`AutoTokenizer` not enforcing `use_fast=True`

This issue is about AutoTokenizer not enforcing use_fast=True.

This works:

$ python -c "from transformers import AutoTokenizer; t=AutoTokenizer.from_pretrained('facebook/opt-13b', use_fast=True); \
assert t.is_fast, 'tokenizer is not fast'; print('Success')" 
Success

now the same code, but a different model ‘facebook/opt-1.3b’ that doesn’t have a fast optimizer:

$ python -c "from transformers import AutoTokenizer; t=AutoTokenizer.from_pretrained('facebook/opt-1.3b', use_fast=True); \
assert t.is_fast, 'tokenizer is not fast'; print('Success')" 
Traceback (most recent call last):
  File "<string>", line 1, in <module>
AssertionError: tokenizer is not fast

now the doc says:

use_fast (bool, optional, defaults to True) — Whether or not to try to load the fast version of the tokenizer.

so it sort of hints with “try to load” that it won’t enforce it. But would you be open to a less ambiguous definition? something like:

use_fast (bool, optional, defaults to True) — Will try to load the fast version of the tokenizer if there is one and 
will quietly fallback onto the normal (slower) tokenizer if the model doesn't provide a fast one.

I think the use_fast arg name is ambiguous - I’d have renamed it to try_to_use_fast since currently if one must use the fast tokenizer one has to additionally check that that AutoTokenizer.from_pretrained returned the slow version.

not sure, open to suggestions.

context: in m4 the codebase currently requires a fast tokenizer.

Thank you!

cc: @ArthurZucker

[ArthurZucker]

It is indeed a bug, the facebook/opt-1.3b tokenizer config is missing the tokenizer_type variable. And the use_fast argument is not passed down properly in that case. The fix is here #20823

[ArthurZucker]

Agreed, the problem is now the inconsistency between two models. If it is only OPT related we can leave it as is, otherwise will have a look