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IQ2_S_R4 (#156)

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* iq2_s_r4: Zen4

* Minor

* iq2_s_r4: NEON

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2024-12-21 11:26:35 +01:00 committed by GitHub
parent 6892554e43
commit 7598ec79a2
10 changed files with 355 additions and 9 deletions
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1
examples/quantize/quantize.cpp
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@ -27,6 +27,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ2_XS_R4",LLAMA_FTYPE_MOSTLY_IQ2_XS_R4,"IQ2_XS repacked", },
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ2_M_R4", LLAMA_FTYPE_MOSTLY_IQ2_M_R4, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "IQ1_BN", LLAMA_FTYPE_MOSTLY_IQ1_BN, " 1.62 bpw quantization (Bitnet)", },

2
ggml/include/ggml.h
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@ -422,6 +422,7 @@ extern "C" {
GGML_TYPE_IQ2_XS_R4 = 217,
GGML_TYPE_IQ3_XXS_R4= 218,
GGML_TYPE_IQ4_NL_R4 = 220,
GGML_TYPE_IQ2_S_R4 = 222,
GGML_TYPE_IQ4_XS_R4 = 223,
GGML_TYPE_BF16_R16 = 230,
GGML_TYPE_Q6_0_R4 = 233,
@ -503,6 +504,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ2_XS_R4 = 216, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ3_XXS_R4= 217, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_NL_R4 = 219, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_S_R4 = 221, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors
GGML_FTYPE_MOSTLY_BF16_R16 = 224, // except 1d tensors
GGML_FTYPE_MOSTLY_Q6_0_R4 = 227, // except 1d tensors

9
ggml/src/ggml-common.h
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@ -428,6 +428,15 @@ typedef struct {
} block_iq2_s;
static_assert(sizeof(block_iq2_s) == sizeof(ggml_half) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
typedef struct {
ggml_half d[4];
uint8_t qs[QK_K/2];
uint8_t qh[QK_K/8];
uint8_t signs[QK_K/2];
uint8_t scales[QK_K/8];
} block_iq2_s_r4;
static_assert(sizeof(block_iq2_s_r4) == 4*sizeof(block_iq2_s), "wrong iq2_s_r4 block size/padding");
// (Almost) "true" 3-bit quantization.
// Due to the need to use blocks as per ggml design, it ends up using
// 3.0625 bpw because of the 16-bit scale for each block of 256.

1
ggml/src/ggml-quants.c
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@ -15201,6 +15201,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
case GGML_TYPE_IQ2_XXS_R4: break;
case GGML_TYPE_IQ2_XS_R4: break;
case GGML_TYPE_IQ3_XXS_R4: break;
case GGML_TYPE_IQ2_S_R4: break;
case GGML_TYPE_Q4_0_R4: break;
case GGML_TYPE_Q5_0_R4: break;
case GGML_TYPE_Q6_0_R4: break;

23
ggml/src/ggml.c
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@ -1096,6 +1096,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.nrows = 1,
.row_meta_size = 0,
},
[GGML_TYPE_IQ2_S_R4] = {
.type_name = "iq2_s_r4",
.blck_size = QK_K,
.type_size = sizeof(block_iq2_s),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq2_s_r4,
.from_float = quantize_row_iq2_s_r4,
.from_float_ref = (ggml_from_float_t)quantize_row_iq2_s_r4_ref,
.vec_dot = vec_dot_iq2_s_r4_q8_k,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
.row_meta_size = 0,
},
[GGML_TYPE_IQ1_S] = {
.type_name = "iq1_s",
.blck_size = QK_K,
@ -4270,6 +4283,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ6_K: wtype = GGML_TYPE_IQ6_K; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break;
case GGML_FTYPE_MOSTLY_IQ2_S_R4: wtype = GGML_TYPE_IQ2_S_R4; break;
case GGML_FTYPE_MOSTLY_Q4_0_4_4: wtype = GGML_TYPE_Q4_0_4_4; break;
case GGML_FTYPE_MOSTLY_Q4_0_4_8: wtype = GGML_TYPE_Q4_0_4_8; break;
case GGML_FTYPE_MOSTLY_Q4_0_8_8: wtype = GGML_TYPE_Q4_0_8_8; break;
@ -10814,6 +10828,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
@ -11277,6 +11292,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
@ -11437,6 +11453,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
@ -14643,6 +14660,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
@ -15043,6 +15061,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
@ -15337,6 +15356,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
@ -15960,6 +15980,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_Q8_K:
case GGML_TYPE_Q8_K64:
case GGML_TYPE_Q8_K16:
@ -22702,6 +22723,7 @@ void ggml_quantize_init(enum ggml_type type) {
switch (type) {
case GGML_TYPE_IQ2_XXS_R4: iq2xs_init_impl(GGML_TYPE_IQ2_XXS); break;
case GGML_TYPE_IQ2_XS_R4: iq2xs_init_impl(GGML_TYPE_IQ2_XS); break;
case GGML_TYPE_IQ2_S_R4: iq2xs_init_impl(GGML_TYPE_IQ2_S); break;
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
@ -22786,6 +22808,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ3_XXS_R4:result = quantize_iq3_xxs_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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_IQ2_S_R4:result = quantize_iq2_s_r4(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_IQ1_BN: result = quantize_iq1_bn (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

