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Diffstat (limited to 'src/cpu/operators/CpuGemmConv2d.cpp')
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diff --git a/src/cpu/operators/CpuGemmConv2d.cpp b/src/cpu/operators/CpuGemmConv2d.cpp new file mode 100644 index 0000000000..55d950ff4a --- /dev/null +++ b/src/cpu/operators/CpuGemmConv2d.cpp @@ -0,0 +1,992 @@ +/* + * Copyright (c) 2021-2024 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/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" + +#include "src/common/utils/Log.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/core/helpers/Utils.h" +#include "src/cpu/kernels/CpuCol2ImKernel.h" +#include "src/cpu/kernels/CpuIm2ColKernel.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/operators/CpuReshape.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 +{ + +/** @section note_CpuGemmConv2d_weight_transformation Weight Transformations in CpuGemmConv2d + * + * A. Terminology + * Throughout CpuGemmConv2d, we use the following terms in ways that may differ from other operators / kernels: + * - "Transform" or "Reshape" of the weights: they both mean all the operations that we perform on the weight + * tensor up until they are consumed by gemm (CpuGemm or CpuGemmLowpMatrixMultiplyCore) + * Note that the specific gemm operator may perform further transformations on the weights, but the + * transformations here only mean those performed in CpuGemmConv2d + * - "Transpose" of weights: The @ref CpuTranspose operation. I.e. transpose of the weights' lowest two + * dimensions + * + * B. Gemm-based conv2d + * We want to convert the 2d convolution op (ignoring bias): + * dst = conv2d(src, weight) + * into a matrix multiplication op: + * gemm_dst = gemm(lhs, rhs) + * + * E.g.: For data layout NHWC + * 3 (hi) <----------> (lo) 0 + * src.shape = [batch, in_h , in_w, in_c] + * weight.shape = [out_c, k_h , k_w, in_c] + * dst.shape = [batch, out_h, out_w, out_c] + * + * This requires three transformations: + * * src -> lhs, transform conv input to gemm lhs; gemm_lhs is a 2d matrix where each row (or column, + * depending on the convention) is a linearized "patch" of the conv_input that corresponds to + * the receptive field of the corresponding output element. + * The convention is to use "column", but to disambiguate from the column vector of a matrix, + * in this documentation we shall use "patch". + * This transform is called im2col (for details see @ref CpuIm2ColKernel) + * * weight -> rhs, transform conv weight to gemm rhs, known as weight transform/reshape (wt) + * * gemm_dst -> dst, transform gemm output back to conv output, known as col2im (for details see + * @ref CpuCol2ImKernel) + * + * This section focuses on the weight transformation and assumes the im2col is already performed + * + * C. Weight Transformation + * After im2col, assume: lhs.shape = [num_patch, patch_size], + * where patch_size is the number of elements in a "patch": patch_size = k_h * k_w * in_c + * num_patch is the number of patches; we can ignore it here (for details see @ref CpuIm2ColKernel) + * + * After wt, rhs should have the shape: rhs = [patch_size, out_c] + * + * Therefore, the weight transformation consists of two steps: + * 1. Collapsing all 3 spatial dimensions: [out_c, k_h, k_w, in_c] -> [out_c, patch_size] + * 2. Transpose the collapsed shape: [out_c, patch_size] -> [patch_size, out_c] + * + * D. Implementation + * There are 4 paths for weight transformation + * + * 1. Path 1: Fixed weight format - no transformation + * The underlying gemm kernel may adopt fixed weight format (isVarWeightsKernel() == true), which requires + * that no weight transformation shall be performed + * Note that this no-transform requirement applies both to this op (CpuGemmConv2d) and the constituent ops, up + * until the fixed format kernels themselves + * + * 2. Path 2: Reinterpret then transpose later + * If the weight tensor has no "holes" (see @ref has_holes), there are two optimizations we can apply: + * - We can ignore the first step (collapsing of spatial dimensions) by simply re-interpreting the shape + * in TensorInfo + * - Instead of performing transpose here, we can pass the transpose flag to the underlying gemm. The gemm + * may then decide to fuse the transpose with any further transformations + * + * 3. Path 3: Reshape then transpose later + * If the weight tensor has holes, then we use a dedicated @ref CpuReshape, followed by transpose later + * + * 4. Path 4: Fused reshape and transpose + * This is only for quantized types for now (TODO: Remove (COMPMID-6596)). We fall back to a legacy + * non-optimized kernel @ref