Fix T5 incorrect weight decay in Trainer and official summarization example
Author: ADAning
Pull request: yes



# What does this PR do?

Official summrization examples use T5 as pretrained model, but the name of T5 layer norm is `layer_norm`, not `layerNorm`:
```
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("t5-small")
for n, p in model.named_parameters():
    print(n)
```
Output:
```
...
encoder.block.0.layer.0.layer_norm.weight
encoder.block.0.layer.1.DenseReluDense.wi.weight
encoder.block.0.layer.1.DenseReluDense.wo.weight
encoder.block.0.layer.1.layer_norm.weight
...
```
In [Official example of summarization](https://github.com/huggingface/transformers/blob/49c8c67fb815a277405f84dea4a66353e19fb347/examples/pytorch/summarization/run_summarization_no_trainer.py#L529), `layer_norm` not included:
```
no_decay = ["bias", "LayerNorm.weight"]
```

A similar problem occurred in [trainer.py](https://github.com/huggingface/transformers/blob/49c8c67fb815a277405f84dea4a66353e19fb347/src/transformers/trainer.py#L970), which may cause `Seq2SeqTrainer` train T5 layer norm with weight decay:
```
if self.optimizer is None:
            decay_parameters = get_parameter_names(opt_model, [nn.LayerNorm])
            decay_parameters = [name for name in decay_parameters if "bias" not in name]
```
Because T5 uses [T5LayerNorm](https://github.com/huggingface/transformers/blob/49c8c67fb815a277405f84dea4a66353e19fb347/src/transformers/models/t5/modeling_t5.py#L239) Layer norm not`nn.LayerNorm`:

```
class T5LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
```


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Models:

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Comments:
_The documentation is not available anymore as the PR was closed or merged._

Thanks a lot for iterating with us!