IQ3_XXS_R4 (#153)
* iq3_xxs_r4: 1st shot on Zen4 PP-512: 107 t/s -> 137 t/s TG-128(1 thread): 2.64 t/s -> 3.44 t/s * iq4_xxs_r4: WIP * iq4_xxs_r4: 1st shot at AVX2 Note: there is a bug in the AVX2 implementation for nrc_y = 1 for IQ quants with blocks of 32. I have fixed it for now by using the nrc_y > 1 implementation (which works) also for nrc_y = 1. * iq3_xxs_r4: NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
GitHub
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8352f12275
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310f8b1d22
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@ -34,6 +34,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "Q2_K_R4", LLAMA_FTYPE_MOSTLY_Q2_K_R4, "Q2_K_S repacked", },
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{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
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{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", },
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{ "IQ3_XXS_R4",LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4,"IQ3_XXS repacked", },
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{ "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", },
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{ "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", },
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{ "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
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@ -418,6 +418,7 @@ extern "C" {
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GGML_TYPE_Q4_K_R4 = 212,
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GGML_TYPE_Q5_K_R4 = 213,
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GGML_TYPE_Q6_K_R4 = 214,
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GGML_TYPE_IQ3_XXS_R4= 218,
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GGML_TYPE_IQ4_NL_R4 = 220,
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GGML_TYPE_IQ4_XS_R4 = 223,
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GGML_TYPE_BF16_R16 = 230,
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@ -496,6 +497,7 @@ extern "C" {
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GGML_FTYPE_MOSTLY_Q4_K_R4 = 212, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q5_K_R4 = 215, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q6_K_R4 = 214, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ3_XXS_R4= 217, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_NL_R4 = 219, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_XS_R4 = 222, // except 1d tensors
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GGML_FTYPE_MOSTLY_BF16_R16 = 224, // except 1d tensors
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@ -423,6 +423,13 @@ typedef struct {
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} block_iq3_xxs;
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static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
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typedef struct {
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ggml_half d[4];
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uint8_t sas[QK_K/2];
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uint8_t qs[QK_K];
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} block_iq3_xxs_r4;
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static_assert(sizeof(block_iq3_xxs_r4) == 4*sizeof(block_iq3_xxs), "wrong iq3_xxs_r4 block size/padding");
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// 3.4375 bpw
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#define IQ3S_N_SCALE QK_K/64
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typedef struct {
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@ -15198,6 +15198,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
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case GGML_TYPE_IQ4_KSS: break;
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case GGML_TYPE_IQ4_NL_R4: break;
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case GGML_TYPE_IQ4_XS_R4: break;
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case GGML_TYPE_IQ3_XXS_R4: break;
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case GGML_TYPE_Q4_0_R4: break;
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case GGML_TYPE_Q5_0_R4: break;
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case GGML_TYPE_Q6_0_R4: break;
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@ -1031,6 +1031,19 @@ 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_XXS_R4] = {
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.type_name = "iq3_xxs_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq3_xxs),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq3_xxs_r4,
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.from_float = quantize_row_iq3_xxs_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq3_xxs_r4_ref,
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.vec_dot = vec_dot_iq3_xxs_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_IQ3_S] = {
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.type_name = "iq3_s",
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.blck_size = QK_K,
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@ -4200,6 +4213,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break;
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case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
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case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
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case GGML_FTYPE_MOSTLY_IQ3_XXS_R4: wtype = GGML_TYPE_IQ3_XXS_R4;break;
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case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
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case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break;
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case GGML_FTYPE_MOSTLY_IQ1_BN: wtype = GGML_TYPE_IQ1_BN; break;
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@ -10741,6 +10755,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -11201,6 +11216,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -11358,6 +11374,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -14561,6 +14578,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -14958,6 +14976,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -15249,6 +15268,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -15869,6 +15889,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ1_BN:
