diff options
| -rw-r--r-- | examples/quantize/quantize.cpp | 1 | ||||
| -rw-r--r-- | ggml/include/ggml.h | 2 | ||||
| -rw-r--r-- | ggml/src/ggml-common.h | 7 | ||||
| -rw-r--r-- | ggml/src/ggml-quants.c | 1 | ||||
| -rw-r--r-- | ggml/src/ggml.c | 23 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_mul_mat.cpp | 172 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_quantize.cpp | 115 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_quantize.h | 6 | ||||
| -rw-r--r-- | include/llama.h | 1 | ||||
| -rw-r--r-- | src/llama.cpp | 32 |
10 files changed, 351 insertions, 9 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index cefe2735..b69b8a6b 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -34,6 +34,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = { { "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_XXS_R4",LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4,"IQ3_XXS repacked", }, { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 1a4e7f19..90d34b40 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -418,6 +418,7 @@ extern "C" { GGML_TYPE_Q4_K_R4 = 212, GGML_TYPE_Q5_K_R4 = 213, GGML_TYPE_Q6_K_R4 = 214, + GGML_TYPE_IQ3_XXS_R4= 218, GGML_TYPE_IQ4_NL_R4 = 220, GGML_TYPE_IQ4_XS_R4 = 223, GGML_TYPE_BF16_R16 = 230, @@ -496,6 +497,7 @@ extern "C" { GGML_FTYPE_MOSTLY_Q4_K_R4 = 212, // except 1d tensors GGML_FTYPE_MOSTLY_Q5_K_R4 = 215, // except 1d tensors GGML_FTYPE_MOSTLY_Q6_K_R4 = 214, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ3_XXS_R4= 217, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_NL_R4 = 219, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors GGML_FTYPE_MOSTLY_BF16_R16 = 224, // except 1d tensors diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index 6e87dbaa..b9da9771 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -423,6 +423,13 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +typedef struct { + ggml_half d[4]; + uint8_t sas[QK_K/2]; + uint8_t qs[QK_K]; +} block_iq3_xxs_r4; +static_assert(sizeof(block_iq3_xxs_r4) == 4*sizeof(block_iq3_xxs), "wrong iq3_xxs_r4 block size/padding"); + // 3.4375 bpw #define IQ3S_N_SCALE QK_K/64 typedef struct { diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index 2b59808b..7821fa79 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -15198,6 +15198,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte case GGML_TYPE_IQ4_KSS: break; case GGML_TYPE_IQ4_NL_R4: break; case GGML_TYPE_IQ4_XS_R4: break; + case GGML_TYPE_IQ3_XXS_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 758ee018..21a82ff3 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1031,6 +1031,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .nrows = 1, .row_meta_size = 0, }, + [GGML_TYPE_IQ3_XXS_R4] = { + .type_name = "iq3_xxs_r4", + .blck_size = QK_K, + .type_size = sizeof(block_iq3_xxs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq3_xxs_r4, + .from_float = quantize_row_iq3_xxs_r4, + .from_float_ref = (ggml_from_float_t)quantize_row_iq3_xxs_r4_ref, + .vec_dot = vec_dot_iq3_xxs_r4_q8_k, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + .row_meta_size = 0, + }, [GGML_TYPE_IQ3_S] = { .type_name = "iq3_s", .blck_size = QK_K, @@ -4200,6 +4213,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; + case GGML_FTYPE_MOSTLY_IQ3_XXS_R4: wtype = GGML_TYPE_IQ3_XXS_R4;break; case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break; case GGML_FTYPE_MOSTLY_IQ1_BN: wtype = GGML_TYPE_IQ1_BN; break; @@ -10741,6 +10755,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -11201,6 +11216,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -11358,6 +11374,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -14561,6 +14578,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -14958,6 +14976,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -15249,6 +15268,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -15869,6 +15889,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ1_BN: @@ -22642,6 +22663,7 @@ void ggml_quantize_init(enum