Q2_K_R4 (#136)
* q2_k_r4: Zen4 PP-512(LLaMA-3.1-8B) = 256 t/s * q3_k_r4: AVX2 * q2_k_r4: AVX2 We get PP-512(LLaMA-3.1-8B) = 287 t/s. Also cherry-picked the q3_k_r4 AVX2 adaptation that I somehow forgot to push upstream. * q2_k_r4: NEON We get PP-512(LLaMA-3.1-8B) = 106.2 t/s. TG-128 is 36.02 t/s, which is ~10% higher than q2_K_S. * Make sure rows per thread are a multiple of 4 --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
680d96ad6f
commit
0f6621d410
|
|
@ -31,6 +31,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
|
|||
{ "IQ2_BN", LLAMA_FTYPE_MOSTLY_IQ2_BN, " 2.00 bpw quantization (Bitnet)", },
|
||||
{ "IQ2_BN_R4",LLAMA_FTYPE_MOSTLY_IQ2_BN_R4," 2.00 bpw quantization (Bitnet)", },
|
||||
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
|
||||
{ "Q2_K_R4", LLAMA_FTYPE_MOSTLY_Q2_K_R4, "Q2_K_S repacked", },
|
||||
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
|
||||
{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", },
|
||||
{ "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", },
|
||||
|
|
|
|||
|
|
@ -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_Q2_K_R4 = 210,
|
||||
GGML_TYPE_Q3_K_R4 = 211,
|
||||
GGML_TYPE_Q4_K_R4 = 212,
|
||||
GGML_TYPE_Q5_K_R4 = 213,
|
||||
|
|
@ -482,6 +483,7 @@ 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_Q2_K_R4 = 210, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q3_K_R4 = 211, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q4_K_R4 = 212, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_K_R4 = 215, // except 1d tensors
|
||||
|
|
|
|||
|
|
@ -276,6 +276,13 @@ typedef struct {
|
|||
} block_q2_K;
|
||||
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
|
||||
|
||||
typedef struct {
|
||||
ggml_half d[8];
|
||||
uint8_t scales[QK_K/4]; // scales and mins, quantized with 4 bits
|
||||
uint8_t qs[QK_K]; // quants
|
||||
} block_q2_k_r4;
|
||||
static_assert(sizeof(block_q2_k_r4) == 8*sizeof(ggml_half) + QK_K/4 + QK_K, "wrong q2_k_r4 block size/padding");
|
||||
|
||||
// 3-bit quantization
|
||||
// weight is represented as x = a * q
|
||||
// 16 blocks of 16 elements each
|
||||
|
|
|
|||
|
|
@ -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_Q2_K_R4: break;
|
||||
case GGML_TYPE_Q3_K_R4: break;
|
||||
case GGML_TYPE_Q4_K_R4: break;
|
||||
case GGML_TYPE_Q5_K_R4: break;
|
||||
|
|
|
|||
|
|
@ -862,6 +862,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
|
|||
.nrows = 1,
|
||||
.row_meta_size = 0,
|
||||
},
|
||||
[GGML_TYPE_Q2_K_R4] = {
|
||||
.type_name = "q2_k_r4",
|
||||
.blck_size = QK_K,
|
||||
.type_size = sizeof(block_q2_K),
|
||||
.is_quantized = true,
|
||||
.to_float = (ggml_to_float_t) dequantize_row_q2_k_r4,
|
||||
.from_float = quantize_row_q2_k_r4,
|
||||
.from_float_ref = (ggml_from_float_t) quantize_row_q2_k_r4_ref,
|
||||
.vec_dot = vec_dot_q2_k_r4_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
.nrows = 1,
|
||||
.row_meta_size = 0,
|
||||
},
|
||||
[GGML_TYPE_Q3_K] = {
|
||||
.type_name = "q3_K",
|
||||
.blck_size = QK_K,
|
||||
|
|
@ -4070,7 +4083,8 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
case GGML_FTYPE_MOSTLY_Q6_0: wtype = GGML_TYPE_Q6_0; break;
|
||||
case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break;
|
||||
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_Q2_K_R4: wtype = GGML_TYPE_Q2_K_R4; break;
|
||||
case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q3_K_R4: wtype = GGML_TYPE_Q3_K_R4; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_K_R4: wtype = GGML_TYPE_Q4_K_R4; break;
|
||||
|
|
@ -10604,6 +10618,7 @@ static void ggml_compute_forward_add(
|
|||
case GGML_TYPE_Q6_0:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -11057,6 +11072,7 @@ static void ggml_compute_forward_add1(
|
|||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -11207,6 +11223,7 @@ static void ggml_compute_forward_acc(
|
|||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -14403,6 +14420,7 @@ static void ggml_compute_forward_out_prod(
|
|||
case GGML_TYPE_Q6_0:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -14793,6 +14811,7 @@ static void ggml_compute_forward_set(
