/* * 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/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" #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::QASYMM8, 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); } // 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(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, const ActivationLayerInfo &act_info, DataType &dt, bool &append_bias, bool &skip_im2col, bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, bool &is_activationlayer_enabled, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows, unsigned int &conv_w, unsigned int &conv_h, const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); DataLayout data_layout = input->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(idx_channel) != input->dimension(idx_channel)); 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())); ARM_COMPUTE_RETURN_ERROR_ON_MSG(data_layout == DataLayout::NHWC && input->data_type() != DataType::F32, "NHWC is only supported for FP32 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(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3)); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } // If we have 1x1 convolution and data layout is NHWC we can disable im2col 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(idx_width); kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(idx_height); mat_weights_cols = weights->dimension(3); mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + ((append_bias && !skip_im2col) ? 1 : 0); skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1); std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width), input->dimension(idx_height), kernel_width, kernel_height, conv_info, dilation); // Check if its a "fully connected" convolution is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); is_interleaved = (!is_fully_connected_convolution && !is_quantized); is_activationlayer_enabled = act_info.enabled(); return Status{}; } } // namespace NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr &memory_manager) : _memory_group(memory_manager), _asm_glue(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(), _activationlayer_function(), _add_bias_kernel(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false), _is_activationlayer_enabled(false), _skip_im2col(false), _is_prepared(false) { } void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info) { 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, is_interleaved, reshape_info); } } void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_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; _data_layout = input->info()->data_layout(); const bool is_nhwc = _data_layout == DataLayout::NHWC; const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, act_info, dt, _append_bias, _skip_im2col, _are_weights_reshaped, kernel_width, kernel_height, _is_fully_connected_convolution, _is_interleaved, _is_quantized, _is_activationlayer_enabled, mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation); ARM_COMPUTE_ERROR_THROW_ON(status); _is_prepared = false; _original_weights = weights; const ITensor *biases_to_use = (_append_bias) ? biases : nullptr; bool run_optimised = dt == DataType::F32; // Reshape weights if needed if(run_optimised) { 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)); _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(idx_height); } else { mat_weights_cols = weights_info.num_kernels(); mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(idx_channel) + (_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)); _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved /* 1xW transpose */); weights = &_weights_reshaped; } } // In case we skip im2col we have to add bias if(!_skip_im2col) { const unsigned int mat_input_cols = mat_weights_rows; const unsigned int mat_input_rows = conv_w * conv_h; // Create tensor to store im2col reshaped inputs 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 && !run_optimised && _is_interleaved) { TensorShape shape_interleaved(shape_im2col); shape_interleaved.set(idx_width, shape_interleaved.x() * 4); shape_interleaved.set(idx_height, std::ceil(shape_interleaved[idx_height] / 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); info_gemm.set_quantization_info(output->info()->quantization_info()); _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); // Configure im2col _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation); } else if(_append_bias) { // Configure add bias kernel _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE); } // Configure matrix multiply if(run_optimised) { _asm_glue.configure(_skip_im2col ? input : &_input_im2col_reshaped, weights, is_nhwc ? output : &_gemm_output, 1.f, 0.f, true); if(!_asm_glue.is_configured()) { ARM_COMPUTE_ERROR("setup_assembly_kernel failed."); } } else { if(_is_interleaved) { // 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, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(idx_height), 0 /* no transpose */, _input_im2col_reshaped.info()->dimension(idx_width))); _input_interleaved_reshaped.allocator()->allocate(); } else { configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, _is_interleaved); } } if(!_skip_im2col) { _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 if(!is_nhwc) { _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(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), "Output shape does not match the expected one"); //Configure Activation Layer if(_is_activationlayer_enabled) { _activationlayer_function.configure(output, nullptr, act_info); } } Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_UNUSED(output); DataType dt{}; bool append_bias{}; bool skip_im2col{}; bool are_weights_reshaped{}; bool is_fully_connected_convolution{}; bool is_interleaved{}; bool is_quantized{}; bool is_activationlayer_enabled{}; 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; const DataLayout data_layout = input->data_layout(); const bool is_nhwc = data_layout == DataLayout::NHWC; const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, act_info, dt, append_bias, skip_im2col, are_weights_reshaped, kernel_width, kernel_height, is_fully_connected_convolution, is_interleaved, is_quantized, is_activationlayer_enabled, mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation); const Size2D kernel_weights = Size2D(kernel_width, kernel_height); ARM_COMPUTE_RETURN_ON_ERROR(status); std::unique_ptr reshaped_weights = weights->clone(); bool optimised_kernel = false; if(dt == DataType::F32) { optimised_kernel = true; } 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); if(!skip_im2col) { // Validate im2col ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false, false, dilation)); } else if(append_bias) { // Validate add bias kernel ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(output, biases, output, ConvertPolicy::SATURATE)); } // 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); // Reshape weights if needed if(optimised_kernel) { ARM_COMPUTE_RETURN_ERROR_ON(are_weights_reshaped); // 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 */)); } else if(!is_quantized) { 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 GEMM interleave and multiply if(is_interleaved) { TensorShape shape_interleaved = shape_im2col; shape_interleaved.set(idx_width, shape_interleaved.x() * 4); shape_interleaved.set(idx_height, 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, 1.f, is_interleaved, GEMMReshapeInfo(shape_im2col[1], // m weights->tensor_shape()[0], // n shape_im2col[0]) /* k */)); } else { ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo())); } } if(!is_nhwc) { ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h))); } ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(idx_width) != conv_w) || (output->dimension(idx_height) != conv_h), "Output shape does not match the expected one"); if(act_info.enabled()) { ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info)); } return Status{}; } void NEGEMMConvolutionLayer::run() { prepare(); _memory_group.acquire(); if(!_skip_im2col) { // Run input reshaping unsigned int _y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); NEScheduler::get().schedule(&_input_im2col_kernel, _y_dim); } // Runs matrix multiply on reshaped matrices if(_asm_glue.is_configured()) { _asm_glue.run(); } else { if(_is_interleaved) { // 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); } } if(_skip_im2col && _append_bias) { NEScheduler::get().schedule(&_add_bias_kernel, Window::DimY); } // Run output stage for quantized case if(_is_quantized) { _gemmlowp_output_stage.run(); } // Reshape output matrix if(_data_layout == DataLayout::NCHW) { NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY); } if(_is_activationlayer_enabled) { _activationlayer_function.run(); } _memory_group.release(); } void NEGEMMConvolutionLayer::prepare() { if(!_is_prepared) { // Run weights reshaping (Runs once for every configure) if(!_are_weights_reshaped) { ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); _weights_reshaped.allocator()->allocate(); _reshape_weights.run(); _reshape_weights = NEConvolutionLayerReshapeWeights(); _original_weights->mark_as_unused(); _are_weights_reshaped = true; } // Run GEMM prepare stage if(_asm_glue.is_configured()) { _asm_glue.prepare(); } else { if(_is_quantized) { _mm_gemmlowp.prepare(); } } // Release weights in case buffer is pretransposed if(!_weights_reshaped.is_used()) { _weights_reshaped.allocator()->free(); } _is_prepared = true; } } } // namespace arm_compute