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IQ5_KS_R4: row-interleaved IQ5_KS (#426)
* iq5_ks_r4: basics * iq5_ks_r4: Zen4 works * iq5_ks_r4: AVX2 works * iq5_ks_r4: NEON * Fix iq5_ks on NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@ -67,6 +67,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KS_R4",LLAMA_FTYPE_MOSTLY_IQ4_KS_R4,"IQ4_KS repacked", },
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{ "IQ5_KS_R4",LLAMA_FTYPE_MOSTLY_IQ5_KS_R4,"IQ5_KS repacked", },
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{ "IQ4_KSS", LLAMA_FTYPE_MOSTLY_IQ4_KSS, " 4.0 bpw non-linear quantization", },
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{ "IQ5_KS", LLAMA_FTYPE_MOSTLY_IQ5_KS, " 5.25 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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@ -452,6 +452,7 @@ extern "C" {
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GGML_TYPE_IQ4_K_R4 = 339,
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GGML_TYPE_IQ5_K_R4 = 340,
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GGML_TYPE_IQ4_KS_R4 = 344,
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GGML_TYPE_IQ5_KS_R4 = 352,
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GGML_TYPE_Q8_KV_R8 = 398,
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GGML_TYPE_Q8_K_R8 = 399,
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GGML_TYPE_COUNT,
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@ -540,6 +541,7 @@ extern "C" {
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GGML_FTYPE_MOSTLY_IQ4_K_R4 = 332, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ5_K_R4 = 333, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_KS_R4 = 337, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ5_KS_R4 = 341, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q8_KV_R8 = 398, // 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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@ -694,6 +694,13 @@ typedef struct {
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} block_iq5_ks;
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static_assert(sizeof(block_iq5_ks) == QK_K/32 + QK_K/2 + QK_K/8, "wrong iq5_ks block size/padding");
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typedef struct {
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uint8_t scales[QK_K/8];
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uint8_t qs[QK_K*2];
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uint8_t qh[QK_K/2];
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} block_iq5_ks_r4;
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static_assert(sizeof(block_iq5_ks_r4) == 4*sizeof(block_iq5_ks), "wrong iq5_ks_r4 block size/padding");
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#endif // GGML_COMMON_DECL
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#endif // GGML_COMMON_DECL
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@ -15451,6 +15451,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
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case GGML_TYPE_IQ4_K_R4: break;
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case GGML_TYPE_IQ5_K_R4: break;
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case GGML_TYPE_IQ4_KS_R4:break;
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case GGML_TYPE_IQ5_KS_R4:break;
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case GGML_TYPE_Q8_KV_R8: break;
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case GGML_TYPE_Q8_K_R8: break;
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case GGML_TYPE_Q8_KV: break;
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@ -1339,6 +1339,23 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
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.vec_dot_type = GGML_TYPE_Q8_K32,
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#else
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.vec_dot_type = GGML_TYPE_Q8_K,
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#endif
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.nrows = 1,
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.row_meta_size = 4,
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},
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[GGML_TYPE_IQ5_KS_R4] = {
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.type_name = "iq5_ks_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq5_ks),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq5_ks_r4,
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.from_float = quantize_row_iq5_ks_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq5_ks_r4_ref,
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.vec_dot = vec_dot_iq5_ks_r4_q8_k,
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#if defined __AVX2__
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.vec_dot_type = GGML_TYPE_Q8_K32,
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#else
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.vec_dot_type = GGML_TYPE_Q8_K,
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#endif
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.nrows = 1,
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.row_meta_size = 4,
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@ -4478,6 +4495,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
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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_KS_R4: wtype = GGML_TYPE_IQ4_KS_R4;break;
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case GGML_FTYPE_MOSTLY_IQ5_KS_R4: wtype = GGML_TYPE_IQ5_KS_R4;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_IQ5_KS: wtype = GGML_TYPE_IQ5_KS; break;
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case GGML_FTYPE_MOSTLY_IQ2_K: wtype = GGML_TYPE_IQ2_K; break;