204
ggml/src/iqk/iqk_mul_mat.cpp
View File

@ -3425,6 +3425,128 @@ static void mul_mat_iq2_xs_r4_q8_k(int n, const void * vx, size_t bx, const Data
}
}
template <int nrc_y>
static void mul_mat_iq2_s_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0);
Q8<nrc_y, block_q8_K> q8(info);
int nbl = n / QK_K;
#ifndef HAVE_FANCY_SIMD
auto smask = _mm256_set1_epi64x(0x8040201008040201);
auto sign_shuffle = _mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, 0x0101010101010101, 0x0000000000000000);
auto m4 = _mm256_set1_epi8(4);
#endif
__m256 acc[nrc_y] = {};
#ifdef HAVE_FANCY_SIMD
__m256i shuffles[2] = {
_mm256_set_epi64x(0x0706070607060706, 0x0302030203020302, 0x0504050405040504, 0x0100010001000100),
_mm256_set_epi64x(0x0f0e0f0e0f0e0f0e, 0x0b0a0b0a0b0a0b0a, 0x0d0c0d0c0d0c0d0c, 0x0908090809080908)
};
__m256i isum[2*nrc_y] = {};
#else
__m256i shuffles[4] = {
MM256_SET_M128I(_mm_set1_epi16(0x0302), _mm_set1_epi16(0x0100)),
MM256_SET_M128I(_mm_set1_epi16(0x0706), _mm_set1_epi16(0x0504)),
MM256_SET_M128I(_mm_set1_epi16(0x0b0a), _mm_set1_epi16(0x0908)),
MM256_SET_M128I(_mm_set1_epi16(0x0f0e), _mm_set1_epi16(0x0d0c)),
};
__m256i isum[nrc_y == 1 ? 4 : nrc_y] = {};
#endif
__m256i qx[4];
auto grid = iq2s_grid;
for (int ix = 0; ix < nrc_x; ix += 4) {
auto iq2 = (const block_iq2_s_r4 *)((const char *)vx + (ix+0)*bx);
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
auto dl = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq2[ibl].d));
auto d4 = _mm256_set_m128(dl, dl);
auto s32 = (const uint32_t *)iq2[ibl].scales;
auto ql = iq2[ibl].qs;
auto qh = iq2[ibl].qh;
for (int ib = 0; ib < QK_K/32; ++ib) {
qx[0] = _mm256_set_epi64x(grid[ql[ 3] | ((qh[0] << 2) & 0x300)], grid[ql[ 2] | ((qh[0] << 4) & 0x300)], grid[ql[ 1] | ((qh[0] << 6) & 0x300)], grid[ql[ 0] | ((qh[0] << 8) & 0x300)]);
qx[1] = _mm256_set_epi64x(grid[ql[ 7] | ((qh[1] << 2) & 0x300)], grid[ql[ 6] | ((qh[1] << 4) & 0x300)], grid[ql[ 5] | ((qh[1] << 6) & 0x300)], grid[ql[ 4] | ((qh[1] << 8) & 0x300)]);
qx[2] = _mm256_set_epi64x(grid[ql[11] | ((qh[2] << 2) & 0x300)], grid[ql[10] | ((qh[2] << 4) & 0x300)], grid[ql[ 9] | ((qh[2] << 6) & 0x300)], grid[ql[ 8] | ((qh[2] << 8) & 0x300)]);
qx[3] = _mm256_set_epi64x(grid[ql[15] | ((qh[3] << 2) & 0x300)], grid[ql[14] | ((qh[3] << 4) & 0x300)], grid[ql[13] | ((qh[3] << 6) & 0x300)], grid[ql[12] | ((qh[3] << 8) & 0x300)]);
ql += 16; qh += 4;
auto signs128 = _mm_loadu_si128((const __m128i*)iq2[ibl].signs + ib);
auto scales = _mm_set1_epi32(s32[ib]);
scales = _mm_and_si128(_mm_unpacklo_epi8(scales, _mm_srli_epi16(scales, 4)), _mm_set1_epi8(0xf));
scales = _mm_or_si128(_mm_slli_epi16(scales, 1), _mm_set1_epi8(1));
auto scales16 = _mm256_cvtepi8_epi16(scales); // 0...7, 0...7
#ifdef HAVE_FANCY_SIMD
__m256i scs[2] = { _mm256_shuffle_epi8(scales16, shuffles[0]), _mm256_shuffle_epi8(scales16, shuffles[1]) };
auto mask = (const __mmask32 *)&signs128;
