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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.
*/
#pragma once
template <typename outT, typename inT, typename nextOPType>
template<inst_set_t instSet>
inline int memCopy<outT, inT, nextOPType>::f(outT* out, inT* inp,
const block_type_t& block, int ld_out, int ld_in) const {
static_assert(
std::is_same<outT, inT>::value,
"input and output data type must be of same type");
// only copy if destination is not the same as source
if (out + block.row_start*ld_out + block.col_start != inp) {
for (int i = block.row_start; i < block.row_start + block.row_size; ++i) {
memcpy(out + block.col_start + i * ld_out,
inp + (i - block.row_start) * ld_in,
block.col_size*sizeof(inT));
}
}
return nextop_.template f<instSet>(out, out, block, ld_out, ld_out);
}
template <typename outT, typename inT, typename nextOPType>
template<inst_set_t instSet>
inline int DoSpmdmOnInpBuffer<outT, inT, nextOPType>::f(outT* out, inT* inp,
const block_type_t& block, int ld_out, int ld_in) const {
assert(B_csc_.NumOfCols() % groups_ == 0);
int n_per_group = B_csc_.NumOfCols() / groups_;
int g = block.col_start / n_per_group;
B_csc_.SpMDM(block, A_ + g * B_csc_.NumOfRows(), lda_, true, inp, ld_in);
return nextop_.template f<instSet>(out, inp, block, ld_out, ld_in);
}
template <bool FUSE_RELU, typename outT, typename inT, typename nextOPType>
template <inst_set_t instSet>
inline int ReQuantizeOutput<FUSE_RELU, outT, inT, nextOPType>::f(
outT* out,
inT* inp,
const block_type_t& block,
int ld_out,
int ld_in) const {
static_assert(
std::is_same<inT, int32_t>::value,
"input data type must be of int32_t type");
if (instSet == inst_set_t::anyarch) {
for (int i = block.row_start; i < block.row_start + block.row_size; ++i) {
for (int j = block.col_start; j < block.col_start + block.col_size; ++j) {
inT raw = inp[(i - block.row_start) * ld_in + (j - block.col_start)];
raw -= Aq_zero_point_ * q_col_offsets_[j];
if (q_row_offsets_) {
raw -= q_row_offsets_[i - block.row_start] * Bq_zero_point_;
}
if (bias_) {
raw += bias_[j];
}
float ab = raw * C_multiplier_;
long rounded = std::lrintf(ab) + C_zero_point_;
out[i * ld_out + j] = std::max(
FUSE_RELU ? static_cast<long>(C_zero_point_) : 0l,
std::min(255l, rounded));
}
}
} else if (instSet == inst_set_t::avx2) {
if (std::is_same<outT, uint8_t>::value) {
// Adoption of implementation at QNNPACK/src/requantization/fp32-sse2.c
// using AVX2 instructions
__m256 multiplier_v = _mm256_set1_ps(C_multiplier_);
__m256i min_v = _mm256_set1_epi8(std::numeric_limits<uint8_t>::min());
__m256i max_v = _mm256_set1_epi8(std::numeric_limits<uint8_t>::max());
__m256i A_zero_point_v = _mm256_set1_epi32(Aq_zero_point_);
__m256i C_zero_point_epi16_v = _mm256_set1_epi16(C_zero_point_);
__m256i C_zero_point_epi8_v = _mm256_set1_epi8(C_zero_point_);
__m256i permute_mask_v =
_mm256_set_epi32(0x07, 0x03, 0x06, 0x02, 0x05, 0x01, 0x04, 0x00);
constexpr int VLEN = 8;
for (int i = block.row_start; i < block.row_start + block.row_size; ++i) {
std::int32_t row_offset = q_row_offsets_
? q_row_offsets_[i - block.row_start] * Bq_zero_point_
: 0;
__m256i row_offset_v = _mm256_set1_epi32(row_offset);
int j = block.col_start;
for (; j < block.col_start + (block.col_size / (VLEN * 4) * (VLEN * 4));
j += (VLEN * 4)) {
__m256i x_v = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(
inp + (i - block.row_start) * ld_in + (j - block.col_start)));
__m256i y_v = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(
inp + (i - block.row_start) * ld_in + (j - block.col_start) +