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@ -11242,6 +11260,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -11715,6 +11734,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -11885,6 +11905,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -15382,6 +15403,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -15792,6 +15814,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -16108,6 +16131,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -16741,6 +16765,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ5_KS:
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case GGML_TYPE_IQ2_K:
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@ -23810,6 +23835,7 @@ size_t ggml_quantize_chunk(
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case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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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_KS_R4:result = quantize_iq4_ks_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ5_KS_R4:result = quantize_iq5_ks_r4(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_IQ5_KS: result = quantize_iq5_ks (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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@ -342,6 +342,7 @@ struct MulMat {
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K_R4:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS_R4:
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case GGML_TYPE_IQ2_S_R4:
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@ -379,6 +380,7 @@ struct MulMat {
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case GGML_TYPE_IQ4_K_R4:
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case GGML_TYPE_IQ5_K_R4:
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case GGML_TYPE_IQ4_KS_R4:
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case GGML_TYPE_IQ5_KS_R4:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS_R4:
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case GGML_TYPE_IQ2_S_R4:
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@ -7353,6 +7355,16 @@ static void mul_mat_iq4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataI
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}
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}
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static inline __m256i prepare_5bit_quants(const __m256i * values, __m256i ql, __m256i qh, __m256i mask) {
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auto q5vl = _mm256_shuffle_epi8(values[0], ql);
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auto q5vh = _mm256_shuffle_epi8(values[1], ql);
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#ifdef HAVE_FANCY_SIMD
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return _mm256_mask_blend_epi8(_mm256_cmpeq_epi8_mask(_mm256_and_si256(qh, mask), mask), q5vl, q5vh);
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#else
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return _mm256_blendv_epi8(q5vl, q5vh, _mm256_cmpeq_epi8(_mm256_and_si256(qh, mask), mask));
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#endif
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}
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template <int nrc_y>
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static void mul_mat_iq5_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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@ -7421,23 +7433,11 @@ static void mul_mat_iq5_k_r4_q8_k(int n, const void * vx, size_t bx, const DataI
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qx[2] = _mm256_and_si256(_mm256_srli_epi16(lbits1, 4), m4);
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qx[3] = _mm256_and_si256(_mm256_srli_epi16(lbits2, 4), m4);
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qx[0] = prepare_5bit_quants(values, qx[0], hb, _mm256_set1_epi8(0x01));
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qx[1] = prepare_5bit_quants(values, qx[1], hb, _mm256_set1_epi8(0x10));
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qx[2] = prepare_5bit_quants(values, qx[2], hb, _mm256_set1_epi8(0x02));
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qx[3] = prepare_5bit_quants(values, qx[3], hb, _mm256_set1_epi8(0x20));
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#ifdef HAVE_FANCY_SIMD
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auto q5vl = _mm256_shuffle_epi8(values[0], qx[0]);
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auto q5vh = _mm256_shuffle_epi8(values[1], qx[0]);
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qx[0] = _mm256_mask_blend_epi8(_mm256_cmpeq_epi8_mask(_mm256_and_si256(hb, _mm256_set1_epi8(0x01)), _mm256_set1_epi8(0x01)), q5vl, q5vh);
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q5vl = _mm256_shuffle_epi8(values[0], qx[1]);
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q5vh = _mm256_shuffle_epi8(values[1], qx[1]);
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qx[1] = _mm256_mask_blend_epi8(_mm256_cmpeq_epi8_mask(_mm256_and_si256(hb, _mm256_set1_epi8(0x10)), _mm256_set1_epi8(0x10)), q5vl, q5vh);
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q5vl = _mm256_shuffle_epi8(values[0], qx[2]);
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q5vh = _mm256_shuffle_epi8(values[1], qx[2]);
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qx[2] = _mm256_mask_blend_epi8(_mm256_cmpeq_epi8_mask(_mm256_and_si256(hb, _mm256_set1_epi8(0x02)), _mm256_set1_epi8(0x02)), q5vl, q5vh);
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q5vl = _mm256_shuffle_epi8(values[0], qx[3]);
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q5vh = _mm256_shuffle_epi8(values[1], qx[3]);
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qx[3] = _mm256_mask_blend_epi8(_mm256_cmpeq_epi8_mask(_mm256_and_si256(hb, _mm256_set1_epi8(0x20)), _mm256_set1_epi8(0x20)), q5vl, q5vh);
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if constexpr (nrc_y == 1) {
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auto shift = _mm256_and_si256(ms, _mm256_slli_epi16(extra, 1)); extra = _mm256_srli_epi16(extra, 1);
