From 29599d01a8f498e33b9c6995bd879473dc02e077 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Tue, 6 Jul 2021 15:01:35 +0100 Subject: Port NEGEMMConvolutionLayer Details: port NEWeightsReshapeKernel to CpuWeightsReshapeKernel port NEGEMMConvolutionLayer to CpuGEMMConvolutionLayer Resolves: COMPMID-4509 Change-Id: I3c7051e2c3f6d808a7ccb898aad70e5b221b9dc3 Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5938 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio --- .../NEON/functions/NEDeconvolutionLayer.cpp | 1 - .../NEON/functions/NEGEMMConvolutionLayer.cpp | 641 ++------------------- src/runtime/cpu/operators/CpuGemmConvolution.cpp | 602 +++++++++++++++++++ src/runtime/cpu/operators/CpuGemmConvolution.h | 197 +++++++ 4 files changed, 860 insertions(+), 581 deletions(-) create mode 100644 src/runtime/cpu/operators/CpuGemmConvolution.cpp create mode 100644 src/runtime/cpu/operators/CpuGemmConvolution.h (limited to 'src/runtime') diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp index 5bd61b4074..712f41f369 100644 --- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp @@ -28,7 +28,6 @@ #include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/NEON/NEScheduler.h" -#include "src/core/NEON/kernels/NEWeightsReshapeKernel.h" #include "src/core/helpers/AutoConfiguration.h" using namespace arm_compute::misc::shape_calculator; diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index 7c06b0adf5..6386a678db 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -26,618 +26,99 @@ #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/helpers/MemoryHelpers.h" +#include "src/runtime/cpu/operators/CpuGemmConvolution.h" -#include "src/core/NEON/kernels/NEWeightsReshapeKernel.h" -#include "src/core/cpu/kernels/CpuCol2ImKernel.h" -#include "src/core/cpu/kernels/CpuIm2ColKernel.h" - -#include -#include +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(); - _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() +struct NEGEMMConvolutionLayer::Impl { - NEScheduler::get().schedule(_weights_reshape_kernel.get(), 3); -} - -NEGEMMConvolutionLayer::~NEGEMMConvolutionLayer() = default; + const ITensor *weights{ nullptr }; + std::unique_ptr op{ nullptr }; + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{ nullptr }; + MemoryRequirements aux_mem_req{}; + WorkspaceData workspace_tensors{}; + bool is_prepared{ false }; +}; NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr &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(), _input(nullptr), _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_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 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 max_activation = type_max.get(); - - 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) + : _impl(std::make_unique()) { - 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 max_activation = type_max.get(); - - const std::set 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 input_qa = input->clone(); - std::unique_ptr 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()); - - return validate_mm(&dummy_input_info, &dummy_weights_info, nullptr, &dummy_output_info, act_info, gemm_3d_depth, skip_im2col); + _impl->weights_manager = weights_manager; + _impl->memory_group = MemoryGroup(memory_manager); } +NEGEMMConvolutionLayer::~NEGEMMConvolutionLayer() = default; 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); - - _input = input; - _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(); - _im2col_kernel->configure(input->info(), _im2col_output.info(), 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(); - _col2im_kernel->configure(gemm_output_to_use->info(), output->info(), 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"); + _impl->weights = weights; + _impl->op = std::make_unique(); + _impl->op->configure(input->info(), weights->info(), (biases != nullptr ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info, 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->prep_pack = + { + { TensorType::ACL_SRC_1, weights }, + { TensorType::ACL_SRC_2, biases }, + }; + _impl->aux_mem_req = _impl->op->workspace(); + _impl->workspace_tensors = manage_workspace(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack); } 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"); - - 