IQ2_K_R4 (#146)
* iq2_k_r4: Zen4 * iq2_k_r4: NEON * iq2_k_r4: better matrix x vector multiplication on NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
GitHub
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commit
c16d352915
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@ -53,6 +53,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KSS", LLAMA_FTYPE_MOSTLY_IQ4_KSS, " 4.0 bpw non-linear quantization", },
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{ "IQ2_K", LLAMA_FTYPE_MOSTLY_IQ2_K, " 2.375 bpw non-linear quantization",},
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{ "IQ2_K_R4", LLAMA_FTYPE_MOSTLY_IQ2_K_R4, "IQ2_K repacked",},
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{ "IQ2_KS", LLAMA_FTYPE_MOSTLY_IQ2_KS, " 2.1875 bpw non-linear quantization",},
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{ "IQ3_K", LLAMA_FTYPE_MOSTLY_IQ3_K, " 3.44 bpw non-linear quantization", },
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{ "IQ3_K_R4", LLAMA_FTYPE_MOSTLY_IQ3_K_R4, "IQ3_K repacked", },
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@ -423,6 +423,7 @@ extern "C" {
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GGML_TYPE_BF16_R16 = 230,
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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_IQ2_K_R4 = 337,
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GGML_TYPE_IQ3_K_R4 = 338,
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GGML_TYPE_IQ4_K_R4 = 339,
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GGML_TYPE_Q8_K_R8 = 399,
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@ -498,6 +499,7 @@ extern "C" {
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GGML_FTYPE_MOSTLY_BF16_R16 = 224, // 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_IQ2_K_R4 = 330, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ3_K_R4 = 331, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_K_R4 = 332, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q8_K_R8 = 399, // except 1d tensors
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@ -521,6 +521,14 @@ typedef struct {
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} block_iq2_k;
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static_assert(sizeof(block_iq2_k) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/32 + QK_K/4, "wrong iq2_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[QK_K/8];
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uint8_t qs[QK_K];
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} block_iq2_k_r4;
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static_assert(sizeof(block_iq2_k_r4) == 4*sizeof(block_iq2_k), "wrong iq2_k_r4 block size/padding");
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typedef struct {
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uint16_t extra;
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uint8_t scales[QK_K/64];
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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_IQ2_K_R4: break;
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case GGML_TYPE_IQ3_K_R4: break;
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case GGML_TYPE_IQ4_K_R4: break;
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case GGML_TYPE_Q8_K_R8: break;
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@ -1308,6 +1308,19 @@ 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_IQ2_K_R4] = {
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.type_name = "iq2_k_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq2_k),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq2_k_r4,
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.from_float = quantize_row_iq2_k_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq2_k_r4_ref,
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.vec_dot = vec_dot_iq2_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_IQ2_KS] = {
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.type_name = "iq2_ks",
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.blck_size = QK_K,
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@ -4173,6 +4186,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_IQ4_KS: wtype = GGML_TYPE_IQ4_KS; break;
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case GGML_FTYPE_MOSTLY_IQ4_KSS: wtype = GGML_TYPE_IQ4_KSS; break;
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case GGML_FTYPE_MOSTLY_IQ2_K: wtype = GGML_TYPE_IQ2_K; break;
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case GGML_FTYPE_MOSTLY_IQ2_K_R4: wtype = GGML_TYPE_IQ2_K_R4; break;
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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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@ -10711,6 +10725,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -11168,6 +11183,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -11322,6 +11338,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -14522,6 +14539,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -14916,6 +14934,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -15204,6 +15223,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -15821,6 +15841,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_K_R4:
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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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@ -22666,6 +22687,7 @@ size_t ggml_quantize_chunk(
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case GGML_TYPE_IQ4_KS: result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_KSS: result = quantize_iq4_kss(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ2_K: result = quantize_iq2_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ2_K_R4:result = quantize_iq2_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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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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@ -182,6 +182,7 @@ struct MulMat {