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shift = _mm256_shuffle_epi8(shift, shift_shuffle);
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@ -7447,23 +7447,6 @@ static void mul_mat_iq5_k_r4_q8_k(int n, const void * vx, size_t bx, const DataI
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qx[3] = _mm256_add_epi8(qx[3], shift);
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}
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#else
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auto q5vl = _mm256_shuffle_epi8(values[0], qx[0]);
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auto q5vh = _mm256_shuffle_epi8(values[1], qx[0]);
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qx[0] = _mm256_blendv_epi8(q5vl, q5vh, _mm256_cmpeq_epi8(_mm256_and_si256(hb, _mm256_set1_epi8(0x01)), _mm256_set1_epi8(0x01)));
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q5vl = _mm256_shuffle_epi8(values[0], qx[1]);
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q5vh = _mm256_shuffle_epi8(values[1], qx[1]);
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qx[1] = _mm256_blendv_epi8(q5vl, q5vh, _mm256_cmpeq_epi8(_mm256_and_si256(hb, _mm256_set1_epi8(0x10)), _mm256_set1_epi8(0x10)));
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q5vl = _mm256_shuffle_epi8(values[0], qx[2]);
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q5vh = _mm256_shuffle_epi8(values[1], qx[2]);
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qx[2] = _mm256_blendv_epi8(q5vl, q5vh, _mm256_cmpeq_epi8(_mm256_and_si256(hb, _mm256_set1_epi8(0x02)), _mm256_set1_epi8(0x02)));
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q5vl = _mm256_shuffle_epi8(values[0], qx[3]);
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q5vh = _mm256_shuffle_epi8(values[1], qx[3]);
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qx[3] = _mm256_blendv_epi8(q5vl, q5vh, _mm256_cmpeq_epi8(_mm256_and_si256(hb, _mm256_set1_epi8(0x20)), _mm256_set1_epi8(0x20)));
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auto shift = _mm256_and_si256(ms, _mm256_slli_epi16(extra, 1)); extra = _mm256_srli_epi16(extra, 1);
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shift = _mm256_shuffle_epi8(shift, shift_shuffle);
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qx[0] = _mm256_add_epi8(qx[0], shift);
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@ -7506,6 +7489,128 @@ static void mul_mat_iq5_k_r4_q8_k(int n, const void * vx, size_t bx, const DataI
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}
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}
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template <int nrc_y>
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static void mul_mat_iq5_ks_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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__m256i values[2];
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{
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auto val1 = _mm_loadu_si128((const __m128i *)iq5nl_values+0);
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auto val2 = _mm_loadu_si128((const __m128i *)iq5nl_values+1);
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values[0] = MM256_SET_M128I(val1, val1);
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values[1] = MM256_SET_M128I(val2, val2);
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#ifdef HAVE_FANCY_SIMD
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values[0] = _mm256_sub_epi8(values[0], _mm256_set1_epi8(-128));
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values[1] = _mm256_sub_epi8(values[1], _mm256_set1_epi8(-128));
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#endif
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}
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int nbl = n / QK_K;
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using helper_t = union { __m256i vec; uint32_t val[8]; };
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#ifndef HAVE_FANCY_SIMD
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helper_t h, h_shift;
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auto s_shuffle = _mm256_set_epi64x(0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908, 0x0706070605040504, 0x0302030201000100);
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#else
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using helper512_t = union { __m512i vec; uint64_t val[8]; };
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helper_t h;
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helper512_t h_shift;
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#endif
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__m256 acc[nrc_y] = {};
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__m256i isum[nrc_y] = {};
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__m256i qx[4];
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for (int ix = 0; ix < nrc_x; ix += 4) {
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auto dptr = (const float *)((const char *)vx + (ix+0)*bx);
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const block_iq5_ks_r4 * iq5 = (const block_iq5_ks_r4 *)(dptr + 4);
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auto d4 = _mm_loadu_ps(dptr);
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for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
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auto scales = _mm256_loadu_si256((const __m256i *)iq5[ibl].scales);
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h.vec = _mm256_sub_epi8(_mm256_and_si256(scales, _mm256_set1_epi8(-2)), _mm256_set1_epi8(127));
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#ifndef HAVE_FANCY_SIMD
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h_shift.vec = _mm256_slli_epi16(_mm256_and_si256(scales, _mm256_set1_epi8(1)), 1);
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{
