mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
785cac7ee5
and correspondingly add an extra ggml_mul_mat operation. As per @ggerganov, this is how things should be done. It seems to be working, but as far as I can tell this results in a ~15% performance penalty for prompt processing. Commiting so I can go and test on othe platforms.
407 lines
12 KiB
C++
407 lines
12 KiB
C++
//
|
|
// 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"
|
|
#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>
|
|
#include <mutex>
|
|
|
|
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 IQ1BNData {
|
|
IQ1BNData();
|
|
std::vector<std::pair<int16_t, bool>> map;
|
|
std::vector<uint16_t> rmap;
|
|
};
|
|
|
|
const IQ1BNData& get_iq1bn_data() {
|
|
static std::mutex mutex;
|
|
std::lock_guard<std::mutex> lock(mutex);
|
|
static IQ1BNData iq1bn;
|
|
return iq1bn;
|
|
}
|
|
|
|
IQ1BNData::IQ1BNData() {
|
|
map.resize(1 << 16, {int16_t(-1), false});
|
|
uint64_t aux64;
|
|
uint8_t * aux8 = (uint8_t *)&aux64;
|
|
std::vector<uint64_t> values;
|
|
values.reserve(6561);
|
|
rmap.reserve(6561);
|
|
for (int i = 0; i < (1 << 16); ++i) {
|
|
bool is_good = true;
|
|
for (int j = 0; j < 8; ++j) {
|
|
aux8[j] = (i >> 2*j) & 3;
|
|
if (aux8[j] == 3u) { is_good = false; break; }
|
|
}
|
|
if (!is_good) continue;
|
|
auto orig = aux64;
|
|
for (int j = 0; j < 8; ++j) aux8[j] = 2 - aux8[j];
|
|
int k = 0;
|
|
for (; k < int(values.size()); ++k) {
|
|
if (values[k] == aux64) break;
|
|
}
|
|
if (k < int(values.size())) {
|
|
map[i] = {k, true};
|
|
} else {
|
|
map[i].first = values.size();
|
|
values.push_back(orig);
|
|
rmap.push_back(i);
|
|
}
|
|
}
|
|
printf("==================== %s: initialized %d grid points\n", __func__, int(rmap.size()));
|
|
}
|
|
|
|
struct IQ1BNQuantizer {
|
|
constexpr static int block_size = QK_IQ1BN;
|
|
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 uint16_t quantize_one_block_1bn(const IQ1BNData& iq1l, const float * xb, int8_t * L, uint8_t * ql, uint8_t * qh);
|
|
};
|
|
|
|
uint16_t IQ1BNQuantizer::quantize_one_block_1bn(const IQ1BNData& iq1bn, const float * xb, int8_t * L, uint8_t * ql, uint8_t * qh) {
|
|
for (int j = 0; j < QK_IQ1BN; ++j) {
|
|
L[j] = fabsf(xb[j]) < 1e-6f ? 1 : xb[j] < 0 ? 0 : 2;
|
|
}
|
|
uint16_t extra = 0;
|
|
for (int k = 0; k < QK_IQ1BN/8; ++k) {
|
|
auto Lk = L + 8*k;
|
|
uint16_t u = 0;
|
|
for (int j = 0; j < 8; ++j) u |= (Lk[j] << 2*j);
|
|
auto& val = iq1bn.map[u];
|
|
GGML_ASSERT(val.first >= 0);
|
|
ql[k] = val.first & 255;
|
|
qh[k/2] |= (val.first >> 8) << 4*(k%2);
|
|
if (val.second) extra |= (1 << k);
|
|
}
|
|
return extra;
|
|
}
|
|
|
|
void IQ1BNQuantizer::quantize_one_row_1bn(const float * src, block_iq1_bn * y, int n_per_row, const float * imatrix) {
|
|
|
|
(void)imatrix;
|
|
|
|
const int nblock = n_per_row/QK_IQ1BN;
|
|
|
|
const auto& iq1bn = get_iq1bn_data();
|
|
|
|
for (int ib = 0; ib < nblock; ++ib) {
|
|
std::memset(&y[ib], 0, sizeof(block_iq1_bn));
|
|
auto xb = src + QK_IQ1BN*ib;
|
|
y[ib].extra = quantize_one_block_1bn(iq1bn, xb, L, y[ib].ql, y[ib].qh);
|
|
}
|
|
}
|
|
|
|
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;
|
|
|
|
//auto max_in_row = row_max(n_per_row, src);
|
|
//printf("%s: max = %g\n", __func__, max_in_row);
|
|
|
|
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);
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
void iq1bn_init_impl(void) {
|
|
get_iq1bn_data();
|
|
}
|
|
|
|
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) {
|
|
float d = iq1bn_fp8_to_float(x[i].extra & 0xff);
|
|
uint8_t extra = x[i].extra >> 8;
|
|
auto qh = x[i].qh;
|
|
auto ql = x[i].ql;
|
|
for (int k = 0; k < QK_IQ1BN/8; ++k) {
|
|
uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
|
|
uint16_t val = iq1bn_grid_u16[idx];
|
|
float dls = extra & (1 << k) ? -d : d;
|
|
for (int j = 0; j < 8; ++j) y[j] = dls * (((val >> 2*j) & 3) - 1);
|
|
y += 8;
|
|
}
|
|
}
|
|
}
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
void ggml_vec_dot_iq1_bn_q8_0 (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");