|
|||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -15077,6 +15096,7 @@ static void ggml_compute_forward_get_rows(
|
|||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -15688,6 +15708,7 @@ static void ggml_compute_forward_clamp(
|
|||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K:
|
||||
|
|
@ -22527,6 +22548,7 @@ size_t ggml_quantize_chunk(
|
|||
case GGML_TYPE_Q6_0: result = quantize_q6_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_Q8_0: result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
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_Q2_K_R4: result = quantize_q2_k_r4(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_Q3_K_R4: result = quantize_q3_k_r4(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;
|
||||
|
|
|
|||
|
|
@ -163,7 +163,10 @@ struct MulMat {
|
|||
static bool prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny);
|
||||
static inline int num_rows(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q5_K_R4:
|
||||
case GGML_TYPE_Q6_K_R4:
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
case GGML_TYPE_Q5_0_R4:
|
||||
|
|
@ -3440,36 +3443,33 @@ static void mul_mat_q5_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn
|
|||
#endif
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
static void mul_mat_q2_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 m4 = _mm256_set1_epi8(0xf);
|
||||
auto m30 = _mm256_set1_epi8(0x30);
|
||||
auto m32 = _mm256_set1_epi8(32);
|
||||
auto mxf = _mm256_set1_epi8(0xf);
|
||||
auto m03 = _mm256_set1_epi8(0x03);
|
||||
auto m04 = _mm256_set1_epi8(0x04);
|
||||
static const uint8_t k_shuff[32] = {0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15};
|
||||
auto shuff = _mm256_loadu_si256((const __m256i *)k_shuff);
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
__m256 d4s[nrc_y];
|
||||
#else
|
||||
auto m1 = _mm256_set1_epi16(1);
|
||||
#endif
|
||||
int nbl = n / QK_K;
|
||||
__m256 acc[nrc_y] = {};
|
||||
__m256i qx[4];
|
||||
int8_t scales[64];
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_q3_k_r4 * iq3 = (const block_q3_k_r4 *)((const char *)vx + (ix+0)*bx);
|
||||
const block_q2_k_r4 * iq2 = (const block_q2_k_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 *)iq3[ibl].d));
|
||||
auto d4 = _mm256_set_m128(dl, dl);
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
d4s[iy] = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl)));
|
||||
}
|
||||
auto slb = _mm256_loadu_si256((const __m256i *)iq3[ibl].scales_l);
|
||||
auto shbits = _mm_loadu_si128((const __m128i *)iq3[ibl].scales_h);
|
||||
auto shb = MM256_SET_M128I(_mm_srli_epi16(shbits, 2), shbits);
|
||||
auto scales1 = _mm256_sub_epi8(_mm256_or_si256(_mm256_and_si256(slb, m4), _mm256_and_si256(_mm256_slli_epi16(shb, 4), m30)), m32);
|
||||
auto scales2 = _mm256_sub_epi8(_mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(slb, 4), m4), _mm256_and_si256(shb, m30)), m32);
|
||||
_mm256_storeu_si256((__m256i *)scales+0, scales1);
|
||||
_mm256_storeu_si256((__m256i *)scales+1, scales2);
|
||||
auto dm = _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)iq2[ibl].d));
|
||||
auto d4 = _mm256_set_m128(_mm256_castps256_ps128(dm), _mm256_castps256_ps128(dm));
|
||||
auto m4 = _mm256_set_m128(_mm256_extractf128_ps(dm, 1), _mm256_extractf128_ps(dm, 1));
|
||||
m4 = _mm256_mul_ps(m4, _mm256_set1_ps(-1.f));
|
||||
auto all_scales1 = _mm256_loadu_si256((const __m256i *)iq2[ibl].scales+0);
|
||||
auto all_scales2 = _mm256_loadu_si256((const __m256i *)iq2[ibl].scales+1);
|
||||
auto scales1 = _mm256_and_si256(_mm256_srli_epi16(all_scales1, 4), mxf);
|
||||
auto scales2 = _mm256_and_si256(_mm256_srli_epi16(all_scales2, 4), mxf);
|
||||
{
|
||||
auto t1 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales1, 0)), shuff); // blocks 0, 1, 2, 3 for each row
|
||||
auto t2 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales1, 1)), shuff); // blocks 4, 5, 6, 7 for each row
|
||||
|
|
@ -3482,16 +3482,158 @@ static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn
|
|||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto bsums = q8.load_bsums(iy, ibl);
|
||||
auto sumi = _mm256_setzero_si256();
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s1, _mm256_shuffle_epi32(bsums, 0x00));
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s2, _mm256_shuffle_epi32(bsums, 0x55));
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s3, _mm256_shuffle_epi32(bsums, 0xaa));
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s4, _mm256_shuffle_epi32(bsums, 0xff));
|
||||
auto d8 = _mm256_set1_ps(q8.scale(iy, ibl));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(m4, d8), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
d4s[iy] = _mm256_mul_ps(d4, d8);
|
||||
#else
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s1, _mm256_shuffle_epi32(bsums, 0x00)));
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s2, _mm256_shuffle_epi32(bsums, 0x55)));
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s3, _mm256_shuffle_epi32(bsums, 0xaa)));
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s4, _mm256_shuffle_epi32(bsums, 0xff)));
|
||||
auto d8 = _mm256_set1_ps(q8.scale(iy, ibl));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(m4, d8), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
if constexpr (nrc_y == 1) {
|
||||
d4 = _mm256_mul_ps(d4, d8);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
all_scales1 = _mm256_and_si256(all_scales1, mxf);
|
||||
all_scales2 = _mm256_and_si256(all_scales2, mxf);
|
||||
_mm256_storeu_si256((__m256i *)scales+0, all_scales1);
|
||||
_mm256_storeu_si256((__m256i *)scales+1, all_scales2);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto iscales = _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i *)(scales + 8*ib)));
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto scales = _mm256_cvtepi32_ps(iscales);
|
||||
#else
|
||||
auto scales = _mm256_mul_ps(d4, _mm256_cvtepi32_ps(iscales));
|
||||
#endif
|
||||
auto lb = _mm256_loadu_si256((const __m256i *)iq2[ibl].qs+ib);
|
||||
qx[0] = _mm256_and_si256(lb, m03);
|
||||
qx[1] = _mm256_and_si256(_mm256_srli_epi16(lb, 2), m03);
|
||||
qx[2] = _mm256_and_si256(_mm256_srli_epi16(lb, 4), m03);
|
||||
qx[3] = _mm256_and_si256(_mm256_srli_epi16(lb, 6), m03);
|
||||
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));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, d4s[iy]), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
#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)));
|
||||
// Quants are in 0...3, so we can add add up all of them as int16_t without overflowing
|
||||
auto sumi = _mm256_madd_epi16(m1, _mm256_add_epi16(sumi1, sumi2));
|
||||
if constexpr (nrc_y == 1) {
|
||||
acc[iy] = _mm256_fmadd_ps(scales, _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
} else {
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), 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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_q3_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 m4 = _mm256_set1_epi8(0xf);
|
||||
auto m30 = _mm256_set1_epi8(0x30);
|
||||
auto m32 = _mm256_set1_epi8(32);
|
||||
auto m03 = _mm256_set1_epi8(0x03);
|
||||
auto m04 = _mm256_set1_epi8(0x04);
|
||||
static const uint8_t k_shuff[32] = {0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15};
|
||||
auto shuff = _mm256_loadu_si256((const __m256i *)k_shuff);
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
__m256 d4s[nrc_y];
|
||||
#else
|
||||
auto m1 = _mm256_set1_epi16(1);
|
||||
#endif
|
||||
int nbl = n / QK_K;
|
||||
__m256 acc[nrc_y] = {};
|
||||
__m256i qx[4];
|
||||
int8_t scales[64];
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_q3_k_r4 * iq3 = (const block_q3_k_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 *)iq3[ibl].d));
|
||||
auto d4 = _mm256_set_m128(dl, dl);
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
d4s[iy] = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl)));
|
||||
}
|
||||
#else
|
||||
if constexpr (nrc_y == 1) {
|
||||
