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Q6_0_R4 (#122) 

* Adding q6_0_r4

We get PP-512(LLaMA-3.1-8B) = 257 t/s on a Ryzen-7950X.

* q6_0_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 95 t/s on M2-Max.
In terms of ops, q6_0_r4 is identical to q5_0_r4
except for loading the high bits being
vld1q_u8_x2 instead of vld1q_u8. It is strange that
this can make a 5% difference in performance, especially
considering that this is amortized (re-used) over 8 columns
in the right matrix. Or am I running out of vector registers?

* Fix AVX2

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2024-12-03 14:48:26 +01:00 committed by GitHub
parent d9593f3689
commit bb699e1e6b
8 changed files with 334 additions and 2 deletions
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1
examples/quantize/quantize.cpp
View File

@ -43,6 +43,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "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 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", },
{ "Q8_0_R4", LLAMA_FTYPE_MOSTLY_Q8_0_R4, " 8.50 bpw 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", },

1
ggml/src/ggml-quants.c
View File

@ -15199,6 +15199,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
case GGML_TYPE_IQ4_NL_X4: break;
case GGML_TYPE_Q4_0_R4: break;
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_0_4_4:
case GGML_TYPE_Q4_0_4_8:

26
ggml/src/ggml.c
View File

@ -1309,6 +1309,23 @@ 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_Q6_0_R4] = {
.type_name = "q6_0_r4",
.blck_size = QK6_0,
.type_size = sizeof(block_q6_0),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q6_0_r4,
.from_float = quantize_row_q6_0_r4,
.from_float_ref = (ggml_from_float_t)quantize_row_q6_0_r4_ref,
.vec_dot = vec_dot_q6_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,
@ -3974,6 +3991,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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_Q5_0_R4: wtype = GGML_TYPE_Q5_0_R4; break;
case GGML_FTYPE_MOSTLY_Q6_0_R4: wtype = GGML_TYPE_Q6_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;
@ -10501,6 +10519,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -10947,6 +10966,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -11090,6 +11110,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -14279,6 +14300,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -14662,6 +14684,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -14939,6 +14962,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -15543,6 +15567,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q6_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@ -22373,6 +22398,7 @@ size_t ggml_quantize_chunk(
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_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;
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;