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@ -22642,6 +22663,7 @@ void ggml_quantize_init(enum ggml_type type) {
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case GGML_TYPE_IQ2_S:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break;
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case GGML_TYPE_IQ3_XXS_R4:
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case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
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case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
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default: // nothing
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@ -22715,6 +22737,7 @@ size_t ggml_quantize_chunk(
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case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ2_XS: result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ3_XXS_R4:result = quantize_iq3_xxs_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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@ -187,6 +187,7 @@ 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_IQ3_XXS_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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case GGML_TYPE_BF16_R16: return 16;
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@ -3212,6 +3213,97 @@ static void mul_mat_iq4_ks_r4_q8_k(int n, const void * vx, size_t bx, const Data
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}
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}
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template <int nrc_y>
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static void mul_mat_iq3_xxs_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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int nbl = n / QK_K;
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#ifndef HAVE_FANCY_SIMD
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auto smask = _mm256_set1_epi64x(0x8040201008040201);
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auto sign_shuffle = _mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, 0x0101010101010101, 0x0000000000000000);
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auto m4 = _mm256_set1_epi8(4);
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auto m1 = _mm256_set1_epi16(1);
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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 iq3 = (const block_iq3_xxs_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_mul_ps(_mm_set1_ps(0.25f), _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq3[ibl].d))); // TODO: absorb the 0.25 factor into d when quantizing/repacking
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auto d4 = _mm256_set_m128(dl, dl);
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for (int ib = 0; ib < QK_K/32; ++ib) {
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qx[0] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+ 7]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 6]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 5]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 4]],
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iq3xxs_grid[iq3[ibl].qs[32*ib+ 3]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 2]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 1]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 0]]);
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qx[1] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+15]], iq3xxs_grid[iq3[ibl].qs[32*ib+14]], iq3xxs_grid[iq3[ibl].qs[32*ib+13]], iq3xxs_grid[iq3[ibl].qs[32*ib+12]],
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iq3xxs_grid[iq3[ibl].qs[32*ib+11]], iq3xxs_grid[iq3[ibl].qs[32*ib+10]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 9]], iq3xxs_grid[iq3[ibl].qs[32*ib+ 8]]);
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qx[2] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+23]], iq3xxs_grid[iq3[ibl].qs[32*ib+22]], iq3xxs_grid[iq3[ibl].qs[32*ib+21]], iq3xxs_grid[iq3[ibl].qs[32*ib+20]],
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iq3xxs_grid[iq3[ibl].qs[32*ib+19]], iq3xxs_grid[iq3[ibl].qs[32*ib+18]], iq3xxs_grid[iq3[ibl].qs[32*ib+17]], iq3xxs_grid[iq3[ibl].qs[32*ib+16]]);
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qx[3] = _mm256_set_epi32(iq3xxs_grid[iq3[ibl].qs[32*ib+31]], iq3xxs_grid[iq3[ibl].qs[32*ib+30]], iq3xxs_grid[iq3[ibl].qs[32*ib+29]], iq3xxs_grid[iq3[ibl].qs[32*ib+28]],
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iq3xxs_grid[iq3[ibl].qs[32*ib+27]], iq3xxs_grid[iq3[ibl].qs[32*ib+26]], iq3xxs_grid[iq3[ibl].qs[32*ib+25]], iq3xxs_grid[iq3[ibl].qs[32*ib+24]]);
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auto sas = _mm_loadu_si128((const __m128i *)iq3[ibl].sas + ib);
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auto scales = _mm_and_si128(sas, _mm_set1_epi8(1));
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#ifdef HAVE_FANCY_SIMD
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scales = _mm_dpbusd_epi32(_mm_set1_epi32(1), scales, _mm_set1_epi32(0x10080402));
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#else
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scales = _mm_maddubs_epi16(scales, _mm_set1_epi32(0x10080402));
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scales = _mm_add_epi32(_mm_madd_epi16(_mm_set1_epi16(1), scales), _mm_set1_epi32(1));
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//auto t1 = _mm_or_si128(_mm_and_si128(scales, _mm_set1_epi32(0x00000001)), _mm_srli_epi32(_mm_and_si128(scales, _mm_set1_epi32(0x00000100)), 7));
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//auto t2 = _mm_or_si128(_mm_srli_epi32(_mm_and_si128(scales, _mm_set1_epi32(0x00010000)), 14), _mm_srli_epi32(_mm_and_si128(scales, _mm_set1_epi32(0x01000000)), 21));
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//scales = _mm_or_si128(_mm_slli_epi32(_mm_or_si128(t1, t2), 1), _mm_set1_epi32(1));
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#endif
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auto scales32 = MM256_SET_M128I(scales, scales);
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auto signs128 = _mm_and_si128(sas, _mm_set1_epi8(-2)); // 0xfe = -2 as signed. Needed to shutup compiler warning.
