Q4_K_R4 (#129)
* Something is still wrong * Simply don't see what is wrong * q4_k_r4: finally works on Zen4 I had forgotten to prevent token_embd.weight being quantized with q4_k_r4! * q4_k_r4: AVX2 We get PP-512(LLaMA-3.1-8B) = 267 t/s on a Ryzen-5975WX. This is ~30% better than Q4_K_S. * q4_k_r4: NEON We get PP-512(LLaMA-3.1-8B) = 110 t/s. Not quite as good as q4_0_r4, but still a massive improvement compared to he 69 t/s for q4_K. * q4_k_r4: slightly better AVX2 PP-512 goes from 267 t/s to 282 t/s on Ryzen-5975WX * Minor * Minor --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
b39bbb0405
commit
13126ce100
|
|
@ -58,6 +58,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
|
|||
{ "IQ5_K", LLAMA_FTYPE_MOSTLY_IQ5_K, " 5.5 bpw non-linear quantization", },
|
||||
{ "IQ6_K", LLAMA_FTYPE_MOSTLY_IQ6_K, " 6.6 bpw non-linear quantization", },
|
||||
{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
|
||||
{ "Q4_K_R4", LLAMA_FTYPE_MOSTLY_Q4_K_R4, "Q4_K_S repacked", },
|
||||
{ "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
|
||||
{ "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
|
||||
{ "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
|
||||
|
|
|
|||
|
|
@ -412,6 +412,7 @@ extern "C" {
|
|||
GGML_TYPE_Q4_0_R4 = 202,
|
||||
GGML_TYPE_Q5_0_R4 = 206,
|
||||
GGML_TYPE_Q8_0_R4 = 208,
|
||||
GGML_TYPE_Q4_K_R4 = 212,
|
||||
GGML_TYPE_IQ4_NL_R4 = 220,
|
||||
GGML_TYPE_IQ4_XS_R4 = 223,
|
||||
GGML_TYPE_Q6_0_R4 = 233,
|
||||
|
|
@ -478,6 +479,7 @@ extern "C" {
|
|||
GGML_FTYPE_MOSTLY_Q4_0_R4 = 202, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q8_0_R4 = 207, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_0_R4 = 208, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q4_K_R4 = 212, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ4_NL_R4 = 219, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q6_0_R4 = 227, // except 1d tensors
|
||||
|
|
|
|||
|
|
@ -299,6 +299,14 @@ typedef struct {
|
|||
} block_q4_K;
|
||||
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
|
||||
|
||||
typedef struct {
|
||||
ggml_half d[8];
|
||||
uint8_t scales_h[QK_K/16];// scales and mins, quantized with 6 bits
|
||||
uint8_t scales_l[QK_K/8]; // scales and mins, quantized with 6 bits
|
||||
uint8_t qs[QK_K*2]; // 4--bit quants
|
||||
} block_q4_k_r4;
|
||||
static_assert(sizeof(block_q4_k_r4) == 8*sizeof(ggml_half) + QK_K/16 + QK_K/8 + QK_K*2, "wrong q4_k_r4 block size/padding");
|
||||
|
||||
// 5-bit quantization
|
||||
// 8 blocks of 32 elements each
|
||||
// weight is represented as x = a * q + b
|
||||
|
|
|
|||
|
|
@ -15202,6 +15202,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
|
|||
case GGML_TYPE_Q5_0_R4: break;
|
||||
case GGML_TYPE_Q6_0_R4: break;
|
||||
case GGML_TYPE_Q8_0_R4: break;
|
||||
case GGML_TYPE_Q4_K_R4: break;
|
||||
case GGML_TYPE_Q4_0_4_4:
|
||||
case GGML_TYPE_Q4_0_4_8:
|
||||
{
|
||||
|
|
|
|||
|
|
@ -888,6 +888,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
|
|||
.nrows = 1,
|
||||
.row_meta_size = 0,
|
||||
},
|
||||
[GGML_TYPE_Q4_K_R4] = {
|
||||
.type_name = "q4_k_r4",
|
||||
.blck_size = QK_K,
|
||||
.type_size = sizeof(block_q4_K),
|
||||
.is_quantized = true,
|
||||
.to_float = (ggml_to_float_t) dequantize_row_q4_k_r4,
|
||||
.from_float = quantize_row_q4_k_r4,
|
||||
.from_float_ref = (ggml_from_float_t) quantize_row_q4_k_r4_ref,
|
||||
.vec_dot = vec_dot_q4_k_r4_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K32,
|
||||
.nrows = 1,
|
||||
.row_meta_size = 0,
|
||||
},
|
||||
[GGML_TYPE_Q5_K] = {
|
||||
.type_name = "q5_K",