CpuWeightsReshapeKernel to perform a fused reshape + transpose + * + * Path 1 is the long term solution that we shall migrate to once (if) we adopt fixed weight format for all gemm + * kernels. + * In the short term, Path 2 is the favored, more performant path. + */ + +namespace +{ +/** Initialize reshaped / transformed weight info + * + * @param[in] weights Input weights + * @param[out] reshaped_weights Transformed weights + */ +void initialize_reshaped_weight_info(const ITensorInfo &weights, ITensorInfo &reshaped_weights) +{ + auto_init_if_empty(reshaped_weights, weights); + if (is_data_type_quantized(weights.data_type())) + { + // WT method: FusedReshapeAndTranspose + reshaped_weights.set_tensor_shape(compute_weights_reshaped_shape(weights, /* has_bias */ false)); + } + else + { + TensorShape collapsed_weights = weights.tensor_shape(); + collapsed_weights.collapse(3); + reshaped_weights.set_tensor_shape(collapsed_weights); + } +} +} // namespace + +CpuGemmConv2d::WeightTransformMethod CpuGemmConv2d::get_wt_method(const ITensorInfo &weights) +{ + // TODO: Extend ReinterpretThenTranspose support for quantized data types COMPMID-6596 + if (is_data_type_quantized(weights.data_type())) + { + return WeightTransformMethod::FusedReshapeAndTranspose; + } + return has_holes(weights) ? WeightTransformMethod::ReshapeThenTranspose + : WeightTransformMethod::ReinterpretThenTranspose; +} + +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(nullptr), + _weights_reshape_and_transpose_kernel(nullptr), + _im2col_kernel(), + _mm_gemm(), + _mm_gemmlowp(), + _col2im_kernel(), + _reshape(), + _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), + _wt_method(WeightTransformMethod::ReshapeThenTranspose), + _run_wt(true), + _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)); + + // 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, fixed_format, weight_format, + false /* pretranspose_B. TODO: COMPMID-6596 */)); + + 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 + { + // 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, fixed_format, weight_format, + true /*pretranspose_B. For fp gemm (wt path 1 - 3), We always pretranspose B (for wt path 1 this + flag is ignored)*/); + // Configure matrix multiply function + _mm_gemm = std::make_unique<CpuGemm>(); + _mm_gemm->configure(src, weights, biases, dst, 1.0f, 1.0f, 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(); + + 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, + false /* pretranspose_B. TODO: COMPMID-6596 */)); + } + else + { + // 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, fixed_format, weight_format, + true /*pretranspose_B. For fp gemm (wt path 1 - 3), We always pretranspose B (for wt path 1 this + flag is ignored)*/); + + // Perform validation step on Matrix multiply function + return CpuGemm::validate(src, weights, biases, dst, 1.0f, 1.0f, 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_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); + + _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(); + + // Initialize reshaped weights + initialize_reshaped_weight_info(*weights, _weights_reshaped); + + // Create tensor to store im2col reshaped inputs + if (!_skip_im2col) + { + const int block_by = arm_compute::block_by(weights_info.weight_format()); + unsigned int input_pad_right = 0; + if (block_by > 1) + { + input_pad_right = + (src->dimension(idx_channel) % block_by) == 0 ? 0 : block_by - (src->dimension(idx_channel) % block_by); + } + // Configure + _im2col_kernel = std::make_unique<kernels::CpuIm2ColKernel>(); + _im2col_kernel->configure(src, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation, + num_groups, input_pad_right); + + // Update GEMM input + gemm_input_to_use = &_im2col_output; + } + + const unsigned int mat_weights_cols = weights->dimension(idx_kernels); + + // 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; + /** @section note_CpuGemmConv2d_weight_use_in_configure Which weights tensor should we use to configure gemm + * + * A. The problem: + * In principle, we should use the weights tensor corresponding to the weights transformation path. I.e.: + * - If no weight transformation (_run_wt == false): Use original weights + * - else: Use transformed weights + * However in practice we have a dilemma: + * - We need to know _run_wt before we can configure gemm with the corresponding weights, but + * - _run_wt depends on isVarWeightsKernel(), which is only known after gemm is configured + * + * B. The decision: + * To simplify the matter, we decide to always use the transformed weights, regardless of _run_wt + * + * This decision requires the following