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Q8_0_R4 (#120)
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* Adding q8_0_r4

We get PP-512(LLaMA-3.1-8B) = 268 t/s on a Ryzen-7950X compared
to 175.6 t/s for Q8_0.

* q8_0_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 112.6 t/s on M2-Max.

* q8_0_r4: Zen4 matrix-vector specialization

---------

Co-authored-by: Iwan Kawrakow <[email protected]>
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@ikawrakow @Kawrakow
ikawrakow and Kawrakow authored Dec 3, 2024
1 parent 239a344 commit ccec009
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Showing 9 changed files with 320 additions and 1 deletion.
1 change: 1 addition & 0 deletions examples/quantize/quantize.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "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", },
{ "Q4_0_R4", LLAMA_FTYPE_MOSTLY_Q4_0_R4, " 4.50 bpw non-linear quantization", },
{ "Q8_0_R4", LLAMA_FTYPE_MOSTLY_Q8_0_R4, " 8.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
{ "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 4.25 bpw non-linear quantization", },
{ "IQ4_KSS", LLAMA_FTYPE_MOSTLY_IQ4_KSS, " 4.0 bpw non-linear quantization", },
Expand Down
2 changes: 2 additions & 0 deletions ggml/include/ggml.h
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Original file line number Diff line number Diff line change
Expand Up @@ -408,6 +408,7 @@ extern "C" {
GGML_TYPE_IQ4_KSS = 146,

GGML_TYPE_Q4_0_R4 = 202,
GGML_TYPE_Q8_0_R4 = 208,
GGML_TYPE_IQ4_NL_X4 = 220,
GGML_TYPE_COUNT,
};
Expand Down Expand Up @@ -469,6 +470,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ4_KSS = 139, // except 1d tensors
//
GGML_FTYPE_MOSTLY_Q4_0_R4 = 202, // except 1d tensors
GGML_FTYPE_MOSTLY_Q8_0_R4 = 207, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_NL_X4 = 219, // except 1d tensors
};

Expand Down
1 change: 1 addition & 0 deletions ggml/src/ggml-quants.c
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Original file line number Diff line number Diff line change
Expand Up @@ -15198,6 +15198,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
case GGML_TYPE_IQ4_KSS: break;
case GGML_TYPE_IQ4_NL_X4: break;
case GGML_TYPE_Q4_0_R4: break;
case GGML_TYPE_Q8_0_R4: break;
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
{
Expand Down
26 changes: 26 additions & 0 deletions ggml/src/ggml.c
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Original file line number Diff line number Diff line change
Expand Up @@ -1275,6 +1275,23 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_1,
#else
.vec_dot_type = GGML_TYPE_Q8_0,
#endif
.nrows = 1,
.row_meta_size = 0,
},
[GGML_TYPE_Q8_0_R4] = {
.type_name = "q8_0_r4",
.blck_size = QK8_0,
.type_size = sizeof(block_q8_0),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q8_0_r4,
.from_float = quantize_row_q8_0_r4,
.from_float_ref = (ggml_from_float_t)quantize_row_q8_0_r4_ref,
.vec_dot = vec_dot_q8_0_r4_q8_0,
#if GGML_USE_IQK_MULMAT && defined __AVX2__
.vec_dot_type = GGML_TYPE_Q8_1,
#else
.vec_dot_type = GGML_TYPE_Q8_0,
#endif
.nrows = 1,
.row_meta_size = 0,
Expand Down Expand Up @@ -3939,6 +3956,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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_Q4_0_R4: wtype = GGML_TYPE_Q4_0_R4; break;
case GGML_FTYPE_MOSTLY_Q8_0_R4: wtype = GGML_TYPE_Q8_0_R4; break;
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ4_KS: wtype = GGML_TYPE_IQ4_KS; break;
case GGML_FTYPE_MOSTLY_IQ4_KSS: wtype = GGML_TYPE_IQ4_KSS; break;
Expand Down Expand Up @@ -10464,6 +10482,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -10908,6 +10927,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -11049,6 +11069,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -14236,6 +14257,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -14617,6 +14639,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -14892,6 +14915,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -15494,6 +15518,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KSS:
Expand Down Expand Up @@ -22322,6 +22347,7 @@ size_t ggml_quantize_chunk(
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_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_Q8_0_R4: result = quantize_q8_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_KS: result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_KSS: result = quantize_iq4_kss(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
Expand Down
194 changes: 194 additions & 0 deletions ggml/src/iqk/iqk_mul_mat.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2283,6 +2283,140 @@ static void mul_mat_q4_0_r4_q8_1(int n, const void * vx, size_t bx, const DataIn
}
#endif