ggml_type type) { case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break; + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; default: // nothing @@ -22715,6 +22737,7 @@ size_t ggml_quantize_chunk( case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ2_XS: result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_XXS_R4:result = quantize_iq3_xxs_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_S: result = quantize_iq1_s (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 f4576319..b6b6da15 100644 --- a/ggml/src/iqk/iqk_mul_mat.cpp +++ b/ggml/src/iqk/iqk_mul_mat.cpp @@ -187,6 +187,7 @@ struct MulMat { case GGML_TYPE_IQ4_K_R4: case GGML_TYPE_IQ5_K_R4: case GGML_TYPE_IQ4_KS_R4: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ2_BN_R4: return 4; case GGML_TYPE_Q8_K_R8: return 8; case GGML_TYPE_BF16_R16: return 16; @@ -3213,6 +3214,97 @@ static void mul_mat_iq4_ks_r4_q8_k(int n, const void * vx, size_t bx, const Data } template <int nrc_y> +static void mul_mat_iq3_xxs_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); + int nbl = n / QK_K; +#ifndef HAVE_FANCY_SIMD + auto smask = _mm256_set1_epi64x(0x8040201008040201); + auto sign_shuffle = _mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, 0x0101010101010101, 0x0000000000000000); + auto m4 = _mm256_set1_epi8(4); + auto m1 = _mm256_set1_epi16(1); +#endif + __m256 acc[nrc_y] = {}; + __m256i isum[nrc_y] = {}; + __m256i qx[4]; + for (int ix = 0; ix < nrc_x; ix += 4) { + auto iq3 = (const block_iq3_xxs_r4 *)((const char *)vx + (ix+0)*bx); + for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256 + auto dl = _mm_mul_ps(_mm_set1_ps(0.25f), _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq3[ibl].d))); // TODO: absorb the 0.25 factor into d when quantizing/repacking + auto d4 = _mm256_set_m128(dl, dl); + for (int ib = 0; ib < QK_K/32; ++ib) { + qx[0] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+ 7]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 6]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 5]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 4]], + iq3xxs_grid[iq3[ibl].qs[32*ib+ 3]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 2]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 1]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 0]]); + qx[1] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+15]], iq3xxs_grid[iq3[ibl].qs[32*ib+14]], iq3xxs_grid[iq3[ibl].qs[32*ib+13]], iq3xxs_grid[iq3[ibl].qs[32*ib+12]], + iq3xxs_grid[iq3[ibl].qs[32*ib+11]], iq3xxs_grid[iq3[ibl].qs[32*ib+10]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 9]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 8]]); + qx[2] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+23]], iq3xxs_grid[iq3[ibl].qs[32*ib+22]], iq3xxs_grid[iq3[ibl].qs[32*ib+21]], iq3xxs_grid[iq3[ibl].qs[32*ib+20]], + iq3xxs_grid[iq3[ibl].qs[32*ib+19]], iq3xxs_grid[iq3[ibl].qs[32*ib+18]], iq3xxs_grid[iq3[ibl].qs[32*ib+17]], iq3xxs_grid[iq3[ibl].qs[32*ib+16]]); + qx[3] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+31]], iq3xxs_grid[iq3[ibl].qs[32*ib+30]], iq3xxs_grid[iq3[ibl].qs[32*ib+29]], iq3xxs_grid[iq3[ibl].qs[32*ib+28]], + iq3xxs_grid[iq3[ibl].qs[32*ib+27]], iq3xxs_grid[iq3[ibl].qs[32*ib+26]], iq3xxs_grid[iq3[ibl].qs[32*ib+25]], iq3xxs_grid[iq3[ibl].qs[32*ib+24]]); + auto sas = _mm_loadu_si128((const __m128i *)iq3[ibl].sas + ib); + auto scales = _mm_and_si128(sas, _mm_set1_epi8(1)); +#ifdef HAVE_FANCY_SIMD + scales = _mm_dpbusd_epi32(_mm_set1_epi32(1), scales, _mm_set1_epi32(0x10080402)); +#else + scales = _mm_maddubs_epi16(scales, _mm_set1_epi32(0x10080402)); + scales = _mm_add_epi32(_mm_madd_epi16(_mm_set1_epi16(1), scales), _mm_set1_epi32(1)); + //auto t1 = _mm_or_si128(_mm_and_si128(scales, _mm_set1_epi32(0x00000001)), _mm_srli_epi32(_mm_and_si128(scales, _mm_set1_epi32(0x00000100)), 7)); + //auto t2 = _mm_or_si128(_mm_srli_epi32(_mm_and_si128(scales, _mm_set1_epi32(0x00010000)), 14), _mm_srli_epi32(_mm_and_si128(scales, _mm_set1_epi32(0x01000000)), 