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 | 24 | ||||
| -rw-r--r-- | ggml/src/iqk/iqk_mul_mat.cpp | 255 | ||||
| -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 | 21 |
10 files changed, 430 insertions, 3 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 2c8b33c2..4c650b87 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -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", }, diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 0ab34f27..2ed0fb1f 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -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 diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index bc34718e..61e8dfd3 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -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 diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index c2fdf6fa..ff857087 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -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; diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 0bb59d2b..6c574933 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -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; diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp index 8ab8b2bd..4316373a 100644 --- a/ggml/src/iqk/iqk_mul_mat.cpp +++ b/ggml/src/iqk/iqk_mul_mat.cpp @@ -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: @@ -3440,6 +3443,116 @@ 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_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 mxf = _mm256_set1_epi8(0xf); + auto m03 = _mm256_set1_epi8(0x03); + 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_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 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 + 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)); + 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); @@ -3450,7 +3563,11 @@ static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn 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]; @@ -3460,9 +3577,15 @@ static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn 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); @@ -3471,6 +3594,9 @@ static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn _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 @@ -3482,16 +3608,32 @@ 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)); 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>; @@ -8361,6 +8530,88 @@ IQK_ALWAYS_INLINE void prepare_q4_k_quants(const uint8x16_t& m4, const uint8x16x } 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); Q8<nrc_y, block_q8_K> q8(info); @@ -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; diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp index 2e59fefe..49e2cf8e 100644 --- a/ggml/src/iqk/iqk_quantize.cpp +++ b/ggml/src/iqk/iqk_quantize.cpp @@ -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); +} + diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h index f3a4d8e2..4a1c31f8 100644 --- a/ggml/src/iqk/iqk_quantize.h +++ b/ggml/src/iqk/iqk_quantize.h @@ -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); diff --git a/include/llama.h b/include/llama.h index f87d13ff..0992b10a 100644 --- a/include/llama.h +++ b/include/llama.h @@ -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 diff --git a/src/llama.cpp b/src/llama.cpp index 9f41724f..6ecf0452 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -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; |