ikawrakow/ik_llama.cpp
1
0
Fork 0
mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-04-28 18:32:04 +00:00
Code Issues Packages Projects Releases Wiki Activity

IQ4_NL_X4 (#118)

Browse Source 
* Adding iq4_nl_x4

Looks very promising - I get PP-512(LLaMA-3.1-8B) = 230 t/s
on the Ryzen-7950X! This is faster than any other quant and
~40% faster than iq4_nl.

* iq4_nl_x4: getting amazing

This Zen4 variant gets us to PP-512(LLaMA-3.1-8B) = 263 t/s!

* iq4_nl_x4: AVX2

Here we gain only 25% compared to iq4_nl

* iq4_nl_x4: NEON

On M2-Max we get PP-512(LLaMA-3.1-8B) = 109.7 t/s, up from
82.4 t/s for iq4_nl.

* iq4_nl_x4: minor NEON improvement and cleanup

This gets us to 110.3 t/s. In comparison,
IQ4_NL_4_4 in mainline llama.cpp achieves 92.3 t/s.

* iq4_nl_x4: NEON specialization for matrix x vector

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-12-02 07:25:39 +01:00
committed by GitHub
parent 6c73f704ca
commit 72d94fbf22
10 changed files with 398 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

114
ggml/src/iqk/iqk_quantize.cpp
View File

@@ -669,12 +669,12 @@ void quantize_row_iq2_k_impl(const float * x, void * vy, int n_per_row, const fl
}
}
void quantize_row_iq2_k_ref(const float * GGML_RESTRICT x, block_iq2_k * GGML_RESTRICT y, int64_t k) {
void quantize_row_iq2_k_ref(const float * x, block_iq2_k * y, int64_t k) {
assert(k % QK_K == 0);
quantize_iq2_k(x, (void *)y, 1, k, nullptr);
}
void quantize_row_iq2_k(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
void quantize_row_iq2_k(const float * x, void * vy, int64_t k) {
assert(k % QK_K == 0);
block_iq2_k * y = (block_iq2_k *)vy;
quantize_row_iq2_k_ref(x, y, k);
@@ -692,7 +692,7 @@ size_t quantize_iq2_k(const float * src, void * dst, int64_t nrows, int64_t n_pe
return nrows * nblock * sizeof(block_iq2_k);
}
void dequantize_row_iq2_k(const block_iq2_k * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
void dequantize_row_iq2_k(const block_iq2_k * x, float * y, int64_t k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
@@ -723,7 +723,7 @@ void dequantize_row_iq2_k(const block_iq2_k * GGML_RESTRICT x, float * GGML_RES
}
void vec_dot_iq2_k_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
void vec_dot_iq2_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
assert(n % QK_K == 0);
assert(nrc == 1);
GGML_UNUSED(nrc);
@@ -967,12 +967,12 @@ void quantize_row_iq2_ks_impl(const float * x, void * vy, int n_per_row, const f
}
}
void quantize_row_iq2_ks_ref(const float * GGML_RESTRICT x, block_iq2_ks * GGML_RESTRICT y, int64_t k) {
void quantize_row_iq2_ks_ref(const float * x, block_iq2_ks * y, int64_t k) {
assert(k % QK_K == 0);
quantize_iq2_ks(x, (void *)y, 1, k, nullptr);
}
void quantize_row_iq2_ks(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
void quantize_row_iq2_ks(const float * x, void * vy, int64_t k) {
assert(k % QK_K == 0);
block_iq2_ks * y = (block_iq2_ks *)vy;
quantize_row_iq2_ks_ref(x, y, k);
@@ -994,7 +994,7 @@ size_t quantize_iq2_ks(const float * src, void * dst, int64_t nrows, int64_t n_p
return nrows * row_size;
}
void dequantize_row_iq2_ks(const block_iq2_ks * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
void dequantize_row_iq2_ks(const block_iq2_ks * x, float * y, int64_t k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
@@ -1334,7 +1334,7 @@ void dequantize_row_iq3_k(const block_iq3_k * x, float * y, int64_t k) {
}
}
void vec_dot_iq3_k_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
void vec_dot_iq3_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
assert(n % QK_K == 0);
assert(nrc == 1);
GGML_UNUSED(nrc);
@@ -3119,4 +3119,102 @@ void vec_dot_iq4_kss_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
