diff options
Diffstat (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLConvolutionLayer.cpp | 288 |
1 files changed, 125 insertions, 163 deletions
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 96d7cc72c2..7767b45a01 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,115 +23,149 @@ */ #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/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/KernelDescriptors.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 "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h" -#include <cmath> -#include <memory> -#include <tuple> +#include "src/common/utils/Log.h" +#include "src/core/CL/ICLKernel.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/operators/ClConv2d.h" +#include "support/Cast.h" 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() +using namespace arm_compute::experimental; +struct CLConvolutionLayer::Impl +{ + MemoryGroup memory_group{}; + std::shared_ptr<IMemoryManager> memory_manager{}; + std::unique_ptr<opencl::IClOperator> op{nullptr}; + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + WorkspaceData<CLTensor> workspace{}; + experimental::MemoryRequirements aux_mem_req{}; + std::unique_ptr<IFunction> func{nullptr}; +}; + +CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) : _impl(std::make_unique<Impl>()) { + _impl->memory_manager = std::move(memory_manager); } 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) +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); + 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) +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)); + 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)); + ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, weights_info, dilation, act_info, + enable_fast_math, num_groups); + + const Conv2dInfo conv2d_info = Conv2dInfo(conv_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)) + switch (opencl::ClConv2d::get_convolution_method(input->info(), weights->info(), output->info(), conv2d_info, + weights_info, CLScheduler::get().target())) { 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::INDIRECT: 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); + auto f = std::make_unique<opencl::ClConv2d>(); + f->configure(compile_context, input->info(), weights->info(), + ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv2d_info, weights_info); + _impl->op = std::move(f); break; } case ConvolutionMethod::FFT: { - auto f = std::make_unique<CLFFTConvolutionLayer>(_memory_manager); + auto f = std::make_unique<CLFFTConvolutionLayer>(_impl->memory_manager); f->configure(compile_context, input, weights, biases, output, conv_info, act_info, enable_fast_math); - _function = std::move(f); + _impl->func = std::move(f); break; } default: ARM_COMPUTE_ERROR("Not supported."); break; } + + if (_impl->op) + { + _impl->memory_group = MemoryGroup(std::move(_impl->memory_manager)); + _impl->aux_mem_req = _impl->op->workspace(); + _impl->run_pack = {{ACL_SRC_0, input}, {ACL_SRC_1, weights}, {ACL_SRC_2, biases}, {ACL_DST, output}}; + _impl->prep_pack = {{ACL_SRC_1, weights}, {ACL_SRC_2, biases}}; + _impl->workspace = + manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack); + } } -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) +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"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!weights->are_values_constant(), "Dynamic weights are not supported"); + 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(); + const GPUTarget gpu_target = CLScheduler::get().target(); + const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, enable_fast_math, num_groups); - switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation, enable_fast_math)) + switch (opencl::ClConv2d::get_convolution_method(input, weights, output, conv2d_info, weights_info, gpu_target)) { 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::INDIRECT: 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)); + ARM_COMPUTE_RETURN_ON_ERROR( + opencl::ClConv2d::validate(input, weights, biases, output, conv2d_info, weights_info)); 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)); + ARM_COMPUTE_RETURN_ON_ERROR(CLFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, + act_info, enable_fast_math)); break; } default: @@ -142,120 +176,48 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo 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) +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); + const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, enable_fast_math, 1); + return opencl::ClConv2d::get_convolution_method(input, weights, output, conv2d_info, weights_info, gpu_target); +} - 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()); - }; +void CLConvolutionLayer::run() +{ + prepare(); - 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; - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); - if(dilation != Size2D(1U, 1U)) + if (_impl->func) { - return ConvolutionMethod::GEMM; + _impl->func->run(); } 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 - { - // 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(gpu_target == GPUTarget::G71) - { - if((weights->dimension(idx_h) > 7) && (input->dimension(idx_c) >= output->dimension(idx_c)) - && (CLFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math))) - { - return ConvolutionMethod::FFT; - } - } - else if(is_data_type_float(input->data_type())) - { - if((weights->dimension(idx_h) >= 5) && (input->dimension(idx_c) >= output->dimension(idx_c)) && (CLDirectConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info))) - { - return ConvolutionMethod::DIRECT; - } - } - 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; - } + _impl->op->run(_impl->run_pack); } } -void CLConvolutionLayer::run() -{ - prepare(); - _function->run(); -} - void CLConvolutionLayer::prepare() { - _function->prepare(); + if (_impl->func) + { + _impl->func->prepare(); + } + else + { + _impl->op->prepare(_impl->prep_pack); + + // Release temporary tensors that are only used in prepare stage + release_temporaries(_impl->aux_mem_req, _impl->workspace); + } } } // namespace arm_compute |