ikawrakow/ik_llama.cpp
1
0
Fork 0
mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-01-26 17:20:01 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
28b4229295e7c818bbed54065cdcd2e66fb86334
ik_llama.cpp/iqk-quantize.cpp

425 lines
18 KiB
C++
Raw Blame History

//
// Copyright 2024 Iwan Kawrakow
//
// 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.
#include "iqk-quantize.h"
#if GGML_USE_IQK_MULMAT
#include "iqk_mul_mat.h"
#endif
#include "ggml-quants.h"
#include "ggml-impl.h"
#define GGML_COMMON_IMPL_C
#include "ggml-common.h"
#include <vector>
#include <utility>
#include <cstdint>
#include <cmath>
#include <array>
#include <algorithm>
#include <cstring>
namespace {
inline int nearest_int(float fval) {
assert(fval <= 4194303.f);
float val = fval + 12582912.f;
int i; memcpy(&i, &val, sizeof(int));
return (i & 0x007fffff) - 0x00400000;
}
struct IQ1BNQuantizer {
int8_t L[QK_IQ1BN];
void quantize_one_row_1bn(const float * src, block_iq1_bn * y, int n_per_row, const float * imatrix);
void quantize_one_row_2bn(const float * src, block_iq2_bn * y, int n_per_row, const float * imatrix);
static inline float row_max(int n_per_row, const float * src) {
float max_in_row = 0;
for (int j = 0; j < n_per_row; ++j) {
float ax = fabsf(src[j]);
max_in_row = std::max(max_in_row, ax);
}
return max_in_row;
}
static constexpr uint8_t k_mult[5] = {81, 27, 9, 3, 1};
};
void IQ1BNQuantizer::quantize_one_row_1bn(const float * src, block_iq1_bn * y, int n_per_row, const float * imatrix) {
static const int k_nb[6] = {1, 3, 9, 27, 81, 243};
(void)imatrix;
const int nblock = n_per_row/QK_IQ1BN;
for (int ib = 0; ib < nblock; ++ib) {
std::memset(&y[ib], 0, sizeof(block_iq1_bn));
auto xb = src + ib*QK_IQ1BN;
int v13 = 0;
for (int i16 = 0; i16 < QK_IQ1BN/16; ++i16) {
for (int k = 0; k < 3; ++k) {
int idx = 0;
for (int j = 0; j < 5; ++j) {
float v = xb[16*i16 + 5*k + j];
int q = fabsf(v) < 1e-6f ? 1 : v < 0 ? 0 : 2;
idx += k_nb[j]*q;
}
idx = (256*idx + k_nb[5] - 1)/k_nb[5];
y[ib].ql[3*i16 + k] = idx;
}
float v = xb[16*i16 + 15];
int q = fabsf(v) < 1e-6f ? 1 : v < 0 ? 0 : 2;
v13 += k_nb[i16]*q;
}
y[ib].extra = (256*v13 + k_nb[5] - 1)/k_nb[5];
}
}
void IQ1BNQuantizer::quantize_one_row_2bn(const float * src, block_iq2_bn * y, int n_per_row, const float * imatrix) {
(void)imatrix;
const int nblock = n_per_row/QK_IQ1BN;
constexpr int Nj = QK_IQ1BN/4;
for (int ib = 0; ib < nblock; ++ib) {
auto xb = src + QK_IQ1BN*ib;
for (int j = 0; j < QK_IQ1BN; ++j) {
L[j] = fabsf(xb[j]) < 1e-6f ? 1 : xb[j] < 0 ? 0 : 2;
}
for (int j = 0; j < Nj; ++j) {
y[ib].qs[j] = L[j] | (L[j + Nj] << 2) | (L[j + 2*Nj] << 4) | (L[j + 3*Nj] << 6);
}
}
}
}
size_t quantize_iq1_bn(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
IQ1BNQuantizer iq1bn;
int nblock = n_per_row/QK_IQ1BN;
block_iq1_bn * y = (block_iq1_bn *)dst;
for (int row = 0; row < nrows; ++row) {
iq1bn.quantize_one_row_1bn(src + row*n_per_row, y, n_per_row, imatrix);
