make test_eager_matches_sdpa_inference less flaky
#34512
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ydshieh
commented
Oct 30, 2024
ydshieh
commented
Oct 30, 2024
ydshieh
commented
Oct 30, 2024
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
gante
approved these changes
Oct 31, 2024
ArthurZucker
approved these changes
Oct 31, 2024
| @@ -1263,6 +1263,9 @@ def test_dola_decoding_sample(self): | |||
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| if model.get_output_embeddings() is None: | |||
| self.skipTest("DoLa is not supported for models that don't have output embeddings") | |||
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| logits_processor_kwargs = self._get_logits_processor_kwargs(do_sample=True, config=model.config) | |||
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do sample is random no?
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I am using the same value as in generation_kwargs = {...} a few line below.
Yes it is random but this method is test_...._sample so makes sense.
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* try * try * try * try * try * try * update * update * update * update * update * update * update --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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* try * try * try * try * try * try * update * update * update * update * update * update * update --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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* try * try * try * try * try * try * update * update * update * update * update * update * update --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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What does this PR do?
With
torch.bfloat16the numerical difference/instability occurs quite often, especially with multiple hidden layers.This PR first changes
test_eager_matches_sdpa_inferenceto create models with only 1 hidden layer.number of failures per 500 runs
Then it relaxes the condition a bit: only checks 80% of the sequences. If the results match on those 80%, the test pass.
This makes the test much less flaky. On 500 runs, it pass (for llama, mistral, idefics2 and Llava)
Finally, change the image size of
llavaandVipLlavafrom30to8so the sequence length is much smaller and avoid numerical issues.