mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-06-28 04:30:15 -05:00
IQ4_XS_R4 (#123)
* Adding iq4_xs_r4 This is a 1st working version on Zen4. We get PP-512(LLaMA-3.1-8B) = 226 t/s, so 16% slower than iq4_nl_x4. * iq4_xs_r4: WIP * iq4_xs_r4: Use AVX2 version for matrix x vector on Zen4 * iq4_xs_r4: NEON We get PP-512(LLaMA-3.1-8B) = 115.6 t/s on M2-Max, up from 68.2 t/s for iq4_xs! * DRY --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
bb699e1e6b
commit
9119023a4b
@ -41,6 +41,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
|
||||
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
|
||||
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
|
||||
{ "IQ4_NL_X4",LLAMA_FTYPE_MOSTLY_IQ4_NL_X4," 4.50 bpw non-linear quantization", },
|
||||
{ "IQ4_XS_R4",LLAMA_FTYPE_MOSTLY_IQ4_XS_R4," 4.25 bpw non-linear quantization", },
|
||||
{ "Q4_0_R4", LLAMA_FTYPE_MOSTLY_Q4_0_R4, " 4.50 bpw quantization", },
|
||||
{ "Q5_0_R4", LLAMA_FTYPE_MOSTLY_Q5_0_R4, " 5.50 bpw quantization", },
|
||||
{ "Q6_0_R4", LLAMA_FTYPE_MOSTLY_Q6_0_R4, " 6.50 bpw quantization", },
|
||||
|
||||
@ -410,7 +410,8 @@ extern "C" {
|
||||
GGML_TYPE_Q4_0_R4 = 202,
|
||||
GGML_TYPE_Q5_0_R4 = 206,
|
||||
GGML_TYPE_Q8_0_R4 = 208,
|
||||
GGML_TYPE_IQ4_NL_X4 = 220,
|
||||
GGML_TYPE_IQ4_NL_X4 = 220, // TODO: rename GGML_TYPE_IQ4_NL_X4 to GGML_TYPE_IQ4_NL_R4
|
||||
GGML_TYPE_IQ4_XS_R4 = 223,
|
||||
GGML_TYPE_Q6_0_R4 = 233,
|
||||
GGML_TYPE_COUNT,
|
||||
};
|
||||
@ -475,6 +476,7 @@ extern "C" {
|
||||
GGML_FTYPE_MOSTLY_Q8_0_R4 = 207, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_0_R4 = 208, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ4_NL_X4 = 219, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q6_0_R4 = 227, // except 1d tensors
|
||||
};
|
||||
|
||||
|
||||
@ -447,6 +447,14 @@ typedef struct {
|
||||
} block_iq4_xs;
|
||||
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
|
||||
|
||||
typedef struct {
|
||||
ggml_half d[4];
|
||||
uint8_t scales_h[QK_K/32];
|
||||
uint8_t scales_l[QK_K/16];
|
||||
uint8_t qs[QK_K*2];
|
||||
} block_iq4_xs_r4;
|
||||
static_assert(sizeof(block_iq4_xs_r4) == 4*sizeof(ggml_half) + QK_K/32 + QK_K/16 + QK_K*2, "wrong iq4_xs_rs block size/padding");
|
||||
|
||||
typedef struct {
|
||||
uint8_t scales[QK_K/32];
|
||||
uint8_t qs[QK_K/2];
|
||||
|
||||
@ -15197,6 +15197,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
|
||||
case GGML_TYPE_IQ4_KS: break;
|
||||
case GGML_TYPE_IQ4_KSS: break;
|
||||
case GGML_TYPE_IQ4_NL_X4: break;
|
||||
case GGML_TYPE_IQ4_XS_R4: break;
|
||||
case GGML_TYPE_Q4_0_R4: break;
|
||||
case GGML_TYPE_Q5_0_R4: break;
|
||||
case GGML_TYPE_Q6_0_R4: break;
|
||||
|
||||
@ -1262,6 +1262,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
|
||||
.nrows = 1,
|
||||
.row_meta_size = 0,
|
||||
},
|
||||
[GGML_TYPE_IQ4_XS_R4] = {
|
||||
.type_name = "iq4_xs_r4",
|
||||
.blck_size = QK_K,
|
||||
.type_size = sizeof(block_iq4_xs),
|
||||
.is_quantized = true,
|
||||
.to_float = (ggml_to_float_t) dequantize_row_iq4_xs_r4,
|
||||
.from_float = quantize_row_iq4_xs_r4,
|
||||
.from_float_ref = (ggml_from_float_t)quantize_row_iq4_xs_r4_ref,
|
||||
.vec_dot = vec_dot_iq4_xs_r4_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
.nrows = 1,
|
||||
.row_meta_size = 0,
|
||||
},
|
||||
[GGML_TYPE_Q4_0_R4] = {
|
||||
.type_name = "q4_0_r4",
|
||||
.blck_size = QK4_NL,
|
||||
@ -3989,6 +4002,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
||||
case GGML_FTYPE_MOSTLY_IQ2_BN: wtype = GGML_TYPE_IQ2_BN; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ4_NL_X4: wtype = GGML_TYPE_IQ4_NL_X4;break;
|
||||
case GGML_FTYPE_MOSTLY_IQ4_XS_R4: wtype = GGML_TYPE_IQ4_XS_R4;break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_0_R4: wtype = GGML_TYPE_Q4_0_R4; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_0_R4: wtype = GGML_TYPE_Q5_0_R4; break;
|
||||
case GGML_FTYPE_MOSTLY_Q6_0_R4: wtype = GGML_TYPE_Q6_0_R4; break;
|
||||