y += nblock;
}
return sizeof(block_iq1_bn)*nblock*nrows;
}
void quantize_row_iq1_bn_reference(const float * x, block_iq1_bn * y, int64_t k) {
quantize_iq1_bn(x, y, 1, k, nullptr);
}
void quantize_row_iq1_bn(const float * x, void * y, int64_t k) {
quantize_iq1_bn(x, y, 1, k, nullptr);
}
void dequantize_row_iq1_bn(const block_iq1_bn * x, float * y, int64_t k) {
assert(k%QK_IQ1BN == 0);
int nblock = k / QK_IQ1BN;
for (int i = 0; i < nblock; ++i) {
uint8_t extra = x[i].extra;
auto ql = x[i].ql;
for (int i16 = 0; i16 < QK_IQ1BN/16; ++i16) {
for (int k = 0; k < 3; ++k) {
for (int j = 0; j < 5; ++j) {
uint8_t v = ql[k]*IQ1BNQuantizer::k_mult[j];
int8_t vs = ((v + (v >> 1)) >> 7);
*y++ = vs - 1;
}
}
ql += 3;
uint8_t v = extra*IQ1BNQuantizer::k_mult[i16];
int8_t vs = ((v + (v >> 1)) >> 7);
*y++ = vs - 1;
}
}
}
size_t quantize_iq2_bn(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
IQ1BNQuantizer iq1bn;
int nblock = n_per_row/QK_IQ1BN;
block_iq2_bn * y = (block_iq2_bn *)dst;
for (int row = 0; row < nrows; ++row) {
iq1bn.quantize_one_row_2bn(src + row*n_per_row, y, n_per_row, imatrix);
y += nblock;
}
return sizeof(block_iq2_bn)*nblock*nrows;
}
void quantize_row_iq2_bn_reference(const float * x, block_iq2_bn * y, int64_t k) {
quantize_iq2_bn(x, y, 1, k, nullptr);
}
void quantize_row_iq2_bn(const float * x, void * y, int64_t k) {
quantize_iq2_bn(x, y, 1, k, nullptr);
}
void dequantize_row_iq2_bn(const block_iq2_bn * x, float * y, int64_t k) {
assert(k%QK_IQ1BN == 0);
int nblock = k / QK_IQ1BN;
auto d1 = 1.f, d2 = 0.25f, d3 = d2*0.25f, d4 = d3*0.25f;
auto m = -1.f;
constexpr int Nj = QK_IQ1BN/4;
for (int i = 0; i < nblock; ++i) {
for (int j = 0; j < Nj; ++j) {
y[j+ 0] = d1*(x[i].qs[j] & 0x03) + m;
y[j+1*Nj] = d2*(x[i].qs[j] & 0x0c) + m;
y[j+2*Nj] = d3*(x[i].qs[j] & 0x30) + m;
y[j+3*Nj] = d4*(x[i].qs[j] & 0xc0) + m;
}
y += QK_IQ1BN;
}
}
namespace {
inline int8_t iq1bn_dequant(uint8_t q, int i) {
uint8_t v = IQ1BNQuantizer::k_mult[i]*q;
//int8_t vs = (v + (v << 1)) >> 8;
int8_t vs = 3*v >> 8;
return vs - 1;
}
}
static const int8_t iq1bn_values[1280] = {
-1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 0, -1, -1, -1, 0, 0, -1, -1, -1, 1, 0,
-1, -1, -1, -1, 1, -1, -1, -1, 0, 1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, 0, -1, -1, 0, -1, 0, -1, -1, 1, -1, 0, -1,
-1, -1, 0, 0, -1, -1, 0, 0, 0, -1, -1, 1, 0, 0, -1, -1, -1, 1, 0, -1, -1, 0, 1, 0, -1, -1, 1, 1, 0, -1, -1, -1,
-1, 1, -1, -1, 0, 0, 0, 0, 0, 0, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, 0, 1, -1, -1, 0, 0, 1, -1, -1, 1, 0, 1,
-1, -1, -1, 1, 1, -1, -1, 0, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 0, -1, 0, -1, -1, 0, -1, 1, -1, -1, 0, -1,
-1, 0, -1, 0, -1, 0, 0, -1, 0, -1, 1, 0, -1, 0, -1, -1, 1, -1, 0, -1, 0, 1, -1, 0, -1, 1, 1, -1, 0, -1, -1, -1,
0, 0, -1, 0, -1, 0, 0, -1, 0, 0, 0, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0, 0, -1, 0, 0, 0, 0, -1, 1, 0, 0, 0,