1 * VLEN));
__m256i z_v = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(
inp + (i - block.row_start) * ld_in + (j - block.col_start) +
2 * VLEN));
__m256i w_v = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(
inp + (i - block.row_start) * ld_in + (j - block.col_start) +
3 * VLEN));
//if (A_zero_pt != 0) {
__m256i col_off_v = _mm256_mullo_epi32(
A_zero_point_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(q_col_offsets_ + j)));
x_v = _mm256_sub_epi32(x_v, col_off_v);
col_off_v = _mm256_mullo_epi32(
A_zero_point_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(q_col_offsets_ + j + VLEN)));
y_v = _mm256_sub_epi32(y_v, col_off_v);
col_off_v = _mm256_mullo_epi32(
A_zero_point_v,
_mm256_loadu_si256(reinterpret_cast<const __m256i*>(
q_col_offsets_ + j + 2 * VLEN)));
z_v = _mm256_sub_epi32(z_v, col_off_v);
col_off_v = _mm256_mullo_epi32(
A_zero_point_v,
_mm256_loadu_si256(reinterpret_cast<const __m256i*>(
q_col_offsets_ + j + 3 * VLEN)));
w_v = _mm256_sub_epi32(w_v, col_off_v);
//}
// if (row_offset != 0) {
x_v = _mm256_sub_epi32(x_v, row_offset_v);
y_v = _mm256_sub_epi32(y_v, row_offset_v);
z_v = _mm256_sub_epi32(z_v, row_offset_v);
w_v = _mm256_sub_epi32(w_v, row_offset_v);
//}
if (bias_) {
x_v = _mm256_add_epi32(
x_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(bias_ + j)));
y_v = _mm256_add_epi32(
y_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(bias_ + j + VLEN)));
z_v = _mm256_add_epi32(
z_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(bias_ + j + 2 * VLEN)));
w_v = _mm256_add_epi32(
w_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(bias_ + j + 3 * VLEN)));
}
/*
* Convert int32_t input to FP32 and multiply by FP32 scale.
* Both operations involve statistically unbiased roundings (with
* default MXCSR rounding mode):
* - Large int32_t values can't be exactly represented as FP32.
* CVTDQ2PS instruction on x86 would round it according to nearest
* FP32 value with ties to even (assuming default MXCSR rounding
* mode).
* - Product of two FP32 values is generally not exactly
* representation as an FP32 value, and will be rounded to nearest
* FP32 value with ties to even with default MXCSR rounding mode.
*/
__m256 x_scaled_v =
_mm256_mul_ps(_mm256_cvtepi32_ps(x_v), multiplier_v);
__m256 y_scaled_v =
_mm256_mul_ps(_mm256_cvtepi32_ps(y_v), multiplier_v);
__m256 z_scaled_v =
_mm256_mul_ps(_mm256_cvtepi32_ps(z_v), multiplier_v);
__m256 w_scaled_v =
_mm256_mul_ps(_mm256_cvtepi32_ps(w_v), multiplier_v);
/*
* Convert scaled FP32 result to int32_t using CVTPS2DQ instruction.
* CVTPS2DQ instruction rounds result according to nearest FP32 value
* with ties to even (assuming default MXCSR rounding mode). However,
* when conversion overflows, it produces INT32_MIN as a result. For
* large positive inputs the result of conversion can become negative,
* which affects the final requantization result. Note that on x86
* SSE2 we have e.g. int32_t(float(INT32_MAX)) == INT32_MIN! This
* happens because float(INT32_MAX) rounds to 2**31, which overflows
* int32_t when it is converted back to integer.
*
* Thankfully, we can prove that overflow never happens in this
* requantization scheme. The largest positive input is INT32_MAX
* (2**31 - 1), which turns into 2**31 when converted to float. The
* largest scale value is 0x1.FFFFFEp-1. When multiplied together, the
* result is 2147483520 (compare to INT32_MAX = 2147483647), which
* fits into int32_t without overflow.