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__m256 v1 = _mm256_mul_ps(_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h.val[4])), _mm_cvtepi8_epi32(_mm_set1_epi32(h.val[0])))),
|
||||
_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[4])), _mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[0])))));
|
||||
__m256 v2 = _mm256_mul_ps(_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h.val[5])), _mm_cvtepi8_epi32(_mm_set1_epi32(h.val[1])))),
|
||||
_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[5])), _mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[1])))));
|
||||
__m256 v3 = _mm256_mul_ps(_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h.val[6])), _mm_cvtepi8_epi32(_mm_set1_epi32(h.val[2])))),
|
||||
_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[6])), _mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[2])))));
|
||||
__m256 v4 = _mm256_mul_ps(_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h.val[7])), _mm_cvtepi8_epi32(_mm_set1_epi32(h.val[3])))),
|
||||
_mm256_cvtepi32_ps(MM256_SET_M128I(_mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[7])), _mm_cvtepi8_epi32(_mm_set1_epi32(h_shift.val[3])))));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto m8 = _mm256_loadu_ps((const float *)q8.y[iy][ibl].bsums);
|
||||
acc[iy] = _mm256_fmadd_ps(v1, _mm256_shuffle_ps(m8, m8, 0x00), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(v2, _mm256_shuffle_ps(m8, m8, 0x55), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(v3, _mm256_shuffle_ps(m8, m8, 0xaa), acc[iy]);
|
||||
acc[iy] = _mm256_fmadd_ps(v4, _mm256_shuffle_ps(m8, m8, 0xff), acc[iy]);
|
||||
}
|
||||
}
|
||||
#else
|
||||
auto shift = _mm256_add_epi8(_mm256_set1_epi8(-64), _mm256_and_si256(scales, _mm256_set1_epi8(1)));
|
||||
h_shift.vec = _mm512_mullo_epi16(_mm512_cvtepi8_epi16(shift), _mm512_cvtepi8_epi16(h.vec));
|
||||
#endif
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto iscales = _mm256_cvtepi8_epi32(_mm_set1_epi32(h.val[ib]));
|
||||
auto ishifts = _mm256_cvtepi16_epi32(_mm_set1_epi64x(h_shift.val[ib]));
|
||||
auto scales_m = _mm256_cvtepi32_ps(ishifts);
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
float m8 = ((const float *)q8.y[iy][ibl].bsums)[ib];
|
||||
acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(m8), acc[iy]);
|
||||
}
|
||||
#endif
|
||||
auto lbits1 = _mm256_loadu_si256((const __m256i *)iq5[ibl].qs+2*ib+0);
|
||||
auto lbits2 = _mm256_loadu_si256((const __m256i *)iq5[ibl].qs+2*ib+1);
|
||||
auto hbits = _mm_loadu_si128((const __m128i *)iq5[ibl].qh+ib);
|
||||
auto hb = MM256_SET_M128I(_mm_srli_epi16(hbits, 2), hbits);
|
||||
qx[0] = _mm256_and_si256(lbits1, m4);
|
||||
qx[1] = _mm256_and_si256(lbits2, m4);
|
||||
qx[2] = _mm256_and_si256(_mm256_srli_epi16(lbits1, 4), m4);
|
||||
qx[3] = _mm256_and_si256(_mm256_srli_epi16(lbits2, 4), m4);
|
||||
|
||||
qx[0] = prepare_5bit_quants(values, qx[0], hb, _mm256_set1_epi8(0x01));
|
||||
qx[1] = prepare_5bit_quants(values, qx[1], hb, _mm256_set1_epi8(0x10));
|
||||
qx[2] = prepare_5bit_quants(values, qx[2], hb, _mm256_set1_epi8(0x02));
|
||||
qx[3] = prepare_5bit_quants(values, qx[3], hb, _mm256_set1_epi8(0x20));
|
||||
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
auto iscales = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm_set1_epi32(h.val[ib])), s_shuffle);
|
||||
auto s1 = _mm256_sign_epi8(qx[0], qx[0]);
|
||||
auto s2 = _mm256_sign_epi8(qx[1], qx[1]);
|
||||
auto s3 = _mm256_sign_epi8(qx[2], qx[2]);
|
||||
auto s4 = _mm256_sign_epi8(qx[3], qx[3]);
|
||||
#endif
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib);
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto sumi = _mm256_setzero_si256();
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[0], _mm256_shuffle_epi32(y, 0x00));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[1], _mm256_shuffle_epi32(y, 0x55));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[2], _mm256_shuffle_epi32(y, 0xaa));
|
||||
sumi = _mm256_dpbusd_epi32(sumi, qx[3], _mm256_shuffle_epi32(y, 0xff));
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(iscales, sumi));
|
||||
#else
|
||||
auto sumi1 = _mm256_maddubs_epi16(s1, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x00), qx[0]));
|
||||
auto sumi2 = _mm256_maddubs_epi16(s2, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x55), qx[1]));
|
||||
auto sumi3 = _mm256_maddubs_epi16(s3, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xaa), qx[2]));
|
||||
auto sumi4 = _mm256_maddubs_epi16(s4, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xff), qx[3]));
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(_mm256_madd_epi16(iscales, sumi1), _mm256_madd_epi16(iscales, sumi2)));
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(_mm256_madd_epi16(iscales, sumi3), _mm256_madd_epi16(iscales, sumi4)));
|
||||
#endif
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_set1_ps(q8.scale(iy, ibl)), _mm256_cvtepi32_ps(isum[iy]), acc[iy]);
|
||||
isum[iy] = _mm256_setzero_si256();
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto sum = _mm_add_ps(_mm256_castps256_ps128(acc[iy]), _mm256_extractf128_ps(acc[iy], 1));
|
||||
acc[iy] = _mm256_setzero_ps();
|
||||
info.store(ix+0, iy, _mm_mul_ps(d4, sum));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Bits>
|
||||
inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, const __m256i * q8, __m256i * sumi) {
|
||||
if (j == 0) {
|
||||
@ -9946,6 +10051,22 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
// For some reason Zen4 does not like this particular function
|
||||
mm.func16 = mul_mat_iq4_ks_r4_q8_k<16>;
|
||||
#endif
|
||||
expected_typeB = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
case GGML_TYPE_IQ5_KS_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq5_ks_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq5_ks_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq5_ks_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq5_ks_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq5_ks_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq5_ks_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq5_ks_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq5_ks_r4_q8_k<8>;
