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Bonsai support (AVX2, generic) (#1570) 

* Bonsai support (AVX2, generic)

* Fix ARM build

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2026-04-02 16:54:08 +02:00 committed by GitHub
parent 73742c5db9
commit 90ec1b80c4
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GPG Key ID: B5690EEEBB952194
11 changed files with 247 additions and 38 deletions
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76
ggml/include/ggml.h
View File

@ -386,45 +386,44 @@ extern "C" {
// NOTE: always add types at the end of the enum to keep backward compatibility
enum ggml_type {
GGML_TYPE_F32 = 0,
GGML_TYPE_F16 = 1,
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
// GGML_TYPE_Q4_2 = 4, support has been removed
// GGML_TYPE_Q4_3 = 5, support has been removed
GGML_TYPE_Q5_0 = 6,
GGML_TYPE_Q5_1 = 7,
GGML_TYPE_Q8_0 = 8,
GGML_TYPE_Q8_1 = 9,
GGML_TYPE_Q2_K = 10,
GGML_TYPE_Q3_K = 11,
GGML_TYPE_Q4_K = 12,
GGML_TYPE_Q5_K = 13,
GGML_TYPE_Q6_K = 14,
GGML_TYPE_Q8_K = 15,
GGML_TYPE_IQ2_XXS = 16,
GGML_TYPE_IQ2_XS = 17,
GGML_TYPE_IQ3_XXS = 18,
GGML_TYPE_IQ1_S = 19,
GGML_TYPE_IQ4_NL = 20,
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_IQ2_S = 22,
GGML_TYPE_IQ4_XS = 23,
GGML_TYPE_I8 = 24,
GGML_TYPE_I16 = 25,
GGML_TYPE_I32 = 26,
GGML_TYPE_I64 = 27,
GGML_TYPE_F64 = 28,
GGML_TYPE_IQ1_M = 29,
GGML_TYPE_BF16 = 30,
GGML_TYPE_Q4_0_4_4 = 31,
GGML_TYPE_Q4_0_4_8 = 32,
GGML_TYPE_Q4_0_8_8 = 33,
GGML_TYPE_MXFP4 = 39, // so we are compatible with mainline
GGML_TYPE_F32 = 0,
GGML_TYPE_F16 = 1,
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
// GGML_TYPE_Q4_2 = 4, support has been removed
// GGML_TYPE_Q4_3 = 5, support has been removed
GGML_TYPE_Q5_0 = 6,
GGML_TYPE_Q5_1 = 7,
GGML_TYPE_Q8_0 = 8,
GGML_TYPE_Q8_1 = 9,
GGML_TYPE_Q2_K = 10,
GGML_TYPE_Q3_K = 11,
GGML_TYPE_Q4_K = 12,
GGML_TYPE_Q5_K = 13,
GGML_TYPE_Q6_K = 14,
GGML_TYPE_Q8_K = 15,
GGML_TYPE_IQ2_XXS = 16,
GGML_TYPE_IQ2_XS = 17,
GGML_TYPE_IQ3_XXS = 18,
GGML_TYPE_IQ1_S = 19,
GGML_TYPE_IQ4_NL = 20,
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_IQ2_S = 22,
GGML_TYPE_IQ4_XS = 23,
GGML_TYPE_I8 = 24,
GGML_TYPE_I16 = 25,
GGML_TYPE_I32 = 26,
GGML_TYPE_I64 = 27,
GGML_TYPE_F64 = 28,
GGML_TYPE_IQ1_M = 29,
GGML_TYPE_BF16 = 30,
GGML_TYPE_Q4_0_4_4 = 31,
GGML_TYPE_Q4_0_4_8 = 32,
GGML_TYPE_Q4_0_8_8 = 33,
GGML_TYPE_I2_S = 36, // So we are able to consume MS BitNet I2_S quants
GGML_TYPE_MXFP4 = 39, // so we are compatible with mainline
GGML_TYPE_Q1_0_G128 = 41, // Bonsai 1-bit quants
//
// So we are able to consume MS BitNet I2_S quants
//
GGML_TYPE_I2_S = 36,
//
GGML_TYPE_Q8_0_X4 = 97,
GGML_TYPE_Q8_1_X4 = 98,
@ -530,6 +529,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_Q4_0_4_4 = 26, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_4_8 = 27, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_8_8 = 28, // except 1d tensors
GGML_FTYPE_MOSTLY_Q1_0_128 = 29, // except 1d tensors
//
GGML_FTYPE_MOSTLY_Q6_0 = 127, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_BN = 128, // except 1d tensors