21)); + //scales = _mm_or_si128(_mm_slli_epi32(_mm_or_si128(t1, t2), 1), _mm_set1_epi32(1)); +#endif + auto scales32 = MM256_SET_M128I(scales, scales); + auto signs128 = _mm_and_si128(sas, _mm_set1_epi8(-2)); // 0xfe = -2 as signed. Needed to shutup compiler warning. + signs128 = _mm_xor_si128(signs128, _mm_srli_epi16(signs128, 1)); +#ifdef HAVE_FANCY_SIMD + auto mask = (const __mmask32 *)&signs128; + for (int iy = 0; iy < nrc_y; ++iy) { + auto y = _mm256_loadu_si256((const __m256i *)q8.y[iy][ibl].qs + ib); + auto sumi1 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[0], _mm256_mask_sub_epi8(y, mask[0], _mm256_setzero_si256(), y)); + auto sumi2 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[1], _mm256_mask_sub_epi8(y, mask[1], _mm256_setzero_si256(), y)); + auto sumi3 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[2], _mm256_mask_sub_epi8(y, mask[2], _mm256_setzero_si256(), y)); + auto sumi4 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[3], _mm256_mask_sub_epi8(y, mask[3], _mm256_setzero_si256(), y)); + auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi1, sumi2), _mm256_unpackhi_epi32(sumi1, sumi2)); // 0,1, 0,1, 0,1, 0,1 + auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi3, sumi4), _mm256_unpackhi_epi32(sumi3, sumi4)); // 2,3, 2,3, 2,3, 2,3 + auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34)); // 0,1,2,3, 0,1,2,3 + isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales32, sumi)); + } +#else + auto signs = MM256_SET_M128I(signs128, signs128); + auto shuffle = sign_shuffle; + auto s1 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1)); + shuffle = _mm256_add_epi8(shuffle, m4); + auto s2 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1)); + shuffle = _mm256_add_epi8(shuffle, m4); + auto s3 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1)); + shuffle = _mm256_add_epi8(shuffle, m4); + auto s4 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1)); + for (int iy = 0; iy < nrc_y; ++iy) { + auto y = _mm256_loadu_si256((const __m256i *)q8.y[iy][ibl].qs + ib); + auto sumi1 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[0], _mm256_sign_epi8(y, s1))); + auto sumi2 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[1], _mm256_sign_epi8(y, s2))); + auto sumi3 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[2], _mm256_sign_epi8(y, s3))); + auto sumi4 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4))); + auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi1, sumi2), _mm256_unpackhi_epi32(sumi1, sumi2)); // 0,1, 0,1, 0,1, 0,1 + auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi3, sumi4), _mm256_unpackhi_epi32(sumi3, sumi4)); // 2,3, 2,3, 2,3, 2,3 + auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34)); // 0,1,2,3, 0,1,2,3 + isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales32, sumi)); + } +#endif + } + for (int iy = 0; iy < nrc_y; ++iy) { + acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(isum[iy]), acc[iy]); + isum[iy] = _mm256_setzero_si256(); + } + } + 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(); + } + } +} + +template <int nrc_y> static void mul_mat_q4_k_r4_q8_k_avx2(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); @@ -4610,15 +4702,17 @@ inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, cons } } +// TODO: find the bug that causes this to be called without HAVE_FANCY_SIMD, which triggers +// writing 4 vvalues into scales, which is of size 2. inline void set_scales_8_iq(int j, const __m256i& all_scales, __m256i * scales) { -#ifdef HAVE_FANCY_SIMD +//#ifdef HAVE_FANCY_SIMD auto shuffle = j == 0 ? _mm256_set_epi64x(0x0302030203020302, 0x0100010001000100, 0x0302030203020302, 0x0100010001000100) : _mm256_set_epi64x(0x0b0a0b0a0b0a0b0a, 0x0908090809080908, 0x0b0a0b0a0b0a0b0a, 0x0908090809080908); scales[0] = _mm256_shuffle_epi8(all_scales, shuffle); scales[1] = _mm256_shuffle_epi8(all_scales, _mm256_add_epi8(shuffle, _mm256_set1_epi8(4))); -#else - set_scales_8(all_scales, j, scales); -#endif +//#else +// set_scales_8(all_scales, j, scales); +//#endif } inline void set_scales_16_iq(const __m256i& all_scales, __m256i * scales) { @@ -5003,11 +5097,15 @@ IQK_NOINLINE void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const template <typename Dequantizer, int nrc_y> static void mul_mat_qX_K_q8_K_IQ(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) { assert(n % QK_K == 0); +#ifdef HAVE_FANCY_SIMD if constexpr (nrc_y == 1) { mul_mat_qX_K_q8_K_IQ_1<Dequantizer>(n, vx, bx, info, nrc_x); } else { mul_mat_qX_K_q8_K_IQ_N<Dequantizer, nrc_y>(n, vx, bx, info, nrc_x); } +#else + mul_mat_qX_K_q8_K_IQ_N<Dequantizer, nrc_y>(n, vx, bx, info, nrc_x); +#endif } //#ifdef HAVE_FANCY_SIMD @@ -6604,6 +6702,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) { mm.funcs[7] = mul_mat_iq4_ks_r4_q8_k<8>; expected_typeB = GGML_TYPE_Q8_K32; break; + case GGML_TYPE_IQ3_XXS_R4: + assert (ne00 % QK_K == 0); + mm.funcs[0] = mul_mat_iq3_xxs_r4_q8_k<1>; + mm.funcs[1] = mul_mat_iq3_xxs_r4_q8_k<2>; + mm.funcs[2] = mul_mat_iq3_xxs_r4_q8_k<3>; + mm.funcs[3] = mul_mat_iq3_xxs_r4_q8_k<4>; + mm.funcs[4] = mul_mat_iq3_xxs_r4_q8_k<5>; + mm.funcs[5] = mul_mat_iq3_xxs_r4_q8_k<6>; + mm.funcs[6] = mul_mat_iq3_xxs_r4_q8_k<7>; + mm.funcs[7] = mul_mat_iq3_xxs_r4_q8_k<8>; + expected_typeB = GGML_TYPE_Q8_K; + break; case GGML_TYPE_Q2_K_R4: assert (ne00 % QK_K == 0); mm.funcs[0] = mul_mat_q2_k_r4_q8_k<1>; @@ -9475,6 +9585,56 @@ void mul_mat_iq4_ks_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i } } +template <int nrc_y> +static void mul_mat_iq3_xxs_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); + int nbl = n / QK_K; + float32x4_t acc[nrc_y] = {}; + int32x4_t isum[nrc_y] = {}; + int8x16_t qx[8]; + SignHelper sh; + for (int ix = 0; ix < nrc_x; ix += 4) { + auto iq3 = (const block_iq3_xxs_r4 *)((const char *)vx + (ix+0)*bx); + for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256 + auto d4 = vmulq_f32(vdupq_n_f32(0.25f), vcvt_f32_f16(vld1_f16((const float16_t *)iq3[ibl].d))); + auto qs = iq3[ibl].qs; + for (int ib = 0; ib < QK_K/32; ++ib) { + auto sas = vld1q_u8(iq3[ibl].sas + 16*ib); + auto scale_bits = vandq_u8(sas, vdupq_n_u8(1)); + auto scales = ggml_vdotq_s32(vdupq_n_s32(1), scale_bits, vreinterpretq_s8_u32(vdupq_n_u32(0x10080402))); + auto signs128 = vandq_u8(sas, vdupq_n_u8(254)); + signs128 = veorq_u8(signs128, vshrq_n_u8(signs128, 1)); + sh.init(); + for (int i = 0; i < 8; ++i) { + qx[i] = vreinterpretq_s8_u32(uint32x4_t{iq3xxs_grid[qs[4*i+0]], iq3xxs_grid[qs[4*i+1]], iq3xxs_grid[qs[4*i+2]], iq3xxs_grid[qs[4*i+3]]}); + sh.apply_signs_1((uint8x16_t *)qx+i, signs128); + } + for (int iy = 0; iy < nrc_y; ++iy) { + auto y = vld1q_s8_x2(q8.y[iy][ibl].qs + 32*ib); + auto sumi1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[0], y.val[0]), qx[1], y.val[1]); + auto sumi2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[2], y.val[0]), qx[3], y.val[1]); + auto sumi3 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[4], y.val[0]), qx[5], y.val[1]); + auto sumi4 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[6], y.val[0]), qx[7], y.val[1]); + auto sumi12 = vpaddq_s32(sumi1, sumi2); + auto sumi34 = vpaddq_s32(sumi3, sumi4); + auto sumi = vpaddq_s32(sumi12, sumi34); + isum[iy] = vmlaq_s32(isum[iy], scales, sumi); + } + qs += 32; + } + 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])); + isum[iy] = vdupq_n_s32(0); + } + } + 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, int k_shift> inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8x16x4_t& i8scales, uint8x16_t extra, int32x4_t * isum) { @@ -10772,6 +10932,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) { SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq4_ks_r4_q8_k); expected_Btype = GGML_TYPE_Q8_K; break; + case GGML_TYPE_IQ3_XXS_R4: + SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq3_xxs_r4_q8_k); + expected_Btype = GGML_TYPE_Q8_K; + 