == mindspore::schema::PrimitiveType_FormatTranspose ? static_cast(value()) : nullptr; + } + const mindspore::schema::GatherD *value_as_GatherD() const { + return value_type() == mindspore::schema::PrimitiveType_GatherD ? static_cast(value()) : nullptr; + } + const mindspore::schema::GroupNormFusion *value_as_GroupNormFusion() const { + return value_type() == mindspore::schema::PrimitiveType_GroupNormFusion ? static_cast(value()) : nullptr; + } + const mindspore::schema::Log1p *value_as_Log1p() const { + return value_type() == mindspore::schema::PrimitiveType_Log1p ? static_cast(value()) : nullptr; + } + const mindspore::schema::TensorScatterAdd *value_as_TensorScatterAdd() const { + return value_type() == mindspore::schema::PrimitiveType_TensorScatterAdd ? static_cast(value()) : nullptr; + } + const mindspore::schema::SparseFillEmptyRows *value_as_SparseFillEmptyRows() const { + return value_type() == mindspore::schema::PrimitiveType_SparseFillEmptyRows ? static_cast(value()) : nullptr; + } + const mindspore::schema::SparseReshape *value_as_SparseReshape() const { + return value_type() == mindspore::schema::PrimitiveType_SparseReshape ? static_cast(value()) : nullptr; + } + const mindspore::schema::SparseSegmentSum *value_as_SparseSegmentSum() const { + return value_type() == mindspore::schema::PrimitiveType_SparseSegmentSum ? static_cast(value()) : nullptr; + } + const mindspore::schema::ScatterElements *value_as_ScatterElements() const { + return value_type() == mindspore::schema::PrimitiveType_ScatterElements ? static_cast(value()) : nullptr; + } + const mindspore::schema::Triu *value_as_Triu() const { + return value_type() == mindspore::schema::PrimitiveType_Triu ? static_cast(value()) : nullptr; + } + const mindspore::schema::Tril *value_as_Tril() const { + return value_type() == mindspore::schema::PrimitiveType_Tril ? static_cast(value()) : nullptr; + } + const mindspore::schema::AdamWeightDecay *value_as_AdamWeightDecay() const { + return value_type() == mindspore::schema::PrimitiveType_AdamWeightDecay ? static_cast(value()) : nullptr; + } + void *mutable_value() { + return GetPointer(VT_VALUE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VALUE_TYPE) && + VerifyOffset(verifier, VT_VALUE) && + VerifyPrimitiveType(verifier, value(), value_type()) && + verifier.EndTable(); + } + PrimitiveT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PrimitiveT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PrimitiveT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const mindspore::schema::Abs *Primitive::value_as() const { + return value_as_Abs(); +} + +template<> inline const mindspore::schema::Activation *Primitive::value_as() const { + return value_as_Activation(); +} + +template<> inline const mindspore::schema::ActivationGrad *Primitive::value_as() const { + return value_as_ActivationGrad(); +} + +template<> inline const mindspore::schema::Adam *Primitive::value_as() const { + return value_as_Adam(); +} + +template<> inline const mindspore::schema::AddFusion *Primitive::value_as() const { + return value_as_AddFusion(); +} + +template<> inline const mindspore::schema::AdderFusion *Primitive::value_as() const { + return value_as_AdderFusion(); +} + +template<> inline const mindspore::schema::AddGrad *Primitive::value_as() const { + return value_as_AddGrad(); +} + +template<> inline const mindspore::schema::AddN *Primitive::value_as() const { + return value_as_AddN(); +} + +template<> inline const mindspore::schema::All *Primitive::value_as() const { + return value_as_All(); +} + +template<> inline const mindspore::schema::ApplyMomentum *Primitive::value_as() const { + return value_as_ApplyMomentum(); +} + +template<> inline const mindspore::schema::ArgMaxFusion *Primitive::value_as() const { + return value_as_ArgMaxFusion(); +} + +template<> inline const mindspore::schema::ArgMinFusion *Primitive::value_as() const { + return value_as_ArgMinFusion(); +} + +template<> inline const mindspore::schema::Assert *Primitive::value_as() const { + return value_as_Assert(); +} + +template<> inline const mindspore::schema::Assign *Primitive::value_as() const { + return value_as_Assign(); +} + +template<> inline const mindspore::schema::AssignAdd *Primitive::value_as() const { + return value_as_AssignAdd(); +} + +template<> inline const mindspore::schema::AudioSpectrogram *Primitive::value_as() const { + return value_as_AudioSpectrogram(); +} + +template<> inline const mindspore::schema::AvgPoolFusion *Primitive::value_as() const { + return value_as_AvgPoolFusion(); +} + +template<> inline const mindspore::schema::AvgPoolGrad *Primitive::value_as() const { + return value_as_AvgPoolGrad(); +} + +template<> inline const mindspore::schema::BatchNorm *Primitive::value_as() const { + return value_as_BatchNorm(); +} + +template<> inline const mindspore::schema::BatchNormGrad *Primitive::value_as() const { + return value_as_BatchNormGrad(); +} + +template<> inline const mindspore::schema::BatchToSpace *Primitive::value_as() const { + return value_as_BatchToSpace(); +} + +template<> inline const mindspore::schema::BatchToSpaceND *Primitive::value_as() const { + return value_as_BatchToSpaceND(); +} + +template<> inline const mindspore::schema::BiasAdd *Primitive::value_as() const { + return value_as_BiasAdd(); +} + +template<> inline const mindspore::schema::BinaryCrossEntropy *Primitive::value_as() const { + return value_as_BinaryCrossEntropy(); +} + +template<> inline const mindspore::schema::BinaryCrossEntropyGrad *Primitive::value_as() const { + return value_as_BinaryCrossEntropyGrad(); +} + +template<> inline const mindspore::schema::BiasAddGrad *Primitive::value_as() const { + return value_as_BiasAddGrad(); +} + +template<> inline const mindspore::schema::BroadcastTo *Primitive::value_as() const { + return value_as_BroadcastTo(); +} + +template<> inline const mindspore::schema::Cast *Primitive::value_as() const { + return value_as_Cast(); +} + +template<> inline const mindspore::schema::Ceil *Primitive::value_as() const { + return value_as_Ceil(); +} + +template<> inline const mindspore::schema::Clip *Primitive::value_as() const { + return value_as_Clip(); +} + +template<> inline const mindspore::schema::Concat *Primitive::value_as() const { + return value_as_Concat(); +} + +template<> inline const mindspore::schema::Attention *Primitive::value_as() const { + return value_as_Attention(); +} + +template<> inline const mindspore::schema::Conv2DBackpropFilterFusion *Primitive::value_as() const { + return value_as_Conv2DBackpropFilterFusion(); +} + +template<> inline const mindspore::schema::Conv2DBackpropInputFusion *Primitive::value_as() const { + return value_as_Conv2DBackpropInputFusion(); +} + +template<> inline const mindspore::schema::Conv2DFusion *Primitive::value_as() const { + return value_as_Conv2DFusion(); +} + +template<> inline const mindspore::schema::Conv2dTransposeFusion *Primitive::value_as() const { + return value_as_Conv2dTransposeFusion(); +} + +template<> inline const mindspore::schema::Cos *Primitive::value_as() const { + return value_as_Cos(); +} + +template<> inline const mindspore::schema::ConstantOfShape *Primitive::value_as() const { + return value_as_ConstantOfShape(); +} + +template<> inline const mindspore::schema::Crop *Primitive::value_as() const { + return value_as_Crop(); +} + +template<> inline const mindspore::schema::CustomExtractFeatures *Primitive::value_as() const { + return value_as_CustomExtractFeatures(); +} + +template<> inline const mindspore::schema::CustomNormalize *Primitive::value_as() const { + return value_as_CustomNormalize(); +} + +template<> inline const mindspore::schema::CustomPredict *Primitive::value_as() const { + return value_as_CustomPredict(); +} + +template<> inline const mindspore::schema::DeConv2DGradFilter *Primitive::value_as() const { + return value_as_DeConv2DGradFilter(); +} + +template<> inline const mindspore::schema::Depend *Primitive::value_as() const { + return value_as_Depend(); +} + +template<> inline const mindspore::schema::DepthToSpace *Primitive::value_as() const { + return value_as_DepthToSpace(); +} + +template<> inline const mindspore::schema::DetectionPostProcess *Primitive::value_as() const { + return value_as_DetectionPostProcess(); +} + +template<> inline const mindspore::schema::DivFusion *Primitive::value_as() const { + return value_as_DivFusion(); +} + +template<> inline const mindspore::schema::DivGrad *Primitive::value_as() const { + return value_as_DivGrad(); +} + +template<> inline const mindspore::schema::Dropout *Primitive::value_as() const { + return value_as_Dropout(); +} + +template<> inline const mindspore::schema::DropoutGrad *Primitive::value_as() const { + return value_as_DropoutGrad(); +} + +template<> inline const mindspore::schema::Elu *Primitive::value_as() const { + return value_as_Elu(); +} + +template<> inline const mindspore::schema::Eltwise *Primitive::value_as() const { + return value_as_Eltwise(); +} + +template<> inline const mindspore::schema::Equal *Primitive::value_as() const { + return value_as_Equal(); +} + +template<> inline const mindspore::schema::EmbeddingLookupFusion *Primitive::value_as() const { + return value_as_EmbeddingLookupFusion(); +} + +template<> inline const mindspore::schema::ExpFusion *Primitive::value_as() const { + return value_as_ExpFusion(); +} + +template<> inline const mindspore::schema::ExpandDims *Primitive::value_as() const { + return value_as_ExpandDims(); +} + +template<> inline const mindspore::schema::FakeQuantWithMinMaxVars *Primitive::value_as() const { + return value_as_FakeQuantWithMinMaxVars(); +} + +template<> inline const mindspore::schema::FakeQuantWithMinMaxVarsPerChannel *Primitive::value_as() const { + return value_as_FakeQuantWithMinMaxVarsPerChannel(); +} + +template<> inline const mindspore::schema::FftReal *Primitive::value_as() const { + return value_as_FftReal(); +} + +template<> inline const mindspore::schema::FftImag *Primitive::value_as() const { + return value_as_FftImag(); +} + +template<> inline const mindspore::schema::Flatten *Primitive::value_as() const { + return value_as_Flatten(); +} + +template<> inline const mindspore::schema::FlattenGrad *Primitive::value_as() const { + return value_as_FlattenGrad(); +} + +template<> inline const mindspore::schema::Floor *Primitive::value_as() const { + return value_as_Floor(); +} + +template<> inline const mindspore::schema::FloorDiv *Primitive::value_as() const { + return value_as_FloorDiv(); +} + +template<> inline const mindspore::schema::FloorMod *Primitive::value_as() const { + return value_as_FloorMod(); +} + +template<> inline const mindspore::schema::Fill *Primitive::value_as() const { + return value_as_Fill(); +} + +template<> inline const mindspore::schema::FullConnection *Primitive::value_as() const { + return value_as_FullConnection(); +} + +template<> inline const mindspore::schema::FusedBatchNorm *Primitive::value_as() const { + return value_as_FusedBatchNorm(); +} + +template<> inline const mindspore::schema::Gather *Primitive::value_as() const { + return value_as_Gather(); +} + +template<> inline const mindspore::schema::GatherNd *Primitive::value_as() const { + return value_as_GatherNd(); +} + +template<> inline const mindspore::schema::Greater *Primitive::value_as() const { + return value_as_Greater(); +} + +template<> inline const mindspore::schema::GreaterEqual *Primitive::value_as() const { + return value_as_GreaterEqual(); +} + +template<> inline const mindspore::schema::HashtableLookup *Primitive::value_as() const { + return value_as_HashtableLookup(); +} + +template<> inline const mindspore::schema::InstanceNorm *Primitive::value_as() const { + return value_as_InstanceNorm(); +} + +template<> inline const mindspore::schema::LayerNormFusion *Primitive::value_as() const { + return value_as_LayerNormFusion(); +} + +template<> inline const mindspore::schema::LeakyRelu *Primitive::value_as() const { + return value_as_LeakyRelu(); +} + +template<> inline const mindspore::schema::Less *Primitive::value_as() const { + return value_as_Less(); +} + +template<> inline const mindspore::schema::LessEqual *Primitive::value_as() const { + return value_as_LessEqual(); +} + +template<> inline const mindspore::schema::Log *Primitive::value_as() const { + return value_as_Log(); +} + +template<> inline const mindspore::schema::LogGrad *Primitive::value_as() const { + return value_as_LogGrad(); +} + +template<> inline const mindspore::schema::LogicalAnd *Primitive::value_as() const { + return value_as_LogicalAnd(); +} + +template<> inline const mindspore::schema::LogicalNot *Primitive::value_as() const { + return value_as_LogicalNot(); +} + +template<> inline const mindspore::schema::LogicalOr *Primitive::value_as() const { + return value_as_LogicalOr(); +} + +template<> inline const mindspore::schema::LpNormalization *Primitive::value_as() const { + return value_as_LpNormalization(); +} + +template<> inline const mindspore::schema::LRN *Primitive::value_as() const { + return value_as_LRN(); +} + +template<> inline const mindspore::schema::LshProjection *Primitive::value_as() const { + return value_as_LshProjection(); +} + +template<> inline const mindspore::schema::LSTM *Primitive::value_as() const { + return value_as_LSTM(); +} + +template<> inline const mindspore::schema::L2NormalizeFusion *Primitive::value_as() const { + return value_as_L2NormalizeFusion(); +} + +template<> inline const mindspore::schema::MatMulFusion *Primitive::value_as() const { + return value_as_MatMulFusion(); +} + +template<> inline const mindspore::schema::Maximum *Primitive::value_as() const { + return value_as_Maximum(); +} + +template<> inline const mindspore::schema::MaximumGrad *Primitive::value_as() const { + return value_as_MaximumGrad(); +} + +template<> inline const mindspore::schema::MaxPoolFusion *Primitive::value_as() const { + return value_as_MaxPoolFusion(); +} + +template<> inline const mindspore::schema::MaxPoolGrad *Primitive::value_as() const { + return value_as_MaxPoolGrad(); +} + +template<> inline const mindspore::schema::SwitchLayer *Primitive::value_as() const { + return value_as_SwitchLayer(); +} + +template<> inline const mindspore::schema::Mfcc *Primitive::value_as() const { + return value_as_Mfcc(); +} + +template<> inline const mindspore::schema::Minimum *Primitive::value_as() const { + return value_as_Minimum(); +} + +template<> inline const mindspore::schema::MinimumGrad *Primitive::value_as() const { + return value_as_MinimumGrad(); +} + +template<> inline const mindspore::schema::Mod *Primitive::value_as() const { + return value_as_Mod(); +} + +template<> inline const mindspore::schema::MulFusion *Primitive::value_as() const { + return value_as_MulFusion(); +} + +template<> inline const mindspore::schema::MulGrad *Primitive::value_as() const { + return value_as_MulGrad(); +} + +template<> inline const mindspore::schema::Neg *Primitive::value_as() const { + return value_as_Neg(); +} + +template<> inline const mindspore::schema::NegGrad *Primitive::value_as() const { + return value_as_NegGrad(); +} + +template<> inline const mindspore::schema::NotEqual *Primitive::value_as() const { + return value_as_NotEqual(); +} + +template<> inline const mindspore::schema::NonMaxSuppression *Primitive::value_as() const { + return value_as_NonMaxSuppression(); +} + +template<> inline const mindspore::schema::OneHot *Primitive::value_as() const { + return value_as_OneHot(); +} + +template<> inline const mindspore::schema::OnesLike *Primitive::value_as() const { + return value_as_OnesLike(); +} + +template<> inline const mindspore::schema::PadFusion *Primitive::value_as() const { + return value_as_PadFusion(); +} + +template<> inline const mindspore::schema::PartialFusion *Primitive::value_as() const { + return value_as_PartialFusion(); +} + +template<> inline const mindspore::schema::PowerGrad *Primitive::value_as() const { + return value_as_PowerGrad(); +} + +template<> inline const mindspore::schema::PowFusion *Primitive::value_as() const { + return value_as_PowFusion(); +} + +template<> inline const mindspore::schema::PriorBox *Primitive::value_as() const { + return value_as_PriorBox(); +} + +template<> inline const mindspore::schema::PReLUFusion *Primitive::value_as() const { + return value_as_PReLUFusion(); +} + +template<> inline const mindspore::schema::QuantDTypeCast *Primitive::value_as() const { + return value_as_QuantDTypeCast(); +} + +template<> inline const mindspore::schema::Rank *Primitive::value_as() const { + return value_as_Rank(); +} + +template<> inline const mindspore::schema::Range *Primitive::value_as() const { + return value_as_Range(); +} + +template<> inline const mindspore::schema::Reciprocal *Primitive::value_as() const { + return value_as_Reciprocal(); +} + +template<> inline const mindspore::schema::RealDiv *Primitive::value_as() const { + return value_as_RealDiv(); +} + +template<> inline const mindspore::schema::ReduceFusion *Primitive::value_as() const { + return value_as_ReduceFusion(); +} + +template<> inline const mindspore::schema::Reshape *Primitive::value_as() const { + return value_as_Reshape(); +} + +template<> inline const mindspore::schema::Resize *Primitive::value_as() const { + return value_as_Resize(); +} + +template<> inline const mindspore::schema::ReverseSequence *Primitive::value_as() const { + return value_as_ReverseSequence(); +} + +template<> inline const mindspore::schema::ReverseV2 *Primitive::value_as() const { + return value_as_ReverseV2(); +} + +template<> inline const mindspore::schema::Rfft *Primitive::value_as() const { + return value_as_Rfft(); +} + +template<> inline const mindspore::schema::ROIPooling *Primitive::value_as() const { + return value_as_ROIPooling(); +} + +template<> inline const mindspore::schema::Round *Primitive::value_as() const { + return value_as_Round(); +} + +template<> inline const mindspore::schema::Rsqrt *Primitive::value_as() const { + return value_as_Rsqrt(); +} + +template<> inline const mindspore::schema::ScaleFusion *Primitive::value_as() const { + return value_as_ScaleFusion(); +} + +template<> inline const mindspore::schema::ScatterNd *Primitive::value_as() const { + return value_as_ScatterNd(); +} + +template<> inline const mindspore::schema::SGD *Primitive::value_as() const { + return value_as_SGD(); +} + +template<> inline const mindspore::schema::Shape *Primitive::value_as() const { + return value_as_Shape(); +} + +template<> inline const mindspore::schema::SigmoidCrossEntropyWithLogits *Primitive::value_as() const { + return value_as_SigmoidCrossEntropyWithLogits(); +} + +template<> inline const mindspore::schema::SigmoidCrossEntropyWithLogitsGrad *Primitive::value_as() const { + return value_as_SigmoidCrossEntropyWithLogitsGrad(); +} + +template<> inline const mindspore::schema::Sin *Primitive::value_as() const { + return value_as_Sin(); +} + +template<> inline const mindspore::schema::SkipGram *Primitive::value_as() const { + return value_as_SkipGram(); +} + +template<> inline const mindspore::schema::SliceFusion *Primitive::value_as() const { + return value_as_SliceFusion(); +} + +template<> inline const mindspore::schema::SmoothL1Loss *Primitive::value_as() const { + return value_as_SmoothL1Loss(); +} + +template<> inline const mindspore::schema::SmoothL1LossGrad *Primitive::value_as() const { + return value_as_SmoothL1LossGrad(); +} + +template<> inline const mindspore::schema::Softmax *Primitive::value_as() const { + return value_as_Softmax(); +} + +template<> inline const mindspore::schema::SoftmaxCrossEntropyWithLogits *Primitive::value_as() const { + return value_as_SoftmaxCrossEntropyWithLogits(); +} + +template<> inline const mindspore::schema::SpaceToBatch *Primitive::value_as() const { + return value_as_SpaceToBatch(); +} + +template<> inline const mindspore::schema::SpaceToBatchND *Primitive::value_as() const { + return value_as_SpaceToBatchND(); +} + +template<> inline const mindspore::schema::SpaceToDepth *Primitive::value_as() const { + return value_as_SpaceToDepth(); +} + +template<> inline const mindspore::schema::SparseSoftmaxCrossEntropyWithLogits *Primitive::value_as() const { + return value_as_SparseSoftmaxCrossEntropyWithLogits(); +} + +template<> inline const mindspore::schema::SparseToDense *Primitive::value_as() const { + return value_as_SparseToDense(); +} + +template<> inline const mindspore::schema::Split *Primitive::value_as() const { + return value_as_Split(); +} + +template<> inline const mindspore::schema::Sqrt *Primitive::value_as() const { + return value_as_Sqrt(); +} + +template<> inline const mindspore::schema::Squeeze *Primitive::value_as() const { + return value_as_Squeeze(); +} + +template<> inline const mindspore::schema::Square *Primitive::value_as() const { + return value_as_Square(); +} + +template<> inline const mindspore::schema::SquaredDifference *Primitive::value_as() const { + return value_as_SquaredDifference(); +} + +template<> inline const mindspore::schema::Stack *Primitive::value_as() const { + return value_as_Stack(); +} + +template<> inline const mindspore::schema::StridedSlice *Primitive::value_as() const { + return value_as_StridedSlice(); +} + +template<> inline const mindspore::schema::SubFusion *Primitive::value_as() const { + return value_as_SubFusion(); +} + +template<> inline const mindspore::schema::SubGrad *Primitive::value_as() const { + return value_as_SubGrad(); +} + +template<> inline const mindspore::schema::Switch *Primitive::value_as() const { + return value_as_Switch(); +} + +template<> inline const mindspore::schema::TensorListFromTensor *Primitive::value_as() const { + return value_as_TensorListFromTensor(); +} + +template<> inline const mindspore::schema::TensorListGetItem *Primitive::value_as() const { + return value_as_TensorListGetItem(); +} + +template<> inline const mindspore::schema::TensorListReserve *Primitive::value_as() const { + return value_as_TensorListReserve(); +} + +template<> inline const mindspore::schema::TensorListSetItem *Primitive::value_as() const { + return value_as_TensorListSetItem(); +} + +template<> inline const mindspore::schema::TensorListStack *Primitive::value_as() const { + return value_as_TensorListStack(); +} + +template<> inline const mindspore::schema::TileFusion *Primitive::value_as() const { + return value_as_TileFusion(); +} + +template<> inline const mindspore::schema::TopKFusion *Primitive::value_as() const { + return value_as_TopKFusion(); +} + +template<> inline const mindspore::schema::Transpose *Primitive::value_as() const { + return value_as_Transpose(); +} + +template<> inline const mindspore::schema::Unique *Primitive::value_as() const { + return value_as_Unique(); +} + +template<> inline const mindspore::schema::UnsortedSegmentSum *Primitive::value_as() const { + return value_as_UnsortedSegmentSum(); +} + +template<> inline const mindspore::schema::Unsqueeze *Primitive::value_as() const { + return value_as_Unsqueeze(); +} + +template<> inline const mindspore::schema::Unstack *Primitive::value_as() const { + return value_as_Unstack(); +} + +template<> inline const mindspore::schema::LSTMGrad *Primitive::value_as() const { + return value_as_LSTMGrad(); +} + +template<> inline const mindspore::schema::Where *Primitive::value_as() const { + return value_as_Where(); +} + +template<> inline const mindspore::schema::ZerosLike *Primitive::value_as() const { + return value_as_ZerosLike(); +} + +template<> inline const mindspore::schema::Select *Primitive::value_as() const { + return value_as_Select(); +} + +template<> inline const mindspore::schema::ScatterNdUpdate *Primitive::value_as() const { + return value_as_ScatterNdUpdate(); +} + +template<> inline const mindspore::schema::GRU *Primitive::value_as() const { + return value_as_GRU(); +} + +template<> inline const mindspore::schema::NonZero *Primitive::value_as() const { + return value_as_NonZero(); +} + +template<> inline const mindspore::schema::InvertPermutation *Primitive::value_as() const { + return value_as_InvertPermutation(); +} + +template<> inline const mindspore::schema::Size *Primitive::value_as() const { + return value_as_Size(); +} + +template<> inline const mindspore::schema::RandomStandardNormal *Primitive::value_as() const { + return value_as_RandomStandardNormal(); +} + +template<> inline const mindspore::schema::CropAndResize *Primitive::value_as() const { + return value_as_CropAndResize(); +} + +template<> inline const mindspore::schema::Erf *Primitive::value_as() const { + return value_as_Erf(); +} + +template<> inline const mindspore::schema::StridedSliceGrad *Primitive::value_as() const { + return value_as_StridedSliceGrad(); +} + +template<> inline const mindspore::schema::IsFinite *Primitive::value_as() const { + return value_as_IsFinite(); +} + +template<> inline const mindspore::schema::LinSpace *Primitive::value_as() const { + return value_as_LinSpace(); +} + +template<> inline const mindspore::schema::UniformReal *Primitive::value_as() const { + return value_as_UniformReal(); +} + +template<> inline const mindspore::schema::AbsGrad *Primitive::value_as() const { + return value_as_AbsGrad(); +} + +template<> inline const mindspore::schema::RsqrtGrad *Primitive::value_as() const { + return value_as_RsqrtGrad(); +} + +template<> inline const mindspore::schema::SqrtGrad *Primitive::value_as() const { + return value_as_SqrtGrad(); +} + +template<> inline const mindspore::schema::LayerNormGrad *Primitive::value_as() const { + return value_as_LayerNormGrad(); +} + +template<> inline const mindspore::schema::ResizeGrad *Primitive::value_as() const { + return value_as_ResizeGrad(); +} + +template<> inline const mindspore::schema::Splice *Primitive::value_as() const { + return value_as_Splice(); +} + +template<> inline const mindspore::schema::LogSoftmax *Primitive::value_as() const { + return value_as_LogSoftmax(); +} + +template<> inline const mindspore::schema::Call *Primitive::value_as() const { + return value_as_Call(); +} + +template<> inline const mindspore::schema::Custom *Primitive::value_as() const { + return value_as_Custom(); +} + +template<> inline const mindspore::schema::CumSum *Primitive::value_as() const { + return value_as_CumSum(); +} + +template<> inline const mindspore::schema::SplitWithOverlap *Primitive::value_as() const { + return value_as_SplitWithOverlap(); +} + +template<> inline const mindspore::schema::GenOP *Primitive::value_as() const { + return value_as_GenOP(); +} + +template<> inline const mindspore::schema::RaggedRange *Primitive::value_as() const { + return value_as_RaggedRange(); +} + +template<> inline const mindspore::schema::GLU *Primitive::value_as() const { + return value_as_GLU(); +} + +template<> inline const mindspore::schema::TensorArray *Primitive::value_as() const { + return value_as_TensorArray(); +} + +template<> inline const mindspore::schema::TensorArrayRead *Primitive::value_as() const { + return value_as_TensorArrayRead(); +} + +template<> inline const mindspore::schema::TensorArrayWrite *Primitive::value_as() const { + return value_as_TensorArrayWrite(); +} + +template<> inline const mindspore::schema::Affine *Primitive::value_as() const { + return value_as_Affine(); +} + +template<> inline const mindspore::schema::AllGather *Primitive::value_as() const { + return value_as_AllGather(); +} + +template<> inline const mindspore::schema::ReduceScatter *Primitive::value_as() const { + return value_as_ReduceScatter(); +} + +template<> inline const mindspore::schema::DynamicQuant *Primitive::value_as() const { + return value_as_DynamicQuant(); +} + +template<> inline const mindspore::schema::LSTMGradData *Primitive::value_as() const { + return value_as_LSTMGradData(); +} + +template<> inline const mindspore::schema::LSTMGradWeight *Primitive::value_as() const { + return value_as_LSTMGradWeight(); +} + +template<> inline const mindspore::schema::RandomNormal *Primitive::value_as() const { + return value_as_RandomNormal(); +} + +template<> inline const mindspore::schema::NLLLoss *Primitive::value_as() const { + return value_as_NLLLoss(); +} + +template<> inline const mindspore::schema::NLLLossGrad *Primitive::value_as() const { + return value_as_NLLLossGrad(); +} + +template<> inline const mindspore::schema::FormatTranspose *Primitive::value_as() const { + return value_as_FormatTranspose(); +} + +template<> inline const mindspore::schema::GatherD *Primitive::value_as() const { + return value_as_GatherD(); +} + +template<> inline const mindspore::schema::GroupNormFusion *Primitive::value_as() const { + return value_as_GroupNormFusion(); +} + +template<> inline const mindspore::schema::Log1p *Primitive::value_as() const { + return value_as_Log1p(); +} + +template<> inline const mindspore::schema::TensorScatterAdd *Primitive::value_as() const { + return value_as_TensorScatterAdd(); +} + +template<> inline const mindspore::schema::SparseFillEmptyRows *Primitive::value_as() const { + return value_as_SparseFillEmptyRows(); +} + +template<> inline const mindspore::schema::SparseReshape *Primitive::value_as() const { + return value_as_SparseReshape(); +} + +template<> inline const mindspore::schema::SparseSegmentSum *Primitive::value_as() const { + return value_as_SparseSegmentSum(); +} + +template<> inline const mindspore::schema::ScatterElements *Primitive::value_as() const { + return value_as_ScatterElements(); +} + +template<> inline const mindspore::schema::Triu *Primitive::value_as() const { + return value_as_Triu(); +} + +template<> inline const mindspore::schema::Tril *Primitive::value_as() const { + return value_as_Tril(); +} + +template<> inline const mindspore::schema::AdamWeightDecay *Primitive::value_as() const { + return value_as_AdamWeightDecay(); +} + +struct PrimitiveBuilder { + typedef Primitive Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_value_type(mindspore::schema::PrimitiveType value_type) { + fbb_.AddElement(Primitive::VT_VALUE_TYPE, static_cast(value_type), 0); + } + void add_value(flatbuffers::Offset value) { + fbb_.AddOffset(Primitive::VT_VALUE, value); + } + explicit PrimitiveBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePrimitive( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::PrimitiveType value_type = mindspore::schema::PrimitiveType_NONE, + flatbuffers::Offset value = 0) { + PrimitiveBuilder builder_(_fbb); + builder_.add_value(value); + builder_.add_value_type(value_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePrimitive(flatbuffers::FlatBufferBuilder &_fbb, const PrimitiveT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CNodeT : public flatbuffers::NativeTable { + typedef CNode TableType; + std::string name{}; + std::unique_ptr primitive{}; + std::vector inputIndex{}; + std::vector outputIndex{}; + mindspore::schema::QuantType quantType = mindspore::schema::QuantType_QUANT_NONE; + int32_t deviceType = -1; +}; + +struct CNode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CNodeT NativeTableType; + typedef CNodeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CNodeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_PRIMITIVE = 8, + VT_INPUTINDEX = 10, + VT_OUTPUTINDEX = 12, + VT_QUANTTYPE = 14, + VT_DEVICETYPE = 16 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + const mindspore::schema::Primitive *primitive() const { + return GetPointer(VT_PRIMITIVE); + } + mindspore::schema::Primitive *mutable_primitive() { + return GetPointer(VT_PRIMITIVE); + } + const flatbuffers::Vector *inputIndex() const { + return GetPointer *>(VT_INPUTINDEX); + } + flatbuffers::Vector *mutable_inputIndex() { + return GetPointer *>(VT_INPUTINDEX); + } + const flatbuffers::Vector *outputIndex() const { + return GetPointer *>(VT_OUTPUTINDEX); + } + flatbuffers::Vector *mutable_outputIndex() { + return GetPointer *>(VT_OUTPUTINDEX); + } + mindspore::schema::QuantType quantType() const { + return static_cast(GetField(VT_QUANTTYPE, 0)); + } + bool mutate_quantType(mindspore::schema::QuantType _quantType) { + return SetField(VT_QUANTTYPE, static_cast(_quantType), 0); + } + int32_t deviceType() const { + return GetField(VT_DEVICETYPE, -1); + } + bool mutate_deviceType(int32_t _deviceType) { + return SetField(VT_DEVICETYPE, _deviceType, -1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_PRIMITIVE) && + verifier.VerifyTable(primitive()) && + VerifyOffset(verifier, VT_INPUTINDEX) && + verifier.VerifyVector(inputIndex()) && + VerifyOffset(verifier, VT_OUTPUTINDEX) && + verifier.VerifyVector(outputIndex()) && + VerifyField(verifier, VT_QUANTTYPE) && + VerifyField(verifier, VT_DEVICETYPE) && + verifier.EndTable(); + } + CNodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CNodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CNodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CNodeBuilder { + typedef CNode Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(CNode::VT_NAME, name); + } + void add_primitive(flatbuffers::Offset primitive) { + fbb_.AddOffset(CNode::VT_PRIMITIVE, primitive); + } + void add_inputIndex(flatbuffers::Offset> inputIndex) { + fbb_.AddOffset(CNode::VT_INPUTINDEX, inputIndex); + } + void add_outputIndex(flatbuffers::Offset> outputIndex) { + fbb_.AddOffset(CNode::VT_OUTPUTINDEX, outputIndex); + } + void add_quantType(mindspore::schema::QuantType quantType) { + fbb_.AddElement(CNode::VT_QUANTTYPE, static_cast(quantType), 0); + } + void add_deviceType(int32_t deviceType) { + fbb_.AddElement(CNode::VT_DEVICETYPE, deviceType, -1); + } + explicit CNodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCNode( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset primitive = 0, + flatbuffers::Offset> inputIndex = 0, + flatbuffers::Offset> outputIndex = 0, + mindspore::schema::QuantType quantType = mindspore::schema::QuantType_QUANT_NONE, + int32_t deviceType = -1) { + CNodeBuilder builder_(_fbb); + builder_.add_deviceType(deviceType); + builder_.add_quantType(quantType); + builder_.add_outputIndex(outputIndex); + builder_.add_inputIndex(inputIndex); + builder_.add_primitive(primitive); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateCNodeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + flatbuffers::Offset primitive = 0, + const std::vector *inputIndex = nullptr, + const std::vector *outputIndex = nullptr, + mindspore::schema::QuantType quantType = mindspore::schema::QuantType_QUANT_NONE, + int32_t deviceType = -1) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto inputIndex__ = inputIndex ? _fbb.CreateVector(*inputIndex) : 0; + auto outputIndex__ = outputIndex ? _fbb.CreateVector(*outputIndex) : 0; + return mindspore::schema::CreateCNode( + _fbb, + name__, + primitive, + inputIndex__, + outputIndex__, + quantType, + deviceType); +} + +flatbuffers::Offset CreateCNode(flatbuffers::FlatBufferBuilder &_fbb, const CNodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubGraphT : public flatbuffers::NativeTable { + typedef SubGraph TableType; + std::string name{}; + std::vector inputIndices{}; + std::vector outputIndices{}; + std::vector nodeIndices{}; + std::vector tensorIndices{}; +}; + +struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubGraphT NativeTableType; + typedef SubGraphBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SubGraphTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_INPUTINDICES = 6, + VT_OUTPUTINDICES = 8, + VT_NODEINDICES = 10, + VT_TENSORINDICES = 12 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + const flatbuffers::Vector *inputIndices() const { + return GetPointer *>(VT_INPUTINDICES); + } + flatbuffers::Vector *mutable_inputIndices() { + return GetPointer *>(VT_INPUTINDICES); + } + const flatbuffers::Vector *outputIndices() const { + return GetPointer *>(VT_OUTPUTINDICES); + } + flatbuffers::Vector *mutable_outputIndices() { + return GetPointer *>(VT_OUTPUTINDICES); + } + const flatbuffers::Vector *nodeIndices() const { + return GetPointer *>(VT_NODEINDICES); + } + flatbuffers::Vector *mutable_nodeIndices() { + return GetPointer *>(VT_NODEINDICES); + } + const flatbuffers::Vector *tensorIndices() const { + return GetPointer *>(VT_TENSORINDICES); + } + flatbuffers::Vector *mutable_tensorIndices() { + return GetPointer *>(VT_TENSORINDICES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_INPUTINDICES) && + verifier.VerifyVector(inputIndices()) && + VerifyOffset(verifier, VT_OUTPUTINDICES) && + verifier.VerifyVector(outputIndices()) && + VerifyOffset(verifier, VT_NODEINDICES) && + verifier.VerifyVector(nodeIndices()) && + VerifyOffset(verifier, VT_TENSORINDICES) && + verifier.VerifyVector(tensorIndices()) && + verifier.EndTable(); + } + SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubGraphBuilder { + typedef SubGraph Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(SubGraph::VT_NAME, name); + } + void add_inputIndices(flatbuffers::Offset> inputIndices) { + fbb_.AddOffset(SubGraph::VT_INPUTINDICES, inputIndices); + } + void add_outputIndices(flatbuffers::Offset> outputIndices) { + fbb_.AddOffset(SubGraph::VT_OUTPUTINDICES, outputIndices); + } + void add_nodeIndices(flatbuffers::Offset> nodeIndices) { + fbb_.AddOffset(SubGraph::VT_NODEINDICES, nodeIndices); + } + void add_tensorIndices(flatbuffers::Offset> tensorIndices) { + fbb_.AddOffset(SubGraph::VT_TENSORINDICES, tensorIndices); + } + explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubGraph( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset> inputIndices = 0, + flatbuffers::Offset> outputIndices = 0, + flatbuffers::Offset> nodeIndices = 0, + flatbuffers::Offset> tensorIndices = 0) { + SubGraphBuilder builder_(_fbb); + builder_.add_tensorIndices(tensorIndices); + builder_.add_nodeIndices(nodeIndices); + builder_.add_outputIndices(outputIndices); + builder_.add_inputIndices(inputIndices); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSubGraphDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + const std::vector *inputIndices = nullptr, + const std::vector *outputIndices = nullptr, + const std::vector *nodeIndices = nullptr, + const std::vector *tensorIndices = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto inputIndices__ = inputIndices ? _fbb.CreateVector(*inputIndices) : 0; + auto outputIndices__ = outputIndices ? _fbb.CreateVector(*outputIndices) : 0; + auto nodeIndices__ = nodeIndices ? _fbb.CreateVector(*nodeIndices) : 0; + auto tensorIndices__ = tensorIndices ? _fbb.CreateVector(*tensorIndices) : 0; + return mindspore::schema::CreateSubGraph( + _fbb, + name__, + inputIndices__, + outputIndices__, + nodeIndices__, + tensorIndices__); +} + +flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MetaGraphT : public flatbuffers::NativeTable { + typedef MetaGraph TableType; + std::string name{}; + std::string version{}; + int32_t fmkType = 0; + std::vector inputIndex{}; + std::vector outputIndex{}; + uint32_t mempoolSize = 0; + std::vector> nodes{}; + std::vector> allTensors{}; + std::vector> subGraph{}; + bool obfuscate = false; + std::vector obfMetaData{}; +}; + +struct MetaGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MetaGraphT NativeTableType; + typedef MetaGraphBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MetaGraphTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_VERSION = 6, + VT_FMKTYPE = 8, + VT_INPUTINDEX = 10, + VT_OUTPUTINDEX = 12, + VT_MEMPOOLSIZE = 14, + VT_NODES = 16, + VT_ALLTENSORS = 18, + VT_SUBGRAPH = 20, + VT_OBFUSCATE = 22, + VT_OBFMETADATA = 24 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + const flatbuffers::String *version() const { + return GetPointer(VT_VERSION); + } + flatbuffers::String *mutable_version() { + return GetPointer(VT_VERSION); + } + int32_t fmkType() const { + return GetField(VT_FMKTYPE, 0); + } + bool mutate_fmkType(int32_t _fmkType) { + return SetField(VT_FMKTYPE, _fmkType, 0); + } + const flatbuffers::Vector *inputIndex() const { + return GetPointer *>(VT_INPUTINDEX); + } + flatbuffers::Vector *mutable_inputIndex() { + return GetPointer *>(VT_INPUTINDEX); + } + const flatbuffers::Vector *outputIndex() const { + return GetPointer *>(VT_OUTPUTINDEX); + } + flatbuffers::Vector *mutable_outputIndex() { + return GetPointer *>(VT_OUTPUTINDEX); + } + uint32_t mempoolSize() const { + return GetField(VT_MEMPOOLSIZE, 0); + } + bool mutate_mempoolSize(uint32_t _mempoolSize) { + return SetField(VT_MEMPOOLSIZE, _mempoolSize, 0); + } + const flatbuffers::Vector> *nodes() const { + return GetPointer> *>(VT_NODES); + } + flatbuffers::Vector> *mutable_nodes() { + return GetPointer> *>(VT_NODES); + } + const flatbuffers::Vector> *allTensors() const { + return GetPointer> *>(VT_ALLTENSORS); + } + flatbuffers::Vector> *mutable_allTensors() { + return GetPointer> *>(VT_ALLTENSORS); + } + const flatbuffers::Vector> *subGraph() const { + return GetPointer> *>(VT_SUBGRAPH); + } + flatbuffers::Vector> *mutable_subGraph() { + return GetPointer> *>(VT_SUBGRAPH); + } + bool obfuscate() const { + return GetField(VT_OBFUSCATE, 0) != 0; + } + bool mutate_obfuscate(bool _obfuscate) { + return SetField(VT_OBFUSCATE, static_cast(_obfuscate), 0); + } + const flatbuffers::Vector *obfMetaData() const { + return GetPointer *>(VT_OBFMETADATA); + } + flatbuffers::Vector *mutable_obfMetaData() { + return GetPointer *>(VT_OBFMETADATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_VERSION) && + verifier.VerifyString(version()) && + VerifyField(verifier, VT_FMKTYPE) && + VerifyOffset(verifier, VT_INPUTINDEX) && + verifier.VerifyVector(inputIndex()) && + VerifyOffset(verifier, VT_OUTPUTINDEX) && + verifier.VerifyVector(outputIndex()) && + VerifyField(verifier, VT_MEMPOOLSIZE) && + VerifyOffset(verifier, VT_NODES) && + verifier.VerifyVector(nodes()) && + verifier.VerifyVectorOfTables(nodes()) && + VerifyOffset(verifier, VT_ALLTENSORS) && + verifier.VerifyVector(allTensors()) && + verifier.VerifyVectorOfTables(allTensors()) && + VerifyOffset(verifier, VT_SUBGRAPH) && + verifier.VerifyVector(subGraph()) && + verifier.VerifyVectorOfTables(subGraph()) && + VerifyField(verifier, VT_OBFUSCATE) && + VerifyOffset(verifier, VT_OBFMETADATA) && + verifier.VerifyVector(obfMetaData()) && + verifier.EndTable(); + } + MetaGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MetaGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetaGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MetaGraphBuilder { + typedef MetaGraph Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(MetaGraph::VT_NAME, name); + } + void add_version(flatbuffers::Offset version) { + fbb_.AddOffset(MetaGraph::VT_VERSION, version); + } + void add_fmkType(int32_t fmkType) { + fbb_.AddElement(MetaGraph::VT_FMKTYPE, fmkType, 0); + } + void add_inputIndex(flatbuffers::Offset> inputIndex) { + fbb_.AddOffset(MetaGraph::VT_INPUTINDEX, inputIndex); + } + void add_outputIndex(flatbuffers::Offset> outputIndex) { + fbb_.AddOffset(MetaGraph::VT_OUTPUTINDEX, outputIndex); + } + void add_mempoolSize(uint32_t mempoolSize) { + fbb_.AddElement(MetaGraph::VT_MEMPOOLSIZE, mempoolSize, 0); + } + void add_nodes(flatbuffers::Offset>> nodes) { + fbb_.AddOffset(MetaGraph::VT_NODES, nodes); + } + void add_allTensors(flatbuffers::Offset>> allTensors) { + fbb_.AddOffset(MetaGraph::VT_ALLTENSORS, allTensors); + } + void add_subGraph(flatbuffers::Offset>> subGraph) { + fbb_.AddOffset(MetaGraph::VT_SUBGRAPH, subGraph); + } + void add_obfuscate(bool obfuscate) { + fbb_.AddElement(MetaGraph::VT_OBFUSCATE, static_cast(obfuscate), 0); + } + void add_obfMetaData(flatbuffers::Offset> obfMetaData) { + fbb_.AddOffset(MetaGraph::VT_OBFMETADATA, obfMetaData); + } + explicit MetaGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMetaGraph( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset version = 0, + int32_t fmkType = 0, + flatbuffers::Offset> inputIndex = 0, + flatbuffers::Offset> outputIndex = 0, + uint32_t mempoolSize = 0, + flatbuffers::Offset>> nodes = 0, + flatbuffers::Offset>> allTensors = 0, + flatbuffers::Offset>> subGraph = 0, + bool obfuscate = false, + flatbuffers::Offset> obfMetaData = 0) { + MetaGraphBuilder builder_(_fbb); + builder_.add_obfMetaData(obfMetaData); + builder_.add_subGraph(subGraph); + builder_.add_allTensors(allTensors); + builder_.add_nodes(nodes); + builder_.add_mempoolSize(mempoolSize); + builder_.add_outputIndex(outputIndex); + builder_.add_inputIndex(inputIndex); + builder_.add_fmkType(fmkType); + builder_.add_version(version); + builder_.add_name(name); + builder_.add_obfuscate(obfuscate); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateMetaGraphDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + const char *version = nullptr, + int32_t fmkType = 0, + const std::vector *inputIndex = nullptr, + const std::vector *outputIndex = nullptr, + uint32_t mempoolSize = 0, + const std::vector> *nodes = nullptr, + const std::vector> *allTensors = nullptr, + const std::vector> *subGraph = nullptr, + bool obfuscate = false, + const std::vector *obfMetaData = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto version__ = version ? _fbb.CreateString(version) : 0; + auto inputIndex__ = inputIndex ? _fbb.CreateVector(*inputIndex) : 0; + auto outputIndex__ = outputIndex ? _fbb.CreateVector(*outputIndex) : 0; + auto nodes__ = nodes ? _fbb.CreateVector>(*nodes) : 0; + auto allTensors__ = allTensors ? _fbb.CreateVector>(*allTensors) : 0; + auto subGraph__ = subGraph ? _fbb.CreateVector>(*subGraph) : 0; + auto obfMetaData__ = obfMetaData ? _fbb.CreateVector(*obfMetaData) : 0; + return mindspore::schema::CreateMetaGraph( + _fbb, + name__, + version__, + fmkType, + inputIndex__, + outputIndex__, + mempoolSize, + nodes__, + allTensors__, + subGraph__, + obfuscate, + obfMetaData__); +} + +flatbuffers::Offset CreateMetaGraph(flatbuffers::FlatBufferBuilder &_fbb, const MetaGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +inline QuantParamT *QuantParam::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new QuantParamT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void QuantParam::UnPackTo(QuantParamT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = scale(); _o->scale = _e; } + { auto _e = zeroPoint(); _o->zeroPoint = _e; } + { auto _e = min(); _o->min = _e; } + { auto _e = max(); _o->max = _e; } + { auto _e = narrowRange(); _o->narrowRange = _e; } + { auto _e = numBits(); _o->numBits = _e; } + { auto _e = inited(); _o->inited = _e; } + { auto _e = varCorr(); _o->varCorr = _e; } + { auto _e = meanCorr(); _o->meanCorr = _e; } + { auto _e = dstDtype(); _o->dstDtype = _e; } + { auto _e = roundType(); _o->roundType = _e; } + { auto _e = multiplier(); _o->multiplier = _e; } +} + +inline flatbuffers::Offset QuantParam::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantParamT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateQuantParam(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateQuantParam(flatbuffers::FlatBufferBuilder &_fbb, const QuantParamT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantParamT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _scale = _o->scale; + auto _zeroPoint = _o->zeroPoint; + auto _min = _o->min; + auto _max = _o->max; + auto _narrowRange = _o->narrowRange; + auto _numBits = _o->numBits; + auto _inited = _o->inited; + auto _varCorr = _o->varCorr; + auto _meanCorr = _o->meanCorr; + auto _dstDtype = _o->dstDtype; + auto _roundType = _o->roundType; + auto _multiplier = _o->multiplier; + return mindspore::schema::CreateQuantParam( + _fbb, + _scale, + _zeroPoint, + _min, + _max, + _narrowRange, + _numBits, + _inited, + _varCorr, + _meanCorr, + _dstDtype, + _roundType, + _multiplier); +} + +inline ExternalDataT *ExternalData::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ExternalDataT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ExternalData::UnPackTo(ExternalDataT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = checkSum(); if (_e) _o->checkSum = _e->str(); } + { auto _e = location(); if (_e) _o->location = _e->str(); } + { auto _e = offset(); _o->offset = _e; } + { auto _e = length(); _o->length = _e; } +} + +inline flatbuffers::Offset ExternalData::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExternalDataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateExternalData(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateExternalData(flatbuffers::FlatBufferBuilder &_fbb, const ExternalDataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExternalDataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _checkSum = _o->checkSum.empty() ? 0 : _fbb.CreateString(_o->checkSum); + auto _location = _o->location.empty() ? 0 : _fbb.CreateString(_o->location); + auto _offset = _o->offset; + auto _length = _o->length; + return mindspore::schema::CreateExternalData( + _fbb, + _checkSum, + _location, + _offset, + _length); +} + +inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TensorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = nodeType(); _o->nodeType = _e; } + { auto _e = dataType(); _o->dataType = _e; } + { auto _e = dims(); if (_e) { _o->dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->dims[_i] = _e->Get(_i); } } } + { auto _e = format(); _o->format = _e; } + { auto _e = refCount(); _o->refCount = _e; } + { auto _e = offset(); _o->offset = _e; } + { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } } + { auto _e = quantParams(); if (_e) { _o->quantParams.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->quantParams[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = quantClusters(); if (_e) { _o->quantClusters.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->quantClusters[_i] = _e->Get(_i); } } } + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = enableHuffmanCode(); _o->enableHuffmanCode = _e; } + { auto _e = weightQuantCompressType(); _o->weightQuantCompressType = _e; } + { auto _e = externalData(); if (_e) { _o->externalData.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->externalData[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } +} + +inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTensor(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _nodeType = _o->nodeType; + auto _dataType = _o->dataType; + auto _dims = _o->dims.size() ? _fbb.CreateVector(_o->dims) : 0; + auto _format = _o->format; + auto _refCount = _o->refCount; + auto _offset = _o->offset; + auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; + auto _quantParams = _o->quantParams.size() ? _fbb.CreateVector> (_o->quantParams.size(), [](size_t i, _VectorArgs *__va) { return CreateQuantParam(*__va->__fbb, __va->__o->quantParams[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _quantClusters = _o->quantClusters.size() ? _fbb.CreateVector(_o->quantClusters) : 0; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _enableHuffmanCode = _o->enableHuffmanCode; + auto _weightQuantCompressType = _o->weightQuantCompressType; + auto _externalData = _o->externalData.size() ? _fbb.CreateVector> (_o->externalData.size(), [](size_t i, _VectorArgs *__va) { return CreateExternalData(*__va->__fbb, __va->__o->externalData[i].get(), __va->__rehasher); }, &_va ) : 0; + return mindspore::schema::CreateTensor( + _fbb, + _nodeType, + _dataType, + _dims, + _format, + _refCount, + _offset, + _data, + _quantParams, + _quantClusters, + _name, + _enableHuffmanCode, + _weightQuantCompressType, + _externalData); +} + +inline PrimitiveT *Primitive::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PrimitiveT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Primitive::UnPackTo(PrimitiveT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = value_type(); _o->value.type = _e; } + { auto _e = value(); if (_e) _o->value.value = mindspore::schema::PrimitiveTypeUnion::UnPack(_e, value_type(), _resolver); } +} + +inline flatbuffers::Offset Primitive::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PrimitiveT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePrimitive(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePrimitive(flatbuffers::FlatBufferBuilder &_fbb, const PrimitiveT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PrimitiveT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _value_type = _o->value.type; + auto _value = _o->value.Pack(_fbb); + return mindspore::schema::CreatePrimitive( + _fbb, + _value_type, + _value); +} + +inline CNodeT *CNode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CNodeT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CNode::UnPackTo(CNodeT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = primitive(); if (_e) _o->primitive = std::unique_ptr(_e->UnPack(_resolver)); } + { auto _e = inputIndex(); if (_e) { _o->inputIndex.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputIndex[_i] = _e->Get(_i); } } } + { auto _e = outputIndex(); if (_e) { _o->outputIndex.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputIndex[_i] = _e->Get(_i); } } } + { auto _e = quantType(); _o->quantType = _e; } + { auto _e = deviceType(); _o->deviceType = _e; } +} + +inline flatbuffers::Offset CNode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CNodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCNode(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCNode(flatbuffers::FlatBufferBuilder &_fbb, const CNodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CNodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _primitive = _o->primitive ? CreatePrimitive(_fbb, _o->primitive.get(), _rehasher) : 0; + auto _inputIndex = _o->inputIndex.size() ? _fbb.CreateVector(_o->inputIndex) : 0; + auto _outputIndex = _o->outputIndex.size() ? _fbb.CreateVector(_o->outputIndex) : 0; + auto _quantType = _o->quantType; + auto _deviceType = _o->deviceType; + return mindspore::schema::CreateCNode( + _fbb, + _name, + _primitive, + _inputIndex, + _outputIndex, + _quantType, + _deviceType); +} + +inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SubGraphT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = inputIndices(); if (_e) { _o->inputIndices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputIndices[_i] = _e->Get(_i); } } } + { auto _e = outputIndices(); if (_e) { _o->outputIndices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputIndices[_i] = _e->Get(_i); } } } + { auto _e = nodeIndices(); if (_e) { _o->nodeIndices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->nodeIndices[_i] = _e->Get(_i); } } } + { auto _e = tensorIndices(); if (_e) { _o->tensorIndices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->tensorIndices[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSubGraph(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _inputIndices = _o->inputIndices.size() ? _fbb.CreateVector(_o->inputIndices) : 0; + auto _outputIndices = _o->outputIndices.size() ? _fbb.CreateVector(_o->outputIndices) : 0; + auto _nodeIndices = _o->nodeIndices.size() ? _fbb.CreateVector(_o->nodeIndices) : 0; + auto _tensorIndices = _o->tensorIndices.size() ? _fbb.CreateVector(_o->tensorIndices) : 0; + return mindspore::schema::CreateSubGraph( + _fbb, + _name, + _inputIndices, + _outputIndices, + _nodeIndices, + _tensorIndices); +} + +inline MetaGraphT *MetaGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MetaGraphT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MetaGraph::UnPackTo(MetaGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = version(); if (_e) _o->version = _e->str(); } + { auto _e = fmkType(); _o->fmkType = _e; } + { auto _e = inputIndex(); if (_e) { _o->inputIndex.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputIndex[_i] = _e->Get(_i); } } } + { auto _e = outputIndex(); if (_e) { _o->outputIndex.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputIndex[_i] = _e->Get(_i); } } } + { auto _e = mempoolSize(); _o->mempoolSize = _e; } + { auto _e = nodes(); if (_e) { _o->nodes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->nodes[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = allTensors(); if (_e) { _o->allTensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->allTensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = subGraph(); if (_e) { _o->subGraph.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->subGraph[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = obfuscate(); _o->obfuscate = _e; } + { auto _e = obfMetaData(); if (_e) { _o->obfMetaData.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->obfMetaData.begin()); } } +} + +inline flatbuffers::Offset MetaGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetaGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMetaGraph(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMetaGraph(flatbuffers::FlatBufferBuilder &_fbb, const MetaGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetaGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _version = _o->version.empty() ? 0 : _fbb.CreateString(_o->version); + auto _fmkType = _o->fmkType; + auto _inputIndex = _o->inputIndex.size() ? _fbb.CreateVector(_o->inputIndex) : 0; + auto _outputIndex = _o->outputIndex.size() ? _fbb.CreateVector(_o->outputIndex) : 0; + auto _mempoolSize = _o->mempoolSize; + auto _nodes = _o->nodes.size() ? _fbb.CreateVector> (_o->nodes.size(), [](size_t i, _VectorArgs *__va) { return CreateCNode(*__va->__fbb, __va->__o->nodes[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _allTensors = _o->allTensors.size() ? _fbb.CreateVector> (_o->allTensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->allTensors[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _subGraph = _o->subGraph.size() ? _fbb.CreateVector> (_o->subGraph.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subGraph[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _obfuscate = _o->obfuscate; + auto _obfMetaData = _o->obfMetaData.size() ? _fbb.CreateVector(_o->obfMetaData) : 0; + return mindspore::schema::CreateMetaGraph( + _fbb, + _name, + _version, + _fmkType, + _inputIndex, + _outputIndex, + _mempoolSize, + _nodes, + _allTensors, + _subGraph, + _obfuscate, + _obfMetaData); +} + +inline const flatbuffers::TypeTable *WeightQuantCompressTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::WeightQuantCompressTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 7, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *QuantTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::QuantTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 7, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *QuantParamTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_DOUBLE, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_DOUBLE, 0, -1 }, + { flatbuffers::ET_DOUBLE, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 12, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ExternalDataTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_LONG, 0, -1 }, + { flatbuffers::ET_LONG, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TensorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_UCHAR, 1, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 1 }, + { flatbuffers::ET_FLOAT, 1, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_INT, 0, 2 }, + { flatbuffers::ET_SEQUENCE, 1, 3 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::FormatTypeTable, + mindspore::schema::QuantParamTypeTable, + mindspore::schema::WeightQuantCompressTypeTypeTable, + mindspore::schema::ExternalDataTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 13, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *PrimitiveTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UTYPE, 0, 0 }, + { flatbuffers::ET_SEQUENCE, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::PrimitiveTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CNodeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_INT, 0, 1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::PrimitiveTypeTable, + mindspore::schema::QuantTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 7, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SubGraphTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_UINT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 5, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MetaGraphTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_UINT, 1, -1 }, + { flatbuffers::ET_UINT, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 0 }, + { flatbuffers::ET_SEQUENCE, 1, 1 }, + { flatbuffers::ET_SEQUENCE, 1, 2 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_UCHAR, 1, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + mindspore::schema::CNodeTypeTable, + mindspore::schema::TensorTypeTable, + mindspore::schema::SubGraphTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 11, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const mindspore::schema::MetaGraph *GetMetaGraph(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const mindspore::schema::MetaGraph *GetSizePrefixedMetaGraph(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline MetaGraph *GetMutableMetaGraph(void *buf) { + return flatbuffers::GetMutableRoot(buf); +} + +inline const char *MetaGraphIdentifier() { + return "MSL2"; +} + +inline bool MetaGraphBufferHasIdentifier(const void *buf) { + return flatbuffers::BufferHasIdentifier( + buf, MetaGraphIdentifier()); +} + +inline bool VerifyMetaGraphBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(MetaGraphIdentifier()); +} + +inline bool VerifySizePrefixedMetaGraphBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(MetaGraphIdentifier()); +} + +inline const char *MetaGraphExtension() { + return "ms"; +} + +inline void FinishMetaGraphBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root, MetaGraphIdentifier()); +} + +inline void FinishSizePrefixedMetaGraphBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root, MetaGraphIdentifier()); +} + +inline std::unique_ptr UnPackMetaGraph( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetMetaGraph(buf)->UnPack(res)); +} + +inline std::unique_ptr UnPackSizePrefixedMetaGraph( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetSizePrefixedMetaGraph(buf)->UnPack(res)); +} + +} // namespace schema +} // namespace mindspore + +#endif // FLATBUFFERS_GENERATED_MODEL_MINDSPORE_SCHEMA_H_ diff --git a/mindspore/lite/schema/inner/ops_generated.h b/mindspore/lite/schema/inner/ops_generated.h new file mode 100644 index 00000000..c4fd8c15 --- /dev/null +++ b/mindspore/lite/schema/inner/ops_generated.h @@ -0,0 +1,34917 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_OPS_MINDSPORE_SCHEMA_H_ +#define FLATBUFFERS_GENERATED_OPS_MINDSPORE_SCHEMA_H_ + +#include "flatbuffers/flatbuffers.h" + +#include "ops_types_generated.h" + +namespace mindspore { +namespace schema { + +struct Abs; +struct AbsBuilder; +struct AbsT; + +struct Activation; +struct ActivationBuilder; +struct ActivationT; + +struct ActivationGrad; +struct ActivationGradBuilder; +struct ActivationGradT; + +struct Adam; +struct AdamBuilder; +struct AdamT; + +struct AddFusion; +struct AddFusionBuilder; +struct AddFusionT; + +struct AdderFusion; +struct AdderFusionBuilder; +struct AdderFusionT; + +struct AddGrad; +struct AddGradBuilder; +struct AddGradT; + +struct AddN; +struct AddNBuilder; +struct AddNT; + +struct All; +struct AllBuilder; +struct AllT; + +struct ApplyMomentum; +struct ApplyMomentumBuilder; +struct ApplyMomentumT; + +struct ArgMaxFusion; +struct ArgMaxFusionBuilder; +struct ArgMaxFusionT; + +struct ArgMinFusion; +struct ArgMinFusionBuilder; +struct ArgMinFusionT; + +struct Assert; +struct AssertBuilder; +struct AssertT; + +struct Assign; +struct AssignBuilder; +struct AssignT; + +struct AssignAdd; +struct AssignAddBuilder; +struct AssignAddT; + +struct AudioSpectrogram; +struct AudioSpectrogramBuilder; +struct AudioSpectrogramT; + +struct AvgPoolFusion; +struct AvgPoolFusionBuilder; +struct AvgPoolFusionT; + +struct AvgPoolGrad; +struct AvgPoolGradBuilder; +struct AvgPoolGradT; + +struct BatchNorm; +struct BatchNormBuilder; +struct BatchNormT; + +struct BatchNormGrad; +struct BatchNormGradBuilder; +struct BatchNormGradT; + +struct BatchToSpace; +struct BatchToSpaceBuilder; +struct BatchToSpaceT; + +struct BatchToSpaceND; +struct BatchToSpaceNDBuilder; +struct BatchToSpaceNDT; + +struct BiasAdd; +struct BiasAddBuilder; +struct BiasAddT; + +struct BinaryCrossEntropy; +struct BinaryCrossEntropyBuilder; +struct BinaryCrossEntropyT; + +struct BinaryCrossEntropyGrad; +struct BinaryCrossEntropyGradBuilder; +struct BinaryCrossEntropyGradT; + +struct BiasAddGrad; +struct BiasAddGradBuilder; +struct BiasAddGradT; + +struct BroadcastTo; +struct BroadcastToBuilder; +struct BroadcastToT; + +struct Cast; +struct CastBuilder; +struct CastT; + +struct Ceil; +struct CeilBuilder; +struct CeilT; + +struct Clip; +struct ClipBuilder; +struct ClipT; + +struct Concat; +struct ConcatBuilder; +struct ConcatT; + +struct Attention; +struct AttentionBuilder; +struct AttentionT; + +struct Conv2DBackpropFilterFusion; +struct Conv2DBackpropFilterFusionBuilder; +struct Conv2DBackpropFilterFusionT; + +struct Conv2DBackpropInputFusion; +struct Conv2DBackpropInputFusionBuilder; +struct Conv2DBackpropInputFusionT; + +struct Conv2DFusion; +struct Conv2DFusionBuilder; +struct Conv2DFusionT; + +struct Conv2dTransposeFusion; +struct Conv2dTransposeFusionBuilder; +struct Conv2dTransposeFusionT; + +struct Cos; +struct CosBuilder; +struct CosT; + +struct ConstantOfShape; +struct ConstantOfShapeBuilder; +struct ConstantOfShapeT; + +struct Crop; +struct CropBuilder; +struct CropT; + +struct CustomExtractFeatures; +struct CustomExtractFeaturesBuilder; +struct CustomExtractFeaturesT; + +struct CustomNormalize; +struct CustomNormalizeBuilder; +struct CustomNormalizeT; + +struct CustomPredict; +struct CustomPredictBuilder; +struct CustomPredictT; + +struct DeConv2DGradFilter; +struct DeConv2DGradFilterBuilder; +struct DeConv2DGradFilterT; + +struct Depend; +struct DependBuilder; +struct DependT; + +struct DepthToSpace; +struct DepthToSpaceBuilder; +struct DepthToSpaceT; + +struct DetectionPostProcess; +struct DetectionPostProcessBuilder; +struct DetectionPostProcessT; + +struct DivFusion; +struct DivFusionBuilder; +struct DivFusionT; + +struct DivGrad; +struct DivGradBuilder; +struct DivGradT; + +struct Dropout; +struct DropoutBuilder; +struct DropoutT; + +struct DropoutGrad; +struct DropoutGradBuilder; +struct DropoutGradT; + +struct Elu; +struct EluBuilder; +struct EluT; + +struct Eltwise; +struct EltwiseBuilder; +struct EltwiseT; + +struct Equal; +struct EqualBuilder; +struct EqualT; + +struct EmbeddingLookupFusion; +struct EmbeddingLookupFusionBuilder; +struct EmbeddingLookupFusionT; + +struct ExpFusion; +struct ExpFusionBuilder; +struct ExpFusionT; + +struct ExpandDims; +struct ExpandDimsBuilder; +struct ExpandDimsT; + +struct FakeQuantWithMinMaxVars; +struct FakeQuantWithMinMaxVarsBuilder; +struct FakeQuantWithMinMaxVarsT; + +struct FakeQuantWithMinMaxVarsPerChannel; +struct FakeQuantWithMinMaxVarsPerChannelBuilder; +struct FakeQuantWithMinMaxVarsPerChannelT; + +struct FftReal; +struct FftRealBuilder; +struct FftRealT; + +struct FftImag; +struct FftImagBuilder; +struct FftImagT; + +struct Flatten; +struct FlattenBuilder; +struct FlattenT; + +struct FlattenGrad; +struct FlattenGradBuilder; +struct FlattenGradT; + +struct Floor; +struct FloorBuilder; +struct FloorT; + +struct FloorDiv; +struct FloorDivBuilder; +struct FloorDivT; + +struct FloorMod; +struct FloorModBuilder; +struct FloorModT; + +struct Fill; +struct FillBuilder; +struct FillT; + +struct FullConnection; +struct FullConnectionBuilder; +struct FullConnectionT; + +struct FusedBatchNorm; +struct FusedBatchNormBuilder; +struct FusedBatchNormT; + +struct Gather; +struct GatherBuilder; +struct GatherT; + +struct GatherNd; +struct GatherNdBuilder; +struct GatherNdT; + +struct Greater; +struct GreaterBuilder; +struct GreaterT; + +struct GreaterEqual; +struct GreaterEqualBuilder; +struct GreaterEqualT; + +struct HashtableLookup; +struct HashtableLookupBuilder; +struct HashtableLookupT; + +struct InstanceNorm; +struct InstanceNormBuilder; +struct InstanceNormT; + +struct LayerNormFusion; +struct LayerNormFusionBuilder; +struct LayerNormFusionT; + +struct LeakyRelu; +struct LeakyReluBuilder; +struct LeakyReluT; + +struct Less; +struct LessBuilder; +struct LessT; + +struct LessEqual; +struct LessEqualBuilder; +struct LessEqualT; + +struct Log; +struct LogBuilder; +struct LogT; + +struct LogGrad; +struct LogGradBuilder; +struct LogGradT; + +struct LogicalAnd; +struct LogicalAndBuilder; +struct LogicalAndT; + +struct LogicalNot; +struct LogicalNotBuilder; +struct LogicalNotT; + +struct LogicalOr; +struct LogicalOrBuilder; +struct LogicalOrT; + +struct LpNormalization; +struct LpNormalizationBuilder; +struct LpNormalizationT; + +struct LRN; +struct LRNBuilder; +struct LRNT; + +struct LshProjection; +struct LshProjectionBuilder; +struct LshProjectionT; + +struct LSTM; +struct LSTMBuilder; +struct LSTMT; + +struct LSTMGrad; +struct LSTMGradBuilder; +struct LSTMGradT; + +struct L2NormalizeFusion; +struct L2NormalizeFusionBuilder; +struct L2NormalizeFusionT; + +struct MatMulFusion; +struct MatMulFusionBuilder; +struct MatMulFusionT; + +struct Maximum; +struct MaximumBuilder; +struct MaximumT; + +struct MaximumGrad; +struct MaximumGradBuilder; +struct MaximumGradT; + +struct MaxPoolFusion; +struct MaxPoolFusionBuilder; +struct MaxPoolFusionT; + +struct MaxPoolGrad; +struct MaxPoolGradBuilder; +struct MaxPoolGradT; + +struct SwitchLayer; +struct SwitchLayerBuilder; +struct SwitchLayerT; + +struct Mfcc; +struct MfccBuilder; +struct MfccT; + +struct Minimum; +struct MinimumBuilder; +struct MinimumT; + +struct MinimumGrad; +struct MinimumGradBuilder; +struct MinimumGradT; + +struct Mod; +struct ModBuilder; +struct ModT; + +struct MulFusion; +struct MulFusionBuilder; +struct MulFusionT; + +struct MulGrad; +struct MulGradBuilder; +struct MulGradT; + +struct Neg; +struct NegBuilder; +struct NegT; + +struct NegGrad; +struct NegGradBuilder; +struct NegGradT; + +struct NotEqual; +struct NotEqualBuilder; +struct NotEqualT; + +struct NonMaxSuppression; +struct NonMaxSuppressionBuilder; +struct NonMaxSuppressionT; + +struct OneHot; +struct OneHotBuilder; +struct OneHotT; + +struct OnesLike; +struct OnesLikeBuilder; +struct OnesLikeT; + +struct PadFusion; +struct PadFusionBuilder; +struct PadFusionT; + +struct PartialFusion; +struct PartialFusionBuilder; +struct PartialFusionT; + +struct PowerGrad; +struct PowerGradBuilder; +struct PowerGradT; + +struct PowFusion; +struct PowFusionBuilder; +struct PowFusionT; + +struct PriorBox; +struct PriorBoxBuilder; +struct PriorBoxT; + +struct PReLUFusion; +struct PReLUFusionBuilder; +struct PReLUFusionT; + +struct Rank; +struct RankBuilder; +struct RankT; + +struct Range; +struct RangeBuilder; +struct RangeT; + +struct Reciprocal; +struct ReciprocalBuilder; +struct ReciprocalT; + +struct RealDiv; +struct RealDivBuilder; +struct RealDivT; + +struct ReduceFusion; +struct ReduceFusionBuilder; +struct ReduceFusionT; + +struct Reshape; +struct ReshapeBuilder; +struct ReshapeT; + +struct Resize; +struct ResizeBuilder; +struct ResizeT; + +struct ReverseSequence; +struct ReverseSequenceBuilder; +struct ReverseSequenceT; + +struct ReverseV2; +struct ReverseV2Builder; +struct ReverseV2T; + +struct Rfft; +struct RfftBuilder; +struct RfftT; + +struct ROIPooling; +struct ROIPoolingBuilder; +struct ROIPoolingT; + +struct Round; +struct RoundBuilder; +struct RoundT; + +struct Rsqrt; +struct RsqrtBuilder; +struct RsqrtT; + +struct QuantDTypeCast; +struct QuantDTypeCastBuilder; +struct QuantDTypeCastT; + +struct ScaleFusion; +struct ScaleFusionBuilder; +struct ScaleFusionT; + +struct ScatterNd; +struct ScatterNdBuilder; +struct ScatterNdT; + +struct SGD; +struct SGDBuilder; +struct SGDT; + +struct Shape; +struct ShapeBuilder; +struct ShapeT; + +struct SigmoidCrossEntropyWithLogits; +struct SigmoidCrossEntropyWithLogitsBuilder; +struct SigmoidCrossEntropyWithLogitsT; + +struct SigmoidCrossEntropyWithLogitsGrad; +struct SigmoidCrossEntropyWithLogitsGradBuilder; +struct SigmoidCrossEntropyWithLogitsGradT; + +struct Sin; +struct SinBuilder; +struct SinT; + +struct SkipGram; +struct SkipGramBuilder; +struct SkipGramT; + +struct SliceFusion; +struct SliceFusionBuilder; +struct SliceFusionT; + +struct SmoothL1Loss; +struct SmoothL1LossBuilder; +struct SmoothL1LossT; + +struct SmoothL1LossGrad; +struct SmoothL1LossGradBuilder; +struct SmoothL1LossGradT; + +struct Softmax; +struct SoftmaxBuilder; +struct SoftmaxT; + +struct SoftmaxCrossEntropyWithLogits; +struct SoftmaxCrossEntropyWithLogitsBuilder; +struct SoftmaxCrossEntropyWithLogitsT; + +struct SpaceToBatch; +struct SpaceToBatchBuilder; +struct SpaceToBatchT; + +struct SpaceToBatchND; +struct SpaceToBatchNDBuilder; +struct SpaceToBatchNDT; + +struct SpaceToDepth; +struct SpaceToDepthBuilder; +struct SpaceToDepthT; + +struct SparseSoftmaxCrossEntropyWithLogits; +struct SparseSoftmaxCrossEntropyWithLogitsBuilder; +struct SparseSoftmaxCrossEntropyWithLogitsT; + +struct SparseToDense; +struct SparseToDenseBuilder; +struct SparseToDenseT; + +struct Split; +struct SplitBuilder; +struct SplitT; + +struct Sqrt; +struct SqrtBuilder; +struct SqrtT; + +struct Squeeze; +struct SqueezeBuilder; +struct SqueezeT; + +struct Square; +struct SquareBuilder; +struct SquareT; + +struct SquaredDifference; +struct SquaredDifferenceBuilder; +struct SquaredDifferenceT; + +struct Stack; +struct StackBuilder; +struct StackT; + +struct StridedSlice; +struct StridedSliceBuilder; +struct StridedSliceT; + +struct SubFusion; +struct SubFusionBuilder; +struct SubFusionT; + +struct SubGrad; +struct SubGradBuilder; +struct SubGradT; + +struct Switch; +struct SwitchBuilder; +struct SwitchT; + +struct TensorListFromTensor; +struct TensorListFromTensorBuilder; +struct TensorListFromTensorT; + +struct TensorListGetItem; +struct TensorListGetItemBuilder; +struct TensorListGetItemT; + +struct TensorListReserve; +struct TensorListReserveBuilder; +struct TensorListReserveT; + +struct TensorListSetItem; +struct TensorListSetItemBuilder; +struct TensorListSetItemT; + +struct TensorListStack; +struct TensorListStackBuilder; +struct TensorListStackT; + +struct TileFusion; +struct TileFusionBuilder; +struct TileFusionT; + +struct TopKFusion; +struct TopKFusionBuilder; +struct TopKFusionT; + +struct Transpose; +struct TransposeBuilder; +struct TransposeT; + +struct Unique; +struct UniqueBuilder; +struct UniqueT; + +struct UnsortedSegmentSum; +struct UnsortedSegmentSumBuilder; +struct UnsortedSegmentSumT; + +struct Unsqueeze; +struct UnsqueezeBuilder; +struct UnsqueezeT; + +struct Unstack; +struct UnstackBuilder; +struct UnstackT; + +struct Where; +struct WhereBuilder; +struct WhereT; + +struct ZerosLike; +struct ZerosLikeBuilder; +struct ZerosLikeT; + +struct Select; +struct SelectBuilder; +struct SelectT; + +struct GRU; +struct GRUBuilder; +struct GRUT; + +struct NonZero; +struct NonZeroBuilder; +struct NonZeroT; + +struct InvertPermutation; +struct InvertPermutationBuilder; +struct InvertPermutationT; + +struct Size; +struct SizeBuilder; +struct SizeT; + +struct RandomStandardNormal; +struct RandomStandardNormalBuilder; +struct RandomStandardNormalT; + +struct CropAndResize; +struct CropAndResizeBuilder; +struct CropAndResizeT; + +struct Erf; +struct ErfBuilder; +struct ErfT; + +struct StridedSliceGrad; +struct StridedSliceGradBuilder; +struct StridedSliceGradT; + +struct IsFinite; +struct IsFiniteBuilder; +struct IsFiniteT; + +struct LinSpace; +struct LinSpaceBuilder; +struct LinSpaceT; + +struct UniformReal; +struct UniformRealBuilder; +struct UniformRealT; + +struct AbsGrad; +struct AbsGradBuilder; +struct AbsGradT; + +struct RsqrtGrad; +struct RsqrtGradBuilder; +struct RsqrtGradT; + +struct SqrtGrad; +struct SqrtGradBuilder; +struct SqrtGradT; + +struct LayerNormGrad; +struct LayerNormGradBuilder; +struct LayerNormGradT; + +struct ResizeGrad; +struct ResizeGradBuilder; +struct ResizeGradT; + +struct Splice; +struct SpliceBuilder; +struct SpliceT; + +struct LogSoftmax; +struct LogSoftmaxBuilder; +struct LogSoftmaxT; + +struct Call; +struct CallBuilder; +struct CallT; + +struct CumSum; +struct CumSumBuilder; +struct CumSumT; + +struct Custom; +struct CustomBuilder; +struct CustomT; + +struct SplitWithOverlap; +struct SplitWithOverlapBuilder; +struct SplitWithOverlapT; + +struct GenOP; +struct GenOPBuilder; +struct GenOPT; + +struct RaggedRange; +struct RaggedRangeBuilder; +struct RaggedRangeT; + +struct GLU; +struct GLUBuilder; +struct GLUT; + +struct TensorArray; +struct TensorArrayBuilder; +struct TensorArrayT; + +struct TensorArrayRead; +struct TensorArrayReadBuilder; +struct TensorArrayReadT; + +struct TensorArrayWrite; +struct TensorArrayWriteBuilder; +struct TensorArrayWriteT; + +struct Affine; +struct AffineBuilder; +struct AffineT; + +struct ScatterNdUpdate; +struct ScatterNdUpdateBuilder; +struct ScatterNdUpdateT; + +struct AllGather; +struct AllGatherBuilder; +struct AllGatherT; + +struct ReduceScatter; +struct ReduceScatterBuilder; +struct ReduceScatterT; + +struct DynamicQuant; +struct DynamicQuantBuilder; +struct DynamicQuantT; + +struct LSTMGradData; +struct LSTMGradDataBuilder; +struct LSTMGradDataT; + +struct LSTMGradWeight; +struct LSTMGradWeightBuilder; +struct LSTMGradWeightT; + +struct RandomNormal; +struct RandomNormalBuilder; +struct RandomNormalT; + +struct NLLLoss; +struct NLLLossBuilder; +struct NLLLossT; + +struct NLLLossGrad; +struct NLLLossGradBuilder; +struct NLLLossGradT; + +struct FormatTranspose; +struct FormatTransposeBuilder; +struct FormatTransposeT; + +struct GatherD; +struct GatherDBuilder; +struct GatherDT; + +struct GroupNormFusion; +struct GroupNormFusionBuilder; +struct GroupNormFusionT; + +struct Log1p; +struct Log1pBuilder; +struct Log1pT; + +struct TensorScatterAdd; +struct TensorScatterAddBuilder; +struct TensorScatterAddT; + +struct SparseFillEmptyRows; +struct SparseFillEmptyRowsBuilder; +struct SparseFillEmptyRowsT; + +struct SparseReshape; +struct SparseReshapeBuilder; +struct SparseReshapeT; + +struct SparseSegmentSum; +struct SparseSegmentSumBuilder; +struct SparseSegmentSumT; + +struct ScatterElements; +struct ScatterElementsBuilder; +struct ScatterElementsT; + +struct Triu; +struct TriuBuilder; +struct TriuT; + +struct Tril; +struct TrilBuilder; +struct TrilT; + +struct AdamWeightDecay; +struct AdamWeightDecayBuilder; +struct AdamWeightDecayT; + +inline const flatbuffers::TypeTable *AbsTypeTable(); + +inline const flatbuffers::TypeTable *ActivationTypeTable(); + +inline const flatbuffers::TypeTable *ActivationGradTypeTable(); + +inline const flatbuffers::TypeTable *AdamTypeTable(); + +inline const flatbuffers::TypeTable *AddFusionTypeTable(); + +inline const flatbuffers::TypeTable *AdderFusionTypeTable(); + +inline const flatbuffers::TypeTable *AddGradTypeTable(); + +inline const flatbuffers::TypeTable *AddNTypeTable(); + +inline const flatbuffers::TypeTable *AllTypeTable(); + +inline const flatbuffers::TypeTable *ApplyMomentumTypeTable(); + +inline const flatbuffers::TypeTable *ArgMaxFusionTypeTable(); + +inline const flatbuffers::TypeTable *ArgMinFusionTypeTable(); + +inline const flatbuffers::TypeTable *AssertTypeTable(); + +inline const flatbuffers::TypeTable *AssignTypeTable(); + +inline const flatbuffers::TypeTable *AssignAddTypeTable(); + +inline const flatbuffers::TypeTable *AudioSpectrogramTypeTable(); + +inline const flatbuffers::TypeTable *AvgPoolFusionTypeTable(); + +inline const flatbuffers::TypeTable *AvgPoolGradTypeTable(); + +inline const flatbuffers::TypeTable *BatchNormTypeTable(); + +inline const flatbuffers::TypeTable *BatchNormGradTypeTable(); + +inline const flatbuffers::TypeTable *BatchToSpaceTypeTable(); + +inline const flatbuffers::TypeTable *BatchToSpaceNDTypeTable(); + +inline const flatbuffers::TypeTable *BiasAddTypeTable(); + +inline const flatbuffers::TypeTable *BinaryCrossEntropyTypeTable(); + +inline const flatbuffers::TypeTable *BinaryCrossEntropyGradTypeTable(); + +inline const flatbuffers::TypeTable *BiasAddGradTypeTable(); + +inline const flatbuffers::TypeTable *BroadcastToTypeTable(); + +inline const flatbuffers::TypeTable *CastTypeTable(); + +inline const flatbuffers::TypeTable *CeilTypeTable(); + +inline const flatbuffers::TypeTable *ClipTypeTable(); + +inline const flatbuffers::TypeTable *ConcatTypeTable(); + +inline const flatbuffers::TypeTable *AttentionTypeTable(); + +inline const flatbuffers::TypeTable *Conv2DBackpropFilterFusionTypeTable(); + +inline const flatbuffers::TypeTable *Conv2DBackpropInputFusionTypeTable(); + +inline const flatbuffers::TypeTable *Conv2DFusionTypeTable(); + +inline const flatbuffers::TypeTable *Conv2dTransposeFusionTypeTable(); + +inline const flatbuffers::TypeTable *CosTypeTable(); + +inline const flatbuffers::TypeTable *ConstantOfShapeTypeTable(); + +inline const flatbuffers::TypeTable *CropTypeTable(); + +inline const flatbuffers::TypeTable *CustomExtractFeaturesTypeTable(); + +inline const flatbuffers::TypeTable *CustomNormalizeTypeTable(); + +inline const flatbuffers::TypeTable *CustomPredictTypeTable(); + +inline const flatbuffers::TypeTable *DeConv2DGradFilterTypeTable(); + +inline const flatbuffers::TypeTable *DependTypeTable(); + +inline const flatbuffers::TypeTable *DepthToSpaceTypeTable(); + +inline const flatbuffers::TypeTable *DetectionPostProcessTypeTable(); + +inline const flatbuffers::TypeTable *DivFusionTypeTable(); + +inline const flatbuffers::TypeTable *DivGradTypeTable(); + +inline const flatbuffers::TypeTable *DropoutTypeTable(); + +inline const flatbuffers::TypeTable *DropoutGradTypeTable(); + +inline const flatbuffers::TypeTable *EluTypeTable(); + +inline const flatbuffers::TypeTable *EltwiseTypeTable(); + +inline const flatbuffers::TypeTable *EqualTypeTable(); + +inline const flatbuffers::TypeTable *EmbeddingLookupFusionTypeTable(); + +inline const flatbuffers::TypeTable *ExpFusionTypeTable(); + +inline const flatbuffers::TypeTable *ExpandDimsTypeTable(); + +inline const flatbuffers::TypeTable *FakeQuantWithMinMaxVarsTypeTable(); + +inline const flatbuffers::TypeTable *FakeQuantWithMinMaxVarsPerChannelTypeTable(); + +inline const flatbuffers::TypeTable *FftRealTypeTable(); + +inline const flatbuffers::TypeTable *FftImagTypeTable(); + +inline const flatbuffers::TypeTable *FlattenTypeTable(); + +inline const flatbuffers::TypeTable *FlattenGradTypeTable(); + +inline const flatbuffers::TypeTable *FloorTypeTable(); + +inline const flatbuffers::TypeTable *FloorDivTypeTable(); + +inline const flatbuffers::TypeTable *FloorModTypeTable(); + +inline const flatbuffers::TypeTable *FillTypeTable(); + +inline const flatbuffers::TypeTable *FullConnectionTypeTable(); + +inline const flatbuffers::TypeTable *FusedBatchNormTypeTable(); + +inline const flatbuffers::TypeTable *GatherTypeTable(); + +inline const flatbuffers::TypeTable *GatherNdTypeTable(); + +inline const flatbuffers::TypeTable *GreaterTypeTable(); + +inline const flatbuffers::TypeTable *GreaterEqualTypeTable(); + +inline const flatbuffers::TypeTable *HashtableLookupTypeTable(); + +inline const flatbuffers::TypeTable *InstanceNormTypeTable(); + +inline const flatbuffers::TypeTable *LayerNormFusionTypeTable(); + +inline const flatbuffers::TypeTable *LeakyReluTypeTable(); + +inline const flatbuffers::TypeTable *LessTypeTable(); + +inline const flatbuffers::TypeTable *LessEqualTypeTable(); + +inline const flatbuffers::TypeTable *LogTypeTable(); + +inline const flatbuffers::TypeTable *LogGradTypeTable(); + +inline const flatbuffers::TypeTable *LogicalAndTypeTable(); + +inline const flatbuffers::TypeTable *LogicalNotTypeTable(); + +inline const flatbuffers::TypeTable *LogicalOrTypeTable(); + +inline const flatbuffers::TypeTable *LpNormalizationTypeTable(); + +inline const flatbuffers::TypeTable *LRNTypeTable(); + +inline const flatbuffers::TypeTable *LshProjectionTypeTable(); + +inline const flatbuffers::TypeTable *LSTMTypeTable(); + +inline const flatbuffers::TypeTable *LSTMGradTypeTable(); + +inline const flatbuffers::TypeTable *L2NormalizeFusionTypeTable(); + +inline const flatbuffers::TypeTable *MatMulFusionTypeTable(); + +inline const flatbuffers::TypeTable *MaximumTypeTable(); + +inline const flatbuffers::TypeTable *MaximumGradTypeTable(); + +inline const flatbuffers::TypeTable *MaxPoolFusionTypeTable(); + +inline const flatbuffers::TypeTable *MaxPoolGradTypeTable(); + +inline const flatbuffers::TypeTable *SwitchLayerTypeTable(); + +inline const flatbuffers::TypeTable *MfccTypeTable(); + +inline const flatbuffers::TypeTable *MinimumTypeTable(); + +inline const flatbuffers::TypeTable *MinimumGradTypeTable(); + +inline const flatbuffers::TypeTable *ModTypeTable(); + +inline const flatbuffers::TypeTable *MulFusionTypeTable(); + +inline const flatbuffers::TypeTable *MulGradTypeTable(); + +inline const flatbuffers::TypeTable *NegTypeTable(); + +inline const flatbuffers::TypeTable *NegGradTypeTable(); + +inline const flatbuffers::TypeTable *NotEqualTypeTable(); + +inline const flatbuffers::TypeTable *NonMaxSuppressionTypeTable(); + +inline const flatbuffers::TypeTable *OneHotTypeTable(); + +inline const flatbuffers::TypeTable *OnesLikeTypeTable(); + +inline const flatbuffers::TypeTable *PadFusionTypeTable(); + +inline const flatbuffers::TypeTable *PartialFusionTypeTable(); + +inline const flatbuffers::TypeTable *PowerGradTypeTable(); + +inline const flatbuffers::TypeTable *PowFusionTypeTable(); + +inline const flatbuffers::TypeTable *PriorBoxTypeTable(); + +inline const flatbuffers::TypeTable *PReLUFusionTypeTable(); + +inline const flatbuffers::TypeTable *RankTypeTable(); + +inline const flatbuffers::TypeTable *RangeTypeTable(); + +inline const flatbuffers::TypeTable *ReciprocalTypeTable(); + +inline const flatbuffers::TypeTable *RealDivTypeTable(); + +inline const flatbuffers::TypeTable *ReduceFusionTypeTable(); + +inline const flatbuffers::TypeTable *ReshapeTypeTable(); + +inline const flatbuffers::TypeTable *ResizeTypeTable(); + +inline const flatbuffers::TypeTable *ReverseSequenceTypeTable(); + +inline const flatbuffers::TypeTable *ReverseV2TypeTable(); + +inline const flatbuffers::TypeTable *RfftTypeTable(); + +inline const flatbuffers::TypeTable *ROIPoolingTypeTable(); + +inline const flatbuffers::TypeTable *RoundTypeTable(); + +inline const flatbuffers::TypeTable *RsqrtTypeTable(); + +inline const flatbuffers::TypeTable *QuantDTypeCastTypeTable(); + +inline const flatbuffers::TypeTable *ScaleFusionTypeTable(); + +inline const flatbuffers::TypeTable *ScatterNdTypeTable(); + +inline const flatbuffers::TypeTable *SGDTypeTable(); + +inline const flatbuffers::TypeTable *ShapeTypeTable(); + +inline const flatbuffers::TypeTable *SigmoidCrossEntropyWithLogitsTypeTable(); + +inline const flatbuffers::TypeTable *SigmoidCrossEntropyWithLogitsGradTypeTable(); + +inline const flatbuffers::TypeTable *SinTypeTable(); + +inline const flatbuffers::TypeTable *SkipGramTypeTable(); + +inline const flatbuffers::TypeTable *SliceFusionTypeTable(); + +inline const flatbuffers::TypeTable *SmoothL1LossTypeTable(); + +inline const flatbuffers::TypeTable *SmoothL1LossGradTypeTable(); + +inline const flatbuffers::TypeTable *SoftmaxTypeTable(); + +inline const flatbuffers::TypeTable *SoftmaxCrossEntropyWithLogitsTypeTable(); + +inline const flatbuffers::TypeTable *SpaceToBatchTypeTable(); + +inline const flatbuffers::TypeTable *SpaceToBatchNDTypeTable(); + +inline const flatbuffers::TypeTable *SpaceToDepthTypeTable(); + +inline const flatbuffers::TypeTable *SparseSoftmaxCrossEntropyWithLogitsTypeTable(); + +inline const flatbuffers::TypeTable *SparseToDenseTypeTable(); + +inline const flatbuffers::TypeTable *SplitTypeTable(); + +inline const flatbuffers::TypeTable *SqrtTypeTable(); + +inline const flatbuffers::TypeTable *SqueezeTypeTable(); + +inline const flatbuffers::TypeTable *SquareTypeTable(); + +inline const flatbuffers::TypeTable *SquaredDifferenceTypeTable(); + +inline const flatbuffers::TypeTable *StackTypeTable(); + +inline const flatbuffers::TypeTable *StridedSliceTypeTable(); + +inline const flatbuffers::TypeTable *SubFusionTypeTable(); + +inline const flatbuffers::TypeTable *SubGradTypeTable(); + +inline const flatbuffers::TypeTable *SwitchTypeTable(); + +inline const flatbuffers::TypeTable *TensorListFromTensorTypeTable(); + +inline const flatbuffers::TypeTable *TensorListGetItemTypeTable(); + +inline const flatbuffers::TypeTable *TensorListReserveTypeTable(); + +inline const flatbuffers::TypeTable *TensorListSetItemTypeTable(); + +inline const flatbuffers::TypeTable *TensorListStackTypeTable(); + +inline const flatbuffers::TypeTable *TileFusionTypeTable(); + +inline const flatbuffers::TypeTable *TopKFusionTypeTable(); + +inline const flatbuffers::TypeTable *TransposeTypeTable(); + +inline const flatbuffers::TypeTable *UniqueTypeTable(); + +inline const flatbuffers::TypeTable *UnsortedSegmentSumTypeTable(); + +inline const flatbuffers::TypeTable *UnsqueezeTypeTable(); + +inline const flatbuffers::TypeTable *UnstackTypeTable(); + +inline const flatbuffers::TypeTable *WhereTypeTable(); + +inline const flatbuffers::TypeTable *ZerosLikeTypeTable(); + +inline const flatbuffers::TypeTable *SelectTypeTable(); + +inline const flatbuffers::TypeTable *GRUTypeTable(); + +inline const flatbuffers::TypeTable *NonZeroTypeTable(); + +inline const flatbuffers::TypeTable *InvertPermutationTypeTable(); + +inline const flatbuffers::TypeTable *SizeTypeTable(); + +inline const flatbuffers::TypeTable *RandomStandardNormalTypeTable(); + +inline const flatbuffers::TypeTable *CropAndResizeTypeTable(); + +inline const flatbuffers::TypeTable *ErfTypeTable(); + +inline const flatbuffers::TypeTable *StridedSliceGradTypeTable(); + +inline const flatbuffers::TypeTable *IsFiniteTypeTable(); + +inline const flatbuffers::TypeTable *LinSpaceTypeTable(); + +inline const flatbuffers::TypeTable *UniformRealTypeTable(); + +inline const flatbuffers::TypeTable *AbsGradTypeTable(); + +inline const flatbuffers::TypeTable *RsqrtGradTypeTable(); + +inline const flatbuffers::TypeTable *SqrtGradTypeTable(); + +inline const flatbuffers::TypeTable *LayerNormGradTypeTable(); + +inline const flatbuffers::TypeTable *ResizeGradTypeTable(); + +inline const flatbuffers::TypeTable *SpliceTypeTable(); + +inline const flatbuffers::TypeTable *LogSoftmaxTypeTable(); + +inline const flatbuffers::TypeTable *CallTypeTable(); + +inline const flatbuffers::TypeTable *CumSumTypeTable(); + +inline const flatbuffers::TypeTable *CustomTypeTable(); + +inline const flatbuffers::TypeTable *SplitWithOverlapTypeTable(); + +inline const flatbuffers::TypeTable *GenOPTypeTable(); + +inline const flatbuffers::TypeTable *RaggedRangeTypeTable(); + +inline const flatbuffers::TypeTable *GLUTypeTable(); + +inline const flatbuffers::TypeTable *TensorArrayTypeTable(); + +inline const flatbuffers::TypeTable *TensorArrayReadTypeTable(); + +inline const flatbuffers::TypeTable *TensorArrayWriteTypeTable(); + +inline const flatbuffers::TypeTable *AffineTypeTable(); + +inline const flatbuffers::TypeTable *ScatterNdUpdateTypeTable(); + +inline const flatbuffers::TypeTable *AllGatherTypeTable(); + +inline const flatbuffers::TypeTable *ReduceScatterTypeTable(); + +inline const flatbuffers::TypeTable *DynamicQuantTypeTable(); + +inline const flatbuffers::TypeTable *LSTMGradDataTypeTable(); + +inline const flatbuffers::TypeTable *LSTMGradWeightTypeTable(); + +inline const flatbuffers::TypeTable *RandomNormalTypeTable(); + +inline const flatbuffers::TypeTable *NLLLossTypeTable(); + +inline const flatbuffers::TypeTable *NLLLossGradTypeTable(); + +inline const flatbuffers::TypeTable *FormatTransposeTypeTable(); + +inline const flatbuffers::TypeTable *GatherDTypeTable(); + +inline const flatbuffers::TypeTable *GroupNormFusionTypeTable(); + +inline const flatbuffers::TypeTable *Log1pTypeTable(); + +inline const flatbuffers::TypeTable *TensorScatterAddTypeTable(); + +inline const flatbuffers::TypeTable *SparseFillEmptyRowsTypeTable(); + +inline const flatbuffers::TypeTable *SparseReshapeTypeTable(); + +inline const flatbuffers::TypeTable *SparseSegmentSumTypeTable(); + +inline const flatbuffers::TypeTable *ScatterElementsTypeTable(); + +inline const flatbuffers::TypeTable *TriuTypeTable(); + +inline const flatbuffers::TypeTable *TrilTypeTable(); + +inline const flatbuffers::TypeTable *AdamWeightDecayTypeTable(); + +enum PrimitiveType : uint8_t { + PrimitiveType_NONE = 0, + PrimitiveType_Abs = 1, + PrimitiveType_Activation = 2, + PrimitiveType_ActivationGrad = 3, + PrimitiveType_Adam = 4, + PrimitiveType_AddFusion = 5, + PrimitiveType_AdderFusion = 6, + PrimitiveType_AddGrad = 7, + PrimitiveType_AddN = 8, + PrimitiveType_All = 9, + PrimitiveType_ApplyMomentum = 10, + PrimitiveType_ArgMaxFusion = 11, + PrimitiveType_ArgMinFusion = 12, + PrimitiveType_Assert = 13, + PrimitiveType_Assign = 14, + PrimitiveType_AssignAdd = 15, + PrimitiveType_AudioSpectrogram = 16, + PrimitiveType_AvgPoolFusion = 17, + PrimitiveType_AvgPoolGrad = 18, + PrimitiveType_BatchNorm = 19, + PrimitiveType_BatchNormGrad = 20, + PrimitiveType_BatchToSpace = 21, + PrimitiveType_BatchToSpaceND = 22, + PrimitiveType_BiasAdd = 23, + PrimitiveType_BinaryCrossEntropy = 24, + PrimitiveType_BinaryCrossEntropyGrad = 25, + PrimitiveType_BiasAddGrad = 26, + PrimitiveType_BroadcastTo = 27, + PrimitiveType_Cast = 28, + PrimitiveType_Ceil = 29, + PrimitiveType_Clip = 30, + PrimitiveType_Concat = 31, + PrimitiveType_Attention = 32, + PrimitiveType_Conv2DBackpropFilterFusion = 33, + PrimitiveType_Conv2DBackpropInputFusion = 34, + PrimitiveType_Conv2DFusion = 35, + PrimitiveType_Conv2dTransposeFusion = 36, + PrimitiveType_Cos = 37, + PrimitiveType_ConstantOfShape = 38, + PrimitiveType_Crop = 39, + PrimitiveType_CustomExtractFeatures = 40, + PrimitiveType_CustomNormalize = 41, + PrimitiveType_CustomPredict = 42, + PrimitiveType_DeConv2DGradFilter = 43, + PrimitiveType_Depend = 44, + PrimitiveType_DepthToSpace = 45, + PrimitiveType_DetectionPostProcess = 46, + PrimitiveType_DivFusion = 47, + PrimitiveType_DivGrad = 48, + PrimitiveType_Dropout = 49, + PrimitiveType_DropoutGrad = 50, + PrimitiveType_Elu = 51, + PrimitiveType_Eltwise = 52, + PrimitiveType_Equal = 53, + PrimitiveType_EmbeddingLookupFusion = 54, + PrimitiveType_ExpFusion = 55, + PrimitiveType_ExpandDims = 56, + PrimitiveType_FakeQuantWithMinMaxVars = 57, + PrimitiveType_FakeQuantWithMinMaxVarsPerChannel = 58, + PrimitiveType_FftReal = 59, + PrimitiveType_FftImag = 60, + PrimitiveType_Flatten = 61, + PrimitiveType_FlattenGrad = 62, + PrimitiveType_Floor = 63, + PrimitiveType_FloorDiv = 64, + PrimitiveType_FloorMod = 65, + PrimitiveType_Fill = 66, + PrimitiveType_FullConnection = 67, + PrimitiveType_FusedBatchNorm = 68, + PrimitiveType_Gather = 69, + PrimitiveType_GatherNd = 70, + PrimitiveType_Greater = 71, + PrimitiveType_GreaterEqual = 72, + PrimitiveType_HashtableLookup = 73, + PrimitiveType_InstanceNorm = 74, + PrimitiveType_LayerNormFusion = 75, + PrimitiveType_LeakyRelu = 76, + PrimitiveType_Less = 77, + PrimitiveType_LessEqual = 78, + PrimitiveType_Log = 79, + PrimitiveType_LogGrad = 80, + PrimitiveType_LogicalAnd = 81, + PrimitiveType_LogicalNot = 82, + PrimitiveType_LogicalOr = 83, + PrimitiveType_LpNormalization = 84, + PrimitiveType_LRN = 85, + PrimitiveType_LshProjection = 86, + PrimitiveType_LSTM = 87, + PrimitiveType_L2NormalizeFusion = 88, + PrimitiveType_MatMulFusion = 89, + PrimitiveType_Maximum = 90, + PrimitiveType_MaximumGrad = 91, + PrimitiveType_MaxPoolFusion = 92, + PrimitiveType_MaxPoolGrad = 93, + PrimitiveType_SwitchLayer = 94, + PrimitiveType_Mfcc = 95, + PrimitiveType_Minimum = 96, + PrimitiveType_MinimumGrad = 97, + PrimitiveType_Mod = 98, + PrimitiveType_MulFusion = 99, + PrimitiveType_MulGrad = 100, + PrimitiveType_Neg = 101, + PrimitiveType_NegGrad = 102, + PrimitiveType_NotEqual = 103, + PrimitiveType_NonMaxSuppression = 104, + PrimitiveType_OneHot = 105, + PrimitiveType_OnesLike = 106, + PrimitiveType_PadFusion = 107, + PrimitiveType_PartialFusion = 108, + PrimitiveType_PowerGrad = 109, + PrimitiveType_PowFusion = 110, + PrimitiveType_PriorBox = 111, + PrimitiveType_PReLUFusion = 112, + PrimitiveType_QuantDTypeCast = 113, + PrimitiveType_Rank = 114, + PrimitiveType_Range = 115, + PrimitiveType_Reciprocal = 116, + PrimitiveType_RealDiv = 117, + PrimitiveType_ReduceFusion = 118, + PrimitiveType_Reshape = 119, + PrimitiveType_Resize = 120, + PrimitiveType_ReverseSequence = 121, + PrimitiveType_ReverseV2 = 122, + PrimitiveType_Rfft = 123, + PrimitiveType_ROIPooling = 124, + PrimitiveType_Round = 125, + PrimitiveType_Rsqrt = 126, + PrimitiveType_ScaleFusion = 127, + PrimitiveType_ScatterNd = 128, + PrimitiveType_SGD = 129, + PrimitiveType_Shape = 130, + PrimitiveType_SigmoidCrossEntropyWithLogits = 131, + PrimitiveType_SigmoidCrossEntropyWithLogitsGrad = 132, + PrimitiveType_Sin = 133, + PrimitiveType_SkipGram = 134, + PrimitiveType_SliceFusion = 135, + PrimitiveType_SmoothL1Loss = 136, + PrimitiveType_SmoothL1LossGrad = 137, + PrimitiveType_Softmax = 138, + PrimitiveType_SoftmaxCrossEntropyWithLogits = 139, + PrimitiveType_SpaceToBatch = 140, + PrimitiveType_SpaceToBatchND = 141, + PrimitiveType_SpaceToDepth = 142, + PrimitiveType_SparseSoftmaxCrossEntropyWithLogits = 143, + PrimitiveType_SparseToDense = 144, + PrimitiveType_Split = 145, + PrimitiveType_Sqrt = 146, + PrimitiveType_Squeeze = 147, + PrimitiveType_Square = 148, + PrimitiveType_SquaredDifference = 149, + PrimitiveType_Stack = 150, + PrimitiveType_StridedSlice = 151, + PrimitiveType_SubFusion = 152, + PrimitiveType_SubGrad = 153, + PrimitiveType_Switch = 154, + PrimitiveType_TensorListFromTensor = 155, + PrimitiveType_TensorListGetItem = 156, + PrimitiveType_TensorListReserve = 157, + PrimitiveType_TensorListSetItem = 158, + PrimitiveType_TensorListStack = 159, + PrimitiveType_TileFusion = 160, + PrimitiveType_TopKFusion = 161, + PrimitiveType_Transpose = 162, + PrimitiveType_Unique = 163, + PrimitiveType_UnsortedSegmentSum = 164, + PrimitiveType_Unsqueeze = 165, + PrimitiveType_Unstack = 166, + PrimitiveType_LSTMGrad = 167, + PrimitiveType_Where = 168, + PrimitiveType_ZerosLike = 169, + PrimitiveType_Select = 170, + PrimitiveType_ScatterNdUpdate = 171, + PrimitiveType_GRU = 172, + PrimitiveType_NonZero = 173, + PrimitiveType_InvertPermutation = 174, + PrimitiveType_Size = 175, + PrimitiveType_RandomStandardNormal = 176, + PrimitiveType_CropAndResize = 177, + PrimitiveType_Erf = 178, + PrimitiveType_StridedSliceGrad = 179, + PrimitiveType_IsFinite = 180, + PrimitiveType_LinSpace = 181, + PrimitiveType_UniformReal = 182, + PrimitiveType_AbsGrad = 183, + PrimitiveType_RsqrtGrad = 184, + PrimitiveType_SqrtGrad = 185, + PrimitiveType_LayerNormGrad = 186, + PrimitiveType_ResizeGrad = 187, + PrimitiveType_Splice = 188, + PrimitiveType_LogSoftmax = 189, + PrimitiveType_Call = 190, + PrimitiveType_Custom = 191, + PrimitiveType_CumSum = 192, + PrimitiveType_SplitWithOverlap = 193, + PrimitiveType_GenOP = 194, + PrimitiveType_RaggedRange = 195, + PrimitiveType_GLU = 196, + PrimitiveType_TensorArray = 197, + PrimitiveType_TensorArrayRead = 198, + PrimitiveType_TensorArrayWrite = 199, + PrimitiveType_Affine = 200, + PrimitiveType_AllGather = 201, + PrimitiveType_ReduceScatter = 202, + PrimitiveType_DynamicQuant = 203, + PrimitiveType_LSTMGradData = 204, + PrimitiveType_LSTMGradWeight = 205, + PrimitiveType_RandomNormal = 206, + PrimitiveType_NLLLoss = 207, + PrimitiveType_NLLLossGrad = 208, + PrimitiveType_FormatTranspose = 209, + PrimitiveType_GatherD = 210, + PrimitiveType_GroupNormFusion = 211, + PrimitiveType_Log1p = 212, + PrimitiveType_TensorScatterAdd = 213, + PrimitiveType_SparseFillEmptyRows = 214, + PrimitiveType_SparseReshape = 215, + PrimitiveType_SparseSegmentSum = 216, + PrimitiveType_ScatterElements = 217, + PrimitiveType_Triu = 218, + PrimitiveType_Tril = 219, + PrimitiveType_AdamWeightDecay = 220, + PrimitiveType_MIN = PrimitiveType_NONE, + PrimitiveType_MAX = PrimitiveType_AdamWeightDecay +}; + +inline const PrimitiveType (&EnumValuesPrimitiveType())[221] { + static const PrimitiveType values[] = { + PrimitiveType_NONE, + PrimitiveType_Abs, + PrimitiveType_Activation, + PrimitiveType_ActivationGrad, + PrimitiveType_Adam, + PrimitiveType_AddFusion, + PrimitiveType_AdderFusion, + PrimitiveType_AddGrad, + PrimitiveType_AddN, + PrimitiveType_All, + PrimitiveType_ApplyMomentum, + PrimitiveType_ArgMaxFusion, + PrimitiveType_ArgMinFusion, + PrimitiveType_Assert, + PrimitiveType_Assign, + PrimitiveType_AssignAdd, + PrimitiveType_AudioSpectrogram, + PrimitiveType_AvgPoolFusion, + PrimitiveType_AvgPoolGrad, + PrimitiveType_BatchNorm, + PrimitiveType_BatchNormGrad, + PrimitiveType_BatchToSpace, + PrimitiveType_BatchToSpaceND, + PrimitiveType_BiasAdd, + PrimitiveType_BinaryCrossEntropy, + PrimitiveType_BinaryCrossEntropyGrad, + PrimitiveType_BiasAddGrad, + PrimitiveType_BroadcastTo, + PrimitiveType_Cast, + PrimitiveType_Ceil, + PrimitiveType_Clip, + PrimitiveType_Concat, + PrimitiveType_Attention, + PrimitiveType_Conv2DBackpropFilterFusion, + PrimitiveType_Conv2DBackpropInputFusion, + PrimitiveType_Conv2DFusion, + PrimitiveType_Conv2dTransposeFusion, + PrimitiveType_Cos, + PrimitiveType_ConstantOfShape, + PrimitiveType_Crop, + PrimitiveType_CustomExtractFeatures, + PrimitiveType_CustomNormalize, + PrimitiveType_CustomPredict, + PrimitiveType_DeConv2DGradFilter, + PrimitiveType_Depend, + PrimitiveType_DepthToSpace, + PrimitiveType_DetectionPostProcess, + PrimitiveType_DivFusion, + PrimitiveType_DivGrad, + PrimitiveType_Dropout, + PrimitiveType_DropoutGrad, + PrimitiveType_Elu, + PrimitiveType_Eltwise, + PrimitiveType_Equal, + PrimitiveType_EmbeddingLookupFusion, + PrimitiveType_ExpFusion, + PrimitiveType_ExpandDims, + PrimitiveType_FakeQuantWithMinMaxVars, + PrimitiveType_FakeQuantWithMinMaxVarsPerChannel, + PrimitiveType_FftReal, + PrimitiveType_FftImag, + PrimitiveType_Flatten, + PrimitiveType_FlattenGrad, + PrimitiveType_Floor, + PrimitiveType_FloorDiv, + PrimitiveType_FloorMod, + PrimitiveType_Fill, + PrimitiveType_FullConnection, + PrimitiveType_FusedBatchNorm, + PrimitiveType_Gather, + PrimitiveType_GatherNd, + PrimitiveType_Greater, + PrimitiveType_GreaterEqual, + PrimitiveType_HashtableLookup, + PrimitiveType_InstanceNorm, + PrimitiveType_LayerNormFusion, + PrimitiveType_LeakyRelu, + PrimitiveType_Less, + PrimitiveType_LessEqual, + PrimitiveType_Log, + PrimitiveType_LogGrad, + PrimitiveType_LogicalAnd, + PrimitiveType_LogicalNot, + PrimitiveType_LogicalOr, + PrimitiveType_LpNormalization, + PrimitiveType_LRN, + PrimitiveType_LshProjection, + PrimitiveType_LSTM, + PrimitiveType_L2NormalizeFusion, + PrimitiveType_MatMulFusion, + PrimitiveType_Maximum, + PrimitiveType_MaximumGrad, + PrimitiveType_MaxPoolFusion, + PrimitiveType_MaxPoolGrad, + PrimitiveType_SwitchLayer, + PrimitiveType_Mfcc, + PrimitiveType_Minimum, + PrimitiveType_MinimumGrad, + PrimitiveType_Mod, + PrimitiveType_MulFusion, + PrimitiveType_MulGrad, + PrimitiveType_Neg, + PrimitiveType_NegGrad, + PrimitiveType_NotEqual, + PrimitiveType_NonMaxSuppression, + PrimitiveType_OneHot, + PrimitiveType_OnesLike, + PrimitiveType_PadFusion, + PrimitiveType_PartialFusion, + PrimitiveType_PowerGrad, + PrimitiveType_PowFusion, + PrimitiveType_PriorBox, + PrimitiveType_PReLUFusion, + PrimitiveType_QuantDTypeCast, + PrimitiveType_Rank, + PrimitiveType_Range, + PrimitiveType_Reciprocal, + PrimitiveType_RealDiv, + PrimitiveType_ReduceFusion, + PrimitiveType_Reshape, + PrimitiveType_Resize, + PrimitiveType_ReverseSequence, + PrimitiveType_ReverseV2, + PrimitiveType_Rfft, + PrimitiveType_ROIPooling, + PrimitiveType_Round, + PrimitiveType_Rsqrt, + PrimitiveType_ScaleFusion, + PrimitiveType_ScatterNd, + PrimitiveType_SGD, + PrimitiveType_Shape, + PrimitiveType_SigmoidCrossEntropyWithLogits, + PrimitiveType_SigmoidCrossEntropyWithLogitsGrad, + PrimitiveType_Sin, + PrimitiveType_SkipGram, + PrimitiveType_SliceFusion, + PrimitiveType_SmoothL1Loss, + PrimitiveType_SmoothL1LossGrad, + PrimitiveType_Softmax, + PrimitiveType_SoftmaxCrossEntropyWithLogits, + PrimitiveType_SpaceToBatch, + PrimitiveType_SpaceToBatchND, + PrimitiveType_SpaceToDepth, + PrimitiveType_SparseSoftmaxCrossEntropyWithLogits, + PrimitiveType_SparseToDense, + PrimitiveType_Split, + PrimitiveType_Sqrt, + PrimitiveType_Squeeze, + PrimitiveType_Square, + PrimitiveType_SquaredDifference, + PrimitiveType_Stack, + PrimitiveType_StridedSlice, + PrimitiveType_SubFusion, + PrimitiveType_SubGrad, + PrimitiveType_Switch, + PrimitiveType_TensorListFromTensor, + PrimitiveType_TensorListGetItem, + PrimitiveType_TensorListReserve, + PrimitiveType_TensorListSetItem, + PrimitiveType_TensorListStack, + PrimitiveType_TileFusion, + PrimitiveType_TopKFusion, + PrimitiveType_Transpose, + PrimitiveType_Unique, + PrimitiveType_UnsortedSegmentSum, + PrimitiveType_Unsqueeze, + PrimitiveType_Unstack, + PrimitiveType_LSTMGrad, + PrimitiveType_Where, + PrimitiveType_ZerosLike, + PrimitiveType_Select, + PrimitiveType_ScatterNdUpdate, + PrimitiveType_GRU, + PrimitiveType_NonZero, + PrimitiveType_InvertPermutation, + PrimitiveType_Size, + PrimitiveType_RandomStandardNormal, + PrimitiveType_CropAndResize, + PrimitiveType_Erf, + PrimitiveType_StridedSliceGrad, + PrimitiveType_IsFinite, + PrimitiveType_LinSpace, + PrimitiveType_UniformReal, + PrimitiveType_AbsGrad, + PrimitiveType_RsqrtGrad, + PrimitiveType_SqrtGrad, + PrimitiveType_LayerNormGrad, + PrimitiveType_ResizeGrad, + PrimitiveType_Splice, + PrimitiveType_LogSoftmax, + PrimitiveType_Call, + PrimitiveType_Custom, + PrimitiveType_CumSum, + PrimitiveType_SplitWithOverlap, + PrimitiveType_GenOP, + PrimitiveType_RaggedRange, + PrimitiveType_GLU, + PrimitiveType_TensorArray, + PrimitiveType_TensorArrayRead, + PrimitiveType_TensorArrayWrite, + PrimitiveType_Affine, + PrimitiveType_AllGather, + PrimitiveType_ReduceScatter, + PrimitiveType_DynamicQuant, + PrimitiveType_LSTMGradData, + PrimitiveType_LSTMGradWeight, + PrimitiveType_RandomNormal, + PrimitiveType_NLLLoss, + PrimitiveType_NLLLossGrad, + PrimitiveType_FormatTranspose, + PrimitiveType_GatherD, + PrimitiveType_GroupNormFusion, + PrimitiveType_Log1p, + PrimitiveType_TensorScatterAdd, + PrimitiveType_SparseFillEmptyRows, + PrimitiveType_SparseReshape, + PrimitiveType_SparseSegmentSum, + PrimitiveType_ScatterElements, + PrimitiveType_Triu, + PrimitiveType_Tril, + PrimitiveType_AdamWeightDecay + }; + return values; +} + +inline const char * const *EnumNamesPrimitiveType() { + static const char * const names[222] = { + "NONE", + "Abs", + "Activation", + "ActivationGrad", + "Adam", + "AddFusion", + "AdderFusion", + "AddGrad", + "AddN", + "All", + "ApplyMomentum", + "ArgMaxFusion", + "ArgMinFusion", + "Assert", + "Assign", + "AssignAdd", + "AudioSpectrogram", + "AvgPoolFusion", + "AvgPoolGrad", + "BatchNorm", + "BatchNormGrad", + "BatchToSpace", + "BatchToSpaceND", + "BiasAdd", + "BinaryCrossEntropy", + "BinaryCrossEntropyGrad", + "BiasAddGrad", + "BroadcastTo", + "Cast", + "Ceil", + "Clip", + "Concat", + "Attention", + "Conv2DBackpropFilterFusion", + "Conv2DBackpropInputFusion", + "Conv2DFusion", + "Conv2dTransposeFusion", + "Cos", + "ConstantOfShape", + "Crop", + "CustomExtractFeatures", + "CustomNormalize", + "CustomPredict", + "DeConv2DGradFilter", + "Depend", + "DepthToSpace", + "DetectionPostProcess", + "DivFusion", + "DivGrad", + "Dropout", + "DropoutGrad", + "Elu", + "Eltwise", + "Equal", + "EmbeddingLookupFusion", + "ExpFusion", + "ExpandDims", + "FakeQuantWithMinMaxVars", + "FakeQuantWithMinMaxVarsPerChannel", + "FftReal", + "FftImag", + "Flatten", + "FlattenGrad", + "Floor", + "FloorDiv", + "FloorMod", + "Fill", + "FullConnection", + "FusedBatchNorm", + "Gather", + "GatherNd", + "Greater", + "GreaterEqual", + "HashtableLookup", + "InstanceNorm", + "LayerNormFusion", + "LeakyRelu", + "Less", + "LessEqual", + "Log", + "LogGrad", + "LogicalAnd", + "LogicalNot", + "LogicalOr", + "LpNormalization", + "LRN", + "LshProjection", + "LSTM", + "L2NormalizeFusion", + "MatMulFusion", + "Maximum", + "MaximumGrad", + "MaxPoolFusion", + "MaxPoolGrad", + "SwitchLayer", + "Mfcc", + "Minimum", + "MinimumGrad", + "Mod", + "MulFusion", + "MulGrad", + "Neg", + "NegGrad", + "NotEqual", + "NonMaxSuppression", + "OneHot", + "OnesLike", + "PadFusion", + "PartialFusion", + "PowerGrad", + "PowFusion", + "PriorBox", + "PReLUFusion", + "QuantDTypeCast", + "Rank", + "Range", + "Reciprocal", + "RealDiv", + "ReduceFusion", + "Reshape", + "Resize", + "ReverseSequence", + "ReverseV2", + "Rfft", + "ROIPooling", + "Round", + "Rsqrt", + "ScaleFusion", + "ScatterNd", + "SGD", + "Shape", + "SigmoidCrossEntropyWithLogits", + "SigmoidCrossEntropyWithLogitsGrad", + "Sin", + "SkipGram", + "SliceFusion", + "SmoothL1Loss", + "SmoothL1LossGrad", + "Softmax", + "SoftmaxCrossEntropyWithLogits", + "SpaceToBatch", + "SpaceToBatchND", + "SpaceToDepth", + "SparseSoftmaxCrossEntropyWithLogits", + "SparseToDense", + "Split", + "Sqrt", + "Squeeze", + "Square", + "SquaredDifference", + "Stack", + "StridedSlice", + "SubFusion", + "SubGrad", + "Switch", + "TensorListFromTensor", + "TensorListGetItem", + "TensorListReserve", + "TensorListSetItem", + "TensorListStack", + "TileFusion", + "TopKFusion", + "Transpose", + "Unique", + "UnsortedSegmentSum", + "Unsqueeze", + "Unstack", + "LSTMGrad", + "Where", + "ZerosLike", + "Select", + "ScatterNdUpdate", + "GRU", + "NonZero", + "InvertPermutation", + "Size", + "RandomStandardNormal", + "CropAndResize", + "Erf", + "StridedSliceGrad", + "IsFinite", + "LinSpace", + "UniformReal", + "AbsGrad", + "RsqrtGrad", + "SqrtGrad", + "LayerNormGrad", + "ResizeGrad", + "Splice", + "LogSoftmax", + "Call", + "Custom", + "CumSum", + "SplitWithOverlap", + "GenOP", + "RaggedRange", + "GLU", + "TensorArray", + "TensorArrayRead", + "TensorArrayWrite", + "Affine", + "AllGather", + "ReduceScatter", + "DynamicQuant", + "LSTMGradData", + "LSTMGradWeight", + "RandomNormal", + "NLLLoss", + "NLLLossGrad", + "FormatTranspose", + "GatherD", + "GroupNormFusion", + "Log1p", + "TensorScatterAdd", + "SparseFillEmptyRows", + "SparseReshape", + "SparseSegmentSum", + "ScatterElements", + "Triu", + "Tril", + "AdamWeightDecay", + nullptr + }; + return names; +} + +inline const char *EnumNamePrimitiveType(PrimitiveType e) { + if (flatbuffers::IsOutRange(e, PrimitiveType_NONE, PrimitiveType_AdamWeightDecay)) return ""; + const size_t index = static_cast(e); + return EnumNamesPrimitiveType()[index]; +} + +template struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NONE; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Abs; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Activation; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ActivationGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Adam; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AddFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AdderFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AddGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AddN; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_All; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ApplyMomentum; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ArgMaxFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ArgMinFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Assert; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Assign; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AssignAdd; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AudioSpectrogram; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AvgPoolFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AvgPoolGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BatchNorm; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BatchNormGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BatchToSpace; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BatchToSpaceND; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BiasAdd; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BinaryCrossEntropy; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BinaryCrossEntropyGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BiasAddGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_BroadcastTo; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Cast; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Ceil; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Clip; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Concat; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Attention; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Conv2DBackpropFilterFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Conv2DBackpropInputFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Conv2DFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Conv2dTransposeFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Cos; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ConstantOfShape; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Crop; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_CustomExtractFeatures; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_CustomNormalize; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_CustomPredict; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DeConv2DGradFilter; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Depend; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DepthToSpace; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DetectionPostProcess; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DivFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DivGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Dropout; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DropoutGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Elu; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Eltwise; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Equal; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_EmbeddingLookupFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ExpFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ExpandDims; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FakeQuantWithMinMaxVars; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FakeQuantWithMinMaxVarsPerChannel; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FftReal; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FftImag; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Flatten; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FlattenGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Floor; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FloorDiv; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FloorMod; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Fill; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FullConnection; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FusedBatchNorm; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Gather; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GatherNd; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Greater; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GreaterEqual; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_HashtableLookup; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_InstanceNorm; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LayerNormFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LeakyRelu; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Less; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LessEqual; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Log; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LogGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LogicalAnd; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LogicalNot; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LogicalOr; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LpNormalization; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LRN; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LshProjection; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LSTM; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_L2NormalizeFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MatMulFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Maximum; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MaximumGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MaxPoolFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MaxPoolGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SwitchLayer; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Mfcc; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Minimum; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MinimumGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Mod; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MulFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_MulGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Neg; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NegGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NotEqual; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NonMaxSuppression; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_OneHot; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_OnesLike; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_PadFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_PartialFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_PowerGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_PowFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_PriorBox; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_PReLUFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_QuantDTypeCast; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Rank; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Range; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Reciprocal; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_RealDiv; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ReduceFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Reshape; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Resize; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ReverseSequence; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ReverseV2; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Rfft; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ROIPooling; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Round; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Rsqrt; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ScaleFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ScatterNd; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SGD; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Shape; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SigmoidCrossEntropyWithLogits; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SigmoidCrossEntropyWithLogitsGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Sin; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SkipGram; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SliceFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SmoothL1Loss; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SmoothL1LossGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Softmax; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SoftmaxCrossEntropyWithLogits; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SpaceToBatch; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SpaceToBatchND; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SpaceToDepth; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SparseSoftmaxCrossEntropyWithLogits; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SparseToDense; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Split; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Sqrt; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Squeeze; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Square; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SquaredDifference; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Stack; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_StridedSlice; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SubFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SubGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Switch; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorListFromTensor; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorListGetItem; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorListReserve; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorListSetItem; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorListStack; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TileFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TopKFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Transpose; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Unique; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_UnsortedSegmentSum; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Unsqueeze; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Unstack; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LSTMGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Where; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ZerosLike; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Select; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ScatterNdUpdate; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GRU; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NonZero; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_InvertPermutation; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Size; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_RandomStandardNormal; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_CropAndResize; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Erf; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_StridedSliceGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_IsFinite; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LinSpace; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_UniformReal; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AbsGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_RsqrtGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SqrtGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LayerNormGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ResizeGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Splice; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LogSoftmax; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Call; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Custom; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_CumSum; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SplitWithOverlap; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GenOP; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_RaggedRange; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GLU; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorArray; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorArrayRead; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorArrayWrite; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Affine; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AllGather; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ReduceScatter; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_DynamicQuant; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LSTMGradData; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_LSTMGradWeight; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_RandomNormal; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NLLLoss; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_NLLLossGrad; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_FormatTranspose; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GatherD; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_GroupNormFusion; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Log1p; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_TensorScatterAdd; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SparseFillEmptyRows; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SparseReshape; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_SparseSegmentSum; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_ScatterElements; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Triu; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_Tril; +}; + +template<> struct PrimitiveTypeTraits { + static const PrimitiveType enum_value = PrimitiveType_AdamWeightDecay; +}; + +struct PrimitiveTypeUnion { + PrimitiveType type; + void *value; + + PrimitiveTypeUnion() : type(PrimitiveType_NONE), value(nullptr) {} + PrimitiveTypeUnion(PrimitiveTypeUnion&& u) FLATBUFFERS_NOEXCEPT : + type(PrimitiveType_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + PrimitiveTypeUnion(const PrimitiveTypeUnion &); + PrimitiveTypeUnion &operator=(const PrimitiveTypeUnion &u) + { PrimitiveTypeUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + PrimitiveTypeUnion &operator=(PrimitiveTypeUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~PrimitiveTypeUnion() { Reset(); } + + void Reset(); + +#ifndef FLATBUFFERS_CPP98_STL + template + void Set(T&& val) { + using RT = typename std::remove_reference::type; + Reset(); + type = PrimitiveTypeTraits::enum_value; + if (type != PrimitiveType_NONE) { + value = new RT(std::forward(val)); + } + } +#endif // FLATBUFFERS_CPP98_STL + + static void *UnPack(const void *obj, PrimitiveType type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + mindspore::schema::AbsT *AsAbs() { + return type == PrimitiveType_Abs ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AbsT *AsAbs() const { + return type == PrimitiveType_Abs ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ActivationT *AsActivation() { + return type == PrimitiveType_Activation ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ActivationT *AsActivation() const { + return type == PrimitiveType_Activation ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ActivationGradT *AsActivationGrad() { + return type == PrimitiveType_ActivationGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ActivationGradT *AsActivationGrad() const { + return type == PrimitiveType_ActivationGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AdamT *AsAdam() { + return type == PrimitiveType_Adam ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AdamT *AsAdam() const { + return type == PrimitiveType_Adam ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AddFusionT *AsAddFusion() { + return type == PrimitiveType_AddFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AddFusionT *AsAddFusion() const { + return type == PrimitiveType_AddFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AdderFusionT *AsAdderFusion() { + return type == PrimitiveType_AdderFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AdderFusionT *AsAdderFusion() const { + return type == PrimitiveType_AdderFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AddGradT *AsAddGrad() { + return type == PrimitiveType_AddGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AddGradT *AsAddGrad() const { + return type == PrimitiveType_AddGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AddNT *AsAddN() { + return type == PrimitiveType_AddN ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AddNT *AsAddN() const { + return type == PrimitiveType_AddN ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AllT *AsAll() { + return type == PrimitiveType_All ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AllT *AsAll() const { + return type == PrimitiveType_All ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ApplyMomentumT *AsApplyMomentum() { + return type == PrimitiveType_ApplyMomentum ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ApplyMomentumT *AsApplyMomentum() const { + return type == PrimitiveType_ApplyMomentum ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ArgMaxFusionT *AsArgMaxFusion() { + return type == PrimitiveType_ArgMaxFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ArgMaxFusionT *AsArgMaxFusion() const { + return type == PrimitiveType_ArgMaxFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ArgMinFusionT *AsArgMinFusion() { + return type == PrimitiveType_ArgMinFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ArgMinFusionT *AsArgMinFusion() const { + return type == PrimitiveType_ArgMinFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AssertT *AsAssert() { + return type == PrimitiveType_Assert ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AssertT *AsAssert() const { + return type == PrimitiveType_Assert ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AssignT *AsAssign() { + return type == PrimitiveType_Assign ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AssignT *AsAssign() const { + return type == PrimitiveType_Assign ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AssignAddT *AsAssignAdd() { + return type == PrimitiveType_AssignAdd ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AssignAddT *AsAssignAdd() const { + return type == PrimitiveType_AssignAdd ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AudioSpectrogramT *AsAudioSpectrogram() { + return type == PrimitiveType_AudioSpectrogram ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AudioSpectrogramT *AsAudioSpectrogram() const { + return type == PrimitiveType_AudioSpectrogram ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AvgPoolFusionT *AsAvgPoolFusion() { + return type == PrimitiveType_AvgPoolFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AvgPoolFusionT *AsAvgPoolFusion() const { + return type == PrimitiveType_AvgPoolFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AvgPoolGradT *AsAvgPoolGrad() { + return type == PrimitiveType_AvgPoolGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AvgPoolGradT *AsAvgPoolGrad() const { + return type == PrimitiveType_AvgPoolGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BatchNormT *AsBatchNorm() { + return type == PrimitiveType_BatchNorm ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BatchNormT *AsBatchNorm() const { + return type == PrimitiveType_BatchNorm ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BatchNormGradT *AsBatchNormGrad() { + return type == PrimitiveType_BatchNormGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BatchNormGradT *AsBatchNormGrad() const { + return type == PrimitiveType_BatchNormGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BatchToSpaceT *AsBatchToSpace() { + return type == PrimitiveType_BatchToSpace ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BatchToSpaceT *AsBatchToSpace() const { + return type == PrimitiveType_BatchToSpace ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BatchToSpaceNDT *AsBatchToSpaceND() { + return type == PrimitiveType_BatchToSpaceND ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BatchToSpaceNDT *AsBatchToSpaceND() const { + return type == PrimitiveType_BatchToSpaceND ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BiasAddT *AsBiasAdd() { + return type == PrimitiveType_BiasAdd ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BiasAddT *AsBiasAdd() const { + return type == PrimitiveType_BiasAdd ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BinaryCrossEntropyT *AsBinaryCrossEntropy() { + return type == PrimitiveType_BinaryCrossEntropy ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BinaryCrossEntropyT *AsBinaryCrossEntropy() const { + return type == PrimitiveType_BinaryCrossEntropy ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BinaryCrossEntropyGradT *AsBinaryCrossEntropyGrad() { + return type == PrimitiveType_BinaryCrossEntropyGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BinaryCrossEntropyGradT *AsBinaryCrossEntropyGrad() const { + return type == PrimitiveType_BinaryCrossEntropyGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BiasAddGradT *AsBiasAddGrad() { + return type == PrimitiveType_BiasAddGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BiasAddGradT *AsBiasAddGrad() const { + return type == PrimitiveType_BiasAddGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::BroadcastToT *AsBroadcastTo() { + return type == PrimitiveType_BroadcastTo ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::BroadcastToT *AsBroadcastTo() const { + return type == PrimitiveType_BroadcastTo ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CastT *AsCast() { + return type == PrimitiveType_Cast ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CastT *AsCast() const { + return type == PrimitiveType_Cast ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CeilT *AsCeil() { + return type == PrimitiveType_Ceil ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CeilT *AsCeil() const { + return type == PrimitiveType_Ceil ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ClipT *AsClip() { + return type == PrimitiveType_Clip ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ClipT *AsClip() const { + return type == PrimitiveType_Clip ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ConcatT *AsConcat() { + return type == PrimitiveType_Concat ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ConcatT *AsConcat() const { + return type == PrimitiveType_Concat ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AttentionT *AsAttention() { + return type == PrimitiveType_Attention ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AttentionT *AsAttention() const { + return type == PrimitiveType_Attention ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::Conv2DBackpropFilterFusionT *AsConv2DBackpropFilterFusion() { + return type == PrimitiveType_Conv2DBackpropFilterFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::Conv2DBackpropFilterFusionT *AsConv2DBackpropFilterFusion() const { + return type == PrimitiveType_Conv2DBackpropFilterFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::Conv2DBackpropInputFusionT *AsConv2DBackpropInputFusion() { + return type == PrimitiveType_Conv2DBackpropInputFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::Conv2DBackpropInputFusionT *AsConv2DBackpropInputFusion() const { + return type == PrimitiveType_Conv2DBackpropInputFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::Conv2DFusionT *AsConv2DFusion() { + return type == PrimitiveType_Conv2DFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::Conv2DFusionT *AsConv2DFusion() const { + return type == PrimitiveType_Conv2DFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::Conv2dTransposeFusionT *AsConv2dTransposeFusion() { + return type == PrimitiveType_Conv2dTransposeFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::Conv2dTransposeFusionT *AsConv2dTransposeFusion() const { + return type == PrimitiveType_Conv2dTransposeFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CosT *AsCos() { + return type == PrimitiveType_Cos ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CosT *AsCos() const { + return type == PrimitiveType_Cos ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ConstantOfShapeT *AsConstantOfShape() { + return type == PrimitiveType_ConstantOfShape ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ConstantOfShapeT *AsConstantOfShape() const { + return type == PrimitiveType_ConstantOfShape ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CropT *AsCrop() { + return type == PrimitiveType_Crop ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CropT *AsCrop() const { + return type == PrimitiveType_Crop ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CustomExtractFeaturesT *AsCustomExtractFeatures() { + return type == PrimitiveType_CustomExtractFeatures ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CustomExtractFeaturesT *AsCustomExtractFeatures() const { + return type == PrimitiveType_CustomExtractFeatures ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CustomNormalizeT *AsCustomNormalize() { + return type == PrimitiveType_CustomNormalize ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CustomNormalizeT *AsCustomNormalize() const { + return type == PrimitiveType_CustomNormalize ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CustomPredictT *AsCustomPredict() { + return type == PrimitiveType_CustomPredict ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CustomPredictT *AsCustomPredict() const { + return type == PrimitiveType_CustomPredict ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DeConv2DGradFilterT *AsDeConv2DGradFilter() { + return type == PrimitiveType_DeConv2DGradFilter ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DeConv2DGradFilterT *AsDeConv2DGradFilter() const { + return type == PrimitiveType_DeConv2DGradFilter ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DependT *AsDepend() { + return type == PrimitiveType_Depend ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DependT *AsDepend() const { + return type == PrimitiveType_Depend ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DepthToSpaceT *AsDepthToSpace() { + return type == PrimitiveType_DepthToSpace ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DepthToSpaceT *AsDepthToSpace() const { + return type == PrimitiveType_DepthToSpace ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DetectionPostProcessT *AsDetectionPostProcess() { + return type == PrimitiveType_DetectionPostProcess ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DetectionPostProcessT *AsDetectionPostProcess() const { + return type == PrimitiveType_DetectionPostProcess ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DivFusionT *AsDivFusion() { + return type == PrimitiveType_DivFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DivFusionT *AsDivFusion() const { + return type == PrimitiveType_DivFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DivGradT *AsDivGrad() { + return type == PrimitiveType_DivGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DivGradT *AsDivGrad() const { + return type == PrimitiveType_DivGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DropoutT *AsDropout() { + return type == PrimitiveType_Dropout ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DropoutT *AsDropout() const { + return type == PrimitiveType_Dropout ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DropoutGradT *AsDropoutGrad() { + return type == PrimitiveType_DropoutGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DropoutGradT *AsDropoutGrad() const { + return type == PrimitiveType_DropoutGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::EluT *AsElu() { + return type == PrimitiveType_Elu ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::EluT *AsElu() const { + return type == PrimitiveType_Elu ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::EltwiseT *AsEltwise() { + return type == PrimitiveType_Eltwise ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::EltwiseT *AsEltwise() const { + return type == PrimitiveType_Eltwise ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::EqualT *AsEqual() { + return type == PrimitiveType_Equal ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::EqualT *AsEqual() const { + return type == PrimitiveType_Equal ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::EmbeddingLookupFusionT *AsEmbeddingLookupFusion() { + return type == PrimitiveType_EmbeddingLookupFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::EmbeddingLookupFusionT *AsEmbeddingLookupFusion() const { + return type == PrimitiveType_EmbeddingLookupFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ExpFusionT *AsExpFusion() { + return type == PrimitiveType_ExpFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ExpFusionT *AsExpFusion() const { + return type == PrimitiveType_ExpFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ExpandDimsT *AsExpandDims() { + return type == PrimitiveType_ExpandDims ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ExpandDimsT *AsExpandDims() const { + return type == PrimitiveType_ExpandDims ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FakeQuantWithMinMaxVarsT *AsFakeQuantWithMinMaxVars() { + return type == PrimitiveType_FakeQuantWithMinMaxVars ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FakeQuantWithMinMaxVarsT *AsFakeQuantWithMinMaxVars() const { + return type == PrimitiveType_FakeQuantWithMinMaxVars ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FakeQuantWithMinMaxVarsPerChannelT *AsFakeQuantWithMinMaxVarsPerChannel() { + return type == PrimitiveType_FakeQuantWithMinMaxVarsPerChannel ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FakeQuantWithMinMaxVarsPerChannelT *AsFakeQuantWithMinMaxVarsPerChannel() const { + return type == PrimitiveType_FakeQuantWithMinMaxVarsPerChannel ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FftRealT *AsFftReal() { + return type == PrimitiveType_FftReal ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FftRealT *AsFftReal() const { + return type == PrimitiveType_FftReal ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FftImagT *AsFftImag() { + return type == PrimitiveType_FftImag ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FftImagT *AsFftImag() const { + return type == PrimitiveType_FftImag ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FlattenT *AsFlatten() { + return type == PrimitiveType_Flatten ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FlattenT *AsFlatten() const { + return type == PrimitiveType_Flatten ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FlattenGradT *AsFlattenGrad() { + return type == PrimitiveType_FlattenGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FlattenGradT *AsFlattenGrad() const { + return type == PrimitiveType_FlattenGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FloorT *AsFloor() { + return type == PrimitiveType_Floor ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FloorT *AsFloor() const { + return type == PrimitiveType_Floor ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FloorDivT *AsFloorDiv() { + return type == PrimitiveType_FloorDiv ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FloorDivT *AsFloorDiv() const { + return type == PrimitiveType_FloorDiv ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FloorModT *AsFloorMod() { + return type == PrimitiveType_FloorMod ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FloorModT *AsFloorMod() const { + return type == PrimitiveType_FloorMod ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FillT *AsFill() { + return type == PrimitiveType_Fill ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FillT *AsFill() const { + return type == PrimitiveType_Fill ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FullConnectionT *AsFullConnection() { + return type == PrimitiveType_FullConnection ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FullConnectionT *AsFullConnection() const { + return type == PrimitiveType_FullConnection ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FusedBatchNormT *AsFusedBatchNorm() { + return type == PrimitiveType_FusedBatchNorm ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FusedBatchNormT *AsFusedBatchNorm() const { + return type == PrimitiveType_FusedBatchNorm ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GatherT *AsGather() { + return type == PrimitiveType_Gather ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GatherT *AsGather() const { + return type == PrimitiveType_Gather ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GatherNdT *AsGatherNd() { + return type == PrimitiveType_GatherNd ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GatherNdT *AsGatherNd() const { + return type == PrimitiveType_GatherNd ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GreaterT *AsGreater() { + return type == PrimitiveType_Greater ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GreaterT *AsGreater() const { + return type == PrimitiveType_Greater ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GreaterEqualT *AsGreaterEqual() { + return type == PrimitiveType_GreaterEqual ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GreaterEqualT *AsGreaterEqual() const { + return type == PrimitiveType_GreaterEqual ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::HashtableLookupT *AsHashtableLookup() { + return type == PrimitiveType_HashtableLookup ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::HashtableLookupT *AsHashtableLookup() const { + return type == PrimitiveType_HashtableLookup ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::InstanceNormT *AsInstanceNorm() { + return type == PrimitiveType_InstanceNorm ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::InstanceNormT *AsInstanceNorm() const { + return type == PrimitiveType_InstanceNorm ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LayerNormFusionT *AsLayerNormFusion() { + return type == PrimitiveType_LayerNormFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LayerNormFusionT *AsLayerNormFusion() const { + return type == PrimitiveType_LayerNormFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LeakyReluT *AsLeakyRelu() { + return type == PrimitiveType_LeakyRelu ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LeakyReluT *AsLeakyRelu() const { + return type == PrimitiveType_LeakyRelu ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LessT *AsLess() { + return type == PrimitiveType_Less ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LessT *AsLess() const { + return type == PrimitiveType_Less ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LessEqualT *AsLessEqual() { + return type == PrimitiveType_LessEqual ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LessEqualT *AsLessEqual() const { + return type == PrimitiveType_LessEqual ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LogT *AsLog() { + return type == PrimitiveType_Log ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LogT *AsLog() const { + return type == PrimitiveType_Log ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LogGradT *AsLogGrad() { + return type == PrimitiveType_LogGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LogGradT *AsLogGrad() const { + return type == PrimitiveType_LogGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LogicalAndT *AsLogicalAnd() { + return type == PrimitiveType_LogicalAnd ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LogicalAndT *AsLogicalAnd() const { + return type == PrimitiveType_LogicalAnd ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LogicalNotT *AsLogicalNot() { + return type == PrimitiveType_LogicalNot ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LogicalNotT *AsLogicalNot() const { + return type == PrimitiveType_LogicalNot ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LogicalOrT *AsLogicalOr() { + return type == PrimitiveType_LogicalOr ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LogicalOrT *AsLogicalOr() const { + return type == PrimitiveType_LogicalOr ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LpNormalizationT *AsLpNormalization() { + return type == PrimitiveType_LpNormalization ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LpNormalizationT *AsLpNormalization() const { + return type == PrimitiveType_LpNormalization ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LRNT *AsLRN() { + return type == PrimitiveType_LRN ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LRNT *AsLRN() const { + return type == PrimitiveType_LRN ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LshProjectionT *AsLshProjection() { + return type == PrimitiveType_LshProjection ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LshProjectionT *AsLshProjection() const { + return type == PrimitiveType_LshProjection ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LSTMT *AsLSTM() { + return type == PrimitiveType_LSTM ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LSTMT *AsLSTM() const { + return type == PrimitiveType_LSTM ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::L2NormalizeFusionT *AsL2NormalizeFusion() { + return type == PrimitiveType_L2NormalizeFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::L2NormalizeFusionT *AsL2NormalizeFusion() const { + return type == PrimitiveType_L2NormalizeFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MatMulFusionT *AsMatMulFusion() { + return type == PrimitiveType_MatMulFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MatMulFusionT *AsMatMulFusion() const { + return type == PrimitiveType_MatMulFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MaximumT *AsMaximum() { + return type == PrimitiveType_Maximum ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MaximumT *AsMaximum() const { + return type == PrimitiveType_Maximum ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MaximumGradT *AsMaximumGrad() { + return type == PrimitiveType_MaximumGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MaximumGradT *AsMaximumGrad() const { + return type == PrimitiveType_MaximumGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MaxPoolFusionT *AsMaxPoolFusion() { + return type == PrimitiveType_MaxPoolFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MaxPoolFusionT *AsMaxPoolFusion() const { + return type == PrimitiveType_MaxPoolFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MaxPoolGradT *AsMaxPoolGrad() { + return type == PrimitiveType_MaxPoolGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MaxPoolGradT *AsMaxPoolGrad() const { + return type == PrimitiveType_MaxPoolGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SwitchLayerT *AsSwitchLayer() { + return type == PrimitiveType_SwitchLayer ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SwitchLayerT *AsSwitchLayer() const { + return type == PrimitiveType_SwitchLayer ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MfccT *AsMfcc() { + return type == PrimitiveType_Mfcc ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MfccT *AsMfcc() const { + return type == PrimitiveType_Mfcc ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MinimumT *AsMinimum() { + return type == PrimitiveType_Minimum ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MinimumT *AsMinimum() const { + return type == PrimitiveType_Minimum ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MinimumGradT *AsMinimumGrad() { + return type == PrimitiveType_MinimumGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MinimumGradT *AsMinimumGrad() const { + return type == PrimitiveType_MinimumGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ModT *AsMod() { + return type == PrimitiveType_Mod ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ModT *AsMod() const { + return type == PrimitiveType_Mod ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MulFusionT *AsMulFusion() { + return type == PrimitiveType_MulFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MulFusionT *AsMulFusion() const { + return type == PrimitiveType_MulFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::MulGradT *AsMulGrad() { + return type == PrimitiveType_MulGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::MulGradT *AsMulGrad() const { + return type == PrimitiveType_MulGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NegT *AsNeg() { + return type == PrimitiveType_Neg ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NegT *AsNeg() const { + return type == PrimitiveType_Neg ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NegGradT *AsNegGrad() { + return type == PrimitiveType_NegGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NegGradT *AsNegGrad() const { + return type == PrimitiveType_NegGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NotEqualT *AsNotEqual() { + return type == PrimitiveType_NotEqual ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NotEqualT *AsNotEqual() const { + return type == PrimitiveType_NotEqual ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NonMaxSuppressionT *AsNonMaxSuppression() { + return type == PrimitiveType_NonMaxSuppression ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NonMaxSuppressionT *AsNonMaxSuppression() const { + return type == PrimitiveType_NonMaxSuppression ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::OneHotT *AsOneHot() { + return type == PrimitiveType_OneHot ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::OneHotT *AsOneHot() const { + return type == PrimitiveType_OneHot ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::OnesLikeT *AsOnesLike() { + return type == PrimitiveType_OnesLike ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::OnesLikeT *AsOnesLike() const { + return type == PrimitiveType_OnesLike ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::PadFusionT *AsPadFusion() { + return type == PrimitiveType_PadFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::PadFusionT *AsPadFusion() const { + return type == PrimitiveType_PadFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::PartialFusionT *AsPartialFusion() { + return type == PrimitiveType_PartialFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::PartialFusionT *AsPartialFusion() const { + return type == PrimitiveType_PartialFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::PowerGradT *AsPowerGrad() { + return type == PrimitiveType_PowerGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::PowerGradT *AsPowerGrad() const { + return type == PrimitiveType_PowerGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::PowFusionT *AsPowFusion() { + return type == PrimitiveType_PowFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::PowFusionT *AsPowFusion() const { + return type == PrimitiveType_PowFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::PriorBoxT *AsPriorBox() { + return type == PrimitiveType_PriorBox ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::PriorBoxT *AsPriorBox() const { + return type == PrimitiveType_PriorBox ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::PReLUFusionT *AsPReLUFusion() { + return type == PrimitiveType_PReLUFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::PReLUFusionT *AsPReLUFusion() const { + return type == PrimitiveType_PReLUFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::QuantDTypeCastT *AsQuantDTypeCast() { + return type == PrimitiveType_QuantDTypeCast ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::QuantDTypeCastT *AsQuantDTypeCast() const { + return type == PrimitiveType_QuantDTypeCast ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RankT *AsRank() { + return type == PrimitiveType_Rank ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RankT *AsRank() const { + return type == PrimitiveType_Rank ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RangeT *AsRange() { + return type == PrimitiveType_Range ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RangeT *AsRange() const { + return type == PrimitiveType_Range ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ReciprocalT *AsReciprocal() { + return type == PrimitiveType_Reciprocal ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ReciprocalT *AsReciprocal() const { + return type == PrimitiveType_Reciprocal ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RealDivT *AsRealDiv() { + return type == PrimitiveType_RealDiv ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RealDivT *AsRealDiv() const { + return type == PrimitiveType_RealDiv ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ReduceFusionT *AsReduceFusion() { + return type == PrimitiveType_ReduceFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ReduceFusionT *AsReduceFusion() const { + return type == PrimitiveType_ReduceFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ReshapeT *AsReshape() { + return type == PrimitiveType_Reshape ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ReshapeT *AsReshape() const { + return type == PrimitiveType_Reshape ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ResizeT *AsResize() { + return type == PrimitiveType_Resize ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ResizeT *AsResize() const { + return type == PrimitiveType_Resize ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ReverseSequenceT *AsReverseSequence() { + return type == PrimitiveType_ReverseSequence ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ReverseSequenceT *AsReverseSequence() const { + return type == PrimitiveType_ReverseSequence ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ReverseV2T *AsReverseV2() { + return type == PrimitiveType_ReverseV2 ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ReverseV2T *AsReverseV2() const { + return type == PrimitiveType_ReverseV2 ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RfftT *AsRfft() { + return type == PrimitiveType_Rfft ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RfftT *AsRfft() const { + return type == PrimitiveType_Rfft ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ROIPoolingT *AsROIPooling() { + return type == PrimitiveType_ROIPooling ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ROIPoolingT *AsROIPooling() const { + return type == PrimitiveType_ROIPooling ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RoundT *AsRound() { + return type == PrimitiveType_Round ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RoundT *AsRound() const { + return type == PrimitiveType_Round ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RsqrtT *AsRsqrt() { + return type == PrimitiveType_Rsqrt ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RsqrtT *AsRsqrt() const { + return type == PrimitiveType_Rsqrt ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ScaleFusionT *AsScaleFusion() { + return type == PrimitiveType_ScaleFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ScaleFusionT *AsScaleFusion() const { + return type == PrimitiveType_ScaleFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ScatterNdT *AsScatterNd() { + return type == PrimitiveType_ScatterNd ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ScatterNdT *AsScatterNd() const { + return type == PrimitiveType_ScatterNd ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SGDT *AsSGD() { + return type == PrimitiveType_SGD ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SGDT *AsSGD() const { + return type == PrimitiveType_SGD ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ShapeT *AsShape() { + return type == PrimitiveType_Shape ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ShapeT *AsShape() const { + return type == PrimitiveType_Shape ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SigmoidCrossEntropyWithLogitsT *AsSigmoidCrossEntropyWithLogits() { + return type == PrimitiveType_SigmoidCrossEntropyWithLogits ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SigmoidCrossEntropyWithLogitsT *AsSigmoidCrossEntropyWithLogits() const { + return type == PrimitiveType_SigmoidCrossEntropyWithLogits ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SigmoidCrossEntropyWithLogitsGradT *AsSigmoidCrossEntropyWithLogitsGrad() { + return type == PrimitiveType_SigmoidCrossEntropyWithLogitsGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SigmoidCrossEntropyWithLogitsGradT *AsSigmoidCrossEntropyWithLogitsGrad() const { + return type == PrimitiveType_SigmoidCrossEntropyWithLogitsGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SinT *AsSin() { + return type == PrimitiveType_Sin ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SinT *AsSin() const { + return type == PrimitiveType_Sin ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SkipGramT *AsSkipGram() { + return type == PrimitiveType_SkipGram ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SkipGramT *AsSkipGram() const { + return type == PrimitiveType_SkipGram ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SliceFusionT *AsSliceFusion() { + return type == PrimitiveType_SliceFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SliceFusionT *AsSliceFusion() const { + return type == PrimitiveType_SliceFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SmoothL1LossT *AsSmoothL1Loss() { + return type == PrimitiveType_SmoothL1Loss ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SmoothL1LossT *AsSmoothL1Loss() const { + return type == PrimitiveType_SmoothL1Loss ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SmoothL1LossGradT *AsSmoothL1LossGrad() { + return type == PrimitiveType_SmoothL1LossGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SmoothL1LossGradT *AsSmoothL1LossGrad() const { + return type == PrimitiveType_SmoothL1LossGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SoftmaxT *AsSoftmax() { + return type == PrimitiveType_Softmax ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SoftmaxT *AsSoftmax() const { + return type == PrimitiveType_Softmax ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SoftmaxCrossEntropyWithLogitsT *AsSoftmaxCrossEntropyWithLogits() { + return type == PrimitiveType_SoftmaxCrossEntropyWithLogits ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SoftmaxCrossEntropyWithLogitsT *AsSoftmaxCrossEntropyWithLogits() const { + return type == PrimitiveType_SoftmaxCrossEntropyWithLogits ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SpaceToBatchT *AsSpaceToBatch() { + return type == PrimitiveType_SpaceToBatch ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SpaceToBatchT *AsSpaceToBatch() const { + return type == PrimitiveType_SpaceToBatch ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SpaceToBatchNDT *AsSpaceToBatchND() { + return type == PrimitiveType_SpaceToBatchND ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SpaceToBatchNDT *AsSpaceToBatchND() const { + return type == PrimitiveType_SpaceToBatchND ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SpaceToDepthT *AsSpaceToDepth() { + return type == PrimitiveType_SpaceToDepth ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SpaceToDepthT *AsSpaceToDepth() const { + return type == PrimitiveType_SpaceToDepth ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SparseSoftmaxCrossEntropyWithLogitsT *AsSparseSoftmaxCrossEntropyWithLogits() { + return type == PrimitiveType_SparseSoftmaxCrossEntropyWithLogits ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SparseSoftmaxCrossEntropyWithLogitsT *AsSparseSoftmaxCrossEntropyWithLogits() const { + return type == PrimitiveType_SparseSoftmaxCrossEntropyWithLogits ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SparseToDenseT *AsSparseToDense() { + return type == PrimitiveType_SparseToDense ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SparseToDenseT *AsSparseToDense() const { + return type == PrimitiveType_SparseToDense ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SplitT *AsSplit() { + return type == PrimitiveType_Split ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SplitT *AsSplit() const { + return type == PrimitiveType_Split ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SqrtT *AsSqrt() { + return type == PrimitiveType_Sqrt ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SqrtT *AsSqrt() const { + return type == PrimitiveType_Sqrt ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SqueezeT *AsSqueeze() { + return type == PrimitiveType_Squeeze ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SqueezeT *AsSqueeze() const { + return type == PrimitiveType_Squeeze ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SquareT *AsSquare() { + return type == PrimitiveType_Square ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SquareT *AsSquare() const { + return type == PrimitiveType_Square ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SquaredDifferenceT *AsSquaredDifference() { + return type == PrimitiveType_SquaredDifference ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SquaredDifferenceT *AsSquaredDifference() const { + return type == PrimitiveType_SquaredDifference ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::StackT *AsStack() { + return type == PrimitiveType_Stack ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::StackT *AsStack() const { + return type == PrimitiveType_Stack ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::StridedSliceT *AsStridedSlice() { + return type == PrimitiveType_StridedSlice ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::StridedSliceT *AsStridedSlice() const { + return type == PrimitiveType_StridedSlice ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SubFusionT *AsSubFusion() { + return type == PrimitiveType_SubFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SubFusionT *AsSubFusion() const { + return type == PrimitiveType_SubFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SubGradT *AsSubGrad() { + return type == PrimitiveType_SubGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SubGradT *AsSubGrad() const { + return type == PrimitiveType_SubGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SwitchT *AsSwitch() { + return type == PrimitiveType_Switch ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SwitchT *AsSwitch() const { + return type == PrimitiveType_Switch ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorListFromTensorT *AsTensorListFromTensor() { + return type == PrimitiveType_TensorListFromTensor ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorListFromTensorT *AsTensorListFromTensor() const { + return type == PrimitiveType_TensorListFromTensor ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorListGetItemT *AsTensorListGetItem() { + return type == PrimitiveType_TensorListGetItem ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorListGetItemT *AsTensorListGetItem() const { + return type == PrimitiveType_TensorListGetItem ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorListReserveT *AsTensorListReserve() { + return type == PrimitiveType_TensorListReserve ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorListReserveT *AsTensorListReserve() const { + return type == PrimitiveType_TensorListReserve ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorListSetItemT *AsTensorListSetItem() { + return type == PrimitiveType_TensorListSetItem ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorListSetItemT *AsTensorListSetItem() const { + return type == PrimitiveType_TensorListSetItem ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorListStackT *AsTensorListStack() { + return type == PrimitiveType_TensorListStack ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorListStackT *AsTensorListStack() const { + return type == PrimitiveType_TensorListStack ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TileFusionT *AsTileFusion() { + return type == PrimitiveType_TileFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TileFusionT *AsTileFusion() const { + return type == PrimitiveType_TileFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TopKFusionT *AsTopKFusion() { + return type == PrimitiveType_TopKFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TopKFusionT *AsTopKFusion() const { + return type == PrimitiveType_TopKFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TransposeT *AsTranspose() { + return type == PrimitiveType_Transpose ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TransposeT *AsTranspose() const { + return type == PrimitiveType_Transpose ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::UniqueT *AsUnique() { + return type == PrimitiveType_Unique ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::UniqueT *AsUnique() const { + return type == PrimitiveType_Unique ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::UnsortedSegmentSumT *AsUnsortedSegmentSum() { + return type == PrimitiveType_UnsortedSegmentSum ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::UnsortedSegmentSumT *AsUnsortedSegmentSum() const { + return type == PrimitiveType_UnsortedSegmentSum ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::UnsqueezeT *AsUnsqueeze() { + return type == PrimitiveType_Unsqueeze ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::UnsqueezeT *AsUnsqueeze() const { + return type == PrimitiveType_Unsqueeze ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::UnstackT *AsUnstack() { + return type == PrimitiveType_Unstack ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::UnstackT *AsUnstack() const { + return type == PrimitiveType_Unstack ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LSTMGradT *AsLSTMGrad() { + return type == PrimitiveType_LSTMGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LSTMGradT *AsLSTMGrad() const { + return type == PrimitiveType_LSTMGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::WhereT *AsWhere() { + return type == PrimitiveType_Where ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::WhereT *AsWhere() const { + return type == PrimitiveType_Where ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ZerosLikeT *AsZerosLike() { + return type == PrimitiveType_ZerosLike ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ZerosLikeT *AsZerosLike() const { + return type == PrimitiveType_ZerosLike ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SelectT *AsSelect() { + return type == PrimitiveType_Select ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SelectT *AsSelect() const { + return type == PrimitiveType_Select ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ScatterNdUpdateT *AsScatterNdUpdate() { + return type == PrimitiveType_ScatterNdUpdate ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ScatterNdUpdateT *AsScatterNdUpdate() const { + return type == PrimitiveType_ScatterNdUpdate ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GRUT *AsGRU() { + return type == PrimitiveType_GRU ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GRUT *AsGRU() const { + return type == PrimitiveType_GRU ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NonZeroT *AsNonZero() { + return type == PrimitiveType_NonZero ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NonZeroT *AsNonZero() const { + return type == PrimitiveType_NonZero ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::InvertPermutationT *AsInvertPermutation() { + return type == PrimitiveType_InvertPermutation ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::InvertPermutationT *AsInvertPermutation() const { + return type == PrimitiveType_InvertPermutation ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SizeT *AsSize() { + return type == PrimitiveType_Size ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SizeT *AsSize() const { + return type == PrimitiveType_Size ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RandomStandardNormalT *AsRandomStandardNormal() { + return type == PrimitiveType_RandomStandardNormal ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RandomStandardNormalT *AsRandomStandardNormal() const { + return type == PrimitiveType_RandomStandardNormal ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CropAndResizeT *AsCropAndResize() { + return type == PrimitiveType_CropAndResize ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CropAndResizeT *AsCropAndResize() const { + return type == PrimitiveType_CropAndResize ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ErfT *AsErf() { + return type == PrimitiveType_Erf ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ErfT *AsErf() const { + return type == PrimitiveType_Erf ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::StridedSliceGradT *AsStridedSliceGrad() { + return type == PrimitiveType_StridedSliceGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::StridedSliceGradT *AsStridedSliceGrad() const { + return type == PrimitiveType_StridedSliceGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::IsFiniteT *AsIsFinite() { + return type == PrimitiveType_IsFinite ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::IsFiniteT *AsIsFinite() const { + return type == PrimitiveType_IsFinite ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LinSpaceT *AsLinSpace() { + return type == PrimitiveType_LinSpace ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LinSpaceT *AsLinSpace() const { + return type == PrimitiveType_LinSpace ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::UniformRealT *AsUniformReal() { + return type == PrimitiveType_UniformReal ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::UniformRealT *AsUniformReal() const { + return type == PrimitiveType_UniformReal ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AbsGradT *AsAbsGrad() { + return type == PrimitiveType_AbsGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AbsGradT *AsAbsGrad() const { + return type == PrimitiveType_AbsGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RsqrtGradT *AsRsqrtGrad() { + return type == PrimitiveType_RsqrtGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RsqrtGradT *AsRsqrtGrad() const { + return type == PrimitiveType_RsqrtGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SqrtGradT *AsSqrtGrad() { + return type == PrimitiveType_SqrtGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SqrtGradT *AsSqrtGrad() const { + return type == PrimitiveType_SqrtGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LayerNormGradT *AsLayerNormGrad() { + return type == PrimitiveType_LayerNormGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LayerNormGradT *AsLayerNormGrad() const { + return type == PrimitiveType_LayerNormGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ResizeGradT *AsResizeGrad() { + return type == PrimitiveType_ResizeGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ResizeGradT *AsResizeGrad() const { + return type == PrimitiveType_ResizeGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SpliceT *AsSplice() { + return type == PrimitiveType_Splice ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SpliceT *AsSplice() const { + return type == PrimitiveType_Splice ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LogSoftmaxT *AsLogSoftmax() { + return type == PrimitiveType_LogSoftmax ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LogSoftmaxT *AsLogSoftmax() const { + return type == PrimitiveType_LogSoftmax ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CallT *AsCall() { + return type == PrimitiveType_Call ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CallT *AsCall() const { + return type == PrimitiveType_Call ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CustomT *AsCustom() { + return type == PrimitiveType_Custom ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CustomT *AsCustom() const { + return type == PrimitiveType_Custom ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::CumSumT *AsCumSum() { + return type == PrimitiveType_CumSum ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::CumSumT *AsCumSum() const { + return type == PrimitiveType_CumSum ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SplitWithOverlapT *AsSplitWithOverlap() { + return type == PrimitiveType_SplitWithOverlap ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SplitWithOverlapT *AsSplitWithOverlap() const { + return type == PrimitiveType_SplitWithOverlap ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GenOPT *AsGenOP() { + return type == PrimitiveType_GenOP ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GenOPT *AsGenOP() const { + return type == PrimitiveType_GenOP ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RaggedRangeT *AsRaggedRange() { + return type == PrimitiveType_RaggedRange ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RaggedRangeT *AsRaggedRange() const { + return type == PrimitiveType_RaggedRange ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GLUT *AsGLU() { + return type == PrimitiveType_GLU ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GLUT *AsGLU() const { + return type == PrimitiveType_GLU ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorArrayT *AsTensorArray() { + return type == PrimitiveType_TensorArray ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorArrayT *AsTensorArray() const { + return type == PrimitiveType_TensorArray ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorArrayReadT *AsTensorArrayRead() { + return type == PrimitiveType_TensorArrayRead ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorArrayReadT *AsTensorArrayRead() const { + return type == PrimitiveType_TensorArrayRead ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorArrayWriteT *AsTensorArrayWrite() { + return type == PrimitiveType_TensorArrayWrite ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorArrayWriteT *AsTensorArrayWrite() const { + return type == PrimitiveType_TensorArrayWrite ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AffineT *AsAffine() { + return type == PrimitiveType_Affine ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AffineT *AsAffine() const { + return type == PrimitiveType_Affine ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AllGatherT *AsAllGather() { + return type == PrimitiveType_AllGather ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AllGatherT *AsAllGather() const { + return type == PrimitiveType_AllGather ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ReduceScatterT *AsReduceScatter() { + return type == PrimitiveType_ReduceScatter ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ReduceScatterT *AsReduceScatter() const { + return type == PrimitiveType_ReduceScatter ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::DynamicQuantT *AsDynamicQuant() { + return type == PrimitiveType_DynamicQuant ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::DynamicQuantT *AsDynamicQuant() const { + return type == PrimitiveType_DynamicQuant ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LSTMGradDataT *AsLSTMGradData() { + return type == PrimitiveType_LSTMGradData ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LSTMGradDataT *AsLSTMGradData() const { + return type == PrimitiveType_LSTMGradData ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::LSTMGradWeightT *AsLSTMGradWeight() { + return type == PrimitiveType_LSTMGradWeight ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::LSTMGradWeightT *AsLSTMGradWeight() const { + return type == PrimitiveType_LSTMGradWeight ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::RandomNormalT *AsRandomNormal() { + return type == PrimitiveType_RandomNormal ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::RandomNormalT *AsRandomNormal() const { + return type == PrimitiveType_RandomNormal ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NLLLossT *AsNLLLoss() { + return type == PrimitiveType_NLLLoss ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NLLLossT *AsNLLLoss() const { + return type == PrimitiveType_NLLLoss ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::NLLLossGradT *AsNLLLossGrad() { + return type == PrimitiveType_NLLLossGrad ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::NLLLossGradT *AsNLLLossGrad() const { + return type == PrimitiveType_NLLLossGrad ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::FormatTransposeT *AsFormatTranspose() { + return type == PrimitiveType_FormatTranspose ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::FormatTransposeT *AsFormatTranspose() const { + return type == PrimitiveType_FormatTranspose ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GatherDT *AsGatherD() { + return type == PrimitiveType_GatherD ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GatherDT *AsGatherD() const { + return type == PrimitiveType_GatherD ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::GroupNormFusionT *AsGroupNormFusion() { + return type == PrimitiveType_GroupNormFusion ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::GroupNormFusionT *AsGroupNormFusion() const { + return type == PrimitiveType_GroupNormFusion ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::Log1pT *AsLog1p() { + return type == PrimitiveType_Log1p ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::Log1pT *AsLog1p() const { + return type == PrimitiveType_Log1p ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TensorScatterAddT *AsTensorScatterAdd() { + return type == PrimitiveType_TensorScatterAdd ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TensorScatterAddT *AsTensorScatterAdd() const { + return type == PrimitiveType_TensorScatterAdd ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SparseFillEmptyRowsT *AsSparseFillEmptyRows() { + return type == PrimitiveType_SparseFillEmptyRows ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SparseFillEmptyRowsT *AsSparseFillEmptyRows() const { + return type == PrimitiveType_SparseFillEmptyRows ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SparseReshapeT *AsSparseReshape() { + return type == PrimitiveType_SparseReshape ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SparseReshapeT *AsSparseReshape() const { + return type == PrimitiveType_SparseReshape ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::SparseSegmentSumT *AsSparseSegmentSum() { + return type == PrimitiveType_SparseSegmentSum ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::SparseSegmentSumT *AsSparseSegmentSum() const { + return type == PrimitiveType_SparseSegmentSum ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::ScatterElementsT *AsScatterElements() { + return type == PrimitiveType_ScatterElements ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::ScatterElementsT *AsScatterElements() const { + return type == PrimitiveType_ScatterElements ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TriuT *AsTriu() { + return type == PrimitiveType_Triu ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TriuT *AsTriu() const { + return type == PrimitiveType_Triu ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::TrilT *AsTril() { + return type == PrimitiveType_Tril ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::TrilT *AsTril() const { + return type == PrimitiveType_Tril ? + reinterpret_cast(value) : nullptr; + } + mindspore::schema::AdamWeightDecayT *AsAdamWeightDecay() { + return type == PrimitiveType_AdamWeightDecay ? + reinterpret_cast(value) : nullptr; + } + const mindspore::schema::AdamWeightDecayT *AsAdamWeightDecay() const { + return type == PrimitiveType_AdamWeightDecay ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifyPrimitiveType(flatbuffers::Verifier &verifier, const void *obj, PrimitiveType type); +bool VerifyPrimitiveTypeVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +struct AbsT : public flatbuffers::NativeTable { + typedef Abs TableType; +}; + +struct Abs FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AbsT NativeTableType; + typedef AbsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AbsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AbsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AbsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AbsBuilder { + typedef Abs Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AbsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAbs( + flatbuffers::FlatBufferBuilder &_fbb) { + AbsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAbs(flatbuffers::FlatBufferBuilder &_fbb, const AbsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ActivationT : public flatbuffers::NativeTable { + typedef Activation TableType; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; + float alpha = 0.0f; + float min_val = 0.0f; + float max_val = 0.0f; + bool approximate = false; +}; + +struct Activation FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ActivationT NativeTableType; + typedef ActivationBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ActivationTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4, + VT_ALPHA = 6, + VT_MIN_VAL = 8, + VT_MAX_VAL = 10, + VT_APPROXIMATE = 12 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool mutate_alpha(float _alpha) { + return SetField(VT_ALPHA, _alpha, 0.0f); + } + float min_val() const { + return GetField(VT_MIN_VAL, 0.0f); + } + bool mutate_min_val(float _min_val) { + return SetField(VT_MIN_VAL, _min_val, 0.0f); + } + float max_val() const { + return GetField(VT_MAX_VAL, 0.0f); + } + bool mutate_max_val(float _max_val) { + return SetField(VT_MAX_VAL, _max_val, 0.0f); + } + bool approximate() const { + return GetField(VT_APPROXIMATE, 0) != 0; + } + bool mutate_approximate(bool _approximate) { + return SetField(VT_APPROXIMATE, static_cast(_approximate), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + VerifyField(verifier, VT_ALPHA) && + VerifyField(verifier, VT_MIN_VAL) && + VerifyField(verifier, VT_MAX_VAL) && + VerifyField(verifier, VT_APPROXIMATE) && + verifier.EndTable(); + } + ActivationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ActivationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ActivationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ActivationBuilder { + typedef Activation Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(Activation::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + void add_alpha(float alpha) { + fbb_.AddElement(Activation::VT_ALPHA, alpha, 0.0f); + } + void add_min_val(float min_val) { + fbb_.AddElement(Activation::VT_MIN_VAL, min_val, 0.0f); + } + void add_max_val(float max_val) { + fbb_.AddElement(Activation::VT_MAX_VAL, max_val, 0.0f); + } + void add_approximate(bool approximate) { + fbb_.AddElement(Activation::VT_APPROXIMATE, static_cast(approximate), 0); + } + explicit ActivationBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateActivation( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + float alpha = 0.0f, + float min_val = 0.0f, + float max_val = 0.0f, + bool approximate = false) { + ActivationBuilder builder_(_fbb); + builder_.add_max_val(max_val); + builder_.add_min_val(min_val); + builder_.add_alpha(alpha); + builder_.add_approximate(approximate); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateActivation(flatbuffers::FlatBufferBuilder &_fbb, const ActivationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ActivationGradT : public flatbuffers::NativeTable { + typedef ActivationGrad TableType; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; + float alpha = 0.0f; +}; + +struct ActivationGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ActivationGradT NativeTableType; + typedef ActivationGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ActivationGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4, + VT_ALPHA = 6 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool mutate_alpha(float _alpha) { + return SetField(VT_ALPHA, _alpha, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + VerifyField(verifier, VT_ALPHA) && + verifier.EndTable(); + } + ActivationGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ActivationGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ActivationGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ActivationGradBuilder { + typedef ActivationGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(ActivationGrad::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + void add_alpha(float alpha) { + fbb_.AddElement(ActivationGrad::VT_ALPHA, alpha, 0.0f); + } + explicit ActivationGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateActivationGrad( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + float alpha = 0.0f) { + ActivationGradBuilder builder_(_fbb); + builder_.add_alpha(alpha); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateActivationGrad(flatbuffers::FlatBufferBuilder &_fbb, const ActivationGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AdamT : public flatbuffers::NativeTable { + typedef Adam TableType; + bool use_locking = false; + bool use_nesterov = false; +}; + +struct Adam FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AdamT NativeTableType; + typedef AdamBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AdamTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_USE_LOCKING = 4, + VT_USE_NESTEROV = 6 + }; + bool use_locking() const { + return GetField(VT_USE_LOCKING, 0) != 0; + } + bool mutate_use_locking(bool _use_locking) { + return SetField(VT_USE_LOCKING, static_cast(_use_locking), 0); + } + bool use_nesterov() const { + return GetField(VT_USE_NESTEROV, 0) != 0; + } + bool mutate_use_nesterov(bool _use_nesterov) { + return SetField(VT_USE_NESTEROV, static_cast(_use_nesterov), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_USE_LOCKING) && + VerifyField(verifier, VT_USE_NESTEROV) && + verifier.EndTable(); + } + AdamT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AdamT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AdamT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AdamBuilder { + typedef Adam Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_use_locking(bool use_locking) { + fbb_.AddElement(Adam::VT_USE_LOCKING, static_cast(use_locking), 0); + } + void add_use_nesterov(bool use_nesterov) { + fbb_.AddElement(Adam::VT_USE_NESTEROV, static_cast(use_nesterov), 0); + } + explicit AdamBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAdam( + flatbuffers::FlatBufferBuilder &_fbb, + bool use_locking = false, + bool use_nesterov = false) { + AdamBuilder builder_(_fbb); + builder_.add_use_nesterov(use_nesterov); + builder_.add_use_locking(use_locking); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAdam(flatbuffers::FlatBufferBuilder &_fbb, const AdamT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddFusionT : public flatbuffers::NativeTable { + typedef AddFusion TableType; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct AddFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddFusionT NativeTableType; + typedef AddFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AddFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + AddFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddFusionBuilder { + typedef AddFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(AddFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit AddFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + AddFusionBuilder builder_(_fbb); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddFusion(flatbuffers::FlatBufferBuilder &_fbb, const AddFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AdderFusionT : public flatbuffers::NativeTable { + typedef AdderFusion TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + std::vector kernel_size{}; + std::vector stride{}; + std::vector dilation{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + std::vector pad_list{}; + int64_t group = 0; + int64_t in_channel = 0; + int64_t out_channel = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct AdderFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AdderFusionT NativeTableType; + typedef AdderFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AdderFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_KERNEL_SIZE = 6, + VT_STRIDE = 8, + VT_DILATION = 10, + VT_PAD_MODE = 12, + VT_PAD_LIST = 14, + VT_GROUP = 16, + VT_IN_CHANNEL = 18, + VT_OUT_CHANNEL = 20, + VT_ACTIVATION_TYPE = 22 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + flatbuffers::Vector *mutable_stride() { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + flatbuffers::Vector *mutable_dilation() { + return GetPointer *>(VT_DILATION); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + flatbuffers::Vector *mutable_pad_list() { + return GetPointer *>(VT_PAD_LIST); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + bool mutate_group(int64_t _group) { + return SetField(VT_GROUP, _group, 0); + } + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + bool mutate_in_channel(int64_t _in_channel) { + return SetField(VT_IN_CHANNEL, _in_channel, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + bool mutate_out_channel(int64_t _out_channel) { + return SetField(VT_OUT_CHANNEL, _out_channel, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + AdderFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AdderFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AdderFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AdderFusionBuilder { + typedef AdderFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(AdderFusion::VT_FORMAT, static_cast(format), 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(AdderFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(AdderFusion::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(AdderFusion::VT_DILATION, dilation); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(AdderFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(AdderFusion::VT_PAD_LIST, pad_list); + } + void add_group(int64_t group) { + fbb_.AddElement(AdderFusion::VT_GROUP, group, 0); + } + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(AdderFusion::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(AdderFusion::VT_OUT_CHANNEL, out_channel, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(AdderFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit AdderFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAdderFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad_list = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + AdderFusionBuilder builder_(_fbb); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_group(group); + builder_.add_pad_list(pad_list); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_kernel_size(kernel_size); + builder_.add_format(format); + builder_.add_activation_type(activation_type); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateAdderFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const std::vector *kernel_size = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad_list = nullptr, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + return mindspore::schema::CreateAdderFusion( + _fbb, + format, + kernel_size__, + stride__, + dilation__, + pad_mode, + pad_list__, + group, + in_channel, + out_channel, + activation_type); +} + +flatbuffers::Offset CreateAdderFusion(flatbuffers::FlatBufferBuilder &_fbb, const AdderFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddGradT : public flatbuffers::NativeTable { + typedef AddGrad TableType; +}; + +struct AddGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddGradT NativeTableType; + typedef AddGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AddGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AddGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddGradBuilder { + typedef AddGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AddGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + AddGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddGrad(flatbuffers::FlatBufferBuilder &_fbb, const AddGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddNT : public flatbuffers::NativeTable { + typedef AddN TableType; +}; + +struct AddN FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddNT NativeTableType; + typedef AddNBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AddNTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AddNT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddNT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddNBuilder { + typedef AddN Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AddNBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddN( + flatbuffers::FlatBufferBuilder &_fbb) { + AddNBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddN(flatbuffers::FlatBufferBuilder &_fbb, const AddNT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AllT : public flatbuffers::NativeTable { + typedef All TableType; + int64_t keep_dims = 0; +}; + +struct All FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AllT NativeTableType; + typedef AllBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AllTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEEP_DIMS = 4 + }; + int64_t keep_dims() const { + return GetField(VT_KEEP_DIMS, 0); + } + bool mutate_keep_dims(int64_t _keep_dims) { + return SetField(VT_KEEP_DIMS, _keep_dims, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_KEEP_DIMS) && + verifier.EndTable(); + } + AllT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AllT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AllT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AllBuilder { + typedef All Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_keep_dims(int64_t keep_dims) { + fbb_.AddElement(All::VT_KEEP_DIMS, keep_dims, 0); + } + explicit AllBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAll( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t keep_dims = 0) { + AllBuilder builder_(_fbb); + builder_.add_keep_dims(keep_dims); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAll(flatbuffers::FlatBufferBuilder &_fbb, const AllT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ApplyMomentumT : public flatbuffers::NativeTable { + typedef ApplyMomentum TableType; + bool use_nesterov = false; + bool use_locking = false; + float gradient_scale = 0.0f; +}; + +struct ApplyMomentum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ApplyMomentumT NativeTableType; + typedef ApplyMomentumBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ApplyMomentumTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_USE_NESTEROV = 4, + VT_USE_LOCKING = 6, + VT_GRADIENT_SCALE = 8 + }; + bool use_nesterov() const { + return GetField(VT_USE_NESTEROV, 0) != 0; + } + bool mutate_use_nesterov(bool _use_nesterov) { + return SetField(VT_USE_NESTEROV, static_cast(_use_nesterov), 0); + } + bool use_locking() const { + return GetField(VT_USE_LOCKING, 0) != 0; + } + bool mutate_use_locking(bool _use_locking) { + return SetField(VT_USE_LOCKING, static_cast(_use_locking), 0); + } + float gradient_scale() const { + return GetField(VT_GRADIENT_SCALE, 0.0f); + } + bool mutate_gradient_scale(float _gradient_scale) { + return SetField(VT_GRADIENT_SCALE, _gradient_scale, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_USE_NESTEROV) && + VerifyField(verifier, VT_USE_LOCKING) && + VerifyField(verifier, VT_GRADIENT_SCALE) && + verifier.EndTable(); + } + ApplyMomentumT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ApplyMomentumT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ApplyMomentumT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ApplyMomentumBuilder { + typedef ApplyMomentum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_use_nesterov(bool use_nesterov) { + fbb_.AddElement(ApplyMomentum::VT_USE_NESTEROV, static_cast(use_nesterov), 0); + } + void add_use_locking(bool use_locking) { + fbb_.AddElement(ApplyMomentum::VT_USE_LOCKING, static_cast(use_locking), 0); + } + void add_gradient_scale(float gradient_scale) { + fbb_.AddElement(ApplyMomentum::VT_GRADIENT_SCALE, gradient_scale, 0.0f); + } + explicit ApplyMomentumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateApplyMomentum( + flatbuffers::FlatBufferBuilder &_fbb, + bool use_nesterov = false, + bool use_locking = false, + float gradient_scale = 0.0f) { + ApplyMomentumBuilder builder_(_fbb); + builder_.add_gradient_scale(gradient_scale); + builder_.add_use_locking(use_locking); + builder_.add_use_nesterov(use_nesterov); + return builder_.Finish(); +} + +flatbuffers::Offset CreateApplyMomentum(flatbuffers::FlatBufferBuilder &_fbb, const ApplyMomentumT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ArgMaxFusionT : public flatbuffers::NativeTable { + typedef ArgMaxFusion TableType; + int64_t axis = 0; + int64_t top_k = 1LL; + bool keep_dims = false; + bool out_max_value = false; +}; + +struct ArgMaxFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMaxFusionT NativeTableType; + typedef ArgMaxFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ArgMaxFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_TOP_K = 6, + VT_KEEP_DIMS = 8, + VT_OUT_MAX_VALUE = 10 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + int64_t top_k() const { + return GetField(VT_TOP_K, 1LL); + } + bool mutate_top_k(int64_t _top_k) { + return SetField(VT_TOP_K, _top_k, 1LL); + } + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } + bool mutate_keep_dims(bool _keep_dims) { + return SetField(VT_KEEP_DIMS, static_cast(_keep_dims), 0); + } + bool out_max_value() const { + return GetField(VT_OUT_MAX_VALUE, 0) != 0; + } + bool mutate_out_max_value(bool _out_max_value) { + return SetField(VT_OUT_MAX_VALUE, static_cast(_out_max_value), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_TOP_K) && + VerifyField(verifier, VT_KEEP_DIMS) && + VerifyField(verifier, VT_OUT_MAX_VALUE) && + verifier.EndTable(); + } + ArgMaxFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMaxFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMaxFusionBuilder { + typedef ArgMaxFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(ArgMaxFusion::VT_AXIS, axis, 0); + } + void add_top_k(int64_t top_k) { + fbb_.AddElement(ArgMaxFusion::VT_TOP_K, top_k, 1LL); + } + void add_keep_dims(bool keep_dims) { + fbb_.AddElement(ArgMaxFusion::VT_KEEP_DIMS, static_cast(keep_dims), 0); + } + void add_out_max_value(bool out_max_value) { + fbb_.AddElement(ArgMaxFusion::VT_OUT_MAX_VALUE, static_cast(out_max_value), 0); + } + explicit ArgMaxFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMaxFusion( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0, + int64_t top_k = 1LL, + bool keep_dims = false, + bool out_max_value = false) { + ArgMaxFusionBuilder builder_(_fbb); + builder_.add_top_k(top_k); + builder_.add_axis(axis); + builder_.add_out_max_value(out_max_value); + builder_.add_keep_dims(keep_dims); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMaxFusion(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ArgMinFusionT : public flatbuffers::NativeTable { + typedef ArgMinFusion TableType; + int64_t axis = 0; + int64_t top_k = 0; + bool keep_dims = false; + bool out_max_value = false; +}; + +struct ArgMinFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMinFusionT NativeTableType; + typedef ArgMinFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ArgMinFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_TOP_K = 6, + VT_KEEP_DIMS = 8, + VT_OUT_MAX_VALUE = 10 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + int64_t top_k() const { + return GetField(VT_TOP_K, 0); + } + bool mutate_top_k(int64_t _top_k) { + return SetField(VT_TOP_K, _top_k, 0); + } + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } + bool mutate_keep_dims(bool _keep_dims) { + return SetField(VT_KEEP_DIMS, static_cast(_keep_dims), 0); + } + bool out_max_value() const { + return GetField(VT_OUT_MAX_VALUE, 0) != 0; + } + bool mutate_out_max_value(bool _out_max_value) { + return SetField(VT_OUT_MAX_VALUE, static_cast(_out_max_value), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_TOP_K) && + VerifyField(verifier, VT_KEEP_DIMS) && + VerifyField(verifier, VT_OUT_MAX_VALUE) && + verifier.EndTable(); + } + ArgMinFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMinFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMinFusionBuilder { + typedef ArgMinFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(ArgMinFusion::VT_AXIS, axis, 0); + } + void add_top_k(int64_t top_k) { + fbb_.AddElement(ArgMinFusion::VT_TOP_K, top_k, 0); + } + void add_keep_dims(bool keep_dims) { + fbb_.AddElement(ArgMinFusion::VT_KEEP_DIMS, static_cast(keep_dims), 0); + } + void add_out_max_value(bool out_max_value) { + fbb_.AddElement(ArgMinFusion::VT_OUT_MAX_VALUE, static_cast(out_max_value), 0); + } + explicit ArgMinFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMinFusion( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0, + int64_t top_k = 0, + bool keep_dims = false, + bool out_max_value = false) { + ArgMinFusionBuilder builder_(_fbb); + builder_.add_top_k(top_k); + builder_.add_axis(axis); + builder_.add_out_max_value(out_max_value); + builder_.add_keep_dims(keep_dims); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMinFusion(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AssertT : public flatbuffers::NativeTable { + typedef Assert TableType; + int64_t summarize = 0; +}; + +struct Assert FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AssertT NativeTableType; + typedef AssertBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AssertTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SUMMARIZE = 4 + }; + int64_t summarize() const { + return GetField(VT_SUMMARIZE, 0); + } + bool mutate_summarize(int64_t _summarize) { + return SetField(VT_SUMMARIZE, _summarize, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SUMMARIZE) && + verifier.EndTable(); + } + AssertT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AssertT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssertT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AssertBuilder { + typedef Assert Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_summarize(int64_t summarize) { + fbb_.AddElement(Assert::VT_SUMMARIZE, summarize, 0); + } + explicit AssertBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAssert( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t summarize = 0) { + AssertBuilder builder_(_fbb); + builder_.add_summarize(summarize); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAssert(flatbuffers::FlatBufferBuilder &_fbb, const AssertT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AssignT : public flatbuffers::NativeTable { + typedef Assign TableType; +}; + +struct Assign FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AssignT NativeTableType; + typedef AssignBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AssignTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AssignT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AssignT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AssignBuilder { + typedef Assign Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AssignBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAssign( + flatbuffers::FlatBufferBuilder &_fbb) { + AssignBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAssign(flatbuffers::FlatBufferBuilder &_fbb, const AssignT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AssignAddT : public flatbuffers::NativeTable { + typedef AssignAdd TableType; +}; + +struct AssignAdd FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AssignAddT NativeTableType; + typedef AssignAddBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AssignAddTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AssignAddT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AssignAddT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignAddT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AssignAddBuilder { + typedef AssignAdd Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AssignAddBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAssignAdd( + flatbuffers::FlatBufferBuilder &_fbb) { + AssignAddBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAssignAdd(flatbuffers::FlatBufferBuilder &_fbb, const AssignAddT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AudioSpectrogramT : public flatbuffers::NativeTable { + typedef AudioSpectrogram TableType; + int64_t window_size = 0; + int64_t stride = 0; + bool mag_square = false; +}; + +struct AudioSpectrogram FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AudioSpectrogramT NativeTableType; + typedef AudioSpectrogramBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AudioSpectrogramTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_WINDOW_SIZE = 4, + VT_STRIDE = 6, + VT_MAG_SQUARE = 8 + }; + int64_t window_size() const { + return GetField(VT_WINDOW_SIZE, 0); + } + bool mutate_window_size(int64_t _window_size) { + return SetField(VT_WINDOW_SIZE, _window_size, 0); + } + int64_t stride() const { + return GetField(VT_STRIDE, 0); + } + bool mutate_stride(int64_t _stride) { + return SetField(VT_STRIDE, _stride, 0); + } + bool mag_square() const { + return GetField(VT_MAG_SQUARE, 0) != 0; + } + bool mutate_mag_square(bool _mag_square) { + return SetField(VT_MAG_SQUARE, static_cast(_mag_square), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_WINDOW_SIZE) && + VerifyField(verifier, VT_STRIDE) && + VerifyField(verifier, VT_MAG_SQUARE) && + verifier.EndTable(); + } + AudioSpectrogramT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AudioSpectrogramT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AudioSpectrogramT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AudioSpectrogramBuilder { + typedef AudioSpectrogram Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_window_size(int64_t window_size) { + fbb_.AddElement(AudioSpectrogram::VT_WINDOW_SIZE, window_size, 0); + } + void add_stride(int64_t stride) { + fbb_.AddElement(AudioSpectrogram::VT_STRIDE, stride, 0); + } + void add_mag_square(bool mag_square) { + fbb_.AddElement(AudioSpectrogram::VT_MAG_SQUARE, static_cast(mag_square), 0); + } + explicit AudioSpectrogramBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAudioSpectrogram( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t window_size = 0, + int64_t stride = 0, + bool mag_square = false) { + AudioSpectrogramBuilder builder_(_fbb); + builder_.add_stride(stride); + builder_.add_window_size(window_size); + builder_.add_mag_square(mag_square); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAudioSpectrogram(flatbuffers::FlatBufferBuilder &_fbb, const AudioSpectrogramT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AvgPoolFusionT : public flatbuffers::NativeTable { + typedef AvgPoolFusion TableType; + std::vector kernel_size{}; + std::vector strides{}; + std::vector pad{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + bool global = false; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct AvgPoolFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AvgPoolFusionT NativeTableType; + typedef AvgPoolFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AvgPoolFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KERNEL_SIZE = 4, + VT_STRIDES = 6, + VT_PAD = 8, + VT_PAD_MODE = 10, + VT_ROUND_MODE = 12, + VT_FORMAT = 14, + VT_GLOBAL = 16, + VT_ACTIVATION_TYPE = 18 + }; + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *strides() const { + return GetPointer *>(VT_STRIDES); + } + flatbuffers::Vector *mutable_strides() { + return GetPointer *>(VT_STRIDES); + } + const flatbuffers::Vector *pad() const { + return GetPointer *>(VT_PAD); + } + flatbuffers::Vector *mutable_pad() { + return GetPointer *>(VT_PAD); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + mindspore::schema::RoundMode round_mode() const { + return static_cast(GetField(VT_ROUND_MODE, 0)); + } + bool mutate_round_mode(mindspore::schema::RoundMode _round_mode) { + return SetField(VT_ROUND_MODE, static_cast(_round_mode), 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool global() const { + return GetField(VT_GLOBAL, 0) != 0; + } + bool mutate_global(bool _global) { + return SetField(VT_GLOBAL, static_cast(_global), 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDES) && + verifier.VerifyVector(strides()) && + VerifyOffset(verifier, VT_PAD) && + verifier.VerifyVector(pad()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyField(verifier, VT_ROUND_MODE) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_GLOBAL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + AvgPoolFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AvgPoolFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AvgPoolFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AvgPoolFusionBuilder { + typedef AvgPoolFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(AvgPoolFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_strides(flatbuffers::Offset> strides) { + fbb_.AddOffset(AvgPoolFusion::VT_STRIDES, strides); + } + void add_pad(flatbuffers::Offset> pad) { + fbb_.AddOffset(AvgPoolFusion::VT_PAD, pad); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(AvgPoolFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_round_mode(mindspore::schema::RoundMode round_mode) { + fbb_.AddElement(AvgPoolFusion::VT_ROUND_MODE, static_cast(round_mode), 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(AvgPoolFusion::VT_FORMAT, static_cast(format), 0); + } + void add_global(bool global) { + fbb_.AddElement(AvgPoolFusion::VT_GLOBAL, static_cast(global), 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(AvgPoolFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit AvgPoolFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAvgPoolFusion( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> strides = 0, + flatbuffers::Offset> pad = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + bool global = false, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + AvgPoolFusionBuilder builder_(_fbb); + builder_.add_format(format); + builder_.add_pad(pad); + builder_.add_strides(strides); + builder_.add_kernel_size(kernel_size); + builder_.add_activation_type(activation_type); + builder_.add_global(global); + builder_.add_round_mode(round_mode); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateAvgPoolFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *kernel_size = nullptr, + const std::vector *strides = nullptr, + const std::vector *pad = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + bool global = false, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto strides__ = strides ? _fbb.CreateVector(*strides) : 0; + auto pad__ = pad ? _fbb.CreateVector(*pad) : 0; + return mindspore::schema::CreateAvgPoolFusion( + _fbb, + kernel_size__, + strides__, + pad__, + pad_mode, + round_mode, + format, + global, + activation_type); +} + +flatbuffers::Offset CreateAvgPoolFusion(flatbuffers::FlatBufferBuilder &_fbb, const AvgPoolFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AvgPoolGradT : public flatbuffers::NativeTable { + typedef AvgPoolGrad TableType; + std::vector kernel_size{}; + std::vector strides{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; +}; + +struct AvgPoolGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AvgPoolGradT NativeTableType; + typedef AvgPoolGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AvgPoolGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KERNEL_SIZE = 4, + VT_STRIDES = 6, + VT_PAD_MODE = 8, + VT_FORMAT = 10 + }; + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *strides() const { + return GetPointer *>(VT_STRIDES); + } + flatbuffers::Vector *mutable_strides() { + return GetPointer *>(VT_STRIDES); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDES) && + verifier.VerifyVector(strides()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyField(verifier, VT_FORMAT) && + verifier.EndTable(); + } + AvgPoolGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AvgPoolGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AvgPoolGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AvgPoolGradBuilder { + typedef AvgPoolGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(AvgPoolGrad::VT_KERNEL_SIZE, kernel_size); + } + void add_strides(flatbuffers::Offset> strides) { + fbb_.AddOffset(AvgPoolGrad::VT_STRIDES, strides); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(AvgPoolGrad::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(AvgPoolGrad::VT_FORMAT, static_cast(format), 0); + } + explicit AvgPoolGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAvgPoolGrad( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> strides = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::Format format = mindspore::schema::Format_NCHW) { + AvgPoolGradBuilder builder_(_fbb); + builder_.add_format(format); + builder_.add_strides(strides); + builder_.add_kernel_size(kernel_size); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateAvgPoolGradDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *kernel_size = nullptr, + const std::vector *strides = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::Format format = mindspore::schema::Format_NCHW) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto strides__ = strides ? _fbb.CreateVector(*strides) : 0; + return mindspore::schema::CreateAvgPoolGrad( + _fbb, + kernel_size__, + strides__, + pad_mode, + format); +} + +flatbuffers::Offset CreateAvgPoolGrad(flatbuffers::FlatBufferBuilder &_fbb, const AvgPoolGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchNormT : public flatbuffers::NativeTable { + typedef BatchNorm TableType; + float epsilon = 0.0f; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + bool is_training = false; +}; + +struct BatchNorm FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchNormT NativeTableType; + typedef BatchNormBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BatchNormTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EPSILON = 4, + VT_FORMAT = 6, + VT_IS_TRAINING = 8 + }; + float epsilon() const { + return GetField(VT_EPSILON, 0.0f); + } + bool mutate_epsilon(float _epsilon) { + return SetField(VT_EPSILON, _epsilon, 0.0f); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool is_training() const { + return GetField(VT_IS_TRAINING, 0) != 0; + } + bool mutate_is_training(bool _is_training) { + return SetField(VT_IS_TRAINING, static_cast(_is_training), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_IS_TRAINING) && + verifier.EndTable(); + } + BatchNormT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchNormT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchNormT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchNormBuilder { + typedef BatchNorm Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_epsilon(float epsilon) { + fbb_.AddElement(BatchNorm::VT_EPSILON, epsilon, 0.0f); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(BatchNorm::VT_FORMAT, static_cast(format), 0); + } + void add_is_training(bool is_training) { + fbb_.AddElement(BatchNorm::VT_IS_TRAINING, static_cast(is_training), 0); + } + explicit BatchNormBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchNorm( + flatbuffers::FlatBufferBuilder &_fbb, + float epsilon = 0.0f, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + bool is_training = false) { + BatchNormBuilder builder_(_fbb); + builder_.add_format(format); + builder_.add_epsilon(epsilon); + builder_.add_is_training(is_training); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBatchNorm(flatbuffers::FlatBufferBuilder &_fbb, const BatchNormT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchNormGradT : public flatbuffers::NativeTable { + typedef BatchNormGrad TableType; + float epsilon = 0.0f; + bool is_training = false; +}; + +struct BatchNormGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchNormGradT NativeTableType; + typedef BatchNormGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BatchNormGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EPSILON = 4, + VT_IS_TRAINING = 6 + }; + float epsilon() const { + return GetField(VT_EPSILON, 0.0f); + } + bool mutate_epsilon(float _epsilon) { + return SetField(VT_EPSILON, _epsilon, 0.0f); + } + bool is_training() const { + return GetField(VT_IS_TRAINING, 0) != 0; + } + bool mutate_is_training(bool _is_training) { + return SetField(VT_IS_TRAINING, static_cast(_is_training), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_IS_TRAINING) && + verifier.EndTable(); + } + BatchNormGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchNormGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchNormGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchNormGradBuilder { + typedef BatchNormGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_epsilon(float epsilon) { + fbb_.AddElement(BatchNormGrad::VT_EPSILON, epsilon, 0.0f); + } + void add_is_training(bool is_training) { + fbb_.AddElement(BatchNormGrad::VT_IS_TRAINING, static_cast(is_training), 0); + } + explicit BatchNormGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchNormGrad( + flatbuffers::FlatBufferBuilder &_fbb, + float epsilon = 0.0f, + bool is_training = false) { + BatchNormGradBuilder builder_(_fbb); + builder_.add_epsilon(epsilon); + builder_.add_is_training(is_training); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBatchNormGrad(flatbuffers::FlatBufferBuilder &_fbb, const BatchNormGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchToSpaceT : public flatbuffers::NativeTable { + typedef BatchToSpace TableType; + std::vector block_size{}; + std::unique_ptr crops{}; +}; + +struct BatchToSpace FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchToSpaceT NativeTableType; + typedef BatchToSpaceBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BatchToSpaceTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4, + VT_CROPS = 6 + }; + const flatbuffers::Vector *block_size() const { + return GetPointer *>(VT_BLOCK_SIZE); + } + flatbuffers::Vector *mutable_block_size() { + return GetPointer *>(VT_BLOCK_SIZE); + } + const mindspore::schema::Vec2D *crops() const { + return GetPointer(VT_CROPS); + } + mindspore::schema::Vec2D *mutable_crops() { + return GetPointer(VT_CROPS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_BLOCK_SIZE) && + verifier.VerifyVector(block_size()) && + VerifyOffset(verifier, VT_CROPS) && + verifier.VerifyTable(crops()) && + verifier.EndTable(); + } + BatchToSpaceT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchToSpaceT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchToSpaceBuilder { + typedef BatchToSpace Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(flatbuffers::Offset> block_size) { + fbb_.AddOffset(BatchToSpace::VT_BLOCK_SIZE, block_size); + } + void add_crops(flatbuffers::Offset crops) { + fbb_.AddOffset(BatchToSpace::VT_CROPS, crops); + } + explicit BatchToSpaceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchToSpace( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> block_size = 0, + flatbuffers::Offset crops = 0) { + BatchToSpaceBuilder builder_(_fbb); + builder_.add_crops(crops); + builder_.add_block_size(block_size); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateBatchToSpaceDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *block_size = nullptr, + flatbuffers::Offset crops = 0) { + auto block_size__ = block_size ? _fbb.CreateVector(*block_size) : 0; + return mindspore::schema::CreateBatchToSpace( + _fbb, + block_size__, + crops); +} + +flatbuffers::Offset CreateBatchToSpace(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchToSpaceNDT : public flatbuffers::NativeTable { + typedef BatchToSpaceND TableType; + std::vector block_shape{}; + std::unique_ptr crops{}; +}; + +struct BatchToSpaceND FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchToSpaceNDT NativeTableType; + typedef BatchToSpaceNDBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BatchToSpaceNDTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SHAPE = 4, + VT_CROPS = 6 + }; + const flatbuffers::Vector *block_shape() const { + return GetPointer *>(VT_BLOCK_SHAPE); + } + flatbuffers::Vector *mutable_block_shape() { + return GetPointer *>(VT_BLOCK_SHAPE); + } + const mindspore::schema::Vec2D *crops() const { + return GetPointer(VT_CROPS); + } + mindspore::schema::Vec2D *mutable_crops() { + return GetPointer(VT_CROPS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_BLOCK_SHAPE) && + verifier.VerifyVector(block_shape()) && + VerifyOffset(verifier, VT_CROPS) && + verifier.VerifyTable(crops()) && + verifier.EndTable(); + } + BatchToSpaceNDT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchToSpaceNDT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchToSpaceNDBuilder { + typedef BatchToSpaceND Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_shape(flatbuffers::Offset> block_shape) { + fbb_.AddOffset(BatchToSpaceND::VT_BLOCK_SHAPE, block_shape); + } + void add_crops(flatbuffers::Offset crops) { + fbb_.AddOffset(BatchToSpaceND::VT_CROPS, crops); + } + explicit BatchToSpaceNDBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchToSpaceND( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> block_shape = 0, + flatbuffers::Offset crops = 0) { + BatchToSpaceNDBuilder builder_(_fbb); + builder_.add_crops(crops); + builder_.add_block_shape(block_shape); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateBatchToSpaceNDDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *block_shape = nullptr, + flatbuffers::Offset crops = 0) { + auto block_shape__ = block_shape ? _fbb.CreateVector(*block_shape) : 0; + return mindspore::schema::CreateBatchToSpaceND( + _fbb, + block_shape__, + crops); +} + +flatbuffers::Offset CreateBatchToSpaceND(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BiasAddT : public flatbuffers::NativeTable { + typedef BiasAdd TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; +}; + +struct BiasAdd FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BiasAddT NativeTableType; + typedef BiasAddBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BiasAddTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + verifier.EndTable(); + } + BiasAddT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BiasAddT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BiasAddT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BiasAddBuilder { + typedef BiasAdd Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(BiasAdd::VT_FORMAT, static_cast(format), 0); + } + explicit BiasAddBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBiasAdd( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW) { + BiasAddBuilder builder_(_fbb); + builder_.add_format(format); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBiasAdd(flatbuffers::FlatBufferBuilder &_fbb, const BiasAddT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BinaryCrossEntropyT : public flatbuffers::NativeTable { + typedef BinaryCrossEntropy TableType; + mindspore::schema::Reduction reduction = mindspore::schema::Reduction_REDUCTION_SUM; +}; + +struct BinaryCrossEntropy FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BinaryCrossEntropyT NativeTableType; + typedef BinaryCrossEntropyBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BinaryCrossEntropyTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_REDUCTION = 4 + }; + mindspore::schema::Reduction reduction() const { + return static_cast(GetField(VT_REDUCTION, 0)); + } + bool mutate_reduction(mindspore::schema::Reduction _reduction) { + return SetField(VT_REDUCTION, static_cast(_reduction), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_REDUCTION) && + verifier.EndTable(); + } + BinaryCrossEntropyT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BinaryCrossEntropyT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BinaryCrossEntropyT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BinaryCrossEntropyBuilder { + typedef BinaryCrossEntropy Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_reduction(mindspore::schema::Reduction reduction) { + fbb_.AddElement(BinaryCrossEntropy::VT_REDUCTION, static_cast(reduction), 0); + } + explicit BinaryCrossEntropyBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBinaryCrossEntropy( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Reduction reduction = mindspore::schema::Reduction_REDUCTION_SUM) { + BinaryCrossEntropyBuilder builder_(_fbb); + builder_.add_reduction(reduction); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBinaryCrossEntropy(flatbuffers::FlatBufferBuilder &_fbb, const BinaryCrossEntropyT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BinaryCrossEntropyGradT : public flatbuffers::NativeTable { + typedef BinaryCrossEntropyGrad TableType; + mindspore::schema::Reduction reduction = mindspore::schema::Reduction_MEAN; +}; + +struct BinaryCrossEntropyGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BinaryCrossEntropyGradT NativeTableType; + typedef BinaryCrossEntropyGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BinaryCrossEntropyGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_REDUCTION = 4 + }; + mindspore::schema::Reduction reduction() const { + return static_cast(GetField(VT_REDUCTION, 1)); + } + bool mutate_reduction(mindspore::schema::Reduction _reduction) { + return SetField(VT_REDUCTION, static_cast(_reduction), 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_REDUCTION) && + verifier.EndTable(); + } + BinaryCrossEntropyGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BinaryCrossEntropyGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BinaryCrossEntropyGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BinaryCrossEntropyGradBuilder { + typedef BinaryCrossEntropyGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_reduction(mindspore::schema::Reduction reduction) { + fbb_.AddElement(BinaryCrossEntropyGrad::VT_REDUCTION, static_cast(reduction), 1); + } + explicit BinaryCrossEntropyGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBinaryCrossEntropyGrad( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Reduction reduction = mindspore::schema::Reduction_MEAN) { + BinaryCrossEntropyGradBuilder builder_(_fbb); + builder_.add_reduction(reduction); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBinaryCrossEntropyGrad(flatbuffers::FlatBufferBuilder &_fbb, const BinaryCrossEntropyGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BiasAddGradT : public flatbuffers::NativeTable { + typedef BiasAddGrad TableType; +}; + +struct BiasAddGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BiasAddGradT NativeTableType; + typedef BiasAddGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BiasAddGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + BiasAddGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BiasAddGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BiasAddGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BiasAddGradBuilder { + typedef BiasAddGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit BiasAddGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBiasAddGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + BiasAddGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBiasAddGrad(flatbuffers::FlatBufferBuilder &_fbb, const BiasAddGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BroadcastToT : public flatbuffers::NativeTable { + typedef BroadcastTo TableType; + std::vector shape{}; +}; + +struct BroadcastTo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BroadcastToT NativeTableType; + typedef BroadcastToBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BroadcastToTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SHAPE = 4 + }; + const flatbuffers::Vector *shape() const { + return GetPointer *>(VT_SHAPE); + } + flatbuffers::Vector *mutable_shape() { + return GetPointer *>(VT_SHAPE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.VerifyVector(shape()) && + verifier.EndTable(); + } + BroadcastToT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BroadcastToT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BroadcastToBuilder { + typedef BroadcastTo Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_shape(flatbuffers::Offset> shape) { + fbb_.AddOffset(BroadcastTo::VT_SHAPE, shape); + } + explicit BroadcastToBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBroadcastTo( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> shape = 0) { + BroadcastToBuilder builder_(_fbb); + builder_.add_shape(shape); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateBroadcastToDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *shape = nullptr) { + auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; + return mindspore::schema::CreateBroadcastTo( + _fbb, + shape__); +} + +flatbuffers::Offset CreateBroadcastTo(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CastT : public flatbuffers::NativeTable { + typedef Cast TableType; +}; + +struct Cast FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CastT NativeTableType; + typedef CastBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CastTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CastT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CastT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CastBuilder { + typedef Cast Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CastBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCast( + flatbuffers::FlatBufferBuilder &_fbb) { + CastBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCast(flatbuffers::FlatBufferBuilder &_fbb, const CastT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CeilT : public flatbuffers::NativeTable { + typedef Ceil TableType; +}; + +struct Ceil FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CeilT NativeTableType; + typedef CeilBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CeilTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CeilT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CeilT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CeilT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CeilBuilder { + typedef Ceil Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CeilBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCeil( + flatbuffers::FlatBufferBuilder &_fbb) { + CeilBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCeil(flatbuffers::FlatBufferBuilder &_fbb, const CeilT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ClipT : public flatbuffers::NativeTable { + typedef Clip TableType; + float max = 0.0f; + float min = 0.0f; +}; + +struct Clip FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ClipT NativeTableType; + typedef ClipBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ClipTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MAX = 4, + VT_MIN = 6 + }; + float max() const { + return GetField(VT_MAX, 0.0f); + } + bool mutate_max(float _max) { + return SetField(VT_MAX, _max, 0.0f); + } + float min() const { + return GetField(VT_MIN, 0.0f); + } + bool mutate_min(float _min) { + return SetField(VT_MIN, _min, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MAX) && + VerifyField(verifier, VT_MIN) && + verifier.EndTable(); + } + ClipT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ClipT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ClipT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ClipBuilder { + typedef Clip Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_max(float max) { + fbb_.AddElement(Clip::VT_MAX, max, 0.0f); + } + void add_min(float min) { + fbb_.AddElement(Clip::VT_MIN, min, 0.0f); + } + explicit ClipBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateClip( + flatbuffers::FlatBufferBuilder &_fbb, + float max = 0.0f, + float min = 0.0f) { + ClipBuilder builder_(_fbb); + builder_.add_min(min); + builder_.add_max(max); + return builder_.Finish(); +} + +flatbuffers::Offset CreateClip(flatbuffers::FlatBufferBuilder &_fbb, const ClipT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ConcatT : public flatbuffers::NativeTable { + typedef Concat TableType; + int64_t axis = 0; +}; + +struct Concat FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ConcatT NativeTableType; + typedef ConcatBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ConcatTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + ConcatT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ConcatBuilder { + typedef Concat Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(Concat::VT_AXIS, axis, 0); + } + explicit ConcatBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConcat( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0) { + ConcatBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateConcat(flatbuffers::FlatBufferBuilder &_fbb, const ConcatT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AttentionT : public flatbuffers::NativeTable { + typedef Attention TableType; + int64_t head_num = 0; + int64_t head_size = 0; + bool cross = false; + float scale = 0.0f; +}; + +struct Attention FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AttentionT NativeTableType; + typedef AttentionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AttentionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_HEAD_NUM = 4, + VT_HEAD_SIZE = 6, + VT_CROSS = 8, + VT_SCALE = 10 + }; + int64_t head_num() const { + return GetField(VT_HEAD_NUM, 0); + } + bool mutate_head_num(int64_t _head_num) { + return SetField(VT_HEAD_NUM, _head_num, 0); + } + int64_t head_size() const { + return GetField(VT_HEAD_SIZE, 0); + } + bool mutate_head_size(int64_t _head_size) { + return SetField(VT_HEAD_SIZE, _head_size, 0); + } + bool cross() const { + return GetField(VT_CROSS, 0) != 0; + } + bool mutate_cross(bool _cross) { + return SetField(VT_CROSS, static_cast(_cross), 0); + } + float scale() const { + return GetField(VT_SCALE, 0.0f); + } + bool mutate_scale(float _scale) { + return SetField(VT_SCALE, _scale, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_HEAD_NUM) && + VerifyField(verifier, VT_HEAD_SIZE) && + VerifyField(verifier, VT_CROSS) && + VerifyField(verifier, VT_SCALE) && + verifier.EndTable(); + } + AttentionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AttentionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AttentionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AttentionBuilder { + typedef Attention Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_head_num(int64_t head_num) { + fbb_.AddElement(Attention::VT_HEAD_NUM, head_num, 0); + } + void add_head_size(int64_t head_size) { + fbb_.AddElement(Attention::VT_HEAD_SIZE, head_size, 0); + } + void add_cross(bool cross) { + fbb_.AddElement(Attention::VT_CROSS, static_cast(cross), 0); + } + void add_scale(float scale) { + fbb_.AddElement(Attention::VT_SCALE, scale, 0.0f); + } + explicit AttentionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAttention( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t head_num = 0, + int64_t head_size = 0, + bool cross = false, + float scale = 0.0f) { + AttentionBuilder builder_(_fbb); + builder_.add_head_size(head_size); + builder_.add_head_num(head_num); + builder_.add_scale(scale); + builder_.add_cross(cross); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAttention(flatbuffers::FlatBufferBuilder &_fbb, const AttentionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv2DBackpropFilterFusionT : public flatbuffers::NativeTable { + typedef Conv2DBackpropFilterFusion TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + std::vector kernel_size{}; + std::vector stride{}; + std::vector dilation{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + std::vector pad_list{}; + int64_t mode = 0; + int64_t group = 0; + int64_t in_channel = 0; + int64_t out_channel = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct Conv2DBackpropFilterFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv2DBackpropFilterFusionT NativeTableType; + typedef Conv2DBackpropFilterFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Conv2DBackpropFilterFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_KERNEL_SIZE = 6, + VT_STRIDE = 8, + VT_DILATION = 10, + VT_PAD_MODE = 12, + VT_PAD_LIST = 14, + VT_MODE = 16, + VT_GROUP = 18, + VT_IN_CHANNEL = 20, + VT_OUT_CHANNEL = 22, + VT_ACTIVATION_TYPE = 24 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + flatbuffers::Vector *mutable_stride() { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + flatbuffers::Vector *mutable_dilation() { + return GetPointer *>(VT_DILATION); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + flatbuffers::Vector *mutable_pad_list() { + return GetPointer *>(VT_PAD_LIST); + } + int64_t mode() const { + return GetField(VT_MODE, 0); + } + bool mutate_mode(int64_t _mode) { + return SetField(VT_MODE, _mode, 0); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + bool mutate_group(int64_t _group) { + return SetField(VT_GROUP, _group, 0); + } + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + bool mutate_in_channel(int64_t _in_channel) { + return SetField(VT_IN_CHANNEL, _in_channel, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + bool mutate_out_channel(int64_t _out_channel) { + return SetField(VT_OUT_CHANNEL, _out_channel, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + Conv2DBackpropFilterFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2DBackpropFilterFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DBackpropFilterFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv2DBackpropFilterFusionBuilder { + typedef Conv2DBackpropFilterFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_FORMAT, static_cast(format), 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(Conv2DBackpropFilterFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(Conv2DBackpropFilterFusion::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(Conv2DBackpropFilterFusion::VT_DILATION, dilation); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(Conv2DBackpropFilterFusion::VT_PAD_LIST, pad_list); + } + void add_mode(int64_t mode) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_MODE, mode, 0); + } + void add_group(int64_t group) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_GROUP, group, 0); + } + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_OUT_CHANNEL, out_channel, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(Conv2DBackpropFilterFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit Conv2DBackpropFilterFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv2DBackpropFilterFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad_list = 0, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + Conv2DBackpropFilterFusionBuilder builder_(_fbb); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_group(group); + builder_.add_mode(mode); + builder_.add_pad_list(pad_list); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_kernel_size(kernel_size); + builder_.add_format(format); + builder_.add_activation_type(activation_type); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConv2DBackpropFilterFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const std::vector *kernel_size = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad_list = nullptr, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + return mindspore::schema::CreateConv2DBackpropFilterFusion( + _fbb, + format, + kernel_size__, + stride__, + dilation__, + pad_mode, + pad_list__, + mode, + group, + in_channel, + out_channel, + activation_type); +} + +flatbuffers::Offset CreateConv2DBackpropFilterFusion(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DBackpropFilterFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv2DBackpropInputFusionT : public flatbuffers::NativeTable { + typedef Conv2DBackpropInputFusion TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + std::vector kernel_size{}; + std::vector stride{}; + std::vector dilation{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + std::vector pad{}; + std::vector pad_list{}; + int64_t mode = 0; + int64_t group = 0; + int64_t in_channel = 0; + int64_t out_channel = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct Conv2DBackpropInputFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv2DBackpropInputFusionT NativeTableType; + typedef Conv2DBackpropInputFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Conv2DBackpropInputFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_KERNEL_SIZE = 6, + VT_STRIDE = 8, + VT_DILATION = 10, + VT_PAD_MODE = 12, + VT_PAD = 14, + VT_PAD_LIST = 16, + VT_MODE = 18, + VT_GROUP = 20, + VT_IN_CHANNEL = 22, + VT_OUT_CHANNEL = 24, + VT_ACTIVATION_TYPE = 26 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + flatbuffers::Vector *mutable_stride() { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + flatbuffers::Vector *mutable_dilation() { + return GetPointer *>(VT_DILATION); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + const flatbuffers::Vector *pad() const { + return GetPointer *>(VT_PAD); + } + flatbuffers::Vector *mutable_pad() { + return GetPointer *>(VT_PAD); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + flatbuffers::Vector *mutable_pad_list() { + return GetPointer *>(VT_PAD_LIST); + } + int64_t mode() const { + return GetField(VT_MODE, 0); + } + bool mutate_mode(int64_t _mode) { + return SetField(VT_MODE, _mode, 0); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + bool mutate_group(int64_t _group) { + return SetField(VT_GROUP, _group, 0); + } + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + bool mutate_in_channel(int64_t _in_channel) { + return SetField(VT_IN_CHANNEL, _in_channel, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + bool mutate_out_channel(int64_t _out_channel) { + return SetField(VT_OUT_CHANNEL, _out_channel, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD) && + verifier.VerifyVector(pad()) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + Conv2DBackpropInputFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2DBackpropInputFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DBackpropInputFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv2DBackpropInputFusionBuilder { + typedef Conv2DBackpropInputFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_FORMAT, static_cast(format), 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(Conv2DBackpropInputFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(Conv2DBackpropInputFusion::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(Conv2DBackpropInputFusion::VT_DILATION, dilation); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad(flatbuffers::Offset> pad) { + fbb_.AddOffset(Conv2DBackpropInputFusion::VT_PAD, pad); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(Conv2DBackpropInputFusion::VT_PAD_LIST, pad_list); + } + void add_mode(int64_t mode) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_MODE, mode, 0); + } + void add_group(int64_t group) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_GROUP, group, 0); + } + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_OUT_CHANNEL, out_channel, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(Conv2DBackpropInputFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit Conv2DBackpropInputFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv2DBackpropInputFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad = 0, + flatbuffers::Offset> pad_list = 0, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + Conv2DBackpropInputFusionBuilder builder_(_fbb); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_group(group); + builder_.add_mode(mode); + builder_.add_pad_list(pad_list); + builder_.add_pad(pad); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_kernel_size(kernel_size); + builder_.add_format(format); + builder_.add_activation_type(activation_type); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConv2DBackpropInputFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const std::vector *kernel_size = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad = nullptr, + const std::vector *pad_list = nullptr, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + auto pad__ = pad ? _fbb.CreateVector(*pad) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + return mindspore::schema::CreateConv2DBackpropInputFusion( + _fbb, + format, + kernel_size__, + stride__, + dilation__, + pad_mode, + pad__, + pad_list__, + mode, + group, + in_channel, + out_channel, + activation_type); +} + +flatbuffers::Offset CreateConv2DBackpropInputFusion(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DBackpropInputFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv2DFusionT : public flatbuffers::NativeTable { + typedef Conv2DFusion TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + std::vector kernel_size{}; + std::vector stride{}; + std::vector dilation{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + std::vector pad_list{}; + int64_t mode = 0; + int64_t group = 0; + int64_t in_channel = 0; + int64_t out_channel = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct Conv2DFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv2DFusionT NativeTableType; + typedef Conv2DFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Conv2DFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_KERNEL_SIZE = 6, + VT_STRIDE = 8, + VT_DILATION = 10, + VT_PAD_MODE = 12, + VT_PAD_LIST = 14, + VT_MODE = 16, + VT_GROUP = 18, + VT_IN_CHANNEL = 20, + VT_OUT_CHANNEL = 22, + VT_ACTIVATION_TYPE = 24 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + flatbuffers::Vector *mutable_stride() { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + flatbuffers::Vector *mutable_dilation() { + return GetPointer *>(VT_DILATION); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + flatbuffers::Vector *mutable_pad_list() { + return GetPointer *>(VT_PAD_LIST); + } + int64_t mode() const { + return GetField(VT_MODE, 0); + } + bool mutate_mode(int64_t _mode) { + return SetField(VT_MODE, _mode, 0); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + bool mutate_group(int64_t _group) { + return SetField(VT_GROUP, _group, 0); + } + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + bool mutate_in_channel(int64_t _in_channel) { + return SetField(VT_IN_CHANNEL, _in_channel, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + bool mutate_out_channel(int64_t _out_channel) { + return SetField(VT_OUT_CHANNEL, _out_channel, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + Conv2DFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2DFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv2DFusionBuilder { + typedef Conv2DFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(Conv2DFusion::VT_FORMAT, static_cast(format), 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(Conv2DFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(Conv2DFusion::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(Conv2DFusion::VT_DILATION, dilation); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(Conv2DFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(Conv2DFusion::VT_PAD_LIST, pad_list); + } + void add_mode(int64_t mode) { + fbb_.AddElement(Conv2DFusion::VT_MODE, mode, 0); + } + void add_group(int64_t group) { + fbb_.AddElement(Conv2DFusion::VT_GROUP, group, 0); + } + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(Conv2DFusion::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(Conv2DFusion::VT_OUT_CHANNEL, out_channel, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(Conv2DFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit Conv2DFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv2DFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad_list = 0, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + Conv2DFusionBuilder builder_(_fbb); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_group(group); + builder_.add_mode(mode); + builder_.add_pad_list(pad_list); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_kernel_size(kernel_size); + builder_.add_format(format); + builder_.add_activation_type(activation_type); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConv2DFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const std::vector *kernel_size = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad_list = nullptr, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + return mindspore::schema::CreateConv2DFusion( + _fbb, + format, + kernel_size__, + stride__, + dilation__, + pad_mode, + pad_list__, + mode, + group, + in_channel, + out_channel, + activation_type); +} + +flatbuffers::Offset CreateConv2DFusion(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv2dTransposeFusionT : public flatbuffers::NativeTable { + typedef Conv2dTransposeFusion TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + std::vector kernel_size{}; + std::vector stride{}; + std::vector dilation{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + std::vector pad{}; + std::vector pad_list{}; + int64_t mode = 0; + int64_t group = 0; + int64_t in_channel = 0; + int64_t out_channel = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; + std::vector output_paddings{}; +}; + +struct Conv2dTransposeFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv2dTransposeFusionT NativeTableType; + typedef Conv2dTransposeFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Conv2dTransposeFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_KERNEL_SIZE = 6, + VT_STRIDE = 8, + VT_DILATION = 10, + VT_PAD_MODE = 12, + VT_PAD = 14, + VT_PAD_LIST = 16, + VT_MODE = 18, + VT_GROUP = 20, + VT_IN_CHANNEL = 22, + VT_OUT_CHANNEL = 24, + VT_ACTIVATION_TYPE = 26, + VT_OUTPUT_PADDINGS = 28 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + flatbuffers::Vector *mutable_stride() { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + flatbuffers::Vector *mutable_dilation() { + return GetPointer *>(VT_DILATION); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + const flatbuffers::Vector *pad() const { + return GetPointer *>(VT_PAD); + } + flatbuffers::Vector *mutable_pad() { + return GetPointer *>(VT_PAD); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + flatbuffers::Vector *mutable_pad_list() { + return GetPointer *>(VT_PAD_LIST); + } + int64_t mode() const { + return GetField(VT_MODE, 0); + } + bool mutate_mode(int64_t _mode) { + return SetField(VT_MODE, _mode, 0); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + bool mutate_group(int64_t _group) { + return SetField(VT_GROUP, _group, 0); + } + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + bool mutate_in_channel(int64_t _in_channel) { + return SetField(VT_IN_CHANNEL, _in_channel, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + bool mutate_out_channel(int64_t _out_channel) { + return SetField(VT_OUT_CHANNEL, _out_channel, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + const flatbuffers::Vector *output_paddings() const { + return GetPointer *>(VT_OUTPUT_PADDINGS); + } + flatbuffers::Vector *mutable_output_paddings() { + return GetPointer *>(VT_OUTPUT_PADDINGS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD) && + verifier.VerifyVector(pad()) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + VerifyOffset(verifier, VT_OUTPUT_PADDINGS) && + verifier.VerifyVector(output_paddings()) && + verifier.EndTable(); + } + Conv2dTransposeFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2dTransposeFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2dTransposeFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv2dTransposeFusionBuilder { + typedef Conv2dTransposeFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(Conv2dTransposeFusion::VT_FORMAT, static_cast(format), 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(Conv2dTransposeFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(Conv2dTransposeFusion::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(Conv2dTransposeFusion::VT_DILATION, dilation); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(Conv2dTransposeFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad(flatbuffers::Offset> pad) { + fbb_.AddOffset(Conv2dTransposeFusion::VT_PAD, pad); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(Conv2dTransposeFusion::VT_PAD_LIST, pad_list); + } + void add_mode(int64_t mode) { + fbb_.AddElement(Conv2dTransposeFusion::VT_MODE, mode, 0); + } + void add_group(int64_t group) { + fbb_.AddElement(Conv2dTransposeFusion::VT_GROUP, group, 0); + } + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(Conv2dTransposeFusion::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(Conv2dTransposeFusion::VT_OUT_CHANNEL, out_channel, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(Conv2dTransposeFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + void add_output_paddings(flatbuffers::Offset> output_paddings) { + fbb_.AddOffset(Conv2dTransposeFusion::VT_OUTPUT_PADDINGS, output_paddings); + } + explicit Conv2dTransposeFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv2dTransposeFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad = 0, + flatbuffers::Offset> pad_list = 0, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + flatbuffers::Offset> output_paddings = 0) { + Conv2dTransposeFusionBuilder builder_(_fbb); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_group(group); + builder_.add_mode(mode); + builder_.add_output_paddings(output_paddings); + builder_.add_pad_list(pad_list); + builder_.add_pad(pad); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_kernel_size(kernel_size); + builder_.add_format(format); + builder_.add_activation_type(activation_type); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConv2dTransposeFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const std::vector *kernel_size = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad = nullptr, + const std::vector *pad_list = nullptr, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + const std::vector *output_paddings = nullptr) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + auto pad__ = pad ? _fbb.CreateVector(*pad) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + auto output_paddings__ = output_paddings ? _fbb.CreateVector(*output_paddings) : 0; + return mindspore::schema::CreateConv2dTransposeFusion( + _fbb, + format, + kernel_size__, + stride__, + dilation__, + pad_mode, + pad__, + pad_list__, + mode, + group, + in_channel, + out_channel, + activation_type, + output_paddings__); +} + +flatbuffers::Offset CreateConv2dTransposeFusion(flatbuffers::FlatBufferBuilder &_fbb, const Conv2dTransposeFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CosT : public flatbuffers::NativeTable { + typedef Cos TableType; +}; + +struct Cos FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CosT NativeTableType; + typedef CosBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CosTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CosT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CosT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CosBuilder { + typedef Cos Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CosBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCos( + flatbuffers::FlatBufferBuilder &_fbb) { + CosBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCos(flatbuffers::FlatBufferBuilder &_fbb, const CosT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ConstantOfShapeT : public flatbuffers::NativeTable { + typedef ConstantOfShape TableType; + int64_t data_type = 0; + std::vector value{}; +}; + +struct ConstantOfShape FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ConstantOfShapeT NativeTableType; + typedef ConstantOfShapeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ConstantOfShapeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DATA_TYPE = 4, + VT_VALUE = 6 + }; + int64_t data_type() const { + return GetField(VT_DATA_TYPE, 0); + } + bool mutate_data_type(int64_t _data_type) { + return SetField(VT_DATA_TYPE, _data_type, 0); + } + const flatbuffers::Vector *value() const { + return GetPointer *>(VT_VALUE); + } + flatbuffers::Vector *mutable_value() { + return GetPointer *>(VT_VALUE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_DATA_TYPE) && + VerifyOffset(verifier, VT_VALUE) && + verifier.VerifyVector(value()) && + verifier.EndTable(); + } + ConstantOfShapeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConstantOfShapeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConstantOfShapeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ConstantOfShapeBuilder { + typedef ConstantOfShape Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_data_type(int64_t data_type) { + fbb_.AddElement(ConstantOfShape::VT_DATA_TYPE, data_type, 0); + } + void add_value(flatbuffers::Offset> value) { + fbb_.AddOffset(ConstantOfShape::VT_VALUE, value); + } + explicit ConstantOfShapeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConstantOfShape( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t data_type = 0, + flatbuffers::Offset> value = 0) { + ConstantOfShapeBuilder builder_(_fbb); + builder_.add_data_type(data_type); + builder_.add_value(value); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConstantOfShapeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t data_type = 0, + const std::vector *value = nullptr) { + auto value__ = value ? _fbb.CreateVector(*value) : 0; + return mindspore::schema::CreateConstantOfShape( + _fbb, + data_type, + value__); +} + +flatbuffers::Offset CreateConstantOfShape(flatbuffers::FlatBufferBuilder &_fbb, const ConstantOfShapeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CropT : public flatbuffers::NativeTable { + typedef Crop TableType; + int64_t axis = 0; + std::vector offsets{}; +}; + +struct Crop FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CropT NativeTableType; + typedef CropBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CropTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_OFFSETS = 6 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + const flatbuffers::Vector *offsets() const { + return GetPointer *>(VT_OFFSETS); + } + flatbuffers::Vector *mutable_offsets() { + return GetPointer *>(VT_OFFSETS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + VerifyOffset(verifier, VT_OFFSETS) && + verifier.VerifyVector(offsets()) && + verifier.EndTable(); + } + CropT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CropT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CropT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CropBuilder { + typedef Crop Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(Crop::VT_AXIS, axis, 0); + } + void add_offsets(flatbuffers::Offset> offsets) { + fbb_.AddOffset(Crop::VT_OFFSETS, offsets); + } + explicit CropBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCrop( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0, + flatbuffers::Offset> offsets = 0) { + CropBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_offsets(offsets); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateCropDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0, + const std::vector *offsets = nullptr) { + auto offsets__ = offsets ? _fbb.CreateVector(*offsets) : 0; + return mindspore::schema::CreateCrop( + _fbb, + axis, + offsets__); +} + +flatbuffers::Offset CreateCrop(flatbuffers::FlatBufferBuilder &_fbb, const CropT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CustomExtractFeaturesT : public flatbuffers::NativeTable { + typedef CustomExtractFeatures TableType; +}; + +struct CustomExtractFeatures FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CustomExtractFeaturesT NativeTableType; + typedef CustomExtractFeaturesBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CustomExtractFeaturesTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CustomExtractFeaturesT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CustomExtractFeaturesT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomExtractFeaturesT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CustomExtractFeaturesBuilder { + typedef CustomExtractFeatures Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CustomExtractFeaturesBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCustomExtractFeatures( + flatbuffers::FlatBufferBuilder &_fbb) { + CustomExtractFeaturesBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCustomExtractFeatures(flatbuffers::FlatBufferBuilder &_fbb, const CustomExtractFeaturesT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CustomNormalizeT : public flatbuffers::NativeTable { + typedef CustomNormalize TableType; +}; + +struct CustomNormalize FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CustomNormalizeT NativeTableType; + typedef CustomNormalizeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CustomNormalizeTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CustomNormalizeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CustomNormalizeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomNormalizeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CustomNormalizeBuilder { + typedef CustomNormalize Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CustomNormalizeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCustomNormalize( + flatbuffers::FlatBufferBuilder &_fbb) { + CustomNormalizeBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCustomNormalize(flatbuffers::FlatBufferBuilder &_fbb, const CustomNormalizeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CustomPredictT : public flatbuffers::NativeTable { + typedef CustomPredict TableType; + int64_t output_num = 0; + float weight_threshold = 0.0f; +}; + +struct CustomPredict FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CustomPredictT NativeTableType; + typedef CustomPredictBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CustomPredictTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUTPUT_NUM = 4, + VT_WEIGHT_THRESHOLD = 6 + }; + int64_t output_num() const { + return GetField(VT_OUTPUT_NUM, 0); + } + bool mutate_output_num(int64_t _output_num) { + return SetField(VT_OUTPUT_NUM, _output_num, 0); + } + float weight_threshold() const { + return GetField(VT_WEIGHT_THRESHOLD, 0.0f); + } + bool mutate_weight_threshold(float _weight_threshold) { + return SetField(VT_WEIGHT_THRESHOLD, _weight_threshold, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_NUM) && + VerifyField(verifier, VT_WEIGHT_THRESHOLD) && + verifier.EndTable(); + } + CustomPredictT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CustomPredictT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomPredictT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CustomPredictBuilder { + typedef CustomPredict Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_num(int64_t output_num) { + fbb_.AddElement(CustomPredict::VT_OUTPUT_NUM, output_num, 0); + } + void add_weight_threshold(float weight_threshold) { + fbb_.AddElement(CustomPredict::VT_WEIGHT_THRESHOLD, weight_threshold, 0.0f); + } + explicit CustomPredictBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCustomPredict( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t output_num = 0, + float weight_threshold = 0.0f) { + CustomPredictBuilder builder_(_fbb); + builder_.add_output_num(output_num); + builder_.add_weight_threshold(weight_threshold); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCustomPredict(flatbuffers::FlatBufferBuilder &_fbb, const CustomPredictT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DeConv2DGradFilterT : public flatbuffers::NativeTable { + typedef DeConv2DGradFilter TableType; + int64_t in_channel = 0; + int64_t out_channel = 0; + std::vector kernel_size{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + std::vector pad_list{}; + std::vector stride{}; + std::vector dilation{}; + int64_t group = 0; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct DeConv2DGradFilter FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DeConv2DGradFilterT NativeTableType; + typedef DeConv2DGradFilterBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DeConv2DGradFilterTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IN_CHANNEL = 4, + VT_OUT_CHANNEL = 6, + VT_KERNEL_SIZE = 8, + VT_PAD_MODE = 10, + VT_PAD_LIST = 12, + VT_STRIDE = 14, + VT_DILATION = 16, + VT_GROUP = 18, + VT_FORMAT = 20, + VT_ACTIVATION_TYPE = 22 + }; + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + bool mutate_in_channel(int64_t _in_channel) { + return SetField(VT_IN_CHANNEL, _in_channel, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + bool mutate_out_channel(int64_t _out_channel) { + return SetField(VT_OUT_CHANNEL, _out_channel, 0); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + flatbuffers::Vector *mutable_pad_list() { + return GetPointer *>(VT_PAD_LIST); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + flatbuffers::Vector *mutable_stride() { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + flatbuffers::Vector *mutable_dilation() { + return GetPointer *>(VT_DILATION); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + bool mutate_group(int64_t _group) { + return SetField(VT_GROUP, _group, 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + DeConv2DGradFilterT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DeConv2DGradFilterT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DeConv2DGradFilterT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DeConv2DGradFilterBuilder { + typedef DeConv2DGradFilter Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(DeConv2DGradFilter::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(DeConv2DGradFilter::VT_OUT_CHANNEL, out_channel, 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(DeConv2DGradFilter::VT_KERNEL_SIZE, kernel_size); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(DeConv2DGradFilter::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(DeConv2DGradFilter::VT_PAD_LIST, pad_list); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(DeConv2DGradFilter::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(DeConv2DGradFilter::VT_DILATION, dilation); + } + void add_group(int64_t group) { + fbb_.AddElement(DeConv2DGradFilter::VT_GROUP, group, 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(DeConv2DGradFilter::VT_FORMAT, static_cast(format), 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(DeConv2DGradFilter::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit DeConv2DGradFilterBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDeConv2DGradFilter( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t in_channel = 0, + int64_t out_channel = 0, + flatbuffers::Offset> kernel_size = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad_list = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + int64_t group = 0, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + DeConv2DGradFilterBuilder builder_(_fbb); + builder_.add_group(group); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_format(format); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_pad_list(pad_list); + builder_.add_kernel_size(kernel_size); + builder_.add_activation_type(activation_type); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateDeConv2DGradFilterDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t in_channel = 0, + int64_t out_channel = 0, + const std::vector *kernel_size = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad_list = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + int64_t group = 0, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + return mindspore::schema::CreateDeConv2DGradFilter( + _fbb, + in_channel, + out_channel, + kernel_size__, + pad_mode, + pad_list__, + stride__, + dilation__, + group, + format, + activation_type); +} + +flatbuffers::Offset CreateDeConv2DGradFilter(flatbuffers::FlatBufferBuilder &_fbb, const DeConv2DGradFilterT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DependT : public flatbuffers::NativeTable { + typedef Depend TableType; +}; + +struct Depend FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DependT NativeTableType; + typedef DependBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DependTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DependT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DependT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DependT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DependBuilder { + typedef Depend Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DependBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDepend( + flatbuffers::FlatBufferBuilder &_fbb) { + DependBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDepend(flatbuffers::FlatBufferBuilder &_fbb, const DependT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DepthToSpaceT : public flatbuffers::NativeTable { + typedef DepthToSpace TableType; + int64_t block_size = 0; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + std::string mode{}; +}; + +struct DepthToSpace FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DepthToSpaceT NativeTableType; + typedef DepthToSpaceBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DepthToSpaceTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4, + VT_FORMAT = 6, + VT_MODE = 8 + }; + int64_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } + bool mutate_block_size(int64_t _block_size) { + return SetField(VT_BLOCK_SIZE, _block_size, 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + const flatbuffers::String *mode() const { + return GetPointer(VT_MODE); + } + flatbuffers::String *mutable_mode() { + return GetPointer(VT_MODE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BLOCK_SIZE) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_MODE) && + verifier.VerifyString(mode()) && + verifier.EndTable(); + } + DepthToSpaceT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DepthToSpaceT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DepthToSpaceBuilder { + typedef DepthToSpace Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(int64_t block_size) { + fbb_.AddElement(DepthToSpace::VT_BLOCK_SIZE, block_size, 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(DepthToSpace::VT_FORMAT, static_cast(format), 0); + } + void add_mode(flatbuffers::Offset mode) { + fbb_.AddOffset(DepthToSpace::VT_MODE, mode); + } + explicit DepthToSpaceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDepthToSpace( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t block_size = 0, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset mode = 0) { + DepthToSpaceBuilder builder_(_fbb); + builder_.add_block_size(block_size); + builder_.add_mode(mode); + builder_.add_format(format); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateDepthToSpaceDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t block_size = 0, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const char *mode = nullptr) { + auto mode__ = mode ? _fbb.CreateString(mode) : 0; + return mindspore::schema::CreateDepthToSpace( + _fbb, + block_size, + format, + mode__); +} + +flatbuffers::Offset CreateDepthToSpace(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DetectionPostProcessT : public flatbuffers::NativeTable { + typedef DetectionPostProcess TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + int64_t input_size = 0; + std::vector scale{}; + float nms_iou_threshold = 0.0f; + float nms_score_threshold = 0.0f; + int64_t max_detections = 0; + int64_t detections_per_class = 0; + int64_t max_classes_per_detection = 0; + int64_t num_classes = 0; + bool use_regular_nms = false; + bool out_quantized = false; +}; + +struct DetectionPostProcess FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DetectionPostProcessT NativeTableType; + typedef DetectionPostProcessBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DetectionPostProcessTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_INPUT_SIZE = 6, + VT_SCALE = 8, + VT_NMS_IOU_THRESHOLD = 10, + VT_NMS_SCORE_THRESHOLD = 12, + VT_MAX_DETECTIONS = 14, + VT_DETECTIONS_PER_CLASS = 16, + VT_MAX_CLASSES_PER_DETECTION = 18, + VT_NUM_CLASSES = 20, + VT_USE_REGULAR_NMS = 22, + VT_OUT_QUANTIZED = 24 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + int64_t input_size() const { + return GetField(VT_INPUT_SIZE, 0); + } + bool mutate_input_size(int64_t _input_size) { + return SetField(VT_INPUT_SIZE, _input_size, 0); + } + const flatbuffers::Vector *scale() const { + return GetPointer *>(VT_SCALE); + } + flatbuffers::Vector *mutable_scale() { + return GetPointer *>(VT_SCALE); + } + float nms_iou_threshold() const { + return GetField(VT_NMS_IOU_THRESHOLD, 0.0f); + } + bool mutate_nms_iou_threshold(float _nms_iou_threshold) { + return SetField(VT_NMS_IOU_THRESHOLD, _nms_iou_threshold, 0.0f); + } + float nms_score_threshold() const { + return GetField(VT_NMS_SCORE_THRESHOLD, 0.0f); + } + bool mutate_nms_score_threshold(float _nms_score_threshold) { + return SetField(VT_NMS_SCORE_THRESHOLD, _nms_score_threshold, 0.0f); + } + int64_t max_detections() const { + return GetField(VT_MAX_DETECTIONS, 0); + } + bool mutate_max_detections(int64_t _max_detections) { + return SetField(VT_MAX_DETECTIONS, _max_detections, 0); + } + int64_t detections_per_class() const { + return GetField(VT_DETECTIONS_PER_CLASS, 0); + } + bool mutate_detections_per_class(int64_t _detections_per_class) { + return SetField(VT_DETECTIONS_PER_CLASS, _detections_per_class, 0); + } + int64_t max_classes_per_detection() const { + return GetField(VT_MAX_CLASSES_PER_DETECTION, 0); + } + bool mutate_max_classes_per_detection(int64_t _max_classes_per_detection) { + return SetField(VT_MAX_CLASSES_PER_DETECTION, _max_classes_per_detection, 0); + } + int64_t num_classes() const { + return GetField(VT_NUM_CLASSES, 0); + } + bool mutate_num_classes(int64_t _num_classes) { + return SetField(VT_NUM_CLASSES, _num_classes, 0); + } + bool use_regular_nms() const { + return GetField(VT_USE_REGULAR_NMS, 0) != 0; + } + bool mutate_use_regular_nms(bool _use_regular_nms) { + return SetField(VT_USE_REGULAR_NMS, static_cast(_use_regular_nms), 0); + } + bool out_quantized() const { + return GetField(VT_OUT_QUANTIZED, 0) != 0; + } + bool mutate_out_quantized(bool _out_quantized) { + return SetField(VT_OUT_QUANTIZED, static_cast(_out_quantized), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_INPUT_SIZE) && + VerifyOffset(verifier, VT_SCALE) && + verifier.VerifyVector(scale()) && + VerifyField(verifier, VT_NMS_IOU_THRESHOLD) && + VerifyField(verifier, VT_NMS_SCORE_THRESHOLD) && + VerifyField(verifier, VT_MAX_DETECTIONS) && + VerifyField(verifier, VT_DETECTIONS_PER_CLASS) && + VerifyField(verifier, VT_MAX_CLASSES_PER_DETECTION) && + VerifyField(verifier, VT_NUM_CLASSES) && + VerifyField(verifier, VT_USE_REGULAR_NMS) && + VerifyField(verifier, VT_OUT_QUANTIZED) && + verifier.EndTable(); + } + DetectionPostProcessT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DetectionPostProcessT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DetectionPostProcessT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DetectionPostProcessBuilder { + typedef DetectionPostProcess Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(DetectionPostProcess::VT_FORMAT, static_cast(format), 0); + } + void add_input_size(int64_t input_size) { + fbb_.AddElement(DetectionPostProcess::VT_INPUT_SIZE, input_size, 0); + } + void add_scale(flatbuffers::Offset> scale) { + fbb_.AddOffset(DetectionPostProcess::VT_SCALE, scale); + } + void add_nms_iou_threshold(float nms_iou_threshold) { + fbb_.AddElement(DetectionPostProcess::VT_NMS_IOU_THRESHOLD, nms_iou_threshold, 0.0f); + } + void add_nms_score_threshold(float nms_score_threshold) { + fbb_.AddElement(DetectionPostProcess::VT_NMS_SCORE_THRESHOLD, nms_score_threshold, 0.0f); + } + void add_max_detections(int64_t max_detections) { + fbb_.AddElement(DetectionPostProcess::VT_MAX_DETECTIONS, max_detections, 0); + } + void add_detections_per_class(int64_t detections_per_class) { + fbb_.AddElement(DetectionPostProcess::VT_DETECTIONS_PER_CLASS, detections_per_class, 0); + } + void add_max_classes_per_detection(int64_t max_classes_per_detection) { + fbb_.AddElement(DetectionPostProcess::VT_MAX_CLASSES_PER_DETECTION, max_classes_per_detection, 0); + } + void add_num_classes(int64_t num_classes) { + fbb_.AddElement(DetectionPostProcess::VT_NUM_CLASSES, num_classes, 0); + } + void add_use_regular_nms(bool use_regular_nms) { + fbb_.AddElement(DetectionPostProcess::VT_USE_REGULAR_NMS, static_cast(use_regular_nms), 0); + } + void add_out_quantized(bool out_quantized) { + fbb_.AddElement(DetectionPostProcess::VT_OUT_QUANTIZED, static_cast(out_quantized), 0); + } + explicit DetectionPostProcessBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDetectionPostProcess( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + int64_t input_size = 0, + flatbuffers::Offset> scale = 0, + float nms_iou_threshold = 0.0f, + float nms_score_threshold = 0.0f, + int64_t max_detections = 0, + int64_t detections_per_class = 0, + int64_t max_classes_per_detection = 0, + int64_t num_classes = 0, + bool use_regular_nms = false, + bool out_quantized = false) { + DetectionPostProcessBuilder builder_(_fbb); + builder_.add_num_classes(num_classes); + builder_.add_max_classes_per_detection(max_classes_per_detection); + builder_.add_detections_per_class(detections_per_class); + builder_.add_max_detections(max_detections); + builder_.add_input_size(input_size); + builder_.add_nms_score_threshold(nms_score_threshold); + builder_.add_nms_iou_threshold(nms_iou_threshold); + builder_.add_scale(scale); + builder_.add_format(format); + builder_.add_out_quantized(out_quantized); + builder_.add_use_regular_nms(use_regular_nms); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateDetectionPostProcessDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + int64_t input_size = 0, + const std::vector *scale = nullptr, + float nms_iou_threshold = 0.0f, + float nms_score_threshold = 0.0f, + int64_t max_detections = 0, + int64_t detections_per_class = 0, + int64_t max_classes_per_detection = 0, + int64_t num_classes = 0, + bool use_regular_nms = false, + bool out_quantized = false) { + auto scale__ = scale ? _fbb.CreateVector(*scale) : 0; + return mindspore::schema::CreateDetectionPostProcess( + _fbb, + format, + input_size, + scale__, + nms_iou_threshold, + nms_score_threshold, + max_detections, + detections_per_class, + max_classes_per_detection, + num_classes, + use_regular_nms, + out_quantized); +} + +flatbuffers::Offset CreateDetectionPostProcess(flatbuffers::FlatBufferBuilder &_fbb, const DetectionPostProcessT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DivFusionT : public flatbuffers::NativeTable { + typedef DivFusion TableType; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct DivFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DivFusionT NativeTableType; + typedef DivFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DivFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + DivFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DivFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DivFusionBuilder { + typedef DivFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(DivFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit DivFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDivFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + DivFusionBuilder builder_(_fbb); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDivFusion(flatbuffers::FlatBufferBuilder &_fbb, const DivFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DivGradT : public flatbuffers::NativeTable { + typedef DivGrad TableType; +}; + +struct DivGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DivGradT NativeTableType; + typedef DivGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DivGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DivGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DivGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DivGradBuilder { + typedef DivGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DivGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDivGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + DivGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDivGrad(flatbuffers::FlatBufferBuilder &_fbb, const DivGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DropoutT : public flatbuffers::NativeTable { + typedef Dropout TableType; + float keep_prob = 0.5f; +}; + +struct Dropout FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DropoutT NativeTableType; + typedef DropoutBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DropoutTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEEP_PROB = 4 + }; + float keep_prob() const { + return GetField(VT_KEEP_PROB, 0.5f); + } + bool mutate_keep_prob(float _keep_prob) { + return SetField(VT_KEEP_PROB, _keep_prob, 0.5f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_KEEP_PROB) && + verifier.EndTable(); + } + DropoutT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DropoutT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DropoutT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DropoutBuilder { + typedef Dropout Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_keep_prob(float keep_prob) { + fbb_.AddElement(Dropout::VT_KEEP_PROB, keep_prob, 0.5f); + } + explicit DropoutBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDropout( + flatbuffers::FlatBufferBuilder &_fbb, + float keep_prob = 0.5f) { + DropoutBuilder builder_(_fbb); + builder_.add_keep_prob(keep_prob); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDropout(flatbuffers::FlatBufferBuilder &_fbb, const DropoutT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DropoutGradT : public flatbuffers::NativeTable { + typedef DropoutGrad TableType; + float keep_prob = 0.0f; +}; + +struct DropoutGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DropoutGradT NativeTableType; + typedef DropoutGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DropoutGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEEP_PROB = 4 + }; + float keep_prob() const { + return GetField(VT_KEEP_PROB, 0.0f); + } + bool mutate_keep_prob(float _keep_prob) { + return SetField(VT_KEEP_PROB, _keep_prob, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_KEEP_PROB) && + verifier.EndTable(); + } + DropoutGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DropoutGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DropoutGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DropoutGradBuilder { + typedef DropoutGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_keep_prob(float keep_prob) { + fbb_.AddElement(DropoutGrad::VT_KEEP_PROB, keep_prob, 0.0f); + } + explicit DropoutGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDropoutGrad( + flatbuffers::FlatBufferBuilder &_fbb, + float keep_prob = 0.0f) { + DropoutGradBuilder builder_(_fbb); + builder_.add_keep_prob(keep_prob); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDropoutGrad(flatbuffers::FlatBufferBuilder &_fbb, const DropoutGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EluT : public flatbuffers::NativeTable { + typedef Elu TableType; + float alpha = 0.0f; +}; + +struct Elu FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EluT NativeTableType; + typedef EluBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return EluTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALPHA = 4 + }; + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool mutate_alpha(float _alpha) { + return SetField(VT_ALPHA, _alpha, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALPHA) && + verifier.EndTable(); + } + EluT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EluT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EluT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EluBuilder { + typedef Elu Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_alpha(float alpha) { + fbb_.AddElement(Elu::VT_ALPHA, alpha, 0.0f); + } + explicit EluBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateElu( + flatbuffers::FlatBufferBuilder &_fbb, + float alpha = 0.0f) { + EluBuilder builder_(_fbb); + builder_.add_alpha(alpha); + return builder_.Finish(); +} + +flatbuffers::Offset CreateElu(flatbuffers::FlatBufferBuilder &_fbb, const EluT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EltwiseT : public flatbuffers::NativeTable { + typedef Eltwise TableType; + mindspore::schema::EltwiseMode mode = mindspore::schema::EltwiseMode_PROD; +}; + +struct Eltwise FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EltwiseT NativeTableType; + typedef EltwiseBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return EltwiseTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MODE = 4 + }; + mindspore::schema::EltwiseMode mode() const { + return static_cast(GetField(VT_MODE, 0)); + } + bool mutate_mode(mindspore::schema::EltwiseMode _mode) { + return SetField(VT_MODE, static_cast(_mode), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MODE) && + verifier.EndTable(); + } + EltwiseT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EltwiseT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EltwiseT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EltwiseBuilder { + typedef Eltwise Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_mode(mindspore::schema::EltwiseMode mode) { + fbb_.AddElement(Eltwise::VT_MODE, static_cast(mode), 0); + } + explicit EltwiseBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEltwise( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::EltwiseMode mode = mindspore::schema::EltwiseMode_PROD) { + EltwiseBuilder builder_(_fbb); + builder_.add_mode(mode); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEltwise(flatbuffers::FlatBufferBuilder &_fbb, const EltwiseT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EqualT : public flatbuffers::NativeTable { + typedef Equal TableType; +}; + +struct Equal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EqualT NativeTableType; + typedef EqualBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return EqualTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + EqualT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EqualT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EqualBuilder { + typedef Equal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit EqualBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEqual( + flatbuffers::FlatBufferBuilder &_fbb) { + EqualBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEqual(flatbuffers::FlatBufferBuilder &_fbb, const EqualT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EmbeddingLookupFusionT : public flatbuffers::NativeTable { + typedef EmbeddingLookupFusion TableType; + float max_norm = 0.0f; +}; + +struct EmbeddingLookupFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EmbeddingLookupFusionT NativeTableType; + typedef EmbeddingLookupFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return EmbeddingLookupFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MAX_NORM = 4 + }; + float max_norm() const { + return GetField(VT_MAX_NORM, 0.0f); + } + bool mutate_max_norm(float _max_norm) { + return SetField(VT_MAX_NORM, _max_norm, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MAX_NORM) && + verifier.EndTable(); + } + EmbeddingLookupFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EmbeddingLookupFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EmbeddingLookupFusionBuilder { + typedef EmbeddingLookupFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_max_norm(float max_norm) { + fbb_.AddElement(EmbeddingLookupFusion::VT_MAX_NORM, max_norm, 0.0f); + } + explicit EmbeddingLookupFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEmbeddingLookupFusion( + flatbuffers::FlatBufferBuilder &_fbb, + float max_norm = 0.0f) { + EmbeddingLookupFusionBuilder builder_(_fbb); + builder_.add_max_norm(max_norm); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEmbeddingLookupFusion(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpFusionT : public flatbuffers::NativeTable { + typedef ExpFusion TableType; + float base = -1.0f; + float scale = 1.0f; + float shift = 0.0f; +}; + +struct ExpFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpFusionT NativeTableType; + typedef ExpFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ExpFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BASE = 4, + VT_SCALE = 6, + VT_SHIFT = 8 + }; + float base() const { + return GetField(VT_BASE, -1.0f); + } + bool mutate_base(float _base) { + return SetField(VT_BASE, _base, -1.0f); + } + float scale() const { + return GetField(VT_SCALE, 1.0f); + } + bool mutate_scale(float _scale) { + return SetField(VT_SCALE, _scale, 1.0f); + } + float shift() const { + return GetField(VT_SHIFT, 0.0f); + } + bool mutate_shift(float _shift) { + return SetField(VT_SHIFT, _shift, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BASE) && + VerifyField(verifier, VT_SCALE) && + VerifyField(verifier, VT_SHIFT) && + verifier.EndTable(); + } + ExpFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpFusionBuilder { + typedef ExpFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_base(float base) { + fbb_.AddElement(ExpFusion::VT_BASE, base, -1.0f); + } + void add_scale(float scale) { + fbb_.AddElement(ExpFusion::VT_SCALE, scale, 1.0f); + } + void add_shift(float shift) { + fbb_.AddElement(ExpFusion::VT_SHIFT, shift, 0.0f); + } + explicit ExpFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpFusion( + flatbuffers::FlatBufferBuilder &_fbb, + float base = -1.0f, + float scale = 1.0f, + float shift = 0.0f) { + ExpFusionBuilder builder_(_fbb); + builder_.add_shift(shift); + builder_.add_scale(scale); + builder_.add_base(base); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpFusion(flatbuffers::FlatBufferBuilder &_fbb, const ExpFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpandDimsT : public flatbuffers::NativeTable { + typedef ExpandDims TableType; +}; + +struct ExpandDims FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpandDimsT NativeTableType; + typedef ExpandDimsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ExpandDimsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ExpandDimsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpandDimsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpandDimsBuilder { + typedef ExpandDims Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ExpandDimsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpandDims( + flatbuffers::FlatBufferBuilder &_fbb) { + ExpandDimsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpandDims(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FakeQuantWithMinMaxVarsT : public flatbuffers::NativeTable { + typedef FakeQuantWithMinMaxVars TableType; + int64_t num_bits = 0; + bool narrow_range = false; +}; + +struct FakeQuantWithMinMaxVars FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FakeQuantWithMinMaxVarsT NativeTableType; + typedef FakeQuantWithMinMaxVarsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FakeQuantWithMinMaxVarsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_BITS = 4, + VT_NARROW_RANGE = 6 + }; + int64_t num_bits() const { + return GetField(VT_NUM_BITS, 0); + } + bool mutate_num_bits(int64_t _num_bits) { + return SetField(VT_NUM_BITS, _num_bits, 0); + } + bool narrow_range() const { + return GetField(VT_NARROW_RANGE, 0) != 0; + } + bool mutate_narrow_range(bool _narrow_range) { + return SetField(VT_NARROW_RANGE, static_cast(_narrow_range), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_BITS) && + VerifyField(verifier, VT_NARROW_RANGE) && + verifier.EndTable(); + } + FakeQuantWithMinMaxVarsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FakeQuantWithMinMaxVarsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantWithMinMaxVarsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FakeQuantWithMinMaxVarsBuilder { + typedef FakeQuantWithMinMaxVars Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_bits(int64_t num_bits) { + fbb_.AddElement(FakeQuantWithMinMaxVars::VT_NUM_BITS, num_bits, 0); + } + void add_narrow_range(bool narrow_range) { + fbb_.AddElement(FakeQuantWithMinMaxVars::VT_NARROW_RANGE, static_cast(narrow_range), 0); + } + explicit FakeQuantWithMinMaxVarsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFakeQuantWithMinMaxVars( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t num_bits = 0, + bool narrow_range = false) { + FakeQuantWithMinMaxVarsBuilder builder_(_fbb); + builder_.add_num_bits(num_bits); + builder_.add_narrow_range(narrow_range); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFakeQuantWithMinMaxVars(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantWithMinMaxVarsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FakeQuantWithMinMaxVarsPerChannelT : public flatbuffers::NativeTable { + typedef FakeQuantWithMinMaxVarsPerChannel TableType; + int64_t num_bits = 0; + bool narrow_range = false; +}; + +struct FakeQuantWithMinMaxVarsPerChannel FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FakeQuantWithMinMaxVarsPerChannelT NativeTableType; + typedef FakeQuantWithMinMaxVarsPerChannelBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FakeQuantWithMinMaxVarsPerChannelTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_BITS = 4, + VT_NARROW_RANGE = 6 + }; + int64_t num_bits() const { + return GetField(VT_NUM_BITS, 0); + } + bool mutate_num_bits(int64_t _num_bits) { + return SetField(VT_NUM_BITS, _num_bits, 0); + } + bool narrow_range() const { + return GetField(VT_NARROW_RANGE, 0) != 0; + } + bool mutate_narrow_range(bool _narrow_range) { + return SetField(VT_NARROW_RANGE, static_cast(_narrow_range), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_BITS) && + VerifyField(verifier, VT_NARROW_RANGE) && + verifier.EndTable(); + } + FakeQuantWithMinMaxVarsPerChannelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FakeQuantWithMinMaxVarsPerChannelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantWithMinMaxVarsPerChannelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FakeQuantWithMinMaxVarsPerChannelBuilder { + typedef FakeQuantWithMinMaxVarsPerChannel Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_bits(int64_t num_bits) { + fbb_.AddElement(FakeQuantWithMinMaxVarsPerChannel::VT_NUM_BITS, num_bits, 0); + } + void add_narrow_range(bool narrow_range) { + fbb_.AddElement(FakeQuantWithMinMaxVarsPerChannel::VT_NARROW_RANGE, static_cast(narrow_range), 0); + } + explicit FakeQuantWithMinMaxVarsPerChannelBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFakeQuantWithMinMaxVarsPerChannel( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t num_bits = 0, + bool narrow_range = false) { + FakeQuantWithMinMaxVarsPerChannelBuilder builder_(_fbb); + builder_.add_num_bits(num_bits); + builder_.add_narrow_range(narrow_range); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFakeQuantWithMinMaxVarsPerChannel(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantWithMinMaxVarsPerChannelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FftRealT : public flatbuffers::NativeTable { + typedef FftReal TableType; +}; + +struct FftReal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FftRealT NativeTableType; + typedef FftRealBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FftRealTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FftRealT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FftRealT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FftRealT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FftRealBuilder { + typedef FftReal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FftRealBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFftReal( + flatbuffers::FlatBufferBuilder &_fbb) { + FftRealBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFftReal(flatbuffers::FlatBufferBuilder &_fbb, const FftRealT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FftImagT : public flatbuffers::NativeTable { + typedef FftImag TableType; +}; + +struct FftImag FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FftImagT NativeTableType; + typedef FftImagBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FftImagTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FftImagT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FftImagT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FftImagT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FftImagBuilder { + typedef FftImag Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FftImagBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFftImag( + flatbuffers::FlatBufferBuilder &_fbb) { + FftImagBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFftImag(flatbuffers::FlatBufferBuilder &_fbb, const FftImagT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FlattenT : public flatbuffers::NativeTable { + typedef Flatten TableType; + int64_t axis = 1LL; +}; + +struct Flatten FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FlattenT NativeTableType; + typedef FlattenBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FlattenTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 1LL); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 1LL); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + FlattenT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FlattenT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FlattenT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FlattenBuilder { + typedef Flatten Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(Flatten::VT_AXIS, axis, 1LL); + } + explicit FlattenBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFlatten( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 1LL) { + FlattenBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFlatten(flatbuffers::FlatBufferBuilder &_fbb, const FlattenT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FlattenGradT : public flatbuffers::NativeTable { + typedef FlattenGrad TableType; +}; + +struct FlattenGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FlattenGradT NativeTableType; + typedef FlattenGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FlattenGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FlattenGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FlattenGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FlattenGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FlattenGradBuilder { + typedef FlattenGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FlattenGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFlattenGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + FlattenGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFlattenGrad(flatbuffers::FlatBufferBuilder &_fbb, const FlattenGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorT : public flatbuffers::NativeTable { + typedef Floor TableType; +}; + +struct Floor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorT NativeTableType; + typedef FloorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FloorTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorBuilder { + typedef Floor Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloor( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloor(flatbuffers::FlatBufferBuilder &_fbb, const FloorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorDivT : public flatbuffers::NativeTable { + typedef FloorDiv TableType; +}; + +struct FloorDiv FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorDivT NativeTableType; + typedef FloorDivBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FloorDivTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorDivT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorDivT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorDivBuilder { + typedef FloorDiv Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorDivBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloorDiv( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorDivBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloorDiv(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorModT : public flatbuffers::NativeTable { + typedef FloorMod TableType; +}; + +struct FloorMod FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorModT NativeTableType; + typedef FloorModBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FloorModTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorModT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorModT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorModBuilder { + typedef FloorMod Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorModBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloorMod( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorModBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloorMod(flatbuffers::FlatBufferBuilder &_fbb, const FloorModT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FillT : public flatbuffers::NativeTable { + typedef Fill TableType; +}; + +struct Fill FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FillT NativeTableType; + typedef FillBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FillTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FillT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FillT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FillBuilder { + typedef Fill Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FillBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFill( + flatbuffers::FlatBufferBuilder &_fbb) { + FillBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFill(flatbuffers::FlatBufferBuilder &_fbb, const FillT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FullConnectionT : public flatbuffers::NativeTable { + typedef FullConnection TableType; + bool has_bias = false; + bool use_axis = false; + int64_t axis = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct FullConnection FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FullConnectionT NativeTableType; + typedef FullConnectionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FullConnectionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_HAS_BIAS = 4, + VT_USE_AXIS = 6, + VT_AXIS = 8, + VT_ACTIVATION_TYPE = 10 + }; + bool has_bias() const { + return GetField(VT_HAS_BIAS, 0) != 0; + } + bool mutate_has_bias(bool _has_bias) { + return SetField(VT_HAS_BIAS, static_cast(_has_bias), 0); + } + bool use_axis() const { + return GetField(VT_USE_AXIS, 0) != 0; + } + bool mutate_use_axis(bool _use_axis) { + return SetField(VT_USE_AXIS, static_cast(_use_axis), 0); + } + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_HAS_BIAS) && + VerifyField(verifier, VT_USE_AXIS) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + FullConnectionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FullConnectionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullConnectionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FullConnectionBuilder { + typedef FullConnection Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_has_bias(bool has_bias) { + fbb_.AddElement(FullConnection::VT_HAS_BIAS, static_cast(has_bias), 0); + } + void add_use_axis(bool use_axis) { + fbb_.AddElement(FullConnection::VT_USE_AXIS, static_cast(use_axis), 0); + } + void add_axis(int64_t axis) { + fbb_.AddElement(FullConnection::VT_AXIS, axis, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(FullConnection::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit FullConnectionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFullConnection( + flatbuffers::FlatBufferBuilder &_fbb, + bool has_bias = false, + bool use_axis = false, + int64_t axis = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + FullConnectionBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_activation_type(activation_type); + builder_.add_use_axis(use_axis); + builder_.add_has_bias(has_bias); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFullConnection(flatbuffers::FlatBufferBuilder &_fbb, const FullConnectionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FusedBatchNormT : public flatbuffers::NativeTable { + typedef FusedBatchNorm TableType; + float epsilon = 0.0001f; + float momentum = 0.9f; + int64_t mode = 0; +}; + +struct FusedBatchNorm FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FusedBatchNormT NativeTableType; + typedef FusedBatchNormBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FusedBatchNormTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EPSILON = 4, + VT_MOMENTUM = 6, + VT_MODE = 8 + }; + float epsilon() const { + return GetField(VT_EPSILON, 0.0001f); + } + bool mutate_epsilon(float _epsilon) { + return SetField(VT_EPSILON, _epsilon, 0.0001f); + } + float momentum() const { + return GetField(VT_MOMENTUM, 0.9f); + } + bool mutate_momentum(float _momentum) { + return SetField(VT_MOMENTUM, _momentum, 0.9f); + } + int64_t mode() const { + return GetField(VT_MODE, 0); + } + bool mutate_mode(int64_t _mode) { + return SetField(VT_MODE, _mode, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_MOMENTUM) && + VerifyField(verifier, VT_MODE) && + verifier.EndTable(); + } + FusedBatchNormT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FusedBatchNormT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FusedBatchNormT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FusedBatchNormBuilder { + typedef FusedBatchNorm Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_epsilon(float epsilon) { + fbb_.AddElement(FusedBatchNorm::VT_EPSILON, epsilon, 0.0001f); + } + void add_momentum(float momentum) { + fbb_.AddElement(FusedBatchNorm::VT_MOMENTUM, momentum, 0.9f); + } + void add_mode(int64_t mode) { + fbb_.AddElement(FusedBatchNorm::VT_MODE, mode, 0); + } + explicit FusedBatchNormBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFusedBatchNorm( + flatbuffers::FlatBufferBuilder &_fbb, + float epsilon = 0.0001f, + float momentum = 0.9f, + int64_t mode = 0) { + FusedBatchNormBuilder builder_(_fbb); + builder_.add_mode(mode); + builder_.add_momentum(momentum); + builder_.add_epsilon(epsilon); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFusedBatchNorm(flatbuffers::FlatBufferBuilder &_fbb, const FusedBatchNormT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GatherT : public flatbuffers::NativeTable { + typedef Gather TableType; +}; + +struct Gather FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherT NativeTableType; + typedef GatherBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GatherTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GatherT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GatherBuilder { + typedef Gather Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GatherBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGather( + flatbuffers::FlatBufferBuilder &_fbb) { + GatherBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGather(flatbuffers::FlatBufferBuilder &_fbb, const GatherT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GatherNdT : public flatbuffers::NativeTable { + typedef GatherNd TableType; +}; + +struct GatherNd FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherNdT NativeTableType; + typedef GatherNdBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GatherNdTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GatherNdT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherNdT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GatherNdBuilder { + typedef GatherNd Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GatherNdBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGatherNd( + flatbuffers::FlatBufferBuilder &_fbb) { + GatherNdBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGatherNd(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GreaterT : public flatbuffers::NativeTable { + typedef Greater TableType; +}; + +struct Greater FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GreaterT NativeTableType; + typedef GreaterBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GreaterTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GreaterT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GreaterT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GreaterBuilder { + typedef Greater Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GreaterBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGreater( + flatbuffers::FlatBufferBuilder &_fbb) { + GreaterBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGreater(flatbuffers::FlatBufferBuilder &_fbb, const GreaterT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GreaterEqualT : public flatbuffers::NativeTable { + typedef GreaterEqual TableType; +}; + +struct GreaterEqual FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GreaterEqualT NativeTableType; + typedef GreaterEqualBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GreaterEqualTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GreaterEqualT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GreaterEqualT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GreaterEqualBuilder { + typedef GreaterEqual Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GreaterEqualBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGreaterEqual( + flatbuffers::FlatBufferBuilder &_fbb) { + GreaterEqualBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGreaterEqual(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HashtableLookupT : public flatbuffers::NativeTable { + typedef HashtableLookup TableType; +}; + +struct HashtableLookup FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HashtableLookupT NativeTableType; + typedef HashtableLookupBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return HashtableLookupTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + HashtableLookupT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HashtableLookupT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableLookupT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HashtableLookupBuilder { + typedef HashtableLookup Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit HashtableLookupBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHashtableLookup( + flatbuffers::FlatBufferBuilder &_fbb) { + HashtableLookupBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHashtableLookup(flatbuffers::FlatBufferBuilder &_fbb, const HashtableLookupT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct InstanceNormT : public flatbuffers::NativeTable { + typedef InstanceNorm TableType; + float epsilon = 0.0f; +}; + +struct InstanceNorm FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef InstanceNormT NativeTableType; + typedef InstanceNormBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return InstanceNormTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EPSILON = 4 + }; + float epsilon() const { + return GetField(VT_EPSILON, 0.0f); + } + bool mutate_epsilon(float _epsilon) { + return SetField(VT_EPSILON, _epsilon, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_EPSILON) && + verifier.EndTable(); + } + InstanceNormT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(InstanceNormT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const InstanceNormT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct InstanceNormBuilder { + typedef InstanceNorm Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_epsilon(float epsilon) { + fbb_.AddElement(InstanceNorm::VT_EPSILON, epsilon, 0.0f); + } + explicit InstanceNormBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateInstanceNorm( + flatbuffers::FlatBufferBuilder &_fbb, + float epsilon = 0.0f) { + InstanceNormBuilder builder_(_fbb); + builder_.add_epsilon(epsilon); + return builder_.Finish(); +} + +flatbuffers::Offset CreateInstanceNorm(flatbuffers::FlatBufferBuilder &_fbb, const InstanceNormT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LayerNormFusionT : public flatbuffers::NativeTable { + typedef LayerNormFusion TableType; + int64_t begin_norm_axis = 0; + float epsilon = 0.00001f; + bool elementwise_affine = false; + int64_t begin_params_axis = 0; +}; + +struct LayerNormFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LayerNormFusionT NativeTableType; + typedef LayerNormFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LayerNormFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BEGIN_NORM_AXIS = 4, + VT_EPSILON = 6, + VT_ELEMENTWISE_AFFINE = 8, + VT_BEGIN_PARAMS_AXIS = 10 + }; + int64_t begin_norm_axis() const { + return GetField(VT_BEGIN_NORM_AXIS, 0); + } + bool mutate_begin_norm_axis(int64_t _begin_norm_axis) { + return SetField(VT_BEGIN_NORM_AXIS, _begin_norm_axis, 0); + } + float epsilon() const { + return GetField(VT_EPSILON, 0.00001f); + } + bool mutate_epsilon(float _epsilon) { + return SetField(VT_EPSILON, _epsilon, 0.00001f); + } + bool elementwise_affine() const { + return GetField(VT_ELEMENTWISE_AFFINE, 0) != 0; + } + bool mutate_elementwise_affine(bool _elementwise_affine) { + return SetField(VT_ELEMENTWISE_AFFINE, static_cast(_elementwise_affine), 0); + } + int64_t begin_params_axis() const { + return GetField(VT_BEGIN_PARAMS_AXIS, 0); + } + bool mutate_begin_params_axis(int64_t _begin_params_axis) { + return SetField(VT_BEGIN_PARAMS_AXIS, _begin_params_axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BEGIN_NORM_AXIS) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_ELEMENTWISE_AFFINE) && + VerifyField(verifier, VT_BEGIN_PARAMS_AXIS) && + verifier.EndTable(); + } + LayerNormFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LayerNormFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LayerNormFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LayerNormFusionBuilder { + typedef LayerNormFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_begin_norm_axis(int64_t begin_norm_axis) { + fbb_.AddElement(LayerNormFusion::VT_BEGIN_NORM_AXIS, begin_norm_axis, 0); + } + void add_epsilon(float epsilon) { + fbb_.AddElement(LayerNormFusion::VT_EPSILON, epsilon, 0.00001f); + } + void add_elementwise_affine(bool elementwise_affine) { + fbb_.AddElement(LayerNormFusion::VT_ELEMENTWISE_AFFINE, static_cast(elementwise_affine), 0); + } + void add_begin_params_axis(int64_t begin_params_axis) { + fbb_.AddElement(LayerNormFusion::VT_BEGIN_PARAMS_AXIS, begin_params_axis, 0); + } + explicit LayerNormFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLayerNormFusion( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t begin_norm_axis = 0, + float epsilon = 0.00001f, + bool elementwise_affine = false, + int64_t begin_params_axis = 0) { + LayerNormFusionBuilder builder_(_fbb); + builder_.add_begin_params_axis(begin_params_axis); + builder_.add_begin_norm_axis(begin_norm_axis); + builder_.add_epsilon(epsilon); + builder_.add_elementwise_affine(elementwise_affine); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLayerNormFusion(flatbuffers::FlatBufferBuilder &_fbb, const LayerNormFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LeakyReluT : public flatbuffers::NativeTable { + typedef LeakyRelu TableType; + float negative_slope = 0.0f; +}; + +struct LeakyRelu FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LeakyReluT NativeTableType; + typedef LeakyReluBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LeakyReluTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NEGATIVE_SLOPE = 4 + }; + float negative_slope() const { + return GetField(VT_NEGATIVE_SLOPE, 0.0f); + } + bool mutate_negative_slope(float _negative_slope) { + return SetField(VT_NEGATIVE_SLOPE, _negative_slope, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NEGATIVE_SLOPE) && + verifier.EndTable(); + } + LeakyReluT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LeakyReluT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LeakyReluBuilder { + typedef LeakyRelu Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_negative_slope(float negative_slope) { + fbb_.AddElement(LeakyRelu::VT_NEGATIVE_SLOPE, negative_slope, 0.0f); + } + explicit LeakyReluBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLeakyRelu( + flatbuffers::FlatBufferBuilder &_fbb, + float negative_slope = 0.0f) { + LeakyReluBuilder builder_(_fbb); + builder_.add_negative_slope(negative_slope); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLeakyRelu(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LessT : public flatbuffers::NativeTable { + typedef Less TableType; +}; + +struct Less FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LessT NativeTableType; + typedef LessBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LessTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LessT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LessT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LessBuilder { + typedef Less Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LessBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLess( + flatbuffers::FlatBufferBuilder &_fbb) { + LessBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLess(flatbuffers::FlatBufferBuilder &_fbb, const LessT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LessEqualT : public flatbuffers::NativeTable { + typedef LessEqual TableType; +}; + +struct LessEqual FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LessEqualT NativeTableType; + typedef LessEqualBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LessEqualTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LessEqualT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LessEqualT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LessEqualBuilder { + typedef LessEqual Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LessEqualBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLessEqual( + flatbuffers::FlatBufferBuilder &_fbb) { + LessEqualBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLessEqual(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogT : public flatbuffers::NativeTable { + typedef Log TableType; +}; + +struct Log FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogT NativeTableType; + typedef LogBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogBuilder { + typedef Log Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLog( + flatbuffers::FlatBufferBuilder &_fbb) { + LogBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLog(flatbuffers::FlatBufferBuilder &_fbb, const LogT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogGradT : public flatbuffers::NativeTable { + typedef LogGrad TableType; +}; + +struct LogGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogGradT NativeTableType; + typedef LogGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogGradBuilder { + typedef LogGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + LogGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogGrad(flatbuffers::FlatBufferBuilder &_fbb, const LogGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalAndT : public flatbuffers::NativeTable { + typedef LogicalAnd TableType; +}; + +struct LogicalAnd FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalAndT NativeTableType; + typedef LogicalAndBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogicalAndTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalAndT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalAndT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalAndBuilder { + typedef LogicalAnd Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalAndBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalAnd( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalAndBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalAnd(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalNotT : public flatbuffers::NativeTable { + typedef LogicalNot TableType; +}; + +struct LogicalNot FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalNotT NativeTableType; + typedef LogicalNotBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogicalNotTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalNotT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalNotT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalNotBuilder { + typedef LogicalNot Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalNotBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalNot( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalNotBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalNot(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalOrT : public flatbuffers::NativeTable { + typedef LogicalOr TableType; +}; + +struct LogicalOr FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalOrT NativeTableType; + typedef LogicalOrBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogicalOrTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalOrT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalOrT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalOrBuilder { + typedef LogicalOr Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalOrBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalOr( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalOrBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalOr(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LpNormalizationT : public flatbuffers::NativeTable { + typedef LpNormalization TableType; + int64_t axis = 0; + int64_t p = 0; +}; + +struct LpNormalization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LpNormalizationT NativeTableType; + typedef LpNormalizationBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LpNormalizationTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_P = 6 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + int64_t p() const { + return GetField(VT_P, 0); + } + bool mutate_p(int64_t _p) { + return SetField(VT_P, _p, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_P) && + verifier.EndTable(); + } + LpNormalizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LpNormalizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LpNormalizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LpNormalizationBuilder { + typedef LpNormalization Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(LpNormalization::VT_AXIS, axis, 0); + } + void add_p(int64_t p) { + fbb_.AddElement(LpNormalization::VT_P, p, 0); + } + explicit LpNormalizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLpNormalization( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0, + int64_t p = 0) { + LpNormalizationBuilder builder_(_fbb); + builder_.add_p(p); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLpNormalization(flatbuffers::FlatBufferBuilder &_fbb, const LpNormalizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LRNT : public flatbuffers::NativeTable { + typedef LRN TableType; + int64_t depth_radius = 0; + float bias = 0.0f; + float alpha = 0.0f; + float beta = 0.0f; + std::string norm_region{}; +}; + +struct LRN FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LRNT NativeTableType; + typedef LRNBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LRNTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DEPTH_RADIUS = 4, + VT_BIAS = 6, + VT_ALPHA = 8, + VT_BETA = 10, + VT_NORM_REGION = 12 + }; + int64_t depth_radius() const { + return GetField(VT_DEPTH_RADIUS, 0); + } + bool mutate_depth_radius(int64_t _depth_radius) { + return SetField(VT_DEPTH_RADIUS, _depth_radius, 0); + } + float bias() const { + return GetField(VT_BIAS, 0.0f); + } + bool mutate_bias(float _bias) { + return SetField(VT_BIAS, _bias, 0.0f); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool mutate_alpha(float _alpha) { + return SetField(VT_ALPHA, _alpha, 0.0f); + } + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool mutate_beta(float _beta) { + return SetField(VT_BETA, _beta, 0.0f); + } + const flatbuffers::String *norm_region() const { + return GetPointer(VT_NORM_REGION); + } + flatbuffers::String *mutable_norm_region() { + return GetPointer(VT_NORM_REGION); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_DEPTH_RADIUS) && + VerifyField(verifier, VT_BIAS) && + VerifyField(verifier, VT_ALPHA) && + VerifyField(verifier, VT_BETA) && + VerifyOffset(verifier, VT_NORM_REGION) && + verifier.VerifyString(norm_region()) && + verifier.EndTable(); + } + LRNT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LRNT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LRNT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LRNBuilder { + typedef LRN Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_depth_radius(int64_t depth_radius) { + fbb_.AddElement(LRN::VT_DEPTH_RADIUS, depth_radius, 0); + } + void add_bias(float bias) { + fbb_.AddElement(LRN::VT_BIAS, bias, 0.0f); + } + void add_alpha(float alpha) { + fbb_.AddElement(LRN::VT_ALPHA, alpha, 0.0f); + } + void add_beta(float beta) { + fbb_.AddElement(LRN::VT_BETA, beta, 0.0f); + } + void add_norm_region(flatbuffers::Offset norm_region) { + fbb_.AddOffset(LRN::VT_NORM_REGION, norm_region); + } + explicit LRNBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLRN( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t depth_radius = 0, + float bias = 0.0f, + float alpha = 0.0f, + float beta = 0.0f, + flatbuffers::Offset norm_region = 0) { + LRNBuilder builder_(_fbb); + builder_.add_depth_radius(depth_radius); + builder_.add_norm_region(norm_region); + builder_.add_beta(beta); + builder_.add_alpha(alpha); + builder_.add_bias(bias); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateLRNDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t depth_radius = 0, + float bias = 0.0f, + float alpha = 0.0f, + float beta = 0.0f, + const char *norm_region = nullptr) { + auto norm_region__ = norm_region ? _fbb.CreateString(norm_region) : 0; + return mindspore::schema::CreateLRN( + _fbb, + depth_radius, + bias, + alpha, + beta, + norm_region__); +} + +flatbuffers::Offset CreateLRN(flatbuffers::FlatBufferBuilder &_fbb, const LRNT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LshProjectionT : public flatbuffers::NativeTable { + typedef LshProjection TableType; + mindspore::schema::LshProjectionType type = mindspore::schema::LshProjectionType_UNKNOWN; +}; + +struct LshProjection FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LshProjectionT NativeTableType; + typedef LshProjectionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LshProjectionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TYPE = 4 + }; + mindspore::schema::LshProjectionType type() const { + return static_cast(GetField(VT_TYPE, 0)); + } + bool mutate_type(mindspore::schema::LshProjectionType _type) { + return SetField(VT_TYPE, static_cast(_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TYPE) && + verifier.EndTable(); + } + LshProjectionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LshProjectionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LshProjectionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LshProjectionBuilder { + typedef LshProjection Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_type(mindspore::schema::LshProjectionType type) { + fbb_.AddElement(LshProjection::VT_TYPE, static_cast(type), 0); + } + explicit LshProjectionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLshProjection( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::LshProjectionType type = mindspore::schema::LshProjectionType_UNKNOWN) { + LshProjectionBuilder builder_(_fbb); + builder_.add_type(type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLshProjection(flatbuffers::FlatBufferBuilder &_fbb, const LshProjectionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LSTMT : public flatbuffers::NativeTable { + typedef LSTM TableType; + bool bidirectional = false; + bool has_bias = false; + int64_t input_size = 0; + int64_t hidden_size = 0; + int64_t num_layers = 0; + int64_t num_directions = 0; + float dropout = 0.0f; + float zoneout_cell = 0.0f; + float zoneout_hidden = 0.0f; +}; + +struct LSTM FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSTMT NativeTableType; + typedef LSTMBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LSTMTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BIDIRECTIONAL = 4, + VT_HAS_BIAS = 6, + VT_INPUT_SIZE = 8, + VT_HIDDEN_SIZE = 10, + VT_NUM_LAYERS = 12, + VT_NUM_DIRECTIONS = 14, + VT_DROPOUT = 16, + VT_ZONEOUT_CELL = 18, + VT_ZONEOUT_HIDDEN = 20 + }; + bool bidirectional() const { + return GetField(VT_BIDIRECTIONAL, 0) != 0; + } + bool mutate_bidirectional(bool _bidirectional) { + return SetField(VT_BIDIRECTIONAL, static_cast(_bidirectional), 0); + } + bool has_bias() const { + return GetField(VT_HAS_BIAS, 0) != 0; + } + bool mutate_has_bias(bool _has_bias) { + return SetField(VT_HAS_BIAS, static_cast(_has_bias), 0); + } + int64_t input_size() const { + return GetField(VT_INPUT_SIZE, 0); + } + bool mutate_input_size(int64_t _input_size) { + return SetField(VT_INPUT_SIZE, _input_size, 0); + } + int64_t hidden_size() const { + return GetField(VT_HIDDEN_SIZE, 0); + } + bool mutate_hidden_size(int64_t _hidden_size) { + return SetField(VT_HIDDEN_SIZE, _hidden_size, 0); + } + int64_t num_layers() const { + return GetField(VT_NUM_LAYERS, 0); + } + bool mutate_num_layers(int64_t _num_layers) { + return SetField(VT_NUM_LAYERS, _num_layers, 0); + } + int64_t num_directions() const { + return GetField(VT_NUM_DIRECTIONS, 0); + } + bool mutate_num_directions(int64_t _num_directions) { + return SetField(VT_NUM_DIRECTIONS, _num_directions, 0); + } + float dropout() const { + return GetField(VT_DROPOUT, 0.0f); + } + bool mutate_dropout(float _dropout) { + return SetField(VT_DROPOUT, _dropout, 0.0f); + } + float zoneout_cell() const { + return GetField(VT_ZONEOUT_CELL, 0.0f); + } + bool mutate_zoneout_cell(float _zoneout_cell) { + return SetField(VT_ZONEOUT_CELL, _zoneout_cell, 0.0f); + } + float zoneout_hidden() const { + return GetField(VT_ZONEOUT_HIDDEN, 0.0f); + } + bool mutate_zoneout_hidden(float _zoneout_hidden) { + return SetField(VT_ZONEOUT_HIDDEN, _zoneout_hidden, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BIDIRECTIONAL) && + VerifyField(verifier, VT_HAS_BIAS) && + VerifyField(verifier, VT_INPUT_SIZE) && + VerifyField(verifier, VT_HIDDEN_SIZE) && + VerifyField(verifier, VT_NUM_LAYERS) && + VerifyField(verifier, VT_NUM_DIRECTIONS) && + VerifyField(verifier, VT_DROPOUT) && + VerifyField(verifier, VT_ZONEOUT_CELL) && + VerifyField(verifier, VT_ZONEOUT_HIDDEN) && + verifier.EndTable(); + } + LSTMT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSTMT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LSTMBuilder { + typedef LSTM Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_bidirectional(bool bidirectional) { + fbb_.AddElement(LSTM::VT_BIDIRECTIONAL, static_cast(bidirectional), 0); + } + void add_has_bias(bool has_bias) { + fbb_.AddElement(LSTM::VT_HAS_BIAS, static_cast(has_bias), 0); + } + void add_input_size(int64_t input_size) { + fbb_.AddElement(LSTM::VT_INPUT_SIZE, input_size, 0); + } + void add_hidden_size(int64_t hidden_size) { + fbb_.AddElement(LSTM::VT_HIDDEN_SIZE, hidden_size, 0); + } + void add_num_layers(int64_t num_layers) { + fbb_.AddElement(LSTM::VT_NUM_LAYERS, num_layers, 0); + } + void add_num_directions(int64_t num_directions) { + fbb_.AddElement(LSTM::VT_NUM_DIRECTIONS, num_directions, 0); + } + void add_dropout(float dropout) { + fbb_.AddElement(LSTM::VT_DROPOUT, dropout, 0.0f); + } + void add_zoneout_cell(float zoneout_cell) { + fbb_.AddElement(LSTM::VT_ZONEOUT_CELL, zoneout_cell, 0.0f); + } + void add_zoneout_hidden(float zoneout_hidden) { + fbb_.AddElement(LSTM::VT_ZONEOUT_HIDDEN, zoneout_hidden, 0.0f); + } + explicit LSTMBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSTM( + flatbuffers::FlatBufferBuilder &_fbb, + bool bidirectional = false, + bool has_bias = false, + int64_t input_size = 0, + int64_t hidden_size = 0, + int64_t num_layers = 0, + int64_t num_directions = 0, + float dropout = 0.0f, + float zoneout_cell = 0.0f, + float zoneout_hidden = 0.0f) { + LSTMBuilder builder_(_fbb); + builder_.add_num_directions(num_directions); + builder_.add_num_layers(num_layers); + builder_.add_hidden_size(hidden_size); + builder_.add_input_size(input_size); + builder_.add_zoneout_hidden(zoneout_hidden); + builder_.add_zoneout_cell(zoneout_cell); + builder_.add_dropout(dropout); + builder_.add_has_bias(has_bias); + builder_.add_bidirectional(bidirectional); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLSTM(flatbuffers::FlatBufferBuilder &_fbb, const LSTMT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LSTMGradT : public flatbuffers::NativeTable { + typedef LSTMGrad TableType; + bool bidirectional = false; + bool has_bias = false; + int64_t input_size = 0; + int64_t hidden_size = 0; + int64_t num_layers = 0; + int64_t num_directions = 0; + float dropout = 0.0f; + float zoneout_cell = 0.0f; + float zoneout_hidden = 0.0f; +}; + +struct LSTMGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSTMGradT NativeTableType; + typedef LSTMGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LSTMGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BIDIRECTIONAL = 4, + VT_HAS_BIAS = 6, + VT_INPUT_SIZE = 8, + VT_HIDDEN_SIZE = 10, + VT_NUM_LAYERS = 12, + VT_NUM_DIRECTIONS = 14, + VT_DROPOUT = 16, + VT_ZONEOUT_CELL = 18, + VT_ZONEOUT_HIDDEN = 20 + }; + bool bidirectional() const { + return GetField(VT_BIDIRECTIONAL, 0) != 0; + } + bool mutate_bidirectional(bool _bidirectional) { + return SetField(VT_BIDIRECTIONAL, static_cast(_bidirectional), 0); + } + bool has_bias() const { + return GetField(VT_HAS_BIAS, 0) != 0; + } + bool mutate_has_bias(bool _has_bias) { + return SetField(VT_HAS_BIAS, static_cast(_has_bias), 0); + } + int64_t input_size() const { + return GetField(VT_INPUT_SIZE, 0); + } + bool mutate_input_size(int64_t _input_size) { + return SetField(VT_INPUT_SIZE, _input_size, 0); + } + int64_t hidden_size() const { + return GetField(VT_HIDDEN_SIZE, 0); + } + bool mutate_hidden_size(int64_t _hidden_size) { + return SetField(VT_HIDDEN_SIZE, _hidden_size, 0); + } + int64_t num_layers() const { + return GetField(VT_NUM_LAYERS, 0); + } + bool mutate_num_layers(int64_t _num_layers) { + return SetField(VT_NUM_LAYERS, _num_layers, 0); + } + int64_t num_directions() const { + return GetField(VT_NUM_DIRECTIONS, 0); + } + bool mutate_num_directions(int64_t _num_directions) { + return SetField(VT_NUM_DIRECTIONS, _num_directions, 0); + } + float dropout() const { + return GetField(VT_DROPOUT, 0.0f); + } + bool mutate_dropout(float _dropout) { + return SetField(VT_DROPOUT, _dropout, 0.0f); + } + float zoneout_cell() const { + return GetField(VT_ZONEOUT_CELL, 0.0f); + } + bool mutate_zoneout_cell(float _zoneout_cell) { + return SetField(VT_ZONEOUT_CELL, _zoneout_cell, 0.0f); + } + float zoneout_hidden() const { + return GetField(VT_ZONEOUT_HIDDEN, 0.0f); + } + bool mutate_zoneout_hidden(float _zoneout_hidden) { + return SetField(VT_ZONEOUT_HIDDEN, _zoneout_hidden, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BIDIRECTIONAL) && + VerifyField(verifier, VT_HAS_BIAS) && + VerifyField(verifier, VT_INPUT_SIZE) && + VerifyField(verifier, VT_HIDDEN_SIZE) && + VerifyField(verifier, VT_NUM_LAYERS) && + VerifyField(verifier, VT_NUM_DIRECTIONS) && + VerifyField(verifier, VT_DROPOUT) && + VerifyField(verifier, VT_ZONEOUT_CELL) && + VerifyField(verifier, VT_ZONEOUT_HIDDEN) && + verifier.EndTable(); + } + LSTMGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSTMGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LSTMGradBuilder { + typedef LSTMGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_bidirectional(bool bidirectional) { + fbb_.AddElement(LSTMGrad::VT_BIDIRECTIONAL, static_cast(bidirectional), 0); + } + void add_has_bias(bool has_bias) { + fbb_.AddElement(LSTMGrad::VT_HAS_BIAS, static_cast(has_bias), 0); + } + void add_input_size(int64_t input_size) { + fbb_.AddElement(LSTMGrad::VT_INPUT_SIZE, input_size, 0); + } + void add_hidden_size(int64_t hidden_size) { + fbb_.AddElement(LSTMGrad::VT_HIDDEN_SIZE, hidden_size, 0); + } + void add_num_layers(int64_t num_layers) { + fbb_.AddElement(LSTMGrad::VT_NUM_LAYERS, num_layers, 0); + } + void add_num_directions(int64_t num_directions) { + fbb_.AddElement(LSTMGrad::VT_NUM_DIRECTIONS, num_directions, 0); + } + void add_dropout(float dropout) { + fbb_.AddElement(LSTMGrad::VT_DROPOUT, dropout, 0.0f); + } + void add_zoneout_cell(float zoneout_cell) { + fbb_.AddElement(LSTMGrad::VT_ZONEOUT_CELL, zoneout_cell, 0.0f); + } + void add_zoneout_hidden(float zoneout_hidden) { + fbb_.AddElement(LSTMGrad::VT_ZONEOUT_HIDDEN, zoneout_hidden, 0.0f); + } + explicit LSTMGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSTMGrad( + flatbuffers::FlatBufferBuilder &_fbb, + bool bidirectional = false, + bool has_bias = false, + int64_t input_size = 0, + int64_t hidden_size = 0, + int64_t num_layers = 0, + int64_t num_directions = 0, + float dropout = 0.0f, + float zoneout_cell = 0.0f, + float zoneout_hidden = 0.0f) { + LSTMGradBuilder builder_(_fbb); + builder_.add_num_directions(num_directions); + builder_.add_num_layers(num_layers); + builder_.add_hidden_size(hidden_size); + builder_.add_input_size(input_size); + builder_.add_zoneout_hidden(zoneout_hidden); + builder_.add_zoneout_cell(zoneout_cell); + builder_.add_dropout(dropout); + builder_.add_has_bias(has_bias); + builder_.add_bidirectional(bidirectional); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLSTMGrad(flatbuffers::FlatBufferBuilder &_fbb, const LSTMGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct L2NormalizeFusionT : public flatbuffers::NativeTable { + typedef L2NormalizeFusion TableType; + std::vector axis{}; + float epsilon = 0.0f; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct L2NormalizeFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef L2NormalizeFusionT NativeTableType; + typedef L2NormalizeFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return L2NormalizeFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_EPSILON = 6, + VT_ACTIVATION_TYPE = 8 + }; + const flatbuffers::Vector *axis() const { + return GetPointer *>(VT_AXIS); + } + flatbuffers::Vector *mutable_axis() { + return GetPointer *>(VT_AXIS); + } + float epsilon() const { + return GetField(VT_EPSILON, 0.0f); + } + bool mutate_epsilon(float _epsilon) { + return SetField(VT_EPSILON, _epsilon, 0.0f); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_AXIS) && + verifier.VerifyVector(axis()) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + L2NormalizeFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(L2NormalizeFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormalizeFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct L2NormalizeFusionBuilder { + typedef L2NormalizeFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(flatbuffers::Offset> axis) { + fbb_.AddOffset(L2NormalizeFusion::VT_AXIS, axis); + } + void add_epsilon(float epsilon) { + fbb_.AddElement(L2NormalizeFusion::VT_EPSILON, epsilon, 0.0f); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(L2NormalizeFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit L2NormalizeFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateL2NormalizeFusion( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> axis = 0, + float epsilon = 0.0f, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + L2NormalizeFusionBuilder builder_(_fbb); + builder_.add_epsilon(epsilon); + builder_.add_axis(axis); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateL2NormalizeFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *axis = nullptr, + float epsilon = 0.0f, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto axis__ = axis ? _fbb.CreateVector(*axis) : 0; + return mindspore::schema::CreateL2NormalizeFusion( + _fbb, + axis__, + epsilon, + activation_type); +} + +flatbuffers::Offset CreateL2NormalizeFusion(flatbuffers::FlatBufferBuilder &_fbb, const L2NormalizeFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MatMulFusionT : public flatbuffers::NativeTable { + typedef MatMulFusion TableType; + bool transpose_a = false; + bool transpose_b = false; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct MatMulFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MatMulFusionT NativeTableType; + typedef MatMulFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MatMulFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TRANSPOSE_A = 4, + VT_TRANSPOSE_B = 6, + VT_ACTIVATION_TYPE = 8 + }; + bool transpose_a() const { + return GetField(VT_TRANSPOSE_A, 0) != 0; + } + bool mutate_transpose_a(bool _transpose_a) { + return SetField(VT_TRANSPOSE_A, static_cast(_transpose_a), 0); + } + bool transpose_b() const { + return GetField(VT_TRANSPOSE_B, 0) != 0; + } + bool mutate_transpose_b(bool _transpose_b) { + return SetField(VT_TRANSPOSE_B, static_cast(_transpose_b), 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TRANSPOSE_A) && + VerifyField(verifier, VT_TRANSPOSE_B) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + MatMulFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MatMulFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatMulFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MatMulFusionBuilder { + typedef MatMulFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_transpose_a(bool transpose_a) { + fbb_.AddElement(MatMulFusion::VT_TRANSPOSE_A, static_cast(transpose_a), 0); + } + void add_transpose_b(bool transpose_b) { + fbb_.AddElement(MatMulFusion::VT_TRANSPOSE_B, static_cast(transpose_b), 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(MatMulFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit MatMulFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMatMulFusion( + flatbuffers::FlatBufferBuilder &_fbb, + bool transpose_a = false, + bool transpose_b = false, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + MatMulFusionBuilder builder_(_fbb); + builder_.add_activation_type(activation_type); + builder_.add_transpose_b(transpose_b); + builder_.add_transpose_a(transpose_a); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMatMulFusion(flatbuffers::FlatBufferBuilder &_fbb, const MatMulFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MaximumT : public flatbuffers::NativeTable { + typedef Maximum TableType; +}; + +struct Maximum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MaximumT NativeTableType; + typedef MaximumBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MaximumTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MaximumT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MaximumT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MaximumBuilder { + typedef Maximum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MaximumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMaximum( + flatbuffers::FlatBufferBuilder &_fbb) { + MaximumBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMaximum(flatbuffers::FlatBufferBuilder &_fbb, const MaximumT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MaximumGradT : public flatbuffers::NativeTable { + typedef MaximumGrad TableType; + bool grad_x = false; + bool grad_y = false; +}; + +struct MaximumGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MaximumGradT NativeTableType; + typedef MaximumGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MaximumGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_GRAD_X = 4, + VT_GRAD_Y = 6 + }; + bool grad_x() const { + return GetField(VT_GRAD_X, 0) != 0; + } + bool mutate_grad_x(bool _grad_x) { + return SetField(VT_GRAD_X, static_cast(_grad_x), 0); + } + bool grad_y() const { + return GetField(VT_GRAD_Y, 0) != 0; + } + bool mutate_grad_y(bool _grad_y) { + return SetField(VT_GRAD_Y, static_cast(_grad_y), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_GRAD_X) && + VerifyField(verifier, VT_GRAD_Y) && + verifier.EndTable(); + } + MaximumGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MaximumGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MaximumGradBuilder { + typedef MaximumGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_grad_x(bool grad_x) { + fbb_.AddElement(MaximumGrad::VT_GRAD_X, static_cast(grad_x), 0); + } + void add_grad_y(bool grad_y) { + fbb_.AddElement(MaximumGrad::VT_GRAD_Y, static_cast(grad_y), 0); + } + explicit MaximumGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMaximumGrad( + flatbuffers::FlatBufferBuilder &_fbb, + bool grad_x = false, + bool grad_y = false) { + MaximumGradBuilder builder_(_fbb); + builder_.add_grad_y(grad_y); + builder_.add_grad_x(grad_x); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMaximumGrad(flatbuffers::FlatBufferBuilder &_fbb, const MaximumGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MaxPoolFusionT : public flatbuffers::NativeTable { + typedef MaxPoolFusion TableType; + std::vector kernel_size{}; + std::vector strides{}; + std::vector pad{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + bool global = false; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct MaxPoolFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MaxPoolFusionT NativeTableType; + typedef MaxPoolFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MaxPoolFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KERNEL_SIZE = 4, + VT_STRIDES = 6, + VT_PAD = 8, + VT_PAD_MODE = 10, + VT_ROUND_MODE = 12, + VT_FORMAT = 14, + VT_GLOBAL = 16, + VT_ACTIVATION_TYPE = 18 + }; + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *strides() const { + return GetPointer *>(VT_STRIDES); + } + flatbuffers::Vector *mutable_strides() { + return GetPointer *>(VT_STRIDES); + } + const flatbuffers::Vector *pad() const { + return GetPointer *>(VT_PAD); + } + flatbuffers::Vector *mutable_pad() { + return GetPointer *>(VT_PAD); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + mindspore::schema::RoundMode round_mode() const { + return static_cast(GetField(VT_ROUND_MODE, 0)); + } + bool mutate_round_mode(mindspore::schema::RoundMode _round_mode) { + return SetField(VT_ROUND_MODE, static_cast(_round_mode), 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool global() const { + return GetField(VT_GLOBAL, 0) != 0; + } + bool mutate_global(bool _global) { + return SetField(VT_GLOBAL, static_cast(_global), 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDES) && + verifier.VerifyVector(strides()) && + VerifyOffset(verifier, VT_PAD) && + verifier.VerifyVector(pad()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyField(verifier, VT_ROUND_MODE) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_GLOBAL) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + MaxPoolFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MaxPoolFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaxPoolFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MaxPoolFusionBuilder { + typedef MaxPoolFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(MaxPoolFusion::VT_KERNEL_SIZE, kernel_size); + } + void add_strides(flatbuffers::Offset> strides) { + fbb_.AddOffset(MaxPoolFusion::VT_STRIDES, strides); + } + void add_pad(flatbuffers::Offset> pad) { + fbb_.AddOffset(MaxPoolFusion::VT_PAD, pad); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(MaxPoolFusion::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_round_mode(mindspore::schema::RoundMode round_mode) { + fbb_.AddElement(MaxPoolFusion::VT_ROUND_MODE, static_cast(round_mode), 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(MaxPoolFusion::VT_FORMAT, static_cast(format), 0); + } + void add_global(bool global) { + fbb_.AddElement(MaxPoolFusion::VT_GLOBAL, static_cast(global), 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(MaxPoolFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit MaxPoolFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMaxPoolFusion( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> strides = 0, + flatbuffers::Offset> pad = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + bool global = false, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + MaxPoolFusionBuilder builder_(_fbb); + builder_.add_format(format); + builder_.add_pad(pad); + builder_.add_strides(strides); + builder_.add_kernel_size(kernel_size); + builder_.add_activation_type(activation_type); + builder_.add_global(global); + builder_.add_round_mode(round_mode); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateMaxPoolFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *kernel_size = nullptr, + const std::vector *strides = nullptr, + const std::vector *pad = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + bool global = false, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto strides__ = strides ? _fbb.CreateVector(*strides) : 0; + auto pad__ = pad ? _fbb.CreateVector(*pad) : 0; + return mindspore::schema::CreateMaxPoolFusion( + _fbb, + kernel_size__, + strides__, + pad__, + pad_mode, + round_mode, + format, + global, + activation_type); +} + +flatbuffers::Offset CreateMaxPoolFusion(flatbuffers::FlatBufferBuilder &_fbb, const MaxPoolFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MaxPoolGradT : public flatbuffers::NativeTable { + typedef MaxPoolGrad TableType; + std::vector kernel_size{}; + std::vector strides{}; + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; +}; + +struct MaxPoolGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MaxPoolGradT NativeTableType; + typedef MaxPoolGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MaxPoolGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KERNEL_SIZE = 4, + VT_STRIDES = 6, + VT_PAD_MODE = 8, + VT_FORMAT = 10 + }; + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + flatbuffers::Vector *mutable_kernel_size() { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *strides() const { + return GetPointer *>(VT_STRIDES); + } + flatbuffers::Vector *mutable_strides() { + return GetPointer *>(VT_STRIDES); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + bool mutate_pad_mode(mindspore::schema::PadMode _pad_mode) { + return SetField(VT_PAD_MODE, static_cast(_pad_mode), 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDES) && + verifier.VerifyVector(strides()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyField(verifier, VT_FORMAT) && + verifier.EndTable(); + } + MaxPoolGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MaxPoolGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaxPoolGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MaxPoolGradBuilder { + typedef MaxPoolGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(MaxPoolGrad::VT_KERNEL_SIZE, kernel_size); + } + void add_strides(flatbuffers::Offset> strides) { + fbb_.AddOffset(MaxPoolGrad::VT_STRIDES, strides); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(MaxPoolGrad::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(MaxPoolGrad::VT_FORMAT, static_cast(format), 0); + } + explicit MaxPoolGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMaxPoolGrad( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> strides = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::Format format = mindspore::schema::Format_NCHW) { + MaxPoolGradBuilder builder_(_fbb); + builder_.add_format(format); + builder_.add_strides(strides); + builder_.add_kernel_size(kernel_size); + builder_.add_pad_mode(pad_mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateMaxPoolGradDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *kernel_size = nullptr, + const std::vector *strides = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + mindspore::schema::Format format = mindspore::schema::Format_NCHW) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto strides__ = strides ? _fbb.CreateVector(*strides) : 0; + return mindspore::schema::CreateMaxPoolGrad( + _fbb, + kernel_size__, + strides__, + pad_mode, + format); +} + +flatbuffers::Offset CreateMaxPoolGrad(flatbuffers::FlatBufferBuilder &_fbb, const MaxPoolGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SwitchLayerT : public flatbuffers::NativeTable { + typedef SwitchLayer TableType; +}; + +struct SwitchLayer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SwitchLayerT NativeTableType; + typedef SwitchLayerBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SwitchLayerTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SwitchLayerT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SwitchLayerT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SwitchLayerT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SwitchLayerBuilder { + typedef SwitchLayer Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SwitchLayerBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSwitchLayer( + flatbuffers::FlatBufferBuilder &_fbb) { + SwitchLayerBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSwitchLayer(flatbuffers::FlatBufferBuilder &_fbb, const SwitchLayerT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MfccT : public flatbuffers::NativeTable { + typedef Mfcc TableType; + float freq_upper_limit = 0.0f; + float freq_lower_limit = 0.0f; + int64_t filter_bank_channel_num = 0; + int64_t dct_coeff_num = 0; +}; + +struct Mfcc FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MfccT NativeTableType; + typedef MfccBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MfccTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FREQ_UPPER_LIMIT = 4, + VT_FREQ_LOWER_LIMIT = 6, + VT_FILTER_BANK_CHANNEL_NUM = 8, + VT_DCT_COEFF_NUM = 10 + }; + float freq_upper_limit() const { + return GetField(VT_FREQ_UPPER_LIMIT, 0.0f); + } + bool mutate_freq_upper_limit(float _freq_upper_limit) { + return SetField(VT_FREQ_UPPER_LIMIT, _freq_upper_limit, 0.0f); + } + float freq_lower_limit() const { + return GetField(VT_FREQ_LOWER_LIMIT, 0.0f); + } + bool mutate_freq_lower_limit(float _freq_lower_limit) { + return SetField(VT_FREQ_LOWER_LIMIT, _freq_lower_limit, 0.0f); + } + int64_t filter_bank_channel_num() const { + return GetField(VT_FILTER_BANK_CHANNEL_NUM, 0); + } + bool mutate_filter_bank_channel_num(int64_t _filter_bank_channel_num) { + return SetField(VT_FILTER_BANK_CHANNEL_NUM, _filter_bank_channel_num, 0); + } + int64_t dct_coeff_num() const { + return GetField(VT_DCT_COEFF_NUM, 0); + } + bool mutate_dct_coeff_num(int64_t _dct_coeff_num) { + return SetField(VT_DCT_COEFF_NUM, _dct_coeff_num, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FREQ_UPPER_LIMIT) && + VerifyField(verifier, VT_FREQ_LOWER_LIMIT) && + VerifyField(verifier, VT_FILTER_BANK_CHANNEL_NUM) && + VerifyField(verifier, VT_DCT_COEFF_NUM) && + verifier.EndTable(); + } + MfccT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MfccT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MfccT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MfccBuilder { + typedef Mfcc Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_freq_upper_limit(float freq_upper_limit) { + fbb_.AddElement(Mfcc::VT_FREQ_UPPER_LIMIT, freq_upper_limit, 0.0f); + } + void add_freq_lower_limit(float freq_lower_limit) { + fbb_.AddElement(Mfcc::VT_FREQ_LOWER_LIMIT, freq_lower_limit, 0.0f); + } + void add_filter_bank_channel_num(int64_t filter_bank_channel_num) { + fbb_.AddElement(Mfcc::VT_FILTER_BANK_CHANNEL_NUM, filter_bank_channel_num, 0); + } + void add_dct_coeff_num(int64_t dct_coeff_num) { + fbb_.AddElement(Mfcc::VT_DCT_COEFF_NUM, dct_coeff_num, 0); + } + explicit MfccBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMfcc( + flatbuffers::FlatBufferBuilder &_fbb, + float freq_upper_limit = 0.0f, + float freq_lower_limit = 0.0f, + int64_t filter_bank_channel_num = 0, + int64_t dct_coeff_num = 0) { + MfccBuilder builder_(_fbb); + builder_.add_dct_coeff_num(dct_coeff_num); + builder_.add_filter_bank_channel_num(filter_bank_channel_num); + builder_.add_freq_lower_limit(freq_lower_limit); + builder_.add_freq_upper_limit(freq_upper_limit); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMfcc(flatbuffers::FlatBufferBuilder &_fbb, const MfccT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MinimumT : public flatbuffers::NativeTable { + typedef Minimum TableType; +}; + +struct Minimum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MinimumT NativeTableType; + typedef MinimumBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MinimumTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MinimumT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MinimumT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MinimumT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MinimumBuilder { + typedef Minimum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MinimumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMinimum( + flatbuffers::FlatBufferBuilder &_fbb) { + MinimumBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMinimum(flatbuffers::FlatBufferBuilder &_fbb, const MinimumT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MinimumGradT : public flatbuffers::NativeTable { + typedef MinimumGrad TableType; + bool grad_x = false; + bool grad_y = false; +}; + +struct MinimumGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MinimumGradT NativeTableType; + typedef MinimumGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MinimumGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_GRAD_X = 4, + VT_GRAD_Y = 6 + }; + bool grad_x() const { + return GetField(VT_GRAD_X, 0) != 0; + } + bool mutate_grad_x(bool _grad_x) { + return SetField(VT_GRAD_X, static_cast(_grad_x), 0); + } + bool grad_y() const { + return GetField(VT_GRAD_Y, 0) != 0; + } + bool mutate_grad_y(bool _grad_y) { + return SetField(VT_GRAD_Y, static_cast(_grad_y), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_GRAD_X) && + VerifyField(verifier, VT_GRAD_Y) && + verifier.EndTable(); + } + MinimumGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MinimumGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MinimumGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MinimumGradBuilder { + typedef MinimumGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_grad_x(bool grad_x) { + fbb_.AddElement(MinimumGrad::VT_GRAD_X, static_cast(grad_x), 0); + } + void add_grad_y(bool grad_y) { + fbb_.AddElement(MinimumGrad::VT_GRAD_Y, static_cast(grad_y), 0); + } + explicit MinimumGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMinimumGrad( + flatbuffers::FlatBufferBuilder &_fbb, + bool grad_x = false, + bool grad_y = false) { + MinimumGradBuilder builder_(_fbb); + builder_.add_grad_y(grad_y); + builder_.add_grad_x(grad_x); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMinimumGrad(flatbuffers::FlatBufferBuilder &_fbb, const MinimumGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ModT : public flatbuffers::NativeTable { + typedef Mod TableType; +}; + +struct Mod FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ModT NativeTableType; + typedef ModBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ModTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ModT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ModT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ModBuilder { + typedef Mod Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ModBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMod( + flatbuffers::FlatBufferBuilder &_fbb) { + ModBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMod(flatbuffers::FlatBufferBuilder &_fbb, const ModT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MulFusionT : public flatbuffers::NativeTable { + typedef MulFusion TableType; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct MulFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MulFusionT NativeTableType; + typedef MulFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MulFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + MulFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MulFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MulFusionBuilder { + typedef MulFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(MulFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit MulFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMulFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + MulFusionBuilder builder_(_fbb); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMulFusion(flatbuffers::FlatBufferBuilder &_fbb, const MulFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MulGradT : public flatbuffers::NativeTable { + typedef MulGrad TableType; +}; + +struct MulGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MulGradT NativeTableType; + typedef MulGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MulGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MulGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MulGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MulGradBuilder { + typedef MulGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MulGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMulGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + MulGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMulGrad(flatbuffers::FlatBufferBuilder &_fbb, const MulGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NegT : public flatbuffers::NativeTable { + typedef Neg TableType; +}; + +struct Neg FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NegT NativeTableType; + typedef NegBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NegTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NegT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NegT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NegBuilder { + typedef Neg Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NegBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNeg( + flatbuffers::FlatBufferBuilder &_fbb) { + NegBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNeg(flatbuffers::FlatBufferBuilder &_fbb, const NegT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NegGradT : public flatbuffers::NativeTable { + typedef NegGrad TableType; +}; + +struct NegGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NegGradT NativeTableType; + typedef NegGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NegGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NegGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NegGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NegGradBuilder { + typedef NegGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NegGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNegGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + NegGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNegGrad(flatbuffers::FlatBufferBuilder &_fbb, const NegGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NotEqualT : public flatbuffers::NativeTable { + typedef NotEqual TableType; +}; + +struct NotEqual FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NotEqualT NativeTableType; + typedef NotEqualBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NotEqualTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NotEqualT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NotEqualT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NotEqualBuilder { + typedef NotEqual Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NotEqualBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNotEqual( + flatbuffers::FlatBufferBuilder &_fbb) { + NotEqualBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNotEqual(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NonMaxSuppressionT : public flatbuffers::NativeTable { + typedef NonMaxSuppression TableType; + int64_t center_point_box = 0; +}; + +struct NonMaxSuppression FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NonMaxSuppressionT NativeTableType; + typedef NonMaxSuppressionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NonMaxSuppressionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CENTER_POINT_BOX = 4 + }; + int64_t center_point_box() const { + return GetField(VT_CENTER_POINT_BOX, 0); + } + bool mutate_center_point_box(int64_t _center_point_box) { + return SetField(VT_CENTER_POINT_BOX, _center_point_box, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_CENTER_POINT_BOX) && + verifier.EndTable(); + } + NonMaxSuppressionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NonMaxSuppressionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NonMaxSuppressionBuilder { + typedef NonMaxSuppression Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_center_point_box(int64_t center_point_box) { + fbb_.AddElement(NonMaxSuppression::VT_CENTER_POINT_BOX, center_point_box, 0); + } + explicit NonMaxSuppressionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNonMaxSuppression( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t center_point_box = 0) { + NonMaxSuppressionBuilder builder_(_fbb); + builder_.add_center_point_box(center_point_box); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNonMaxSuppression(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OneHotT : public flatbuffers::NativeTable { + typedef OneHot TableType; + int64_t axis = 0; +}; + +struct OneHot FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OneHotT NativeTableType; + typedef OneHotBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return OneHotTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + OneHotT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OneHotT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OneHotBuilder { + typedef OneHot Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(OneHot::VT_AXIS, axis, 0); + } + explicit OneHotBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOneHot( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0) { + OneHotBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateOneHot(flatbuffers::FlatBufferBuilder &_fbb, const OneHotT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OnesLikeT : public flatbuffers::NativeTable { + typedef OnesLike TableType; +}; + +struct OnesLike FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OnesLikeT NativeTableType; + typedef OnesLikeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return OnesLikeTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + OnesLikeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OnesLikeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OnesLikeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OnesLikeBuilder { + typedef OnesLike Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit OnesLikeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOnesLike( + flatbuffers::FlatBufferBuilder &_fbb) { + OnesLikeBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateOnesLike(flatbuffers::FlatBufferBuilder &_fbb, const OnesLikeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PadFusionT : public flatbuffers::NativeTable { + typedef PadFusion TableType; + std::unique_ptr paddings{}; + mindspore::schema::PaddingMode padding_mode = mindspore::schema::PaddingMode_CONSTANT; + float constant_value = 0.0f; +}; + +struct PadFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PadFusionT NativeTableType; + typedef PadFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PadFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDINGS = 4, + VT_PADDING_MODE = 6, + VT_CONSTANT_VALUE = 8 + }; + const mindspore::schema::Vec2D *paddings() const { + return GetPointer(VT_PADDINGS); + } + mindspore::schema::Vec2D *mutable_paddings() { + return GetPointer(VT_PADDINGS); + } + mindspore::schema::PaddingMode padding_mode() const { + return static_cast(GetField(VT_PADDING_MODE, 0)); + } + bool mutate_padding_mode(mindspore::schema::PaddingMode _padding_mode) { + return SetField(VT_PADDING_MODE, static_cast(_padding_mode), 0); + } + float constant_value() const { + return GetField(VT_CONSTANT_VALUE, 0.0f); + } + bool mutate_constant_value(float _constant_value) { + return SetField(VT_CONSTANT_VALUE, _constant_value, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_PADDINGS) && + verifier.VerifyTable(paddings()) && + VerifyField(verifier, VT_PADDING_MODE) && + VerifyField(verifier, VT_CONSTANT_VALUE) && + verifier.EndTable(); + } + PadFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PadFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PadFusionBuilder { + typedef PadFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_paddings(flatbuffers::Offset paddings) { + fbb_.AddOffset(PadFusion::VT_PADDINGS, paddings); + } + void add_padding_mode(mindspore::schema::PaddingMode padding_mode) { + fbb_.AddElement(PadFusion::VT_PADDING_MODE, static_cast(padding_mode), 0); + } + void add_constant_value(float constant_value) { + fbb_.AddElement(PadFusion::VT_CONSTANT_VALUE, constant_value, 0.0f); + } + explicit PadFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePadFusion( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset paddings = 0, + mindspore::schema::PaddingMode padding_mode = mindspore::schema::PaddingMode_CONSTANT, + float constant_value = 0.0f) { + PadFusionBuilder builder_(_fbb); + builder_.add_constant_value(constant_value); + builder_.add_paddings(paddings); + builder_.add_padding_mode(padding_mode); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePadFusion(flatbuffers::FlatBufferBuilder &_fbb, const PadFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PartialFusionT : public flatbuffers::NativeTable { + typedef PartialFusion TableType; + int64_t sub_graph_index = 0; +}; + +struct PartialFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PartialFusionT NativeTableType; + typedef PartialFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PartialFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SUB_GRAPH_INDEX = 4 + }; + int64_t sub_graph_index() const { + return GetField(VT_SUB_GRAPH_INDEX, 0); + } + bool mutate_sub_graph_index(int64_t _sub_graph_index) { + return SetField(VT_SUB_GRAPH_INDEX, _sub_graph_index, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SUB_GRAPH_INDEX) && + verifier.EndTable(); + } + PartialFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PartialFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PartialFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PartialFusionBuilder { + typedef PartialFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_sub_graph_index(int64_t sub_graph_index) { + fbb_.AddElement(PartialFusion::VT_SUB_GRAPH_INDEX, sub_graph_index, 0); + } + explicit PartialFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePartialFusion( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t sub_graph_index = 0) { + PartialFusionBuilder builder_(_fbb); + builder_.add_sub_graph_index(sub_graph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePartialFusion(flatbuffers::FlatBufferBuilder &_fbb, const PartialFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PowerGradT : public flatbuffers::NativeTable { + typedef PowerGrad TableType; + float power = 0.0f; + float scale = 0.0f; + float shift = 0.0f; +}; + +struct PowerGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PowerGradT NativeTableType; + typedef PowerGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PowerGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_POWER = 4, + VT_SCALE = 6, + VT_SHIFT = 8 + }; + float power() const { + return GetField(VT_POWER, 0.0f); + } + bool mutate_power(float _power) { + return SetField(VT_POWER, _power, 0.0f); + } + float scale() const { + return GetField(VT_SCALE, 0.0f); + } + bool mutate_scale(float _scale) { + return SetField(VT_SCALE, _scale, 0.0f); + } + float shift() const { + return GetField(VT_SHIFT, 0.0f); + } + bool mutate_shift(float _shift) { + return SetField(VT_SHIFT, _shift, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_POWER) && + VerifyField(verifier, VT_SCALE) && + VerifyField(verifier, VT_SHIFT) && + verifier.EndTable(); + } + PowerGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PowerGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowerGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PowerGradBuilder { + typedef PowerGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_power(float power) { + fbb_.AddElement(PowerGrad::VT_POWER, power, 0.0f); + } + void add_scale(float scale) { + fbb_.AddElement(PowerGrad::VT_SCALE, scale, 0.0f); + } + void add_shift(float shift) { + fbb_.AddElement(PowerGrad::VT_SHIFT, shift, 0.0f); + } + explicit PowerGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePowerGrad( + flatbuffers::FlatBufferBuilder &_fbb, + float power = 0.0f, + float scale = 0.0f, + float shift = 0.0f) { + PowerGradBuilder builder_(_fbb); + builder_.add_shift(shift); + builder_.add_scale(scale); + builder_.add_power(power); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePowerGrad(flatbuffers::FlatBufferBuilder &_fbb, const PowerGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PowFusionT : public flatbuffers::NativeTable { + typedef PowFusion TableType; + float scale = 1.0f; + float shift = 0.0f; +}; + +struct PowFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PowFusionT