|
||||
.blck_size = QK_K,
|
||||
|
|
@ -4020,8 +4033,9 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_K_R4: wtype = GGML_TYPE_Q4_K_R4; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
|
||||
|
|
@ -10550,6 +10564,7 @@ static void ggml_compute_forward_add(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -10999,6 +11014,7 @@ static void ggml_compute_forward_add1(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -11145,6 +11161,7 @@ static void ggml_compute_forward_acc(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -14337,6 +14354,7 @@ static void ggml_compute_forward_out_prod(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -14723,6 +14741,7 @@ static void ggml_compute_forward_set(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -15003,6 +15022,7 @@ static void ggml_compute_forward_get_rows(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -15610,6 +15630,7 @@ static void ggml_compute_forward_clamp(
|
|||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
|
|
@ -22445,6 +22466,7 @@ size_t ggml_quantize_chunk(
|
|||
case GGML_TYPE_Q2_K: result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q3_K: result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q4_K: result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q4_K_R4: result = quantize_q4_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q5_K: result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q6_K: result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
|
|
|
|||
|
|
@ -3003,8 +3003,8 @@ static void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const Data
|
|||
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;
|
||||
using helper_t = union { __m512i vec; uint32_t val[16]; };
|
||||
helper_t h;
|
||||
__m512 acc[2*nrc_y] = {};
|
||||
__m512i qx[4];
|
||||
for (int ix = 0; ix < nrc_x; ix += 8) {
|
||||
|
|
@ -3016,20 +3016,22 @@ static void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const Data
|
|||
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 sl_l = MM256_SET_M128I(_mm_srli_epi16(slbits_l, 4), slbits_l);
|
||||
auto sh_l = MM256_SET_M128I(_mm_srli_epi16(shbits_l, 4), shbits_l);
|
||||
auto slb = _mm512_and_si512(_mm512_inserti32x8(_mm512_castsi256_si512(sl_l), sh_l, 1), m4);
|
||||
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));
|
||||
auto sl_h = MM256_SET_M128I(slbits_h, _mm_slli_epi16(slbits_h, 4));
|
||||
auto sh_h = MM256_SET_M128I(shbits_h, _mm_slli_epi16(shbits_h, 4));
|
||||
auto shb = _mm512_and_si512(_mm512_inserti32x8(_mm512_castsi256_si512(sl_h), sh_h, 1), _mm512_set1_epi8(0x30));
|
||||
h.vec = _mm512_sub_epi8(_mm512_or_si512(slb, shb), _mm512_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 iscales = _mm512_cvtepi8_epi32(_mm_blend_epi32(_mm_set1_epi32(h.val[ib+0]), _mm_set1_epi32(h.val[ib+8]), 0x0c));
|
||||
//auto scales1 = _mm256_cvtepi8_epi32(_mm_set1_epi32(h.val[ib+0]));
|
||||
//auto scales2 = _mm256_cvtepi8_epi32(_mm_set1_epi32(h.val[ib+8]));
|
||||
//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)),
|
||||
|
|
@ -3073,6 +3075,179 @@ static void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const Data
|
|||
}
|
||||
#endif
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_q4_k_r4_q8_k_avx2(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 mf = _mm256_set1_epi8(0xf);
|
||||
auto m3 = _mm256_set1_epi8(0x30);
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
auto m1 = _mm256_set1_epi16(1);
|
||||
#endif
|
||||
int nbl = n / QK_K;
|
||||
union { __m256i vec; uint32_t val[8]; } hd;
|
||||
