IQ2_XS_R4 (#155)
* iq2_xs_r4: Zen4 * iq2_xs_r4: AVX2 * iq2_xs_r4: slightly better matrix x vector on AVX2 * iq2_xs_r4: NEON - not much better than iq2_xs * iq2_xs_r4: slightly better NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
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@ -24,6 +24,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS, " 2.06 bpw quantization", },
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{ "IQ2_XXS_R4",LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4,"IQ2_XXS repacked", },
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{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", },
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{ "IQ2_XS_R4",LLAMA_FTYPE_MOSTLY_IQ2_XS_R4,"IQ2_XS repacked", },
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{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
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{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
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{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
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@ -505,7 +506,7 @@ int main(int argc, char ** argv) {
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if (!params.ignore_imatrix_rules && imatrix_data.empty() &&
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(params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
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params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 ||
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params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
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params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4 ||
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params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
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params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M)) {
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fprintf(stderr, "\n==========================================================================================================\n");
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@ -419,6 +419,7 @@ extern "C" {
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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_IQ2_XXS_R4= 216,
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GGML_TYPE_IQ2_XS_R4 = 217,
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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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@ -499,6 +500,7 @@ extern "C" {
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GGML_FTYPE_MOSTLY_Q5_K_R4 = 213, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q6_K_R4 = 214, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ2_XXS_R4= 215, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ2_XS_R4 = 216, // 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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@ -412,6 +412,13 @@ typedef struct {
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} block_iq2_xs;
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static_assert(sizeof(block_iq2_xs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
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typedef struct {
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ggml_half d[4];
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uint16_t qs[QK_K/2];
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uint8_t scales[QK_K/8];
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} block_iq2_xs_r4;
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static_assert(sizeof(block_iq2_xs_r4) == 4*sizeof(block_iq2_xs), "wrong iq2_xs_r4 block size/padding");
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// 2.5625 bpw quants
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typedef struct {
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ggml_half d;
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@ -15199,6 +15199,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
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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_IQ2_XXS_R4: break;
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case GGML_TYPE_IQ2_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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@ -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_IQ2_XS_R4] = {
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.type_name = "iq2_xs_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq2_xs),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq2_xs_r4,
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.from_float = quantize_row_iq2_xs_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq2_xs_r4_ref,