|
||||
#ifndef HAVE_FANCY_SIMD
|
||||
// For some reason Zen4 does not like this particular function
|
||||
mm.func16 = mul_mat_iq5_ks_r4_q8_k<16>;
|
||||
#endif
|
||||
expected_typeB = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
@ -11086,7 +11207,8 @@ struct DequantizerIQ4KS final : public BaseDequantizer<block_iq4_ks, true> {
|
||||
};
|
||||
|
||||
struct DequantizerIQ5KS final : public BaseDequantizer<block_iq5_ks, true> {
|
||||
DequantizerIQ5KS(const void * vx, size_t bx, int nrc) : BaseDequantizer(vx, bx, nrc), values(vld1q_s8_x2(iq5nl_values)) {}
|
||||
DequantizerIQ5KS(const void * vx, size_t bx, int nrc) : BaseDequantizer(vx, bx, nrc),
|
||||
values(vld1q_s8_x4(iq5nl_values)) {}
|
||||
|
||||
constexpr static int num_blocks() { return 8; }
|
||||
constexpr static bool should_scale_quants() { return false; }
|
||||
@ -11095,7 +11217,11 @@ struct DequantizerIQ5KS final : public BaseDequantizer<block_iq5_ks, true> {
|
||||
inline int32x4x2_t new_block(int i, const Q8& q8, float32x4_t * acc) {
|
||||
(void)q8;
|
||||
(void)acc;
|
||||
auto scales16 = vaddq_s16(vreinterpretq_s16_u16(vandq_u16(vmovl_u8(vld1_u8(x[i].scales)), mask)), m127);
|
||||
auto sas8 = vld1_u8(x[i].scales);
|
||||
auto scales16 = vaddq_s16(vreinterpretq_s16_u16(vandq_u16(vmovl_u8(sas8), mask)), m127);
|
||||
hbits = vld1q_u8_x2(x[i].qh);
|
||||
sas = vcombine_u8(sas8, sas8);
|
||||
sas = vshlq_n_u8(vandq_u8(sas, vdupq_n_u8(1)), 5);
|
||||
int32x4x2_t scales = {vmovl_s16(vget_low_s16(scales16)), vmovl_s16(vget_high_s16(scales16))};
|
||||
return scales;
|
||||
}
|
||||
@ -11105,27 +11231,29 @@ struct DequantizerIQ5KS final : public BaseDequantizer<block_iq5_ks, true> {
|
||||
if (j == 1) {
|
||||
for (int k = 0; k < 2; ++k) hbits.val[k] = vshrq_n_u8(hbits.val[k], 4);
|
||||
}
|
||||
bits.b1.val[0] = vorrq_u8(bits.b1.val[0], vandq_u8(vshlq_n_u8(hbits.val[0], 4), hm));
|
||||
bits.b1.val[1] = vorrq_u8(bits.b1.val[1], vandq_u8(vshlq_n_u8(hbits.val[1], 4), hm));
|
||||
bits.b1.val[2] = vorrq_u8(bits.b1.val[2], vandq_u8(vshlq_n_u8(hbits.val[0], 3), hm));
|
||||
bits.b1.val[3] = vorrq_u8(bits.b1.val[3], vandq_u8(vshlq_n_u8(hbits.val[1], 3), hm));
|
||||
bits.b2.val[0] = vorrq_u8(bits.b2.val[0], vandq_u8(vshlq_n_u8(hbits.val[0], 2), hm));
|
||||
bits.b2.val[1] = vorrq_u8(bits.b2.val[1], vandq_u8(vshlq_n_u8(hbits.val[1], 2), hm));
|
||||
bits.b2.val[2] = vorrq_u8(bits.b2.val[2], vandq_u8(vshlq_n_u8(hbits.val[0], 1), hm));
|
||||
bits.b2.val[3] = vorrq_u8(bits.b2.val[3], vandq_u8(vshlq_n_u8(hbits.val[1], 1), hm));
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
bits.b1.val[k] = vqtbl2q_s8(values, bits.b1.val[k]);
|
||||
bits.b2.val[k] = vqtbl2q_s8(values, bits.b2.val[k]);
|
||||
}
|
||||
auto shift = vdupq_n_u8((x[i].scales[4*j+0] & 1) << 5);
|
||||
bits.b1.val[0] = vaddq_u8(shift, vorrq_u8(bits.b1.val[0], vandq_u8(vshlq_n_u8(hbits.val[0], 4), hm)));
|
||||
bits.b1.val[1] = vaddq_u8(shift, vorrq_u8(bits.b1.val[1], vandq_u8(vshlq_n_u8(hbits.val[1], 4), hm)));
|
||||
shift = vdupq_n_u8((x[i].scales[4*j+1] & 1) << 5);
|
||||
bits.b1.val[2] = vaddq_u8(shift, vorrq_u8(bits.b1.val[2], vandq_u8(vshlq_n_u8(hbits.val[0], 3), hm)));
|
||||
bits.b1.val[3] = vaddq_u8(shift, vorrq_u8(bits.b1.val[3], vandq_u8(vshlq_n_u8(hbits.val[1], 3), hm)));
|
||||
for (int k = 0; k < 4; ++k) bits.b1.val[k] = vqtbl4q_s8(values, bits.b1.val[k]);
|
||||
shift = vdupq_n_u8((x[i].scales[4*j+2] & 1) << 5);
|
||||
bits.b2.val[0] = vaddq_u8(shift, vorrq_u8(bits.b2.val[0], vandq_u8(vshlq_n_u8(hbits.val[0], 2), hm)));
|
||||
bits.b2.val[1] = vaddq_u8(shift, vorrq_u8(bits.b2.val[1], vandq_u8(vshlq_n_u8(hbits.val[1], 2), hm)));
|
||||
shift = vdupq_n_u8((x[i].scales[4*j+3] & 1) << 5);
|
||||
bits.b2.val[2] = vaddq_u8(shift, vorrq_u8(bits.b2.val[2], vandq_u8(vshlq_n_u8(hbits.val[0], 1), hm)));
|
||||
bits.b2.val[3] = vaddq_u8(shift, vorrq_u8(bits.b2.val[3], vandq_u8(vshlq_n_u8(hbits.val[1], 1), hm)));
|
||||
for (int k = 0; k < 4; ++k) bits.b2.val[k] = vqtbl4q_s8(values, bits.b2.val[k]);
|
||||
}
|
||||
|
||||
Q4bits bits;
|
||||
const int8x16x2_t values;
|
||||
const uint8x16_t hshuff = vreinterpretq_u8_u32(uint32x4_t{0x09010800, 0x0b030a02, 0x0d050c04, 0x0f070e06});
|
||||
const int8x16x4_t values;
|
||||
const uint8x16_t hm = vdupq_n_u8(0x10);
|
||||
const uint16x8_t mask = vdupq_n_u16(254);
|
||||
const int16x8_t m127 = vdupq_n_s16(-127);
|
||||
uint8x16x2_t hbits;
|
||||
uint8x16_t sas;
|
||||
|
||||
};
|
||||
|
||||
@ -13068,6 +13196,91 @@ void mul_mat_iq4_ks_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq5_ks_r4_q8_k_neon(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 m10 = vdupq_n_u8(0x10);
|
||||
auto values = vld1q_s8_x2(iq5nl_values);
|
||||
int nbl = n / QK_K;
|
||||
int8x16_t qx[8];
|
||||
int16x8x4_t iscales;
|
||||
int32x4x4_t scales;
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
int32x4_t isum[nrc_y] = {};
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
auto dptr = (const float *)((const char *)vx + ix*bx);
|
||||
auto d4 = vld1q_f32(dptr);
|
||||
const block_iq5_ks_r4 * iq5 = (const block_iq5_ks_r4 *)(dptr + 4);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
auto sas = vld1q_u8_x2(iq5[ibl].scales);
|
||||
auto scale = vandq_u8(sas.val[0], vdupq_n_u8(254));
|
||||
iscales.val[0] = vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8 (scale))), vdupq_n_s16(-127));
|
||||
iscales.val[1] = vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(scale))), vdupq_n_s16(-127));
|
||||
scale = vandq_u8(sas.val[1], vdupq_n_u8(254));
|
||||
iscales.val[2] = vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8 (scale))), vdupq_n_s16(-127));
|
||||
iscales.val[3] = vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(scale))), vdupq_n_s16(-127));
|
||||
// Adding the block shifts costs us ~9% in performance drop.
|
||||
// Is there a better way?
