Simplify the implementation of jitter noise in moe models
Author: jiangwangyi
Pull request: yes

# What does this PR do?

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This PR simplifies the implementation of jitter noise in both gptsan_japanese and switch_transformers.

The former implementation is:
```Python
if self.jitter_noise > 0:
    # Get the lower and upper bound of the uniform distribution
    # Adapted from: https://stackoverflow.com/questions/44328530/how-to-get-a-uniform-distribution-in-a-range-r1-r2-in-pytorch
    distrib_lower_bound = 1.0 - self.jitter_noise
    distrib_upper_bound = 1.0 + self.jitter_noise

    uniform_distrib = torch.rand(hidden_states.shape, device=hidden_states.device, dtype=self.dtype)
    uniform_distrib = uniform_distrib * (distrib_lower_bound - distrib_upper_bound)

    uniform_distrib = uniform_distrib + distrib_upper_bound
    # Multiply the token inputs by the uniform distribution - adding some noise
    hidden_states *= uniform_distrib
```
The simplified implementation is:
```Python
if self.jitter_noise > 0:
    # Multiply the token inputs by the uniform distribution - adding some noise
    hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
```

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## Who can review?

cc @ArthurZucker @younesbelkada

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