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.vec_dot = vec_dot_iq2_xs_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_XXS] = {
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.type_name = "iq3_xxs",
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.blck_size = QK_K,
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@ -4226,6 +4239,7 @@ 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_XXS_R4: wtype = GGML_TYPE_IQ2_XXS_R4;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_IQ2_XS_R4: wtype = GGML_TYPE_IQ2_XS_R4;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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@ -10769,6 +10783,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -11231,6 +11246,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -11390,6 +11406,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -14595,6 +14612,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -14994,6 +15012,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -15287,6 +15306,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -15909,6 +15929,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XXS_R4:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_XS_R4:
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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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@ -22680,6 +22701,7 @@ void ggml_quantize_init(enum ggml_type type) {
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switch (type) {
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case GGML_TYPE_IQ2_XXS_R4: iq2xs_init_impl(GGML_TYPE_IQ2_XXS); break;
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case GGML_TYPE_IQ2_XS_R4: iq2xs_init_impl(GGML_TYPE_IQ2_XS); break;
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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_IQ2_S:
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@ -22759,6 +22781,7 @@ 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_XXS_R4:result = quantize_iq2_xxs_r4(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_IQ2_XS_R4:result = quantize_iq2_xs_r4(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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@ -3300,6 +3300,131 @@ static void mul_mat_iq2_xxs_r4_q8_k(int n, const void * vx, size_t bx, const Dat
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}
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}
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template <int nrc_y>
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static void mul_mat_iq2_xs_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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#endif
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__m256 acc[nrc_y] = {};
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#ifdef HAVE_FANCY_SIMD
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__m256i shuffles[2] = {
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_mm256_set_epi64x(0x0706070607060706, 0x0302030203020302, 0x0504050405040504, 0x0100010001000100),
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_mm256_set_epi64x(0x0f0e0f0e0f0e0f0e, 0x0b0a0b0a0b0a0b0a, 0x0d0c0d0c0d0c0d0c, 0x0908090809080908)
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};
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__m256i isum[2*nrc_y] = {};
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#else
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__m256i shuffles[4] = {
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MM256_SET_M128I(_mm_set1_epi16(0x0302), _mm_set1_epi16(0x0100)),
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MM256_SET_M128I(_mm_set1_epi16(0x0706), _mm_set1_epi16(0x0504)),
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MM256_SET_M128I(_mm_set1_epi16(0x0b0a), _mm_set1_epi16(0x0908)),
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MM256_SET_M128I(_mm_set1_epi16(0x0f0e), _mm_set1_epi16(0x0d0c)),
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};
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__m256i isum[nrc_y == 1 ? 4 : nrc_y] = {};
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#endif
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auto s_shuffle = _mm_set_epi64x(0x0f0d0b0907050301, 0x0e0c0a0806040200);
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__m256i qx[4];
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union { __m256i vec; uint16_t val[16]; } helper;
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for (int ix = 0; ix < nrc_x; ix += 4) {
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auto iq2 = (const block_iq2_xs_r4 *)((const char *)vx + (ix+0)*bx);
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for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