10
ggml/src/ggml-common.h
View File

@ -541,6 +541,16 @@ typedef struct {
} block_iq1_m_r4;
static_assert(sizeof(block_iq1_m_r4) == 28, "wrong iq1_m_r4 block size/padding");
//
// Bonsai
//
#define QK1_0_G128 128
typedef struct {
ggml_half d;
uint8_t qs[QK1_0_G128 / 8];
} block_q1_0_g128;
static_assert(sizeof(block_q1_0_g128) == sizeof(ggml_half) + QK1_0_G128 / 8, "wrong q1_0_g128 block size/padding");
//
// Bitnet and TriLM - implemented as 1.625 bpw
//

1
ggml/src/ggml-quants.c
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@ -15435,6 +15435,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
case GGML_TYPE_IQ2_KT: break;
case GGML_TYPE_IQ3_KT: break;
case GGML_TYPE_IQ4_KT: break;
case GGML_TYPE_Q1_0_G128: break;
case GGML_TYPE_IQ3_K: break;
case GGML_TYPE_IQ3_KS: break;
case GGML_TYPE_IQ2_KL: break;

22
ggml/src/ggml.c
View File

@ -1677,6 +1677,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.nrows = 1,
.row_meta_size = 4,
},
[GGML_TYPE_Q1_0_G128] = {
.type_name = "q1_0_g128",
.blck_size = QK1_0_G128,
.type_size = sizeof(block_q1_0_g128),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q1_0_g128,
.from_float = quantize_row_q1_0_g128,
.from_float_ref = (ggml_from_float_t)quantize_row_q1_0_g128_ref,
.vec_dot = vec_dot_q1_0_g128_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0_X4,
.nrows = 1,
.row_meta_size = 0,
},
[GGML_TYPE_IQ3_K] = {
.type_name = "iq3_k",
.blck_size = QK_K,
@ -4900,6 +4913,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ2_KT: wtype = GGML_TYPE_IQ2_KT; break;
case GGML_FTYPE_MOSTLY_IQ3_KT: wtype = GGML_TYPE_IQ3_KT; break;
case GGML_FTYPE_MOSTLY_IQ4_KT: wtype = GGML_TYPE_IQ4_KT; break;
case GGML_FTYPE_MOSTLY_Q1_0_128: wtype = GGML_TYPE_Q1_0_G128;break;
case GGML_FTYPE_MOSTLY_IQ3_K: wtype = GGML_TYPE_IQ3_K; break;
case GGML_FTYPE_MOSTLY_IQ3_KS: wtype = GGML_TYPE_IQ3_KS; break;
case GGML_FTYPE_MOSTLY_IQ2_KL: wtype = GGML_TYPE_IQ2_KL; break;
@ -12817,6 +12831,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -13370,6 +13385,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -13549,6 +13565,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -17874,6 +17891,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -18297,6 +18315,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -18626,6 +18645,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -19383,6 +19403,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_Q1_0_G128:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_KS:
case GGML_TYPE_IQ2_KL:
@ -28464,6 +28485,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ2_KT: result = quantize_iq2_kt (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ3_KT: result = quantize_iq3_kt (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_KT: result = quantize_iq4_kt (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_Q1_0_G128: result = quantize_q1_0_g128(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ3_K: result = quantize_iq3_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ3_KS: result = quantize_iq3_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ2_KL: result = quantize_iq2_kl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

