IQ4_NL_X4 (#118)
* Adding iq4_nl_x4 Looks very promising - I get PP-512(LLaMA-3.1-8B) = 230 t/s on the Ryzen-7950X! This is faster than any other quant and ~40% faster than iq4_nl. * iq4_nl_x4: getting amazing This Zen4 variant gets us to PP-512(LLaMA-3.1-8B) = 263 t/s! * iq4_nl_x4: AVX2 Here we gain only 25% compared to iq4_nl * iq4_nl_x4: NEON On M2-Max we get PP-512(LLaMA-3.1-8B) = 109.7 t/s, up from 82.4 t/s for iq4_nl. * iq4_nl_x4: minor NEON improvement and cleanup This gets us to 110.3 t/s. In comparison, IQ4_NL_4_4 in mainline llama.cpp achieves 92.3 t/s. * iq4_nl_x4: NEON specialization for matrix x vector --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
GitHub
parent
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commit
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@ -40,6 +40,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
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{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
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{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
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{ "IQ4_NL_X4",LLAMA_FTYPE_MOSTLY_IQ4_NL_X4," 4.50 bpw non-linear quantization", },
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{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KSS", LLAMA_FTYPE_MOSTLY_IQ4_KSS, " 4.0 bpw non-linear quantization", },
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@ -406,6 +406,8 @@ extern "C" {
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GGML_TYPE_IQ4_KS = 144,
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GGML_TYPE_IQ2_KS = 145,
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GGML_TYPE_IQ4_KSS = 146,
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GGML_TYPE_IQ4_NL_X4 = 220,
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GGML_TYPE_COUNT,
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};
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@ -464,6 +466,8 @@ extern "C" {
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GGML_FTYPE_MOSTLY_IQ4_KS = 137, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ2_KS = 138, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_KSS = 139, // except 1d tensors
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//
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GGML_FTYPE_MOSTLY_IQ4_NL_X4 = 219, // except 1d tensors
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};
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// available tensor operations:
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@ -419,6 +419,11 @@ typedef struct {
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uint8_t qs[QK4_NL/2];
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} block_iq4_nl;
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static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding");
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typedef struct {
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ggml_half d[4];
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uint8_t qs[2*QK4_NL];
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} block_iq4_nl_x4;
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static_assert(sizeof(block_iq4_nl_x4) == 4*sizeof(ggml_half) + 2*QK4_NL, "wrong iq4_nl_x4 block size/padding");
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typedef struct {
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ggml_half d;
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@ -15196,6 +15196,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
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case GGML_TYPE_IQ6_K: break;
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case GGML_TYPE_IQ4_KS: break;
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case GGML_TYPE_IQ4_KSS: break;
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case GGML_TYPE_IQ4_NL_X4: break;
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case GGML_TYPE_Q4_0_4_4:
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case GGML_TYPE_Q4_0_4_8:
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{
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@ -1245,6 +1245,23 @@ 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_IQ4_NL_X4] = {
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.type_name = "iq4_nl_x4",
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.blck_size = QK4_NL,
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.type_size = sizeof(block_iq4_nl),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq4_nl_x4,
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.from_float = quantize_row_iq4_nl_x4,
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.from_float_ref = (ggml_from_float_t)quantize_row_iq4_nl_x4_ref,
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.vec_dot = vec_dot_iq4_nl_x4_q8_0,
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#if GGML_USE_IQK_MULMAT && defined __AVX2__
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.vec_dot_type = GGML_TYPE_Q8_1,
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#else
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.vec_dot_type = GGML_TYPE_Q8_0,
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#endif
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.nrows = 1,
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.row_meta_size = 0,
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},
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};
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// For internal test use
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@ -3903,6 +3920,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_IQ1_BN: wtype = GGML_TYPE_IQ1_BN; break;
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case GGML_FTYPE_MOSTLY_IQ2_BN: wtype = GGML_TYPE_IQ2_BN; break;
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case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
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case GGML_FTYPE_MOSTLY_IQ4_NL_X4: wtype = GGML_TYPE_IQ4_NL_X4;break;
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case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
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case GGML_FTYPE_MOSTLY_IQ4_KS: wtype = GGML_TYPE_IQ4_KS; break;
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case GGML_FTYPE_MOSTLY_IQ4_KSS: wtype = GGML_TYPE_IQ4_KSS; break;
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@ -10426,6 +10444,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -10868,6 +10887,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -11007,6 +11027,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -14192,6 +14213,7 @@ static void ggml_compute_forward_out_prod(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -14571,6 +14593,7 @@ static void ggml_compute_forward_set(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -14844,6 +14867,7 @@ static void ggml_compute_forward_get_rows(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -15444,6 +15468,7 @@ static void ggml_compute_forward_clamp(
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case GGML_TYPE_IQ1_BN:
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case GGML_TYPE_IQ2_BN:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_NL_X4:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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@ -22270,6 +22295,7 @@ size_t ggml_quantize_chunk(
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case GGML_TYPE_IQ1_BN: result = quantize_iq1_bn (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ2_BN: result = quantize_iq2_bn (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_NL_X4: result = quantize_iq4_nl_x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_KS: result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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case GGML_TYPE_IQ4_KSS: result = quantize_iq4_kss(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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@ -23,6 +23,7 @@
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#include "ggml-impl.h"
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#include "ggml-quants.h"
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#include "iqk_mul_mat.h"
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#include "iqk_quantize.h"
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#define GGML_COMMON_IMPL_C
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#include "ggml-common.h"
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@ -87,6 +88,16 @@ struct DataInfo {
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inline void store(int ix, int iy, float result) const {
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*(dst_row(iy) + ix) = result;
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}
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#ifdef __AVX__
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inline void store(int ix, int iy, __m128 result) const {
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_mm_storeu_ps(dst_row(iy) + ix, result);
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}
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#endif
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#ifdef __ARM_NEON
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inline void store(int ix, int iy, float32x4_t result) const {
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vst1q_f32(dst_row(iy) + ix, result);
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}
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#endif
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inline float * dst_row(int iy) const {
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if (!row_mapping) return s + (cur_y + iy)*bs;
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int i12 = row_mapping[cur_y + iy].i2;
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@ -2068,6 +2079,112 @@ static void mul_mat_qX_K_q8_K_T(int n, const void * vx, size_t bx, const DataInf
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#endif // Zen4 or vanilla AVX2
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#ifdef HAVE_FANCY_SIMD
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template <int nrc_y>
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static void mul_mat_iq4_nl_x4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
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GGML_ASSERT(nrc_x%8 == 0);
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Q8<nrc_y, block_q8_1_x4> q8(info);
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auto m4 = _mm512_set1_epi8(0xf);
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auto values = load_iq4nl_values_512();
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int nb = n / QK4_NL;
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GGML_ASSERT(nb%4 == 0);
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__m512 acc[2*nrc_y] = {};
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__m512i qx[4];
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for (int ix = 0; ix < nrc_x; ix += 8) {
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const block_iq4_nl_x4 * iq4l = (const block_iq4_nl_x4 *)((const char *)vx + (ix+0)*bx);
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const block_iq4_nl_x4 * iq4h = (const block_iq4_nl_x4 *)((const char *)vx + (ix+4)*bx);
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for (int ib4 = 0; ib4 < nb/4; ++ib4) {
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for (int k = 0; k < 4; ++k) {
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auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4l[4*ib4+k].d));
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auto scales1 = _mm256_set_m128(scales128, scales128);
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scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4h[4*ib4+k].d));
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auto scales2 = _mm256_set_m128(scales128, scales128);
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auto scales = _mm512_insertf32x8(_mm512_castps256_ps512(scales1), scales2, 1);
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auto scales_m = _mm512_mul_ps(scales, _mm512_set1_ps(-64.f));
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auto bits1 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[4*ib4+k].qs+0)),
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_mm256_loadu_si256((const __m256i *)iq4h[4*ib4+k].qs+0), 1);
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auto bits2 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[4*ib4+k].qs+1)),
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_mm256_loadu_si256((const __m256i *)iq4h[4*ib4+k].qs+1), 1);
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qx[0] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits1, m4));
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qx[1] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits2, m4));
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qx[2] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits1, 4), m4));
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qx[3] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits2, 4), m4));
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for (int iy = 0; iy < nrc_y; ++iy) {
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auto y8 = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
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auto y = _mm512_inserti32x8(_mm512_castsi256_si512(y8), y8, 1);
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auto sumi = _mm512_setzero_si512();
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sumi = _mm512_dpbusd_epi32(sumi, qx[0], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x00)));
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sumi = _mm512_dpbusd_epi32(sumi, qx[1], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x55)));
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sumi = _mm512_dpbusd_epi32(sumi, qx[2], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xaa)));
