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| author | Kawrakow <iwankawrakow@gmail.com> | 2024-12-02 07:25:39 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-12-02 07:25:39 +0100 |
| commit | 6d0462d4a39085a9f9da04e0a5fc7cc9d4578818 (patch) | |
| tree | b7fd71bda09bb8e2315feff8b6128ad0b7cbefc7 /ggml/src/ggml-quants.c | |
| parent | 8ad84b9fab9570c36220cb791f9a67a4d2c7fd2f (diff) | |
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>
Diffstat (limited to 'ggml/src/ggml-quants.c')
| -rw-r--r-- | ggml/src/ggml-quants.c | 1 |
1 files changed, 1 insertions, 0 deletions
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index d18b1981..376c97f8 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -15196,6 +15196,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte case GGML_TYPE_IQ6_K: break; case GGML_TYPE_IQ4_KS: break; case GGML_TYPE_IQ4_KSS: break; + case GGML_TYPE_IQ4_NL_X4: break; case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: { |