IQ3_K_R4 (#145)
* iq3_k_r4 WIP * iq3_k_r4: Zen4 * iq3_k_r4: AVX2 * iq3_k_r4: NEON * iq3_k_r4: faster matrix x vector multiplication on NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
GitHub
parent
1095cd27a9
commit
b52e2e2934
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@ -55,6 +55,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ2_K", LLAMA_FTYPE_MOSTLY_IQ2_K, " 2.375 bpw non-linear quantization",},
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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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{ "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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@ -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_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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GGML_TYPE_COUNT,
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@ -497,6 +498,7 @@ 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_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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};
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@ -538,6 +538,16 @@ typedef struct {
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} block_iq3_k;
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static_assert(sizeof(block_iq3_k) == sizeof(ggml_half) + 2*sizeof(uint16_t) + QK_K/32 + QK_K/4 + QK_K/8, "wrong iq3_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/32];
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uint8_t scales_l[QK_K/8];
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uint8_t qs[QK_K];
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uint8_t qh[QK_K/2];
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} block_iq3_k_r4;
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static_assert(sizeof(block_iq3_k_r4) == 4*sizeof(block_iq3_k), "wrong iq3_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_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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case GGML_TYPE_BF16_R16: break;
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@ -1360,6 +1360,19 @@ 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_IQ3_K_R4] = {
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.type_name = "iq3_k_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq3_k),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq3_k_r4,
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.from_float = quantize_row_iq3_k_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq3_k_r4_ref,
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.vec_dot = vec_dot_iq3_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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@ -4163,6 +4176,7 @@ 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_IQ3_K_R4: wtype = GGML_TYPE_IQ3_K_R4; 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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@ -10700,6 +10714,7 @@ 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_IQ3_K_R4:
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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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@ -11156,6 +11171,7 @@ 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_IQ3_K_R4:
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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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@ -11309,6 +11325,7 @@ 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_IQ3_K_R4:
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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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@ -14508,6 +14525,7 @@ 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_IQ3_K_R4:
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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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@ -14901,6 +14919,7 @@ 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_IQ3_K_R4:
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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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@ -15188,6 +15207,7 @@ 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_IQ3_K_R4:
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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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@ -15804,6 +15824,7 @@ 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_IQ3_K_R4:
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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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@ -22648,6 +22669,7 @@ 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_IQ3_K_R4:result = quantize_iq3_k_r4(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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@ -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_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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case GGML_TYPE_Q8_K_R8: return 8;
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@ -3956,6 +3957,119 @@ 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_iq3_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(8);
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auto m03 = _mm256_set1_epi8(0x03);
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auto m04 = _mm256_set1_epi8(0x04);
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auto smask = _mm256_set_epi64x(0x0808080808080808, 0x0404040404040404, 0x0202020202020202, 0x0101010101010101);
