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iq1_m: basics

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This commit is contained in:
Iwan Kawrakow 2024-03-22 19:19:12 +02:00
parent 43139cc528
commit 2a2d66de46
8 changed files with 366 additions and 71 deletions
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11
examples/quantize/quantize.cpp
View file

@ -26,6 +26,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.81 bpw quantization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
{ "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", },
@ -320,10 +321,12 @@ int main(int argc, char ** argv) {
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) {
fprintf(stderr, "\n===============================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "===============================================================================================\n\n\n");
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
fprintf(stderr, "\n==========================================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "==========================================================================================================\n\n\n");
return 1;
}

9
ggml-common.h
View file

@ -377,6 +377,15 @@ typedef struct {
} block_iq1_s;
static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
// 1.8125 bpw
typedef struct {
ggml_half d;
uint8_t qs[QK_K/8]; // grid index, low 8 bits
uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8)
uint8_t scales[QK_K/32]; // 4-bit block scales
} block_iq1_m;
static_assert(sizeof(block_iq1_m) == sizeof(ggml_half) + QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
// Non-linear quants
#define QK4_NL 32
typedef struct {

329
ggml-quants.c
View file

@ -3474,6 +3474,51 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in
}
}
void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
float delta[4];
uint16_t idx[4];
for (int i = 0; i < nb; i++) {
const float d1 = GGML_FP16_TO_FP32(x[i].d);
const float d2 = d1 / 16;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
for (int ib = 0; ib < QK_K/32; ++ib) {
const float dl1 = d1 * (2*(x[i].scales[ib] & 0x0f) + 1);
const float dl2 = d2 * (2*(x[i].scales[ib] & 0xf0) + 16);
idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
idx[3] = qs[3] | ((qh[1] << 4) & 0x700);
delta[0] = qh[0] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[1] = qh[0] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[2] = qh[1] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[3] = qh[1] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
for (int l = 0; l < 2; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
for (int j = 0; j < 8; ++j) {
y[j] = dl1 * (grid[j] + delta[l]);
}
y += 8;
}
for (int l = 2; l < 4; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
for (int j = 0; j < 8; ++j) {
y[j] = dl2 * (grid[j] + delta[l]);
}
y += 8;
}
qs += 4;
qh += 2;
}
}
}
static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) {
@ -9695,6 +9740,140 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void
#endif
}
// TODO
void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(n % QK_K == 0);
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_iq1_s * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
#if defined __ARM_NEON
ggml_int8x16x4_t q1b;
ggml_int8x16x4_t q8b;
float sumf = 0;
for (int i = 0; i < nb; ++i) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint16_t * qh = x[i].qh;
int sumi1 = 0, sumi2 = 0, sumi3 = 0;
for (int ib = 0; ib < QK_K/32; ib += 2) {
q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700)))));
q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700)))));
q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700)))));
q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700)))));
qs += 8;
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]);
const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]);
const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
sumi1 += vaddvq_s32(p1) * ls1;
sumi2 += vaddvq_s32(p2) * ls2;
sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1)
+ (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1);
}
sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3);
}
*s = sumf;
#elif defined __AVX2__
__m256 accum = _mm256_setzero_ps();
float accum1 = 0;
for (int i = 0; i < nb; ++i) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint16_t * qh = x[i].qh;
__m256i sumi = _mm256_setzero_si256();
int sumi1 = 0;
for (int ib = 0; ib < QK_K/32; ib += 2) {
const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)],
iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]);
const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)],
iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]);
qs += 8;
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1));
const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2));
sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2));
sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1
+ (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2;
}
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum);
accum1 += d * sumi1;
}
*s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
#else
float sumf = 0;
for (int i = 0; i < nb; i++) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint16_t * qh = x[i].qh;
int sumi = 0, sumi1 = 0;
