diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp | 658 |
1 files changed, 75 insertions, 583 deletions
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index 2876c254fa..c8f65d2fd9 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -26,617 +26,109 @@ #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 "arm_compute/runtime/Tensor.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 "src/core/helpers/MemoryHelpers.h" +#include "src/cpu/operators/CpuGemmConv2d.h" -#include <set> -#include <tuple> +using namespace arm_compute::experimental; namespace arm_compute { -using namespace arm_compute::misc::shape_calculator; - -NEConvolutionLayerReshapeWeights::~NEConvolutionLayerReshapeWeights() = default; -NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights() noexcept - : _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 = std::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) +struct NEGEMMConvolutionLayer::Impl { - 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() + const ITensor *weights{nullptr}; + std::unique_ptr<cpu::CpuGemmConv2d> op{nullptr}; + ITensorPack run_pack{}; + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{nullptr}; + MemoryRequirements aux_mem_req{}; + WorkspaceData<Tensor> workspace_tensors{}; + bool is_prepared{false}; +}; + +NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager, + IWeightsManager *weights_manager) + : _impl(std::make_unique<Impl>()) { - NEScheduler::get().schedule(_weights_reshape_kernel.get(), 3); + _impl->weights_manager = weights_manager; + _impl->memory_group = MemoryGroup(memory_manager); } - 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), _original_output(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _gemm_output_3d(), _tmp_output(), - _data_layout(DataLayout::NCHW), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_prepared(false) +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, + bool enable_fast_math, + unsigned int num_groups) { -} - -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, false, false, act_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, false, false, act_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()); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - return validate_mm(&dummy_input_info, &dummy_weights_info, nullptr, &dummy_output_info, act_info, gemm_3d_depth, skip_im2col); + _impl->weights = weights; + _impl->op = std::make_unique<cpu::CpuGemmConv2d>(); + _impl->op->configure(input->info(), weights->info(), (biases != nullptr ? biases->info() : nullptr), output->info(), + conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups); + + _impl->run_pack = {{TensorType::ACL_SRC_0, input}, + {TensorType::ACL_SRC_1, weights}, + {TensorType::ACL_SRC_2, biases}, + {TensorType::ACL_DST, output}}; + _impl->aux_mem_req = _impl->op->workspace(); + _impl->workspace_tensors = + manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->run_pack); } -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) +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, + bool enable_fast_math, + 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; - _original_output = output; - _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 = std::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); - _gemm_output_3d.allocator()->init(info_gemm); - _memory_group.manage(&_gemm_output); - - // Update GEMM output - gemm_output_to_use = &_gemm_output; - } - else - { - TensorInfo out_info{ *output->info() }; - out_info.set_data_type(output_data_type).set_data_layout(input->info()->data_layout()).set_is_resizable(true); - _gemm_output.allocator()->init(out_info); - _gemm_output_3d.allocator()->init(out_info); - _memory_group.manage(&_gemm_output); - - // Update GEMM output - gemm_output_to_use = &_gemm_output_3d; - } - - // 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 = std::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); - } - } - else - { - // Configure reshape layer - _reshape_layer.configure(gemm_output_to_use, output); - } - - if(_is_quantized && !_skip_col2im) - { - _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"); + return cpu::CpuGemmConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, + enable_fast_math, num_groups); } -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) +Status NEGEMMConvolutionLayer::has_opt_impl(arm_compute::WeightFormat &expected_weight_format, + const ITensorInfo *src, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *dst, + const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, + const Size2D &dilation, + const ActivationLayerInfo &act_info, + const bool enable_fast_math) { - 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"); - - 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 CPU, the batch size is on the fourth dimension - 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{}; + return cpu::CpuGemmConv2d::has_opt_impl(expected_weight_format, src, weights, biases, dst, conv_info, weights_info, + dilation, act_info, enable_fast_math); } void NEGEMMConvolutionLayer::run() { prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - bool out_has_padding = _skip_col2im && (_original_output->info()->padding().bottom != 0 || _original_output->info()->padding().top != 0); - - 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); - } - - // Handle the case where output has top/bottom padding - const ITensor *out_to_use = out_has_padding ? &_gemm_output : _original_output; - _gemm_output_3d.info()->extend_padding(out_to_use->info()->padding()); - _gemm_output_3d.allocator()->import_memory(out_to_use->buffer()); - - // 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(); - } - } - else if(out_has_padding) - { - _reshape_layer.run(); - } - - _gemm_output_3d.allocator()->free(); + MemoryGroupResourceScope scope_mg(_impl->memory_group); + _impl->op->run(_impl->run_pack); } void NEGEMMConvolutionLayer::prepare() { - if(!_is_prepared) + if (!_impl->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(); - } + _impl->op->prepare(_impl->run_pack); - _is_prepared = true; + // Release temporary tensors that are only used in prepare stage + release_temporaries<Tensor>(_impl->aux_mem_req, _impl->workspace_tensors); + _impl->is_prepared = true; } } } // namespace arm_compute |