conditions: + * 1. The underlying gemm where isVarWeightsKernel() == true, must guarantee that: + * A. Ignore the flag to transpose weights (GEMMInfo::pretranspose_B) + * B. Use weights/B tensor passed to it at prepare() or run() instead of that passed at configure() + * 2. CpuGemmConv2d where isVarWeightsKernel() == true, must guarantee that: + * A. Pass original weights instead of reshaped or reinterpreted weights + * + * C. Future actions: + * Condition 2 is a given, based on our implementation. + * If condition 1 cannot hold, we must make changes to the underlying gemm to: + * 1. Either expose isVarWeightsKernel() before gemm is configured somehow, or + * 2. Take in an additional "original_weights" tensor info at configure + */ + 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()); + + // Can only decide isVarWeightsKernel after gemm is configured + _run_wt = !isVarWeightsKernel(); + + 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 = std::make_unique<CpuReshape>(); + _reshape->configure(gemm_output_to_use, dst); + } + + // Check lifetime + _aux_mem[Im2ColOutput] = + MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size()); + // Add WeightsReshaped memory requirement to workspace + // Note that in case of WeightTransformMethod::ReinterpretThenTranspose, we do not need to allocate this memory + // However since we cannot determine weight transformation method until prepare (see prepare()), we will have to + // settle with allocating more + if (_run_wt) + { + // Check if GEMM transforms weights + // If weight is further transformed by underlying gemm after ReshapeThenTranspose then we can free + // WeightsReshaped in prepare + // Otherwise WeightsReshaped is the final transformation of weights and needs to persist + bool gemm_trans_wei = _aux_mem[GemmAsmPretransposedRHS].size > 0; + gemm_trans_wei = _mm_gemm != nullptr ? _aux_mem[GemmTransposed1xWRHS].size > 0 : gemm_trans_wei; + gemm_trans_wei = _mm_gemmlowp != nullptr ? _aux_mem[GemmLowpTransposed1xWRHS].size > 0 : gemm_trans_wei; + + _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; + + /** @section note_CpuGemmConv2d_weight_use_in_has_opt_impl Which weights tensor should we use for has_opt_impl + * + * For the pretranspose_B flag, this shares a similar problem and thus the same decision as that of + * @ref note_CpuGemmConv2d_weight_use_in_configure + * + * But for the weights, we shall always use the original instead of reshaped weights here + */ + 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, + fixed_format, weights_info.weight_format(), true /* pretranspose_B */); + + 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); + + // Initialize reshaped weights + initialize_reshaped_weight_info(*weights, weights_reshaped_info); + // No need to call CpuReshape::validate() or CpuTranspose::validate() as the dst info is auto-configured from the + // src + weights_to_use = &weights_reshaped_info; + + if (!skip_im2col) + { + const int block_by = arm_compute::block_by(weights_info.weight_format()); + int input_pad_right = 0; + if (block_by > 1) + { + input_pad_right = + (src->dimension(idx_channel) % block_by) == 0 ? 0 : block_by - (src->dimension(idx_channel) % block_by); + mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * + (weights->dimension(idx_channel) + input_pad_right); + } + + // 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, num_groups, input_pad_right)); + 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; + + // See note_CpuGemmConv2d_weight_use_in_configure regarding the choice of the weights + 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); + + bool out_has_padding = _skip_col2im && (dst->info()->padding().bottom != 0 || dst->info()->padding().top != 0); + if (!_skip_im2col) + { + // Run input reshaping + unsigned int hint_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); + unsigned int x_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); + unsigned int hint_dim_iterations = _im2col_kernel->window().num_iterations(hint_dim); + unsigned int x_dim_iterations = _im2col_kernel->window().num_iterations(x_dim); + if (hint_dim_iterations < NEScheduler::get().num_threads() && x_dim_iterations > hint_dim_iterations) + { + hint_dim = x_dim; + } + ITensorPack pack = {{TensorType::ACL_SRC, src}, {TensorType::ACL_DST, im2col_output.get()}}; + NEScheduler::get().schedule_op(_im2col_kernel.get(), hint_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; + } + + ITensorPack gemm_pack = tensors; + gemm_pack.add_const_tensor(TensorType::ACL_SRC_0, gemm_input_to_use); + gemm_pack.add_tensor(TensorType::ACL_DST, gemm_output_to_use); + // Allocate reshaped