194
ggml/src/iqk/iqk_mul_mat.cpp
View File

@ -2400,6 +2400,128 @@ static void mul_mat_q5_0_r4_q8_1(int n, const void * vx, size_t bx, const DataIn
}
#endif
template <int nrc_y>
static void mul_mat_q6_0_r4_q8_1_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_1_x4> q8(info);
auto m4 = _mm256_set1_epi8(0xf);
auto m6 = _mm256_set1_epi8(0x30);
#ifndef HAVE_FANCY_SIMD
auto m1 = _mm256_set1_epi16(1);
#endif
int nb = n / QK6_0;
GGML_ASSERT(nb%4 == 0);
__m256 acc[nrc_y] = {};
for (int ix = 0; ix < nrc_x; ix += 4) {
const block_q6_0_r4 * iq6 = (const block_q6_0_r4 *)((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 *)iq6[4*ib4+k].d));
auto scales = _mm256_set_m128(scales128, scales128);
auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-16.f));
auto bits1 = _mm256_loadu_si256((const __m256i *)iq6[4*ib4+k].qs+0);
auto bits2 = _mm256_loadu_si256((const __m256i *)iq6[4*ib4+k].qs+1);
auto hbits = _mm256_loadu_si256((const __m256i *)iq6[4*ib4+k].qh);
auto q1 = _mm256_and_si256(bits1, m4) | _mm256_and_si256(_mm256_slli_epi16(hbits, 4), m6);
auto q2 = _mm256_and_si256(bits2, m4) | _mm256_and_si256(_mm256_slli_epi16(hbits, 2), m6);
auto q3 = _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4) | _mm256_and_si256(hbits, m6);
auto q4 = _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4) | _mm256_and_si256(_mm256_srli_epi16(hbits, 2), m6);;
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
#ifdef HAVE_FANCY_SIMD
auto sumi = _mm256_dpbusd_epi32(_mm256_setzero_si256(), q1, _mm256_shuffle_epi32(y, 0x00));
sumi = _mm256_dpbusd_epi32(sumi, q2, _mm256_shuffle_epi32(y, 0x55));
sumi = _mm256_dpbusd_epi32(sumi, q3, _mm256_shuffle_epi32(y, 0xaa));
sumi = _mm256_dpbusd_epi32(sumi, q4, _mm256_shuffle_epi32(y, 0xff));
#else
auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(q1, _mm256_shuffle_epi32(y, 0x00)),
_mm256_maddubs_epi16(q2, _mm256_shuffle_epi32(y, 0x55)));
auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(q3, _mm256_shuffle_epi32(y, 0xaa)),
_mm256_maddubs_epi16(q4, _mm256_shuffle_epi32(y, 0xff)));
auto sumi = _mm256_add_epi32(_mm256_madd_epi16(m1, sumi1), _mm256_madd_epi16(m1, sumi2));
#endif
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(sumi), 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();
}
}
}
#ifdef HAVE_FANCY_SIMD
template <int nrc_y>
static void mul_mat_q6_0_r4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
if constexpr (nrc_y == 1) {
mul_mat_q6_0_r4_q8_1_avx2<1>(n, vx, bx, info, nrc_x);
} else {
GGML_ASSERT(nrc_x%8 == 0);
Q8<nrc_y, block_q8_1_x4> q8(info);
auto m4 = _mm512_set1_epi8(0xf);
auto m6 = _mm512_set1_epi8(0x30);
int nb = n / QK6_0;
GGML_ASSERT(nb%4 == 0);
__m512 acc[2*nrc_y] = {};
__m512i qx[4];
for (int ix = 0; ix < nrc_x; ix += 8) {
const block_q6_0_r4 * iq6l = (const block_q6_0_r4 *)((const char *)vx + (ix+0)*bx);
const block_q6_0_r4 * iq6h = (const block_q6_0_r4 *)((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 *)iq6l[4*ib4+k].d));
auto scales1 = _mm256_set_m128(scales128, scales128);
scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq6h[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(-16.f));
auto bits1 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq6l[4*ib4+k].qs+0)),
_mm256_loadu_si256((const __m256i *)iq6h[4*ib4+k].qs+0), 1);
auto bits2 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq6l[4*ib4+k].qs+1)),
_mm256_loadu_si256((const __m256i *)iq6h[4*ib4+k].qs+1), 1);