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case GGML_TYPE_Q8_0_R4:
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case GGML_TYPE_IQ4_NL_R4:
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case GGML_TYPE_IQ4_XS_R4:
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case GGML_TYPE_IQ2_K_R4:
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case GGML_TYPE_IQ3_K_R4:
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ2_BN_R4: return 4;
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@ -3957,6 +3958,108 @@ static void mul_mat_bf16_r16_bf16(int n, const void * vx, size_t bx, const DataI
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}
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#endif
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template <int nrc_y>
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static void mul_mat_iq2_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 ms = _mm256_set1_epi8(4);
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auto m03 = _mm256_set1_epi8(0x03);
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auto shift_shuffle = _mm256_set_epi64x(0x0707070706060606, 0x0505050504040404, 0x0303030302020202, 0x0101010100000000);
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static const uint8_t kvalues_iq2nl[32] = {1, 19, 33, 49, 6, 24, 38, 54, 1, 19, 33, 49, 6, 24, 38, 54, 1, 19, 33, 49, 6, 24, 38, 54, 1, 19, 33, 49, 6, 24, 38, 54};
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auto values = _mm256_loadu_si256((const __m256i*)kvalues_iq2nl);
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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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#ifndef HAVE_FANCY_SIMD
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auto s_shuffle = _mm256_set_epi64x(0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908, 0x0706070605040504, 0x0302030201000100);
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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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uint64_t stored_scales[8];
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for (int ix = 0; ix < nrc_x; ix += 4) {
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const block_iq2_k_r4 * iq2 = (const block_iq2_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 *)iq2[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 *)iq2[ibl].extra);
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auto slbits = _mm256_loadu_si256((const __m256i *)iq2[ibl].scales);
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auto i8scales1 = _mm256_add_epi8(_mm256_and_si256(slbits, m4), _mm256_set1_epi8(-8));
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auto i8scales2 = _mm256_add_epi8(_mm256_and_si256(_mm256_srli_epi16(slbits, 4), m4), _mm256_set1_epi8(-8));
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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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__m256i isum[nrc_y] = {};
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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_mullo_epi16(_mm256_set1_epi16(-32), 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_mullo_epi16(_mm256_set1_epi16(-32), 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_mullo_epi16(_mm256_set1_epi16(-32), 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_mullo_epi16(_mm256_set1_epi16(-32), 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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#ifdef HAVE_FANCY_SIMD
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isum[iy] = _mm256_dpwssd_epi32(isum[iy], s1, _mm256_shuffle_epi32(bsums, 0x00));
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isum[iy] = _mm256_dpwssd_epi32(isum[iy], s2, _mm256_shuffle_epi32(bsums, 0x55));
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isum[iy] = _mm256_dpwssd_epi32(isum[iy], s3, _mm256_shuffle_epi32(bsums, 0xaa));
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isum[iy] = _mm256_dpwssd_epi32(isum[iy], s4, _mm256_shuffle_epi32(bsums, 0xff));
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#else
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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s1, _mm256_shuffle_epi32(bsums, 0x00)));
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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s2, _mm256_shuffle_epi32(bsums, 0x55)));
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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s3, _mm256_shuffle_epi32(bsums, 0xaa)));
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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s4, _mm256_shuffle_epi32(bsums, 0xff)));
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#endif
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}
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}
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for (int ib = 0; ib < QK_K/32; ++ib) {
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#ifdef HAVE_FANCY_SIMD
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auto scales = _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i *)(stored_scales + ib)));
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#else
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auto scales = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm_set1_epi64x(stored_scales[ib])), s_shuffle);
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#endif
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auto lb = _mm256_loadu_si256((const __m256i *)iq2[ibl].qs+ib);