for (int ib = 0; ib < QK_K/32; ++ib) {
const int ls = 2*((qh[ib] >> 12) & 7) + 1;
const int delta = qh[ib] & 0x8000 ? -1 : 1;
int lsum = 0;
for (int l = 0; l < 4; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
for (int j = 0; j < 8; ++j) {
lsum += q8[j] * grid[j];
}
q8 += 8;
}
sumi += ls * lsum;
sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
qs += 4;
}
sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
}
*s = sumf;
#endif
}
void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(nrc == 1);
UNUSED(nrc);
@ -9938,17 +10117,17 @@ static iq2_entry_t iq2_data[4] = {
};
static inline int iq2_data_index(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S);
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
return type == GGML_TYPE_IQ2_XXS ? 0 :
type == GGML_TYPE_IQ2_XS ? 1 :
type == GGML_TYPE_IQ1_S ? 2 : 3;
type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 2 : 3;
}
static inline int iq2_grid_size(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S);
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
return type == GGML_TYPE_IQ2_XXS ? 256 :
type == GGML_TYPE_IQ2_XS ? 512 :
type == GGML_TYPE_IQ1_S ? NGRID_IQ1S : 1024;
type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? NGRID_IQ1S : 1024;
}
static int iq2_compare_func(const void * left, const void * right) {
@ -10214,10 +10393,10 @@ void iq2xs_init_impl(enum ggml_type type) {
const int kmap_size = 43692;
//const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2;
const int nwant = type == GGML_TYPE_IQ1_S ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
const int nwant = type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 :
type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 :
type == GGML_TYPE_IQ1_S ? kgrid_1bit_2048 : kgrid_2bit_1024;
type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? kgrid_1bit_2048 : kgrid_2bit_1024;
uint64_t * kgrid_q2xs;
int * kmap_q2xs;
uint16_t * kneighbors_q2xs;
@ -10314,7 +10493,7 @@ void iq2xs_init_impl(enum ggml_type type) {
}
void iq2xs_free_impl(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S);
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
const int gindex = iq2_data_index(type);
if (iq2_data[gindex].grid) {
free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL;
@ -11520,7 +11699,16 @@ static int iq1_sort_helper(const void * left, const void * right) {
}
#define IQ1S_BLOCK_SIZE 32
static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
#define IQ1M_BLOCK_SIZE 16
static void quantize_row_iq1_impl(enum ggml_type type, const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
float * scales,
float * weight,
float * sumx,
float * sumw,
float * pairs,
int8_t * L,
uint16_t * index,
int8_t * shifts) {
const int gindex = iq2_data_index(GGML_TYPE_IQ1_S);
@ -11536,26 +11724,28 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
const int nbl = n/QK_K;
block_iq1_s * y = vy;
const int block_size = type == GGML_TYPE_IQ1_S ? IQ1S_BLOCK_SIZE : IQ1M_BLOCK_SIZE;
const float x_p[3] = {-1 + IQ1S_DELTA, IQ1S_DELTA, 1 + IQ1S_DELTA};
const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA};
float scales[QK_K/IQ1S_BLOCK_SIZE];
float weight[IQ1S_BLOCK_SIZE];
int8_t L[IQ1S_BLOCK_SIZE];
float sumx[IQ1S_BLOCK_SIZE+1];
float sumw[IQ1S_BLOCK_SIZE+1];
float pairs[2*IQ1S_BLOCK_SIZE];
int * idx = (int *)(pairs + 1);
uint16_t index[IQ1S_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
for (int ibl = 0; ibl < nbl; ++ibl) {
if (type == GGML_TYPE_IQ1_S) {
block_iq1_s * y = vy;
y[ibl].d = GGML_FP32_TO_FP16(0.f);
memset(y[ibl].qs, 0, QK_K/8);
memset(y[ibl].qh, 0, QK_K/16);
} else {
block_iq1_m * y = vy;
y[ibl].d = GGML_FP32_TO_FP16(0.f);
memset(y[ibl].qs, 0, QK_K/8);
memset(y[ibl].qh, 0, QK_K/16);
memset(y[ibl].scales, 0, QK_K/32);
}
float max_scale = 0;
@ -11564,15 +11754,15 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
float sigma2 = 2*sumx2/QK_K;
for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) {
const float * xb = xbl + IQ1S_BLOCK_SIZE*ib;
const float * qw = quant_weights + QK_K*ibl + IQ1S_BLOCK_SIZE*ib;
for (int i = 0; i < IQ1S_BLOCK_SIZE; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
for (int ib = 0; ib < QK_K/block_size; ++ib) {
const float * xb = xbl + block_size*ib;
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
float max = fabsf(xb[0]);
for (int i = 1; i < IQ1S_BLOCK_SIZE; ++i) max = MAX(max, fabsf(xb[i]));
for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
if (!max) {
scales[ib] = 0;
memset(L, 1, IQ1S_BLOCK_SIZE);
memset(L, 1, block_size);
continue;
}
// Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
@ -11581,31 +11771,32 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
// in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
// Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
// for each possible and score for each split.
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) {
for (int j = 0; j < block_size; ++j) {
pairs[2*j] = xb[j];
idx[2*j] = j;
}
qsort(pairs, IQ1S_BLOCK_SIZE, 2*sizeof(float), iq1_sort_helper);
qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
{
sumx[0] = sumw[0] = 0;
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) {
for (int j = 0; j < block_size; ++j) {
int i = idx[2*j];
sumx[j+1] = sumx[j] + weight[i]*xb[i];
sumw[j+1] = sumw[j] + weight[i];
}
}
float best_score = 0, scale = max;
// TODO: we need two shifts per block for IQ1_M.