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auto dl = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq2[ibl].d));
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auto d4 = _mm256_set_m128(dl, dl);
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auto s32 = (const uint32_t *)iq2[ibl].scales;
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for (int ib = 0; ib < QK_K/32; ++ib) {
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auto val = _mm256_loadu_si256((const __m256i *)iq2[ibl].qs + ib);
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helper.vec = _mm256_and_si256(val, _mm256_set1_epi16(511));
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qx[0] = _mm256_set_epi64x(iq2xs_grid[helper.val[ 3]], iq2xs_grid[helper.val[ 2]], iq2xs_grid[helper.val[ 1]], iq2xs_grid[helper.val[ 0]]);
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qx[1] = _mm256_set_epi64x(iq2xs_grid[helper.val[ 7]], iq2xs_grid[helper.val[ 6]], iq2xs_grid[helper.val[ 5]], iq2xs_grid[helper.val[ 4]]);
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qx[2] = _mm256_set_epi64x(iq2xs_grid[helper.val[11]], iq2xs_grid[helper.val[10]], iq2xs_grid[helper.val[ 9]], iq2xs_grid[helper.val[ 8]]);
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qx[3] = _mm256_set_epi64x(iq2xs_grid[helper.val[15]], iq2xs_grid[helper.val[14]], iq2xs_grid[helper.val[13]], iq2xs_grid[helper.val[12]]);
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auto signs16 = _mm256_srli_epi16(val, 9);
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signs16 = _mm256_xor_si256(signs16, _mm256_slli_epi16(signs16, 1));
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auto signs128 = _mm_or_si128(_mm256_castsi256_si128(signs16), _mm_slli_epi16(_mm256_extracti128_si256(signs16, 1), 8));
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signs128 = _mm_shuffle_epi8(signs128, s_shuffle);
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auto scales = _mm_set1_epi32(s32[ib]);
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scales = _mm_and_si128(_mm_unpacklo_epi8(scales, _mm_srli_epi16(scales, 4)), _mm_set1_epi8(0xf));
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scales = _mm_or_si128(_mm_slli_epi16(scales, 1), _mm_set1_epi8(1));
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auto scales16 = _mm256_cvtepi8_epi16(scales); // 0...7, 0...7
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#ifdef HAVE_FANCY_SIMD
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__m256i scs[2] = { _mm256_shuffle_epi8(scales16, shuffles[0]), _mm256_shuffle_epi8(scales16, shuffles[1]) };
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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)); // blocks: 0,0,0,0, 1,1,1,1, row 0
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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)); // blocks: 2,2,2,2, 3,3,3,3, row 1
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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)); // blocks: 4,4,4,4, 5,5,5,5, row 2
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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)); // blocks: 6,6,6,6, 7,7,7,7, row 3
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auto s12 = _mm256_packs_epi32(sumi1, sumi2); // 0,0,0,0, 2,2,2,2, 1,1,1,1, 3,3,3,3
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auto s34 = _mm256_packs_epi32(sumi3, sumi4); // 4,4,4,4, 6,6,6,6, 5,5,5,5, 7,7,7,7
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isum[2*iy+0] = _mm256_add_epi32(isum[2*iy+0], _mm256_madd_epi16(scs[0], s12));
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isum[2*iy+1] = _mm256_add_epi32(isum[2*iy+1], _mm256_madd_epi16(scs[1], s34));
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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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__m256i scs[4] = {
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_mm256_shuffle_epi8(scales16, shuffles[0]), _mm256_shuffle_epi8(scales16, shuffles[1]),
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_mm256_shuffle_epi8(scales16, shuffles[2]), _mm256_shuffle_epi8(scales16, shuffles[3]),
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};
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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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if constexpr (nrc_y == 1) {
|
||||
isum[0] = _mm256_add_epi32(isum[0], _mm256_madd_epi16(scs[0], _mm256_maddubs_epi16(qx[0], _mm256_sign_epi8(y, s1))));
|
||||
isum[1] = _mm256_add_epi32(isum[1], _mm256_madd_epi16(scs[1], _mm256_maddubs_epi16(qx[1], _mm256_sign_epi8(y, s2))));
|
||||
isum[2] = _mm256_add_epi32(isum[2], _mm256_madd_epi16(scs[2], _mm256_maddubs_epi16(qx[2], _mm256_sign_epi8(y, s3))));
|
||||
isum[3] = _mm256_add_epi32(isum[3], _mm256_madd_epi16(scs[3], _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4))));
|
