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Diffstat (limited to 'src/runtime/cpu/operators/CpuConv2d.cpp')
-rw-r--r-- | src/runtime/cpu/operators/CpuConv2d.cpp | 253 |
1 files changed, 0 insertions, 253 deletions
diff --git a/src/runtime/cpu/operators/CpuConv2d.cpp b/src/runtime/cpu/operators/CpuConv2d.cpp deleted file mode 100644 index cff9238308..0000000000 --- a/src/runtime/cpu/operators/CpuConv2d.cpp +++ /dev/null @@ -1,253 +0,0 @@ -/* - * 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 "src/runtime/cpu/operators/CpuConv2d.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "arm_compute/runtime/NEON/functions/NEFFTConvolutionLayer.h" -#include "src/runtime/cpu/operators/CpuDirectConv2d.h" -#include "src/runtime/cpu/operators/CpuGemm.h" -#include "src/runtime/cpu/operators/CpuGemmConv2d.h" -#include "src/runtime/cpu/operators/CpuGemmDirectConv2d.h" -#include "src/runtime/cpu/operators/CpuWinogradConv2d.h" - -namespace arm_compute -{ -namespace cpu -{ -CpuConv2d::CpuConv2d() - : _function() -{ -} - -CpuConv2d::~CpuConv2d() = default; - -void CpuConv2d::configure(ITensorInfo *input, ITensorInfo *weights, const ITensorInfo *biases, 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) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_UNUSED(num_groups); - ARM_COMPUTE_ERROR_THROW_ON(CpuConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, - enable_fast_math, num_groups)); - - const Conv2dInfo info(conv_info, dilation, act_info, enable_fast_math, num_groups); - switch(CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info, enable_fast_math)) - { - case ConvolutionMethod::WINOGRAD: - { - auto f = std::make_unique<CpuWinogradConv2d>(); - f->configure(input, weights, biases, output, conv_info, act_info, enable_fast_math); - _function = std::move(f); - break; - } - case ConvolutionMethod::GEMM: - { - auto f = std::make_unique<CpuGemmConv2d>(); - f->configure(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math); - _function = std::move(f); - break; - } - case ConvolutionMethod::GEMM_CONV2D: - { - auto f = std::make_unique<CpuGemmDirectConv2d>(); - f->configure(input, weights, biases, output, info); - _function = std::move(f); - break; - } - case ConvolutionMethod::DIRECT: - { - auto f = std::make_unique<CpuDirectConv2d>(); - f->configure(input, weights, biases, output, conv_info, act_info); - _function = std::move(f); - break; - } - default: - ARM_COMPUTE_ERROR("Not supported."); - break; - } - - _aux_mem = _function->workspace(); -} - -Status CpuConv2d::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_MSG((num_groups != 1), "Grouping (num_groups != 1) is not supported on Neon"); - - const Conv2dInfo info(conv_info, dilation, act_info, enable_fast_math, num_groups); - switch(CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info, enable_fast_math)) - { - case ConvolutionMethod::WINOGRAD: - ARM_COMPUTE_RETURN_ON_ERROR(CpuWinogradConv2d::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math)); - break; - case ConvolutionMethod::GEMM: - ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math)); - break; - case ConvolutionMethod::GEMM_CONV2D: - ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmDirectConv2d::validate(input, weights, biases, output, info)); - break; - case ConvolutionMethod::DIRECT: - ARM_COMPUTE_RETURN_ON_ERROR(CpuDirectConv2d::validate(input, weights, biases, output, conv_info, act_info)); - break; - default: - ARM_COMPUTE_ERROR("Not supported."); - break; - } - - return Status{}; -} - -ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, - const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, weights); - ARM_COMPUTE_UNUSED(weights_info); - - 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); - - const Conv2dInfo info(conv_info, dilation, act_info, enable_fast_math, 1); - - /* Input spatial dims, kernel size, IFM/OFM, conv info*/ - using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo>; - 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)), ConvolutionMethod::GEMM), - // VGG16 / VGG19 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U)), ConvolutionMethod::GEMM), - // Mobilenet 224 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)), ConvolutionMethod::GEMM), - // Mobilenet 160 - ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)), ConvolutionMethod::GEMM) - }; - - const auto find_config = [&](ConfigurationMethod c) - { - const ConvolutionConfiguration config = c.first; - const PadStrideInfo info = std::get<3>(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(); - }; - - 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 - { - // SRGAN - // Output might not be initialized when it is an internal tensor of the layer using the convolution - if(input->total_size() > 1e7 && (weights->dimension(idx_h) > 7) - && (CpuDirectConv2d::validate(input, weights, nullptr, output, conv_info, act_info))) - { - return ConvolutionMethod::DIRECT; - } - if((weights->dimension(idx_h) > 7) && (input->dimension(idx_c) > output->dimension(idx_c)) && (NEFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info))) - { - return ConvolutionMethod::FFT; - } - if(input->dimension(idx_c) < 16) - { - return ConvolutionMethod::GEMM; - } - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - // This heuristics only applies to F16 data type on A55r1 - if(NEScheduler::get().cpu_info().get_cpu_model() == CPUModel::A55r1 && enable_fast_math && input->data_type() == DataType::F16) - { - // Exclude known bad winograd configs (and defaults to GEMM) - const std::vector<ConvolutionConfiguration> known_bad_winograd_f16_with_fastmath_configs = - { - // Squeezenet_V1_1 fire2 and fire3 - ConvolutionConfiguration(Size2D(56U, 56U), Size2D(3U, 3U), Size2D(16U, 64U), PadStrideInfo(1U, 1U, 1U, 1U)), - // Squeezenet_V1_1 fire6 and fire7 - ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(48U, 192U), PadStrideInfo(1U, 1U, 1U, 1U)), - // Squeezenet_V1_1 fire8 and fire9 - ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(64U, 256U), PadStrideInfo(1U, 1U, 1U, 1U)), - }; - const auto find_conv_config = [&](ConvolutionConfiguration c) - { - const PadStrideInfo info = std::get<3>(c); - - return std::get<0>(c) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) && std::get<1>(c) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) - && std::get<2>(c) == 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(); - }; - - bool found_bad = std::find_if(known_bad_winograd_f16_with_fastmath_configs.begin(), known_bad_winograd_f16_with_fastmath_configs.end(), - find_conv_config) - != known_bad_winograd_f16_with_fastmath_configs.end(); - if(found_bad) - { - return ConvolutionMethod::GEMM; - } - } -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - // For 1x1 convolutions run the default GEMM - if(weights->dimension(idx_w) == 1 && weights->dimension(idx_h) == 1) - { - return ConvolutionMethod::GEMM; - } - - if(bool(CpuWinogradConv2d::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math))) - { - return ConvolutionMethod::WINOGRAD; - } - if(bool(CpuGemmDirectConv2d::validate(input, weights, nullptr, output, info))) - { - return ConvolutionMethod::GEMM_CONV2D; - } - return ConvolutionMethod::GEMM; - } -} - -void CpuConv2d::run(ITensorPack &tensors) -{ - prepare(tensors); - _function->run(tensors); -} - -void CpuConv2d::prepare(ITensorPack &tensors) -{ - _function->prepare(tensors); -} - -experimental::MemoryRequirements CpuConv2d::workspace() const -{ - return _aux_mem; -} -} // namespace cpu -} // namespace arm_compute |