@ -10517,6 +10531,7 @@ static void ggml_compute_forward_add(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -10964,6 +10979,7 @@ static void ggml_compute_forward_add1(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -11108,6 +11124,7 @@ static void ggml_compute_forward_acc(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -14298,6 +14315,7 @@ static void ggml_compute_forward_out_prod(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -14682,6 +14700,7 @@ static void ggml_compute_forward_set(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -14960,6 +14979,7 @@ static void ggml_compute_forward_get_rows(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -15565,6 +15585,7 @@ static void ggml_compute_forward_clamp(
|
||||
case GGML_TYPE_IQ2_BN:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
case GGML_TYPE_Q6_0_R4:
|
||||
@ -22396,6 +22417,7 @@ size_t ggml_quantize_chunk(
|
||||
case GGML_TYPE_IQ2_BN: result = quantize_iq2_bn (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;
|
||||
case GGML_TYPE_IQ4_NL_X4: result = quantize_iq4_nl_x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ4_XS_R4: result = quantize_iq4_xs_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q4_0_R4: result = quantize_q4_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q5_0_R4: result = quantize_q5_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q6_0_R4: result = quantize_q6_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
|
||||
@ -2656,6 +2656,172 @@ static void mul_mat_q8_0_r4_q8_1(int n, const void * vx, size_t bx, const DataIn
|
||||
}
|
||||
#endif
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_iq4_xs_r4_q8_k_avx2(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%8 == 0);
|
||||
Q8<nrc_y, block_q8_K> q8(info);
|
||||
auto m4 = _mm256_set1_epi8(0xf);
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
auto m1 = _mm256_set1_epi16(1);
|
||||
#endif
|
||||
auto values128 = _mm_loadu_si128((const __m128i *)iq4k_values);
|
||||
auto values = MM256_SET_M128I(values128, values128);
|
||||
//auto values = load_iq4nl_values_256();
|
||||
int nbl = n / QK_K;
|
||||
using helper_t = union { __m256i vec; uint32_t val[8]; };
|
||||
helper_t h;
|
||||
__m256 acc[nrc_y] = {};
|
||||
__m256i qx[4];
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_iq4_xs_r4 * iq4 = (const block_iq4_xs_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 *)iq4[ibl].d));
|
||||
auto d4 = _mm256_set_m128(dl, dl);
|
||||
auto slbits = _mm_loadu_si128((const __m128i *)iq4[ibl].scales_l);
|
||||
auto sl = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(slbits, 4), slbits), _mm256_set1_epi8(0xf));
|
||||
auto aux64 = (const uint64_t *)iq4[ibl].scales_h;
|
||||
auto shbits = _mm_set_epi64x(aux64[0] >> 2, aux64[0]);
|
||||
auto sh = _mm256_and_si256(MM256_SET_M128I(shbits, _mm_slli_epi16(shbits, 4)), _mm256_set1_epi8(0x30));
|
||||
h.vec = _mm256_sub_epi8(_mm256_or_si256(sl, sh), _mm256_set1_epi8(32));
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto iscales = _mm256_cvtepi8_epi32(_mm_set1_epi32(h.val[ib]));
|
||||
auto scales = _mm256_mul_ps(d4, _mm256_cvtepi32_ps(iscales));
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-64.f));
|
||||
#endif
|
||||
auto bits1 = _mm256_loadu_si256((const __m256i *)iq4[ibl].qs+2*ib+0);
|
||||
auto bits2 = _mm256_loadu_si256((const __m256i *)iq4[ibl].qs+2*ib+1);
|
||||
qx[0] = _mm256_shuffle_epi8(values, _mm256_and_si256(bits1, m4));
|
||||
qx[1] = _mm256_shuffle_epi8(values, _mm256_and_si256(bits2, m4));
|
||||
qx[2] = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4));
|
||||
qx[3] = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4));
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
auto s1 = _mm256_sign_epi8(qx[0], qx[0]);
|
||||
auto s2 = _mm256_sign_epi8(qx[1], qx[1]);
|
||||
auto s3 = _mm256_sign_epi8(qx[2], qx[2]);
|
||||
auto s4 = _mm256_sign_epi8(qx[3], qx[3]);
|
||||
#endif
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib);
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto sumi = _mm256_setzero_si256();
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[0], _mm256_shuffle_epi32(y, 0x00));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[1], _mm256_shuffle_epi32(y, 0x55));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[2], _mm256_shuffle_epi32(y, 0xaa));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[3], _mm256_shuffle_epi32(y, 0xff));
|
||||
float d8 = q8.scale(iy, ibl);
|
||||
float m8 = d8 * (q8.y[iy][ibl].bsums[2*ib+0] + q8.y[iy][ibl].bsums[2*ib+1]);
