| /* |
| * Copyright (c) 2022-2024 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
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| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
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| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
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| * SOFTWARE. |
| */ |
| #ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE_H |
| #define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE_H |
| |
| #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/Conv2dAttributes.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h" |
| |
| #include "tests/CL/CLAccessor.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| #include "tests/validation/reference/Permute.h" |
| #include "tests/validation/Validation.h" |
| |
| using namespace arm_compute::experimental::dynamic_fusion; |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| 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); |
| } |
| } |
| |
| } // namespace |
| |
| /** General Conv2d fixture |
| * Adapted from tests/validation/fixtures/ConvolutionLayerFixture.h |
| * TODO: Parameterize to be fully backend agnostic: COMPMID-5760; remove Gpu from name |
| */ |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionGpuConv2dValidationGenericFixture : 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 |
| |
| void setup(TensorShape input_shape, |
| TensorShape weights_shape, |
| TensorShape bias_shape, |
| TensorShape output_shape, |
| const PadStrideInfo &info, |
| const Size2D &dilation, |
| DataType data_type, |
| DataLayout data_layout, |
| QuantizationInfo quantization_info, |
| QuantizationInfo weight_quantization_info) |
| { |
| ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout |
| const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, dilation); |
| _data_type = data_type; |
| _data_layout = data_layout; |
| _is_quantized = is_data_type_quantized_asymmetric(data_type); |
| _quantization_info = quantization_info; |
| _weight_quantization_info = weight_quantization_info; |
| _bias_data_type = _is_quantized ? DataType::S32 : data_type; |
| _target = compute_target(input_shape, weights_shape, bias_shape, conv2d_attr); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr); |
| } |
| |
| protected: |
| // Given input is in nchw format |
| TensorType compute_target(TensorShape input_shape, |
| TensorShape weights_shape, |
| const TensorShape &bias_shape, |
| Conv2dAttributes conv2d_attr) |
| { |
| ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC); |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| CLScheduler::get().default_reinit(); |
| |
| // Create a new workload sketch |
| auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| auto context = GpuWorkloadContext{&cl_compile_ctx}; |
| GpuWorkloadSketch sketch{&context}; |
| |
| // Create sketch tensors |
| ITensorInfo *input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout)); |
| ITensorInfo *weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout)); |
| ITensorInfo *bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout)); |
| ITensorInfo *dst_info = context.create_tensor_info(); |
| |
| ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, weight_info, bias_info, 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()) |
| { |
| CLTensor *tensor = std::get<0>(data); |
| TensorInfo info = std::get<1>(data); |
| AuxMemoryInfo aux_mem_req = std::get<2>(data); |
| tensor->allocator()->init(info, aux_mem_req.alignment); |
| tensor->allocator()->allocate(); // Use ACL allocated memory |
| } |
| // 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, |
| Conv2dAttributes conv2d_attr) |
| { |
| // Create reference |
| SimpleTensor<T> src{input_shape, _data_type, 1, _quantization_info}; |
| SimpleTensor<T> weight{weights_shape, _data_type, 1, _weight_quantization_info}; |
| SimpleTensor<TBias> bias{bias_shape, _data_type, 1, _quantization_info}; |
| |
| 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(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, |
| conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom, |
| DimensionRoundingType{}); |
| auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, |
| legacy_pad_stride, conv2d_attr.dilation()); |
| return dst_nchw; |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataType _bias_data_type{}; |
| DataLayout _data_layout{}; |
| QuantizationInfo _quantization_info{}; |
| QuantizationInfo _weight_quantization_info{}; |
| bool _is_quantized = false; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionGpuConv2dValidationFixture |
| : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| void setup(TensorShape input_shape, |
| TensorShape weights_shape, |
| TensorShape output_shape, |
| TensorShape bias_shape, |
| const PadStrideInfo &info, |
| const Size2D &dialation, |
| DataType data_type, |
| DataLayout data_layout, |
| QuantizationInfo quantization_info) |
| { |
| DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup( |
| input_shape, weights_shape, output_shape, bias_shape, info, dialation, data_type, data_layout, |