d4 = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(0, ibl)));
|
||||
}
|
||||
#endif
|
||||
auto slb = _mm256_loadu_si256((const __m256i *)iq3[ibl].scales_l);
|
||||
auto shbits = _mm_loadu_si128((const __m128i *)iq3[ibl].scales_h);
|
||||
auto shb = MM256_SET_M128I(_mm_srli_epi16(shbits, 2), shbits);
|
||||
auto scales1 = _mm256_sub_epi8(_mm256_or_si256(_mm256_and_si256(slb, m4), _mm256_and_si256(_mm256_slli_epi16(shb, 4), m30)), m32);
|
||||
auto scales2 = _mm256_sub_epi8(_mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(slb, 4), m4), _mm256_and_si256(shb, m30)), m32);
|
||||
_mm256_storeu_si256((__m256i *)scales+0, scales1);
|
||||
_mm256_storeu_si256((__m256i *)scales+1, scales2);
|
||||
{
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
auto min = _mm256_mul_ps(d4, _mm256_set1_ps(-4.f));
|
||||
#endif
|
||||
auto t1 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales1, 0)), shuff); // blocks 0, 1, 2, 3 for each row
|
||||
auto t2 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales1, 1)), shuff); // blocks 4, 5, 6, 7 for each row
|
||||
auto t3 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales2, 0)), shuff); // blocks 8, 9, 10, 11 for each row
|
||||
auto t4 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales2, 1)), shuff); // blocks 12, 13, 14, 15 for each row
|
||||
auto s1 = MM256_SET_M128I(_mm256_extracti128_si256(t3, 0), _mm256_extracti128_si256(t1, 0)); // blocks 0, 1, 8, 9
|
||||
auto s2 = MM256_SET_M128I(_mm256_extracti128_si256(t3, 1), _mm256_extracti128_si256(t1, 1)); // blocks 2, 3, 10, 11
|
||||
auto s3 = MM256_SET_M128I(_mm256_extracti128_si256(t4, 0), _mm256_extracti128_si256(t2, 0)); // blocks 4, 5, 12, 13
|
||||
auto s4 = MM256_SET_M128I(_mm256_extracti128_si256(t4, 1), _mm256_extracti128_si256(t2, 1)); // blocks 6, 7, 14, 15
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto bsums = q8.load_bsums(iy, ibl);
|
||||
auto sumi = _mm256_setzero_si256();
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s1, _mm256_shuffle_epi32(bsums, 0x00));
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s2, _mm256_shuffle_epi32(bsums, 0x55));
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s3, _mm256_shuffle_epi32(bsums, 0xaa));
|
||||
sumi = _mm256_dpwssd_epi32(sumi, s4, _mm256_shuffle_epi32(bsums, 0xff));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4s[iy], _mm256_set1_ps(-4.f)), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
#else
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s1, _mm256_shuffle_epi32(bsums, 0x00)));
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s2, _mm256_shuffle_epi32(bsums, 0x55)));
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s3, _mm256_shuffle_epi32(bsums, 0xaa)));
|
||||
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(s4, _mm256_shuffle_epi32(bsums, 0xff)));
|
||||
if constexpr (nrc_y == 1) {
|
||||
acc[iy] = _mm256_fmadd_ps(min, _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
} else {
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(min, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto iscales = _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i *)(scales + 8*ib)));
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto scales = _mm256_cvtepi32_ps(iscales);
|
||||
#else
|
||||
auto scales = _mm256_mul_ps(d4, _mm256_cvtepi32_ps(iscales));
|
||||
#endif
|
||||
auto lb = _mm256_loadu_si256((const __m256i *)iq3[ibl].qs+ib);
|
||||
auto hbits = _mm_loadu_si128((const __m128i *)iq3[ibl].qh+ib);
|
||||
auto hb = MM256_SET_M128I(hbits, _mm_slli_epi16(hbits, 4));
|
||||
|
|
@ -3501,12 +3643,27 @@ static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn
|
|||
qx[3] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 6), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 5)));
|
||||
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));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, d4s[iy]), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
#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)));
|
||||
