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authorKawrakow <iwankawrakow@gmail.com>2024-12-03 12:59:22 +0100
committerGitHub <noreply@github.com>2024-12-03 12:59:22 +0100
commitc5bf589367cd609f4c0ff73a6534bbde7902abe8 (patch)
treefa17f82c717d535222c1843fc9fca2d66f4d6ea7 /ggml/src/ggml.c
parentccec00939a30aa7762a232ac4dcadba985ef9ee4 (diff)
Q5_0_R4 (#121)
* Adding q5_0_r4 We get PP-512(LLaMA-3.1-8B) = 256.7 t/s on a Ryzen-7950X. We even get TG-128 improvement to 11.7 t/s from 11.1 t/s. * q5_0_r4: NEON We get PP-512(LLaMA-3.1-8B) = 99.6 t/s on M2-Max, up from 71.0 t/s for Q5_0. The difference to mainline llama.cpp is no longer funny: they get 26.5 t/s for Q5_0. For TG, we are nor able to fully saturate memory bandwidth and arrive at 22.1 t/s @ 8 threads. Mainline llama.cpp gets 20.6 t/s for Q5_0. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml/src/ggml.c')
-rw-r--r--ggml/src/ggml.c26
1 files changed, 26 insertions, 0 deletions
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index fd65ae67..0eb76a07 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1296,6 +1296,23 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.nrows = 1,
.row_meta_size = 0,
},
+ [GGML_TYPE_Q5_0_R4] = {
+ .type_name = "q5_0_r4",
+ .blck_size = QK5_0,
+ .type_size = sizeof(block_q5_0),
+ .is_quantized = true,
+ .to_float = (ggml_to_float_t) dequantize_row_q5_0_r4,
+ .from_float = quantize_row_q5_0_r4,
+ .from_float_ref = (ggml_from_float_t)quantize_row_q5_0_r4_ref,
+ .vec_dot = vec_dot_q5_0_r4_q8_0,
+#if GGML_USE_IQK_MULMAT && defined __AVX2__
+ .vec_dot_type = GGML_TYPE_Q8_1,
+#else
+ .vec_dot_type = GGML_TYPE_Q8_0,
+#endif
+ .nrows = 1,
+ .row_meta_size = 0,
+ },
};
// For internal test use
@@ -3956,6 +3973,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
case GGML_FTYPE_MOSTLY_IQ4_NL_X4: wtype = GGML_TYPE_IQ4_NL_X4;break;
case GGML_FTYPE_MOSTLY_Q4_0_R4: wtype = GGML_TYPE_Q4_0_R4; break;
+ case GGML_FTYPE_MOSTLY_Q5_0_R4: wtype = GGML_TYPE_Q5_0_R4; break;
case GGML_FTYPE_MOSTLY_Q8_0_R4: wtype = GGML_TYPE_Q8_0_R4; break;
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ4_KS: wtype = GGML_TYPE_IQ4_KS; break;
@@ -10482,6 +10500,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -10927,6 +10946,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -11069,6 +11089,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -14257,6 +14278,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -14639,6 +14661,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -14915,6 +14938,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -15518,6 +15542,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_NL_X4:
case GGML_TYPE_Q4_0_R4:
+ case GGML_TYPE_Q5_0_R4:
case GGML_TYPE_Q8_0_R4:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ4_KS:
@@ -22347,6 +22372,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
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;
case GGML_TYPE_Q4_0_R4: result = quantize_q4_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+ case GGML_TYPE_Q5_0_R4: result = quantize_q5_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_Q8_0_R4: result = quantize_q8_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_KS: result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;