|
|
|
|
const block_iq1_bn * x = (const block_iq1_bn *)vx;
|
|
const block_q8_0 * y = (const block_q8_0 *)vy;
|
|
int nblock = n / QK_IQ1BN;
|
|
|
|
float sumf = 0;
|
|
|
|
for (int i = 0; i < nblock; ++i) {
|
|
auto qh = x[i].qh;
|
|
auto ql = x[i].ql;
|
|
auto q8 = y[2*i+0].qs;
|
|
int16_t sumi1 = 0;
|
|
for (int k = 0; k < 4; ++k) {
|
|
uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
|
|
uint16_t val = iq1bn_grid_u16[idx];
|
|
int16_t sl = 0;
|
|
for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
|
|
sumi1 += x[i].extra & (1 << k) ? -sl : sl;
|
|
q8 += 8;
|
|
}
|
|
q8 = y[2*i+1].qs;
|
|
int16_t sumi2 = 0;
|
|
for (int k = 4; k < 8; ++k) {
|
|
uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
|
|
uint16_t val = iq1bn_grid_u16[idx];
|
|
int16_t sl = 0;
|
|
for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
|
|
sumi2 += x[i].extra & (1 << k) ? -sl : sl;
|
|
q8 += 8;
|
|
}
|
|
sumf += GGML_FP16_TO_FP32(y[2*i+0].d) * sumi1 + GGML_FP16_TO_FP32(y[2*i+1].d) * sumi2;
|
|
}
|
|
|
|
*s = sumf;
|
|
|
|
}
|
|
|
|
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");
|
|
|
|
const block_iq1_bn * x = (const block_iq1_bn *)vx;
|
|
const block_q8_K64 * y = (const block_q8_K64 *)vy;
|
|
int nblock = n / QK_IQ1BN;
|
|
|
|
float sumf = 0;
|
|
|
|
for (int i = 0; i < nblock; ++i) {
|
|
auto qh = x[i].qh;
|
|
auto ql = x[i].ql;
|
|
auto q8 = y[i].qs;
|
|
int sumi = 0;
|
|
for (int k = 0; k < 4; ++k) {
|
|
uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
|
|
uint16_t val = iq1bn_grid_u16[idx];
|
|
int16_t sl = 0;
|
|
for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
|
|
sumi += x[i].extra & (1 << k) ? -sl : sl;
|
|
q8 += 8;
|
|
}
|
|
for (int k = 4; k < 8; ++k) {
|
|
uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
|
|
uint16_t val = iq1bn_grid_u16[idx];
|
|
int16_t sl = 0;
|
|
for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
|
|
sumi += x[i].extra & (1 << k) ? -sl : sl;
|
|
q8 += 8;
|
|
}
|
|
sumf += y[i].d * sumi;
|
|
}
|
|
|
|
*s = sumf;
|
|
}
|
|
|
|
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_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");
|
|
|
|
constexpr int Nj = QK_IQ1BN/4;
|
|
|
|
const block_iq2_bn * x = (const block_iq2_bn *)vx;
|
|
const block_q8_K64 * y = (const block_q8_K64 *)vy;
|
|
int nblock = n / QK_IQ1BN;
|
|
|
|
float sumf = 0;
|
|
|
|
for (int i = 0; i < nblock; ++i) {
|
|
auto q8 = y[i].qs;
|
|
int s0 = 0, s1 = 0, s2 = 0, s3 = 0, s4 = 0;
|
|
for (int j = 0; j < Nj; ++j) {
|
|
s1 += q8[j+ 0] * (x[i].qs[j] & 0x03);
|
|
s2 += q8[j+1*Nj] * (x[i].qs[j] & 0x0c);
|
|
s3 += q8[j+2*Nj] * (x[i].qs[j] & 0x30);
|
|
s4 += q8[j+3*Nj] * (x[i].qs[j] & 0xc0);
|
|
s0 += q8[j] + q8[j+1*Nj] + q8[j+2*Nj] + q8[j+3*Nj];
|
|
}
|
|
sumf += y[i].d * (s1 + 0.25f*s2 + 0.0625*s3 + 0.015625*s4 - s0);
|
|
}
|
|
|
|
*s = sumf;
|
|
|
|
}
|
|
|
|
void quantize_row_q8_K64_reference(const float * x, block_q8_K64 * y, int64_t k) {
|
|
assert(k % 64 == 0);
|
|
const int64_t nb = k / 64;
|
|
|
|
// Check if a row-wise scale works. It almost does, PPL is only ~0.02 higher
|
|
//float amax = 0;
|
|
//for (int j = 0; j < k; ++j) {
|
|
// float ax = fabsf(x[j]);
|
|
// amax = MAX(ax, amax);
|
|
//}
|
|
|
|
//float d = amax/127;
|
|
//float id = d ? 1/d : 0.f;
|
|
|
|
//for (int i = 0; i < nb; i++) {
|
|
// for (int j = 0; j < 64; ++j) y[i].qs[j] = nearest_int(id*x[j]);
|
|
// y[i].d = d;
|
|
// x += 64;
|
|
//}
|
|
|
|
for (int i = 0; i < nb; i++) {
|
|
|
|
float max = 0;
|
|
float amax = 0;
|
|
for (int j = 0; j < 64; ++j) {
|
|
float ax = fabsf(x[j]);
|
|
if (ax > amax) {
|
|
amax = ax; max = x[j];
|
|
}
|
|
}
|
|
if (!amax) {
|
|
y[i].d = 0;
|
|
memset(y[i].qs, 0, 64);
|
|
x += 64;
|
|
continue;
|
|
}
|
|
const float iscale = -127.f/max;
|
|
for (int j = 0; j < 64; ++j) {
|
|
int v = nearest_int(iscale*x[j]);
|
|
y[i].qs[j] = MIN(127, v);
|
|
}
|
|
y[i].d = 1/iscale;
|
|
x += 64;
|
|
}
|
|
}
|
|
|
|
void quantize_row_q8_K64(const float * x, void * y, int64_t k) {
|
|
quantize_row_q8_K64_reference(x, (block_q8_K64 *)y, k);
|
|
}
|
|
|