||||
} else {
|
||||
auto sumi1 = _mm256_madd_epi16(scs[0], _mm256_maddubs_epi16(qx[0], _mm256_sign_epi8(y, s1))); // blocks 4x0, 4x1, row 0
|
||||
auto sumi2 = _mm256_madd_epi16(scs[1], _mm256_maddubs_epi16(qx[1], _mm256_sign_epi8(y, s2))); // blocks 4x2, 4x3, row 1
|
||||
auto sumi3 = _mm256_madd_epi16(scs[2], _mm256_maddubs_epi16(qx[2], _mm256_sign_epi8(y, s3))); // blocks 4x4, 4x5, row 2
|
||||
auto sumi4 = _mm256_madd_epi16(scs[3], _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4))); // blocks 4x6, 4x7, row 3
|
||||
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], sumi);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
#ifdef HAVE_FANCY_SIMD
|
||||
auto sumi = _mm256_hadd_epi32(isum[2*iy+0], isum[2*iy+1]);
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
isum[2*iy+0] = isum[2*iy+1] = _mm256_setzero_si256();
|
||||
#else
|
||||
if constexpr (nrc_y == 1) {
|
||||
auto s12 = _mm256_add_epi32(_mm256_unpacklo_epi32(isum[0], isum[1]), _mm256_unpackhi_epi32(isum[0], isum[1]));
|
||||
auto s34 = _mm256_add_epi32(_mm256_unpacklo_epi32(isum[2], isum[3]), _mm256_unpackhi_epi32(isum[2], isum[3]));
|
||||
auto sumi = _mm256_add_epi32(_mm256_unpacklo_epi64(s12, s34), _mm256_unpackhi_epi64(s12, s34));
|
||||
acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(sumi), acc[iy]);
|
||||
isum[0] = isum[1] = isum[2] = isum[3] = _mm256_setzero_si256();
|
||||
} else {
|
||||
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();
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
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, _mm_mul_ps(_mm_set1_ps(0.125f), sum));
|
||||
acc[iy] = _mm256_setzero_ps();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
|
|
@ -6801,6 +6926,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
mm.funcs[7] = mul_mat_iq2_xxs_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ2_XS_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq2_xs_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq2_xs_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq2_xs_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq2_xs_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq2_xs_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq2_xs_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq2_xs_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq2_xs_r4_q8_k<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ3_XXS_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq3_xxs_r4_q8_k<1>;
|
||||
|
|
@ -9734,6 +9871,73 @@ static void mul_mat_iq2_xxs_r4_q8_k(int n, const void * vx, size_t bx, const Dat
|
|||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
static void mul_mat_iq2_xs_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;
|
||||
static const uint8_t k_shuff[16] = {1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31};
|
||||
auto shuff = vld1q_u8(k_shuff);
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
int32x4_t isum[2*nrc_y] = {};
|
||||
int8x16_t qx[8];
|
||||
uint16x8x4_t scales16;
|
||||
SignHelper sh;
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
auto iq2 = (const block_iq2_xs_r4 *)((const char *)vx + (ix+0)*bx);
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
|
||||
auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq2[ibl].d));
|
||||
auto qs = iq2[ibl].qs;
|
||||
for (int is = 0; is < 2; ++is) {
|
||||
auto scale_bits = vld1q_u8(iq2[ibl].scales + 16*is);
|
||||
auto scales1 = vandq_u8(scale_bits, vdupq_n_u8(0xf));
|
||||
auto scales2 = vshrq_n_u8(scale_bits, 4);
|
||||
scales1 = vorrq_u8(vshlq_n_u8(scales1, 1), vdupq_n_u8(1));
|
||||
scales2 = vorrq_u8(vshlq_n_u8(scales2, 1), vdupq_n_u8(1));
|
||||
auto s1 = vzip1q_u8(scales1, scales2);
|
||||
auto s2 = vzip2q_u8(scales1, scales2);
|
||||
scales16.val[0] = vmovl_u8(vget_low_u8 (s1));
|
||||
scales16.val[1] = vmovl_u8(vget_high_u8(s1));
|
||||
scales16.val[2] = vmovl_u8(vget_low_u8 (s2));
|
||||
scales16.val[3] = vmovl_u8(vget_high_u8(s2));
|
||||
for (int ib = 0; ib < QK_K/64; ++ib) {
|
||||
auto v = vld1q_u8_x2((const uint8_t *)qs);
|
||||
auto signs128 = vandq_u8(vqtbl2q_u8(v, shuff), 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_u64(uint64x2_t{iq2xs_grid[qs[2*i+0] & 511], iq2xs_grid[qs[2*i+1] & 511]});
|
||||
sh.apply_signs_1((uint8x16_t *)qx+i, signs128);
|
||||
}
|
||||
auto s32_1 = vmovl_u16(vget_low_u16 (scales16.val[ib]));
|
||||
auto s32_2 = vmovl_u16(vget_high_u16(scales16.val[ib]));
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ibl].qs + 128*is + 32*ib);
|
||||
auto sumi1 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[0], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[1], y.val[1]));
|
||||
auto sumi2 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[2], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[3], y.val[1]));
|
||||
auto sumi3 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[4], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[5], y.val[1]));