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(d8)), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(m8), acc[iy]);
|
||||
#else
|
||||
auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(s1, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x00), qx[0])),
|
||||
_mm256_maddubs_epi16(s2, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x55), qx[1])));
|
||||
auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(s3, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xaa), qx[2])),
|
||||
_mm256_maddubs_epi16(s4, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xff), qx[3])));
|
||||
auto sumi = _mm256_add_epi32(_mm256_madd_epi16(m1, sumi1), _mm256_madd_epi16(m1, sumi2));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
//auto sumi1 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[0], _mm256_shuffle_epi32(y, 0x00))),
|
||||
// _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[1], _mm256_shuffle_epi32(y, 0x55))));
|
||||
//auto sumi2 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[2], _mm256_shuffle_epi32(y, 0xaa))),
|
||||
// _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[3], _mm256_shuffle_epi32(y, 0xff))));
|
||||
//auto sumi = _mm256_add_epi32(sumi1, sumi2);
|
||||
//float d8 = q8.scale(iy, ibl);
|
||||
//float m8 = d8 * (q8.y[iy][ibl].bsums[2*ib+0] + q8.y[iy][ibl].bsums[2*ib+1]);
|
||||
//acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(d8)), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
//acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(m8), acc[iy]);
|
||||
#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));
|
||||
acc[iy] = _mm256_setzero_ps();
|
||||
info.store(ix+0, iy, sum);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
template <int nrc_y>
|
||||
static void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
if constexpr (nrc_y == 1){
|
||||
mul_mat_iq4_xs_r4_q8_k_avx2<1>(n, vx, bx, info, nrc_x);
|
||||
} else {
|
||||
GGML_ASSERT(nrc_x%8 == 0);
|
||||
Q8<nrc_y, block_q8_K> q8(info);
|
||||
auto m4 = _mm512_set1_epi8(0xf);
|
||||
auto values = load_iq4nl_values_512();
|
||||
int nbl = n / QK_K;
|
||||
using helper_t = union { __m256i vec; uint32_t val[8]; };
|
||||
helper_t hl, hh;
|
||||
__m512 acc[2*nrc_y] = {};
|
||||
__m512i qx[4];
|
||||
for (int ix = 0; ix < nrc_x; ix += 8) {
|
||||
const block_iq4_xs_r4 * iq4l = (const block_iq4_xs_r4 *)((const char *)vx + (ix+0)*bx);
|
||||
const block_iq4_xs_r4 * iq4h = (const block_iq4_xs_r4 *)((const char *)vx + (ix+4)*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
|
||||
auto dl = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4l[ibl].d));
|
||||
auto dh = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4h[ibl].d));
|
||||
auto d4 = _mm512_insertf32x8(_mm512_castps256_ps512(_mm256_set_m128(dl, dl)), _mm256_set_m128(dh, dh), 1);
|
||||
auto slbits_l = _mm_loadu_si128((const __m128i *)iq4l[ibl].scales_l);
|
||||
auto shbits_l = _mm_loadu_si128((const __m128i *)iq4h[ibl].scales_l);
|
||||
auto sl_l = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(slbits_l, 4), slbits_l), _mm256_set1_epi8(0xf));
|
||||
auto sh_l = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(shbits_l, 4), shbits_l), _mm256_set1_epi8(0xf));
|
||||
auto aux64 = (const uint64_t *)iq4l[ibl].scales_h;
|
||||
auto slbits_h = _mm_set_epi64x(aux64[0] >> 2, aux64[0]);
|
||||
aux64 = (const uint64_t *)iq4h[ibl].scales_h;
|
||||
auto shbits_h = _mm_set_epi64x(aux64[0] >> 2, aux64[0]);
|
||||
auto sl_h = _mm256_and_si256(MM256_SET_M128I(slbits_h, _mm_slli_epi16(slbits_h, 4)), _mm256_set1_epi8(0x30));
|
||||
auto sh_h = _mm256_and_si256(MM256_SET_M128I(shbits_h, _mm_slli_epi16(shbits_h, 4)), _mm256_set1_epi8(0x30));
|
||||
hl.vec = _mm256_sub_epi8(_mm256_or_si256(sl_l, sl_h), _mm256_set1_epi8(32));
|
||||
hh.vec = _mm256_sub_epi8(_mm256_or_si256(sh_l, sh_h), _mm256_set1_epi8(32));
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto scales1 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hl.val[ib]));
|
||||
auto scales2 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hh.val[ib]));
|
||||
auto iscales = _mm512_inserti32x8(_mm512_castsi256_si512(scales1), scales2, 1);
|
||||
auto scales = _mm512_mul_ps(d4, _mm512_cvtepi32_ps(iscales));
|
||||
auto scales_m = _mm512_mul_ps(scales, _mm512_set1_ps(-64.f));
|