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = _mm256_loadu_si256((const __m256i *)q8.y[iy][ibl].qs + ib);
auto sumi1 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[0], _mm256_mask_sub_epi8(y, mask[0], _mm256_setzero_si256(), y)); // blocks: 0,0,0,0, 1,1,1,1, row 0
auto sumi2 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[1], _mm256_mask_sub_epi8(y, mask[1], _mm256_setzero_si256(), y)); // blocks: 2,2,2,2, 3,3,3,3, row 1
auto sumi3 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[2], _mm256_mask_sub_epi8(y, mask[2], _mm256_setzero_si256(), y)); // blocks: 4,4,4,4, 5,5,5,5, row 2
auto sumi4 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[3], _mm256_mask_sub_epi8(y, mask[3], _mm256_setzero_si256(), y)); // blocks: 6,6,6,6, 7,7,7,7, row 3
auto s12 = _mm256_packs_epi32(sumi1, sumi2); // 0,0,0,0, 2,2,2,2, 1,1,1,1, 3,3,3,3
auto s34 = _mm256_packs_epi32(sumi3, sumi4); // 4,4,4,4, 6,6,6,6, 5,5,5,5, 7,7,7,7
isum[2*iy+0] = _mm256_add_epi32(isum[2*iy+0], _mm256_madd_epi16(scs[0], s12));
isum[2*iy+1] = _mm256_add_epi32(isum[2*iy+1], _mm256_madd_epi16(scs[1], s34));
}
#else
auto signs = MM256_SET_M128I(signs128, signs128);
auto shuffle = sign_shuffle;
auto s1 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
shuffle = _mm256_add_epi8(shuffle, m4);
auto s2 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
shuffle = _mm256_add_epi8(shuffle, m4);
auto s3 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
shuffle = _mm256_add_epi8(shuffle, m4);
auto s4 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
__m256i scs[4] = {
_mm256_shuffle_epi8(scales16, shuffles[0]), _mm256_shuffle_epi8(scales16, shuffles[1]),
_mm256_shuffle_epi8(scales16, shuffles[2]), _mm256_shuffle_epi8(scales16, shuffles[3]),
};
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = _mm256_loadu_si256((const __m256i *)q8.y[iy][ibl].qs + ib);
if constexpr (nrc_y == 1) {
isum[0] = _mm256_add_epi32(isum[0], _mm256_madd_epi16(scs[0], _mm256_maddubs_epi16(qx[0], _mm256_sign_epi8(y, s1))));
isum[1] = _mm256_add_epi32(isum[1], _mm256_madd_epi16(scs[1], _mm256_maddubs_epi16(qx[1], _mm256_sign_epi8(y, s2))));
isum[2] = _mm256_add_epi32(isum[2], _mm256_madd_epi16(scs[2], _mm256_maddubs_epi16(qx[2], _mm256_sign_epi8(y, s3))));
isum[3] = _mm256_add_epi32(isum[3], _mm256_madd_epi16(scs[3], _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4))));
} else {
auto sumi1 = _mm256_madd_epi16(scs[0], _mm256_maddubs_epi16(qx[0], _mm256_sign_epi8(y, s1))); // blocks 4x0, 4x1, row 0
auto sumi2 = _mm256_madd_epi16(scs[1], _mm256_maddubs_epi16(qx[1], _mm256_sign_epi8(y, s2))); // blocks 4x2, 4x3, row 1
auto sumi3 = _mm256_madd_epi16(scs[2], _mm256_maddubs_epi16(qx[2], _mm256_sign_epi8(y, s3))); // blocks 4x4, 4x5, row 2
auto sumi4 = _mm256_madd_epi16(scs[3], _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4))); // blocks 4x6, 4x7, row 3
auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi1, sumi2), _mm256_unpackhi_epi32(sumi1, sumi2)); // 0,1, 0,1, 0,1, 0,1
auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi3, sumi4), _mm256_unpackhi_epi32(sumi3, sumi4)); // 2,3, 2,3, 2,3, 2,3
auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34)); // 0,1,2,3, 0,1,2,3
isum[iy] = _mm256_add_epi32(isum[iy], sumi);
}
}
#endif
}
for (int iy = 0; iy < nrc_y; ++iy) {
#ifdef HAVE_FANCY_SIMD
auto sumi = _mm256_hadd_epi32(isum[2*iy+0], isum[2*iy+1]);
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
isum[2*iy+0] = isum[2*iy+1] = _mm256_setzero_si256();
#else
if constexpr (nrc_y == 1) {
auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(isum[0], isum[1]), _mm256_unpackhi_epi32(isum[0], isum[1]));
auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(isum[2], isum[3]), _mm256_unpackhi_epi32(isum[2], isum[3]));
auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34));
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
isum[0] = isum[1] = isum[2] = isum[3] = _mm256_setzero_si256();
} else {
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(isum[iy]), acc[iy]);
isum[iy] = _mm256_setzero_si256();
}
#endif
}
}
for (int iy = 0; iy < nrc_y; ++iy) {
auto sum = _mm_add_ps(_mm256_castps256_ps128(acc[iy]), _mm256_extractf128_ps(acc[iy], 1));
info.store(ix, iy, _mm_mul_ps(_mm_set1_ps(0.125f), sum));
acc[iy] = _mm256_setzero_ps();
}
}
}
template <int nrc_y>
static void mul_mat_iq3_xxs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0);
@ -6938,6 +7060,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
mm.funcs[7] = mul_mat_iq2_xs_r4_q8_k<8>;
expected_typeB = GGML_TYPE_Q8_K;
break;
case GGML_TYPE_IQ2_S_R4:
assert (ne00 % QK_K == 0);
mm.funcs[0] = mul_mat_iq2_s_r4_q8_k<1>;
mm.funcs[1] = mul_mat_iq2_s_r4_q8_k<2>;
mm.funcs[2] = mul_mat_iq2_s_r4_q8_k<3>;
mm.funcs[3] = mul_mat_iq2_s_r4_q8_k<4>;
mm.funcs[4] = mul_mat_iq2_s_r4_q8_k<5>;
mm.funcs[5] = mul_mat_iq2_s_r4_q8_k<6>;
mm.funcs[6] = mul_mat_iq2_s_r4_q8_k<7>;
mm.funcs[7] = mul_mat_iq2_s_r4_q8_k<8>;
expected_typeB = GGML_TYPE_Q8_K;
break;
case GGML_TYPE_IQ3_XXS_R4:
assert (ne00 % QK_K == 0);
mm.funcs[0] = mul_mat_iq3_xxs_r4_q8_k<1>;
@ -9938,6 +10072,72 @@ static void mul_mat_iq2_xs_r4_q8_k(int n, const void * vx, size_t bx, const Data
}
}
template <int nrc_y>
static void mul_mat_iq2_s_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0);
Q8<nrc_y, block_q8_K> q8(info);
int nbl = n / QK_K;
float32x4_t acc[nrc_y] = {};
int32x4_t isum[2*nrc_y] = {};
int8x16_t qx[8];
uint16x8x4_t scales16;
SignHelper sh;
for (int ix = 0; ix < nrc_x; ix += 4) {
auto iq2 = (const block_iq2_s_r4 *)((const char *)vx + (ix+0)*bx);
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq2[ibl].d));
auto qs = iq2[ibl].qs;
auto qh = iq2[ibl].qh;
for (int is = 0; is < 2; ++is) {
auto scale_bits = vld1q_u8(iq2[ibl].scales + 16*is);