|
||||
auto sumi4 = vpaddq_s32(ggml_vdotq_s32(vdupq_n_s32(0), qx[6], y.val[0]), ggml_vdotq_s32(vdupq_n_s32(0), qx[7], y.val[1]));
|
||||
auto sumi12 = vpaddq_s32(sumi1, sumi2); // blocks 0,1,2,3 in rows 0,1
|
||||
auto sumi34 = vpaddq_s32(sumi3, sumi4); // blocks 4,5,6,7 in rows 2,3
|
||||
isum[2*iy+0] = vmlaq_s32(isum[2*iy+0], s32_1, sumi12);
|
||||
isum[2*iy+1] = vmlaq_s32(isum[2*iy+1], s32_2, sumi34);
|
||||
}
|
||||
qs += 16;
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto sumi = vpaddq_s32(isum[2*iy+0], isum[2*iy+1]);
|
||||
acc[iy] = vfmaq_f32(acc[iy], vmulq_f32(d4, vdupq_n_f32(q8.scale(iy, ibl))), vcvtq_f32_s32(sumi));
|
||||
isum[2*iy] = isum[2*iy+1] = vdupq_n_s32(0);
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
info.store(ix, iy, vmulq_f32(vdupq_n_f32(0.125f), acc[iy]));
|
||||
acc[iy] = vdupq_n_f32(0.f);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
|
|
@ -11085,6 +11289,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
|||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_xxs_r4_q8_k);
|
||||
expected_Btype = GGML_TYPE_Q8_K;
|
||||
break;
|
||||
case GGML_TYPE_IQ2_XS_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_xs_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;
|
||||
|
|
|
|||
|
|
@ -5303,18 +5303,6 @@ struct Repack {
|
|||
};
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq2_xxs_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq2_xxs_r4_ref(const float * x, block_iq2_xxs_r4 * y, int64_t k) {
|
||||
quantize_iq2_xxs_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq2_xxs_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq2_xxs_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline uint8_t scrambled_sign(uint8_t s) {
|
||||
static const uint8_t k_table[128] = {
|
||||
|
|
@ -5331,6 +5319,18 @@ inline uint8_t scrambled_sign(uint8_t s) {
|
|||
}
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq2_xxs_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq2_xxs_r4_ref(const float * x, block_iq2_xxs_r4 * y, int64_t k) {
|
||||
quantize_iq2_xxs_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq2_xxs_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq2_xxs_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
static void repack_iq2_xxs(int nrows, int n_per_row, const block_iq2_xxs * x, block_iq2_xxs_r4 * y) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
|
|
@ -5419,6 +5419,93 @@ void vec_dot_iq2_xxs_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_
|
|||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq2_xs_r4
|
||||
//
|
||||
|
||||
void quantize_row_iq2_xs_r4_ref(const float * x, block_iq2_xs_r4 * y, int64_t k) {
|
||||
quantize_iq2_xs_r4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq2_xs_r4(const float * x, void * y, int64_t k) {
|
||||
quantize_iq2_xs_r4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
static void repack_iq2_xs(int nrows, int n_per_row, const block_iq2_xs * x, block_iq2_xs_r4 * y) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
int nblock = n_per_row/QK_K;
|
||||
const block_iq2_xs * x4[4];
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
y[ibl].d[k] = x4[k][ibl].d;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
uint16_t v = x4[k][ibl].qs[4*ib+i];
|
||||
uint8_t s = v >> 9;
|
||||
y[ibl].qs[16*ib+4*k+i] = (v & 511) | (scrambled_sign(s) << 9);
|
||||
}
|
||||
y[ibl].scales[4*ib+k] = x4[k][ibl].scales[ib];
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq2_xs_r4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
char * qcur = (char *)dst;
|
||||
auto row_size = ggml_row_size(GGML_TYPE_IQ2_XS, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq2_xs(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq2_xs(4, n_per_row, (const block_iq2_xs *)qtmp.data(), (block_iq2_xs_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq2_xs_r4(const block_iq2_xs_r4 * x, float * y, int64_t k) {
|
||||
auto n_per_row = k/4;
|
||||
float * y4[4] = {y, y + n_per_row, y + 2*n_per_row, y + 3*n_per_row};
|
||||
int nblock = n_per_row/QK_K;
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
const float d = 0.125f*GGML_FP16_TO_FP32(x[ibl].d[k]);
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
float dl1 = d * (2*(x[ibl].scales[4*ib+k] & 0xf) + 1);
|
||||
float dl2 = d * (2*(x[ibl].scales[4*ib+k] >> 4) + 1);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
auto val = (const int8_t *)(iq2xs_grid + (x[ibl].qs[16*ib+4*k+i] & 511));
|
||||
auto signs = x[ibl].qs[16*ib+4*k+i] >> 9;
|
||||
signs ^= (signs << 1);
|
||||
float dl = i < 2 ? dl1 : dl2;
|
||||
for (int j = 0; j < 8; ++j) y4[k][QK_K*ibl+32*ib+8*i+j] = dl * val[j] * (signs & (1 << j) ? -1 : 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq2_xs_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
|
||||
#if GGML_USE_IQK_MULMAT
|
||||
