fix: convert past_length to int in _prefill for static cache compatib…
Author: silicon-dispatch-100
Pull request: yes
Labels: name: Code agent slop

…ility

StaticCache.get_seq_length() returns self.cumulative_length which is intentionally a device tensor (for torch.compile). But _prefill uses it in arithmetic that produces next_sequence_length, which is later passed to prepare_inputs_for_generation(next_sequence_length: int | None).

When next_sequence_length is a tensor instead of int, the slice input_ids[:, -next_sequence_length:] produces wrong (0-length) results, causing model.forward to crash on view(1, 0, -1, 128).

Fix: convert past_length to Python int in _prefill before arithmetic. This preserves cumulative_length as tensor on the model (for compile) while ensuring correct slice behavior in the generation loop.

Fixes #46858

# What does this PR do?

Fixes a bug where `_prefill()` passes a `torch.Tensor` as `next_sequence_length` to
`prepare_inputs_for_generation()` (which expects `int | None`), causing incorrect
slicing that produces 0-length `input_ids` on the second `generate()` call with
static cache.

`StaticLayer.get_seq_length()` returns `self.cumulative_length` — a device tensor by
design (comment: "Very important that it's a tensor here, to avoid recompiling"). But
`_prefill` uses this in int arithmetic and passes the result to 
`prepare_inputs_for_generation(next_sequence_length: int | None)`.

On non-CUDA backends where `torch.compile` retraces on shape changes, the tensor type
causes `input_ids[:, -tensor([4096]):]` to produce `(1, 0)` shaped tensors instead of
`(1, 4096)`.

## Fix

2-line addition in `_prefill()`:
```python
if isinstance(past_length, torch.Tensor):
    past_length = int(past_length.item())
```

## Who is affected?

Any user running `model.generate()` multiple times with:
- `cache_implementation="static"`
- `torch.compile` (any backend except CUDA with `reduce-overhead` which masks the bug)

This includes custom accelerator backends (QAIC, XPU, etc.) and potentially
CPU backend with inductor.

## Tests

- `test_prefill_static_cache_tensor_seq_length`: Verifies no crash on 3 consecutive calls
- `test_prefill_next_sequence_length_type`: Verifies the type contract is maintained

## Related

- `StaticLayer.get_seq_length()` return type annotation says `int` but returns `Tensor`
  (separate issue — the tensor is intentional for torch.compile, but annotation is misleading)

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Fixes # (issue)

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Comments:
 The CI failures are unrelated to this change. This PR only adds 2 lines
  (isinstance check + int conversion) in `_prefill` which is a no-op when
  `get_seq_length()` already returns an int (as it does on CUDA). The failing
  tests appear to be pre-existing/flaky failures unrelated to generation or
  static cache.

  Happy to rebase if needed once the unrelated failures are resolved.


**CI Dashboard:** [View test results in Grafana](https://transformers-ci.lor-e.huggingface.cool/d/pytest-observability-by-pr/pytest-observability-branch?var-pr=46863)