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Diffstat (limited to 'tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h')
| -rw-r--r-- | tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h | 216 |
1 files changed, 216 insertions, 0 deletions
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..b0522488b4 --- /dev/null +++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h @@ -0,0 +1,216 @@ +/* + * 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 ARM_COMPUTE_TEST_DYNAMIC_FUSION_FIXTURE +#define ARM_COMPUTE_TEST_DYNAMIC_FUSION_FIXTURE + +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" + +#include "arm_compute/runtime/CL/CLScheduler.h" + +#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h" +#include "arm_compute/dynamic_fusion/sketch/OperatorAttributes.h" +#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h" +#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h" + +#include "src/gpu/cl/operators/ClAdd.h" +#include "src/gpu/cl/operators/ClConv2d.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/ConvolutionLayer.h" +#include "tests/validation/reference/ElementwiseOperations.h" +#include "tests/validation/reference/Permute.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 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 + + template <typename...> + 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: + 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, 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 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(); + FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, &dst_info, conv2d_attr); + + // 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(); // Use ACL allocated memory + } + // Construct user tensors + CLTensor t_input{}; + CLTensor t_weight{}; + CLTensor t_bias{}; + CLTensor 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(CLAccessor(t_input), 0); + fill(CLAccessor(t_weight), 1); + fill(CLAccessor(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 _weights_data_type{}; + DataType _bias_data_type{}; + DataType _output_data_type{}; + DataLayout _data_layout{}; + QuantizationInfo _quantization_info{}; + QuantizationInfo _weight_quantization_info{}; + bool _is_quantized = false; + bool _is_bfloat16 = false; + bool _mixed_layout = false; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class DynamicFusionGpuConv2dValidationFixture : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + 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); + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_DYNAMIC_FUSION_FIXTURE */ |