[WIP] T5v1.1 & MT5
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Maybe wrong model config for T5.1.1. For instance, T5.1.1.small should have num_layers=8 and num_heads=6.

See https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/models/gin/models/t5.1.1.small.gin<|||||>> Maybe wrong model config for T5.1.1. For instance, T5.1.1.small should have num_layers=8 and num_heads=6.
> 
> See https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/models/gin/models/t5.1.1.small.gin

Thanks yeah, I implemented that. 

The new model structure is now equal to mesh t5 v1.1. 

If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal. 

There is still quite some work to do: write more tests, lots of cleaning and better design, and check if mT5 works with it.<|||||>> If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal.

Hi, `check_t5_against_hf.py` still fails if I use a longer input text instead of `Hello there`, like `Hello there. Let's put more words in more languages than I originally thought.`
<|||||>> > If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal.
> 
> Hi, `check_t5_against_hf.py` still fails if I use a longer input text instead of `Hello there`, like `Hello there. Let's put more words in more languages than I originally thought.`

Hmm, it works for me - do you experience that for T5v1.1 or mT5?<|||||>> > > If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal.
> > 
> > 
> > Hi, `check_t5_against_hf.py` still fails if I use a longer input text instead of `Hello there`, like `Hello there. Let's put more words in more languages than I originally thought.`
> 
> Hmm, it works for me - do you experience that for T5v1.1 or mT5?

Aha, the checking is OK now. Yesterday I made a mistake that when I changed the test input sentence in the check script, I didn't update the input length for MTF model from 4 to a longer value like 128. So actually the MTF model and PyTorch model received different inputs, and of course got different results.

Besides, if I add the z-loss to the CE loss at last, it differs from MTF score again. I just found MTF ignores z-loss when not training ([code](https://github.com/tensorflow/mesh/blob/4f82ba1275e4c335348019fee7974d11ac0c9649/mesh_tensorflow/transformer/transformer.py#L781)). So I think MTF model score does not include z-loss, but its training does, which is absent from HF T5 training. Well, this is absolutely not a blocking issue now.

Appreciate your great work :) <|||||>closing in favor of https://github.com/huggingface/transformers/pull/8552.