| /* |
| * Copyright (c) 2022 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 |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * 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 |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE |
| #define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE |
| |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h" |
| #include "arm_compute/dynamic_fusion/sketch/attributes/DepthwiseConv2dAttributes.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuDepthwiseConv2d.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h" |
| |
| #include "tests/CL/CLAccessor.h" |
| |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/framework/Macros.h" |
| |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/reference/DepthwiseConvolutionLayer.h" |
| |
| using namespace arm_compute::experimental::dynamic_fusion; |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionGpuDepthwiseConv2dValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value |
| || std::is_same<typename std::decay<T>::type, int8_t>::value, |
| int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T |
| |
| template <typename...> |
| void setup(TensorShape input_shape, Size2D kernel_size, const PadStrideInfo &pad_stride, const Size2D &dilation, |
| const unsigned int depth_multiplier, const DataType data_type, const DataLayout data_layout) |
| { |
| ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion depthwise conv2d only supports NHWC layout |
| |
| DepthwiseConv2dAttributes dwc_conv2d_attr; |
| const Padding2D padding_2d(pad_stride.pad_left(), pad_stride.pad_right(), pad_stride.pad_top(), pad_stride.pad_bottom()); |
| dwc_conv2d_attr.pad(padding_2d) |
| .stride(Size2D(pad_stride.stride().first, pad_stride.stride().second)) |
| .dilation(dilation) |
| .depth_multiplier(depth_multiplier) |
| .dimension_rounding_type(pad_stride.round()); |
| |
| // Calculate Output and Weight Shapes |
| TensorShape weights_shape = TensorShape(kernel_size.width, kernel_size.height); |
| |
| const TensorInfo in_info(input_shape, 1, data_type); |
| const TensorInfo we_info(weights_shape, 1, data_type); |
| |
| const ConvolutionInfo info{ pad_stride, depth_multiplier, ActivationLayerInfo(), dilation }; |
| const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(in_info, we_info, info); |
| |
| weights_shape.set(2, output_shape.z()); |
| const TensorShape bias_shape = TensorShape(weights_shape[2]); |
| |
| _data_type = data_type; |
| _data_layout = data_layout; |
| _target = compute_target(input_shape, weights_shape, bias_shape, dwc_conv2d_attr); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, dwc_conv2d_attr); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::F16: |
| { |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f }; |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| // Given input is in nchw format |
| TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, const DepthwiseConv2dAttributes dwc_conv2d_attr) |
| { |
| ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC); |
| |
| // Our test shapes are assumed in NCHW data layout, thus the permutation |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| |
| // Create a new workload sketch |
| auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; |
| GpuWorkloadSketch sketch{ &gpu_ctx }; |
| |
| // Create sketch tensors |
| auto input_info = sketch.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout)); |
| auto weight_info = sketch.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout)); |
| auto bias_info = sketch.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout)); |
| auto dst_info = sketch.create_tensor_info(); |
| |
| auto ans_info = sketch.create_tensor_info(); |
| |
| FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, &ans_info, dwc_conv2d_attr); |
| GpuOutput::create_op(sketch, &ans_info, &dst_info); |
| |
| // Configure runtime |
| ClWorkloadRuntime runtime; |
| runtime.configure(sketch); |
| |
| // (Important) Allocate auxiliary tensor memory if there are any |
| for(auto &data : runtime.get_auxiliary_tensors()) |
| { |
| auto tensor = data.first; |
| const auto aux_mem_req = data.second; |
| tensor->allocator()->init(*data.first->info(), aux_mem_req.alignment); |
| tensor->allocator()->allocate(); |
| } |
| |
| // Construct user tensors |
| TensorType t_input{}; |
| TensorType t_weight{}; |
| TensorType t_bias{}; |
| TensorType t_dst{}; |
| |
| // Initialize user tensors |
| t_input.allocator()->init(input_info); |
| t_weight.allocator()->init(weight_info); |
| t_bias.allocator()->init(bias_info); |
| t_dst.allocator()->init(dst_info); |
| |
| // Allocate and fill user tensors |
| t_input.allocator()->allocate(); |
| t_weight.allocator()->allocate(); |
| t_bias.allocator()->allocate(); |
| t_dst.allocator()->allocate(); |
| |
| fill(AccessorType(t_input), 0); |
| fill(AccessorType(t_weight), 1); |
| fill(AccessorType(t_bias), 2); |
| |
| // Run runtime |
| runtime.run({ &t_input, &t_weight, &t_bias, &t_dst }); |
| return t_dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, |
| const TensorShape &output_shape, DepthwiseConv2dAttributes dwc_conv2d_attr) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, _data_type, 1 }; |
| SimpleTensor<T> weight{ weights_shape, _data_type, 1 }; |
| SimpleTensor<TBias> bias{ bias_shape, _data_type, 1 }; |
| |
| fill(src, 0); |
| fill(weight, 1); |
| fill(bias, 2); |
| |
| auto src_nchw = src; |
| auto weights_nchw = weight; |
| auto bias_nchw = bias; |
| auto output_shape_nchw = output_shape; |
| |
| PadStrideInfo legacy_pad_stride(dwc_conv2d_attr.stride().x(), dwc_conv2d_attr.stride().y(), dwc_conv2d_attr.pad().left, dwc_conv2d_attr.pad().right, dwc_conv2d_attr.pad().top, |
| dwc_conv2d_attr.pad().bottom, |
| DimensionRoundingType{}); |
| auto dst_nchw = reference::depthwise_convolution(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, dwc_conv2d_attr.depth_multiplier(), dwc_conv2d_attr.dilation()); |
| return dst_nchw; |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataLayout _data_layout{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionGpuDepthwiseConv2dValidationFixture : public DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, Size2D kernel_size, const PadStrideInfo &info, const Size2D &dilation, const unsigned int depth_multiplier, DataType data_type, DataLayout data_layout) |
| { |
| DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, kernel_size, info, dilation, |
| depth_multiplier, data_type, data_layout); |
| } |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE */ |