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https://github.com/ikawrakow/ik_llama.cpp.git
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IQ2_XXS_R4 (#154)
* iq2_xxs_r4: Zen4 Disapointing gain: 134.7 t/s -> 151.1 t/s for PP-512 TG-128 is better: 3.45 -> 4.61 t/s @ 1 thread * Minor * iq2_xxs_r4: NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@ -22,6 +22,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
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{ "Q6_0", LLAMA_FTYPE_MOSTLY_Q6_0, " 6.5 bpw quantization", },
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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_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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@ -503,7 +504,7 @@ 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 ||
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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_IQ1_S ||
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params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_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_IQ2_XXS_R4= 216,
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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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@ -495,8 +496,9 @@ extern "C" {
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GGML_FTYPE_MOSTLY_Q2_K_R4 = 210, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q3_K_R4 = 211, // except 1d tensors
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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_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_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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@ -397,6 +397,13 @@ typedef struct {
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} block_iq2_xxs;
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static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t), "wrong iq2_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/2];
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} block_iq2_xxs_r4;
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static_assert(sizeof(block_iq2_xxs_r4) == 4*sizeof(block_iq2_xxs), "wrong iq2_xxs_r4 block size/padding");
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// 2.3125 bpw quants
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typedef struct {
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ggml_half d;
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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_IQ2_XXS_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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@ -1005,6 +1005,19 @@ 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_XXS_R4] = {
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.type_name = "iq2_xxs_r4",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq2_xxs),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq2_xxs_r4,
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.from_float = quantize_row_iq2_xxs_r4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq2_xxs_r4_ref,
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.vec_dot = vec_dot_iq2_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_IQ2_XS] = {
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.type_name = "iq2_xs",
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.blck_size = QK_K,
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@ -4211,6 +4224,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_Q6_K_R4: wtype = GGML_TYPE_Q6_K_R4; break;
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case GGML_FTYPE_MOSTLY_Q8_K_R8: wtype = GGML_TYPE_Q8_K_R8; break;
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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_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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@ -10753,6 +10767,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_Q6_K_R4:
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case GGML_TYPE_Q8_K_R8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -11214,6 +11229,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_Q6_K_R4:
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case GGML_TYPE_Q8_K_R8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -11372,6 +11388,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_Q6_K_R4:
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case GGML_TYPE_Q8_K_R8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -14576,6 +14593,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_Q6_K_R4:
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case GGML_TYPE_Q8_K_R8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -14974,6 +14992,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_Q6_K_R4:
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case GGML_TYPE_Q8_K_R8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -15266,6 +15285,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_Q6_K_R4:
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case GGML_TYPE_Q8_K_R8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -15887,6 +15907,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_Q8_K_R8:
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case GGML_TYPE_Q8_KR8:
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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_IQ3_XXS:
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case GGML_TYPE_IQ3_XXS_R4:
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@ -22658,6 +22679,7 @@ void ggml_quantize_init(enum ggml_type type) {
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ggml_critical_section_start();
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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_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ2_S:
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@ -22735,6 +22757,7 @@ size_t ggml_quantize_chunk(
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case GGML_TYPE_Q6_K_R4: result = quantize_q6_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_Q8_K_R8: result = quantize_q8_k_r8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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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_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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@ -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_IQ2_XXS_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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@ -3213,6 +3214,92 @@ 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_iq2_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 iq2 = (const block_iq2_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_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 qs = iq2[ibl].qs;
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for (int ib = 0; ib < QK_K/32; ++ib) {
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qx[0] = _mm256_set_epi64x(iq2xxs_grid[qs[ 3]], iq2xxs_grid[qs[ 2]], iq2xxs_grid[qs[ 1]], iq2xxs_grid[qs[ 0]]);
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qx[1] = _mm256_set_epi64x(iq2xxs_grid[qs[ 7]], iq2xxs_grid[qs[ 6]], iq2xxs_grid[qs[ 5]], iq2xxs_grid[qs[ 4]]);
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qx[2] = _mm256_set_epi64x(iq2xxs_grid[qs[11]], iq2xxs_grid[qs[10]], iq2xxs_grid[qs[ 9]], iq2xxs_grid[qs[ 8]]);
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qx[3] = _mm256_set_epi64x(iq2xxs_grid[qs[15]], iq2xxs_grid[qs[14]], iq2xxs_grid[qs[13]], iq2xxs_grid[qs[12]]);
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qs += 16;
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auto sas = _mm_loadu_si128((const __m128i *)iq2[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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#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)));
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auto sumi4 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(qx[3], _mm256_sign_epi8(y, s4)));
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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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#endif
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}
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for (int iy = 0; iy < nrc_y; ++iy) {
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acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(isum[iy]), acc[iy]);
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isum[iy] = _mm256_setzero_si256();
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}
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}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto sum = _mm_add_ps(_mm256_castps256_ps128(acc[iy]), _mm256_extractf128_ps(acc[iy], 1));
|
||||
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);
|
||||
@ -6702,6 +6789,18 @@ 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_IQ2_XXS_R4:
|
||||
assert (ne00 % QK_K == 0);
|
||||
mm.funcs[0] = mul_mat_iq2_xxs_r4_q8_k<1>;
|
||||
mm.funcs[1] = mul_mat_iq2_xxs_r4_q8_k<2>;
|
||||
mm.funcs[2] = mul_mat_iq2_xxs_r4_q8_k<3>;
|
||||
mm.funcs[3] = mul_mat_iq2_xxs_r4_q8_k<4>;
|
||||
mm.funcs[4] = mul_mat_iq2_xxs_r4_q8_k<5>;
|
||||
mm.funcs[5] = mul_mat_iq2_xxs_r4_q8_k<6>;
|
||||
mm.funcs[6] = mul_mat_iq2_xxs_r4_q8_k<7>;
|
||||
mm.funcs[7] = mul_mat_iq2_xxs_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>;
|
||||
@ -9585,6 +9684,56 @@ 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_iq2_xxs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
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 iq2 = (const block_iq2_xxs_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 ib = 0; ib < QK_K/32; ++ib) {
|
||||
auto sas = vld1q_u8(iq2[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_u64(uint64x2_t{iq2xxs_grid[qs[2*i+0]], iq2xxs_grid[qs[2*i+1]]});
|
||||
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 += 16;
|
||||
}
|
||||
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, 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);
|
||||
@ -10932,6 +11081,10 @@ 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_IQ2_XXS_R4:
|
||||
SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_xxs_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;
|
||||
|
||||
@ -5303,6 +5303,122 @@ 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] = {
|
||||
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,
|
||||
};
|
||||
return k_table[s];
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
int nblock = n_per_row/QK_K;
|
||||
const block_iq2_xxs * x4[4];
|
||||
uint32_t aux32[2];
|
||||
const uint8_t * aux8 = (const uint8_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;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
std::memcpy(aux32, x4[k][ibl].qs + 4*ib, 2*sizeof(uint32_t));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
y[ibl].qs[16*ib+4*k+i] = aux8[i];
|
||||
}
|
||||
uint8_t scale = aux32[1] >> 28;
|
||||
uint8_t s1 = (scrambled_sign((aux32[1] >> 0) & 127) << 1) | ((scale >> 0) & 1);
|
||||
uint8_t s2 = (scrambled_sign((aux32[1] >> 7) & 127) << 1) | ((scale >> 1) & 1);