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signs128 = _mm_xor_si128(signs128, _mm_srli_epi16(signs128, 1));
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#ifdef HAVE_FANCY_SIMD
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auto mask = (const __mmask32 *)&signs128;
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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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auto sumi1 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[0], _mm256_mask_sub_epi8(y, mask[0], _mm256_setzero_si256(), y));
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auto sumi2 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[1], _mm256_mask_sub_epi8(y, mask[1], _mm256_setzero_si256(), y));
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auto sumi3 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[2], _mm256_mask_sub_epi8(y, mask[2], _mm256_setzero_si256(), y));
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auto sumi4 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), qx[3], _mm256_mask_sub_epi8(y, mask[3], _mm256_setzero_si256(), y));
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auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi1, sumi2), _mm256_unpackhi_epi32(sumi1, sumi2)); // 0,1, 0,1, 0,1, 0,1
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auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi3, sumi4), _mm256_unpackhi_epi32(sumi3, sumi4)); // 2,3, 2,3, 2,3, 2,3
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auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34)); // 0,1,2,3, 0,1,2,3
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isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales32, sumi));
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}
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#else
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auto signs = MM256_SET_M128I(signs128, signs128);
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auto shuffle = sign_shuffle;
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auto s1 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
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shuffle = _mm256_add_epi8(shuffle, m4);
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auto s2 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
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shuffle = _mm256_add_epi8(shuffle, m4);
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auto s3 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
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shuffle = _mm256_add_epi8(shuffle, m4);
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auto s4 = _mm256_or_si256(_mm256_cmpeq_epi8(_mm256_and_si256(_mm256_shuffle_epi8(signs, shuffle), smask), smask), _mm256_set1_epi8(1));
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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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auto sumi1 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[0], _mm256_sign_epi8(y, s1)));
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auto sumi2 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[1], _mm256_sign_epi8(y, s2)));
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auto sumi3 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[2], _mm256_sign_epi8(y, s3)));
|
||||
auto sumi4 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4)));
|
||||
auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi1, sumi2), _mm256_unpackhi_epi32(sumi1, sumi2)); // 0,1, 0,1, 0,1, 0,1
|
||||
auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(sumi3, sumi4), _mm256_unpackhi_epi32(sumi3, sumi4)); // 2,3, 2,3, 2,3, 2,3
|
||||
auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34)); // 0,1,2,3, 0,1,2,3
|
||||
isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales32, sumi));
|
||||
}
|
||||
#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]);
|
||||
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));
|
||||
info.store(ix, iy, sum);
|
||||
acc[iy] = _mm256_setzero_ps();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_q4_k_r4_q8_k_avx2(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
|
|
@ -4610,15 +4702,17 @@ inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, cons
|
|||
}
|
||||
}
|
||||
|
||||
// TODO: find the bug that causes this to be called without HAVE_FANCY_SIMD, which triggers
|
||||
// writing 4 vvalues into scales, which is of size 2.