|
||||
sas.val[0] = vshlq_n_u8(vandq_u8(sas.val[0], vdupq_n_u8(1)), 1);
|
||||
sas.val[1] = vshlq_n_u8(vandq_u8(sas.val[1], vdupq_n_u8(1)), 1);
|
||||
{
|
||||
auto s16_1 = vmulq_s16(iscales.val[0], vmovl_u8(vget_low_u8 (sas.val[0])));
|
||||
auto s16_2 = vmulq_s16(iscales.val[1], vmovl_u8(vget_high_u8(sas.val[0])));
|
||||
auto s16_3 = vmulq_s16(iscales.val[2], vmovl_u8(vget_low_u8 (sas.val[1])));
|
||||
auto s16_4 = vmulq_s16(iscales.val[3], vmovl_u8(vget_high_u8(sas.val[1])));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto bsums = vld1q_s16_x2(q8.y[iy][ibl].bsums);
|
||||
auto bs = vpaddq_s16(bsums.val[0], bsums.val[1]);
|
||||
auto b8 = vget_low_s16(bs);
|
||||
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 = vget_high_s16(bs);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_3), b8, 0);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_3), b8, 1);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_4), b8, 2);
|
||||
isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_4), b8, 3);
|
||||
}
|
||||
}
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
scales.val[0] = vmovl_s16(vget_low_s16 (iscales.val[2*is+0]));
|
||||
scales.val[1] = vmovl_s16(vget_high_s16(iscales.val[2*is+0]));
|
||||
scales.val[2] = vmovl_s16(vget_low_s16 (iscales.val[2*is+1]));
|
||||
scales.val[3] = vmovl_s16(vget_high_s16(iscales.val[2*is+1]));
|
||||
for (int ib = 0; ib < 4; ++ib) {
|
||||
auto lbits = vld1q_u8_x4(iq5[ibl].qs + 256*is + 64*ib);
|
||||
auto hbits = vld1q_u8(iq5[ibl].qh + 64*is + 16*ib);
|
||||
qx[0] = vorrq_u8(vandq_u8(lbits.val[0], m4), vandq_u8(m10, vshlq_n_u8(hbits, 4)));
|
||||
qx[1] = vorrq_u8(vandq_u8(lbits.val[1], m4), vandq_u8(m10, vshlq_n_u8(hbits, 2)));
|
||||
qx[2] = vorrq_u8(vandq_u8(lbits.val[2], m4), vandq_u8(m10, hbits));
|
||||
qx[3] = vorrq_u8(vandq_u8(lbits.val[3], m4), vandq_u8(m10, vshrq_n_u8(hbits, 2)));
|
||||
qx[4] = vorrq_u8(vshrq_n_u8(lbits.val[0], 4), vandq_u8(m10, vshlq_n_u8(hbits, 3)));
|
||||
qx[5] = vorrq_u8(vshrq_n_u8(lbits.val[1], 4), vandq_u8(m10, vshlq_n_u8(hbits, 1)));
|
||||
qx[6] = vorrq_u8(vshrq_n_u8(lbits.val[2], 4), vandq_u8(m10, vshrq_n_u8(hbits, 1)));
|
||||
qx[7] = vorrq_u8(vshrq_n_u8(lbits.val[3], 4), vandq_u8(m10, vshrq_n_u8(hbits, 3)));
|
||||
for (int l = 0; l < 8; ++l) qx[l] = vqtbl2q_s8(values, qx[l]);
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ibl].qs+128*is+32*ib);
|
||||
auto sumi = interleaved_dotq(qx, y);
|
||||
isum[iy] = vmlaq_s32(isum[iy], scales.val[ib], sumi);
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
acc[iy] = vfmaq_f32(acc[iy], vdupq_n_f32(q8.scale(iy, ibl)), vcvtq_f32_s32(isum[iy]));
|
||||
isum[iy] = vdupq_n_s32(0);
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
info.store(ix, iy, vmulq_f32(d4, acc[iy]));
|
||||
acc[iy] = vdupq_n_f32(0.f);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_iq2_xxs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
@ -15274,6 +15487,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq5_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ5_KS_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq5_ks_r4_q8_k_neon);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0_R8:
|
||||
SET_MUL_MAT_FUNCTIONS_T(m, mul_mat_qx_r8_q8_0, Q4_0_R8_Dequantizer);
|
||||
expected_Btype = GGML_TYPE_Q8_0_X4;
|
||||
|
||||
@ -5628,7 +5628,8 @@ void quantize_row_iq5_k_r4(const float * x, void * y, int64_t k) {
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline void convert_iq5_k(const block_iq5_k& x, uint8_t * L) {
|
||||
template <typename Block>
|
||||
inline void convert_iq5_k(const Block& x, uint8_t * L) {
|
||||
const uint8_t * qs = x.qs;
|
||||
const uint8_t * qh = x.qh;
|
||||
int shift = 0;
|
||||
@ -5751,6 +5752,126 @@ void vec_dot_iq5_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t
|
||||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq5_ks_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq5_ks_r4_ref(const float * x, block_iq5_ks_r4 * y, int64_t k) {
|
||||
quantize_iq5_ks_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq5_ks_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq5_ks_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
static void repack_iq5_ks(int nrows, int n_per_row, const block_iq5_ks * x, block_iq5_ks_r4 * y, [[maybe_unused]] bool online) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
auto row_size = ggml_row_size(GGML_TYPE_IQ5_KS, n_per_row);
|
||||
int nblock = n_per_row/QK_K;
|
||||
const block_iq5_ks * x4[4];
|
||||
uint8_t L[QK_K];
|
||||
char * cy = (char *)y;
|
||||
const char * cx = (const char *)x;
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
float * dptr = (float *)cy;
|
||||
block_iq5_ks_r4 * y = (block_iq5_ks_r4 *)(dptr + 4);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
auto dk = (const float *)(cx + k*row_size);
|
||||
dptr[k] = dk[0];
|
||||
x4[k] = (const block_iq5_ks *)(dk + 1);
|
||||
}