int besti1 = -1, besti2 = -1, best_shift = 0;
for (int i1 = 0; i1 <= IQ1S_BLOCK_SIZE; ++i1) {
for (int i2 = i1; i2 <= IQ1S_BLOCK_SIZE; ++i2) {
float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_p[2];
float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_p[2]*x_p[2];
for (int i1 = 0; i1 <= block_size; ++i1) {
for (int i2 = i1; i2 <= block_size; ++i2) {
float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[block_size] - sumx[i2])*x_p[2];
float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[block_size] - sumw[i2])*x_p[2]*x_p[2];
if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
scale = sumqx/sumq2; best_score = scale*sumqx;
besti1 = i1; besti2 = i2; best_shift = 1;
}
sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_m[2];
sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_m[2]*x_m[2];
sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[block_size] - sumx[i2])*x_m[2];
sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[block_size] - sumw[i2])*x_m[2]*x_m[2];
if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
scale = sumqx/sumq2; best_score = scale*sumqx;
besti1 = i1; besti2 = i2; best_shift = -1;
@ -11615,14 +11806,14 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0);
for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0;
for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
for (int j = besti2; j < IQ1S_BLOCK_SIZE; ++j) L[idx[2*j]] = 2;
for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
if (scale < 0) {
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) L[j] = 2 - L[j];
for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
scale = -scale; best_shift = -best_shift;
}
bool all_on_grid = true;
const float * xx = best_shift == 1 ? x_p : x_m;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) {
for (int k = 0; k < block_size/8; ++k) {
uint16_t u = 0;
for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
int grid_index = kmap_q2xs[u];
@ -11636,7 +11827,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
}
if (!all_on_grid) {
float sumqx = 0, sumq2 = 0;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) {
for (int k = 0; k < block_size/8; ++k) {
const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j];
@ -11647,12 +11838,20 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
}
if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2;
}
if (type == GGML_TYPE_IQ1_S) {
block_iq1_s * y = vy;
uint16_t h = 0;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) {
y[ibl].qs[(IQ1S_BLOCK_SIZE/8)*ib + k] = index[k] & 255;
for (int k = 0; k < block_size/8; ++k) {
y[ibl].qs[(block_size/8)*ib + k] = index[k] & 255;
h |= (index[k] >> 8) << 3*k;
}
y[ibl].qh[ib] = h;
} else {
block_iq1_m * y = vy;
y[ibl].qs[2*ib + 0] = index[0] & 255;
y[ibl].qs[2*ib + 1] = index[1] & 255;
y[ibl].qh[ib] = (index[0] >> 8) | ((index[1] >> 8) << 4);
}
GGML_ASSERT(scale >= 0);
scales[ib] = scale;
shifts[ib] = best_shift;
@ -11660,28 +11859,74 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
}
if (!max_scale) {
memset(y[ibl].qs, 0, QK_K/8);
continue;
}
if (type == GGML_TYPE_IQ1_S) {
float d = max_scale/15;
y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.085f is another fudge factor. Don't ask me why it is needed.
block_iq1_s * y = vy;
y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed.
float id = 1/d;
for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) {
for (int ib = 0; ib < QK_K/block_size; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib]-1));
l = MAX(0, MIN(7, l));
if (shifts[ib] == -1) l |= 8;
y[ibl].qh[ib] |= (l << 12);
}
} else {
block_iq1_m * y = vy;
float d = max_scale/31;
y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed.
float id = 1/d;
for (int ib = 0; ib < QK_K/block_size; ib += 2) {
int l1 = nearest_int(0.5f*(id*scales[ib+0]-1));
l1 = MAX(0, MIN(15, l1));
int l2 = nearest_int(0.5f*(id*scales[ib+1]-1));
l2 = MAX(0, MIN(15, l2));
y[ibl].scales[ib/2] = l1 | (l2 << 4);
// TODO: we need two shifts per block for IQ1_M.
// For now we use the same shift for both groups of 8 in the block, thus wasting 1 pet per 16 weights.
if (shifts[ib+0] == -1) y[ibl].qh[ib+0] |= 0x88;
if (shifts[ib+1] == -1) y[ibl].qh[ib+1] |= 0x88;
}
}
}
}
size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
GGML_ASSERT(n_per_row%QK_K == 0);
float scales[QK_K/IQ1S_BLOCK_SIZE];
float weight[IQ1S_BLOCK_SIZE];
int8_t L[IQ1S_BLOCK_SIZE];
float sumx[IQ1S_BLOCK_SIZE+1];
float sumw[IQ1S_BLOCK_SIZE+1];
float pairs[2*IQ1S_BLOCK_SIZE];
uint16_t index[IQ1S_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights);
quantize_row_iq1_impl(GGML_TYPE_IQ1_S, src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts);
src += n_per_row;
qrow += nblock*sizeof(block_iq1_s);
}
return nrow * nblock * sizeof(block_iq1_s);
}
size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
GGML_ASSERT(n_per_row%QK_K == 0);
float scales[QK_K/IQ1M_BLOCK_SIZE];
float weight[IQ1M_BLOCK_SIZE];
int8_t L[IQ1M_BLOCK_SIZE];
float sumx[IQ1M_BLOCK_SIZE+1];
float sumw[IQ1M_BLOCK_SIZE+1];
float pairs[2*IQ1M_BLOCK_SIZE];
uint16_t index[IQ1M_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1M_BLOCK_SIZE];
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq1_impl(GGML_TYPE_IQ1_M, src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts);
src += n_per_row;
qrow += nblock*sizeof(block_iq1_s);
}

