From e6630e4063fc3aa4312a2c8d094318b09ad2c3f5 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Thu, 18 Jan 2018 15:50:39 +0000 Subject: COMPMID-790 - NEON: Add QASYMM8 support to Convolution Change-Id: Iec82a91ad351cfe8d07d0976a24bd42f4703177a Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116833 Tested-by: Jenkins Reviewed-by: Anthony Barbier Reviewed-by: Gian Marco Iodice --- src/runtime/NEON/functions/NEConvolutionLayer.cpp | 194 ++++++++++++++++------ 1 file changed, 143 insertions(+), 51 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 8f7d940fca..bb685c62d6 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -29,6 +29,7 @@ #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" @@ -46,10 +47,10 @@ namespace arm_compute { namespace { -TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool has_bias) +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) + (has_bias ? 1 : 0); + 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 @@ -69,14 +70,16 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I transpose1xW)); // Check if bias are present, if yes they will be embedded to the weights matrix - const bool _has_bias = (biases != nullptr); + 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(), _has_bias)); + 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); @@ -88,30 +91,35 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I } else { - _weights_reshape_kernel.configure(weights, biases, output); + _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::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); + 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(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); } - // Check if bias are present, if yes they will be embedded to the weights matrix - const bool has_bias = (biases != nullptr); - // Checks performed when biases are present - if(has_bias) + if(append_bias) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); @@ -120,7 +128,7 @@ Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, co if(transpose1xW) { - TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, has_bias)); + 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)); } @@ -148,10 +156,10 @@ void NEConvolutionLayerReshapeWeights::run() namespace { -TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool has_bias, bool is_fully_connected_convolution) +TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool append_bias, bool is_fully_connected_convolution) { unsigned int mat_weights_cols = weights->dimension(3); - unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0); + unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0); if(is_fully_connected_convolution) { @@ -167,45 +175,84 @@ TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool has } 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 &has_bias, - bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, bool &is_fully_connected_convolution, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows, + 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::QS16, DataType::F16, DataType::F32); + 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_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) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + if(is_quantized) + { + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_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); } - dt = input->data_type(); - has_bias = (biases != nullptr); + 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) + (has_bias ? 1 : 0); + 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(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(), - _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) + : _memory_group(std::move(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(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) @@ -221,14 +268,15 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, 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, _has_bias, _are_weights_reshaped, + 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_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) @@ -264,7 +312,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, { if(_are_weights_reshaped) { - if(_is_fully_connected_convolution) + if(_is_fully_connected_convolution || _is_quantized) { mat_weights_cols = weights_info.num_kernels(); mat_weights_rows = weights->info()->dimension(1); @@ -273,14 +321,14 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, { 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 + (_has_bias ? 1 : 0); + mat_weights_rows = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0); } } else { TensorShape reshaped_weights_shape; - if(_is_fully_connected_convolution) + if(_is_fully_connected_convolution || _is_quantized) { reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows }; } @@ -294,7 +342,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, // 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, !_is_fully_connected_convolution /* 1xW transpose */); + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */); weights = &_weights_reshaped; } } @@ -324,12 +372,18 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape()); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, mat_input_rows); - _gemm_output.allocator()->init(_input_im2col_reshaped.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm)); + 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 - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); + // Configure im2col + _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias); + // Configure matrix multiply #if defined(__arm__) || defined(__aarch64__) if(_mm_optimised_kernel != nullptr) { @@ -357,22 +411,44 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, else #endif /* defined(__arm__) || defined(__aarch64__) */ { - if(_is_fully_connected_convolution) + if(_is_interleaved_transposed) { - _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f); + // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel + _memory_group.manage(&_input_interleaved_reshaped); + _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 { - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f); - _input_interleaved_reshaped.allocator()->allocate(); + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output); } } _input_im2col_reshaped.allocator()->allocate(); - _output_col2im_kernel.configure(&_gemm_output, output, Size2D(conv_w, conv_h)); + + // Configure output stage for quantized case + if(_is_quantized) + { + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_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->info()->quantization_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) { @@ -384,9 +460,11 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo const WeightsInfo &weights_info) { DataType dt{}; - bool has_bias{}; + 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; @@ -394,8 +472,8 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo unsigned int conv_w = 0; unsigned int conv_h = 0; - Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, has_bias, are_weights_reshaped, kernel_width, kernel_height, - is_fully_connected_convolution, mat_weights_cols, mat_weights_rows, + 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); @@ -428,7 +506,7 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo 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, has_bias, is_fully_connected_convolution)); + 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(); } @@ -439,13 +517,13 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo { const unsigned int transpose_width = 16 / input->element_size(); mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->dimension(0) / transpose_width + (has_bias ? 1 : 0); + mat_weights_rows = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0); } else { TensorShape reshaped_weights_shape; - if(is_fully_connected_convolution) + if(is_fully_connected_convolution || is_quantized) { reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows }; } @@ -458,7 +536,7 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo } // Create tensor to store the reshaped weights - reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution)); + 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(); } @@ -472,7 +550,7 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo 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, has_bias)); + 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()); @@ -481,7 +559,7 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm); // Validate GEMM interleave and multiply - if(!is_fully_connected_convolution) + if(is_interleaved_transposed) { TensorShape shape_interleaved = shape_im2col; shape_interleaved.set(0, shape_interleaved.x() * 4); @@ -523,13 +601,27 @@ void NEConvolutionLayer::run() } else { - if(!_is_fully_connected_convolution) + if(_is_interleaved_transposed) { // Run interleave NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY); } - NEScheduler::get().schedule(&_mm_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) + { + _gemmlowp_output_stage.run(); } // Reshape output matrix -- cgit v1.2.1