| /* |
| * Copyright (c) 2017-2020 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/Size2D.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include "src/core/NEON/kernels/NECol2ImKernel.h" |
| #include "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h" |
| #include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h" |
| #include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h" |
| #include "src/core/NEON/kernels/NEIm2ColKernel.h" |
| #include "src/core/NEON/kernels/NEWeightsReshapeKernel.h" |
| #include "support/MemorySupport.h" |
| |
| #include <set> |
| #include <tuple> |
| |
| namespace arm_compute |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| |
| NEConvolutionLayerReshapeWeights::~NEConvolutionLayerReshapeWeights() = default; |
| NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights() |
| : _weights_reshape_kernel() |
| { |
| } |
| |
| void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output) |
| { |
| // 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())); |
| const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); |
| const ITensor *biases_to_use = (append_biases) ? biases : nullptr; |
| |
| _weights_reshape_kernel = arm_compute::support::cpp14::make_unique<NEWeightsReshapeKernel>(); |
| _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) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, |
| DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, |
| DataType::BFLOAT16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| |
| if(biases != nullptr) |
| { |
| const int idx_kernels = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES); |
| 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(idx_kernels)); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| if((output != nullptr) && (output->total_size() != 0)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); |
| |
| NEWeightsReshapeKernel::validate(weights, biases, output); |
| } |
| |
| return Status{}; |
| } |
| |
| void NEConvolutionLayerReshapeWeights::run() |
| { |
| NEScheduler::get().schedule(_weights_reshape_kernel.get(), 3); |
| } |
| |
| NEGEMMConvolutionLayer::~NEGEMMConvolutionLayer() = default; |
| |
| NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager, IWeightsManager *weights_manager) |
| : _memory_group(memory_manager), _weights_manager(weights_manager), _reshape_weights(), _reshape_weights_managed(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), |
| _col2im_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _skip_im2col(false), |
| _skip_col2im(false), _is_quantized(false), _is_prepared(false) |
| { |
| } |
| |
| void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info, int gemm_3d_depth) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output == nullptr ? nullptr : output->info(), |
| act_info, gemm_3d_depth, _skip_im2col)); |
| |
| // Create GEMMInfo structure |
| const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, |
| gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */, |
| false, GEMMLowpOutputStageInfo(), false, false, act_info); |
| |
| // Supported activations in GEMM |
| const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, |
| ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, |
| ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU |
| }; |
| |
| 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 iqinfo = input->info()->quantization_info(); |
| const QuantizationInfo wqinfo = weights->info()->quantization_info(); |
| const QuantizationInfo oqinfo = (output->info()->total_size() == 0) ? iqinfo : output->info()->quantization_info(); |
| const UniformQuantizationInfo uiqinfo = iqinfo.uniform(); |
| const UniformQuantizationInfo uoqinfo = oqinfo.uniform(); |
| const DataType data_type = input->info()->data_type(); |
| |
| input->info()->set_quantization_info(QuantizationInfo(uiqinfo.scale, -uiqinfo.offset)); |
| if(!is_data_type_quantized_per_channel(weights->info()->data_type())) |
| { |
| const UniformQuantizationInfo uwqinfo = wqinfo.uniform(); |
| weights->info()->set_quantization_info(QuantizationInfo(uwqinfo.scale, -uwqinfo.offset)); |
| } |
| |
| // Merge activation with output stage |
| PixelValue type_min{}; |
| PixelValue type_max{}; |
| std::tie(type_min, type_max) = get_min_max(data_type); |
| int32_t min_activation = type_min.get<int32_t>(); |
| int32_t max_activation = type_max.get<int32_t>(); |
| |
| if(supported_acts.count(act_info.activation()) != 0) |
| { |
| std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act_info, data_type, uoqinfo); |
| } |
| |
| GEMMLowpOutputStageInfo output_info; |
| output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| output_info.gemmlowp_offset = uoqinfo.offset; |
| output_info.gemmlowp_min_bound = min_activation; |
| output_info.gemmlowp_max_bound = max_activation; |