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
convert_iq5_k(x4[k][ibl], L);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
y[ibl].scales[4*ib+k] = x4[k][ibl].scales[ib];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y[ibl].qs[64*ib+4*k+i+ 0] = (L[32*ib+i+ 0] & 0xf) | ((L[32*ib+i+ 8] & 0xf) << 4); // 0....3 + 8...11 from each row
|
||||
y[ibl].qs[64*ib+4*k+i+16] = (L[32*ib+i+16] & 0xf) | ((L[32*ib+i+24] & 0xf) << 4); // 16...19 + 24...27 from each row
|
||||
y[ibl].qs[64*ib+4*k+i+32] = (L[32*ib+i+ 4] & 0xf) | ((L[32*ib+i+12] & 0xf) << 4); // 4....7 + 12...15 from each row
|
||||
y[ibl].qs[64*ib+4*k+i+48] = (L[32*ib+i+20] & 0xf) | ((L[32*ib+i+28] & 0xf) << 4); // 20...23 + 28...31 from each row
|
||||
y[ibl].qh[16*ib+4*k+i ] = ((L[32*ib+i+ 0] >> 4) << 0) | ((L[32*ib+i+ 8] >> 4) << 1) | ((L[32*ib+i+16] >> 4) << 2) | ((L[32*ib+i+24] >> 4) << 3)
|
||||
| ((L[32*ib+i+ 4] >> 4) << 4) | ((L[32*ib+i+12] >> 4) << 5) | ((L[32*ib+i+20] >> 4) << 6) | ((L[32*ib+i+28] >> 4) << 7);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
cx += 4*row_size;
|
||||
cy += 4*row_size;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq5_ks_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_IQ5_KS, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq5_ks(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq5_ks(4, n_per_row, (const block_iq5_ks *)qtmp.data(), (block_iq5_ks_r4 *)qcur, false);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq5_ks_r4(const block_iq5_ks_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};
|
||||
//auto row_size = ggml_row_size(GGML_TYPE_IQ5_KS, n_per_row);
|
||||
int nblock = n_per_row/QK_K;
|
||||
const float * dptr = (const float *)x;
|
||||
x = (const block_iq5_ks_r4 *)(dptr + 4);
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
const float d = dptr[k];
|
||||
//if (!isfinite(d)) {
|
||||
// printf("Oops: d = %g for ibl = %d, k = %d\n", d, ibl, k); exit(1);
|
||||
//}
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
uint8_t sc = x[ibl].scales[4*ib+k];
|
||||
float dl = d * ((sc & 254) - 127);
|
||||
//if (!isfinite(dl)) {
|
||||
// printf("Oops: dl = %g for ibl = %d, k = %d, ib = %d, d = %g, sc = %u\n", dl, ibl, k, ib, d, sc); exit(1);
|
||||
//}
|
||||
auto values = iq5nl_values + ((sc & 1) << 5);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y4[k][QK_K*ibl+32*ib+i+ 0] = dl * values[(x[ibl].qs[64*ib+4*k+i+ 0] & 0xf) | (((x[ibl].qh[16*ib+4*k+i] >> 0) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 8] = dl * values[(x[ibl].qs[64*ib+4*k+i+ 0] >> 4) | (((x[ibl].qh[16*ib+4*k+i] >> 1) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+16] = dl * values[(x[ibl].qs[64*ib+4*k+i+16] & 0xf) | (((x[ibl].qh[16*ib+4*k+i] >> 2) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+24] = dl * values[(x[ibl].qs[64*ib+4*k+i+16] >> 4) | (((x[ibl].qh[16*ib+4*k+i] >> 3) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+ 4] = dl * values[(x[ibl].qs[64*ib+4*k+i+32] & 0xf) | (((x[ibl].qh[16*ib+4*k+i] >> 4) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+12] = dl * values[(x[ibl].qs[64*ib+4*k+i+32] >> 4) | (((x[ibl].qh[16*ib+4*k+i] >> 5) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+20] = dl * values[(x[ibl].qs[64*ib+4*k+i+48] & 0xf) | (((x[ibl].qh[16*ib+4*k+i] >> 6) & 1) << 4)];
|
||||
y4[k][QK_K*ibl+32*ib+i+28] = dl * values[(x[ibl].qs[64*ib+4*k+i+48] >> 4) | (((x[ibl].qh[16*ib+4*k+i] >> 7) & 1) << 4)];
|
||||
}
|
||||
//for (int i = 0; i < 32; ++i) {
|
||||
// if (!isfinite(y4[k][QK_K*ibl+32*ib+i])) {
|
||||
// printf("Oops: y4[%d][%d, %d, %d] = %g\n", k, ibl, ib, i, y4[k][QK_K*ibl+32*ib+i]);
|
||||
// printf("d = %g, dl = %g\n", d, dl);
|
||||
// exit(1);
|
||||
// }
|
||||
//}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq5_ks_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_IQ5_KS_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);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= q8_k_r8
|
||||
//
|
||||
@ -7182,6 +7303,7 @@ const Repack * get_repack_info(ggml_type type) {
|
||||
{ GGML_TYPE_IQ5_K, { GGML_TYPE_IQ5_K_R4, 4, (Repack::repack_func)repack_iq5_k} },
|
||||
{ GGML_TYPE_IQ4_XS, { GGML_TYPE_IQ4_XS_R8, 8, (Repack::repack_func)repack_iq4_xs} },
|
||||
{ GGML_TYPE_IQ4_KS, { GGML_TYPE_IQ4_KS_R4, 4, (Repack::repack_func)repack_iq4_ks} },
|
||||
{ GGML_TYPE_IQ5_KS, { GGML_TYPE_IQ5_KS_R4, 4, (Repack::repack_func)repack_iq5_ks} },
|
||||
{ GGML_TYPE_IQ4_NL, { GGML_TYPE_IQ4_NL_R4, 4, (Repack::repack_func)repack_iq4_nl} },
|
||||
{ GGML_TYPE_IQ2_BN, { GGML_TYPE_IQ2_BN_R4, 4, (Repack::repack_func)repack_iq2_bn} },
|
||||
{ GGML_TYPE_IQ2_XXS,{ GGML_TYPE_IQ2_XXS_R4,4, (Repack::repack_func)repack_iq2_xxs} },
|
||||
|
||||
@ -181,6 +181,12 @@ size_t quantize_iq4_ks_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT
|
||||
void dequantize_row_iq4_ks_r4(const block_iq4_ks_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq4_ks_r4_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