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; diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp index 874c6d55..8d2439ed 100644 --- a/ggml/src/iqk/iqk_quantize.cpp +++ b/ggml/src/iqk/iqk_quantize.cpp @@ -5303,6 +5303,119 @@ struct Repack { }; } +// +// ========================================= iq3_xxs_r4 +// + +void quantize_row_iq3_xxs_r4_ref(const float * x, block_iq3_xxs_r4 * y, int64_t k) { + quantize_iq3_xxs_r4(x, (void *)y, 4, k/4, nullptr); +} + +void quantize_row_iq3_xxs_r4(const float * x, void * y, int64_t k) { + quantize_iq3_xxs_r4(x, y, 4, k/4, nullptr); +} + +namespace { +} + +static void repack_iq3_xxs(int nrows, int n_per_row, const block_iq3_xxs * x, block_iq3_xxs_r4 * y) { + GGML_ASSERT(nrows%4 == 0); + GGML_ASSERT(n_per_row%QK_K == 0); + static uint8_t k_table[128] = { + 0x00, 0x7f, 0x7e, 0x01, 0x7c, 0x03, 0x02, 0x7d, 0x78, 0x07, 0x06, 0x79, 0x04, 0x7b, 0x7a, 0x05, + 0x70, 0x0f, 0x0e, 0x71, 0x0c, 0x73, 0x72, 0x0d, 0x08, 0x77, 0x76, 0x09, 0x74, 0x0b, 0x0a, 0x75, + 0x60, 0x1f, 0x1e, 0x61, 0x1c, 0x63, 0x62, 0x1d, 0x18, 0x67, 0x66, 0x19, 0x64, 0x1b, 0x1a, 0x65, + 0x10, 0x6f, 0x6e, 0x11, 0x6c, 0x13, 0x12, 0x6d, 0x68, 0x17, 0x16, 0x69, 0x14, 0x6b, 0x6a, 0x15, + 0x40, 0x3f, 0x3e, 0x41, 0x3c, 0x43, 0x42, 0x3d, 0x38, 0x47, 0x46, 0x39, 0x44, 0x3b, 0x3a, 0x45, + 0x30, 0x4f, 0x4e, 0x31, 0x4c, 0x33, 0x32, 0x4d, 0x48, 0x37, 0x36, 0x49, 0x34, 0x4b, 0x4a, 0x35, + 0x20, 0x5f, 0x5e, 0x21, 0x5c, 0x23, 0x22, 0x5d, 0x58, 0x27, 0x26, 0x59, 0x24, 0x5b, 0x5a, 0x25, + 0x50, 0x2f, 0x2e, 0x51, 0x2c, 0x53, 0x52, 0x2d, 0x28, 0x57, 0x56, 0x29, 0x54, 0x2b, 0x2a, 0x55, + }; + int nblock = n_per_row/QK_K; + const block_iq3_xxs * x4[4]; + uint32_t aux32; + 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) { + auto ysas = (uint32_t *)y[ibl].sas; + for (int k = 0; k < 4; ++k) { + y[ibl].d[k] = x4[k][ibl].d; + auto xsas = x4[k][ibl].qs + QK_K/4; + for (int ib = 0; ib < QK_K/32; ++ib) { + for (int i = 0; i < 8; ++i) { + y[ibl].qs[32*ib+8*k+i] = x4[k][ibl].qs[8*ib+i]; + } + std::memcpy(&aux32, xsas + 4*ib, 4); + uint8_t scale = aux32 >> 28; + uint8_t s1 = (k_table[(aux32 >> 0) & 127] << 1) | ((scale >> 0) & 1); + uint8_t s2 = (k_table[(aux32 >> 7) & 127] << 1) | ((scale >> 1) & 1); + uint8_t s3 = (k_table[(aux32 >> 14) & 127] << 1) | ((scale >> 2) & 1); + uint8_t s4 = (k_table[(aux32 >> 21) & 127] << 1) | ((scale >> 3) & 1); + aux32 = uint32_t(s1) | (uint32_t(s2) << 8) | (uint32_t(s3) << 16) | (uint32_t(s4) << 24); + ysas[4*ib+k] = aux32; + } + } + } + x += 4*nblock; + y += nblock; + } +} + +size_t quantize_iq3_xxs_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_IQ3_XXS, n_per_row); + std::vector<char> qtmp(4*row_size); + for (int row = 0; row < nrows; row += 4) { + quantize_iq3_xxs(src, (void *)qtmp.data(), 4, n_per_row, imatrix); + repack_iq3_xxs(4, n_per_row, (const block_iq3_xxs *)qtmp.data(), (block_iq3_xxs_r4 *)qcur); + qcur += 4*row_size; + src += 4*n_per_row; + } + return nrows*row_size; +} + +void dequantize_row_iq3_xxs_r4(const block_iq3_xxs_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; + uint32_t s32; + const uint8_t * s8 = (const uint8_t *)&s32; + for (int ibl = 0; ibl < nblock; ++ibl) { + const uint32_t * sas = (const uint32_t *)x[ibl].sas; + for (int k = 0; k < 4; ++k) { + const float d = 0.25f*GGML_FP16_TO_FP32(x[ibl].d[k]); + for (int ib = 0; ib < QK_K/32; ++ib) { + uint32_t aux32 = sas[4*ib+k]; + s32 = aux32 & 0x01010101; + uint8_t scale = s8[0] | (s8[1] << 1) | (s8[2] << 2) | (s8[3] << 3); + float dl = d*(2*scale+1); + aux32 &= 0xfefefefe; + aux32 ^= (aux32 >> 1); + for (int i = 0; i < 8; ++i) { + auto val = (const int8_t *)(iq3xxs_grid + x[ibl].qs[32*ib+8*k+i]); + for (int j = 0; j < 4; ++j) y4[k][QK_K*ibl+32*ib+4*i+j] = dl * val[j] * (aux32 & (1 << j) ? -1 : 1); + aux32 >>= 4; + } + } + } + } +} + +void vec_dot_iq3_xxs_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_IQ3_XXS_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); +} + void iqk_repack_tensor(struct ggml_tensor * tensor) { constexpr int kChunk = 8; if (!tensor) return; @@ -5313,7 +5426,9 @@ void iqk_repack_tensor(struct ggml_tensor * tensor) { { GGML_TYPE_IQ2_K, { GGML_TYPE_IQ2_K_R4, 4, (Repack::repack_func)repack_iq2_k} }, { GGML_TYPE_IQ3_K, { GGML_TYPE_IQ3_K_R4, 4, (Repack::repack_func)repack_iq3_k} }, { GGML_TYPE_IQ4_K, { GGML_TYPE_IQ4_K_R4, 4, (Repack::repack_func)repack_iq4_k} }, + { GGML_TYPE_IQ5_K, { GGML_TYPE_IQ5_K_R4, 4, (Repack::repack_func)repack_iq5_k} }, { GGML_TYPE_IQ4_XS, { GGML_TYPE_IQ4_XS_R4, 4, (Repack::repack_func)repack_iq4_xs} }, + { GGML_TYPE_IQ4_KS, { GGML_TYPE_IQ4_KS_R4, 4, (Repack::repack_func)repack_iq4_ks} }, { GGML_TYPE_IQ4_NL, { GGML_TYPE_IQ4_NL_R4, 4, (Repack::repack_func)repack_iq4_nl} }, { GGML_TYPE_IQ2_BN, { GGML_TYPE_IQ2_BN_R4, 4, (Repack::repack_func)repack_iq2_bn} }, { GGML_TYPE_Q2_K, { GGML_TYPE_Q2_K_R4, 4, (Repack::repack_func)repack_q2_k} }, diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h index bcec8432..af15f1dd 100644 --- a/ggml/src/iqk/iqk_quantize.h +++ b/ggml/src/iqk/iqk_quantize.h @@ -169,6 +169,12 @@ size_t quantize_iq4_ks_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT void dequantize_row_iq4_ks_r4(const block_iq4_ks_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); void vec_dot_iq4_ks_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_iq3_xxs_r4_ref(const float * GGML_RESTRICT x, block_iq3_xxs_r4 * GGML_RESTRICT y, int64_t k); +void quantize_row_iq3_xxs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +size_t quantize_iq3_xxs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +void dequantize_row_iq3_xxs_r4(const block_iq3_xxs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void vec_dot_iq3_xxs_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_q8_k_r8_ref(const float * GGML_RESTRICT x, block_q8_k_r8 * GGML_RESTRICT y, int64_t k); void quantize_row_q8_k_r8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); size_t quantize_q8_k_r8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); diff --git a/include/llama.h b/include/llama.h index 5480f4e9..e009ed4e 100644 --- a/include/llama.h +++ b/include/llama.h @@ -188,6 +188,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_Q4_K_R4 = 214, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_K_R4 = 216, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q6_K_R4 = 218, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 = 223, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 = 225, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 = 230, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 335, // except 1d tensors diff --git a/src/llama.cpp b/src/llama.cpp index 3f0ceb0a..0171539d 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -3854,6 +3854,7 @@ struct llama_model_loader { case GGML_TYPE_IQ2_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KS; break; case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break; case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; + case GGML_TYPE_IQ3_XXS_R4: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4; break; case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break; case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break; @@ -4583,6 +4584,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return "IQ3_XXS - 3.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: return "IQ3_XXS_R4 - 3.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S: return "IQ1_S - 1.5625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; @@ -15792,7 +15794,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || - ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) { + ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4 || + ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) { new_type = !qs.has_output ? GGML_TYPE_IQ4_K : GGML_TYPE_Q5_K; } else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || @@ -15818,6 +15821,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = GGML_TYPE_IQ3_S; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) { + new_type = GGML_TYPE_IQ3_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_BN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_BN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_BN_R4) { new_type = GGML_TYPE_IQ4_NL; } @@ -15930,6 +15936,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n 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; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) { + new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K_R4 : qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ3_K_R4 + : !qs.has_imatrix ? GGML_TYPE_IQ3_K_R4 : GGML_TYPE_IQ3_XXS_R4; + } else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 2) { new_type = GGML_TYPE_IQ4_K; } @@ -15984,6 +15994,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n } else if (qs.model.hparams.n_gqa() >= 4) { if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_IQ3_XXS) new_type = GGML_TYPE_IQ3_S; + else if (new_type == GGML_TYPE_Q2_K_R4 || new_type == GGML_TYPE_IQ3_XXS_R4) new_type = GGML_TYPE_IQ3_K_R4; else if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_IQ3_S ) new_type = GGML_TYPE_Q4_K; else if (new_type == GGML_TYPE_Q3_K_R4) new_type = GGML_TYPE_Q4_K_R4; else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K; @@ -16003,7 +16014,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) { new_type = GGML_TYPE_IQ3_XXS; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) { new_type = GGML_TYPE_IQ2_S; } } else if (name.find("attn_q.weight") != std::string::npos) { @@ -16011,7 +16022,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) { new_type = GGML_TYPE_IQ3_XXS; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) { new_type = GGML_TYPE_IQ2_S; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) { @@ -16032,6 +16043,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n 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; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 && !qs.has_imatrix) { + new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K_R4 : GGML_TYPE_IQ3_K_R4; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K @@ -16093,12 +16107,13 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n 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_Q2_K_R4|| ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || - ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) { + ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) { new_type = GGML_TYPE_Q5_K; } } else { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) new_type = GGML_TYPE_IQ3_K_R4; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = 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_IQ3_M ) new_type = GGML_TYPE_IQ4_K; @@ -16166,7 +16181,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n 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_Q2_K_R4 || new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4|| - new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_R4) { + new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_R4 || + new_type == GGML_TYPE_IQ3_XXS_R4) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -16189,6 +16205,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n case GGML_TYPE_IQ2_KS: case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_XXS_R4: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: @@ -16320,6 +16337,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break; case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: default_type = GGML_TYPE_IQ3_XXS_R4; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break; case LLAMA_FTYPE_MOSTLY_IQ1_BN: default_type = GGML_TYPE_IQ1_BN; break; @@ -16769,6 +16787,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_KS; else chunk_size_multiplier = 4; } + else if (new_type == GGML_TYPE_IQ3_XXS_R4) { + if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ3_XXS; + else chunk_size_multiplier = 4; + } else if (new_type == GGML_TYPE_BF16_R16) { if (tensor->ne[1] % 16 != 0) new_type = GGML_TYPE_BF16; else chunk_size_multiplier = 16; |