diff options
| -rw-r--r-- | examples/quantize/quantize.cpp | 1 | ||||
| -rw-r--r-- | ggml/include/ggml.h | 4 | ||||
| -rw-r--r-- | ggml/src/ggml-common.h | 8 | ||||
| -rw-r--r-- | ggml/src/ggml-quants.c | 1 | ||||
| -rw-r--r-- | ggml/src/ggml.c | 22 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_mul_mat.cpp | 271 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_quantize.cpp | 108 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_quantize.h | 6 | ||||
| -rw-r--r-- | include/llama.h | 1 | ||||
| -rw-r--r-- | src/llama.cpp | 20 |
10 files changed, 421 insertions, 21 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index a8b4a44e..f8ce3edd 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -41,6 +41,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = { { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", }, { "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", }, + { "IQ4_XS_R4",LLAMA_FTYPE_MOSTLY_IQ4_XS_R4," 4.25 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", }, diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 99c39b9c..09f92eb9 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -410,7 +410,8 @@ extern "C" { GGML_TYPE_Q4_0_R4 = 202, GGML_TYPE_Q5_0_R4 = 206, GGML_TYPE_Q8_0_R4 = 208, - GGML_TYPE_IQ4_NL_X4 = 220, + GGML_TYPE_IQ4_NL_X4 = 220, // TODO: rename GGML_TYPE_IQ4_NL_X4 to GGML_TYPE_IQ4_NL_R4 + GGML_TYPE_IQ4_XS_R4 = 223, GGML_TYPE_Q6_0_R4 = 233, GGML_TYPE_COUNT, }; @@ -475,6 +476,7 @@ extern "C" { GGML_FTYPE_MOSTLY_Q8_0_R4 = 207, // except 1d tensors GGML_FTYPE_MOSTLY_Q5_0_R4 = 208, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_NL_X4 = 219, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors GGML_FTYPE_MOSTLY_Q6_0_R4 = 227, // except 1d tensors }; diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index fb87a602..aa41bf55 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -448,6 +448,14 @@ typedef struct { static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); typedef struct { + ggml_half d[4]; + uint8_t scales_h[QK_K/32]; + uint8_t scales_l[QK_K/16]; + uint8_t qs[QK_K*2]; +} block_iq4_xs_r4; +static_assert(sizeof(block_iq4_xs_r4) == 4*sizeof(ggml_half) + QK_K/32 + QK_K/16 + QK_K*2, "wrong iq4_xs_rs block size/padding"); + +typedef struct { uint8_t scales[QK_K/32]; uint8_t qs[QK_K/2]; } block_iq4_ks; diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index 94950a36..4fdd2c36 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -15197,6 +15197,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte case GGML_TYPE_IQ4_KS: break; case GGML_TYPE_IQ4_KSS: break; case GGML_TYPE_IQ4_NL_X4: break; + case GGML_TYPE_IQ4_XS_R4: break; case GGML_TYPE_Q4_0_R4: break; case GGML_TYPE_Q5_0_R4: break; case GGML_TYPE_Q6_0_R4: break; diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 203b1b57..f4320e99 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1262,6 +1262,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .nrows = 1, .row_meta_size = 0, }, + [GGML_TYPE_IQ4_XS_R4] = { + .type_name = "iq4_xs_r4", + .blck_size = QK_K, + .type_size = sizeof(block_iq4_xs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq4_xs_r4, + .from_float = quantize_row_iq4_xs_r4, + .from_float_ref = (ggml_from_float_t)quantize_row_iq4_xs_r4_ref, + .vec_dot = vec_dot_iq4_xs_r4_q8_k, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + .row_meta_size = 0, + }, [GGML_TYPE_Q4_0_R4] = { .type_name = "q4_0_r4", .blck_size = QK4_NL, @@ -3989,6 +4002,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ2_BN: wtype = GGML_TYPE_IQ2_BN; break; 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_IQ4_XS_R4: wtype = GGML_TYPE_IQ4_XS_R4;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; @@ -10517,6 +10531,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -10964,6 +10979,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -11108,6 +11124,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -14298,6 +14315,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -14682,6 +14700,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -14960,6 +14979,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -15565,6 +15585,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ2_BN: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL_X4: + case GGML_TYPE_IQ4_XS_R4: case GGML_TYPE_Q4_0_R4: case GGML_TYPE_Q5_0_R4: case GGML_TYPE_Q6_0_R4: @@ -22396,6 +22417,7 @@ size_t ggml_quantize_chunk( case GGML_TYPE_IQ2_BN: result = quantize_iq2_bn (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; 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_IQ4_XS_R4: result = quantize_iq4_xs_r4(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; diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp index f827e460..faa4cab7 100644 --- a/ggml/src/iqk/iqk_mul_mat.cpp +++ b/ggml/src/iqk/iqk_mul_mat.cpp @@ -2656,6 +2656,172 @@ static void mul_mat_q8_0_r4_q8_1(int n, const void * vx, size_t bx, const DataIn } #endif +template <int nrc_y> +static void mul_mat_iq4_xs_r4_q8_k_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_K> q8(info); + auto m4 = _mm256_set1_epi8(0xf); +#ifndef HAVE_FANCY_SIMD + auto m1 = _mm256_set1_epi16(1); +#endif + auto values128 = _mm_loadu_si128((const __m128i *)iq4k_values); + auto values = MM256_SET_M128I(values128, values128); + //auto values = load_iq4nl_values_256(); + int nbl = n / QK_K; + using helper_t = union { __m256i vec; uint32_t val[8]; }; + helper_t h; + __m256 acc[nrc_y] = {}; + __m256i qx[4]; + for (int ix = 0; ix < nrc_x; ix += 4) { + const block_iq4_xs_r4 * iq4 = (const block_iq4_xs_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 *)iq4[ibl].d)); + auto d4 = _mm256_set_m128(dl, dl); + auto slbits = _mm_loadu_si128((const __m128i *)iq4[ibl].scales_l); + auto sl = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(slbits, 4), slbits), _mm256_set1_epi8(0xf)); + auto aux64 = (const uint64_t *)iq4[ibl].scales_h; + auto shbits = _mm_set_epi64x(aux64[0] >> 2, aux64[0]); + auto sh = _mm256_and_si256(MM256_SET_M128I(shbits, _mm_slli_epi16(shbits, 4)), _mm256_set1_epi8(0x30)); + h.vec = _mm256_sub_epi8(_mm256_or_si256(sl, sh), _mm256_set1_epi8(32)); + for (int ib = 0; ib < QK_K/32; ++ib) { + auto iscales = _mm256_cvtepi8_epi32(_mm_set1_epi32(h.val[ib])); + auto scales = _mm256_mul_ps(d4, _mm256_cvtepi32_ps(iscales)); +#ifdef HAVE_FANCY_SIMD + auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-64.f)); +#endif + auto bits1 = _mm256_loadu_si256((const __m256i *)iq4[ibl].qs+2*ib+0); + auto bits2 = _mm256_loadu_si256((const __m256i *)iq4[ibl].qs+2*ib+1); + qx[0] = _mm256_shuffle_epi8(values, _mm256_and_si256(bits1, m4)); + qx[1] = _mm256_shuffle_epi8(values, _mm256_and_si256(bits2, m4)); + qx[2] = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4)); + qx[3] = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4)); +#ifndef HAVE_FANCY_SIMD + auto s1 = _mm256_sign_epi8(qx[0], qx[0]); + auto s2 = _mm256_sign_epi8(qx[1], qx[1]); + auto s3 = _mm256_sign_epi8(qx[2], qx[2]); + auto s4 = _mm256_sign_epi8(qx[3], qx[3]); +#endif + 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)); + float d8 = q8.scale(iy, ibl); + float m8 = d8 * (q8.y[iy][ibl].bsums[2*ib+0] + q8.y[iy][ibl].bsums[2*ib+1]); + acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(d8)), _mm256_cvtepi32_ps(sumi), acc[iy]); + acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(m8), acc[iy]); +#else + auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(s1, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x00), qx[0])), + _mm256_maddubs_epi16(s2, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x55), qx[1]))); + auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(s3, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xaa), qx[2])), + _mm256_maddubs_epi16(s4, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xff), qx[3]))); + auto sumi = _mm256_add_epi32(_mm256_madd_epi16(m1, sumi1), _mm256_madd_epi16(m1, sumi2)); + acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]); + //auto sumi1 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[0], _mm256_shuffle_epi32(y, 0x00))), + // _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[1], _mm256_shuffle_epi32(y, 0x55)))); + //auto sumi2 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[2], _mm256_shuffle_epi32(y, 0xaa))), + // _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[3], _mm256_shuffle_epi32(y, 0xff)))); + //auto sumi = _mm256_add_epi32(sumi1, sumi2); + //float d8 = q8.scale(iy, ibl); + //float m8 = d8 * (q8.y[iy][ibl].bsums[2*ib+0] + q8.y[iy][ibl].bsums[2*ib+1]); + //acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(d8)), _mm256_cvtepi32_ps(sumi), acc[iy]); + //acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(m8), 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); + } + } +} + +#ifdef HAVE_FANCY_SIMD +template <int nrc_y> +static void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) { + if constexpr (nrc_y == 1){ + mul_mat_iq4_xs_r4_q8_k_avx2<1>(n, vx, bx, info, nrc_x); + } else { + GGML_ASSERT(nrc_x%8 == 0); + Q8<nrc_y, block_q8_K> q8(info); + auto m4 = _mm512_set1_epi8(0xf); + auto values = load_iq4nl_values_512(); + int nbl = n / QK_K; + using helper_t = union { __m256i vec; uint32_t val[8]; }; + helper_t hl, hh; + __m512 acc[2*nrc_y] = {}; + __m512i qx[4]; + for (int ix = 0; ix < nrc_x; ix += 8) { + const block_iq4_xs_r4 * iq4l = (const block_iq4_xs_r4 *)((const char *)vx + (ix+0)*bx); + const block_iq4_xs_r4 * iq4h = (const block_iq4_xs_r4 *)((const char *)vx + (ix+4)*bx); + for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256 + auto dl = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4l[ibl].d)); + auto dh = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4h[ibl].d)); + auto d4 = _mm512_insertf32x8(_mm512_castps256_ps512(_mm256_set_m128(dl, dl)), _mm256_set_m128(dh, dh), 1); + auto slbits_l = _mm_loadu_si128((const __m128i *)iq4l[ibl].scales_l); + auto shbits_l = _mm_loadu_si128((const __m128i *)iq4h[ibl].scales_l); + auto sl_l = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(slbits_l, 4), slbits_l), _mm256_set1_epi8(0xf)); + auto sh_l = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(shbits_l, 4), shbits_l), _mm256_set1_epi8(0xf)); + auto aux64 = (const uint64_t *)iq4l[ibl].scales_h; + auto slbits_h = _mm_set_epi64x(aux64[0] >> 2, aux64[0]); + aux64 = (const uint64_t *)iq4h[ibl].scales_h; + auto shbits_h = _mm_set_epi64x(aux64[0] >> 2, aux64[0]); + auto sl_h = _mm256_and_si256(MM256_SET_M128I(slbits_h, _mm_slli_epi16(slbits_h, 4)), _mm256_set1_epi8(0x30)); + auto sh_h = _mm256_and_si256(MM256_SET_M128I(shbits_h, _mm_slli_epi16(shbits_h, 4)), _mm256_set1_epi8(0x30)); + hl.vec = _mm256_sub_epi8(_mm256_or_si256(sl_l, sl_h), _mm256_set1_epi8(32)); + hh.vec = _mm256_sub_epi8(_mm256_or_si256(sh_l, sh_h), _mm256_set1_epi8(32)); + for (int ib = 0; ib < QK_K/32; ++ib) { + auto scales1 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hl.val[ib])); + auto scales2 = _mm256_cvtepi8_epi32(_mm_set1_epi32(hh.val[ib])); + auto iscales = _mm512_inserti32x8(_mm512_castsi256_si512(scales1), scales2, 1); + auto scales = _mm512_mul_ps(d4, _mm512_cvtepi32_ps(iscales)); + auto scales_m = _mm512_mul_ps(scales, _mm512_set1_ps(-64.f)); + auto bits1 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[ibl].qs+2*ib+0)), + _mm256_loadu_si256((const __m256i *)iq4h[ibl].qs+2*ib+0), 