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(cpu::kernels::CpuIm2ColKernel::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(cpu::kernels::CpuCol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h))); - } - - return Status{}; + return cpu::CpuGemmConvolution::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, num_groups); } 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); - ITensorPack pack = - { - { TensorType::ACL_SRC, _input }, - { TensorType::ACL_DST, &_im2col_output } - }; - NEScheduler::get().schedule_op(_im2col_kernel.get(), y_dim, _im2col_kernel->window(), pack); - } - - // 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) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, &_gemm_output }, - { TensorType::ACL_DST, _original_output } - }; - NEScheduler::get().schedule_op(_col2im_kernel.get(), Window::DimY, _col2im_kernel->window(), pack); - } - 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(); - } + _impl->op->prepare(_impl->prep_pack); + auto has_reshape = std::find_if(_impl->aux_mem_req.begin(), + _impl->aux_mem_req.end(), + [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; }); - // Prepare GEMM - _is_quantized ? _mm_gemmlowp.prepare() : _mm_gemm.prepare(); - if(!_weights_reshaped.is_used()) + if(has_reshape != std::end(_impl->aux_mem_req)) { - _weights_reshaped.allocator()->free(); + _impl->weights->mark_as_unused(); } - - _is_prepared = true; + for(auto &ws : _impl->workspace_tensors) + { + const int slot = ws.first; + for(auto &m : _impl->aux_mem_req) + { + if(m.slot == slot && m.lifetime == MemoryLifetime::Prepare) + { + auto tensor = ws.second.get(); + tensor->allocator()->free(); + break; + } + } + } + _impl->is_prepared = true; } } } // namespace arm_compute diff --git a/src/runtime/cpu/operators/CpuGemmConvolution.cpp b/src/runtime/cpu/operators/CpuGemmConvolution.cpp new file mode 100644 index 0000000000..a0424b1c63 --- /dev/null +++ b/src/runtime/cpu/operators/CpuGemmConvolution.cpp @@ -0,0 +1,602 @@ +/* + * Copyright (c) 2021 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 "src/runtime/cpu/operators/CpuGemmConvolution.h" + +#include "arm_compute/core/Size2D.h" +#include "arm_compute/core/TensorInfo.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/cpu/kernels/CpuCol2ImKernel.h" +#include "src/core/cpu/kernels/CpuIm2ColKernel.h" +#include "src/core/cpu/kernels/CpuReshapeKernel.h" +#include "src/core/cpu/kernels/CpuWeightsReshapeKernel.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/runtime/cpu/operators/CpuGemm.h" +#include "src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h" +#include "src/runtime/cpu/operators/CpuGemmLowpOutputStage.h" +#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h" + +#include +#include + +using namespace arm_compute::misc::shape_calculator; +using namespace arm_compute::experimental; + +namespace arm_compute +{ +namespace cpu +{ +CpuGemmConvolution::CpuGemmConvolution() + : _weights_reshape_kernel(nullptr), _im2col_kernel(), _mm_gemm(), _mm_gemmlowp(), _col2im_kernel(), _reshape_kernel(), _im2col_output(), _weights_reshaped(), _gemm_output(), _gemm_output_3d(), + _data_layout(DataLayout::NCHW), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_prepared(false), _aux_mem(AuxTensorIdx::Count) +{ +} +CpuGemmConvolution::~CpuGemmConvolution() = default; + +void CpuGemmConvolution::configure_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act_info, int gemm_3d_depth) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights); + ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, 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 supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, + ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, + ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU + }; + + if(_is_quantized) + { + TensorInfo tmp_src{ *src }; + TensorInfo tmp_weights{ *weights }; + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() + // Extract and negate input and weights offset + const QuantizationInfo iqinfo = src->quantization_info(); + const QuantizationInfo wqinfo = weights->quantization_info(); + const QuantizationInfo oqinfo = (dst->total_size() == 0) ? iqinfo : dst->quantization_info(); + const UniformQuantizationInfo uiqinfo = iqinfo.uniform(); + const UniformQuantizationInfo uoqinfo = oqinfo.uniform(); + const DataType data_type = src->data_type(); + + tmp_src.set_quantization_info(QuantizationInfo(uiqinfo.scale, -uiqinfo.offset)); + if(!is_data_type_quantized_per_channel(tmp_weights.data_type())) + { + const UniformQuantizationInfo uwqinfo = wqinfo.uniform(); + tmp_weights.