__m256 acc[nrc_y] = {};
|
||||
__m256i qx[4];
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_q4_k_r4 * iq4 = (const block_q4_k_r4 *)((const char *)vx + (ix+0)*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
|
||||
auto dl = _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)iq4[ibl].d));
|
||||
auto d4 = _mm256_set_m128(_mm256_castps256_ps128(dl), _mm256_castps256_ps128(dl));
|
||||
auto m4 = _mm256_mul_ps(_mm256_set1_ps(-1.0f), _mm256_set_m128(_mm256_extractf128_ps(dl, 1), _mm256_extractf128_ps(dl, 1)));
|
||||
if constexpr (nrc_y == 1) {
|
||||
d4 = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(0, ibl)));
|
||||
}
|
||||
auto lbits = _mm256_loadu_si256((const __m256i *)iq4[ibl].scales_l);
|
||||
auto hbits128 = _mm_loadu_si128((const __m128i *)iq4[ibl].scales_h);
|
||||
auto hbits = MM256_SET_M128I(hbits128, _mm_slli_epi16(hbits128, 4));
|
||||
hd.vec = _mm256_or_si256(_mm256_and_si256(lbits, mf), _mm256_and_si256(hbits, m3));
|
||||
auto mins = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lbits, 4), mf), _mm256_and_si256(_mm256_srli_epi16(hbits, 2), m3));
|
||||
auto shuffle = _mm256_set1_epi64x(0x0000000400000000);
|
||||
auto c1 = _mm256_mul_ps(m4, _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(_mm256_castsi256_si128(_mm256_permutevar8x32_epi32(mins, shuffle)))));
|
||||
shuffle = _mm256_add_epi32(shuffle, _mm256_set1_epi32(1));
|
||||
auto c2 = _mm256_mul_ps(m4, _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(_mm256_castsi256_si128(_mm256_permutevar8x32_epi32(mins, shuffle)))));
|
||||
shuffle = _mm256_add_epi32(shuffle, _mm256_set1_epi32(1));
|
||||
auto c3 = _mm256_mul_ps(m4, _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(_mm256_castsi256_si128(_mm256_permutevar8x32_epi32(mins, shuffle)))));
|
||||
shuffle = _mm256_add_epi32(shuffle, _mm256_set1_epi32(1));
|
||||
auto c4 = _mm256_mul_ps(m4, _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(_mm256_castsi256_si128(_mm256_permutevar8x32_epi32(mins, shuffle)))));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto bs = _mm256_loadu_ps((const float *)q8.y[iy][ibl].bsums);
|
||||
acc[iy] = _mm256_fmadd_ps(c1, _mm256_shuffle_ps(bs, bs, 0x00), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(c2, _mm256_shuffle_ps(bs, bs, 0x55), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(c3, _mm256_shuffle_ps(bs, bs, 0xaa), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(c4, _mm256_shuffle_ps(bs, bs, 0xff), acc[iy]);
|
||||
}
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto scales_d = _mm256_mul_ps(d4, _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(_mm_set1_epi32(hd.val[ib]))));
|
||||
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_and_si256(bits1, mf);
|
||||
qx[1] = _mm256_and_si256(bits2, mf);
|
||||
qx[2] = _mm256_and_si256(_mm256_srli_epi16(bits1, 4), mf);
|
||||
qx[3] = _mm256_and_si256(_mm256_srli_epi16(bits2, 4), mf);
|
||||
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));
|
||||
#else
|
||||
auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(qx[0], _mm256_shuffle_epi32(y, 0x00)),
|
||||
_mm256_maddubs_epi16(qx[1], _mm256_shuffle_epi32(y, 0x55)));
|
||||
auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(qx[2], _mm256_shuffle_epi32(y, 0xaa)),
|
||||
_mm256_maddubs_epi16(qx[3], _mm256_shuffle_epi32(y, 0xff)));
|
||||
auto sumi = _mm256_madd_epi16(m1, _mm256_add_epi16(sumi1, sumi2));
|
||||
#endif
|
||||
if constexpr (nrc_y == 1) {
|
||||
acc[iy] = _mm256_fmadd_ps(scales_d, _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
} else {
|
||||