auto scales1 = vandq_u8(scale_bits, vdupq_n_u8(0xf));
auto scales2 = vshrq_n_u8(scale_bits, 4);
scales1 = vorrq_u8(vshlq_n_u8(scales1, 1), vdupq_n_u8(1));
scales2 = vorrq_u8(vshlq_n_u8(scales2, 1), vdupq_n_u8(1));
auto s1 = vzip1q_u8(scales1, scales2);
auto s2 = vzip2q_u8(scales1, scales2);
scales16.val[0] = vmovl_u8(vget_low_u8 (s1));
scales16.val[1] = vmovl_u8(vget_high_u8(s1));
scales16.val[2] = vmovl_u8(vget_low_u8 (s2));
scales16.val[3] = vmovl_u8(vget_high_u8(s2));
for (int ib = 0; ib < QK_K/64; ++ib) {
auto signs128 = vld1q_u8(iq2[ibl].signs + 64*is + 16*ib);
sh.init();
for (int i = 0; i < 4; ++i) {
qx[2*i+0] = vreinterpretq_s8_u64(uint64x2_t{iq2s_grid[qs[4*i+0] | ((qh[i] << 8) & 0x300)], iq2s_grid[qs[4*i+1] | ((qh[i] << 6) & 0x300)]});
sh.apply_signs_1((uint8x16_t *)qx+2*i+0, signs128);
qx[2*i+1] = vreinterpretq_s8_u64(uint64x2_t{iq2s_grid[qs[4*i+2] | ((qh[i] << 4) & 0x300)], iq2s_grid[qs[4*i+3] | ((qh[i] << 2) & 0x300)]});
sh.apply_signs_1((uint8x16_t *)qx+2*i+1, signs128);
}
qs += 16; qh += 4;
auto s32_1 = vmovl_u16(vget_low_u16 (scales16.val[ib]));
auto s32_2 = vmovl_u16(vget_high_u16(scales16.val[ib]));
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = vld1q_s8_x2(q8.y[iy][ibl].qs + 128*is + 32*ib);
auto sumi1 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[0], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[1], y.val[1]));
auto sumi2 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[2], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[3], y.val[1]));
auto sumi3 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[4], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[5], y.val[1]));
auto sumi4 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[6], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[7], y.val[1]));
auto sumi12 = vpaddq_s32(sumi1, sumi2); // blocks 0,1,2,3 in rows 0,1
auto sumi34 = vpaddq_s32(sumi3, sumi4); // blocks 4,5,6,7 in rows 2,3
isum[2*iy+0] = vmlaq_s32(isum[2*iy+0], s32_1, sumi12);
isum[2*iy+1] = vmlaq_s32(isum[2*iy+1], s32_2, sumi34);
}
}
}
for (int iy = 0; iy < nrc_y; ++iy) {
auto sumi = vpaddq_s32(isum[2*iy+0], isum[2*iy+1]);
acc[iy] = vfmaq_f32(acc[iy], vmulq_f32(d4, vdupq_n_f32(q8.scale(iy, ibl))), vcvtq_f32_s32(sumi));
isum[2*iy] = isum[2*iy+1] = vdupq_n_s32(0);
}
}
for (int iy = 0; iy < nrc_y; ++iy) {
info.store(ix, iy, vmulq_f32(vdupq_n_f32(0.125f), acc[iy]));
acc[iy] = vdupq_n_f32(0.f);
}
}
}
template <int nrc_y>
static void mul_mat_iq3_xxs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0);
@ -11293,6 +11493,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_xs_r4_q8_k);
expected_Btype = GGML_TYPE_Q8_K;
break;
case GGML_TYPE_IQ2_S_R4:
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_s_r4_q8_k);
expected_Btype = GGML_TYPE_Q8_K;
break;
case GGML_TYPE_IQ3_XXS_R4:
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq3_xxs_r4_q8_k);
expected_Btype = GGML_TYPE_Q8_K;