GGML_UNUSED(by);
}
//
// ========================================= x4
//
void quantize_row_iq4_nl_x4_ref(const float * x, block_iq4_nl_x4 * y, int64_t k) {
// we assume we are called with 4 rows
quantize_iq4_nl_x4(x, (void *)y, 4, k/4, nullptr);
}
void quantize_row_iq4_nl_x4(const float * x, void * y, int64_t k) {
// we assume we are called with 4 rows
quantize_iq4_nl_x4(x, y, 4, k/4, nullptr);
}
static void repack_iq4_nl(int nrows, int n_per_row, const block_iq4_nl * x, block_iq4_nl_x4 * y) {
GGML_ASSERT(nrows%4 == 0);
GGML_ASSERT(n_per_row%QK4_NL == 0);
int nblock = n_per_row/QK4_NL;
const block_iq4_nl * x4[4];
for (int row = 0; row < nrows; row += 4) {
for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
for (int ib = 0; ib < nblock; ++ib) {
for (int k = 0; k < 4; ++k) y[ib].d[k] = x4[k][ib].d;
for (int k = 0; k < 4; ++k) for (int i = 0; i < 4; ++i) {
y[ib].qs[4*k+i+ 0] = (x4[k][ib].qs[i+0] & 0xf) | ((x4[k][ib].qs[i+ 8] & 0x0f) << 4); // 0....3 + 8...11 from each row
y[ib].qs[4*k+i+16] = (x4[k][ib].qs[i+0] >> 4) | ((x4[k][ib].qs[i+ 8] & 0xf0)); // 16...19 + 24...27 from each row
y[ib].qs[4*k+i+32] = (x4[k][ib].qs[i+4] & 0xf) | ((x4[k][ib].qs[i+12] & 0x0f) << 4); // 4....7 + 12...15 from each row
y[ib].qs[4*k+i+48] = (x4[k][ib].qs[i+4] >> 4) | ((x4[k][ib].qs[i+12] & 0xf0)); // 20...23 + 28...31 from each row
}
}
x += 4*nblock;
y += nblock;
}
}
size_t quantize_iq4_nl_x4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
GGML_ASSERT(nrows%4 == 0);
auto row_size_nl = ggml_row_size(GGML_TYPE_IQ4_NL, n_per_row);
std::vector<char> qtmp(4*row_size_nl);
//std::vector<float> check1(4*n_per_row), check2(4*n_per_row);
char * qrow = (char *)dst;
for (int row = 0; row < nrows; row += 4) {
quantize_iq4_nl(src, qtmp.data(), 4, n_per_row, imatrix);
repack_iq4_nl(4, n_per_row, (const block_iq4_nl *)qtmp.data(), (block_iq4_nl_x4 *)qrow);
//dequantize_row_iq4_nl_x4((const block_iq4_nl_x4 *)qrow, check1.data(), 4*n_per_row);
//dequantize_row_iq4_nl((const block_iq4_nl *)qtmp.data(), check2.data(), 4*n_per_row);
//for (int k = 0; k < 4; ++k) {
// auto x1 = check1.data() + k*n_per_row;
// auto x2 = check2.data() + k*n_per_row;
// int nbad = 0;
// for (int j = 0; j < n_per_row; ++j) {
// if (std::abs(x1[j] - x2[j]) > 1e-8) {
// printf("Oops: %g vs %g\n", x1[j], x2[j]);
// if (++nbad > 20) GGML_ABORT("fatal error");
// }
// }
//}
src += 4*n_per_row;
qrow += 4*row_size_nl;
}
return nrows*row_size_nl;
}
void dequantize_row_iq4_nl_x4(const block_iq4_nl_x4 * x, float * y, int64_t k) {
// we assume we are called with 4 rows
int n_per_row = k/4;
int nb = n_per_row/QK4_NL;
float * yk[4];
for (int k = 0; k < 4; ++k) yk[k] = y + k*n_per_row;
for (int ib = 0; ib < nb; ++ib) {
for (int k = 0; k < 4; ++k) {
float scale = GGML_FP16_TO_FP32(x[ib].d[k]);
for (int i = 0; i < 4; ++i) {
yk[k][QK4_NL*ib+i+ 0] = scale * iq4k_values[x[ib].qs[4*k+i+ 0] & 0xf];
yk[k][QK4_NL*ib+i+ 8] = scale * iq4k_values[x[ib].qs[4*k+i+ 0] >> 4];
yk[k][QK4_NL*ib+i+16] = scale * iq4k_values[x[ib].qs[4*k+i+16] & 0xf];
yk[k][QK4_NL*ib+i+24] = scale * iq4k_values[x[ib].qs[4*k+i+16] >> 4];
yk[k][QK4_NL*ib+i+ 4] = scale * iq4k_values[x[ib].qs[4*k+i+32] & 0xf];
yk[k][QK4_NL*ib+i+12] = scale * iq4k_values[x[ib].qs[4*k+i+32] >> 4];
yk[k][QK4_NL*ib+i+20] = scale * iq4k_values[x[ib].qs[4*k+i+48] & 0xf];
yk[k][QK4_NL*ib+i+28] = scale * iq4k_values[x[ib].qs[4*k+i+48] >> 4];
}
}
}
}
void vec_dot_iq4_nl_x4_q8_0(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
#if GGML_USE_IQK_MULMAT
if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ4_NL_X4, vx, 0, GGML_TYPE_Q8_0, vy, 0, s, 0, 0, 1)) {
return;
}
#endif
GGML_ASSERT(n%QK4_NL == 0);
GGML_ASSERT(nrc == 1);
GGML_UNUSED(bs);
GGML_UNUSED(bx);
GGML_UNUSED(by);
}