1); + auto bits2 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[ibl].qs+2*ib+1)), + _mm256_loadu_si256((const __m256i *)iq4h[ibl].qs+2*ib+1), 1); + qx[0] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits1, m4)); + qx[1] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits2, m4)); + qx[2] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits1, 4), m4)); + qx[3] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits2, 4), m4)); + for (int iy = 0; iy < nrc_y; ++iy) { + auto y8 = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib); + auto y = _mm512_inserti32x8(_mm512_castsi256_si512(y8), y8, 1); + auto sumi = _mm512_setzero_si512(); + sumi = _mm512_dpbusd_epi32(sumi, qx[0], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x00))); + sumi = _mm512_dpbusd_epi32(sumi, qx[1], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x55))); + sumi = _mm512_dpbusd_epi32(sumi, qx[2], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xaa))); + sumi = _mm512_dpbusd_epi32(sumi, qx[3], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xff))); + float d8 = q8.scale(iy, ibl); + float m8 = d8 * (q8.y[iy][ibl].bsums[2*ib+0] + q8.y[iy][ibl].bsums[2*ib+1]); + acc[2*iy+0] = _mm512_fmadd_ps(_mm512_mul_ps(scales, _mm512_set1_ps(d8)), _mm512_cvtepi32_ps(sumi), acc[2*iy+0]); + acc[2*iy+1] = _mm512_fmadd_ps(scales_m, _mm512_set1_ps(m8), acc[2*iy+1]); + } + } + } + for (int iy = 0; iy < nrc_y; ++iy) { + auto sum512 = _mm512_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_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) { + mul_mat_iq4_xs_r4_q8_k_avx2<nrc_y>(n, vx, bx, info, nrc_x); +} +#endif + template <typename Bits> inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, const __m256i * q8, __m256i * sumi) { if (j == 0) { @@ -4625,6 +4791,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) { mm.funcs[7] = mul_mat_iq4_nl_x4_q8_1<8>; expected_typeB = GGML_TYPE_Q8_1; break; + case GGML_TYPE_IQ4_XS_R4: + assert (ne00 % QK_K == 0); + mm.funcs[0] = mul_mat_iq4_xs_r4_q8_k<1>; + mm.funcs[1] = mul_mat_iq4_xs_r4_q8_k<2>; + mm.funcs[2] = mul_mat_iq4_xs_r4_q8_k<3>; + mm.funcs[3] = mul_mat_iq4_xs_r4_q8_k<4>; + mm.funcs[4] = mul_mat_iq4_xs_r4_q8_k<5>; + mm.funcs[5] = mul_mat_iq4_xs_r4_q8_k<6>; + mm.funcs[6] = mul_mat_iq4_xs_r4_q8_k<7>; + mm.funcs[7] = mul_mat_iq4_xs_r4_q8_k<8>; + expected_typeB = GGML_TYPE_Q8_K; + break; case GGML_TYPE_Q4_0_R4: assert (ne00 % QK4_NL == 0); mm.funcs[0] = mul_mat_q4_0_r4_q8_1<1>; @@ -7075,6 +7253,30 @@ static void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const DataIn } } +IQK_ALWAYS_INLINE int32x4_t interleaved_dotq(const int8x16_t * qx, const int8x16x2_t& y) { + 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); + return sumi; +} + +IQK_ALWAYS_INLINE void prepare_iq4_nl_quants(const int8x16_t& values, const uint8x16_t& m4, const uint8x16x4_t& bits, int8x16_t * qx) { + qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4)); // 0...3 from the 4 rows + qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19 + qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4)); // 4...7 + qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23 + qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11 + qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27 + qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15 + qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31 +} + template <int nrc_y> void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) { GGML_ASSERT(nrc_x%4 == 0); @@ -7091,25 +7293,10 @@ void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& i for (int k = 0; k < 4; ++k) { auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[4*ib4+k].d)); auto bits = vld1q_u8_x4(iq4[4*ib4+k].qs); - qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4)); // 