87
ggml/src/iqk/iqk_quantize.cpp
View File

@ -5506,6 +5506,93 @@ void vec_dot_iq2_xs_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t
GGML_UNUSED(by);
}
//
// ========================================= iq2_s_r4
//
void quantize_row_iq2_s_r4_ref(const float * x, block_iq2_s_r4 * y, int64_t k) {
quantize_iq2_s_r4(x, (void *)y, 4, k/4, nullptr);
}
void quantize_row_iq2_s_r4(const float * x, void * y, int64_t k) {
quantize_iq2_s_r4(x, y, 4, k/4, nullptr);
}
static void repack_iq2_s(int nrows, int n_per_row, const block_iq2_s * x, block_iq2_s_r4 * y) {
GGML_ASSERT(nrows%4 == 0);
GGML_ASSERT(n_per_row%QK_K == 0);
int nblock = n_per_row/QK_K;
const block_iq2_s * x4[4];
for (int row = 0; row < nrows; row += 4) {
for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
for (int ibl = 0; ibl < nblock; ++ibl) {
for (int k = 0; k < 4; ++k) {
auto signs = x4[k][ibl].qs + QK_K/8;
y[ibl].d[k] = x4[k][ibl].d;
for (int ib = 0; ib < QK_K/32; ++ib) {
y[ibl].scales[4*ib+k] = x4[k][ibl].scales[ib];
for (int i = 0; i < 4; ++i) {
y[ibl].qs[16*ib+4*k+i] = x4[k][ibl].qs[4*ib+i];
y[ibl].signs[16*ib+4*k+i] = signs[4*ib+i];
}
y[ibl].qh[4*ib+k] = x4[k][ibl].qh[ib];
}
}
}
x += 4*nblock;
y += nblock;
}
}
size_t quantize_iq2_s_r4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
GGML_ASSERT(nrows%4 == 0);
GGML_ASSERT(n_per_row%QK_K == 0);
char * qcur = (char *)dst;
auto row_size = ggml_row_size(GGML_TYPE_IQ2_S, n_per_row);
std::vector<char> qtmp(4*row_size);
for (int row = 0; row < nrows; row += 4) {
quantize_iq2_s(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
repack_iq2_s(4, n_per_row, (const block_iq2_s *)qtmp.data(), (block_iq2_s_r4 *)qcur);
qcur += 4*row_size;
src += 4*n_per_row;
}
return nrows*row_size;
}
void dequantize_row_iq2_s_r4(const block_iq2_s_r4 * x, float * y, int64_t k) {
auto n_per_row = k/4;
float * y4[4] = {y, y + n_per_row, y + 2*n_per_row, y + 3*n_per_row};
int nblock = n_per_row/QK_K;
for (int ibl = 0; ibl < nblock; ++ibl) {
for (int k = 0; k < 4; ++k) {
const float d = 0.125f*GGML_FP16_TO_FP32(x[ibl].d[k]);
for (int ib = 0; ib < QK_K/32; ++ib) {
float dl1 = d * (2*(x[ibl].scales[4*ib+k] & 0xf) + 1);
float dl2 = d * (2*(x[ibl].scales[4*ib+k] >> 4) + 1);
for (int i = 0; i < 4; ++i) {
auto val = (const int8_t *)(iq2s_grid + (x[ibl].qs[16*ib+4*k+i] | ((x[ibl].qh[4*ib+k] << (8 - 2*i)) & 0x300)));
auto signs = x[ibl].signs[16*ib+4*k+i];
float dl = i < 2 ? dl1 : dl2;
for (int j = 0; j < 8; ++j) y4[k][QK_K*ibl+32*ib+8*i+j] = dl * val[j] * (signs & (1 << j) ? -1 : 1);
}
}
}
}
}
void vec_dot_iq2_s_r4_q8_k(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_IQ2_S_R4, vx, 0, GGML_TYPE_Q8_K, 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);
}
//
// ========================================= iq3_xxs_r4
//