|
||||
inline void set_scales_8_iq(int j, const __m256i& all_scales, __m256i * scales) {
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
//#ifdef HAVE_FANCY_SIMD
|
||||
auto shuffle = j == 0 ? _mm256_set_epi64x(0x0302030203020302, 0x0100010001000100, 0x0302030203020302, 0x0100010001000100)
|
||||
: _mm256_set_epi64x(0x0b0a0b0a0b0a0b0a, 0x0908090809080908, 0x0b0a0b0a0b0a0b0a, 0x0908090809080908);
|
||||
scales[0] = _mm256_shuffle_epi8(all_scales, shuffle);
|
||||
scales[1] = _mm256_shuffle_epi8(all_scales, _mm256_add_epi8(shuffle, _mm256_set1_epi8(4)));
|
||||
#else
|
||||
set_scales_8(all_scales, j, scales);
|
||||
#endif
|
||||
//#else
|
||||
// set_scales_8(all_scales, j, scales);
|
||||
//#endif
|
||||
}
|
||||
|
||||
inline void set_scales_16_iq(const __m256i& all_scales, __m256i * scales) {
|
||||
|
|
@ -5003,11 +5097,15 @@ IQK_NOINLINE void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const
|
|||
template <typename Dequantizer, int nrc_y>
|
||||
static void mul_mat_qX_K_q8_K_IQ(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
assert(n % QK_K == 0);
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
if constexpr (nrc_y == 1) {
|
||||
mul_mat_qX_K_q8_K_IQ_1<Dequantizer>(n, vx, bx, info, nrc_x);
|
||||
} else {
|
||||
mul_mat_qX_K_q8_K_IQ_N<Dequantizer, nrc_y>(n, vx, bx, info, nrc_x);
|
||||
}
|
||||
#else
|
||||
mul_mat_qX_K_q8_K_IQ_N<Dequantizer, nrc_y>(n, vx, bx, info, nrc_x);
|
||||
#endif
|
||||
}
|
||||
|
||||
//#ifdef HAVE_FANCY_SIMD
|
||||
|
|
@ -6604,6 +6702,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
mm.funcs[7] = mul_mat_iq4_ks_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K32;
|
||||
break;
|
||||
case GGML_TYPE_IQ3_XXS_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq3_xxs_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq3_xxs_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq3_xxs_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq3_xxs_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq3_xxs_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq3_xxs_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq3_xxs_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq3_xxs_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_q2_k_r4_q8_k<1>;
|
||||
|
|
@ -9475,6 +9585,56 @@ void mul_mat_iq4_ks_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
|
|||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_iq3_xxs_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);
|
||||
int nbl = n / QK_K;
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
int32x4_t isum[nrc_y] = {};
|
||||
int8x16_t qx[8];
|
||||
SignHelper sh;
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
auto iq3 = (const block_iq3_xxs_r4 *)((const char *)vx + (ix+0)*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
|
||||
auto d4 = vmulq_f32(vdupq_n_f32(0.25f), vcvt_f32_f16(vld1_f16((const float16_t *)iq3[ibl].d)));
|
||||
auto qs = iq3[ibl].qs;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto sas = vld1q_u8(iq3[ibl].sas + 16*ib);
|
||||
auto scale_bits = vandq_u8(sas, vdupq_n_u8(1));
|
||||
auto scales = ggml_vdotq_s32(vdupq_n_s32(1), scale_bits, vreinterpretq_s8_u32(vdupq_n_u32(0x10080402)));
|
||||
auto signs128 = vandq_u8(sas, vdupq_n_u8(254));
|
||||
signs128 = veorq_u8(signs128, vshrq_n_u8(signs128, 1));
|
||||
sh.init();
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
qx[i] = vreinterpretq_s8_u32(uint32x4_t{iq3xxs_grid[qs[4*i+0]], iq3xxs_grid[qs[4*i+1]], iq3xxs_grid[qs[4*i+2]], iq3xxs_grid[qs[4*i+3]]});
|
||||
sh.apply_signs_1((uint8x16_t *)qx+i, signs128);
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ibl].qs + 32*ib);
|
||||
auto sumi1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[0], y.val[0]), qx[1], y.val[1]);
|
||||
auto sumi2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[2], y.val[0]), qx[3], y.val[1]);
|
||||
