| /* |
| * 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/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 <cmath> |
| #include <tuple> |
| |
| 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<IMemoryManager> 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) |
| { |
| 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<unsigned int>(transpose_width), static_cast<unsigned int>(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<IMemoryManager> &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<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false); |
| #elif defined(__aarch64__) |
| GemmInterleaved<sgemm_12x8, float, float> 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) |
| { |
| // 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<NEGEMMAArch32Kernel>(); |
| } |
| #elif defined(__aarch64__) |
| if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32) |
| { |
| _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>(); |
| } |
| #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<unsigned int>(transpose_width), |
| static_cast<unsigned int>(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<NEGEMMAArch64NativeKernel>(); |
| } |
| 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 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<ITensorInfo> 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<unsigned int>(transpose_width), |
| static_cast<unsigned int>(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 NEConvolutionLayer::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 |