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IQ4_K_R4 (#138)
* iq4_k_r4: WIP * iq4_k_r4: Zen4 and hopefully AVX2 On Zen4 we get PP-512(LLaMA-3.1-8B) = 232.6 t/s, up from 182.2 t/s for iq4_k. Applying the extra shift costs a ~6 performance penalty. * iq4_k_r4: AVX2 PP-512 = 227.60 t/s. The shifts are really costly. * iq4_k_r4: NEON We get PP-512(LLaMA-3.1-8B) = 108 t/s, up from 58.2 t/s for iq4_k. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@ -57,6 +57,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ3_K", LLAMA_FTYPE_MOSTLY_IQ3_K, " 3.44 bpw non-linear quantization", },
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{ "IQ3_KL", LLAMA_FTYPE_MOSTLY_IQ3_KL, " 4 bpw non-linear quantization mix",},
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{ "IQ4_K", LLAMA_FTYPE_MOSTLY_IQ4_K, " 4.5 bpw non-linear quantization", },
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{ "IQ4_K_R4", LLAMA_FTYPE_MOSTLY_IQ4_K_R4, "IQ4_K repacked", },
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{ "IQ5_K", LLAMA_FTYPE_MOSTLY_IQ5_K, " 5.5 bpw non-linear quantization", },
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{ "IQ6_K", LLAMA_FTYPE_MOSTLY_IQ6_K, " 6.6 bpw non-linear quantization", },
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{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
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@ -421,6 +421,7 @@ extern "C" {
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GGML_TYPE_IQ4_XS_R4 = 223,
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GGML_TYPE_Q6_0_R4 = 233,
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GGML_TYPE_IQ2_BN_R4 = 335,
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GGML_TYPE_IQ4_K_R4 = 339,
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GGML_TYPE_COUNT,
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};
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@ -492,6 +493,7 @@ extern "C" {
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GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q6_0_R4 = 227, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ2_BN_R4 = 329, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_K_R4 = 332, // except 1d tensors
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};
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// available tensor operations:
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@ -541,6 +541,15 @@ typedef struct {
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} block_iq4_k;
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static_assert(sizeof(block_iq4_k) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/2 + 3*QK_K/64, "wrong iq4_k block size/padding");
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typedef struct {
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ggml_half d[4];
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uint8_t extra[8];
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uint8_t scales_h[QK_K/16];
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uint8_t scales_l[QK_K/8];
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uint8_t qs[QK_K*2];
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} block_iq4_k_r4;
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static_assert(sizeof(block_iq4_k_r4) == 4*sizeof(block_iq4_k), "wrong iq4_k_r4 block size/padding");
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typedef struct {
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ggml_half d;
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uint16_t extra;
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@ -15207,6 +15207,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
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case GGML_TYPE_Q4_K_R4: break;
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case GGML_TYPE_Q5_K_R4: break;
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case GGML_TYPE_Q6_K_R4: break;
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case GGML_TYPE_IQ4_K_R4: break;
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case GGML_TYPE_Q4_0_4_4:
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case GGML_TYPE_Q4_0_4_8:
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{
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@ -1313,6 +1313,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
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.nrows = 1,
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.row_meta_size = 0,
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},
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[GGML_TYPE_IQ4_K_R4] = {
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.type_name = "iq4_k_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq4_k),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq4_k_r4,
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.from_float = quantize_row_iq4_k_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq4_k_r4_ref,
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.vec_dot = vec_dot_iq4_k_r4_q8_k,
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.vec_dot_type = GGML_TYPE_Q8_K,
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.nrows = 1,
