OPTForCausalLM lm_head input size should be config.word_embed_proj_dim
Author: vfbd
Pull request: yes

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The input size of `lm_head` in `OPTForCausalLM` should be `config.word_embed_proj_dim`, not `config.hidden_size`. This is because, like the comment above the changed line says, `lm_head.weight` is tied to `model.decoder.embed_tokens.weight`, so the input size of lm_head should be the output size of embed_tokens (which is `config.word_embed_proj_dim`) and vice-versa.


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Comments:
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Hi!
Thanks for pointing out the issue :)
@patrickvonplaten @ArthurZucker  Do we have tests with models that doesn't have the same `hidden_dim` and `word_embed_proj_dim` ? Wondering why the tests are still passing

For extra information, we noticed this on KoboldAI and our software automatically saves the model the first time it is downloaded to the cache. We do that to help our users store the model in the most suitable format for them for later offline use. So if all tests pass also check the model after it has been saved using huggingface transformers rather than the converters.

Regarding tests, we should probably add a fast test that randomly initializes a model with `word_embed_proj_dim` != `hidden_size`. Essentially, we could add a test like the following: https://github.com/huggingface/transformers/blob/ee393c009a243bbb86fa11d2efa771a1704d85cf/tests/models/opt/test_modeling_opt.py#L235
Only that we overwrite the `word_embed_proj_dim` variable with something != hidden_size before initializing a random model.
@vfbd would cool if you could add a test for this - if you have no time that's totally fine as well and we could add a test afterwards

> Regarding tests, we should probably add a fast test that randomly initializes a model with `word_embed_proj_dim` != `hidden_size`. Essentially, we could add a test like the following:
> 
> https://github.com/huggingface/transformers/blob/ee393c009a243bbb86fa11d2efa771a1704d85cf/tests/models/opt/test_modeling_opt.py#L235
> 
> 
> Only that we overwrite the `word_embed_proj_dim` variable with something != hidden_size before initializing a random model.
> @vfbd would cool if you could add a test for this - if you have no time that's totally fine as well and we could add a test afterwards



I think I can take care of that in my FLAX PR, or should I rather create a new PR? 

> in

A new PR would be great :-)

Any updates on this?

Thanks for the ping @mrseeker - this is good for merge IMO :-)