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sumi = _mm512_dpbusd_epi32(sumi, qx[3], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xff)));
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auto dy = _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k]));
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acc[2*iy+0] = _mm512_fmadd_ps(_mm512_mul_ps(scales, dy), _mm512_cvtepi32_ps(sumi), acc[2*iy+0]);
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acc[2*iy+1] = _mm512_fmadd_ps(scales_m, _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[2*iy+1]);
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}
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}
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}
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for (int iy = 0; iy < nrc_y; ++iy) {
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auto sum512 = _mm512_add_ps(acc[2*iy+0], acc[2*iy+1]);
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acc[2*iy+0] = acc[2*iy+1] = _mm512_setzero_ps();
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auto sum1 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 0), _mm512_extractf32x4_ps(sum512, 1));
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auto sum2 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 2), _mm512_extractf32x4_ps(sum512, 3));
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info.store(ix+0, iy, sum1);
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info.store(ix+4, iy, sum2);
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}
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}
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}
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#else
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template <int nrc_y>
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static void mul_mat_iq4_nl_x4_q8_1(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_1_x4> q8(info);
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auto m4 = _mm256_set1_epi8(0xf);
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auto m1 = _mm256_set1_epi16(1);
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auto values = load_iq4nl_values_256();
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int nb = n / QK4_NL;
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GGML_ASSERT(nb%4 == 0);
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__m256 acc[nrc_y] = {};
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//__m256 acc[2*nrc_y] = {};
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for (int ix = 0; ix < nrc_x; ix += 4) {
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const block_iq4_nl_x4 * iq4 = (const block_iq4_nl_x4 *)((const char *)vx + ix*bx);
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for (int ib4 = 0; ib4 < nb/4; ++ib4) {
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for (int k = 0; k < 4; ++k) {
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auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4[4*ib4+k].d));
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auto scales = _mm256_set_m128(scales128, scales128);
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auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-64.f));
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auto bits1 = _mm256_loadu_si256((const __m256i *)iq4[4*ib4+k].qs+0);
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auto bits2 = _mm256_loadu_si256((const __m256i *)iq4[4*ib4+k].qs+1);
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auto q1 = _mm256_shuffle_epi8(values, _mm256_and_si256(bits1, m4));
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auto q2 = _mm256_shuffle_epi8(values, _mm256_and_si256(bits2, m4));
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auto q3 = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4));
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auto q4 = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4));
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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][ib4].qs+k);
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auto sumi1 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q1, _mm256_shuffle_epi32(y, 0x00))),
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_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q2, _mm256_shuffle_epi32(y, 0x55))));
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auto sumi2 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q3, _mm256_shuffle_epi32(y, 0xaa))),
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_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q4, _mm256_shuffle_epi32(y, 0xff))));
