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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 <iomanip>
#include <iostream>
#include "fbgemm/Fbgemm.h"
namespace fbgemm {
template <typename T, typename accT>
PackAMatrix<T, accT>::PackAMatrix(
matrix_op_t trans,
int32_t nRow,
int32_t nCol,
const T* smat,
int32_t ld,
inpType* pmat,
int groups,
const BlockingFactors* params)
: PackMatrix<PackAMatrix<T, accT>, T, accT>(
nRow,
nCol,
pmat,
groups,
params),
trans_(trans),
smat_(smat),
ld_(ld) {
if (!cpuinfo_initialize()) {
throw std::runtime_error("Failed to initialize cpuinfo!");
}
if ((!fbgemmHasAvx512VnniSupport() && !fbgemmHasAvx512Support() &&
!fbgemmHasAvx2Support())) {
assert(0 && "unknown architecure");
}
if (params) {
BaseType::brow_ = params->MCB;
BaseType::bcol_ = params->KCB;
row_interleave_B_ = params->ROW_INTERLEAVE;
} else {
if (fbgemmHasAvx512VnniSupport()) {
BaseType::brow_ = PackingTraits<T, accT, inst_set_t::avx512_vnni>::MCB;
BaseType::bcol_ = PackingTraits<T, accT, inst_set_t::avx512_vnni>::KCB;
row_interleave_B_ =
PackingTraits<T, accT, inst_set_t::avx512_vnni>::ROW_INTERLEAVE;
} 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 {
// AVX2
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;
}
}
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));
}
}
template <typename T, typename accT>
void PackAMatrix<T, accT>::pack(const block_type_t& block) {
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_};
BaseType::packedBlock(block_p);
bool tr = (trans_ == matrix_op_t::Transpose);
T* out = BaseType::getBuf();
if (tr) {
// TODO: should print warning because this path is not optimized yet
for (int i = block.row_start; i < block.row_start + block.row_size; ++i) {
int buf_idx = i - block.row_start;
for (int j = block.col_start; j < block.col_start + block.col_size; ++j) {
T val = smat_[i + j * ld_];
out[buf_idx * BaseType::blockColSize() + (j - block.col_start)] = val;
}
// zero fill
// Please note that we zero fill, not zero_pt fill, because for
// requantization original, i.e., not padded, dimensions are used. If we
// were to use padded dimensions for requantization, we would zero_pt
// fill.
// For example, consider the following dot product:
// A = .3(5-15), .3(20-15) //.3 is scale and 15 is zero_pt
// B = .4(1+10), .4(4+10) // .4 is scale and -10 is zero_pt
//
// numElements(A) = 2 and numElements(B) = 2
//
// Dot product is (real): -3*4.4+1.5*5.6 = -4.8
// Dot product is (quantized): 5*1+20*4 = 85
//
// requantization: .3*.4(85 - (5+20)*(-10) - (1+4)*(15) +
// numElements(A)*(15)(-10)) = -4.8
//
// In the above adding one more element zero in the quantized domain,
// i.e., the quantized vectors become:
// A_q = 5, 20, 0
// B_q = 1, 4, 0
//
// and requantization with numElements(A) = 2 will produce the same
// answer (-4.8).
//
// Also in the above adding one more element zero_pt in the quantized
// domain, i.e., the quantized vectors become:
// A_q = 5, 20, 15
// B_q = 1, 4, -10
//
// and requantization with numElements(A) = 3 will produce the same
// answer (-4.8).
for (int j = block.col_size; j < block_p.col_size; ++j) {
out[buf_idx * BaseType::blockColSize() + j] = 0;
}
}
} else {
for (int i = block.row_start; i < block.row_start + block.row_size; ++i) {
int buf_idx = i - block.row_start;
memcpy(
out + buf_idx * BaseType::blockColSize(),
smat_ + i * ld_ + block.col_start,
block.col_size * sizeof(T));
// zero fill
for (int j = block.col_size; j < block_p.col_size; ++j) {
out[buf_idx * BaseType::blockColSize() + j] = 0;
}
}
}
}
template <typename T, typename accT>
int32_t PackAMatrix<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 PackAMatrix<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 class PackAMatrix<uint8_t, int32_t>;
template class PackAMatrix<uint8_t, int16_t>;
} // namespace fbgemm
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