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 max_activation = type_max.get(); + + 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 = (tmp_weights.data_type() == DataType::QSYMM8_PER_CHANNEL); + quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, output_info); + + _mm_gemmlowp = std::make_unique(); + _mm_gemmlowp->configure(&tmp_src, &tmp_weights, biases, dst, GEMMInfo(false, false, true, gemm_3d_depth, _skip_im2col, false, output_info, false, false, act_info)); + + auto mm_mem_req = _mm_gemmlowp->workspace(); + for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont) + { + _aux_mem[cont] = mm_mem_req[cont]; + } + } + else + { + // Configure matrix multiply function + _mm_gemm = std::make_unique(); + _mm_gemm->configure(src, weights, biases, dst, 1.0f, 0.0f, gemm_info); + auto mm_mem_req = _mm_gemm->workspace(); + for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont) + { + _aux_mem[cont] = mm_mem_req[cont]; + } + } +} + +Status CpuGemmConvolution::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, + const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col) +{ + const DataType data_type = src->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 = src->quantization_info(); + const QuantizationInfo &wqinfo = weights->quantization_info(); + const QuantizationInfo &oqinfo = (dst->total_size() == 0) ? iqinfo : dst->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 max_activation = type_max.get(); + + const std::set 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 input_qa = src->clone(); + std::unique_ptr 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 CpuGemmLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, dst, 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 CpuGemm::validate(src, weights, nullptr, dst, 1.0f, 0.0f, gemm_info); + } +} + +Status CpuGemmConvolution::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 CpuGemmConvolution::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, + const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); + ARM_COMPUTE_UNUSED(num_groups, weights_info); + ARM_COMPUTE_ERROR_THROW_ON(CpuGemmConvolution::validate(src, + weights, + biases, + dst, + conv_info, + weights_info, + dilation, + act_info, + num_groups)); + + const DataType data_type = src->data_type(); + const DataLayout data_layout = src->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->dimension(idx_width); + const unsigned int kernel_height = weights->dimension(idx_height); + + _is_prepared = weights_info.retain_internal_weights(); + _is_quantized = is_data_type_quantized_asymmetric(src->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 ITensorInfo *gemm_input_to_use = src; + ITensorInfo *gemm_output_to_use = dst; + + // Get convolved dimensions + unsigned int conv_w = 0; + unsigned int conv_h = 0; + std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), + src->dimension(idx_height), + kernel_width, + kernel_height, + conv_info, + dilation); + ARM_COMPUTE_ERROR_ON_MSG((dst->dimension(idx_width) != conv_w) || (dst->dimension(idx_height) != conv_h), + "Output shape does not match the expected one"); + + // Check if GEMM3D is supported + if(data_layout == DataLayout::NHWC) + { + _skip_col2im = bool(validate_gemm3d(src, 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; + } + } + 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->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 CpuGemm + _weights_reshape_kernel = std::make_unique(); + _weights_reshape_kernel->configure(weights, nullptr, &_weights_reshaped); + _weights_reshaped.set_quantization_info(weights->quantization_info()); + + // Create tensor to store im2col reshaped inputs + if(!_skip_im2col) + { + // Configure + _im2col_kernel = std::make_unique(); + _im2col_kernel->configure(src, &_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.tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, conv_w * conv_h); + + _gemm_output = TensorInfo(shape_gemm, 1, output_data_type); + _gemm_output.