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auto d4d8 = _mm256_mul_ps(scales, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
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acc[iy] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), acc[iy]);
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acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[iy]);
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//acc[2*iy+0] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), acc[2*iy+0]);
|
||||
//acc[2*iy+1] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[2*iy+1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
//auto sum256 = _mm256_add_ps(acc[2*iy+0], acc[2*iy+1]);
|
||||
//acc[2*iy+0] = acc[2*iy+1] = _mm256_setzero_ps();
|
||||
//auto sum = _mm_add_ps(_mm256_castps256_ps128(sum256), _mm256_extractf128_ps(sum256, 1));
|
||||
//info.store(ix, iy, sum);
|
||||
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();
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
template <typename Bits>
|
||||
inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, const __m256i * q8, __m256i * sumi) {
|
||||
if (j == 0) {
|
||||
|
|
@ -4025,6 +4142,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
MulMat::set_functions<IQ4_NL_Unpacker>(mm);
|
||||
expected_typeB = GGML_TYPE_Q8_1;
|
||||
break;
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
assert (ne00 % QK4_NL == 0);
|
||||
mm.funcs[0] = mul_mat_iq4_nl_x4_q8_1<1>;
|
||||
mm.funcs[1] = mul_mat_iq4_nl_x4_q8_1<2>;
|
||||
mm.funcs[2] = mul_mat_iq4_nl_x4_q8_1<3>;
|
||||
mm.funcs[3] = mul_mat_iq4_nl_x4_q8_1<4>;
|
||||
mm.funcs[4] = mul_mat_iq4_nl_x4_q8_1<5>;
|
||||
mm.funcs[5] = mul_mat_iq4_nl_x4_q8_1<6>;
|
||||
mm.funcs[6] = mul_mat_iq4_nl_x4_q8_1<7>;
|
||||
mm.funcs[7] = mul_mat_iq4_nl_x4_q8_1<8>;
|
||||
expected_typeB = GGML_TYPE_Q8_1;
|
||||
break;
|
||||
|
||||
default:
|
||||
return false;
|
||||
|
|
@ -6427,6 +6556,96 @@ static void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const DataIn
|
|||
}
|
||||
}
|
||||
|
||||
template <int nrc_y>
|
||||
void mul_mat_iq4_nl_x4_q8_0(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_0_x4> q8(info);
|
||||
auto m4 = vdupq_n_u8(0xf);
|
||||
auto values = vld1q_s8(iq4k_values);
|
||||
int nb = n / QK4_NL;
|
||||
GGML_ASSERT(nb%4 == 0);
|
||||
int8x16_t qx[8];
|
||||
float32x4_t acc[nrc_y] = {};
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
const block_iq4_nl_x4 * iq4 = (const block_iq4_nl_x4 *)((const char *)vx + ix*bx);
|
||||
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[4*ib4+k].d));
|
||||
auto bits = vld1q_u8_x4(iq4[4*ib4+k].qs);
|
||||
qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4)); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19
|
||||
qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4)); // 4...7
|
||||
qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23
|
||||
qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11
|
||||
qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27
|
||||
qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15
|
||||
qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
auto y = vld1q_s8_x2(q8.y[iy][ib4].qs+32*k);
|
||||
auto sumi = vdupq_n_s32(0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[0], y.val[0], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[1], y.val[1], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[2], y.val[0], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[3], y.val[1], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[4], y.val[0], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[5], y.val[1], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[6], y.val[0], 3);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[7], y.val[1], 3);
|
||||
auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
|
||||
acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi));
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int iy = 0; iy < nrc_y; ++iy) {
|
||||
info.store(ix, iy, acc[iy]);
|
||||
acc[iy] = vdupq_n_f32(0.f);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void mul_mat_iq4_nl_x4_q8_0_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
|
||||
GGML_ASSERT(nrc_x%4 == 0);
|
||||
Q8<1, block_q8_0_x4> q8(info);
|
||||
auto m4 = vdupq_n_u8(0xf);
|
||||
auto values = vld1q_s8(iq4k_values);
|
||||
int nb = n / QK4_NL;
|
||||
GGML_ASSERT(nb%4 == 0);
|
||||
int8x16_t qx[8];
|
||||
for (int ix = 0; ix < nrc_x; ix += 4) {
|
||||
auto acc = vdupq_n_f32(0.f);
|
||||
const block_iq4_nl_x4 * iq4 = (const block_iq4_nl_x4 *)((const char *)vx + ix*bx);
|
||||
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
|
||||
auto y1 = vld1q_s8_x4(q8.y[0][ib4].qs);
|
||||
auto y2 = vld1q_s8_x4(q8.y[0][ib4].qs+64);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[4*ib4+k].d));
|
||||
auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[0][ib4].d[k])));
|
||||
auto sumi = vdupq_n_s32(0);
|
||||
const auto yval = k < 2 ? y1.val + 2*k : y2.val + 2*(k-2);
|
||||
auto bits = vld1q_u8_x4(iq4[4*ib4+k].qs);
|
||||
qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4)); // 0...3 from the 4 rows
|
||||
qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4)); // 16..19
|
||||
sumi = vdotq_laneq_s32(sumi, qx[0], yval[0], 0);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[1], yval[1], 0);
|
||||
qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4)); // 4...7
|
||||
qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4)); // 20..23
|
||||
sumi = vdotq_laneq_s32(sumi, qx[2], yval[0], 1);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[3], yval[1], 1);
|
||||
qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4)); // 8..11
|
||||
qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4)); // 24..27
|
||||
sumi = vdotq_laneq_s32(sumi, qx[4], yval[0], 2);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[5], yval[1], 2);
|
||||
qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4)); // 12..15
|
||||
qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4)); // 28..31
|
||||
sumi = vdotq_laneq_s32(sumi, qx[6], yval[0], 3);
|
||||
sumi = vdotq_laneq_s32(sumi, qx[7], yval[1], 3);
|
||||
acc = vfmaq_f32(acc, d4d8, vcvtq_f32_s32(sumi));
|
||||
}
|
||||
}
|
||||
info.store(ix, 0, acc);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Dequantizer> void MulMat::set_functions(MulMat& m) {
|
||||
if constexpr (std::is_same_v<Dequantizer, DequantizerQ40> || std::is_same_v<Dequantizer, DequantizerQ50> ||
|
||||
std::is_same_v<Dequantizer, DequantizerQ80> || std::is_same_v<Dequantizer, DequantizerIQ4NL> ||
|
||||
|
|
@ -6596,6 +6815,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
|||
MulMat::set_functions<DequantizerIQ4NL>(m);
|
||||
expected_Btype = GGML_TYPE_Q8_0;
|
||||
break;
|
||||
case GGML_TYPE_IQ4_NL_X4:
|
||||
m.funcs[0] = mul_mat_iq4_nl_x4_q8_0_1;
|
||||
m.funcs[1] = mul_mat_iq4_nl_x4_q8_0<2>;
|
||||
m.funcs[2] = mul_mat_iq4_nl_x4_q8_0<3>;
|
||||
m.funcs[3] = mul_mat_iq4_nl_x4_q8_0<4>;
|
||||
m.funcs[4] = mul_mat_iq4_nl_x4_q8_0<5>;
|
||||
m.funcs[5] = mul_mat_iq4_nl_x4_q8_0<6>;
|
||||
m.funcs[6] = mul_mat_iq4_nl_x4_q8_0<7>;
|
||||
m.funcs[7] = mul_mat_iq4_nl_x4_q8_0<8>;
|
||||
expected_Btype = GGML_TYPE_Q8_0;
|
||||
break;
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -669,12 +669,12 @@ void quantize_row_iq2_k_impl(const float * x, void * vy, int n_per_row, const fl
|
|||
}
|
||||
}
|
||||
|
||||
void quantize_row_iq2_k_ref(const float * GGML_RESTRICT x, block_iq2_k * GGML_RESTRICT y, int64_t k) {
|
||||
void quantize_row_iq2_k_ref(const float * x, block_iq2_k * y, int64_t k) {
|
||||
assert(k % QK_K == 0);
|
||||
quantize_iq2_k(x, (void *)y, 1, k, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq2_k(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
|
||||
void quantize_row_iq2_k(const float * x, void * vy, int64_t k) {
|
||||
assert(k % QK_K == 0);
|
||||
block_iq2_k * y = (block_iq2_k *)vy;
|
||||
quantize_row_iq2_k_ref(x, y, k);
|
||||
|
|
@ -692,7 +692,7 @@ size_t quantize_iq2_k(const float * src, void * dst, int64_t nrows, int64_t n_pe
|
|||
return nrows * nblock * sizeof(block_iq2_k);
|
||||
}
|
||||
|
||||
void dequantize_row_iq2_k(const block_iq2_k * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
|
||||
void dequantize_row_iq2_k(const block_iq2_k * x, float * y, int64_t k) {
|
||||
assert(k % QK_K == 0);
|
||||
const int nb = k / QK_K;
|
||||
|
||||
|
|
@ -723,7 +723,7 @@ void dequantize_row_iq2_k(const block_iq2_k * GGML_RESTRICT x, float * GGML_RES
|
|||
|
||||
}
|
||||
|
||||
void vec_dot_iq2_k_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 vec_dot_iq2_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
assert(nrc == 1);
|
||||
GGML_UNUSED(nrc);
|
||||
|
|
@ -967,12 +967,12 @@ void quantize_row_iq2_ks_impl(const float * x, void * vy, int n_per_row, const f
|
|||
}
|
||||
}
|
||||
|
||||
void quantize_row_iq2_ks_ref(const float * GGML_RESTRICT x, block_iq2_ks * GGML_RESTRICT y, int64_t k) {
|
||||
void quantize_row_iq2_ks_ref(const float * x, block_iq2_ks * y, int64_t k) {
|
||||
assert(k % QK_K == 0);
|
||||
quantize_iq2_ks(x, (void *)y, 1, k, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq2_ks(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
|
||||
void quantize_row_iq2_ks(const float * x, void * vy, int64_t k) {
|
||||
assert(k % QK_K == 0);
|
||||
block_iq2_ks * y = (block_iq2_ks *)vy;
|
||||
quantize_row_iq2_ks_ref(x, y, k);
|
||||
|
|
@ -994,7 +994,7 @@ size_t quantize_iq2_ks(const float * src, void * dst, int64_t nrows, int64_t n_p
|
|||
return nrows * row_size;
|
||||
}
|
||||
|
||||