float d8 = q8.scale(iy, ibl);
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales_d, _mm256_set1_ps(d8)), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
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_q4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
//mul_mat_q4_k_r4_q8_k_avx2<nrc_y>(n, vx, bx, info, nrc_x);
|
||||
if constexpr (nrc_y == 1){
|
||||
mul_mat_q4_k_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 mf = _mm512_set1_epi8(0xf);
|
||||
int nbl = n / QK_K;
|
||||
using helper_t = union { __m512i vec; uint32_t val[16]; };
|
||||
helper_t hd, hm;
|
||||
__m512 acc[2*nrc_y] = {};
|
||||
__m512i qx[4];
|
||||
for (int ix = 0; ix < nrc_x; ix += 8) {
|
||||
const block_q4_k_r4 * iq4l = (const block_q4_k_r4 *)((const char *)vx + (ix+0)*bx);
|
||||
const block_q4_k_r4 * iq4h = (const block_q4_k_r4 *)((const char *)vx + (ix+4)*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
|
||||
auto d1 = _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)iq4l[ibl].d));
|
||||
auto d2 = _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)iq4h[ibl].d));
|
||||
auto dl = _mm256_castps256_ps128(d1);
|
||||
auto ml = _mm256_extractf128_ps(d1, 1);
|
||||
auto dh = _mm256_castps256_ps128(d2);
|
||||
auto mh = _mm256_extractf128_ps(d2, 1);
|
||||
auto d4 = _mm512_insertf32x8(_mm512_castps256_ps512(_mm256_set_m128(dl, dl)), _mm256_set_m128(dh, dh), 1);
|
||||
auto m4 = _mm512_insertf32x8(_mm512_castps256_ps512(_mm256_set_m128(ml, ml)), _mm256_set_m128(mh, mh), 1);
|
||||
auto slbits_l = _mm256_loadu_si256((const __m256i *)iq4l[ibl].scales_l);
|
||||
auto shbits_l = _mm256_loadu_si256((const __m256i *)iq4h[ibl].scales_l);
|
||||
auto slb = _mm512_inserti32x8(_mm512_castsi256_si512(slbits_l), shbits_l, 1);
|
||||
auto sld = _mm512_and_si512(slb, mf);
|
||||
auto slm = _mm512_and_si512(_mm512_srli_epi16(slb, 4), mf);
|
||||
auto slbits_h = _mm_loadu_si128((const __m128i *)iq4l[ibl].scales_h);
|
||||
auto shbits_h = _mm_loadu_si128((const __m128i *)iq4h[ibl].scales_h);
|
||||
auto slbits_h2 = MM256_SET_M128I(_mm_srli_epi16(slbits_h, 4), slbits_h);
|
||||
auto shbits_h2 = MM256_SET_M128I(_mm_srli_epi16(shbits_h, 4), shbits_h);
|
||||
auto shb = _mm512_inserti32x8(_mm512_castsi256_si512(slbits_h2), shbits_h2, 1);
|
||||
auto shd = _mm512_and_si512(_mm512_slli_epi16(shb, 4), _mm512_set1_epi8(0x30));
|
||||
auto shm = _mm512_and_si512(_mm512_slli_epi16(shb, 2), _mm512_set1_epi8(0x30));
|
||||
hd.vec = _mm512_or_si512(sld, shd);
|
||||
hm.vec = _mm512_or_si512(slm, shm);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto scales1 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hd.val[ib+0]));
|
||||
auto scales2 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hd.val[ib+8]));
|
||||
auto iscales = _mm512_inserti32x8(_mm512_castsi256_si512(scales1), scales2, 1);
|
||||
auto scales = _mm512_mul_ps(d4, _mm512_cvtepi32_ps(iscales));
|
||||
scales1 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hm.val[ib+0]));
|
||||
scales2 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hm.val[ib+8]));
|
||||
iscales = _mm512_inserti32x8(_mm512_castsi256_si512(scales1), scales2, 1);
|
||||
auto scales_m = _mm512_mul_ps(m4, _mm512_cvtepi32_ps(iscales));
|
||||
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_and_si512(bits1, mf);
|
||||
qx[1] = _mm512_and_si512(bits2, mf);
|
||||
qx[2] = _mm512_and_si512(_mm512_srli_epi16(bits1, 4), mf);
|
||||
qx[3] = _mm512_and_si512(_mm512_srli_epi16(bits2, 4), mf);
|
||||
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 = ((const float *)q8.y[iy][ibl].bsums)[ib];
