diff options
Diffstat (limited to 'src/runtime/cpu/operators/CpuGemmConv2d.cpp')
| -rw-r--r-- | src/runtime/cpu/operators/CpuGemmConv2d.cpp | 612 |
1 files changed, 0 insertions, 612 deletions
diff --git a/src/runtime/cpu/operators/CpuGemmConv2d.cpp b/src/runtime/cpu/operators/CpuGemmConv2d.cpp deleted file mode 100644 index a81dd8a661..0000000000 --- a/src/runtime/cpu/operators/CpuGemmConv2d.cpp +++ /dev/null @@ -1,612 +0,0 @@ -/* - * 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/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/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 <set> -#include <tuple> - -using namespace arm_compute::misc::shape_calculator; -using namespace arm_compute::experimental; - -namespace arm_compute -{ -namespace cpu -{ -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) -{ - 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)); - - // 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); - - // 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)); - - 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) -{ - 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); - - 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)); - - 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<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; - configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, act_info, enable_fast_math, gemm_3d_depth); - - 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::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; - 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, enable_fast_math, 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 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); - 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) - { - // 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(); - - // Prepare GEMM - 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; -} -} // namespace cpu -} // namespace arm_compute
\ No newline at end of file |