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auto shift_shuffle = _mm256_set_epi64x(0x0707070706060606, 0x0505050504040404, 0x0303030302020202, 0x0101010100000000);
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auto values128 = _mm_loadu_si128((const __m128i *)iq3nl_values);
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auto values = MM256_SET_M128I(values128, values128);
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values = _mm256_add_epi8(values, _mm256_set1_epi8(64));
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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_iq3_k_r4 * iq3 = (const block_iq3_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 *)iq3[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 *)iq3[ibl].extra);
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auto slbits = _mm256_loadu_si256((const __m256i *)iq3[ibl].scales_l);
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auto sl1 = _mm256_add_epi8(_mm256_slli_epi16(_mm256_and_si256(slbits, m4), 1), _mm256_set1_epi8(1));
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auto sl2 = _mm256_add_epi8(_mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(slbits, 4), m4), 1), _mm256_set1_epi8(1));
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auto sh = _mm256_set1_epi64x(((const uint64_t *)iq3[ibl].scales_h)[0]);
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auto sh1 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(sh, smask), smask), _mm256_set1_epi8(1));
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auto sh2 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_srli_epi16(sh, 4), smask), smask), _mm256_set1_epi8(1));
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auto i8scales1 = _mm256_sign_epi8(sl1, sh1);
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auto i8scales2 = _mm256_sign_epi8(sl2, sh2);
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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(-64), 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(-64), 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(-64), 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(-64), 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 *)iq3[ibl].qs+ib);
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auto hbits = _mm_loadu_si128((const __m128i *)iq3[ibl].qh+ib);
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auto hb = MM256_SET_M128I(hbits, _mm_slli_epi16(hbits, 4));
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auto shift = _mm256_and_si256(ms, _mm256_slli_epi16(extra, 3)); extra = _mm256_srli_epi16(extra, 1);
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shift = _mm256_shuffle_epi8(shift, shift_shuffle);
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qx[0] = _mm256_or_si256(_mm256_and_si256(lb, m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 2)));
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qx[1] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 2), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 3)));
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qx[2] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 4), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 4)));
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qx[3] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 6), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 5)));
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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));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[3], _mm256_shuffle_epi32(y, 0xff));
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales, sumi));
|
||||
#else
|
||||
auto sumi1 = _mm256_maddubs_epi16(qx[0], _mm256_shuffle_epi32(y, 0x00));
|
||||
auto sumi2 = _mm256_maddubs_epi16(qx[1], _mm256_shuffle_epi32(y, 0x55));
|
||||
auto sumi3 = _mm256_maddubs_epi16(qx[2], _mm256_shuffle_epi32(y, 0xaa));
|
||||
auto sumi4 = _mm256_maddubs_epi16(qx[3], _mm256_shuffle_epi32(y, 0xff));
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(_mm256_madd_epi16(scales, sumi1), _mm256_madd_epi16(scales, sumi2)));
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(_mm256_madd_epi16(scales, sumi3), _mm256_madd_epi16(scales, sumi4)));
|
||||
#endif
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(isum[iy]), acc[iy]);
|
||||
}
|
||||
}
|
||||
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_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);
|
||||
|
|
@ -6167,6 +6281,18 @@ 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_IQ3_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq3_k_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq3_k_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq3_k_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq3_k_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq3_k_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq3_k_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq3_k_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq3_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>;
|
||||
|
|
@ -8843,6 +8969,161 @@ void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
|
|||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
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));
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
Q8<nrc_y, block_q8_K> q8(info);
|
||||
auto m4 = vdupq_n_u8(0xf);
|
||||
auto ms = nrc_y == 1 ? vdupq_n_u8(4) : vdupq_n_u8(8);
|
||||
auto m03 = vdupq_n_u8(0x03);
|
||||
auto m04 = vdupq_n_u8(0x04);
|
||||
uint8x16x2_t shift_shuffle = {
|
||||
vreinterpretq_u8_u64(uint64x2_t{0x0101010100000000, 0x0303030302020202}),
|
||||
vreinterpretq_u8_u64(uint64x2_t{0x0505050504040404, 0x0707070706060606})
|
||||