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.row_meta_size = 0,
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},
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[GGML_TYPE_IQ5_K] = {
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.type_name = "iq5_k",
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.blck_size = QK_K,
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@ -4114,6 +4127,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_IQ2_KS: wtype = GGML_TYPE_IQ2_KS; break;
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case GGML_FTYPE_MOSTLY_IQ3_K: wtype = GGML_TYPE_IQ3_K; break;
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case GGML_FTYPE_MOSTLY_IQ4_K: wtype = GGML_TYPE_IQ4_K; break;
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case GGML_FTYPE_MOSTLY_IQ4_K_R4: wtype = GGML_TYPE_IQ4_K_R4; break;
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case GGML_FTYPE_MOSTLY_IQ5_K: wtype = GGML_TYPE_IQ5_K; break;
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case GGML_FTYPE_MOSTLY_IQ6_K: wtype = GGML_TYPE_IQ6_K; break;
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case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
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@ -10649,6 +10663,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -11103,6 +11118,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -11254,6 +11270,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -14451,6 +14468,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -14842,6 +14860,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -15127,6 +15146,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -15739,6 +15759,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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case GGML_TYPE_IQ4_K:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K:
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case GGML_TYPE_IQ6_K:
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case GGML_TYPE_IQ3_S:
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@ -22581,6 +22602,7 @@ size_t ggml_quantize_chunk(
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case GGML_TYPE_IQ2_KS: result = quantize_iq2_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ3_K: result = quantize_iq3_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_K: result = quantize_iq4_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_K_R4:result = quantize_iq4_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ5_K: result = quantize_iq5_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ6_K: result = quantize_iq6_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_Q4_0_4_4: result = quantize_q4_0_4x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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@ -3785,6 +3785,125 @@ static void mul_mat_q6_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn
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}
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}
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template <int nrc_y>
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static void mul_mat_iq4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
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GGML_ASSERT(nrc_x%4 == 0);
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Q8<nrc_y, block_q8_K> q8(info);
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auto m4 = _mm256_set1_epi8(0xf);
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auto m30 = _mm256_set1_epi8(0x30);
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auto m32 = _mm256_set1_epi8(32);
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auto ms = _mm256_set1_epi8(4);
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//auto shift_shuffle = _mm256_set_epi64x(0x0303030302020202, 0x0101010100000000, 0x0303030302020202, 0x0101010100000000);
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auto shift_shuffle = _mm256_set_epi64x(0x0707070706060606, 0x0505050504040404, 0x0303030302020202, 0x0101010100000000);
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#ifdef HAVE_FANCY_SIMD
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auto values = load_iq4nl_values_256();
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__m256 d4s[nrc_y];
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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};
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auto shuff = _mm256_loadu_si256((const __m256i *)k_shuff);
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#else
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auto m1 = _mm256_set1_epi16(1);
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auto values128 = _mm_loadu_si128((const __m128i *)iq4k_values);
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auto values = MM256_SET_M128I(values128, values128);
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#endif
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int nbl = n / QK_K;
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__m256 acc[nrc_y] = {};
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__m256i qx[4];
