| /* |
| * Copyright (c) 2021-2022 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 <set> |
| #include <tuple> |
| |
| 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_gemm::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<ITensorInfo *>(), fixed_format, weight_format); |
| |
| // 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) |
| { |
| 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>(); |
| 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 = (tmp_weights.data_type() == DataType::QSYMM8_PER_CHANNEL); |
| quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, output_info); |
| |
| _mm_gemmlowp = std::make_unique<CpuGemmLowpMatrixMultiplyCore>(); |
| _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<ITensorInfo *>(), 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<CpuGemm>(); |
| _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_gemm::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<ITensorInfo *>(), 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>(); |
| 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 = src->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 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<kernels::CpuWeightsReshapeKernel>(); |
| _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<kernels::CpuIm2ColKernel>(); |
| _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_gemm::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<kernels::CpuCol2ImKernel>(); |
| _col2im_kernel->configure(gemm_output_to_use, dst, Size2D(conv_w, conv_h)); |
| } |
| else |
| { |
| // Configure reshape layer |
| _reshape_kernel = std::make_unique<kernels::CpuReshapeKernel>(); |
| _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_gemm::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_gemm::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<ITensorInfo *>(), 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); |
| 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, 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_gemm::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 |