|
||||
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_fmadd_ps(_mm512_set1_ps(-0.5f), acc[2*iy+1], acc[2*iy+0]);
|
||||
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_q4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
mul_mat_q4_k_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) {
|
||||
|
|
@ -5068,6 +5243,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
mm.funcs[7] = mul_mat_iq4_xs_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_q4_k_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_q4_k_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_q4_k_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_q4_k_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_q4_k_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_q4_k_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_q4_k_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_q4_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
assert (ne00 % QK4_NL == 0);
|
||||
mm.funcs[0] = mul_mat_q4_0_r4_q8_1<1>;
|
||||
|
|
@ -7726,6 +7913,86 @@ void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
|
|||
}
|
||||
}
|
||||
|
||||
IQK_ALWAYS_INLINE void prepare_q4_k_quants(const uint8x16_t& m4, const uint8x16x4_t& bits, int8x16_t * qx) {
|
||||
qx[0] = vandq_u8(bits.val[0], m4); // 0...3 from the 4 rows
|
||||
qx[1] = vandq_u8(bits.val[1], m4); // 16..19
|
||||
qx[2] = vandq_u8(bits.val[2], m4); // 4...7
|
||||
qx[3] = vandq_u8(bits.val[3], m4); // 20..23
|
||||
qx[4] = vshrq_n_u8(bits.val[0], 4); // 8..11
|
||||
qx[5] = vshrq_n_u8(bits.val[1], 4); // 24..27
|
||||
qx[6] = vshrq_n_u8(bits.val[2], 4); // 12..15
|
||||
qx[7] = vshrq_n_u8(bits.val[3], 4); // 28..31
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_q4_k_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 mf = vdupq_n_u8(0xf);
|
||||
auto m3 = vdupq_n_u8(0x30);
|
||||
int nbl = n / QK_K;
|
||||
int8x16_t qx[8];
|
||||
int8x16x4_t iscales;
|
||||
float32x4x4_t scales;
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_q4_k_r4 * iq4 = (const block_q4_k_r4 *)((const char *)vx + ix*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[ibl].d));
|
||||
auto m4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[ibl].d+4));
|
||||
m4 = vmulq_f32(m4, vdupq_n_f32(-1.f));
|
||||
if constexpr (nrc_y == 1) {
|
||||
d4 = vmulq_f32(d4, vdupq_n_f32(q8.scale(0, ibl)));
|
||||
}
|
||||
auto sl = vld1q_u8_x2(iq4[ibl].scales_l);
|
||||
auto sh = vld1q_u8(iq4[ibl].scales_h);
|
||||
iscales.val[0] = vorrq_u8(vandq_u8(sl.val[0], mf), vandq_u8(vshlq_n_u8(sh, 4), m3));
|
||||
iscales.val[1] = vorrq_u8(vandq_u8(sl.val[1], mf), vandq_u8(sh, m3));
|
||||
iscales.val[2] = vorrq_u8(vshrq_n_u8(sl.val[0], 4), vandq_u8(vshlq_n_u8(sh, 2), m3));
|
||||
iscales.val[3] = vorrq_u8(vshrq_n_u8(sl.val[1], 4), vandq_u8(vshrq_n_u8(sh, 2), m3));
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
auto iscales16_1 = vmovl_s8(vget_low_s8(iscales.val[is+2]));
|
||||
auto iscales16_2 = vmovl_s8(vget_high_s8(iscales.val[is+2]));
|
||||
scales.val[0] = vmulq_f32(m4, vcvtq_f32_s32(vmovl_s16(vget_low_s16(iscales16_1))));
|
||||
scales.val[1] = vmulq_f32(m4, vcvtq_f32_s32(vmovl_s16(vget_high_s16(iscales16_1))));
|
||||
scales.val[2] = vmulq_f32(m4, vcvtq_f32_s32(vmovl_s16(vget_low_s16(iscales16_2))));
|
||||
scales.val[3] = vmulq_f32(m4, vcvtq_f32_s32(vmovl_s16(vget_high_s16(iscales16_2))));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto m8 = vld1q_f32((const float *)q8.y[iy][ibl].bsums + 4*is);
|
||||
acc[iy] = vmlaq_laneq_f32(acc[iy], scales.val[0], m8, 0);