||||
auto bits1 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[ibl].qs+2*ib+0)),
|
||||
_mm256_loadu_si256((const __m256i *)iq4h[ibl].qs+2*ib+0), 1);
|
||||
auto bits2 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[ibl].qs+2*ib+1)),
|
||||
_mm256_loadu_si256((const __m256i *)iq4h[ibl].qs+2*ib+1), 1);
|
||||
qx[0] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits1, m4));
|
||||
qx[1] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits2, m4));
|
||||
qx[2] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits1, 4), m4));
|
||||
qx[3] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits2, 4), m4));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y8 = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib);
|
||||
auto y = _mm512_inserti32x8(_mm512_castsi256_si512(y8), y8, 1);
|
||||
auto sumi = _mm512_setzero_si512();
|
||||
sumi = _mm512_dpbusd_epi32(sumi, qx[0], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x00)));
|
||||
sumi = _mm512_dpbusd_epi32(sumi, qx[1], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x55)));
|
||||
sumi = _mm512_dpbusd_epi32(sumi, qx[2], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xaa)));
|
||||
sumi = _mm512_dpbusd_epi32(sumi, qx[3], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xff)));
|
||||
float d8 = q8.scale(iy, ibl);
|
||||
float m8 = d8 * (q8.y[iy][ibl].bsums[2*ib+0] + q8.y[iy][ibl].bsums[2*ib+1]);
|
||||
acc[2*iy+0] = _mm512_fmadd_ps(_mm512_mul_ps(scales, _mm512_set1_ps(d8)), _mm512_cvtepi32_ps(sumi), acc[2*iy+0]);
|
||||
acc[2*iy+1] = _mm512_fmadd_ps(scales_m, _mm512_set1_ps(m8), acc[2*iy+1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto sum512 = _mm512_add_ps(acc[2*iy+0], acc[2*iy+1]);
|
||||
acc[2*iy+0] = acc[2*iy+1] = _mm512_setzero_ps();
|
||||
auto sum1 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 0), _mm512_extractf32x4_ps(sum512, 1));
|
||||
auto sum2 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 2), _mm512_extractf32x4_ps(sum512, 3));
|
||||
info.store(ix+0, iy, sum1);
|
||||
info.store(ix+4, iy, sum2);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
#else
|
||||
template <int nrc_y>
|
||||
static void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
mul_mat_iq4_xs_r4_q8_k_avx2<nrc_y>(n, vx, bx, info, nrc_x);
|
||||
}
|
||||
#endif
|
||||
|
||||
template <typename Bits>
|
||||
inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, const __m256i * q8, __m256i * sumi) {
|
||||
if (j == 0) {
|
||||
@ -4625,6 +4791,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
||||
mm.funcs[7] = mul_mat_iq4_nl_x4_q8_1<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_1;
|
||||
break;
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq4_xs_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq4_xs_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq4_xs_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq4_xs_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq4_xs_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq4_xs_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq4_xs_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq4_xs_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
assert (ne00 % QK4_NL == 0);
|
||||
mm.funcs[0] = mul_mat_q4_0_r4_q8_1<1>;
|
||||
@ -7075,6 +7253,30 @@ static void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const DataIn
|
||||
}
|
||||
}
|
||||
|
||||
IQK_ALWAYS_INLINE int32x4_t interleaved_dotq(const int8x16_t * qx, const int8x16x2_t& y) {
|
||||
auto sumi = vdupq_n_s32(0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[0], y.val[0], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[1], y.val[1], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[2], y.val[0], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[3], y.val[1], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[4], y.val[0], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[5], y.val[1], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[6], y.val[0], 3);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[7], y.val[1], 3);
|
||||
return sumi;
|
||||
}
|
||||
|
||||
IQK_ALWAYS_INLINE void prepare_iq4_nl_quants(const int8x16_t& values, const uint8x16_t& m4, const uint8x16x4_t& bits, int8x16_t * qx) {
|
||||
qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4)); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19
|
||||
qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4)); // 4...7
|
||||
qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23
|
||||
qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11
|
||||
qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27
|
||||
qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15
|
||||
qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
@ -7091,25 +7293,10 @@ void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& i
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[4*ib4+k].d));
|
||||
auto bits = vld1q_u8_x4(iq4[4*ib4+k].qs);
|
||||
qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4)); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19
|
||||
qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4)); // 4...7
|
||||
qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23
|
||||
qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11
|
||||
qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27
|
||||
qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15
|
||||
qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31
|
||||
prepare_iq4_nl_quants(values, m4, bits, qx);
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ib4].qs+32*k);
|
||||
auto sumi = vdupq_n_s32(0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[0], y.val[0], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[1], y.val[1], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[2], y.val[0], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[3], y.val[1], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[4], y.val[0], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[5], y.val[1], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[6], y.val[0], 3);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[7], y.val[1], 3);
|
||||
auto sumi = interleaved_dotq(qx, y);
|
||||
auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
|
||||
acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi));
|
||||
}
|
||||
@ -7122,6 +7309,45 @@ void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& i
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq4_xs_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);
|
||||
auto m4 = vdupq_n_u8(0xf);
|
||||
auto values = vld1q_s8(iq4k_values);
|
||||
int nbl = n / QK_K;
|
||||
int8x16_t qx[8];
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_iq4_xs_r4 * iq4 = (const block_iq4_xs_r4 *)((const char *)vx + ix*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
const uint32_t * scales_l = (const uint32_t *)iq4[ibl].scales_l;
|
||||
const uint32_t * scales_h = (const uint32_t *)iq4[ibl].scales_h;
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[ibl].d));
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto ul = (scales_l[ib%4] >> 4*(ib/4)) & 0x0f0f0f0f;
|
||||
auto uh = (scales_h[ib%2] >> 2*(ib/2)) & 0x03030303;
|
||||
auto sl8 = vsub_s8(vreinterpret_s8_s32(vdup_n_s32(ul | (uh << 4))), vdup_n_s8(32));
|
||||
auto sl16 = vmovl_s8(sl8);
|
||||
auto sl32 = vmovl_s16(vget_low_s16(sl16));
|
||||
auto scales = vmulq_f32(d4, vcvtq_f32_s32(sl32));
|
||||
auto bits = vld1q_u8_x4(iq4[ibl].qs + 64*ib);
|
||||
prepare_iq4_nl_quants(values, m4, bits, qx);
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ibl].qs+32*ib);
|
||||
auto sumi = interleaved_dotq(qx, y);
|
||||
auto d4d8 = vmulq_f32(scales, vdupq_n_f32(q8.scale(iy, ibl)));
|
||||
acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi));
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
info.store(ix, iy, acc[iy]);
|
||||
acc[iy] = vdupq_n_f32(0.f);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void mul_mat_iq4_nl_x4_q8_0_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
Q8<1, block_q8_0_x4> q8(info);
|
||||
@ -7529,6 +7755,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
||||
m.funcs[7] = mul_mat_iq4_nl_x4_q8_0<8>;
|
||||
expected_Btype = GGML_TYPE_Q8_0;
|
||||
break;
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
m.funcs[0] = mul_mat_iq4_nl_x4_q8_0_1;
|
||||
m.funcs[1] = mul_mat_iq4_xs_r4_q8_k<2>;
|
||||
m.funcs[2] = mul_mat_iq4_xs_r4_q8_k<3>;
|
||||
m.funcs[3] = mul_mat_iq4_xs_r4_q8_k<4>;
|
||||
m.funcs[4] = mul_mat_iq4_xs_r4_q8_k<5>;