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int8_t stored_scales[64];
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for (int ix = 0; ix < nrc_x; ix += 4) {
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const block_iq4_k_r4 * iq4 = (const block_iq4_k_r4 *)((const char *)vx + (ix+0)*bx);
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for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
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auto dl = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4[ibl].d));
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auto d4 = _mm256_set_m128(dl, dl);
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auto extra = _mm256_set1_epi64x(*(const uint64_t *)iq4[ibl].extra);
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#ifdef HAVE_FANCY_SIMD
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for (int iy = 0; iy < nrc_y; ++iy) {
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d4s[iy] = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl)));
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}
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#else
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if constexpr (nrc_y == 1) {
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d4 = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(0, ibl)));
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}
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#endif
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auto slbits = _mm256_loadu_si256((const __m256i *)iq4[ibl].scales_l);
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auto sl1 = _mm256_and_si256(slbits, m4);
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auto sl2 = _mm256_and_si256(_mm256_srli_epi16(slbits, 4), m4);
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auto shbits = _mm_loadu_si128((const __m128i*)iq4[ibl].scales_h);
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auto sh = MM256_SET_M128I(_mm_srli_epi16(shbits, 2), shbits);
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auto i8scales1 = _mm256_sub_epi8(_mm256_or_si256(sl1, _mm256_and_si256(m30, _mm256_slli_epi16(sh, 4))), m32);
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auto i8scales2 = _mm256_sub_epi8(_mm256_or_si256(sl2, _mm256_and_si256(m30, sh)), m32);
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_mm256_storeu_si256((__m256i *)stored_scales+0, i8scales1);
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_mm256_storeu_si256((__m256i *)stored_scales+1, i8scales2);
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#ifdef HAVE_FANCY_SIMD
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{
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auto t1 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales1, 0)), shuff); // blocks 0, 1, 2, 3 for each row
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auto t2 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales1, 1)), shuff); // blocks 4, 5, 6, 7 for each row
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auto t3 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales2, 0)), shuff); // blocks 8, 9, 10, 11 for each row
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auto t4 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales2, 1)), shuff); // blocks 12, 13, 14, 15 for each row
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auto s1 = MM256_SET_M128I(_mm256_extracti128_si256(t3, 0), _mm256_extracti128_si256(t1, 0)); // blocks 0, 1, 8, 9
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auto s2 = MM256_SET_M128I(_mm256_extracti128_si256(t3, 1), _mm256_extracti128_si256(t1, 1)); // blocks 2, 3, 10, 11
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auto s3 = MM256_SET_M128I(_mm256_extracti128_si256(t4, 0), _mm256_extracti128_si256(t2, 0)); // blocks 4, 5, 12, 13
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auto s4 = MM256_SET_M128I(_mm256_extracti128_si256(t4, 1), _mm256_extracti128_si256(t2, 1)); // blocks 6, 7, 14, 15
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for (int iy = 0; iy < nrc_y; ++iy) {
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auto bsums = q8.load_bsums(iy, ibl);
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auto sumi = _mm256_setzero_si256();
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sumi = _mm256_dpwssd_epi32(sumi, s1, _mm256_shuffle_epi32(bsums, 0x00));
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sumi = _mm256_dpwssd_epi32(sumi, s2, _mm256_shuffle_epi32(bsums, 0x55));
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sumi = _mm256_dpwssd_epi32(sumi, s3, _mm256_shuffle_epi32(bsums, 0xaa));
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sumi = _mm256_dpwssd_epi32(sumi, s4, _mm256_shuffle_epi32(bsums, 0xff));
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acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4s[iy], _mm256_set1_ps(-128.f)), _mm256_cvtepi32_ps(sumi), acc[iy]);
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}
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}
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#endif
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for (int ib = 0; ib < QK_K/32; ++ib) {
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auto iscales = _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i *)(stored_scales + 8*ib)));
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#ifdef HAVE_FANCY_SIMD
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auto scales = _mm256_cvtepi32_ps(iscales);
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#else
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auto scales = _mm256_mul_ps(d4, _mm256_cvtepi32_ps(iscales));
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#endif
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auto bits1 = _mm256_loadu_si256((const __m256i *)iq4[ibl].qs+2*ib+0);
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auto bits2 = _mm256_loadu_si256((const __m256i *)iq4[ibl].qs+2*ib+1);
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auto shift = _mm256_and_si256(ms, _mm256_slli_epi16(extra, 2)); extra = _mm256_srli_epi16(extra, 1);
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shift = _mm256_shuffle_epi8(shift, shift_shuffle);
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qx[0] = _mm256_add_epi8(shift, _mm256_shuffle_epi8(values, _mm256_and_si256(bits1, m4)));
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qx[1] = _mm256_add_epi8(shift, _mm256_shuffle_epi8(values, _mm256_and_si256(bits2, m4)));
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qx[2] = _mm256_add_epi8(shift, _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4)));
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qx[3] = _mm256_add_epi8(shift, _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4)));
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#ifndef HAVE_FANCY_SIMD
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auto s1 = _mm256_sign_epi8(qx[0], qx[0]);
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auto s2 = _mm256_sign_epi8(qx[1], qx[1]);
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auto s3 = _mm256_sign_epi8(qx[2], qx[2]);
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auto s4 = _mm256_sign_epi8(qx[3], qx[3]);
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#endif
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for (int iy = 0; iy < nrc_y; ++iy) {
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auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib);
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#ifdef HAVE_FANCY_SIMD
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auto sumi = _mm256_setzero_si256();
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sumi = _mm256_dpbusd_epi32(sumi, qx[0], _mm256_shuffle_epi32(y, 0x00));
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sumi = _mm256_dpbusd_epi32(sumi, qx[1], _mm256_shuffle_epi32(y, 0x55));
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sumi = _mm256_dpbusd_epi32(sumi, qx[2], _mm256_shuffle_epi32(y, 0xaa));
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sumi = _mm256_dpbusd_epi32(sumi, qx[3], _mm256_shuffle_epi32(y, 0xff));
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acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, d4s[iy]), _mm256_cvtepi32_ps(sumi), acc[iy]);
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#else
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auto sumi1 = _mm256_maddubs_epi16(s1, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x00), qx[0]));
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auto sumi2 = _mm256_maddubs_epi16(s2, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x55), qx[1]));
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auto sumi3 = _mm256_maddubs_epi16(s3, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xaa), qx[2]));
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auto sumi4 = _mm256_maddubs_epi16(s4, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xff), qx[3]));
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auto sumi = _mm256_add_epi32(_mm256_add_epi32(_mm256_madd_epi16(m1, sumi1), _mm256_madd_epi16(m1, sumi2)),
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_mm256_add_epi32(_mm256_madd_epi16(m1, sumi3), _mm256_madd_epi16(m1, sumi4)));
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acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
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#endif
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}
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}
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}
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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 <typename Bits>
|
||||
inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, const __m256i * q8, __m256i * sumi) {
|
||||
if (j == 0) {
|
||||
@ -5804,18 +5923,6 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
||||
mm.funcs[7] = mul_mat_q3_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_q4_k_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_q4_k_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_q4_k_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_q4_k_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_q4_k_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_q4_k_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_q4_k_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_q4_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
case GGML_TYPE_Q5_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_q5_k_r4_q8_k<1>;
|
||||
@ -5840,6 +5947,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
||||
mm.funcs[7] = mul_mat_q6_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ4_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq4_k_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq4_k_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq4_k_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq4_k_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq4_k_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq4_k_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq4_k_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq4_k_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>;
|
||||
@ -8516,6 +8635,139 @@ void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
Q8<nrc_y, block_q8_K> q8(info);
|
||||
auto m4 = vdupq_n_u8(0xf);
|
||||
auto m3 = vdupq_n_u8(0x30);
|
||||
auto ms = vdupq_n_u8(4);
|
||||
auto m32 = vdupq_n_s8(-32);
|
||||
uint8x16x2_t shift_shuffle = {
|
||||
vreinterpretq_u8_u64(uint64x2_t{0x0101010100000000, 0x0303030302020202}),
|
||||
vreinterpretq_u8_u64(uint64x2_t{0x0505050504040404, 0x0707070706060606})
|
||||
};
|
||||
auto values = vld1q_s8(iq4k_values);
|
||||
int nbl = n / QK_K;
|
||||
int8x16_t qx[4];
|
||||
int8x16x4_t i8scales;
|
||||
int16x8x4_t i16scales;
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_iq4_k_r4 * iq4 = (const block_iq4_k_r4 *)((const char *)vx + ix*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[ibl].d));
|
||||
auto extra8 = vld1_u8(iq4[ibl].extra);
|
||||
uint8x16_t extra;
|
||||
if constexpr (nrc_y == 1) {
|
||||
extra = vcombine_u8(extra8, vshr_n_u8(extra8,1));
|
||||
} else {
|
||||
extra = vcombine_u8(extra8, extra8);
|
||||
}
|
||||
auto sl = vld1q_u8_x2(iq4[ibl].scales_l);
|
||||
auto sh = vld1q_u8(iq4[ibl].scales_h);
|
||||
i8scales.val[0] = vaddq_s8(vorrq_u8(vandq_u8(sl.val[0], m4), vandq_u8(vshlq_n_u8(sh, 4), m3)), m32);
|
||||
i8scales.val[1] = vaddq_s8(vorrq_u8(vandq_u8(sl.val[1], m4), vandq_u8(vshlq_n_u8(sh, 2), m3)), m32);
|
||||
i8scales.val[2] = vaddq_s8(vorrq_u8(vshrq_n_u8(sl.val[0], 4), vandq_u8(sh, m3)), m32);
|
||||
i8scales.val[3] = vaddq_s8(vorrq_u8(vshrq_n_u8(sl.val[1], 4), vandq_u8(vshrq_n_u8(sh, 2), m3)), m32);
|
||||
int32x4_t isum[nrc_y] = {};
|
||||
if constexpr (nrc_y == 1) {
|
||||
auto s8_1 = vmulq_s8(i8scales.val[0], vandq_u8(ms, vshlq_n_u8(extra, 2)));
|
||||
auto s8_2 = vmulq_s8(i8scales.val[1], vandq_u8(ms, extra));
|
||||
auto s16_1 = vmovl_s8(vget_low_s8 (s8_1));
|
||||
auto s16_2 = vmovl_s8(vget_high_s8(s8_1));
|
||||
auto s16_3 = vmovl_s8(vget_low_s8 (s8_2));
|
||||
auto s16_4 = vmovl_s8(vget_high_s8(s8_2));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto b8 = vld1_s16(q8.y[iy][ibl].bsums);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_1), b8, 0);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_1), b8, 1);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_2), b8, 2);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_2), b8, 3);
|
||||
b8 = vld1_s16(q8.y[iy][ibl].bsums+4);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_3), b8, 0);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_3), b8, 1);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_4), b8, 2);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_4), b8, 3);
|
||||
}
|
||||
s8_1 = vmulq_s8(i8scales.val[2], vandq_u8(ms, vshrq_n_u8(extra, 2)));
|
||||
s8_2 = vmulq_s8(i8scales.val[3], vandq_u8(ms, vshrq_n_u8(extra, 4)));
|
||||
s16_1 = vmovl_s8(vget_low_s8 (s8_1));
|
||||
s16_2 = vmovl_s8(vget_high_s8(s8_1));
|
||||
s16_3 = vmovl_s8(vget_low_s8 (s8_2));
|
||||
s16_4 = vmovl_s8(vget_high_s8(s8_2));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto b8 = vld1_s16(q8.y[iy][ibl].bsums+8);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_1), b8, 0);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_1), b8, 1);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_2), b8, 2);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_2), b8, 3);
|
||||
b8 = vld1_s16(q8.y[iy][ibl].bsums+12);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_3), b8, 0);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_3), b8, 1);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_4), b8, 2);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_4), b8, 3);
|
||||
}
|
||||
}
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
|
||||
i16scales.val[1] = vmovl_s8(vget_high_s8(i8scales.val[2*is+0]));
|
||||
i16scales.val[2] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+1]));
|
||||
i16scales.val[3] = vmovl_s8(vget_high_s8(i8scales.val[2*is+1]));
|
||||
for (int ib = 0; ib < 4; ++ib) {
|
||||
auto bits = vld1q_u8_x4(iq4[ibl].qs + 256*is + 64*ib);
|
||||
uint8x16_t shifts;
|
||||
if constexpr (nrc_y == 1) {
|
||||
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[2], m4)); // 4...7
|
||||
qx[2] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11
|
||||
qx[3] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15
|
||||
} else {
|
||||