6
ggml/src/iqk/iqk_quantize.h
View File

@ -181,6 +181,12 @@ size_t quantize_iq2_xs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT
void dequantize_row_iq2_xs_r4(const block_iq2_xs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_iq2_xs_r4_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 quantize_row_iq2_s_r4_ref(const float * GGML_RESTRICT x, block_iq2_s_r4 * GGML_RESTRICT y, int64_t k);
void quantize_row_iq2_s_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
size_t quantize_iq2_s_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
void dequantize_row_iq2_s_r4(const block_iq2_s_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_iq2_s_r4_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 quantize_row_iq3_xxs_r4_ref(const float * GGML_RESTRICT x, block_iq3_xxs_r4 * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_xxs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
size_t quantize_iq3_xxs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);

1
include/llama.h
View File

@ -192,6 +192,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ2_XS_R4 = 220, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 = 223, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 = 225, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M_R4 = 229, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 = 230, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 335, // except 1d tensors
LLAMA_FTYPE_MOSTLY_BF16_R16 = 232, // except 1d tensors

30
src/llama.cpp
View File

@ -3854,7 +3854,8 @@ struct llama_model_loader {
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
case GGML_TYPE_IQ2_XS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS_R4; break;
case GGML_TYPE_IQ2_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KS; break;
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_M; break;
case GGML_TYPE_IQ2_S_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_M_R4;break;
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
case GGML_TYPE_IQ3_XXS_R4: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4; break;
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
@ -4586,6 +4587,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ2_KS: return "IQ2_KS - 2.1875 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_M_R4: return "IQ2_M_R4 - 2.7 bpw";
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_IQ3_XXS_R4: return "IQ3_XXS_R4 - 3.0625 bpw";
@ -15799,7 +15801,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4 ||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) {
ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4) {
new_type = !qs.has_output ? GGML_TYPE_IQ4_K : GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) {
@ -15823,7 +15825,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) {
new_type = GGML_TYPE_Q2_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4) {
new_type = GGML_TYPE_IQ3_S;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
@ -15896,22 +15898,24 @@ 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_IQ1_M ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) {
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4 ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4) {
bool is_iq2_m = ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4;
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_IQ4_K;
else if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_IQ3_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || is_iq2_m ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
++qs.i_attention_wv;
}
else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
new_type = GGML_TYPE_Q4_K;
}
else if (name.find("attn_qkv.weight") != std::string::npos) {
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_XXS : GGML_TYPE_IQ2_K;
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || is_iq2_m ? GGML_TYPE_IQ3_XXS : GGML_TYPE_IQ2_K;
}
else if (name.find("ffn_down") != std::string::npos) {
if (qs.i_ffn_down < qs.n_ffn_down/8) {
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || is_iq2_m ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
}
++qs.i_ffn_down;
}
@ -15920,7 +15924,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = GGML_TYPE_Q5_K;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || is_iq2_m) new_type = GGML_TYPE_IQ3_S;
}
}
} else if (name.find("attn_v.weight") != std::string::npos) {
@ -16190,7 +16194,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4 ||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4||
new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_R4 ||
new_type == GGML_TYPE_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4 || new_type == GGML_TYPE_IQ2_XS_R4) {
new_type == GGML_TYPE_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4 || new_type == GGML_TYPE_IQ2_XS_R4 ||
new_type == GGML_TYPE_IQ2_S_R4) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
@ -16214,6 +16219,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_IQ2_XS_R4:
case GGML_TYPE_IQ2_KS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_XXS_R4:
case GGML_TYPE_IQ3_S:
@ -16348,6 +16354,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ2_KS: default_type = GGML_TYPE_IQ2_KS; break;
case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
case LLAMA_FTYPE_MOSTLY_IQ2_M_R4:default_type = GGML_TYPE_IQ2_S_R4;break;
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: default_type = GGML_TYPE_IQ3_XXS_R4; break;
case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
@ -16701,6 +16708,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type == GGML_TYPE_IQ2_XS ||
new_type == GGML_TYPE_IQ2_XS_R4 ||
new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ2_S_R4||
new_type == GGML_TYPE_IQ1_S ||
(new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
(new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0))) {
@ -16809,6 +16817,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_XS;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_IQ2_S_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_S;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_IQ3_XXS_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ3_XXS;
else chunk_size_multiplier = 4;