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/*
* Copyright (c) Facebook, Inc. and its affiliates.
* All rights reserved.
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <cpuinfo.h>
#include <cassert>
#include <cmath>
#include <cstring>
#include <iomanip>
#include <iostream>
#include <stdexcept>
#include "OptimizedKernelsAvx2.h"
#include "fbgemm/Fbgemm.h"
#include "fbgemm/QuantUtilsAvx2.h"
namespace fbgemm {
template <typename T, typename accT>
PackAWithQuantRowOffset<T, accT>::PackAWithQuantRowOffset(
matrix_op_t trans,
int32_t nRow,
int32_t nCol,
const float* smat,
int32_t ld,
inpType* pmat,
float scale,
int32_t zero_pt,
int groups,
int32_t* row_offset,
const BlockingFactors* params)
: PackMatrix<PackAWithQuantRowOffset<T, accT>, T, accT>(
nRow,
nCol,
pmat,
groups,
params),
trans_(trans),
smat_(smat),
ld_(ld),
scale_(scale),
zero_pt_(zero_pt),
row_offset_(row_offset) {
if (!cpuinfo_initialize()) {
throw std::runtime_error("Failed to initialize cpuinfo!");
}
rowOffsetAllocatedHere = false;
if (params) {
if (fbgemmHasAvx512Support() || fbgemmHasAvx2Support()) {
BaseType::brow_ = params->MCB;
BaseType::bcol_ = params->KCB;
row_interleave_B_ = params->ROW_INTERLEAVE;
} else {
// TODO: Have default slower path
assert(0 && "unsupported architecure");
}
} else {
if (fbgemmHasAvx512Support()) {
BaseType::brow_ = PackingTraits<T, accT, inst_set_t::avx512>::MCB;
BaseType::bcol_ = PackingTraits<T, accT, inst_set_t::avx512>::KCB;
row_interleave_B_ =
PackingTraits<T, accT, inst_set_t::avx512>::ROW_INTERLEAVE;
} else if (fbgemmHasAvx2Support()) {
BaseType::brow_ = PackingTraits<T, accT, inst_set_t::avx2>::MCB;
BaseType::bcol_ = PackingTraits<T, accT, inst_set_t::avx2>::KCB;
row_interleave_B_ =
PackingTraits<T, accT, inst_set_t::avx2>::ROW_INTERLEAVE;
} else {
// TODO: Have default slower path
assert(0 && "unknown architecure");
}
}
if (BaseType::numCols() % groups != 0) {
throw std::runtime_error(
"groups = " + std::to_string(groups) +
" does not divide numCols = " + std::to_string(BaseType::numCols()));
}
if (pmat) {
BaseType::buf_ = pmat;
} else {
BaseType::bufAllocatedHere_ = true;
BaseType::buf_ = (T*)fbgemmAlignedAlloc(
64, BaseType::brow_ * BaseType::bcol_ * sizeof(T));
}
if (!row_offset_) {
rowOffsetAllocatedHere = true;
row_offset_ = reinterpret_cast<int32_t*>(
fbgemmAlignedAlloc(64, BaseType::brow_ * sizeof(accT)));
}
}
template <typename T, typename accT>
void PackAWithQuantRowOffset<T, accT>::pack(const block_type_t& block) {
// assert(block.row_start % BaseType::blockRowSize() == 0);
assert(block.row_size <= BaseType::blockRowSize());
assert(block.col_size <= BaseType::blockColSize());
block_type_t block_p = {block.row_start,
block.row_size,
block.col_start,
(block.col_size + row_interleave_B_ - 1) /
row_interleave_B_ * row_interleave_B_};
assert(block_p.col_size <= BaseType::blockColSize());
BaseType::packedBlock(block_p);
T* out = BaseType::getBuf();
bool tr = (trans_ == matrix_op_t::Transpose);
// accumulate into row offset?
