| /* |
| * Copyright (c) 2017-2021, 2023 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/CpuConv2d.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "arm_compute/runtime/NEON/functions/NEFFTConvolutionLayer.h" |
| #include "src/common/utils/Log.h" |
| #include "src/cpu/operators/CpuDirectConv2d.h" |
| #include "src/cpu/operators/CpuGemm.h" |
| #include "src/cpu/operators/CpuGemmConv2d.h" |
| #include "src/cpu/operators/CpuGemmDirectConv2d.h" |
| #include "src/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)); |
| |
| ARM_COMPUTE_LOG_PARAMS(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(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 |