-1, -1, 1, 0, 0, -1, 0, 1, 0, 0, -1, 1, 1, 0, 0, -1, -1, -1, 1, 0, -1, 0, -1, 1, 0, -1, 1, -1, 1, 0, -1, -1,
0, 1, 0, -1, 0, 0, 1, 0, -1, 1, 0, 1, 0, -1, -1, 1, 1, 0, -1, 0, 1, 1, 0, -1, 1, 1, 1, 0, -1, -1, -1, -1,
1, -1, 0, -1, -1, 1, -1, 1, -1, -1, 1, -1, 0, 0, 0, 0, 0, -1, 0, -1, 1, -1, 0, 0, -1, 1, -1, 1, 0, -1, 1, -1,
-1, 1, -1, 1, -1, 0, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 0, 1, -1, 0, -1, 0, 1, -1, 1, -1, 0, 1, -1, -1, 0,
0, 1, -1, 0, 0, 0, 1, -1, 1, 0, 0, 1, -1, -1, 1, 0, 1, -1, 0, 1, 0, 1, -1, 1, 1, 0, 1, -1, -1, -1, 1, 1,
-1, 0, -1, 1, 1, -1, 1, -1, 1, 1, -1, 0, 0, 0, 0, 0, -1, 0, 1, 1, -1, 0, 0, 1, 1, -1, 1, 0, 1, 1, -1, -1,
1, 1, 1, -1, 0, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, 0, 1, -1, -1, -1, 0, -1, 0, -1,
-1, 0, 0, 0, -1, -1, 0, 1, 0, -1, -1, 0, -1, 1, -1, -1, 0, 0, 1, -1, -1, 0, 1, 1, -1, -1, 0, -1, -1, 0, -1, 0,
0, -1, 0, -1, 0, 1, -1, 0, -1, 0, -1, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 1, 0, 0, -1, 0, -1, 1,
0, -1, 0, 0, 1, 0, -1, 0, 1, 1, 0, -1, 0, -1, -1, 1, -1, 0, 0, -1, 1, -1, 0, 1, -1, 1, -1, 0, -1, 0, 1, -1,
0, 0, 0, 1, -1, 0, 1, 0, 1, -1, 0, -1, 1, 1, -1, 0, 0, 1, 1, -1, 0, 1, 1, 1, -1, 0, -1, -1, -1, 0, 0, 0,
-1, -1, 0, 0, 1, -1, -1, 0, 0, -1, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1, 0, 0, -1, 1, -1,
0, 0, 0, 1, -1, 0, 0, 1, 1, -1, 0, 0, -1, -1, 0, 0, 0, 0, -1, 0, 0, 0, 1, -1, 0, 0, 0, -1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, -1, -1, 1, 0, 0, 0, -1,
1, 0, 0, 1, -1, 1, 0, 0, -1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, -1, 1, 1, 0,
0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, -1, -1, -1, 1, 0, 0, -1, -1, 1, 0, 1, -1, -1, 1, 0, -1, 0, -1, 1, 0, 0,
0, -1, 1, 0, 1, 0, -1, 1, 0, -1, 1, -1, 1, 0, 0, 1, -1, 1, 0, 1, 1, -1, 1, 0, -1, -1, 0, 1, 0, 0, -1, 0,
1, 0, 1, -1, 0, 1, 0, -1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, -1, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 1, 0, 1, 0, -1, -1, 1, 1, 0, 0, -1, 1, 1, 0, 1, -1, 1, 1, 0, -1, 0, 1, 1, 0, 0, 0,
1, 1, 0, 1, 0, 1, 1, 0, -1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, -1, -1, -1, -1, 1, 0, -1, -1, -1,
1, 1, -1, -1, -1, 1, -1, 0, -1, -1, 1, 0, 0, -1, -1, 1, 1, 0, -1, -1, 1, -1, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0,
1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 0, -1, 1, 0, -1, 0, -1, 1, 1, -1, 0, -1, 1, -1, 0, 0, -1, 1, 0, 0, 0,
-1, 1, 1, 0, 0, -1, 1, -1, 1, 0, -1, 1, 0, 1, 0, -1, 1, 1, 1, 0, -1, 1, -1, -1, 1, -1, 1, 0, -1, 1, -1, 1,
1, -1, 1, -1, 1, -1, 0, 1, -1, 1, 0, 0, 1, -1, 1, 1, 0, 1, -1, 1, -1, 1, 1, -1, 1, 0, 0, 0, 0, 0, 0, 1,
1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 0, 1, 0, -1, -1, 0, 1, 1, -1, -1, 0, 1, -1, 0, -1, 0, 1, 0, 0, -1, 0,
1, 1, 0, -1, 0, 1, -1, 1, -1, 0, 1, 0, 1, -1, 0, 1, 1, 1, -1, 0, 1, -1, -1, 0, 0, 1, 0, -1, 0, 0, 1, 1,
-1, 0, 0, 1, -1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, -1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 1, -1, -1, 1, 0, 1, 0, -1, 1, 0, 1, 1, -1, 1, 0, 1, -1, 0, 1, 0, 1, 0, 0, 1, 0, 1,