0...3 from the 4 rows - qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19 - qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4)); // 4...7 - qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23 - qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11 - qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27 - qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15 - qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31 + prepare_iq4_nl_quants(values, m4, bits, qx); 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 sumi = interleaved_dotq(qx, y); 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)); } @@ -7122,6 +7309,45 @@ void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& i } } +template <int nrc_y> +void mul_mat_iq4_xs_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 = vdupq_n_u8(0xf); + auto values = vld1q_s8(iq4k_values); + int nbl = n / QK_K; + int8x16_t qx[8]; + float32x4_t acc[nrc_y] = {}; + for (int ix = 0; ix < nrc_x; ix += 4) { + const block_iq4_xs_r4 * iq4 = (const block_iq4_xs_r4 *)((const char *)vx + ix*bx); + for (int ibl = 0; ibl < nbl; ++ibl) { + const uint32_t * scales_l = (const uint32_t *)iq4[ibl].scales_l; + const uint32_t * scales_h = (const uint32_t *)iq4[ibl].scales_h; + auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[ibl].d)); + for (int ib = 0; ib < QK_K/32; ++ib) { + auto ul = (scales_l[ib%4] >> 4*(ib/4)) & 0x0f0f0f0f; + auto uh = (scales_h[ib%2] >> 2*(ib/2)) & 0x03030303; + auto sl8 = vsub_s8(vreinterpret_s8_s32(vdup_n_s32(ul | (uh << 4))), vdup_n_s8(32)); + auto sl16 = vmovl_s8(sl8); + auto sl32 = vmovl_s16(vget_low_s16(sl16)); + auto scales = vmulq_f32(d4, vcvtq_f32_s32(sl32)); + auto bits = vld1q_u8_x4(iq4[ibl].qs + 64*ib); + prepare_iq4_nl_quants(values, m4, bits, qx); + for (int iy = 0; iy < nrc_y; ++iy) { + auto y = vld1q_s8_x2(q8.y[iy][ibl].qs+32*ib); + auto sumi = interleaved_dotq(qx, y); + auto d4d8 = vmulq_f32(scales, vdupq_n_f32(q8.scale(iy, ibl))); + acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi)); + } + } + } + for (int iy = 0; iy < nrc_y; ++iy) { + info.store(ix, iy, acc[iy]); + acc[iy] = vdupq_n_f32(0.f); + } + } +} + void mul_mat_iq4_nl_x4_q8_0_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) { GGML_ASSERT(nrc_x%4 == 0); Q8<1, block_q8_0_x4> q8(info); @@ -7529,6 +7755,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) { m.funcs[7] = mul_mat_iq4_nl_x4_q8_0<8>; expected_Btype = GGML_TYPE_Q8_0; break; + case GGML_TYPE_IQ4_XS_R4: + m.funcs[0] = mul_mat_iq4_nl_x4_q8_0_1; + m.funcs[1] = mul_mat_iq4_xs_r4_q8_k<2>; + m.funcs[2] = mul_mat_iq4_xs_r4_q8_k<3>; + m.funcs[3] = mul_mat_iq4_xs_r4_q8_k<4>; + m.funcs[4] = mul_mat_iq4_xs_r4_q8_k<5>; + m.funcs[5] = mul_mat_iq4_xs_r4_q8_k<6>; + m.funcs[6] = mul_mat_iq4_xs_r4_q8_k<7>; + m.funcs[7] = mul_mat_iq4_xs_r4_q8_k<8>; + expected_Btype = GGML_TYPE_Q8_K; + break; case GGML_TYPE_Q4_0_R4: m.funcs[0] = mul_mat_q4_0_r4_q8_0<1>; m.funcs[1] = mul_mat_q4_0_r4_q8_0<2>; diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp index f2e6a45e..acef04db 100644 --- a/ggml/src/iqk/iqk_quantize.cpp +++ b/ggml/src/iqk/iqk_quantize.cpp @@ -3572,3 +3572,111 @@ void vec_dot_q6_0_r4_q8_0(int n, float * s, size_t bs, const void * vx, size_t b GGML_UNUSED(bx); GGML_UNUSED(by); } + +// +// ========================================= iq4_xs_r4 +// + +void quantize_row_iq4_xs_r4_ref(const float * x, block_iq4_xs_r4 * y, int64_t k) { + quantize_iq4_xs_r4(x, (void *)y, 4, k/4, nullptr); +} + +void quantize_row_iq4_xs_r4(const float * x, void * y, int64_t k) { + quantize_iq4_xs_r4(x, y, 4, k/4, nullptr); +} + +static void repack_iq4_xs(int nrows, int n_per_row, const block_iq4_xs * x, block_iq4_xs_r4 * y) { + GGML_ASSERT(nrows%4 == 0); + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + const block_iq4_xs * x4[4]; + for (int row = 0; row < nrows; row += 4) { + for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k; + for (int ibl = 0; ibl < nblock; ++ibl) { + std::memset(y[ibl].scales_l, 0, QK_K/16); + std::memset(y[ibl].scales_h, 0, QK_K/32); + for (int k = 0; k < 4; ++k) { + y[ibl].d[k] = x4[k][ibl].d; + for (int ib = 0; ib < QK_K/32; ++ib) { + uint8_t sl = (x4[k][ibl].scales_l[ib/2] >> 4*(ib%2)) & 0xf; + uint8_t sh = (x4[k][ibl].scales_h >> 2*ib) & 3; + int i = 4*ib + k; + y[ibl].scales_l[i%16] |= (sl << 4*(i/16)); + y[ibl].scales_h[i%8 ] |= (sh << 2*(i/8)); + } + } + for (int ib = 0; ib < QK_K/32; ++ib) { + for (int k = 0; k < 4; ++k) for (int i = 0; i < 4; ++i) { + y[ibl].qs[64*ib+4*k+i+ 0] = (x4[k][ibl].qs[16*ib+i+0] & 0xf) | ((x4[k][ibl].qs[16*ib+i+ 8] & 0x0f) << 4); // 0....3 + 8...11 from each row + y[ibl].qs[64*ib+4*k+i+16] = (x4[k][ibl].qs[16*ib+i+0] >> 4) | ((x4[k][ibl].qs[16*ib+i+ 8] & 0xf0)); // 16...19 + 24...27 from each row + y[ibl].qs[64*ib+4*k+i+32] = (x4[k][ibl].qs[16*ib+i+4] & 0xf) | ((x4[k][ibl].qs[16*ib+i+12] & 0x0f) << 4); // 4....7 + 12...15 from each row + y[ibl].qs[64*ib+4*k+i+48] = (x4[k][ibl].qs[16*ib+i+4] >> 4) | ((x4[k][ibl].qs[16*ib+i+12] & 0xf0)); // 20...23 + 28...31 from each row + } + } + } + x += 4*nblock; + y += nblock; + } +} + +size_t quantize_iq4_xs_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_IQ4_XS, n_per_row); + std::vector<char> qtmp(4*row_size); + for (int row = 0; row < nrows; row += 4) { + quantize_iq4_xs(src, (void *)qtmp.data(), 4, n_per_row, imatrix); + repack_iq4_xs(4, n_per_row, (const block_iq4_xs *)qtmp.data(), (block_iq4_xs_r4 *)qcur); + qcur += 4*row_size; + src += 4*n_per_row; + } + return nrows*row_size; +} + +void dequantize_row_iq4_xs_r4(const block_iq4_xs_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]); + for (int ib = 0; ib < QK_K/32; ++ib) { + int is = 4*ib + k; + float dl = d * ((((x[ibl].scales_l[is%16] >> 4*(is/16)) & 0xf) | (((x[ibl].scales_h[is%8] >> 2*(is/8)) & 3) << 4)) - 32); + for (int i = 0; i < 4; ++i) { + y4[k][QK_K*ibl+32*ib+i+ 0] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+ 0] & 0xf]; + y4[k][QK_K*ibl+32*ib+i+ 8] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+ 0] >> 4]; + y4[k][QK_K*ibl+32*ib+i+16] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+16] & 0xf]; + y4[k][QK_K*ibl+32*ib+i+24] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+16] >> 4]; + y4[k][QK_K*ibl+32*ib+i+ 4] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+32] & 0xf]; + y4[k][QK_K*ibl+32*ib+i+12] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+32] >> 4]; + y4[k][QK_K*ibl+32*ib+i+20] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+48] & 0xf]; + y4[k][QK_K*ibl+32*ib+i+28] = dl * iq4k_values[x[ibl].qs[64*ib+4*k+i+48] >> 4]; + } + } + } + //dequantize_row_iq4_xs(x + ib, ytmp, QK_K); + //for (int k = 0; k < 4; ++k) { + // for (int l = 0; l < 16; ++l) { + // for (int i = 0; i < 4; ++i) { + // //y4[k][ib*kBlockSize + i + 16*(l%4) + 4*(l/4)] = ytmp[16*l + 4*k + i]; + // y4[k][ib*kBlockSize + i + 8*(l%8) + 4*(l/8)] = ytmp[16*l + 4*k + i]; + // } + // } + //} + } +} + +void vec_dot_iq4_xs_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_IQ4_XS_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); +} diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h index 3349c675..ad2294c5 100644 --- a/ggml/src/iqk/iqk_quantize.h +++ b/ggml/src/iqk/iqk_quantize.h @@ -93,6 +93,12 @@ size_t quantize_q6_0_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT ds 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); +void quantize_row_iq4_xs_r4_ref(const float * GGML_RESTRICT x, block_iq4_xs_r4 * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_xs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +size_t