auto sumi3 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[4], y.val[0]), qx[5], y.val[1]);
|
||||
auto sumi4 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[6], y.val[0]), qx[7], y.val[1]);
|
||||
auto sumi12 = vpaddq_s32(sumi1, sumi2);
|
||||
auto sumi34 = vpaddq_s32(sumi3, sumi4);
|
||||
auto sumi = vpaddq_s32(sumi12, sumi34);
|
||||
isum[iy] = vmlaq_s32(isum[iy], scales, sumi);
|
||||
}
|
||||
qs += 32;
|
||||
}
|
||||
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]));
|
||||
isum[iy] = vdupq_n_s32(0);
|
||||
}
|
||||
}
|
||||
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, int k_shift>
|
||||
inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8x16x4_t& i8scales, uint8x16_t extra,
|
||||
int32x4_t * isum) {
|
||||
|
|
@ -10772,6 +10932,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
|||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq4_ks_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ3_XXS_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq3_xxs_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_Q2_K_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_q2_k_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
|
|
|
|||
|
|
@ -5303,6 +5303,119 @@ struct Repack {
|
|||
};
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq3_xxs_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq3_xxs_r4_ref(const float * x, block_iq3_xxs_r4 * y, int64_t k) {
|
||||
quantize_iq3_xxs_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq3_xxs_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq3_xxs_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
namespace {
|
||||
}
|
||||
|
||||
static void repack_iq3_xxs(int nrows, int n_per_row, const block_iq3_xxs * x, block_iq3_xxs_r4 * y) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
static uint8_t k_table[128] = {
|
||||
0x00, 0x7f, 0x7e, 0x01, 0x7c, 0x03, 0x02, 0x7d, 0x78, 0x07, 0x06, 0x79, 0x04, 0x7b, 0x7a, 0x05,
|
||||
0x70, 0x0f, 0x0e, 0x71, 0x0c, 0x73, 0x72, 0x0d, 0x08, 0x77, 0x76, 0x09, 0x74, 0x0b, 0x0a, 0x75,
|
||||
0x60, 0x1f, 0x1e, 0x61, 0x1c, 0x63, 0x62, 0x1d, 0x18, 0x67, 0x66, 0x19, 0x64, 0x1b, 0x1a, 0x65,
|
||||
0x10, 0x6f, 0x6e, 0x11, 0x6c, 0x13, 0x12, 0x6d, 0x68, 0x17, 0x16, 0x69, 0x14, 0x6b, 0x6a, 0x15,
|
||||
0x40, 0x3f, 0x3e, 0x41, 0x3c, 0x43, 0x42, 0x3d, 0x38, 0x47, 0x46, 0x39, 0x44, 0x3b, 0x3a, 0x45,
|
||||
0x30, 0x4f, 0x4e, 0x31, 0x4c, 0x33, 0x32, 0x4d, 0x48, 0x37, 0x36, 0x49, 0x34, 0x4b, 0x4a, 0x35,
|
||||
0x20, 0x5f, 0x5e, 0x21, 0x5c, 0x23, 0x22, 0x5d, 0x58, 0x27, 0x26, 0x59, 0x24, 0x5b, 0x5a, 0x25,
|
||||
0x50, 0x2f, 0x2e, 0x51, 0x2c, 0x53, 0x52, 0x2d, 0x28, 0x57, 0x56, 0x29, 0x54, 0x2b, 0x2a, 0x55,
|
||||
};
|
||||
int nblock = n_per_row/QK_K;
|
||||
const block_iq3_xxs * x4[4];
|
||||
uint32_t aux32;
|
||||
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) {
|
||||
auto ysas = (uint32_t *)y[ibl].sas;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k] = x4[k][ibl].d;
|
||||
auto xsas = x4[k][ibl].qs + QK_K/4;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
y[ibl].qs[32*ib+8*k+i] = x4[k][ibl].qs[8*ib+i];
|
||||
}
|
||||
std::memcpy(&aux32, xsas + 4*ib, 4);
|
||||
uint8_t scale = aux32 >> 28;
|
||||
uint8_t s1 = (k_table[(aux32 >> 0) & 127] << 1) | ((scale >> 0) & 1);
|
||||
uint8_t s2 = (k_table[(aux32 >> 7) & 127] << 1) | ((scale >> 1) & 1);
|
||||
uint8_t s3 = (k_table[(aux32 >> 14) & 127] << 1) | ((scale >> 2) & 1);
|
||||
uint8_t s4 = (k_table[(aux32 >> 21) & 127] << 1) | ((scale >> 3) & 1);
|
||||
aux32 = uint32_t(s1) | (uint32_t(s2) << 8) | (uint32_t(s3) << 16) | (uint32_t(s4) << 24);
|
||||
ysas[4*ib+k] = aux32;
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq3_xxs_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_XXS, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq3_xxs(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq3_xxs(4, n_per_row, (const block_iq3_xxs *)qtmp.data(), (block_iq3_xxs_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq3_xxs_r4(const block_iq3_xxs_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;