|
||||
|
||||
void quantize_row_iq5_ks_r4_ref(const float * GGML_RESTRICT x, block_iq5_ks_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq5_ks_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq5_ks_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void dequantize_row_iq5_ks_r4(const block_iq5_ks_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq5_ks_r4_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
|
||||
|
||||
void quantize_row_iq2_xxs_r4_ref(const float * GGML_RESTRICT x, block_iq2_xxs_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq2_xxs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq2_xxs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
|
||||
@ -220,6 +220,7 @@ extern "C" {
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_K_R4 = 340, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ5_K_R4 = 341, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_KS_R4 = 345, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ5_KS_R4 = 350, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q8_KV_R8 = 398, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q8_K_R8 = 399, // except 1d tensors
|
||||
|
||||
|
||||
@ -4372,6 +4372,7 @@ struct llama_model_loader {
|
||||
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
|
||||
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
|
||||
case GGML_TYPE_IQ4_KS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS_R4; break;
|
||||
case GGML_TYPE_IQ5_KS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ5_KS_R4; break;
|
||||
case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
|
||||
case GGML_TYPE_IQ5_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ5_KS; break;
|
||||
case GGML_TYPE_IQ2_K: ftype = LLAMA_FTYPE_MOSTLY_IQ2_K; break;
|
||||
@ -5109,6 +5110,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS_R4:return "IQ4_KS_R4 - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ5_KS_R4:return "IQ5_KS_R4 - 5.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: return "IQ4_KSS - 4.0 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ5_KS: return "IQ5_KS - 5.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K: return "IQ2_K - 2.375 bpw";
|
||||
@ -18621,7 +18623,8 @@ static ggml_type change_type_if_necessary(ggml_type new_type, int nx, int ny) {
|
||||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4||
|
||||
new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_R4 ||
|
||||
new_type == GGML_TYPE_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4 || new_type == GGML_TYPE_IQ2_XS_R4 ||
|
||||
new_type == GGML_TYPE_IQ2_S_R4|| new_type == GGML_TYPE_IQ3_S_R4|| new_type == GGML_TYPE_IQ5_KS) {
|
||||
new_type == GGML_TYPE_IQ2_S_R4|| new_type == GGML_TYPE_IQ3_S_R4||
|
||||
new_type == GGML_TYPE_IQ5_KS || new_type == GGML_TYPE_IQ5_KS_R4) {
|
||||
if (nx % QK_K != 0) {
|
||||
LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
|
||||
convert_incompatible_tensor = true;
|
||||
@ -18664,6 +18667,7 @@ static ggml_type change_type_if_necessary(ggml_type new_type, int nx, int ny) {
|
||||
case GGML_TYPE_IQ4_K_R4:
|
||||
case GGML_TYPE_Q4_K_R4:
|
||||
case GGML_TYPE_IQ5_KS:
|
||||
case GGML_TYPE_IQ5_KS_R4:
|
||||
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
|
||||
case GGML_TYPE_IQ5_K:
|
||||
case GGML_TYPE_IQ5_K_R4:
|
||||
@ -18708,6 +18712,7 @@ static std::pair<ggml_type, int> interleaved_properties(ggml_type type) {
|
||||
{ GGML_TYPE_IQ3_K_R4, { GGML_TYPE_IQ3_K, 4} },
|
||||
{ GGML_TYPE_IQ4_K_R4, { GGML_TYPE_IQ4_K, 4} },
|
||||
{ GGML_TYPE_IQ4_KS_R4, { GGML_TYPE_IQ4_KS, 4} },
|
||||
{ GGML_TYPE_IQ5_KS_R4, { GGML_TYPE_IQ5_KS, 4} },
|
||||
{ GGML_TYPE_IQ5_K_R4, { GGML_TYPE_IQ5_K, 4} },
|
||||
{ GGML_TYPE_Q8_KV_R8, { GGML_TYPE_Q8_KV, 8} },
|
||||
{ GGML_TYPE_Q8_K_R8, { GGML_TYPE_Q8_K, 8} },
|
||||
@ -19254,6 +19259,7 @@ static llama_ftype repacked_ftype(llama_ftype ftype) {
|
||||
{ LLAMA_FTYPE_MOSTLY_IQ4_K, LLAMA_FTYPE_MOSTLY_IQ4_K_R4 },
|
||||
{ LLAMA_FTYPE_MOSTLY_IQ5_K, LLAMA_FTYPE_MOSTLY_IQ5_K_R4 },
|
||||
{ LLAMA_FTYPE_MOSTLY_IQ4_KS, LLAMA_FTYPE_MOSTLY_IQ4_KS_R4 },
|
||||
{ LLAMA_FTYPE_MOSTLY_IQ5_KS, LLAMA_FTYPE_MOSTLY_IQ5_KS_R4 },
|
||||
{ LLAMA_FTYPE_MOSTLY_Q8_KV, LLAMA_FTYPE_MOSTLY_Q8_KV_R8 },
|
||||
};
|
||||
if (auto it = k_map.find(ftype); it != k_map.end()) return it->second;
|
||||
@ -19323,6 +19329,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: default_type = GGML_TYPE_IQ4_KS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS_R4:default_type = GGML_TYPE_IQ4_KS_R4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ5_KS_R4:default_type = GGML_TYPE_IQ5_KS_R4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: default_type = GGML_TYPE_IQ4_KSS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ5_KS: default_type = GGML_TYPE_IQ5_KS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K: default_type = GGML_TYPE_IQ2_K; break;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user