Fixed resize_token_embedding issue #21053

[susnato]

What does this PR do?

There was a typo in Line 449 in huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_tf_gpt2.py where the code was doing a check between input_ids and self.vocab_size but resize_token_embeddings change self.config.vocab_size so we were getting the error described in the issue, to overcome this I replaced it with self.config.vocab_size and it worked.

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Who can review?

Anyone in the community is free to review the PR once the tests have passed.
@sgugger

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[sgugger]

cc @gante and @Rocketknight1

[gante]

This is absolutely correct. self.vocab_size can easily get stale when the vocabulary gets updated, and the check should be done against the config.

(there are other models with this issue, where the fix needs to be slightly different, so I'll have a look very soon)

[gante]

@sgugger feel free to merge if you approve. As I wrote above, other models have a similar problem (which require a more elaborate fix)

[gante]

@susnato can you remove the Fixes https://github.com/huggingface/transformers/issues/21053 at the top? That way, the issue stays open and I'll likely won't forget to fix the other models :)

[susnato]

@susnato can you remove the Fixes https://github.com/huggingface/transformers/issues/21053 at the top? That way, the issue stays open and I'll likely won't forget to fix the other models :)

Hi, @gante I removed the line...is it ok now?

[sgugger]

Thanks for the fix!

[susnato]

This is absolutely correct. self.vocab_size can easily get stale when the vocabulary gets updated, and the check should be done against the config.

(there are other models with this issue, where the fix needs to be slightly different, so I'll have a look very soon)

Hi, @gante if you want, I would be happy to look into this and fix if I can.

[gante]

@susnato sounds good!

My plan consists in removing all references to self.vocab_size, deleting the variable whenever it is a variable that is set at __init__ time from the config (if needed, store the config in self.config instead, since it will hold the mutable vocabulary size).

If you search for "tf.cast(self.vocab_size", you will find all matches that will likely have to be touched.

[susnato]

@susnato sounds good!

My plan consists in removing all references to self.vocab_size, deleting the variable whenever it is a variable that is set at __init__ time from the config (if needed, store the config in self.config instead, since it will hold the mutable vocabulary size).

If you search for "tf.cast(self.vocab_size", you will find all matches that will likely have to be touched.

Hi @gante I am going to check for all models in src/transformers/models/modeling_tf_<model>.py to remove references of self.vocab_size and also I found some references of self.vocab_size in some of the <model>MLMHead, I need to change them too right?

[gante]

@susnato yes. If we look at the corresponding PT implementation e.g. for Albert, the layer classes store self.config = config for future use, as opposed to individual attributes of config. Making the switch here protects us from errors like the one that originated this PR :)