// Quants are in 0...8, so we can add add up all of them as int16_t without overflowing
|
||||
auto sumi = _mm256_madd_epi16(m1, _mm256_add_epi16(sumi1, sumi2));
|
||||
if constexpr (nrc_y == 1) {
|
||||
acc[iy] = _mm256_fmadd_ps(scales, _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
} else {
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
}
|
||||
#endif
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -5625,6 +5782,18 @@ 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_Q2_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_q2_k_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_q2_k_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_q2_k_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_q2_k_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_q2_k_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_q2_k_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_q2_k_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_q2_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_q3_k_r4_q8_k<1>;
|
||||
|
|
@ -8360,6 +8529,88 @@ IQK_ALWAYS_INLINE void prepare_q4_k_quants(const uint8x16_t& m4, const uint8x16x
|
|||
qx[7] = vshrq_n_u8(bits.val[3], 4); // 28..31
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_q2_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(0x0f);
|
||||
auto m03 = vdupq_n_u8(0x03);
|
||||
int nbl = n / QK_K;
|
||||
int8x16_t qx[4];
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
int16x8x4_t i16scales;
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_q2_k_r4 * iq2 = (const block_q2_k_r4 *)((const char *)vx + ix*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
int32x4_t isum[nrc_y] = {};
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq2[ibl].d));
|
||||
auto m4 = vmulq_f32(vdupq_n_f32(-1.f), vcvt_f32_f16(vld1_f16((const float16_t *)iq2[ibl].d+4)));
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
auto sl = vld1q_u8_x2(iq2[ibl].scales + 32*is);
|
||||
auto m = vshrq_n_u8(sl.val[0], 4);
|
||||
i16scales.val[0] = vmovl_u8(vget_low_u8 (m));
|
||||
i16scales.val[1] = vmovl_u8(vget_high_u8(m));
|
||||
m = vshrq_n_u8(sl.val[1], 4);
|
||||
i16scales.val[2] = vmovl_u8(vget_low_u8 (m));
|
||||
i16scales.val[3] = vmovl_u8(vget_high_u8(m));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto sumi = vdupq_n_s32(0);
|
||||
auto bsums = vld1q_s16(q8.y[iy][ibl].bsums + 8*is);
|
||||
auto b8 = vget_low_s16(bsums);
|
||||
//auto bsums = q8.load_bsums(iy, ibl);
|
||||
//auto b8 = vget_low_s16(bsums.val[0]);
|
||||
sumi = vmlal_lane_s16(sumi, vget_low_s16 (i16scales.val[0]), b8, 0);
|
||||
sumi = vmlal_lane_s16(sumi, vget_high_s16(i16scales.val[0]), b8, 1);
|
||||
sumi = vmlal_lane_s16(sumi, vget_low_s16 (i16scales.val[1]), b8, 2);
|
||||
sumi = vmlal_lane_s16(sumi, vget_high_s16(i16scales.val[1]), b8, 3);
|
||||
b8 = vget_high_s16(bsums);
|
||||
sumi = vmlal_lane_s16(sumi, vget_low_s16 (i16scales.val[2]), b8, 0);
|
||||
sumi = vmlal_lane_s16(sumi, vget_high_s16(i16scales.val[2]), b8, 1);
|
||||
sumi = vmlal_lane_s16(sumi, vget_low_s16 (i16scales.val[3]), b8, 2);
|
||||
sumi = vmlal_lane_s16(sumi, vget_high_s16(i16scales.val[3]), b8, 3);
|
||||
acc[iy] = vfmaq_f32(acc[iy], vmulq_f32(m4, vdupq_n_f32(q8.scale(iy, ibl))), vcvtq_f32_s32(sumi));
|
||||
}
|
||||
m = vandq_u8(sl.val[0], mf);
|
||||
i16scales.val[0] = vmovl_u8(vget_low_u8 (m));
|
||||
i16scales.val[1] = vmovl_u8(vget_high_u8(m));
|
||||
m = vandq_u8(sl.val[1], mf);
|
||||
i16scales.val[2] = vmovl_u8(vget_low_u8 (m));
|
||||
i16scales.val[3] = vmovl_u8(vget_high_u8(m));
|
||||
for (int ib = 0; ib < 4; ++ib) {
|
||||
auto bits = vld1q_u8_x2(iq2[ibl].qs + 128*is + 32*ib);
|
||||
auto scales = vmovl_s16(vget_low_s16 (i16scales.val[ib]));
|
||||
qx[0] = vreinterpretq_s8_u8(vandq_u8( bits.val[0], m03));
|
||||
qx[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(bits.val[0], 2), m03));
|
||||
qx[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(bits.val[0], 4), m03));
|
||||
qx[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(bits.val[0], 6), m03));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8(q8.y[iy][ibl].qs+128*is+32*ib);