|
||||
uint32_t s32;
|
||||
const uint8_t * s8 = (const uint8_t *)&s32;
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
const uint32_t * sas = (const uint32_t *)x[ibl].sas;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
const float d = 0.25f*GGML_FP16_TO_FP32(x[ibl].d[k]);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
uint32_t aux32 = sas[4*ib+k];
|
||||
s32 = aux32 & 0x01010101;
|
||||
uint8_t scale = s8[0] | (s8[1] << 1) | (s8[2] << 2) | (s8[3] << 3);
|
||||
float dl = d*(2*scale+1);
|
||||
aux32 &= 0xfefefefe;
|
||||
aux32 ^= (aux32 >> 1);
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
auto val = (const int8_t *)(iq3xxs_grid + x[ibl].qs[32*ib+8*k+i]);
|
||||
for (int j = 0; j < 4; ++j) y4[k][QK_K*ibl+32*ib+4*i+j] = dl * val[j] * (aux32 & (1 << j) ? -1 : 1);
|
||||
aux32 >>= 4;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq3_xxs_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_XXS_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);
|
||||
}
|
||||
|
||||
void iqk_repack_tensor(struct ggml_tensor * tensor) {
|
||||
constexpr int kChunk = 8;
|
||||
if (!tensor) return;
|
||||
|
|
@ -5313,7 +5426,9 @@ void iqk_repack_tensor(struct ggml_tensor * tensor) {
|
|||
{ GGML_TYPE_IQ2_K, { GGML_TYPE_IQ2_K_R4, 4, (Repack::repack_func)repack_iq2_k} },
|
||||
{ GGML_TYPE_IQ3_K, { GGML_TYPE_IQ3_K_R4, 4, (Repack::repack_func)repack_iq3_k} },
|
||||
{ GGML_TYPE_IQ4_K, { GGML_TYPE_IQ4_K_R4, 4, (Repack::repack_func)repack_iq4_k} },
|
||||
{ GGML_TYPE_IQ5_K, { GGML_TYPE_IQ5_K_R4, 4, (Repack::repack_func)repack_iq5_k} },
|
||||
{ GGML_TYPE_IQ4_XS, { GGML_TYPE_IQ4_XS_R4, 4, (Repack::repack_func)repack_iq4_xs} },
|
||||
{ GGML_TYPE_IQ4_KS, { GGML_TYPE_IQ4_KS_R4, 4, (Repack::repack_func)repack_iq4_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_Q2_K, { GGML_TYPE_Q2_K_R4, 4, (Repack::repack_func)repack_q2_k} },
|
||||
|
|
|
|||
|
|
@ -169,6 +169,12 @@ 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_iq3_xxs_r4_ref(const float * GGML_RESTRICT x, block_iq3_xxs_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq3_xxs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq3_xxs_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_xxs_r4(const block_iq3_xxs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq3_xxs_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);
|
||||
|
|
|
|||
|
|
@ -188,6 +188,7 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q4_K_R4 = 214, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_K_R4 = 216, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_K_R4 = 218, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 = 223, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 = 225, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 = 230, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_0_R4 = 335, // except 1d tensors
|
||||
|
|
|
|||
|
|
@ -3854,6 +3854,7 @@ struct llama_model_loader {
|
|||
case GGML_TYPE_IQ2_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KS; break;
|
||||
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
|
||||
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
|
||||
case GGML_TYPE_IQ3_XXS_R4: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4; break;
|
||||
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
|
||||
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
|
||||
case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break;
|
||||
|
|
@ -4583,6 +4584,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return "IQ3_XXS - 3.0625 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: return "IQ3_XXS_R4 - 3.0625 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_S: return "IQ1_S - 1.5625 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
|
||||
|
|
@ -15792,7 +15794,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_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 ||
