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Diffstat (limited to 'tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h')
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diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h new file mode 100644 index 0000000000..1f4e223b93 --- /dev/null +++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h @@ -0,0 +1,411 @@ +/* + * Copyright (c) 2022-2024 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 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 |