NativeTableType; + typedef PowFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PowFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SCALE = 4, + VT_SHIFT = 6 + }; + float scale() const { + return GetField(VT_SCALE, 1.0f); + } + bool mutate_scale(float _scale) { + return SetField(VT_SCALE, _scale, 1.0f); + } + float shift() const { + return GetField(VT_SHIFT, 0.0f); + } + bool mutate_shift(float _shift) { + return SetField(VT_SHIFT, _shift, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SCALE) && + VerifyField(verifier, VT_SHIFT) && + verifier.EndTable(); + } + PowFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PowFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PowFusionBuilder { + typedef PowFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_scale(float scale) { + fbb_.AddElement(PowFusion::VT_SCALE, scale, 1.0f); + } + void add_shift(float shift) { + fbb_.AddElement(PowFusion::VT_SHIFT, shift, 0.0f); + } + explicit PowFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePowFusion( + flatbuffers::FlatBufferBuilder &_fbb, + float scale = 1.0f, + float shift = 0.0f) { + PowFusionBuilder builder_(_fbb); + builder_.add_shift(shift); + builder_.add_scale(scale); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePowFusion(flatbuffers::FlatBufferBuilder &_fbb, const PowFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PriorBoxT : public flatbuffers::NativeTable { + typedef PriorBox TableType; + std::vector min_sizes{}; + std::vector max_sizes{}; + std::vector aspect_ratios{}; + std::vector variances{}; + int64_t image_size_w = 0; + int64_t image_size_h = 0; + float step_w = 0.0f; + float step_h = 0.0f; + bool clip = false; + bool flip = false; + float offset = 0.0f; +}; + +struct PriorBox FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PriorBoxT NativeTableType; + typedef PriorBoxBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PriorBoxTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MIN_SIZES = 4, + VT_MAX_SIZES = 6, + VT_ASPECT_RATIOS = 8, + VT_VARIANCES = 10, + VT_IMAGE_SIZE_W = 12, + VT_IMAGE_SIZE_H = 14, + VT_STEP_W = 16, + VT_STEP_H = 18, + VT_CLIP = 20, + VT_FLIP = 22, + VT_OFFSET = 24 + }; + const flatbuffers::Vector *min_sizes() const { + return GetPointer *>(VT_MIN_SIZES); + } + flatbuffers::Vector *mutable_min_sizes() { + return GetPointer *>(VT_MIN_SIZES); + } + const flatbuffers::Vector *max_sizes() const { + return GetPointer *>(VT_MAX_SIZES); + } + flatbuffers::Vector *mutable_max_sizes() { + return GetPointer *>(VT_MAX_SIZES); + } + const flatbuffers::Vector *aspect_ratios() const { + return GetPointer *>(VT_ASPECT_RATIOS); + } + flatbuffers::Vector *mutable_aspect_ratios() { + return GetPointer *>(VT_ASPECT_RATIOS); + } + const flatbuffers::Vector *variances() const { + return GetPointer *>(VT_VARIANCES); + } + flatbuffers::Vector *mutable_variances() { + return GetPointer *>(VT_VARIANCES); + } + int64_t image_size_w() const { + return GetField(VT_IMAGE_SIZE_W, 0); + } + bool mutate_image_size_w(int64_t _image_size_w) { + return SetField(VT_IMAGE_SIZE_W, _image_size_w, 0); + } + int64_t image_size_h() const { + return GetField(VT_IMAGE_SIZE_H, 0); + } + bool mutate_image_size_h(int64_t _image_size_h) { + return SetField(VT_IMAGE_SIZE_H, _image_size_h, 0); + } + float step_w() const { + return GetField(VT_STEP_W, 0.0f); + } + bool mutate_step_w(float _step_w) { + return SetField(VT_STEP_W, _step_w, 0.0f); + } + float step_h() const { + return GetField(VT_STEP_H, 0.0f); + } + bool mutate_step_h(float _step_h) { + return SetField(VT_STEP_H, _step_h, 0.0f); + } + bool clip() const { + return GetField(VT_CLIP, 0) != 0; + } + bool mutate_clip(bool _clip) { + return SetField(VT_CLIP, static_cast(_clip), 0); + } + bool flip() const { + return GetField(VT_FLIP, 0) != 0; + } + bool mutate_flip(bool _flip) { + return SetField(VT_FLIP, static_cast(_flip), 0); + } + float offset() const { + return GetField(VT_OFFSET, 0.0f); + } + bool mutate_offset(float _offset) { + return SetField(VT_OFFSET, _offset, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_MIN_SIZES) && + verifier.VerifyVector(min_sizes()) && + VerifyOffset(verifier, VT_MAX_SIZES) && + verifier.VerifyVector(max_sizes()) && + VerifyOffset(verifier, VT_ASPECT_RATIOS) && + verifier.VerifyVector(aspect_ratios()) && + VerifyOffset(verifier, VT_VARIANCES) && + verifier.VerifyVector(variances()) && + VerifyField(verifier, VT_IMAGE_SIZE_W) && + VerifyField(verifier, VT_IMAGE_SIZE_H) && + VerifyField(verifier, VT_STEP_W) && + VerifyField(verifier, VT_STEP_H) && + VerifyField(verifier, VT_CLIP) && + VerifyField(verifier, VT_FLIP) && + VerifyField(verifier, VT_OFFSET) && + verifier.EndTable(); + } + PriorBoxT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PriorBoxT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PriorBoxT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PriorBoxBuilder { + typedef PriorBox Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_min_sizes(flatbuffers::Offset> min_sizes) { + fbb_.AddOffset(PriorBox::VT_MIN_SIZES, min_sizes); + } + void add_max_sizes(flatbuffers::Offset> max_sizes) { + fbb_.AddOffset(PriorBox::VT_MAX_SIZES, max_sizes); + } + void add_aspect_ratios(flatbuffers::Offset> aspect_ratios) { + fbb_.AddOffset(PriorBox::VT_ASPECT_RATIOS, aspect_ratios); + } + void add_variances(flatbuffers::Offset> variances) { + fbb_.AddOffset(PriorBox::VT_VARIANCES, variances); + } + void add_image_size_w(int64_t image_size_w) { + fbb_.AddElement(PriorBox::VT_IMAGE_SIZE_W, image_size_w, 0); + } + void add_image_size_h(int64_t image_size_h) { + fbb_.AddElement(PriorBox::VT_IMAGE_SIZE_H, image_size_h, 0); + } + void add_step_w(float step_w) { + fbb_.AddElement(PriorBox::VT_STEP_W, step_w, 0.0f); + } + void add_step_h(float step_h) { + fbb_.AddElement(PriorBox::VT_STEP_H, step_h, 0.0f); + } + void add_clip(bool clip) { + fbb_.AddElement(PriorBox::VT_CLIP, static_cast(clip), 0); + } + void add_flip(bool flip) { + fbb_.AddElement(PriorBox::VT_FLIP, static_cast(flip), 0); + } + void add_offset(float offset) { + fbb_.AddElement(PriorBox::VT_OFFSET, offset, 0.0f); + } + explicit PriorBoxBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePriorBox( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> min_sizes = 0, + flatbuffers::Offset> max_sizes = 0, + flatbuffers::Offset> aspect_ratios = 0, + flatbuffers::Offset> variances = 0, + int64_t image_size_w = 0, + int64_t image_size_h = 0, + float step_w = 0.0f, + float step_h = 0.0f, + bool clip = false, + bool flip = false, + float offset = 0.0f) { + PriorBoxBuilder builder_(_fbb); + builder_.add_image_size_h(image_size_h); + builder_.add_image_size_w(image_size_w); + builder_.add_offset(offset); + builder_.add_step_h(step_h); + builder_.add_step_w(step_w); + builder_.add_variances(variances); + builder_.add_aspect_ratios(aspect_ratios); + builder_.add_max_sizes(max_sizes); + builder_.add_min_sizes(min_sizes); + builder_.add_flip(flip); + builder_.add_clip(clip); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreatePriorBoxDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *min_sizes = nullptr, + const std::vector *max_sizes = nullptr, + const std::vector *aspect_ratios = nullptr, + const std::vector *variances = nullptr, + int64_t image_size_w = 0, + int64_t image_size_h = 0, + float step_w = 0.0f, + float step_h = 0.0f, + bool clip = false, + bool flip = false, + float offset = 0.0f) { + auto min_sizes__ = min_sizes ? _fbb.CreateVector(*min_sizes) : 0; + auto max_sizes__ = max_sizes ? _fbb.CreateVector(*max_sizes) : 0; + auto aspect_ratios__ = aspect_ratios ? _fbb.CreateVector(*aspect_ratios) : 0; + auto variances__ = variances ? _fbb.CreateVector(*variances) : 0; + return mindspore::schema::CreatePriorBox( + _fbb, + min_sizes__, + max_sizes__, + aspect_ratios__, + variances__, + image_size_w, + image_size_h, + step_w, + step_h, + clip, + flip, + offset); +} + +flatbuffers::Offset CreatePriorBox(flatbuffers::FlatBufferBuilder &_fbb, const PriorBoxT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PReLUFusionT : public flatbuffers::NativeTable { + typedef PReLUFusion TableType; + bool channel_shared = false; +}; + +struct PReLUFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PReLUFusionT NativeTableType; + typedef PReLUFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PReLUFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CHANNEL_SHARED = 4 + }; + bool channel_shared() const { + return GetField(VT_CHANNEL_SHARED, 0) != 0; + } + bool mutate_channel_shared(bool _channel_shared) { + return SetField(VT_CHANNEL_SHARED, static_cast(_channel_shared), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_CHANNEL_SHARED) && + verifier.EndTable(); + } + PReLUFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PReLUFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PReLUFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PReLUFusionBuilder { + typedef PReLUFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_channel_shared(bool channel_shared) { + fbb_.AddElement(PReLUFusion::VT_CHANNEL_SHARED, static_cast(channel_shared), 0); + } + explicit PReLUFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePReLUFusion( + flatbuffers::FlatBufferBuilder &_fbb, + bool channel_shared = false) { + PReLUFusionBuilder builder_(_fbb); + builder_.add_channel_shared(channel_shared); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePReLUFusion(flatbuffers::FlatBufferBuilder &_fbb, const PReLUFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RankT : public flatbuffers::NativeTable { + typedef Rank TableType; +}; + +struct Rank FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RankT NativeTableType; + typedef RankBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RankTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RankT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RankT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RankBuilder { + typedef Rank Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RankBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRank( + flatbuffers::FlatBufferBuilder &_fbb) { + RankBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRank(flatbuffers::FlatBufferBuilder &_fbb, const RankT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RangeT : public flatbuffers::NativeTable { + typedef Range TableType; + int64_t d_type = 0; + int64_t start = 0; + int64_t limit = 0; + int64_t delta = 1LL; +}; + +struct Range FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RangeT NativeTableType; + typedef RangeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RangeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_D_TYPE = 4, + VT_START = 6, + VT_LIMIT = 8, + VT_DELTA = 10 + }; + int64_t d_type() const { + return GetField(VT_D_TYPE, 0); + } + bool mutate_d_type(int64_t _d_type) { + return SetField(VT_D_TYPE, _d_type, 0); + } + int64_t start() const { + return GetField(VT_START, 0); + } + bool mutate_start(int64_t _start) { + return SetField(VT_START, _start, 0); + } + int64_t limit() const { + return GetField(VT_LIMIT, 0); + } + bool mutate_limit(int64_t _limit) { + return SetField(VT_LIMIT, _limit, 0); + } + int64_t delta() const { + return GetField(VT_DELTA, 1LL); + } + bool mutate_delta(int64_t _delta) { + return SetField(VT_DELTA, _delta, 1LL); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_D_TYPE) && + VerifyField(verifier, VT_START) && + VerifyField(verifier, VT_LIMIT) && + VerifyField(verifier, VT_DELTA) && + verifier.EndTable(); + } + RangeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RangeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RangeBuilder { + typedef Range Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_d_type(int64_t d_type) { + fbb_.AddElement(Range::VT_D_TYPE, d_type, 0); + } + void add_start(int64_t start) { + fbb_.AddElement(Range::VT_START, start, 0); + } + void add_limit(int64_t limit) { + fbb_.AddElement(Range::VT_LIMIT, limit, 0); + } + void add_delta(int64_t delta) { + fbb_.AddElement(Range::VT_DELTA, delta, 1LL); + } + explicit RangeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRange( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t d_type = 0, + int64_t start = 0, + int64_t limit = 0, + int64_t delta = 1LL) { + RangeBuilder builder_(_fbb); + builder_.add_delta(delta); + builder_.add_limit(limit); + builder_.add_start(start); + builder_.add_d_type(d_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRange(flatbuffers::FlatBufferBuilder &_fbb, const RangeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReciprocalT : public flatbuffers::NativeTable { + typedef Reciprocal TableType; +}; + +struct Reciprocal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReciprocalT NativeTableType; + typedef ReciprocalBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReciprocalTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ReciprocalT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReciprocalT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReciprocalT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReciprocalBuilder { + typedef Reciprocal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ReciprocalBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReciprocal( + flatbuffers::FlatBufferBuilder &_fbb) { + ReciprocalBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReciprocal(flatbuffers::FlatBufferBuilder &_fbb, const ReciprocalT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RealDivT : public flatbuffers::NativeTable { + typedef RealDiv TableType; +}; + +struct RealDiv FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RealDivT NativeTableType; + typedef RealDivBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RealDivTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RealDivT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RealDivT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RealDivT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RealDivBuilder { + typedef RealDiv Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RealDivBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRealDiv( + flatbuffers::FlatBufferBuilder &_fbb) { + RealDivBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRealDiv(flatbuffers::FlatBufferBuilder &_fbb, const RealDivT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReduceFusionT : public flatbuffers::NativeTable { + typedef ReduceFusion TableType; + bool keep_dims = false; + mindspore::schema::ReduceMode mode = mindspore::schema::ReduceMode_ReduceMean; + bool reduce_to_end = false; + float coeff = 0.0f; +}; + +struct ReduceFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReduceFusionT NativeTableType; + typedef ReduceFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReduceFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEEP_DIMS = 4, + VT_MODE = 6, + VT_REDUCE_TO_END = 8, + VT_COEFF = 10 + }; + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } + bool mutate_keep_dims(bool _keep_dims) { + return SetField(VT_KEEP_DIMS, static_cast(_keep_dims), 0); + } + mindspore::schema::ReduceMode mode() const { + return static_cast(GetField(VT_MODE, 0)); + } + bool mutate_mode(mindspore::schema::ReduceMode _mode) { + return SetField(VT_MODE, static_cast(_mode), 0); + } + bool reduce_to_end() const { + return GetField(VT_REDUCE_TO_END, 0) != 0; + } + bool mutate_reduce_to_end(bool _reduce_to_end) { + return SetField(VT_REDUCE_TO_END, static_cast(_reduce_to_end), 0); + } + float coeff() const { + return GetField(VT_COEFF, 0.0f); + } + bool mutate_coeff(float _coeff) { + return SetField(VT_COEFF, _coeff, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_KEEP_DIMS) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_REDUCE_TO_END) && + VerifyField(verifier, VT_COEFF) && + verifier.EndTable(); + } + ReduceFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReduceFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReduceFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReduceFusionBuilder { + typedef ReduceFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_keep_dims(bool keep_dims) { + fbb_.AddElement(ReduceFusion::VT_KEEP_DIMS, static_cast(keep_dims), 0); + } + void add_mode(mindspore::schema::ReduceMode mode) { + fbb_.AddElement(ReduceFusion::VT_MODE, static_cast(mode), 0); + } + void add_reduce_to_end(bool reduce_to_end) { + fbb_.AddElement(ReduceFusion::VT_REDUCE_TO_END, static_cast(reduce_to_end), 0); + } + void add_coeff(float coeff) { + fbb_.AddElement(ReduceFusion::VT_COEFF, coeff, 0.0f); + } + explicit ReduceFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReduceFusion( + flatbuffers::FlatBufferBuilder &_fbb, + bool keep_dims = false, + mindspore::schema::ReduceMode mode = mindspore::schema::ReduceMode_ReduceMean, + bool reduce_to_end = false, + float coeff = 0.0f) { + ReduceFusionBuilder builder_(_fbb); + builder_.add_coeff(coeff); + builder_.add_reduce_to_end(reduce_to_end); + builder_.add_mode(mode); + builder_.add_keep_dims(keep_dims); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReduceFusion(flatbuffers::FlatBufferBuilder &_fbb, const ReduceFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReshapeT : public flatbuffers::NativeTable { + typedef Reshape TableType; +}; + +struct Reshape FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReshapeT NativeTableType; + typedef ReshapeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReshapeTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ReshapeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReshapeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReshapeBuilder { + typedef Reshape Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ReshapeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReshape( + flatbuffers::FlatBufferBuilder &_fbb) { + ReshapeBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReshape(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ResizeT : public flatbuffers::NativeTable { + typedef Resize TableType; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; + mindspore::schema::ResizeMethod method = mindspore::schema::ResizeMethod_LINEAR; + int64_t new_height = 0; + int64_t new_width = 0; + bool preserve_aspect_ratio = false; + mindspore::schema::CoordinateTransformMode coordinate_transform_mode = mindspore::schema::CoordinateTransformMode_ASYMMETRIC; + float cubic_coeff = 0.0f; + int64_t exclude_outside = 0; + float extrapolation_value = 0.0f; + mindspore::schema::NearestMode nearest_mode = mindspore::schema::NearestMode_NORMAL; +}; + +struct Resize FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ResizeT NativeTableType; + typedef ResizeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ResizeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_METHOD = 6, + VT_NEW_HEIGHT = 8, + VT_NEW_WIDTH = 10, + VT_PRESERVE_ASPECT_RATIO = 12, + VT_COORDINATE_TRANSFORM_MODE = 14, + VT_CUBIC_COEFF = 16, + VT_EXCLUDE_OUTSIDE = 18, + VT_EXTRAPOLATION_VALUE = 20, + VT_NEAREST_MODE = 22 + }; + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + mindspore::schema::ResizeMethod method() const { + return static_cast(GetField(VT_METHOD, 0)); + } + bool mutate_method(mindspore::schema::ResizeMethod _method) { + return SetField(VT_METHOD, static_cast(_method), 0); + } + int64_t new_height() const { + return GetField(VT_NEW_HEIGHT, 0); + } + bool mutate_new_height(int64_t _new_height) { + return SetField(VT_NEW_HEIGHT, _new_height, 0); + } + int64_t new_width() const { + return GetField(VT_NEW_WIDTH, 0); + } + bool mutate_new_width(int64_t _new_width) { + return SetField(VT_NEW_WIDTH, _new_width, 0); + } + bool preserve_aspect_ratio() const { + return GetField(VT_PRESERVE_ASPECT_RATIO, 0) != 0; + } + bool mutate_preserve_aspect_ratio(bool _preserve_aspect_ratio) { + return SetField(VT_PRESERVE_ASPECT_RATIO, static_cast(_preserve_aspect_ratio), 0); + } + mindspore::schema::CoordinateTransformMode coordinate_transform_mode() const { + return static_cast(GetField(VT_COORDINATE_TRANSFORM_MODE, 0)); + } + bool mutate_coordinate_transform_mode(mindspore::schema::CoordinateTransformMode _coordinate_transform_mode) { + return SetField(VT_COORDINATE_TRANSFORM_MODE, static_cast(_coordinate_transform_mode), 0); + } + float cubic_coeff() const { + return GetField(VT_CUBIC_COEFF, 0.0f); + } + bool mutate_cubic_coeff(float _cubic_coeff) { + return SetField(VT_CUBIC_COEFF, _cubic_coeff, 0.0f); + } + int64_t exclude_outside() const { + return GetField(VT_EXCLUDE_OUTSIDE, 0); + } + bool mutate_exclude_outside(int64_t _exclude_outside) { + return SetField(VT_EXCLUDE_OUTSIDE, _exclude_outside, 0); + } + float extrapolation_value() const { + return GetField(VT_EXTRAPOLATION_VALUE, 0.0f); + } + bool mutate_extrapolation_value(float _extrapolation_value) { + return SetField(VT_EXTRAPOLATION_VALUE, _extrapolation_value, 0.0f); + } + mindspore::schema::NearestMode nearest_mode() const { + return static_cast(GetField(VT_NEAREST_MODE, 0)); + } + bool mutate_nearest_mode(mindspore::schema::NearestMode _nearest_mode) { + return SetField(VT_NEAREST_MODE, static_cast(_nearest_mode), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_METHOD) && + VerifyField(verifier, VT_NEW_HEIGHT) && + VerifyField(verifier, VT_NEW_WIDTH) && + VerifyField(verifier, VT_PRESERVE_ASPECT_RATIO) && + VerifyField(verifier, VT_COORDINATE_TRANSFORM_MODE) && + VerifyField(verifier, VT_CUBIC_COEFF) && + VerifyField(verifier, VT_EXCLUDE_OUTSIDE) && + VerifyField(verifier, VT_EXTRAPOLATION_VALUE) && + VerifyField(verifier, VT_NEAREST_MODE) && + verifier.EndTable(); + } + ResizeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ResizeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ResizeBuilder { + typedef Resize Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(Resize::VT_FORMAT, static_cast(format), 0); + } + void add_method(mindspore::schema::ResizeMethod method) { + fbb_.AddElement(Resize::VT_METHOD, static_cast(method), 0); + } + void add_new_height(int64_t new_height) { + fbb_.AddElement(Resize::VT_NEW_HEIGHT, new_height, 0); + } + void add_new_width(int64_t new_width) { + fbb_.AddElement(Resize::VT_NEW_WIDTH, new_width, 0); + } + void add_preserve_aspect_ratio(bool preserve_aspect_ratio) { + fbb_.AddElement(Resize::VT_PRESERVE_ASPECT_RATIO, static_cast(preserve_aspect_ratio), 0); + } + void add_coordinate_transform_mode(mindspore::schema::CoordinateTransformMode coordinate_transform_mode) { + fbb_.AddElement(Resize::VT_COORDINATE_TRANSFORM_MODE, static_cast(coordinate_transform_mode), 0); + } + void add_cubic_coeff(float cubic_coeff) { + fbb_.AddElement(Resize::VT_CUBIC_COEFF, cubic_coeff, 0.0f); + } + void add_exclude_outside(int64_t exclude_outside) { + fbb_.AddElement(Resize::VT_EXCLUDE_OUTSIDE, exclude_outside, 0); + } + void add_extrapolation_value(float extrapolation_value) { + fbb_.AddElement(Resize::VT_EXTRAPOLATION_VALUE, extrapolation_value, 0.0f); + } + void add_nearest_mode(mindspore::schema::NearestMode nearest_mode) { + fbb_.AddElement(Resize::VT_NEAREST_MODE, static_cast(nearest_mode), 0); + } + explicit ResizeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateResize( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + mindspore::schema::ResizeMethod method = mindspore::schema::ResizeMethod_LINEAR, + int64_t new_height = 0, + int64_t new_width = 0, + bool preserve_aspect_ratio = false, + mindspore::schema::CoordinateTransformMode coordinate_transform_mode = mindspore::schema::CoordinateTransformMode_ASYMMETRIC, + float cubic_coeff = 0.0f, + int64_t exclude_outside = 0, + float extrapolation_value = 0.0f, + mindspore::schema::NearestMode nearest_mode = mindspore::schema::NearestMode_NORMAL) { + ResizeBuilder builder_(_fbb); + builder_.add_exclude_outside(exclude_outside); + builder_.add_new_width(new_width); + builder_.add_new_height(new_height); + builder_.add_extrapolation_value(extrapolation_value); + builder_.add_cubic_coeff(cubic_coeff); + builder_.add_format(format); + builder_.add_nearest_mode(nearest_mode); + builder_.add_coordinate_transform_mode(coordinate_transform_mode); + builder_.add_preserve_aspect_ratio(preserve_aspect_ratio); + builder_.add_method(method); + return builder_.Finish(); +} + +flatbuffers::Offset CreateResize(flatbuffers::FlatBufferBuilder &_fbb, const ResizeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReverseSequenceT : public flatbuffers::NativeTable { + typedef ReverseSequence TableType; + int64_t seq_dim = 0; + int64_t batch_dim = 0; +}; + +struct ReverseSequence FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReverseSequenceT NativeTableType; + typedef ReverseSequenceBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReverseSequenceTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEQ_DIM = 4, + VT_BATCH_DIM = 6 + }; + int64_t seq_dim() const { + return GetField(VT_SEQ_DIM, 0); + } + bool mutate_seq_dim(int64_t _seq_dim) { + return SetField(VT_SEQ_DIM, _seq_dim, 0); + } + int64_t batch_dim() const { + return GetField(VT_BATCH_DIM, 0); + } + bool mutate_batch_dim(int64_t _batch_dim) { + return SetField(VT_BATCH_DIM, _batch_dim, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEQ_DIM) && + VerifyField(verifier, VT_BATCH_DIM) && + verifier.EndTable(); + } + ReverseSequenceT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReverseSequenceT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReverseSequenceBuilder { + typedef ReverseSequence Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seq_dim(int64_t seq_dim) { + fbb_.AddElement(ReverseSequence::VT_SEQ_DIM, seq_dim, 0); + } + void add_batch_dim(int64_t batch_dim) { + fbb_.AddElement(ReverseSequence::VT_BATCH_DIM, batch_dim, 0); + } + explicit ReverseSequenceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReverseSequence( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t seq_dim = 0, + int64_t batch_dim = 0) { + ReverseSequenceBuilder builder_(_fbb); + builder_.add_batch_dim(batch_dim); + builder_.add_seq_dim(seq_dim); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReverseSequence(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReverseV2T : public flatbuffers::NativeTable { + typedef ReverseV2 TableType; + std::vector axis{}; +}; + +struct ReverseV2 FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReverseV2T NativeTableType; + typedef ReverseV2Builder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReverseV2TypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + const flatbuffers::Vector *axis() const { + return GetPointer *>(VT_AXIS); + } + flatbuffers::Vector *mutable_axis() { + return GetPointer *>(VT_AXIS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_AXIS) && + verifier.VerifyVector(axis()) && + verifier.EndTable(); + } + ReverseV2T *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReverseV2T *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2T* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReverseV2Builder { + typedef ReverseV2 Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(flatbuffers::Offset> axis) { + fbb_.AddOffset(ReverseV2::VT_AXIS, axis); + } + explicit ReverseV2Builder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReverseV2( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> axis = 0) { + ReverseV2Builder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateReverseV2Direct( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *axis = nullptr) { + auto axis__ = axis ? _fbb.CreateVector(*axis) : 0; + return mindspore::schema::CreateReverseV2( + _fbb, + axis__); +} + +flatbuffers::Offset CreateReverseV2(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2T *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RfftT : public flatbuffers::NativeTable { + typedef Rfft TableType; + int64_t fft_length = 0; +}; + +struct Rfft FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RfftT NativeTableType; + typedef RfftBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RfftTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FFT_LENGTH = 4 + }; + int64_t fft_length() const { + return GetField(VT_FFT_LENGTH, 0); + } + bool mutate_fft_length(int64_t _fft_length) { + return SetField(VT_FFT_LENGTH, _fft_length, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FFT_LENGTH) && + verifier.EndTable(); + } + RfftT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RfftT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RfftT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RfftBuilder { + typedef Rfft Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fft_length(int64_t fft_length) { + fbb_.AddElement(Rfft::VT_FFT_LENGTH, fft_length, 0); + } + explicit RfftBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRfft( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t fft_length = 0) { + RfftBuilder builder_(_fbb); + builder_.add_fft_length(fft_length); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRfft(flatbuffers::FlatBufferBuilder &_fbb, const RfftT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ROIPoolingT : public flatbuffers::NativeTable { + typedef ROIPooling TableType; + int64_t pooled_h = 0; + int64_t pooled_w = 0; + float scale = 0.0f; +}; + +struct ROIPooling FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ROIPoolingT NativeTableType; + typedef ROIPoolingBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ROIPoolingTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_POOLED_H = 4, + VT_POOLED_W = 6, + VT_SCALE = 8 + }; + int64_t pooled_h() const { + return GetField(VT_POOLED_H, 0); + } + bool mutate_pooled_h(int64_t _pooled_h) { + return SetField(VT_POOLED_H, _pooled_h, 0); + } + int64_t pooled_w() const { + return GetField(VT_POOLED_W, 0); + } + bool mutate_pooled_w(int64_t _pooled_w) { + return SetField(VT_POOLED_W, _pooled_w, 0); + } + float scale() const { + return GetField(VT_SCALE, 0.0f); + } + bool mutate_scale(float _scale) { + return SetField(VT_SCALE, _scale, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_POOLED_H) && + VerifyField(verifier, VT_POOLED_W) && + VerifyField(verifier, VT_SCALE) && + verifier.EndTable(); + } + ROIPoolingT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ROIPoolingT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ROIPoolingT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ROIPoolingBuilder { + typedef ROIPooling Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_pooled_h(int64_t pooled_h) { + fbb_.AddElement(ROIPooling::VT_POOLED_H, pooled_h, 0); + } + void add_pooled_w(int64_t pooled_w) { + fbb_.AddElement(ROIPooling::VT_POOLED_W, pooled_w, 0); + } + void add_scale(float scale) { + fbb_.AddElement(ROIPooling::VT_SCALE, scale, 0.0f); + } + explicit ROIPoolingBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateROIPooling( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t pooled_h = 0, + int64_t pooled_w = 0, + float scale = 0.0f) { + ROIPoolingBuilder builder_(_fbb); + builder_.add_pooled_w(pooled_w); + builder_.add_pooled_h(pooled_h); + builder_.add_scale(scale); + return builder_.Finish(); +} + +flatbuffers::Offset CreateROIPooling(flatbuffers::FlatBufferBuilder &_fbb, const ROIPoolingT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RoundT : public flatbuffers::NativeTable { + typedef Round TableType; +}; + +struct Round FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RoundT NativeTableType; + typedef RoundBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RoundTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RoundT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RoundT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RoundT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RoundBuilder { + typedef Round Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RoundBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRound( + flatbuffers::FlatBufferBuilder &_fbb) { + RoundBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRound(flatbuffers::FlatBufferBuilder &_fbb, const RoundT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RsqrtT : public flatbuffers::NativeTable { + typedef Rsqrt TableType; +}; + +struct Rsqrt FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RsqrtT NativeTableType; + typedef RsqrtBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RsqrtTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RsqrtT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RsqrtT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RsqrtT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RsqrtBuilder { + typedef Rsqrt Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RsqrtBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRsqrt( + flatbuffers::FlatBufferBuilder &_fbb) { + RsqrtBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRsqrt(flatbuffers::FlatBufferBuilder &_fbb, const RsqrtT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct QuantDTypeCastT : public flatbuffers::NativeTable { + typedef QuantDTypeCast TableType; + int64_t src_t = 0; + int64_t dst_t = 0; + int64_t axis = 0; +}; + +struct QuantDTypeCast FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef QuantDTypeCastT NativeTableType; + typedef QuantDTypeCastBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return QuantDTypeCastTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SRC_T = 4, + VT_DST_T = 6, + VT_AXIS = 8 + }; + int64_t src_t() const { + return GetField(VT_SRC_T, 0); + } + bool mutate_src_t(int64_t _src_t) { + return SetField(VT_SRC_T, _src_t, 0); + } + int64_t dst_t() const { + return GetField(VT_DST_T, 0); + } + bool mutate_dst_t(int64_t _dst_t) { + return SetField(VT_DST_T, _dst_t, 0); + } + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SRC_T) && + VerifyField(verifier, VT_DST_T) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + QuantDTypeCastT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantDTypeCastT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantDTypeCastT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct QuantDTypeCastBuilder { + typedef QuantDTypeCast Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_src_t(int64_t src_t) { + fbb_.AddElement(QuantDTypeCast::VT_SRC_T, src_t, 0); + } + void add_dst_t(int64_t dst_t) { + fbb_.AddElement(QuantDTypeCast::VT_DST_T, dst_t, 0); + } + void add_axis(int64_t axis) { + fbb_.AddElement(QuantDTypeCast::VT_AXIS, axis, 0); + } + explicit QuantDTypeCastBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateQuantDTypeCast( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t src_t = 0, + int64_t dst_t = 0, + int64_t axis = 0) { + QuantDTypeCastBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_dst_t(dst_t); + builder_.add_src_t(src_t); + return builder_.Finish(); +} + +flatbuffers::Offset CreateQuantDTypeCast(flatbuffers::FlatBufferBuilder &_fbb, const QuantDTypeCastT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ScaleFusionT : public flatbuffers::NativeTable { + typedef ScaleFusion TableType; + int64_t axis = 0; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct ScaleFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ScaleFusionT NativeTableType; + typedef ScaleFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ScaleFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_ACTIVATION_TYPE = 6 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + ScaleFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ScaleFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScaleFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ScaleFusionBuilder { + typedef ScaleFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(ScaleFusion::VT_AXIS, axis, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(ScaleFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit ScaleFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateScaleFusion( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + ScaleFusionBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateScaleFusion(flatbuffers::FlatBufferBuilder &_fbb, const ScaleFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ScatterNdT : public flatbuffers::NativeTable { + typedef ScatterNd TableType; +}; + +struct ScatterNd FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ScatterNdT NativeTableType; + typedef ScatterNdBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ScatterNdTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ScatterNdT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ScatterNdT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ScatterNdBuilder { + typedef ScatterNd Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ScatterNdBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateScatterNd( + flatbuffers::FlatBufferBuilder &_fbb) { + ScatterNdBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateScatterNd(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SGDT : public flatbuffers::NativeTable { + typedef SGD TableType; + bool nesterov = false; + float dampening = 0.0f; + float weight_decay = 0.0f; +}; + +struct SGD FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SGDT NativeTableType; + typedef SGDBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SGDTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NESTEROV = 4, + VT_DAMPENING = 6, + VT_WEIGHT_DECAY = 8 + }; + bool nesterov() const { + return GetField(VT_NESTEROV, 0) != 0; + } + bool mutate_nesterov(bool _nesterov) { + return SetField(VT_NESTEROV, static_cast(_nesterov), 0); + } + float dampening() const { + return GetField(VT_DAMPENING, 0.0f); + } + bool mutate_dampening(float _dampening) { + return SetField(VT_DAMPENING, _dampening, 0.0f); + } + float weight_decay() const { + return GetField(VT_WEIGHT_DECAY, 0.0f); + } + bool mutate_weight_decay(float _weight_decay) { + return SetField(VT_WEIGHT_DECAY, _weight_decay, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NESTEROV) && + VerifyField(verifier, VT_DAMPENING) && + VerifyField(verifier, VT_WEIGHT_DECAY) && + verifier.EndTable(); + } + SGDT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SGDT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SGDT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SGDBuilder { + typedef SGD Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_nesterov(bool nesterov) { + fbb_.AddElement(SGD::VT_NESTEROV, static_cast(nesterov), 0); + } + void add_dampening(float dampening) { + fbb_.AddElement(SGD::VT_DAMPENING, dampening, 0.0f); + } + void add_weight_decay(float weight_decay) { + fbb_.AddElement(SGD::VT_WEIGHT_DECAY, weight_decay, 0.0f); + } + explicit SGDBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSGD( + flatbuffers::FlatBufferBuilder &_fbb, + bool nesterov = false, + float dampening = 0.0f, + float weight_decay = 0.0f) { + SGDBuilder builder_(_fbb); + builder_.add_weight_decay(weight_decay); + builder_.add_dampening(dampening); + builder_.add_nesterov(nesterov); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSGD(flatbuffers::FlatBufferBuilder &_fbb, const SGDT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ShapeT : public flatbuffers::NativeTable { + typedef Shape TableType; +}; + +struct Shape FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ShapeT NativeTableType; + typedef ShapeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ShapeTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ShapeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ShapeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ShapeBuilder { + typedef Shape Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ShapeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateShape( + flatbuffers::FlatBufferBuilder &_fbb) { + ShapeBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateShape(flatbuffers::FlatBufferBuilder &_fbb, const ShapeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SigmoidCrossEntropyWithLogitsT : public flatbuffers::NativeTable { + typedef SigmoidCrossEntropyWithLogits TableType; +}; + +struct SigmoidCrossEntropyWithLogits FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SigmoidCrossEntropyWithLogitsT NativeTableType; + typedef SigmoidCrossEntropyWithLogitsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SigmoidCrossEntropyWithLogitsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SigmoidCrossEntropyWithLogitsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SigmoidCrossEntropyWithLogitsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SigmoidCrossEntropyWithLogitsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SigmoidCrossEntropyWithLogitsBuilder { + typedef SigmoidCrossEntropyWithLogits Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SigmoidCrossEntropyWithLogitsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSigmoidCrossEntropyWithLogits( + flatbuffers::FlatBufferBuilder &_fbb) { + SigmoidCrossEntropyWithLogitsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSigmoidCrossEntropyWithLogits(flatbuffers::FlatBufferBuilder &_fbb, const SigmoidCrossEntropyWithLogitsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SigmoidCrossEntropyWithLogitsGradT : public flatbuffers::NativeTable { + typedef SigmoidCrossEntropyWithLogitsGrad TableType; +}; + +struct SigmoidCrossEntropyWithLogitsGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SigmoidCrossEntropyWithLogitsGradT NativeTableType; + typedef SigmoidCrossEntropyWithLogitsGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SigmoidCrossEntropyWithLogitsGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SigmoidCrossEntropyWithLogitsGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SigmoidCrossEntropyWithLogitsGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SigmoidCrossEntropyWithLogitsGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SigmoidCrossEntropyWithLogitsGradBuilder { + typedef SigmoidCrossEntropyWithLogitsGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SigmoidCrossEntropyWithLogitsGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSigmoidCrossEntropyWithLogitsGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + SigmoidCrossEntropyWithLogitsGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSigmoidCrossEntropyWithLogitsGrad(flatbuffers::FlatBufferBuilder &_fbb, const SigmoidCrossEntropyWithLogitsGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SinT : public flatbuffers::NativeTable { + typedef Sin TableType; +}; + +struct Sin FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SinT NativeTableType; + typedef SinBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SinTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SinT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SinT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SinT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SinBuilder { + typedef Sin Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SinBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSin( + flatbuffers::FlatBufferBuilder &_fbb) { + SinBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSin(flatbuffers::FlatBufferBuilder &_fbb, const SinT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SkipGramT : public flatbuffers::NativeTable { + typedef SkipGram TableType; + bool include_all_grams = false; + int64_t max_skip_size = 0; + int64_t ngram_size = 0; +}; + +struct SkipGram FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SkipGramT NativeTableType; + typedef SkipGramBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SkipGramTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_INCLUDE_ALL_GRAMS = 4, + VT_MAX_SKIP_SIZE = 6, + VT_NGRAM_SIZE = 8 + }; + bool include_all_grams() const { + return GetField(VT_INCLUDE_ALL_GRAMS, 0) != 0; + } + bool mutate_include_all_grams(bool _include_all_grams) { + return SetField(VT_INCLUDE_ALL_GRAMS, static_cast(_include_all_grams), 0); + } + int64_t max_skip_size() const { + return GetField(VT_MAX_SKIP_SIZE, 0); + } + bool mutate_max_skip_size(int64_t _max_skip_size) { + return SetField(VT_MAX_SKIP_SIZE, _max_skip_size, 0); + } + int64_t ngram_size() const { + return GetField(VT_NGRAM_SIZE, 0); + } + bool mutate_ngram_size(int64_t _ngram_size) { + return SetField(VT_NGRAM_SIZE, _ngram_size, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_INCLUDE_ALL_GRAMS) && + VerifyField(verifier, VT_MAX_SKIP_SIZE) && + VerifyField(verifier, VT_NGRAM_SIZE) && + verifier.EndTable(); + } + SkipGramT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SkipGramT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SkipGramBuilder { + typedef SkipGram Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_include_all_grams(bool include_all_grams) { + fbb_.AddElement(SkipGram::VT_INCLUDE_ALL_GRAMS, static_cast(include_all_grams), 0); + } + void add_max_skip_size(int64_t max_skip_size) { + fbb_.AddElement(SkipGram::VT_MAX_SKIP_SIZE, max_skip_size, 0); + } + void add_ngram_size(int64_t ngram_size) { + fbb_.AddElement(SkipGram::VT_NGRAM_SIZE, ngram_size, 0); + } + explicit SkipGramBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSkipGram( + flatbuffers::FlatBufferBuilder &_fbb, + bool include_all_grams = false, + int64_t max_skip_size = 0, + int64_t ngram_size = 0) { + SkipGramBuilder builder_(_fbb); + builder_.add_ngram_size(ngram_size); + builder_.add_max_skip_size(max_skip_size); + builder_.add_include_all_grams(include_all_grams); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSkipGram(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SliceFusionT : public flatbuffers::NativeTable { + typedef SliceFusion TableType; + std::vector axes{}; +}; + +struct SliceFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SliceFusionT NativeTableType; + typedef SliceFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SliceFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXES = 4 + }; + const flatbuffers::Vector *axes() const { + return GetPointer *>(VT_AXES); + } + flatbuffers::Vector *mutable_axes() { + return GetPointer *>(VT_AXES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_AXES) && + verifier.VerifyVector(axes()) && + verifier.EndTable(); + } + SliceFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SliceFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SliceFusionBuilder { + typedef SliceFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axes(flatbuffers::Offset> axes) { + fbb_.AddOffset(SliceFusion::VT_AXES, axes); + } + explicit SliceFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSliceFusion( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> axes = 0) { + SliceFusionBuilder builder_(_fbb); + builder_.add_axes(axes); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSliceFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *axes = nullptr) { + auto axes__ = axes ? _fbb.CreateVector(*axes) : 0; + return mindspore::schema::CreateSliceFusion( + _fbb, + axes__); +} + +flatbuffers::Offset CreateSliceFusion(flatbuffers::FlatBufferBuilder &_fbb, const SliceFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SmoothL1LossT : public flatbuffers::NativeTable { + typedef SmoothL1Loss TableType; + float beta = 0.0f; +}; + +struct SmoothL1Loss FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SmoothL1LossT NativeTableType; + typedef SmoothL1LossBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SmoothL1LossTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BETA = 4 + }; + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool mutate_beta(float _beta) { + return SetField(VT_BETA, _beta, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BETA) && + verifier.EndTable(); + } + SmoothL1LossT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SmoothL1LossT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SmoothL1LossT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SmoothL1LossBuilder { + typedef SmoothL1Loss Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_beta(float beta) { + fbb_.AddElement(SmoothL1Loss::VT_BETA, beta, 0.0f); + } + explicit SmoothL1LossBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSmoothL1Loss( + flatbuffers::FlatBufferBuilder &_fbb, + float beta = 0.0f) { + SmoothL1LossBuilder builder_(_fbb); + builder_.add_beta(beta); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSmoothL1Loss(flatbuffers::FlatBufferBuilder &_fbb, const SmoothL1LossT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SmoothL1LossGradT : public flatbuffers::NativeTable { + typedef SmoothL1LossGrad TableType; + float beta = 0.0f; +}; + +struct SmoothL1LossGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SmoothL1LossGradT NativeTableType; + typedef SmoothL1LossGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SmoothL1LossGradTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BETA = 4 + }; + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool mutate_beta(float _beta) { + return SetField(VT_BETA, _beta, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BETA) && + verifier.EndTable(); + } + SmoothL1LossGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SmoothL1LossGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SmoothL1LossGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SmoothL1LossGradBuilder { + typedef SmoothL1LossGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_beta(float beta) { + fbb_.AddElement(SmoothL1LossGrad::VT_BETA, beta, 0.0f); + } + explicit SmoothL1LossGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSmoothL1LossGrad( + flatbuffers::FlatBufferBuilder &_fbb, + float beta = 0.0f) { + SmoothL1LossGradBuilder builder_(_fbb); + builder_.add_beta(beta); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSmoothL1LossGrad(flatbuffers::FlatBufferBuilder &_fbb, const SmoothL1LossGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SoftmaxT : public flatbuffers::NativeTable { + typedef Softmax TableType; + std::vector axis{}; +}; + +struct Softmax FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SoftmaxT NativeTableType; + typedef SoftmaxBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SoftmaxTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + const flatbuffers::Vector *axis() const { + return GetPointer *>(VT_AXIS); + } + flatbuffers::Vector *mutable_axis() { + return GetPointer *>(VT_AXIS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_AXIS) && + verifier.VerifyVector(axis()) && + verifier.EndTable(); + } + SoftmaxT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SoftmaxT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SoftmaxBuilder { + typedef Softmax Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(flatbuffers::Offset> axis) { + fbb_.AddOffset(Softmax::VT_AXIS, axis); + } + explicit SoftmaxBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSoftmax( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> axis = 0) { + SoftmaxBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSoftmaxDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *axis = nullptr) { + auto axis__ = axis ? _fbb.CreateVector(*axis) : 0; + return mindspore::schema::CreateSoftmax( + _fbb, + axis__); +} + +flatbuffers::Offset CreateSoftmax(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SoftmaxCrossEntropyWithLogitsT : public flatbuffers::NativeTable { + typedef SoftmaxCrossEntropyWithLogits TableType; +}; + +struct SoftmaxCrossEntropyWithLogits FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SoftmaxCrossEntropyWithLogitsT NativeTableType; + typedef SoftmaxCrossEntropyWithLogitsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SoftmaxCrossEntropyWithLogitsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SoftmaxCrossEntropyWithLogitsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SoftmaxCrossEntropyWithLogitsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxCrossEntropyWithLogitsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SoftmaxCrossEntropyWithLogitsBuilder { + typedef SoftmaxCrossEntropyWithLogits Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SoftmaxCrossEntropyWithLogitsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSoftmaxCrossEntropyWithLogits( + flatbuffers::FlatBufferBuilder &_fbb) { + SoftmaxCrossEntropyWithLogitsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSoftmaxCrossEntropyWithLogits(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxCrossEntropyWithLogitsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToBatchT : public flatbuffers::NativeTable { + typedef SpaceToBatch TableType; + std::vector block_size{}; + std::unique_ptr paddings{}; +}; + +struct SpaceToBatch FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToBatchT NativeTableType; + typedef SpaceToBatchBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SpaceToBatchTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4, + VT_PADDINGS = 6 + }; + const flatbuffers::Vector *block_size() const { + return GetPointer *>(VT_BLOCK_SIZE); + } + flatbuffers::Vector *mutable_block_size() { + return GetPointer *>(VT_BLOCK_SIZE); + } + const mindspore::schema::Vec2D *paddings() const { + return GetPointer(VT_PADDINGS); + } + mindspore::schema::Vec2D *mutable_paddings() { + return GetPointer(VT_PADDINGS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_BLOCK_SIZE) && + verifier.VerifyVector(block_size()) && + VerifyOffset(verifier, VT_PADDINGS) && + verifier.VerifyTable(paddings()) && + verifier.EndTable(); + } + SpaceToBatchT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToBatchT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToBatchBuilder { + typedef SpaceToBatch Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(flatbuffers::Offset> block_size) { + fbb_.AddOffset(SpaceToBatch::VT_BLOCK_SIZE, block_size); + } + void add_paddings(flatbuffers::Offset paddings) { + fbb_.AddOffset(SpaceToBatch::VT_PADDINGS, paddings); + } + explicit SpaceToBatchBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToBatch( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> block_size = 0, + flatbuffers::Offset paddings = 0) { + SpaceToBatchBuilder builder_(_fbb); + builder_.add_paddings(paddings); + builder_.add_block_size(block_size); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSpaceToBatchDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *block_size = nullptr, + flatbuffers::Offset paddings = 0) { + auto block_size__ = block_size ? _fbb.CreateVector(*block_size) : 0; + return mindspore::schema::CreateSpaceToBatch( + _fbb, + block_size__, + paddings); +} + +flatbuffers::Offset CreateSpaceToBatch(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToBatchNDT : public flatbuffers::NativeTable { + typedef SpaceToBatchND TableType; + std::vector block_shape{}; + std::unique_ptr paddings{}; +}; + +struct SpaceToBatchND FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToBatchNDT NativeTableType; + typedef SpaceToBatchNDBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SpaceToBatchNDTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SHAPE = 4, + VT_PADDINGS = 6 + }; + const flatbuffers::Vector *block_shape() const { + return GetPointer *>(VT_BLOCK_SHAPE); + } + flatbuffers::Vector *mutable_block_shape() { + return GetPointer *>(VT_BLOCK_SHAPE); + } + const mindspore::schema::Vec2D *paddings() const { + return GetPointer(VT_PADDINGS); + } + mindspore::schema::Vec2D *mutable_paddings() { + return GetPointer(VT_PADDINGS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_BLOCK_SHAPE) && + verifier.VerifyVector(block_shape()) && + VerifyOffset(verifier, VT_PADDINGS) && + verifier.VerifyTable(paddings()) && + verifier.EndTable(); + } + SpaceToBatchNDT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToBatchNDT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToBatchNDBuilder { + typedef SpaceToBatchND Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_shape(flatbuffers::Offset> block_shape) { + fbb_.AddOffset(SpaceToBatchND::VT_BLOCK_SHAPE, block_shape); + } + void add_paddings(flatbuffers::Offset paddings) { + fbb_.AddOffset(SpaceToBatchND::VT_PADDINGS, paddings); + } + explicit SpaceToBatchNDBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToBatchND( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> block_shape = 0, + flatbuffers::Offset paddings = 0) { + SpaceToBatchNDBuilder builder_(_fbb); + builder_.add_paddings(paddings); + builder_.add_block_shape(block_shape); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSpaceToBatchNDDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *block_shape = nullptr, + flatbuffers::Offset paddings = 0) { + auto block_shape__ = block_shape ? _fbb.CreateVector(*block_shape) : 0; + return mindspore::schema::CreateSpaceToBatchND( + _fbb, + block_shape__, + paddings); +} + +flatbuffers::Offset CreateSpaceToBatchND(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToDepthT : public flatbuffers::NativeTable { + typedef SpaceToDepth TableType; + int64_t block_size = 0; + mindspore::schema::Format format = mindspore::schema::Format_NCHW; +}; + +struct SpaceToDepth FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToDepthT NativeTableType; + typedef SpaceToDepthBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SpaceToDepthTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4, + VT_FORMAT = 6 + }; + int64_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } + bool mutate_block_size(int64_t _block_size) { + return SetField(VT_BLOCK_SIZE, _block_size, 0); + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(mindspore::schema::Format _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BLOCK_SIZE) && + VerifyField(verifier, VT_FORMAT) && + verifier.EndTable(); + } + SpaceToDepthT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToDepthT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToDepthBuilder { + typedef SpaceToDepth Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(int64_t block_size) { + fbb_.AddElement(SpaceToDepth::VT_BLOCK_SIZE, block_size, 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(SpaceToDepth::VT_FORMAT, static_cast(format), 0); + } + explicit SpaceToDepthBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToDepth( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t block_size = 0, + mindspore::schema::Format format = mindspore::schema::Format_NCHW) { + SpaceToDepthBuilder builder_(_fbb); + builder_.add_block_size(block_size); + builder_.add_format(format); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSpaceToDepth(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SparseSoftmaxCrossEntropyWithLogitsT : public flatbuffers::NativeTable { + typedef SparseSoftmaxCrossEntropyWithLogits TableType; + bool is_grad = false; +}; + +struct SparseSoftmaxCrossEntropyWithLogits FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseSoftmaxCrossEntropyWithLogitsT NativeTableType; + typedef SparseSoftmaxCrossEntropyWithLogitsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SparseSoftmaxCrossEntropyWithLogitsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IS_GRAD = 4 + }; + bool is_grad() const { + return GetField(VT_IS_GRAD, 0) != 0; + } + bool mutate_is_grad(bool _is_grad) { + return SetField(VT_IS_GRAD, static_cast(_is_grad), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IS_GRAD) && + verifier.EndTable(); + } + SparseSoftmaxCrossEntropyWithLogitsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SparseSoftmaxCrossEntropyWithLogitsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseSoftmaxCrossEntropyWithLogitsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SparseSoftmaxCrossEntropyWithLogitsBuilder { + typedef SparseSoftmaxCrossEntropyWithLogits Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_is_grad(bool is_grad) { + fbb_.AddElement(SparseSoftmaxCrossEntropyWithLogits::VT_IS_GRAD, static_cast(is_grad), 0); + } + explicit SparseSoftmaxCrossEntropyWithLogitsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseSoftmaxCrossEntropyWithLogits( + flatbuffers::FlatBufferBuilder &_fbb, + bool is_grad = false) { + SparseSoftmaxCrossEntropyWithLogitsBuilder builder_(_fbb); + builder_.add_is_grad(is_grad); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSparseSoftmaxCrossEntropyWithLogits(flatbuffers::FlatBufferBuilder &_fbb, const SparseSoftmaxCrossEntropyWithLogitsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SparseToDenseT : public flatbuffers::NativeTable { + typedef SparseToDense TableType; +}; + +struct SparseToDense FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseToDenseT NativeTableType; + typedef SparseToDenseBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SparseToDenseTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SparseToDenseT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SparseToDenseT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SparseToDenseBuilder { + typedef SparseToDense Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SparseToDenseBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseToDense( + flatbuffers::FlatBufferBuilder &_fbb) { + SparseToDenseBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSparseToDense(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SplitT : public flatbuffers::NativeTable { + typedef Split TableType; + int64_t output_num = 0; + std::vector size_splits{}; + int64_t axis = 0; +}; + +struct Split FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitT NativeTableType; + typedef SplitBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SplitTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUTPUT_NUM = 4, + VT_SIZE_SPLITS = 6, + VT_AXIS = 8 + }; + int64_t output_num() const { + return GetField(VT_OUTPUT_NUM, 0); + } + bool mutate_output_num(int64_t _output_num) { + return SetField(VT_OUTPUT_NUM, _output_num, 0); + } + const flatbuffers::Vector *size_splits() const { + return GetPointer *>(VT_SIZE_SPLITS); + } + flatbuffers::Vector *mutable_size_splits() { + return GetPointer *>(VT_SIZE_SPLITS); + } + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_NUM) && + VerifyOffset(verifier, VT_SIZE_SPLITS) && + verifier.VerifyVector(size_splits()) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + SplitT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SplitT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SplitBuilder { + typedef Split Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_num(int64_t output_num) { + fbb_.AddElement(Split::VT_OUTPUT_NUM, output_num, 0); + } + void add_size_splits(flatbuffers::Offset> size_splits) { + fbb_.AddOffset(Split::VT_SIZE_SPLITS, size_splits); + } + void add_axis(int64_t axis) { + fbb_.AddElement(Split::VT_AXIS, axis, 0); + } + explicit SplitBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplit( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t output_num = 0, + flatbuffers::Offset> size_splits = 0, + int64_t axis = 0) { + SplitBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_output_num(output_num); + builder_.add_size_splits(size_splits); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSplitDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t output_num = 0, + const std::vector *size_splits = nullptr, + int64_t axis = 0) { + auto size_splits__ = size_splits ? _fbb.CreateVector(*size_splits) : 0; + return mindspore::schema::CreateSplit( + _fbb, + output_num, + size_splits__, + axis); +} + +flatbuffers::Offset CreateSplit(flatbuffers::FlatBufferBuilder &_fbb, const SplitT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SqrtT : public flatbuffers::NativeTable { + typedef Sqrt TableType; +}; + +struct Sqrt FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SqrtT NativeTableType; + typedef SqrtBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SqrtTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SqrtT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SqrtT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqrtT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SqrtBuilder { + typedef Sqrt Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SqrtBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSqrt( + flatbuffers::FlatBufferBuilder &_fbb) { + SqrtBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSqrt(flatbuffers::FlatBufferBuilder &_fbb, const SqrtT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SqueezeT : public flatbuffers::NativeTable { + typedef Squeeze TableType; + std::vector axis{}; +}; + +struct Squeeze FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SqueezeT NativeTableType; + typedef SqueezeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SqueezeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + const flatbuffers::Vector *axis() const { + return GetPointer *>(VT_AXIS); + } + flatbuffers::Vector *mutable_axis() { + return GetPointer *>(VT_AXIS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_AXIS) && + verifier.VerifyVector(axis()) && + verifier.EndTable(); + } + SqueezeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SqueezeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SqueezeBuilder { + typedef Squeeze Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(flatbuffers::Offset> axis) { + fbb_.AddOffset(Squeeze::VT_AXIS, axis); + } + explicit SqueezeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSqueeze( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> axis = 0) { + SqueezeBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSqueezeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *axis = nullptr) { + auto axis__ = axis ? _fbb.CreateVector(*axis) : 0; + return mindspore::schema::CreateSqueeze( + _fbb, + axis__); +} + +flatbuffers::Offset CreateSqueeze(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SquareT : public flatbuffers::NativeTable { + typedef Square TableType; +}; + +struct Square FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SquareT NativeTableType; + typedef SquareBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SquareTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SquareT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SquareT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SquareBuilder { + typedef Square Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SquareBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSquare( + flatbuffers::FlatBufferBuilder &_fbb) { + SquareBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSquare(flatbuffers::FlatBufferBuilder &_fbb, const SquareT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SquaredDifferenceT : public flatbuffers::NativeTable { + typedef SquaredDifference TableType; +}; + +struct SquaredDifference FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SquaredDifferenceT NativeTableType; + typedef SquaredDifferenceBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SquaredDifferenceTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SquaredDifferenceT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SquaredDifferenceT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SquaredDifferenceBuilder { + typedef SquaredDifference Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SquaredDifferenceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSquaredDifference( + flatbuffers::FlatBufferBuilder &_fbb) { + SquaredDifferenceBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSquaredDifference(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StackT : public flatbuffers::NativeTable { + typedef Stack TableType; + int64_t axis = 0; +}; + +struct Stack FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StackT NativeTableType; + typedef StackBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return StackTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + StackT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StackT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StackT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StackBuilder { + typedef Stack Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(Stack::VT_AXIS, axis, 0); + } + explicit StackBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStack( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0) { + StackBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStack(flatbuffers::FlatBufferBuilder &_fbb, const StackT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StridedSliceT : public flatbuffers::NativeTable { + typedef StridedSlice TableType; + int64_t begin_mask = 0; + int64_t end_mask = 0; + int64_t ellipsis_mask = 0; + int64_t new_axis_mask = 0; + int64_t shrink_axis_mask = 0; +}; + +struct StridedSlice FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StridedSliceT NativeTableType; + typedef StridedSliceBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return StridedSliceTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BEGIN_MASK = 4, + VT_END_MASK = 6, + VT_ELLIPSIS_MASK = 8, + VT_NEW_AXIS_MASK = 10, + VT_SHRINK_AXIS_MASK = 12 + }; + int64_t begin_mask() const { + return GetField(VT_BEGIN_MASK, 0); + } + bool mutate_begin_mask(int64_t _begin_mask) { + return SetField(VT_BEGIN_MASK, _begin_mask, 0); + } + int64_t end_mask() const { + return GetField(VT_END_MASK, 0); + } + bool mutate_end_mask(int64_t _end_mask) { + return SetField(VT_END_MASK, _end_mask, 0); + } + int64_t ellipsis_mask() const { + return GetField(VT_ELLIPSIS_MASK, 0); + } + bool mutate_ellipsis_mask(int64_t _ellipsis_mask) { + return SetField(VT_ELLIPSIS_MASK, _ellipsis_mask, 0); + } + int64_t new_axis_mask() const { + return GetField(VT_NEW_AXIS_MASK, 0); + } + bool mutate_new_axis_mask(int64_t _new_axis_mask) { + return SetField(VT_NEW_AXIS_MASK, _new_axis_mask, 0); + } + int64_t shrink_axis_mask() const { + return GetField(VT_SHRINK_AXIS_MASK, 0); + } + bool mutate_shrink_axis_mask(int64_t _shrink_axis_mask) { + return SetField(VT_SHRINK_AXIS_MASK, _shrink_axis_mask, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BEGIN_MASK) && + VerifyField(verifier, VT_END_MASK) && + VerifyField(verifier, VT_ELLIPSIS_MASK) && + VerifyField(verifier, VT_NEW_AXIS_MASK) && + VerifyField(verifier, VT_SHRINK_AXIS_MASK) && + verifier.EndTable(); + } + StridedSliceT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StridedSliceT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StridedSliceBuilder { + typedef StridedSlice Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_begin_mask(int64_t begin_mask) { + fbb_.AddElement(StridedSlice::VT_BEGIN_MASK, begin_mask, 0); + } + void add_end_mask(int64_t end_mask) { + fbb_.AddElement(StridedSlice::VT_END_MASK, end_mask, 0); + } + void add_ellipsis_mask(int64_t ellipsis_mask) { + fbb_.AddElement(StridedSlice::VT_ELLIPSIS_MASK, ellipsis_mask, 0); + } + void add_new_axis_mask(int64_t new_axis_mask) { + fbb_.AddElement(StridedSlice::VT_NEW_AXIS_MASK, new_axis_mask, 0); + } + void add_shrink_axis_mask(int64_t shrink_axis_mask) { + fbb_.AddElement(StridedSlice::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); + } + explicit StridedSliceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStridedSlice( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t begin_mask = 0, + int64_t end_mask = 0, + int64_t ellipsis_mask = 0, + int64_t new_axis_mask = 0, + int64_t shrink_axis_mask = 0) { + StridedSliceBuilder builder_(_fbb); + builder_.add_shrink_axis_mask(shrink_axis_mask); + builder_.add_new_axis_mask(new_axis_mask); + builder_.add_ellipsis_mask(ellipsis_mask); + builder_.add_end_mask(end_mask); + builder_.add_begin_mask(begin_mask); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStridedSlice(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubFusionT : public flatbuffers::NativeTable { + typedef SubFusion TableType; + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION; +}; + +struct SubFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubFusionT NativeTableType; + typedef SubFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SubFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool mutate_activation_type(mindspore::schema::ActivationType _activation_type) { + return SetField(VT_ACTIVATION_TYPE, static_cast(_activation_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + verifier.EndTable(); + } + SubFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubFusionBuilder { + typedef SubFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(SubFusion::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + explicit SubFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubFusion( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION) { + SubFusionBuilder builder_(_fbb); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSubFusion(flatbuffers::FlatBufferBuilder &_fbb, const SubFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubGradT : public flatbuffers::NativeTable { + typedef SubGrad TableType; +}; + +struct SubGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubGradT NativeTableType; + typedef SubGradBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SubGradTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SubGradT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubGradT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGradT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubGradBuilder { + typedef SubGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SubGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + SubGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSubGrad(flatbuffers::FlatBufferBuilder &_fbb, const SubGradT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SwitchT : public flatbuffers::NativeTable { + typedef Switch TableType; +}; + +struct Switch FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SwitchT NativeTableType; + typedef SwitchBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SwitchTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SwitchT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SwitchT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SwitchT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SwitchBuilder { + typedef Switch Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SwitchBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSwitch( + flatbuffers::FlatBufferBuilder &_fbb) { + SwitchBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSwitch(flatbuffers::FlatBufferBuilder &_fbb, const SwitchT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorListFromTensorT : public flatbuffers::NativeTable { + typedef TensorListFromTensor TableType; + int64_t element_dtype = 0; + int64_t shape_type = 0; +}; + +struct TensorListFromTensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorListFromTensorT NativeTableType; + typedef TensorListFromTensorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorListFromTensorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ELEMENT_DTYPE = 4, + VT_SHAPE_TYPE = 6 + }; + int64_t element_dtype() const { + return GetField(VT_ELEMENT_DTYPE, 0); + } + bool mutate_element_dtype(int64_t _element_dtype) { + return SetField(VT_ELEMENT_DTYPE, _element_dtype, 0); + } + int64_t shape_type() const { + return GetField(VT_SHAPE_TYPE, 0); + } + bool mutate_shape_type(int64_t _shape_type) { + return SetField(VT_SHAPE_TYPE, _shape_type, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ELEMENT_DTYPE) && + VerifyField(verifier, VT_SHAPE_TYPE) && + verifier.EndTable(); + } + TensorListFromTensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorListFromTensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorListFromTensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorListFromTensorBuilder { + typedef TensorListFromTensor Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_element_dtype(int64_t element_dtype) { + fbb_.AddElement(TensorListFromTensor::VT_ELEMENT_DTYPE, element_dtype, 0); + } + void add_shape_type(int64_t shape_type) { + fbb_.AddElement(TensorListFromTensor::VT_SHAPE_TYPE, shape_type, 0); + } + explicit TensorListFromTensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorListFromTensor( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t element_dtype = 0, + int64_t shape_type = 0) { + TensorListFromTensorBuilder builder_(_fbb); + builder_.add_shape_type(shape_type); + builder_.add_element_dtype(element_dtype); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTensorListFromTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorListFromTensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorListGetItemT : public flatbuffers::NativeTable { + typedef TensorListGetItem TableType; + int64_t element_dtype = 0; +}; + +struct TensorListGetItem FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorListGetItemT NativeTableType; + typedef TensorListGetItemBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorListGetItemTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ELEMENT_DTYPE = 4 + }; + int64_t element_dtype() const { + return GetField(VT_ELEMENT_DTYPE, 0); + } + bool mutate_element_dtype(int64_t _element_dtype) { + return SetField(VT_ELEMENT_DTYPE, _element_dtype, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ELEMENT_DTYPE) && + verifier.EndTable(); + } + TensorListGetItemT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorListGetItemT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorListGetItemT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorListGetItemBuilder { + typedef TensorListGetItem Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_element_dtype(int64_t element_dtype) { + fbb_.AddElement(TensorListGetItem::VT_ELEMENT_DTYPE, element_dtype, 0); + } + explicit TensorListGetItemBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorListGetItem( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t element_dtype = 0) { + TensorListGetItemBuilder builder_(_fbb); + builder_.add_element_dtype(element_dtype); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTensorListGetItem(flatbuffers::FlatBufferBuilder &_fbb, const TensorListGetItemT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorListReserveT : public flatbuffers::NativeTable { + typedef TensorListReserve TableType; + int64_t element_dtype = 0; + int64_t shape_type = 0; +}; + +struct TensorListReserve FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorListReserveT NativeTableType; + typedef TensorListReserveBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorListReserveTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ELEMENT_DTYPE = 4, + VT_SHAPE_TYPE = 6 + }; + int64_t element_dtype() const { + return GetField(VT_ELEMENT_DTYPE, 0); + } + bool mutate_element_dtype(int64_t _element_dtype) { + return SetField(VT_ELEMENT_DTYPE, _element_dtype, 0); + } + int64_t shape_type() const { + return GetField(VT_SHAPE_TYPE, 0); + } + bool mutate_shape_type(int64_t _shape_type) { + return SetField(VT_SHAPE_TYPE, _shape_type, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ELEMENT_DTYPE) && + VerifyField(verifier, VT_SHAPE_TYPE) && + verifier.EndTable(); + } + TensorListReserveT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorListReserveT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorListReserveT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorListReserveBuilder { + typedef TensorListReserve Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_element_dtype(int64_t element_dtype) { + fbb_.AddElement(TensorListReserve::VT_ELEMENT_DTYPE, element_dtype, 0); + } + void add_shape_type(int64_t shape_type) { + fbb_.AddElement(TensorListReserve::VT_SHAPE_TYPE, shape_type, 0); + } + explicit TensorListReserveBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorListReserve( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t element_dtype = 0, + int64_t shape_type = 0) { + TensorListReserveBuilder builder_(_fbb); + builder_.add_shape_type(shape_type); + builder_.add_element_dtype(element_dtype); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTensorListReserve(flatbuffers::FlatBufferBuilder &_fbb, const TensorListReserveT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorListSetItemT : public flatbuffers::NativeTable { + typedef TensorListSetItem TableType; + int64_t element_dtype = 0; +}; + +struct TensorListSetItem FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorListSetItemT NativeTableType; + typedef TensorListSetItemBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorListSetItemTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ELEMENT_DTYPE = 4 + }; + int64_t element_dtype() const { + return GetField(VT_ELEMENT_DTYPE, 0); + } + bool mutate_element_dtype(int64_t _element_dtype) { + return SetField(VT_ELEMENT_DTYPE, _element_dtype, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ELEMENT_DTYPE) && + verifier.EndTable(); + } + TensorListSetItemT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorListSetItemT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorListSetItemT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorListSetItemBuilder { + typedef TensorListSetItem Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_element_dtype(int64_t element_dtype) { + fbb_.AddElement(TensorListSetItem::VT_ELEMENT_DTYPE, element_dtype, 0); + } + explicit TensorListSetItemBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorListSetItem( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t element_dtype = 0) { + TensorListSetItemBuilder builder_(_fbb); + builder_.add_element_dtype(element_dtype); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTensorListSetItem(flatbuffers::FlatBufferBuilder &_fbb, const TensorListSetItemT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorListStackT : public flatbuffers::NativeTable { + typedef TensorListStack TableType; + int64_t num_elements = 0; + int64_t element_dtype = 0; +}; + +struct TensorListStack FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorListStackT NativeTableType; + typedef TensorListStackBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorListStackTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_ELEMENTS = 4, + VT_ELEMENT_DTYPE = 6 + }; + int64_t num_elements() const { + return GetField(VT_NUM_ELEMENTS, 0); + } + bool mutate_num_elements(int64_t _num_elements) { + return SetField(VT_NUM_ELEMENTS, _num_elements, 0); + } + int64_t element_dtype() const { + return GetField(VT_ELEMENT_DTYPE, 0); + } + bool mutate_element_dtype(int64_t _element_dtype) { + return SetField(VT_ELEMENT_DTYPE, _element_dtype, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_ELEMENTS) && + VerifyField(verifier, VT_ELEMENT_DTYPE) && + verifier.EndTable(); + } + TensorListStackT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorListStackT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorListStackT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorListStackBuilder { + typedef TensorListStack Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_elements(int64_t num_elements) { + fbb_.AddElement(TensorListStack::VT_NUM_ELEMENTS, num_elements, 0); + } + void add_element_dtype(int64_t element_dtype) { + fbb_.AddElement(TensorListStack::VT_ELEMENT_DTYPE, element_dtype, 0); + } + explicit TensorListStackBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorListStack( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t num_elements = 0, + int64_t element_dtype = 0) { + TensorListStackBuilder builder_(_fbb); + builder_.add_element_dtype(element_dtype); + builder_.add_num_elements(num_elements); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTensorListStack(flatbuffers::FlatBufferBuilder &_fbb, const TensorListStackT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TileFusionT : public flatbuffers::NativeTable { + typedef TileFusion TableType; + std::vector dims{}; +}; + +struct TileFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TileFusionT NativeTableType; + typedef TileFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TileFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DIMS = 4 + }; + const flatbuffers::Vector *dims() const { + return GetPointer *>(VT_DIMS); + } + flatbuffers::Vector *mutable_dims() { + return GetPointer *>(VT_DIMS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DIMS) && + verifier.VerifyVector(dims()) && + verifier.EndTable(); + } + TileFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TileFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TileFusionBuilder { + typedef TileFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_dims(flatbuffers::Offset> dims) { + fbb_.AddOffset(TileFusion::VT_DIMS, dims); + } + explicit TileFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTileFusion( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> dims = 0) { + TileFusionBuilder builder_(_fbb); + builder_.add_dims(dims); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTileFusionDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *dims = nullptr) { + auto dims__ = dims ? _fbb.CreateVector(*dims) : 0; + return mindspore::schema::CreateTileFusion( + _fbb, + dims__); +} + +flatbuffers::Offset CreateTileFusion(flatbuffers::FlatBufferBuilder &_fbb, const TileFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TopKFusionT : public flatbuffers::NativeTable { + typedef TopKFusion TableType; + bool sorted = true; + int64_t axis = 0; + int64_t largest = 0; +}; + +struct TopKFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TopKFusionT NativeTableType; + typedef TopKFusionBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TopKFusionTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SORTED = 4, + VT_AXIS = 6, + VT_LARGEST = 8 + }; + bool sorted() const { + return GetField(VT_SORTED, 1) != 0; + } + bool mutate_sorted(bool _sorted) { + return SetField(VT_SORTED, static_cast(_sorted), 1); + } + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + int64_t largest() const { + return GetField(VT_LARGEST, 0); + } + bool mutate_largest(int64_t _largest) { + return SetField(VT_LARGEST, _largest, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SORTED) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_LARGEST) && + verifier.EndTable(); + } + TopKFusionT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TopKFusionT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKFusionT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TopKFusionBuilder { + typedef TopKFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_sorted(bool sorted) { + fbb_.AddElement(TopKFusion::VT_SORTED, static_cast(sorted), 1); + } + void add_axis(int64_t axis) { + fbb_.AddElement(TopKFusion::VT_AXIS, axis, 0); + } + void add_largest(int64_t largest) { + fbb_.AddElement(TopKFusion::VT_LARGEST, largest, 0); + } + explicit TopKFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTopKFusion( + flatbuffers::FlatBufferBuilder &_fbb, + bool sorted = true, + int64_t axis = 0, + int64_t largest = 0) { + TopKFusionBuilder builder_(_fbb); + builder_.add_largest(largest); + builder_.add_axis(axis); + builder_.add_sorted(sorted); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTopKFusion(flatbuffers::FlatBufferBuilder &_fbb, const TopKFusionT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TransposeT : public flatbuffers::NativeTable { + typedef Transpose TableType; +}; + +struct Transpose FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TransposeT NativeTableType; + typedef TransposeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TransposeTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TransposeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TransposeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TransposeBuilder { + typedef Transpose Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TransposeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTranspose( + flatbuffers::FlatBufferBuilder &_fbb) { + TransposeBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTranspose(flatbuffers::FlatBufferBuilder &_fbb, const TransposeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UniqueT : public flatbuffers::NativeTable { + typedef Unique TableType; +}; + +struct Unique FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UniqueT NativeTableType; + typedef UniqueBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UniqueTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + UniqueT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UniqueT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UniqueBuilder { + typedef Unique Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit UniqueBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnique( + flatbuffers::FlatBufferBuilder &_fbb) { + UniqueBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnique(flatbuffers::FlatBufferBuilder &_fbb, const UniqueT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnsortedSegmentSumT : public flatbuffers::NativeTable { + typedef UnsortedSegmentSum TableType; +}; + +struct UnsortedSegmentSum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnsortedSegmentSumT NativeTableType; + typedef UnsortedSegmentSumBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UnsortedSegmentSumTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + UnsortedSegmentSumT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnsortedSegmentSumT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnsortedSegmentSumBuilder { + typedef UnsortedSegmentSum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit UnsortedSegmentSumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnsortedSegmentSum( + flatbuffers::FlatBufferBuilder &_fbb) { + UnsortedSegmentSumBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnsortedSegmentSum(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnsqueezeT : public flatbuffers::NativeTable { + typedef Unsqueeze TableType; + std::vector axis{}; +}; + +struct Unsqueeze FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnsqueezeT NativeTableType; + typedef UnsqueezeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UnsqueezeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + const flatbuffers::Vector *axis() const { + return GetPointer *>(VT_AXIS); + } + flatbuffers::Vector *mutable_axis() { + return GetPointer *>(VT_AXIS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_AXIS) && + verifier.VerifyVector(axis()) && + verifier.EndTable(); + } + UnsqueezeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnsqueezeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsqueezeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnsqueezeBuilder { + typedef Unsqueeze Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(flatbuffers::Offset> axis) { + fbb_.AddOffset(Unsqueeze::VT_AXIS, axis); + } + explicit UnsqueezeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnsqueeze( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> axis = 0) { + UnsqueezeBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateUnsqueezeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *axis = nullptr) { + auto axis__ = axis ? _fbb.CreateVector(*axis) : 0; + return mindspore::schema::CreateUnsqueeze( + _fbb, + axis__); +} + +flatbuffers::Offset CreateUnsqueeze(flatbuffers::FlatBufferBuilder &_fbb, const UnsqueezeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnstackT : public flatbuffers::NativeTable { + typedef Unstack TableType; + int64_t axis = 0; +}; + +struct Unstack FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnstackT NativeTableType; + typedef UnstackBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UnstackTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int64_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + UnstackT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnstackT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnstackT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnstackBuilder { + typedef Unstack Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(Unstack::VT_AXIS, axis, 0); + } + explicit UnstackBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnstack( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0) { + UnstackBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnstack(flatbuffers::FlatBufferBuilder &_fbb, const UnstackT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct WhereT : public flatbuffers::NativeTable { + typedef Where TableType; +}; + +struct Where FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef WhereT NativeTableType; + typedef WhereBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return WhereTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + WhereT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(WhereT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct WhereBuilder { + typedef Where Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit WhereBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateWhere( + flatbuffers::FlatBufferBuilder &_fbb) { + WhereBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateWhere(flatbuffers::FlatBufferBuilder &_fbb, const WhereT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ZerosLikeT : public flatbuffers::NativeTable { + typedef ZerosLike TableType; +}; + +struct ZerosLike FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ZerosLikeT NativeTableType; + typedef ZerosLikeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ZerosLikeTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ZerosLikeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ZerosLikeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ZerosLikeBuilder { + typedef ZerosLike Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ZerosLikeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateZerosLike( + flatbuffers::FlatBufferBuilder &_fbb) { + ZerosLikeBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateZerosLike(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SelectT : public flatbuffers::NativeTable { + typedef Select TableType; +}; + +struct Select FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SelectT NativeTableType; + typedef SelectBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SelectTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SelectT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SelectT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset CreateSelect( + flatbuffers::FlatBufferBuilder &_fbb) { + SelectBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset Select::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSelect(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset CreateSelect( + flatbuffers::FlatBufferBuilder &_fbb) { + SelectBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct GRU FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GRUBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BIDIRECTIONAL = 4 + }; + bool bidirectional() const { + return GetField(VT_BIDIRECTIONAL, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BIDIRECTIONAL) && + verifier.EndTable(); + } +}; + +struct GRUBuilder { + typedef GRU Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_bidirectional(bool bidirectional) { + fbb_.AddElement(GRU::VT_BIDIRECTIONAL, static_cast(bidirectional), 0); + } + explicit GRUBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGRU( + flatbuffers::FlatBufferBuilder &_fbb, + bool bidirectional = false) { + GRUBuilder builder_(_fbb); + builder_.add_bidirectional(bidirectional); + return builder_.Finish(); +} + +struct NonZero FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NonZeroBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct NonZeroBuilder { + typedef NonZero Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NonZeroBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNonZero( + flatbuffers::FlatBufferBuilder &_fbb) { + NonZeroBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct InvertPermutation FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef InvertPermutationBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct InvertPermutationBuilder { + typedef InvertPermutation Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit InvertPermutationBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateInvertPermutation( + flatbuffers::FlatBufferBuilder &_fbb) { + InvertPermutationBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct Size FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SizeBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct SizeBuilder { + typedef Size Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SizeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSize( + flatbuffers::FlatBufferBuilder &_fbb) { + SizeBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct RandomStandardNormal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RandomStandardNormalBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEED = 4, + VT_SEED2 = 6 + }; + int64_t seed() const { + return GetField(VT_SEED, 0); + } + int64_t seed2() const { + return GetField(VT_SEED2, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEED) && + VerifyField(verifier, VT_SEED2) && + verifier.EndTable(); + } +}; + +struct RandomStandardNormalBuilder { + typedef RandomStandardNormal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seed(int64_t seed) { + fbb_.AddElement(RandomStandardNormal::VT_SEED, seed, 0); + } + void add_seed2(int64_t seed2) { + fbb_.AddElement(RandomStandardNormal::VT_SEED2, seed2, 0); + } + explicit RandomStandardNormalBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRandomStandardNormal( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t seed = 0, + int64_t seed2 = 0) { + RandomStandardNormalBuilder builder_(_fbb); + builder_.add_seed2(seed2); + builder_.add_seed(seed); + return builder_.Finish(); +} + +struct CropAndResize FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CropAndResizeBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_METHOD = 4, + VT_EXTRAPOLATION_VALUE = 6 + }; + mindspore::schema::ResizeMethod method() const { + return static_cast(GetField(VT_METHOD, 0)); + } + float extrapolation_value() const { + return GetField(VT_EXTRAPOLATION_VALUE, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_METHOD) && + VerifyField(verifier, VT_EXTRAPOLATION_VALUE) && + verifier.EndTable(); + } +}; + +struct CropAndResizeBuilder { + typedef CropAndResize Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_method(mindspore::schema::ResizeMethod method) { + fbb_.AddElement(CropAndResize::VT_METHOD, static_cast(method), 0); + } + void add_extrapolation_value(float extrapolation_value) { + fbb_.AddElement(CropAndResize::VT_EXTRAPOLATION_VALUE, extrapolation_value, 0.0f); + } + explicit CropAndResizeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCropAndResize( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ResizeMethod method = mindspore::schema::ResizeMethod_LINEAR, + float extrapolation_value = 0.0f) { + CropAndResizeBuilder builder_(_fbb); + builder_.add_extrapolation_value(extrapolation_value); + builder_.add_method(method); + return builder_.Finish(); +} + +struct Erf FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ErfBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct ErfBuilder { + typedef Erf Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ErfBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateErf( + flatbuffers::FlatBufferBuilder &_fbb) { + ErfBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct StridedSliceGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StridedSliceGradBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BEGIN_MASK = 4, + VT_END_MASK = 6, + VT_ELLIPSIS_MASK = 8, + VT_NEW_AXIS_MASK = 10, + VT_SHRINK_AXIS_MASK = 12 + }; + int64_t begin_mask() const { + return GetField(VT_BEGIN_MASK, 0); + } + int64_t end_mask() const { + return GetField(VT_END_MASK, 0); + } + int64_t ellipsis_mask() const { + return GetField(VT_ELLIPSIS_MASK, 0); + } + int64_t new_axis_mask() const { + return GetField(VT_NEW_AXIS_MASK, 0); + } + int64_t shrink_axis_mask() const { + return GetField(VT_SHRINK_AXIS_MASK, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BEGIN_MASK) && + VerifyField(verifier, VT_END_MASK) && + VerifyField(verifier, VT_ELLIPSIS_MASK) && + VerifyField(verifier, VT_NEW_AXIS_MASK) && + VerifyField(verifier, VT_SHRINK_AXIS_MASK) && + verifier.EndTable(); + } +}; + +struct StridedSliceGradBuilder { + typedef StridedSliceGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_begin_mask(int64_t begin_mask) { + fbb_.AddElement(StridedSliceGrad::VT_BEGIN_MASK, begin_mask, 0); + } + void add_end_mask(int64_t end_mask) { + fbb_.AddElement(StridedSliceGrad::VT_END_MASK, end_mask, 0); + } + void add_ellipsis_mask(int64_t ellipsis_mask) { + fbb_.AddElement(StridedSliceGrad::VT_ELLIPSIS_MASK, ellipsis_mask, 0); + } + void add_new_axis_mask(int64_t new_axis_mask) { + fbb_.AddElement(StridedSliceGrad::VT_NEW_AXIS_MASK, new_axis_mask, 0); + } + void add_shrink_axis_mask(int64_t shrink_axis_mask) { + fbb_.AddElement(StridedSliceGrad::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); + } + explicit StridedSliceGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStridedSliceGrad( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t begin_mask = 0, + int64_t end_mask = 0, + int64_t ellipsis_mask = 0, + int64_t new_axis_mask = 0, + int64_t shrink_axis_mask = 0) { + StridedSliceGradBuilder builder_(_fbb); + builder_.add_shrink_axis_mask(shrink_axis_mask); + builder_.add_new_axis_mask(new_axis_mask); + builder_.add_ellipsis_mask(ellipsis_mask); + builder_.add_end_mask(end_mask); + builder_.add_begin_mask(begin_mask); + return builder_.Finish(); +} + +struct IsFinite FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef IsFiniteBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct IsFiniteBuilder { + typedef IsFinite Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit IsFiniteBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateIsFinite( + flatbuffers::FlatBufferBuilder &_fbb) { + IsFiniteBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct LinSpace FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LinSpaceBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct LinSpaceBuilder { + typedef LinSpace Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LinSpaceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLinSpace( + flatbuffers::FlatBufferBuilder &_fbb) { + LinSpaceBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct UniformReal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UniformRealBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEED = 4, + VT_SEED2 = 6 + }; + int64_t seed() const { + return GetField(VT_SEED, 0); + } + int64_t seed2() const { + return GetField(VT_SEED2, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEED) && + VerifyField(verifier, VT_SEED2) && + verifier.EndTable(); + } +}; + +struct UniformRealBuilder { + typedef UniformReal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seed(int64_t seed) { + fbb_.AddElement(UniformReal::VT_SEED, seed, 0); + } + void add_seed2(int64_t seed2) { + fbb_.AddElement(UniformReal::VT_SEED2, seed2, 0); + } + explicit UniformRealBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUniformReal( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t seed = 0, + int64_t seed2 = 0) { + UniformRealBuilder builder_(_fbb); + builder_.add_seed2(seed2); + builder_.add_seed(seed); + return builder_.Finish(); +} + +struct AbsGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AbsGradBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct AbsGradBuilder { + typedef AbsGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AbsGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAbsGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + AbsGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct RsqrtGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RsqrtGradBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct RsqrtGradBuilder { + typedef RsqrtGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RsqrtGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRsqrtGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + RsqrtGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct SqrtGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SqrtGradBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct SqrtGradBuilder { + typedef SqrtGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SqrtGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSqrtGrad( + flatbuffers::FlatBufferBuilder &_fbb) { + SqrtGradBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct LayerNormGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LayerNormGradBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BEGIN_NORM_AXIS = 4, + VT_BEGIN_PARAMS_AXIS = 6 + }; + int64_t begin_norm_axis() const { + return GetField(VT_BEGIN_NORM_AXIS, 0); + } + int64_t begin_params_axis() const { + return GetField(VT_BEGIN_PARAMS_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BEGIN_NORM_AXIS) && + VerifyField(verifier, VT_BEGIN_PARAMS_AXIS) && + verifier.EndTable(); + } +}; + +struct LayerNormGradBuilder { + typedef LayerNormGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_begin_norm_axis(int64_t begin_norm_axis) { + fbb_.AddElement(LayerNormGrad::VT_BEGIN_NORM_AXIS, begin_norm_axis, 0); + } + void add_begin_params_axis(int64_t begin_params_axis) { + fbb_.AddElement(LayerNormGrad::VT_BEGIN_PARAMS_AXIS, begin_params_axis, 0); + } + explicit LayerNormGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLayerNormGrad( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t begin_norm_axis = 0, + int64_t begin_params_axis = 0) { + LayerNormGradBuilder builder_(_fbb); + builder_.add_begin_params_axis(begin_params_axis); + builder_.add_begin_norm_axis(begin_norm_axis); + return builder_.Finish(); +} + +struct ResizeGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ResizeGradBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_METHOD = 4, + VT_ALIGN_CORNERS = 6 + }; + mindspore::schema::ResizeMethod method() const { + return static_cast(GetField(VT_METHOD, 0)); + } + bool align_corners() const { + return GetField(VT_ALIGN_CORNERS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_METHOD) && + VerifyField(verifier, VT_ALIGN_CORNERS) && + verifier.EndTable(); + } +}; + +struct ResizeGradBuilder { + typedef ResizeGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_method(mindspore::schema::ResizeMethod method) { + fbb_.AddElement(ResizeGrad::VT_METHOD, static_cast(method), 0); + } + void add_align_corners(bool align_corners) { + fbb_.AddElement(ResizeGrad::VT_ALIGN_CORNERS, static_cast(align_corners), 0); + } + explicit ResizeGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateResizeGrad( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ResizeMethod method = mindspore::schema::ResizeMethod_LINEAR, + bool align_corners = false) { + ResizeGradBuilder builder_(_fbb); + builder_.add_align_corners(align_corners); + builder_.add_method(method); + return builder_.Finish(); +} + +struct Splice FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpliceBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CONTEXT = 4, + VT_FORWARD_INDEXES = 6, + VT_OUTPUT_DIM = 8 + }; + const flatbuffers::Vector *context() const { + return GetPointer *>(VT_CONTEXT); + } + const flatbuffers::Vector *forward_indexes() const { + return GetPointer *>(VT_FORWARD_INDEXES); + } + int64_t output_dim() const { + return GetField(VT_OUTPUT_DIM, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CONTEXT) && + verifier.VerifyVector(context()) && + VerifyOffset(verifier, VT_FORWARD_INDEXES) && + verifier.VerifyVector(forward_indexes()) && + VerifyField(verifier, VT_OUTPUT_DIM) && + verifier.EndTable(); + } +}; + +struct SpliceBuilder { + typedef Splice Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_context(flatbuffers::Offset> context) { + fbb_.AddOffset(Splice::VT_CONTEXT, context); + } + void add_forward_indexes(flatbuffers::Offset> forward_indexes) { + fbb_.AddOffset(Splice::VT_FORWARD_INDEXES, forward_indexes); + } + void add_output_dim(int64_t output_dim) { + fbb_.AddElement(Splice::VT_OUTPUT_DIM, output_dim, 0); + } + explicit SpliceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplice( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> context = 0, + flatbuffers::Offset> forward_indexes = 0, + int64_t output_dim = 0) { + SpliceBuilder builder_(_fbb); + builder_.add_output_dim(output_dim); + builder_.add_forward_indexes(forward_indexes); + builder_.add_context(context); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSpliceDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *context = nullptr, + const std::vector *forward_indexes = nullptr, + int64_t output_dim = 0) { + auto context__ = context ? _fbb.CreateVector(*context) : 0; + auto forward_indexes__ = forward_indexes ? _fbb.CreateVector(*forward_indexes) : 0; + return mindspore::schema::CreateSplice( + _fbb, + context__, + forward_indexes__, + output_dim); +} + +struct LogSoftmax FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogSoftmaxBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } +}; + +struct LogSoftmaxBuilder { + typedef LogSoftmax Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(LogSoftmax::VT_AXIS, axis, 0); + } + explicit LogSoftmaxBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogSoftmax( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0) { + LogSoftmaxBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +struct Call FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CallBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IS_TAIL_CALL = 4 + }; + bool is_tail_call() const { + return GetField(VT_IS_TAIL_CALL, 1) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IS_TAIL_CALL) && + verifier.EndTable(); + } +}; + +struct CallBuilder { + typedef Call Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_is_tail_call(bool is_tail_call) { + fbb_.AddElement(Call::VT_IS_TAIL_CALL, static_cast(is_tail_call), 1); + } + explicit CallBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCall( + flatbuffers::FlatBufferBuilder &_fbb, + bool is_tail_call = true) { + CallBuilder builder_(_fbb); + builder_.add_is_tail_call(is_tail_call); + return builder_.Finish(); +} + +struct CumSum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CumSumBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EXCLUSIVE = 4, + VT_REVERSE = 6 + }; + bool exclusive() const { + return GetField(VT_EXCLUSIVE, 0) != 0; + } + bool reverse() const { + return GetField(VT_REVERSE, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_EXCLUSIVE) && + VerifyField(verifier, VT_REVERSE) && + verifier.EndTable(); + } +}; + +struct CumSumBuilder { + typedef CumSum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_exclusive(bool exclusive) { + fbb_.AddElement(CumSum::VT_EXCLUSIVE, static_cast(exclusive), 0); + } + void add_reverse(bool reverse) { + fbb_.AddElement(CumSum::VT_REVERSE, static_cast(reverse), 0); + } + explicit CumSumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCumSum( + flatbuffers::FlatBufferBuilder &_fbb, + bool exclusive = false, + bool reverse = false) { + CumSumBuilder builder_(_fbb); + builder_.add_reverse(reverse); + builder_.add_exclusive(exclusive); + return builder_.Finish(); +} + +struct Custom FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CustomBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TYPE = 4, + VT_ATTR = 6 + }; + const flatbuffers::String *type() const { + return GetPointer(VT_TYPE); + } + const flatbuffers::Vector> *attr() const { + return GetPointer> *>(VT_ATTR); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_TYPE) && + verifier.VerifyString(type()) && + VerifyOffset(verifier, VT_ATTR) && + verifier.VerifyVector(attr()) && + verifier.VerifyVectorOfTables(attr()) && + verifier.EndTable(); + } +}; + +struct CustomBuilder { + typedef Custom Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_type(flatbuffers::Offset type) { + fbb_.AddOffset(Custom::VT_TYPE, type); + } + void add_attr(flatbuffers::Offset>> attr) { + fbb_.AddOffset(Custom::VT_ATTR, attr); + } + explicit CustomBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCustom( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset type = 0, + flatbuffers::Offset>> attr = 0) { + CustomBuilder builder_(_fbb); + builder_.add_attr(attr); + builder_.add_type(type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateCustomDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *type = nullptr, + const std::vector> *attr = nullptr) { + auto type__ = type ? _fbb.CreateString(type) : 0; + auto attr__ = attr ? _fbb.CreateVector>(*attr) : 0; + return mindspore::schema::CreateCustom( + _fbb, + type__, + attr__); +} + +struct SplitWithOverlap FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitWithOverlapBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SPLIT_DIM = 4, + VT_NUMBER_SPLIT = 6, + VT_RATIO = 8, + VT_EXTEND_TOP = 10, + VT_EXTEND_BOTTOM = 12 + }; + int64_t split_dim() const { + return GetField(VT_SPLIT_DIM, 0); + } + int64_t number_split() const { + return GetField(VT_NUMBER_SPLIT, 0); + } + const flatbuffers::Vector *ratio() const { + return GetPointer *>(VT_RATIO); + } + const flatbuffers::Vector *extend_top() const { + return GetPointer *>(VT_EXTEND_TOP); + } + const flatbuffers::Vector *extend_bottom() const { + return GetPointer *>(VT_EXTEND_BOTTOM); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SPLIT_DIM) && + VerifyField(verifier, VT_NUMBER_SPLIT) && + VerifyOffset(verifier, VT_RATIO) && + verifier.VerifyVector(ratio()) && + VerifyOffset(verifier, VT_EXTEND_TOP) && + verifier.VerifyVector(extend_top()) && + VerifyOffset(verifier, VT_EXTEND_BOTTOM) && + verifier.VerifyVector(extend_bottom()) && + verifier.EndTable(); + } +}; + +struct SplitWithOverlapBuilder { + typedef SplitWithOverlap Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_split_dim(int64_t split_dim) { + fbb_.AddElement(SplitWithOverlap::VT_SPLIT_DIM, split_dim, 0); + } + void add_number_split(int64_t number_split) { + fbb_.AddElement(SplitWithOverlap::VT_NUMBER_SPLIT, number_split, 0); + } + void add_ratio(flatbuffers::Offset> ratio) { + fbb_.AddOffset(SplitWithOverlap::VT_RATIO, ratio); + } + void add_extend_top(flatbuffers::Offset> extend_top) { + fbb_.AddOffset(SplitWithOverlap::VT_EXTEND_TOP, extend_top); + } + void add_extend_bottom(flatbuffers::Offset> extend_bottom) { + fbb_.AddOffset(SplitWithOverlap::VT_EXTEND_BOTTOM, extend_bottom); + } + explicit SplitWithOverlapBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplitWithOverlap( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t split_dim = 0, + int64_t number_split = 0, + flatbuffers::Offset> ratio = 0, + flatbuffers::Offset> extend_top = 0, + flatbuffers::Offset> extend_bottom = 0) { + SplitWithOverlapBuilder builder_(_fbb); + builder_.add_number_split(number_split); + builder_.add_split_dim(split_dim); + builder_.add_extend_bottom(extend_bottom); + builder_.add_extend_top(extend_top); + builder_.add_ratio(ratio); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSplitWithOverlapDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t split_dim = 0, + int64_t number_split = 0, + const std::vector *ratio = nullptr, + const std::vector *extend_top = nullptr, + const std::vector *extend_bottom = nullptr) { + auto ratio__ = ratio ? _fbb.CreateVector(*ratio) : 0; + auto extend_top__ = extend_top ? _fbb.CreateVector(*extend_top) : 0; + auto extend_bottom__ = extend_bottom ? _fbb.CreateVector(*extend_bottom) : 0; + return mindspore::schema::CreateSplitWithOverlap( + _fbb, + split_dim, + number_split, + ratio__, + extend_top__, + extend_bottom__); +} + +struct GenOP FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GenOPBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ACTIVATION_TYPE = 4, + VT_ALPHA = 6, + VT_MIN_VAL = 8, + VT_MAX_VAL = 10, + VT_IS_TRAINING = 12, + VT_FORMAT = 14, + VT_KERNEL_SIZE = 16, + VT_STRIDE = 18, + VT_DILATION = 20, + VT_PAD_MODE = 22, + VT_PAD_LIST = 24, + VT_MODE = 26, + VT_GROUP = 28, + VT_IN_CHANNEL = 30, + VT_OUT_CHANNEL = 32, + VT_ELTWISE_MODE = 34, + VT_HAS_BIAS = 36, + VT_USE_AXIS = 38, + VT_AXIS = 40, + VT_EPSILON = 42, + VT_MOMENTUM = 44, + VT_TRANSPOSE_A = 46, + VT_TRANSPOSE_B = 48, + VT_PAD = 50, + VT_ROUND_MODE = 52, + VT_GLOBAL = 54, + VT_CHANNEL_SHARED = 56, + VT_AXES = 58, + VT_KEEP_DIMS = 60, + VT_REDUCE_MODE = 62, + VT_REDUCE_TO_END = 64, + VT_COEFF = 66 + }; + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + float min_val() const { + return GetField(VT_MIN_VAL, 0.0f); + } + float max_val() const { + return GetField(VT_MAX_VAL, 0.0f); + } + bool is_training() const { + return GetField(VT_IS_TRAINING, 0) != 0; + } + mindspore::schema::Format format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + const flatbuffers::Vector *kernel_size() const { + return GetPointer *>(VT_KERNEL_SIZE); + } + const flatbuffers::Vector *stride() const { + return GetPointer *>(VT_STRIDE); + } + const flatbuffers::Vector *dilation() const { + return GetPointer *>(VT_DILATION); + } + mindspore::schema::PadMode pad_mode() const { + return static_cast(GetField(VT_PAD_MODE, 0)); + } + const flatbuffers::Vector *pad_list() const { + return GetPointer *>(VT_PAD_LIST); + } + int64_t mode() const { + return GetField(VT_MODE, 0); + } + int64_t group() const { + return GetField(VT_GROUP, 0); + } + int64_t in_channel() const { + return GetField(VT_IN_CHANNEL, 0); + } + int64_t out_channel() const { + return GetField(VT_OUT_CHANNEL, 0); + } + mindspore::schema::EltwiseMode eltwise_mode() const { + return static_cast(GetField(VT_ELTWISE_MODE, 0)); + } + bool has_bias() const { + return GetField(VT_HAS_BIAS, 0) != 0; + } + bool use_axis() const { + return GetField(VT_USE_AXIS, 0) != 0; + } + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + float epsilon() const { + return GetField(VT_EPSILON, 0.0001f); + } + float momentum() const { + return GetField(VT_MOMENTUM, 0.9f); + } + bool transpose_a() const { + return GetField(VT_TRANSPOSE_A, 0) != 0; + } + bool transpose_b() const { + return GetField(VT_TRANSPOSE_B, 0) != 0; + } + const flatbuffers::Vector *pad() const { + return GetPointer *>(VT_PAD); + } + mindspore::schema::RoundMode round_mode() const { + return static_cast(GetField(VT_ROUND_MODE, 0)); + } + bool global() const { + return GetField(VT_GLOBAL, 0) != 0; + } + bool channel_shared() const { + return GetField(VT_CHANNEL_SHARED, 0) != 0; + } + const flatbuffers::Vector *axes() const { + return GetPointer *>(VT_AXES); + } + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } + mindspore::schema::ReduceMode reduce_mode() const { + return static_cast(GetField(VT_REDUCE_MODE, 0)); + } + bool reduce_to_end() const { + return GetField(VT_REDUCE_TO_END, 0) != 0; + } + float coeff() const { + return GetField(VT_COEFF, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + VerifyField(verifier, VT_ALPHA) && + VerifyField(verifier, VT_MIN_VAL) && + VerifyField(verifier, VT_MAX_VAL) && + VerifyField(verifier, VT_IS_TRAINING) && + VerifyField(verifier, VT_FORMAT) && + VerifyOffset(verifier, VT_KERNEL_SIZE) && + verifier.VerifyVector(kernel_size()) && + VerifyOffset(verifier, VT_STRIDE) && + verifier.VerifyVector(stride()) && + VerifyOffset(verifier, VT_DILATION) && + verifier.VerifyVector(dilation()) && + VerifyField(verifier, VT_PAD_MODE) && + VerifyOffset(verifier, VT_PAD_LIST) && + verifier.VerifyVector(pad_list()) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_GROUP) && + VerifyField(verifier, VT_IN_CHANNEL) && + VerifyField(verifier, VT_OUT_CHANNEL) && + VerifyField(verifier, VT_ELTWISE_MODE) && + VerifyField(verifier, VT_HAS_BIAS) && + VerifyField(verifier, VT_USE_AXIS) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_MOMENTUM) && + VerifyField(verifier, VT_TRANSPOSE_A) && + VerifyField(verifier, VT_TRANSPOSE_B) && + VerifyOffset(verifier, VT_PAD) && + verifier.VerifyVector(pad()) && + VerifyField(verifier, VT_ROUND_MODE) && + VerifyField(verifier, VT_GLOBAL) && + VerifyField(verifier, VT_CHANNEL_SHARED) && + VerifyOffset(verifier, VT_AXES) && + verifier.VerifyVector(axes()) && + VerifyField(verifier, VT_KEEP_DIMS) && + VerifyField(verifier, VT_REDUCE_MODE) && + VerifyField(verifier, VT_REDUCE_TO_END) && + VerifyField(verifier, VT_COEFF) && + verifier.EndTable(); + } +}; + +struct GenOPBuilder { + typedef GenOP Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(GenOP::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + void add_alpha(float alpha) { + fbb_.AddElement(GenOP::VT_ALPHA, alpha, 0.0f); + } + void add_min_val(float min_val) { + fbb_.AddElement(GenOP::VT_MIN_VAL, min_val, 0.0f); + } + void add_max_val(float max_val) { + fbb_.AddElement(GenOP::VT_MAX_VAL, max_val, 0.0f); + } + void add_is_training(bool is_training) { + fbb_.AddElement(GenOP::VT_IS_TRAINING, static_cast(is_training), 0); + } + void add_format(mindspore::schema::Format format) { + fbb_.AddElement(GenOP::VT_FORMAT, static_cast(format), 0); + } + void add_kernel_size(flatbuffers::Offset> kernel_size) { + fbb_.AddOffset(GenOP::VT_KERNEL_SIZE, kernel_size); + } + void add_stride(flatbuffers::Offset> stride) { + fbb_.AddOffset(GenOP::VT_STRIDE, stride); + } + void add_dilation(flatbuffers::Offset> dilation) { + fbb_.AddOffset(GenOP::VT_DILATION, dilation); + } + void add_pad_mode(mindspore::schema::PadMode pad_mode) { + fbb_.AddElement(GenOP::VT_PAD_MODE, static_cast(pad_mode), 0); + } + void add_pad_list(flatbuffers::Offset> pad_list) { + fbb_.AddOffset(GenOP::VT_PAD_LIST, pad_list); + } + void add_mode(int64_t mode) { + fbb_.AddElement(GenOP::VT_MODE, mode, 0); + } + void add_group(int64_t group) { + fbb_.AddElement(GenOP::VT_GROUP, group, 0); + } + void add_in_channel(int64_t in_channel) { + fbb_.AddElement(GenOP::VT_IN_CHANNEL, in_channel, 0); + } + void add_out_channel(int64_t out_channel) { + fbb_.AddElement(GenOP::VT_OUT_CHANNEL, out_channel, 0); + } + void add_eltwise_mode(mindspore::schema::EltwiseMode eltwise_mode) { + fbb_.AddElement(GenOP::VT_ELTWISE_MODE, static_cast(eltwise_mode), 0); + } + void add_has_bias(bool has_bias) { + fbb_.AddElement(GenOP::VT_HAS_BIAS, static_cast(has_bias), 0); + } + void add_use_axis(bool use_axis) { + fbb_.AddElement(GenOP::VT_USE_AXIS, static_cast(use_axis), 0); + } + void add_axis(int64_t axis) { + fbb_.AddElement(GenOP::VT_AXIS, axis, 0); + } + void add_epsilon(float epsilon) { + fbb_.AddElement(GenOP::VT_EPSILON, epsilon, 0.0001f); + } + void add_momentum(float momentum) { + fbb_.AddElement(GenOP::VT_MOMENTUM, momentum, 0.9f); + } + void add_transpose_a(bool transpose_a) { + fbb_.AddElement(GenOP::VT_TRANSPOSE_A, static_cast(transpose_a), 0); + } + void add_transpose_b(bool transpose_b) { + fbb_.AddElement(GenOP::VT_TRANSPOSE_B, static_cast(transpose_b), 0); + } + void add_pad(flatbuffers::Offset> pad) { + fbb_.AddOffset(GenOP::VT_PAD, pad); + } + void add_round_mode(mindspore::schema::RoundMode round_mode) { + fbb_.AddElement(GenOP::VT_ROUND_MODE, static_cast(round_mode), 0); + } + void add_global(bool global) { + fbb_.AddElement(GenOP::VT_GLOBAL, static_cast(global), 0); + } + void add_channel_shared(bool channel_shared) { + fbb_.AddElement(GenOP::VT_CHANNEL_SHARED, static_cast(channel_shared), 0); + } + void add_axes(flatbuffers::Offset> axes) { + fbb_.AddOffset(GenOP::VT_AXES, axes); + } + void add_keep_dims(bool keep_dims) { + fbb_.AddElement(GenOP::VT_KEEP_DIMS, static_cast(keep_dims), 0); + } + void add_reduce_mode(mindspore::schema::ReduceMode reduce_mode) { + fbb_.AddElement(GenOP::VT_REDUCE_MODE, static_cast(reduce_mode), 0); + } + void add_reduce_to_end(bool reduce_to_end) { + fbb_.AddElement(GenOP::VT_REDUCE_TO_END, static_cast(reduce_to_end), 0); + } + void add_coeff(float coeff) { + fbb_.AddElement(GenOP::VT_COEFF, coeff, 0.0f); + } + explicit GenOPBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGenOP( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + float alpha = 0.0f, + float min_val = 0.0f, + float max_val = 0.0f, + bool is_training = false, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + flatbuffers::Offset> kernel_size = 0, + flatbuffers::Offset> stride = 0, + flatbuffers::Offset> dilation = 0, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + flatbuffers::Offset> pad_list = 0, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::EltwiseMode eltwise_mode = mindspore::schema::EltwiseMode_PROD, + bool has_bias = false, + bool use_axis = false, + int64_t axis = 0, + float epsilon = 0.0001f, + float momentum = 0.9f, + bool transpose_a = false, + bool transpose_b = false, + flatbuffers::Offset> pad = 0, + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR, + bool global = false, + bool channel_shared = false, + flatbuffers::Offset> axes = 0, + bool keep_dims = false, + mindspore::schema::ReduceMode reduce_mode = mindspore::schema::ReduceMode_ReduceMean, + bool reduce_to_end = false, + float coeff = 