Fix big model inference for T5 models in float16
# What does this PR do?

This PR fixes big model inference for large T5 models. The problem is that T5 models have some weights kept in float32, which interferes with the computation of `infer_auto_device_map`. Accelerate adds the functionality to deal with this in [this PR](https://github.com/huggingface/accelerate/pull/1179), and a patch release will be out soon with the fix in a release.

When this is done, this PR can be merged so the fix can be used. With this I can do
```py
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained('google/flan-ul2')
model = T5ForConditionalGeneration.from_pretrained('google/flan-ul2', device_map = 'auto', torch_dtype=torch.float16)
input_string = 'Answer the following question by reasoning step by step. I start with 10 bananas. A monkey eats three of them, and then gives me an avocado. How many bananas do I have left?'
inputs = tokenizer(input_string, return_tensors = 'pt').to('cuda:0')
outputs = model.generate(inputs['input_ids'], max_length = 200)
print(tokenizer.decode(outputs[0]))
```
whereas before this went OOM.
_The documentation is not available anymore as the PR was closed or merged._