void dequantize_row_iq2_ks(const block_iq2_ks * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
|
||||
void dequantize_row_iq2_ks(const block_iq2_ks * x, float * y, int64_t k) {
|
||||
assert(k % QK_K == 0);
|
||||
const int nb = k / QK_K;
|
||||
|
||||
|
|
@ -1334,7 +1334,7 @@ void dequantize_row_iq3_k(const block_iq3_k * x, float * y, int64_t k) {
|
|||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq3_k_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 vec_dot_iq3_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
assert(nrc == 1);
|
||||
GGML_UNUSED(nrc);
|
||||
|
|
@ -3119,4 +3119,102 @@ void vec_dot_iq4_kss_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
|
|||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
//
|
||||
// ========================================= x4
|
||||
//
|
||||
void quantize_row_iq4_nl_x4_ref(const float * x, block_iq4_nl_x4 * y, int64_t k) {
|
||||
// we assume we are called with 4 rows
|
||||
quantize_iq4_nl_x4(x, (void *)y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq4_nl_x4(const float * x, void * y, int64_t k) {
|
||||
// we assume we are called with 4 rows
|
||||
quantize_iq4_nl_x4(x, y, 4, k/4, nullptr);
|
||||
}
|
||||
|
||||
static void repack_iq4_nl(int nrows, int n_per_row, const block_iq4_nl * x, block_iq4_nl_x4 * y) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
GGML_ASSERT(n_per_row%QK4_NL == 0);
|
||||
int nblock = n_per_row/QK4_NL;
|
||||
const block_iq4_nl * x4[4];
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
|
||||
for (int ib = 0; ib < nblock; ++ib) {
|
||||
for (int k = 0; k < 4; ++k) y[ib].d[k] = x4[k][ib].d;
|
||||
for (int k = 0; k < 4; ++k) for (int i = 0; i < 4; ++i) {
|
||||
y[ib].qs[4*k+i+ 0] = (x4[k][ib].qs[i+0] & 0xf) | ((x4[k][ib].qs[i+ 8] & 0x0f) << 4); // 0....3 + 8...11 from each row
|
||||
y[ib].qs[4*k+i+16] = (x4[k][ib].qs[i+0] >> 4) | ((x4[k][ib].qs[i+ 8] & 0xf0)); // 16...19 + 24...27 from each row
|
||||
y[ib].qs[4*k+i+32] = (x4[k][ib].qs[i+4] & 0xf) | ((x4[k][ib].qs[i+12] & 0x0f) << 4); // 4....7 + 12...15 from each row
|
||||
y[ib].qs[4*k+i+48] = (x4[k][ib].qs[i+4] >> 4) | ((x4[k][ib].qs[i+12] & 0xf0)); // 20...23 + 28...31 from each row
|
||||
}
|
||||
}
|
||||
x += 4*nblock;
|
||||
y += nblock;
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq4_nl_x4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
|
||||
GGML_ASSERT(nrows%4 == 0);
|
||||
auto row_size_nl = ggml_row_size(GGML_TYPE_IQ4_NL, n_per_row);
|
||||
std::vector<char> qtmp(4*row_size_nl);
|
||||
//std::vector<float> check1(4*n_per_row), check2(4*n_per_row);
|
||||
char * qrow = (char *)dst;
|
||||
for (int row = 0; row < nrows; row += 4) {
|
||||
quantize_iq4_nl(src, qtmp.data(), 4, n_per_row, imatrix);
|
||||
repack_iq4_nl(4, n_per_row, (const block_iq4_nl *)qtmp.data(), (block_iq4_nl_x4 *)qrow);
|
||||
//dequantize_row_iq4_nl_x4((const block_iq4_nl_x4 *)qrow, check1.data(), 4*n_per_row);
|
||||
//dequantize_row_iq4_nl((const block_iq4_nl *)qtmp.data(), check2.data(), 4*n_per_row);
|
||||
//for (int k = 0; k < 4; ++k) {
|
||||
// auto x1 = check1.data() + k*n_per_row;
|
||||
// auto x2 = check2.data() + k*n_per_row;
|
||||
// int nbad = 0;
|
||||
// for (int j = 0; j < n_per_row; ++j) {
|
||||
// if (std::abs(x1[j] - x2[j]) > 1e-8) {
|
||||
// printf("Oops: %g vs %g\n", x1[j], x2[j]);
|
||||
// if (++nbad > 20) GGML_ABORT("fatal error");
|
||||
// }
|
||||
// }
|
||||
//}
|
||||
src += 4*n_per_row;
|
||||
qrow += 4*row_size_nl;
|
||||
}
|
||||
return nrows*row_size_nl;
|
||||
}
|
||||
|
||||
void dequantize_row_iq4_nl_x4(const block_iq4_nl_x4 * x, float * y, int64_t k) {
|
||||
// we assume we are called with 4 rows
|
||||
int n_per_row = k/4;
|
||||
int nb = n_per_row/QK4_NL;
|
||||
float * yk[4];
|
||||
for (int k = 0; k < 4; ++k) yk[k] = y + k*n_per_row;
|
||||
for (int ib = 0; ib < nb; ++ib) {
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
float scale = GGML_FP16_TO_FP32(x[ib].d[k]);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
yk[k][QK4_NL*ib+i+ 0] = scale * iq4k_values[x[ib].qs[4*k+i+ 0] & 0xf];
|
||||
yk[k][QK4_NL*ib+i+ 8] = scale * iq4k_values[x[ib].qs[4*k+i+ 0] >> 4];
|
||||
yk[k][QK4_NL*ib+i+16] = scale * iq4k_values[x[ib].qs[4*k+i+16] & 0xf];
|
||||
yk[k][QK4_NL*ib+i+24] = scale * iq4k_values[x[ib].qs[4*k+i+16] >> 4];
|
||||
yk[k][QK4_NL*ib+i+ 4] = scale * iq4k_values[x[ib].qs[4*k+i+32] & 0xf];
|
||||
yk[k][QK4_NL*ib+i+12] = scale * iq4k_values[x[ib].qs[4*k+i+32] >> 4];
|
||||
yk[k][QK4_NL*ib+i+20] = scale * iq4k_values[x[ib].qs[4*k+i+48] & 0xf];
|
||||
yk[k][QK4_NL*ib+i+28] = scale * iq4k_values[x[ib].qs[4*k+i+48] >> 4];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq4_nl_x4_q8_0(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_IQ4_NL_X4, vx, 0, GGML_TYPE_Q8_0, 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);