auto hbits1 = _mm256_loadu_si256((const __m256i *)iq6l[4*ib4+k].qh);
auto hbits2 = _mm256_loadu_si256((const __m256i *)iq6h[4*ib4+k].qh);
auto hb = _mm512_inserti32x8(_mm512_castsi256_si512(hbits1), hbits2, 1);
qx[0] = _mm512_and_si512(bits1, m4) | _mm512_and_si512(_mm512_slli_epi16(hb, 4), m6);
qx[1] = _mm512_and_si512(bits2, m4) | _mm512_and_si512(_mm512_slli_epi16(hb, 2), m6);;
qx[2] = _mm512_and_si512(_mm512_srli_epi16(bits1, 4), m4) | _mm512_and_si512(hb, m6);
qx[3] = _mm512_and_si512(_mm512_srli_epi16(bits2, 4), m4) | _mm512_and_si512(_mm512_srli_epi16(hb, 2), m6);
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_q6_0_r4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
mul_mat_q6_0_r4_q8_1_avx2<nrc_y>(n, vx, bx, info, nrc_x);
}
#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) {
@ -4527,6 +4649,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
mm.funcs[7] = mul_mat_q5_0_r4_q8_1<8>;
expected_typeB = GGML_TYPE_Q8_1;
break;
case GGML_TYPE_Q6_0_R4:
assert (ne00 % QK4_NL == 0);
mm.funcs[0] = mul_mat_q6_0_r4_q8_1<1>;
mm.funcs[1] = mul_mat_q6_0_r4_q8_1<2>;
mm.funcs[2] = mul_mat_q6_0_r4_q8_1<3>;
mm.funcs[3] = mul_mat_q6_0_r4_q8_1<4>;
mm.funcs[4] = mul_mat_q6_0_r4_q8_1<5>;
mm.funcs[5] = mul_mat_q6_0_r4_q8_1<6>;
mm.funcs[6] = mul_mat_q6_0_r4_q8_1<7>;
mm.funcs[7] = mul_mat_q6_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>;
@ -7129,6 +7263,55 @@ void mul_mat_q5_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& inf
}
}
template <int nrc_y>
void mul_mat_q6_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);
auto m4 = vdupq_n_u8(0x0f);
auto m6 = vdupq_n_u8(0x30);
auto m32 = vdupq_n_s8(-32);
int nb = n / QK6_0;
GGML_ASSERT(nb%4 == 0);
int8x16_t qx[8];
float32x4_t acc[nrc_y] = {};
for (int ix = 0; ix < nrc_x; ix += 4) {
const block_q6_0_r4 * iq6 = (const block_q6_0_r4 *)((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 *)iq6[4*ib4+k].d));
auto lbits = vld1q_u8_x4(iq6[4*ib4+k].qs);
auto hbits = vld1q_u8_x2(iq6[4*ib4+k].qh);
qx[0] = vaddq_s8(vandq_u8(lbits.val[0], m4) | vandq_u8(vshlq_n_u8(hbits.val[0], 4), m6), m32); // 0...3
qx[1] = vaddq_s8(vandq_u8(lbits.val[1], m4) | vandq_u8(vshlq_n_u8(hbits.val[1], 4), m6), m32); // 16..19
qx[2] = vaddq_s8(vandq_u8(lbits.val[2], m4) | vandq_u8(vshlq_n_u8(hbits.val[0], 2), m6), m32); // 4...7
qx[3] = vaddq_s8(vandq_u8(lbits.val[3], m4) | vandq_u8(vshlq_n_u8(hbits.val[1], 2), m6), m32); // 20..23
qx[4] = vaddq_s8(vshrq_n_u8(lbits.val[0], 4)| vandq_u8(hbits.val[0], m6), m32); // 8..11
qx[5] = vaddq_s8(vshrq_n_u8(lbits.val[1], 4)| vandq_u8(hbits.val[1], m6), m32); // 24..27
qx[6] = vaddq_s8(vshrq_n_u8(lbits.val[2], 4)| vandq_u8(vshrq_n_u8(hbits.val[0], 2), m6), m32); // 12..15
qx[7] = vaddq_s8(vshrq_n_u8(lbits.val[3], 4)| vandq_u8(vshrq_n_u8(hbits.val[1], 2), m6), m32); // 28..31
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 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 <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);
@ -7368,6 +7551,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
m.funcs[7] = mul_mat_q5_0_r4_q8_0<8>;
expected_Btype = GGML_TYPE_Q8_0;
break;
case GGML_TYPE_Q6_0_R4:
m.funcs[0] = mul_mat_q6_0_r4_q8_0<1>;
m.funcs[1] = mul_mat_q6_0_r4_q8_0<2>;
m.funcs[2] = mul_mat_q6_0_r4_q8_0<3>;
m.funcs[3] = mul_mat_q6_0_r4_q8_0<4>;
m.funcs[4] = mul_mat_q6_0_r4_q8_0<5>;
m.funcs[5] = mul_mat_q6_0_r4_q8_0<6>;
m.funcs[6] = mul_mat_q6_0_r4_q8_0<7>;
m.funcs[7] = mul_mat_q6_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>;

