From 6acc6add8412c6d3841a49684610fc5a6526312e Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Fri, 2 Feb 2018 17:19:18 +0000 Subject: COMPMID-846: Create a ConvolutionLayer for NEON Change-Id: I98bbef40bfac5b05134be4ef9fb54d14c0c9e8e8 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118806 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- src/runtime/NEON/functions/NEConvolutionLayer.cpp | 636 ++-------------------- 1 file changed, 51 insertions(+), 585 deletions(-) (limited to 'src/runtime/NEON/functions/NEConvolutionLayer.cpp') diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp index 335267522b..0a491589ff 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -23,630 +23,96 @@ */ #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" -#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h" -#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h" -#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64NativeKernel.h" #include "arm_compute/core/PixelValue.h" -#include "arm_compute/core/Size2D.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" #include "support/ToolchainSupport.h" -namespace arm_compute -{ -#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp" -#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp" -#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp" -} // namespace arm_compute - #include #include namespace arm_compute { -namespace -{ -TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool append_bias) -{ - const unsigned int mat_weights_cols = weights->dimension(3); - const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0); - return TensorShape(mat_weights_cols, mat_weights_rows); -} -} // namespace - -NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) -{ -} - -void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW) -{ - // Perform validation step - ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output); - ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(), - (biases != nullptr) ? biases->info() : nullptr, - output->info(), - transpose1xW)); - - // Check if bias are present, if yes they will be embedded to the weights matrix - const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); - //const unsigned bias_element = (append_biases) ? 1 : 0; - const ITensor *biases_to_use = (append_biases) ? biases : nullptr; - - _transpose1xW = transpose1xW; - - if(transpose1xW) - { - // Create tensor to store the reshaped weights - TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), append_biases)); - - _weights_reshaped.allocator()->init(info_wr); - _memory_group.manage(&_weights_reshaped); - - _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped); - _weights_transposed_kernel.configure(&_weights_reshaped, output); - - _weights_reshaped.allocator()->allocate(); - } - else - { - _weights_reshape_kernel.configure(weights, biases_to_use, output); - } - - output->info()->set_quantization_info(weights->info()->quantization_info()); -} - -Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - if(!is_data_type_quantized_asymmetric(weights->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); - } - // Check if bias are present, if yes they will be embedded to the weights matrix - const bool append_bias = (biases != nullptr); - - if(append_bias) - { - ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type())); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - // Checks performed when biases are present - if(append_bias) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(transpose1xW) - { - TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias)); - ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped)); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output)); - } - - return Status{}; -} - -void NEConvolutionLayerReshapeWeights::run() -{ - _memory_group.acquire(); - - NEScheduler::get().schedule(&_weights_reshape_kernel, 3); - - if(_transpose1xW) - { - NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY); - } - - _memory_group.release(); -} - -namespace -{ -TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool append_bias, bool is_fully_connected_convolution) +NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr memory_manager) + : _memory_manager(std::move(memory_manager)), _function() { - unsigned int mat_weights_cols = weights->dimension(3); - unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0); - - if(is_fully_connected_convolution) - { - // Create tensor to store the reshaped weights - return TensorShape(mat_weights_cols, mat_weights_rows); - } - else - { - // Create tensor to store transposed weights - const float transpose_width = 16.0f / weights->element_size(); - return TensorShape(mat_weights_rows * static_cast(transpose_width), static_cast(std::ceil(mat_weights_cols / transpose_width))); - } } -Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt, - bool &append_bias, - bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, - bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized, - unsigned int &mat_weights_cols, unsigned int &mat_weights_rows, - unsigned int &conv_w, unsigned int &conv_h) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2)); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type())); - - dt = input->data_type(); - is_quantized = is_data_type_quantized_asymmetric(dt); - - if(biases != nullptr) - { - if(is_quantized) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); - ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - append_bias = (biases != nullptr) && (!is_quantized); - are_weights_reshaped = weights_info.are_reshaped(); - kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0); - kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1); - mat_weights_cols = weights->dimension(3); - mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0); - - std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, - conv_info); - - // Check if its a "fully connected" convolution - is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - is_interleaved_transposed = (!is_fully_connected_convolution && !is_quantized); - - return Status{}; -} -} // namespace - -NEConvolutionLayer::NEConvolutionLayer(const std::shared_ptr &memory_manager) - : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager), - _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false), - _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false) -{ -} - -void NEConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output) -{ - if(_is_quantized) - { - // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() - // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info = input->info()->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); - - input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); - weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); - - _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); - - // Revert back QuantizatioInfo as input and weights could be used in other convolution layers - input->info()->set_quantization_info(input_quantization_info); - weights->info()->set_quantization_info(weights_quantization_info); - } - else - { - _mm_kernel.configure(input, weights, output, 1.f); - } -} - -void NEConvolutionLayer::configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K) -{ - ARM_COMPUTE_UNUSED(ci); - ARM_COMPUTE_UNUSED(M); - ARM_COMPUTE_UNUSED(N); - ARM_COMPUTE_UNUSED(K); -#if defined(__arm__) || defined(__aarch64__) -#if defined(__arm__) - GemmInterleaved gemm(&ci, M, N, K, false, false); -#elif defined(__aarch64__) - GemmInterleaved gemm(&ci, M, N, K, false, false); -#endif /* defined(__arm__) || defined(__aarch64__) */ - - constexpr size_t alignment = 4096; - _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); - _memory_group.manage(&_workspace); -#endif /* defined(__arm__) || defined(__aarch64__) */ -} - -void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info)); - DataType dt{}; - unsigned int kernel_width = 0; - unsigned int kernel_height = 0; - unsigned int mat_weights_cols = 0; - unsigned int mat_weights_rows = 0; - unsigned int conv_w = 0; - unsigned int conv_h = 0; - - Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped, - kernel_width, kernel_height, - _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized, - mat_weights_cols, mat_weights_rows, conv_w, conv_h); - - ARM_COMPUTE_ERROR_THROW_ON(status); - - const unsigned int fixed_point_position = input->info()->fixed_point_position(); - const ITensor *biases_to_use = (_append_bias) ? biases : nullptr; - -#if defined(__arm__) - if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32) - { - _mm_optimised_kernel = support::cpp14::make_unique(); - } -#elif defined(__aarch64__) - if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32) - { - _mm_optimised_kernel = support::cpp14::make_unique(); - } -#endif /* defined(__arm__) || defined(__aarch64__) */ - - // Reshape weights if needed - if(_mm_optimised_kernel != nullptr) + switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, + weights_info)) { - if(_are_weights_reshaped) + case ConvolutionMethod::WINOGRAD: { - mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->info()->dimension(1); + auto f = arm_compute::support::cpp14::make_unique(_memory_manager); + f->configure(input, weights, biases, output, conv_info); + _function = std::move(f); + break; } - else + case ConvolutionMethod::GEMM: { - TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows }; - - // Create tensor to store the reshaped weights - _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position)); - _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */); - weights = &_weights_reshaped; + auto f = arm_compute::support::cpp14::make_unique(_memory_manager); + f->configure(input, weights, biases, output, conv_info, weights_info); + _function = std::move(f); + break; } - } - else - { - if(_are_weights_reshaped) + case ConvolutionMethod::DIRECT: { - if(_is_fully_connected_convolution || _is_quantized) - { - mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->info()->dimension(1); - } - else - { - const unsigned int transpose_width = 16 / input->info()->element_size(); - mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0); - } + auto f = arm_compute::support::cpp14::make_unique(_memory_manager); + f->configure(input, weights, biases, output, conv_info); + _function = std::move(f); + break; } - else - { - TensorShape reshaped_weights_shape; - - if(_is_fully_connected_convolution || _is_quantized) - { - reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows }; - } - else - { - // Create tensor to store transposed weights - const float transpose_width = 16.0f / input->info()->element_size(); - reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast(transpose_width), - static_cast(std::ceil(mat_weights_cols / transpose_width)) }; - } - - // Create tensor to store the reshaped weights - _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position)); - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */); - weights = &_weights_reshaped; - } - } - - // Create tensor to store im2col reshaped inputs - const unsigned int mat_input_cols = mat_weights_rows; - const unsigned int mat_input_rows = conv_w * conv_h; - - TensorShape shape_im2col(input->info()->tensor_shape()); - shape_im2col.set(0, mat_input_cols); - shape_im2col.set(1, mat_input_rows); - shape_im2col.set(2, 1); - _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col)); - _memory_group.manage(&_input_im2col_reshaped); - - // Create tensor (interleave) to prepare input tensor for GEMM - if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr) - { - TensorShape shape_interleaved(shape_im2col); - shape_interleaved.set(0, shape_interleaved.x() * 4); - shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved)); - _memory_group.manage(&_input_interleaved_reshaped); - } - - // Create GEMM output tensor - TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape()); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, mat_input_rows); - const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt; - // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. - TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position()); - info_gemm.set_quantization_info(output->info()->quantization_info()); - _gemm_output.allocator()->init(info_gemm); - _memory_group.manage(&_gemm_output); - - // Configure kernels - // Configure im2col - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias); - - // Configure matrix multiply - if(_mm_optimised_kernel != nullptr) - { - struct CPUInfo ci = NEScheduler::get().cpu_info(); - - const int M = _gemm_output.info()->tensor_shape().y(); - const int N = _gemm_output.info()->tensor_shape().x(); - const int K = _input_im2col_reshaped.info()->tensor_shape().x(); - -#if defined(__aarch64__) - if((N <= 128) && (K <= 128)) - { - _mm_optimised_kernel = support::cpp14::make_unique(); - } - else -#endif /* defined(__aarch64__) */ - { - configure_asm_mm(ci, M, N, K); - } - - // Configure matrix multiplication kernel - _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace); - - _workspace.allocator()->allocate(); - } - else - { - if(_is_interleaved_transposed) - { - // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - - // Configure GEMM - configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); - _input_interleaved_reshaped.allocator()->allocate(); - } - else - { - configure_mm(&_input_im2col_reshaped, weights, &_gemm_output); - } - } - - _input_im2col_reshaped.allocator()->allocate(); - - // Configure output stage for quantized case - if(_is_quantized) - { - const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info(); - - float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; - int output_multiplier, output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _memory_group.manage(&_tmp_output); - _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset); - } - - // Configure Col2Im - _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h)); - if(_is_quantized) - { - _tmp_output.allocator()->allocate(); - } - _gemm_output.allocator()->allocate(); - - ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); - - // Allocate intermediate tensor - if(!_are_weights_reshaped) - { - _weights_reshaped.allocator()->allocate(); + default: + ARM_COMPUTE_ERROR("Not supported."); + break; } } Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { - DataType dt{}; - bool append_bias{}; - bool are_weights_reshaped{}; - bool is_fully_connected_convolution{}; - bool is_interleaved_transposed{}; - bool is_quantized{}; - unsigned int kernel_width = 0; - unsigned int kernel_height = 0; - unsigned int mat_weights_cols = 0; - unsigned int mat_weights_rows = 0; - unsigned int conv_w = 0; - unsigned int conv_h = 0; - - Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height, - is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows, - conv_w, conv_h); - - ARM_COMPUTE_RETURN_ON_ERROR(status); - - std::unique_ptr reshaped_weights = weights->clone(); - bool optimised_kernel = false; - -#if defined(__arm__) - if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32) + switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info)) { - optimised_kernel = true; - } -#elif defined(__aarch64__) - if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32) - { - optimised_kernel = true; - } -#endif /* defined(__arm__) || defined(__aarch64__) */ - - // Reshape weights if needed - if(optimised_kernel) - { - if(are_weights_reshaped) - { - mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->dimension(1); - } - else - { - TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows }; - - // Create tensor to store the reshaped weights - reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution)); - ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */)); - weights = reshaped_weights.get(); - } - } - else - { - if(are_weights_reshaped) - { - const unsigned int transpose_width = 16 / input->element_size(); - mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0); - } - else - { - TensorShape reshaped_weights_shape; - - if(is_fully_connected_convolution || is_quantized) - { - reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows }; - } - else - { - // Create tensor to store transposed weights - const float transpose_width = 16.0f / input->element_size(); - reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast(transpose_width), - static_cast(std::ceil(mat_weights_cols / transpose_width)) }; - } - - // Create tensor to store the reshaped weights - reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution)); - ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */)); - weights = reshaped_weights.get(); - } - } - - // Validate im2col - const unsigned int mat_input_cols = mat_weights_rows; - const unsigned int mat_input_rows = conv_w * conv_h; - TensorShape shape_im2col = input->tensor_shape(); - shape_im2col.set(0, mat_input_cols); - shape_im2col.set(1, mat_input_rows); - shape_im2col.set(2, 1); - TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col); - ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias)); - - // Create GEMM output tensor - TensorShape shape_gemm(im2_col_info.tensor_shape()); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, mat_input_rows); - TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm); - - // Validate GEMM interleave and multiply - if(is_interleaved_transposed) - { - TensorShape shape_interleaved = shape_im2col; - shape_interleaved.set(0, shape_interleaved.x() * 4); - shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info)); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info)); + case ConvolutionMethod::WINOGRAD: + //Validate Winograd + NEWinogradLayer::validate(input, weights, biases, output, conv_info); + break; + case ConvolutionMethod::GEMM: + //Validate Gemm-based Convolution + NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info); + break; + case ConvolutionMethod::DIRECT: + //Validate Gemm-based Convolution + NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info); + default: + ARM_COMPUTE_ERROR("Not supported."); + break; } - ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h))); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one"); - return Status{}; } -void NEConvolutionLayer::run() +ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info) { - // Run weights reshaping (Runs once for every configure) - if(!_are_weights_reshaped) - { - _are_weights_reshaped = true; - _reshape_weights.run(); - } - - _memory_group.acquire(); - - // Run input reshaping - NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY); - - // Runs matrix multiply on reshaped matrices - if(_mm_optimised_kernel != nullptr) - { - NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY); - } - else - { - if(_is_interleaved_transposed) - { - // Run interleave - NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY); - } - - // Runs matrix multiply on reshaped matrices - if(_is_quantized) - { - _mm_gemmlowp.run(); - } - else - { - NEScheduler::get().schedule(&_mm_kernel, Window::DimY); - } - } - - // Run output stage for quantized case - if(_is_quantized) + ARM_COMPUTE_UNUSED(output); + ARM_COMPUTE_UNUSED(weights_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) && (biases != nullptr)) { - _gemmlowp_output_stage.run(); + return ConvolutionMethod::WINOGRAD; } + return ConvolutionMethod::GEMM; +} - // Reshape output matrix - NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY); - - _memory_group.release(); +void NEConvolutionLayer::run() +{ + _function->run(); } } // namespace arm_compute -- cgit v1.2.1