| quantization_info, quantization_info); |
| } |
| }; |
| |
| /** Specific Conv2d method: Direct Conv2d fixture |
| * Adapted from tests/validation/fixtures/DirectConvolutionLayerFixture.h |
| * TODO: Parameterize to be fully backend agnostic: COMPMID-5760 |
| */ |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionDirectConv2dValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| using TBias = |
| typename std::conditional<std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T>::type; |
| |
| void setup(TensorShape input_shape, |
| int stride_x, |
| int stride_y, |
| int pad_x, |
| int pad_y, |
| unsigned int kernel_size, |
| unsigned int num_kernels, |
| DataType data_type, |
| QuantizationInfo quantization_info, |
| DataLayout data_layout) |
| { |
| ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout |
| |
| TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); |
| const TensorShape bias_shape(num_kernels); |
| const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); |
| const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| |
| const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, {1U, 1U} /* dilation */); |
| |
| TensorInfo input_info = TensorInfo(input_shape, 1, data_type); |
| TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type); |
| |
| const TensorShape output_shape = |
| misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info); |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type, |
| bias_data_type, quantization_info, data_layout); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, |
| bias_data_type, quantization_info); |
| } |
| |
| protected: |
| TensorType compute_target(TensorShape input_shape, |
| TensorShape weights_shape, |
| const TensorShape &bias_shape, |
| TensorShape output_shape, |
| const Conv2dAttributes &conv2d_attr, |
| DataType data_type, |
| DataType bias_data_type, |
| QuantizationInfo quantization_info, |
| const DataLayout &data_layout) |
| { |
| ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); |
| ARM_COMPUTE_UNUSED(quantization_info); |
| // Dataset shapes are in NCHW layout |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| |
| auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| auto context = GpuWorkloadContext{&cl_compile_ctx}; |
| GpuWorkloadSketch sketch{&context}; |
| |
| // Create sketch tensors |
| auto input_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, data_layout)); |
| auto weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, data_type, data_layout)); |
| auto bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, bias_data_type, data_layout)); |
| auto dst_info = context.create_tensor_info(); |
| |
| ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, weight_info, bias_info, conv2d_attr); |
| GpuOutput::create_op(sketch, ans_info, dst_info); |
| |
| // Configure runtime |
| ClWorkloadRuntime runtime; |
| runtime.configure(sketch); |
| |
| for (auto &data : runtime.get_auxiliary_tensors()) |
| { |
| CLTensor *tensor = std::get<0>(data); |
| TensorInfo info = std::get<1>(data); |
| AuxMemoryInfo aux_mem_req = std::get<2>(data); |
| tensor->allocator()->init(info, aux_mem_req.alignment); |
| tensor->allocator()->allocate(); // Use ACL allocated memory |
| } |
| // 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); |
| |
| ARM_COMPUTE_ASSERT(t_input.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(t_weight.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(t_bias.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable()); |
| |
| // Allocate and fill user tensors |
| t_input.allocator()->allocate(); |
| t_weight.allocator()->allocate(); |
| t_bias.allocator()->allocate(); |
| t_dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_ASSERT(!t_input.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!t_weight.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!t_bias.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable()); |
| |
| 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, |
| const PadStrideInfo &info, |
| DataType data_type, |
| DataType bias_data_type, |
| QuantizationInfo quantization_info) |
| { |
| // Create reference |
| SimpleTensor<T> src{input_shape, data_type, 1, quantization_info}; |
| SimpleTensor<T> weights{weights_shape, data_type, 1, quantization_info}; |
| SimpleTensor<TBias> bias{bias_shape, bias_data_type, 1, quantization_info}; |
| |
| // Fill reference |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info); |
| return dst; |
| } |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DynamicFusionDirectConv2dValidationFixture |
| : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| void setup(TensorShape input_shape, |
| int stride_x, |
| int stride_y, |
| int pad_x, |
| int pad_y, |
| unsigned int kernel_size, |
| unsigned int num_kernels, |
| DataType data_type, |
| DataLayout data_layout) |
| { |
| DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup( |
| input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, QuantizationInfo(), |
| data_layout); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE_H |