97
ggml/src/iqk/iqk_quantize.cpp
View File

@ -3475,3 +3475,100 @@ void vec_dot_q5_0_r4_q8_0(int n, float * s, size_t bs, const void * vx, size_t b
GGML_UNUSED(bx);
GGML_UNUSED(by);
}
//
// ========================================= q6_0_r4
//
void quantize_row_q6_0_r4_ref(const float * x, block_q6_0_r4 * y, int64_t k) {
// we assume we are called with 4 rows
quantize_q6_0_r4(x, (void *)y, 4, k/4, nullptr);
}
void quantize_row_q6_0_r4(const float * x, void * y, int64_t k) {
// we assume we are called with 4 rows
quantize_q6_0_r4(x, y, 4, k/4, nullptr);
}
static inline void convert_q6_0(const block_q6_0& x, uint8_t * L) {
for (int j = 0; j < QK6_0/2; ++j) {
const uint8_t h = x.qh[j%(QK6_0/4)] >> 4*(j/(QK6_0/4));
L[j ] = (x.qs[j] & 0x0F) | ((h << 4) & 0x30);
L[j + QK6_0/2] = (x.qs[j] >> 4) | ((h << 2) & 0x30);
}
}
static void repack_q6_0(int nrows, int n_per_row, const block_q6_0 * x, block_q6_0_r4 * y) {
GGML_ASSERT(nrows%4 == 0);
GGML_ASSERT(n_per_row%QK5_0 == 0);
int nblock = n_per_row/QK6_0;
const block_q6_0 * x4[4];
uint8_t L[QK6_0];
for (int row = 0; row < nrows; row += 4) {
for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
for (int ib = 0; ib < nblock; ++ib) {
std::memset(y[ib].qh, 0, QK6_0);
for (int k = 0; k < 4; ++k) {
y[ib].d[k] = x4[k][ib].d;
convert_q6_0(x4[k][ib], L);
for (int l = 0; l < 4; ++l) {
int l1 = 4*(l/2) + 16*(l%2), l2 = l1 + 8;
for (int i = 0; i < 4; ++i) {
y[ib].qs[4*k+i+16*l] = (L[i + l1] & 0xf) | ((L[i + l2] & 0xf) << 4);
y[ib].qh[4*k+i+16*(l%2)] |= ((L[i + l1] >> 4) | ((L[i + l2] >> 4) << 4)) << 2*(l/2);
}
}
}
}
x += 4*nblock;
y += nblock;
}
}
size_t quantize_q6_0_r4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
GGML_ASSERT(nrows%4 == 0);
auto row_size_0 = ggml_row_size(GGML_TYPE_Q6_0, n_per_row);
std::vector<char> qtmp(4*row_size_0);
char * qrow = (char *)dst;
for (int row = 0; row < nrows; row += 4) {
quantize_q6_0(src, qtmp.data(), 4, n_per_row, imatrix);
repack_q6_0(4, n_per_row, (const block_q6_0 *)qtmp.data(), (block_q6_0_r4 *)qrow);
src += 4*n_per_row;
qrow += 4*row_size_0;
}
return nrows*row_size_0;
}
void dequantize_row_q6_0_r4(const block_q6_0_r4 * x, float * y, int64_t k) {
// we assume we are called with 4 rows
int n_per_row = k/4;
int nb = n_per_row/QK6_0;
float * yk[4];
for (int k = 0; k < 4; ++k) yk[k] = y + k*n_per_row;
for (int ib = 0; ib < nb; ++ib) {
for (int k = 0; k < 4; ++k) {
float d = GGML_FP16_TO_FP32(x[ib].d[k]);
float m = -32*d;
for (int l = 0; l < 4; ++l) {
int ll = 16*(l%2) + 4*(l/2);
for (int i = 0; i < 4; ++i) {
yk[k][QK4_0*ib+i+ll+0] = d * ((x[ib].qs[4*k+i+16*l] & 0xf) | (((x[ib].qh[4*k+i+16*(l%2)] >> (2*(l/2)+0)) & 3) << 4)) + m;
yk[k][QK4_0*ib+i+ll+8] = d * ((x[ib].qs[4*k+i+16*l] >> 4) | (((x[ib].qh[4*k+i+16*(l%2)] >> (2*(l/2)+4)) & 3) << 4)) + m;
}
}
}
}
}
void vec_dot_q6_0_r4_q8_0(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_Q6_0_R4, vx, 0, GGML_TYPE_Q8_0, 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);
}

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

@ -87,6 +87,12 @@ size_t quantize_q5_0_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
void dequantize_row_q5_0_r4(const block_q5_0_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_q5_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_q6_0_r4_ref(const float * GGML_RESTRICT x, block_q6_0_r4 * GGML_RESTRICT y, int64_t k);
void quantize_row_q6_0_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
size_t quantize_q6_0_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
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);
#ifdef __cplusplus
}
#endif

1
include/llama.h
View File

@ -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_Q6_0_R4 = 235, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};

10
src/llama.cpp
View File

@ -3852,6 +3852,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ4_NL_X4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_X4;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;
case GGML_TYPE_Q8_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q8_0_R4; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
@ -4560,6 +4561,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:return "IQ4_NL_X4 - 4.5 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";
case LLAMA_FTYPE_MOSTLY_Q8_0_R4: return "Q8_0_R4 - 8.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 4.25 bpw";
@ -15783,6 +15785,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (new_type == GGML_TYPE_Q5_0_R4) {
new_type = GGML_TYPE_Q5_0;
}
else if (new_type == GGML_TYPE_Q6_0_R4) {
new_type = GGML_TYPE_Q6_0;
}
else if (new_type == GGML_TYPE_Q8_0_R4) {
new_type = GGML_TYPE_Q8_0;
}
@ -16180,6 +16185,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:default_type = GGML_TYPE_IQ4_NL_X4;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;
case LLAMA_FTYPE_MOSTLY_Q8_0_R4: default_type = GGML_TYPE_Q8_0_R4; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ4_KS: default_type = GGML_TYPE_IQ4_KS; break;
@ -16546,8 +16552,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_Q5_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q5_0;
else if (new_type == GGML_TYPE_Q6_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q6_0;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_Q8_0_R4) {