weights if required + auto weights = gemm_pack.get_const_tensor(TensorType::ACL_SRC_1); + ARM_COMPUTE_ERROR_ON_NULLPTR(weights); + // Re-interpreted weights. Only tensor shape is changed. Only memory import, no allocation + const bool use_reinterpreted_wei = (_run_wt && _wt_method == WeightTransformMethod::ReinterpretThenTranspose); + CpuAuxTensorHandler reinterpreted_wei( + _weights_reshaped, *weights, + /* import only if we chose the ReinterpretThenTranspose path, because otherwise the weight may have been freed */ + !use_reinterpreted_wei); + + const bool use_reshaped_wei = (_run_wt && (_wt_method == WeightTransformMethod::ReshapeThenTranspose || + _wt_method == WeightTransformMethod::FusedReshapeAndTranspose)); + CpuAuxTensorHandler reshaped_wei(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors, + false /* pack_inject */, !use_reshaped_wei /* bypass_alloc */, + !use_reshaped_wei /* bypass_import */ + ); + // Update the weights to use if it has been reshaped + if (use_reinterpreted_wei) + { + gemm_pack.add_const_tensor(TensorType::ACL_SRC_1, reinterpreted_wei.get()); + } + else if (use_reshaped_wei) + { + gemm_pack.add_const_tensor(TensorType::ACL_SRC_1, reshaped_wei.get()); + } + + // Runs CpuGemm or CpuGemmLowpMatrixMultiplyCore functions + _is_quantized ? _mm_gemmlowp->run(gemm_pack) : _mm_gemm->run(gemm_pack); + + // 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}}; + _reshape->run(pack); + } + } + else if (out_has_padding) + { + ITensorPack pack = {{TensorType::ACL_SRC, gemm_output_to_use}, {TensorType::ACL_DST, dst}}; + _reshape->run(pack); + } +} + +void CpuGemmConv2d::prepare(ITensorPack &tensors) +{ + if (!_is_prepared) + { + auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); + // Determine which weights reshape path to take + // Note that this decision can only occur at prepare instead of configure because it relies on the presence of + // any holes in the weight tensor, which may change after configure (e.g. from extending padding) + if (_run_wt) + { + _wt_method = get_wt_method(*(weights->info())); + switch (_wt_method) + { + case (WeightTransformMethod::FusedReshapeAndTranspose): + { + ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("Perform weight transformation: FusedReshapeAndTranspose"); + _weights_reshape_and_transpose_kernel = std::make_unique<kernels::CpuWeightsReshapeKernel>(); + _weights_reshape_and_transpose_kernel->configure(weights->info(), nullptr, &_weights_reshaped); + break; + } + case (WeightTransformMethod::ReshapeThenTranspose): + { + ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("Perform weight transformation: ReshapeThenTranspose"); + _weights_reshape = std::make_unique<CpuReshape>(); + _weights_reshape->configure(weights->info(), &_weights_reshaped); + break; + } + case (WeightTransformMethod::ReinterpretThenTranspose): + { + ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("Perform weight transformation: ReinterpretThenTranspose"); + // Nothing to configure + break; + } + default: + { + ARM_COMPUTE_ERROR("Unsupported weight transform method"); + } + } + } + else + { + ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("No weight transformation is performed"); + } + ITensorPack gemm_pack = tensors; + // Allocate reshaped weights if required + CpuAuxTensorHandler reinterpreted_wei( + _weights_reshaped, + *weights); // Re-interpreted weights. Only tensor shape is changed. No allocation + CpuAuxTensorHandler reshaped_wei(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors); + // Run weights reshape if required + if (_run_wt) + { + switch (_wt_method) + { + case (WeightTransformMethod::FusedReshapeAndTranspose): + { + ITensorPack pack = {{TensorType::ACL_SRC, weights}, {TensorType::ACL_DST, reshaped_wei.get()}}; + NEScheduler::get().schedule_op(_weights_reshape_and_transpose_kernel.get(), Window::DimW, + _weights_reshape_and_transpose_kernel->window(), pack); + weights->mark_as_unused(); + gemm_pack.add_const_tensor(TensorType::ACL_SRC_1, reshaped_wei.get()); + break; + } + case (WeightTransformMethod::ReshapeThenTranspose): + { + ITensorPack pack = {{TensorType::ACL_SRC, weights}, {TensorType::ACL_DST, reshaped_wei.get()}}; + _weights_reshape->run(pack); + weights->mark_as_unused(); + gemm_pack.add_const_tensor(TensorType::ACL_SRC_1, reshaped_wei.get()); + break; + } + case (WeightTransformMethod::ReinterpretThenTranspose): + { + gemm_pack.add_const_tensor(TensorType::ACL_SRC_1, reinterpreted_wei.get()); + // Nothing to run + break; + } + default: + { + ARM_COMPUTE_ERROR("Unsupported weight transform method"); + } + } + } + _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 |