| output_info.is_quantized_per_channel = (weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL); |
| quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, output_info); |
| |
| _mm_gemmlowp.configure(input, weights, biases, output, GEMMInfo(false, false, true, gemm_3d_depth, _skip_im2col, false, output_info)); |
| |
| // Revert back QuantizatioInfo as input and weights could be used in other convolution layers |
| input->info()->set_quantization_info(iqinfo); |
| weights->info()->set_quantization_info(wqinfo); |
| } |
| else |
| { |
| // Configure matrix multiply function |
| _mm_gemm.configure(input, weights, biases, output, 1.0f, 0.0f, gemm_info); |
| } |
| } |
| |
| Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, |
| const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col) |
| { |
| const DataType data_type = input->data_type(); |
| const bool is_quantized = is_data_type_quantized_asymmetric(data_type); |
| const bool is_activation_enabled = act_info.enabled(); |
| |
| // Create GEMMInfo structure |
| const GEMMInfo gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, |
| gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */, |
| false, GEMMLowpOutputStageInfo(), false, false, act_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 &iqinfo = input->quantization_info(); |
| const QuantizationInfo &wqinfo = weights->quantization_info(); |
| const QuantizationInfo &oqinfo = (output->total_size() == 0) ? iqinfo : output->quantization_info(); |
| const UniformQuantizationInfo uoqinfo = oqinfo.uniform(); |
| |
| // Merge activation with output stage |
| PixelValue type_min{}; |
| PixelValue type_max{}; |
| std::tie(type_min, type_max) = get_min_max(data_type); |
| int32_t min_activation = type_min.get<int32_t>(); |
| int32_t max_activation = type_max.get<int32_t>(); |
| |
| const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, |
| ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, |
| ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU |
| }; |
| if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0) |
| { |
| std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act_info, data_type, uoqinfo); |
| } |
| |
| GEMMLowpOutputStageInfo output_info; |
| output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| output_info.gemmlowp_offset = uoqinfo.offset; |
| output_info.gemmlowp_min_bound = min_activation; |
| output_info.gemmlowp_max_bound = max_activation; |
| output_info.is_quantized_per_channel = (weights->data_type() == DataType::QSYMM8_PER_CHANNEL); |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, output_info)); |
| |
| // Perform validation step on GEMMLowp |
| std::unique_ptr<ITensorInfo> input_qa = input->clone(); |
| std::unique_ptr<ITensorInfo> weights_qa = weights->clone(); |
| input_qa->set_quantization_info(QuantizationInfo(iqinfo.uniform().scale, -iqinfo.uniform().offset)); |
| weights_qa->set_quantization_info(QuantizationInfo(wqinfo.uniform().scale, -wqinfo.uniform().offset)); |
| return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, GEMMInfo(false, false, true, gemm_3d_depth, skip_im2col, false, output_info)); |
| } |
| else |
| { |
| // Perform validation step on Matrix multiply function |
| return NEGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info); |
| } |
| } |
| |
| Status NEGEMMConvolutionLayer::validate_gemm3d(const ITensorInfo *input_info, const ITensorInfo *weights_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col) |
| { |
| const DataType data_type = input_info->data_type(); |
| const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth; |
| const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U; |
| |
| // Set dummy tensor shapes for the validation |
| const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type, input_info->quantization_info()); |
| const TensorInfo dummy_weights_info(TensorShape(4U, 4U), 1, data_type, weights_info->quantization_info()); |
| const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, data_type, input_info->quantization_info()); |
| |
| return validate_mm(&dummy_input_info, &dummy_weights_info, nullptr, &dummy_output_info, act_info, gemm_3d_depth, skip_im2col); |
| } |
| |
| 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, unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_UNUSED(num_groups, weights_info); |
| ARM_COMPUTE_ERROR_THROW_ON(NEGEMMConvolutionLayer::validate(input->info(), |
| weights->info(), |
| biases != nullptr ? biases->info() : nullptr, |
| output->info(), |
| conv_info, |
| weights_info, |
| dilation, |
| act_info, |
| num_groups)); |
| |
| const DataType data_type = input->info()->data_type(); |
| const DataLayout data_layout = input->info()->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_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); |
| |
