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| author | Kawrakow <48489457+ikawrakow@users.noreply.github.com> | 2024-01-30 15:14:12 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-01-30 15:14:12 +0200 |
| commit | f4d7e5497485ce6ce0e322533930b7da4657dd2d (patch) | |
| tree | 78b30048cb4a9c78d5cf3e231a1ac3e9ed190577 /tests/test-quantize-perf.cpp | |
| parent | 2256f36b79a932a478d4dcdf02c1e5a60056e5f3 (diff) | |
SOTA 3-bit quants (#5196)
* iq3_xxs: quantize/dequantize
RMSE seems a bit high-ish at about half-way between q2_K and
q3_K, so need to check more.
* iq3_xxs: CUDA dequantize works
* iq2_xxs: tuning quantization
* iq3_xxs: starting to look better
PPL on wiki.test.raw
LLaMA-v1-7B: 6.4218
LLaMA-v2-7B: 6.3560
Mistral-7B : 6.0717
This is better than Q3_K_XS, with a 5% reduction in quantized model
size.
* iq3_xxs: CUDA dot product
We have
PP-512: 5891 t/s
TG-128: 143.9 t/s
* iq3_xxs: scalar and AVX2 dot products
* iq3_xxs: ARM_NEON and Metal
Metal performance is decent, ARM_NEON is pathetic
* iq3_xxs: slightly better grid points
* Faster iq3_xxs and iq2_xs dot products on CUDA
* iq3_xxs: add some quant mix
* iq3_xxs: fix failing quantization test
Dot product still fails. Is this real?
* iq3_xxs: hopefully fix ROCm
* iq3_xxs: failing tests
This time the dot product accuracy did find an actual bug
in the AVX2 implementation.
* Add IQ3_XXS to test-backend-ops
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'tests/test-quantize-perf.cpp')
| -rw-r--r-- | tests/test-quantize-perf.cpp | 2 |
1 files changed, 2 insertions, 0 deletions
diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index 09d410b7..8ec81734 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -278,6 +278,8 @@ int main(int argc, char * argv[]) { if (qfns.from_float && qfns.to_float) { printf("%s\n", ggml_type_name(type)); + ggml_quantize_init(type); + if (params.op_quantize_row_q_reference) { printf(" quantize_row_q_reference\n"); for (size_t size : params.test_sizes) { |