|
||||
auto sumi = interleaved_dotq(qx, y);
|
||||
isum[iy] = vmlaq_s32(isum[iy], scales, sumi);
|
||||
}
|
||||
scales = vmovl_s16(vget_high_s16(i16scales.val[ib]));
|
||||
qx[0] = vreinterpretq_s8_u8(vandq_u8( bits.val[1], m03));
|
||||
qx[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(bits.val[1], 2), m03));
|
||||
qx[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(bits.val[1], 4), m03));
|
||||
qx[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(bits.val[1], 6), m03));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8(q8.y[iy][ibl].qs+128*is+32*ib+16);
|
||||
auto sumi = interleaved_dotq(qx, y);
|
||||
isum[iy] = vmlaq_s32(isum[iy], scales, sumi);
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
acc[iy] = vfmaq_f32(acc[iy], vmulq_f32(d4, vdupq_n_f32(q8.scale(iy, ibl))), vcvtq_f32_s32(isum[iy]));
|
||||
}
|
||||
}
|
||||
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_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
|
|
@ -9025,6 +9276,10 @@ 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_K32;
|
||||
break;
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_q2_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_q3_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
|
|
|
|||
|
|
@ -4437,3 +4437,118 @@ void vec_dot_q3_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
|
|||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= q2_k_r4
|
||||
//
|
||||
|
||||
void quantize_row_q2_k_r4_ref(const float * x, block_q2_k_r4 * y, int64_t k) {
|
||||
quantize_q3_k_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_q2_k_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_q2_k_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline void convert_q2_k(const block_q2_K& x, uint8_t * L) {
|
||||
|
||||
const uint8_t * qs = x.qs;
|
||||
for (int n = 0; n < QK_K; n += 128) {
|
||||
for (int j = 0; j < 32; ++j) {
|
||||
L[n + j + 0] = (qs[j] >> 0) & 0x3;
|
||||
L[n + j + 32] = (qs[j] >> 2) & 0x3;
|
||||
L[n + j + 64] = (qs[j] >> 4) & 0x3;
|
||||
L[n + j + 96] = (qs[j] >> 6) & 0x3;
|
||||
}
|
||||
qs += 32;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void repack_q2_k(int nrows, int n_per_row, const block_q2_K * x, block_q2_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_q2_K * x4[4];
|
||||
uint8_t L[QK_K];
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k+0] = x4[k][ibl].d;
|
||||
y[ibl].d[k+4] = x4[k][ibl].dmin;
|
||||
for (int ib = 0; ib < QK_K/16; ++ib) {
|
||||
y[ibl].scales[4*ib+k] = x4[k][ibl].scales[ib];
|
||||
}
|
||||
convert_q2_k(x4[k][ibl], L);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y[ibl].qs[32*ib+4*k+i+ 0] = ((L[32*ib+i+ 0] & 0x3) << 0) | ((L[32*ib+i+ 4] & 0x3) << 2) | ((L[32*ib+i+ 8] & 0x3) << 4) | ((L[32*ib+i+12] & 0x3) << 6);
|
||||
y[ibl].qs[32*ib+4*k+i+16] = ((L[32*ib+i+16] & 0x3) << 0) | ((L[32*ib+i+20] & 0x3) << 2) | ((L[32*ib+i+24] & 0x3) << 4) | ((L[32*ib+i+28] & 0x3) << 6);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_q2_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_Q2_K, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_q2_K(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_q2_k(4, n_per_row, (const block_q2_K *)qtmp.data(), (block_q2_k_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_q2_k_r4(const block_q2_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]);
|
||||
auto ql = x[ibl].qs;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
float dl1 = d * (x[ibl].scales[8*ib + k + 0] & 0xf);
|
||||
float ml1 = m * (x[ibl].scales[8*ib + k + 0] >> 4);
|
||||
float dl2 = d * (x[ibl].scales[8*ib + k + 4] & 0xf);
|
||||
float ml2 = m * (x[ibl].scales[8*ib + k + 4] >> 4);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y4[k][QK_K*ibl+32*ib+i+ 0] = dl1 * ((ql[4*k+i+ 0] >> 0) & 3) - ml1;
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl1 * ((ql[4*k+i+ 0] >> 2) & 3) - ml1;
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl1 * ((ql[4*k+i+ 0] >> 4) & 3) - ml1;