|
||||
acc[iy] = vmlaq_laneq_f32(acc[iy], scales.val[1], m8, 1);
|
||||
acc[iy] = vmlaq_laneq_f32(acc[iy], scales.val[2], m8, 2);
|
||||
acc[iy] = vmlaq_laneq_f32(acc[iy], scales.val[3], m8, 3);
|
||||
}
|
||||
iscales16_1 = vmovl_s8(vget_low_s8(iscales.val[is]));
|
||||
iscales16_2 = vmovl_s8(vget_high_s8(iscales.val[is]));
|
||||
scales.val[0] = vmulq_f32(d4, vcvtq_f32_s32(vmovl_s16(vget_low_s16(iscales16_1))));
|
||||
scales.val[1] = vmulq_f32(d4, vcvtq_f32_s32(vmovl_s16(vget_high_s16(iscales16_1))));
|
||||
scales.val[2] = vmulq_f32(d4, vcvtq_f32_s32(vmovl_s16(vget_low_s16(iscales16_2))));
|
||||
scales.val[3] = vmulq_f32(d4, vcvtq_f32_s32(vmovl_s16(vget_high_s16(iscales16_2))));
|
||||
for (int ib = 0; ib < 4; ++ib) {
|
||||
auto bits = vld1q_u8_x4(iq4[ibl].qs + 256*is + 64*ib);
|
||||
prepare_q4_k_quants(mf, bits, qx);
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ibl].qs+128*is+32*ib);
|
||||
auto sumi = interleaved_dotq(qx, y);
|
||||
if constexpr (nrc_y == 1) {
|
||||
acc[iy] = vfmaq_f32(acc[iy], scales.val[ib], vcvtq_f32_s32(sumi));
|
||||
} else {
|
||||
auto d4d8 = vmulq_f32(scales.val[ib], 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_r4_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);
|
||||
|
|
@ -8123,6 +8390,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
|||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq4_xs_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_q4_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
SET_MUL_MAT_FUNCTIONS_T(m, mul_mat_qx_r4_q8_0, Q4_0_R4_Dequantizer);
|
||||
expected_Btype = GGML_TYPE_Q8_0;
|
||||
|
|
|
|||
|
|
@ -3942,3 +3942,124 @@ void vec_dot_iq2_bn_r4_q8_K64(int n, float * s, size_t bs, const void * vx, size
|
|||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= q4_k_r4
|
||||
//
|
||||
|
||||
void quantize_row_q4_k_r4_ref(const float * x, block_q4_k_r4 * y, int64_t k) {
|
||||
quantize_q4_k_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_q4_k_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_q4_k_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t& d, uint8_t& m) {
|
||||
if (j < 4) {
|
||||
d = q[j] & 63; m = q[j + 4] & 63;
|
||||
} else {
|
||||
d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
|
||||
m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
|
||||
}
|
||||
}
|
||||
inline void convert_q4_k(const block_q4_K& x, uint8_t * L, uint8_t * Ld, uint8_t * Lm) {
|
||||
for (int ib64 = 0; ib64 < QK_K/64; ++ib64) {
|
||||
get_scale_min_k4(2*ib64+0, x.scales, Ld[2*ib64+0], Lm[2*ib64+0]);
|
||||
get_scale_min_k4(2*ib64+1, x.scales, Ld[2*ib64+1], Lm[2*ib64+1]);
|
||||
for (int j = 0; j < 32; ++j) {
|
||||
L[64*ib64+j+ 0] = x.qs[32*ib64+j] & 0xf;
|
||||
L[64*ib64+j+32] = x.qs[32*ib64+j] >> 4;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void repack_q4_k(int nrows, int n_per_row, const block_q4_K * x, block_q4_k_r4 * y) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
int nblock = n_per_row/QK_K;
|
||||
const block_q4_K * x4[4];
|
||||
uint8_t L[QK_K], Ld[QK_K/32], Lm[QK_K/32];
|
||||
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/8);
|
||||
std::memset(y[ibl].scales_h, 0, QK_K/16);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k+0] = x4[k][ibl].d;
|
||||
y[ibl].d[k+4] = x4[k][ibl].dmin;
|
||||
convert_q4_k(x4[k][ibl], L, Ld, Lm);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
y[ibl].scales_l[4*ib+k] = (Ld[ib] & 0xf) | ((Lm[ib] & 0xf) << 4);
|
||||
uint8_t h = (Ld[ib] >> 4) | ((Lm[ib] >> 4) << 2);
|
||||