shifts = vandq_u8(ms, vshlq_n_u8(extra, 2));
|
||||
auto shift = vqtbl1q_u8(shifts, shift_shuffle.val[0]);
|
||||
extra = vshrq_n_u8(extra, 1);
|
||||
qx[0] = vaddq_s8(shift, vqtbl1q_s8(values, vandq_u8(bits.val[0], m4))); // 0...3 from the 4 rows
|
||||
qx[1] = vaddq_s8(shift, vqtbl1q_s8(values, vandq_u8(bits.val[2], m4))); // 4...7
|
||||
qx[2] = vaddq_s8(shift, vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4))); // 8..11
|
||||
qx[3] = vaddq_s8(shift, vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4))); // 12..15
|
||||
}
|
||||
auto scales = vmovl_s16(vget_low_s16 (i16scales.val[ib]));
|
||||
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);
|
||||
}
|
||||
if constexpr (nrc_y == 1) {
|
||||
qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19
|
||||
qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23
|
||||
qx[2] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27
|
||||
qx[3] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31
|
||||
} else {
|
||||
auto shift = vqtbl1q_u8(shifts, shift_shuffle.val[1]);
|
||||
qx[0] = vaddq_s8(shift, vqtbl1q_s8(values, vandq_u8(bits.val[1], m4))); // 16..19
|
||||
qx[1] = vaddq_s8(shift, vqtbl1q_s8(values, vandq_u8(bits.val[3], m4))); // 20..23
|
||||
qx[2] = vaddq_s8(shift, vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4))); // 24..27
|
||||
qx[3] = vaddq_s8(shift, vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4))); // 28..31
|
||||
}
|
||||
scales = vmovl_s16(vget_high_s16(i16scales.val[ib]));
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
IQK_ALWAYS_INLINE void prepare_q4_k_quants(const uint8x16_t& m4, const uint8x16x4_t& bits, int8x16_t * qx) {
|
||||
qx[0] = vandq_u8(bits.val[0], m4); // 0...3 from the 4 rows
|
||||
qx[1] = vandq_u8(bits.val[1], m4); // 16..19
|
||||
@ -9294,6 +9546,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_q6_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ4_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq4_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0_R4:
|
||||
SET_MUL_MAT_FUNCTIONS_T(m, mul_mat_qx_r4_q8_0, Q4_0_R4_Dequantizer);
|
||||
expected_Btype = GGML_TYPE_Q8_0;
|
||||
|
||||
@ -4552,3 +4552,117 @@ void vec_dot_q2_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
|
||||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq4_k_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq4_k_r4_ref(const float * x, block_iq4_k_r4 * y, int64_t k) {
|
||||
quantize_iq4_k_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq4_k_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq4_k_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
static void repack_iq4_k(int nrows, int n_per_row, const block_iq4_k * x, block_iq4_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_iq4_k * 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].extra, 0, 8);
|
||||
std::memset(y[ibl].scales_l, 0, QK_K/8);
|
||||
std::memset(y[ibl].scales_h, 0, QK_K/16);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k] = x4[k][ibl].d;
|
||||
auto extra = x4[k][ibl].extra;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
if (extra & 1) y[ibl].extra[k+0] |= (1 << ib);
|
||||
if (extra & 2) y[ibl].extra[k+4] |= (1 << ib);
|
||||
extra >>= 2;
|
||||
uint8_t sl1 = x4[k][ibl].scales_l[ib] & 0xf;
|
||||
uint8_t sl2 = x4[k][ibl].scales_l[ib] >> 4;
|
||||
uint8_t sh = x4[k][ibl].scales_h[ib/2] >> 4*(ib%2);
|
||||
uint8_t sh1 = (sh >> 0) & 3;
|
||||
uint8_t sh2 = (sh >> 2) & 3;
|
||||
int i = 8*ib + k;
|
||||
y[ibl].scales_l[i%32] |= (sl1 << 4*(i/32));
|
||||
y[ibl].scales_h[i%16] |= (sh1 << 2*(i/16));
|
||||
i += 4;
|
||||
y[ibl].scales_l[i%32] |= (sl2 << 4*(i/32));
|
||||
y[ibl].scales_h[i%16] |= (sh2 << 2*(i/16));
|
||||
}
|
||||
}
|
||||
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_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_IQ4_K, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq4_k(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq4_k(4, n_per_row, (const block_iq4_k *)qtmp.data(), (block_iq4_k_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq4_k_r4(const block_iq4_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]);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
int is = 8*ib + k;
|
||||
float dl1 = d * ((((x[ibl].scales_l[is%32] >> 4*(is/32)) & 0xf) | (((x[ibl].scales_h[is%16] >> 2*(is/16)) & 3) << 4)) - 32);
|
||||
is += 4;
|
||||
float dl2 = d * ((((x[ibl].scales_l[is%32] >> 4*(is/32)) & 0xf) | (((x[ibl].scales_h[is%16] >> 2*(is/16)) & 3) << 4)) - 32);
|
||||
auto values1 = iq4k_values + (x[ibl].extra[k+0] & (1 << ib) ? 16 : 0);
|
||||
auto values2 = iq4k_values + (x[ibl].extra[k+4] & (1 << ib) ? 16 : 0);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y4[k][QK_K*ibl+32*ib+i+ 0] = dl1 * values1[x[ibl].qs[64*ib+4*k+i+ 0] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl1 * values1[x[ibl].qs[64*ib+4*k+i+ 0] >> 4];
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl2 * values2[x[ibl].qs[64*ib+4*k+i+16] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl2 * values2[x[ibl].qs[64*ib+4*k+i+16] >> 4];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl1 * values1[x[ibl].qs[64*ib+4*k+i+32] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl1 * values1[x[ibl].qs[64*ib+4*k+i+32] >> 4];
