| /* |
| * Copyright (c) 2017-2021 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 "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" |
| |
| #include "arm_compute/core/PixelValue.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/CL/CLScheduler.h" |
| |
| #include <cmath> |
| #include <memory> |
| #include <tuple> |
| |
| namespace arm_compute |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| |
| CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_manager(std::move(memory_manager)), _function() |
| { |
| } |
| |
| CLConvolutionLayer::~CLConvolutionLayer() = default; |
| |
| void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, |
| const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups); |
| } |
| |
| void CLConvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, |
| const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info, |
| enable_fast_math, num_groups)); |
| |
| switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, |
| weights_info, act_info, CLScheduler::get().target(), dilation, enable_fast_math)) |
| { |
| case ConvolutionMethod::WINOGRAD: |
| { |
| ARM_COMPUTE_ERROR_ON(num_groups != 1); |
| auto f = std::make_unique<CLWinogradConvolutionLayer>(_memory_manager); |
| f->configure(compile_context, input, weights, biases, output, conv_info, act_info, enable_fast_math); |
| _function = std::move(f); |
| break; |
| } |
| case ConvolutionMethod::DIRECT: |
| { |
| ARM_COMPUTE_ERROR_ON(num_groups != 1); |
| auto f = std::make_unique<CLDirectConvolutionLayer>(); |
| f->configure(compile_context, input, weights, biases, output, conv_info, act_info); |
| _function = std::move(f); |
| break; |
| } |
| case ConvolutionMethod::GEMM: |
| { |
| auto f = std::make_unique<CLGEMMConvolutionLayer>(_memory_manager); |
| f->configure(compile_context, input, weights, biases, output, conv_info, weights_info, dilation, act_info, num_groups); |
| _function = std::move(f); |
| break; |
| } |
| case ConvolutionMethod::FFT: |
| { |
| auto f = std::make_unique<CLFFTConvolutionLayer>(_memory_manager); |
| f->configure(compile_context, input, weights, biases, output, conv_info, act_info, enable_fast_math); |
| _function = std::move(f); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported."); |
| break; |
| } |
| } |
| |
| Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); |
| |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| |
| switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation, enable_fast_math)) |
| { |
| case ConvolutionMethod::WINOGRAD: |
| { |
| //Validate Winograd |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups != 1, "Grouping (num_groups != 1) with CLWinogradConvolutionLayer is not supported"); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math)); |
| break; |
| } |
| case ConvolutionMethod::DIRECT: |
| { |
| // Validate direct convolution layer |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups != 1, "Grouping (num_groups != 1) with CLDirectConvolutionLayer is not supported"); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info)); |
| break; |
| } |
| case ConvolutionMethod::GEMM: |
| { |
| // Validate gemm-based convolution layer |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, num_groups)); |
| break; |
| } |
| case ConvolutionMethod::FFT: |
| { |
| // Validate FFT-based convolution layer |
| ARM_COMPUTE_RETURN_ON_ERROR(CLFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported."); |
| break; |
| } |
| |
| return Status{}; |
| } |
| |
| ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation, bool enable_fast_math) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(weights); |
| ARM_COMPUTE_UNUSED(weights_info); |
| ARM_COMPUTE_UNUSED(gpu_target); |
| |
| const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| /* Input spatial dims, kernel size, IFM/OFM, conv info*/ |
| using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo, DataLayout>; |
| using ConfigurationMethod = std::pair<ConvolutionConfiguration, ConvolutionMethod>; |
| |
| const std::vector<ConfigurationMethod> known_configs = |
| { |
| // Alexnet |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U), DataLayout::NCHW), ConvolutionMethod::DIRECT), |
| // VGG16 / VGG19 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U), DataLayout::NCHW), ConvolutionMethod::DIRECT), |
| // Mobilenet 224 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), |
| // Mobilenet 160 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), |
| // Mobilenet 224 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), |
| // Mobilenet 160 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), |
| }; |
| |
| const auto find_config = [&](ConfigurationMethod c) |
| { |
| const ConvolutionConfiguration config = c.first; |
| const PadStrideInfo info = std::get<3>(config); |
| const DataLayout data_layout = std::get<4>(config); |
| |
| return std::get<0>(config) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) && std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) |
| && std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() |
| && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride() && (data_layout == input->data_layout()); |
| }; |
| |
| std::vector<ConfigurationMethod>::const_iterator found; |
| if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) |
| { |
| return (*found).second; |
| } |
| |
| if(dilation != Size2D(1U, 1U)) |
| { |
| return ConvolutionMethod::GEMM; |
| } |
| else |
| { |
| if(input->data_layout() == DataLayout::NCHW) |
| { |
| // SRGAN |
| if((input->dimension(idx_h) > 720U) && (output->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv_info.pad_top() < 3) |
| && (CLDirectConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info))) |
| { |
| return ConvolutionMethod::DIRECT; |
| } |
| if((weights->dimension(idx_h) > 5) && (input->dimension(idx_c) > output->dimension(idx_c)) && (CLFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math))) |
| { |
| return ConvolutionMethod::FFT; |
| } |
| if(input->dimension(idx_c) < 16) |
| { |
| return ConvolutionMethod::GEMM; |
| } |
| return bool(CLWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; |
| } |
| else |
| { |
| const bool is_direct_valid = bool(CLDirectConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info)); |
| const bool is_wino_valid = bool(CLWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)); |
| |
| // SRGAN case |
| if((input->dimension(idx_h) > 720U) && (output->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv_info.pad_top() < 3) |
| && is_direct_valid) |
| { |
| return ConvolutionMethod::DIRECT; |
| } |
| |
| // Floating-point case: GeMM/Direct/Winograd |
| if(is_data_type_float(input->data_type())) |
| { |
| const bool is_large_kernel_sz = (weights->dimension(idx_w) >= 5) && (weights->dimension(idx_h) >= 5); |
| const bool is_ifm_gt_eq_16 = input->dimension(idx_c) >= 16; |
| |
| // Large kernel size with IFMs >= OFMs |
| if(is_large_kernel_sz) |
| { |
| // First check if we can use Winograd |
| if(is_wino_valid && is_ifm_gt_eq_16) |
| { |
| return ConvolutionMethod::WINOGRAD; |
| } |
| |
| if(is_direct_valid) |
| { |
| return ConvolutionMethod::DIRECT; |
| } |
| |
| // Default implementation for floating-point-data-type |
| return ConvolutionMethod::GEMM; |
| } |
| else // Small kernel size |
| { |
| return is_wino_valid && is_ifm_gt_eq_16 ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; |
| } |
| } |
| |
| // Generic case for quantized. Only GeMM |
| return ConvolutionMethod::GEMM; |
| } |
| } |
| } |
| |
| void CLConvolutionLayer::run() |
| { |
| prepare(); |
| _function->run(); |
| } |
| |
| void CLConvolutionLayer::prepare() |
| { |
| _function->prepare(); |
| } |
| } // namespace arm_compute |