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// Copyright 2015 Google Inc. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// unpack.h: unpacking the result blocks computed by compute.h,
// storing them into the destination matrix.
#ifndef GEMMLOWP_INTERNAL_UNPACK_H_
#define GEMMLOWP_INTERNAL_UNPACK_H_
#include "allocator.h"
#include "block_params.h"
#include "output.h"
#include "pack.h"
#include <cmath>
namespace gemmlowp {
class PackedResult {
public:
PackedResult(Allocator* _allocator, const BlockParams& _block_params)
: allocator_(_allocator), block_params_(_block_params) {
matrix_handle_ = allocator_->Reserve<std::int32_t>(block_params_.l2_rows *
block_params_.l2_cols);
}
~PackedResult() {}
MatrixMap<std::int32_t, MapOrder::ColMajor> Map() {
return MatrixMap<std::int32_t, MapOrder::ColMajor>(
allocator_->GetPointer<std::int32_t>(matrix_handle_),
block_params_.l2_rows, block_params_.l2_cols, block_params_.l2_rows);
}
MatrixMap<const std::int32_t, MapOrder::ColMajor> Map() const {
return MatrixMap<const std::int32_t, MapOrder::ColMajor>(
allocator_->GetPointer<const std::int32_t>(matrix_handle_),
block_params_.l2_rows, block_params_.l2_cols, block_params_.l2_rows);
}
private:
Allocator* allocator_;
Allocator::Handle matrix_handle_;
const BlockParams& block_params_;
};
template <std::uint32_t numerator, std::uint32_t denominator>
std::int32_t RoundingMultiplyByConstantFraction(std::int32_t x) {
if (numerator == denominator) {
return x;
}
// We'll use only signed arithmetic here. This is
// simpler (since this function operates on signed int32's) and
// more friendly to ARM NEON, where this allows us to use the
// VQRDMULH instruction.
static const std::int32_t int_quotient =
(numerator + denominator / 2) / denominator;
static const std::int32_t remaining_numerator =
numerator - int_quotient * denominator;
static const std::int32_t scaled_remaining_numerator =
static_cast<std::int32_t>(
(static_cast<std::int64_t>(remaining_numerator) * (1ll << 31)) /
denominator);
const std::int64_t scaled_remaining_product =
static_cast<std::int64_t>(x) *
static_cast<std::int64_t>(scaled_remaining_numerator);
const std::int32_t scaled_remaining_product_nudge =
(scaled_remaining_product > 0 ? 1 : -1) * (1 << 30);
const std::int32_t remaining_product = static_cast<std::int32_t>(
(scaled_remaining_product + scaled_remaining_product_nudge) / (1u << 31));
return x * int_quotient + remaining_product;
}
template <typename BitDepthParams, typename ResultBlockType,
typename PackedResultType, typename LhsOffset, typename RhsOffset,
typename OutputPipelineType>
struct UnpackResultImplGeneric {
static void Unpack(ResultBlockType* dst, const PackedResultType& src,
int depth, const std::int32_t* lhs_sums_of_each_slice,
const std::int32_t* rhs_sums_of_each_slice,
const LhsOffset& lhs_offset, const RhsOffset& rhs_offset,
const OutputPipelineType& output_pipeline) {
auto src_map = src.Map();
// No top-level blocking in the depth dimension at the moment.
// Too much loss of precision.
const int kLhsBits = BitDepthParams::LhsBitDepth::kBits;
const int kRhsBits = BitDepthParams::RhsBitDepth::kBits;
const std::int32_t kLhsMax = (1 << kLhsBits) - 1;
const std::int32_t kRhsMax = (1 << kRhsBits) - 1;
OutputPipelineExecutor<OutputPipelineType, FragmentInt32x1x1>
output_pipeline_executor(output_pipeline);
for (int c = 0; c < dst->cols(); c++) {
for (int r = 0; r < dst->rows(); r++) {
// To understand this code, read
// doc/low-precision.txt
// doc/less-than-8-bit.txt
// We have 4 terms to sum: xx, x1, 1x, 11.
// In case of requantization, we first need to scale them back
// to the original scale, using RoundingMultiplyByConstantFraction.
std::int32_t raw_xx = src_map(r, c);
std::int32_t raw_x1 = lhs_sums_of_each_slice[r] * rhs_offset(c);
std::int32_t raw_1x = rhs_sums_of_each_slice[c] * lhs_offset(r);
std::int32_t term_xx =
RoundingMultiplyByConstantFraction<255 * 255, kLhsMax * kRhsMax>(
raw_xx);
std::int32_t term_x1 =
RoundingMultiplyByConstantFraction<255, kLhsMax>(raw_x1);
std::int32_t term_1x =
RoundingMultiplyByConstantFraction<255, kRhsMax>(raw_1x);
std::int32_t term_11 = lhs_offset(r) * rhs_offset(c) * depth;
// Sum the 4 terms.
FragmentInt32x1x1 sum = term_xx + term_x1 + term_1x + term_11;
output_pipeline_executor.Execute(sum, dst, r, c);
}
}
}
};
template <typename BitDepthParams, typename ResultBlockType,
typename PackedResultType, typename LhsOffset, typename RhsOffset,
typename OutputPipelineType>
struct UnpackResultImpl
: UnpackResultImplGeneric<BitDepthParams, ResultBlockType, PackedResultType,
LhsOffset, RhsOffset, OutputPipelineType> {};
template <typename BitDepthParams, typename ResultBlockType,
typename PackedResultType, typename LhsOffset, typename RhsOffset,
typename OutputPipelineType>
void UnpackResult(ResultBlockType* dst, const PackedResultType& src, int depth,
const std::int32_t* lhs_sums_of_each_slice,
const std::int32_t* rhs_sums_of_each_slice,
const LhsOffset& lhs_offset, const RhsOffset& rhs_offset,
const OutputPipelineType& output_pipeline) {
ScopedProfilingLabel label("unpack");
UnpackResultImpl<BitDepthParams, ResultBlockType, PackedResultType,
LhsOffset, RhsOffset, OutputPipelineType>::Unpack(
dst, src, depth, lhs_sums_of_each_slice, rhs_sums_of_each_slice,
lhs_offset, rhs_offset, output_pipeline);
}
} // namespace gemmlowp
#ifdef GEMMLOWP_NEON
#include "unpack_neon.h"
#endif
#endif // GEMMLOWP_INTERNAL_UNPACK_H_