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl2 * values2[x[ibl].qs[64*ib+4*k+i+48] & 0xf];
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl2 * values2[x[ibl].qs[64*ib+4*k+i+48] >> 4];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq4_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_IQ4_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);
|
||||
}
|
||||
|
||||
|
||||
@ -139,6 +139,12 @@ size_t quantize_q6_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
|
||||
void dequantize_row_q6_k_r4(const block_q6_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_q6_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_iq4_k_r4_ref(const float * GGML_RESTRICT x, block_iq4_k_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq4_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq4_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_iq4_k_r4(const block_iq4_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq4_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 iqk_quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
void quantize_row_q8_K64_ref(const float * GGML_RESTRICT x, block_q8_K64 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_q8_K64(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
|
||||
@ -190,8 +190,9 @@ extern "C" {
|
||||
LLAMA_FTYPE_MOSTLY_Q6_K_R4 = 218, // 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 = 235, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_BN_R4 = 237, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 335, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_BN_R4 = 337, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_K_R4 = 340, // except 1d tensors
|
||||
|
||||
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
|
||||
};
|
||||
|
||||
@ -3866,6 +3866,7 @@ struct llama_model_loader {
|
||||
case GGML_TYPE_IQ2_K: ftype = LLAMA_FTYPE_MOSTLY_IQ2_K; break;
|
||||
case GGML_TYPE_IQ3_K: ftype = LLAMA_FTYPE_MOSTLY_IQ3_K; break;
|
||||
case GGML_TYPE_IQ4_K: ftype = LLAMA_FTYPE_MOSTLY_IQ4_K; break;
|
||||
case GGML_TYPE_IQ4_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_K_R4;break;
|
||||
case GGML_TYPE_IQ5_K: ftype = LLAMA_FTYPE_MOSTLY_IQ5_K; break;
|
||||
case GGML_TYPE_IQ6_K: ftype = LLAMA_FTYPE_MOSTLY_IQ6_K; break;
|
||||
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
|
||||
@ -4582,6 +4583,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K: return "IQ3_K - 3.4325 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_KL: return "IQ3_KL - 4 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_K: return "IQ4_K - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_K_R4: return "IQ4_K_R4 - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ5_K: return "IQ5_K - 5.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ6_K: return "IQ6_K - 6.6 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_BN: return "IQ1_BN - 1.625 bpw Bitnet";
|
||||
@ -15810,6 +15812,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
else if (new_type == GGML_TYPE_Q6_K_R4) {
|
||||
new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ4_K_R4) {
|
||||
new_type = GGML_TYPE_IQ4_K;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_Q4_0_R4) {
|
||||
new_type = GGML_TYPE_Q4_0;
|
||||
}
|
||||
@ -15894,6 +15899,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_K && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4 && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
}
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
||||
use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
|
||||
@ -16020,7 +16028,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
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_Q2_K_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4|| ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
@ -16090,7 +16098,8 @@ 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_Q2_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) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
@ -16127,6 +16136,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
case GGML_TYPE_IQ4_XS_R4:
|
||||
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
|
||||
case GGML_TYPE_IQ4_K:
|
||||
case GGML_TYPE_IQ4_K_R4:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
|
||||
case GGML_TYPE_IQ5_K:
|
||||
@ -16255,6 +16265,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K: default_type = GGML_TYPE_IQ3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_KL: default_type = GGML_TYPE_IQ3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_K: default_type = GGML_TYPE_IQ4_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_K_R4:default_type = GGML_TYPE_IQ4_K_R4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ5_K: default_type = GGML_TYPE_IQ5_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ6_K: default_type = GGML_TYPE_IQ6_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
|
||||
@ -16653,6 +16664,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_BN;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ4_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_K;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
|
||||
fflush(stdout);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user