#ifdef HAVE_FANCY_SIMD
template <int nrc_y>
static void mul_mat_q8_0_r4_q8_1(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_1_x4> q8(info);
int nb = n / QK8_0;
GGML_ASSERT(nb%4 == 0);
if constexpr (nrc_y == 1) {
auto m127 = _mm256_set1_epi8(127);
auto m1 = _mm256_set1_epi16(1);
__m256 acc[nrc_y] = {};
for (int ix = 0; ix < nrc_x; ix += 4) {
const block_q8_0_x4 * iq8 = (const block_q8_0_x4 *)((const char *)vx + ix*bx);
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
for (int k = 0; k < 4; ++k) {
auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq8[4*ib4+k].d));
auto scales = _mm256_set_m128(scales128, scales128);
auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-63.5f));
auto q1 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+0), m127);
auto q2 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+1), m127);
auto q3 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+2), m127);
auto q4 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+3), m127);
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
auto sumi1 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q1, _mm256_shuffle_epi32(y, 0x00))),
_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q2, _mm256_shuffle_epi32(y, 0x55))));
auto sumi2 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q3, _mm256_shuffle_epi32(y, 0xaa))),
_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q4, _mm256_shuffle_epi32(y, 0xff))));
auto d4d8 = _mm256_mul_ps(scales, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
acc[iy] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), acc[iy]);
acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), 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));
info.store(ix, iy, sum);
acc[iy] = _mm256_setzero_ps();
}
}
} else {
__m512 acc[2*nrc_y] = {};
__m512i qx[4];
auto m127 = _mm512_set1_epi8(127);
for (int ix = 0; ix < nrc_x; ix += 8) {
const block_q8_0_x4 * q8l = (const block_q8_0_x4 *)((const char *)vx + (ix+0)*bx);
const block_q8_0_x4 * q8h = (const block_q8_0_x4 *)((const char *)vx + (ix+4)*bx);
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
for (int k = 0; k < 4; ++k) {
auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)q8l[4*ib4+k].d));
auto scales1 = _mm256_set_m128(scales128, scales128);
scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)q8h[4*ib4+k].d));
auto scales2 = _mm256_set_m128(scales128, scales128);
auto scales = _mm512_insertf32x8(_mm512_castps256_ps512(scales1), scales2, 1);
auto scales_m = _mm512_mul_ps(scales, _mm512_set1_ps(-63.5f));
qx[0] = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)q8l[4*ib4+k].qs+0)),
_mm256_loadu_si256((const __m256i *)q8h[4*ib4+k].qs+0), 1);
qx[1] = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)q8l[4*ib4+k].qs+1)),
_mm256_loadu_si256((const __m256i *)q8h[4*ib4+k].qs+1), 1);
qx[2] = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)q8l[4*ib4+k].qs+2)),
_mm256_loadu_si256((const __m256i *)q8h[4*ib4+k].qs+2), 1);
qx[3] = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)q8l[4*ib4+k].qs+3)),
_mm256_loadu_si256((const __m256i *)q8h[4*ib4+k].qs+3), 1);
qx[0] = _mm512_add_epi8(qx[0], m127);
qx[1] = _mm512_add_epi8(qx[1], m127);
qx[2] = _mm512_add_epi8(qx[2], m127);
qx[3] = _mm512_add_epi8(qx[3], m127);
for (int iy = 0; iy < nrc_y; ++iy) {
auto y8 = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
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)));
auto dy = _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k]));
acc[2*iy+0] = _mm512_fmadd_ps(_mm512_mul_ps(scales, dy), _mm512_cvtepi32_ps(sumi), acc[2*iy+0]);
acc[2*iy+1] = _mm512_fmadd_ps(scales_m, _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), 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_q8_0_r4_q8_1(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_1_x4> q8(info);
auto m127 = _mm256_set1_epi8(127);
auto m1 = _mm256_set1_epi16(1);
int nb = n / QK8_0;
GGML_ASSERT(nb%4 == 0);
__m256 acc[nrc_y] = {};
for (int ix = 0; ix < nrc_x; ix += 4) {
const block_q8_0_x4 * iq8 = (const block_q8_0_x4 *)((const char *)vx + ix*bx);
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
for (int k = 0; k < 4; ++k) {
auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq8[4*ib4+k].d));
auto scales = _mm256_set_m128(scales128, scales128);
auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-63.5f));
auto q1 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+0), m127);
auto q2 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+1), m127);
auto q3 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+2), m127);
auto q4 = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)iq8[4*ib4+k].qs+3), m127);
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
auto sumi1 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q1, _mm256_shuffle_epi32(y, 0x00))),
_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q2, _mm256_shuffle_epi32(y, 0x55))));
auto sumi2 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q3, _mm256_shuffle_epi32(y, 0xaa))),
_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q4, _mm256_shuffle_epi32(y, 0xff))));
auto d4d8 = _mm256_mul_ps(scales, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
acc[iy] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), acc[iy]);
acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), 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));
info.store(ix, iy, sum);
acc[iy] = _mm256_setzero_ps();
}
}
}
#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) {
Expand Down Expand Up @@ -4264,6 +4398,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
mm.funcs[7] = mul_mat_q4_0_r4_q8_1<8>;
expected_typeB = GGML_TYPE_Q8_1;
break;
case GGML_TYPE_Q8_0_R4:
assert (ne00 % QK4_NL == 0);
mm.funcs[0] = mul_mat_q8_0_r4_q8_1<1>;
mm.funcs[1] = mul_mat_q8_0_r4_q8_1<2>;
mm.funcs[2] = mul_mat_q8_0_r4_q8_1<3>;
mm.funcs[3] = mul_mat_q8_0_r4_q8_1<4>;
mm.funcs[4] = mul_mat_q8_0_r4_q8_1<5>;
mm.funcs[5] = mul_mat_q8_0_r4_q8_1<6>;
mm.funcs[6] = mul_mat_q8_0_r4_q8_1<7>;
mm.funcs[7] = mul_mat_q8_0_r4_q8_1<8>;
expected_typeB = GGML_TYPE_Q8_1;
break;

