[whisper] compile compatibility with long-form decoding (#31772)

* [whisper] compile compatibility with long-form decoding

* clarify comment

* fix after rebase

* finalise

* fix bsz

* fix cache split

* remove contiguous

* style

* finish

* update doc

* prevent cuda graph trace

docs/source/en/model_doc/whisper.md

@@ -72,7 +72,7 @@ Here is a step-by-step guide to transcribing an audio sample using a pre-trained
 ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
 ```
 
-Whisper is compatible with the following optimisations:
+Whisper is compatible with the following optimisations for both short and long-form generation:
 - [PyTorch Scaled Dot Product Attention (SDPA)](../perf_infer_gpu_one#pytorch-scaled-dot-product-attention): flash attention and memory-efficient attention kernels. Enabled by default for `torch>=2.1.1`.
 - [Flash Attention 2](../perf_infer_gpu_one#flashattention-2): improved implementation of flash attention through better parallelism and work partitioning. 
 - [torch.compile](../llm_optims#static-kv-cache-and-torchcompile): JIT-compile the forward pass to dispatch to efficient fused kernels.
@@ -101,7 +101,8 @@ As an example, the following codesnippet enables SDPA and `torch.compile` for up
 ... ).input_features
 
 >>> # Compile the forward pass
->>> _ = model.generate(input_features)
+>>> for _ in range(2):
+>>>     model.generate(input_features)
 
 >>> # Generate token ids using compiled graph (fast!)
 >>> predicted_ids = model.generate(input_features)