1, 0, 1, 0, 1, -1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, -1, -1, -1, 1, 1, 0, -1, -1, 1, 1, 1, -1,
-1, 1, 1, -1, 0, -1, 1, 1, 0, 0, -1, 1, 1, 1, 0, -1, 1, 1, -1, 1, -1, 1, 1, 0, 1, -1, 1, 1, 1, 1, -1, 1,
1, 0, 0, 0, 0, 0, -1, -1, 0, 1, 1, 0, -1, 0, 1, 1, 1, -1, 0, 1, 1, -1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1,
0, 0, 1, 1, -1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, -1, -1, 1, 1, 1, 0, -1, 1, 1, 1, 1, -1, 1,
1, 1, -1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, -1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
};
void ggml_vec_dot_iq1_bn_q8_K64(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
GGML_UNUSED(bs);
GGML_UNUSED(bx);
GGML_UNUSED(by);
GGML_UNUSED(nrc);
static_assert(QK_IQ1BN == 64, "This dot product implementation for iq1_bn requires a block size of 64");
#if GGML_USE_IQK_MULMAT
if (iqk_mul_mat(GGML_TASK_TYPE_COMPUTE, 1, 1, n, GGML_TYPE_IQ1_BN, vx, 0, GGML_TYPE_Q8_K64, vy, 0, s, 0, 0, 1)) {
return;
}
#endif
const block_iq1_bn * x = (const block_iq1_bn *)vx;
const float * d8 = (const float *)vy;
const int8_t * q8 = (const int8_t *)(d8 + 4);
int nblock = n / QK_IQ1BN;
int sumi[8] = {};
int8_t q1[16];
for (int ii = 0; ii < nblock; ii += 32) {
int16_t sum16[8] = {};
int nb = std::min(ii + 32, nblock);
for (int i = ii; i < nb; ++i) {
auto ql = x[i].ql;
const int8_t * extra = iq1bn_values + 5*x[i].extra;
for (int i16 = 0; i16 < QK_IQ1BN/16; ++i16) {
for (int k = 0; k < 3; ++k) {
uint8_t q = *ql++;
const int8_t * vs = iq1bn_values + 5*q;
for (int j = 0; j < 5; ++j) q1[5*k+j] = vs[j];
}
q1[15] = extra[i16];
// We collect 8 q8 values per block into each element of sum16
// => 32 x 8 = 256 values in each loop over i, so this cannot overflow the int16_t range
// (q8 is in -127...127, and hence the sum is in -32512...32512
for (int j = 0; j < 8; ++j) sum16[j] += q8[2*j+0]*q1[2*j+0] + q8[2*j+1]*q1[2*j+1];
q8 += 16;
}
}
for (int j = 0; j < 8; ++j) sumi[j] += sum16[j];
}
*s = d8[0] * (sumi[0] + sumi[1]) + d8[1] * (sumi[2] + sumi[3]) + d8[2] * (sumi[4] + sumi[5]) + d8[3] * (sumi[6] + sumi[7]);
}
void ggml_vec_dot_iq2_bn_q8_K64(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
GGML_ASSERT(nrc == 1);
GGML_UNUSED(bs);
GGML_UNUSED(bx);
GGML_UNUSED(by);
GGML_UNUSED(nrc);
static_assert(QK_IQ1BN == 64, "This dot product implementation for iq2_bn requires a block size of 64");
if (iqk_mul_mat(GGML_TASK_TYPE_COMPUTE, 1, 1, n, GGML_TYPE_IQ2_BN, vx, 0, GGML_TYPE_Q8_K64, vy, 0, s, 0, 0, 1)) {
return;
}
constexpr int Nj = QK_IQ1BN/4;
const block_iq2_bn * x = (const block_iq2_bn *)vx;
int nblock = n / QK_IQ1BN;
const float * d = (const float *)vy;
const int8_t * q8 = (const int8_t *)(d + 4);
int sum[16] = { };
int sum0[4] = { };
for (int i = 0; i < nblock; ++i) {
for (int j = 0; j < Nj/4; ++j) {
for (int l = 0; l < 4; ++l) {