y[ibl].scales_h[(4*ib+k)%16] |= (h << 4*((4*ib+k)/16));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y[ibl].qs[64*ib+4*k+i+ 0] = L[32*ib+i+ 0] | (L[32*ib+i+ 8] << 4);
|
||||
y[ibl].qs[64*ib+4*k+i+16] = L[32*ib+i+16] | (L[32*ib+i+24] << 4);
|
||||
y[ibl].qs[64*ib+4*k+i+32] = L[32*ib+i+ 4] | (L[32*ib+i+12] << 4);
|
||||
y[ibl].qs[64*ib+4*k+i+48] = L[32*ib+i+20] | (L[32*ib+i+28] << 4);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_q4_k_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_Q4_K, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_q4_K(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_q4_k(4, n_per_row, (const block_q4_K *)qtmp.data(), (block_q4_k_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_q4_k_r4(const block_q4_k_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+0]);
|
||||
const float m = GGML_FP16_TO_FP32(x[ibl].d[k+4]);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
int is = 4*ib + k;
|
||||
float dl = d * ((x[ibl].scales_l[is] & 0xf) | (((x[ibl].scales_h[is%16] >> 4*(is/16)) & 0x03) << 4));
|
||||
float ml = m * ((x[ibl].scales_l[is] >> 4) | (((x[ibl].scales_h[is%16] >> 4*(is/16)) & 0x0c) << 2));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y4[k][QK_K*ibl+32*ib+i+ 0] = dl * (x[ibl].qs[64*ib+4*k+i+ 0] & 0xf) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl * (x[ibl].qs[64*ib+4*k+i+ 0] >> 4) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl * (x[ibl].qs[64*ib+4*k+i+16] & 0xf) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl * (x[ibl].qs[64*ib+4*k+i+16] >> 4) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl * (x[ibl].qs[64*ib+4*k+i+32] & 0xf) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl * (x[ibl].qs[64*ib+4*k+i+32] >> 4) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl * (x[ibl].qs[64*ib+4*k+i+48] & 0xf) - ml;
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl * (x[ibl].qs[64*ib+4*k+i+48] >> 4) - ml;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_q4_k_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_Q4_K_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);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -109,6 +109,12 @@ void dequantize_row_iq2_bn_r4(const block_iq2_bn * GGML_RESTRICT x, float * GG
|
|||
size_t quantize_iq2_bn_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void vec_dot_iq2_bn_r4_q8_K64(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_q4_k_r4_ref(const float * GGML_RESTRICT x, block_q4_k_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_q4_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_q4_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void dequantize_row_q4_k_r4(const block_q4_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_q4_k_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 iqk_quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
void quantize_row_q8_K64_ref(const float * GGML_RESTRICT x, block_q8_K64 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_q8_K64(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
|
|
|
|||
|
|
@ -183,6 +183,7 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q4_0_R4 = 202, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q8_0_R4 = 207, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_0_R4 = 208, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_K_R4 = 214, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 = 225, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 = 230, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 235, // except 1d tensors
|
||||
|
|
|
|||
|
|
@ -3837,6 +3837,7 @@ struct llama_model_loader {