*/
__m256i x_rounded_v = _mm256_cvtps_epi32(x_scaled_v);
__m256i y_rounded_v = _mm256_cvtps_epi32(y_scaled_v);
__m256i z_rounded_v = _mm256_cvtps_epi32(z_scaled_v);
__m256i w_rounded_v = _mm256_cvtps_epi32(w_scaled_v);
/*
* Standard final sequence on x86 AVX2:
* - Pack to int16_t and saturate
* - Add zero point
* - Pack to uint8_t and saturate
* - Clamp between qmin and qmax
*/
__m256i xy_packed_v = _mm256_adds_epi16(
_mm256_packs_epi32(x_rounded_v, y_rounded_v),
C_zero_point_epi16_v);
__m256i zw_packed_v = _mm256_adds_epi16(
_mm256_packs_epi32(z_rounded_v, w_rounded_v),
C_zero_point_epi16_v);
__m256i xyzw_packed_v = _mm256_packus_epi16(xy_packed_v, zw_packed_v);
__m256i xyzw_clamped_v = _mm256_max_epu8(
FUSE_RELU ? C_zero_point_epi8_v : min_v,
_mm256_min_epu8(xyzw_packed_v, max_v));
/*
* xyzw_clamped_v has results in the following layout so we need to
* permute: x0-3 y0-3 z0-3 w0-3 x4-7 y4-7 z4-7 w4-7
*/
xyzw_clamped_v =
_mm256_permutevar8x32_epi32(xyzw_clamped_v, permute_mask_v);
/*
* 4x CVTDQ2PS
* 4x MULPS
* 4x CVTPS2DQ
* 2x PACKSSDW
* 1x PACKUSWB
* 2x PADDW
* 1x PMAXUB
* 1x PMINUB
* 1x PERMD
* ---------------------
* 20 instructions total
*/
_mm256_storeu_si256(
reinterpret_cast<__m256i*>(out + i * ld_out + j), xyzw_clamped_v);
} // j loop vectorized and unrolled 4x
for (; j < block.col_start + (block.col_size / VLEN * VLEN);
j += VLEN) {
__m256i x_v = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(
inp + (i - block.row_start) * ld_in + (j - block.col_start)));
//if (A_zero_pt != 0) {
__m256i col_off_v = _mm256_mullo_epi32(
A_zero_point_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(q_col_offsets_ + j)));
x_v = _mm256_sub_epi32(x_v, col_off_v);
//}
// if (row_offset != 0) {
x_v = _mm256_sub_epi32(x_v, row_offset_v);
//}
if (bias_) {
x_v = _mm256_add_epi32(
x_v,
_mm256_loadu_si256(
reinterpret_cast<const __m256i*>(bias_ + j)));
}
__m256 x_scaled_v =
_mm256_mul_ps(_mm256_cvtepi32_ps(x_v), multiplier_v);
__m256i x_rounded_v = _mm256_cvtps_epi32(x_scaled_v);
__m256i x_packed_v = _mm256_adds_epi16(
_mm256_packs_epi32(x_rounded_v, _mm256_setzero_si256()),
C_zero_point_epi16_v);
x_packed_v = _mm256_packus_epi16(x_packed_v, _mm256_setzero_si256());
__m256i x_clamped_v = _mm256_max_epu8(
FUSE_RELU ? C_zero_point_epi8_v : min_v,
_mm256_min_epu8(x_packed_v, max_v));
/*
* x_clamped_v has results in the following layout so we need to
* permute: x0-3 garbage0-11 x4-7 garbage12-23
*/
x_clamped_v =
_mm256_permutevar8x32_epi32(x_clamped_v, permute_mask_v);
/*
* 1x CVTDQ2PS
* 1x MULPS
* 1x CVTPS2DQ
* 1x PACKSSDW
* 1x PACKUSWB
* 1x PADDW
* 1x PMAXUB
* 1x PMINUB
* 1x PERMD
* ---------------------
* 9 instructions total
*/
_mm_storel_epi64(
reinterpret_cast<__m128i*>(out + i * ld_out + j),
_mm256_castsi256_si128(x_clamped_v));
} // j loop vectorized
for (; j < block.col_start + block.col_size; ++j) {
int32_t raw =
inp[(i - block.row_start) * ld_in + (j - block.col_start)];
// if (A_zero_pt != 0) {
raw -= Aq_zero_point_ * q_col_offsets_[j];
//}
raw -= row_offset;
if (bias_) {
raw += bias_[j];
}
float ab = raw * C_multiplier_;
long rounded = std::lrintf(ab) + C_zero_point_;
out[i * ld_out + j] = std::max(
FUSE_RELU ? static_cast<long>(C_zero_point_) : 0l,
std::min(255l, rounded));
} // j loop remainder
} // i loop
} else {
assert(0 && "Not supported yet");
}
} else {
assert(0 && "Not supported yet");
}
return nextop_.template f<instSet>(out, out, block, ld_out, ld_out);
}
template <bool FUSE_RELU, typename outT, typename inT, typename nextOPType>
template <inst_set_t instSet>
inline int ReQuantizeForFloat<FUSE_RELU, outT, inT, nextOPType>::f(
outT* out,
inT* inp,
const block_type_t& block,
int ld_out,
int ld_in) const {
static_assert(
std::is_same<int32_t, inT>::value,
"input data type is of not expected type");
static_assert(
std::is_same<float, outT>::value,
"output data type is of not expected type");
for (int i = block.row_start; i < block.row_start + block.row_size; ++i) {
for (int j = block.col_start; j < block.col_start + block.col_size; ++j) {
inT raw = inp[(i - block.row_start) * ld_in + j - block.col_start];
raw -= Aq_zero_point_ * q_col_offsets_[j];
raw -= q_row_offsets_[i - block.row_start] * Bq_zero_point_;
float res = raw * Aq_scale_ * Bq_scale_;
if (bias_) {
res += bias_[j];
}
out[i * ld_out + j] = res;
if (FUSE_RELU) {
out[i * ld_out + j] = std::max<outT>(0.0f, out[i * ld_out + j]);
}
}
}
return nextop_.template f<instSet>(out, out, block, ld_out, ld_out);
}
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