};
|
||||
uint8x16x2_t smask = { vcombine_u8(vdup_n_u8(1), vdup_n_u8(2)), vcombine_u8(vdup_n_u8(4), vdup_n_u8(8)) };
|
||||
auto values = vld1q_s8(iq3nl_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_iq3_k_r4 * iq3 = (const block_iq3_k_r4 *)((const char *)vx + ix*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq3[ibl].d));
|
||||
auto extra8 = vld1_u8(iq3[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(iq3[ibl].scales_l);
|
||||
auto sh8 = vld1_u8(iq3[ibl].scales_h);
|
||||
auto sh = vcombine_u8(sh8, sh8);
|
||||
i8scales.val[0] = vaddq_s8(vshlq_n_u8(vandq_u8(sl.val[0], m4), 1), vdupq_n_s8(1));
|
||||
i8scales.val[1] = vaddq_s8(vshlq_n_u8(vandq_u8(sl.val[1], m4), 1), vdupq_n_s8(1));
|
||||
i8scales.val[2] = vaddq_s8(vshlq_n_u8(vshrq_n_u8(sl.val[0], 4), 1), vdupq_n_s8(1));
|
||||
i8scales.val[3] = vaddq_s8(vshlq_n_u8(vshrq_n_u8(sl.val[1], 4), 1), vdupq_n_s8(1));
|
||||
i8scales.val[0] = vmulq_s8(i8scales.val[0], vorrq_u8(vceqq_u8(vandq_u8(sh, smask.val[0]), smask.val[0]), vdupq_n_u8(1)));
|
||||
i8scales.val[1] = vmulq_s8(i8scales.val[1], vorrq_u8(vceqq_u8(vandq_u8(sh, smask.val[1]), smask.val[1]), vdupq_n_u8(1)));
|
||||
sh = vshrq_n_u8(sh, 4);
|
||||
i8scales.val[2] = vmulq_s8(i8scales.val[2], vorrq_u8(vceqq_u8(vandq_u8(sh, smask.val[0]), smask.val[0]), vdupq_n_u8(1)));
|
||||
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);
|
||||
}
|
||||
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 lbits = vld1q_u8_x2(iq3[ibl].qs + 128*is + 32*ib);
|
||||
auto hbits = vld1q_u8(iq3[ibl].qh + 64*is + 16*ib);
|
||||
qx[0] = vorrq_u8(vandq_u8( lbits.val[0], m03), vandq_u8(m04, vshlq_n_u8(hbits, 2)));
|
||||
qx[1] = vorrq_u8(vandq_u8(vshrq_n_u8(lbits.val[0], 2), m03), vandq_u8(m04, vshlq_n_u8(hbits, 1)));
|
||||
qx[2] = vorrq_u8(vandq_u8(vshrq_n_u8(lbits.val[0], 4), m03), vandq_u8(m04, hbits));
|
||||
qx[3] = vorrq_u8(vandq_u8(vshrq_n_u8(lbits.val[0], 6), m03), vandq_u8(m04, vshrq_n_u8(hbits, 1)));
|
||||
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, 3));
|
||||
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] = vorrq_u8(vandq_u8( lbits.val[1], m03), vandq_u8(m04, vshrq_n_u8(hbits, 2)));
|
||||
qx[1] = vorrq_u8(vandq_u8(vshrq_n_u8(lbits.val[1], 2), m03), vandq_u8(m04, vshrq_n_u8(hbits, 3)));
|
||||
qx[2] = vorrq_u8(vandq_u8(vshrq_n_u8(lbits.val[1], 4), m03), vandq_u8(m04, vshrq_n_u8(hbits, 4)));
|
||||
qx[3] = vorrq_u8(vandq_u8(vshrq_n_u8(lbits.val[1], 6), m03), vandq_u8(m04, vshrq_n_u8(hbits, 5)));
|
||||
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_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);
|
||||
|
|
@ -8880,42 +9161,7 @@ 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) {
|
||||
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);
|
||||
}
|
||||
iq3_4_add_shift(ibl, q8, i8scales, extra, ms, isum);
|
||||
}
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
|
||||
|
|
@ -9803,6 +10049,10 @@ 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_IQ3_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq3_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;
|
||||
|
|
|
|||
|
|
@ -4795,3 +4795,139 @@ void repack_bf16_bf16_r16(const void * GGML_RESTRICT src, void * GGML_RESTRICT d
|
|||
repack_bf16(nrows, n_per_row, (const ggml_bf16_t *)src, (ggml_bf16_t *)dst);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq3_k_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq3_k_r4_ref(const float * x, block_iq3_k_r4 * y, int64_t k) {
|
||||
quantize_iq3_k_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq3_k_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq3_k_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline void convert_iq3_k(const block_iq3_k& x, uint8_t * L) {
|
||||
const uint8_t * qs = x.qs;
|
||||
const uint8_t * qh = x.qh;
|
||||
for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
|
||||
int shift_l = 2*(ib32%4);
|
||||
int shift_h = ib32%8;
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
L[j+ 0] = ((qs[j+ 0] >> shift_l) & 3) | (((qh[j+ 0] >> shift_h) & 1) << 2);
|
||||
L[j+16] = ((qs[j+16] >> shift_l) & 3) | (((qh[j+16] >> shift_h) & 1) << 2);
|
||||
}
|
||||
L += 32;
|
||||
if (shift_l == 6) qs += 32;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void repack_iq3_k(int nrows, int n_per_row, const block_iq3_k * x, block_iq3_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_iq3_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_l, 0, QK_K/8);
|
||||
std::memset(y[ibl].scales_h, 0, QK_K/32);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k] = x4[k][ibl].d;
|
||||
auto extra = x4[k][ibl].extra;
|
||||
uint16_t sh = x4[k][ibl].scales_h;
|
||||
convert_iq3_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_l[ib] & 0xf;
|
||||
uint8_t sl2 = x4[k][ibl].scales_l[ib] >> 4;
|
||||
uint8_t sh1 = (sh >> 0) & 1;
|
||||
uint8_t sh2 = (sh >> 1) & 1;
|
||||
sh >>= 2;
|
||||
int i = 8*ib + k;
|
||||
y[ibl].scales_l[i%32] |= (sl1 << 4*(i/32));
|
||||
y[ibl].scales_h[i%8 ] |= (sh1 << (i/8));
|
||||
i += 4;
|
||||
y[ibl].scales_l[i%32] |= (sl2 << 4*(i/32));
|
||||
y[ibl].scales_h[i%8 ] |= (sh2 << (i/8));
|
||||
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);
|
||||
y[ibl].qh[16*ib+4*k+i+ 0] = ((L[32*ib+i+ 0] >> 2) << 0) | ((L[32*ib+i+ 4] >> 2) << 1) | ((L[32*ib+i+ 8] >> 2) << 2) | ((L[32*ib+i+12] >> 2) << 3)
|
||||
| ((L[32*ib+i+16] >> 2) << 4) | ((L[32*ib+i+20] >> 2) << 5) | ((L[32*ib+i+24] >> 2) << 6) | ((L[32*ib+i+28] >> 2) << 7);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq3_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_IQ3_K, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq3_k(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq3_k(4, n_per_row, (const block_iq3_k *)qtmp.data(), (block_iq3_k_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq3_k_r4(const block_iq3_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;