if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ2_XS_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);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq3_xxs_r4
|
||||
//
|
||||
|
|
|
|||
|
|
@ -175,6 +175,12 @@ size_t quantize_iq2_xxs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT
|
|||
void dequantize_row_iq2_xxs_r4(const block_iq2_xxs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq2_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_iq2_xs_r4_ref(const float * GGML_RESTRICT x, block_iq2_xs_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq2_xs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq2_xs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void dequantize_row_iq2_xs_r4(const block_iq2_xs_r4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq2_xs_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);
|
||||
|
|
|
|||
|
|
@ -189,6 +189,7 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q5_K_R4 = 216, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_K_R4 = 218, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 = 219, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_XS_R4 = 220, // 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
|
||||
|
|
|
|||
|
|
@ -3852,6 +3852,7 @@ struct llama_model_loader {
|
|||
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
|
||||
case GGML_TYPE_IQ2_XXS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4; break;
|
||||
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
|
||||
case GGML_TYPE_IQ2_XS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS_R4; break;
|
||||
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;
|
||||
|
|
@ -4581,6 +4582,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_IQ2_XXS: return "IQ2_XXS - 2.0625 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4:return "IQ2_XXS_R4 - 2.0625 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XS_R4:return "IQ2_XS_R4 - 2.3125 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_KS: return "IQ2_KS - 2.1875 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw";
|
||||
|
|
@ -15797,10 +15799,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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_IQ3_XXS_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_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_IQ2_XXS_R4) {
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) {
|
||||
new_type = !qs.has_output ? GGML_TYPE_IQ4_K_R4 : GGML_TYPE_Q5_K_R4;
|
||||
}
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
|
|
@ -15818,7 +15820,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
} else {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) {
|
||||
new_type = GGML_TYPE_Q2_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
|
||||
|
|
@ -15894,7 +15896,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
}
|
||||
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_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_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) {
|
||||
if (name.find("attn_v.weight") != std::string::npos) {
|
||||
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_IQ4_K;
|
||||
else if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_IQ3_K;
|
||||
|
|
@ -16188,7 +16190,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4) {
|
||||
new_type == GGML_TYPE_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4 || new_type == GGML_TYPE_IQ2_XS_R4) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
|
|
@ -16209,6 +16211,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XXS_R4:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ2_XS_R4:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ2_S:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
|
|
@ -16341,6 +16344,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4:default_type = GGML_TYPE_IQ2_XXS_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XS: default_type = GGML_TYPE_IQ2_XS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XS_R4:default_type = GGML_TYPE_IQ2_XS_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_KS: default_type = GGML_TYPE_IQ2_KS; break;
|
||||
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;
|
||||
|
|
@ -16695,6 +16699,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
(new_type == GGML_TYPE_IQ2_XXS ||
|
||||
new_type == GGML_TYPE_IQ2_XXS_R4 ||
|
||||
new_type == GGML_TYPE_IQ2_XS ||
|
||||
new_type == GGML_TYPE_IQ2_XS_R4 ||
|
||||
new_type == GGML_TYPE_IQ2_S ||
|
||||
new_type == GGML_TYPE_IQ1_S ||
|
||||
(new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
|
||||
|
|
@ -16800,6 +16805,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_XXS;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ2_XS_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_XS;
|
||||
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;
|
||||
|
|
|
|||
Loading…
Reference in New Issue