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review.mlplatform.org / ml / ComputeLibrary / 1e91d71aaadd2154a708de1bc95f3c937b1e718a / . / src / cpu / operators / CpuGemmConv2d.cpp
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/*
* 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