80
ggml/src/iqk/iqk_gemm_1bit.cpp
View File

@ -1445,6 +1445,63 @@ IQK_NOINLINE void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const
}
}
template <int nrc_y>
static void mul_mat_q1_0_g128_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
constexpr int n4 = QK1_0_G128 / QK8_0;
Q8<nrc_y, block_q8_0> q8(info);
const block_q8_0_x4 * y[nrc_y];
for (int iy = 0; iy < nrc_y; ++iy) {
y[iy] = (const block_q8_0_x4 *)info.src1_row(iy);
}
__m256i shuffle[4] = {
_mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, 0x0101010101010101, 0x0000000000000000),
_mm256_set_epi64x(0x0707070707070707, 0x0606060606060606, 0x0505050505050505, 0x0404040404040404),
_mm256_set_epi64x(0x0b0b0b0b0b0b0b0b, 0x0a0a0a0a0a0a0a0a, 0x0909090909090909, 0x0808080808080808),
_mm256_set_epi64x(0x0f0f0f0f0f0f0f0f, 0x0e0e0e0e0e0e0e0e, 0x0d0d0d0d0d0d0d0d, 0x0c0c0c0c0c0c0c0c),
};
auto mask = _mm256_set1_epi64x(0x8040201008040201);
auto mp1 = _mm256_set1_epi8( 1);
auto mm1 = _mm256_set1_epi8(-1);
auto m1 = _mm256_set1_epi16(1);
int nb = n / QK1_0_G128;
__m256i qx[4];
__m256i sumi[4];
for (int ix = 0; ix < nrc_x; ++ix) {
auto x = (const block_q1_0_g128 *)((const char *)vx + ix*bx);
__m256 acc[nrc_y] = {};
for (int ib = 0; ib < nb; ++ib) {
float d = GGML_FP16_TO_FP32(x[ib].d);
auto vd = _mm256_set1_ps(d);
auto bits128 = _mm_loadu_si128((const __m128i *)x[ib].qs);
auto bits = MM256_SET_M128I(bits128, bits128);
for (int k = 0; k < 4; ++k) {
qx[k] = _mm256_shuffle_epi8(bits, shuffle[k]);
qx[k] = _mm256_cmpeq_epi8(_mm256_and_si256(qx[k], mask), mask);
qx[k] = _mm256_or_si256(_mm256_and_si256(qx[k], mp1), _mm256_andnot_si256(qx[k], mm1));
}
for (int iy = 0; iy < nrc_y; ++iy) {
for (int k = 0; k < n4; ++k) {
auto qy = _mm256_loadu_si256((const __m256i *)y[iy][ib].qs + k);
#ifdef HAVE_VNNI256
sumi[k] = _mm256_dpbusd_epi32(_mm256_setzero_si256(), mp1, _mm256_sign_epi8(qy, qx[k]));
#else
sumi[k] = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(mp1, _mm256_sign_epi8(qy, qx[k])));
#endif
}
sumi[0] = _mm256_madd_epi16(m1, _mm256_packs_epi32(sumi[0], sumi[1]));
sumi[2] = _mm256_madd_epi16(m1, _mm256_packs_epi32(sumi[2], sumi[3]));
sumi[0] = _mm256_madd_epi16(m1, _mm256_packs_epi32(sumi[0], sumi[2]));
auto dy = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)y[iy][ib].d));
auto dxy= _mm256_mul_ps(vd, _mm256_set_m128(dy, dy));
acc[iy] = _mm256_fmadd_ps(dxy, _mm256_cvtepi32_ps(sumi[0]), acc[iy]);
}
}
for (int iy = 0; iy < nrc_y; ++iy) {
info.store(ix, iy, hsum_float_8(acc[iy]));
}
}
}
template <int nrc_y>
static void mul_mat_iq2_bn_r4_q8_k16_avx2(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
if (nrc_x%4) {
@ -1903,6 +1960,11 @@ bool iqk_set_kernels_1bit(int ne00, int typeA, int typeB, std::array<mul_mat_t,
IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq2_bn_r4_q8_k16, funcs);
expected_typeB = GGML_TYPE_Q8_K16;
break;
case GGML_TYPE_Q1_0_G128:
if (ne00 % QK1_0_G128 != 0) return false;
expected_typeB = GGML_TYPE_Q8_0_X4;
IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_q1_0_g128_q8_0, funcs);
break;
default:
return false;
@ -2279,6 +2341,19 @@ static void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const DataIn
}
}
template <int nrc_y>
static void mul_mat_q1_0_g128_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
Q8<nrc_y, block_q8_0_x4> q8(info);
for (int ix = 0; ix < nrc_x; ++ix) {
auto x = (const block_q1_0_g128 *)((const char *)vx + ix*bx);
for (int iy = 0; iy < nrc_y; ++iy) {
float s;
vec_dot_q1_0_g128_q8_0(n, &s, 0, x, bx, q8.y[iy], 0, 1);
info.store(ix, iy, s);
}
}
}
template <int nrc_y>
static void mul_mat_iq1_s_r4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0);
@ -2831,6 +2906,11 @@ bool iqk_set_kernels_1bit(int ne00, int typeA, int typeB, std::array<mul_mat_t,
func16 = mul_mat_iq1_m_r4_q8_0<16>;
expected_Btype = GGML_TYPE_Q8_K128;
break;
case GGML_TYPE_Q1_0_G128:
if (ne00 % QK1_0_G128 != 0) return false;
expected_Btype = GGML_TYPE_Q8_0_X4;
IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_q1_0_g128_q8_0, funcs);
break;
default:
return false;
}