sum[4*j + 0] += q8[4*j + l + 0] * (x[i].qs[4*j+l] & 0x03);
sum[4*j + 1] += q8[4*j + l + 1*Nj] * (x[i].qs[4*j+l] & 0x0c);
sum[4*j + 2] += q8[4*j + l + 2*Nj] * (x[i].qs[4*j+l] & 0x30);
sum[4*j + 3] += q8[4*j + l + 3*Nj] * (x[i].qs[4*j+l] & 0xc0);
sum0[j] += q8[4*j + l] + q8[4*j + l + 1*Nj] + q8[4*j + l + 2*Nj] + q8[4*j + l + 3*Nj];
}
}
q8 += QK_IQ1BN;
}
float sumf = 0;
for (int j = 0; j < 4; ++j) {
sumf += d[j] * (sum[4*j + 0] + 0.25f*sum[4*j + 1] + 0.0625*sum[4*j + 2] + 0.015625*sum[4*j + 3] - sum0[j]);
}
*s = sumf;
}
void quantize_row_q8_K64_reference(const float * x, block_q8_K64 * y, int64_t k) {
float * dptr = (float *)y;
auto qs = (int8_t *)(dptr + 4);
#ifdef __ARM_NEON
static const uint8_t k_shuffle[16] = {0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60};
auto shuffle = vld1q_u8(k_shuffle);
float32x4_t max[4] = { };
for (int j = 0; j < k; j += 16) {
for (int i = 0; i < 4; ++i) {
auto val = vld1q_f32(x + j + 4*i);
val = vabsq_f32(val);
max[i] = vmaxq_f32(max[i], val);
}
}
float32x4_t vid[4];
for (int i = 0; i < 4; ++i) {
dptr[i] = vmaxvq_f32(max[i])/127;
float id = dptr[i] > 0 ? 1/dptr[i] : 0.f;
vid[i] = vdupq_n_f32(id);
}
int8x16x4_t q;
for (int j = 0; j < k; j += 16) {
for (int i = 0; i < 4; ++i) {
auto val = vld1q_f32(x + j + 4*i);
val = vmulq_f32(vid[i], val);
q.val[i] = vreinterpretq_s8_s32(vcvtnq_s32_f32(val));
}
auto qi = vqtbl4q_s8(q, shuffle);
vst1q_s8(qs, qi);
qs += 16;
}
#elif defined __AVX__
__m128 max[4] = {};
__m128 sign_bit = _mm_set1_ps(-0.f);
for (int j = 0; j < k; j += 16) {
for (int i = 0; i < 4; ++i) {
auto val = _mm_loadu_ps(x + j + 4*i);
val = _mm_andnot_ps(sign_bit, val);
max[i] = _mm_max_ps(max[i], val);
}
}
__m128 vid[4];
for (int i = 0; i < 4; ++i) {
max[i] = _mm_max_ps(max[i], _mm_movehl_ps(max[i], max[i]));
max[i] = _mm_max_ss(max[i], _mm_movehdup_ps(max[i]));
float maxi = _mm_cvtss_f32(max[i]);
dptr[i] = maxi/127;
float id = dptr[i] > 0 ? 1/dptr[i] : 0.f;
vid[i] = _mm_set1_ps(id);
}
__m128i q[4];
for (int j = 0; j < k; j += 16) {
for (int i = 0; i < 4; ++i) {
auto val = _mm_loadu_ps(x + j + 4*i);
val = _mm_round_ps(_mm_mul_ps(vid[i], val), _MM_ROUND_NEAREST);
q[i] = _mm_cvtps_epi32(val);
}
auto q1 = _mm_packs_epi32(q[0], q[1]);
auto q2 = _mm_packs_epi32(q[2], q[3]);
auto qi = _mm_packs_epi16(q1, q2);
_mm_storeu_si128((__m128i *)qs, qi);
qs += 16;
}
#else
float aux[4] = {0.f, 0.f, 0.f, 0.f};
for (int j = 0; j < k; j += 16) {
for (int i = 0; i < 4; ++i) {
for (int l = 0; l < 4; ++l) {
float ax = fabsf(x[j+4*i+l]);
aux[i] = std::max(aux[i], ax);
}
}
}
for (int i = 0; i < 4; ++i) {
dptr[i] = aux[i]/127;
aux[i] = dptr[i] > 0 ? 1/dptr[i] : 0.f;
}
for (int j = 0; j < k; j += 16) {
for (int i = 0; i < 4; ++i) {
for (int l = 0; l < 4; ++l) qs[j+4*i+l] = nearest_int(aux[i]*x[j+4*i+l]);
}
}
#endif
}
void quantize_row_q8_K64(const float * x, void * y, int64_t k) {
quantize_row_q8_K64_reference(x, (block_q8_K64 *)y, k);
}
Reference in New Issue View Git Blame Copy Permalink