|
|||
case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
|
||||
case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
|
||||
case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
|
||||
case GGML_TYPE_Q4_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_R4; break;
|
||||
case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
|
||||
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
|
||||
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
|
||||
|
|
@ -4546,6 +4547,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_R4: return "Q4_K_R4";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
|
||||
|
|
@ -15786,6 +15788,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (new_type == GGML_TYPE_IQ4_XS_R4) {
|
||||
new_type = GGML_TYPE_IQ4_XS;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q4_K_R4) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q4_0_R4) {
|
||||
new_type = GGML_TYPE_Q4_0;
|
||||
}
|
||||
|
|
@ -15870,6 +15875,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
||||
use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_R4 && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
|
||||
if (qs.model.hparams.n_vocab >= 127999 && (qs.model.type == MODEL_8B || qs.model.type == MODEL_70B))
|
||||
new_type = GGML_TYPE_Q6_K;
|
||||
|
|
@ -15964,6 +15970,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_R4 && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
|
||||
&& qs.has_imatrix && i_layer < n_layer/8) {
|
||||
// Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
|
||||
|
|
@ -15983,7 +15992,7 @@ 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_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
|
|
@ -16051,8 +16060,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_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 || new_type == GGML_TYPE_IQ4_K || new_type == GGML_TYPE_IQ2_K ||
|
||||
new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K ||
|
||||
new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ4_KS ||
|
||||
new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K || new_type == GGML_TYPE_Q4_K_R4 ||
|
||||
new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ4_KS || new_type == GGML_TYPE_IQ4_XS_R4 ||
|
||||
new_type == GGML_TYPE_IQ2_KS || new_type == GGML_TYPE_IQ4_KSS) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
|
|
@ -16085,8 +16094,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_IQ3_K:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
|
||||
case GGML_TYPE_IQ4_K:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
|
||||
case GGML_TYPE_IQ5_K:
|
||||
case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q6_0; break;
|
||||
|
|
@ -16179,6 +16190,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_Q3_K_L: default_type = GGML_TYPE_Q3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_M: default_type = GGML_TYPE_Q4_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_R4: default_type = GGML_TYPE_Q4_K_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: default_type = GGML_TYPE_Q5_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: default_type = GGML_TYPE_Q6_K; break;
|
||||
|
|
@ -16581,6 +16593,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q8_0;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q4_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_K;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ2_BN_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_BN;
|
||||
else chunk_size_multiplier = 4;
|
||||
|
|
|
|||
Loading…
Reference in New Issue