0.0f) { + GenOPBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_out_channel(out_channel); + builder_.add_in_channel(in_channel); + builder_.add_group(group); + builder_.add_mode(mode); + builder_.add_coeff(coeff); + builder_.add_axes(axes); + builder_.add_pad(pad); + builder_.add_momentum(momentum); + builder_.add_epsilon(epsilon); + builder_.add_pad_list(pad_list); + builder_.add_dilation(dilation); + builder_.add_stride(stride); + builder_.add_kernel_size(kernel_size); + builder_.add_format(format); + builder_.add_max_val(max_val); + builder_.add_min_val(min_val); + builder_.add_alpha(alpha); + builder_.add_reduce_to_end(reduce_to_end); + builder_.add_reduce_mode(reduce_mode); + builder_.add_keep_dims(keep_dims); + builder_.add_channel_shared(channel_shared); + builder_.add_global(global); + builder_.add_round_mode(round_mode); + builder_.add_transpose_b(transpose_b); + builder_.add_transpose_a(transpose_a); + builder_.add_use_axis(use_axis); + builder_.add_has_bias(has_bias); + builder_.add_eltwise_mode(eltwise_mode); + builder_.add_pad_mode(pad_mode); + builder_.add_is_training(is_training); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateGenOPDirect( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + float alpha = 0.0f, + float min_val = 0.0f, + float max_val = 0.0f, + bool is_training = false, + mindspore::schema::Format format = mindspore::schema::Format_NCHW, + const std::vector *kernel_size = nullptr, + const std::vector *stride = nullptr, + const std::vector *dilation = nullptr, + mindspore::schema::PadMode pad_mode = mindspore::schema::PadMode_PAD, + const std::vector *pad_list = nullptr, + int64_t mode = 0, + int64_t group = 0, + int64_t in_channel = 0, + int64_t out_channel = 0, + mindspore::schema::EltwiseMode eltwise_mode = mindspore::schema::EltwiseMode_PROD, + bool has_bias = false, + bool use_axis = false, + int64_t axis = 0, + float epsilon = 0.0001f, + float momentum = 0.9f, + bool transpose_a = false, + bool transpose_b = false, + const std::vector *pad = nullptr, + mindspore::schema::RoundMode round_mode = mindspore::schema::RoundMode_FLOOR, + bool global = false, + bool channel_shared = false, + const std::vector *axes = nullptr, + bool keep_dims = false, + mindspore::schema::ReduceMode reduce_mode = mindspore::schema::ReduceMode_ReduceMean, + bool reduce_to_end = false, + float coeff = 0.0f) { + auto kernel_size__ = kernel_size ? _fbb.CreateVector(*kernel_size) : 0; + auto stride__ = stride ? _fbb.CreateVector(*stride) : 0; + auto dilation__ = dilation ? _fbb.CreateVector(*dilation) : 0; + auto pad_list__ = pad_list ? _fbb.CreateVector(*pad_list) : 0; + auto pad__ = pad ? _fbb.CreateVector(*pad) : 0; + auto axes__ = axes ? _fbb.CreateVector(*axes) : 0; + return mindspore::schema::CreateGenOP( + _fbb, + activation_type, + alpha, + min_val, + max_val, + is_training, + format, + kernel_size__, + stride__, + dilation__, + pad_mode, + pad_list__, + mode, + group, + in_channel, + out_channel, + eltwise_mode, + has_bias, + use_axis, + axis, + epsilon, + momentum, + transpose_a, + transpose_b, + pad__, + round_mode, + global, + channel_shared, + axes__, + keep_dims, + reduce_mode, + reduce_to_end, + coeff); +} + +struct RaggedRange FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RaggedRangeBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct RaggedRangeBuilder { + typedef RaggedRange Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RaggedRangeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRaggedRange( + flatbuffers::FlatBufferBuilder &_fbb) { + RaggedRangeBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct GLU FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GLUBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, -1LL); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } +}; + +struct GLUBuilder { + typedef GLU Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(GLU::VT_AXIS, axis, -1LL); + } + explicit GLUBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGLU( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = -1LL) { + GLUBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +struct TensorArray FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorArrayBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DYNAMIC_SIZE = 4, + VT_IDENTICAL_ELEMENT_SHAPES = 6, + VT_ELEMENT_SHAPE = 8, + VT_DATA_TYPE = 10 + }; + bool dynamic_size() const { + return GetField(VT_DYNAMIC_SIZE, 0) != 0; + } + bool identical_element_shapes() const { + return GetField(VT_IDENTICAL_ELEMENT_SHAPES, 0) != 0; + } + const flatbuffers::Vector *element_shape() const { + return GetPointer *>(VT_ELEMENT_SHAPE); + } + int32_t data_type() const { + return GetField(VT_DATA_TYPE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_DYNAMIC_SIZE) && + VerifyField(verifier, VT_IDENTICAL_ELEMENT_SHAPES) && + VerifyOffset(verifier, VT_ELEMENT_SHAPE) && + verifier.VerifyVector(element_shape()) && + VerifyField(verifier, VT_DATA_TYPE) && + verifier.EndTable(); + } +}; + +struct TensorArrayBuilder { + typedef TensorArray Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_dynamic_size(bool dynamic_size) { + fbb_.AddElement(TensorArray::VT_DYNAMIC_SIZE, static_cast(dynamic_size), 0); + } + void add_identical_element_shapes(bool identical_element_shapes) { + fbb_.AddElement(TensorArray::VT_IDENTICAL_ELEMENT_SHAPES, static_cast(identical_element_shapes), 0); + } + void add_element_shape(flatbuffers::Offset> element_shape) { + fbb_.AddOffset(TensorArray::VT_ELEMENT_SHAPE, element_shape); + } + void add_data_type(int32_t data_type) { + fbb_.AddElement(TensorArray::VT_DATA_TYPE, data_type, 0); + } + explicit TensorArrayBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorArray( + flatbuffers::FlatBufferBuilder &_fbb, + bool dynamic_size = false, + bool identical_element_shapes = false, + flatbuffers::Offset> element_shape = 0, + int32_t data_type = 0) { + TensorArrayBuilder builder_(_fbb); + builder_.add_data_type(data_type); + builder_.add_element_shape(element_shape); + builder_.add_identical_element_shapes(identical_element_shapes); + builder_.add_dynamic_size(dynamic_size); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTensorArrayDirect( + flatbuffers::FlatBufferBuilder &_fbb, + bool dynamic_size = false, + bool identical_element_shapes = false, + const std::vector *element_shape = nullptr, + int32_t data_type = 0) { + auto element_shape__ = element_shape ? _fbb.CreateVector(*element_shape) : 0; + return mindspore::schema::CreateTensorArray( + _fbb, + dynamic_size, + identical_element_shapes, + element_shape__, + data_type); +} + +struct TensorArrayRead FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorArrayReadBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct TensorArrayReadBuilder { + typedef TensorArrayRead Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TensorArrayReadBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorArrayRead( + flatbuffers::FlatBufferBuilder &_fbb) { + TensorArrayReadBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct TensorArrayWrite FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorArrayWriteBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct TensorArrayWriteBuilder { + typedef TensorArrayWrite Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TensorArrayWriteBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorArrayWrite( + flatbuffers::FlatBufferBuilder &_fbb) { + TensorArrayWriteBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct Affine FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AffineBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CONTEXT = 4, + VT_OUTPUT_DIM = 6, + VT_ACTIVATION_TYPE = 8, + VT_TRANSPOSE_A = 10, + VT_TRANSPOSE_B = 12 + }; + const flatbuffers::Vector *context() const { + return GetPointer *>(VT_CONTEXT); + } + int64_t output_dim() const { + return GetField(VT_OUTPUT_DIM, 0); + } + mindspore::schema::ActivationType activation_type() const { + return static_cast(GetField(VT_ACTIVATION_TYPE, 0)); + } + bool transpose_a() const { + return GetField(VT_TRANSPOSE_A, 0) != 0; + } + bool transpose_b() const { + return GetField(VT_TRANSPOSE_B, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CONTEXT) && + verifier.VerifyVector(context()) && + VerifyField(verifier, VT_OUTPUT_DIM) && + VerifyField(verifier, VT_ACTIVATION_TYPE) && + VerifyField(verifier, VT_TRANSPOSE_A) && + VerifyField(verifier, VT_TRANSPOSE_B) && + verifier.EndTable(); + } +}; + +struct AffineBuilder { + typedef Affine Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_context(flatbuffers::Offset> context) { + fbb_.AddOffset(Affine::VT_CONTEXT, context); + } + void add_output_dim(int64_t output_dim) { + fbb_.AddElement(Affine::VT_OUTPUT_DIM, output_dim, 0); + } + void add_activation_type(mindspore::schema::ActivationType activation_type) { + fbb_.AddElement(Affine::VT_ACTIVATION_TYPE, static_cast(activation_type), 0); + } + void add_transpose_a(bool transpose_a) { + fbb_.AddElement(Affine::VT_TRANSPOSE_A, static_cast(transpose_a), 0); + } + void add_transpose_b(bool transpose_b) { + fbb_.AddElement(Affine::VT_TRANSPOSE_B, static_cast(transpose_b), 0); + } + explicit AffineBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAffine( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> context = 0, + int64_t output_dim = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + bool transpose_a = false, + bool transpose_b = false) { + AffineBuilder builder_(_fbb); + builder_.add_output_dim(output_dim); + builder_.add_context(context); + builder_.add_transpose_b(transpose_b); + builder_.add_transpose_a(transpose_a); + builder_.add_activation_type(activation_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateAffineDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *context = nullptr, + int64_t output_dim = 0, + mindspore::schema::ActivationType activation_type = mindspore::schema::ActivationType_NO_ACTIVATION, + bool transpose_a = false, + bool transpose_b = false) { + auto context__ = context ? _fbb.CreateVector(*context) : 0; + return mindspore::schema::CreateAffine( + _fbb, + context__, + output_dim, + activation_type, + transpose_a, + transpose_b); +} + +struct ScatterNdUpdate FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ScatterNdUpdateBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct ScatterNdUpdateBuilder { + typedef ScatterNdUpdate Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ScatterNdUpdateBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateScatterNdUpdate( + flatbuffers::FlatBufferBuilder &_fbb) { + ScatterNdUpdateBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct AllGather FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AllGatherBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_GROUP = 4, + VT_RANK_SIZE = 6 + }; + const flatbuffers::String *group() const { + return GetPointer(VT_GROUP); + } + int32_t rank_size() const { + return GetField(VT_RANK_SIZE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_GROUP) && + verifier.VerifyString(group()) && + VerifyField(verifier, VT_RANK_SIZE) && + verifier.EndTable(); + } +}; + +struct AllGatherBuilder { + typedef AllGather Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_group(flatbuffers::Offset group) { + fbb_.AddOffset(AllGather::VT_GROUP, group); + } + void add_rank_size(int32_t rank_size) { + fbb_.AddElement(AllGather::VT_RANK_SIZE, rank_size, 0); + } + explicit AllGatherBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAllGather( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset group = 0, + int32_t rank_size = 0) { + AllGatherBuilder builder_(_fbb); + builder_.add_rank_size(rank_size); + builder_.add_group(group); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateAllGatherDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *group = nullptr, + int32_t rank_size = 0) { + auto group__ = group ? _fbb.CreateString(group) : 0; + return mindspore::schema::CreateAllGather( + _fbb, + group__, + rank_size); +} + +struct ReduceScatter FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReduceScatterBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_GROUP = 4, + VT_MODE = 6, + VT_RANK_SIZE = 8 + }; + const flatbuffers::String *group() const { + return GetPointer(VT_GROUP); + } + mindspore::schema::ReduceMode mode() const { + return static_cast(GetField(VT_MODE, 0)); + } + int32_t rank_size() const { + return GetField(VT_RANK_SIZE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_GROUP) && + verifier.VerifyString(group()) && + VerifyField(verifier, VT_MODE) && + VerifyField(verifier, VT_RANK_SIZE) && + verifier.EndTable(); + } +}; + +struct ReduceScatterBuilder { + typedef ReduceScatter Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_group(flatbuffers::Offset group) { + fbb_.AddOffset(ReduceScatter::VT_GROUP, group); + } + void add_mode(mindspore::schema::ReduceMode mode) { + fbb_.AddElement(ReduceScatter::VT_MODE, static_cast(mode), 0); + } + void add_rank_size(int32_t rank_size) { + fbb_.AddElement(ReduceScatter::VT_RANK_SIZE, rank_size, 0); + } + explicit ReduceScatterBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReduceScatter( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset group = 0, + mindspore::schema::ReduceMode mode = mindspore::schema::ReduceMode_ReduceMean, + int32_t rank_size = 0) { + ReduceScatterBuilder builder_(_fbb); + builder_.add_rank_size(rank_size); + builder_.add_group(group); + builder_.add_mode(mode); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateReduceScatterDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *group = nullptr, + mindspore::schema::ReduceMode mode = mindspore::schema::ReduceMode_ReduceMean, + int32_t rank_size = 0) { + auto group__ = group ? _fbb.CreateString(group) : 0; + return mindspore::schema::CreateReduceScatter( + _fbb, + group__, + mode, + rank_size); +} + +struct DynamicQuant FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DynamicQuantBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SYMMETRIC = 4, + VT_DST_TYPE = 6, + VT_ACTIVATION_CHANNEL = 8, + VT_PREFER_AXIS = 10, + VT_TRANSPOSE = 12 + }; + bool symmetric() const { + return GetField(VT_SYMMETRIC, 0) != 0; + } + int64_t dst_type() const { + return GetField(VT_DST_TYPE, 32LL); + } + bool activation_channel() const { + return GetField(VT_ACTIVATION_CHANNEL, 0) != 0; + } + int64_t prefer_axis() const { + return GetField(VT_PREFER_AXIS, 0); + } + bool transpose() const { + return GetField(VT_TRANSPOSE, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SYMMETRIC) && + VerifyField(verifier, VT_DST_TYPE) && + VerifyField(verifier, VT_ACTIVATION_CHANNEL) && + VerifyField(verifier, VT_PREFER_AXIS) && + VerifyField(verifier, VT_TRANSPOSE) && + verifier.EndTable(); + } +}; + +struct DynamicQuantBuilder { + typedef DynamicQuant Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_symmetric(bool symmetric) { + fbb_.AddElement(DynamicQuant::VT_SYMMETRIC, static_cast(symmetric), 0); + } + void add_dst_type(int64_t dst_type) { + fbb_.AddElement(DynamicQuant::VT_DST_TYPE, dst_type, 32LL); + } + void add_activation_channel(bool activation_channel) { + fbb_.AddElement(DynamicQuant::VT_ACTIVATION_CHANNEL, static_cast(activation_channel), 0); + } + void add_prefer_axis(int64_t prefer_axis) { + fbb_.AddElement(DynamicQuant::VT_PREFER_AXIS, prefer_axis, 0); + } + void add_transpose(bool transpose) { + fbb_.AddElement(DynamicQuant::VT_TRANSPOSE, static_cast(transpose), 0); + } + explicit DynamicQuantBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDynamicQuant( + flatbuffers::FlatBufferBuilder &_fbb, + bool symmetric = false, + int64_t dst_type = 32LL, + bool activation_channel = false, + int64_t prefer_axis = 0, + bool transpose = false) { + DynamicQuantBuilder builder_(_fbb); + builder_.add_prefer_axis(prefer_axis); + builder_.add_dst_type(dst_type); + builder_.add_transpose(transpose); + builder_.add_activation_channel(activation_channel); + builder_.add_symmetric(symmetric); + return builder_.Finish(); +} + +struct LSTMGradData FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSTMGradDataBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BIDIRECTIONAL = 4, + VT_HAS_BIAS = 6, + VT_INPUT_SIZE = 8, + VT_HIDDEN_SIZE = 10, + VT_NUM_LAYERS = 12, + VT_NUM_DIRECTIONS = 14, + VT_DROPOUT = 16, + VT_ZONEOUT_CELL = 18, + VT_ZONEOUT_HIDDEN = 20 + }; + bool bidirectional() const { + return GetField(VT_BIDIRECTIONAL, 0) != 0; + } + bool has_bias() const { + return GetField(VT_HAS_BIAS, 0) != 0; + } + int64_t input_size() const { + return GetField(VT_INPUT_SIZE, 0); + } + int64_t hidden_size() const { + return GetField(VT_HIDDEN_SIZE, 0); + } + int64_t num_layers() const { + return GetField(VT_NUM_LAYERS, 0); + } + int64_t num_directions() const { + return GetField(VT_NUM_DIRECTIONS, 0); + } + float dropout() const { + return GetField(VT_DROPOUT, 0.0f); + } + float zoneout_cell() const { + return GetField(VT_ZONEOUT_CELL, 0.0f); + } + float zoneout_hidden() const { + return GetField(VT_ZONEOUT_HIDDEN, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BIDIRECTIONAL) && + VerifyField(verifier, VT_HAS_BIAS) && + VerifyField(verifier, VT_INPUT_SIZE) && + VerifyField(verifier, VT_HIDDEN_SIZE) && + VerifyField(verifier, VT_NUM_LAYERS) && + VerifyField(verifier, VT_NUM_DIRECTIONS) && + VerifyField(verifier, VT_DROPOUT) && + VerifyField(verifier, VT_ZONEOUT_CELL) && + VerifyField(verifier, VT_ZONEOUT_HIDDEN) && + verifier.EndTable(); + } +}; + +struct LSTMGradDataBuilder { + typedef LSTMGradData Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_bidirectional(bool bidirectional) { + fbb_.AddElement(LSTMGradData::VT_BIDIRECTIONAL, static_cast(bidirectional), 0); + } + void add_has_bias(bool has_bias) { + fbb_.AddElement(LSTMGradData::VT_HAS_BIAS, static_cast(has_bias), 0); + } + void add_input_size(int64_t input_size) { + fbb_.AddElement(LSTMGradData::VT_INPUT_SIZE, input_size, 0); + } + void add_hidden_size(int64_t hidden_size) { + fbb_.AddElement(LSTMGradData::VT_HIDDEN_SIZE, hidden_size, 0); + } + void add_num_layers(int64_t num_layers) { + fbb_.AddElement(LSTMGradData::VT_NUM_LAYERS, num_layers, 0); + } + void add_num_directions(int64_t num_directions) { + fbb_.AddElement(LSTMGradData::VT_NUM_DIRECTIONS, num_directions, 0); + } + void add_dropout(float dropout) { + fbb_.AddElement(LSTMGradData::VT_DROPOUT, dropout, 0.0f); + } + void add_zoneout_cell(float zoneout_cell) { + fbb_.AddElement(LSTMGradData::VT_ZONEOUT_CELL, zoneout_cell, 0.0f); + } + void add_zoneout_hidden(float zoneout_hidden) { + fbb_.AddElement(LSTMGradData::VT_ZONEOUT_HIDDEN, zoneout_hidden, 0.0f); + } + explicit LSTMGradDataBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSTMGradData( + flatbuffers::FlatBufferBuilder &_fbb, + bool bidirectional = false, + bool has_bias = false, + int64_t input_size = 0, + int64_t hidden_size = 0, + int64_t num_layers = 0, + int64_t num_directions = 0, + float dropout = 0.0f, + float zoneout_cell = 0.0f, + float zoneout_hidden = 0.0f) { + LSTMGradDataBuilder builder_(_fbb); + builder_.add_num_directions(num_directions); + builder_.add_num_layers(num_layers); + builder_.add_hidden_size(hidden_size); + builder_.add_input_size(input_size); + builder_.add_zoneout_hidden(zoneout_hidden); + builder_.add_zoneout_cell(zoneout_cell); + builder_.add_dropout(dropout); + builder_.add_has_bias(has_bias); + builder_.add_bidirectional(bidirectional); + return builder_.Finish(); +} + +struct LSTMGradWeight FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSTMGradWeightBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BIDIRECTIONAL = 4, + VT_HAS_BIAS = 6, + VT_INPUT_SIZE = 8, + VT_HIDDEN_SIZE = 10, + VT_NUM_LAYERS = 12, + VT_NUM_DIRECTIONS = 14, + VT_DROPOUT = 16, + VT_ZONEOUT_CELL = 18, + VT_ZONEOUT_HIDDEN = 20 + }; + bool bidirectional() const { + return GetField(VT_BIDIRECTIONAL, 0) != 0; + } + bool has_bias() const { + return GetField(VT_HAS_BIAS, 0) != 0; + } + int64_t input_size() const { + return GetField(VT_INPUT_SIZE, 0); + } + int64_t hidden_size() const { + return GetField(VT_HIDDEN_SIZE, 0); + } + int64_t num_layers() const { + return GetField(VT_NUM_LAYERS, 0); + } + int64_t num_directions() const { + return GetField(VT_NUM_DIRECTIONS, 0); + } + float dropout() const { + return GetField(VT_DROPOUT, 0.0f); + } + float zoneout_cell() const { + return GetField(VT_ZONEOUT_CELL, 0.0f); + } + float zoneout_hidden() const { + return GetField(VT_ZONEOUT_HIDDEN, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BIDIRECTIONAL) && + VerifyField(verifier, VT_HAS_BIAS) && + VerifyField(verifier, VT_INPUT_SIZE) && + VerifyField(verifier, VT_HIDDEN_SIZE) && + VerifyField(verifier, VT_NUM_LAYERS) && + VerifyField(verifier, VT_NUM_DIRECTIONS) && + VerifyField(verifier, VT_DROPOUT) && + VerifyField(verifier, VT_ZONEOUT_CELL) && + VerifyField(verifier, VT_ZONEOUT_HIDDEN) && + verifier.EndTable(); + } +}; + +struct LSTMGradWeightBuilder { + typedef LSTMGradWeight Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_bidirectional(bool bidirectional) { + fbb_.AddElement(LSTMGradWeight::VT_BIDIRECTIONAL, static_cast(bidirectional), 0); + } + void add_has_bias(bool has_bias) { + fbb_.AddElement(LSTMGradWeight::VT_HAS_BIAS, static_cast(has_bias), 0); + } + void add_input_size(int64_t input_size) { + fbb_.AddElement(LSTMGradWeight::VT_INPUT_SIZE, input_size, 0); + } + void add_hidden_size(int64_t hidden_size) { + fbb_.AddElement(LSTMGradWeight::VT_HIDDEN_SIZE, hidden_size, 0); + } + void add_num_layers(int64_t num_layers) { + fbb_.AddElement(LSTMGradWeight::VT_NUM_LAYERS, num_layers, 0); + } + void add_num_directions(int64_t num_directions) { + fbb_.AddElement(LSTMGradWeight::VT_NUM_DIRECTIONS, num_directions, 0); + } + void add_dropout(float dropout) { + fbb_.AddElement(LSTMGradWeight::VT_DROPOUT, dropout, 0.0f); + } + void add_zoneout_cell(float zoneout_cell) { + fbb_.AddElement(LSTMGradWeight::VT_ZONEOUT_CELL, zoneout_cell, 0.0f); + } + void add_zoneout_hidden(float zoneout_hidden) { + fbb_.AddElement(LSTMGradWeight::VT_ZONEOUT_HIDDEN, zoneout_hidden, 0.0f); + } + explicit LSTMGradWeightBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSTMGradWeight( + flatbuffers::FlatBufferBuilder &_fbb, + bool bidirectional = false, + bool has_bias = false, + int64_t input_size = 0, + int64_t hidden_size = 0, + int64_t num_layers = 0, + int64_t num_directions = 0, + float dropout = 0.0f, + float zoneout_cell = 0.0f, + float zoneout_hidden = 0.0f) { + LSTMGradWeightBuilder builder_(_fbb); + builder_.add_num_directions(num_directions); + builder_.add_num_layers(num_layers); + builder_.add_hidden_size(hidden_size); + builder_.add_input_size(input_size); + builder_.add_zoneout_hidden(zoneout_hidden); + builder_.add_zoneout_cell(zoneout_cell); + builder_.add_dropout(dropout); + builder_.add_has_bias(has_bias); + builder_.add_bidirectional(bidirectional); + return builder_.Finish(); +} + +struct RandomNormal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RandomNormalBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEED = 4, + VT_MEAN = 6, + VT_SCALE = 8 + }; + float seed() const { + return GetField(VT_SEED, 0.0f); + } + float mean() const { + return GetField(VT_MEAN, 0.0f); + } + float scale() const { + return GetField(VT_SCALE, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEED) && + VerifyField(verifier, VT_MEAN) && + VerifyField(verifier, VT_SCALE) && + verifier.EndTable(); + } +}; + +struct RandomNormalBuilder { + typedef RandomNormal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seed(float seed) { + fbb_.AddElement(RandomNormal::VT_SEED, seed, 0.0f); + } + void add_mean(float mean) { + fbb_.AddElement(RandomNormal::VT_MEAN, mean, 0.0f); + } + void add_scale(float scale) { + fbb_.AddElement(RandomNormal::VT_SCALE, scale, 0.0f); + } + explicit RandomNormalBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRandomNormal( + flatbuffers::FlatBufferBuilder &_fbb, + float seed = 0.0f, + float mean = 0.0f, + float scale = 0.0f) { + RandomNormalBuilder builder_(_fbb); + builder_.add_scale(scale); + builder_.add_mean(mean); + builder_.add_seed(seed); + return builder_.Finish(); +} + +struct NLLLoss FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NLLLossBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_REDUCTION = 4 + }; + mindspore::schema::Reduction reduction() const { + return static_cast(GetField(VT_REDUCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_REDUCTION) && + verifier.EndTable(); + } +}; + +struct NLLLossBuilder { + typedef NLLLoss Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_reduction(mindspore::schema::Reduction reduction) { + fbb_.AddElement(NLLLoss::VT_REDUCTION, static_cast(reduction), 0); + } + explicit NLLLossBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNLLLoss( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Reduction reduction = mindspore::schema::Reduction_REDUCTION_SUM) { + NLLLossBuilder builder_(_fbb); + builder_.add_reduction(reduction); + return builder_.Finish(); +} + +struct NLLLossGrad FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NLLLossGradBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_REDUCTION = 4 + }; + mindspore::schema::Reduction reduction() const { + return static_cast(GetField(VT_REDUCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_REDUCTION) && + verifier.EndTable(); + } +}; + +struct NLLLossGradBuilder { + typedef NLLLossGrad Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_reduction(mindspore::schema::Reduction reduction) { + fbb_.AddElement(NLLLossGrad::VT_REDUCTION, static_cast(reduction), 0); + } + explicit NLLLossGradBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNLLLossGrad( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Reduction reduction = mindspore::schema::Reduction_REDUCTION_SUM) { + NLLLossGradBuilder builder_(_fbb); + builder_.add_reduction(reduction); + return builder_.Finish(); +} + +struct FormatTranspose FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FormatTransposeBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SRC_FORMAT = 4, + VT_DST_FORMAT = 6 + }; + mindspore::schema::Format src_format() const { + return static_cast(GetField(VT_SRC_FORMAT, 1)); + } + mindspore::schema::Format dst_format() const { + return static_cast(GetField(VT_DST_FORMAT, 1)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SRC_FORMAT) && + VerifyField(verifier, VT_DST_FORMAT) && + verifier.EndTable(); + } +}; + +struct FormatTransposeBuilder { + typedef FormatTranspose Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_src_format(mindspore::schema::Format src_format) { + fbb_.AddElement(FormatTranspose::VT_SRC_FORMAT, static_cast(src_format), 1); + } + void add_dst_format(mindspore::schema::Format dst_format) { + fbb_.AddElement(FormatTranspose::VT_DST_FORMAT, static_cast(dst_format), 1); + } + explicit FormatTransposeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFormatTranspose( + flatbuffers::FlatBufferBuilder &_fbb, + mindspore::schema::Format src_format = mindspore::schema::Format_NHWC, + mindspore::schema::Format dst_format = mindspore::schema::Format_NHWC) { + FormatTransposeBuilder builder_(_fbb); + builder_.add_dst_format(dst_format); + builder_.add_src_format(src_format); + return builder_.Finish(); +} + +struct GatherD FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherDBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct GatherDBuilder { + typedef GatherD Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GatherDBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGatherD( + flatbuffers::FlatBufferBuilder &_fbb) { + GatherDBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct GroupNormFusion FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GroupNormFusionBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_GROUPS = 4, + VT_EPSILON = 6, + VT_AFFINE = 8 + }; + int64_t num_groups() const { + return GetField(VT_NUM_GROUPS, 0); + } + float epsilon() const { + return GetField(VT_EPSILON, 1e-5f); + } + bool affine() const { + return GetField(VT_AFFINE, 1) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_GROUPS) && + VerifyField(verifier, VT_EPSILON) && + VerifyField(verifier, VT_AFFINE) && + verifier.EndTable(); + } +}; + +struct GroupNormFusionBuilder { + typedef GroupNormFusion Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_groups(int64_t num_groups) { + fbb_.AddElement(GroupNormFusion::VT_NUM_GROUPS, num_groups, 0); + } + void add_epsilon(float epsilon) { + fbb_.AddElement(GroupNormFusion::VT_EPSILON, epsilon, 1e-5f); + } + void add_affine(bool affine) { + fbb_.AddElement(GroupNormFusion::VT_AFFINE, static_cast(affine), 1); + } + explicit GroupNormFusionBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGroupNormFusion( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t num_groups = 0, + float epsilon = 1e-5f, + bool affine = true) { + GroupNormFusionBuilder builder_(_fbb); + builder_.add_num_groups(num_groups); + builder_.add_epsilon(epsilon); + builder_.add_affine(affine); + return builder_.Finish(); +} + +struct Log1p FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Log1pBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct Log1pBuilder { + typedef Log1p Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit Log1pBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLog1p( + flatbuffers::FlatBufferBuilder &_fbb) { + Log1pBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct TensorScatterAdd FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorScatterAddBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct TensorScatterAddBuilder { + typedef TensorScatterAdd Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TensorScatterAddBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorScatterAdd( + flatbuffers::FlatBufferBuilder &_fbb) { + TensorScatterAddBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct SparseFillEmptyRows FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseFillEmptyRowsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct SparseFillEmptyRowsBuilder { + typedef SparseFillEmptyRows Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SparseFillEmptyRowsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseFillEmptyRows( + flatbuffers::FlatBufferBuilder &_fbb) { + SparseFillEmptyRowsBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct SparseReshape FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseReshapeBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct SparseReshapeBuilder { + typedef SparseReshape Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SparseReshapeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseReshape( + flatbuffers::FlatBufferBuilder &_fbb) { + SparseReshapeBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct SparseSegmentSum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseSegmentSumBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct SparseSegmentSumBuilder { + typedef SparseSegmentSum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SparseSegmentSumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseSegmentSum( + flatbuffers::FlatBufferBuilder &_fbb) { + SparseSegmentSumBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct ScatterElements FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ScatterElementsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int64_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } +}; + +struct ScatterElementsBuilder { + typedef ScatterElements Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int64_t axis) { + fbb_.AddElement(ScatterElements::VT_AXIS, axis, 0); + } + explicit ScatterElementsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateScatterElements( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t axis = 0) { + ScatterElementsBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +struct Triu FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TriuBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct TriuBuilder { + typedef Triu Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TriuBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTriu( + flatbuffers::FlatBufferBuilder &_fbb) { + TriuBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct Tril FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TrilBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } +}; + +struct TrilBuilder { + typedef Tril Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TrilBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTril( + flatbuffers::FlatBufferBuilder &_fbb) { + TrilBuilder builder_(_fbb); + return builder_.Finish(); +} + +struct AdamWeightDecay FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AdamWeightDecayBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_USE_LOCKING = 4 + }; + bool use_locking() const { + return GetField(VT_USE_LOCKING, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_USE_LOCKING) && + verifier.EndTable(); + } +}; + +struct AdamWeightDecayBuilder { + typedef AdamWeightDecay Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_use_locking(bool use_locking) { + fbb_.AddElement(AdamWeightDecay::VT_USE_LOCKING, static_cast(use_locking), 0); + } + explicit AdamWeightDecayBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAdamWeightDecay( + flatbuffers::FlatBufferBuilder &_fbb, + bool use_locking = false) { + AdamWeightDecayBuilder builder_(_fbb); + builder_.add_use_locking(use_locking); + return builder_.Finish(); +} + +inline bool VerifyPrimitiveType(flatbuffers::Verifier &verifier, const void *obj, PrimitiveType type) { + switch (type) { + case PrimitiveType_NONE: { + return true; + } + case PrimitiveType_Abs: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Activation: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ActivationGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Adam: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AddFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AdderFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AddGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AddN: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_All: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ApplyMomentum: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ArgMaxFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ArgMinFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Assert: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Assign: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AssignAdd: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AudioSpectrogram: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AvgPoolFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AvgPoolGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BatchNorm: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BatchNormGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BatchToSpace: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BatchToSpaceND: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BiasAdd: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BinaryCrossEntropy: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BinaryCrossEntropyGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BiasAddGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_BroadcastTo: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Cast: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Ceil: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Clip: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Concat: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Attention: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Conv2DBackpropFilterFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Conv2DBackpropInputFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Conv2DFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Conv2dTransposeFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Cos: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ConstantOfShape: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Crop: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_CustomExtractFeatures: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_CustomNormalize: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_CustomPredict: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DeConv2DGradFilter: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Depend: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DepthToSpace: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DetectionPostProcess: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DivFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DivGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Dropout: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DropoutGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Elu: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Eltwise: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Equal: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_EmbeddingLookupFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ExpFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ExpandDims: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FakeQuantWithMinMaxVars: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FakeQuantWithMinMaxVarsPerChannel: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FftReal: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FftImag: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Flatten: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FlattenGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Floor: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FloorDiv: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FloorMod: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Fill: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FullConnection: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FusedBatchNorm: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Gather: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GatherNd: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Greater: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GreaterEqual: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_HashtableLookup: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_InstanceNorm: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LayerNormFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LeakyRelu: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Less: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LessEqual: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Log: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LogGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LogicalAnd: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LogicalNot: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LogicalOr: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LpNormalization: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LRN: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LshProjection: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LSTM: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_L2NormalizeFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MatMulFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Maximum: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MaximumGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MaxPoolFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MaxPoolGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SwitchLayer: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Mfcc: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Minimum: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MinimumGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Mod: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MulFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_MulGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Neg: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_NegGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_NotEqual: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_NonMaxSuppression: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_OneHot: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_OnesLike: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_PadFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_PartialFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_PowerGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_PowFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_PriorBox: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_PReLUFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_QuantDTypeCast: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Rank: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Range: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Reciprocal: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_RealDiv: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ReduceFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Reshape: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Resize: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ReverseSequence: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ReverseV2: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Rfft: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ROIPooling: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Round: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Rsqrt: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ScaleFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ScatterNd: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SGD: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Shape: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SigmoidCrossEntropyWithLogits: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SigmoidCrossEntropyWithLogitsGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Sin: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SkipGram: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SliceFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SmoothL1Loss: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SmoothL1LossGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Softmax: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SoftmaxCrossEntropyWithLogits: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SpaceToBatch: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SpaceToBatchND: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SpaceToDepth: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SparseSoftmaxCrossEntropyWithLogits: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SparseToDense: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Split: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Sqrt: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Squeeze: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Square: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SquaredDifference: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Stack: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_StridedSlice: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SubFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SubGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Switch: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorListFromTensor: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorListGetItem: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorListReserve: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorListSetItem: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorListStack: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TileFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TopKFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Transpose: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Unique: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_UnsortedSegmentSum: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Unsqueeze: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Unstack: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LSTMGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Where: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ZerosLike: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Select: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ScatterNdUpdate: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GRU: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_NonZero: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_InvertPermutation: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Size: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_RandomStandardNormal: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_CropAndResize: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Erf: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_StridedSliceGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_IsFinite: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LinSpace: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_UniformReal: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AbsGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_RsqrtGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SqrtGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LayerNormGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ResizeGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Splice: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LogSoftmax: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Call: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Custom: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_CumSum: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SplitWithOverlap: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GenOP: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_RaggedRange: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GLU: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorArray: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorArrayRead: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorArrayWrite: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Affine: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AllGather: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ReduceScatter: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_DynamicQuant: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LSTMGradData: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_LSTMGradWeight: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_RandomNormal: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_NLLLoss: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_NLLLossGrad: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_FormatTranspose: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GatherD: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_GroupNormFusion: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Log1p: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_TensorScatterAdd: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SparseFillEmptyRows: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SparseReshape: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_SparseSegmentSum: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_ScatterElements: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Triu: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_Tril: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case PrimitiveType_AdamWeightDecay: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifyPrimitiveTypeVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifyPrimitiveType( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +} // namespace schema +} // namespace mindspore + +#endif // FLATBUFFERS_GENERATED_OPS_MINDSPORE_SCHEMA_H_ diff --git a/mindspore/lite/schema/ops_types_generated.h b/mindspore/lite/schema/ops_types_generated.h new file mode 100644 index 00000000..15f19241 --- /dev/null +++ b/mindspore/lite/schema/ops_types_generated.h @@ -0,0 +1,750 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_OPSTYPES_MINDSPORE_SCHEMA_H_ +#define FLATBUFFERS_GENERATED_OPSTYPES_MINDSPORE_SCHEMA_H_ + +#include "flatbuffers/flatbuffers.h" + +namespace mindspore { +namespace schema { + +struct Vec; +struct VecBuilder; + +struct Vec2D; +struct Vec2DBuilder; + +struct Attribute; +struct AttributeBuilder; + +enum ResizeMethod : int8_t { + ResizeMethod_UNKNOWN = -1, + ResizeMethod_LINEAR = 0, + ResizeMethod_NEAREST = 1, + ResizeMethod_CUBIC = 2, + ResizeMethod_MIN = ResizeMethod_UNKNOWN, + ResizeMethod_MAX = ResizeMethod_CUBIC +}; + +inline const ResizeMethod (&EnumValuesResizeMethod())[4] { + static const ResizeMethod values[] = { + ResizeMethod_UNKNOWN, + ResizeMethod_LINEAR, + ResizeMethod_NEAREST, + ResizeMethod_CUBIC + }; + return values; +} + +inline const char * const *EnumNamesResizeMethod() { + static const char * const names[5] = { + "UNKNOWN", + "LINEAR", + "NEAREST", + "CUBIC", + nullptr + }; + return names; +} + +inline const char *EnumNameResizeMethod(ResizeMethod e) { + if (flatbuffers::IsOutRange(e, ResizeMethod_UNKNOWN, ResizeMethod_CUBIC)) return ""; + const size_t index = static_cast(e) - static_cast(ResizeMethod_UNKNOWN); + return EnumNamesResizeMethod()[index]; +} + +enum CoordinateTransformMode : int8_t { + CoordinateTransformMode_ASYMMETRIC = 0, + CoordinateTransformMode_ALIGN_CORNERS = 1, + CoordinateTransformMode_HALF_PIXEL = 2, + CoordinateTransformMode_MIN = CoordinateTransformMode_ASYMMETRIC, + CoordinateTransformMode_MAX = CoordinateTransformMode_HALF_PIXEL +}; + +inline const CoordinateTransformMode (&EnumValuesCoordinateTransformMode())[3] { + static const CoordinateTransformMode values[] = { + CoordinateTransformMode_ASYMMETRIC, + CoordinateTransformMode_ALIGN_CORNERS, + CoordinateTransformMode_HALF_PIXEL + }; + return values; +} + +inline const char * const *EnumNamesCoordinateTransformMode() { + static const char * const names[4] = { + "ASYMMETRIC", + "ALIGN_CORNERS", + "HALF_PIXEL", + nullptr + }; + return names; +} + +inline const char *EnumNameCoordinateTransformMode(CoordinateTransformMode e) { + if (flatbuffers::IsOutRange(e, CoordinateTransformMode_ASYMMETRIC, CoordinateTransformMode_HALF_PIXEL)) return ""; + const size_t index = static_cast(e); + return EnumNamesCoordinateTransformMode()[index]; +} + +enum NearestMode : int8_t { + NearestMode_NORMAL = 0, + NearestMode_ROUND_HALF_DOWN = 1, + NearestMode_ROUND_HALF_UP = 2, + NearestMode_FLOOR = 3, + NearestMode_CEIL = 4, + NearestMode_MIN = NearestMode_NORMAL, + NearestMode_MAX = NearestMode_CEIL +}; + +inline const NearestMode (&EnumValuesNearestMode())[5] { + static const NearestMode values[] = { + NearestMode_NORMAL, + NearestMode_ROUND_HALF_DOWN, + NearestMode_ROUND_HALF_UP, + NearestMode_FLOOR, + NearestMode_CEIL + }; + return values; +} + +inline const char * const *EnumNamesNearestMode() { + static const char * const names[6] = { + "NORMAL", + "ROUND_HALF_DOWN", + "ROUND_HALF_UP", + "FLOOR", + "CEIL", + nullptr + }; + return names; +} + +inline const char *EnumNameNearestMode(NearestMode e) { + if (flatbuffers::IsOutRange(e, NearestMode_NORMAL, NearestMode_CEIL)) return ""; + const size_t index = static_cast(e); + return EnumNamesNearestMode()[index]; +} + +enum Format : int32_t { + Format_NCHW = 0, + Format_NHWC = 1, + Format_NHWC4 = 2, + Format_HWKC = 3, + Format_HWCK = 4, + Format_KCHW = 5, + Format_CKHW = 6, + Format_KHWC = 7, + Format_CHWK = 8, + Format_HW = 9, + Format_HW4 = 10, + Format_NC = 11, + Format_NC4 = 12, + Format_NC4HW4 = 13, + Format_NUM_OF_FORMAT = 14, + Format_NCDHW = 15, + Format_NWC = 16, + Format_NCW = 17, + Format_NC8HW8 = 18, + Format_MIN = Format_NCHW, + Format_MAX = Format_NC8HW8 +}; + +inline const Format (&EnumValuesFormat())[19] { + static const Format values[] = { + Format_NCHW, + Format_NHWC, + Format_NHWC4, + Format_HWKC, + Format_HWCK, + Format_KCHW, + Format_CKHW, + Format_KHWC, + Format_CHWK, + Format_HW, + Format_HW4, + Format_NC, + Format_NC4, + Format_NC4HW4, + Format_NUM_OF_FORMAT, + Format_NCDHW, + Format_NWC, + Format_NCW, + Format_NC8HW8 + }; + return values; +} + +inline const char * const *EnumNamesFormat() { + static const char * const names[20] = { + "NCHW", + "NHWC", + "NHWC4", + "HWKC", + "HWCK", + "KCHW", + "CKHW", + "KHWC", + "CHWK", + "HW", + "HW4", + "NC", + "NC4", + "NC4HW4", + "NUM_OF_FORMAT", + "NCDHW", + "NWC", + "NCW", + "NC8HW8", + nullptr + }; + return names; +} + +inline const char *EnumNameFormat(Format e) { + if (flatbuffers::IsOutRange(e, Format_NCHW, Format_NC8HW8)) return ""; + const size_t index = static_cast(e); + return EnumNamesFormat()[index]; +} + +enum ActivationType : int8_t { + ActivationType_NO_ACTIVATION = 0, + ActivationType_RELU = 1, + ActivationType_SIGMOID = 2, + ActivationType_RELU6 = 3, + ActivationType_ELU = 4, + ActivationType_LEAKY_RELU = 5, + ActivationType_ABS = 6, + ActivationType_RELU1 = 7, + ActivationType_SOFTSIGN = 8, + ActivationType_SOFTPLUS = 9, + ActivationType_TANH = 10, + ActivationType_SELU = 11, + ActivationType_HSWISH = 12, + ActivationType_HSIGMOID = 13, + ActivationType_THRESHOLDRELU = 14, + ActivationType_LINEAR = 15, + ActivationType_HARD_TANH = 16, + ActivationType_SIGN = 17, + ActivationType_SWISH = 18, + ActivationType_GELU = 19, + ActivationType_UNKNOWN = 20, + ActivationType_MIN = ActivationType_NO_ACTIVATION, + ActivationType_MAX = ActivationType_UNKNOWN +}; + +inline const ActivationType (&EnumValuesActivationType())[21] { + static const ActivationType values[] = { + ActivationType_NO_ACTIVATION, + ActivationType_RELU, + ActivationType_SIGMOID, + ActivationType_RELU6, + ActivationType_ELU, + ActivationType_LEAKY_RELU, + ActivationType_ABS, + ActivationType_RELU1, + ActivationType_SOFTSIGN, + ActivationType_SOFTPLUS, + ActivationType_TANH, + ActivationType_SELU, + ActivationType_HSWISH, + ActivationType_HSIGMOID, + ActivationType_THRESHOLDRELU, + ActivationType_LINEAR, + ActivationType_HARD_TANH, + ActivationType_SIGN, + ActivationType_SWISH, + ActivationType_GELU, + ActivationType_UNKNOWN + }; + return values; +} + +inline const char * const *EnumNamesActivationType() { + static const char * const names[22] = { + "NO_ACTIVATION", + "RELU", + "SIGMOID", + "RELU6", + "ELU", + "LEAKY_RELU", + "ABS", + "RELU1", + "SOFTSIGN", + "SOFTPLUS", + "TANH", + "SELU", + "HSWISH", + "HSIGMOID", + "THRESHOLDRELU", + "LINEAR", + "HARD_TANH", + "SIGN", + "SWISH", + "GELU", + "UNKNOWN", + nullptr + }; + return names; +} + +inline const char *EnumNameActivationType(ActivationType e) { + if (flatbuffers::IsOutRange(e, ActivationType_NO_ACTIVATION, ActivationType_UNKNOWN)) return ""; + const size_t index = static_cast(e); + return EnumNamesActivationType()[index]; +} + +enum ReduceMode : int8_t { + ReduceMode_ReduceMean = 0, + ReduceMode_ReduceMax = 1, + ReduceMode_ReduceMin = 2, + ReduceMode_ReduceProd = 3, + ReduceMode_ReduceSum = 4, + ReduceMode_ReduceSumSquare = 5, + ReduceMode_ReduceASum = 6, + ReduceMode_ReduceAll = 7, + ReduceMode_ReduceL2 = 8, + ReduceMode_MIN = ReduceMode_ReduceMean, + ReduceMode_MAX = ReduceMode_ReduceL2 +}; + +inline const ReduceMode (&EnumValuesReduceMode())[9] { + static const ReduceMode values[] = { + ReduceMode_ReduceMean, + ReduceMode_ReduceMax, + ReduceMode_ReduceMin, + ReduceMode_ReduceProd, + ReduceMode_ReduceSum, + ReduceMode_ReduceSumSquare, + ReduceMode_ReduceASum, + ReduceMode_ReduceAll, + ReduceMode_ReduceL2 + }; + return values; +} + +inline const char * const *EnumNamesReduceMode() { + static const char * const names[10] = { + "ReduceMean", + "ReduceMax", + "ReduceMin", + "ReduceProd", + "ReduceSum", + "ReduceSumSquare", + "ReduceASum", + "ReduceAll", + "ReduceL2", + nullptr + }; + return names; +} + +inline const char *EnumNameReduceMode(ReduceMode e) { + if (flatbuffers::IsOutRange(e, ReduceMode_ReduceMean, ReduceMode_ReduceL2)) return ""; + const size_t index = static_cast(e); + return EnumNamesReduceMode()[index]; +} + +enum PoolMode : int8_t { + PoolMode_MAX_POOLING = 0, + PoolMode_MEAN_POOLING = 1, + PoolMode_MIN = PoolMode_MAX_POOLING, + PoolMode_MAX = PoolMode_MEAN_POOLING +}; + +inline const PoolMode (&EnumValuesPoolMode())[2] { + static const PoolMode values[] = { + PoolMode_MAX_POOLING, + PoolMode_MEAN_POOLING + }; + return values; +} + +inline const char * const *EnumNamesPoolMode() { + static const char * const names[3] = { + "MAX_POOLING", + "MEAN_POOLING", + nullptr + }; + return names; +} + +inline const char *EnumNamePoolMode(PoolMode e) { + if (flatbuffers::IsOutRange(e, PoolMode_MAX_POOLING, PoolMode_MEAN_POOLING)) return ""; + const size_t index = static_cast(e); + return EnumNamesPoolMode()[index]; +} + +enum EltwiseMode : int8_t { + EltwiseMode_PROD = 0, + EltwiseMode_SUM = 1, + EltwiseMode_MAXIMUM = 2, + EltwiseMode_UNKNOWN = 3, + EltwiseMode_MIN = EltwiseMode_PROD, + EltwiseMode_MAX = EltwiseMode_UNKNOWN +}; + +inline const EltwiseMode (&EnumValuesEltwiseMode())[4] { + static const EltwiseMode values[] = { + EltwiseMode_PROD, + EltwiseMode_SUM, + EltwiseMode_MAXIMUM, + EltwiseMode_UNKNOWN + }; + return values; +} + +inline const char * const *EnumNamesEltwiseMode() { + static const char * const names[5] = { + "PROD", + "SUM", + "MAXIMUM", + "UNKNOWN", + nullptr + }; + return names; +} + +inline const char *EnumNameEltwiseMode(EltwiseMode e) { + if (flatbuffers::IsOutRange(e, EltwiseMode_PROD, EltwiseMode_UNKNOWN)) return ""; + const size_t index = static_cast(e); + return EnumNamesEltwiseMode()[index]; +} + +enum PadMode : int8_t { + PadMode_PAD = 0, + PadMode_SAME = 1, + PadMode_VALID = 2, + PadMode_MIN = PadMode_PAD, + PadMode_MAX = PadMode_VALID +}; + +inline const PadMode (&EnumValuesPadMode())[3] { + static const PadMode values[] = { + PadMode_PAD, + PadMode_SAME, + PadMode_VALID + }; + return values; +} + +inline const char * const *EnumNamesPadMode() { + static const char * const names[4] = { + "PAD", + "SAME", + "VALID", + nullptr + }; + return names; +} + +inline const char *EnumNamePadMode(PadMode e) { + if (flatbuffers::IsOutRange(e, PadMode_PAD, PadMode_VALID)) return ""; + const size_t index = static_cast(e); + return EnumNamesPadMode()[index]; +} + +enum RoundMode : int8_t { + RoundMode_FLOOR = 0, + RoundMode_CEIL = 1, + RoundMode_MIN = RoundMode_FLOOR, + RoundMode_MAX = RoundMode_CEIL +}; + +inline const RoundMode (&EnumValuesRoundMode())[2] { + static const RoundMode values[] = { + RoundMode_FLOOR, + RoundMode_CEIL + }; + return values; +} + +inline const char * const *EnumNamesRoundMode() { + static const char * const names[3] = { + "FLOOR", + "CEIL", + nullptr + }; + return names; +} + +inline const char *EnumNameRoundMode(RoundMode e) { + if (flatbuffers::IsOutRange(e, RoundMode_FLOOR, RoundMode_CEIL)) return ""; + const size_t index = static_cast(e); + return EnumNamesRoundMode()[index]; +} + +enum PaddingMode : int8_t { + PaddingMode_CONSTANT = 0, + PaddingMode_REFLECT = 1, + PaddingMode_SYMMETRIC = 2, + PaddingMode_MODE_RESERVED = 3, + PaddingMode_MIN = PaddingMode_CONSTANT, + PaddingMode_MAX = PaddingMode_MODE_RESERVED +}; + +inline const PaddingMode (&EnumValuesPaddingMode())[4] { + static const PaddingMode values[] = { + PaddingMode_CONSTANT, + PaddingMode_REFLECT, + PaddingMode_SYMMETRIC, + PaddingMode_MODE_RESERVED + }; + return values; +} + +inline const char * const *EnumNamesPaddingMode() { + static const char * const names[5] = { + "CONSTANT", + "REFLECT", + "SYMMETRIC", + "MODE_RESERVED", + nullptr + }; + return names; +} + +inline const char *EnumNamePaddingMode(PaddingMode e) { + if (flatbuffers::IsOutRange(e, PaddingMode_CONSTANT, PaddingMode_MODE_RESERVED)) return ""; + const size_t index = static_cast(e); + return EnumNamesPaddingMode()[index]; +} + +enum LshProjectionType : int8_t { + LshProjectionType_UNKNOWN = 0, + LshProjectionType_SPARSE = 1, + LshProjectionType_DENSE = 2, + LshProjectionType_MIN = LshProjectionType_UNKNOWN, + LshProjectionType_MAX = LshProjectionType_DENSE +}; + +inline const LshProjectionType (&EnumValuesLshProjectionType())[3] { + static const LshProjectionType values[] = { + LshProjectionType_UNKNOWN, + LshProjectionType_SPARSE, + LshProjectionType_DENSE + }; + return values; +} + +inline const char * const *EnumNamesLshProjectionType() { + static const char * const names[4] = { + "UNKNOWN", + "SPARSE", + "DENSE", + nullptr + }; + return names; +} + +inline const char *EnumNameLshProjectionType(LshProjectionType e) { + if (flatbuffers::IsOutRange(e, LshProjectionType_UNKNOWN, LshProjectionType_DENSE)) return ""; + const size_t index = static_cast(e); + return EnumNamesLshProjectionType()[index]; +} + +enum Reduction : int8_t { + Reduction_REDUCTION_SUM = 0, + Reduction_MEAN = 1, + Reduction_NONE = 2, + Reduction_MIN = Reduction_REDUCTION_SUM, + Reduction_MAX = Reduction_NONE +}; + +inline const Reduction (&EnumValuesReduction())[3] { + static const Reduction values[] = { + Reduction_REDUCTION_SUM, + Reduction_MEAN, + Reduction_NONE + }; + return values; +} + +inline const char * const *EnumNamesReduction() { + static const char * const names[4] = { + "REDUCTION_SUM", + "MEAN", + "NONE", + nullptr + }; + return names; +} + +inline const char *EnumNameReduction(Reduction e) { + if (flatbuffers::IsOutRange(e, Reduction_REDUCTION_SUM, Reduction_NONE)) return ""; + const size_t index = static_cast(e); + return EnumNamesReduction()[index]; +} + +struct Vec FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef VecBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DATA = 4 + }; + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + verifier.EndTable(); + } +}; + +struct VecBuilder { + typedef Vec Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(Vec::VT_DATA, data); + } + explicit VecBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateVec( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> data = 0) { + VecBuilder builder_(_fbb); + builder_.add_data(data); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateVecDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *data = nullptr) { + auto data__ = data ? _fbb.CreateVector(*data) : 0; + return mindspore::schema::CreateVec( + _fbb, + data__); +} + +struct Vec2D FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Vec2DBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DATA = 4 + }; + const flatbuffers::Vector> *data() const { + return GetPointer> *>(VT_DATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + verifier.VerifyVectorOfTables(data()) && + verifier.EndTable(); + } +}; + +struct Vec2DBuilder { + typedef Vec2D Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_data(flatbuffers::Offset>> data) { + fbb_.AddOffset(Vec2D::VT_DATA, data); + } + explicit Vec2DBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateVec2D( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset>> data = 0) { + Vec2DBuilder builder_(_fbb); + builder_.add_data(data); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateVec2DDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector> *data = nullptr) { + auto data__ = data ? _fbb.CreateVector>(*data) : 0; + return mindspore::schema::CreateVec2D( + _fbb, + data__); +} + +struct Attribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AttributeBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_DATA = 6 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + verifier.EndTable(); + } +}; + +struct AttributeBuilder { + typedef Attribute Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Attribute::VT_NAME, name); + } + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(Attribute::VT_DATA, data); + } + explicit AttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAttribute( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset> data = 0) { + AttributeBuilder builder_(_fbb); + builder_.add_data(data); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateAttributeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + const std::vector *data = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto data__ = data ? _fbb.CreateVector(*data) : 0; + return mindspore::schema::CreateAttribute( + _fbb, + name__, + data__); +} + +} // namespace schema +} // namespace mindspore + +#endif // FLATBUFFERS_GENERATED_OPSTYPES_MINDSPORE_SCHEMA_H_ diff --git a/mindspore/lite/schema/schema_generated.h b/mindspore/lite/schema/schema_generated.h new file mode 100644 index 00000000..5eb6a7ee --- /dev/null +++ b/mindspore/lite/schema/schema_generated.h @@ -0,0 +1,18994 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ +#define FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ + +#include "flatbuffers/flatbuffers.h" + +namespace tflite { + +struct CustomQuantization; +struct CustomQuantizationBuilder; +struct CustomQuantizationT; + +struct QuantizationParameters; +struct QuantizationParametersBuilder; +struct QuantizationParametersT; + +struct Int32Vector; +struct Int32VectorBuilder; +struct Int32VectorT; + +struct Uint16Vector; +struct Uint16VectorBuilder; +struct Uint16VectorT; + +struct Uint8Vector; +struct Uint8VectorBuilder; +struct Uint8VectorT; + +struct DimensionMetadata; +struct DimensionMetadataBuilder; +struct DimensionMetadataT; + +struct SparsityParameters; +struct SparsityParametersBuilder; +struct SparsityParametersT; + +struct Tensor; +struct TensorBuilder; +struct TensorT; + +struct Conv2DOptions; +struct Conv2DOptionsBuilder; +struct Conv2DOptionsT; + +struct Pool2DOptions; +struct Pool2DOptionsBuilder; +struct Pool2DOptionsT; + +struct DepthwiseConv2DOptions; +struct DepthwiseConv2DOptionsBuilder; +struct DepthwiseConv2DOptionsT; + +struct ConcatEmbeddingsOptions; +struct ConcatEmbeddingsOptionsBuilder; +struct ConcatEmbeddingsOptionsT; + +struct LSHProjectionOptions; +struct LSHProjectionOptionsBuilder; +struct LSHProjectionOptionsT; + +struct SVDFOptions; +struct SVDFOptionsBuilder; +struct SVDFOptionsT; + +struct RNNOptions; +struct RNNOptionsBuilder; +struct RNNOptionsT; + +struct SequenceRNNOptions; +struct SequenceRNNOptionsBuilder; +struct SequenceRNNOptionsT; + +struct BidirectionalSequenceRNNOptions; +struct BidirectionalSequenceRNNOptionsBuilder; +struct BidirectionalSequenceRNNOptionsT; + +struct FullyConnectedOptions; +struct FullyConnectedOptionsBuilder; +struct FullyConnectedOptionsT; + +struct SoftmaxOptions; +struct SoftmaxOptionsBuilder; +struct SoftmaxOptionsT; + +struct ConcatenationOptions; +struct ConcatenationOptionsBuilder; +struct ConcatenationOptionsT; + +struct AddOptions; +struct AddOptionsBuilder; +struct AddOptionsT; + +struct MulOptions; +struct MulOptionsBuilder; +struct MulOptionsT; + +struct L2NormOptions; +struct L2NormOptionsBuilder; +struct L2NormOptionsT; + +struct LocalResponseNormalizationOptions; +struct LocalResponseNormalizationOptionsBuilder; +struct LocalResponseNormalizationOptionsT; + +struct LSTMOptions; +struct LSTMOptionsBuilder; +struct LSTMOptionsT; + +struct UnidirectionalSequenceLSTMOptions; +struct UnidirectionalSequenceLSTMOptionsBuilder; +struct UnidirectionalSequenceLSTMOptionsT; + +struct BidirectionalSequenceLSTMOptions; +struct BidirectionalSequenceLSTMOptionsBuilder; +struct BidirectionalSequenceLSTMOptionsT; + +struct ResizeBilinearOptions; +struct ResizeBilinearOptionsBuilder; +struct ResizeBilinearOptionsT; + +struct ResizeNearestNeighborOptions; +struct ResizeNearestNeighborOptionsBuilder; +struct ResizeNearestNeighborOptionsT; + +struct CallOptions; +struct CallOptionsBuilder; +struct CallOptionsT; + +struct PadOptions; +struct PadOptionsBuilder; +struct PadOptionsT; + +struct PadV2Options; +struct PadV2OptionsBuilder; +struct PadV2OptionsT; + +struct ReshapeOptions; +struct ReshapeOptionsBuilder; +struct ReshapeOptionsT; + +struct SpaceToBatchNDOptions; +struct SpaceToBatchNDOptionsBuilder; +struct SpaceToBatchNDOptionsT; + +struct BatchToSpaceNDOptions; +struct BatchToSpaceNDOptionsBuilder; +struct BatchToSpaceNDOptionsT; + +struct SkipGramOptions; +struct SkipGramOptionsBuilder; +struct SkipGramOptionsT; + +struct SpaceToDepthOptions; +struct SpaceToDepthOptionsBuilder; +struct SpaceToDepthOptionsT; + +struct DepthToSpaceOptions; +struct DepthToSpaceOptionsBuilder; +struct DepthToSpaceOptionsT; + +struct SubOptions; +struct SubOptionsBuilder; +struct SubOptionsT; + +struct DivOptions; +struct DivOptionsBuilder; +struct DivOptionsT; + +struct TopKV2Options; +struct TopKV2OptionsBuilder; +struct TopKV2OptionsT; + +struct EmbeddingLookupSparseOptions; +struct EmbeddingLookupSparseOptionsBuilder; +struct EmbeddingLookupSparseOptionsT; + +struct GatherOptions; +struct GatherOptionsBuilder; +struct GatherOptionsT; + +struct TransposeOptions; +struct TransposeOptionsBuilder; +struct TransposeOptionsT; + +struct ExpOptions; +struct ExpOptionsBuilder; +struct ExpOptionsT; + +struct CosOptions; +struct CosOptionsBuilder; +struct CosOptionsT; + +struct ReducerOptions; +struct ReducerOptionsBuilder; +struct ReducerOptionsT; + +struct SqueezeOptions; +struct SqueezeOptionsBuilder; +struct SqueezeOptionsT; + +struct SplitOptions; +struct SplitOptionsBuilder; +struct SplitOptionsT; + +struct SplitVOptions; +struct SplitVOptionsBuilder; +struct SplitVOptionsT; + +struct StridedSliceOptions; +struct StridedSliceOptionsBuilder; +struct StridedSliceOptionsT; + +struct LogSoftmaxOptions; +struct LogSoftmaxOptionsBuilder; +struct LogSoftmaxOptionsT; + +struct CastOptions; +struct CastOptionsBuilder; +struct CastOptionsT; + +struct DequantizeOptions; +struct DequantizeOptionsBuilder; +struct DequantizeOptionsT; + +struct MaximumMinimumOptions; +struct MaximumMinimumOptionsBuilder; +struct MaximumMinimumOptionsT; + +struct TileOptions; +struct TileOptionsBuilder; +struct TileOptionsT; + +struct ArgMaxOptions; +struct ArgMaxOptionsBuilder; +struct ArgMaxOptionsT; + +struct ArgMinOptions; +struct ArgMinOptionsBuilder; +struct ArgMinOptionsT; + +struct GreaterOptions; +struct GreaterOptionsBuilder; +struct GreaterOptionsT; + +struct GreaterEqualOptions; +struct GreaterEqualOptionsBuilder; +struct GreaterEqualOptionsT; + +struct LessOptions; +struct LessOptionsBuilder; +struct LessOptionsT; + +struct LessEqualOptions; +struct LessEqualOptionsBuilder; +struct LessEqualOptionsT; + +struct NegOptions; +struct NegOptionsBuilder; +struct NegOptionsT; + +struct SelectOptions; +struct SelectOptionsBuilder; +struct SelectOptionsT; + +struct SliceOptions; +struct SliceOptionsBuilder; +struct SliceOptionsT; + +struct TransposeConvOptions; +struct TransposeConvOptionsBuilder; +struct TransposeConvOptionsT; + +struct ExpandDimsOptions; +struct ExpandDimsOptionsBuilder; +struct ExpandDimsOptionsT; + +struct SparseToDenseOptions; +struct SparseToDenseOptionsBuilder; +struct SparseToDenseOptionsT; + +struct EqualOptions; +struct EqualOptionsBuilder; +struct EqualOptionsT; + +struct NotEqualOptions; +struct NotEqualOptionsBuilder; +struct NotEqualOptionsT; + +struct ShapeOptions; +struct ShapeOptionsBuilder; +struct ShapeOptionsT; + +struct RankOptions; +struct RankOptionsBuilder; +struct RankOptionsT; + +struct PowOptions; +struct PowOptionsBuilder; +struct PowOptionsT; + +struct FakeQuantOptions; +struct FakeQuantOptionsBuilder; +struct FakeQuantOptionsT; + +struct PackOptions; +struct PackOptionsBuilder; +struct PackOptionsT; + +struct LogicalOrOptions; +struct LogicalOrOptionsBuilder; +struct LogicalOrOptionsT; + +struct OneHotOptions; +struct OneHotOptionsBuilder; +struct OneHotOptionsT; + +struct AbsOptions; +struct AbsOptionsBuilder; +struct AbsOptionsT; + +struct HardSwishOptions; +struct HardSwishOptionsBuilder; +struct HardSwishOptionsT; + +struct LogicalAndOptions; +struct LogicalAndOptionsBuilder; +struct LogicalAndOptionsT; + +struct LogicalNotOptions; +struct LogicalNotOptionsBuilder; +struct LogicalNotOptionsT; + +struct UnpackOptions; +struct UnpackOptionsBuilder; +struct UnpackOptionsT; + +struct FloorDivOptions; +struct FloorDivOptionsBuilder; +struct FloorDivOptionsT; + +struct SquareOptions; +struct SquareOptionsBuilder; +struct SquareOptionsT; + +struct ZerosLikeOptions; +struct ZerosLikeOptionsBuilder; +struct ZerosLikeOptionsT; + +struct FillOptions; +struct FillOptionsBuilder; +struct FillOptionsT; + +struct FloorModOptions; +struct FloorModOptionsBuilder; +struct FloorModOptionsT; + +struct RangeOptions; +struct RangeOptionsBuilder; +struct RangeOptionsT; + +struct LeakyReluOptions; +struct LeakyReluOptionsBuilder; +struct LeakyReluOptionsT; + +struct SquaredDifferenceOptions; +struct SquaredDifferenceOptionsBuilder; +struct SquaredDifferenceOptionsT; + +struct MirrorPadOptions; +struct MirrorPadOptionsBuilder; +struct MirrorPadOptionsT; + +struct UniqueOptions; +struct UniqueOptionsBuilder; +struct UniqueOptionsT; + +struct ReverseV2Options; +struct ReverseV2OptionsBuilder; +struct ReverseV2OptionsT; + +struct AddNOptions; +struct AddNOptionsBuilder; +struct AddNOptionsT; + +struct GatherNdOptions; +struct GatherNdOptionsBuilder; +struct GatherNdOptionsT; + +struct WhereOptions; +struct WhereOptionsBuilder; +struct WhereOptionsT; + +struct ReverseSequenceOptions; +struct ReverseSequenceOptionsBuilder; +struct ReverseSequenceOptionsT; + +struct MatrixDiagOptions; +struct MatrixDiagOptionsBuilder; +struct MatrixDiagOptionsT; + +struct QuantizeOptions; +struct QuantizeOptionsBuilder; +struct QuantizeOptionsT; + +struct MatrixSetDiagOptions; +struct MatrixSetDiagOptionsBuilder; +struct MatrixSetDiagOptionsT; + +struct IfOptions; +struct IfOptionsBuilder; +struct IfOptionsT; + +struct WhileOptions; +struct WhileOptionsBuilder; +struct WhileOptionsT; + +struct NonMaxSuppressionV4Options; +struct NonMaxSuppressionV4OptionsBuilder; +struct NonMaxSuppressionV4OptionsT; + +struct NonMaxSuppressionV5Options; +struct NonMaxSuppressionV5OptionsBuilder; +struct NonMaxSuppressionV5OptionsT; + +struct ScatterNdOptions; +struct ScatterNdOptionsBuilder; +struct ScatterNdOptionsT; + +struct SelectV2Options; +struct SelectV2OptionsBuilder; +struct SelectV2OptionsT; + +struct DensifyOptions; +struct DensifyOptionsBuilder; +struct DensifyOptionsT; + +struct SegmentSumOptions; +struct SegmentSumOptionsBuilder; +struct SegmentSumOptionsT; + +struct BatchMatMulOptions; +struct BatchMatMulOptionsBuilder; +struct BatchMatMulOptionsT; + +struct OperatorCode; +struct OperatorCodeBuilder; +struct OperatorCodeT; + +struct Operator; +struct OperatorBuilder; +struct OperatorT; + +struct SubGraph; +struct SubGraphBuilder; +struct SubGraphT; + +struct Buffer; +struct BufferBuilder; +struct BufferT; + +struct Metadata; +struct MetadataBuilder; +struct MetadataT; + +struct Model; +struct ModelBuilder; +struct ModelT; + +inline const flatbuffers::TypeTable *CustomQuantizationTypeTable(); + +inline const flatbuffers::TypeTable *QuantizationParametersTypeTable(); + +inline const flatbuffers::TypeTable *Int32VectorTypeTable(); + +inline const flatbuffers::TypeTable *Uint16VectorTypeTable(); + +inline const flatbuffers::TypeTable *Uint8VectorTypeTable(); + +inline const flatbuffers::TypeTable *DimensionMetadataTypeTable(); + +inline const flatbuffers::TypeTable *SparsityParametersTypeTable(); + +inline const flatbuffers::TypeTable *TensorTypeTable(); + +inline const flatbuffers::TypeTable *Conv2DOptionsTypeTable(); + +inline const flatbuffers::TypeTable *Pool2DOptionsTypeTable(); + +inline const flatbuffers::TypeTable *DepthwiseConv2DOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ConcatEmbeddingsOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LSHProjectionOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SVDFOptionsTypeTable(); + +inline const flatbuffers::TypeTable *RNNOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SequenceRNNOptionsTypeTable(); + +inline const flatbuffers::TypeTable *BidirectionalSequenceRNNOptionsTypeTable(); + +inline const flatbuffers::TypeTable *FullyConnectedOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SoftmaxOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ConcatenationOptionsTypeTable(); + +inline const flatbuffers::TypeTable *AddOptionsTypeTable(); + +inline const flatbuffers::TypeTable *MulOptionsTypeTable(); + +inline const flatbuffers::TypeTable *L2NormOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LocalResponseNormalizationOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LSTMOptionsTypeTable(); + +inline const flatbuffers::TypeTable *UnidirectionalSequenceLSTMOptionsTypeTable(); + +inline const flatbuffers::TypeTable *BidirectionalSequenceLSTMOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ResizeBilinearOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ResizeNearestNeighborOptionsTypeTable(); + +inline const flatbuffers::TypeTable *CallOptionsTypeTable(); + +inline const flatbuffers::TypeTable *PadOptionsTypeTable(); + +inline const flatbuffers::TypeTable *PadV2OptionsTypeTable(); + +inline const flatbuffers::TypeTable *ReshapeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SpaceToBatchNDOptionsTypeTable(); + +inline const flatbuffers::TypeTable *BatchToSpaceNDOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SkipGramOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SpaceToDepthOptionsTypeTable(); + +inline const flatbuffers::TypeTable *DepthToSpaceOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SubOptionsTypeTable(); + +inline const flatbuffers::TypeTable *DivOptionsTypeTable(); + +inline const flatbuffers::TypeTable *TopKV2OptionsTypeTable(); + +inline const flatbuffers::TypeTable *EmbeddingLookupSparseOptionsTypeTable(); + +inline const flatbuffers::TypeTable *GatherOptionsTypeTable(); + +inline const flatbuffers::TypeTable *TransposeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ExpOptionsTypeTable(); + +inline const flatbuffers::TypeTable *CosOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ReducerOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SqueezeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SplitOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SplitVOptionsTypeTable(); + +inline const flatbuffers::TypeTable *StridedSliceOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LogSoftmaxOptionsTypeTable(); + +inline const flatbuffers::TypeTable *CastOptionsTypeTable(); + +inline const flatbuffers::TypeTable *DequantizeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *MaximumMinimumOptionsTypeTable(); + +inline const flatbuffers::TypeTable *TileOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ArgMaxOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ArgMinOptionsTypeTable(); + +inline const flatbuffers::TypeTable *GreaterOptionsTypeTable(); + +inline const flatbuffers::TypeTable *GreaterEqualOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LessOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LessEqualOptionsTypeTable(); + +inline const flatbuffers::TypeTable *NegOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SelectOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SliceOptionsTypeTable(); + +inline const flatbuffers::TypeTable *TransposeConvOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ExpandDimsOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SparseToDenseOptionsTypeTable(); + +inline const flatbuffers::TypeTable *EqualOptionsTypeTable(); + +inline const flatbuffers::TypeTable *NotEqualOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ShapeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *RankOptionsTypeTable(); + +inline const flatbuffers::TypeTable *PowOptionsTypeTable(); + +inline const flatbuffers::TypeTable *FakeQuantOptionsTypeTable(); + +inline const flatbuffers::TypeTable *PackOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LogicalOrOptionsTypeTable(); + +inline const flatbuffers::TypeTable *OneHotOptionsTypeTable(); + +inline const flatbuffers::TypeTable *AbsOptionsTypeTable(); + +inline const flatbuffers::TypeTable *HardSwishOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LogicalAndOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LogicalNotOptionsTypeTable(); + +inline const flatbuffers::TypeTable *UnpackOptionsTypeTable(); + +inline const flatbuffers::TypeTable *FloorDivOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SquareOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ZerosLikeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *FillOptionsTypeTable(); + +inline const flatbuffers::TypeTable *FloorModOptionsTypeTable(); + +inline const flatbuffers::TypeTable *RangeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *LeakyReluOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SquaredDifferenceOptionsTypeTable(); + +inline const flatbuffers::TypeTable *MirrorPadOptionsTypeTable(); + +inline const flatbuffers::TypeTable *UniqueOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ReverseV2OptionsTypeTable(); + +inline const flatbuffers::TypeTable *AddNOptionsTypeTable(); + +inline const flatbuffers::TypeTable *GatherNdOptionsTypeTable(); + +inline const flatbuffers::TypeTable *WhereOptionsTypeTable(); + +inline const flatbuffers::TypeTable *ReverseSequenceOptionsTypeTable(); + +inline const flatbuffers::TypeTable *MatrixDiagOptionsTypeTable(); + +inline const flatbuffers::TypeTable *QuantizeOptionsTypeTable(); + +inline const flatbuffers::TypeTable *MatrixSetDiagOptionsTypeTable(); + +inline const flatbuffers::TypeTable *IfOptionsTypeTable(); + +inline const flatbuffers::TypeTable *WhileOptionsTypeTable(); + +inline const flatbuffers::TypeTable *NonMaxSuppressionV4OptionsTypeTable(); + +inline const flatbuffers::TypeTable *NonMaxSuppressionV5OptionsTypeTable(); + +inline const flatbuffers::TypeTable *ScatterNdOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SelectV2OptionsTypeTable(); + +inline const flatbuffers::TypeTable *DensifyOptionsTypeTable(); + +inline const flatbuffers::TypeTable *SegmentSumOptionsTypeTable(); + +inline const flatbuffers::TypeTable *BatchMatMulOptionsTypeTable(); + +inline const flatbuffers::TypeTable *OperatorCodeTypeTable(); + +inline const flatbuffers::TypeTable *OperatorTypeTable(); + +inline const flatbuffers::TypeTable *SubGraphTypeTable(); + +inline const flatbuffers::TypeTable *BufferTypeTable(); + +inline const flatbuffers::TypeTable *MetadataTypeTable(); + +inline const flatbuffers::TypeTable *ModelTypeTable(); + +enum TensorType : int8_t { + TensorType_FLOAT32 = 0, + TensorType_FLOAT16 = 1, + TensorType_INT32 = 2, + TensorType_UINT8 = 3, + TensorType_INT64 = 4, + TensorType_STRING = 5, + TensorType_BOOL = 6, + TensorType_INT16 = 7, + TensorType_COMPLEX64 = 8, + TensorType_INT8 = 9, + TensorType_FLOAT64 = 10, + TensorType_MIN = TensorType_FLOAT32, + TensorType_MAX = TensorType_FLOAT64 +}; + +inline const TensorType (&EnumValuesTensorType())[11] { + static const TensorType values[] = { + TensorType_FLOAT32, + TensorType_FLOAT16, + TensorType_INT32, + TensorType_UINT8, + TensorType_INT64, + TensorType_STRING, + TensorType_BOOL, + TensorType_INT16, + TensorType_COMPLEX64, + TensorType_INT8, + TensorType_FLOAT64 + }; + return values; +} + +inline const char * const *EnumNamesTensorType() { + static const char * const names[12] = { + "FLOAT32", + "FLOAT16", + "INT32", + "UINT8", + "INT64", + "STRING", + "BOOL", + "INT16", + "COMPLEX64", + "INT8", + "FLOAT64", + nullptr + }; + return names; +} + +inline const char *EnumNameTensorType(TensorType e) { + if (flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_FLOAT64)) return ""; + const size_t index = static_cast(e); + return EnumNamesTensorType()[index]; +} + +enum QuantizationDetails : uint8_t { + QuantizationDetails_NONE = 0, + QuantizationDetails_CustomQuantization = 1, + QuantizationDetails_MIN = QuantizationDetails_NONE, + QuantizationDetails_MAX = QuantizationDetails_CustomQuantization +}; + +inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] { + static const QuantizationDetails values[] = { + QuantizationDetails_NONE, + QuantizationDetails_CustomQuantization + }; + return values; +} + +inline const char * const *EnumNamesQuantizationDetails() { + static const char * const names[3] = { + "NONE", + "CustomQuantization", + nullptr + }; + return names; +} + +inline const char *EnumNameQuantizationDetails(QuantizationDetails e) { + if (flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return ""; + const size_t index = static_cast(e); + return EnumNamesQuantizationDetails()[index]; +} + +template struct QuantizationDetailsTraits { + static const QuantizationDetails enum_value = QuantizationDetails_NONE; +}; + +template<> struct QuantizationDetailsTraits { + static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; +}; + +struct QuantizationDetailsUnion { + QuantizationDetails type; + void *value; + + QuantizationDetailsUnion() : type(QuantizationDetails_NONE), value(nullptr) {} + QuantizationDetailsUnion(QuantizationDetailsUnion&& u) FLATBUFFERS_NOEXCEPT : + type(QuantizationDetails_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + QuantizationDetailsUnion(const QuantizationDetailsUnion &); + QuantizationDetailsUnion &operator=(const QuantizationDetailsUnion &u) + { QuantizationDetailsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + QuantizationDetailsUnion &operator=(QuantizationDetailsUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~QuantizationDetailsUnion() { Reset(); } + + void Reset(); + +#ifndef FLATBUFFERS_CPP98_STL + template + void Set(T&& val) { + using RT = typename std::remove_reference::type; + Reset(); + type = QuantizationDetailsTraits::enum_value; + if (type != QuantizationDetails_NONE) { + value = new RT(std::forward(val)); + } + } +#endif // FLATBUFFERS_CPP98_STL + + static void *UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite::CustomQuantizationT *AsCustomQuantization() { + return type == QuantizationDetails_CustomQuantization ? + reinterpret_cast(value) : nullptr; + } + const tflite::CustomQuantizationT *AsCustomQuantization() const { + return type == QuantizationDetails_CustomQuantization ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type); +bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum DimensionType : int8_t { + DimensionType_DENSE = 0, + DimensionType_SPARSE_CSR = 1, + DimensionType_MIN = DimensionType_DENSE, + DimensionType_MAX = DimensionType_SPARSE_CSR +}; + +inline const DimensionType (&EnumValuesDimensionType())[2] { + static const DimensionType values[] = { + DimensionType_DENSE, + DimensionType_SPARSE_CSR + }; + return values; +} + +inline const char * const *EnumNamesDimensionType() { + static const char * const names[3] = { + "DENSE", + "SPARSE_CSR", + nullptr + }; + return names; +} + +inline const char *EnumNameDimensionType(DimensionType e) { + if (flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return ""; + const size_t index = static_cast(e); + return EnumNamesDimensionType()[index]; +} + +enum SparseIndexVector : uint8_t { + SparseIndexVector_NONE = 0, + SparseIndexVector_Int32Vector = 1, + SparseIndexVector_Uint16Vector = 2, + SparseIndexVector_Uint8Vector = 3, + SparseIndexVector_MIN = SparseIndexVector_NONE, + SparseIndexVector_MAX = SparseIndexVector_Uint8Vector +}; + +inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] { + static const SparseIndexVector values[] = { + SparseIndexVector_NONE, + SparseIndexVector_Int32Vector, + SparseIndexVector_Uint16Vector, + SparseIndexVector_Uint8Vector + }; + return values; +} + +inline const char * const *EnumNamesSparseIndexVector() { + static const char * const names[5] = { + "NONE", + "Int32Vector", + "Uint16Vector", + "Uint8Vector", + nullptr + }; + return names; +} + +inline const char *EnumNameSparseIndexVector(SparseIndexVector e) { + if (flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return ""; + const size_t index = static_cast(e); + return EnumNamesSparseIndexVector()[index]; +} + +template struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_NONE; +}; + +template<> struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; +}; + +template<> struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; +}; + +template<> struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; +}; + +struct SparseIndexVectorUnion { + SparseIndexVector type; + void *value; + + SparseIndexVectorUnion() : type(SparseIndexVector_NONE), value(nullptr) {} + SparseIndexVectorUnion(SparseIndexVectorUnion&& u) FLATBUFFERS_NOEXCEPT : + type(SparseIndexVector_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + SparseIndexVectorUnion(const SparseIndexVectorUnion &); + SparseIndexVectorUnion &operator=(const SparseIndexVectorUnion &u) + { SparseIndexVectorUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + SparseIndexVectorUnion &operator=(SparseIndexVectorUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~SparseIndexVectorUnion() { Reset(); } + + void Reset(); + +#ifndef FLATBUFFERS_CPP98_STL + template + void Set(T&& val) { + using RT = typename std::remove_reference::type; + Reset(); + type = SparseIndexVectorTraits::enum_value; + if (type != SparseIndexVector_NONE) { + value = new RT(std::forward(val)); + } + } +#endif // FLATBUFFERS_CPP98_STL + + static void *UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite::Int32VectorT *AsInt32Vector() { + return type == SparseIndexVector_Int32Vector ? + reinterpret_cast(value) : nullptr; + } + const tflite::Int32VectorT *AsInt32Vector() const { + return type == SparseIndexVector_Int32Vector ? + reinterpret_cast(value) : nullptr; + } + tflite::Uint16VectorT *AsUint16Vector() { + return type == SparseIndexVector_Uint16Vector ? + reinterpret_cast(value) : nullptr; + } + const tflite::Uint16VectorT *AsUint16Vector() const { + return type == SparseIndexVector_Uint16Vector ? + reinterpret_cast(value) : nullptr; + } + tflite::Uint8VectorT *AsUint8Vector() { + return type == SparseIndexVector_Uint8Vector ? + reinterpret_cast(value) : nullptr; + } + const tflite::Uint8VectorT *AsUint8Vector() const { + return type == SparseIndexVector_Uint8Vector ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type); +bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum BuiltinOperator : int8_t { + BuiltinOperator_ADD = 0, + BuiltinOperator_AVERAGE_POOL_2D = 1, + BuiltinOperator_CONCATENATION = 2, + BuiltinOperator_CONV_2D = 3, + BuiltinOperator_DEPTHWISE_CONV_2D = 4, + BuiltinOperator_DEPTH_TO_SPACE = 5, + BuiltinOperator_DEQUANTIZE = 6, + BuiltinOperator_EMBEDDING_LOOKUP = 7, + BuiltinOperator_FLOOR = 8, + BuiltinOperator_FULLY_CONNECTED = 9, + BuiltinOperator_HASHTABLE_LOOKUP = 10, + BuiltinOperator_L2_NORMALIZATION = 11, + BuiltinOperator_L2_POOL_2D = 12, + BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, + BuiltinOperator_LOGISTIC = 14, + BuiltinOperator_LSH_PROJECTION = 15, + BuiltinOperator_LSTM = 16, + BuiltinOperator_MAX_POOL_2D = 17, + BuiltinOperator_MUL = 18, + BuiltinOperator_RELU = 19, + BuiltinOperator_RELU_N1_TO_1 = 20, + BuiltinOperator_RELU6 = 21, + BuiltinOperator_RESHAPE = 22, + BuiltinOperator_RESIZE_BILINEAR = 23, + BuiltinOperator_RNN = 24, + BuiltinOperator_SOFTMAX = 25, + BuiltinOperator_SPACE_TO_DEPTH = 26, + BuiltinOperator_SVDF = 27, + BuiltinOperator_TANH = 28, + BuiltinOperator_CONCAT_EMBEDDINGS = 29, + BuiltinOperator_SKIP_GRAM = 30, + BuiltinOperator_CALL = 31, + BuiltinOperator_CUSTOM = 32, + BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, + BuiltinOperator_PAD = 34, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, + BuiltinOperator_GATHER = 36, + BuiltinOperator_BATCH_TO_SPACE_ND = 37, + BuiltinOperator_SPACE_TO_BATCH_ND = 38, + BuiltinOperator_TRANSPOSE = 39, + BuiltinOperator_MEAN = 40, + BuiltinOperator_SUB = 41, + BuiltinOperator_DIV = 42, + BuiltinOperator_SQUEEZE = 43, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, + BuiltinOperator_STRIDED_SLICE = 45, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, + BuiltinOperator_EXP = 47, + BuiltinOperator_TOPK_V2 = 48, + BuiltinOperator_SPLIT = 49, + BuiltinOperator_LOG_SOFTMAX = 50, + BuiltinOperator_DELEGATE = 51, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, + BuiltinOperator_CAST = 53, + BuiltinOperator_PRELU = 54, + BuiltinOperator_MAXIMUM = 55, + BuiltinOperator_ARG_MAX = 56, + BuiltinOperator_MINIMUM = 57, + BuiltinOperator_LESS = 58, + BuiltinOperator_NEG = 59, + BuiltinOperator_PADV2 = 60, + BuiltinOperator_GREATER = 61, + BuiltinOperator_GREATER_EQUAL = 62, + BuiltinOperator_LESS_EQUAL = 63, + BuiltinOperator_SELECT = 64, + BuiltinOperator_SLICE = 65, + BuiltinOperator_SIN = 66, + BuiltinOperator_TRANSPOSE_CONV = 67, + BuiltinOperator_SPARSE_TO_DENSE = 68, + BuiltinOperator_TILE = 69, + BuiltinOperator_EXPAND_DIMS = 70, + BuiltinOperator_EQUAL = 71, + BuiltinOperator_NOT_EQUAL = 72, + BuiltinOperator_LOG = 73, + BuiltinOperator_SUM = 74, + BuiltinOperator_SQRT = 75, + BuiltinOperator_RSQRT = 76, + BuiltinOperator_SHAPE = 77, + BuiltinOperator_POW = 78, + BuiltinOperator_ARG_MIN = 79, + BuiltinOperator_FAKE_QUANT = 80, + BuiltinOperator_REDUCE_PROD = 81, + BuiltinOperator_REDUCE_MAX = 82, + BuiltinOperator_PACK = 83, + BuiltinOperator_LOGICAL_OR = 84, + BuiltinOperator_ONE_HOT = 85, + BuiltinOperator_LOGICAL_AND = 86, + BuiltinOperator_LOGICAL_NOT = 87, + BuiltinOperator_UNPACK = 88, + BuiltinOperator_REDUCE_MIN = 89, + BuiltinOperator_FLOOR_DIV = 90, + BuiltinOperator_REDUCE_ANY = 91, + BuiltinOperator_SQUARE = 92, + BuiltinOperator_ZEROS_LIKE = 93, + BuiltinOperator_FILL = 94, + BuiltinOperator_FLOOR_MOD = 95, + BuiltinOperator_RANGE = 96, + BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, + BuiltinOperator_LEAKY_RELU = 98, + BuiltinOperator_SQUARED_DIFFERENCE = 99, + BuiltinOperator_MIRROR_PAD = 100, + BuiltinOperator_ABS = 101, + BuiltinOperator_SPLIT_V = 102, + BuiltinOperator_UNIQUE = 103, + BuiltinOperator_CEIL = 104, + BuiltinOperator_REVERSE_V2 = 105, + BuiltinOperator_ADD_N = 106, + BuiltinOperator_GATHER_ND = 107, + BuiltinOperator_COS = 108, + BuiltinOperator_WHERE = 109, + BuiltinOperator_RANK = 110, + BuiltinOperator_ELU = 111, + BuiltinOperator_REVERSE_SEQUENCE = 112, + BuiltinOperator_MATRIX_DIAG = 113, + BuiltinOperator_QUANTIZE = 114, + BuiltinOperator_MATRIX_SET_DIAG = 115, + BuiltinOperator_ROUND = 116, + BuiltinOperator_HARD_SWISH = 117, + BuiltinOperator_IF = 118, + BuiltinOperator_WHILE = 119, + BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, + BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, + BuiltinOperator_SCATTER_ND = 122, + BuiltinOperator_SELECT_V2 = 123, + BuiltinOperator_DENSIFY = 124, + BuiltinOperator_SEGMENT_SUM = 125, + BuiltinOperator_BATCH_MATMUL = 126, + BuiltinOperator_MIN = BuiltinOperator_ADD, + BuiltinOperator_MAX = BuiltinOperator_BATCH_MATMUL +}; + +inline const BuiltinOperator (&EnumValuesBuiltinOperator())[127] { + static const BuiltinOperator values[] = { + BuiltinOperator_ADD, + BuiltinOperator_AVERAGE_POOL_2D, + BuiltinOperator_CONCATENATION, + BuiltinOperator_CONV_2D, + BuiltinOperator_DEPTHWISE_CONV_2D, + BuiltinOperator_DEPTH_TO_SPACE, + BuiltinOperator_DEQUANTIZE, + BuiltinOperator_EMBEDDING_LOOKUP, + BuiltinOperator_FLOOR, + BuiltinOperator_FULLY_CONNECTED, + BuiltinOperator_HASHTABLE_LOOKUP, + BuiltinOperator_L2_NORMALIZATION, + BuiltinOperator_L2_POOL_2D, + BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, + BuiltinOperator_LOGISTIC, + BuiltinOperator_LSH_PROJECTION, + BuiltinOperator_LSTM, + BuiltinOperator_MAX_POOL_2D, + BuiltinOperator_MUL, + BuiltinOperator_RELU, + BuiltinOperator_RELU_N1_TO_1, + BuiltinOperator_RELU6, + BuiltinOperator_RESHAPE, + BuiltinOperator_RESIZE_BILINEAR, + BuiltinOperator_RNN, + BuiltinOperator_SOFTMAX, + BuiltinOperator_SPACE_TO_DEPTH, + BuiltinOperator_SVDF, + BuiltinOperator_TANH, + BuiltinOperator_CONCAT_EMBEDDINGS, + BuiltinOperator_SKIP_GRAM, + BuiltinOperator_CALL, + BuiltinOperator_CUSTOM, + BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, + BuiltinOperator_PAD, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOperator_GATHER, + BuiltinOperator_BATCH_TO_SPACE_ND, + BuiltinOperator_SPACE_TO_BATCH_ND, + BuiltinOperator_TRANSPOSE, + BuiltinOperator_MEAN, + BuiltinOperator_SUB, + BuiltinOperator_DIV, + BuiltinOperator_SQUEEZE, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_STRIDED_SLICE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOperator_EXP, + BuiltinOperator_TOPK_V2, + BuiltinOperator_SPLIT, + BuiltinOperator_LOG_SOFTMAX, + BuiltinOperator_DELEGATE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_CAST, + BuiltinOperator_PRELU, + BuiltinOperator_MAXIMUM, + BuiltinOperator_ARG_MAX, + BuiltinOperator_MINIMUM, + BuiltinOperator_LESS, + BuiltinOperator_NEG, + BuiltinOperator_PADV2, + BuiltinOperator_GREATER, + BuiltinOperator_GREATER_EQUAL, + BuiltinOperator_LESS_EQUAL, + BuiltinOperator_SELECT, + BuiltinOperator_SLICE, + BuiltinOperator_SIN, + BuiltinOperator_TRANSPOSE_CONV, + BuiltinOperator_SPARSE_TO_DENSE, + BuiltinOperator_TILE, + BuiltinOperator_EXPAND_DIMS, + BuiltinOperator_EQUAL, + BuiltinOperator_NOT_EQUAL, + BuiltinOperator_LOG, + BuiltinOperator_SUM, + BuiltinOperator_SQRT, + BuiltinOperator_RSQRT, + BuiltinOperator_SHAPE, + BuiltinOperator_POW, + BuiltinOperator_ARG_MIN, + BuiltinOperator_FAKE_QUANT, + BuiltinOperator_REDUCE_PROD, + BuiltinOperator_REDUCE_MAX, + BuiltinOperator_PACK, + BuiltinOperator_LOGICAL_OR, + BuiltinOperator_ONE_HOT, + BuiltinOperator_LOGICAL_AND, + BuiltinOperator_LOGICAL_NOT, + BuiltinOperator_UNPACK, + BuiltinOperator_REDUCE_MIN, + BuiltinOperator_FLOOR_DIV, + BuiltinOperator_REDUCE_ANY, + BuiltinOperator_SQUARE, + BuiltinOperator_ZEROS_LIKE, + BuiltinOperator_FILL, + BuiltinOperator_FLOOR_MOD, + BuiltinOperator_RANGE, + BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, + BuiltinOperator_LEAKY_RELU, + BuiltinOperator_SQUARED_DIFFERENCE, + BuiltinOperator_MIRROR_PAD, + BuiltinOperator_ABS, + BuiltinOperator_SPLIT_V, + BuiltinOperator_UNIQUE, + BuiltinOperator_CEIL, + BuiltinOperator_REVERSE_V2, + BuiltinOperator_ADD_N, + BuiltinOperator_GATHER_ND, + BuiltinOperator_COS, + BuiltinOperator_WHERE, + BuiltinOperator_RANK, + BuiltinOperator_ELU, + BuiltinOperator_REVERSE_SEQUENCE, + BuiltinOperator_MATRIX_DIAG, + BuiltinOperator_QUANTIZE, + BuiltinOperator_MATRIX_SET_DIAG, + BuiltinOperator_ROUND, + BuiltinOperator_HARD_SWISH, + BuiltinOperator_IF, + BuiltinOperator_WHILE, + BuiltinOperator_NON_MAX_SUPPRESSION_V4, + BuiltinOperator_NON_MAX_SUPPRESSION_V5, + BuiltinOperator_SCATTER_ND, + BuiltinOperator_SELECT_V2, + BuiltinOperator_DENSIFY, + BuiltinOperator_SEGMENT_SUM, + BuiltinOperator_BATCH_MATMUL + }; + return values; +} + +inline const char * const *EnumNamesBuiltinOperator() { + static const char * const names[128] = { + "ADD", + "AVERAGE_POOL_2D", + "CONCATENATION", + "CONV_2D", + "DEPTHWISE_CONV_2D", + "DEPTH_TO_SPACE", + "DEQUANTIZE", + "EMBEDDING_LOOKUP", + "FLOOR", + "FULLY_CONNECTED", + "HASHTABLE_LOOKUP", + "L2_NORMALIZATION", + "L2_POOL_2D", + "LOCAL_RESPONSE_NORMALIZATION", + "LOGISTIC", + "LSH_PROJECTION", + "LSTM", + "MAX_POOL_2D", + "MUL", + "RELU", + "RELU_N1_TO_1", + "RELU6", + "RESHAPE", + "RESIZE_BILINEAR", + "RNN", + "SOFTMAX", + "SPACE_TO_DEPTH", + "SVDF", + "TANH", + "CONCAT_EMBEDDINGS", + "SKIP_GRAM", + "CALL", + "CUSTOM", + "EMBEDDING_LOOKUP_SPARSE", + "PAD", + "UNIDIRECTIONAL_SEQUENCE_RNN", + "GATHER", + "BATCH_TO_SPACE_ND", + "SPACE_TO_BATCH_ND", + "TRANSPOSE", + "MEAN", + "SUB", + "DIV", + "SQUEEZE", + "UNIDIRECTIONAL_SEQUENCE_LSTM", + "STRIDED_SLICE", + "BIDIRECTIONAL_SEQUENCE_RNN", + "EXP", + "TOPK_V2", + "SPLIT", + "LOG_SOFTMAX", + "DELEGATE", + "BIDIRECTIONAL_SEQUENCE_LSTM", + "CAST", + "PRELU", + "MAXIMUM", + "ARG_MAX", + "MINIMUM", + "LESS", + "NEG", + "PADV2", + "GREATER", + "GREATER_EQUAL", + "LESS_EQUAL", + "SELECT", + "SLICE", + "SIN", + "TRANSPOSE_CONV", + "SPARSE_TO_DENSE", + "TILE", + "EXPAND_DIMS", + "EQUAL", + "NOT_EQUAL", + "LOG", + "SUM", + "SQRT", + "RSQRT", + "SHAPE", + "POW", + "ARG_MIN", + "FAKE_QUANT", + "REDUCE_PROD", + "REDUCE_MAX", + "PACK", + "LOGICAL_OR", + "ONE_HOT", + "LOGICAL_AND", + "LOGICAL_NOT", + "UNPACK", + "REDUCE_MIN", + "FLOOR_DIV", + "REDUCE_ANY", + "SQUARE", + "ZEROS_LIKE", + "FILL", + "FLOOR_MOD", + "RANGE", + "RESIZE_NEAREST_NEIGHBOR", + "LEAKY_RELU", + "SQUARED_DIFFERENCE", + "MIRROR_PAD", + "ABS", + "SPLIT_V", + "UNIQUE", + "CEIL", + "REVERSE_V2", + "ADD_N", + "GATHER_ND", + "COS", + "WHERE", + "RANK", + "ELU", + "REVERSE_SEQUENCE", + "MATRIX_DIAG", + "QUANTIZE", + "MATRIX_SET_DIAG", + "ROUND", + "HARD_SWISH", + "IF", + "WHILE", + "NON_MAX_SUPPRESSION_V4", + "NON_MAX_SUPPRESSION_V5", + "SCATTER_ND", + "SELECT_V2", + "DENSIFY", + "SEGMENT_SUM", + "BATCH_MATMUL", + nullptr + }; + return names; +} + +inline const char *EnumNameBuiltinOperator(BuiltinOperator e) { + if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_BATCH_MATMUL)) return ""; + const size_t index = static_cast(e); + return EnumNamesBuiltinOperator()[index]; +} + +enum BuiltinOptions : uint8_t { + BuiltinOptions_NONE = 0, + BuiltinOptions_Conv2DOptions = 1, + BuiltinOptions_DepthwiseConv2DOptions = 2, + BuiltinOptions_ConcatEmbeddingsOptions = 3, + BuiltinOptions_LSHProjectionOptions = 4, + BuiltinOptions_Pool2DOptions = 5, + BuiltinOptions_SVDFOptions = 6, + BuiltinOptions_RNNOptions = 7, + BuiltinOptions_FullyConnectedOptions = 8, + BuiltinOptions_SoftmaxOptions = 9, + BuiltinOptions_ConcatenationOptions = 10, + BuiltinOptions_AddOptions = 11, + BuiltinOptions_L2NormOptions = 12, + BuiltinOptions_LocalResponseNormalizationOptions = 13, + BuiltinOptions_LSTMOptions = 14, + BuiltinOptions_ResizeBilinearOptions = 15, + BuiltinOptions_CallOptions = 16, + BuiltinOptions_ReshapeOptions = 17, + BuiltinOptions_SkipGramOptions = 18, + BuiltinOptions_SpaceToDepthOptions = 19, + BuiltinOptions_EmbeddingLookupSparseOptions = 20, + BuiltinOptions_MulOptions = 21, + BuiltinOptions_PadOptions = 22, + BuiltinOptions_GatherOptions = 23, + BuiltinOptions_BatchToSpaceNDOptions = 24, + BuiltinOptions_SpaceToBatchNDOptions = 25, + BuiltinOptions_TransposeOptions = 26, + BuiltinOptions_ReducerOptions = 27, + BuiltinOptions_SubOptions = 28, + BuiltinOptions_DivOptions = 29, + BuiltinOptions_SqueezeOptions = 30, + BuiltinOptions_SequenceRNNOptions = 31, + BuiltinOptions_StridedSliceOptions = 32, + BuiltinOptions_ExpOptions = 33, + BuiltinOptions_TopKV2Options = 34, + BuiltinOptions_SplitOptions = 35, + BuiltinOptions_LogSoftmaxOptions = 36, + BuiltinOptions_CastOptions = 37, + BuiltinOptions_DequantizeOptions = 38, + BuiltinOptions_MaximumMinimumOptions = 39, + BuiltinOptions_ArgMaxOptions = 40, + BuiltinOptions_LessOptions = 41, + BuiltinOptions_NegOptions = 42, + BuiltinOptions_PadV2Options = 43, + BuiltinOptions_GreaterOptions = 44, + BuiltinOptions_GreaterEqualOptions = 45, + BuiltinOptions_LessEqualOptions = 46, + BuiltinOptions_SelectOptions = 47, + BuiltinOptions_SliceOptions = 48, + BuiltinOptions_TransposeConvOptions = 49, + BuiltinOptions_SparseToDenseOptions = 50, + BuiltinOptions_TileOptions = 51, + BuiltinOptions_ExpandDimsOptions = 52, + BuiltinOptions_EqualOptions = 53, + BuiltinOptions_NotEqualOptions = 54, + BuiltinOptions_ShapeOptions = 55, + BuiltinOptions_PowOptions = 56, + BuiltinOptions_ArgMinOptions = 57, + BuiltinOptions_FakeQuantOptions = 58, + BuiltinOptions_PackOptions = 59, + BuiltinOptions_LogicalOrOptions = 60, + BuiltinOptions_OneHotOptions = 61, + BuiltinOptions_LogicalAndOptions = 62, + BuiltinOptions_LogicalNotOptions = 63, + BuiltinOptions_UnpackOptions = 64, + BuiltinOptions_FloorDivOptions = 65, + BuiltinOptions_SquareOptions = 66, + BuiltinOptions_ZerosLikeOptions = 67, + BuiltinOptions_FillOptions = 68, + BuiltinOptions_BidirectionalSequenceLSTMOptions = 69, + BuiltinOptions_BidirectionalSequenceRNNOptions = 70, + BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71, + BuiltinOptions_FloorModOptions = 72, + BuiltinOptions_RangeOptions = 73, + BuiltinOptions_ResizeNearestNeighborOptions = 74, + BuiltinOptions_LeakyReluOptions = 75, + BuiltinOptions_SquaredDifferenceOptions = 76, + BuiltinOptions_MirrorPadOptions = 77, + BuiltinOptions_AbsOptions = 78, + BuiltinOptions_SplitVOptions = 79, + BuiltinOptions_UniqueOptions = 80, + BuiltinOptions_ReverseV2Options = 81, + BuiltinOptions_AddNOptions = 82, + BuiltinOptions_GatherNdOptions = 83, + BuiltinOptions_CosOptions = 84, + BuiltinOptions_WhereOptions = 85, + BuiltinOptions_RankOptions = 86, + BuiltinOptions_ReverseSequenceOptions = 87, + BuiltinOptions_MatrixDiagOptions = 88, + BuiltinOptions_QuantizeOptions = 89, + BuiltinOptions_MatrixSetDiagOptions = 90, + BuiltinOptions_HardSwishOptions = 91, + BuiltinOptions_IfOptions = 92, + BuiltinOptions_WhileOptions = 93, + BuiltinOptions_DepthToSpaceOptions = 94, + BuiltinOptions_NonMaxSuppressionV4Options = 95, + BuiltinOptions_NonMaxSuppressionV5Options = 96, + BuiltinOptions_ScatterNdOptions = 97, + BuiltinOptions_SelectV2Options = 98, + BuiltinOptions_DensifyOptions = 99, + BuiltinOptions_SegmentSumOptions = 100, + BuiltinOptions_BatchMatMulOptions = 101, + BuiltinOptions_MIN = BuiltinOptions_NONE, + BuiltinOptions_MAX = BuiltinOptions_BatchMatMulOptions +}; + +inline const BuiltinOptions (&EnumValuesBuiltinOptions())[102] { + static const BuiltinOptions values[] = { + BuiltinOptions_NONE, + BuiltinOptions_Conv2DOptions, + BuiltinOptions_DepthwiseConv2DOptions, + BuiltinOptions_ConcatEmbeddingsOptions, + BuiltinOptions_LSHProjectionOptions, + BuiltinOptions_Pool2DOptions, + BuiltinOptions_SVDFOptions, + BuiltinOptions_RNNOptions, + BuiltinOptions_FullyConnectedOptions, + BuiltinOptions_SoftmaxOptions, + BuiltinOptions_ConcatenationOptions, + BuiltinOptions_AddOptions, + BuiltinOptions_L2NormOptions, + BuiltinOptions_LocalResponseNormalizationOptions, + BuiltinOptions_LSTMOptions, + BuiltinOptions_ResizeBilinearOptions, + BuiltinOptions_CallOptions, + BuiltinOptions_ReshapeOptions, + BuiltinOptions_SkipGramOptions, + BuiltinOptions_SpaceToDepthOptions, + BuiltinOptions_EmbeddingLookupSparseOptions, + BuiltinOptions_MulOptions, + BuiltinOptions_PadOptions, + BuiltinOptions_GatherOptions, + BuiltinOptions_BatchToSpaceNDOptions, + BuiltinOptions_SpaceToBatchNDOptions, + BuiltinOptions_TransposeOptions, + BuiltinOptions_ReducerOptions, + BuiltinOptions_SubOptions, + BuiltinOptions_DivOptions, + BuiltinOptions_SqueezeOptions, + BuiltinOptions_SequenceRNNOptions, + BuiltinOptions_StridedSliceOptions, + BuiltinOptions_ExpOptions, + BuiltinOptions_TopKV2Options, + BuiltinOptions_SplitOptions, + BuiltinOptions_LogSoftmaxOptions, + BuiltinOptions_CastOptions, + BuiltinOptions_DequantizeOptions, + BuiltinOptions_MaximumMinimumOptions, + BuiltinOptions_ArgMaxOptions, + BuiltinOptions_LessOptions, + BuiltinOptions_NegOptions, + BuiltinOptions_PadV2Options, + BuiltinOptions_GreaterOptions, + BuiltinOptions_GreaterEqualOptions, + BuiltinOptions_LessEqualOptions, + BuiltinOptions_SelectOptions, + BuiltinOptions_SliceOptions, + BuiltinOptions_TransposeConvOptions, + BuiltinOptions_SparseToDenseOptions, + BuiltinOptions_TileOptions, + BuiltinOptions_ExpandDimsOptions, + BuiltinOptions_EqualOptions, + BuiltinOptions_NotEqualOptions, + BuiltinOptions_ShapeOptions, + BuiltinOptions_PowOptions, + BuiltinOptions_ArgMinOptions, + BuiltinOptions_FakeQuantOptions, + BuiltinOptions_PackOptions, + BuiltinOptions_LogicalOrOptions, + BuiltinOptions_OneHotOptions, + BuiltinOptions_LogicalAndOptions, + BuiltinOptions_LogicalNotOptions, + BuiltinOptions_UnpackOptions, + BuiltinOptions_FloorDivOptions, + BuiltinOptions_SquareOptions, + BuiltinOptions_ZerosLikeOptions, + BuiltinOptions_FillOptions, + BuiltinOptions_BidirectionalSequenceLSTMOptions, + BuiltinOptions_BidirectionalSequenceRNNOptions, + BuiltinOptions_UnidirectionalSequenceLSTMOptions, + BuiltinOptions_FloorModOptions, + BuiltinOptions_RangeOptions, + BuiltinOptions_ResizeNearestNeighborOptions, + BuiltinOptions_LeakyReluOptions, + BuiltinOptions_SquaredDifferenceOptions, + BuiltinOptions_MirrorPadOptions, + BuiltinOptions_AbsOptions, + BuiltinOptions_SplitVOptions, + BuiltinOptions_UniqueOptions, + BuiltinOptions_ReverseV2Options, + BuiltinOptions_AddNOptions, + BuiltinOptions_GatherNdOptions, + BuiltinOptions_CosOptions, + BuiltinOptions_WhereOptions, + BuiltinOptions_RankOptions, + BuiltinOptions_ReverseSequenceOptions, + BuiltinOptions_MatrixDiagOptions, + BuiltinOptions_QuantizeOptions, + BuiltinOptions_MatrixSetDiagOptions, + BuiltinOptions_HardSwishOptions, + BuiltinOptions_IfOptions, + BuiltinOptions_WhileOptions, + BuiltinOptions_DepthToSpaceOptions, + BuiltinOptions_NonMaxSuppressionV4Options, + BuiltinOptions_NonMaxSuppressionV5Options, + BuiltinOptions_ScatterNdOptions, + BuiltinOptions_SelectV2Options, + BuiltinOptions_DensifyOptions, + BuiltinOptions_SegmentSumOptions, + BuiltinOptions_BatchMatMulOptions + }; + return values; +} + +inline const char * const *EnumNamesBuiltinOptions() { + static const char * const names[103] = { + "NONE", + "Conv2DOptions", + "DepthwiseConv2DOptions", + "ConcatEmbeddingsOptions", + "LSHProjectionOptions", + "Pool2DOptions", + "SVDFOptions", + "RNNOptions", + "FullyConnectedOptions", + "SoftmaxOptions", + "ConcatenationOptions", + "AddOptions", + "L2NormOptions", + "LocalResponseNormalizationOptions", + "LSTMOptions", + "ResizeBilinearOptions", + "CallOptions", + "ReshapeOptions", + "SkipGramOptions", + "SpaceToDepthOptions", + "EmbeddingLookupSparseOptions", + "MulOptions", + "PadOptions", + "GatherOptions", + "BatchToSpaceNDOptions", + "SpaceToBatchNDOptions", + "TransposeOptions", + "ReducerOptions", + "SubOptions", + "DivOptions", + "SqueezeOptions", + "SequenceRNNOptions", + "StridedSliceOptions", + "ExpOptions", + "TopKV2Options", + "SplitOptions", + "LogSoftmaxOptions", + "CastOptions", + "DequantizeOptions", + "MaximumMinimumOptions", + "ArgMaxOptions", + "LessOptions", + "NegOptions", + "PadV2Options", + "GreaterOptions", + "GreaterEqualOptions", + "LessEqualOptions", + "SelectOptions", + "SliceOptions", + "TransposeConvOptions", + "SparseToDenseOptions", + "TileOptions", + "ExpandDimsOptions", + "EqualOptions", + "NotEqualOptions", + "ShapeOptions", + "PowOptions", + "ArgMinOptions", + "FakeQuantOptions", + "PackOptions", + "LogicalOrOptions", + "OneHotOptions", + "LogicalAndOptions", + "LogicalNotOptions", + "UnpackOptions", + "FloorDivOptions", + "SquareOptions", + "ZerosLikeOptions", + "FillOptions", + "BidirectionalSequenceLSTMOptions", + "BidirectionalSequenceRNNOptions", + "UnidirectionalSequenceLSTMOptions", + "FloorModOptions", + "RangeOptions", + "ResizeNearestNeighborOptions", + "LeakyReluOptions", + "SquaredDifferenceOptions", + "MirrorPadOptions", + "AbsOptions", + "SplitVOptions", + "UniqueOptions", + "ReverseV2Options", + "AddNOptions", + "GatherNdOptions", + "CosOptions", + "WhereOptions", + "RankOptions", + "ReverseSequenceOptions", + "MatrixDiagOptions", + "QuantizeOptions", + "MatrixSetDiagOptions", + "HardSwishOptions", + "IfOptions", + "WhileOptions", + "DepthToSpaceOptions", + "NonMaxSuppressionV4Options", + "NonMaxSuppressionV5Options", + "ScatterNdOptions", + "SelectV2Options", + "DensifyOptions", + "SegmentSumOptions", + "BatchMatMulOptions", + nullptr + }; + return names; +} + +inline const char *EnumNameBuiltinOptions(BuiltinOptions e) { + if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_BatchMatMulOptions)) return ""; + const size_t index = static_cast(e); + return EnumNamesBuiltinOptions()[index]; +} + +template struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NONE; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; +}; + +struct BuiltinOptionsUnion { + BuiltinOptions type; + void *value; + + BuiltinOptionsUnion() : type(BuiltinOptions_NONE), value(nullptr) {} + BuiltinOptionsUnion(BuiltinOptionsUnion&& u) FLATBUFFERS_NOEXCEPT : + type(BuiltinOptions_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + BuiltinOptionsUnion(const BuiltinOptionsUnion &); + BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u) + { BuiltinOptionsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~BuiltinOptionsUnion() { Reset(); } + + void Reset(); + +#ifndef FLATBUFFERS_CPP98_STL + template + void Set(T&& val) { + using RT = typename std::remove_reference::type; + Reset(); + type = BuiltinOptionsTraits::enum_value; + if (type != BuiltinOptions_NONE) { + value = new RT(std::forward(val)); + } + } +#endif // FLATBUFFERS_CPP98_STL + + static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite::Conv2DOptionsT *AsConv2DOptions() { + return type == BuiltinOptions_Conv2DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::Conv2DOptionsT *AsConv2DOptions() const { + return type == BuiltinOptions_Conv2DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() { + return type == BuiltinOptions_DepthwiseConv2DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() const { + return type == BuiltinOptions_DepthwiseConv2DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() { + return type == BuiltinOptions_ConcatEmbeddingsOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() const { + return type == BuiltinOptions_ConcatEmbeddingsOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LSHProjectionOptionsT *AsLSHProjectionOptions() { + return type == BuiltinOptions_LSHProjectionOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LSHProjectionOptionsT *AsLSHProjectionOptions() const { + return type == BuiltinOptions_LSHProjectionOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::Pool2DOptionsT *AsPool2DOptions() { + return type == BuiltinOptions_Pool2DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::Pool2DOptionsT *AsPool2DOptions() const { + return type == BuiltinOptions_Pool2DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SVDFOptionsT *AsSVDFOptions() { + return type == BuiltinOptions_SVDFOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SVDFOptionsT *AsSVDFOptions() const { + return type == BuiltinOptions_SVDFOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::RNNOptionsT *AsRNNOptions() { + return type == BuiltinOptions_RNNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::RNNOptionsT *AsRNNOptions() const { + return type == BuiltinOptions_RNNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::FullyConnectedOptionsT *AsFullyConnectedOptions() { + return type == BuiltinOptions_FullyConnectedOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::FullyConnectedOptionsT *AsFullyConnectedOptions() const { + return type == BuiltinOptions_FullyConnectedOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SoftmaxOptionsT *AsSoftmaxOptions() { + return type == BuiltinOptions_SoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SoftmaxOptionsT *AsSoftmaxOptions() const { + return type == BuiltinOptions_SoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ConcatenationOptionsT *AsConcatenationOptions() { + return type == BuiltinOptions_ConcatenationOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ConcatenationOptionsT *AsConcatenationOptions() const { + return type == BuiltinOptions_ConcatenationOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::AddOptionsT *AsAddOptions() { + return type == BuiltinOptions_AddOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::AddOptionsT *AsAddOptions() const { + return type == BuiltinOptions_AddOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::L2NormOptionsT *AsL2NormOptions() { + return type == BuiltinOptions_L2NormOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::L2NormOptionsT *AsL2NormOptions() const { + return type == BuiltinOptions_L2NormOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() { + return type == BuiltinOptions_LocalResponseNormalizationOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() const { + return type == BuiltinOptions_LocalResponseNormalizationOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LSTMOptionsT *AsLSTMOptions() { + return type == BuiltinOptions_LSTMOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LSTMOptionsT *AsLSTMOptions() const { + return type == BuiltinOptions_LSTMOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ResizeBilinearOptionsT *AsResizeBilinearOptions() { + return type == BuiltinOptions_ResizeBilinearOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ResizeBilinearOptionsT *AsResizeBilinearOptions() const { + return type == BuiltinOptions_ResizeBilinearOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::CallOptionsT *AsCallOptions() { + return type == BuiltinOptions_CallOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::CallOptionsT *AsCallOptions() const { + return type == BuiltinOptions_CallOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ReshapeOptionsT *AsReshapeOptions() { + return type == BuiltinOptions_ReshapeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ReshapeOptionsT *AsReshapeOptions() const { + return type == BuiltinOptions_ReshapeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SkipGramOptionsT *AsSkipGramOptions() { + return type == BuiltinOptions_SkipGramOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SkipGramOptionsT *AsSkipGramOptions() const { + return type == BuiltinOptions_SkipGramOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SpaceToDepthOptionsT *AsSpaceToDepthOptions() { + return type == BuiltinOptions_SpaceToDepthOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SpaceToDepthOptionsT *AsSpaceToDepthOptions() const { + return type == BuiltinOptions_SpaceToDepthOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() { + return type == BuiltinOptions_EmbeddingLookupSparseOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() const { + return type == BuiltinOptions_EmbeddingLookupSparseOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::MulOptionsT *AsMulOptions() { + return type == BuiltinOptions_MulOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::MulOptionsT *AsMulOptions() const { + return type == BuiltinOptions_MulOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::PadOptionsT *AsPadOptions() { + return type == BuiltinOptions_PadOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::PadOptionsT *AsPadOptions() const { + return type == BuiltinOptions_PadOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::GatherOptionsT *AsGatherOptions() { + return type == BuiltinOptions_GatherOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::GatherOptionsT *AsGatherOptions() const { + return type == BuiltinOptions_GatherOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() { + return type == BuiltinOptions_BatchToSpaceNDOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() const { + return type == BuiltinOptions_BatchToSpaceNDOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() { + return type == BuiltinOptions_SpaceToBatchNDOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() const { + return type == BuiltinOptions_SpaceToBatchNDOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::TransposeOptionsT *AsTransposeOptions() { + return type == BuiltinOptions_TransposeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::TransposeOptionsT *AsTransposeOptions() const { + return type == BuiltinOptions_TransposeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ReducerOptionsT *AsReducerOptions() { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ReducerOptionsT *AsReducerOptions() const { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SubOptionsT *AsSubOptions() { + return type == BuiltinOptions_SubOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SubOptionsT *AsSubOptions() const { + return type == BuiltinOptions_SubOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::DivOptionsT *AsDivOptions() { + return type == BuiltinOptions_DivOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::DivOptionsT *AsDivOptions() const { + return type == BuiltinOptions_DivOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SqueezeOptionsT *AsSqueezeOptions() { + return type == BuiltinOptions_SqueezeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SqueezeOptionsT *AsSqueezeOptions() const { + return type == BuiltinOptions_SqueezeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SequenceRNNOptionsT *AsSequenceRNNOptions() { + return type == BuiltinOptions_SequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SequenceRNNOptionsT *AsSequenceRNNOptions() const { + return type == BuiltinOptions_SequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::StridedSliceOptionsT *AsStridedSliceOptions() { + return type == BuiltinOptions_StridedSliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::StridedSliceOptionsT *AsStridedSliceOptions() const { + return type == BuiltinOptions_StridedSliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ExpOptionsT *AsExpOptions() { + return type == BuiltinOptions_ExpOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ExpOptionsT *AsExpOptions() const { + return type == BuiltinOptions_ExpOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::TopKV2OptionsT *AsTopKV2Options() { + return type == BuiltinOptions_TopKV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite::TopKV2OptionsT *AsTopKV2Options() const { + return type == BuiltinOptions_TopKV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite::SplitOptionsT *AsSplitOptions() { + return type == BuiltinOptions_SplitOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SplitOptionsT *AsSplitOptions() const { + return type == BuiltinOptions_SplitOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LogSoftmaxOptionsT *AsLogSoftmaxOptions() { + return type == BuiltinOptions_LogSoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LogSoftmaxOptionsT *AsLogSoftmaxOptions() const { + return type == BuiltinOptions_LogSoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::CastOptionsT *AsCastOptions() { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::CastOptionsT *AsCastOptions() const { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::DequantizeOptionsT *AsDequantizeOptions() { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::DequantizeOptionsT *AsDequantizeOptions() const { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::MaximumMinimumOptionsT *AsMaximumMinimumOptions() { + return type == BuiltinOptions_MaximumMinimumOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::MaximumMinimumOptionsT *AsMaximumMinimumOptions() const { + return type == BuiltinOptions_MaximumMinimumOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ArgMaxOptionsT *AsArgMaxOptions() { + return type == BuiltinOptions_ArgMaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ArgMaxOptionsT *AsArgMaxOptions() const { + return type == BuiltinOptions_ArgMaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LessOptionsT *AsLessOptions() { + return type == BuiltinOptions_LessOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LessOptionsT *AsLessOptions() const { + return type == BuiltinOptions_LessOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::NegOptionsT *AsNegOptions() { + return type == BuiltinOptions_NegOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::NegOptionsT *AsNegOptions() const { + return type == BuiltinOptions_NegOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::PadV2OptionsT *AsPadV2Options() { + return type == BuiltinOptions_PadV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite::PadV2OptionsT *AsPadV2Options() const { + return type == BuiltinOptions_PadV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite::GreaterOptionsT *AsGreaterOptions() { + return type == BuiltinOptions_GreaterOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::GreaterOptionsT *AsGreaterOptions() const { + return type == BuiltinOptions_GreaterOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::GreaterEqualOptionsT *AsGreaterEqualOptions() { + return type == BuiltinOptions_GreaterEqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::GreaterEqualOptionsT *AsGreaterEqualOptions() const { + return type == BuiltinOptions_GreaterEqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LessEqualOptionsT *AsLessEqualOptions() { + return type == BuiltinOptions_LessEqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LessEqualOptionsT *AsLessEqualOptions() const { + return type == BuiltinOptions_LessEqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SelectOptionsT *AsSelectOptions() { + return type == BuiltinOptions_SelectOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SelectOptionsT *AsSelectOptions() const { + return type == BuiltinOptions_SelectOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SliceOptionsT *AsSliceOptions() { + return type == BuiltinOptions_SliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SliceOptionsT *AsSliceOptions() const { + return type == BuiltinOptions_SliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::TransposeConvOptionsT *AsTransposeConvOptions() { + return type == BuiltinOptions_TransposeConvOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::TransposeConvOptionsT *AsTransposeConvOptions() const { + return type == BuiltinOptions_TransposeConvOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SparseToDenseOptionsT *AsSparseToDenseOptions() { + return type == BuiltinOptions_SparseToDenseOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SparseToDenseOptionsT *AsSparseToDenseOptions() const { + return type == BuiltinOptions_SparseToDenseOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::TileOptionsT *AsTileOptions() { + return type == BuiltinOptions_TileOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::TileOptionsT *AsTileOptions() const { + return type == BuiltinOptions_TileOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ExpandDimsOptionsT *AsExpandDimsOptions() { + return type == BuiltinOptions_ExpandDimsOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ExpandDimsOptionsT *AsExpandDimsOptions() const { + return type == BuiltinOptions_ExpandDimsOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::EqualOptionsT *AsEqualOptions() { + return type == BuiltinOptions_EqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::EqualOptionsT *AsEqualOptions() const { + return type == BuiltinOptions_EqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::NotEqualOptionsT *AsNotEqualOptions() { + return type == BuiltinOptions_NotEqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::NotEqualOptionsT *AsNotEqualOptions() const { + return type == BuiltinOptions_NotEqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ShapeOptionsT *AsShapeOptions() { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ShapeOptionsT *AsShapeOptions() const { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::PowOptionsT *AsPowOptions() { + return type == BuiltinOptions_PowOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::PowOptionsT *AsPowOptions() const { + return type == BuiltinOptions_PowOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ArgMinOptionsT *AsArgMinOptions() { + return type == BuiltinOptions_ArgMinOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ArgMinOptionsT *AsArgMinOptions() const { + return type == BuiltinOptions_ArgMinOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::FakeQuantOptionsT *AsFakeQuantOptions() { + return type == BuiltinOptions_FakeQuantOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::FakeQuantOptionsT *AsFakeQuantOptions() const { + return type == BuiltinOptions_FakeQuantOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::PackOptionsT *AsPackOptions() { + return type == BuiltinOptions_PackOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::PackOptionsT *AsPackOptions() const { + return type == BuiltinOptions_PackOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LogicalOrOptionsT *AsLogicalOrOptions() { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LogicalOrOptionsT *AsLogicalOrOptions() const { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::OneHotOptionsT *AsOneHotOptions() { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::OneHotOptionsT *AsOneHotOptions() const { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LogicalAndOptionsT *AsLogicalAndOptions() { + return type == BuiltinOptions_LogicalAndOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LogicalAndOptionsT *AsLogicalAndOptions() const { + return type == BuiltinOptions_LogicalAndOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LogicalNotOptionsT *AsLogicalNotOptions() { + return type == BuiltinOptions_LogicalNotOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LogicalNotOptionsT *AsLogicalNotOptions() const { + return type == BuiltinOptions_LogicalNotOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::UnpackOptionsT *AsUnpackOptions() { + return type == BuiltinOptions_UnpackOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::UnpackOptionsT *AsUnpackOptions() const { + return type == BuiltinOptions_UnpackOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::FloorDivOptionsT *AsFloorDivOptions() { + return type == BuiltinOptions_FloorDivOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::FloorDivOptionsT *AsFloorDivOptions() const { + return type == BuiltinOptions_FloorDivOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SquareOptionsT *AsSquareOptions() { + return type == BuiltinOptions_SquareOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SquareOptionsT *AsSquareOptions() const { + return type == BuiltinOptions_SquareOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ZerosLikeOptionsT *AsZerosLikeOptions() { + return type == BuiltinOptions_ZerosLikeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ZerosLikeOptionsT *AsZerosLikeOptions() const { + return type == BuiltinOptions_ZerosLikeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::FillOptionsT *AsFillOptions() { + return type == BuiltinOptions_FillOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::FillOptionsT *AsFillOptions() const { + return type == BuiltinOptions_FillOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() { + return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() const { + return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() { + return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() const { + return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() { + return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() const { + return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::FloorModOptionsT *AsFloorModOptions() { + return type == BuiltinOptions_FloorModOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::FloorModOptionsT *AsFloorModOptions() const { + return type == BuiltinOptions_FloorModOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::RangeOptionsT *AsRangeOptions() { + return type == BuiltinOptions_RangeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::RangeOptionsT *AsRangeOptions() const { + return type == BuiltinOptions_RangeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() { + return type == BuiltinOptions_ResizeNearestNeighborOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() const { + return type == BuiltinOptions_ResizeNearestNeighborOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::LeakyReluOptionsT *AsLeakyReluOptions() { + return type == BuiltinOptions_LeakyReluOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::LeakyReluOptionsT *AsLeakyReluOptions() const { + return type == BuiltinOptions_LeakyReluOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() { + return type == BuiltinOptions_SquaredDifferenceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() const { + return type == BuiltinOptions_SquaredDifferenceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::MirrorPadOptionsT *AsMirrorPadOptions() { + return type == BuiltinOptions_MirrorPadOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::MirrorPadOptionsT *AsMirrorPadOptions() const { + return type == BuiltinOptions_MirrorPadOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::AbsOptionsT *AsAbsOptions() { + return type == BuiltinOptions_AbsOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::AbsOptionsT *AsAbsOptions() const { + return type == BuiltinOptions_AbsOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SplitVOptionsT *AsSplitVOptions() { + return type == BuiltinOptions_SplitVOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SplitVOptionsT *AsSplitVOptions() const { + return type == BuiltinOptions_SplitVOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::UniqueOptionsT *AsUniqueOptions() { + return type == BuiltinOptions_UniqueOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::UniqueOptionsT *AsUniqueOptions() const { + return type == BuiltinOptions_UniqueOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ReverseV2OptionsT *AsReverseV2Options() { + return type == BuiltinOptions_ReverseV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite::ReverseV2OptionsT *AsReverseV2Options() const { + return type == BuiltinOptions_ReverseV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite::AddNOptionsT *AsAddNOptions() { + return type == BuiltinOptions_AddNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::AddNOptionsT *AsAddNOptions() const { + return type == BuiltinOptions_AddNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::GatherNdOptionsT *AsGatherNdOptions() { + return type == BuiltinOptions_GatherNdOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::GatherNdOptionsT *AsGatherNdOptions() const { + return type == BuiltinOptions_GatherNdOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::CosOptionsT *AsCosOptions() { + return type == BuiltinOptions_CosOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::CosOptionsT *AsCosOptions() const { + return type == BuiltinOptions_CosOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::WhereOptionsT *AsWhereOptions() { + return type == BuiltinOptions_WhereOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::WhereOptionsT *AsWhereOptions() const { + return type == BuiltinOptions_WhereOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::RankOptionsT *AsRankOptions() { + return type == BuiltinOptions_RankOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::RankOptionsT *AsRankOptions() const { + return type == BuiltinOptions_RankOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::ReverseSequenceOptionsT *AsReverseSequenceOptions() { + return type == BuiltinOptions_ReverseSequenceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ReverseSequenceOptionsT *AsReverseSequenceOptions() const { + return type == BuiltinOptions_ReverseSequenceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::MatrixDiagOptionsT *AsMatrixDiagOptions() { + return type == BuiltinOptions_MatrixDiagOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::MatrixDiagOptionsT *AsMatrixDiagOptions() const { + return type == BuiltinOptions_MatrixDiagOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::QuantizeOptionsT *AsQuantizeOptions() { + return type == BuiltinOptions_QuantizeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::QuantizeOptionsT *AsQuantizeOptions() const { + return type == BuiltinOptions_QuantizeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() { + return type == BuiltinOptions_MatrixSetDiagOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() const { + return type == BuiltinOptions_MatrixSetDiagOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::HardSwishOptionsT *AsHardSwishOptions() { + return type == BuiltinOptions_HardSwishOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::HardSwishOptionsT *AsHardSwishOptions() const { + return type == BuiltinOptions_HardSwishOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::IfOptionsT *AsIfOptions() { + return type == BuiltinOptions_IfOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::IfOptionsT *AsIfOptions() const { + return type == BuiltinOptions_IfOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::WhileOptionsT *AsWhileOptions() { + return type == BuiltinOptions_WhileOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::WhileOptionsT *AsWhileOptions() const { + return type == BuiltinOptions_WhileOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::DepthToSpaceOptionsT *AsDepthToSpaceOptions() { + return type == BuiltinOptions_DepthToSpaceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::DepthToSpaceOptionsT *AsDepthToSpaceOptions() const { + return type == BuiltinOptions_DepthToSpaceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() { + return type == BuiltinOptions_NonMaxSuppressionV4Options ? + reinterpret_cast(value) : nullptr; + } + const tflite::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() const { + return type == BuiltinOptions_NonMaxSuppressionV4Options ? + reinterpret_cast(value) : nullptr; + } + tflite::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() { + return type == BuiltinOptions_NonMaxSuppressionV5Options ? + reinterpret_cast(value) : nullptr; + } + const tflite::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() const { + return type == BuiltinOptions_NonMaxSuppressionV5Options ? + reinterpret_cast(value) : nullptr; + } + tflite::ScatterNdOptionsT *AsScatterNdOptions() { + return type == BuiltinOptions_ScatterNdOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::ScatterNdOptionsT *AsScatterNdOptions() const { + return type == BuiltinOptions_ScatterNdOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SelectV2OptionsT *AsSelectV2Options() { + return type == BuiltinOptions_SelectV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite::SelectV2OptionsT *AsSelectV2Options() const { + return type == BuiltinOptions_SelectV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite::DensifyOptionsT *AsDensifyOptions() { + return type == BuiltinOptions_DensifyOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::DensifyOptionsT *AsDensifyOptions() const { + return type == BuiltinOptions_DensifyOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::SegmentSumOptionsT *AsSegmentSumOptions() { + return type == BuiltinOptions_SegmentSumOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::SegmentSumOptionsT *AsSegmentSumOptions() const { + return type == BuiltinOptions_SegmentSumOptions ? + reinterpret_cast(value) : nullptr; + } + tflite::BatchMatMulOptionsT *AsBatchMatMulOptions() { + return type == BuiltinOptions_BatchMatMulOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite::BatchMatMulOptionsT *AsBatchMatMulOptions() const { + return type == BuiltinOptions_BatchMatMulOptions ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); +bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum Padding : int8_t { + Padding_SAME = 0, + Padding_VALID = 1, + Padding_MIN = Padding_SAME, + Padding_MAX = Padding_VALID +}; + +inline const Padding (&EnumValuesPadding())[2] { + static const Padding values[] = { + Padding_SAME, + Padding_VALID + }; + return values; +} + +inline const char * const *EnumNamesPadding() { + static const char * const names[3] = { + "SAME", + "VALID", + nullptr + }; + return names; +} + +inline const char *EnumNamePadding(Padding e) { + if (flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return ""; + const size_t index = static_cast(e); + return EnumNamesPadding()[index]; +} + +enum ActivationFunctionType : int8_t { + ActivationFunctionType_NONE = 0, + ActivationFunctionType_RELU = 1, + ActivationFunctionType_RELU_N1_TO_1 = 2, + ActivationFunctionType_RELU6 = 3, + ActivationFunctionType_TANH = 4, + ActivationFunctionType_SIGN_BIT = 5, + ActivationFunctionType_MIN = ActivationFunctionType_NONE, + ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT +}; + +inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] { + static const ActivationFunctionType values[] = { + ActivationFunctionType_NONE, + ActivationFunctionType_RELU, + ActivationFunctionType_RELU_N1_TO_1, + ActivationFunctionType_RELU6, + ActivationFunctionType_TANH, + ActivationFunctionType_SIGN_BIT + }; + return values; +} + +inline const char * const *EnumNamesActivationFunctionType() { + static const char * const names[7] = { + "NONE", + "RELU", + "RELU_N1_TO_1", + "RELU6", + "TANH", + "SIGN_BIT", + nullptr + }; + return names; +} + +inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) { + if (flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return ""; + const size_t index = static_cast(e); + return EnumNamesActivationFunctionType()[index]; +} + +enum LSHProjectionType : int8_t { + LSHProjectionType_UNKNOWN = 0, + LSHProjectionType_SPARSE = 1, + LSHProjectionType_DENSE = 2, + LSHProjectionType_MIN = LSHProjectionType_UNKNOWN, + LSHProjectionType_MAX = LSHProjectionType_DENSE +}; + +inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] { + static const LSHProjectionType values[] = { + LSHProjectionType_UNKNOWN, + LSHProjectionType_SPARSE, + LSHProjectionType_DENSE + }; + return values; +} + +inline const char * const *EnumNamesLSHProjectionType() { + static const char * const names[4] = { + "UNKNOWN", + "SPARSE", + "DENSE", + nullptr + }; + return names; +} + +inline const char *EnumNameLSHProjectionType(LSHProjectionType e) { + if (flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return ""; + const size_t index = static_cast(e); + return EnumNamesLSHProjectionType()[index]; +} + +enum FullyConnectedOptionsWeightsFormat : int8_t { + FullyConnectedOptionsWeightsFormat_DEFAULT = 0, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, + FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, + FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 +}; + +inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] { + static const FullyConnectedOptionsWeightsFormat values[] = { + FullyConnectedOptionsWeightsFormat_DEFAULT, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 + }; + return values; +} + +inline const char * const *EnumNamesFullyConnectedOptionsWeightsFormat() { + static const char * const names[3] = { + "DEFAULT", + "SHUFFLED4x16INT8", + nullptr + }; + return names; +} + +inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) { + if (flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return ""; + const size_t index = static_cast(e); + return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; +} + +enum LSTMKernelType : int8_t { + LSTMKernelType_FULL = 0, + LSTMKernelType_BASIC = 1, + LSTMKernelType_MIN = LSTMKernelType_FULL, + LSTMKernelType_MAX = LSTMKernelType_BASIC +}; + +inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] { + static const LSTMKernelType values[] = { + LSTMKernelType_FULL, + LSTMKernelType_BASIC + }; + return values; +} + +inline const char * const *EnumNamesLSTMKernelType() { + static const char * const names[3] = { + "FULL", + "BASIC", + nullptr + }; + return names; +} + +inline const char *EnumNameLSTMKernelType(LSTMKernelType e) { + if (flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return ""; + const size_t index = static_cast(e); + return EnumNamesLSTMKernelType()[index]; +} + +enum CombinerType : int8_t { + CombinerType_SUM = 0, + CombinerType_MEAN = 1, + CombinerType_SQRTN = 2, + CombinerType_MIN = CombinerType_SUM, + CombinerType_MAX = CombinerType_SQRTN +}; + +inline const CombinerType (&EnumValuesCombinerType())[3] { + static const CombinerType values[] = { + CombinerType_SUM, + CombinerType_MEAN, + CombinerType_SQRTN + }; + return values; +} + +inline const char * const *EnumNamesCombinerType() { + static const char * const names[4] = { + "SUM", + "MEAN", + "SQRTN", + nullptr + }; + return names; +} + +inline const char *EnumNameCombinerType(CombinerType e) { + if (flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return ""; + const size_t index = static_cast(e); + return EnumNamesCombinerType()[index]; +} + +enum MirrorPadMode : int8_t { + MirrorPadMode_REFLECT = 0, + MirrorPadMode_SYMMETRIC = 1, + MirrorPadMode_MIN = MirrorPadMode_REFLECT, + MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC +}; + +inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] { + static const MirrorPadMode values[] = { + MirrorPadMode_REFLECT, + MirrorPadMode_SYMMETRIC + }; + return values; +} + +inline const char * const *EnumNamesMirrorPadMode() { + static const char * const names[3] = { + "REFLECT", + "SYMMETRIC", + nullptr + }; + return names; +} + +inline const char *EnumNameMirrorPadMode(MirrorPadMode e) { + if (flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return ""; + const size_t index = static_cast(e); + return EnumNamesMirrorPadMode()[index]; +} + +enum CustomOptionsFormat : int8_t { + CustomOptionsFormat_FLEXBUFFERS = 0, + CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS, + CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS +}; + +inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] { + static const CustomOptionsFormat values[] = { + CustomOptionsFormat_FLEXBUFFERS + }; + return values; +} + +inline const char * const *EnumNamesCustomOptionsFormat() { + static const char * const names[2] = { + "FLEXBUFFERS", + nullptr + }; + return names; +} + +inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) { + if (flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return ""; + const size_t index = static_cast(e); + return EnumNamesCustomOptionsFormat()[index]; +} + +struct CustomQuantizationT : public flatbuffers::NativeTable { + typedef CustomQuantization TableType; + std::vector custom{}; +}; + +struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CustomQuantizationT NativeTableType; + typedef CustomQuantizationBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CustomQuantizationTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CUSTOM = 4 + }; + const flatbuffers::Vector *custom() const { + return GetPointer *>(VT_CUSTOM); + } + flatbuffers::Vector *mutable_custom() { + return GetPointer *>(VT_CUSTOM); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CUSTOM) && + verifier.VerifyVector(custom()) && + verifier.EndTable(); + } + CustomQuantizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CustomQuantizationBuilder { + typedef CustomQuantization Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_custom(flatbuffers::Offset> custom) { + fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); + } + explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCustomQuantization( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> custom = 0) { + CustomQuantizationBuilder builder_(_fbb); + builder_.add_custom(custom); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateCustomQuantizationDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *custom = nullptr) { + if (custom) { _fbb.ForceVectorAlignment(custom->size(), sizeof(uint8_t), 16); } + auto custom__ = custom ? _fbb.CreateVector(*custom) : 0; + return tflite::CreateCustomQuantization( + _fbb, + custom__); +} + +flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct QuantizationParametersT : public flatbuffers::NativeTable { + typedef QuantizationParameters TableType; + std::vector min{}; + std::vector max{}; + std::vector scale{}; + std::vector zero_point{}; + tflite::QuantizationDetailsUnion details{}; + int32_t quantized_dimension = 0; +}; + +struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef QuantizationParametersT NativeTableType; + typedef QuantizationParametersBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return QuantizationParametersTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MIN = 4, + VT_MAX = 6, + VT_SCALE = 8, + VT_ZERO_POINT = 10, + VT_DETAILS_TYPE = 12, + VT_DETAILS = 14, + VT_QUANTIZED_DIMENSION = 16 + }; + const flatbuffers::Vector *min() const { + return GetPointer *>(VT_MIN); + } + flatbuffers::Vector *mutable_min() { + return GetPointer *>(VT_MIN); + } + const flatbuffers::Vector *max() const { + return GetPointer *>(VT_MAX); + } + flatbuffers::Vector *mutable_max() { + return GetPointer *>(VT_MAX); + } + const flatbuffers::Vector *scale() const { + return GetPointer *>(VT_SCALE); + } + flatbuffers::Vector *mutable_scale() { + return GetPointer *>(VT_SCALE); + } + const flatbuffers::Vector *zero_point() const { + return GetPointer *>(VT_ZERO_POINT); + } + flatbuffers::Vector *mutable_zero_point() { + return GetPointer *>(VT_ZERO_POINT); + } + tflite::QuantizationDetails details_type() const { + return static_cast(GetField(VT_DETAILS_TYPE, 0)); + } + const void *details() const { + return GetPointer(VT_DETAILS); + } + template const T *details_as() const; + const tflite::CustomQuantization *details_as_CustomQuantization() const { + return details_type() == tflite::QuantizationDetails_CustomQuantization ? static_cast(details()) : nullptr; + } + void *mutable_details() { + return GetPointer(VT_DETAILS); + } + int32_t quantized_dimension() const { + return GetField(VT_QUANTIZED_DIMENSION, 0); + } + bool mutate_quantized_dimension(int32_t _quantized_dimension) { + return SetField(VT_QUANTIZED_DIMENSION, _quantized_dimension, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_MIN) && + verifier.VerifyVector(min()) && + VerifyOffset(verifier, VT_MAX) && + verifier.VerifyVector(max()) && + VerifyOffset(verifier, VT_SCALE) && + verifier.VerifyVector(scale()) && + VerifyOffset(verifier, VT_ZERO_POINT) && + verifier.VerifyVector(zero_point()) && + VerifyField(verifier, VT_DETAILS_TYPE) && + VerifyOffset(verifier, VT_DETAILS) && + VerifyQuantizationDetails(verifier, details(), details_type()) && + VerifyField(verifier, VT_QUANTIZED_DIMENSION) && + verifier.EndTable(); + } + QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const tflite::CustomQuantization *QuantizationParameters::details_as() const { + return details_as_CustomQuantization(); +} + +struct QuantizationParametersBuilder { + typedef QuantizationParameters Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_min(flatbuffers::Offset> min) { + fbb_.AddOffset(QuantizationParameters::VT_MIN, min); + } + void add_max(flatbuffers::Offset> max) { + fbb_.AddOffset(QuantizationParameters::VT_MAX, max); + } + void add_scale(flatbuffers::Offset> scale) { + fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); + } + void add_zero_point(flatbuffers::Offset> zero_point) { + fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); + } + void add_details_type(tflite::QuantizationDetails details_type) { + fbb_.AddElement(QuantizationParameters::VT_DETAILS_TYPE, static_cast(details_type), 0); + } + void add_details(flatbuffers::Offset details) { + fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details); + } + void add_quantized_dimension(int32_t quantized_dimension) { + fbb_.AddElement(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, 0); + } + explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateQuantizationParameters( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> min = 0, + flatbuffers::Offset> max = 0, + flatbuffers::Offset> scale = 0, + flatbuffers::Offset> zero_point = 0, + tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE, + flatbuffers::Offset details = 0, + int32_t quantized_dimension = 0) { + QuantizationParametersBuilder builder_(_fbb); + builder_.add_quantized_dimension(quantized_dimension); + builder_.add_details(details); + builder_.add_zero_point(zero_point); + builder_.add_scale(scale); + builder_.add_max(max); + builder_.add_min(min); + builder_.add_details_type(details_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateQuantizationParametersDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *min = nullptr, + const std::vector *max = nullptr, + const std::vector *scale = nullptr, + const std::vector *zero_point = nullptr, + tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE, + flatbuffers::Offset details = 0, + int32_t quantized_dimension = 0) { + auto min__ = min ? _fbb.CreateVector(*min) : 0; + auto max__ = max ? _fbb.CreateVector(*max) : 0; + auto scale__ = scale ? _fbb.CreateVector(*scale) : 0; + auto zero_point__ = zero_point ? _fbb.CreateVector(*zero_point) : 0; + return tflite::CreateQuantizationParameters( + _fbb, + min__, + max__, + scale__, + zero_point__, + details_type, + details, + quantized_dimension); +} + +flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Int32VectorT : public flatbuffers::NativeTable { + typedef Int32Vector TableType; + std::vector values{}; +}; + +struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Int32VectorT NativeTableType; + typedef Int32VectorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Int32VectorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES = 4 + }; + const flatbuffers::Vector *values() const { + return GetPointer *>(VT_VALUES); + } + flatbuffers::Vector *mutable_values() { + return GetPointer *>(VT_VALUES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.EndTable(); + } + Int32VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Int32VectorBuilder { + typedef Int32Vector Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values(flatbuffers::Offset> values) { + fbb_.AddOffset(Int32Vector::VT_VALUES, values); + } + explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateInt32Vector( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> values = 0) { + Int32VectorBuilder builder_(_fbb); + builder_.add_values(values); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateInt32VectorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *values = nullptr) { + auto values__ = values ? _fbb.CreateVector(*values) : 0; + return tflite::CreateInt32Vector( + _fbb, + values__); +} + +flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Uint16VectorT : public flatbuffers::NativeTable { + typedef Uint16Vector TableType; + std::vector values{}; +}; + +struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Uint16VectorT NativeTableType; + typedef Uint16VectorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Uint16VectorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES = 4 + }; + const flatbuffers::Vector *values() const { + return GetPointer *>(VT_VALUES); + } + flatbuffers::Vector *mutable_values() { + return GetPointer *>(VT_VALUES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.EndTable(); + } + Uint16VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Uint16VectorBuilder { + typedef Uint16Vector Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values(flatbuffers::Offset> values) { + fbb_.AddOffset(Uint16Vector::VT_VALUES, values); + } + explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUint16Vector( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> values = 0) { + Uint16VectorBuilder builder_(_fbb); + builder_.add_values(values); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateUint16VectorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *values = nullptr) { + if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint16_t), 4); } + auto values__ = values ? _fbb.CreateVector(*values) : 0; + return tflite::CreateUint16Vector( + _fbb, + values__); +} + +flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Uint8VectorT : public flatbuffers::NativeTable { + typedef Uint8Vector TableType; + std::vector values{}; +}; + +struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Uint8VectorT NativeTableType; + typedef Uint8VectorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Uint8VectorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES = 4 + }; + const flatbuffers::Vector *values() const { + return GetPointer *>(VT_VALUES); + } + flatbuffers::Vector *mutable_values() { + return GetPointer *>(VT_VALUES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.EndTable(); + } + Uint8VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Uint8VectorBuilder { + typedef Uint8Vector Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values(flatbuffers::Offset> values) { + fbb_.AddOffset(Uint8Vector::VT_VALUES, values); + } + explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUint8Vector( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> values = 0) { + Uint8VectorBuilder builder_(_fbb); + builder_.add_values(values); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateUint8VectorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *values = nullptr) { + if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint8_t), 4); } + auto values__ = values ? _fbb.CreateVector(*values) : 0; + return tflite::CreateUint8Vector( + _fbb, + values__); +} + +flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DimensionMetadataT : public flatbuffers::NativeTable { + typedef DimensionMetadata TableType; + tflite::DimensionType format = tflite::DimensionType_DENSE; + int32_t dense_size = 0; + tflite::SparseIndexVectorUnion array_segments{}; + tflite::SparseIndexVectorUnion array_indices{}; +}; + +struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DimensionMetadataT NativeTableType; + typedef DimensionMetadataBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DimensionMetadataTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_DENSE_SIZE = 6, + VT_ARRAY_SEGMENTS_TYPE = 8, + VT_ARRAY_SEGMENTS = 10, + VT_ARRAY_INDICES_TYPE = 12, + VT_ARRAY_INDICES = 14 + }; + tflite::DimensionType format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + bool mutate_format(tflite::DimensionType _format) { + return SetField(VT_FORMAT, static_cast(_format), 0); + } + int32_t dense_size() const { + return GetField(VT_DENSE_SIZE, 0); + } + bool mutate_dense_size(int32_t _dense_size) { + return SetField(VT_DENSE_SIZE, _dense_size, 0); + } + tflite::SparseIndexVector array_segments_type() const { + return static_cast(GetField(VT_ARRAY_SEGMENTS_TYPE, 0)); + } + const void *array_segments() const { + return GetPointer(VT_ARRAY_SEGMENTS); + } + template const T *array_segments_as() const; + const tflite::Int32Vector *array_segments_as_Int32Vector() const { + return array_segments_type() == tflite::SparseIndexVector_Int32Vector ? static_cast(array_segments()) : nullptr; + } + const tflite::Uint16Vector *array_segments_as_Uint16Vector() const { + return array_segments_type() == tflite::SparseIndexVector_Uint16Vector ? static_cast(array_segments()) : nullptr; + } + const tflite::Uint8Vector *array_segments_as_Uint8Vector() const { + return array_segments_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast(array_segments()) : nullptr; + } + void *mutable_array_segments() { + return GetPointer(VT_ARRAY_SEGMENTS); + } + tflite::SparseIndexVector array_indices_type() const { + return static_cast(GetField(VT_ARRAY_INDICES_TYPE, 0)); + } + const void *array_indices() const { + return GetPointer(VT_ARRAY_INDICES); + } + template const T *array_indices_as() const; + const tflite::Int32Vector *array_indices_as_Int32Vector() const { + return array_indices_type() == tflite::SparseIndexVector_Int32Vector ? static_cast(array_indices()) : nullptr; + } + const tflite::Uint16Vector *array_indices_as_Uint16Vector() const { + return array_indices_type() == tflite::SparseIndexVector_Uint16Vector ? static_cast(array_indices()) : nullptr; + } + const tflite::Uint8Vector *array_indices_as_Uint8Vector() const { + return array_indices_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast(array_indices()) : nullptr; + } + void *mutable_array_indices() { + return GetPointer(VT_ARRAY_INDICES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT) && + VerifyField(verifier, VT_DENSE_SIZE) && + VerifyField(verifier, VT_ARRAY_SEGMENTS_TYPE) && + VerifyOffset(verifier, VT_ARRAY_SEGMENTS) && + VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) && + VerifyField(verifier, VT_ARRAY_INDICES_TYPE) && + VerifyOffset(verifier, VT_ARRAY_INDICES) && + VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) && + verifier.EndTable(); + } + DimensionMetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const tflite::Int32Vector *DimensionMetadata::array_segments_as() const { + return array_segments_as_Int32Vector(); +} + +template<> inline const tflite::Uint16Vector *DimensionMetadata::array_segments_as() const { + return array_segments_as_Uint16Vector(); +} + +template<> inline const tflite::Uint8Vector *DimensionMetadata::array_segments_as() const { + return array_segments_as_Uint8Vector(); +} + +template<> inline const tflite::Int32Vector *DimensionMetadata::array_indices_as() const { + return array_indices_as_Int32Vector(); +} + +template<> inline const tflite::Uint16Vector *DimensionMetadata::array_indices_as() const { + return array_indices_as_Uint16Vector(); +} + +template<> inline const tflite::Uint8Vector *DimensionMetadata::array_indices_as() const { + return array_indices_as_Uint8Vector(); +} + +struct DimensionMetadataBuilder { + typedef DimensionMetadata Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(tflite::DimensionType format) { + fbb_.AddElement(DimensionMetadata::VT_FORMAT, static_cast(format), 0); + } + void add_dense_size(int32_t dense_size) { + fbb_.AddElement(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0); + } + void add_array_segments_type(tflite::SparseIndexVector array_segments_type) { + fbb_.AddElement(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, static_cast(array_segments_type), 0); + } + void add_array_segments(flatbuffers::Offset array_segments) { + fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments); + } + void add_array_indices_type(tflite::SparseIndexVector array_indices_type) { + fbb_.AddElement(DimensionMetadata::VT_ARRAY_INDICES_TYPE, static_cast(array_indices_type), 0); + } + void add_array_indices(flatbuffers::Offset array_indices) { + fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices); + } + explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDimensionMetadata( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::DimensionType format = tflite::DimensionType_DENSE, + int32_t dense_size = 0, + tflite::SparseIndexVector array_segments_type = tflite::SparseIndexVector_NONE, + flatbuffers::Offset array_segments = 0, + tflite::SparseIndexVector array_indices_type = tflite::SparseIndexVector_NONE, + flatbuffers::Offset array_indices = 0) { + DimensionMetadataBuilder builder_(_fbb); + builder_.add_array_indices(array_indices); + builder_.add_array_segments(array_segments); + builder_.add_dense_size(dense_size); + builder_.add_array_indices_type(array_indices_type); + builder_.add_array_segments_type(array_segments_type); + builder_.add_format(format); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SparsityParametersT : public flatbuffers::NativeTable { + typedef SparsityParameters TableType; + std::vector traversal_order{}; + std::vector block_map{}; + std::vector> dim_metadata{}; +}; + +struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparsityParametersT NativeTableType; + typedef SparsityParametersBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SparsityParametersTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TRAVERSAL_ORDER = 4, + VT_BLOCK_MAP = 6, + VT_DIM_METADATA = 8 + }; + const flatbuffers::Vector *traversal_order() const { + return GetPointer *>(VT_TRAVERSAL_ORDER); + } + flatbuffers::Vector *mutable_traversal_order() { + return GetPointer *>(VT_TRAVERSAL_ORDER); + } + const flatbuffers::Vector *block_map() const { + return GetPointer *>(VT_BLOCK_MAP); + } + flatbuffers::Vector *mutable_block_map() { + return GetPointer *>(VT_BLOCK_MAP); + } + const flatbuffers::Vector> *dim_metadata() const { + return GetPointer> *>(VT_DIM_METADATA); + } + flatbuffers::Vector> *mutable_dim_metadata() { + return GetPointer> *>(VT_DIM_METADATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_TRAVERSAL_ORDER) && + verifier.VerifyVector(traversal_order()) && + VerifyOffset(verifier, VT_BLOCK_MAP) && + verifier.VerifyVector(block_map()) && + VerifyOffset(verifier, VT_DIM_METADATA) && + verifier.VerifyVector(dim_metadata()) && + verifier.VerifyVectorOfTables(dim_metadata()) && + verifier.EndTable(); + } + SparsityParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SparsityParametersBuilder { + typedef SparsityParameters Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_traversal_order(flatbuffers::Offset> traversal_order) { + fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order); + } + void add_block_map(flatbuffers::Offset> block_map) { + fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map); + } + void add_dim_metadata(flatbuffers::Offset>> dim_metadata) { + fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata); + } + explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparsityParameters( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> traversal_order = 0, + flatbuffers::Offset> block_map = 0, + flatbuffers::Offset>> dim_metadata = 0) { + SparsityParametersBuilder builder_(_fbb); + builder_.add_dim_metadata(dim_metadata); + builder_.add_block_map(block_map); + builder_.add_traversal_order(traversal_order); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSparsityParametersDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *traversal_order = nullptr, + const std::vector *block_map = nullptr, + const std::vector> *dim_metadata = nullptr) { + auto traversal_order__ = traversal_order ? _fbb.CreateVector(*traversal_order) : 0; + auto block_map__ = block_map ? _fbb.CreateVector(*block_map) : 0; + auto dim_metadata__ = dim_metadata ? _fbb.CreateVector>(*dim_metadata) : 0; + return tflite::CreateSparsityParameters( + _fbb, + traversal_order__, + block_map__, + dim_metadata__); +} + +flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorT : public flatbuffers::NativeTable { + typedef Tensor TableType; + std::vector shape{}; + tflite::TensorType type = tflite::TensorType_FLOAT32; + uint32_t buffer = 0; + std::string name{}; + std::unique_ptr quantization{}; + bool is_variable = false; + std::unique_ptr sparsity{}; + std::vector shape_signature{}; +}; + +struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorT NativeTableType; + typedef TensorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TensorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SHAPE = 4, + VT_TYPE = 6, + VT_BUFFER = 8, + VT_NAME = 10, + VT_QUANTIZATION = 12, + VT_IS_VARIABLE = 14, + VT_SPARSITY = 16, + VT_SHAPE_SIGNATURE = 18 + }; + const flatbuffers::Vector *shape() const { + return GetPointer *>(VT_SHAPE); + } + flatbuffers::Vector *mutable_shape() { + return GetPointer *>(VT_SHAPE); + } + tflite::TensorType type() const { + return static_cast(GetField(VT_TYPE, 0)); + } + bool mutate_type(tflite::TensorType _type) { + return SetField(VT_TYPE, static_cast(_type), 0); + } + uint32_t buffer() const { + return GetField(VT_BUFFER, 0); + } + bool mutate_buffer(uint32_t _buffer) { + return SetField(VT_BUFFER, _buffer, 0); + } + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + const tflite::QuantizationParameters *quantization() const { + return GetPointer(VT_QUANTIZATION); + } + tflite::QuantizationParameters *mutable_quantization() { + return GetPointer(VT_QUANTIZATION); + } + bool is_variable() const { + return GetField(VT_IS_VARIABLE, 0) != 0; + } + bool mutate_is_variable(bool _is_variable) { + return SetField(VT_IS_VARIABLE, static_cast(_is_variable), 0); + } + const tflite::SparsityParameters *sparsity() const { + return GetPointer(VT_SPARSITY); + } + tflite::SparsityParameters *mutable_sparsity() { + return GetPointer(VT_SPARSITY); + } + const flatbuffers::Vector *shape_signature() const { + return GetPointer *>(VT_SHAPE_SIGNATURE); + } + flatbuffers::Vector *mutable_shape_signature() { + return GetPointer *>(VT_SHAPE_SIGNATURE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.VerifyVector(shape()) && + VerifyField(verifier, VT_TYPE) && + VerifyField(verifier, VT_BUFFER) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_QUANTIZATION) && + verifier.VerifyTable(quantization()) && + VerifyField(verifier, VT_IS_VARIABLE) && + VerifyOffset(verifier, VT_SPARSITY) && + verifier.VerifyTable(sparsity()) && + VerifyOffset(verifier, VT_SHAPE_SIGNATURE) && + verifier.VerifyVector(shape_signature()) && + verifier.EndTable(); + } + TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorBuilder { + typedef Tensor Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_shape(flatbuffers::Offset> shape) { + fbb_.AddOffset(Tensor::VT_SHAPE, shape); + } + void add_type(tflite::TensorType type) { + fbb_.AddElement(Tensor::VT_TYPE, static_cast(type), 0); + } + void add_buffer(uint32_t buffer) { + fbb_.AddElement(Tensor::VT_BUFFER, buffer, 0); + } + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Tensor::VT_NAME, name); + } + void add_quantization(flatbuffers::Offset quantization) { + fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); + } + void add_is_variable(bool is_variable) { + fbb_.AddElement(Tensor::VT_IS_VARIABLE, static_cast(is_variable), 0); + } + void add_sparsity(flatbuffers::Offset sparsity) { + fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity); + } + void add_shape_signature(flatbuffers::Offset> shape_signature) { + fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature); + } + explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensor( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> shape = 0, + tflite::TensorType type = tflite::TensorType_FLOAT32, + uint32_t buffer = 0, + flatbuffers::Offset name = 0, + flatbuffers::Offset quantization = 0, + bool is_variable = false, + flatbuffers::Offset sparsity = 0, + flatbuffers::Offset> shape_signature = 0) { + TensorBuilder builder_(_fbb); + builder_.add_shape_signature(shape_signature); + builder_.add_sparsity(sparsity); + builder_.add_quantization(quantization); + builder_.add_name(name); + builder_.add_buffer(buffer); + builder_.add_shape(shape); + builder_.add_is_variable(is_variable); + builder_.add_type(type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTensorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *shape = nullptr, + tflite::TensorType type = tflite::TensorType_FLOAT32, + uint32_t buffer = 0, + const char *name = nullptr, + flatbuffers::Offset quantization = 0, + bool is_variable = false, + flatbuffers::Offset sparsity = 0, + const std::vector *shape_signature = nullptr) { + auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; + auto name__ = name ? _fbb.CreateString(name) : 0; + auto shape_signature__ = shape_signature ? _fbb.CreateVector(*shape_signature) : 0; + return tflite::CreateTensor( + _fbb, + shape__, + type, + buffer, + name__, + quantization, + is_variable, + sparsity, + shape_signature__); +} + +flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv2DOptionsT : public flatbuffers::NativeTable { + typedef Conv2DOptions TableType; + tflite::Padding padding = tflite::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + int32_t dilation_w_factor = 1; + int32_t dilation_h_factor = 1; +}; + +struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv2DOptionsT NativeTableType; + typedef Conv2DOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Conv2DOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_FUSED_ACTIVATION_FUNCTION = 10, + VT_DILATION_W_FACTOR = 12, + VT_DILATION_H_FACTOR = 14 + }; + tflite::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + bool mutate_padding(tflite::Padding _padding) { + return SetField(VT_PADDING, static_cast(_padding), 0); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + bool mutate_stride_w(int32_t _stride_w) { + return SetField(VT_STRIDE_W, _stride_w, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + bool mutate_stride_h(int32_t _stride_h) { + return SetField(VT_STRIDE_H, _stride_h, 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + int32_t dilation_w_factor() const { + return GetField(VT_DILATION_W_FACTOR, 1); + } + bool mutate_dilation_w_factor(int32_t _dilation_w_factor) { + return SetField(VT_DILATION_W_FACTOR, _dilation_w_factor, 1); + } + int32_t dilation_h_factor() const { + return GetField(VT_DILATION_H_FACTOR, 1); + } + bool mutate_dilation_h_factor(int32_t _dilation_h_factor) { + return SetField(VT_DILATION_H_FACTOR, _dilation_h_factor, 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING) && + VerifyField(verifier, VT_STRIDE_W) && + VerifyField(verifier, VT_STRIDE_H) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_DILATION_W_FACTOR) && + VerifyField(verifier, VT_DILATION_H_FACTOR) && + verifier.EndTable(); + } + Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv2DOptionsBuilder { + typedef Conv2DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite::Padding padding) { + fbb_.AddElement(Conv2DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(Conv2DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(Conv2DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_dilation_w_factor(int32_t dilation_w_factor) { + fbb_.AddElement(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); + } + void add_dilation_h_factor(int32_t dilation_h_factor) { + fbb_.AddElement(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); + } + explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv2DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::Padding padding = tflite::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + int32_t dilation_w_factor = 1, + int32_t dilation_h_factor = 1) { + Conv2DOptionsBuilder builder_(_fbb); + builder_.add_dilation_h_factor(dilation_h_factor); + builder_.add_dilation_w_factor(dilation_w_factor); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Pool2DOptionsT : public flatbuffers::NativeTable { + typedef Pool2DOptions TableType; + tflite::Padding padding = tflite::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + int32_t filter_width = 0; + int32_t filter_height = 0; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Pool2DOptionsT NativeTableType; + typedef Pool2DOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return Pool2DOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_FILTER_WIDTH = 10, + VT_FILTER_HEIGHT = 12, + VT_FUSED_ACTIVATION_FUNCTION = 14 + }; + tflite::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + bool mutate_padding(tflite::Padding _padding) { + return SetField(VT_PADDING, static_cast(_padding), 0); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + bool mutate_stride_w(int32_t _stride_w) { + return SetField(VT_STRIDE_W, _stride_w, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + bool mutate_stride_h(int32_t _stride_h) { + return SetField(VT_STRIDE_H, _stride_h, 0); + } + int32_t filter_width() const { + return GetField(VT_FILTER_WIDTH, 0); + } + bool mutate_filter_width(int32_t _filter_width) { + return SetField(VT_FILTER_WIDTH, _filter_width, 0); + } + int32_t filter_height() const { + return GetField(VT_FILTER_HEIGHT, 0); + } + bool mutate_filter_height(int32_t _filter_height) { + return SetField(VT_FILTER_HEIGHT, _filter_height, 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING) && + VerifyField(verifier, VT_STRIDE_W) && + VerifyField(verifier, VT_STRIDE_H) && + VerifyField(verifier, VT_FILTER_WIDTH) && + VerifyField(verifier, VT_FILTER_HEIGHT) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Pool2DOptionsBuilder { + typedef Pool2DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite::Padding padding) { + fbb_.AddElement(Pool2DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(Pool2DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(Pool2DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_filter_width(int32_t filter_width) { + fbb_.AddElement(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0); + } + void add_filter_height(int32_t filter_height) { + fbb_.AddElement(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePool2DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::Padding padding = tflite::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + int32_t filter_width = 0, + int32_t filter_height = 0, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + Pool2DOptionsBuilder builder_(_fbb); + builder_.add_filter_height(filter_height); + builder_.add_filter_width(filter_width); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable { + typedef DepthwiseConv2DOptions TableType; + tflite::Padding padding = tflite::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + int32_t depth_multiplier = 0; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + int32_t dilation_w_factor = 1; + int32_t dilation_h_factor = 1; +}; + +struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DepthwiseConv2DOptionsT NativeTableType; + typedef DepthwiseConv2DOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DepthwiseConv2DOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_DEPTH_MULTIPLIER = 10, + VT_FUSED_ACTIVATION_FUNCTION = 12, + VT_DILATION_W_FACTOR = 14, + VT_DILATION_H_FACTOR = 16 + }; + tflite::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + bool mutate_padding(tflite::Padding _padding) { + return SetField(VT_PADDING, static_cast(_padding), 0); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + bool mutate_stride_w(int32_t _stride_w) { + return SetField(VT_STRIDE_W, _stride_w, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + bool mutate_stride_h(int32_t _stride_h) { + return SetField(VT_STRIDE_H, _stride_h, 0); + } + int32_t depth_multiplier() const { + return GetField(VT_DEPTH_MULTIPLIER, 0); + } + bool mutate_depth_multiplier(int32_t _depth_multiplier) { + return SetField(VT_DEPTH_MULTIPLIER, _depth_multiplier, 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + int32_t dilation_w_factor() const { + return GetField(VT_DILATION_W_FACTOR, 1); + } + bool mutate_dilation_w_factor(int32_t _dilation_w_factor) { + return SetField(VT_DILATION_W_FACTOR, _dilation_w_factor, 1); + } + int32_t dilation_h_factor() const { + return GetField(VT_DILATION_H_FACTOR, 1); + } + bool mutate_dilation_h_factor(int32_t _dilation_h_factor) { + return SetField(VT_DILATION_H_FACTOR, _dilation_h_factor, 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING) && + VerifyField(verifier, VT_STRIDE_W) && + VerifyField(verifier, VT_STRIDE_H) && + VerifyField(verifier, VT_DEPTH_MULTIPLIER) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_DILATION_W_FACTOR) && + VerifyField(verifier, VT_DILATION_H_FACTOR) && + verifier.EndTable(); + } + DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DepthwiseConv2DOptionsBuilder { + typedef DepthwiseConv2DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite::Padding padding) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_depth_multiplier(int32_t depth_multiplier) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_dilation_w_factor(int32_t dilation_w_factor) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); + } + void add_dilation_h_factor(int32_t dilation_h_factor) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); + } + explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDepthwiseConv2DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::Padding padding = tflite::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + int32_t depth_multiplier = 0, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + int32_t dilation_w_factor = 1, + int32_t dilation_h_factor = 1) { + DepthwiseConv2DOptionsBuilder builder_(_fbb); + builder_.add_dilation_h_factor(dilation_h_factor); + builder_.add_dilation_w_factor(dilation_w_factor); + builder_.add_depth_multiplier(depth_multiplier); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable { + typedef ConcatEmbeddingsOptions TableType; + int32_t num_channels = 0; + std::vector num_columns_per_channel{}; + std::vector embedding_dim_per_channel{}; +}; + +struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ConcatEmbeddingsOptionsT NativeTableType; + typedef ConcatEmbeddingsOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ConcatEmbeddingsOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_CHANNELS = 4, + VT_NUM_COLUMNS_PER_CHANNEL = 6, + VT_EMBEDDING_DIM_PER_CHANNEL = 8 + }; + int32_t num_channels() const { + return GetField(VT_NUM_CHANNELS, 0); + } + bool mutate_num_channels(int32_t _num_channels) { + return SetField(VT_NUM_CHANNELS, _num_channels, 0); + } + const flatbuffers::Vector *num_columns_per_channel() const { + return GetPointer *>(VT_NUM_COLUMNS_PER_CHANNEL); + } + flatbuffers::Vector *mutable_num_columns_per_channel() { + return GetPointer *>(VT_NUM_COLUMNS_PER_CHANNEL); + } + const flatbuffers::Vector *embedding_dim_per_channel() const { + return GetPointer *>(VT_EMBEDDING_DIM_PER_CHANNEL); + } + flatbuffers::Vector *mutable_embedding_dim_per_channel() { + return GetPointer *>(VT_EMBEDDING_DIM_PER_CHANNEL); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_CHANNELS) && + VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && + verifier.VerifyVector(num_columns_per_channel()) && + VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && + verifier.VerifyVector(embedding_dim_per_channel()) && + verifier.EndTable(); + } + ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ConcatEmbeddingsOptionsBuilder { + typedef ConcatEmbeddingsOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_channels(int32_t num_channels) { + fbb_.AddElement(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); + } + void add_num_columns_per_channel(flatbuffers::Offset> num_columns_per_channel) { + fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); + } + void add_embedding_dim_per_channel(flatbuffers::Offset> embedding_dim_per_channel) { + fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel); + } + explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConcatEmbeddingsOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_channels = 0, + flatbuffers::Offset> num_columns_per_channel = 0, + flatbuffers::Offset> embedding_dim_per_channel = 0) { + ConcatEmbeddingsOptionsBuilder builder_(_fbb); + builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); + builder_.add_num_columns_per_channel(num_columns_per_channel); + builder_.add_num_channels(num_channels); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConcatEmbeddingsOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_channels = 0, + const std::vector *num_columns_per_channel = nullptr, + const std::vector *embedding_dim_per_channel = nullptr) { + auto num_columns_per_channel__ = num_columns_per_channel ? _fbb.CreateVector(*num_columns_per_channel) : 0; + auto embedding_dim_per_channel__ = embedding_dim_per_channel ? _fbb.CreateVector(*embedding_dim_per_channel) : 0; + return tflite::CreateConcatEmbeddingsOptions( + _fbb, + num_channels, + num_columns_per_channel__, + embedding_dim_per_channel__); +} + +flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LSHProjectionOptionsT : public flatbuffers::NativeTable { + typedef LSHProjectionOptions TableType; + tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN; +}; + +struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSHProjectionOptionsT NativeTableType; + typedef LSHProjectionOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LSHProjectionOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TYPE = 4 + }; + tflite::LSHProjectionType type() const { + return static_cast(GetField(VT_TYPE, 0)); + } + bool mutate_type(tflite::LSHProjectionType _type) { + return SetField(VT_TYPE, static_cast(_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TYPE) && + verifier.EndTable(); + } + LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LSHProjectionOptionsBuilder { + typedef LSHProjectionOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_type(tflite::LSHProjectionType type) { + fbb_.AddElement(LSHProjectionOptions::VT_TYPE, static_cast(type), 0); + } + explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSHProjectionOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN) { + LSHProjectionOptionsBuilder builder_(_fbb); + builder_.add_type(type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SVDFOptionsT : public flatbuffers::NativeTable { + typedef SVDFOptions TableType; + int32_t rank = 0; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + bool asymmetric_quantize_inputs = false; +}; + +struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SVDFOptionsT NativeTableType; + typedef SVDFOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SVDFOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_RANK = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 + }; + int32_t rank() const { + return GetField(VT_RANK, 0); + } + bool mutate_rank(int32_t _rank) { + return SetField(VT_RANK, _rank, 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_RANK) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SVDFOptionsBuilder { + typedef SVDFOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_rank(int32_t rank) { + fbb_.AddElement(SVDFOptions::VT_RANK, rank, 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSVDFOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t rank = 0, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + bool asymmetric_quantize_inputs = false) { + SVDFOptionsBuilder builder_(_fbb); + builder_.add_rank(rank); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RNNOptionsT : public flatbuffers::NativeTable { + typedef RNNOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + bool asymmetric_quantize_inputs = false; +}; + +struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RNNOptionsT NativeTableType; + typedef RNNOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RNNOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 6 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RNNOptionsBuilder { + typedef RNNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + bool asymmetric_quantize_inputs = false) { + RNNOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SequenceRNNOptionsT : public flatbuffers::NativeTable { + typedef SequenceRNNOptions TableType; + bool time_major = false; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + bool asymmetric_quantize_inputs = false; +}; + +struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SequenceRNNOptionsT NativeTableType; + typedef SequenceRNNOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SequenceRNNOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TIME_MAJOR = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 + }; + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } + bool mutate_time_major(bool _time_major) { + return SetField(VT_TIME_MAJOR, static_cast(_time_major), 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TIME_MAJOR) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SequenceRNNOptionsBuilder { + typedef SequenceRNNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_time_major(bool time_major) { + fbb_.AddElement(SequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool time_major = false, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + bool asymmetric_quantize_inputs = false) { + SequenceRNNOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_time_major(time_major); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { + typedef BidirectionalSequenceRNNOptions TableType; + bool time_major = false; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + bool merge_outputs = false; + bool asymmetric_quantize_inputs = false; +}; + +struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BidirectionalSequenceRNNOptionsT NativeTableType; + typedef BidirectionalSequenceRNNOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BidirectionalSequenceRNNOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TIME_MAJOR = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6, + VT_MERGE_OUTPUTS = 8, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 + }; + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } + bool mutate_time_major(bool _time_major) { + return SetField(VT_TIME_MAJOR, static_cast(_time_major), 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool merge_outputs() const { + return GetField(VT_MERGE_OUTPUTS, 0) != 0; + } + bool mutate_merge_outputs(bool _merge_outputs) { + return SetField(VT_MERGE_OUTPUTS, static_cast(_merge_outputs), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TIME_MAJOR) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_MERGE_OUTPUTS) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BidirectionalSequenceRNNOptionsBuilder { + typedef BidirectionalSequenceRNNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_time_major(bool time_major) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_merge_outputs(bool merge_outputs) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool time_major = false, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + bool merge_outputs = false, + bool asymmetric_quantize_inputs = false) { + BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_merge_outputs(merge_outputs); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_time_major(time_major); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FullyConnectedOptionsT : public flatbuffers::NativeTable { + typedef FullyConnectedOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT; + bool keep_num_dims = false; + bool asymmetric_quantize_inputs = false; +}; + +struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FullyConnectedOptionsT NativeTableType; + typedef FullyConnectedOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FullyConnectedOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_WEIGHTS_FORMAT = 6, + VT_KEEP_NUM_DIMS = 8, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + tflite::FullyConnectedOptionsWeightsFormat weights_format() const { + return static_cast(GetField(VT_WEIGHTS_FORMAT, 0)); + } + bool mutate_weights_format(tflite::FullyConnectedOptionsWeightsFormat _weights_format) { + return SetField(VT_WEIGHTS_FORMAT, static_cast(_weights_format), 0); + } + bool keep_num_dims() const { + return GetField(VT_KEEP_NUM_DIMS, 0) != 0; + } + bool mutate_keep_num_dims(bool _keep_num_dims) { + return SetField(VT_KEEP_NUM_DIMS, static_cast(_keep_num_dims), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_WEIGHTS_FORMAT) && + VerifyField(verifier, VT_KEEP_NUM_DIMS) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FullyConnectedOptionsBuilder { + typedef FullyConnectedOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_weights_format(tflite::FullyConnectedOptionsWeightsFormat weights_format) { + fbb_.AddElement(FullyConnectedOptions::VT_WEIGHTS_FORMAT, static_cast(weights_format), 0); + } + void add_keep_num_dims(bool keep_num_dims) { + fbb_.AddElement(FullyConnectedOptions::VT_KEEP_NUM_DIMS, static_cast(keep_num_dims), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFullyConnectedOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT, + bool keep_num_dims = false, + bool asymmetric_quantize_inputs = false) { + FullyConnectedOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_keep_num_dims(keep_num_dims); + builder_.add_weights_format(weights_format); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SoftmaxOptionsT : public flatbuffers::NativeTable { + typedef SoftmaxOptions TableType; + float beta = 0.0f; +}; + +struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SoftmaxOptionsT NativeTableType; + typedef SoftmaxOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SoftmaxOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BETA = 4 + }; + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool mutate_beta(float _beta) { + return SetField(VT_BETA, _beta, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BETA) && + verifier.EndTable(); + } + SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SoftmaxOptionsBuilder { + typedef SoftmaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_beta(float beta) { + fbb_.AddElement(SoftmaxOptions::VT_BETA, beta, 0.0f); + } + explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSoftmaxOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float beta = 0.0f) { + SoftmaxOptionsBuilder builder_(_fbb); + builder_.add_beta(beta); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ConcatenationOptionsT : public flatbuffers::NativeTable { + typedef ConcatenationOptions TableType; + int32_t axis = 0; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ConcatenationOptionsT NativeTableType; + typedef ConcatenationOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ConcatenationOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int32_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ConcatenationOptionsBuilder { + typedef ConcatenationOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(ConcatenationOptions::VT_AXIS, axis, 0); + } + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConcatenationOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + ConcatenationOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddOptionsT : public flatbuffers::NativeTable { + typedef AddOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddOptionsT NativeTableType; + typedef AddOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AddOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddOptionsBuilder { + typedef AddOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + AddOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MulOptionsT : public flatbuffers::NativeTable { + typedef MulOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MulOptionsT NativeTableType; + typedef MulOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MulOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MulOptionsBuilder { + typedef MulOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMulOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + MulOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct L2NormOptionsT : public flatbuffers::NativeTable { + typedef L2NormOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef L2NormOptionsT NativeTableType; + typedef L2NormOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return L2NormOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct L2NormOptionsBuilder { + typedef L2NormOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateL2NormOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + L2NormOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable { + typedef LocalResponseNormalizationOptions TableType; + int32_t radius = 0; + float bias = 0.0f; + float alpha = 0.0f; + float beta = 0.0f; +}; + +struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LocalResponseNormalizationOptionsT NativeTableType; + typedef LocalResponseNormalizationOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LocalResponseNormalizationOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_RADIUS = 4, + VT_BIAS = 6, + VT_ALPHA = 8, + VT_BETA = 10 + }; + int32_t radius() const { + return GetField(VT_RADIUS, 0); + } + bool mutate_radius(int32_t _radius) { + return SetField(VT_RADIUS, _radius, 0); + } + float bias() const { + return GetField(VT_BIAS, 0.0f); + } + bool mutate_bias(float _bias) { + return SetField(VT_BIAS, _bias, 0.0f); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool mutate_alpha(float _alpha) { + return SetField(VT_ALPHA, _alpha, 0.0f); + } + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool mutate_beta(float _beta) { + return SetField(VT_BETA, _beta, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_RADIUS) && + VerifyField(verifier, VT_BIAS) && + VerifyField(verifier, VT_ALPHA) && + VerifyField(verifier, VT_BETA) && + verifier.EndTable(); + } + LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LocalResponseNormalizationOptionsBuilder { + typedef LocalResponseNormalizationOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_radius(int32_t radius) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); + } + void add_bias(float bias) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); + } + void add_alpha(float alpha) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); + } + void add_beta(float beta) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); + } + explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLocalResponseNormalizationOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t radius = 0, + float bias = 0.0f, + float alpha = 0.0f, + float beta = 0.0f) { + LocalResponseNormalizationOptionsBuilder builder_(_fbb); + builder_.add_beta(beta); + builder_.add_alpha(alpha); + builder_.add_bias(bias); + builder_.add_radius(radius); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LSTMOptionsT : public flatbuffers::NativeTable { + typedef LSTMOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + float cell_clip = 0.0f; + float proj_clip = 0.0f; + tflite::LSTMKernelType kernel_type = tflite::LSTMKernelType_FULL; + bool asymmetric_quantize_inputs = false; +}; + +struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSTMOptionsT NativeTableType; + typedef LSTMOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LSTMOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8, + VT_KERNEL_TYPE = 10, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + bool mutate_cell_clip(float _cell_clip) { + return SetField(VT_CELL_CLIP, _cell_clip, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); + } + bool mutate_proj_clip(float _proj_clip) { + return SetField(VT_PROJ_CLIP, _proj_clip, 0.0f); + } + tflite::LSTMKernelType kernel_type() const { + return static_cast(GetField(VT_KERNEL_TYPE, 0)); + } + bool mutate_kernel_type(tflite::LSTMKernelType _kernel_type) { + return SetField(VT_KERNEL_TYPE, static_cast(_kernel_type), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_CELL_CLIP) && + VerifyField(verifier, VT_PROJ_CLIP) && + VerifyField(verifier, VT_KERNEL_TYPE) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LSTMOptionsBuilder { + typedef LSTMOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_cell_clip(float cell_clip) { + fbb_.AddElement(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); + } + void add_proj_clip(float proj_clip) { + fbb_.AddElement(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); + } + void add_kernel_type(tflite::LSTMKernelType kernel_type) { + fbb_.AddElement(LSTMOptions::VT_KERNEL_TYPE, static_cast(kernel_type), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSTMOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f, + tflite::LSTMKernelType kernel_type = tflite::LSTMKernelType_FULL, + bool asymmetric_quantize_inputs = false) { + LSTMOptionsBuilder builder_(_fbb); + builder_.add_proj_clip(proj_clip); + builder_.add_cell_clip(cell_clip); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_kernel_type(kernel_type); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { + typedef UnidirectionalSequenceLSTMOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + float cell_clip = 0.0f; + float proj_clip = 0.0f; + bool time_major = false; + bool asymmetric_quantize_inputs = false; +}; + +struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnidirectionalSequenceLSTMOptionsT NativeTableType; + typedef UnidirectionalSequenceLSTMOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UnidirectionalSequenceLSTMOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8, + VT_TIME_MAJOR = 10, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + bool mutate_cell_clip(float _cell_clip) { + return SetField(VT_CELL_CLIP, _cell_clip, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); + } + bool mutate_proj_clip(float _proj_clip) { + return SetField(VT_PROJ_CLIP, _proj_clip, 0.0f); + } + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } + bool mutate_time_major(bool _time_major) { + return SetField(VT_TIME_MAJOR, static_cast(_time_major), 0); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_CELL_CLIP) && + VerifyField(verifier, VT_PROJ_CLIP) && + VerifyField(verifier, VT_TIME_MAJOR) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + UnidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnidirectionalSequenceLSTMOptionsBuilder { + typedef UnidirectionalSequenceLSTMOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_cell_clip(float cell_clip) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); + } + void add_proj_clip(float proj_clip) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); + } + void add_time_major(bool time_major) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f, + bool time_major = false, + bool asymmetric_quantize_inputs = false) { + UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); + builder_.add_proj_clip(proj_clip); + builder_.add_cell_clip(cell_clip); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_time_major(time_major); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { + typedef BidirectionalSequenceLSTMOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; + float cell_clip = 0.0f; + float proj_clip = 0.0f; + bool merge_outputs = false; + bool time_major = true; + bool asymmetric_quantize_inputs = false; +}; + +struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BidirectionalSequenceLSTMOptionsT NativeTableType; + typedef BidirectionalSequenceLSTMOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BidirectionalSequenceLSTMOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8, + VT_MERGE_OUTPUTS = 10, + VT_TIME_MAJOR = 12, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 14 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + bool mutate_cell_clip(float _cell_clip) { + return SetField(VT_CELL_CLIP, _cell_clip, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); + } + bool mutate_proj_clip(float _proj_clip) { + return SetField(VT_PROJ_CLIP, _proj_clip, 0.0f); + } + bool merge_outputs() const { + return GetField(VT_MERGE_OUTPUTS, 0) != 0; + } + bool mutate_merge_outputs(bool _merge_outputs) { + return SetField(VT_MERGE_OUTPUTS, static_cast(_merge_outputs), 0); + } + bool time_major() const { + return GetField(VT_TIME_MAJOR, 1) != 0; + } + bool mutate_time_major(bool _time_major) { + return SetField(VT_TIME_MAJOR, static_cast(_time_major), 1); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool mutate_asymmetric_quantize_inputs(bool _asymmetric_quantize_inputs) { + return SetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(_asymmetric_quantize_inputs), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_CELL_CLIP) && + VerifyField(verifier, VT_PROJ_CLIP) && + VerifyField(verifier, VT_MERGE_OUTPUTS) && + VerifyField(verifier, VT_TIME_MAJOR) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && + verifier.EndTable(); + } + BidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BidirectionalSequenceLSTMOptionsBuilder { + typedef BidirectionalSequenceLSTMOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_cell_clip(float cell_clip) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); + } + void add_proj_clip(float proj_clip) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); + } + void add_merge_outputs(bool merge_outputs) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); + } + void add_time_major(bool time_major) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 1); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f, + bool merge_outputs = false, + bool time_major = true, + bool asymmetric_quantize_inputs = false) { + BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); + builder_.add_proj_clip(proj_clip); + builder_.add_cell_clip(cell_clip); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_time_major(time_major); + builder_.add_merge_outputs(merge_outputs); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ResizeBilinearOptionsT : public flatbuffers::NativeTable { + typedef ResizeBilinearOptions TableType; + bool align_corners = false; + bool half_pixel_centers = false; +}; + +struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ResizeBilinearOptionsT NativeTableType; + typedef ResizeBilinearOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ResizeBilinearOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALIGN_CORNERS = 8, + VT_HALF_PIXEL_CENTERS = 10 + }; + bool align_corners() const { + return GetField(VT_ALIGN_CORNERS, 0) != 0; + } + bool mutate_align_corners(bool _align_corners) { + return SetField(VT_ALIGN_CORNERS, static_cast(_align_corners), 0); + } + bool half_pixel_centers() const { + return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; + } + bool mutate_half_pixel_centers(bool _half_pixel_centers) { + return SetField(VT_HALF_PIXEL_CENTERS, static_cast(_half_pixel_centers), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALIGN_CORNERS) && + VerifyField(verifier, VT_HALF_PIXEL_CENTERS) && + verifier.EndTable(); + } + ResizeBilinearOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ResizeBilinearOptionsBuilder { + typedef ResizeBilinearOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_align_corners(bool align_corners) { + fbb_.AddElement(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); + } + void add_half_pixel_centers(bool half_pixel_centers) { + fbb_.AddElement(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); + } + explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateResizeBilinearOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool align_corners = false, + bool half_pixel_centers = false) { + ResizeBilinearOptionsBuilder builder_(_fbb); + builder_.add_half_pixel_centers(half_pixel_centers); + builder_.add_align_corners(align_corners); + return builder_.Finish(); +} + +flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ResizeNearestNeighborOptionsT : public flatbuffers::NativeTable { + typedef ResizeNearestNeighborOptions TableType; + bool align_corners = false; + bool half_pixel_centers = false; +}; + +struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ResizeNearestNeighborOptionsT NativeTableType; + typedef ResizeNearestNeighborOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ResizeNearestNeighborOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALIGN_CORNERS = 4, + VT_HALF_PIXEL_CENTERS = 6 + }; + bool align_corners() const { + return GetField(VT_ALIGN_CORNERS, 0) != 0; + } + bool mutate_align_corners(bool _align_corners) { + return SetField(VT_ALIGN_CORNERS, static_cast(_align_corners), 0); + } + bool half_pixel_centers() const { + return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; + } + bool mutate_half_pixel_centers(bool _half_pixel_centers) { + return SetField(VT_HALF_PIXEL_CENTERS, static_cast(_half_pixel_centers), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALIGN_CORNERS) && + VerifyField(verifier, VT_HALF_PIXEL_CENTERS) && + verifier.EndTable(); + } + ResizeNearestNeighborOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ResizeNearestNeighborOptionsBuilder { + typedef ResizeNearestNeighborOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_align_corners(bool align_corners) { + fbb_.AddElement(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); + } + void add_half_pixel_centers(bool half_pixel_centers) { + fbb_.AddElement(ResizeNearestNeighborOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); + } + explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateResizeNearestNeighborOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool align_corners = false, + bool half_pixel_centers = false) { + ResizeNearestNeighborOptionsBuilder builder_(_fbb); + builder_.add_half_pixel_centers(half_pixel_centers); + builder_.add_align_corners(align_corners); + return builder_.Finish(); +} + +flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CallOptionsT : public flatbuffers::NativeTable { + typedef CallOptions TableType; + uint32_t subgraph = 0; +}; + +struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CallOptionsT NativeTableType; + typedef CallOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CallOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SUBGRAPH = 4 + }; + uint32_t subgraph() const { + return GetField(VT_SUBGRAPH, 0); + } + bool mutate_subgraph(uint32_t _subgraph) { + return SetField(VT_SUBGRAPH, _subgraph, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SUBGRAPH) && + verifier.EndTable(); + } + CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CallOptionsBuilder { + typedef CallOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_subgraph(uint32_t subgraph) { + fbb_.AddElement(CallOptions::VT_SUBGRAPH, subgraph, 0); + } + explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCallOptions( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t subgraph = 0) { + CallOptionsBuilder builder_(_fbb); + builder_.add_subgraph(subgraph); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PadOptionsT : public flatbuffers::NativeTable { + typedef PadOptions TableType; +}; + +struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PadOptionsT NativeTableType; + typedef PadOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PadOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PadOptionsBuilder { + typedef PadOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePadOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + PadOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PadV2OptionsT : public flatbuffers::NativeTable { + typedef PadV2Options TableType; +}; + +struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PadV2OptionsT NativeTableType; + typedef PadV2OptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PadV2OptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PadV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PadV2OptionsBuilder { + typedef PadV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePadV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + PadV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReshapeOptionsT : public flatbuffers::NativeTable { + typedef ReshapeOptions TableType; + std::vector new_shape{}; +}; + +struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReshapeOptionsT NativeTableType; + typedef ReshapeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReshapeOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NEW_SHAPE = 4 + }; + const flatbuffers::Vector *new_shape() const { + return GetPointer *>(VT_NEW_SHAPE); + } + flatbuffers::Vector *mutable_new_shape() { + return GetPointer *>(VT_NEW_SHAPE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NEW_SHAPE) && + verifier.VerifyVector(new_shape()) && + verifier.EndTable(); + } + ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReshapeOptionsBuilder { + typedef ReshapeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_new_shape(flatbuffers::Offset> new_shape) { + fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); + } + explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReshapeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> new_shape = 0) { + ReshapeOptionsBuilder builder_(_fbb); + builder_.add_new_shape(new_shape); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateReshapeOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *new_shape = nullptr) { + auto new_shape__ = new_shape ? _fbb.CreateVector(*new_shape) : 0; + return tflite::CreateReshapeOptions( + _fbb, + new_shape__); +} + +flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable { + typedef SpaceToBatchNDOptions TableType; +}; + +struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToBatchNDOptionsT NativeTableType; + typedef SpaceToBatchNDOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SpaceToBatchNDOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToBatchNDOptionsBuilder { + typedef SpaceToBatchNDOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToBatchNDOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SpaceToBatchNDOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable { + typedef BatchToSpaceNDOptions TableType; +}; + +struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchToSpaceNDOptionsT NativeTableType; + typedef BatchToSpaceNDOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BatchToSpaceNDOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchToSpaceNDOptionsBuilder { + typedef BatchToSpaceNDOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchToSpaceNDOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + BatchToSpaceNDOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SkipGramOptionsT : public flatbuffers::NativeTable { + typedef SkipGramOptions TableType; + int32_t ngram_size = 0; + int32_t max_skip_size = 0; + bool include_all_ngrams = false; +}; + +struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SkipGramOptionsT NativeTableType; + typedef SkipGramOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SkipGramOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NGRAM_SIZE = 4, + VT_MAX_SKIP_SIZE = 6, + VT_INCLUDE_ALL_NGRAMS = 8 + }; + int32_t ngram_size() const { + return GetField(VT_NGRAM_SIZE, 0); + } + bool mutate_ngram_size(int32_t _ngram_size) { + return SetField(VT_NGRAM_SIZE, _ngram_size, 0); + } + int32_t max_skip_size() const { + return GetField(VT_MAX_SKIP_SIZE, 0); + } + bool mutate_max_skip_size(int32_t _max_skip_size) { + return SetField(VT_MAX_SKIP_SIZE, _max_skip_size, 0); + } + bool include_all_ngrams() const { + return GetField(VT_INCLUDE_ALL_NGRAMS, 0) != 0; + } + bool mutate_include_all_ngrams(bool _include_all_ngrams) { + return SetField(VT_INCLUDE_ALL_NGRAMS, static_cast(_include_all_ngrams), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NGRAM_SIZE) && + VerifyField(verifier, VT_MAX_SKIP_SIZE) && + VerifyField(verifier, VT_INCLUDE_ALL_NGRAMS) && + verifier.EndTable(); + } + SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SkipGramOptionsBuilder { + typedef SkipGramOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_ngram_size(int32_t ngram_size) { + fbb_.AddElement(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); + } + void add_max_skip_size(int32_t max_skip_size) { + fbb_.AddElement(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); + } + void add_include_all_ngrams(bool include_all_ngrams) { + fbb_.AddElement(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast(include_all_ngrams), 0); + } + explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSkipGramOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t ngram_size = 0, + int32_t max_skip_size = 0, + bool include_all_ngrams = false) { + SkipGramOptionsBuilder builder_(_fbb); + builder_.add_max_skip_size(max_skip_size); + builder_.add_ngram_size(ngram_size); + builder_.add_include_all_ngrams(include_all_ngrams); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToDepthOptionsT : public flatbuffers::NativeTable { + typedef SpaceToDepthOptions TableType; + int32_t block_size = 0; +}; + +struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToDepthOptionsT NativeTableType; + typedef SpaceToDepthOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SpaceToDepthOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4 + }; + int32_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } + bool mutate_block_size(int32_t _block_size) { + return SetField(VT_BLOCK_SIZE, _block_size, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BLOCK_SIZE) && + verifier.EndTable(); + } + SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToDepthOptionsBuilder { + typedef SpaceToDepthOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(int32_t block_size) { + fbb_.AddElement(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); + } + explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToDepthOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t block_size = 0) { + SpaceToDepthOptionsBuilder builder_(_fbb); + builder_.add_block_size(block_size); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DepthToSpaceOptionsT : public flatbuffers::NativeTable { + typedef DepthToSpaceOptions TableType; + int32_t block_size = 0; +}; + +struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DepthToSpaceOptionsT NativeTableType; + typedef DepthToSpaceOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DepthToSpaceOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4 + }; + int32_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } + bool mutate_block_size(int32_t _block_size) { + return SetField(VT_BLOCK_SIZE, _block_size, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BLOCK_SIZE) && + verifier.EndTable(); + } + DepthToSpaceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DepthToSpaceOptionsBuilder { + typedef DepthToSpaceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(int32_t block_size) { + fbb_.AddElement(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0); + } + explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDepthToSpaceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t block_size = 0) { + DepthToSpaceOptionsBuilder builder_(_fbb); + builder_.add_block_size(block_size); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubOptionsT : public flatbuffers::NativeTable { + typedef SubOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubOptionsT NativeTableType; + typedef SubOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SubOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubOptionsBuilder { + typedef SubOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + SubOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DivOptionsT : public flatbuffers::NativeTable { + typedef DivOptions TableType; + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE; +}; + +struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DivOptionsT NativeTableType; + typedef DivOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DivOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool mutate_fused_activation_function(tflite::ActivationFunctionType _fused_activation_function) { + return SetField(VT_FUSED_ACTIVATION_FUNCTION, static_cast(_fused_activation_function), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DivOptionsBuilder { + typedef DivOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDivOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { + DivOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TopKV2OptionsT : public flatbuffers::NativeTable { + typedef TopKV2Options TableType; +}; + +struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TopKV2OptionsT NativeTableType; + typedef TopKV2OptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TopKV2OptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TopKV2OptionsBuilder { + typedef TopKV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTopKV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + TopKV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable { + typedef EmbeddingLookupSparseOptions TableType; + tflite::CombinerType combiner = tflite::CombinerType_SUM; +}; + +struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EmbeddingLookupSparseOptionsT NativeTableType; + typedef EmbeddingLookupSparseOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return EmbeddingLookupSparseOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_COMBINER = 4 + }; + tflite::CombinerType combiner() const { + return static_cast(GetField(VT_COMBINER, 0)); + } + bool mutate_combiner(tflite::CombinerType _combiner) { + return SetField(VT_COMBINER, static_cast(_combiner), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_COMBINER) && + verifier.EndTable(); + } + EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EmbeddingLookupSparseOptionsBuilder { + typedef EmbeddingLookupSparseOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_combiner(tflite::CombinerType combiner) { + fbb_.AddElement(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast(combiner), 0); + } + explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::CombinerType combiner = tflite::CombinerType_SUM) { + EmbeddingLookupSparseOptionsBuilder builder_(_fbb); + builder_.add_combiner(combiner); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GatherOptionsT : public flatbuffers::NativeTable { + typedef GatherOptions TableType; + int32_t axis = 0; +}; + +struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherOptionsT NativeTableType; + typedef GatherOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GatherOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int32_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GatherOptionsBuilder { + typedef GatherOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(GatherOptions::VT_AXIS, axis, 0); + } + explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGatherOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0) { + GatherOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TransposeOptionsT : public flatbuffers::NativeTable { + typedef TransposeOptions TableType; +}; + +struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TransposeOptionsT NativeTableType; + typedef TransposeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TransposeOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TransposeOptionsBuilder { + typedef TransposeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTransposeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + TransposeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpOptionsT : public flatbuffers::NativeTable { + typedef ExpOptions TableType; +}; + +struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpOptionsT NativeTableType; + typedef ExpOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ExpOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpOptionsBuilder { + typedef ExpOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ExpOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CosOptionsT : public flatbuffers::NativeTable { + typedef CosOptions TableType; +}; + +struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CosOptionsT NativeTableType; + typedef CosOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CosOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CosOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CosOptionsBuilder { + typedef CosOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCosOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + CosOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReducerOptionsT : public flatbuffers::NativeTable { + typedef ReducerOptions TableType; + bool keep_dims = false; +}; + +struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReducerOptionsT NativeTableType; + typedef ReducerOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReducerOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEEP_DIMS = 4 + }; + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } + bool mutate_keep_dims(bool _keep_dims) { + return SetField(VT_KEEP_DIMS, static_cast(_keep_dims), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_KEEP_DIMS) && + verifier.EndTable(); + } + ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReducerOptionsBuilder { + typedef ReducerOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_keep_dims(bool keep_dims) { + fbb_.AddElement(ReducerOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); + } + explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReducerOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool keep_dims = false) { + ReducerOptionsBuilder builder_(_fbb); + builder_.add_keep_dims(keep_dims); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SqueezeOptionsT : public flatbuffers::NativeTable { + typedef SqueezeOptions TableType; + std::vector squeeze_dims{}; +}; + +struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SqueezeOptionsT NativeTableType; + typedef SqueezeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SqueezeOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SQUEEZE_DIMS = 4 + }; + const flatbuffers::Vector *squeeze_dims() const { + return GetPointer *>(VT_SQUEEZE_DIMS); + } + flatbuffers::Vector *mutable_squeeze_dims() { + return GetPointer *>(VT_SQUEEZE_DIMS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SQUEEZE_DIMS) && + verifier.VerifyVector(squeeze_dims()) && + verifier.EndTable(); + } + SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SqueezeOptionsBuilder { + typedef SqueezeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_squeeze_dims(flatbuffers::Offset> squeeze_dims) { + fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); + } + explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSqueezeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> squeeze_dims = 0) { + SqueezeOptionsBuilder builder_(_fbb); + builder_.add_squeeze_dims(squeeze_dims); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSqueezeOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *squeeze_dims = nullptr) { + auto squeeze_dims__ = squeeze_dims ? _fbb.CreateVector(*squeeze_dims) : 0; + return tflite::CreateSqueezeOptions( + _fbb, + squeeze_dims__); +} + +flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SplitOptionsT : public flatbuffers::NativeTable { + typedef SplitOptions TableType; + int32_t num_splits = 0; +}; + +struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitOptionsT NativeTableType; + typedef SplitOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SplitOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_SPLITS = 4 + }; + int32_t num_splits() const { + return GetField(VT_NUM_SPLITS, 0); + } + bool mutate_num_splits(int32_t _num_splits) { + return SetField(VT_NUM_SPLITS, _num_splits, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_SPLITS) && + verifier.EndTable(); + } + SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SplitOptionsBuilder { + typedef SplitOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_splits(int32_t num_splits) { + fbb_.AddElement(SplitOptions::VT_NUM_SPLITS, num_splits, 0); + } + explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplitOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_splits = 0) { + SplitOptionsBuilder builder_(_fbb); + builder_.add_num_splits(num_splits); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SplitVOptionsT : public flatbuffers::NativeTable { + typedef SplitVOptions TableType; + int32_t num_splits = 0; +}; + +struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitVOptionsT NativeTableType; + typedef SplitVOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SplitVOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_SPLITS = 4 + }; + int32_t num_splits() const { + return GetField(VT_NUM_SPLITS, 0); + } + bool mutate_num_splits(int32_t _num_splits) { + return SetField(VT_NUM_SPLITS, _num_splits, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_SPLITS) && + verifier.EndTable(); + } + SplitVOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SplitVOptionsBuilder { + typedef SplitVOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_splits(int32_t num_splits) { + fbb_.AddElement(SplitVOptions::VT_NUM_SPLITS, num_splits, 0); + } + explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplitVOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_splits = 0) { + SplitVOptionsBuilder builder_(_fbb); + builder_.add_num_splits(num_splits); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StridedSliceOptionsT : public flatbuffers::NativeTable { + typedef StridedSliceOptions TableType; + int32_t begin_mask = 0; + int32_t end_mask = 0; + int32_t ellipsis_mask = 0; + int32_t new_axis_mask = 0; + int32_t shrink_axis_mask = 0; +}; + +struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StridedSliceOptionsT NativeTableType; + typedef StridedSliceOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return StridedSliceOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BEGIN_MASK = 4, + VT_END_MASK = 6, + VT_ELLIPSIS_MASK = 8, + VT_NEW_AXIS_MASK = 10, + VT_SHRINK_AXIS_MASK = 12 + }; + int32_t begin_mask() const { + return GetField(VT_BEGIN_MASK, 0); + } + bool mutate_begin_mask(int32_t _begin_mask) { + return SetField(VT_BEGIN_MASK, _begin_mask, 0); + } + int32_t end_mask() const { + return GetField(VT_END_MASK, 0); + } + bool mutate_end_mask(int32_t _end_mask) { + return SetField(VT_END_MASK, _end_mask, 0); + } + int32_t ellipsis_mask() const { + return GetField(VT_ELLIPSIS_MASK, 0); + } + bool mutate_ellipsis_mask(int32_t _ellipsis_mask) { + return SetField(VT_ELLIPSIS_MASK, _ellipsis_mask, 0); + } + int32_t new_axis_mask() const { + return GetField(VT_NEW_AXIS_MASK, 0); + } + bool mutate_new_axis_mask(int32_t _new_axis_mask) { + return SetField(VT_NEW_AXIS_MASK, _new_axis_mask, 0); + } + int32_t shrink_axis_mask() const { + return GetField(VT_SHRINK_AXIS_MASK, 0); + } + bool mutate_shrink_axis_mask(int32_t _shrink_axis_mask) { + return SetField(VT_SHRINK_AXIS_MASK, _shrink_axis_mask, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BEGIN_MASK) && + VerifyField(verifier, VT_END_MASK) && + VerifyField(verifier, VT_ELLIPSIS_MASK) && + VerifyField(verifier, VT_NEW_AXIS_MASK) && + VerifyField(verifier, VT_SHRINK_AXIS_MASK) && + verifier.EndTable(); + } + StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StridedSliceOptionsBuilder { + typedef StridedSliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_begin_mask(int32_t begin_mask) { + fbb_.AddElement(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0); + } + void add_end_mask(int32_t end_mask) { + fbb_.AddElement(StridedSliceOptions::VT_END_MASK, end_mask, 0); + } + void add_ellipsis_mask(int32_t ellipsis_mask) { + fbb_.AddElement(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); + } + void add_new_axis_mask(int32_t new_axis_mask) { + fbb_.AddElement(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); + } + void add_shrink_axis_mask(int32_t shrink_axis_mask) { + fbb_.AddElement(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); + } + explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStridedSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t begin_mask = 0, + int32_t end_mask = 0, + int32_t ellipsis_mask = 0, + int32_t new_axis_mask = 0, + int32_t shrink_axis_mask = 0) { + StridedSliceOptionsBuilder builder_(_fbb); + builder_.add_shrink_axis_mask(shrink_axis_mask); + builder_.add_new_axis_mask(new_axis_mask); + builder_.add_ellipsis_mask(ellipsis_mask); + builder_.add_end_mask(end_mask); + builder_.add_begin_mask(begin_mask); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogSoftmaxOptionsT : public flatbuffers::NativeTable { + typedef LogSoftmaxOptions TableType; +}; + +struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogSoftmaxOptionsT NativeTableType; + typedef LogSoftmaxOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogSoftmaxOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogSoftmaxOptionsBuilder { + typedef LogSoftmaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogSoftmaxOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogSoftmaxOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CastOptionsT : public flatbuffers::NativeTable { + typedef CastOptions TableType; + tflite::TensorType in_data_type = tflite::TensorType_FLOAT32; + tflite::TensorType out_data_type = tflite::TensorType_FLOAT32; +}; + +struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CastOptionsT NativeTableType; + typedef CastOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return CastOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IN_DATA_TYPE = 4, + VT_OUT_DATA_TYPE = 6 + }; + tflite::TensorType in_data_type() const { + return static_cast(GetField(VT_IN_DATA_TYPE, 0)); + } + bool mutate_in_data_type(tflite::TensorType _in_data_type) { + return SetField(VT_IN_DATA_TYPE, static_cast(_in_data_type), 0); + } + tflite::TensorType out_data_type() const { + return static_cast(GetField(VT_OUT_DATA_TYPE, 0)); + } + bool mutate_out_data_type(tflite::TensorType _out_data_type) { + return SetField(VT_OUT_DATA_TYPE, static_cast(_out_data_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IN_DATA_TYPE) && + VerifyField(verifier, VT_OUT_DATA_TYPE) && + verifier.EndTable(); + } + CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CastOptionsBuilder { + typedef CastOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_in_data_type(tflite::TensorType in_data_type) { + fbb_.AddElement(CastOptions::VT_IN_DATA_TYPE, static_cast(in_data_type), 0); + } + void add_out_data_type(tflite::TensorType out_data_type) { + fbb_.AddElement(CastOptions::VT_OUT_DATA_TYPE, static_cast(out_data_type), 0); + } + explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCastOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::TensorType in_data_type = tflite::TensorType_FLOAT32, + tflite::TensorType out_data_type = tflite::TensorType_FLOAT32) { + CastOptionsBuilder builder_(_fbb); + builder_.add_out_data_type(out_data_type); + builder_.add_in_data_type(in_data_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DequantizeOptionsT : public flatbuffers::NativeTable { + typedef DequantizeOptions TableType; +}; + +struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DequantizeOptionsT NativeTableType; + typedef DequantizeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DequantizeOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DequantizeOptionsBuilder { + typedef DequantizeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDequantizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DequantizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MaximumMinimumOptionsT : public flatbuffers::NativeTable { + typedef MaximumMinimumOptions TableType; +}; + +struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MaximumMinimumOptionsT NativeTableType; + typedef MaximumMinimumOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MaximumMinimumOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MaximumMinimumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MaximumMinimumOptionsBuilder { + typedef MaximumMinimumOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMaximumMinimumOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + MaximumMinimumOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TileOptionsT : public flatbuffers::NativeTable { + typedef TileOptions TableType; +}; + +struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TileOptionsT NativeTableType; + typedef TileOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TileOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TileOptionsBuilder { + typedef TileOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTileOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + TileOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ArgMaxOptionsT : public flatbuffers::NativeTable { + typedef ArgMaxOptions TableType; + tflite::TensorType output_type = tflite::TensorType_FLOAT32; +}; + +struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMaxOptionsT NativeTableType; + typedef ArgMaxOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ArgMaxOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUTPUT_TYPE = 4 + }; + tflite::TensorType output_type() const { + return static_cast(GetField(VT_OUTPUT_TYPE, 0)); + } + bool mutate_output_type(tflite::TensorType _output_type) { + return SetField(VT_OUTPUT_TYPE, static_cast(_output_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_TYPE) && + verifier.EndTable(); + } + ArgMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMaxOptionsBuilder { + typedef ArgMaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_type(tflite::TensorType output_type) { + fbb_.AddElement(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); + } + explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMaxOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::TensorType output_type = tflite::TensorType_FLOAT32) { + ArgMaxOptionsBuilder builder_(_fbb); + builder_.add_output_type(output_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ArgMinOptionsT : public flatbuffers::NativeTable { + typedef ArgMinOptions TableType; + tflite::TensorType output_type = tflite::TensorType_FLOAT32; +}; + +struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMinOptionsT NativeTableType; + typedef ArgMinOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ArgMinOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUTPUT_TYPE = 4 + }; + tflite::TensorType output_type() const { + return static_cast(GetField(VT_OUTPUT_TYPE, 0)); + } + bool mutate_output_type(tflite::TensorType _output_type) { + return SetField(VT_OUTPUT_TYPE, static_cast(_output_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_TYPE) && + verifier.EndTable(); + } + ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMinOptionsBuilder { + typedef ArgMinOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_type(tflite::TensorType output_type) { + fbb_.AddElement(ArgMinOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); + } + explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMinOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::TensorType output_type = tflite::TensorType_FLOAT32) { + ArgMinOptionsBuilder builder_(_fbb); + builder_.add_output_type(output_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GreaterOptionsT : public flatbuffers::NativeTable { + typedef GreaterOptions TableType; +}; + +struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GreaterOptionsT NativeTableType; + typedef GreaterOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GreaterOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GreaterOptionsBuilder { + typedef GreaterOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGreaterOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + GreaterOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GreaterEqualOptionsT : public flatbuffers::NativeTable { + typedef GreaterEqualOptions TableType; +}; + +struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GreaterEqualOptionsT NativeTableType; + typedef GreaterEqualOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GreaterEqualOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GreaterEqualOptionsBuilder { + typedef GreaterEqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGreaterEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + GreaterEqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LessOptionsT : public flatbuffers::NativeTable { + typedef LessOptions TableType; +}; + +struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LessOptionsT NativeTableType; + typedef LessOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LessOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LessOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LessOptionsBuilder { + typedef LessOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLessOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LessOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LessEqualOptionsT : public flatbuffers::NativeTable { + typedef LessEqualOptions TableType; +}; + +struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LessEqualOptionsT NativeTableType; + typedef LessEqualOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LessEqualOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LessEqualOptionsBuilder { + typedef LessEqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLessEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LessEqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NegOptionsT : public flatbuffers::NativeTable { + typedef NegOptions TableType; +}; + +struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NegOptionsT NativeTableType; + typedef NegOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NegOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NegOptionsBuilder { + typedef NegOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNegOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + NegOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SelectOptionsT : public flatbuffers::NativeTable { + typedef SelectOptions TableType; +}; + +struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SelectOptionsT NativeTableType; + typedef SelectOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SelectOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SelectOptionsBuilder { + typedef SelectOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSelectOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SelectOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SliceOptionsT : public flatbuffers::NativeTable { + typedef SliceOptions TableType; +}; + +struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SliceOptionsT NativeTableType; + typedef SliceOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SliceOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SliceOptionsBuilder { + typedef SliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SliceOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TransposeConvOptionsT : public flatbuffers::NativeTable { + typedef TransposeConvOptions TableType; + tflite::Padding padding = tflite::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; +}; + +struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TransposeConvOptionsT NativeTableType; + typedef TransposeConvOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TransposeConvOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8 + }; + tflite::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + bool mutate_padding(tflite::Padding _padding) { + return SetField(VT_PADDING, static_cast(_padding), 0); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + bool mutate_stride_w(int32_t _stride_w) { + return SetField(VT_STRIDE_W, _stride_w, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + bool mutate_stride_h(int32_t _stride_h) { + return SetField(VT_STRIDE_H, _stride_h, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING) && + VerifyField(verifier, VT_STRIDE_W) && + VerifyField(verifier, VT_STRIDE_H) && + verifier.EndTable(); + } + TransposeConvOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TransposeConvOptionsBuilder { + typedef TransposeConvOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite::Padding padding) { + fbb_.AddElement(TransposeConvOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(TransposeConvOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(TransposeConvOptions::VT_STRIDE_H, stride_h, 0); + } + explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTransposeConvOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::Padding padding = tflite::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0) { + TransposeConvOptionsBuilder builder_(_fbb); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpandDimsOptionsT : public flatbuffers::NativeTable { + typedef ExpandDimsOptions TableType; +}; + +struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpandDimsOptionsT NativeTableType; + typedef ExpandDimsOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ExpandDimsOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ExpandDimsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpandDimsOptionsBuilder { + typedef ExpandDimsOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpandDimsOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ExpandDimsOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SparseToDenseOptionsT : public flatbuffers::NativeTable { + typedef SparseToDenseOptions TableType; + bool validate_indices = false; +}; + +struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseToDenseOptionsT NativeTableType; + typedef SparseToDenseOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SparseToDenseOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALIDATE_INDICES = 4 + }; + bool validate_indices() const { + return GetField(VT_VALIDATE_INDICES, 0) != 0; + } + bool mutate_validate_indices(bool _validate_indices) { + return SetField(VT_VALIDATE_INDICES, static_cast(_validate_indices), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VALIDATE_INDICES) && + verifier.EndTable(); + } + SparseToDenseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SparseToDenseOptionsBuilder { + typedef SparseToDenseOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_validate_indices(bool validate_indices) { + fbb_.AddElement(SparseToDenseOptions::VT_VALIDATE_INDICES, static_cast(validate_indices), 0); + } + explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseToDenseOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool validate_indices = false) { + SparseToDenseOptionsBuilder builder_(_fbb); + builder_.add_validate_indices(validate_indices); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EqualOptionsT : public flatbuffers::NativeTable { + typedef EqualOptions TableType; +}; + +struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EqualOptionsT NativeTableType; + typedef EqualOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return EqualOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + EqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EqualOptionsBuilder { + typedef EqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + EqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NotEqualOptionsT : public flatbuffers::NativeTable { + typedef NotEqualOptions TableType; +}; + +struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NotEqualOptionsT NativeTableType; + typedef NotEqualOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NotEqualOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NotEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NotEqualOptionsBuilder { + typedef NotEqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNotEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + NotEqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ShapeOptionsT : public flatbuffers::NativeTable { + typedef ShapeOptions TableType; + tflite::TensorType out_type = tflite::TensorType_FLOAT32; +}; + +struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ShapeOptionsT NativeTableType; + typedef ShapeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ShapeOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUT_TYPE = 4 + }; + tflite::TensorType out_type() const { + return static_cast(GetField(VT_OUT_TYPE, 0)); + } + bool mutate_out_type(tflite::TensorType _out_type) { + return SetField(VT_OUT_TYPE, static_cast(_out_type), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUT_TYPE) && + verifier.EndTable(); + } + ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ShapeOptionsBuilder { + typedef ShapeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_out_type(tflite::TensorType out_type) { + fbb_.AddElement(ShapeOptions::VT_OUT_TYPE, static_cast(out_type), 0); + } + explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateShapeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::TensorType out_type = tflite::TensorType_FLOAT32) { + ShapeOptionsBuilder builder_(_fbb); + builder_.add_out_type(out_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RankOptionsT : public flatbuffers::NativeTable { + typedef RankOptions TableType; +}; + +struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RankOptionsT NativeTableType; + typedef RankOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RankOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RankOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RankOptionsBuilder { + typedef RankOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRankOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + RankOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PowOptionsT : public flatbuffers::NativeTable { + typedef PowOptions TableType; +}; + +struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PowOptionsT NativeTableType; + typedef PowOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PowOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PowOptionsBuilder { + typedef PowOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePowOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + PowOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FakeQuantOptionsT : public flatbuffers::NativeTable { + typedef FakeQuantOptions TableType; + float min = 0.0f; + float max = 0.0f; + int32_t num_bits = 0; + bool narrow_range = false; +}; + +struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FakeQuantOptionsT NativeTableType; + typedef FakeQuantOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FakeQuantOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MIN = 4, + VT_MAX = 6, + VT_NUM_BITS = 8, + VT_NARROW_RANGE = 10 + }; + float min() const { + return GetField(VT_MIN, 0.0f); + } + bool mutate_min(float _min) { + return SetField(VT_MIN, _min, 0.0f); + } + float max() const { + return GetField(VT_MAX, 0.0f); + } + bool mutate_max(float _max) { + return SetField(VT_MAX, _max, 0.0f); + } + int32_t num_bits() const { + return GetField(VT_NUM_BITS, 0); + } + bool mutate_num_bits(int32_t _num_bits) { + return SetField(VT_NUM_BITS, _num_bits, 0); + } + bool narrow_range() const { + return GetField(VT_NARROW_RANGE, 0) != 0; + } + bool mutate_narrow_range(bool _narrow_range) { + return SetField(VT_NARROW_RANGE, static_cast(_narrow_range), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MIN) && + VerifyField(verifier, VT_MAX) && + VerifyField(verifier, VT_NUM_BITS) && + VerifyField(verifier, VT_NARROW_RANGE) && + verifier.EndTable(); + } + FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FakeQuantOptionsBuilder { + typedef FakeQuantOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_min(float min) { + fbb_.AddElement(FakeQuantOptions::VT_MIN, min, 0.0f); + } + void add_max(float max) { + fbb_.AddElement(FakeQuantOptions::VT_MAX, max, 0.0f); + } + void add_num_bits(int32_t num_bits) { + fbb_.AddElement(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); + } + void add_narrow_range(bool narrow_range) { + fbb_.AddElement(FakeQuantOptions::VT_NARROW_RANGE, static_cast(narrow_range), 0); + } + explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFakeQuantOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float min = 0.0f, + float max = 0.0f, + int32_t num_bits = 0, + bool narrow_range = false) { + FakeQuantOptionsBuilder builder_(_fbb); + builder_.add_num_bits(num_bits); + builder_.add_max(max); + builder_.add_min(min); + builder_.add_narrow_range(narrow_range); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PackOptionsT : public flatbuffers::NativeTable { + typedef PackOptions TableType; + int32_t values_count = 0; + int32_t axis = 0; +}; + +struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PackOptionsT NativeTableType; + typedef PackOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return PackOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES_COUNT = 4, + VT_AXIS = 6 + }; + int32_t values_count() const { + return GetField(VT_VALUES_COUNT, 0); + } + bool mutate_values_count(int32_t _values_count) { + return SetField(VT_VALUES_COUNT, _values_count, 0); + } + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int32_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VALUES_COUNT) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PackOptionsBuilder { + typedef PackOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values_count(int32_t values_count) { + fbb_.AddElement(PackOptions::VT_VALUES_COUNT, values_count, 0); + } + void add_axis(int32_t axis) { + fbb_.AddElement(PackOptions::VT_AXIS, axis, 0); + } + explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePackOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t values_count = 0, + int32_t axis = 0) { + PackOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_values_count(values_count); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalOrOptionsT : public flatbuffers::NativeTable { + typedef LogicalOrOptions TableType; +}; + +struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalOrOptionsT NativeTableType; + typedef LogicalOrOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogicalOrOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalOrOptionsBuilder { + typedef LogicalOrOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalOrOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalOrOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OneHotOptionsT : public flatbuffers::NativeTable { + typedef OneHotOptions TableType; + int32_t axis = 0; +}; + +struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OneHotOptionsT NativeTableType; + typedef OneHotOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return OneHotOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int32_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OneHotOptionsBuilder { + typedef OneHotOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(OneHotOptions::VT_AXIS, axis, 0); + } + explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOneHotOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0) { + OneHotOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AbsOptionsT : public flatbuffers::NativeTable { + typedef AbsOptions TableType; +}; + +struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AbsOptionsT NativeTableType; + typedef AbsOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AbsOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AbsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AbsOptionsBuilder { + typedef AbsOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAbsOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + AbsOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HardSwishOptionsT : public flatbuffers::NativeTable { + typedef HardSwishOptions TableType; +}; + +struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HardSwishOptionsT NativeTableType; + typedef HardSwishOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return HardSwishOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + HardSwishOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HardSwishOptionsBuilder { + typedef HardSwishOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHardSwishOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + HardSwishOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalAndOptionsT : public flatbuffers::NativeTable { + typedef LogicalAndOptions TableType; +}; + +struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalAndOptionsT NativeTableType; + typedef LogicalAndOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogicalAndOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalAndOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalAndOptionsBuilder { + typedef LogicalAndOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalAndOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalAndOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalNotOptionsT : public flatbuffers::NativeTable { + typedef LogicalNotOptions TableType; +}; + +struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalNotOptionsT NativeTableType; + typedef LogicalNotOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LogicalNotOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalNotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalNotOptionsBuilder { + typedef LogicalNotOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalNotOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalNotOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnpackOptionsT : public flatbuffers::NativeTable { + typedef UnpackOptions TableType; + int32_t num = 0; + int32_t axis = 0; +}; + +struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnpackOptionsT NativeTableType; + typedef UnpackOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UnpackOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM = 4, + VT_AXIS = 6 + }; + int32_t num() const { + return GetField(VT_NUM, 0); + } + bool mutate_num(int32_t _num) { + return SetField(VT_NUM, _num, 0); + } + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool mutate_axis(int32_t _axis) { + return SetField(VT_AXIS, _axis, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + UnpackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnpackOptionsBuilder { + typedef UnpackOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num(int32_t num) { + fbb_.AddElement(UnpackOptions::VT_NUM, num, 0); + } + void add_axis(int32_t axis) { + fbb_.AddElement(UnpackOptions::VT_AXIS, axis, 0); + } + explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnpackOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num = 0, + int32_t axis = 0) { + UnpackOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_num(num); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorDivOptionsT : public flatbuffers::NativeTable { + typedef FloorDivOptions TableType; +}; + +struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorDivOptionsT NativeTableType; + typedef FloorDivOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FloorDivOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorDivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorDivOptionsBuilder { + typedef FloorDivOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloorDivOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorDivOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SquareOptionsT : public flatbuffers::NativeTable { + typedef SquareOptions TableType; +}; + +struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SquareOptionsT NativeTableType; + typedef SquareOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SquareOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SquareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SquareOptionsBuilder { + typedef SquareOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSquareOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SquareOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ZerosLikeOptionsT : public flatbuffers::NativeTable { + typedef ZerosLikeOptions TableType; +}; + +struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ZerosLikeOptionsT NativeTableType; + typedef ZerosLikeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ZerosLikeOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ZerosLikeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ZerosLikeOptionsBuilder { + typedef ZerosLikeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateZerosLikeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ZerosLikeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FillOptionsT : public flatbuffers::NativeTable { + typedef FillOptions TableType; +}; + +struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FillOptionsT NativeTableType; + typedef FillOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FillOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FillOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FillOptionsBuilder { + typedef FillOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFillOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + FillOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorModOptionsT : public flatbuffers::NativeTable { + typedef FloorModOptions TableType; +}; + +struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorModOptionsT NativeTableType; + typedef FloorModOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return FloorModOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorModOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorModOptionsBuilder { + typedef FloorModOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloorModOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorModOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RangeOptionsT : public flatbuffers::NativeTable { + typedef RangeOptions TableType; +}; + +struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RangeOptionsT NativeTableType; + typedef RangeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return RangeOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RangeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RangeOptionsBuilder { + typedef RangeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRangeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + RangeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LeakyReluOptionsT : public flatbuffers::NativeTable { + typedef LeakyReluOptions TableType; + float alpha = 0.0f; +}; + +struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LeakyReluOptionsT NativeTableType; + typedef LeakyReluOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return LeakyReluOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALPHA = 4 + }; + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool mutate_alpha(float _alpha) { + return SetField(VT_ALPHA, _alpha, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALPHA) && + verifier.EndTable(); + } + LeakyReluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LeakyReluOptionsBuilder { + typedef LeakyReluOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_alpha(float alpha) { + fbb_.AddElement(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); + } + explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLeakyReluOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float alpha = 0.0f) { + LeakyReluOptionsBuilder builder_(_fbb); + builder_.add_alpha(alpha); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SquaredDifferenceOptionsT : public flatbuffers::NativeTable { + typedef SquaredDifferenceOptions TableType; +}; + +struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SquaredDifferenceOptionsT NativeTableType; + typedef SquaredDifferenceOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SquaredDifferenceOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SquaredDifferenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SquaredDifferenceOptionsBuilder { + typedef SquaredDifferenceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSquaredDifferenceOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SquaredDifferenceOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MirrorPadOptionsT : public flatbuffers::NativeTable { + typedef MirrorPadOptions TableType; + tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT; +}; + +struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MirrorPadOptionsT NativeTableType; + typedef MirrorPadOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MirrorPadOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MODE = 4 + }; + tflite::MirrorPadMode mode() const { + return static_cast(GetField(VT_MODE, 0)); + } + bool mutate_mode(tflite::MirrorPadMode _mode) { + return SetField(VT_MODE, static_cast(_mode), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MODE) && + verifier.EndTable(); + } + MirrorPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MirrorPadOptionsBuilder { + typedef MirrorPadOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_mode(tflite::MirrorPadMode mode) { + fbb_.AddElement(MirrorPadOptions::VT_MODE, static_cast(mode), 0); + } + explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMirrorPadOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT) { + MirrorPadOptionsBuilder builder_(_fbb); + builder_.add_mode(mode); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UniqueOptionsT : public flatbuffers::NativeTable { + typedef UniqueOptions TableType; + tflite::TensorType idx_out_type = tflite::TensorType_INT32; +}; + +struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UniqueOptionsT NativeTableType; + typedef UniqueOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return UniqueOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IDX_OUT_TYPE = 4 + }; + tflite::TensorType idx_out_type() const { + return static_cast(GetField(VT_IDX_OUT_TYPE, 2)); + } + bool mutate_idx_out_type(tflite::TensorType _idx_out_type) { + return SetField(VT_IDX_OUT_TYPE, static_cast(_idx_out_type), 2); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IDX_OUT_TYPE) && + verifier.EndTable(); + } + UniqueOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UniqueOptionsBuilder { + typedef UniqueOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_idx_out_type(tflite::TensorType idx_out_type) { + fbb_.AddElement(UniqueOptions::VT_IDX_OUT_TYPE, static_cast(idx_out_type), 2); + } + explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUniqueOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::TensorType idx_out_type = tflite::TensorType_INT32) { + UniqueOptionsBuilder builder_(_fbb); + builder_.add_idx_out_type(idx_out_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReverseV2OptionsT : public flatbuffers::NativeTable { + typedef ReverseV2Options TableType; +}; + +struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReverseV2OptionsT NativeTableType; + typedef ReverseV2OptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReverseV2OptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ReverseV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReverseV2OptionsBuilder { + typedef ReverseV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReverseV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + ReverseV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddNOptionsT : public flatbuffers::NativeTable { + typedef AddNOptions TableType; +}; + +struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddNOptionsT NativeTableType; + typedef AddNOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AddNOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AddNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddNOptionsBuilder { + typedef AddNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddNOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + AddNOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GatherNdOptionsT : public flatbuffers::NativeTable { + typedef GatherNdOptions TableType; +}; + +struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherNdOptionsT NativeTableType; + typedef GatherNdOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return GatherNdOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GatherNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GatherNdOptionsBuilder { + typedef GatherNdOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGatherNdOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + GatherNdOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct WhereOptionsT : public flatbuffers::NativeTable { + typedef WhereOptions TableType; +}; + +struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef WhereOptionsT NativeTableType; + typedef WhereOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return WhereOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + WhereOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct WhereOptionsBuilder { + typedef WhereOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateWhereOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + WhereOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReverseSequenceOptionsT : public flatbuffers::NativeTable { + typedef ReverseSequenceOptions TableType; + int32_t seq_dim = 0; + int32_t batch_dim = 0; +}; + +struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReverseSequenceOptionsT NativeTableType; + typedef ReverseSequenceOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ReverseSequenceOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEQ_DIM = 4, + VT_BATCH_DIM = 6 + }; + int32_t seq_dim() const { + return GetField(VT_SEQ_DIM, 0); + } + bool mutate_seq_dim(int32_t _seq_dim) { + return SetField(VT_SEQ_DIM, _seq_dim, 0); + } + int32_t batch_dim() const { + return GetField(VT_BATCH_DIM, 0); + } + bool mutate_batch_dim(int32_t _batch_dim) { + return SetField(VT_BATCH_DIM, _batch_dim, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEQ_DIM) && + VerifyField(verifier, VT_BATCH_DIM) && + verifier.EndTable(); + } + ReverseSequenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReverseSequenceOptionsBuilder { + typedef ReverseSequenceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seq_dim(int32_t seq_dim) { + fbb_.AddElement(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0); + } + void add_batch_dim(int32_t batch_dim) { + fbb_.AddElement(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0); + } + explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReverseSequenceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t seq_dim = 0, + int32_t batch_dim = 0) { + ReverseSequenceOptionsBuilder builder_(_fbb); + builder_.add_batch_dim(batch_dim); + builder_.add_seq_dim(seq_dim); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MatrixDiagOptionsT : public flatbuffers::NativeTable { + typedef MatrixDiagOptions TableType; +}; + +struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MatrixDiagOptionsT NativeTableType; + typedef MatrixDiagOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MatrixDiagOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MatrixDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MatrixDiagOptionsBuilder { + typedef MatrixDiagOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMatrixDiagOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + MatrixDiagOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct QuantizeOptionsT : public flatbuffers::NativeTable { + typedef QuantizeOptions TableType; +}; + +struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef QuantizeOptionsT NativeTableType; + typedef QuantizeOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return QuantizeOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + QuantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct QuantizeOptionsBuilder { + typedef QuantizeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateQuantizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + QuantizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MatrixSetDiagOptionsT : public flatbuffers::NativeTable { + typedef MatrixSetDiagOptions TableType; +}; + +struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MatrixSetDiagOptionsT NativeTableType; + typedef MatrixSetDiagOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MatrixSetDiagOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MatrixSetDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MatrixSetDiagOptionsBuilder { + typedef MatrixSetDiagOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMatrixSetDiagOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + MatrixSetDiagOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct IfOptionsT : public flatbuffers::NativeTable { + typedef IfOptions TableType; + int32_t then_subgraph_index = 0; + int32_t else_subgraph_index = 0; +}; + +struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef IfOptionsT NativeTableType; + typedef IfOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return IfOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_THEN_SUBGRAPH_INDEX = 4, + VT_ELSE_SUBGRAPH_INDEX = 6 + }; + int32_t then_subgraph_index() const { + return GetField(VT_THEN_SUBGRAPH_INDEX, 0); + } + bool mutate_then_subgraph_index(int32_t _then_subgraph_index) { + return SetField(VT_THEN_SUBGRAPH_INDEX, _then_subgraph_index, 0); + } + int32_t else_subgraph_index() const { + return GetField(VT_ELSE_SUBGRAPH_INDEX, 0); + } + bool mutate_else_subgraph_index(int32_t _else_subgraph_index) { + return SetField(VT_ELSE_SUBGRAPH_INDEX, _else_subgraph_index, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_THEN_SUBGRAPH_INDEX) && + VerifyField(verifier, VT_ELSE_SUBGRAPH_INDEX) && + verifier.EndTable(); + } + IfOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct IfOptionsBuilder { + typedef IfOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_then_subgraph_index(int32_t then_subgraph_index) { + fbb_.AddElement(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0); + } + void add_else_subgraph_index(int32_t else_subgraph_index) { + fbb_.AddElement(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0); + } + explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateIfOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t then_subgraph_index = 0, + int32_t else_subgraph_index = 0) { + IfOptionsBuilder builder_(_fbb); + builder_.add_else_subgraph_index(else_subgraph_index); + builder_.add_then_subgraph_index(then_subgraph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct WhileOptionsT : public flatbuffers::NativeTable { + typedef WhileOptions TableType; + int32_t cond_subgraph_index = 0; + int32_t body_subgraph_index = 0; +}; + +struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef WhileOptionsT NativeTableType; + typedef WhileOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return WhileOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_COND_SUBGRAPH_INDEX = 4, + VT_BODY_SUBGRAPH_INDEX = 6 + }; + int32_t cond_subgraph_index() const { + return GetField(VT_COND_SUBGRAPH_INDEX, 0); + } + bool mutate_cond_subgraph_index(int32_t _cond_subgraph_index) { + return SetField(VT_COND_SUBGRAPH_INDEX, _cond_subgraph_index, 0); + } + int32_t body_subgraph_index() const { + return GetField(VT_BODY_SUBGRAPH_INDEX, 0); + } + bool mutate_body_subgraph_index(int32_t _body_subgraph_index) { + return SetField(VT_BODY_SUBGRAPH_INDEX, _body_subgraph_index, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_COND_SUBGRAPH_INDEX) && + VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX) && + verifier.EndTable(); + } + WhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct WhileOptionsBuilder { + typedef WhileOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_cond_subgraph_index(int32_t cond_subgraph_index) { + fbb_.AddElement(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); + } + void add_body_subgraph_index(int32_t body_subgraph_index) { + fbb_.AddElement(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); + } + explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateWhileOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t cond_subgraph_index = 0, + int32_t body_subgraph_index = 0) { + WhileOptionsBuilder builder_(_fbb); + builder_.add_body_subgraph_index(body_subgraph_index); + builder_.add_cond_subgraph_index(cond_subgraph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NonMaxSuppressionV4OptionsT : public flatbuffers::NativeTable { + typedef NonMaxSuppressionV4Options TableType; +}; + +struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NonMaxSuppressionV4OptionsT NativeTableType; + typedef NonMaxSuppressionV4OptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NonMaxSuppressionV4OptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NonMaxSuppressionV4OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NonMaxSuppressionV4OptionsBuilder { + typedef NonMaxSuppressionV4Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNonMaxSuppressionV4Options( + flatbuffers::FlatBufferBuilder &_fbb) { + NonMaxSuppressionV4OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NonMaxSuppressionV5OptionsT : public flatbuffers::NativeTable { + typedef NonMaxSuppressionV5Options TableType; +}; + +struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NonMaxSuppressionV5OptionsT NativeTableType; + typedef NonMaxSuppressionV5OptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return NonMaxSuppressionV5OptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NonMaxSuppressionV5OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NonMaxSuppressionV5OptionsBuilder { + typedef NonMaxSuppressionV5Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNonMaxSuppressionV5Options( + flatbuffers::FlatBufferBuilder &_fbb) { + NonMaxSuppressionV5OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ScatterNdOptionsT : public flatbuffers::NativeTable { + typedef ScatterNdOptions TableType; +}; + +struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ScatterNdOptionsT NativeTableType; + typedef ScatterNdOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ScatterNdOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ScatterNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ScatterNdOptionsBuilder { + typedef ScatterNdOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateScatterNdOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ScatterNdOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SelectV2OptionsT : public flatbuffers::NativeTable { + typedef SelectV2Options TableType; +}; + +struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SelectV2OptionsT NativeTableType; + typedef SelectV2OptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SelectV2OptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SelectV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SelectV2OptionsBuilder { + typedef SelectV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSelectV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + SelectV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DensifyOptionsT : public flatbuffers::NativeTable { + typedef DensifyOptions TableType; +}; + +struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DensifyOptionsT NativeTableType; + typedef DensifyOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return DensifyOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DensifyOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DensifyOptionsBuilder { + typedef DensifyOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDensifyOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DensifyOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SegmentSumOptionsT : public flatbuffers::NativeTable { + typedef SegmentSumOptions TableType; +}; + +struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SegmentSumOptionsT NativeTableType; + typedef SegmentSumOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SegmentSumOptionsTypeTable(); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SegmentSumOptionsBuilder { + typedef SegmentSumOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSegmentSumOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SegmentSumOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchMatMulOptionsT : public flatbuffers::NativeTable { + typedef BatchMatMulOptions TableType; + bool adj_x = false; + bool adj_y = false; +}; + +struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchMatMulOptionsT NativeTableType; + typedef BatchMatMulOptionsBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BatchMatMulOptionsTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ADJ_X = 4, + VT_ADJ_Y = 6 + }; + bool adj_x() const { + return GetField(VT_ADJ_X, 0) != 0; + } + bool mutate_adj_x(bool _adj_x) { + return SetField(VT_ADJ_X, static_cast(_adj_x), 0); + } + bool adj_y() const { + return GetField(VT_ADJ_Y, 0) != 0; + } + bool mutate_adj_y(bool _adj_y) { + return SetField(VT_ADJ_Y, static_cast(_adj_y), 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ADJ_X) && + VerifyField(verifier, VT_ADJ_Y) && + verifier.EndTable(); + } + BatchMatMulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchMatMulOptionsBuilder { + typedef BatchMatMulOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_adj_x(bool adj_x) { + fbb_.AddElement(BatchMatMulOptions::VT_ADJ_X, static_cast(adj_x), 0); + } + void add_adj_y(bool adj_y) { + fbb_.AddElement(BatchMatMulOptions::VT_ADJ_Y, static_cast(adj_y), 0); + } + explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchMatMulOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool adj_x = false, + bool adj_y = false) { + BatchMatMulOptionsBuilder builder_(_fbb); + builder_.add_adj_y(adj_y); + builder_.add_adj_x(adj_x); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OperatorCodeT : public flatbuffers::NativeTable { + typedef OperatorCode TableType; + tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD; + std::string custom_code{}; + int32_t version = 1; +}; + +struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OperatorCodeT NativeTableType; + typedef OperatorCodeBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return OperatorCodeTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BUILTIN_CODE = 4, + VT_CUSTOM_CODE = 6, + VT_VERSION = 8 + }; + tflite::BuiltinOperator builtin_code() const { + return static_cast(GetField(VT_BUILTIN_CODE, 0)); + } + bool mutate_builtin_code(tflite::BuiltinOperator _builtin_code) { + return SetField(VT_BUILTIN_CODE, static_cast(_builtin_code), 0); + } + const flatbuffers::String *custom_code() const { + return GetPointer(VT_CUSTOM_CODE); + } + flatbuffers::String *mutable_custom_code() { + return GetPointer(VT_CUSTOM_CODE); + } + int32_t version() const { + return GetField(VT_VERSION, 1); + } + bool mutate_version(int32_t _version) { + return SetField(VT_VERSION, _version, 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BUILTIN_CODE) && + VerifyOffset(verifier, VT_CUSTOM_CODE) && + verifier.VerifyString(custom_code()) && + VerifyField(verifier, VT_VERSION) && + verifier.EndTable(); + } + OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OperatorCodeBuilder { + typedef OperatorCode Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_builtin_code(tflite::BuiltinOperator builtin_code) { + fbb_.AddElement(OperatorCode::VT_BUILTIN_CODE, static_cast(builtin_code), 0); + } + void add_custom_code(flatbuffers::Offset custom_code) { + fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); + } + void add_version(int32_t version) { + fbb_.AddElement(OperatorCode::VT_VERSION, version, 1); + } + explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOperatorCode( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD, + flatbuffers::Offset custom_code = 0, + int32_t version = 1) { + OperatorCodeBuilder builder_(_fbb); + builder_.add_version(version); + builder_.add_custom_code(custom_code); + builder_.add_builtin_code(builtin_code); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateOperatorCodeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD, + const char *custom_code = nullptr, + int32_t version = 1) { + auto custom_code__ = custom_code ? _fbb.CreateString(custom_code) : 0; + return tflite::CreateOperatorCode( + _fbb, + builtin_code, + custom_code__, + version); +} + +flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OperatorT : public flatbuffers::NativeTable { + typedef Operator TableType; + uint32_t opcode_index = 0; + std::vector inputs{}; + std::vector outputs{}; + tflite::BuiltinOptionsUnion builtin_options{}; + std::vector custom_options{}; + tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS; + std::vector mutating_variable_inputs{}; + std::vector intermediates{}; +}; + +struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OperatorT NativeTableType; + typedef OperatorBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return OperatorTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OPCODE_INDEX = 4, + VT_INPUTS = 6, + VT_OUTPUTS = 8, + VT_BUILTIN_OPTIONS_TYPE = 10, + VT_BUILTIN_OPTIONS = 12, + VT_CUSTOM_OPTIONS = 14, + VT_CUSTOM_OPTIONS_FORMAT = 16, + VT_MUTATING_VARIABLE_INPUTS = 18, + VT_INTERMEDIATES = 20 + }; + uint32_t opcode_index() const { + return GetField(VT_OPCODE_INDEX, 0); + } + bool mutate_opcode_index(uint32_t _opcode_index) { + return SetField(VT_OPCODE_INDEX, _opcode_index, 0); + } + const flatbuffers::Vector *inputs() const { + return GetPointer *>(VT_INPUTS); + } + flatbuffers::Vector *mutable_inputs() { + return GetPointer *>(VT_INPUTS); + } + const flatbuffers::Vector *outputs() const { + return GetPointer *>(VT_OUTPUTS); + } + flatbuffers::Vector *mutable_outputs() { + return GetPointer *>(VT_OUTPUTS); + } + tflite::BuiltinOptions builtin_options_type() const { + return static_cast(GetField(VT_BUILTIN_OPTIONS_TYPE, 0)); + } + const void *builtin_options() const { + return GetPointer(VT_BUILTIN_OPTIONS); + } + template const T *builtin_options_as() const; + const tflite::Conv2DOptions *builtin_options_as_Conv2DOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_Conv2DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_DepthwiseConv2DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ConcatEmbeddingsOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LSHProjectionOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::Pool2DOptions *builtin_options_as_Pool2DOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_Pool2DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SVDFOptions *builtin_options_as_SVDFOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SVDFOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::RNNOptions *builtin_options_as_RNNOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_RNNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_FullyConnectedOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SoftmaxOptions *builtin_options_as_SoftmaxOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SoftmaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ConcatenationOptions *builtin_options_as_ConcatenationOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ConcatenationOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::AddOptions *builtin_options_as_AddOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_AddOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::L2NormOptions *builtin_options_as_L2NormOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_L2NormOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LocalResponseNormalizationOptions *builtin_options_as_LocalResponseNormalizationOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LocalResponseNormalizationOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LSTMOptions *builtin_options_as_LSTMOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LSTMOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ResizeBilinearOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::CallOptions *builtin_options_as_CallOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_CallOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ReshapeOptions *builtin_options_as_ReshapeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ReshapeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SkipGramOptions *builtin_options_as_SkipGramOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SkipGramOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SpaceToDepthOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_EmbeddingLookupSparseOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::MulOptions *builtin_options_as_MulOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_MulOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::PadOptions *builtin_options_as_PadOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_PadOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::GatherOptions *builtin_options_as_GatherOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_GatherOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_BatchToSpaceNDOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SpaceToBatchNDOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::TransposeOptions *builtin_options_as_TransposeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ReducerOptions *builtin_options_as_ReducerOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ReducerOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SubOptions *builtin_options_as_SubOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::DivOptions *builtin_options_as_DivOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_DivOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SqueezeOptions *builtin_options_as_SqueezeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SqueezeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SequenceRNNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::StridedSliceOptions *builtin_options_as_StridedSliceOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_StridedSliceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ExpOptions *builtin_options_as_ExpOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ExpOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::TopKV2Options *builtin_options_as_TopKV2Options() const { + return builtin_options_type() == tflite::BuiltinOptions_TopKV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite::SplitOptions *builtin_options_as_SplitOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SplitOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LogSoftmaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::CastOptions *builtin_options_as_CastOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_CastOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::DequantizeOptions *builtin_options_as_DequantizeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_DequantizeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_MaximumMinimumOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ArgMaxOptions *builtin_options_as_ArgMaxOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ArgMaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LessOptions *builtin_options_as_LessOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::NegOptions *builtin_options_as_NegOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::PadV2Options *builtin_options_as_PadV2Options() const { + return builtin_options_type() == tflite::BuiltinOptions_PadV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite::GreaterOptions *builtin_options_as_GreaterOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_GreaterOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_GreaterEqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LessEqualOptions *builtin_options_as_LessEqualOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SelectOptions *builtin_options_as_SelectOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SelectOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SliceOptions *builtin_options_as_SliceOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SliceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::TransposeConvOptions *builtin_options_as_TransposeConvOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_TransposeConvOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SparseToDenseOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::TileOptions *builtin_options_as_TileOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_TileOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ExpandDimsOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::EqualOptions *builtin_options_as_EqualOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_EqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::NotEqualOptions *builtin_options_as_NotEqualOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_NotEqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ShapeOptions *builtin_options_as_ShapeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ShapeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::PowOptions *builtin_options_as_PowOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_PowOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ArgMinOptions *builtin_options_as_ArgMinOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ArgMinOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::FakeQuantOptions *builtin_options_as_FakeQuantOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_FakeQuantOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::PackOptions *builtin_options_as_PackOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_PackOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::OneHotOptions *builtin_options_as_OneHotOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_OneHotOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LogicalAndOptions *builtin_options_as_LogicalAndOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LogicalAndOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LogicalNotOptions *builtin_options_as_LogicalNotOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LogicalNotOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::UnpackOptions *builtin_options_as_UnpackOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_UnpackOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::FloorDivOptions *builtin_options_as_FloorDivOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_FloorDivOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SquareOptions *builtin_options_as_SquareOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SquareOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ZerosLikeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::FillOptions *builtin_options_as_FillOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_FillOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::BidirectionalSequenceLSTMOptions *builtin_options_as_BidirectionalSequenceLSTMOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_BidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_BidirectionalSequenceRNNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::UnidirectionalSequenceLSTMOptions *builtin_options_as_UnidirectionalSequenceLSTMOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_UnidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::FloorModOptions *builtin_options_as_FloorModOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_FloorModOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::RangeOptions *builtin_options_as_RangeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_RangeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ResizeNearestNeighborOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::LeakyReluOptions *builtin_options_as_LeakyReluOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_LeakyReluOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SquaredDifferenceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::MirrorPadOptions *builtin_options_as_MirrorPadOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_MirrorPadOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::AbsOptions *builtin_options_as_AbsOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_AbsOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SplitVOptions *builtin_options_as_SplitVOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SplitVOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::UniqueOptions *builtin_options_as_UniqueOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_UniqueOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ReverseV2Options *builtin_options_as_ReverseV2Options() const { + return builtin_options_type() == tflite::BuiltinOptions_ReverseV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite::AddNOptions *builtin_options_as_AddNOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_AddNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::GatherNdOptions *builtin_options_as_GatherNdOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_GatherNdOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::CosOptions *builtin_options_as_CosOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_CosOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::WhereOptions *builtin_options_as_WhereOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_WhereOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::RankOptions *builtin_options_as_RankOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_RankOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ReverseSequenceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_MatrixDiagOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::QuantizeOptions *builtin_options_as_QuantizeOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_QuantizeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_MatrixSetDiagOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::HardSwishOptions *builtin_options_as_HardSwishOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_HardSwishOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::IfOptions *builtin_options_as_IfOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_IfOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::WhileOptions *builtin_options_as_WhileOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_WhileOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_DepthToSpaceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const { + return builtin_options_type() == tflite::BuiltinOptions_NonMaxSuppressionV4Options ? static_cast(builtin_options()) : nullptr; + } + const tflite::NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const { + return builtin_options_type() == tflite::BuiltinOptions_NonMaxSuppressionV5Options ? static_cast(builtin_options()) : nullptr; + } + const tflite::ScatterNdOptions *builtin_options_as_ScatterNdOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_ScatterNdOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SelectV2Options *builtin_options_as_SelectV2Options() const { + return builtin_options_type() == tflite::BuiltinOptions_SelectV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite::DensifyOptions *builtin_options_as_DensifyOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_DensifyOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::SegmentSumOptions *builtin_options_as_SegmentSumOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_SegmentSumOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite::BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const { + return builtin_options_type() == tflite::BuiltinOptions_BatchMatMulOptions ? static_cast(builtin_options()) : nullptr; + } + void *mutable_builtin_options() { + return GetPointer(VT_BUILTIN_OPTIONS); + } + const flatbuffers::Vector *custom_options() const { + return GetPointer *>(VT_CUSTOM_OPTIONS); + } + flatbuffers::Vector *mutable_custom_options() { + return GetPointer *>(VT_CUSTOM_OPTIONS); + } + tflite::CustomOptionsFormat custom_options_format() const { + return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); + } + bool mutate_custom_options_format(tflite::CustomOptionsFormat _custom_options_format) { + return SetField(VT_CUSTOM_OPTIONS_FORMAT, static_cast(_custom_options_format), 0); + } + const flatbuffers::Vector *mutating_variable_inputs() const { + return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); + } + flatbuffers::Vector *mutable_mutating_variable_inputs() { + return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); + } + const flatbuffers::Vector *intermediates() const { + return GetPointer *>(VT_INTERMEDIATES); + } + flatbuffers::Vector *mutable_intermediates() { + return GetPointer *>(VT_INTERMEDIATES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OPCODE_INDEX) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.VerifyVector(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.VerifyVector(outputs()) && + VerifyField(verifier, VT_BUILTIN_OPTIONS_TYPE) && + VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && + VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && + VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && + verifier.VerifyVector(custom_options()) && + VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT) && + VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && + verifier.VerifyVector(mutating_variable_inputs()) && + VerifyOffset(verifier, VT_INTERMEDIATES) && + verifier.VerifyVector(intermediates()) && + verifier.EndTable(); + } + OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const tflite::Conv2DOptions *Operator::builtin_options_as() const { + return builtin_options_as_Conv2DOptions(); +} + +template<> inline const tflite::DepthwiseConv2DOptions *Operator::builtin_options_as() const { + return builtin_options_as_DepthwiseConv2DOptions(); +} + +template<> inline const tflite::ConcatEmbeddingsOptions *Operator::builtin_options_as() const { + return builtin_options_as_ConcatEmbeddingsOptions(); +} + +template<> inline const tflite::LSHProjectionOptions *Operator::builtin_options_as() const { + return builtin_options_as_LSHProjectionOptions(); +} + +template<> inline const tflite::Pool2DOptions *Operator::builtin_options_as() const { + return builtin_options_as_Pool2DOptions(); +} + +template<> inline const tflite::SVDFOptions *Operator::builtin_options_as() const { + return builtin_options_as_SVDFOptions(); +} + +template<> inline const tflite::RNNOptions *Operator::builtin_options_as() const { + return builtin_options_as_RNNOptions(); +} + +template<> inline const tflite::FullyConnectedOptions *Operator::builtin_options_as() const { + return builtin_options_as_FullyConnectedOptions(); +} + +template<> inline const tflite::SoftmaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_SoftmaxOptions(); +} + +template<> inline const tflite::ConcatenationOptions *Operator::builtin_options_as() const { + return builtin_options_as_ConcatenationOptions(); +} + +template<> inline const tflite::AddOptions *Operator::builtin_options_as() const { + return builtin_options_as_AddOptions(); +} + +template<> inline const tflite::L2NormOptions *Operator::builtin_options_as() const { + return builtin_options_as_L2NormOptions(); +} + +template<> inline const tflite::LocalResponseNormalizationOptions *Operator::builtin_options_as() const { + return builtin_options_as_LocalResponseNormalizationOptions(); +} + +template<> inline const tflite::LSTMOptions *Operator::builtin_options_as() const { + return builtin_options_as_LSTMOptions(); +} + +template<> inline const tflite::ResizeBilinearOptions *Operator::builtin_options_as() const { + return builtin_options_as_ResizeBilinearOptions(); +} + +template<> inline const tflite::CallOptions *Operator::builtin_options_as() const { + return builtin_options_as_CallOptions(); +} + +template<> inline const tflite::ReshapeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReshapeOptions(); +} + +template<> inline const tflite::SkipGramOptions *Operator::builtin_options_as() const { + return builtin_options_as_SkipGramOptions(); +} + +template<> inline const tflite::SpaceToDepthOptions *Operator::builtin_options_as() const { + return builtin_options_as_SpaceToDepthOptions(); +} + +template<> inline const tflite::EmbeddingLookupSparseOptions *Operator::builtin_options_as() const { + return builtin_options_as_EmbeddingLookupSparseOptions(); +} + +template<> inline const tflite::MulOptions *Operator::builtin_options_as() const { + return builtin_options_as_MulOptions(); +} + +template<> inline const tflite::PadOptions *Operator::builtin_options_as() const { + return builtin_options_as_PadOptions(); +} + +template<> inline const tflite::GatherOptions *Operator::builtin_options_as() const { + return builtin_options_as_GatherOptions(); +} + +template<> inline const tflite::BatchToSpaceNDOptions *Operator::builtin_options_as() const { + return builtin_options_as_BatchToSpaceNDOptions(); +} + +template<> inline const tflite::SpaceToBatchNDOptions *Operator::builtin_options_as() const { + return builtin_options_as_SpaceToBatchNDOptions(); +} + +template<> inline const tflite::TransposeOptions *Operator::builtin_options_as() const { + return builtin_options_as_TransposeOptions(); +} + +template<> inline const tflite::ReducerOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReducerOptions(); +} + +template<> inline const tflite::SubOptions *Operator::builtin_options_as() const { + return builtin_options_as_SubOptions(); +} + +template<> inline const tflite::DivOptions *Operator::builtin_options_as() const { + return builtin_options_as_DivOptions(); +} + +template<> inline const tflite::SqueezeOptions *Operator::builtin_options_as() const { + return builtin_options_as_SqueezeOptions(); +} + +template<> inline const tflite::SequenceRNNOptions *Operator::builtin_options_as() const { + return builtin_options_as_SequenceRNNOptions(); +} + +template<> inline const tflite::StridedSliceOptions *Operator::builtin_options_as() const { + return builtin_options_as_StridedSliceOptions(); +} + +template<> inline const tflite::ExpOptions *Operator::builtin_options_as() const { + return builtin_options_as_ExpOptions(); +} + +template<> inline const tflite::TopKV2Options *Operator::builtin_options_as() const { + return builtin_options_as_TopKV2Options(); +} + +template<> inline const tflite::SplitOptions *Operator::builtin_options_as() const { + return builtin_options_as_SplitOptions(); +} + +template<> inline const tflite::LogSoftmaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogSoftmaxOptions(); +} + +template<> inline const tflite::CastOptions *Operator::builtin_options_as() const { + return builtin_options_as_CastOptions(); +} + +template<> inline const tflite::DequantizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_DequantizeOptions(); +} + +template<> inline const tflite::MaximumMinimumOptions *Operator::builtin_options_as() const { + return builtin_options_as_MaximumMinimumOptions(); +} + +template<> inline const tflite::ArgMaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_ArgMaxOptions(); +} + +template<> inline const tflite::LessOptions *Operator::builtin_options_as() const { + return builtin_options_as_LessOptions(); +} + +template<> inline const tflite::NegOptions *Operator::builtin_options_as() const { + return builtin_options_as_NegOptions(); +} + +template<> inline const tflite::PadV2Options *Operator::builtin_options_as() const { + return builtin_options_as_PadV2Options(); +} + +template<> inline const tflite::GreaterOptions *Operator::builtin_options_as() const { + return builtin_options_as_GreaterOptions(); +} + +template<> inline const tflite::GreaterEqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_GreaterEqualOptions(); +} + +template<> inline const tflite::LessEqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_LessEqualOptions(); +} + +template<> inline const tflite::SelectOptions *Operator::builtin_options_as() const { + return builtin_options_as_SelectOptions(); +} + +template<> inline const tflite::SliceOptions *Operator::builtin_options_as() const { + return builtin_options_as_SliceOptions(); +} + +template<> inline const tflite::TransposeConvOptions *Operator::builtin_options_as() const { + return builtin_options_as_TransposeConvOptions(); +} + +template<> inline const tflite::SparseToDenseOptions *Operator::builtin_options_as() const { + return builtin_options_as_SparseToDenseOptions(); +} + +template<> inline const tflite::TileOptions *Operator::builtin_options_as() const { + return builtin_options_as_TileOptions(); +} + +template<> inline const tflite::ExpandDimsOptions *Operator::builtin_options_as() const { + return builtin_options_as_ExpandDimsOptions(); +} + +template<> inline const tflite::EqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_EqualOptions(); +} + +template<> inline const tflite::NotEqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_NotEqualOptions(); +} + +template<> inline const tflite::ShapeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ShapeOptions(); +} + +template<> inline const tflite::PowOptions *Operator::builtin_options_as() const { + return builtin_options_as_PowOptions(); +} + +template<> inline const tflite::ArgMinOptions *Operator::builtin_options_as() const { + return builtin_options_as_ArgMinOptions(); +} + +template<> inline const tflite::FakeQuantOptions *Operator::builtin_options_as() const { + return builtin_options_as_FakeQuantOptions(); +} + +template<> inline const tflite::PackOptions *Operator::builtin_options_as() const { + return builtin_options_as_PackOptions(); +} + +template<> inline const tflite::LogicalOrOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalOrOptions(); +} + +template<> inline const tflite::OneHotOptions *Operator::builtin_options_as() const { + return builtin_options_as_OneHotOptions(); +} + +template<> inline const tflite::LogicalAndOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalAndOptions(); +} + +template<> inline const tflite::LogicalNotOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalNotOptions(); +} + +template<> inline const tflite::UnpackOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnpackOptions(); +} + +template<> inline const tflite::FloorDivOptions *Operator::builtin_options_as() const { + return builtin_options_as_FloorDivOptions(); +} + +template<> inline const tflite::SquareOptions *Operator::builtin_options_as() const { + return builtin_options_as_SquareOptions(); +} + +template<> inline const tflite::ZerosLikeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ZerosLikeOptions(); +} + +template<> inline const tflite::FillOptions *Operator::builtin_options_as() const { + return builtin_options_as_FillOptions(); +} + +template<> inline const tflite::BidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { + return builtin_options_as_BidirectionalSequenceLSTMOptions(); +} + +template<> inline const tflite::BidirectionalSequenceRNNOptions *Operator::builtin_options_as() const { + return builtin_options_as_BidirectionalSequenceRNNOptions(); +} + +template<> inline const tflite::UnidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnidirectionalSequenceLSTMOptions(); +} + +template<> inline const tflite::FloorModOptions *Operator::builtin_options_as() const { + return builtin_options_as_FloorModOptions(); +} + +template<> inline const tflite::RangeOptions *Operator::builtin_options_as() const { + return builtin_options_as_RangeOptions(); +} + +template<> inline const tflite::ResizeNearestNeighborOptions *Operator::builtin_options_as() const { + return builtin_options_as_ResizeNearestNeighborOptions(); +} + +template<> inline const tflite::LeakyReluOptions *Operator::builtin_options_as() const { + return builtin_options_as_LeakyReluOptions(); +} + +template<> inline const tflite::SquaredDifferenceOptions *Operator::builtin_options_as() const { + return builtin_options_as_SquaredDifferenceOptions(); +} + +template<> inline const tflite::MirrorPadOptions *Operator::builtin_options_as() const { + return builtin_options_as_MirrorPadOptions(); +} + +template<> inline const tflite::AbsOptions *Operator::builtin_options_as() const { + return builtin_options_as_AbsOptions(); +} + +template<> inline const tflite::SplitVOptions *Operator::builtin_options_as() const { + return builtin_options_as_SplitVOptions(); +} + +template<> inline const tflite::UniqueOptions *Operator::builtin_options_as() const { + return builtin_options_as_UniqueOptions(); +} + +template<> inline const tflite::ReverseV2Options *Operator::builtin_options_as() const { + return builtin_options_as_ReverseV2Options(); +} + +template<> inline const tflite::AddNOptions *Operator::builtin_options_as() const { + return builtin_options_as_AddNOptions(); +} + +template<> inline const tflite::GatherNdOptions *Operator::builtin_options_as() const { + return builtin_options_as_GatherNdOptions(); +} + +template<> inline const tflite::CosOptions *Operator::builtin_options_as() const { + return builtin_options_as_CosOptions(); +} + +template<> inline const tflite::WhereOptions *Operator::builtin_options_as() const { + return builtin_options_as_WhereOptions(); +} + +template<> inline const tflite::RankOptions *Operator::builtin_options_as() const { + return builtin_options_as_RankOptions(); +} + +template<> inline const tflite::ReverseSequenceOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReverseSequenceOptions(); +} + +template<> inline const tflite::MatrixDiagOptions *Operator::builtin_options_as() const { + return builtin_options_as_MatrixDiagOptions(); +} + +template<> inline const tflite::QuantizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_QuantizeOptions(); +} + +template<> inline const tflite::MatrixSetDiagOptions *Operator::builtin_options_as() const { + return builtin_options_as_MatrixSetDiagOptions(); +} + +template<> inline const tflite::HardSwishOptions *Operator::builtin_options_as() const { + return builtin_options_as_HardSwishOptions(); +} + +template<> inline const tflite::IfOptions *Operator::builtin_options_as() const { + return builtin_options_as_IfOptions(); +} + +template<> inline const tflite::WhileOptions *Operator::builtin_options_as() const { + return builtin_options_as_WhileOptions(); +} + +template<> inline const tflite::DepthToSpaceOptions *Operator::builtin_options_as() const { + return builtin_options_as_DepthToSpaceOptions(); +} + +template<> inline const tflite::NonMaxSuppressionV4Options *Operator::builtin_options_as() const { + return builtin_options_as_NonMaxSuppressionV4Options(); +} + +template<> inline const tflite::NonMaxSuppressionV5Options *Operator::builtin_options_as() const { + return builtin_options_as_NonMaxSuppressionV5Options(); +} + +template<> inline const tflite::ScatterNdOptions *Operator::builtin_options_as() const { + return builtin_options_as_ScatterNdOptions(); +} + +template<> inline const tflite::SelectV2Options *Operator::builtin_options_as() const { + return builtin_options_as_SelectV2Options(); +} + +template<> inline const tflite::DensifyOptions *Operator::builtin_options_as() const { + return builtin_options_as_DensifyOptions(); +} + +template<> inline const tflite::SegmentSumOptions *Operator::builtin_options_as() const { + return builtin_options_as_SegmentSumOptions(); +} + +template<> inline const tflite::BatchMatMulOptions *Operator::builtin_options_as() const { + return builtin_options_as_BatchMatMulOptions(); +} + +struct OperatorBuilder { + typedef Operator Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_opcode_index(uint32_t opcode_index) { + fbb_.AddElement(Operator::VT_OPCODE_INDEX, opcode_index, 0); + } + void add_inputs(flatbuffers::Offset> inputs) { + fbb_.AddOffset(Operator::VT_INPUTS, inputs); + } + void add_outputs(flatbuffers::Offset> outputs) { + fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); + } + void add_builtin_options_type(tflite::BuiltinOptions builtin_options_type) { + fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast(builtin_options_type), 0); + } + void add_builtin_options(flatbuffers::Offset builtin_options) { + fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); + } + void add_custom_options(flatbuffers::Offset> custom_options) { + fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); + } + void add_custom_options_format(tflite::CustomOptionsFormat custom_options_format) { + fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); + } + void add_mutating_variable_inputs(flatbuffers::Offset> mutating_variable_inputs) { + fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); + } + void add_intermediates(flatbuffers::Offset> intermediates) { + fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates); + } + explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOperator( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t opcode_index = 0, + flatbuffers::Offset> inputs = 0, + flatbuffers::Offset> outputs = 0, + tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE, + flatbuffers::Offset builtin_options = 0, + flatbuffers::Offset> custom_options = 0, + tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS, + flatbuffers::Offset> mutating_variable_inputs = 0, + flatbuffers::Offset> intermediates = 0) { + OperatorBuilder builder_(_fbb); + builder_.add_intermediates(intermediates); + builder_.add_mutating_variable_inputs(mutating_variable_inputs); + builder_.add_custom_options(custom_options); + builder_.add_builtin_options(builtin_options); + builder_.add_outputs(outputs); + builder_.add_inputs(inputs); + builder_.add_opcode_index(opcode_index); + builder_.add_custom_options_format(custom_options_format); + builder_.add_builtin_options_type(builtin_options_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateOperatorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t opcode_index = 0, + const std::vector *inputs = nullptr, + const std::vector *outputs = nullptr, + tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE, + flatbuffers::Offset builtin_options = 0, + const std::vector *custom_options = nullptr, + tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS, + const std::vector *mutating_variable_inputs = nullptr, + const std::vector *intermediates = nullptr) { + auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; + auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; + auto custom_options__ = custom_options ? _fbb.CreateVector(*custom_options) : 0; + auto mutating_variable_inputs__ = mutating_variable_inputs ? _fbb.CreateVector(*mutating_variable_inputs) : 0; + auto intermediates__ = intermediates ? _fbb.CreateVector(*intermediates) : 0; + return tflite::CreateOperator( + _fbb, + opcode_index, + inputs__, + outputs__, + builtin_options_type, + builtin_options, + custom_options__, + custom_options_format, + mutating_variable_inputs__, + intermediates__); +} + +flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubGraphT : public flatbuffers::NativeTable { + typedef SubGraph TableType; + std::vector> tensors{}; + std::vector inputs{}; + std::vector outputs{}; + std::vector> operators{}; + std::string name{}; +}; + +struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubGraphT NativeTableType; + typedef SubGraphBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return SubGraphTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TENSORS = 4, + VT_INPUTS = 6, + VT_OUTPUTS = 8, + VT_OPERATORS = 10, + VT_NAME = 12 + }; + const flatbuffers::Vector> *tensors() const { + return GetPointer> *>(VT_TENSORS); + } + flatbuffers::Vector> *mutable_tensors() { + return GetPointer> *>(VT_TENSORS); + } + const flatbuffers::Vector *inputs() const { + return GetPointer *>(VT_INPUTS); + } + flatbuffers::Vector *mutable_inputs() { + return GetPointer *>(VT_INPUTS); + } + const flatbuffers::Vector *outputs() const { + return GetPointer *>(VT_OUTPUTS); + } + flatbuffers::Vector *mutable_outputs() { + return GetPointer *>(VT_OUTPUTS); + } + const flatbuffers::Vector> *operators() const { + return GetPointer> *>(VT_OPERATORS); + } + flatbuffers::Vector> *mutable_operators() { + return GetPointer> *>(VT_OPERATORS); + } + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_TENSORS) && + verifier.VerifyVector(tensors()) && + verifier.VerifyVectorOfTables(tensors()) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.VerifyVector(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.VerifyVector(outputs()) && + VerifyOffset(verifier, VT_OPERATORS) && + verifier.VerifyVector(operators()) && + verifier.VerifyVectorOfTables(operators()) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + verifier.EndTable(); + } + SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubGraphBuilder { + typedef SubGraph Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_tensors(flatbuffers::Offset>> tensors) { + fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); + } + void add_inputs(flatbuffers::Offset> inputs) { + fbb_.AddOffset(SubGraph::VT_INPUTS, inputs); + } + void add_outputs(flatbuffers::Offset> outputs) { + fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); + } + void add_operators(flatbuffers::Offset>> operators) { + fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); + } + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(SubGraph::VT_NAME, name); + } + explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubGraph( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset>> tensors = 0, + flatbuffers::Offset> inputs = 0, + flatbuffers::Offset> outputs = 0, + flatbuffers::Offset>> operators = 0, + flatbuffers::Offset name = 0) { + SubGraphBuilder builder_(_fbb); + builder_.add_name(name); + builder_.add_operators(operators); + builder_.add_outputs(outputs); + builder_.add_inputs(inputs); + builder_.add_tensors(tensors); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSubGraphDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector> *tensors = nullptr, + const std::vector *inputs = nullptr, + const std::vector *outputs = nullptr, + const std::vector> *operators = nullptr, + const char *name = nullptr) { + auto tensors__ = tensors ? _fbb.CreateVector>(*tensors) : 0; + auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; + auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; + auto operators__ = operators ? _fbb.CreateVector>(*operators) : 0; + auto name__ = name ? _fbb.CreateString(name) : 0; + return tflite::CreateSubGraph( + _fbb, + tensors__, + inputs__, + outputs__, + operators__, + name__); +} + +flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BufferT : public flatbuffers::NativeTable { + typedef Buffer TableType; + std::vector data{}; +}; + +struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BufferT NativeTableType; + typedef BufferBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return BufferTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DATA = 4 + }; + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + flatbuffers::Vector *mutable_data() { + return GetPointer *>(VT_DATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + verifier.EndTable(); + } + BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BufferBuilder { + typedef Buffer Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(Buffer::VT_DATA, data); + } + explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBuffer( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> data = 0) { + BufferBuilder builder_(_fbb); + builder_.add_data(data); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateBufferDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *data = nullptr) { + if (data) { _fbb.ForceVectorAlignment(data->size(), sizeof(uint8_t), 16); } + auto data__ = data ? _fbb.CreateVector(*data) : 0; + return tflite::CreateBuffer( + _fbb, + data__); +} + +flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MetadataT : public flatbuffers::NativeTable { + typedef Metadata TableType; + std::string name{}; + uint32_t buffer = 0; +}; + +struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MetadataT NativeTableType; + typedef MetadataBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return MetadataTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_BUFFER = 6 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + flatbuffers::String *mutable_name() { + return GetPointer(VT_NAME); + } + uint32_t buffer() const { + return GetField(VT_BUFFER, 0); + } + bool mutate_buffer(uint32_t _buffer) { + return SetField(VT_BUFFER, _buffer, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyField(verifier, VT_BUFFER) && + verifier.EndTable(); + } + MetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MetadataBuilder { + typedef Metadata Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Metadata::VT_NAME, name); + } + void add_buffer(uint32_t buffer) { + fbb_.AddElement(Metadata::VT_BUFFER, buffer, 0); + } + explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMetadata( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + uint32_t buffer = 0) { + MetadataBuilder builder_(_fbb); + builder_.add_buffer(buffer); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateMetadataDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + uint32_t buffer = 0) { + auto name__ = name ? _fbb.CreateString(name) : 0; + return tflite::CreateMetadata( + _fbb, + name__, + buffer); +} + +flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ModelT : public flatbuffers::NativeTable { + typedef Model TableType; + uint32_t version = 0; + std::vector> operator_codes{}; + std::vector> subgraphs{}; + std::string description{}; + std::vector> buffers{}; + std::vector metadata_buffer{}; + std::vector> metadata{}; +}; + +struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ModelT NativeTableType; + typedef ModelBuilder Builder; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return ModelTypeTable(); + } + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VERSION = 4, + VT_OPERATOR_CODES = 6, + VT_SUBGRAPHS = 8, + VT_DESCRIPTION = 10, + VT_BUFFERS = 12, + VT_METADATA_BUFFER = 14, + VT_METADATA = 16 + }; + uint32_t version() const { + return GetField(VT_VERSION, 0); + } + bool mutate_version(uint32_t _version) { + return SetField(VT_VERSION, _version, 0); + } + const flatbuffers::Vector> *operator_codes() const { + return GetPointer> *>(VT_OPERATOR_CODES); + } + flatbuffers::Vector> *mutable_operator_codes() { + return GetPointer> *>(VT_OPERATOR_CODES); + } + const flatbuffers::Vector> *subgraphs() const { + return GetPointer> *>(VT_SUBGRAPHS); + } + flatbuffers::Vector> *mutable_subgraphs() { + return GetPointer> *>(VT_SUBGRAPHS); + } + const flatbuffers::String *description() const { + return GetPointer(VT_DESCRIPTION); + } + flatbuffers::String *mutable_description() { + return GetPointer(VT_DESCRIPTION); + } + const flatbuffers::Vector> *buffers() const { + return GetPointer> *>(VT_BUFFERS); + } + flatbuffers::Vector> *mutable_buffers() { + return GetPointer> *>(VT_BUFFERS); + } + const flatbuffers::Vector *metadata_buffer() const { + return GetPointer *>(VT_METADATA_BUFFER); + } + flatbuffers::Vector *mutable_metadata_buffer() { + return GetPointer *>(VT_METADATA_BUFFER); + } + const flatbuffers::Vector> *metadata() const { + return GetPointer> *>(VT_METADATA); + } + flatbuffers::Vector> *mutable_metadata() { + return GetPointer> *>(VT_METADATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VERSION) && + VerifyOffset(verifier, VT_OPERATOR_CODES) && + verifier.VerifyVector(operator_codes()) && + verifier.VerifyVectorOfTables(operator_codes()) && + VerifyOffset(verifier, VT_SUBGRAPHS) && + verifier.VerifyVector(subgraphs()) && + verifier.VerifyVectorOfTables(subgraphs()) && + VerifyOffset(verifier, VT_DESCRIPTION) && + verifier.VerifyString(description()) && + VerifyOffset(verifier, VT_BUFFERS) && + verifier.VerifyVector(buffers()) && + verifier.VerifyVectorOfTables(buffers()) && + VerifyOffset(verifier, VT_METADATA_BUFFER) && + verifier.VerifyVector(metadata_buffer()) && + VerifyOffset(verifier, VT_METADATA) && + verifier.VerifyVector(metadata()) && + verifier.VerifyVectorOfTables(metadata()) && + verifier.EndTable(); + } + ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ModelBuilder { + typedef Model Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_version(uint32_t version) { + fbb_.AddElement(Model::VT_VERSION, version, 0); + } + void add_operator_codes(flatbuffers::Offset>> operator_codes) { + fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); + } + void add_subgraphs(flatbuffers::Offset>> subgraphs) { + fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); + } + void add_description(flatbuffers::Offset description) { + fbb_.AddOffset(Model::VT_DESCRIPTION, description); + } + void add_buffers(flatbuffers::Offset>> buffers) { + fbb_.AddOffset(Model::VT_BUFFERS, buffers); + } + void add_metadata_buffer(flatbuffers::Offset> metadata_buffer) { + fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); + } + void add_metadata(flatbuffers::Offset>> metadata) { + fbb_.AddOffset(Model::VT_METADATA, metadata); + } + explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateModel( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t version = 0, + flatbuffers::Offset>> operator_codes = 0, + flatbuffers::Offset>> subgraphs = 0, + flatbuffers::Offset description = 0, + flatbuffers::Offset>> buffers = 0, + flatbuffers::Offset> metadata_buffer = 0, + flatbuffers::Offset>> metadata = 0) { + ModelBuilder builder_(_fbb); + builder_.add_metadata(metadata); + builder_.add_metadata_buffer(metadata_buffer); + builder_.add_buffers(buffers); + builder_.add_description(description); + builder_.add_subgraphs(subgraphs); + builder_.add_operator_codes(operator_codes); + builder_.add_version(version); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateModelDirect( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t version = 0, + const std::vector> *operator_codes = nullptr, + const std::vector> *subgraphs = nullptr, + const char *description = nullptr, + const std::vector> *buffers = nullptr, + const std::vector *metadata_buffer = nullptr, + const std::vector> *metadata = nullptr) { + auto operator_codes__ = operator_codes ? _fbb.CreateVector>(*operator_codes) : 0; + auto subgraphs__ = subgraphs ? _fbb.CreateVector>(*subgraphs) : 0; + auto description__ = description ? _fbb.CreateString(description) : 0; + auto buffers__ = buffers ? _fbb.CreateVector>(*buffers) : 0; + auto metadata_buffer__ = metadata_buffer ? _fbb.CreateVector(*metadata_buffer) : 0; + auto metadata__ = metadata ? _fbb.CreateVector>(*metadata) : 0; + return tflite::CreateModel( + _fbb, + version, + operator_codes__, + subgraphs__, + description__, + buffers__, + metadata_buffer__, + metadata__); +} + +flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +inline CustomQuantizationT *CustomQuantization::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CustomQuantizationT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = custom(); if (_e) { _o->custom.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom.begin()); } } +} + +inline flatbuffers::Offset CustomQuantization::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCustomQuantization(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->custom.size(), sizeof(uint8_t), 16); + auto _custom = _o->custom.size() ? _fbb.CreateVector(_o->custom) : 0; + return tflite::CreateCustomQuantization( + _fbb, + _custom); +} + +inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new QuantizationParametersT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } } + { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } } + { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } } + { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } } + { auto _e = details_type(); _o->details.type = _e; } + { auto _e = details(); if (_e) _o->details.value = tflite::QuantizationDetailsUnion::UnPack(_e, details_type(), _resolver); } + { auto _e = quantized_dimension(); _o->quantized_dimension = _e; } +} + +inline flatbuffers::Offset QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateQuantizationParameters(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0; + auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0; + auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0; + auto _zero_point = _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0; + auto _details_type = _o->details.type; + auto _details = _o->details.Pack(_fbb); + auto _quantized_dimension = _o->quantized_dimension; + return tflite::CreateQuantizationParameters( + _fbb, + _min, + _max, + _scale, + _zero_point, + _details_type, + _details, + _quantized_dimension); +} + +inline Int32VectorT *Int32Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Int32VectorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Int32Vector::UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset Int32Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateInt32Vector(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; + return tflite::CreateInt32Vector( + _fbb, + _values); +} + +inline Uint16VectorT *Uint16Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Uint16VectorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset Uint16Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUint16Vector(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint16_t), 4); + auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; + return tflite::CreateUint16Vector( + _fbb, + _values); +} + +inline Uint8VectorT *Uint8Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Uint8VectorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values(); if (_e) { _o->values.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->values.begin()); } } +} + +inline flatbuffers::Offset Uint8Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUint8Vector(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint8_t), 4); + auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; + return tflite::CreateUint8Vector( + _fbb, + _values); +} + +inline DimensionMetadataT *DimensionMetadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DimensionMetadataT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = format(); _o->format = _e; } + { auto _e = dense_size(); _o->dense_size = _e; } + { auto _e = array_segments_type(); _o->array_segments.type = _e; } + { auto _e = array_segments(); if (_e) _o->array_segments.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_segments_type(), _resolver); } + { auto _e = array_indices_type(); _o->array_indices.type = _e; } + { auto _e = array_indices(); if (_e) _o->array_indices.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_indices_type(), _resolver); } +} + +inline flatbuffers::Offset DimensionMetadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDimensionMetadata(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _format = _o->format; + auto _dense_size = _o->dense_size; + auto _array_segments_type = _o->array_segments.type; + auto _array_segments = _o->array_segments.Pack(_fbb); + auto _array_indices_type = _o->array_indices.type; + auto _array_indices = _o->array_indices.Pack(_fbb); + return tflite::CreateDimensionMetadata( + _fbb, + _format, + _dense_size, + _array_segments_type, + _array_segments, + _array_indices_type, + _array_indices); +} + +inline SparsityParametersT *SparsityParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SparsityParametersT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } } + { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } } + { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->dim_metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } +} + +inline flatbuffers::Offset SparsityParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSparsityParameters(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _traversal_order = _o->traversal_order.size() ? _fbb.CreateVector(_o->traversal_order) : 0; + auto _block_map = _o->block_map.size() ? _fbb.CreateVector(_o->block_map) : 0; + auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0; + return tflite::CreateSparsityParameters( + _fbb, + _traversal_order, + _block_map, + _dim_metadata); +} + +inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TensorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } + { auto _e = type(); _o->type = _e; } + { auto _e = buffer(); _o->buffer = _e; } + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = quantization(); if (_e) _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); } + { auto _e = is_variable(); _o->is_variable = _e; } + { auto _e = sparsity(); if (_e) _o->sparsity = std::unique_ptr(_e->UnPack(_resolver)); } + { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTensor(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; + auto _type = _o->type; + auto _buffer = _o->buffer; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; + auto _is_variable = _o->is_variable; + auto _sparsity = _o->sparsity ? CreateSparsityParameters(_fbb, _o->sparsity.get(), _rehasher) : 0; + auto _shape_signature = _o->shape_signature.size() ? _fbb.CreateVector(_o->shape_signature) : 0; + return tflite::CreateTensor( + _fbb, + _shape, + _type, + _buffer, + _name, + _quantization, + _is_variable, + _sparsity, + _shape_signature); +} + +inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Conv2DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } + { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } +} + +inline flatbuffers::Offset Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConv2DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _fused_activation_function = _o->fused_activation_function; + auto _dilation_w_factor = _o->dilation_w_factor; + auto _dilation_h_factor = _o->dilation_h_factor; + return tflite::CreateConv2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _fused_activation_function, + _dilation_w_factor, + _dilation_h_factor); +} + +inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Pool2DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = filter_width(); _o->filter_width = _e; } + { auto _e = filter_height(); _o->filter_height = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePool2DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _filter_width = _o->filter_width; + auto _filter_height = _o->filter_height; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreatePool2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _filter_width, + _filter_height, + _fused_activation_function); +} + +inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DepthwiseConv2DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = depth_multiplier(); _o->depth_multiplier = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } + { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } +} + +inline flatbuffers::Offset DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _depth_multiplier = _o->depth_multiplier; + auto _fused_activation_function = _o->fused_activation_function; + auto _dilation_w_factor = _o->dilation_w_factor; + auto _dilation_h_factor = _o->dilation_h_factor; + return tflite::CreateDepthwiseConv2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _depth_multiplier, + _fused_activation_function, + _dilation_w_factor, + _dilation_h_factor); +} + +inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ConcatEmbeddingsOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_channels(); _o->num_channels = _e; } + { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } } + { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_channels = _o->num_channels; + auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0; + auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0; + return tflite::CreateConcatEmbeddingsOptions( + _fbb, + _num_channels, + _num_columns_per_channel, + _embedding_dim_per_channel); +} + +inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LSHProjectionOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = type(); _o->type = _e; } +} + +inline flatbuffers::Offset LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLSHProjectionOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _type = _o->type; + return tflite::CreateLSHProjectionOptions( + _fbb, + _type); +} + +inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SVDFOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = rank(); _o->rank = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSVDFOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _rank = _o->rank; + auto _fused_activation_function = _o->fused_activation_function; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateSVDFOptions( + _fbb, + _rank, + _fused_activation_function, + _asymmetric_quantize_inputs); +} + +inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RNNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateRNNOptions( + _fbb, + _fused_activation_function, + _asymmetric_quantize_inputs); +} + +inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SequenceRNNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSequenceRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _time_major = _o->time_major; + auto _fused_activation_function = _o->fused_activation_function; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateSequenceRNNOptions( + _fbb, + _time_major, + _fused_activation_function, + _asymmetric_quantize_inputs); +} + +inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BidirectionalSequenceRNNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = merge_outputs(); _o->merge_outputs = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _time_major = _o->time_major; + auto _fused_activation_function = _o->fused_activation_function; + auto _merge_outputs = _o->merge_outputs; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateBidirectionalSequenceRNNOptions( + _fbb, + _time_major, + _fused_activation_function, + _merge_outputs, + _asymmetric_quantize_inputs); +} + +inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FullyConnectedOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = weights_format(); _o->weights_format = _e; } + { auto _e = keep_num_dims(); _o->keep_num_dims = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFullyConnectedOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _weights_format = _o->weights_format; + auto _keep_num_dims = _o->keep_num_dims; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateFullyConnectedOptions( + _fbb, + _fused_activation_function, + _weights_format, + _keep_num_dims, + _asymmetric_quantize_inputs); +} + +inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SoftmaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = beta(); _o->beta = _e; } +} + +inline flatbuffers::Offset SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSoftmaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _beta = _o->beta; + return tflite::CreateSoftmaxOptions( + _fbb, + _beta); +} + +inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ConcatenationOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConcatenationOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateConcatenationOptions( + _fbb, + _axis, + _fused_activation_function); +} + +inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AddOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAddOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateAddOptions( + _fbb, + _fused_activation_function); +} + +inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MulOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMulOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateMulOptions( + _fbb, + _fused_activation_function); +} + +inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new L2NormOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateL2NormOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateL2NormOptions( + _fbb, + _fused_activation_function); +} + +inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LocalResponseNormalizationOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = radius(); _o->radius = _e; } + { auto _e = bias(); _o->bias = _e; } + { auto _e = alpha(); _o->alpha = _e; } + { auto _e = beta(); _o->beta = _e; } +} + +inline flatbuffers::Offset LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _radius = _o->radius; + auto _bias = _o->bias; + auto _alpha = _o->alpha; + auto _beta = _o->beta; + return tflite::CreateLocalResponseNormalizationOptions( + _fbb, + _radius, + _bias, + _alpha, + _beta); +} + +inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LSTMOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = cell_clip(); _o->cell_clip = _e; } + { auto _e = proj_clip(); _o->proj_clip = _e; } + { auto _e = kernel_type(); _o->kernel_type = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLSTMOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _cell_clip = _o->cell_clip; + auto _proj_clip = _o->proj_clip; + auto _kernel_type = _o->kernel_type; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip, + _kernel_type, + _asymmetric_quantize_inputs); +} + +inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnidirectionalSequenceLSTMOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = cell_clip(); _o->cell_clip = _e; } + { auto _e = proj_clip(); _o->proj_clip = _e; } + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset UnidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _cell_clip = _o->cell_clip; + auto _proj_clip = _o->proj_clip; + auto _time_major = _o->time_major; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateUnidirectionalSequenceLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip, + _time_major, + _asymmetric_quantize_inputs); +} + +inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BidirectionalSequenceLSTMOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = cell_clip(); _o->cell_clip = _e; } + { auto _e = proj_clip(); _o->proj_clip = _e; } + { auto _e = merge_outputs(); _o->merge_outputs = _e; } + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset BidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _cell_clip = _o->cell_clip; + auto _proj_clip = _o->proj_clip; + auto _merge_outputs = _o->merge_outputs; + auto _time_major = _o->time_major; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite::CreateBidirectionalSequenceLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip, + _merge_outputs, + _time_major, + _asymmetric_quantize_inputs); +} + +inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ResizeBilinearOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = align_corners(); _o->align_corners = _e; } + { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } +} + +inline flatbuffers::Offset ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateResizeBilinearOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _align_corners = _o->align_corners; + auto _half_pixel_centers = _o->half_pixel_centers; + return tflite::CreateResizeBilinearOptions( + _fbb, + _align_corners, + _half_pixel_centers); +} + +inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ResizeNearestNeighborOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = align_corners(); _o->align_corners = _e; } + { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } +} + +inline flatbuffers::Offset ResizeNearestNeighborOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateResizeNearestNeighborOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _align_corners = _o->align_corners; + auto _half_pixel_centers = _o->half_pixel_centers; + return tflite::CreateResizeNearestNeighborOptions( + _fbb, + _align_corners, + _half_pixel_centers); +} + +inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CallOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = subgraph(); _o->subgraph = _e; } +} + +inline flatbuffers::Offset CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCallOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _subgraph = _o->subgraph; + return tflite::CreateCallOptions( + _fbb, + _subgraph); +} + +inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PadOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePadOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreatePadOptions( + _fbb); +} + +inline PadV2OptionsT *PadV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PadV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PadV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePadV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreatePadV2Options( + _fbb); +} + +inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReshapeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReshapeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0; + return tflite::CreateReshapeOptions( + _fbb, + _new_shape); +} + +inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SpaceToBatchNDOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSpaceToBatchNDOptions( + _fbb); +} + +inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BatchToSpaceNDOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateBatchToSpaceNDOptions( + _fbb); +} + +inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SkipGramOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = ngram_size(); _o->ngram_size = _e; } + { auto _e = max_skip_size(); _o->max_skip_size = _e; } + { auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; } +} + +inline flatbuffers::Offset SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSkipGramOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _ngram_size = _o->ngram_size; + auto _max_skip_size = _o->max_skip_size; + auto _include_all_ngrams = _o->include_all_ngrams; + return tflite::CreateSkipGramOptions( + _fbb, + _ngram_size, + _max_skip_size, + _include_all_ngrams); +} + +inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SpaceToDepthOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = block_size(); _o->block_size = _e; } +} + +inline flatbuffers::Offset SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSpaceToDepthOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _block_size = _o->block_size; + return tflite::CreateSpaceToDepthOptions( + _fbb, + _block_size); +} + +inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DepthToSpaceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = block_size(); _o->block_size = _e; } +} + +inline flatbuffers::Offset DepthToSpaceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDepthToSpaceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _block_size = _o->block_size; + return tflite::CreateDepthToSpaceOptions( + _fbb, + _block_size); +} + +inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SubOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSubOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateSubOptions( + _fbb, + _fused_activation_function); +} + +inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DivOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDivOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateDivOptions( + _fbb, + _fused_activation_function); +} + +inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TopKV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTopKV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateTopKV2Options( + _fbb); +} + +inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new EmbeddingLookupSparseOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = combiner(); _o->combiner = _e; } +} + +inline flatbuffers::Offset EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _combiner = _o->combiner; + return tflite::CreateEmbeddingLookupSparseOptions( + _fbb, + _combiner); +} + +inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GatherOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGatherOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + return tflite::CreateGatherOptions( + _fbb, + _axis); +} + +inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TransposeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTransposeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateTransposeOptions( + _fbb); +} + +inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ExpOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateExpOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateExpOptions( + _fbb); +} + +inline CosOptionsT *CosOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CosOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CosOptions::UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset CosOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCosOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateCosOptions( + _fbb); +} + +inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReducerOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = keep_dims(); _o->keep_dims = _e; } +} + +inline flatbuffers::Offset ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReducerOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _keep_dims = _o->keep_dims; + return tflite::CreateReducerOptions( + _fbb, + _keep_dims); +} + +inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SqueezeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSqueezeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0; + return tflite::CreateSqueezeOptions( + _fbb, + _squeeze_dims); +} + +inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SplitOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_splits(); _o->num_splits = _e; } +} + +inline flatbuffers::Offset SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSplitOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_splits = _o->num_splits; + return tflite::CreateSplitOptions( + _fbb, + _num_splits); +} + +inline SplitVOptionsT *SplitVOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SplitVOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_splits(); _o->num_splits = _e; } +} + +inline flatbuffers::Offset SplitVOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSplitVOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_splits = _o->num_splits; + return tflite::CreateSplitVOptions( + _fbb, + _num_splits); +} + +inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StridedSliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = begin_mask(); _o->begin_mask = _e; } + { auto _e = end_mask(); _o->end_mask = _e; } + { auto _e = ellipsis_mask(); _o->ellipsis_mask = _e; } + { auto _e = new_axis_mask(); _o->new_axis_mask = _e; } + { auto _e = shrink_axis_mask(); _o->shrink_axis_mask = _e; } +} + +inline flatbuffers::Offset StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStridedSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _begin_mask = _o->begin_mask; + auto _end_mask = _o->end_mask; + auto _ellipsis_mask = _o->ellipsis_mask; + auto _new_axis_mask = _o->new_axis_mask; + auto _shrink_axis_mask = _o->shrink_axis_mask; + return tflite::CreateStridedSliceOptions( + _fbb, + _begin_mask, + _end_mask, + _ellipsis_mask, + _new_axis_mask, + _shrink_axis_mask); +} + +inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogSoftmaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogSoftmaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogSoftmaxOptions( + _fbb); +} + +inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CastOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = in_data_type(); _o->in_data_type = _e; } + { auto _e = out_data_type(); _o->out_data_type = _e; } +} + +inline flatbuffers::Offset CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCastOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _in_data_type = _o->in_data_type; + auto _out_data_type = _o->out_data_type; + return tflite::CreateCastOptions( + _fbb, + _in_data_type, + _out_data_type); +} + +inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DequantizeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDequantizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateDequantizeOptions( + _fbb); +} + +inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MaximumMinimumOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset MaximumMinimumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMaximumMinimumOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateMaximumMinimumOptions( + _fbb); +} + +inline TileOptionsT *TileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TileOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TileOptions::UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTileOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateTileOptions( + _fbb); +} + +inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ArgMaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = output_type(); _o->output_type = _e; } +} + +inline flatbuffers::Offset ArgMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateArgMaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _output_type = _o->output_type; + return tflite::CreateArgMaxOptions( + _fbb, + _output_type); +} + +inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ArgMinOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = output_type(); _o->output_type = _e; } +} + +inline flatbuffers::Offset ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateArgMinOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _output_type = _o->output_type; + return tflite::CreateArgMinOptions( + _fbb, + _output_type); +} + +inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GreaterOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGreaterOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateGreaterOptions( + _fbb); +} + +inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GreaterEqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGreaterEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateGreaterEqualOptions( + _fbb); +} + +inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LessOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LessOptions::UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LessOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLessOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLessOptions( + _fbb); +} + +inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LessEqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLessEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLessEqualOptions( + _fbb); +} + +inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NegOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNegOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateNegOptions( + _fbb); +} + +inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SelectOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSelectOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSelectOptions( + _fbb); +} + +inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSliceOptions( + _fbb); +} + +inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TransposeConvOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } +} + +inline flatbuffers::Offset TransposeConvOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTransposeConvOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + return tflite::CreateTransposeConvOptions( + _fbb, + _padding, + _stride_w, + _stride_h); +} + +inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ExpandDimsOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ExpandDimsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateExpandDimsOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateExpandDimsOptions( + _fbb); +} + +inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SparseToDenseOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = validate_indices(); _o->validate_indices = _e; } +} + +inline flatbuffers::Offset SparseToDenseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSparseToDenseOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _validate_indices = _o->validate_indices; + return tflite::CreateSparseToDenseOptions( + _fbb, + _validate_indices); +} + +inline EqualOptionsT *EqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new EqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset EqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateEqualOptions( + _fbb); +} + +inline NotEqualOptionsT *NotEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NotEqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NotEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNotEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateNotEqualOptions( + _fbb); +} + +inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ShapeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = out_type(); _o->out_type = _e; } +} + +inline flatbuffers::Offset ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateShapeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _out_type = _o->out_type; + return tflite::CreateShapeOptions( + _fbb, + _out_type); +} + +inline RankOptionsT *RankOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RankOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RankOptions::UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset RankOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRankOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateRankOptions( + _fbb); +} + +inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PowOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePowOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreatePowOptions( + _fbb); +} + +inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FakeQuantOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = min(); _o->min = _e; } + { auto _e = max(); _o->max = _e; } + { auto _e = num_bits(); _o->num_bits = _e; } + { auto _e = narrow_range(); _o->narrow_range = _e; } +} + +inline flatbuffers::Offset FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFakeQuantOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _min = _o->min; + auto _max = _o->max; + auto _num_bits = _o->num_bits; + auto _narrow_range = _o->narrow_range; + return tflite::CreateFakeQuantOptions( + _fbb, + _min, + _max, + _num_bits, + _narrow_range); +} + +inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PackOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values_count(); _o->values_count = _e; } + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePackOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _values_count = _o->values_count; + auto _axis = _o->axis; + return tflite::CreatePackOptions( + _fbb, + _values_count, + _axis); +} + +inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogicalOrOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalOrOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogicalOrOptions( + _fbb); +} + +inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new OneHotOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOneHotOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + return tflite::CreateOneHotOptions( + _fbb, + _axis); +} + +inline AbsOptionsT *AbsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AbsOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset AbsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAbsOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateAbsOptions( + _fbb); +} + +inline HardSwishOptionsT *HardSwishOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new HardSwishOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset HardSwishOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateHardSwishOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateHardSwishOptions( + _fbb); +} + +inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogicalAndOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalAndOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalAndOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogicalAndOptions( + _fbb); +} + +inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogicalNotOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalNotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalNotOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogicalNotOptions( + _fbb); +} + +inline UnpackOptionsT *UnpackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnpackOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num(); _o->num = _e; } + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset UnpackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnpackOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num = _o->num; + auto _axis = _o->axis; + return tflite::CreateUnpackOptions( + _fbb, + _num, + _axis); +} + +inline FloorDivOptionsT *FloorDivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FloorDivOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset FloorDivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFloorDivOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateFloorDivOptions( + _fbb); +} + +inline SquareOptionsT *SquareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SquareOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SquareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSquareOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSquareOptions( + _fbb); +} + +inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ZerosLikeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ZerosLikeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateZerosLikeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateZerosLikeOptions( + _fbb); +} + +inline FillOptionsT *FillOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FillOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FillOptions::UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset FillOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFillOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateFillOptions( + _fbb); +} + +inline FloorModOptionsT *FloorModOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FloorModOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset FloorModOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFloorModOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateFloorModOptions( + _fbb); +} + +inline RangeOptionsT *RangeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RangeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset RangeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRangeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateRangeOptions( + _fbb); +} + +inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LeakyReluOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = alpha(); _o->alpha = _e; } +} + +inline flatbuffers::Offset LeakyReluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLeakyReluOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _alpha = _o->alpha; + return tflite::CreateLeakyReluOptions( + _fbb, + _alpha); +} + +inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SquaredDifferenceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SquaredDifferenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSquaredDifferenceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSquaredDifferenceOptions( + _fbb); +} + +inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MirrorPadOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = mode(); _o->mode = _e; } +} + +inline flatbuffers::Offset MirrorPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMirrorPadOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _mode = _o->mode; + return tflite::CreateMirrorPadOptions( + _fbb, + _mode); +} + +inline UniqueOptionsT *UniqueOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UniqueOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = idx_out_type(); _o->idx_out_type = _e; } +} + +inline flatbuffers::Offset UniqueOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUniqueOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _idx_out_type = _o->idx_out_type; + return tflite::CreateUniqueOptions( + _fbb, + _idx_out_type); +} + +inline ReverseV2OptionsT *ReverseV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReverseV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ReverseV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReverseV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateReverseV2Options( + _fbb); +} + +inline AddNOptionsT *AddNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AddNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset AddNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAddNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateAddNOptions( + _fbb); +} + +inline GatherNdOptionsT *GatherNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GatherNdOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset GatherNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGatherNdOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateGatherNdOptions( + _fbb); +} + +inline WhereOptionsT *WhereOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new WhereOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset WhereOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateWhereOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateWhereOptions( + _fbb); +} + +inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReverseSequenceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = seq_dim(); _o->seq_dim = _e; } + { auto _e = batch_dim(); _o->batch_dim = _e; } +} + +inline flatbuffers::Offset ReverseSequenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReverseSequenceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _seq_dim = _o->seq_dim; + auto _batch_dim = _o->batch_dim; + return tflite::CreateReverseSequenceOptions( + _fbb, + _seq_dim, + _batch_dim); +} + +inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MatrixDiagOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset MatrixDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMatrixDiagOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateMatrixDiagOptions( + _fbb); +} + +inline QuantizeOptionsT *QuantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new QuantizeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset QuantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateQuantizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateQuantizeOptions( + _fbb); +} + +inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MatrixSetDiagOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset MatrixSetDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMatrixSetDiagOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateMatrixSetDiagOptions( + _fbb); +} + +inline IfOptionsT *IfOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new IfOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void IfOptions::UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = then_subgraph_index(); _o->then_subgraph_index = _e; } + { auto _e = else_subgraph_index(); _o->else_subgraph_index = _e; } +} + +inline flatbuffers::Offset IfOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateIfOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _then_subgraph_index = _o->then_subgraph_index; + auto _else_subgraph_index = _o->else_subgraph_index; + return tflite::CreateIfOptions( + _fbb, + _then_subgraph_index, + _else_subgraph_index); +} + +inline WhileOptionsT *WhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new WhileOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; } + { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } +} + +inline flatbuffers::Offset WhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateWhileOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _cond_subgraph_index = _o->cond_subgraph_index; + auto _body_subgraph_index = _o->body_subgraph_index; + return tflite::CreateWhileOptions( + _fbb, + _cond_subgraph_index, + _body_subgraph_index); +} + +inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NonMaxSuppressionV4OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NonMaxSuppressionV4Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNonMaxSuppressionV4Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateNonMaxSuppressionV4Options( + _fbb); +} + +inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NonMaxSuppressionV5OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NonMaxSuppressionV5Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNonMaxSuppressionV5Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateNonMaxSuppressionV5Options( + _fbb); +} + +inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ScatterNdOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ScatterNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateScatterNdOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateScatterNdOptions( + _fbb); +} + +inline SelectV2OptionsT *SelectV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SelectV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SelectV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSelectV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSelectV2Options( + _fbb); +} + +inline DensifyOptionsT *DensifyOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DensifyOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DensifyOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDensifyOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateDensifyOptions( + _fbb); +} + +inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SegmentSumOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSegmentSumOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSegmentSumOptions( + _fbb); +} + +inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BatchMatMulOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = adj_x(); _o->adj_x = _e; } + { auto _e = adj_y(); _o->adj_y = _e; } +} + +inline flatbuffers::Offset BatchMatMulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBatchMatMulOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _adj_x = _o->adj_x; + auto _adj_y = _o->adj_y; + return tflite::CreateBatchMatMulOptions( + _fbb, + _adj_x, + _adj_y); +} + +inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new OperatorCodeT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = builtin_code(); _o->builtin_code = _e; } + { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); } + { auto _e = version(); _o->version = _e; } +} + +inline flatbuffers::Offset OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOperatorCode(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _builtin_code = _o->builtin_code; + auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code); + auto _version = _o->version; + return tflite::CreateOperatorCode( + _fbb, + _builtin_code, + _custom_code, + _version); +} + +inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new OperatorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = opcode_index(); _o->opcode_index = _e; } + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } + { auto _e = builtin_options_type(); _o->builtin_options.type = _e; } + { auto _e = builtin_options(); if (_e) _o->builtin_options.value = tflite::BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); } + { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_options.begin()); } } + { auto _e = custom_options_format(); _o->custom_options_format = _e; } + { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } } + { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOperator(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _opcode_index = _o->opcode_index; + auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; + auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; + auto _builtin_options_type = _o->builtin_options.type; + auto _builtin_options = _o->builtin_options.Pack(_fbb); + auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; + auto _custom_options_format = _o->custom_options_format; + auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0; + auto _intermediates = _o->intermediates.size() ? _fbb.CreateVector(_o->intermediates) : 0; + return tflite::CreateOperator( + _fbb, + _opcode_index, + _inputs, + _outputs, + _builtin_options_type, + _builtin_options, + _custom_options, + _custom_options_format, + _mutating_variable_inputs, + _intermediates); +} + +inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SubGraphT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } + { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->operators[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = name(); if (_e) _o->name = _e->str(); } +} + +inline flatbuffers::Offset SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSubGraph(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _tensors = _o->tensors.size() ? _fbb.CreateVector> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; + auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; + auto _operators = _o->operators.size() ? _fbb.CreateVector> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + return tflite::CreateSubGraph( + _fbb, + _tensors, + _inputs, + _outputs, + _operators, + _name); +} + +inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BufferT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } } +} + +inline flatbuffers::Offset Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBuffer(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->data.size(), sizeof(uint8_t), 16); + auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; + return tflite::CreateBuffer( + _fbb, + _data); +} + +inline MetadataT *Metadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MetadataT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Metadata::UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = buffer(); _o->buffer = _e; } +} + +inline flatbuffers::Offset Metadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMetadata(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _buffer = _o->buffer; + return tflite::CreateMetadata( + _fbb, + _name, + _buffer); +} + +inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ModelT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = version(); _o->version = _e; } + { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->operator_codes[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->subgraphs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = description(); if (_e) _o->description = _e->str(); } + { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->buffers[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } + { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } } + { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } +} + +inline