quantize_iq4_xs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +void dequantize_row_iq4_xs_r4(const block_iq4_xs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void vec_dot_iq4_xs_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); + #ifdef __cplusplus } #endif diff --git a/include/llama.h b/include/llama.h index bf843ad2..77c988a5 100644 --- a/include/llama.h +++ b/include/llama.h @@ -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_IQ4_XS_R4 = 230, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 235, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file diff --git a/src/llama.cpp b/src/llama.cpp index f307fd89..e2abc235 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -3850,6 +3850,7 @@ struct llama_model_loader { case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break; case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break; case GGML_TYPE_IQ4_NL_X4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_X4;break; + case GGML_TYPE_IQ4_XS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS_R4;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; @@ -4559,6 +4560,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:return "IQ4_NL_X4 - 4.5 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ4_XS_R4:return "IQ4_XS_R4 - 4.25 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"; @@ -15779,6 +15781,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (new_type == GGML_TYPE_IQ4_NL_X4) { new_type = GGML_TYPE_IQ4_NL; } + else if (new_type == GGML_TYPE_IQ4_XS_R4) { + new_type = GGML_TYPE_IQ4_XS; + } else if (new_type == GGML_TYPE_Q4_0_R4) { new_type = GGML_TYPE_Q4_0; } @@ -15852,7 +15857,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; - else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || + else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || + ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && qs.model.hparams.n_gqa() >= 2) { new_type = GGML_TYPE_IQ5_K; } @@ -15883,6 +15889,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K; else if (new_type == GGML_TYPE_IQ4_NL) new_type = GGML_TYPE_Q5_K; else if (new_type == GGML_TYPE_IQ4_NL_X4) new_type = GGML_TYPE_Q5_K; + else if (new_type == GGML_TYPE_IQ4_XS_R4) new_type = GGML_TYPE_Q5_K; else if (new_type == GGML_TYPE_Q5_K) new_type = GGML_TYPE_Q6_K; } ++qs.i_attention_wv; @@ -15947,7 +15954,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n } else if (i_layer < n_layer/8 && !qs.has_imatrix && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || - ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4)) { + ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS || + ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4)) { new_type = GGML_TYPE_Q5_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; @@ -15973,7 +15981,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_K || - ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4) { + ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || + ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4) { new_type = GGML_TYPE_Q5_K; } } else { @@ -16183,6 +16192,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ2_BN: default_type = GGML_TYPE_IQ2_BN; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:default_type = GGML_TYPE_IQ4_NL_X4;break; + case LLAMA_FTYPE_MOSTLY_IQ4_XS_R4:default_type = GGML_TYPE_IQ4_XS_R4;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; @@ -16548,6 +16558,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_NL; else chunk_size_multiplier = 4; } + else if (new_type == GGML_TYPE_IQ4_XS_R4) { + if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_XS; + else chunk_size_multiplier = 4; + } else if (new_type == GGML_TYPE_Q4_0_R4) { if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0; else chunk_size_multiplier = 4; |