3
ggml-quants.h
View file

@ -72,6 +72,7 @@ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
@ -94,6 +95,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
void ggml_vec_dot_iq2_s_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 ggml_vec_dot_iq3_xxs_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 ggml_vec_dot_iq1_s_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 ggml_vec_dot_iq1_m_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 ggml_vec_dot_iq4_nl_q8_0 (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 ggml_vec_dot_iq4_xs_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 ggml_vec_dot_iq3_s_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);
@ -104,6 +106,7 @@ size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);

30
ggml.c
View file

@ -783,7 +783,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.nrows = 1,
},
[GGML_TYPE_IQ1_S] = {
.type_name = "iq1_s",
.type_name = "iq1_m",
.blck_size = QK_K,
.type_size = sizeof(block_iq1_m),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq1_m,
.from_float = NULL,
.from_float_reference = NULL,
.vec_dot = ggml_vec_dot_iq1_m_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ1_M] = {
.type_name = "iq1_m",
.blck_size = QK_K,
.type_size = sizeof(block_iq1_s),
.is_quantized = true,
@ -2539,6 +2551,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break;
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
@ -8135,6 +8148,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -8417,6 +8431,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -8544,6 +8559,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -11447,6 +11463,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -11638,6 +11655,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -11861,6 +11879,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -12564,6 +12583,7 @@ static void ggml_compute_forward_alibi(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -12652,6 +12672,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -20306,7 +20327,8 @@ void ggml_quantize_init(enum ggml_type type) {
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
default: // nothing
@ -20331,7 +20353,8 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) {
return
type == GGML_TYPE_IQ2_XXS ||
type == GGML_TYPE_IQ2_XS ||
type == GGML_TYPE_IQ1_S;
type == GGML_TYPE_IQ1_S ||
type == GGML_TYPE_IQ1_M;
}
size_t ggml_quantize_chunk(
@ -20375,6 +20398,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
#if QK_K == 64
case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

12
ggml.h
View file

@ -364,11 +364,12 @@ extern "C" {
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_IQ2_S = 22,
GGML_TYPE_IQ4_XS = 23,
GGML_TYPE_I8 = 24,
GGML_TYPE_I16 = 25,
GGML_TYPE_I32 = 26,
GGML_TYPE_I64 = 27,
GGML_TYPE_F64 = 28,
GGML_TYPE_IQ1_M = 24,
GGML_TYPE_I8 = 25,
GGML_TYPE_I16 = 26,
GGML_TYPE_I32 = 27,
GGML_TYPE_I64 = 28,
GGML_TYPE_F64 = 29,
GGML_TYPE_COUNT,
};
@ -408,6 +409,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors
};
// available tensor operations:

18
llama.cpp
View file

@ -3018,6 +3018,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
@ -3413,6 +3414,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_M :return "IQ1_M - 1.8125 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
@ -12447,7 +12449,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = GGML_TYPE_Q8_0;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q5_K;
}
else if (new_type != GGML_TYPE_Q8_0) {
@ -12458,7 +12461,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
new_type = qs.params->token_embedding_type;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q2_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
@ -12469,7 +12473,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
}
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
if (name.find("attn_v.weight") != std::string::npos) {
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
@ -12488,7 +12492,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (qs.model.hparams.n_expert == 8) {
new_type = GGML_TYPE_Q5_K;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS;
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
}
}
@ -12655,7 +12659,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) {
new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
new_type == GGML_TYPE_IQ1_M) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
@ -12673,6 +12678,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
@ -12754,6 +12760,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
@ -12944,6 +12951,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type == GGML_TYPE_IQ2_XS ||
new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ1_S ||
new_type == GGML_TYPE_IQ1_M ||
(new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
LLAMA_LOG_ERROR("\n\n============================================================\n");
LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);

1
llama.h
View file

@ -117,6 +117,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};