|
||||
uint8_t s3 = (scrambled_sign((aux32[1] >> 14) & 127) << 1) | ((scale >> 2) & 1);
|
||||
uint8_t s4 = (scrambled_sign((aux32[1] >> 21) & 127) << 1) | ((scale >> 3) & 1);
|
||||
aux32[1] = uint32_t(s1) | (uint32_t(s2) << 8) | (uint32_t(s3) << 16) | (uint32_t(s4) << 24);
|
||||
ysas[4*ib+k] = aux32[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq2_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_IQ2_XXS, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size);
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq2_xxs(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq2_xxs(4, n_per_row, (const block_iq2_xxs *)qtmp.data(), (block_iq2_xxs_r4 *)qcur);
|
||||
qcur += 4*row_size;
|
||||
src += 4*n_per_row;
|
||||
}
|
||||
return nrows*row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq2_xxs_r4(const block_iq2_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 = 0.125f*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 < 4; ++i) {
|
||||
auto val = (const int8_t *)(iq2xxs_grid + x[ibl].qs[16*ib+4*k+i]);
|
||||
for (int j = 0; j < 8; ++j) y4[k][QK_K*ibl+32*ib+8*i+j] = dl * val[j] * (aux32 & (1 << j) ? -1 : 1);
|
||||
aux32 >>= 8;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq2_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_IQ2_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);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= iq3_xxs_r4
|
||||
//
|
||||
@ -5321,16 +5437,6 @@ 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;
|
||||
@ -5347,10 +5453,10 @@ static void repack_iq3_xxs(int nrows, int n_per_row, const block_iq3_xxs * x, bl
|
||||
}
|
||||
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);
|
||||
uint8_t s1 = (scrambled_sign((aux32 >> 0) & 127) << 1) | ((scale >> 0) & 1);
|
||||
uint8_t s2 = (scrambled_sign((aux32 >> 7) & 127) << 1) | ((scale >> 1) & 1);
|
||||
uint8_t s3 = (scrambled_sign((aux32 >> 14) & 127) << 1) | ((scale >> 2) & 1);
|
||||
uint8_t s4 = (scrambled_sign((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;
|
||||
}
|
||||
|
||||
@ -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_iq2_xxs_r4_ref(const float * GGML_RESTRICT x, block_iq2_xxs_r4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq2_xxs_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq2_xxs_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
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_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);
|
||||
|
||||
@ -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_IQ2_XXS_R4 = 219, // 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
|
||||
|
||||
@ -3850,6 +3850,7 @@ struct llama_model_loader {
|
||||
case GGML_TYPE_Q6_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q6_K_R4; break;
|
||||
case GGML_TYPE_Q8_K_R8: ftype = LLAMA_FTYPE_MOSTLY_Q8_K_R8; break;
|
||||
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_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KS; break;
|
||||
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
|
||||
@ -4578,6 +4579,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K_R4: return "Q6_K_R4";
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_K_R8: return "Q8_K_R8";
|
||||
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_KS: return "IQ2_KS - 2.1875 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw";
|
||||
@ -15798,6 +15800,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
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) {
|
||||
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 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS_R4) && !qs.has_output) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
@ -15812,7 +15817,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
new_type = qs.params->token_embedding_type;
|
||||
} 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_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4) {
|
||||
new_type = GGML_TYPE_Q2_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
|
||||
@ -15887,8 +15893,8 @@ 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_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_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) {
|
||||
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;
|
||||
@ -16182,7 +16188,7 @@ 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_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
@ -16201,6 +16207,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
||||
if (convert_incompatible_tensor) {
|
||||
switch (new_type) {
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XXS_R4:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ2_S:
|
||||
@ -16332,6 +16339,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K_R4: default_type = GGML_TYPE_Q6_K_R4; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_K_R8: default_type = GGML_TYPE_Q8_K_R8; break;
|
||||
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_KS: default_type = GGML_TYPE_IQ2_KS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break;
|
||||
@ -16685,6 +16693,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
}
|
||||
if (!params->ignore_imatrix_rules && !imatrix &&
|
||||
(new_type == GGML_TYPE_IQ2_XXS ||
|
||||
new_type == GGML_TYPE_IQ2_XXS_R4 ||
|
||||
new_type == GGML_TYPE_IQ2_XS ||
|
||||
new_type == GGML_TYPE_IQ2_S ||
|
||||
new_type == GGML_TYPE_IQ1_S ||
|
||||
@ -16787,6 +16796,10 @@ 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_IQ2_XXS_R4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_XXS;
|
||||
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…
x
Reference in New Issue
Block a user