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Diffstat (limited to 'tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h')
| -rw-r--r-- | tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h | 246 |
1 files changed, 246 insertions, 0 deletions
diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h new file mode 100644 index 0000000000..ca4de11a15 --- /dev/null +++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h @@ -0,0 +1,246 @@ +/* + * 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_DEPTHWISECONV2DFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_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/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/reference/DepthwiseConvolutionLayer.h" +#include "tests/validation/Validation.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 + + 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 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, 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()) + { + 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, + 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: + 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 // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H |