bool row_offset_acc =
(block.col_start % (this->numCols() / this->numGroups())) != 0;
int32_t* row_offset_buf = getRowOffsetBuffer();
float* smat_transposed = new float[block.row_size * block.col_size];
if (tr) {
transpose_simd(
block.col_size,
block.row_size,
smat_ + block.col_start * ld_ + block.row_start,
ld_,
smat_transposed,
block.col_size);
}
const float* smat_temp =
tr ? smat_transposed : smat_ + block.row_start * ld_ + block.col_start;
int32_t ld_temp = tr ? block.col_size : ld_;
static_assert(
std::is_same<T, uint8_t>::value,
"PackAWithQuantRowOffset<T, accT>::pack only works for T == uint8_t");
// Only scale and zero points are used in QuantizeAvx2
TensorQuantizationParams qparams;
qparams.scale = scale_;
qparams.zero_point = zero_pt_;
for (int i = 0; i < block.row_size; ++i) {
QuantizeAvx2(
smat_temp + i * ld_temp,
out + i * BaseType::blockColSize(),
block.col_size,
qparams);
int32_t row_sum = row_offset_acc ? row_offset_buf[i] : 0;
row_sum += reduceAvx2(out + i * BaseType::blockColSize(), block.col_size);
row_offset_buf[i] = row_sum;
// zero fill
// Please see the comment in PackAMatrix.cc on zero vs zero_pt fill.
for (int j = block.col_start + block.col_size; j < block_p.col_size; ++j) {
out[i * BaseType::blockColSize() + j] = 0;
}
}
delete[] smat_transposed;
}
template <typename T, typename accT>
int32_t PackAWithQuantRowOffset<T, accT>::addr(int32_t r, int32_t c) const {
int32_t block_row_id = r / BaseType::blockRowSize();
int32_t brow_offset = (block_row_id * BaseType::blockCols()) *
(BaseType::blockRowSize() * BaseType::blockColSize());
int32_t block_col_id = c / BaseType::blockColSize();
int32_t bcol_offset =
block_col_id * BaseType::blockRowSize() * BaseType::blockColSize();
int32_t block_offset = brow_offset + bcol_offset;
int32_t inblock_offset =
(r % BaseType::blockRowSize()) * BaseType::blockColSize() +
(c % BaseType::blockColSize());
int32_t index = block_offset + inblock_offset;
return index;
}
template <typename T, typename accT>
void PackAWithQuantRowOffset<T, accT>::printPackedMatrix(std::string name) {
std::cout << name << ":"
<< "[" << BaseType::numPackedRows() << ", "
<< BaseType::numPackedCols() << "]" << std::endl;
T* out = BaseType::getBuf();
for (auto r = 0; r < BaseType::numPackedRows(); ++r) {
for (auto c = 0; c < BaseType::numPackedCols(); ++c) {
T val = out[addr(r, c)];
if (std::is_integral<T>::value) {
// cast to int64 because cout doesn't print int8_t type directly
std::cout << std::setw(5) << static_cast<int64_t>(val) << " ";
} else {
std::cout << std::setw(5) << val << " ";
}
}
std::cout << std::endl;
}
std::cout << std::endl;
}
template <typename T, typename accT>
int PackAWithQuantRowOffset<T, accT>::rowOffsetBufferSize(
const BlockingFactors* params) {
if (cpuinfo_initialize()) {
if (params) {
return params->MCB;
} else {
if (fbgemmHasAvx512Support()) {
return PackingTraits<T, accT, inst_set_t::avx512>::MCB;
} else if (fbgemmHasAvx2Support()) {
return PackingTraits<T, accT, inst_set_t::avx2>::MCB;
} else {
assert(0 && "unsupported architecture");
return -1;
}
}
} else {
throw std::runtime_error("Failed to initialize cpuinfo!");
}
}
template class PackAWithQuantRowOffset<uint8_t, int32_t>;
} // namespace fbgemm
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