| /* |
| * 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/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/common/utils/Log.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/helpers/MemoryHelpers.h" |
| #include "src/cpu/kernels/CpuWinogradConv2dKernel.h" |
| #include "src/cpu/operators/CpuActivation.h" |
| #include "src/cpu/operators/CpuPermute.h" |
| #include "src/cpu/operators/CpuWinogradConv2d.h" |
| #include "src/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)); |
| ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math); |
| |
| // 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 |