diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-20 21:39:25 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-25 16:23:15 +0000 |
commit | 7891a73ef36f4ad7b71069b3c57694f85bb79454 (patch) | |
tree | 5b08692989e28ce63de2937d8d92ea5176589dbe /src/runtime/cpu/operators/CpuWinogradConv2d.cpp | |
parent | a46c9c98c2b1d70acc7c6eee00e2cdc2a1e209a6 (diff) | |
download | ComputeLibrary-7891a73ef36f4ad7b71069b3c57694f85bb79454.tar.gz |
Move CPU/GPU files from Core/Runtime to the respective backend folders
Legacy structure contained two libraries core/runtime with two backends
in each.
We reduce the core/runtime libraries to a single library thus merging
the backend files
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/runtime/cpu/operators/CpuWinogradConv2d.cpp')
-rw-r--r-- | src/runtime/cpu/operators/CpuWinogradConv2d.cpp | 839 |
1 files changed, 0 insertions, 839 deletions
diff --git a/src/runtime/cpu/operators/CpuWinogradConv2d.cpp b/src/runtime/cpu/operators/CpuWinogradConv2d.cpp deleted file mode 100644 index 253280a951..0000000000 --- a/src/runtime/cpu/operators/CpuWinogradConv2d.cpp +++ /dev/null @@ -1,839 +0,0 @@ -/* - * Copyright (c) 2021 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. - */ -#include "src/runtime/cpu/operators/CpuWinogradConv2d.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "arm_compute/runtime/FunctionDescriptors.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "src/core/CPP/Validate.h" -#include "src/core/NEON/kernels/convolution/common/utils.hpp" -#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp" -#include "src/core/cpu/kernels/CpuWinogradConv2dKernel.h" -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/cpu/operators/CpuActivation.h" -#include "src/runtime/cpu/operators/CpuPermute.h" -#include "src/runtime/cpu/operators/CpuWinogradConv2d.h" -#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h" - -#include "support/Cast.h" - -#include <set> - -namespace arm_compute -{ -namespace cpu -{ -using namespace arm_compute::experimental; -using namespace arm_compute::utils::cast; - -namespace -{ -arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info) -{ - switch(act_info.activation()) - { - case ActivationLayerInfo::ActivationFunction::RELU: - { - return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b()); - } - case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU: - { - return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b()); - } - default: - { - return arm_gemm::Activation(arm_gemm::Activation::Type::None); - } - } -} - -inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32); - - if(src->data_type() == DataType::F32) - { - if(input_dims.width > 4 && input_dims.height > 4) - { - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info))); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info))); - } - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - else if(src->data_type() == DataType::F16) - { - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info))); - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Status validate_kernel_5x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, dst, winograd_info))); - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Status validate_kernel_3x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, dst, winograd_info))); - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Status validate_kernel_1x3(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, dst, winograd_info))); - - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Status validate_kernel_5x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, dst, winograd_info))); - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} -inline Status validate_kernel_1x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, dst, winograd_info))); - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Status validate_kernel_7x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, dst, winograd_info))); - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Status validate_kernel_1x7(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, - const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(src, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, dst, winograd_info))); - - if(act_info.enabled()) - { - CpuActivation::validate(dst, nullptr, act_info); - } - return Status{}; -} - -inline Tensor4DShape internal_get_input_shape(const ITensorInfo *src) -{ - const DataLayout data_layout = src->data_layout(); - const int in_width = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)); - const int in_height = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)); - const int in_channels = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL)); - const int in_batches = src->dimension(3); - - return Tensor4DShape{ in_batches, in_height, in_width, in_channels }; -} - -Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info) -{ - ARM_COMPUTE_UNUSED(dst); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides."); - if(biases != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - return ICpuWinogradConv2dTransformWeightsKernel::validate(src, weights); -} -Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type) -{ - Size2D output_tile = Size2D{}; - if(kernel_dims == Size2D(3U, 3U)) - { - output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); - if(data_type == DataType::F16) - { - output_tile = Size2D(4U, 4U); - } - } - else if(kernel_dims == Size2D(5U, 5U)) - { - output_tile = Size2D(2U, 2U); - } - else if(kernel_dims == Size2D(1U, 3U)) - { - output_tile = Size2D(1U, 6U); - } - else if(kernel_dims == Size2D(3U, 1U)) - { - output_tile = Size2D(6U, 1U); - } - else if(kernel_dims == Size2D(1U, 5U)) - { - output_tile = Size2D(1U, 4U); - } - else if(kernel_dims == Size2D(5U, 1U)) - { - output_tile = Size2D(4U, 1U); - } - else if(kernel_dims == Size2D(7U, 1U)) - { - output_tile = Size2D(2U, 1U); - } - else if(kernel_dims == Size2D(1U, 7U)) - { - output_tile = Size2D(1U, 2U); - } - return output_tile; -} - -bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type) -{ - // Check if we want to configure a Winograd configuration which requires fast math - using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>; - - const std::vector<WinogradConfiguration> fast_math_winograd_f16 = - { - WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3)) - }; - - const std::vector<WinogradConfiguration> fast_math_winograd_f32 = - { - WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)), - WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)) - }; - - auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height), - std::pair<int, int>(kernel_size.width, kernel_size.height)); - - switch(data_type) - { - case DataType::F16: - return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end(); - case DataType::F32: - return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end(); - default: - return false; - } -} - -inline bool fuse_function_supported(const ActivationLayerInfo &act_info) -{ - return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU; -} - -} // namespace - -CpuWinogradConv2d::CpuWinogradConv2d() - : _gemm_function(std::make_unique<CpuGemm>()), - _activation_func(std::make_unique<CpuActivation>()), - _permute_input(std::make_unique<CpuPermute>()), - _permute_output(std::make_unique<CpuPermute>()), - _permute_weights(std::make_unique<CpuPermute>()), - _transform_input_kernel(nullptr), - _transform_weights_kernel(nullptr), - _transform_output_kernel(nullptr), - _data_layout(), - _aux_mem(AuxTensorIdx::Count), - _input_nhwc(), - _output_nhwc(), - _input_workspace(), - _kernel_storage(), - _output_workspace(), - _input_transformed(), - _output_transformed(), - _weights_hwio(), - _run_activation(false), - _is_prepared(false) -{ -} - -CpuWinogradConv2d::~CpuWinogradConv2d() = default; - -void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, - const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info)); - - // Get indices for the width and height - _data_layout = src->data_layout(); - const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); - const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); - const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); - - const Size2D input_dims = Size2D(src->dimension(width_idx), src->dimension(height_idx)); - const Size2D kernel_size = Size2D(weights->dimension(width_idx), weights->dimension(height_idx)); - const DataType data_type = src->data_type(); - const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type); - - // Check if the Winograd configuration requires fast math - if(!enable_fast_math) - { - ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type), - "This Winograd configuration requires enable_fast_math=true"); - } - - _is_prepared = false; - - std::unique_ptr<ICpuWinogradConv2dTransformInputKernel> transform_input_kernel; - std::unique_ptr<ICpuWinogradConv2dTransformWeightsKernel> transform_weights_kernel; - std::unique_ptr<ICpuWinogradConv2dTransformOutputKernel> transform_output_kernel; - - int n_gemms = 1; - int N_BLOCK = 1; // Size of block used by GEMM. - if(data_type == DataType::F32) - { - if(kernel_size == Size2D(3, 3)) - { - if(src->dimension(width_idx) > 4 && src->dimension(height_idx) > 4) - { - using config = CpuWinogradConv2dConfiguration<float, float, 4, 4, 3, 3>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else - { - using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 3, 3>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - } - else if(kernel_size == Size2D(5, 5)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 5, 5>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(1, 3)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 6, 1, 3, 1>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(3, 1)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 1, 6, 1, 3>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(1, 5)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 4, 1, 5, 1>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(5, 1)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 1, 4, 1, 5>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(1, 7)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 2, 1, 7, 1>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(7, 1)) - { - using config = CpuWinogradConv2dConfiguration<float, float, 1, 2, 1, 7>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else - { - ARM_COMPUTE_ERROR("Not supported."); - } - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - else if(data_type == DataType::F16) - { - if(kernel_size == Size2D(3, 3)) - { - using config = CpuWinogradConv2dConfiguration<__fp16, __fp16, 4, 4, 3, 3>; - transform_input_kernel = std::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = std::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else - { - ARM_COMPUTE_ERROR("Not supported."); - } - } -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - else - { - ARM_COMPUTE_ERROR("Not supported."); - } - - const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID; - const bool use_same_padding = use_padding_type == PADDING_SAME; - - // Get convolved dimensions - const int in_channels = src->dimension(channel_idx); - const int out_channels = dst->dimension(channel_idx); - - const Tensor4DShape in_shape(internal_get_input_shape(src)); - const size_t data_type_size = src->element_size(); - // Get the memory required to instantiate a new Winograd operator. - constexpr size_t storage_alignment = 64; - - // Kernel Storage - const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size; - - // Input storage - const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size; - - // Output storage - const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size; - const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels); - const int output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels); - const auto output_shape = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME); - const int input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME); - - // Configure GEMM - const int tile_rows = iceildiv(output_shape.first, output_tile.height); - const int tile_cols = iceildiv(output_shape.second, output_tile.width); - const int m = in_shape.n_batches * tile_rows * tile_cols; - const int k = in_shape.n_channels; - const int n = out_channels; - const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK); - const int output_matrix_row_stride = kernel_matrix_row_stride; - - TensorShape a_shape(k, m, 1, n_gemms); - Strides a_strides(data_type_size); - a_strides.set(1, a_strides[0] * k); - //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0. - a_strides.set(2, 0); - a_strides.set(3, data_type_size * input_matrix_stride); - - TensorShape b_shape(n, k, n_gemms); - Strides b_strides(data_type_size); - b_strides.set(1, data_type_size * kernel_matrix_row_stride); - b_strides.set(2, data_type_size * kernel_matrix_stride); - - TensorShape d_shape(n, m, 1, n_gemms); - Strides d_strides(data_type_size); - d_strides.set(1, data_type_size * output_matrix_row_stride); - //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0. - d_strides.set(2, 0); - d_strides.set(3, data_type_size * output_matrix_stride); - - TensorInfo a_info{}; - TensorInfo b_info{}; - TensorInfo d_info{}; - a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size); - b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size); - d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size); - - _input_transformed = a_info; - _kernel_storage = b_info; - _output_transformed = d_info; - - const ITensorInfo *input_to_use = src; - ITensorInfo *output_to_use = dst; - PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U); - const unsigned int max_num_threads = NEScheduler::get().num_threads(); - - // Configure the kernel to transform the input tensor from NCHW -> NHWC - if(_data_layout == DataLayout::NCHW) - { - _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U)); - input_to_use = &_input_nhwc; - weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U); - } - - // Configure input transform kernel - transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, - &_input_transformed, input_matrix_stride, &_input_workspace); - const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads); - TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8); - _input_workspace = input_workspace_info; - - // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map] - _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector); - transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels); - - // Configure GEMM function - _gemm_function->configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f); - - // Configure output transform function - // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method - if(_data_layout == DataLayout::NCHW) - { - // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output() - TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0), - dst->dimension(1), dst->dimension(3)), - 1, dst->data_type()); - _output_nhwc = info; - output_to_use = &_output_nhwc; - } - const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info); - - transform_output_kernel->configure(biases, - &_output_transformed, - output_matrix_stride, - output_to_use, - in_shape.n_batches, - output_shape.first, - output_shape.second, - out_channels, - &_output_workspace, - activation); - - const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads); - TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8); - _output_workspace = output_workspace_info; - - // Reorder the convoluted output to ACL's ordering NCHW - if(_data_layout == DataLayout::NCHW) - { - _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U)); - } - - _transform_input_kernel = std::move(transform_input_kernel); - _transform_weights_kernel = std::move(transform_weights_kernel); - _transform_output_kernel = std::move(transform_output_kernel); - - //Configure Activation Layer - _run_activation = act_info.enabled() && !fuse_function_supported(act_info); - if(_run_activation) - { - _activation_func->configure(dst, nullptr, act_info); - } - - auto asm_mem_req = _gemm_function->workspace(); - _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace]; - _aux_mem[Pretranspose] = asm_mem_req[Pretranspose]; - _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS]; - _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS]; - _aux_mem[TempResult] = asm_mem_req[TempResult]; - - // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps. - _aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, input_storage_size, storage_alignment); - _aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, output_storage_size, storage_alignment); - _aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size)); - _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size()); - _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, kernel_storage_size, storage_alignment); - if(_data_layout == DataLayout::NCHW) - { - _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size()); - _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size()); - } -} - -Status CpuWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, - const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info)); - - // Get indices for the width and height - const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); - - // Input shape, kernel size and output tile - const Size2D input_dims = Size2D(src->dimension(idx_width), src->dimension(idx_height)); - const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height)); - const DataType data_type = src->data_type(); - const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type); - - // Check if the Winograd configuration requires fast math - if(!enable_fast_math) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type), - "This Winograd configuration requires enable_fast_math=true"); - } - - const WinogradInfo winograd_info = WinogradInfo(output_tile, - kernel_size, - input_dims, - conv_info, - src->data_layout()); - - // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info); - const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape); - // Validate filter transform - const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); - const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); - // Validate batched matrix multiply - TensorShape batched_mm_output_shape = input0.tensor_shape(); - batched_mm_output_shape[0] = input1.tensor_shape()[0]; - const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); - - if(kernel_size == Size2D(3, 3)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported"); - return validate_kernel_3x3(input_dims, src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else if(kernel_size == Size2D(5, 5)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported"); - return validate_kernel_5x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - if(kernel_size == Size2D(3, 1)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported"); - return validate_kernel_3x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else if(kernel_size == Size2D(1, 3)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported"); - return validate_kernel_1x3(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else if(kernel_size == Size2D(5, 1)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported"); - return validate_kernel_5x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else if(kernel_size == Size2D(1, 5)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported"); - return validate_kernel_1x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else if(kernel_size == Size2D(7, 1)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported"); - return validate_kernel_7x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else if(kernel_size == Size2D(1, 7)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported"); - return validate_kernel_1x7(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info); - } - else - { - ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported"); - } -} - -void CpuWinogradConv2d::run(ITensorPack &tensors) -{ - prepare(tensors); - - auto a = tensors.get_const_tensor(ACL_SRC_0); - auto c = tensors.get_const_tensor(ACL_SRC_2); - auto d = tensors.get_tensor(ACL_DST); - - CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true); - CpuAuxTensorHandler input_transformed(offset_int_vec(TransformedInput), _input_transformed, tensors, true); - CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true); - - const bool is_nchw = _data_layout == DataLayout::NCHW; - if(is_nchw) - { - //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC - ITensorPack pack{ { ACL_SRC, a }, { ACL_DST, input_nhwc.get() } }; - _permute_input->run(pack); - } - - // Transform input tensor to the winograd domain - ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : a }, { ACL_DST, input_transformed.get() }, { ACL_INT, input_workspace.get() } }; - NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, _transform_input_kernel->window(), transform_input_pack); - - CpuAuxTensorHandler output_transformed(offset_int_vec(TransformedOutput), _output_transformed, tensors, true); - CpuAuxTensorHandler weights_transformed(offset_int_vec(TransformedWeights), _kernel_storage, tensors, true); - - // Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs - ITensorPack gemm_pack = tensors; - gemm_pack.add_const_tensor(ACL_SRC, input_transformed.get()); - gemm_pack.add_const_tensor(ACL_SRC_1, weights_transformed.get()); - gemm_pack.add_const_tensor(ACL_BIAS, nullptr); - gemm_pack.add_tensor(ACL_DST, output_transformed.get()); - _gemm_function->run(gemm_pack); - - // Transform output tensor to the spatial domain - CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true); - CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true); - ITensorPack transform_output_pack{ { ACL_SRC_0, c }, { ACL_SRC_1, output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : d }, { ACL_INT, output_workspace.get() } }; - NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, _transform_output_kernel->window(), transform_output_pack); - - if(is_nchw) - { - // Reorder the convoluted output to ACL's ordering NCHW - ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, d } }; - _permute_output->run(pack); - } - - if(_run_activation) - { - ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } }; - _activation_func->run(pack); - } -} - -void CpuWinogradConv2d::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - // Permute weights - const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1); - ITensor *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights))); - ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux); - - CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux); - ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } }; - _permute_weights->run(permute_tensors); - - // Transform weights - ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights))); - ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf); - - CpuAuxTensorHandler transformed_weights(_kernel_storage, *weights_transf); - ITensorPack transform_tensors{ { ACL_SRC, permuted_weights.get() }, { ACL_DST, transformed_weights.get() } }; - NEScheduler::get().schedule_op(_transform_weights_kernel.get(), Window::DimX, _transform_weights_kernel->window(), transform_tensors); - - ITensorPack gemm_pack = tensors; - gemm_pack.add_const_tensor(ACL_SRC_1, transformed_weights.get()); - _gemm_function->prepare(gemm_pack); - - _is_prepared = true; - } -} - -experimental::MemoryRequirements CpuWinogradConv2d::workspace() const -{ - return _aux_mem; -} -} // namespace cpu -} // namespace arm_compute
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