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -63,6 +63,12 @@ void vec_dot_iq2_ks_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void
|
|||
|
||||
void iqk_quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
|
||||
void quantize_row_iq4_nl_x4_ref(const float * GGML_RESTRICT x, block_iq4_nl_x4 * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq4_nl_x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq4_nl_x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void dequantize_row_iq4_nl_x4(const block_iq4_nl_x4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq4_nl_x4_q8_0(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);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -179,6 +179,8 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_IQ3_KL = 146, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_KS = 147, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_KSS = 148, // except 1d tensors
|
||||
//
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 = 225, // except 1d tensors
|
||||
|
||||
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
|
||||
};
|
||||
|
|
|
|||
|
|
@ -3849,6 +3849,7 @@ struct llama_model_loader {
|
|||
case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break;
|
||||
case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break;
|
||||
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
|
||||
case GGML_TYPE_IQ4_NL_X4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_X4;break;
|
||||
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
|
||||
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
|
||||
case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
|
||||
|
|
@ -4553,6 +4554,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
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";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:return "IQ4_NL_X4 - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: return "IQ4_KSS - 4.0 bpw";
|
||||
|
|
@ -15766,6 +15768,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type == GGML_TYPE_Q4_0_8_8) {
|
||||
new_type = GGML_TYPE_Q4_0;
|
||||
}
|
||||
else if (new_type == GGML_TYPE_IQ4_NL_X4) {
|
||||
new_type = GGML_TYPE_IQ4_NL;
|
||||
}
|
||||
}
|
||||
} 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 ||
|
||||
|
|
@ -15827,7 +15832,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : 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_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
}
|
||||
|
|
@ -15840,7 +15845,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
|
||||
if (qs.model.hparams.n_vocab >= 127999 && (qs.model.type == MODEL_8B || qs.model.type == MODEL_70B))
|
||||
new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
}
|
||||
if (qs.model.type == MODEL_70B) {
|
||||
// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
|
||||
// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
|
||||
|
|
@ -15857,6 +15862,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
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_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_IQ4_NL) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_IQ4_NL_X4) new_type = GGML_TYPE_Q5_K;
|
||||
else if (new_type == GGML_TYPE_Q5_K) new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
++qs.i_attention_wv;
|
||||
|
|
@ -15919,8 +15925,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
}
|
||||
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && !qs.has_imatrix) {
|
||||
else if (i_layer < n_layer/8 && !qs.has_imatrix &&
|
||||
(ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4)) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
||||
|
|
@ -15943,7 +15950,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_K ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K) {
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
|
|
@ -16152,6 +16159,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_IQ1_BN: default_type = GGML_TYPE_IQ1_BN; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_BN: default_type = GGML_TYPE_IQ2_BN; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:default_type = GGML_TYPE_IQ4_NL_X4;break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: default_type = GGML_TYPE_IQ4_KS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: default_type = GGML_TYPE_IQ4_KSS; break;
|
||||
|
|
@ -16509,6 +16517,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
|
||||
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
|
||||
}
|
||||
if (new_type == GGML_TYPE_IQ4_NL_X4) {
|
||||
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_NL;
|
||||
else chunk_size_multiplier = 4;
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
|
||||
fflush(stdout);
|
||||
|
|
|
|||
Loading…
Reference in New Issue