| /* |
| * Copyright (c) 2023 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
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| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
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| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
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| * SOFTWARE. |
| */ |
| |
| #ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE |
| #define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE |
| |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h" |
| |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/reference/ActivationLayer.h" |
| |
| using namespace arm_compute::experimental::dynamic_fusion; |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename... TArgs> |
| class DynamicFusionActivationValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, bool fuse, DataType data_type, ActivationLayerInfo act_info, TArgs... args) |
| { |
| _fuse = fuse; |
| _data_type = data_type; |
| _function = act_info.activation(); |
| _target = compute_target(shape, args...); |
| _reference = compute_reference(shape, act_info); |
| } |
| |
| protected: |
| std::vector<T> get_boundary_values(T min, T max) |
| { |
| // This function will return a vector filled with the following values that can |
| // represent two partitions derived from equivalent partitioning. |
| // * Lower partition: min, min + delta, lower quarter (nominal), center - delta |
| // * Upper partition: center, center + delta, upper quarter (nominal), max - delta, max |
| const auto delta = is_data_type_float(_data_type) ? T(0.1f) : T(1); |
| const auto center_value = (min + max) / 2; |
| const auto lower_quarter = (min + center_value) / 2; |
| const auto upper_quarter = (center_value + max) / 2; |
| |
| std::vector<T> boundary_values{}; |
| |
| // To ensure all the inserted values are within the given range after subtracing/adding delta |
| auto insert_values = [&boundary_values, &min, &max](const std::initializer_list<T> &new_values) |
| { |
| for(auto &v : new_values) |
| { |
| if(v >= min && v <= max) |
| { |
| boundary_values.emplace_back(v); |
| } |
| } |
| }; |
| |
| insert_values({ min, static_cast<T>(min + delta), static_cast<T>(lower_quarter), static_cast<T>(center_value - delta) }); // lower partition |
| insert_values({ static_cast<T>(center_value), static_cast<T>(center_value + delta), static_cast<T>(upper_quarter), static_cast<T>(max - delta), max }); // upper partition |
| |
| return boundary_values; |
| } |
| |
| template <typename U> |
| void fill(U &&tensor) |
| { |
| float min_bound = 0; |
| float max_bound = 0; |
| std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<T>(_function, _data_type); |
| library->fill_static_values(tensor, get_boundary_values(static_cast<T>(min_bound), static_cast<T>(max_bound))); |
| } |
| |
| TensorType compute_target(const TensorShape &shape, TArgs... args) |
| { |
| // Create a new workload sketch |
| CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| GpuWorkloadContext gpu_ctx{ &cl_compile_ctx }; |
| GpuWorkloadSketch sketch{ &gpu_ctx }; |
| |
| // Create sketch tensors |
| TensorInfo src_info = sketch.create_tensor_info(TensorInfo(shape, 1, _data_type)); |
| TensorInfo dst_info = sketch.create_tensor_info(TensorInfo(shape, 1, _data_type)); |
| |
| ITensorInfo *ans_0_info = FunctionType::create_op(sketch, &src_info, args...); |
| if(_fuse) |
| { |
| ITensorInfo *ans_1_info = FunctionType::create_op(sketch, ans_0_info, args...); |
| GpuOutput::create_op(sketch, ans_1_info, &dst_info); |
| } |
| else |
| { |
| GpuOutput::create_op(sketch, ans_0_info, &dst_info); |
| } |
| |
| // Configure runtime |
| ClWorkloadRuntime runtime; |
| runtime.configure(sketch); |
| |
| // Construct user tensors |
| TensorType t_src{}; |
| TensorType t_dst{}; |
| |
| // Initialize user tensors |
| t_src.allocator()->init(src_info); |
| t_dst.allocator()->init(dst_info); |
| |
| // Allocate and fill user tensors |
| t_src.allocator()->allocate(); |
| t_dst.allocator()->allocate(); |
| |
| fill(AccessorType(t_src)); |
| |
| // Run runtime |
| runtime.run({ &t_src, &t_dst }); |
| |
| return t_dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &shape, ActivationLayerInfo act_info) |
| { |
| // Create reference |
| SimpleTensor<T> src{ shape, _data_type, 1 }; |
| |
| // Fill reference |
| fill(src); |
| |
| auto tmp = reference::activation_layer<T>(src, act_info); |
| |
| if(_fuse) |
| { |
| auto dst = reference::activation_layer<T>(tmp, act_info); |
| return dst; |
| } |
| else |
| { |
| return tmp; |
| } |
| } |
| |
| protected: |
| ActivationLayerInfo::ActivationFunction _function{}; |
| bool _fuse{ false }; |
| DataType _data_type{}; |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionSigmoidValidationFixture : public DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, bool fuse, DataType data_type) |
| { |
| ActivationLayerInfo act_info{ ActivationLayerInfo::ActivationFunction::LOGISTIC }; |
| DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, fuse, data_type, act_info); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionTanhValidationFixture : public DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, bool fuse, DataType data_type) |
| { |
| ActivationLayerInfo act_info{ ActivationLayerInfo::ActivationFunction::TANH }; |
| DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, fuse, data_type, act_info); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| |
| #endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE */ |