🚨🚨🚨 Optimize Top P Sampler and fix edge case
# What does this PR do?

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This PR does the following:
1. Fixes #18976
2. Optimizes the Top P sampler Pytorch implementation by removing the need to clone an intermediate tensor and shifting things to right.
3. Add edge case test to PT, TF, FLAX



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## Who can review?

@gante @patrickvonplaten 

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Models:

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_The documentation is not available anymore as the PR was closed or merged._<|||||>@gante the proposed PT implementation passes the edge case. I also added the edge case locally and verified that the existing FLAX implementation passes the edge case with no change required in its implementation. 

However, the TF implementation passes the edge case when `use_xla` is True but fails when it is false in my local machine. Hence, I reverted the addition of edge case to TF and FLAX in my PR. It seems that the behavior changes when using xla for TF. 

Can you please confirm if just replacing 0.7 with 0.8 in this test succeeds in your local machine?
https://github.com/huggingface/transformers/blob/a86acb75ad832fd604a1d5b5e5089f299aae5df4/tests/generation/test_generation_tf_logits_process.py#L192<|||||>I was investigating on TF's behavior and found this:

This is the input distribution to the test:
https://github.com/huggingface/transformers/blob/a86acb75ad832fd604a1d5b5e5089f299aae5df4/tests/generation/test_generation_tf_logits_process.py#L190

The above goes to TFTopPLogitsWrapper which takes a cumsum here:
https://github.com/huggingface/transformers/blob/a86acb75ad832fd604a1d5b5e5089f299aae5df4/src/transformers/generation_tf_logits_process.py#L173

This `cumulative_probs` gets different value for `use_xla` as True or False in the unittest. 
1. When `use_xla` is True then `cumulative_probs` is [[0.5, 0.8,  0.90000004, 1.],
       [0.29999998, 0.59999996, 0.8499999 , 0.99999994]]
2. When `use_xla` is False then `cumulative_probs` is [[0.5, 0.79999995, 0.9, 1. ],
 [0.3, 0.6, 0.85, 1. ]

This is causing an extra sample to get be sampled in the 1st batch when `use_xla` is False as 0.79999995 is < 0.8.

How should we proceed forward? This issue of changing behavior is not there in PT and FLAX so should we go ahead with just PT and FLAX for this PR and raise this as a separate TF issue in transformers repo?<|||||>@ekagra-ranjan we could add an if/else depending on whether `use_xla` is True or not, and set `top_p` to `0.8` or `0.79999995` accordingly. 

However, since this edge case has such low impact in practice, it's okay if we take the simpler path and simply set `top_p` to `0.79999995`. It won't test the edge case with XLA, but at least it is tested once (with eager execution, i.e. with `use_xla=False`).

P.S.: TF's softmax is known to have these minor numerical instabilities.<|||||>@gante Thank you for your reviews! Edge case test for FLAX and TF have been added and are passing<|||||>@sgugger Sure, done.