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout()); + _gemm_output_3d = TensorInfo(_gemm_output); + + // Update GEMM output + gemm_output_to_use = &_gemm_output; + } + else + { + _gemm_output_3d = TensorInfo(*dst); + _gemm_output_3d.set_data_type(output_data_type).set_data_layout(src->data_layout()).set_is_resizable(true); + _gemm_output = TensorInfo(_gemm_output_3d); + + // 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_reshaped, biases, gemm_output_to_use, act_info, gemm_3d_depth); + + if(!_skip_col2im && _data_layout == DataLayout::NCHW) + { + // Configure col2im + _col2im_kernel = std::make_unique(); + _col2im_kernel->configure(gemm_output_to_use, dst, Size2D(conv_w, conv_h)); + } + else + { + // Configure reshape layer + _reshape_kernel = std::make_unique(); + _reshape_kernel->configure(gemm_output_to_use, dst); + } + + _aux_mem[Im2ColOutput] = MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size()); + _aux_mem[WeightsReshaped] = MemoryInfo(offset_int_vec(WeightsReshaped), MemoryLifetime::Prepare, _weights_reshaped.total_size()); + _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size()); + _aux_mem[GemmOutput3d] = MemoryInfo(offset_int_vec(GemmOutput3d), MemoryLifetime::Temporary, _gemm_output_3d.total_size()); +} + +Status CpuGemmConvolution::validate(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, unsigned int num_groups) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); + 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(src, 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(src, weights); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Grouping (num_groups != 1) is not supported"); + + const DataLayout data_layout = src->data_layout(); + const DataType data_type = src->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 = src; + const ITensorInfo *gemm_output_to_use = dst; + 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(src->dimension(idx_width), + src->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(src, 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(src, weights, act_info, conv_h, skip_im2col))) + { + skip_im2col = false; + skip_col2im = false; + } + } + + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != src->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(src, 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); + + 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 = src->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(src->quantization_info()); + ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuIm2ColKernel::validate(src, &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(dst->tensor_shape(), 1, output_data_type); + } + info_gemm.set_quantization_info(dst->quantization_info()).set_data_layout(src->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(kernels::CpuCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h))); + } + + return Status{}; +} + +void CpuGemmConvolution::run(ITensorPack &tensors) +{ + prepare(tensors); + + auto src = tensors.get_const_tensor(ACL_SRC_0); + auto weights = tensors.get_const_tensor(ACL_SRC_1); + auto biases = tensors.get_const_tensor(ACL_SRC_2); + auto dst = tensors.get_tensor(ACL_DST); + auto gemm_input_to_use = src; + + CpuAuxTensorHandler im2col_output(offset_int_vec(Im2ColOutput), _im2col_output, tensors, false); + CpuAuxTensorHandler gemm_output(offset_int_vec(GemmOutput), _gemm_output, tensors, false); + + bool out_has_padding = _skip_col2im && (dst->info()->padding().bottom != 0 || dst->info()->padding().top != 0); + if(!_skip_im2col) + { + // Run input reshaping + unsigned int y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); + ITensorPack pack = + { + { TensorType::ACL_SRC, src }, + { TensorType::ACL_DST, im2col_output.get() } + }; + NEScheduler::get().schedule_op(_im2col_kernel.get(), y_dim, _im2col_kernel->window(), pack); + gemm_input_to_use = im2col_output.get(); + } + + // Handle the case where output has top/bottom padding + const ITensor *out_to_use = out_has_padding ? gemm_output.get() : dst; + _gemm_output_3d.extend_padding(out_to_use->info()->padding()); + CpuAuxTensorHandler gemm_output_3d(offset_int_vec(GemmOutput3d), _gemm_output_3d, tensors, true); + auto gemm_output_to_use = gemm_output.get(); + if(_skip_im2col) + { + gemm_output_to_use = gemm_output_3d.get(); + } + if(_skip_col2im && !out_has_padding) + { + gemm_output_to_use = dst; + } + + // Runs CpuGemm or CpuGemmLowpMatrixMultiplyCore functions + ITensorPack pack_mm = + { + { TensorType::ACL_SRC_0, gemm_input_to_use }, + { TensorType::ACL_SRC_1, weights }, + { TensorType::ACL_SRC_2, biases }, + { TensorType::ACL_DST, gemm_output_to_use } + }; + if(_is_quantized) + { + // Run gemmlowp + _mm_gemmlowp->run(pack_mm); + } + else + { + // Run gemm + _mm_gemm->run(pack_mm); + } + + // Reshape output matrix + if(!