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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_and_si256(lb, m03);
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qx[1] = _mm256_and_si256(_mm256_srli_epi16(lb, 2), m03);
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qx[2] = _mm256_and_si256(_mm256_srli_epi16(lb, 4), m03);
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qx[3] = _mm256_and_si256(_mm256_srli_epi16(lb, 6), m03);
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qx[0] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[0], shift));
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qx[1] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[1], shift));
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qx[2] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[2], shift));
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qx[3] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[3], shift));
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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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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales, sumi));
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#else
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auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(qx[0], _mm256_shuffle_epi32(y, 0x00)),
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_mm256_maddubs_epi16(qx[1], _mm256_shuffle_epi32(y, 0x55)));
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auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(qx[2], _mm256_shuffle_epi32(y, 0xaa)),
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_mm256_maddubs_epi16(qx[3], _mm256_shuffle_epi32(y, 0xff)));
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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(_mm256_madd_epi16(scales, sumi1), _mm256_madd_epi16(scales, sumi2)));
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#endif
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}
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}
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for (int iy = 0; iy < nrc_y; ++iy) {
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acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(isum[iy]), acc[iy]);
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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 <int nrc_y>
|
||||
static void mul_mat_iq3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
|
|
@ -6281,6 +6384,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
mm.funcs[7] = mul_mat_iq4_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ2_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq2_k_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq2_k_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq2_k_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq2_k_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq2_k_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq2_k_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq2_k_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq2_k_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ3_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq3_k_r4_q8_k<1>;
|
||||
|
|
@ -8969,11 +9084,19 @@ void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
|
|||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
template <int nrc_y, bool is_iq2k>
|
||||
inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8x16x4_t& i8scales, uint8x16_t extra,
|
||||
uint8x16_t ms, int32x4_t * isum) {
|
||||
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));
|
||||
int32x4_t * isum) {
|
||||
auto ms = is_iq2k ? vdupq_n_s8(5) : vdupq_n_s8(4);
|
||||
int8x16_t s8_1, s8_2;
|
||||
if constexpr (is_iq2k) {
|
||||
auto m1 = vdupq_n_u8(1);
|
||||
s8_1 = vmulq_s8(i8scales.val[0], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
|
||||
s8_2 = vmulq_s8(i8scales.val[1], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
|
||||
} else {
|
||||
s8_1 = vmulq_s8(i8scales.val[0], vandq_u8(ms, vshlq_n_u8(extra, 2)));
|
||||
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));
|
||||
|
|
@ -8990,8 +9113,14 @@ inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8
|
|||
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)));
|
||||
if constexpr (is_iq2k) {
|
||||
auto m1 = vdupq_n_u8(1);
|
||||
s8_1 = vmulq_s8(i8scales.val[2], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
|
||||
s8_2 = vmulq_s8(i8scales.val[3], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
|
||||
} else {
|
||||
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));
|
||||
|
|
@ -9010,6 +9139,111 @@ inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8
|
|||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq2_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 m03 = vdupq_n_u8(0x03);
|
||||
auto ms = vdupq_n_u8(4);
|
||||
uint8x16x2_t shift_shuffle = {
|
||||
vreinterpretq_u8_u64(uint64x2_t{0x0101010100000000, 0x0303030302020202}),
|
||||
vreinterpretq_u8_u64(uint64x2_t{0x0505050504040404, 0x0707070706060606})
|
||||
};
|
||||
auto values8 = vld1_s8(iq2nl_values);
|
||||
auto values = vcombine_s8(values8, values8);
|
||||
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_iq2_k_r4 * iq2 = (const block_iq2_k_r4 *)((const char *)vx + ix*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq2[ibl].d));
|
||||
auto extra8 = vld1_u8(iq2[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(iq2[ibl].scales);
|
||||
i8scales.val[0] = vaddq_s8(vandq_u8(sl.val[0], m4), vdupq_n_s8(-8));
|
||||
i8scales.val[1] = vaddq_s8(vandq_u8(sl.val[1], m4), vdupq_n_s8(-8));
|
||||
i8scales.val[2] = vaddq_s8(vshrq_n_u8(sl.val[0], 4), vdupq_n_s8(-8));
|
||||
i8scales.val[3] = vaddq_s8(vshrq_n_u8(sl.val[1], 4), vdupq_n_s8(-8));
|
||||
int32x4_t isum[nrc_y] = {};
|
||||
if constexpr (nrc_y == 1) {
|