|
||||
m.funcs[5] = mul_mat_iq4_xs_r4_q8_k<6>;
|
||||
m.funcs[6] = mul_mat_iq4_xs_r4_q8_k<7>;
|
||||
m.funcs[7] = mul_mat_iq4_xs_r4_q8_k<8>;
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
m.funcs[0] = mul_mat_q4_0_r4_q8_0<1>;
|
||||
m.funcs[1] = mul_mat_q4_0_r4_q8_0<2>;
|
||||
|
||||
@ -3572,3 +3572,111 @@ void vec_dot_q6_0_r4_q8_0(int n, float * s, size_t bs, const void * vx, size_t b
|
||||
GGML_UNUSED(bx);
|
||||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq4_xs_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq4_xs_r4_ref(const float * x, block_iq4_xs_r4 * y, int64_t k) {
|
||||
quantize_iq4_xs_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq4_xs_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq4_xs_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
static void repack_iq4_xs(int nrows, int n_per_row, const block_iq4_xs * x, block_iq4_xs_r4 * y) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
int nblock = n_per_row/QK_K;
|
||||
const block_iq4_xs * 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) {
|
||||
std::memset(y[ibl].scales_l, 0, QK_K/16);
|
||||
std::memset(y[ibl].scales_h, 0, QK_K/32);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k] = x4[k][ibl].d;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
uint8_t sl = (x4[k][ibl].scales_l[ib/2] >> 4*(ib%2)) & 0xf;
|
||||
uint8_t sh = (x4[k][ibl].scales_h >> 2*ib) & 3;
|
||||
int i = 4*ib + k;
|
||||
y[ibl].scales_l[i%16] |= (sl << 4*(i/16));
|
||||
y[ibl].scales_h[i%8 ] |= (sh << 2*(i/8));
|
||||
}
|
||||
}
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
for (int k = 0; k < 4; ++k) for (int i = 0; i < 4; ++i) {
|
||||
y[ibl].qs[64*ib+4*k+i+ 0] = (x4[k][ibl].qs[16*ib+i+0] & 0xf) | ((x4[k][ibl].qs[16*ib+i+ 8] & 0x0f) << 4); // 0....3 + 8...11 from each row
|
||||
y[ibl].qs[64*ib+4*k+i+16] = (x4[k][ibl].qs[16*ib+i+0] >> 4) | ((x4[k][ibl].qs[16*ib+i+ 8] & 0xf0)); // 16...19 + 24...27 from each row
|
||||
y[ibl].qs[64*ib+4*k+i+32] = (x4[k][ibl].qs[16*ib+i+4] & 0xf) | ((x4[k][ibl].qs[16*ib+i+12] & 0x0f) << 4); // 4....7 + 12...15 from each row
|
||||
y[ibl].qs[64*ib+4*k+i+48] = (x4[k][ibl].qs[16*ib+i+4] >> 4) | ((x4[k][ibl].qs[16*ib+i+12] & 0xf0)); // 20...23 + 28...31 from each row
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq4_xs_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_IQ4_XS, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq4_xs(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq4_xs(4, n_per_row, (const block_iq4_xs *)qtmp.data(), (block_iq4_xs_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq4_xs_r4(const block_iq4_xs_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 = GGML_FP16_TO_FP32(x[ibl].d[k]);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
int is = 4*ib + k;
|
||||
float dl = d * ((((x[ibl].scales_l[is%16] >> 4*(is/16)) & 0xf) | (((x[ibl].scales_h[is%8] >> 2*(is/8)) & 3) << 4)) - 32);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y4[k][QK_K*ibl+32*ib+i+ 0] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+ 0] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+ 0] >> 4];
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+16] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+16] >> 4];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+32] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+32] >> 4];
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+48] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+48] >> 4];
|
||||
}
|
||||
}
|
||||
}
|
||||
//dequantize_row_iq4_xs(x + ib, ytmp, QK_K);
|
||||
//for (int k = 0; k < 4; ++k) {
|
||||
// for (int l = 0; l < 16; ++l) {
|
||||
// for (int i = 0; i < 4; ++i) {
|
||||
// //y4[k][ib*kBlockSize + i + 16*(l%4) + 4*(l/4)] = ytmp[16*l + 4*k + i];
|
||||
// y4[k][ib*kBlockSize + i + 8*(l%8) + 4*(l/8)] = ytmp[16*l + 4*k + i];
|
||||
// }
|
||||
// }
|
||||
//}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq4_xs_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_IQ4_XS_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);
|
||||
}
|
||||
|
||||
@ -93,6 +93,12 @@ size_t quantize_q6_0_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
|
||||