|
||||
auto qh = x[ibl].qh;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
int is = 8*ib + k;
|
||||
float dl1 = d * (2*((x[ibl].scales_l[is%32] >> 4*(is/32)) & 0xf) + 1) * ((x[ibl].scales_h[is%8] >> (is/8)) & 1 ? -1 : 1);
|
||||
is += 4;
|
||||
float dl2 = d * (2*((x[ibl].scales_l[is%32] >> 4*(is/32)) & 0xf) + 1) * ((x[ibl].scales_h[is%8] >> (is/8)) & 1 ? -1 : 1);
|
||||
auto values1 = iq3nl_values + (x[ibl].extra[k+0] & (1 << ib) ? 8 : 0);
|
||||
auto values2 = iq3nl_values + (x[ibl].extra[k+4] & (1 << ib) ? 8 : 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) | ((qh[4*k+i] << 2) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl1 * values1[((ql[4*k+i+ 0] >> 2) & 3) | ((qh[4*k+i] << 1) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl1 * values1[((ql[4*k+i+ 0] >> 4) & 3) | ((qh[4*k+i] << 0) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl1 * values1[((ql[4*k+i+ 0] >> 6) & 3) | ((qh[4*k+i] >> 1) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl2 * values2[((ql[4*k+i+16] >> 0) & 3) | ((qh[4*k+i] >> 2) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl2 * values2[((ql[4*k+i+16] >> 2) & 3) | ((qh[4*k+i] >> 3) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl2 * values2[((ql[4*k+i+16] >> 4) & 3) | ((qh[4*k+i] >> 4) & 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl2 * values2[((ql[4*k+i+16] >> 6) & 3) | ((qh[4*k+i] >> 5) & 4)];
|
||||
}
|
||||
ql += 32;
|
||||
qh += 16;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq3_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_IQ3_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);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -145,6 +145,12 @@ size_t quantize_iq4_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT d
|
|||
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 quantize_row_iq3_k_r4_ref(const float * GGML_RESTRICT x, block_iq3_k_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq3_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq3_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_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_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_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
|
||||
|
||||
|
|
|
|||
|
|
@ -3867,6 +3867,7 @@ struct llama_model_loader {
|
|||
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_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;
|
||||
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;
|
||||
|
|
@ -4585,6 +4586,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
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_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";
|
||||
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";
|
||||
|
|
@ -15763,7 +15765,7 @@ 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_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_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 ||
|
||||
|
|
@ -15820,6 +15822,9 @@ 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_IQ3_K_R4) {
|
||||
new_type = GGML_TYPE_IQ3_K;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ4_K_R4) {
|
||||
new_type = GGML_TYPE_IQ4_K;
|
||||
}
|
||||
|
|
@ -15892,6 +15897,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_K && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ4_K_R4;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ5_K : GGML_TYPE_IQ4_K;
|
||||
}
|
||||
|
|
@ -16039,7 +16047,7 @@ 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_IQ4_K_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4|| ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
|
|
@ -16110,7 +16118,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
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new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ4_KS || new_type == GGML_TYPE_IQ4_XS_R4 ||
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||||
new_type == GGML_TYPE_IQ2_KS || new_type == GGML_TYPE_IQ4_KSS || new_type == GGML_TYPE_Q6_K_R4 ||
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||||
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4 ||
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||||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8) {
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||||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4) {
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||||
int nx = tensor->ne[0];
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||||
int ny = tensor->ne[1];
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if (nx % QK_K != 0) {
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||||
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@ -16142,6 +16150,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_Q3_K_R4:
|
||||
case GGML_TYPE_IQ2_K:
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||||
case GGML_TYPE_IQ3_K:
|
||||
case GGML_TYPE_IQ3_K_R4:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_XS_R4:
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||||
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|
@ -16277,6 +16286,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
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_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;
|
||||
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;
|
||||
|
|
@ -16685,6 +16695,10 @@ 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_IQ3_K_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ3_K;
|
||||
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;
|
||||
|
|
|
|||
Loading…
Reference in New Issue