/* * Copyright (c) 2021-2023 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/cpu/operators/CpuGemmConv2d.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/common/utils/Log.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/cpu/kernels/CpuCol2ImKernel.h" #include "src/cpu/kernels/CpuIm2ColKernel.h" #include "src/cpu/kernels/CpuReshapeKernel.h" #include "src/cpu/kernels/CpuWeightsReshapeKernel.h" #include "src/cpu/operators/CpuGemm.h" #include "src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h" #include "src/cpu/operators/CpuGemmLowpOutputStage.h" #include "src/cpu/utils/CpuAuxTensorHandler.h" #include #include using namespace arm_compute::misc::shape_calculator; using namespace arm_compute::experimental; namespace arm_compute { namespace cpu { CpuGemmConv2d::SkipInfo CpuGemmConv2d::skip_im_col_info(const ITensorInfo *src, const ITensorInfo *weights, const PadStrideInfo &conv_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { 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 unsigned int kernel_width = weights->dimension(idx_width); const unsigned int kernel_height = weights->dimension(idx_height); 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); const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); if(skip_im2col) { const bool skip_col2im = (data_layout == DataLayout::NHWC && (bool(CpuGemmConv2d::validate_gemm3d(src, weights, act_info, conv_h, /*skip_im2col*/ true)))); if(skip_col2im) { return { true, true }; } } else { const bool skip_col2im = (data_layout == DataLayout::NHWC && (bool(CpuGemmConv2d::validate_gemm3d(src, weights, act_info, conv_h, /*skip_im2col*/ false)))); if(skip_col2im) { return { false, true }; } } // Default case when we cannot reinterpret the input and output as 3D. return { false, false }; } CpuGemmConv2d::CpuGemmConv2d() : _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) { } CpuGemmConv2d::~CpuGemmConv2d() = default; void CpuGemmConv2d::configure_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act_info, bool enable_fast_math, int gemm_3d_depth, bool fixed_format, arm_compute::WeightFormat weight_format) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights); ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, act_info, enable_fast_math, gemm_3d_depth, _skip_im2col, fixed_format, weight_format)); // 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, enable_fast_math, false, act_info, experimental::PostOpList(), fixed_format, weight_format); // 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, enable_fast_math, false, act_info, experimental::PostOpList(), fixed_format, weight_format)); 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 CpuGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ActivationLayerInfo &act_info, bool enable_fast_math, int gemm_3d_depth, bool skip_im2col, bool fixed_format, arm_compute::WeightFormat weight_format) { 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, enable_fast_math, false, act_info, experimental::PostOpList(), fixed_format, weight_format); 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, enable_fast_math, 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 CpuGemmConv2d::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, false, gemm_3d_depth, skip_im2col); } void CpuGemmConv2d::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, bool enable_fast_math, unsigned int num_groups) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); ARM_COMPUTE_UNUSED(num_groups, weights_info); ARM_COMPUTE_ERROR_THROW_ON(CpuGemmConv2d::validate(src, weights, biases, dst, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups)); ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, weights_info, dilation, act_info, enable_fast_math, 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 const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info, dilation, act_info); _skip_im2col = skip_info.skip_im2col; _skip_col2im = skip_info.skip_col2im; // 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; const bool fixed_format = weights_info.weight_format() != arm_compute::WeightFormat::UNSPECIFIED; configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, act_info, enable_fast_math, gemm_3d_depth, fixed_format, weights_info.weight_format()); 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); } // Check if GEMM transforms weights // Modernise through COMPMID-4535 bool gemm_trans_wei = _aux_mem[1].size > 0; // Asm Pretranspose gemm_trans_wei = _mm_gemm != nullptr ? _aux_mem[3].size > 0 : gemm_trans_wei; // Tranpose RHS gemm_trans_wei = _mm_gemmlowp != nullptr ? _aux_mem[5].size > 0 : gemm_trans_wei; // Transpose RHS // Check lifetime _aux_mem[Im2ColOutput] = MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size()); _aux_mem[WeightsReshaped] = MemoryInfo(offset_int_vec(WeightsReshaped), gemm_trans_wei ? MemoryLifetime::Prepare : MemoryLifetime::Persistent, _weights_reshaped.total_size()); _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size()); } Status CpuGemmConv2d::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) { 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 unsigned int kernel_width = weights->dimension(idx_width); const unsigned int kernel_height = weights->dimension(idx_height); 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); const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info, dilation, act_info); const bool skip_im2col = skip_info.skip_im2col; const bool skip_col2im = skip_info.skip_col2im; const unsigned int gemm_3d_depth = skip_col2im ? conv_h : 0; const bool fixed_format = weights_info.weight_format() != arm_compute::WeightFormat::UNSPECIFIED; 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, enable_fast_math, false, act_info, experimental::PostOpList(), fixed_format, weights_info.weight_format()); return CpuGemm::has_opt_impl(expected_weight_format, src, weights, biases, dst, gemm_info); } Status CpuGemmConv2d::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, bool enable_fast_math, 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); if (!is_fixed_format(weights_info.weight_format())) { 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; // 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 const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info, dilation, act_info); const bool skip_im2col = skip_info.skip_im2col, skip_col2im = skip_info.skip_col2im; 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) != dst->dimension(idx_channel)); 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, weights->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, 1)); 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; const bool fixed_format = weights_info.weight_format() != arm_compute::WeightFormat::UNSPECIFIED; ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, act_info, enable_fast_math, skip_col2im ? conv_h : 0, skip_im2col, fixed_format, weights_info.weight_format())); // 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 CpuGemmConv2d::run(ITensorPack &tensors) { prepare(tensors); auto src = tensors.get_const_tensor(ACL_SRC_0); 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); CpuAuxTensorHandler reshaped_wei(offset_int_vec(WeightsReshaped), _weights_reshaped, 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; Tensor gemm3d; _gemm_output_3d.extend_padding(out_to_use->info()->padding()); gemm3d.allocator()->soft_init(_gemm_output_3d); gemm3d.allocator()->import_memory(out_to_use->buffer()); auto gemm_output_to_use = gemm_output.get(); if(_skip_im2col) { gemm_output_to_use = &gemm3d; } if(_skip_col2im && !out_has_padding) { gemm_output_to_use = dst; } // Runs CpuGemm or CpuGemmLowpMatrixMultiplyCore functions ITensorPack pack_mm = tensors; pack_mm.add_const_tensor(TensorType::ACL_SRC_0, gemm_input_to_use); if(!this->isVarWeightsKernel()) { pack_mm.add_const_tensor(TensorType::ACL_SRC_1, reshaped_wei.get()); } pack_mm.add_tensor(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 CpuGemmConv2d::prepare(ITensorPack &tensors) { if(!_is_prepared) { // Variable weights executions that use fixed-format kernels // need no reshaping of the weights. if(this->isVarWeightsKernel()) { _is_quantized ? _mm_gemmlowp->prepare(tensors) : _mm_gemm->prepare(tensors); _is_prepared = true; return; } // Run weights reshaping and mark original weights tensor as unused CpuAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors); 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); weights->mark_as_unused(); ITensorPack gemm_pack = tensors; gemm_pack.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get()); _is_quantized ? _mm_gemmlowp->prepare(gemm_pack) : _mm_gemm->prepare(gemm_pack); _is_prepared = true; } } experimental::MemoryRequirements CpuGemmConv2d::workspace() const { return _aux_mem; } bool CpuGemmConv2d::isVarWeightsKernel() const { return _mm_gemm && _mm_gemm->isVarWeightsKernel(); } } // namespace cpu } // namespace arm_compute