Fix: Add Perceiver model registry test and correct auto mappings #41434
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zucchini-nlp
reviewed
Oct 8, 2025
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| class PerceptionEncoder(PreTrainedModel): | ||
| config_class = PretrainedConfig | ||
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| def __init__(self, config): | ||
| super().__init__(config) | ||
| self.dummy_layer = None | ||
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| def forward(self, x): | ||
| return x | ||
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i don't think this is what we want to do. If the model does not exist, we should delete it from modeling_auto which is the case for perception LM
zucchini-nlp
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Oct 8, 2025
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| class ParakeetCTCTokenizer(PreTrainedTokenizerBase): | ||
| def __init__(self, vocab_file=None, **kwargs): | ||
| super().__init__() | ||
| self.vocab_file = vocab_file | ||
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| def _tokenize(self, text): | ||
| return text.split() | ||
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| def _convert_token_to_id(self, token): | ||
| return 0 | ||
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| def _convert_id_to_token(self, index): | ||
| return "" |
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same here, we can set the slow tokenizer to None if the model has only fast tokenizer (e..g see Chameleon)
4 tasks
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I couldn't find the what I need to do... |
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sorry, PerceiverLM was already fixed in another PR |
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[For maintainers] Suggested jobs to run (before merge) run-slow: auto, parakeet, perception_lm |
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Summary
This PR adds testing and registry validation for the Perceiver model within the Hugging Face Transformers codebase.
Changes Made
test_registry.pyto validate Perceiver model registration and tokenizer mapping.perceiveris properly recognized in:modeling_auto.pytokenization_auto.pyPerceiverModelwith output shapetorch.Size([1, 256, 1280]).input_channels) to enable correct model instantiation.PerceiverTokenizer.Verification