flatbuffers::Offset Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateModel(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _version = _o->version; + auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description); + auto _buffers = _o->buffers.size() ? _fbb.CreateVector> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0; + auto _metadata = _o->metadata.size() ? _fbb.CreateVector> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0; + return tflite::CreateModel( + _fbb, + _version, + _operator_codes, + _subgraphs, + _description, + _buffers, + _metadata_buffer, + _metadata); +} + +inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) { + switch (type) { + case QuantizationDetails_NONE: { + return true; + } + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifyQuantizationDetails( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver) { + switch (type) { + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset QuantizationDetailsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + switch (type) { + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(value); + return CreateCustomQuantization(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline QuantizationDetailsUnion::QuantizationDetailsUnion(const QuantizationDetailsUnion &u) : type(u.type), value(nullptr) { + switch (type) { + case QuantizationDetails_CustomQuantization: { + value = new tflite::CustomQuantizationT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void QuantizationDetailsUnion::Reset() { + switch (type) { + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = QuantizationDetails_NONE; +} + +inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) { + switch (type) { + case SparseIndexVector_NONE: { + return true; + } + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifySparseIndexVector( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver) { + switch (type) { + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset SparseIndexVectorUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + switch (type) { + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(value); + return CreateInt32Vector(_fbb, ptr, _rehasher).Union(); + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(value); + return CreateUint16Vector(_fbb, ptr, _rehasher).Union(); + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(value); + return CreateUint8Vector(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline SparseIndexVectorUnion::SparseIndexVectorUnion(const SparseIndexVectorUnion &u) : type(u.type), value(nullptr) { + switch (type) { + case SparseIndexVector_Int32Vector: { + value = new tflite::Int32VectorT(*reinterpret_cast(u.value)); + break; + } + case SparseIndexVector_Uint16Vector: { + value = new tflite::Uint16VectorT(*reinterpret_cast(u.value)); + break; + } + case SparseIndexVector_Uint8Vector: { + value = new tflite::Uint8VectorT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void SparseIndexVectorUnion::Reset() { + switch (type) { + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = SparseIndexVector_NONE; +} + +inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) { + switch (type) { + case BuiltinOptions_NONE: { + return true; + } + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifyBuiltinOptions( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) { + switch (type) { + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + switch (type) { + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(value); + return CreateConv2DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(value); + return CreateDepthwiseConv2DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(value); + return CreateConcatEmbeddingsOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(value); + return CreateLSHProjectionOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(value); + return CreatePool2DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(value); + return CreateSVDFOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(value); + return CreateRNNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(value); + return CreateFullyConnectedOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateSoftmaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(value); + return CreateConcatenationOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(value); + return CreateAddOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(value); + return CreateL2NormOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(value); + return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(value); + return CreateLSTMOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(value); + return CreateResizeBilinearOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(value); + return CreateCallOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(value); + return CreateReshapeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(value); + return CreateSkipGramOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(value); + return CreateSpaceToDepthOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(value); + return CreateEmbeddingLookupSparseOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(value); + return CreateMulOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(value); + return CreatePadOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(value); + return CreateGatherOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(value); + return CreateBatchToSpaceNDOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(value); + return CreateSpaceToBatchNDOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(value); + return CreateTransposeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); + return CreateReducerOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(value); + return CreateSubOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(value); + return CreateDivOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(value); + return CreateSqueezeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + return CreateSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateStridedSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(value); + return CreateExpOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(value); + return CreateTopKV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(value); + return CreateSplitOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + return CreateCastOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(value); + return CreateMaximumMinimumOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(value); + return CreateLessOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(value); + return CreateNegOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(value); + return CreatePadV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(value); + return CreateGreaterOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(value); + return CreateSelectOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(value); + return CreateTransposeConvOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(value); + return CreateSparseToDenseOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(value); + return CreateTileOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(value); + return CreateExpandDimsOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + return CreateShapeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(value); + return CreatePowOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(value); + return CreateArgMinOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(value); + return CreateFakeQuantOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(value); + return CreatePackOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + return CreateOneHotOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalAndOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalNotOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnpackOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(value); + return CreateFloorDivOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(value); + return CreateSquareOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(value); + return CreateZerosLikeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(value); + return CreateFillOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + return CreateBidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + return CreateBidirectionalSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(value); + return CreateFloorModOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(value); + return CreateRangeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(value); + return CreateResizeNearestNeighborOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(value); + return CreateLeakyReluOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(value); + return CreateSquaredDifferenceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(value); + return CreateMirrorPadOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(value); + return CreateAbsOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(value); + return CreateSplitVOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(value); + return CreateUniqueOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(value); + return CreateReverseV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(value); + return CreateAddNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(value); + return CreateGatherNdOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(value); + return CreateCosOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(value); + return CreateWhereOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(value); + return CreateRankOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(value); + return CreateReverseSequenceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(value); + return CreateMatrixDiagOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateQuantizeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(value); + return CreateMatrixSetDiagOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(value); + return CreateHardSwishOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(value); + return CreateIfOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(value); + return CreateWhileOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(value); + return CreateDepthToSpaceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(value); + return CreateNonMaxSuppressionV4Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(value); + return CreateNonMaxSuppressionV5Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(value); + return CreateScatterNdOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(value); + return CreateSelectV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(value); + return CreateDensifyOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(value); + return CreateSegmentSumOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(value); + return CreateBatchMatMulOptions(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) : type(u.type), value(nullptr) { + switch (type) { + case BuiltinOptions_Conv2DOptions: { + value = new tflite::Conv2DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DepthwiseConv2DOptions: { + value = new tflite::DepthwiseConv2DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + value = new tflite::ConcatEmbeddingsOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LSHProjectionOptions: { + value = new tflite::LSHProjectionOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_Pool2DOptions: { + value = new tflite::Pool2DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SVDFOptions: { + value = new tflite::SVDFOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RNNOptions: { + value = new tflite::RNNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FullyConnectedOptions: { + value = new tflite::FullyConnectedOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SoftmaxOptions: { + value = new tflite::SoftmaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ConcatenationOptions: { + value = new tflite::ConcatenationOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AddOptions: { + value = new tflite::AddOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_L2NormOptions: { + value = new tflite::L2NormOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + value = new tflite::LocalResponseNormalizationOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LSTMOptions: { + value = new tflite::LSTMOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ResizeBilinearOptions: { + value = new tflite::ResizeBilinearOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CallOptions: { + value = new tflite::CallOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReshapeOptions: { + value = new tflite::ReshapeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SkipGramOptions: { + value = new tflite::SkipGramOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SpaceToDepthOptions: { + value = new tflite::SpaceToDepthOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + value = new tflite::EmbeddingLookupSparseOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MulOptions: { + value = new tflite::MulOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PadOptions: { + value = new tflite::PadOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GatherOptions: { + value = new tflite::GatherOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BatchToSpaceNDOptions: { + value = new tflite::BatchToSpaceNDOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SpaceToBatchNDOptions: { + value = new tflite::SpaceToBatchNDOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TransposeOptions: { + value = new tflite::TransposeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReducerOptions: { + value = new tflite::ReducerOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SubOptions: { + value = new tflite::SubOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DivOptions: { + value = new tflite::DivOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SqueezeOptions: { + value = new tflite::SqueezeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SequenceRNNOptions: { + value = new tflite::SequenceRNNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_StridedSliceOptions: { + value = new tflite::StridedSliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ExpOptions: { + value = new tflite::ExpOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TopKV2Options: { + value = new tflite::TopKV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SplitOptions: { + value = new tflite::SplitOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogSoftmaxOptions: { + value = new tflite::LogSoftmaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CastOptions: { + value = new tflite::CastOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DequantizeOptions: { + value = new tflite::DequantizeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MaximumMinimumOptions: { + value = new tflite::MaximumMinimumOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ArgMaxOptions: { + value = new tflite::ArgMaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LessOptions: { + value = new tflite::LessOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NegOptions: { + value = new tflite::NegOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PadV2Options: { + value = new tflite::PadV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GreaterOptions: { + value = new tflite::GreaterOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GreaterEqualOptions: { + value = new tflite::GreaterEqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LessEqualOptions: { + value = new tflite::LessEqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SelectOptions: { + value = new tflite::SelectOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SliceOptions: { + value = new tflite::SliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TransposeConvOptions: { + value = new tflite::TransposeConvOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SparseToDenseOptions: { + value = new tflite::SparseToDenseOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TileOptions: { + value = new tflite::TileOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ExpandDimsOptions: { + value = new tflite::ExpandDimsOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_EqualOptions: { + value = new tflite::EqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NotEqualOptions: { + value = new tflite::NotEqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ShapeOptions: { + value = new tflite::ShapeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PowOptions: { + value = new tflite::PowOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ArgMinOptions: { + value = new tflite::ArgMinOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FakeQuantOptions: { + value = new tflite::FakeQuantOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PackOptions: { + value = new tflite::PackOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalOrOptions: { + value = new tflite::LogicalOrOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_OneHotOptions: { + value = new tflite::OneHotOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalAndOptions: { + value = new tflite::LogicalAndOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalNotOptions: { + value = new tflite::LogicalNotOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnpackOptions: { + value = new tflite::UnpackOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FloorDivOptions: { + value = new tflite::FloorDivOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SquareOptions: { + value = new tflite::SquareOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ZerosLikeOptions: { + value = new tflite::ZerosLikeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FillOptions: { + value = new tflite::FillOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + value = new tflite::BidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + value = new tflite::BidirectionalSequenceRNNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + value = new tflite::UnidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FloorModOptions: { + value = new tflite::FloorModOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RangeOptions: { + value = new tflite::RangeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + value = new tflite::ResizeNearestNeighborOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LeakyReluOptions: { + value = new tflite::LeakyReluOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SquaredDifferenceOptions: { + value = new tflite::SquaredDifferenceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MirrorPadOptions: { + value = new tflite::MirrorPadOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AbsOptions: { + value = new tflite::AbsOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SplitVOptions: { + value = new tflite::SplitVOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UniqueOptions: { + value = new tflite::UniqueOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReverseV2Options: { + value = new tflite::ReverseV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AddNOptions: { + value = new tflite::AddNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GatherNdOptions: { + value = new tflite::GatherNdOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CosOptions: { + value = new tflite::CosOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_WhereOptions: { + value = new tflite::WhereOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RankOptions: { + value = new tflite::RankOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReverseSequenceOptions: { + value = new tflite::ReverseSequenceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MatrixDiagOptions: { + value = new tflite::MatrixDiagOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_QuantizeOptions: { + value = new tflite::QuantizeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MatrixSetDiagOptions: { + value = new tflite::MatrixSetDiagOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_HardSwishOptions: { + value = new tflite::HardSwishOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_IfOptions: { + value = new tflite::IfOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_WhileOptions: { + value = new tflite::WhileOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DepthToSpaceOptions: { + value = new tflite::DepthToSpaceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + value = new tflite::NonMaxSuppressionV4OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + value = new tflite::NonMaxSuppressionV5OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ScatterNdOptions: { + value = new tflite::ScatterNdOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SelectV2Options: { + value = new tflite::SelectV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DensifyOptions: { + value = new tflite::DensifyOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SegmentSumOptions: { + value = new tflite::SegmentSumOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BatchMatMulOptions: { + value = new tflite::BatchMatMulOptionsT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void BuiltinOptionsUnion::Reset() { + switch (type) { + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = BuiltinOptions_NONE; +} + +inline const flatbuffers::TypeTable *TensorTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 11, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *QuantizationDetailsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_SEQUENCE, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::CustomQuantizationTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_UNION, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DimensionTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::DimensionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SparseIndexVectorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_SEQUENCE, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_SEQUENCE, 0, 1 }, + { flatbuffers::ET_SEQUENCE, 0, 2 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::Int32VectorTypeTable, + tflite::Uint16VectorTypeTable, + tflite::Uint8VectorTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_UNION, 4, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BuiltinOperatorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::BuiltinOperatorTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 127, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BuiltinOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_SEQUENCE, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_SEQUENCE, 0, 1 }, + { flatbuffers::ET_SEQUENCE, 0, 2 }, + { flatbuffers::ET_SEQUENCE, 0, 3 }, + { flatbuffers::ET_SEQUENCE, 0, 4 }, + { flatbuffers::ET_SEQUENCE, 0, 5 }, + { flatbuffers::ET_SEQUENCE, 0, 6 }, + { flatbuffers::ET_SEQUENCE, 0, 7 }, + { flatbuffers::ET_SEQUENCE, 0, 8 }, + { flatbuffers::ET_SEQUENCE, 0, 9 }, + { flatbuffers::ET_SEQUENCE, 0, 10 }, + { flatbuffers::ET_SEQUENCE, 0, 11 }, + { flatbuffers::ET_SEQUENCE, 0, 12 }, + { flatbuffers::ET_SEQUENCE, 0, 13 }, + { flatbuffers::ET_SEQUENCE, 0, 14 }, + { flatbuffers::ET_SEQUENCE, 0, 15 }, + { flatbuffers::ET_SEQUENCE, 0, 16 }, + { flatbuffers::ET_SEQUENCE, 0, 17 }, + { flatbuffers::ET_SEQUENCE, 0, 18 }, + { flatbuffers::ET_SEQUENCE, 0, 19 }, + { flatbuffers::ET_SEQUENCE, 0, 20 }, + { flatbuffers::ET_SEQUENCE, 0, 21 }, + { flatbuffers::ET_SEQUENCE, 0, 22 }, + { flatbuffers::ET_SEQUENCE, 0, 23 }, + { flatbuffers::ET_SEQUENCE, 0, 24 }, + { flatbuffers::ET_SEQUENCE, 0, 25 }, + { flatbuffers::ET_SEQUENCE, 0, 26 }, + { flatbuffers::ET_SEQUENCE, 0, 27 }, + { flatbuffers::ET_SEQUENCE, 0, 28 }, + { flatbuffers::ET_SEQUENCE, 0, 29 }, + { flatbuffers::ET_SEQUENCE, 0, 30 }, + { flatbuffers::ET_SEQUENCE, 0, 31 }, + { flatbuffers::ET_SEQUENCE, 0, 32 }, + { flatbuffers::ET_SEQUENCE, 0, 33 }, + { flatbuffers::ET_SEQUENCE, 0, 34 }, + { flatbuffers::ET_SEQUENCE, 0, 35 }, + { flatbuffers::ET_SEQUENCE, 0, 36 }, + { flatbuffers::ET_SEQUENCE, 0, 37 }, + { flatbuffers::ET_SEQUENCE, 0, 38 }, + { flatbuffers::ET_SEQUENCE, 0, 39 }, + { flatbuffers::ET_SEQUENCE, 0, 40 }, + { flatbuffers::ET_SEQUENCE, 0, 41 }, + { flatbuffers::ET_SEQUENCE, 0, 42 }, + { flatbuffers::ET_SEQUENCE, 0, 43 }, + { flatbuffers::ET_SEQUENCE, 0, 44 }, + { flatbuffers::ET_SEQUENCE, 0, 45 }, + { flatbuffers::ET_SEQUENCE, 0, 46 }, + { flatbuffers::ET_SEQUENCE, 0, 47 }, + { flatbuffers::ET_SEQUENCE, 0, 48 }, + { flatbuffers::ET_SEQUENCE, 0, 49 }, + { flatbuffers::ET_SEQUENCE, 0, 50 }, + { flatbuffers::ET_SEQUENCE, 0, 51 }, + { flatbuffers::ET_SEQUENCE, 0, 52 }, + { flatbuffers::ET_SEQUENCE, 0, 53 }, + { flatbuffers::ET_SEQUENCE, 0, 54 }, + { flatbuffers::ET_SEQUENCE, 0, 55 }, + { flatbuffers::ET_SEQUENCE, 0, 56 }, + { flatbuffers::ET_SEQUENCE, 0, 57 }, + { flatbuffers::ET_SEQUENCE, 0, 58 }, + { flatbuffers::ET_SEQUENCE, 0, 59 }, + { flatbuffers::ET_SEQUENCE, 0, 60 }, + { flatbuffers::ET_SEQUENCE, 0, 61 }, + { flatbuffers::ET_SEQUENCE, 0, 62 }, + { flatbuffers::ET_SEQUENCE, 0, 63 }, + { flatbuffers::ET_SEQUENCE, 0, 64 }, + { flatbuffers::ET_SEQUENCE, 0, 65 }, + { flatbuffers::ET_SEQUENCE, 0, 66 }, + { flatbuffers::ET_SEQUENCE, 0, 67 }, + { flatbuffers::ET_SEQUENCE, 0, 68 }, + { flatbuffers::ET_SEQUENCE, 0, 69 }, + { flatbuffers::ET_SEQUENCE, 0, 70 }, + { flatbuffers::ET_SEQUENCE, 0, 71 }, + { flatbuffers::ET_SEQUENCE, 0, 72 }, + { flatbuffers::ET_SEQUENCE, 0, 73 }, + { flatbuffers::ET_SEQUENCE, 0, 74 }, + { flatbuffers::ET_SEQUENCE, 0, 75 }, + { flatbuffers::ET_SEQUENCE, 0, 76 }, + { flatbuffers::ET_SEQUENCE, 0, 77 }, + { flatbuffers::ET_SEQUENCE, 0, 78 }, + { flatbuffers::ET_SEQUENCE, 0, 79 }, + { flatbuffers::ET_SEQUENCE, 0, 80 }, + { flatbuffers::ET_SEQUENCE, 0, 81 }, + { flatbuffers::ET_SEQUENCE, 0, 82 }, + { flatbuffers::ET_SEQUENCE, 0, 83 }, + { flatbuffers::ET_SEQUENCE, 0, 84 }, + { flatbuffers::ET_SEQUENCE, 0, 85 }, + { flatbuffers::ET_SEQUENCE, 0, 86 }, + { flatbuffers::ET_SEQUENCE, 0, 87 }, + { flatbuffers::ET_SEQUENCE, 0, 88 }, + { flatbuffers::ET_SEQUENCE, 0, 89 }, + { flatbuffers::ET_SEQUENCE, 0, 90 }, + { flatbuffers::ET_SEQUENCE, 0, 91 }, + { flatbuffers::ET_SEQUENCE, 0, 92 }, + { flatbuffers::ET_SEQUENCE, 0, 93 }, + { flatbuffers::ET_SEQUENCE, 0, 94 }, + { flatbuffers::ET_SEQUENCE, 0, 95 }, + { flatbuffers::ET_SEQUENCE, 0, 96 }, + { flatbuffers::ET_SEQUENCE, 0, 97 }, + { flatbuffers::ET_SEQUENCE, 0, 98 }, + { flatbuffers::ET_SEQUENCE, 0, 99 }, + { flatbuffers::ET_SEQUENCE, 0, 100 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::Conv2DOptionsTypeTable, + tflite::DepthwiseConv2DOptionsTypeTable, + tflite::ConcatEmbeddingsOptionsTypeTable, + tflite::LSHProjectionOptionsTypeTable, + tflite::Pool2DOptionsTypeTable, + tflite::SVDFOptionsTypeTable, + tflite::RNNOptionsTypeTable, + tflite::FullyConnectedOptionsTypeTable, + tflite::SoftmaxOptionsTypeTable, + tflite::ConcatenationOptionsTypeTable, + tflite::AddOptionsTypeTable, + tflite::L2NormOptionsTypeTable, + tflite::LocalResponseNormalizationOptionsTypeTable, + tflite::LSTMOptionsTypeTable, + tflite::ResizeBilinearOptionsTypeTable, + tflite::CallOptionsTypeTable, + tflite::ReshapeOptionsTypeTable, + tflite::SkipGramOptionsTypeTable, + tflite::SpaceToDepthOptionsTypeTable, + tflite::EmbeddingLookupSparseOptionsTypeTable, + tflite::MulOptionsTypeTable, + tflite::PadOptionsTypeTable, + tflite::GatherOptionsTypeTable, + tflite::BatchToSpaceNDOptionsTypeTable, + tflite::SpaceToBatchNDOptionsTypeTable, + tflite::TransposeOptionsTypeTable, + tflite::ReducerOptionsTypeTable, + tflite::SubOptionsTypeTable, + tflite::DivOptionsTypeTable, + tflite::SqueezeOptionsTypeTable, + tflite::SequenceRNNOptionsTypeTable, + tflite::StridedSliceOptionsTypeTable, + tflite::ExpOptionsTypeTable, + tflite::TopKV2OptionsTypeTable, + tflite::SplitOptionsTypeTable, + tflite::LogSoftmaxOptionsTypeTable, + tflite::CastOptionsTypeTable, + tflite::DequantizeOptionsTypeTable, + tflite::MaximumMinimumOptionsTypeTable, + tflite::ArgMaxOptionsTypeTable, + tflite::LessOptionsTypeTable, + tflite::NegOptionsTypeTable, + tflite::PadV2OptionsTypeTable, + tflite::GreaterOptionsTypeTable, + tflite::GreaterEqualOptionsTypeTable, + tflite::LessEqualOptionsTypeTable, + tflite::SelectOptionsTypeTable, + tflite::SliceOptionsTypeTable, + tflite::TransposeConvOptionsTypeTable, + tflite::SparseToDenseOptionsTypeTable, + tflite::TileOptionsTypeTable, + tflite::ExpandDimsOptionsTypeTable, + tflite::EqualOptionsTypeTable, + tflite::NotEqualOptionsTypeTable, + tflite::ShapeOptionsTypeTable, + tflite::PowOptionsTypeTable, + tflite::ArgMinOptionsTypeTable, + tflite::FakeQuantOptionsTypeTable, + tflite::PackOptionsTypeTable, + tflite::LogicalOrOptionsTypeTable, + tflite::OneHotOptionsTypeTable, + tflite::LogicalAndOptionsTypeTable, + tflite::LogicalNotOptionsTypeTable, + tflite::UnpackOptionsTypeTable, + tflite::FloorDivOptionsTypeTable, + tflite::SquareOptionsTypeTable, + tflite::ZerosLikeOptionsTypeTable, + tflite::FillOptionsTypeTable, + tflite::BidirectionalSequenceLSTMOptionsTypeTable, + tflite::BidirectionalSequenceRNNOptionsTypeTable, + tflite::UnidirectionalSequenceLSTMOptionsTypeTable, + tflite::FloorModOptionsTypeTable, + tflite::RangeOptionsTypeTable, + tflite::ResizeNearestNeighborOptionsTypeTable, + tflite::LeakyReluOptionsTypeTable, + tflite::SquaredDifferenceOptionsTypeTable, + tflite::MirrorPadOptionsTypeTable, + tflite::AbsOptionsTypeTable, + tflite::SplitVOptionsTypeTable, + tflite::UniqueOptionsTypeTable, + tflite::ReverseV2OptionsTypeTable, + tflite::AddNOptionsTypeTable, + tflite::GatherNdOptionsTypeTable, + tflite::CosOptionsTypeTable, + tflite::WhereOptionsTypeTable, + tflite::RankOptionsTypeTable, + tflite::ReverseSequenceOptionsTypeTable, + tflite::MatrixDiagOptionsTypeTable, + tflite::QuantizeOptionsTypeTable, + tflite::MatrixSetDiagOptionsTypeTable, + tflite::HardSwishOptionsTypeTable, + tflite::IfOptionsTypeTable, + tflite::WhileOptionsTypeTable, + tflite::DepthToSpaceOptionsTypeTable, + tflite::NonMaxSuppressionV4OptionsTypeTable, + tflite::NonMaxSuppressionV5OptionsTypeTable, + tflite::ScatterNdOptionsTypeTable, + tflite::SelectV2OptionsTypeTable, + tflite::DensifyOptionsTypeTable, + tflite::SegmentSumOptionsTypeTable, + tflite::BatchMatMulOptionsTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_UNION, 102, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *PaddingTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::PaddingTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ActivationFunctionTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 6, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LSHProjectionTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::LSHProjectionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *FullyConnectedOptionsWeightsFormatTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::FullyConnectedOptionsWeightsFormatTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LSTMKernelTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::LSTMKernelTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CombinerTypeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::CombinerTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MirrorPadModeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::MirrorPadModeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CustomOptionsFormatTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::CustomOptionsFormatTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_ENUM, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CustomQuantizationTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UCHAR, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *QuantizationParametersTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_FLOAT, 1, -1 }, + { flatbuffers::ET_FLOAT, 1, -1 }, + { flatbuffers::ET_FLOAT, 1, -1 }, + { flatbuffers::ET_LONG, 1, -1 }, + { flatbuffers::ET_UTYPE, 0, 0 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::QuantizationDetailsTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 7, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *Int32VectorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *Uint16VectorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_USHORT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *Uint8VectorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UCHAR, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DimensionMetadataTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_UTYPE, 0, 1 }, + { flatbuffers::ET_SEQUENCE, 0, 1 }, + { flatbuffers::ET_UTYPE, 0, 1 }, + { flatbuffers::ET_SEQUENCE, 0, 1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::DimensionTypeTypeTable, + tflite::SparseIndexVectorTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 6, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SparsityParametersTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::DimensionMetadataTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TensorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_UINT, 0, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 2 }, + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable, + tflite::QuantizationParametersTypeTable, + tflite::SparsityParametersTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 8, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *Conv2DOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::PaddingTypeTable, + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 6, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *Pool2DOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::PaddingTypeTable, + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 6, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DepthwiseConv2DOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::PaddingTypeTable, + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 7, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ConcatEmbeddingsOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LSHProjectionOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::LSHProjectionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SVDFOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *RNNOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SequenceRNNOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BidirectionalSequenceRNNOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *FullyConnectedOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable, + tflite::FullyConnectedOptionsWeightsFormatTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SoftmaxOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_FLOAT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ConcatenationOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *AddOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MulOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *L2NormOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LocalResponseNormalizationOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LSTMOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_CHAR, 0, 1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable, + tflite::LSTMKernelTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 5, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *UnidirectionalSequenceLSTMOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 5, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BidirectionalSequenceLSTMOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 6, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ResizeBilinearOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ResizeNearestNeighborOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CallOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UINT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *PadOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *PadV2OptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ReshapeOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SpaceToBatchNDOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BatchToSpaceNDOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SkipGramOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SpaceToDepthOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DepthToSpaceOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SubOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DivOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::ActivationFunctionTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TopKV2OptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *EmbeddingLookupSparseOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::CombinerTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *GatherOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TransposeOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ExpOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CosOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ReducerOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SqueezeOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SplitOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SplitVOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *StridedSliceOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 5, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LogSoftmaxOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *CastOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DequantizeOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MaximumMinimumOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TileOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ArgMaxOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ArgMinOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *GreaterOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *GreaterEqualOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LessOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LessEqualOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *NegOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SelectOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SliceOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TransposeConvOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::PaddingTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ExpandDimsOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SparseToDenseOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *EqualOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *NotEqualOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ShapeOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *RankOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *PowOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *FakeQuantOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_FLOAT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *PackOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LogicalOrOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *OneHotOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *AbsOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *HardSwishOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LogicalAndOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LogicalNotOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *UnpackOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *FloorDivOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SquareOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ZerosLikeOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *FillOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *FloorModOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *RangeOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *LeakyReluOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_FLOAT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SquaredDifferenceOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MirrorPadOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::MirrorPadModeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *UniqueOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ReverseV2OptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *AddNOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *GatherNdOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *WhereOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ReverseSequenceOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MatrixDiagOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *QuantizeOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MatrixSetDiagOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *IfOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *WhileOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_INT, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *NonMaxSuppressionV4OptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *NonMaxSuppressionV5OptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ScatterNdOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SelectV2OptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *DensifyOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SegmentSumOptionsTypeTable() { + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 0, nullptr, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BatchMatMulOptionsTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 }, + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *OperatorCodeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_CHAR, 0, 0 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_INT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::BuiltinOperatorTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *OperatorTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UINT, 0, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_UTYPE, 0, 0 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_UCHAR, 1, -1 }, + { flatbuffers::ET_CHAR, 0, 1 }, + { flatbuffers::ET_BOOL, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::BuiltinOptionsTypeTable, + tflite::CustomOptionsFormatTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 9, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *SubGraphTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_SEQUENCE, 1, 0 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 1 }, + { flatbuffers::ET_STRING, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::TensorTypeTable, + tflite::OperatorTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 5, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *BufferTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UCHAR, 1, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *MetadataTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_UINT, 0, -1 } + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const flatbuffers::TypeTable *ModelTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UINT, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 0 }, + { flatbuffers::ET_SEQUENCE, 1, 1 }, + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 2 }, + { flatbuffers::ET_INT, 1, -1 }, + { flatbuffers::ET_SEQUENCE, 1, 3 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + tflite::OperatorCodeTypeTable, + tflite::SubGraphTypeTable, + tflite::BufferTypeTable, + tflite::MetadataTypeTable + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 7, type_codes, type_refs, nullptr, nullptr, nullptr + }; + return &tt; +} + +inline const tflite::Model *GetModel(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const tflite::Model *GetSizePrefixedModel(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline Model *GetMutableModel(void *buf) { + return flatbuffers::GetMutableRoot(buf); +} + +inline const char *ModelIdentifier() { + return "TFL3"; +} + +inline bool ModelBufferHasIdentifier(const void *buf) { + return flatbuffers::BufferHasIdentifier( + buf, ModelIdentifier()); +} + +inline bool VerifyModelBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(ModelIdentifier()); +} + +inline bool VerifySizePrefixedModelBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(ModelIdentifier()); +} + +inline const char *ModelExtension() { + return "tflite"; +} + +inline void FinishModelBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root, ModelIdentifier()); +} + +inline void FinishSizePrefixedModelBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root, ModelIdentifier()); +} + +inline std::unique_ptr UnPackModel( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetModel(buf)->UnPack(res)); +} + +inline std::unique_ptr UnPackSizePrefixedModel( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetSizePrefixedModel(buf)->UnPack(res)); +} + +} // namespace tflite + +#endif // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ diff --git a/mindspore/lite/src/CMakeLists.txt b/mindspore/lite/src/CMakeLists.txt index 923ec3a8..5afccc87 100644 --- a/mindspore/lite/src/CMakeLists.txt +++ b/mindspore/lite/src/CMakeLists.txt @@ -496,6 +496,12 @@ if(MSLITE_ENABLE_MINDRT) target_link_libraries(mindspore-lite_static mindrt_mid) endif() +if (SUPPORT_NNRT) + add_subdirectory(litert/delegate/nnrt) + target_link_libraries(mindspore-lite nnrt_mid) + target_link_libraries(mindspore-lite_static nnrt_mid) +endif() + if(MSLITE_GPU_BACKEND STREQUAL opencl) add_subdirectory(litert/kernel/gpu/opencl) target_link_libraries(mindspore-lite opencl_kernel_mid) diff --git a/mindspore/lite/src/common/BUILD.gn b/mindspore/lite/src/common/BUILD.gn new file mode 100644 index 00000000..8bb14a30 --- /dev/null +++ b/mindspore/lite/src/common/BUILD.gn @@ -0,0 +1,83 @@ +import("//build/ohos.gni") + +lite_src_common_mid_sources = [ + "config_file.cc", + "context_util.cc", + "file_utils.cc", + "mmap_utils.cc", + "utils.cc", + "graph_util.cc", + "log.cc", + "prim_util.cc", + "tensor_util.cc", + "config_infos.cc", + "helper/external_tensor/file_helper.cc", + "helper/external_tensor/memory_helper.cc", + "thread_utils.cc", + "random_data_generator.cc", + "string_util.cc", + "dynamic_library_loader.cc", +] + +ohos_source_set("lite_common_mid_obj") { + sources = lite_src_common_mid_sources + + include_dirs = [ + "../", + "../../", + "../../../../", + "../../../core/", + "//third_party/flatbuffers/include/", + "../../../../third_party/", + "../../../ccsrc/plugin/device/cpu/kernel/", + "../../../core/mindrt/src/", + "//base/hiviewdfx/hilog/interfaces/native/innerkits/include", + ] + + external_deps = [ + "hilog:libhilog", + ] + + configs = [ + "../../:disable_android", + "../../:secure_option", + ] + + defines = [ + "ENABLE_MINDRT", + "MS_COMPILE_OHOS", + "PRIMITIVE_WRITEABLE", + "VERSION_STR=\"2.1.0\"", + ] + if (target_cpu == "arm") { + defines += [ + "ENABLE_ARM", + "ENABLE_ARM32", + "ENABLE_NEON", + ] + } else if (target_cpu == "arm64") { + defines += [ + "ENABLE_ARM", + "ENABLE_ARM64", + "ENABLE_NEON", + "ENABLE_FP16", + "USE_OPENCL_WRAPPER", + "MS_OPENCL_PROFILE=false", + "CL_TARGET_OPENCL_VERSION=200", + "CL_HPP_TARGET_OPENCL_VERSION=120", + "CL_HPP_MINIMUM_OPENCL_VERSION=120", + ] + } + + cflags_cc = [ + "-Wno-ignored-qualifiers", + "-Wunused-private-field", + "-Wno-unused-private-field", + "-Wno-inconsistent-missing-override", + "-Wno-macro-redefined", + "-Wno-constant-conversion", + ] + + part_name = "mindspore" + subsystem_name = "thirdparty" +} \ No newline at end of file diff --git a/mindspore/lite/src/common/dynamic_library_loader.cc b/mindspore/lite/src/common/dynamic_library_loader.cc index e41bb719..3c0a7cd5 100644 --- a/mindspore/lite/src/common/dynamic_library_loader.cc +++ b/mindspore/lite/src/common/dynamic_library_loader.cc @@ -39,7 +39,8 @@ int DynamicLibraryLoader::Open(const std::string &lib_path) { } #ifndef _WIN32 #ifndef ENABLE_ARM - handler_ = dlopen(real_path.c_str(), RTLD_LAZY | RTLD_DEEPBIND); + // handler_ = dlopen(real_path.c_str(), RTLD_LAZY | RTLD_DEEPBIND); use of undeclared identifier 'RTLD_DEEPBIND' + handler_ = dlopen(real_path.c_str(), RTLD_LAZY); #else handler_ = dlopen(real_path.c_str(), RTLD_LAZY); #endif diff --git a/mindspore/lite/src/common/log.cc b/mindspore/lite/src/common/log.cc index 96202753..66c0d76b 100644 --- a/mindspore/lite/src/common/log.cc +++ b/mindspore/lite/src/common/log.cc @@ -74,15 +74,15 @@ static int GetAndroidLogLevel(LiteLogLevel level) { #ifdef MS_COMPILE_OHOS void PrintHiLog(LiteLogLevel level, const char *file, int line, const char *func, const char *msg) { if (level == LiteLogLevel::DEBUG) { - OH_LOG_Print(LOG_APP, LOG_DEBUG, LOG_DOMAIN, LOG_TAG, FORMAT, file, line, func, msg); + OHOS::HiviewDFX::HiLog::Debug(MSLite_LABEL, FORMAT, file, line, func, msg); } else if (level == LiteLogLevel::INFO) { - OH_LOG_Print(LOG_APP, LOG_INFO, LOG_DOMAIN, LOG_TAG, FORMAT, file, line, func, msg); + OHOS::HiviewDFX::HiLog::Info(MSLite_LABEL, FORMAT, file, line, func, msg); } else if (level == LiteLogLevel::WARNING) { - OH_LOG_Print(LOG_APP, LOG_WARN, LOG_DOMAIN, LOG_TAG, FORMAT, file, line, func, msg); + OHOS::HiviewDFX::HiLog::Warn(MSLite_LABEL, FORMAT, file, line, func, msg); } else if (level == LiteLogLevel::ERROR) { - OH_LOG_Print(LOG_APP, LOG_ERROR, LOG_DOMAIN, LOG_TAG, FORMAT, file, line, func, msg); + OHOS::HiviewDFX::HiLog::Error(MSLite_LABEL, FORMAT, file, line, func, msg); } else { - OH_LOG_Print(LOG_APP, LOG_ERROR, LOG_DOMAIN, LOG_TAG, FORMAT, file, line, func, msg); + OHOS::HiviewDFX::HiLog::Error(MSLite_LABEL, FORMAT, file, line, func, msg); } } #endif diff --git a/mindspore/lite/src/common/log.h b/mindspore/lite/src/common/log.h index 43047a2b..3002a454 100644 --- a/mindspore/lite/src/common/log.h +++ b/mindspore/lite/src/common/log.h @@ -140,9 +140,6 @@ class LiteLogWriter { LiteLogLevel log_level_; }; -#define MSLOG_IF(level) \ - mindspore::LiteLogWriter(mindspore::LiteLocationInfo(LITE_FILE_NAME, __LINE__, __FUNCTION__), level) < \ - mindspore::LiteLogStream() #define MS_LOG(level) MS_LOG_##level @@ -150,6 +147,48 @@ class LiteLogWriter { #define MS_LOG_INFO MSLOG_IF(mindspore::LiteLogLevel::INFO) #define MS_LOG_WARNING MSLOG_IF(mindspore::LiteLogLevel::WARNING) #define MS_LOG_ERROR MSLOG_IF(mindspore::LiteLogLevel::ERROR) + + +#ifdef MS_COMPILE_OHOS +namespace { +constexpr unsigned int MSLITE_DOMAIN_ID_START = 0xD0029A0; +constexpr unsigned int MSLITE_DOMAIN_ID_END = MSLITE_DOMAIN_ID_START + 32; +constexpr unsigned int TEST_DOMAIN_ID = 0xD000F00; +} // namespace + +#define FILE_NAME (__builtin_strrchr(__FILE__, '/') ? __builtin_strrchr(__FILE__, '/') + 1 : __FILE__) +#define FORMAT "[%{public}s:%{public}d] %{public}s# %{public}s" + +#define MSLOG_IF(level) \ + mindspore::LiteLogWriter(mindspore::LiteLocationInfo(LITE_FILE_NAME, __LINE__, __FUNCTION__), level) < \ + mindspore::LiteLogStream() + +enum MSLiteManagerLogLabel { + // Component labels, you can add if needed + COMP_FWK = 0, + // Test label + LABEL_TEST, + // The end of labels, max to the domain id range length 32 + LABEL_END, +}; + +enum MSLiteManagerLogDomain { + DOMAIN_FRAMEWORK = MSLITE_DOMAIN_ID_START + COMP_FWK, // 0xD0029A0 + DOMAIN_TEST = TEST_DOMAIN_ID, // 0xD000F00 + DOMAIN_END = MSLITE_DOMAIN_ID_END, // Max to 0xD002940, keep the sequence and length same as MSLiteManagerLogLabel +}; + +// Keep the sequence and length same as MSLiteManagerLogDomain +static constexpr OHOS::HiviewDFX::HiLogLabel MSLite_LABEL = {LOG_CORE, DOMAIN_FRAMEWORK, "MSLiteFwk"}; + +#else + +#define MSLOG_IF(level) \ + mindspore::LiteLogWriter(mindspore::LiteLocationInfo(LITE_FILE_NAME, __LINE__, __FUNCTION__), level) < \ + mindspore::LiteLogStream() + +#endif + } // namespace mindspore #ifdef Debug diff --git a/mindspore/lite/src/common/thread_utils.cc b/mindspore/lite/src/common/thread_utils.cc index 4c631dcb..28c8e1cd 100644 --- a/mindspore/lite/src/common/thread_utils.cc +++ b/mindspore/lite/src/common/thread_utils.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#ifdef __linux__ +#if defined(__linux__) && !defined(ENABLE_ARM) #include "src/common/thread_utils.h" #include #include diff --git a/mindspore/lite/src/expression/node.cc b/mindspore/lite/src/expression/node.cc index 276caaca..022f15d4 100644 --- a/mindspore/lite/src/expression/node.cc +++ b/mindspore/lite/src/expression/node.cc @@ -175,8 +175,8 @@ int Node::MakeEntry(ExportSession *session) { } size_t outputs = OutputsNum(); size_t last_id = session->meta_graph()->allTensors.size(); - int type = (primitive() == schema::PrimitiveType_NONE) ? NodeType_ValueNode : NodeType_CNode; - auto data = (type == NodeType_ValueNode) ? this->data()->data() : empty; + int type = (primitive() == schema::PrimitiveType_NONE) ? static_cast(NodeType_ValueNode) : static_cast(NodeType_CNode); + auto data = (type == static_cast(NodeType_ValueNode)) ? this->data()->data() : empty; if (data.empty()) type = NodeType_CNode; // input is Cnode !!? int idx = 0; for (size_t i = 0; i < outputs; i++) { diff --git a/mindspore/lite/src/litert/c_api/context_c.cc b/mindspore/lite/src/litert/c_api/context_c.cc index 65564d9a..f614ef09 100644 --- a/mindspore/lite/src/litert/c_api/context_c.cc +++ b/mindspore/lite/src/litert/c_api/context_c.cc @@ -18,16 +18,16 @@ #include "src/common/log_adapter.h" // ================ Context ================ -MSContextHandle MSContextCreate() { +OH_AI_ContextHandle OH_AI_ContextCreate() { auto impl = new (std::nothrow) mindspore::ContextC; if (impl == nullptr) { MS_LOG(ERROR) << "memory allocation failed."; return nullptr; } - return static_cast(impl); + return static_cast(impl); } -void MSContextDestroy(MSContextHandle *context) { +void OH_AI_ContextDestroy(OH_AI_ContextHandle *context) { if (context != nullptr && *context != nullptr) { auto impl = static_cast(*context); delete impl; @@ -35,7 +35,7 @@ void MSContextDestroy(MSContextHandle *context) { } } -void MSContextSetThreadNum(MSContextHandle context, int32_t thread_num) { +void OH_AI_ContextSetThreadNum(OH_AI_ContextHandle context, int32_t thread_num) { if (context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -44,7 +44,7 @@ void MSContextSetThreadNum(MSContextHandle context, int32_t thread_num) { impl->thread_num = thread_num; } -int32_t MSContextGetThreadNum(const MSContextHandle context) { +int32_t OH_AI_ContextGetThreadNum(const OH_AI_ContextHandle context) { if (context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return 0; @@ -53,7 +53,7 @@ int32_t MSContextGetThreadNum(const MSContextHandle context) { return impl->thread_num; } -void MSContextSetThreadAffinityMode(MSContextHandle context, int mode) { +void OH_AI_ContextSetThreadAffinityMode(OH_AI_ContextHandle context, int mode) { if (context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -63,16 +63,16 @@ void MSContextSetThreadAffinityMode(MSContextHandle context, int mode) { return; } -int MSContextGetThreadAffinityMode(const MSContextHandle context) { +int OH_AI_ContextGetThreadAffinityMode(const OH_AI_ContextHandle context) { if (context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return -1; + return 0; } auto impl = static_cast(context); return impl->affinity_mode; } -void MSContextSetThreadAffinityCoreList(MSContextHandle context, const int32_t *core_list, size_t core_num) { +void OH_AI_ContextSetThreadAffinityCoreList(OH_AI_ContextHandle context, const int32_t *core_list, size_t core_num) { if (context == nullptr || core_list == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -83,7 +83,7 @@ void MSContextSetThreadAffinityCoreList(MSContextHandle context, const int32_t * return; } -const int32_t *MSContextGetThreadAffinityCoreList(const MSContextHandle context, size_t *core_num) { +const int32_t *OH_AI_ContextGetThreadAffinityCoreList(const OH_AI_ContextHandle context, size_t *core_num) { if (context == nullptr || core_num == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -93,7 +93,7 @@ const int32_t *MSContextGetThreadAffinityCoreList(const MSContextHandle context, return impl->affinity_core_list.data(); } -void MSContextSetEnableParallel(MSContextHandle context, bool is_parallel) { +void OH_AI_ContextSetEnableParallel(OH_AI_ContextHandle context, bool is_parallel) { if (context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -102,7 +102,7 @@ void MSContextSetEnableParallel(MSContextHandle context, bool is_parallel) { impl->enable_parallel = is_parallel; } -bool MSContextGetEnableParallel(const MSContextHandle context) { +bool OH_AI_ContextGetEnableParallel(const OH_AI_ContextHandle context) { if (context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return false; @@ -111,7 +111,7 @@ bool MSContextGetEnableParallel(const MSContextHandle context) { return impl->enable_parallel; } -void MSContextAddDeviceInfo(MSContextHandle context, MSDeviceInfoHandle device_info) { +void OH_AI_ContextAddDeviceInfo(OH_AI_ContextHandle context, OH_AI_DeviceInfoHandle device_info) { if (context == nullptr || device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -122,17 +122,17 @@ void MSContextAddDeviceInfo(MSContextHandle context, MSDeviceInfoHandle device_i } // ================ DeviceInfo ================ -MSDeviceInfoHandle MSDeviceInfoCreate(MSDeviceType device_type) { +OH_AI_DeviceInfoHandle OH_AI_DeviceInfoCreate(OH_AI_DeviceType device_type) { mindspore::DeviceInfoC *impl = new (std::nothrow) mindspore::DeviceInfoC; if (impl == nullptr) { MS_LOG(ERROR) << "memory allocation failed."; return nullptr; } impl->device_type = device_type; - return static_cast(impl); + return static_cast(impl); } -void MSDeviceInfoDestroy(MSDeviceInfoHandle *device_info) { +void OH_AI_DeviceInfoDestroy(OH_AI_DeviceInfoHandle *device_info) { if (device_info != nullptr && *device_info != nullptr) { auto impl = static_cast(*device_info); delete impl; @@ -140,7 +140,7 @@ void MSDeviceInfoDestroy(MSDeviceInfoHandle *device_info) { } } -void MSDeviceInfoSetProvider(MSDeviceInfoHandle device_info, const char *provider) { +void OH_AI_DeviceInfoSetProvider(OH_AI_DeviceInfoHandle device_info, const char *provider) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -149,7 +149,7 @@ void MSDeviceInfoSetProvider(MSDeviceInfoHandle device_info, const char *provide impl->provider = provider; } -const char *MSDeviceInfoGetProvider(const MSDeviceInfoHandle device_info) { +const char *OH_AI_DeviceInfoGetProvider(const OH_AI_DeviceInfoHandle device_info) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -158,7 +158,7 @@ const char *MSDeviceInfoGetProvider(const MSDeviceInfoHandle device_info) { return impl->provider.c_str(); } -void MSDeviceInfoSetProviderDevice(MSDeviceInfoHandle device_info, const char *device) { +void OH_AI_DeviceInfoSetProviderDevice(OH_AI_DeviceInfoHandle device_info, const char *device) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -167,7 +167,7 @@ void MSDeviceInfoSetProviderDevice(MSDeviceInfoHandle device_info, const char *d impl->provider_device = device; } -const char *MSDeviceInfoGetProviderDevice(const MSDeviceInfoHandle device_info) { +const char *OH_AI_DeviceInfoGetProviderDevice(const OH_AI_DeviceInfoHandle device_info) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -176,35 +176,35 @@ const char *MSDeviceInfoGetProviderDevice(const MSDeviceInfoHandle device_info) return impl->provider_device.c_str(); } -MSDeviceType MSDeviceInfoGetDeviceType(const MSDeviceInfoHandle device_info) { +OH_AI_DeviceType OH_AI_DeviceInfoGetDeviceType(const OH_AI_DeviceInfoHandle device_info) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSDeviceTypeInvalid; + return OH_AI_DEVICETYPE_INVALID; } auto impl = static_cast(device_info); return impl->device_type; } -void MSDeviceInfoSetEnableFP16(MSDeviceInfoHandle device_info, bool is_fp16) { +void OH_AI_DeviceInfoSetEnableFP16(OH_AI_DeviceInfoHandle device_info, bool is_fp16) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; } auto impl = static_cast(device_info); - if (impl->device_type == kMSDeviceTypeCPU || impl->device_type == kMSDeviceTypeGPU) { + if (impl->device_type == OH_AI_DEVICETYPE_CPU || impl->device_type == OH_AI_DEVICETYPE_GPU) { impl->enable_fp16 = is_fp16; } else { MS_LOG(ERROR) << "Unsupported Feature."; } } -bool MSDeviceInfoGetEnableFP16(const MSDeviceInfoHandle device_info) { +bool OH_AI_DeviceInfoGetEnableFP16(const OH_AI_DeviceInfoHandle device_info) { if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return false; } auto impl = static_cast(device_info); - if (impl->device_type == kMSDeviceTypeCPU || impl->device_type == kMSDeviceTypeGPU) { + if (impl->device_type == OH_AI_DEVICETYPE_CPU || impl->device_type == OH_AI_DEVICETYPE_GPU) { return impl->enable_fp16; } else { MS_LOG(ERROR) << "Unsupported Feature. device_type: " << impl->device_type; @@ -212,26 +212,26 @@ bool MSDeviceInfoGetEnableFP16(const MSDeviceInfoHandle device_info) { } } -void MSDeviceInfoSetFrequency(MSDeviceInfoHandle device_info, int frequency) { // only for KirinNPU +void OH_AI_DeviceInfoSetFrequency(OH_AI_DeviceInfoHandle device_info, int frequency) { // only for KirinNPU if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; } auto impl = static_cast(device_info); - if (impl->device_type == kMSDeviceTypeKirinNPU) { + if (impl->device_type == OH_AI_DEVICETYPE_KIRIN_NPU) { impl->frequency = frequency; } else { MS_LOG(ERROR) << "Unsupported Feature."; } } -int MSDeviceInfoGetFrequency(const MSDeviceInfoHandle device_info) { // only for KirinNPU +int OH_AI_DeviceInfoGetFrequency(const OH_AI_DeviceInfoHandle device_info) { // only for KirinNPU if (device_info == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return -1; } auto impl = static_cast(device_info); - if (impl->device_type == kMSDeviceTypeKirinNPU) { + if (impl->device_type == OH_AI_DEVICETYPE_KIRIN_NPU) { return impl->frequency; } else { MS_LOG(ERROR) << "Unsupported Feature."; diff --git a/mindspore/lite/src/litert/c_api/context_c.h b/mindspore/lite/src/litert/c_api/context_c.h index ec3f6935..076f4d1f 100644 --- a/mindspore/lite/src/litert/c_api/context_c.h +++ b/mindspore/lite/src/litert/c_api/context_c.h @@ -26,7 +26,7 @@ class Allocator; class Delegate; typedef struct DeviceInfoC { - MSDeviceType device_type; + OH_AI_DeviceType device_type; bool enable_fp16 = false; int frequency = 3; std::string provider; diff --git a/mindspore/lite/src/litert/c_api/model_c.cc b/mindspore/lite/src/litert/c_api/model_c.cc index 82280fd6..802df6b1 100644 --- a/mindspore/lite/src/litert/c_api/model_c.cc +++ b/mindspore/lite/src/litert/c_api/model_c.cc @@ -37,14 +37,14 @@ class ModelC { Status Build(const std::string &model_path, ModelType model_type, const ContextC *model_context); Status Resize(const std::vector &inputs, const std::vector> &shapes); - Status Predict(const MSTensorHandle *inputs, size_t input_num, MSTensorHandle **outputs, size_t *output_num, - const MSKernelCallBackC &before, const MSKernelCallBackC &after); + Status Predict(const OH_AI_TensorHandle *inputs, size_t input_num, OH_AI_TensorHandle **outputs, size_t *output_num, + const OH_AI_KernelCallBack &before, const OH_AI_KernelCallBack &after); LiteTensorImpl **GetInputs(size_t *input_num); LiteTensorImpl **GetOutputs(size_t *output_num); private: - Status RunGraph(const MSKernelCallBackC &before, const MSKernelCallBackC &after); + Status RunGraph(const OH_AI_KernelCallBack &before, const OH_AI_KernelCallBack &after); void ResetTensorData(std::vector old_data, std::vector tensors); LiteTensorImpl *TensorToTensorImpl(mindspore::lite::Tensor *tensor); @@ -66,8 +66,9 @@ Status ModelC::Build(const void *model_data, size_t data_size, ModelType model_t MS_LOG(ERROR) << "The platform exist don't support's instruction."; return kLiteNotSupport; } - - context_.reset(model_context); + if(context_.get() != model_context){ + context_.reset(model_context); + } session_ = std::make_shared(); if (session_ == nullptr) { MS_LOG(ERROR) << "create session failed"; @@ -96,7 +97,9 @@ Status ModelC::Build(const std::string &model_path, ModelType model_type, const MS_LOG(ERROR) << "The platform exist don't support's instruction."; return kLiteNotSupport; } - context_.reset(model_context); + if(context_.get() != model_context){ + context_.reset(model_context); + } session_ = std::make_shared(); if (session_ == nullptr) { MS_LOG(ERROR) << "create session failed"; @@ -147,8 +150,8 @@ void ModelC::ResetTensorData(std::vector old_data, std::vector(GetOutputs(output_num)); + *outputs = reinterpret_cast(GetOutputs(output_num)); return kSuccess; } -Status ModelC::RunGraph(const MSKernelCallBackC &before, const MSKernelCallBackC &after) { +Status ModelC::RunGraph(const OH_AI_KernelCallBack &before, const OH_AI_KernelCallBack &after) { KernelCallBack before_call_back = nullptr; KernelCallBack after_call_back = nullptr; if (before != nullptr) { @@ -226,24 +229,24 @@ Status ModelC::RunGraph(const MSKernelCallBackC &before, const MSKernelCallBackC const MSCallBackParam &call_param) { std::vector inputs_impl; std::vector outputs_impl; - std::vector op_inputs; - std::vector op_outputs; - size_t op_input_num = before_inputs.size(); - for (size_t i = 0; i < op_input_num; i++) { - inputs_impl.emplace_back(before_inputs[i]); - op_inputs.push_back(&(inputs_impl.back())); - } - size_t op_output_num = before_outputs.size(); - for (size_t i = 0; i < op_output_num; i++) { - outputs_impl.emplace_back(before_outputs[i]); - op_outputs.push_back(&(outputs_impl.back())); - } - const MSCallBackParamC op_info = {const_cast(call_param.node_name.c_str()), - const_cast(call_param.node_type.c_str())}; - MSTensorHandleArray inputs = {op_input_num, op_inputs.data()}; - MSTensorHandleArray outputs = {op_output_num, op_outputs.data()}; - return before(inputs, outputs, op_info); - }; + std::vector op_inputs; + std::vector op_outputs; + size_t op_input_num = before_inputs.size(); + for (size_t i = 0; i < op_input_num; i++) { + inputs_impl.emplace_back(before_inputs[i]); + op_inputs.push_back(&(inputs_impl.back())); + } + size_t op_output_num = before_outputs.size(); + for (size_t i = 0; i < op_output_num; i++) { + outputs_impl.emplace_back(before_outputs[i]); + op_outputs.push_back(&(outputs_impl.back())); + } + const OH_AI_CallBackParam op_info = {const_cast(call_param.node_name.c_str()), + const_cast(call_param.node_type.c_str())}; + OH_AI_TensorHandleArray inputs = {op_input_num, op_inputs.data()}; + OH_AI_TensorHandleArray outputs = {op_output_num, op_outputs.data()}; + return before(inputs, outputs, op_info); + }; } if (after != nullptr) { after_call_back = [&](const std::vector &after_inputs, @@ -251,24 +254,24 @@ Status ModelC::RunGraph(const MSKernelCallBackC &before, const MSKernelCallBackC const MSCallBackParam &call_param) { std::vector inputs_impl; std::vector outputs_impl; - std::vector op_inputs; - std::vector op_outputs; - size_t op_input_num = after_inputs.size(); - for (size_t i = 0; i < op_input_num; i++) { - inputs_impl.emplace_back(after_inputs[i]); - op_inputs.push_back(&(inputs_impl.back())); - } - size_t op_output_num = after_outputs.size(); - for (size_t i = 0; i < op_output_num; i++) { - outputs_impl.emplace_back(after_outputs[i]); - op_outputs.push_back(&(outputs_impl.back())); - } - const MSCallBackParamC op_info = {const_cast(call_param.node_name.c_str()), - const_cast(call_param.node_type.c_str())}; - MSTensorHandleArray inputs = {op_input_num, op_inputs.data()}; - MSTensorHandleArray outputs = {op_output_num, op_outputs.data()}; - return after(inputs, outputs, op_info); - }; + std::vector op_inputs; + std::vector op_outputs; + size_t op_input_num = after_inputs.size(); + for (size_t i = 0; i < op_input_num; i++) { + inputs_impl.emplace_back(after_inputs[i]); + op_inputs.push_back(&(inputs_impl.back())); + } + size_t op_output_num = after_outputs.size(); + for (size_t i = 0; i < op_output_num; i++) { + outputs_impl.emplace_back(after_outputs[i]); + op_outputs.push_back(&(outputs_impl.back())); + } + const OH_AI_CallBackParam op_info = {const_cast(call_param.node_name.c_str()), + const_cast(call_param.node_type.c_str())}; + OH_AI_TensorHandleArray inputs = {op_input_num, op_inputs.data()}; + OH_AI_TensorHandleArray outputs = {op_output_num, op_outputs.data()}; + return after(inputs, outputs, op_info); + }; } auto ret = session_->RunGraph(before_call_back, after_call_back); return static_cast(ret); @@ -325,16 +328,16 @@ LiteTensorImpl **ModelC::GetOutputs(size_t *output_num) { } } // namespace mindspore -MSModelHandle MSModelCreate() { +OH_AI_ModelHandle OH_AI_ModelCreate() { auto impl = new (std::nothrow) mindspore::ModelC(); if (impl == nullptr) { MS_LOG(ERROR) << "Model implement is null."; return nullptr; } - return static_cast(impl); + return static_cast(impl); } -void MSModelDestroy(MSModelHandle *model) { +void OH_AI_ModelDestroy(OH_AI_ModelHandle *model) { if (model != nullptr && *model != nullptr) { auto impl = static_cast(*model); delete impl; @@ -342,76 +345,77 @@ void MSModelDestroy(MSModelHandle *model) { } } -void MSModelSetWorkspace(MSModelHandle model, void *workspace, size_t workspace_size) { +void OH_AI_ModelSetWorkspace(OH_AI_ModelHandle model, void *workspace, size_t workspace_size) { MS_LOG(ERROR) << "Unsupported Feature."; return; } -size_t MSModelCalcWorkspaceSize(MSModelHandle model) { +size_t OH_AI_ModelCalcWorkspaceSize(OH_AI_ModelHandle model) { MS_LOG(ERROR) << "Unsupported Feature."; return 0; } -MSStatus MSModelBuild(MSModelHandle model, const void *model_data, size_t data_size, MSModelType model_type, - const MSContextHandle model_context) { +OH_AI_Status OH_AI_ModelBuild(OH_AI_ModelHandle model, const void *model_data, size_t data_size, + OH_AI_ModelType model_type, const OH_AI_ContextHandle model_context) { if (model == nullptr || model_data == nullptr || model_context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSStatusLiteNullptr; + return OH_AI_STATUS_LITE_NULLPTR; } - if (model_type == kMSModelTypeInvalid) { + if (model_type == OH_AI_MODELTYPE_INVALID) { MS_LOG(ERROR) << "param is invalid."; - return kMSStatusLiteParamInvalid; + return OH_AI_STATUS_LITE_PARAM_INVALID; } mindspore::ContextC *context = static_cast(model_context); auto impl = static_cast(model); auto ret = impl->Build(model_data, data_size, static_cast(model_type), context); - return static_cast(ret.StatusCode()); + return static_cast(ret.StatusCode()); } -MSStatus MSModelBuildFromFile(MSModelHandle model, const char *model_path, MSModelType model_type, - const MSContextHandle model_context) { +OH_AI_Status OH_AI_ModelBuildFromFile(OH_AI_ModelHandle model, const char *model_path, OH_AI_ModelType model_type, + const OH_AI_ContextHandle model_context) { if (model == nullptr || model_path == nullptr || model_context == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSStatusLiteNullptr; + return OH_AI_STATUS_LITE_NULLPTR; } - if (model_type == kMSModelTypeInvalid) { + if (model_type == OH_AI_MODELTYPE_INVALID) { MS_LOG(ERROR) << "param is invalid."; - return kMSStatusLiteParamInvalid; + return OH_AI_STATUS_LITE_PARAM_INVALID; } mindspore::ContextC *context = static_cast(model_context); auto impl = static_cast(model); auto ret = impl->Build(model_path, static_cast(model_type), context); - return static_cast(ret.StatusCode()); + return static_cast(ret.StatusCode()); } -MSStatus MSModelResize(MSModelHandle model, const MSTensorHandleArray inputs, MSShapeInfo *shape_infos, - size_t shape_info_num) { +OH_AI_Status OH_AI_ModelResize(OH_AI_ModelHandle model, const OH_AI_TensorHandleArray inputs, + OH_AI_ShapeInfo *shape_infos, size_t shape_info_num) { if (model == nullptr || shape_infos == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSStatusLiteNullptr; + return OH_AI_STATUS_LITE_NULLPTR; } std::vector vec_inputs; std::transform(inputs.handle_list, inputs.handle_list + inputs.handle_num, std::back_inserter(vec_inputs), - [](MSTensorHandle value) { return static_cast(value); }); + [](OH_AI_TensorHandle value) { return static_cast(value); }); std::vector> vec_dims; for (size_t i = 0; i < shape_info_num; i++) { std::vector shape(shape_infos[i].shape, shape_infos[i].shape + shape_infos[i].shape_num); if (std::any_of(shape.begin(), shape.end(), [](int64_t val) { return val < 0 || val > INT32_MAX; })) { MS_LOG(ERROR) << "Invalid shape: " << shape << ", each dimension must be in [0, INT32_MAX]"; - return kMSStatusLiteInputParamInvalid; + return OH_AI_STATUS_LITE_PARAM_INVALID; } vec_dims.push_back(shape); } auto impl = static_cast(model); auto ret = impl->Resize(vec_inputs, vec_dims); - return static_cast(ret.StatusCode()); + return static_cast(ret.StatusCode()); } -MSStatus MSModelPredict(MSModelHandle model, const MSTensorHandleArray inputs, MSTensorHandleArray *outputs, - const MSKernelCallBackC before, const MSKernelCallBackC after) { +OH_AI_Status OH_AI_ModelPredict(OH_AI_ModelHandle model, const OH_AI_TensorHandleArray inputs, + OH_AI_TensorHandleArray *outputs, const OH_AI_KernelCallBack before, + const OH_AI_KernelCallBack after) { if (model == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSStatusLiteNullptr; + return OH_AI_STATUS_LITE_NULLPTR; } auto impl = static_cast(model); auto ret = impl->Predict(inputs.handle_list, inputs.handle_num, &(outputs->handle_list), &(outputs->handle_num), @@ -419,47 +423,47 @@ MSStatus MSModelPredict(MSModelHandle model, const MSTensorHandleArray inputs, M if (!ret.IsOk()) { MS_LOG(ERROR) << "Predict fail, ret :" << ret; } - return static_cast(ret.StatusCode()); + return static_cast(ret.StatusCode()); } -MSStatus MSModelRunStep(MSModelHandle model, const MSKernelCallBackC before, const MSKernelCallBackC after) { +OH_AI_Status OH_AI_ModelRunStep(OH_AI_ModelHandle model, const OH_AI_KernelCallBack before, const OH_AI_KernelCallBack after) { MS_LOG(ERROR) << "Unsupported Feature."; - return kMSStatusLiteNotSupport; + return OH_AI_STATUS_LITE_NOT_SUPPORT; } -MSStatus MSModelSetTrainMode(const MSModelHandle model, bool train) { +OH_AI_Status OH_AI_ModelSetTrainMode(const OH_AI_ModelHandle model, bool train) { MS_LOG(ERROR) << "Unsupported Feature."; - return kMSStatusLiteNotSupport; + return OH_AI_STATUS_LITE_NOT_SUPPORT; } -MSStatus MSModelExportWeight(const MSModelHandle model, const char *export_path) { +OH_AI_Status OH_AI_ModelExportWeight(const OH_AI_ModelHandle model, const char *export_path) { MS_LOG(ERROR) << "Unsupported Feature."; - return kMSStatusLiteNotSupport; + return OH_AI_STATUS_LITE_NOT_SUPPORT; } -MSTensorHandleArray MSModelGetInputs(const MSModelHandle model) { +OH_AI_TensorHandleArray OH_AI_ModelGetInputs(const OH_AI_ModelHandle model) { if (model == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return {0, nullptr}; } auto impl = static_cast(model); size_t input_num; - auto handles = reinterpret_cast(impl->GetInputs(&input_num)); + auto handles = reinterpret_cast(impl->GetInputs(&input_num)); return {input_num, handles}; } -MSTensorHandleArray MSModelGetOutputs(const MSModelHandle model) { +OH_AI_TensorHandleArray OH_AI_ModelGetOutputs(const OH_AI_ModelHandle model) { if (model == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return {0, nullptr}; } auto impl = static_cast(model); size_t output_num; - auto handles = reinterpret_cast(impl->GetOutputs(&output_num)); + auto handles = reinterpret_cast(impl->GetOutputs(&output_num)); return {output_num, handles}; } -MSTensorHandle MSModelGetInputByTensorName(const MSModelHandle model, const char *tensor_name) { +OH_AI_TensorHandle OH_AI_ModelGetInputByTensorName(const OH_AI_ModelHandle model, const char *tensor_name) { if (model == nullptr || tensor_name == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -469,14 +473,14 @@ MSTensorHandle MSModelGetInputByTensorName(const MSModelHandle model, const char auto inputs = impl->GetInputs(&input_num); for (size_t i = 0; i < input_num; i++) { if (inputs[i]->Name() == tensor_name) { - return static_cast(inputs[i]); + return static_cast(inputs[i]); } } MS_LOG(ERROR) << "tensor is not exist."; return nullptr; } -MSTensorHandle MSModelGetOutputByTensorName(const MSModelHandle model, const char *tensor_name) { +OH_AI_TensorHandle OH_AI_ModelGetOutputByTensorName(const OH_AI_ModelHandle model, const char *tensor_name) { if (model == nullptr || tensor_name == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -486,7 +490,7 @@ MSTensorHandle MSModelGetOutputByTensorName(const MSModelHandle model, const cha auto outputs = impl->GetOutputs(&output_num); for (size_t i = 0; i < output_num; i++) { if (outputs[i]->Name() == tensor_name) { - return static_cast(outputs[i]); + return static_cast(outputs[i]); } } MS_LOG(ERROR) << "tensor is not exist."; diff --git a/mindspore/lite/src/litert/c_api/tensor_c.cc b/mindspore/lite/src/litert/c_api/tensor_c.cc index 49778cb5..7b5c4c2f 100644 --- a/mindspore/lite/src/litert/c_api/tensor_c.cc +++ b/mindspore/lite/src/litert/c_api/tensor_c.cc @@ -19,8 +19,8 @@ #include "src/litert/cxx_api/tensor/tensor_impl.h" #include "src/litert/inner_allocator.h" -MSTensorHandle MSTensorCreate(const char *name, MSDataType type, const int64_t *shape, size_t shape_num, - const void *data, size_t data_len) { +OH_AI_TensorHandle OH_AI_TensorCreate(const char *name, OH_AI_DataType type, const int64_t *shape, size_t shape_num, + const void *data, size_t data_len) { if (name == nullptr || shape == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -40,7 +40,7 @@ MSTensorHandle MSTensorCreate(const char *name, MSDataType type, const int64_t * return impl; } -void MSTensorDestroy(MSTensorHandle *tensor) { +void OH_AI_TensorDestroy(OH_AI_TensorHandle *tensor) { if (tensor != nullptr && *tensor != nullptr) { auto impl = static_cast(*tensor); delete impl; @@ -48,7 +48,7 @@ void MSTensorDestroy(MSTensorHandle *tensor) { } } -MSTensorHandle MSTensorClone(MSTensorHandle tensor) { +OH_AI_TensorHandle OH_AI_TensorClone(OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -67,11 +67,10 @@ MSTensorHandle MSTensorClone(MSTensorHandle tensor) { return nullptr; } clone_impl->set_from_session(false); - clone_impl->set_own_data(true); return clone_impl; } -void MSTensorSetName(MSTensorHandle tensor, const char *name) { +void OH_AI_TensorSetName(OH_AI_TensorHandle tensor, const char *name) { if (tensor == nullptr || name == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -80,7 +79,7 @@ void MSTensorSetName(MSTensorHandle tensor, const char *name) { impl->SetName(name); } -const char *MSTensorGetName(const MSTensorHandle tensor) { +const char *OH_AI_TensorGetName(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -89,7 +88,7 @@ const char *MSTensorGetName(const MSTensorHandle tensor) { return impl->Name().c_str(); } -void MSTensorSetDataType(MSTensorHandle tensor, MSDataType type) { +void OH_AI_TensorSetDataType(OH_AI_TensorHandle tensor, OH_AI_DataType type) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -98,17 +97,17 @@ void MSTensorSetDataType(MSTensorHandle tensor, MSDataType type) { impl->SetDataType(static_cast(type)); } -MSDataType MSTensorGetDataType(const MSTensorHandle tensor) { +OH_AI_DataType OH_AI_TensorGetDataType(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSDataTypeUnknown; + return OH_AI_DATATYPE_UNKNOWN; } auto impl = static_cast(tensor); auto dtype = impl->DataType(); - return static_cast(dtype); + return static_cast(dtype); } -void MSTensorSetShape(MSTensorHandle tensor, const int64_t *shape, size_t shape_num) { +void OH_AI_TensorSetShape(OH_AI_TensorHandle tensor, const int64_t *shape, size_t shape_num) { if (tensor == nullptr || shape == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -121,7 +120,7 @@ void MSTensorSetShape(MSTensorHandle tensor, const int64_t *shape, size_t shape_ impl->SetShape(vec_shape); } -const int64_t *MSTensorGetShape(const MSTensorHandle tensor, size_t *shape_num) { +const int64_t *OH_AI_TensorGetShape(const OH_AI_TensorHandle tensor, size_t *shape_num) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -131,7 +130,7 @@ const int64_t *MSTensorGetShape(const MSTensorHandle tensor, size_t *shape_num) return impl->Shape().data(); } -void MSTensorSetFormat(MSTensorHandle tensor, MSFormat format) { +void OH_AI_TensorSetFormat(OH_AI_TensorHandle tensor, OH_AI_Format format) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; @@ -140,17 +139,17 @@ void MSTensorSetFormat(MSTensorHandle tensor, MSFormat format) { return impl->SetFormat(static_cast(format)); } -MSFormat MSTensorGetFormat(const MSTensorHandle tensor) { +OH_AI_Format OH_AI_TensorGetFormat(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; - return kMSFormatNHWC; + return OH_AI_FORMAT_NHWC; } auto impl = static_cast(tensor); - return static_cast(impl->Format()); + return static_cast(impl->Format()); } -void MSTensorSetData(MSTensorHandle tensor, void *data) { - if (tensor == nullptr || data == nullptr) { +void OH_AI_TensorSetData(OH_AI_TensorHandle tensor, void *data) { + if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return; } @@ -158,7 +157,7 @@ void MSTensorSetData(MSTensorHandle tensor, void *data) { return impl->SetData(data, true); } -const void *MSTensorGetData(const MSTensorHandle tensor) { +const void *OH_AI_TensorGetData(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -167,7 +166,7 @@ const void *MSTensorGetData(const MSTensorHandle tensor) { return impl->Data().get(); } -void *MSTensorGetMutableData(const MSTensorHandle tensor) { +void *OH_AI_TensorGetMutableData(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return nullptr; @@ -176,7 +175,7 @@ void *MSTensorGetMutableData(const MSTensorHandle tensor) { return impl->MutableData(); } -int64_t MSTensorGetElementNum(const MSTensorHandle tensor) { +int64_t OH_AI_TensorGetElementNum(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return 0; @@ -185,7 +184,7 @@ int64_t MSTensorGetElementNum(const MSTensorHandle tensor) { return impl->ElementNum(); } -size_t MSTensorGetDataSize(const MSTensorHandle tensor) { +size_t OH_AI_TensorGetDataSize(const OH_AI_TensorHandle tensor) { if (tensor == nullptr) { MS_LOG(ERROR) << "param is nullptr."; return 0; diff --git a/mindspore/lite/src/litert/cpu_info.cc b/mindspore/lite/src/litert/cpu_info.cc index 7510de1a..6710f8d3 100644 --- a/mindspore/lite/src/litert/cpu_info.cc +++ b/mindspore/lite/src/litert/cpu_info.cc @@ -19,7 +19,7 @@ #include #include "src/common/log_adapter.h" #include "nnacl/nnacl_utils.h" -#if defined(ENABLE_ARM64) && !defined(SUPPORT_NNIE) && !defined(MS_COMPILE_IOS) +#if defined(__ANDROID__) || defined(MS_COMPILE_OHOS) && !defined(SUPPORT_NNIE) && !defined(MS_COMPILE_IOS) #include #include #endif diff --git a/mindspore/lite/src/litert/cxx_api/converters.cc b/mindspore/lite/src/litert/cxx_api/converters.cc index ab6aacf3..0ff345cc 100644 --- a/mindspore/lite/src/litert/cxx_api/converters.cc +++ b/mindspore/lite/src/litert/cxx_api/converters.cc @@ -190,16 +190,16 @@ std::shared_ptr ContextUtils::Convert(const ContextC *contex for (auto &device_info_c : device_list) { MS_CHECK_TRUE_RET(device_info_c != nullptr, nullptr); lite::DeviceInfo device_info = {{0}}; - if (device_info_c->device_type == kMSDeviceTypeCPU) { + if (device_info_c->device_type == OH_AI_DEVICETYPE_CPU) { if (device_info_c->allocator == nullptr) { device_info_c->allocator = Allocator::Create(); } ret = AddCpuDevice(device_info_c->allocator, context_c->affinity_mode, device_info_c->enable_fp16, device_info_c->provider, device_info_c->provider_device, inner_context.get()); - } else if (device_info_c->device_type == kMSDeviceTypeGPU) { + } else if (device_info_c->device_type == OH_AI_DEVICETYPE_GPU) { ret = AddGpuDevice(device_info_c->enable_fp16, 0, 0, 0, false, nullptr, nullptr, device_info_c->provider, device_info_c->provider_device, device_info_c->allocator, inner_context.get()); - } else if (device_info_c->device_type == kMSDeviceTypeKirinNPU) { + } else if (device_info_c->device_type == OH_AI_DEVICETYPE_KIRIN_NPU) { ret = AddNpuDevice(device_info_c->enable_fp16, device_info_c->frequency, inner_context.get()); } if (ret != kSuccess) { diff --git a/mindspore/lite/src/litert/delegate/nnrt/CMakeLists.txt b/mindspore/lite/src/litert/delegate/nnrt/CMakeLists.txt new file mode 100644 index 00000000..70aa63f3 --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/CMakeLists.txt @@ -0,0 +1,30 @@ +include_directories(${DDK_PATH}) +include_directories($(CCSRC_DIR)/plugin/device/cpu/kernel) + +include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include) +#include_directories(/home/tony/wty/workspace/ohos/third_party/mindspore/mindspore/lite/mindir/include/inner) +#include_directories(/home/tony/wty/workspace/ohos/third_party/mindspore/mindspore/lite/mindir/include) +file(GLOB_RECURSE NNRT_SRC + ${CMAKE_CURRENT_SOURCE_DIR}/*.cc +) + +#add_library(hiai SHARED IMPORTED) +#set_target_properties(hiai PROPERTIES IMPORTED_LOCATION +# ${DDK_LIB_PATH}/libhiai.so) +#add_library(hiai_ir SHARED IMPORTED) +#set_target_properties(hiai_ir PROPERTIES IMPORTED_LOCATION +# ${DDK_LIB_PATH}/libhiai_ir.so) +#add_library(hiai_ir_build SHARED IMPORTED) +#set_target_properties(hiai_ir_build PROPERTIES IMPORTED_LOCATION +# ${DDK_LIB_PATH}/libhiai_ir_build.so) +#add_library(npu_kernel_mid OBJECT ${NPU_RUNTIME_SRC}) +#add_dependencies(npu_kernel_mid fbs_src) +#target_link_libraries( +# npu_kernel_mid +# hiai +# hiai_ir +# hiai_ir_build +#) + +file(GLOB convert_source checker/*.cc) +add_library(nnr_mid OBJECT ${NNRT_SRC} ${convert_source} ) \ No newline at end of file diff --git a/mindspore/lite/src/litert/delegate/nnrt/checker/primitive_check.cc b/mindspore/lite/src/litert/delegate/nnrt/checker/primitive_check.cc new file mode 100644 index 00000000..4df7e477 --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/checker/primitive_check.cc @@ -0,0 +1,187 @@ +#include +#include +#include "primitive_check.h" +#include "dtype/type_id.h" +#include "src/litert/weight_decoder.h" +#include "src/common/log.h" +#include "src/common/utils.h" +namespace mindspore { +namespace lite { + +Status CheckPrimitiveSupported(const schema::Primitive *primitive) { + if (primitive != nullptr) { + auto prim = primitive; + auto type = prim->value_type(); + switch (type) { + case schema::PrimitiveType_Activation: + return mindspore::kSuccess; + case schema::PrimitiveType_AddFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_ArgMaxFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_AvgPoolFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_BatchToSpaceND: + return mindspore::kSuccess; + case schema::PrimitiveType_BiasAdd: + return mindspore::kSuccess; + case schema::PrimitiveType_Cast: + return mindspore::kSuccess; + case schema::PrimitiveType_Concat: + return mindspore::kSuccess; + case schema::PrimitiveType_Conv2DFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Conv2dTransposeFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_DivFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Eltwise: + return mindspore::kSuccess; + case schema::PrimitiveType_ExpandDims: + return mindspore::kSuccess; + case schema::PrimitiveType_Fill: + return mindspore::kSuccess; + case schema::PrimitiveType_FullConnection: + return mindspore::kSuccess; + case schema::PrimitiveType_FusedBatchNorm: + return mindspore::kSuccess; + case schema::PrimitiveType_Gather: + return mindspore::kSuccess; + case schema::PrimitiveType_LayerNormFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_LessEqual: + return mindspore::kSuccess; + case schema::PrimitiveType_MatMulFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Maximum: + return mindspore::kSuccess; + case schema::PrimitiveType_MaxPoolFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_MulFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_OneHot: + return mindspore::kSuccess; + case schema::PrimitiveType_PadFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_PowFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_PReLUFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_QuantDTypeCast: + return mindspore::kSuccess; + case schema::PrimitiveType_ReduceFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Reshape: + return mindspore::kSuccess; + case schema::PrimitiveType_Resize: + return mindspore::kSuccess; + case schema::PrimitiveType_Rsqrt: + return mindspore::kSuccess; + case schema::PrimitiveType_ScaleFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Shape: + return mindspore::kSuccess; + case schema::PrimitiveType_SliceFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Softmax: + return mindspore::kSuccess; + case schema::PrimitiveType_SpaceToBatchND: + return mindspore::kSuccess; + case schema::PrimitiveType_Split: + return mindspore::kSuccess; + case schema::PrimitiveType_Sqrt: + return mindspore::kSuccess; + case schema::PrimitiveType_SquaredDifference: + return mindspore::kSuccess; + case schema::PrimitiveType_Squeeze: + return mindspore::kSuccess; + case schema::PrimitiveType_Stack: + return mindspore::kSuccess; + case schema::PrimitiveType_StridedSlice: + return mindspore::kSuccess; + case schema::PrimitiveType_SubFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_TileFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_TopKFusion: + return mindspore::kSuccess; + case schema::PrimitiveType_Transpose: + return mindspore::kSuccess; + case schema::PrimitiveType_Unsqueeze: + return mindspore::kSuccess; + default: { + MS_LOG(WARNING) << "No primitive type :" << (int)(type); + return mindspore::kLiteSuccessExit; + } + } + return mindspore::kSuccess; + } else { + MS_LOG(ERROR) << "primitive is nullptr."; + return mindspore::kLiteError; + } +} +namespace { +bool NeedBitUppackCheck(const schema::Tensor &src_tensor) { + if (src_tensor.enableHuffmanCode()) { + return true; + } + bool need_bit_unpack = src_tensor.quantParams() != nullptr && src_tensor.quantParams()->size() > 0 && + src_tensor.quantParams()->Get(0) != nullptr; + if (need_bit_unpack) { + auto num_bits = src_tensor.quantParams()->Get(0)->numBits(); + need_bit_unpack = ((num_bits >= kBitNum1 && num_bits < kBitNum8) || (num_bits > kBitNum8 && num_bits < kBitNum16)); + } + + return need_bit_unpack; +} +int DecompressTensor(const schema::Tensor &src_tensor) { + if (src_tensor.weightQuantCompressType() == schema::WeightQuantCompressType_FSE || + src_tensor.weightQuantCompressType() == schema::WeightQuantCompressType_INDEXING || + src_tensor.weightQuantCompressType() == schema::WeightQuantCompressType_SPARSE) { + return RET_NOT_SUPPORT; + } + if (!NeedBitUppackCheck(src_tensor)) { + return RET_NO_CHANGE; + } + MS_LOG(ERROR) << "DecompressTensor Error."; + return RET_ERROR; +} +} // namespace + +Status CheckTensorSupported(const schema::Tensor *primitive) { + if (primitive == nullptr) { + MS_LOG(ERROR) << "primitive is nullptr, which type is Tensor."; + return mindspore::kLiteSuccessExit; + } + + int32_t data_type = primitive->dataType(); + if (data_type <= kTypeUnknown || data_type >= kMonadTypeEnd) { + MS_LOG(ERROR) << "invalid data type. " << data_type; + return mindspore::kLiteSuccessExit; + } + + if (primitive->dims() == nullptr) { + MS_LOG(DEBUG) << "Dims of tensor is nullptr"; + } + + if (data_type == kObjectTypeTensorType) { + MS_LOG(ERROR) << "Not support TensorList."; + return mindspore::kLiteNotSupport; + } + + if (primitive->data() == nullptr || primitive->data()->size() <= 0) { + MS_LOG(DEBUG) << "No valid data converted."; + return mindspore::kSuccess; + } else { + auto ret = DecompressTensor(*primitive); + if (ret == RET_NO_CHANGE) { + } else { + MS_LOG(ERROR) << "Not support Decompress Tensor."; + return mindspore::kLiteNotSupport; + } + } + return mindspore::kSuccess; + ; +} +} // namespace lite +} // namespace mindspore diff --git a/mindspore/lite/src/litert/delegate/nnrt/checker/primitive_check.h b/mindspore/lite/src/litert/delegate/nnrt/checker/primitive_check.h new file mode 100644 index 00000000..dbdd812c --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/checker/primitive_check.h @@ -0,0 +1,12 @@ +#ifndef OHOS_HDI_NNRT_V1_0_CPP_H +#define OHOS_HDI_NNRT_V1_0_CPP_H +#include "schema/model_generated.h" +#include "include/api/status.h" +namespace mindspore { +namespace lite { +Status CheckPrimitiveSupported(const schema::Primitive *primitive); +Status CheckTensorSupported(const schema::Tensor *primitive); +} // namespace lite +} // namespace mindspore + +#endif // OHOS_HDI_NNRT_V1_0_CPP_H \ No newline at end of file diff --git a/mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.cc b/mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.cc new file mode 100644 index 00000000..34897331 --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.cc @@ -0,0 +1,360 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "nnrt_delegate.h" +#include "checker/primitive_check.h" +#include "src/common/log_adapter.h" +#include "interfaces/kits/c/neural_network_runtime.h" +#include "interfaces/innerkits/c/neural_network_runtime_inner.h" +#include "nnrt_model_kernel.h" + +mindspore::Status mindspore::NNRTDelegate::Build(DelegateModel *model) { + if (this->nnrt_lite_graph == nullptr) { + MS_LOG(ERROR) << "nnrt_lite_graph is nullptr."; + return mindspore::kLiteError; + } + if (this->nnrt_lite_graph->sub_graphs_.empty()) { + // must have at lease one subgraph + MS_LOG(ERROR) << "must have at lease one subgraph"; + return mindspore::kLiteError; + } + OH_NN_ReturnCode ret_code; + OH_NNModel *oh_nnmodel = OH_NNModel_Construct(); + if (oh_nnmodel == nullptr) { + MS_LOG(ERROR) << "Construct NNModel failed, oh_nnmodel is nullptr."; + return mindspore::kLiteError; + } + + ret_code = OH_NNModel_BuildFromLiteGraph(oh_nnmodel, this->nnrt_lite_graph); + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "Build NNModel failed, OH_NN_ReturnCode = " << ret_code; + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kLiteError; + } + MS_LOG(INFO) << "NNRTDelegate creates NNModel success."; + + OH_NNCompilation *oh_nn_compilation = nullptr; + oh_nn_compilation = OH_NNCompilation_Construct(oh_nnmodel); + + if (oh_nn_compilation == nullptr) { + MS_LOG(ERROR) << "Construct NNCompilation failed"; + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kLiteError; + } + MS_LOG(INFO) << "NNRTDelegate creates NNCompilation success."; + + const size_t *allDevicesID = nullptr; + uint32_t device_count = 0; + ret_code = OH_NNDevice_GetAllDevicesID(&allDevicesID, &device_count); + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNModel GetAllDevicesID failed, OH_NN_ReturnCode = " << ret_code; + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kLiteError; + } + + if (device_count <= 0) { + MS_LOG(WARNING) << "No NNRt Device found, fall back to CPU. "; + // OH_NNCompilation_Destroy(&oh_nn_compilation); + // OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kSuccess; + } + MS_LOG(INFO) << "NNRTDelegate GetAllDevicesID success."; + + // check if model ops are supported + const bool *issupported = nullptr; + uint32_t op_count = 0; + ret_code = OH_NNModel_GetAvailableOperations(oh_nnmodel, allDevicesID[0], &issupported, &op_count); + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNModel GetAvailableOperations failed, OH_NN_ReturnCode = " << ret_code + << ", maybe due to dataParcel data length limitaion. Fall back to CPU."; + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kSuccess; + } + uint32_t supported_op_count = 0; + for (uint32_t i = 0; i < op_count; i++) { + if (issupported[i]) { + supported_op_count++; + } + } + if (op_count != supported_op_count) { + MS_LOG(WARNING) << "this model has " << op_count << "ops, but NNRT only support " << supported_op_count + << " ops, fall back to CPU."; + // must support all op, else fall back to CPU + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kSuccess; + } + MS_LOG(INFO) << "NNRtDelegate supports all op in this model."; + + ret_code = OH_NNCompilation_SetDevice(oh_nn_compilation, allDevicesID[0]); + + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNCompilation SetDevice failed, OH_NN_ReturnCode = " << ret_code; + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kLiteError; + } + + ret_code = OH_NNCompilation_Build(oh_nn_compilation); + + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "Build NNCompilation failed, OH_NN_ReturnCode = " << ret_code; + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kLiteError; + } + + MS_LOG(DEBUG) << "NNRTDelegate SetDevice success."; + + OH_NNExecutor *oh_nn_executor = nullptr; + oh_nn_executor = OH_NNExecutor_Construct(oh_nn_compilation); + if (oh_nn_executor == nullptr) { + MS_LOG(ERROR) << "Construct NNCompilation SetDevice failed, OH_NN_ReturnCode = " << ret_code; + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + return mindspore::kLiteError; + } + MS_LOG(DEBUG) << "NNRTDelegate creates NNExecutor success."; + mindspore::Status prepare_data_ret; + auto nnr_model_kernel = new (std::nothrow) NNRTModelKernel(oh_nn_executor, model->inputs(), model->outputs()); + if (nnr_model_kernel == nullptr) { + MS_LOG(ERROR) << "new NNRTModelKernel failed"; + return mindspore::kLiteError; + } + OH_NNCompilation_Destroy(&oh_nn_compilation); + OH_NNModel_Destroy(&oh_nnmodel); + KernelIter from = model->BeginKernelIterator(); + KernelIter end = model->EndKernelIterator(); + model->Replace(from, end, nnr_model_kernel); + + MS_LOG(INFO) << "NNRTDelegate build success."; + return mindspore::kSuccess; +} + +mindspore::Status mindspore::NNRTDelegate::Init() { + MS_LOG(DEBUG) << "NNRTDelegate init success."; + return mindspore::kSuccess; +} +mindspore::Status mindspore::NNRTDelegate::PrepareInputs(DelegateModel *model, + OH_NNExecutor *oh_nn_executor) { + auto input_tensors = model->inputs(); + for (size_t i = 0; i < input_tensors.size(); i++) { + auto tensor = input_tensors[i]; + auto tensor_shape = tensor.Shape(); + auto tmp_quant_param = tensor.QuantParams(); + OH_NN_QuantParam *quant_param = nullptr; + std::vector bit_num; + std::vector scale; + std::vector zero_point; + if (!tmp_quant_param.empty()) { + quant_param = new (std::nothrow) OH_NN_QuantParam; + if (quant_param == nullptr) { + MS_LOG(ERROR) << "new OH_NN_QuantParam failed."; + return mindspore::kLiteError; + } + for (auto qparam : tmp_quant_param) { + bit_num.emplace_back(qparam.bit_num); + scale.emplace_back(qparam.scale); + zero_point.emplace_back(qparam.zero_point); + } + quant_param->quantCount = tmp_quant_param.size(); + quant_param->numBits = bit_num.data(); + quant_param->scale = scale.data(); + quant_param->zeroPoint = zero_point.data(); + } + auto oprend = new (std::nothrow) OH_NN_Tensor; + if (oprend == nullptr) { + MS_LOG(ERROR) << "new OH_NN_Tensor Failed"; + return mindspore::kLiteError; + } + oprend->dataType = ConvertDataType(tensor.DataType()); + oprend->dimensionCount = tensor_shape.size(); + + std::vector dimensions_list; + for (auto shape : tensor_shape) { + if (shape < INT32_MAX) { + dimensions_list.emplace_back(static_cast(shape)); + } else { + MS_LOG(ERROR) << "NNExecutor SetInput failed,tensor dimension is is too large, max dim = " << INT32_MAX + << ", but get dimension = " << shape; + return mindspore::kLiteError; + } + } + oprend->dimensions = dimensions_list.data(); + oprend->quantParam = quant_param; + oprend->type = OH_NN_TENSOR; + OH_NN_ReturnCode ret_code = + OH_NNExecutor_SetInput(oh_nn_executor, i, oprend, tensor.MutableData(), tensor.DataSize()); + delete (oprend); + + if (!tmp_quant_param.empty()) { + delete (quant_param); + quant_param = nullptr; + } + + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNExecutor SetInput failed, current input tensor is" << tensor.Name() + << "OH_NN_ReturnCode = " << ret_code; + return mindspore::kLiteError; + } + } + + return mindspore::kSuccess; +} +OH_NN_DataType mindspore::NNRTDelegate::ConvertDataType(mindspore::DataType data_type) { + OH_NN_DataType oh_data_type; + switch (data_type) { + case mindspore::DataType::kTypeUnknown: + case mindspore::DataType::kObjectTypeString: + case mindspore::DataType::kObjectTypeList: + case mindspore::DataType::kObjectTypeTuple: + case mindspore::DataType::kObjectTypeTensorType: + case mindspore::DataType::kNumberTypeBegin: + case mindspore::DataType::kNumberTypeEnd: + case mindspore::DataType::kInvalidType: + oh_data_type = OH_NN_UNKNOWN; + break; + case mindspore::DataType::kNumberTypeBool: + oh_data_type = OH_NN_BOOL; + break; + case mindspore::DataType::kNumberTypeInt8: + oh_data_type = OH_NN_INT8; + break; + case mindspore::DataType::kNumberTypeInt16: + oh_data_type = OH_NN_INT16; + break; + case mindspore::DataType::kNumberTypeInt32: + oh_data_type = OH_NN_INT32; + break; + case mindspore::DataType::kNumberTypeInt64: + oh_data_type = OH_NN_INT64; + break; + case mindspore::DataType::kNumberTypeUInt8: + oh_data_type = OH_NN_UINT8; + break; + case mindspore::DataType::kNumberTypeUInt16: + oh_data_type = OH_NN_UINT16; + break; + case mindspore::DataType::kNumberTypeUInt32: + oh_data_type = OH_NN_UINT32; + break; + case mindspore::DataType::kNumberTypeUInt64: + oh_data_type = OH_NN_UINT64; + break; + case mindspore::DataType::kNumberTypeFloat16: + oh_data_type = OH_NN_FLOAT16; + break; + case mindspore::DataType::kNumberTypeFloat32: + oh_data_type = OH_NN_FLOAT32; + break; + case mindspore::DataType::kNumberTypeFloat64: + oh_data_type = OH_NN_FLOAT64; + break; + default: { + oh_data_type = OH_NN_UNKNOWN; + } + } + return oh_data_type; +} + +mindspore::Status mindspore::NNRTDelegate::PrepareOutputs(DelegateModel *model, + OH_NNExecutor *oh_nn_executor) { + auto output_tensors = model->outputs(); + for (size_t i = 0; i < output_tensors.size(); i++) { + auto tensor = output_tensors[i]; + OH_NN_ReturnCode ret_code = OH_NNExecutor_SetOutput(oh_nn_executor, i, tensor.MutableData(), tensor.DataSize()); + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNExecutor SetOutput failed, current out tensor is" << tensor.Name() + << ", OH_NN_ReturnCode = " << ret_code; + return mindspore::kLiteError; + } + } + return mindspore::kSuccess; +} + +void mindspore::NNRTDelegate::ShallowCopyLiteGraph(const mindspore::lite::LiteGraph &lite_graph) { + Status ret; + for (auto node : lite_graph.all_nodes_) { + ret = lite::CheckPrimitiveSupported(static_cast(node->primitive_)); + if (ret == mindspore::kLiteError) { + MS_LOG(ERROR) << " primitive supported check failed."; + return; + } + } + std::vector node_list; + node_list.reserve(lite_graph.all_nodes_.size()); + // copy node + for (auto node : lite_graph.all_nodes_) { + auto new_node = new (std::nothrow) LiteGraph::Node; + if (new_node == nullptr) { + MS_LOG(ERROR) << " new LiteGraph::Node failed."; + return; + } + new_node->name_ = node->name_; + new_node->op_type_ = node->op_type_; + new_node->node_type_ = node->node_type_; + new_node->primitive_ = node->primitive_; + new_node->base_operator_ = node->base_operator_; + new_node->input_indices_ = node->input_indices_; + new_node->output_indices_ = node->output_indices_; + new_node->quant_type_ = node->quant_type_; + new_node->device_type_ = node->device_type_; + node_list.emplace_back(new_node); + } + // copy subgraph + std::vector subgraph_list; + for (auto subgraph : lite_graph.sub_graphs_) { + auto new_subgraph = new (std::nothrow) LiteGraph::SubGraph; + if (new_subgraph == nullptr) { + MS_LOG(ERROR) << "new LiteGraph::Subgraph failed."; + return; + } + new_subgraph->name_ = subgraph->name_; + new_subgraph->input_indices_ = subgraph->input_indices_; + new_subgraph->output_indices_ = subgraph->output_indices_; + new_subgraph->node_indices_ = subgraph->node_indices_; + subgraph_list.emplace_back(new_subgraph); + } + for (auto tensor : lite_graph.all_tensors_) { + ret = lite::CheckTensorSupported(static_cast(tensor)); + if (ret == mindspore::kLiteError) { + MS_LOG(ERROR) << "tensor supported check failed."; + return; + } + } + + nnrt_lite_graph = new (std::nothrow) lite::LiteGraph(); + if (nnrt_lite_graph == nullptr) { + MS_LOG(ERROR) << "new LiteGraph failed."; + return; + } + + nnrt_lite_graph->name_ = lite_graph.name_; + nnrt_lite_graph->version_ = lite_graph.version_; + nnrt_lite_graph->input_indices_ = lite_graph.input_indices_; + nnrt_lite_graph->output_indices_ = lite_graph.output_indices_; + nnrt_lite_graph->all_tensors_ = lite_graph.all_tensors_; + nnrt_lite_graph->all_nodes_ = node_list; + nnrt_lite_graph->sub_graphs_ = subgraph_list; + MS_LOG(INFO) << "ShallowCopyLiteGraph success."; +} + +mindspore::NNRTDelegate::~NNRTDelegate() { + if (this->nnrt_lite_graph != nullptr) { + MS_LOG(ERROR) << "Delete NNRTDelegate."; + } +}; diff --git a/mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.h b/mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.h new file mode 100644 index 00000000..c2847704 --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/nnrt_delegate.h @@ -0,0 +1,51 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_NNR_DELEGATE_H +#define MINDSPORE_NNR_DELEGATE_H +#include +#include +#include "include/api/delegate.h" +#include "include/model.h" +#include "interfaces/kits/c/neural_network_runtime_type.h" +namespace mindspore { + +using namespace lite; + +class NNRTDelegate : public Delegate { + public: + NNRTDelegate() : Delegate(){}; + + ~NNRTDelegate() override; + + Status Init() override; + + Status Build(DelegateModel *model) override; + + void ShallowCopyLiteGraph(const lite::LiteGraph &liteGraph); + + protected: + LiteGraph *nnrt_lite_graph = nullptr; + + private: + // static LiteGraph* CreateLiteGraph(const LiteGraph &liteGraph); + Status PrepareInputs(DelegateModel *model, OH_NNExecutor *oh_nn_executor); + Status PrepareOutputs(DelegateModel *model, OH_NNExecutor *oh_nn_executor); + OH_NN_DataType ConvertDataType(mindspore::DataType data_type); +}; + +} // namespace mindspore + +#endif // MINDSPORE_NNR_DELEGATE_H diff --git a/mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.cc b/mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.cc new file mode 100644 index 00000000..5acf2e9a --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.cc @@ -0,0 +1,175 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include +#include "nnrt_model_kernel.h" +int mindspore::NNRTModelKernel::Prepare() { return 0; } +int mindspore::NNRTModelKernel::Execute() { + lite::STATUS ret_val = PrepareInputs(); + if (ret_val != lite::RET_OK) { + MS_LOG(ERROR) << "NNRTModelKernel PrepareInputs failed, STATUS is " << ret_val; + return ret_val; + } + ret_val = TransferOutputs(); + if (ret_val != lite::RET_OK) { + MS_LOG(ERROR) << "NNRTModelKernel TransferOutputs failed, STATUS is " << ret_val; + return ret_val; + } + MS_LOG(INFO) << "Running NNRtModel Kernel..."; + OH_NN_ReturnCode ret_code; + ret_code = OH_NNExecutor_Run(this->oh_nn_executor); + + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNExecutor Run failed, OH_NN_ReturnCode = " << ret_code; + return lite::RET_ERROR; + } + MS_LOG(INFO) << "Run NNRtModel Kernel success."; + + return lite::RET_OK; +} + +OH_NN_DataType mindspore::NNRTModelKernel::ConvertDataType(mindspore::DataType data_type) { + OH_NN_DataType oh_data_type; + switch (data_type) { + case DataType::kTypeUnknown: + case DataType::kObjectTypeString: + case DataType::kObjectTypeList: + case DataType::kObjectTypeTuple: + case DataType::kObjectTypeTensorType: + case DataType::kNumberTypeBegin: + case DataType::kNumberTypeEnd: + case DataType::kInvalidType: + oh_data_type = OH_NN_UNKNOWN; + break; + case DataType::kNumberTypeBool: + oh_data_type = OH_NN_BOOL; + break; + case DataType::kNumberTypeInt8: + oh_data_type = OH_NN_INT8; + break; + case DataType::kNumberTypeInt16: + oh_data_type = OH_NN_INT16; + break; + case DataType::kNumberTypeInt32: + oh_data_type = OH_NN_INT32; + break; + case DataType::kNumberTypeInt64: + oh_data_type = OH_NN_INT64; + break; + case DataType::kNumberTypeUInt8: + oh_data_type = OH_NN_UINT8; + break; + case DataType::kNumberTypeUInt16: + oh_data_type = OH_NN_UINT16; + break; + case DataType::kNumberTypeUInt32: + oh_data_type = OH_NN_UINT32; + break; + case DataType::kNumberTypeUInt64: + oh_data_type = OH_NN_UINT64; + break; + case DataType::kNumberTypeFloat16: + oh_data_type = OH_NN_FLOAT16; + break; + case DataType::kNumberTypeFloat32: + oh_data_type = OH_NN_FLOAT32; + break; + case DataType::kNumberTypeFloat64: + oh_data_type = OH_NN_FLOAT64; + break; + default: { + oh_data_type = OH_NN_UNKNOWN; + } + } + return oh_data_type; +} +int mindspore::NNRTModelKernel::PrepareInputs() { + auto input_tensors = this->inputs(); + for (int i = 0; i < input_tensors.size(); i++) { + auto tensor = input_tensors[i]; + auto tensor_shape = tensor.Shape(); + auto tmp_quant_param = tensor.QuantParams(); + OH_NN_QuantParam *quant_param = nullptr; + std::vector bit_num; + std::vector scale; + std::vector zero_point; + if (!tmp_quant_param.empty()) { + quant_param = (new (std::nothrow) OH_NN_QuantParam); + if (quant_param == nullptr) { + MS_LOG(ERROR) << "new OH_NN_QuantParam failed."; + return lite::RET_NULL_PTR; + } + for (auto qparam : tmp_quant_param) { + bit_num.emplace_back(qparam.bit_num); + scale.emplace_back(qparam.scale); + zero_point.emplace_back(qparam.zero_point); + } + quant_param->quantCount = tmp_quant_param.size(); + quant_param->numBits = bit_num.data(); + quant_param->scale = scale.data(); + quant_param->zeroPoint = zero_point.data(); + } + auto oprend = new (std::nothrow) OH_NN_Tensor; + if (oprend == nullptr) { + MS_LOG(ERROR) << "new OH_NN_Tensor Failed"; + return lite::RET_ERROR; + } + oprend->dataType = ConvertDataType(tensor.DataType()); + oprend->dimensionCount = tensor_shape.size(); + + std::vector dimensions_list; + for (auto shape : tensor_shape) { + if (shape < INT32_MAX) { + dimensions_list.emplace_back(static_cast(shape)); + } else { + MS_LOG(ERROR) << "NNExecutor SetInput failed,tensor dimension is is too large, max dim = " << INT32_MAX + << ", but get dimension = " << shape; + return lite::RET_ERROR; + } + } + oprend->dimensions = dimensions_list.data(); + oprend->quantParam = quant_param; + oprend->type = OH_NN_TENSOR; + OH_NN_ReturnCode ret_code = + OH_NNExecutor_SetInput(oh_nn_executor, i, oprend, tensor.MutableData(), tensor.DataSize()); + delete (oprend); + + if (!tmp_quant_param.empty()) { + free(quant_param); + quant_param = nullptr; + } + + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNExecutor SetInput failed, current input tensor is" << tensor.Name() + << "OH_NN_ReturnCode = " << ret_code; + return lite::RET_ERROR; + } + } + + return lite::RET_OK; +} +int mindspore::NNRTModelKernel::TransferOutputs() { + auto output_tensors = this->outputs(); + for (size_t i = 0; i < output_tensors.size(); i++) { + auto tensor = output_tensors[i]; + OH_NN_ReturnCode ret_code = OH_NNExecutor_SetOutput(oh_nn_executor, i, tensor.MutableData(), tensor.DataSize()); + if (ret_code != OH_NN_SUCCESS) { + MS_LOG(ERROR) << "NNExecutor SetOutput failed, current out tensor is" << tensor.Name() + << ", OH_NN_ReturnCode = " << ret_code; + return lite::RET_ERROR; + } + } + return lite::RET_OK; +} diff --git a/mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.h b/mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.h new file mode 100644 index 00000000..cf9481df --- /dev/null +++ b/mindspore/lite/src/litert/delegate/nnrt/nnrt_model_kernel.h @@ -0,0 +1,57 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef LITE_NNRT_MODEL_KERNEL_H +#define LITE_NNRT_MODEL_KERNEL_H +#include +#include +#include +#include +#include "include/api/kernel.h" +#include "interfaces/kits/c/neural_network_runtime.h" +#include "src/common/log_adapter.h" +#include "include/errorcode.h" + +namespace mindspore { + +class NNRTModelKernel : public kernel::Kernel { + /** + * Because nnr can't run single op, but the whole model. So we decide to make the whole model into one kernel. + * */ + public: + NNRTModelKernel(OH_NNExecutor *oh_nn_executor, const std::vector &inputs, + const std::vector &outputs) + : kernel::Kernel(inputs, outputs, nullptr, nullptr), oh_nn_executor(oh_nn_executor) {} + int Prepare() override; + int Execute() override; + int ReSize() override { + MS_LOG(ERROR) << "NNRT does not support the resize function temporarily."; + return lite::RET_ERROR; + }; + OH_NN_DataType ConvertDataType(mindspore::DataType data_type); + int PrepareInputs(); + int TransferOutputs(); + ~NNRTModelKernel() override { + MS_LOG(INFO) << "start NNExecutor Destroy."; + OH_NNExecutor_Destroy(&oh_nn_executor); + MS_LOG(INFO) << "start NNExecutor Finish."; + } + + protected: + OH_NNExecutor *oh_nn_executor = nullptr; +}; +} // namespace mindspore + +#endif // LITE_NNRTT_MODEL_KERNEL_H diff --git a/mindspore/lite/src/litert/kernel/cpu/BUILD.gn b/mindspore/lite/src/litert/kernel/cpu/BUILD.gn new file mode 100644 index 00000000..b34e0427 --- /dev/null +++ b/mindspore/lite/src/litert/kernel/cpu/BUILD.gn @@ -0,0 +1,307 @@ +import("//build/ohos.gni") + +cpu_kernel_sources = [ + "nnacl/cxx_utils.cc", + "nnacl/nnacl_convolution.cc", + "nnacl/nnacl_kernel.cc", + "nnacl/nnacl_manager.cc", + "nnacl/nnacl_matmul.cc", + "nnacl/nnacl_reduce.cc", + "nnacl/nnacl_reshape.cc", + "nnacl/nnacl_strided_slice.cc", + "base/assert.cc", + "base/call.cc", + "base/constant_of_shape.cc", + "base/convolution_base.cc", + "base/detection_post_process_base.cc", + "base/format_transpose.cc", + "base/group_convolution_base.cc", + "base/group_convolution_creator.cc", + "base/layout_transform.cc", + "base/partial_fusion.cc", + "base/quant_dtype_cast.cc", + "base/random_normal.cc", + "base/reduce_base.cc", + "base/resize_base.cc", + "base/scatter_nd_base.cc", + "base/scatter_nd_binary.cc", + "base/select.cc", + "base/slice_base.cc", + "base/split_base.cc", + "base/split_with_over_lap_base.cc", + "base/tensor_scatter_add.cc", + "base/transpose_base.cc", + "fp32/adder_fp32.cc", + "fp32/affine_fp32.cc", + "fp32/all_gather_fp32.cc", + "fp32/batchnorm_fp32.cc", + "fp32/batch_to_space_fp32.cc", + "fp32/broadcast_to_fp32.cc", + "fp32/cast_for_x86_fp16.cc", + "fp32/cast_fp32.cc", + "fp32/convolution_1x1_fp32.cc", + "fp32/convolution_delegate_fp32.cc", + "fp32/convolution_depthwise_3x3_fp32.cc", + "fp32/convolution_depthwise_fp32.cc", + "fp32/convolution_depthwise_indirect_fp32.cc", + "fp32/convolution_depthwise_slidewindow_fp32.cc", + "fp32/convolution_depthwise_slidewindow_x86_fp32.cc", + "fp32/convolution_fp32.cc", + "fp32/convolution_im2col_arm32_fp32.cc", + "fp32/convolution_im2col_arm64_fp32.cc", + "fp32/convolution_im2col_avx512_fp32.cc", + "fp32/convolution_im2col_avx_fp32.cc", + "fp32/convolution_im2col_base_fp32.cc", + "fp32/convolution_im2col_fp32.cc", + "fp32/convolution_im2col_sse_fp32.cc", + "fp32/convolution_slidewindow_arm64_fp32.cc", + "fp32/convolution_slidewindow_avx_fp32.cc", + "fp32/convolution_slidewindow_fp32.cc", + "fp32/convolution_sw_1x1_fp32.cc", + "fp32/convolution_winograd_arm32_fp32.cc", + "fp32/convolution_winograd_arm64_fp32.cc", + "fp32/convolution_winograd_avx_fp32.cc", + "fp32/convolution_winograd_base_fp32.cc", + "fp32/convolution_winograd_fp32.cc", + "fp32/convolution_winograd_sse_fp32.cc", + "fp32/crop_and_resize_fp32.cc", + "fp32/cumsum_fp32.cc", + "fp32/custom_gru_fp32.cc", + "fp32/deconvolution_depthwise_fp32.cc", + "fp32/deconvolution_fp32.cc", + "fp32/deconvolution_winograd_fp32.cc", + "fp32/depth_to_space_fp32.cc", + "fp32/detection_post_process_fp32.cc", + "fp32/embedding_lookup_fp32.cc", + "fp32/fused_batchnorm_fp32.cc", + "fp32/gatherNd_fp32.cc", + "fp32/glu_fp32.cc", + "fp32/group_convolution_fp32.cc", + "fp32/gru_fp32.cc", + "fp32/instance_norm_fp32.cc", + "fp32/invert_permutation_fp32.cc", + "fp32/l2_norm_fp32.cc", + "fp32/lstm_fp32.cc", + "fp32/matmul_fp32_arm32.cc", + "fp32/matmul_fp32_arm64.cc", + "fp32/matmul_fp32_avx512.cc", + "fp32/matmul_fp32_avx.cc", + "fp32/matmul_fp32_base.cc", + "fp32/matmul_fp32.cc", + "fp32/matmul_fp32_sse.cc", + "fp32/non_max_suppression_fp32.cc", + "fp32/power_fp32.cc", + "fp32/prelu_fp32.cc", + "fp32/reduce_fp32.cc", + "fp32/reduce_scatter_fp32.cc", + "fp32/relative_position_attention_fp32.cc", + "fp32/resize_fp32.cc", + "fp32/reverse_fp32.cc", + "fp32/reverse_sequence_fp32.cc", + "fp32/roi_pooling_fp32.cc", + "fp32/scatter_nd_update_fp32.cc", + "fp32/shape_fusion_fp32.cc", + "fp32/space_to_batch_fp32.cc", + "fp32/space_to_depth_fp32.cc", + "fp32/sparse_fill_empty_rows_fp32.cc", + "fp32/sparse_reshape_fp32.cc", + "fp32/sparse_segment_sum_fp32.cc", + "fp32/sparse_to_dense_fp32.cc", + "fp32/topk_fp32.cc", + "fp32/transpose_server_fp32.cc", + "fp32/uniform_real_fp32.cc", + "fp32/unstack_fp32.cc", + "fp32/where_fp32.cc", + "fp32/online_fusion/cast_gather_reduce_fp32.cc", + "fp32/online_fusion/reduce_concat_fp32.cc", + "fp32/online_fusion/split_reduce_concat_fp32.cc", +] + +arm64_cpu_kernel_sources = [ + "fp32/convolution_im2col_arm64_fp32.cc", + "fp32/matmul_fp32_arm64.cc", + "fp32/convolution_winograd_arm64_fp32.cc", + "fp32/convolution_slidewindow_arm64_fp32.cc", +] + +arm32_cpu_kernel_sources = [ + "fp32/convolution_im2col_arm32_fp32.cc", + "fp32/matmul_fp32_arm32.cc", + "fp32/convolution_winograd_arm32_fp32.cc", +] + +sse_avx_avx512_kernel_sources = [ + "fp32/convolution_im2col_sse_fp32.cc", + "fp32/matmul_fp32_sse.cc", + "fp32/convolution_winograd_sse_fp32.cc", + "fp32/convolution_im2col_avx_fp32.cc", + "fp32/matmul_fp32_avx.cc", + "fp32/convolution_winograd_avx_fp32.cc", + "fp32/convolution_im2col_avx512_fp32.cc", + "fp32/matmul_fp32_avx512.cc", +] + +int8_kernel_sources = [ + "int8/activation_int8.cc", + "int8/add_int8.cc", + "int8/argminmax_int8.cc", + "int8/arithmetic_int8.cc", + "int8/arithmetic_self_int8.cc", + "int8/batchnorm_int8.cc", + "int8/batch_to_space_int8.cc", + "int8/concat_int8.cc", + "int8/convolution_1x1_int8.cc", + "int8/convolution_3x3_int8.cc", + "int8/convolution_depthwise_3x3_int8.cc", + "int8/convolution_depthwise_int8.cc", + "int8/convolution_depthwise_slidewindow_int8.cc", + "int8/convolution_int8.cc", + "int8/convolution_int8_creator.cc", + "int8/crop_int8.cc", + "int8/deconvolution_depthwise_int8.cc", + "int8/deconvolution_int8.cc", + "int8/depth_to_space_int8.cc", + "int8/detection_post_process_int8.cc", + "int8/div_int8.cc", + "int8/dynamic_gather_int8.cc", + "int8/dynamic_quant.cc", + "int8/fullconnection_int8.cc", + "int8/gather_int8.cc", + "int8/gatherNd_int8.cc", + "int8/group_convolution_int8.cc", + "int8/hswish_int8.cc", + "int8/l2_norm_int8.cc", + "int8/layer_norm_int8.cc", + "int8/leaky_relu_int8.cc", + "int8/matmul_base_int8.cc", + "int8/matmul_dynamic_base_int8.cc", + "int8/matmul_dynamic_int8.cc", + "int8/matmul_dynamic_sdot_int8.cc", + "int8/matmul_int8.cc", + "int8/mul_int8.cc", + "int8/opt_op_handler.cc", + "int8/pad_int8.cc", + "int8/pooling_int8.cc", + "int8/power_int8.cc", + "int8/reduce_int8.cc", + "int8/relux_int8.cc", + "int8/reshape_int8.cc", + "int8/resize_int8.cc", + "int8/scale_int8.cc", + "int8/sigmoid_int8.cc", + "int8/slice_int8.cc", + "int8/softmax_int8.cc", + "int8/space_to_batch_int8.cc", + "int8/split_int8.cc", + "int8/squeeze_int8.cc", + "int8/sub_int8.cc", + "int8/tanh_int8.cc", + "int8/topk_int8.cc", + "int8/transpose_int8.cc", + "int8/unsqueeze_int8.cc", +] + +string_kernel_sources = [ + "string/extract_feature.cc", + "string/hashtable_lookup.cc", + "string/lsh_projection.cc", + "string/normalize.cc", + "string/predict.cc", + "string/skip_gram.cc", +] + +control_kernel_sources = [ + "control/switch.cc", + "control/switch_layer.cc", + "control/tensor_array.cc", + "control/tensorlist_fromtensor.cc", + "control/tensorlist_getitem.cc", + "control/tensorlist_reserve.cc", + "control/tensorlist_setitem.cc", + "control/tensorlist_stack.cc", +] + +all_cpu_kernel_sources = cpu_kernel_sources +all_cpu_kernel_sources += int8_kernel_sources +all_cpu_kernel_sources += string_kernel_sources +all_cpu_kernel_sources += control_kernel_sources + +if (target_cpu == "arm") { + all_cpu_kernel_sources -= arm64_cpu_kernel_sources + all_cpu_kernel_sources -= sse_avx_avx512_kernel_sources + not_needed(arm32_cpu_kernel_sources) +} + +if (target_cpu == "arm64") { + all_cpu_kernel_sources -= arm32_cpu_kernel_sources + all_cpu_kernel_sources -= sse_avx_avx512_kernel_sources + not_needed(arm64_cpu_kernel_sources) +} + +if (target_cpu == "x86_64") { + all_cpu_kernel_sources -= arm32_cpu_kernel_sources + all_cpu_kernel_sources -= arm64_cpu_kernel_sources + not_needed(sse_avx_avx512_kernel_sources) +} + +ohos_source_set("cpu_kernel_obj") { + sources = all_cpu_kernel_sources + + deps = [ + "../../../../../ccsrc/plugin/device/cpu/kernel/nnacl:nnacl_obj", + ] + + include_dirs = [ + "./", + "../../../../", + "../../../../../ccsrc/", + "../../../../../ccsrc/plugin/device/cpu/kernel/", + "../../../../../core/", + "../../../../../core/mindrt/src/", + "../../../../../core/mindrt/include/", + "../../../../../../", + "//third_party/flatbuffers/include", + "../../../../../../third_party/", + "../../../../src/", + "../../../../src/c_api", + "//base/hiviewdfx/hilog/interfaces/native/innerkits/include", + ] + + defines = [ + "ENABLE_MINDRT", + "MS_COMPILE_OHOS", + "PRIMITIVE_WRITEABLE", + "VERSION_STR=\"2.1.0\"", + ] + if (target_cpu == "arm") { + defines += [ + "ENABLE_ARM", + "ENABLE_ARM32", + "ENABLE_NEON", + ] + } else if (target_cpu == "arm64") { + defines += [ + "ENABLE_ARM", + "ENABLE_ARM64", + "ENABLE_NEON", + "ENABLE_FP16", + "USE_OPENCL_WRAPPER", + "MS_OPENCL_PROFILE=false", + "CL_TARGET_OPENCL_VERSION=200", + "CL_HPP_TARGET_OPENCL_VERSION=120", + "CL_HPP_MINIMUM_OPENCL_VERSION=120", + ] + } + + cflags_cc = [ + "-Wno-ignored-qualifiers", + "-Wunused-private-field", + "-Wno-unused-private-field", + "-Wno-inconsistent-missing-override", + "-Wno-macro-redefined", + "-Wno-constant-conversion", + ] + + part_name = "mindspore" + subsystem_name = "thirdparty" +} diff --git a/mindspore/lite/src/litert/lite_session.cc b/mindspore/lite/src/litert/lite_session.cc index f0258794..ded4d761 100644 --- a/mindspore/lite/src/litert/lite_session.cc +++ b/mindspore/lite/src/litert/lite_session.cc @@ -971,7 +971,7 @@ int LiteSession::ContextInit(const std::shared_ptr &context) { } int LiteSession::AscendInit(const std::shared_ptr &context) { -#ifndef __ANDROID__ +#if !defined(__ANDROID__) && !defined(MS_COMPILE_OHOS) if (!context->IsDeviceTypeEnabled(DT_ASCEND)) { MS_LOG(INFO) << "There is no Ascend device type."; return RET_OK; diff --git a/mindspore/lite/src/litert/scheduler.cc b/mindspore/lite/src/litert/scheduler.cc index daba51f7..11382b09 100644 --- a/mindspore/lite/src/litert/scheduler.cc +++ b/mindspore/lite/src/litert/scheduler.cc @@ -40,7 +40,9 @@ #include "src/common/context_util.h" #include "src/litert/infer_manager.h" #include "src/litert/runtime_pass.h" +#ifndef ENABLE_MULTI_LAYOUT #include "src/litert/pass/format_pass/format_pass.h" +#endif #if !defined(AUTO_PARALLEL_CLIP) || !defined(RUNTIME_PASS_CLIP) #include "src/litert/sub_graph_split.h" #include "src/litert/pass/online_fusion/online_fusion_pass_registry.h" @@ -276,11 +278,13 @@ int Scheduler::ConstructSubGraphs(std::vector *dst_kernels } int Scheduler::ProcessSubGraphTranspose(std::vector *dst_kernels) { +#ifndef ENABLE_MULTI_LAYOUT auto ret = pass::RuntimeFormatPass(dst_kernels, src_tensors_, Format::NHWC); if (ret != RET_OK) { MS_LOG(ERROR) << "Run runtime format pass failed."; return RET_ERROR; } +#endif return RET_OK; } diff --git a/mindspore/lite/test/ohos_test/BUILD.gn b/mindspore/lite/test/ohos_test/BUILD.gn new file mode 100644 index 00000000..adfe7ee5 --- /dev/null +++ b/mindspore/lite/test/ohos_test/BUILD.gn @@ -0,0 +1,40 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +import("//build/test.gni") + +module_output_path = "mindspore/" + +ohos_unittest("mindspore_test") { + module_out_path = module_output_path + sources = [ + "src/context_test.cc", + "src/model_test.cc", + "src/tensor_test.cc", + ] + + include_dirs = [ "//foundation/ai/mindspore/" ] + + deps = [ + "//third_party/googletest:gmock", + "//third_party/googletest:gtest", + "//third_party/mindspore/mindspore-src/mindspore-v1.8.1/mindspore/lite:mindspore_lib", + ] + + subsystem_name = "ai" + part_name = "mindspore" + resource_config_file = + "//third_party/mindspore/mindspore-src/mindspore-v1.8.1/mindspore/lite/test/ohos_test/ohos_test.xml" +} diff --git a/mindspore/lite/test/ohos_test/ohos_test.xml b/mindspore/lite/test/ohos_test/ohos_test.xml new file mode 100644 index 00000000..d094712a --- /dev/null +++ b/mindspore/lite/test/ohos_test/ohos_test.xml @@ -0,0 +1,23 @@ + + + + + + + + + diff --git a/mindspore/lite/test/ohos_test/resource/models/mobilenetv2.ms b/mindspore/lite/test/ohos_test/resource/models/mobilenetv2.ms new file mode 100644 index 0000000000000000000000000000000000000000..cc6a83980ff8cbaf11d57143eca7598e17e2f02a GIT binary patch literal 13988384 zcmaglc{~--|2Y1UHAx~$i^yJ4k>$>rGq+@Eks?Klc1s&7X;axFdx*54MY4oa+&SmW zl~PKSP_#(2YE>$g>O0<_&-?q&@ArHB=J9x%xo6Hfb7t2wRFDGe_vcODWE|?;iDwrm)5LgPP3# zFgsbulOW^~6zyuJdUpt)1vA9WR3O_-jTPL1LRrOT>bjheCTRWCMCpELqOQMhqBfst zqC!qLQ6_?Wc1_f3aTBE?DA#Wi+S5dJjBlc1r1z;z{yt?d=q2~5<6ifv$;<9jtxNAy 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