|
||||
|
|
@ -15818,6 +15821,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
||||
new_type = GGML_TYPE_IQ3_S;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) {
|
||||
new_type = GGML_TYPE_IQ3_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_BN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_BN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_BN_R4) {
|
||||
new_type = GGML_TYPE_IQ4_NL;
|
||||
}
|
||||
|
|
@ -15930,6 +15936,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ3_K
|
||||
: !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K_R4 : qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ3_K_R4
|
||||
: !qs.has_imatrix ? GGML_TYPE_IQ3_K_R4 : GGML_TYPE_IQ3_XXS_R4;
|
||||
}
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ4_K;
|
||||
}
|
||||
|
|
@ -15984,6 +15994,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
}
|
||||
else if (qs.model.hparams.n_gqa() >= 4) {
|
||||
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
|
||||
else if (new_type == GGML_TYPE_Q2_K_R4 || new_type == GGML_TYPE_IQ3_XXS_R4) new_type = GGML_TYPE_IQ3_K_R4;
|
||||
else if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_IQ3_S ) new_type = GGML_TYPE_Q4_K;
|
||||
else if (new_type == GGML_TYPE_Q3_K_R4) new_type = GGML_TYPE_Q4_K_R4;
|
||||
else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K;
|
||||
|
|
@ -16003,7 +16014,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
|
||||
new_type = GGML_TYPE_IQ3_XXS;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) {
|
||||
new_type = GGML_TYPE_IQ2_S;
|
||||
}
|
||||
} else if (name.find("attn_q.weight") != std::string::npos) {
|
||||
|
|
@ -16011,7 +16022,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
|
||||
new_type = GGML_TYPE_IQ3_XXS;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) {
|
||||
new_type = GGML_TYPE_IQ2_S;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
|
||||
|
|
@ -16032,6 +16043,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
|
||||
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 && !qs.has_imatrix) {
|
||||
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K_R4 : GGML_TYPE_IQ3_K_R4;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
||||
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
|
||||
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
|
||||
|
|
@ -16093,12 +16107,13 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4|| ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4) new_type = GGML_TYPE_IQ3_K_R4;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_IQ4_K;
|
||||
|
|
@ -16166,7 +16181,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type == GGML_TYPE_IQ2_KS || new_type == GGML_TYPE_IQ4_KSS || new_type == GGML_TYPE_Q6_K_R4 ||
|
||||
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4 ||
|
||||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4||
|
||||
new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_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) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
|
|
@ -16189,6 +16205,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ2_S:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ3_XXS_R4:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
|
|
@ -16320,6 +16337,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: default_type = GGML_TYPE_IQ3_XXS_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ1_BN: default_type = GGML_TYPE_IQ1_BN; break;
|
||||
|
|
@ -16769,6 +16787,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_KS;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ3_XXS_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ3_XXS;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_BF16_R16) {
|
||||
if (tensor->ne[1] % 16 != 0) new_type = GGML_TYPE_BF16;
|
||||
else chunk_size_multiplier = 16;
|
||||
|
|
|
|||
Loading…
Reference in New Issue