ikawrakow/ik_llama.cpp
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ik_llama.cpp/iqk-quantize.cpp
Kawrakow 90a6071a93 bitnet: fix scalar dot product for 1.625 bpw 
I had not adjusted after going to 4 q8 scales per row.
2024-06-25 08:31:12 +02:00

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//
// 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 "iqk_mul_mat.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) {
uint8_t extra = x[i].extra;
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) ? -1 : 1;
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");
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;
}
constexpr uint16_t k_magic = 0xaaaa;
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] = {};
for (int i = 0; i < nblock; ++i) {
auto qh = x[i].qh;
auto ql = x[i].ql;
auto extra = x[i].extra;
for (int j = 0; j < QK_IQ1BN/16; ++j) {
uint16_t idx1 = ql[2*j+0] | ((qh[j] << 8) & 0x0f00);
uint16_t idx2 = ql[2*j+1] | ((qh[j] << 4) & 0x0f00);
uint16_t val1 = extra & 1 ? k_magic - iq1bn_grid_u16[idx1] : iq1bn_grid_u16[idx1];
uint16_t val2 = extra & 2 ? k_magic - iq1bn_grid_u16[idx2] : iq1bn_grid_u16[idx2];
extra >>= 2;
for (int k = 0; k < 8; ++k) {
sumi[k] += q8[k+0] * (((val1 >> 2*k) & 3) - 1);
sumi[k] += q8[k+8] * (((val2 >> 2*k) & 3) - 1);
}
q8 += 16;
}
}
*s = d8[0] * (sumi[0] + sumi[4]) + d8[1] * (sumi[1] + sumi[5]) + d8[2] * (sumi[2] + sumi[6]) + d8[3] * (sumi[3] + 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) {
//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;
//}
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);
}
}
}
float * dptr = (float *)y;
for (int i = 0; i < 4; ++i) {
dptr[i] = aux[i]/127;
aux[i] = dptr[i] > 0 ? 1/dptr[i] : 0.f;
}
auto qs = (int8_t *)(dptr + 4);
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]);
}
}
}
void quantize_row_q8_K64(const float * x, void * y, int64_t k) {
quantize_row_q8_K64_reference(x, (block_q8_K64 *)y, k);
}
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