_skip_col2im) + { + if(_data_layout == DataLayout::NCHW) + { + ITensorPack pack = + { + { TensorType::ACL_SRC, gemm_output.get() }, + { TensorType::ACL_DST, dst } + }; + NEScheduler::get().schedule_op(_col2im_kernel.get(), Window::DimY, _col2im_kernel->window(), pack); + } + else + { + ITensorPack pack = + { + { TensorType::ACL_SRC, gemm_output_to_use }, + { TensorType::ACL_DST, dst } + }; + NEScheduler::get().schedule_op(_reshape_kernel.get(), Window::DimY, _reshape_kernel->window(), pack); + } + } + else if(out_has_padding) + { + ITensorPack pack = + { + { TensorType::ACL_SRC, gemm_output_to_use }, + { TensorType::ACL_DST, dst } + }; + NEScheduler::get().schedule_op(_reshape_kernel.get(), Window::DimY, _reshape_kernel->window(), pack); + } +} + +void CpuGemmConvolution::prepare(ITensorPack &tensors) +{ + if(!_is_prepared) + { + // Run weights reshaping and mark original weights tensor as unused + ITensor *weights_reshaped_p = utils::cast::polymorphic_downcast(tensors.get_tensor(offset_int_vec(WeightsReshaped))); + CpuAuxTensorHandler weights_reshaped(_weights_reshaped, *weights_reshaped_p); + auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); + ITensorPack pack = + { + { TensorType::ACL_SRC, weights }, + { TensorType::ACL_DST, weights_reshaped.get() } + }; + NEScheduler::get().schedule_op(_weights_reshape_kernel.get(), 3, _weights_reshape_kernel->window(), pack); + tensors.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get()); + + // Prepare GEMM + _is_quantized ? _mm_gemmlowp->prepare(tensors) : _mm_gemm->prepare(tensors); + _is_prepared = true; + } +} +experimental::MemoryRequirements CpuGemmConvolution::workspace() const +{ + return _aux_mem; +} +} // namespace cpu +} // namespace arm_compute \ No newline at end of file diff --git a/src/runtime/cpu/operators/CpuGemmConvolution.h b/src/runtime/cpu/operators/CpuGemmConvolution.h new file mode 100644 index 0000000000..8b41cb4a91 --- /dev/null +++ b/src/runtime/cpu/operators/CpuGemmConvolution.h @@ -0,0 +1,197 @@ +/* + * Copyright (c) 2021 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. + */ +#ifndef ARM_COMPUTE_CPU_GEMMCONVOLUTION_H +#define ARM_COMPUTE_CPU_GEMMCONVOLUTION_H + +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "src/runtime/cpu/ICpuOperator.h" + +#include + +namespace arm_compute +{ +namespace cpu +{ +class CpuGemm; +class CpuGemmLowpMatrixMultiplyCore; +class CpuGemmLowpOutputStage; +namespace kernels +{ +class CpuWeightsReshapeKernel; +class CpuIm2ColKernel; +class CpuCol2ImKernel; +class CpuReshapeKernel; +} // namespace kernels + +/** Basic function to compute the convolution layer. This function calls the following kernels/functions: + * + * -# @ref cpu::kernels::CpuIm2ColKernel + * -# @ref CpuGemm (if the data type is BFLOAT16/FP16/FP32) + * -# @ref CpuGemmLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED) + * -# @ref CpuGemmLowpOutputStage (if the data type is QASYMM8/QASYMM8_SIGNED) + * -# @ref cpu::kernels::CpuCol2ImKernel (if NCHW data layout) + * -# @ref kernels::CpuWeightsReshapeKernel + * + */ +class CpuGemmConvolution : public ICpuOperator +{ +public: + /** Constructor */ + CpuGemmConvolution(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CpuGemmConvolution(const CpuGemmConvolution &) = delete; + /** Prevent instances of this class from being moved (As this class contains non movable objects) */ + CpuGemmConvolution(CpuGemmConvolution &&) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CpuGemmConvolution &operator=(const CpuGemmConvolution &) = delete; + /** Prevent instances of this class from being moved (As this class contains non movable objects) */ + CpuGemmConvolution &operator=(CpuGemmConvolution &&) = delete; + /** Destructor */ + ~CpuGemmConvolution(); + /** Set the input and output tensors. + * + * Valid data layouts: + * - NHWC + * - NCHW + * + * Valid data type configurations: + * |src0 |src1 |src2 |dst | + * |:--------------|:------------------|:--------|:--------------| + * |F16 |F16 |F16 |F16 | + * |F32 |F32 |F32 |F32 | + * |BFLOAT16 |BFLOAT16 |BFLOAT16 |BFLOAT16 | + * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | + * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 | + * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | + * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED | + * + * @param[in] src Source tensor info. 3 lower dimensions represent a single input [width, height, IFM], + * while every optional dimension from 4 and above represent a batch of inputs. + * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. + * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. + * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. + * @param[out] dst Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. + * Data types supported: Same as @p input. + * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. + * @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights + * tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. + * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported + */ + void configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), + const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to CpuGemmConvolution::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1); + + // Inherited methods overridden: + void run(ITensorPack &tensors) override; + void prepare(ITensorPack &tensors) override; + experimental::MemoryRequirements workspace() const override; + +private: + /** Configures the appropriate matrix multiply routine + * + * @param[in] src Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. + * @param[in] weights Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. + * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. + * @param[out] dst Output tensor info. Data types supported: Same as @p input, + * except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type. + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. + * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1) + */ + void configure_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), + int gemm_3d_depth = 1); + /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines + * + * @param[in] src Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. + * @param[in] weights Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. + * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. + * @param[in] dst Output tensor info. Data types supported: Same as @p input, + * except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type. + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. + * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1) + * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false) + * + * @return a status + */ + static Status validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ActivationLayerInfo &act_info = ActivationLayerInfo(), + int gemm_3d_depth = 1, bool skip_im2col = false); + /** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref CpuGemmMLowpMatrixMultiplyCore + * + * @param[in] src Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. + * @param[in] weights Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. + * @param[in] act_info Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. + * @param[in] gemm_3d_depth Depth of GEMM 3D + * @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout + * + * @return a status + */ + static Status validate_gemm3d(const ITensorInfo *src, const ITensorInfo *weights, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col); + + enum AuxTensorIdx + { + // CpuGemmLowpMatrixMultiplyCore has up to 8 internal tensors + Im2ColOutput = 9, + WeightsReshaped, + GemmOutput, + GemmOutput3d, + Count + }; + + std::unique_ptr _weights_reshape_kernel; + std::unique_ptr _im2col_kernel; + std::unique_ptr _mm_gemm; + std::unique_ptr _mm_gemmlowp; + std::unique_ptr _col2im_kernel; + std::unique_ptr _reshape_kernel; + + TensorInfo _im2col_output; + TensorInfo _weights_reshaped; + TensorInfo _gemm_output; + TensorInfo _gemm_output_3d; + + DataLayout _data_layout; + + bool _skip_im2col; + bool _skip_col2im; + bool _is_quantized; + bool _is_prepared; + + experimental::MemoryRequirements _aux_mem{ Count }; +}; +} // namespace cpu +} // namespace arm_compute +#endif /* ARM_COMPUTE_CPU_GEMMCONVOLUTION_H */ -- cgit v1.2.1