From e52a3000d2c13bc1b66ca66b3d12b6b836982394 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Wed, 11 Apr 2018 15:59:10 +0100 Subject: COMPMID-1026 - Add support for 4x4 output tile in CLWinogradConvolutionLayer The performance achieved can be found at the following confluence page: https://confluence.arm.com/display/MLENG/GEMM-based+convolution+vs+Winograd-based+convolution+on+OpenCL Change-Id: I4b690cfdd4eb4ff0cd17b14fdd49ccaa1d1dc85c Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127729 Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- src/runtime/CL/functions/CLConvolutionLayer.cpp | 37 ++++++++++++++++------ .../CL/functions/CLWinogradConvolutionLayer.cpp | 18 ++++++++--- src/runtime/NEON/functions/NEConvolutionLayer.cpp | 8 +++-- 3 files changed, 47 insertions(+), 16 deletions(-) (limited to 'src/runtime') diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index bcb5424aab..643e24d638 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -48,9 +48,16 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c 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)); - switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, + switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, weights_info, act_info, CLScheduler::get().target(), dilation)) { + case ConvolutionMethod::WINOGRAD: + { + auto f = arm_compute::support::cpp14::make_unique(); + f->configure(input, weights, biases, output, conv_info); + _function = std::move(f); + break; + } case ConvolutionMethod::DIRECT: { auto f = arm_compute::support::cpp14::make_unique(); @@ -79,8 +86,14 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo //Configure if the parameters match the direct convolution or the gemm-based const GPUTarget gpu_target = CLScheduler::get().target(); - switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, act_info, gpu_target, dilation)) + switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation)) { + case ConvolutionMethod::WINOGRAD: + { + //Validate Winograd + CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info); + break; + } case ConvolutionMethod::DIRECT: { // Validate direct convolution layer @@ -101,19 +114,25 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo return Status{}; } -ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, +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) { - ARM_COMPUTE_UNUSED(input); - ARM_COMPUTE_UNUSED(weights); - ARM_COMPUTE_UNUSED(biases); + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_ERROR_ON_NULLPTR(weights); ARM_COMPUTE_UNUSED(output); - ARM_COMPUTE_UNUSED(conv_info); ARM_COMPUTE_UNUSED(weights_info); ARM_COMPUTE_UNUSED(gpu_target); - ARM_COMPUTE_UNUSED(dilation); - ARM_COMPUTE_UNUSED(act_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); + + if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (input->dimension(idx_c) > 3) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) + && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled())) + { + return ConvolutionMethod::WINOGRAD; + } return ConvolutionMethod::GEMM; } diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index 0aa7f8d1b5..86ccddac88 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -44,13 +44,18 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); // Input shape - const TensorShape input_shape = input->info()->tensor_shape(); + const TensorShape input_shape = input->info()->tensor_shape(); + const unsigned int input_w = input->info()->tensor_shape()[idx_width]; + const unsigned int input_h = input->info()->tensor_shape()[idx_height]; // Kernel size const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height]; - const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + //Winograd output tile + const Size2D output_tile = (Size2D(kernel_w, kernel_h) == Size2D(3U, 3U) && input_w <= 4 && input_h <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + + const WinogradInfo winograd_info = WinogradInfo(output_tile, Size2D(kernel_w, kernel_h), Size2D(input_shape[idx_width], input_shape[idx_height]), conv_info, @@ -95,13 +100,18 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); // Input shape - const TensorShape input_shape = input->tensor_shape(); + const TensorShape input_shape = input->tensor_shape(); + const unsigned int input_w = input->tensor_shape()[idx_width]; + const unsigned int input_h = input->tensor_shape()[idx_height]; // Kernel size const unsigned int kernel_w = weights->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->tensor_shape()[idx_height]; - const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + //Winograd output tile + const Size2D output_tile = (Size2D(kernel_w, kernel_h) == Size2D(3U, 3U) && input_w <= 4 && input_h <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + + const WinogradInfo winograd_info = WinogradInfo(output_tile, Size2D(kernel_w, kernel_h), Size2D(input_shape[idx_width], input_shape[idx_height]), conv_info, diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp index afc354533d..b0603e92d2 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -109,10 +109,12 @@ ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo * ARM_COMPUTE_ERROR_ON_NULLPTR(weights); ARM_COMPUTE_UNUSED(output); ARM_COMPUTE_UNUSED(weights_info); - ARM_COMPUTE_UNUSED(act_info); - if((input->data_type() == DataType::F32) && (weights->dimension(0) == 3) && (weights->dimension(1) == 3) && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) - && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U))) + 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); + + if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) && (weights->num_dimensions() <= 4) + && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled())) { //FIXME Until COMPMID-1041 is implemented Winograd is slower than GEMM on A53. if(Scheduler::get().cpu_info().get_cpu_model() != CPUModel::A53) -- cgit v1.2.1