default:
return false;
Expand Down Expand Up @@ -6805,6 +6951,43 @@ void mul_mat_q4_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& inf
}
}

template <int nrc_y>
void mul_mat_q8_0_r4_q8_0(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_0_x4> q8(info);
int nb = n / QK8_0;
GGML_ASSERT(nb%4 == 0);
float32x4_t acc[nrc_y] = {};
for (int ix = 0; ix < nrc_x; ix += 4) {
const block_q8_0_x4 * iq8 = (const block_q8_0_x4 *)((const char *)vx + ix*bx);
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
for (int k = 0; k < 4; ++k) {
auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq8[4*ib4+k].d));
auto qx1 = vld1q_s8_x4(iq8[4*ib4+k].qs);
auto qx2 = vld1q_s8_x4(iq8[4*ib4+k].qs+64);
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, qx1.val[0], y.val[0], 0);
sumi = vdotq_laneq_s32(sumi, qx1.val[1], y.val[1], 0);
sumi = vdotq_laneq_s32(sumi, qx1.val[2], y.val[0], 1);
sumi = vdotq_laneq_s32(sumi, qx1.val[3], y.val[1], 1);
sumi = vdotq_laneq_s32(sumi, qx2.val[0], y.val[0], 2);
sumi = vdotq_laneq_s32(sumi, qx2.val[1], y.val[1], 2);
sumi = vdotq_laneq_s32(sumi, qx2.val[2], y.val[0], 3);
sumi = vdotq_laneq_s32(sumi, qx2.val[3], y.val[1], 3);
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));
}
}
}
for (int iy = 0; iy < nrc_y; ++iy) {
info.store(ix, iy, acc[iy]);
acc[iy] = vdupq_n_f32(0.f);
}
}
}

template <typename Dequantizer> void MulMat::set_functions(MulMat& m) {
if constexpr (std::is_same_v<Dequantizer, DequantizerQ40> || std::is_same_v<Dequantizer, DequantizerQ50> ||
std::is_same_v<Dequantizer, DequantizerQ80> || std::is_same_v<Dequantizer, DequantizerIQ4NL> ||
Expand Down Expand Up @@ -6996,6 +7179,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
m.funcs[7] = mul_mat_q4_0_r4_q8_0<8>;
expected_Btype = GGML_TYPE_Q8_0;
break;
case GGML_TYPE_Q8_0_R4:
m.funcs[0] = mul_mat_q8_0_r4_q8_0<1>;
m.funcs[1] = mul_mat_q8_0_r4_q8_0<2>;
m.funcs[2] = mul_mat_q8_0_r4_q8_0<3>;
m.funcs[3] = mul_mat_q8_0_r4_q8_0<4>;
m.funcs[4] = mul_mat_q8_0_r4_q8_0<5>;
m.funcs[5] = mul_mat_q8_0_r4_q8_0<6>;
m.funcs[6] = mul_mat_q8_0_r4_q8_0<7>;
m.funcs[7] = mul_mat_q8_0_r4_q8_0<8>;
expected_Btype = GGML_TYPE_Q8_0;
break;
default:
return false;
}
Expand Down
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