||||
iq3_4_add_shift<nrc_y, true>(ibl, q8, i8scales, extra, isum);
|
||||
}
|
||||
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 scales = vmovl_s16(vget_low_s16 (i16scales.val[ib]));
|
||||
auto bits = vld1q_u8_x2(iq2[ibl].qs + 128*is + 32*ib);
|
||||
qx[0] = vandq_u8( bits.val[0], m03);
|
||||
qx[1] = vandq_u8(vshrq_n_u8(bits.val[0], 2), m03);
|
||||
qx[2] = vandq_u8(vshrq_n_u8(bits.val[0], 4), m03);
|
||||
qx[3] = vandq_u8(vshrq_n_u8(bits.val[0], 6), m03);
|
||||
uint8x16_t shifts;
|
||||
if constexpr (nrc_y == 1) {
|
||||
qx[0] = vqtbl1q_s8(values, qx[0]); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, qx[1]); // 4...7
|
||||
qx[2] = vqtbl1q_s8(values, qx[2]); // 8..11
|
||||
qx[3] = vqtbl1q_s8(values, qx[3]); // 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] = vqtbl1q_s8(values, vaddq_u8(shift, qx[0])); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, vaddq_u8(shift, qx[1])); // 4...7
|
||||
qx[2] = vqtbl1q_s8(values, vaddq_u8(shift, qx[2])); // 8..11
|
||||
qx[3] = vqtbl1q_s8(values, vaddq_u8(shift, qx[3])); // 12..15
|
||||
}
|
||||
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);
|
||||
}
|
||||
qx[0] = vandq_u8( bits.val[1], m03);
|
||||
qx[1] = vandq_u8(vshrq_n_u8(bits.val[1], 2), m03);
|
||||
qx[2] = vandq_u8(vshrq_n_u8(bits.val[1], 4), m03);
|
||||
qx[3] = vandq_u8(vshrq_n_u8(bits.val[1], 6), m03);
|
||||
if constexpr (nrc_y == 1) {
|
||||
qx[0] = vqtbl1q_s8(values, qx[0]); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, qx[1]); // 4...7
|
||||
qx[2] = vqtbl1q_s8(values, qx[2]); // 8..11
|
||||
qx[3] = vqtbl1q_s8(values, qx[3]); // 12..15
|
||||
} else {
|
||||
auto shift = vqtbl1q_u8(shifts, shift_shuffle.val[1]);
|
||||
qx[0] = vqtbl1q_s8(values, vaddq_u8(shift, qx[0])); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, vaddq_u8(shift, qx[1])); // 4...7
|
||||
qx[2] = vqtbl1q_s8(values, vaddq_u8(shift, qx[2])); // 8..11
|
||||
qx[3] = vqtbl1q_s8(values, vaddq_u8(shift, qx[3])); // 12..15
|
||||
}
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
|
|
@ -9054,7 +9288,7 @@ void mul_mat_iq3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& in
|
|||
i8scales.val[3] = vmulq_s8(i8scales.val[3], vorrq_u8(vceqq_u8(vandq_u8(sh, smask.val[1]), smask.val[1]), vdupq_n_u8(1)));
|
||||
int32x4_t isum[nrc_y] = {};
|
||||
if constexpr (nrc_y == 1) {
|
||||
iq3_4_add_shift(ibl, q8, i8scales, extra, ms, isum);
|
||||
iq3_4_add_shift<nrc_y, false>(ibl, q8, i8scales, extra, isum);
|
||||
}
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
|
||||
|
|
@ -9161,7 +9395,7 @@ void mul_mat_iq4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& in
|
|||
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) {
|
||||
iq3_4_add_shift(ibl, q8, i8scales, extra, ms, isum);
|
||||
iq3_4_add_shift<nrc_y, false>(ibl, q8, i8scales, extra, isum);
|
||||
}
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
|
||||
|
|
@ -10049,6 +10283,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
|||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_q8_k_r8_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_KR8;
|
||||
break;
|
||||
case GGML_TYPE_IQ2_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ3_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq3_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
|
|
|
|||
|
|
@ -4931,3 +4931,126 @@ void vec_dot_iq3_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t
|
|||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq2_k_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq2_k_r4_ref(const float * x, block_iq2_k_r4 * y, int64_t k) {
|
||||
quantize_iq2_k_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq2_k_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq2_k_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline void convert_iq2_k(const block_iq2_k& x, uint8_t * L) {
|
||||
const uint8_t * qs = x.qs;
|
||||
for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
|
||||
int shift_l = 2*(ib32%4);
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
L[j+ 0] = ((qs[j+ 0] >> shift_l) & 3);
|
||||
L[j+16] = ((qs[j+16] >> shift_l) & 3);
|
||||
}
|
||||
L += 32;
|
||||
if (shift_l == 6) qs += 32;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void repack_iq2_k(int nrows, int n_per_row, const block_iq2_k * x, block_iq2_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_iq2_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) {
|
||||
std::memset(y[ibl].extra, 0, 8);
|
||||
std::memset(y[ibl].scales, 0, QK_K/8);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k] = x4[k][ibl].d;
|
||||
auto extra = x4[k][ibl].extra;
|
||||
convert_iq2_k(x4[k][ibl], L);
|
||||
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[ib] & 0xf;
|
||||
uint8_t sl2 = x4[k][ibl].scales[ib] >> 4;
|
||||
int i = 8*ib + k;
|
||||
y[ibl].scales[i%32] |= (sl1 << 4*(i/32));
|
||||
i += 4;
|
||||
y[ibl].scales[i%32] |= (sl2 << 4*(i/32));
|
||||
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_iq2_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_IQ2_K, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq2_k(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq2_k(4, n_per_row, (const block_iq2_k *)qtmp.data(), (block_iq2_k_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq2_k_r4(const block_iq2_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]);
|
||||
auto ql = x[ibl].qs;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
int is = 8*ib + k;
|
||||
float dl1 = d * (((x[ibl].scales[is%32] >> 4*(is/32)) & 0xf) - 8);
|
||||
is += 4;
|
||||
float dl2 = d * (((x[ibl].scales[is%32] >> 4*(is/32)) & 0xf) - 8);
|
||||
auto values1 = iq2nl_values + (x[ibl].extra[k+0] & (1 << ib) ? 4 : 0);
|
||||