| const unsigned int kernel_width = weights->info()->dimension(idx_width); |
| const unsigned int kernel_height = weights->info()->dimension(idx_height); |
| |
| _is_prepared = weights_info.retain_internal_weights(); |
| _original_weights = weights; |
| _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| _data_layout = data_layout; |
| _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); |
| |
| const ITensor *gemm_input_to_use = input; |
| ITensor *gemm_output_to_use = output; |
| |
| // Get convolved dimensions |
| unsigned int conv_w = 0; |
| unsigned int conv_h = 0; |
| std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(idx_width), |
| input->info()->dimension(idx_height), |
| kernel_width, |
| kernel_height, |
| conv_info, |
| dilation); |
| |
| // Check if GEMM3D is supported |
| if(data_layout == DataLayout::NHWC) |
| { |
| _skip_col2im = bool(validate_gemm3d(input->info(), weights->info(), act_info, conv_h, true)); |
| // If not supported, we need to perform im2col and col2im (or reshape layer) |
| if(!_skip_col2im) |
| { |
| _skip_im2col = false; |
| } |
| } |
| else |
| { |
| _skip_col2im = false; |
| } |
| |
| // Get parameters from conv_info |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| |
| unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels); |
| |
| // _weights_reshaped will be auto configured in the kernel. |
| // Just append biases and do not transpose 1xW as it will be reshaped in NEGEMM |
| const ITensor *weights_to_use = weights; |
| |
| if(_weights_manager && _weights_manager->are_weights_managed(weights)) |
| { |
| _reshape_weights_managed.configure(weights, nullptr); |
| weights_to_use = _weights_manager->acquire(weights, &_reshape_weights_managed); |
| } |
| else |
| { |
| _reshape_weights.configure(weights, nullptr, &_weights_reshaped); |
| weights_to_use = &_weights_reshaped; |
| } |
| |
| // Create tensor to store im2col reshaped inputs |
| if(!_skip_im2col) |
| { |
| _memory_group.manage(&_im2col_output); |
| |
| // Configure |
| _im2col_kernel = arm_compute::support::cpp14::make_unique<NEIm2ColKernel>(); |
| _im2col_kernel->configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation); |
| |
| // Update GEMM input |
| gemm_input_to_use = &_im2col_output; |
| } |
| |
| // Create temporary GEMM output tensor in case we cannot skip col2im |
| const DataType output_data_type = data_type == DataType::BFLOAT16 ? DataType::F32 : data_type; |
| if(!_skip_col2im) |
| { |
| TensorShape shape_gemm; |
| |
| // Calculate GEMM output shape |
| shape_gemm = _im2col_output.info()->tensor_shape(); |
| shape_gemm.set(0, mat_weights_cols); |
| shape_gemm.set(1, conv_w * conv_h); |
| |
| // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. |
| TensorInfo info_gemm(shape_gemm, 1, output_data_type); |
| info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout()); |
| _gemm_output.allocator()->init(info_gemm); |
| _memory_group.manage(&_gemm_output); |
| |
| // Update GEMM output |
| gemm_output_to_use = &_gemm_output; |
| } |
| |
| // Configure GEMM |
| // In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix |
| const unsigned int gemm_3d_depth = _skip_col2im ? conv_h : 0; |
| configure_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, act_info, gemm_3d_depth); |
| |
| if(!_skip_im2col) |
| { |
| _im2col_output.allocator()->allocate(); |
| } |
| |
| if(!_skip_col2im) |
| { |
| if(_data_layout == DataLayout::NCHW) |
| { |
| // Configure col2im |
| _col2im_kernel = arm_compute::support::cpp14::make_unique<NECol2ImKernel>(); |
| _col2im_kernel->configure(gemm_output_to_use, output, Size2D(conv_w, conv_h)); |
| } |
| else |
| { |
| // Configure reshape layer |
| _reshape_layer.configure(gemm_output_to_use, output); |
| } |
| } |
| |
| if(_is_quantized && !_skip_col2im) |
| { |
| _tmp_output.allocator()->allocate(); |
| } |
| |
| if(!_skip_col2im || _is_quantized) |
| { |
| _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"); |
| } |
| |
| 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, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::BFLOAT16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Grouping (num_groups != 1) is not supported on NEON"); |
| |
| const DataLayout data_layout = input->data_layout(); |
| const DataType data_type = input->data_type(); |
| 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); |
| const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); |
| |
| const unsigned int kernel_width = weights->dimension(idx_width); |
| const unsigned int kernel_height = weights->dimension(idx_height); |
| |
| TensorInfo im2col_reshaped_info{}; |
| TensorInfo info_gemm{}; |
| TensorInfo tmp_info{}; |
| TensorInfo weights_reshaped_info{}; |
| const ITensorInfo *gemm_input_to_use = input; |
| const ITensorInfo *gemm_output_to_use = output; |
| const ITensorInfo *weights_to_use = weights; |