2
ggml/src/iqk/iqk_mul_mat.cpp
View File

@ -941,6 +941,7 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
case GGML_TYPE_IQ1_BN:
case GGML_TYPE_IQ2_BN:
case GGML_TYPE_IQ2_BN_R4:
case GGML_TYPE_Q1_0_G128:
return iqk_set_kernels_1bit(ne00, typeA, typeB, mm.funcs, mm.func16);
default:
@ -1032,6 +1033,7 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ1_S_R4:
case GGML_TYPE_IQ1_M_R4:
case GGML_TYPE_Q1_0_G128:
return iqk_set_kernels_1bit(ne00, typeA, typeB, m.funcs, m.func16);
case GGML_TYPE_IQ1_KT:
case GGML_TYPE_IQ2_KT:

85
ggml/src/iqk/iqk_quantize.cpp
View File

@ -9872,6 +9872,91 @@ void vec_dot_iq4_kt_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx
}
void quantize_row_q1_0_g128_ref(const float * x, block_q1_0_g128 * y, int64_t k) {
quantize_row_q1_0_g128(x, y, k);
}
void quantize_row_q1_0_g128(const float * x, void * vy, int64_t k) {
assert(k % QK1_0_G128 == 0);
int nb = k / QK1_0_G128;
auto y = (block_q1_0_g128 *)vy;
for (int ib = 0; ib < nb; ++ib) {
float sum = 0;
for (int j = 0; j < QK1_0_G128; ++j) sum += std::abs(x[j]);
float d = sum / QK1_0_G128;
y[ib].d = GGML_FP32_TO_FP16(d);
std::memset(y[ib].qs, 0, QK1_0_G128/8);
for (int j = 0; j < QK1_0_G128; ++j) {
if (x[j] >= 0.0f) {
y[ib].qs[j / 8] |= (1 << (j % 8));
}
}
x += QK1_0_G128;
}
}
size_t quantize_q1_0_g128(const float * src, void * dst, int64_t nrows, int64_t n_per_row, [[maybe_unused]] const float * imatrix) {
GGML_ASSERT(n_per_row % QK1_0_G128 == 0);
int64_t ntot = nrows * n_per_row;
quantize_row_q1_0_g128(src, dst, ntot);
int64_t nblock = ntot / QK1_0_G128;
return nblock * sizeof(block_q1_0_g128);
}
void dequantize_row_q1_0_g128(const block_q1_0_g128 * x, float * y, int64_t k) {
assert(k % QK1_0_G128 == 0);
constexpr uint8_t k_mask[8] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80};
int nb = k / QK1_0_G128;
for (int ib = 0; ib < nb; ++ib) {
float d = GGML_FP16_TO_FP32(x[ib].d);
for (int i = 0; i < QK1_0_G128/8; ++i) {
for (int j = 0; j < 8; ++j) {
*y++ = x[ib].qs[i] & k_mask[j] ? d : -d;
}
}
}
}
void vec_dot_q1_0_g128_q8_0(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
assert(n % QK1_0_G128 == 0);
assert(nrc == 1);
GGML_UNUSED(nrc);
GGML_UNUSED(bx);
GGML_UNUSED(by);
GGML_UNUSED(bs);
int nb = n / QK1_0_G128;
constexpr uint8_t k_mask[8] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80};
constexpr int n4 = QK1_0_G128 / QK8_0;
auto x = (const block_q1_0_g128 *)vx;
auto y = (const block_q8_0_x4 *)vy;
int16_t sumi[QK1_0_G128/8];
float sumf = 0;
for (int ib = 0; ib < nb; ++ib) {
auto dx = GGML_FP16_TO_FP32(x[ib].d);
auto qx = x[ib].qs;
auto qy = y[ib].qs;
for (int k = 0; k < QK1_0_G128/8; ++k) {
uint8_t bits = qx[k];
int16_t s = 0;
for (int j = 0; j < 8; ++j) {
s += (bits & k_mask[j] ? qy[j] : -qy[j]);
}
qy += 8;
sumi[k] = s;
}
auto s = sumi;
for (int k = 0; k < n4; ++k) {
float dy = GGML_FP16_TO_FP32(y[ib].d[k]);
sumf += dx*dy*(s[0] + s[1] + s[2] + s[3]);
s += 4;
}
}
*s = sumf;
}
namespace {
template <typename Block>
inline int check_row_for_blocks_256_fp16(int nblock, const Block * x) {