void dequantize_row_q6_0_r4(const block_q6_0_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_q6_0_r4_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 quantize_row_iq4_xs_r4_ref(const float * GGML_RESTRICT x, block_iq4_xs_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq4_xs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq4_xs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void dequantize_row_iq4_xs_r4(const block_iq4_xs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq4_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);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -184,6 +184,7 @@ extern "C" {
|
||||
LLAMA_FTYPE_MOSTLY_Q8_0_R4 = 207, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_0_R4 = 208, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 = 225, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 = 230, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 235, // except 1d tensors
|
||||
|
||||
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
|
||||
|
||||
@ -3850,6 +3850,7 @@ struct llama_model_loader {
|
||||
case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break;
|
||||
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
|
||||
case GGML_TYPE_IQ4_NL_X4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_X4;break;
|
||||
case GGML_TYPE_IQ4_XS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS_R4;break;
|
||||
case GGML_TYPE_Q4_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_R4; break;
|
||||
case GGML_TYPE_Q5_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q5_0_R4; break;
|
||||
case GGML_TYPE_Q6_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q6_0_R4; break;
|
||||
@ -4559,6 +4560,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:return "IQ4_NL_X4 - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS_R4:return "IQ4_XS_R4 - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_0_R4: return "Q4_0_R4 - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_0_R4: return "Q5_0_R4 - 5.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_0_R4: return "Q6_0_R4 - 6.5 bpw";
|
||||
@ -15779,6 +15781,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
else if (new_type == GGML_TYPE_IQ4_NL_X4) {
|
||||
new_type = GGML_TYPE_IQ4_NL;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ4_XS_R4) {
|
||||
new_type = GGML_TYPE_IQ4_XS;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q4_0_R4) {
|
||||
new_type = GGML_TYPE_Q4_0;
|
||||
}
|
||||
@ -15852,7 +15857,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 ||
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
}
|
||||
@ -15883,6 +15889,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_IQ4_NL) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_IQ4_NL_X4) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_IQ4_XS_R4) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_Q5_K) new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
++qs.i_attention_wv;
|
||||
@ -15947,7 +15954,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
}
|
||||
else if (i_layer < n_layer/8 && !qs.has_imatrix &&
|
||||
(ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4)) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4)) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
||||
@ -15973,7 +15981,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_K ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
@ -16183,6 +16192,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_BN: default_type = GGML_TYPE_IQ2_BN; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:default_type = GGML_TYPE_IQ4_NL_X4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS_R4:default_type = GGML_TYPE_IQ4_XS_R4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_0_R4: default_type = GGML_TYPE_Q4_0_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_0_R4: default_type = GGML_TYPE_Q5_0_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_0_R4: default_type = GGML_TYPE_Q6_0_R4; break;
|
||||
@ -16548,6 +16558,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_NL;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ4_XS_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_XS;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q4_0_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
|
||||
else chunk_size_multiplier = 4;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user