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authorKawrakow <iwankawrakow@gmail.com>2024-10-16 15:18:26 +0300
committerGitHub <noreply@github.com>2024-10-16 15:18:26 +0300
commit76b97c80645362ac65a2e33043fd8d46bdaf8c56 (patch)
treeb2b8ab9efb91a6ce4dd9d0fccbc9e11141ca1d80 /ggml/src/iqk/iqk_quantize.h
parent993ca95e9e3108f0352fa2a3384cab0775c7f7c1 (diff)
Adding IQ4_KSS: 4.0 bpw quants (#89)
* iq4_kss: WIP * iq4_kss: CUDA dequantize works So we can run perplexity. Sadly, the result does not look good on the bpw vs quantization error plot. * iq4_kss: slightly better quantization * iq4_kss: another small quantization improvement * iq4_kss: CUDA works TG-128 performance is very decent with 131 t/s for LLaMA-3.1-8B. In comparison, we have 123 t/s for q4_0 and 128 t/s for iq4_ks. I.e., the reduced model size more than offsets the additional bit fiddling required for iq4_kss. * iq4_kss: new bit arrangement - CUDA and Zen4 work Did not lose performance on CUDA. Zen4 is decent, but not great: PP-512(LLaMA-3.1-8B) = 163 t/s. TG-128 is of course better than other 4-bit quants due to smaller model size. We get 14.5 t/s @ 8 threads. * iq4_kss: ARM_NEON. Predictably very slow * iq4_kss: Metal PP is not too bad - just 10% slower than q4_0. But TG is 30% slower, i.e., predictably bad. * iq4_kss: somewhat faster Metal dot product 45.75 t/s -> 48.75 t/s. Still 22% slower than q4_0 * iq4_kss: AVX2 Bad, but better than I expected. PP-512(LLaMA-3.1-8B) = 167 t/s on the Ryzen-5950X. I.e., with 32 AVX2 threads we get the performance of 16 Zen4 threads. * iq4_kss: very slightly faster Metal dot product 48.7 t/s -> 49.3 t/s --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml/src/iqk/iqk_quantize.h')
-rw-r--r--ggml/src/iqk/iqk_quantize.h6
1 files changed, 6 insertions, 0 deletions
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index eb562779..e0dde0d8 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -61,6 +61,12 @@ size_t quantize_iq4_ks(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst
void dequantize_row_iq4_ks(const block_iq4_ks * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_iq4_ks_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_iq4_kss_ref(const float * GGML_RESTRICT x, block_iq4_kss * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_kss(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_iq4_kss(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+void dequantize_row_iq4_kss(const block_iq4_kss * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void vec_dot_iq4_kss_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_ks_ref(const float * GGML_RESTRICT x, block_iq2_ks * GGML_RESTRICT y, int64_t k);
void quantize_row_iq2_ks(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
size_t quantize_iq2_ks(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);