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 --- .../NEON/functions/NEGEMMConvolutionLayer.cpp | 652 +++++++++++++++++++++ 1 file changed, 652 insertions(+) create mode 100644 src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp (limited to 'src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp') diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp new file mode 100644 index 0000000000..d0a16ef40d --- /dev/null +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -0,0 +1,652 @@ +/* + * Copyright (c) 2017-2018 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 "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.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::TensorShape get_reshaped_weights_shape(const arm_compute::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 arm_compute::TensorShape(mat_weights_cols, mat_weights_rows); +} +} // namespace + +namespace arm_compute +{ +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_RETURN_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) +{ + 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 + +NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(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 NEGEMMConvolutionLayer::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 NEGEMMConvolutionLayer::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 NEGEMMConvolutionLayer::configure(const 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); + + 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) + { + if(_are_weights_reshaped) + { + mat_weights_cols = weights_info.num_kernels(); + mat_weights_rows = weights->info()->dimension(1); + } + else + { + 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; + } + } + else + { + if(_are_weights_reshaped) + { + 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); + } + } + 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(); + } +} + +Status NEGEMMConvolutionLayer::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) + { + 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)); + } + + 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 NEGEMMConvolutionLayer::run() +{ + // 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) + { + _gemmlowp_output_stage.run(); + } + + // Reshape output matrix + NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY); + + _memory_group.release(); +} +} // namespace arm_compute -- cgit v1.2.1