From afd38f0c617d6f89b2b4532c6c44f116617e2b6f Mon Sep 17 00:00:00 2001 From: Felix Thomasmathibalan Date: Wed, 27 Sep 2023 17:46:17 +0100 Subject: Apply clang-format on repository Code is formatted as per a revised clang format configuration file(not part of this delivery). Version 14.0.6 is used. Exclusion List: - files with .cl extension - files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...) And the following directories - compute_kernel_writer/validation/ - tests/ - include/ - src/core/NEON/kernels/convolution/ - src/core/NEON/kernels/arm_gemm/ - src/core/NEON/kernels/arm_conv/ - data/ There will be a follow up for formatting of .cl files and the files under tests/ and compute_kernel_writer/validation/. Signed-off-by: Felix Thomasmathibalan Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391 Benchmark: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gunes Bayir --- src/cpu/operators/CpuConv2d.cpp | 140 ++++++++++++++++++++++++++-------------- 1 file changed, 92 insertions(+), 48 deletions(-) (limited to 'src/cpu/operators/CpuConv2d.cpp') diff --git a/src/cpu/operators/CpuConv2d.cpp b/src/cpu/operators/CpuConv2d.cpp index 16ac16b3ba..19311733db 100644 --- a/src/cpu/operators/CpuConv2d.cpp +++ b/src/cpu/operators/CpuConv2d.cpp @@ -22,8 +22,10 @@ * SOFTWARE. */ #include "src/cpu/operators/CpuConv2d.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" + #include "arm_compute/runtime/NEON/functions/NEFFTConvolutionLayer.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" + #include "src/common/utils/Log.h" #include "src/cpu/operators/CpuDirectConv2d.h" #include "src/cpu/operators/CpuGemm.h" @@ -35,26 +37,35 @@ namespace arm_compute { namespace cpu { -CpuConv2d::CpuConv2d() - : _function() +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) +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_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); + 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)) + switch (CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info, + enable_fast_math)) { case ConvolutionMethod::WINOGRAD: { @@ -92,19 +103,30 @@ void CpuConv2d::configure(ITensorInfo *input, ITensorInfo *weights, const ITenso _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) +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)) + 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)); + 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)); + 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)); @@ -120,9 +142,14 @@ Status CpuConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, 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) +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); @@ -137,35 +164,46 @@ ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, co using ConvolutionConfiguration = std::tuple; using ConfigurationMethod = std::pair; - const std::vector known_configs = - { + const std::vector known_configs = { // Alexnet - ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U)), ConvolutionMethod::GEMM), + 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), + 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), + 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) - }; + 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(); + 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::const_iterator found; - if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) + if ((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) { return (*found).second; } - if(dilation != Size2D(1U, 1U)) + if (dilation != Size2D(1U, 1U)) { return ConvolutionMethod::GEMM; } @@ -173,43 +211,49 @@ ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, co { // 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))) + 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) + 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) + 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 known_bad_winograd_f16_with_fastmath_configs = - { + const std::vector 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)), + 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)), + 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)), + 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(); + 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) + 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; } @@ -217,16 +261,16 @@ ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, co #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC // For 1x1 convolutions run the default GEMM - if(weights->dimension(idx_w) == 1 && weights->dimension(idx_h) == 1) + 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))) + 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))) + if (bool(CpuGemmDirectConv2d::validate(input, weights, nullptr, output, info))) { return ConvolutionMethod::GEMM_CONV2D; } -- cgit v1.2.1