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

@ -295,6 +295,12 @@ size_t quantize_q8_KV_r8(const float * GGML_RESTRICT src, void * GGML_RESTRICT d
void dequantize_row_q8_KV_r8(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_q8_KV_r8_q8_KV(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_q1_0_g128_ref(const float * GGML_RESTRICT x, block_q1_0_g128 * GGML_RESTRICT y, int64_t k);
void quantize_row_q1_0_g128(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
size_t quantize_q1_0_g128(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
void dequantize_row_q1_0_g128(const block_q1_0_g128 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_q1_0_g128_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 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);

1
include/llama.h
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@ -189,6 +189,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // except 1d tensors
LLAMA_FTYPE_MOSTLY_MXFP4 = 38, // except 1d tensors, 38 to be compatible with mainline
LLAMA_FTYPE_MOSTLY_Q1_0_G128 = 41, // except 1d tensors, 38 to be compatible with mainline
//
LLAMA_FTYPE_MOSTLY_Q6_0 = 135, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_BN = 136, // except 1d tensors

1
src/llama-model.cpp
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@ -1678,6 +1678,7 @@ std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_Q6_0_R4: return "Q6_0_R4 - 6.5 bpw";
case LLAMA_FTYPE_MOSTLY_Q8_0_R8: return "Q8_0_R8 - 8.5 bpw";
case LLAMA_FTYPE_MOSTLY_MXFP4: return "MXFP4 - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_Q1_0_G128:return "Q1_0_G128 - 1.125 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";
case LLAMA_FTYPE_MOSTLY_IQ4_KS_R4:return "IQ4_KS_R4 - 4.25 bpw";

1
src/llama-quantize.cpp
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@ -988,6 +988,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_Q6_0_R4: default_type = GGML_TYPE_Q6_0_R4; break;
case LLAMA_FTYPE_MOSTLY_Q8_0_R8: default_type = GGML_TYPE_Q8_0_R8; break;
case LLAMA_FTYPE_MOSTLY_MXFP4: default_type = GGML_TYPE_MXFP4; break;
case LLAMA_FTYPE_MOSTLY_Q1_0_G128: default_type = GGML_TYPE_Q1_0_G128; 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;
case LLAMA_FTYPE_MOSTLY_IQ4_KS_R4:default_type = GGML_TYPE_IQ4_KS_R4;break;