auto values2 = iq2nl_values + (x[ibl].extra[k+4] & (1 << ib) ? 4 : 0);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y4[k][QK_K*ibl+32*ib+i+ 0] = dl1 * values1[(ql[4*k+i+ 0] >> 0) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl1 * values1[(ql[4*k+i+ 0] >> 2) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl1 * values1[(ql[4*k+i+ 0] >> 4) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl1 * values1[(ql[4*k+i+ 0] >> 6) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl2 * values2[(ql[4*k+i+16] >> 0) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl2 * values2[(ql[4*k+i+16] >> 2) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl2 * values2[(ql[4*k+i+16] >> 4) & 3];
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl2 * values2[(ql[4*k+i+16] >> 6) & 3];
|
||||
}
|
||||
ql += 32;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq2_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_IQ2_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);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -151,6 +151,12 @@ size_t quantize_iq3_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT d
|
|||
void dequantize_row_iq3_k_r4(const block_iq3_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq3_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_iq2_k_r4_ref(const float * GGML_RESTRICT x, block_iq2_k_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq2_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq2_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_iq2_k_r4(const block_iq2_k_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq2_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_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);
|
||||
|
|
|
|||
|
|
@ -193,6 +193,7 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 335, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_BF16_R16 = 232, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_BN_R4 = 337, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_K_R4 = 338, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ3_K_R4 = 339, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_K_R4 = 340, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q8_K_R8 = 399, // except 1d tensors
|
||||
|
|
|
|||
|
|
@ -3866,6 +3866,7 @@ struct llama_model_loader {
|
|||
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
|
||||
case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
|
||||
case GGML_TYPE_IQ2_K: ftype = LLAMA_FTYPE_MOSTLY_IQ2_K; break;
|
||||
case GGML_TYPE_IQ2_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_K_R4;break;
|
||||
case GGML_TYPE_IQ3_K: ftype = LLAMA_FTYPE_MOSTLY_IQ3_K; break;
|
||||
case GGML_TYPE_IQ3_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ3_K_R4;break;
|
||||
case GGML_TYPE_IQ4_K: ftype = LLAMA_FTYPE_MOSTLY_IQ4_K; break;
|
||||
|
|
@ -4585,6 +4586,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: return "IQ4_KSS - 4.0 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K: return "IQ2_K - 2.375 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K_R4: return "IQ2_K_R4 - 2.375 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K: return "IQ3_K - 3.4325 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K_R4: return "IQ3_K_R4 - 3.4325 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_KL: return "IQ3_KL - 4 bpw";
|
||||
|
|
@ -15765,7 +15767,7 @@ 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_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_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 ||
|
||||
|
|
@ -15822,6 +15824,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (new_type == GGML_TYPE_Q8_K_R8) {
|
||||
new_type = GGML_TYPE_Q8_0;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ2_K_R4) {
|
||||
new_type = GGML_TYPE_IQ2_K;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ3_K_R4) {
|
||||
new_type = GGML_TYPE_IQ3_K;
|
||||
}
|
||||
|
|
@ -15881,6 +15886,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ4_K : GGML_TYPE_IQ3_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ4_K_R4 : GGML_TYPE_IQ3_K_R4;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
|
|
@ -16047,7 +16055,8 @@ 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_IQ4_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_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) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
|
|
@ -16057,6 +16066,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K ) new_type = GGML_TYPE_IQ3_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) new_type = GGML_TYPE_IQ3_K_R4;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL ) new_type = GGML_TYPE_IQ4_KS;
|
||||
}
|
||||
} else {
|
||||
|
|
@ -16118,7 +16128,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_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) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
|
|
@ -16149,6 +16160,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_K_R4:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
case GGML_TYPE_IQ3_K_R4:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
|
|
@ -16285,6 +16297,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: default_type = GGML_TYPE_IQ4_KS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: default_type = GGML_TYPE_IQ4_KSS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K: default_type = GGML_TYPE_IQ2_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K_R4:default_type = GGML_TYPE_IQ2_K_R4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K: default_type = GGML_TYPE_IQ3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K_R4:default_type = GGML_TYPE_IQ3_K_R4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_KL: default_type = GGML_TYPE_IQ3_K; break;
|
||||
|
|
@ -16695,6 +16708,10 @@ 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_IQ2_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_K;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ3_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ3_K;
|
||||
else chunk_size_multiplier = 4;
|
||||
|
|
|
|||
Loading…
Reference in New Issue