| |
| const bool append_bias = false; |
| const bool is_quantized = is_data_type_quantized_asymmetric(data_type); |
| const bool is_bf16 = data_type == DataType::BFLOAT16; |
| bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); |
| |
| // Get convolved dimensions |
| unsigned int conv_w = 0; |
| unsigned int conv_h = 0; |
| |
| 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 GEMM3D is supported |
| bool skip_col2im = false; |
| if(data_layout == DataLayout::NHWC) |
| { |
| skip_col2im = bool(validate_gemm3d(input, weights, act_info, conv_h, true)); |
| // If not supported, we need to perform im2col and col2im (or reshape layer) |
| if(!skip_col2im) |
| { |
| skip_im2col = false; |
| } |
| } |
| |
| if(skip_col2im) |
| { |
| // If not supported, we need to perform im2col and col2im (or reshape layer) |
| if(!bool(validate_gemm3d(input, weights, act_info, conv_h, skip_im2col))) |
| { |
| skip_im2col = false; |
| skip_col2im = false; |
| } |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != input->dimension(idx_channel)); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| |
| // Validate biases |
| if(biases != nullptr) |
| { |
| if(is_quantized) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| } |
| else if(is_bf16) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| unsigned int mat_weights_cols = weights->dimension(idx_kernels); |
| unsigned int mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel); |
| |
| // Output tensor auto inizialization if not yet initialized |
| ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, nullptr, nullptr)); |
| weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, append_bias), 1, data_type); |
| weights_reshaped_info.set_quantization_info(weights->quantization_info()); |
| weights_to_use = &weights_reshaped_info; |
| |
| if(!skip_im2col) |
| { |
| // Create tensor info for im2col reshaped inputs |
| // For NEON the batch size is on the fourth dimension |
| // TODO (giaiod01): Auto-initialize the output shape of im2col COMPMID-1482 |
| TensorShape shape_im2col = input->tensor_shape(); |
| shape_im2col.set(0, mat_weights_rows); |
| shape_im2col.set(1, conv_w * conv_h); |
| shape_im2col.set(2, 1); |
| |
| im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type); |
| im2col_reshaped_info.set_quantization_info(input->quantization_info()); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation)); |
| gemm_input_to_use = &im2col_reshaped_info; |
| } |
| |
| // Create temporary GEMM output tensor in case we cannot skip col2im |
| const DataType output_data_type = data_type == DataType::BFLOAT16 ? DataType::F32 : data_type; |
| if(!skip_col2im) |
| { |
| TensorShape shape_gemm = gemm_input_to_use->tensor_shape(); |
| shape_gemm.set(0, mat_weights_cols); |
| shape_gemm.set(1, conv_w * conv_h); |
| info_gemm = TensorInfo(shape_gemm, 1, output_data_type); |
| } |
| else |
| { |
| info_gemm = TensorInfo(output->tensor_shape(), 1, output_data_type); |
| } |
| info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout()); |
| gemm_output_to_use = &info_gemm; |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, act_info, skip_col2im ? conv_h : 0, skip_im2col)); |
| |
| // Validate Col2Im/ReshapeLayer |
| if(!skip_col2im && (data_layout == DataLayout::NCHW)) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h))); |
| } |
| |
| return Status{}; |
| } |
| |
| void NEGEMMConvolutionLayer::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(!_skip_im2col) |
| { |
| // Run input reshaping |
| unsigned int y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| NEScheduler::get().schedule(_im2col_kernel.get(), y_dim); |
| } |
| |
| // Runs NEGEMM or NEGEMMLowpMatrixMultiplyCore functions |
| if(_is_quantized) |
| { |
| // Run gemmlowp |
| _mm_gemmlowp.run(); |
| } |
| else |
| { |
| // Run gemm |
| _mm_gemm.run(); |
| } |
| |
| // Reshape output matrix |
| if(!_skip_col2im) |
| { |
| if(_data_layout == DataLayout::NCHW) |
| { |
| NEScheduler::get().schedule(_col2im_kernel.get(), Window::DimY); |
| } |
| else |
| { |
| _reshape_layer.run(); |
| } |
| } |
| } |
| |
| void NEGEMMConvolutionLayer::prepare() |
| { |
| if(!_is_prepared) |
| { |
| if(_weights_manager && _weights_manager->are_weights_managed(_original_weights)) |
| { |
| _weights_manager->run(_original_weights, &_reshape_weights_managed); |
| } |
| else |
| { |
| // Run weights reshaping and mark original weights tensor as unused |
| _weights_reshaped.allocator()->allocate(); |
| _reshape_weights.run(); |
| _original_weights->mark_as_unused(); |
| } |
| |
| // Prepare GEMM |
| _is_quantized ? _mm_gemmlowp.prepare() : _mm_gemm.prepare(); |
| if(!_weights_reshaped.is_used()) |
| { |
| _weights_reshaped.allocator()->free(); |
| } |
| |
| _is_prepared = true; |
| } |
| } |
| } // namespace arm_compute |