[perf] optimize whisper GPU performance
Author: JaredforReal
Pull request: yes

# What does this PR do?

## Optimize WhisperFeatureExtractor GPU performance

### Problem
The current `WhisperFeatureExtractor._torch_extract_fbank_features()` has significant performance overhead when using GPU:
- Creates `torch.hann_window()` on every call
- Transfers `mel_filters` from CPU→GPU on every call  
- Forces GPU→CPU synchronization even when downstream ops need GPU tensors
- Multiple unnecessary device transfers cause ~300ms latency

### Solution
Implement lazy caching mechanism to reuse GPU tensors across calls:

**Key Changes:**
1. **Add caching** (`_ensure_gpu_cache`): Cache `hann_window` and `mel_filters` on target device
2. **Optimize mel_filters**: Pre-transpose and make contiguous for faster matmul
3. **Support tensor input**: Accept both `np.ndarray` and `torch.Tensor` inputs
4. **Add `return_tensors` parameter**: Keep results on GPU when `device != "cpu"` and `return_tensors="pt"`
5. **Minor optimizations**: Use `torch.amax()` instead of chained `.max()` calls

### Performance Impact
- **First call**: One-time cache initialization overhead
- **Subsequent calls**: Significant speedup by eliminating repeated CPU↔GPU transfers
- **vLLM use case**: Reduces feature extraction from ~300ms to ~1-2ms (150-300x faster)

### Backward Compatibility
✅ Fully backward compatible:
- Default behavior unchanged (`device="cpu"`)
- Existing code works without modifications
- All 19 core tests pass

### Testing
- ✅ All existing CPU tests pass
- ✅ Numerical precision maintained (1e-5 tolerance)
- ✅ Works with both numpy and torch inputs
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Fixes # (issue)


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Comments:
**[For maintainers]** Suggested jobs to run (before merge)

run-slow: glmasr, whisper

cc @eustlb @ebezzam