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Refactor generation benchmark to compare with AWQ and HQQ #128

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merged 3 commits into from
Mar 21, 2024

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@dacorvo dacorvo commented Mar 21, 2024

The same generation metrics can now be evaluated on models quantized using quanto, BitdsAndBytes, hqq and AutoAWQ.

Example on princeton-nlp/Sheared-LLaMA-1.3B with int4 weights and float16 activations on an Nvidia A10:

library perplexity prediction latency
fp16 8.85 0.83 % 24 ms
quanto 9.99 0.81 % 69 ms
bnb 9.35 0.82 % 35 ms
hqq 9.05 0.83 % 96 ms
AutoAWQ 9.02 0.19 % 7 ms

@dacorvo dacorvo merged commit 96871c1 into main Mar 21, 2024
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@dacorvo dacorvo deleted the benchmark_libs branch March 21, 2024 14:37
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