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl1 * ((ql[4*k+i+ 0] >> 6) & 3) - ml1;
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl2 * ((ql[4*k+i+16] >> 0) & 3) - ml2;
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl2 * ((ql[4*k+i+16] >> 2) & 3) - ml2;
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl2 * ((ql[4*k+i+16] >> 4) & 3) - ml2;
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl2 * ((ql[4*k+i+16] >> 6) & 3) - ml2;
|
||||
}
|
||||
ql += 32;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_q2_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_Q2_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);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -115,6 +115,12 @@ size_t quantize_q3_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
|
|||
void dequantize_row_q3_k_r4(const block_q3_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_q3_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 quantize_row_q2_k_r4_ref(const float * GGML_RESTRICT x, block_q2_k_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_q2_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_q2_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_q2_k_r4(const block_q2_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_q2_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 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);
|
||||
|
|
|
|||
|
|
@ -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_Q2_K_R4 = 210, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_R4 = 211, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_K_R4 = 214, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_K_R4 = 216, // except 1d tensors
|
||||
|
|
|
|||
|
|
@ -4545,6 +4545,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_Q6_0: return "Q6_0";
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K_R4: return "Q2_K_R4";
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
|
||||
|
|
@ -15794,6 +15795,9 @@ 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_Q2_K_R4) {
|
||||
new_type = GGML_TYPE_Q2_K;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q3_K_R4) {
|
||||
new_type = GGML_TYPE_Q3_K;
|
||||
}
|
||||
|
|
@ -15859,6 +15863,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4 && qs.model.hparams.n_gqa() >= 4) {
|
||||
new_type = GGML_TYPE_Q4_K_R4;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ3_K
|
||||
: !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
|
||||
|
|
@ -15950,6 +15957,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
|
||||
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4) {
|
||||
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K_R4;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
|
||||
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
|
||||
}
|
||||
|
|
@ -16009,7 +16019,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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_Q4_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
|
|
@ -16079,7 +16090,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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 || new_type == GGML_TYPE_Q6_K_R4 ||
|
||||
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4) {
|
||||
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
|
|
@ -16106,6 +16117,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
|
|
@ -16204,6 +16216,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
// K-quants
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: default_type = GGML_TYPE_Q2_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K_R4: default_type = GGML_TYPE_Q2_K_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XS: default_type = GGML_TYPE_IQ3_S; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
|
||||
|
|
@ -16616,6 +16629,10 @@ 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_Q2_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q2_K;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q3_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q3_K;
|
||||
else chunk_size_multiplier = 4;
|
||||
|
|
|
|||
Loading…
Reference in New Issue