eneration: meta-safe _prepare_special_tokens + regression tests

[moonrunnerkc]

Summary

What / Why

This PR makes generation/utils.py::_prepare_special_tokens meta-safe.

In assisted decoding, special-token tensors could be created on the meta device and then accessed via .item() or .cpu().numpy(), which triggers:

RuntimeError: Tensor.item() cannot be called on meta tensors

This patch avoids unsafe operations by rebuilding safe tensors on the requested device using Python IDs from GenerationConfig, and introduces a clear error type for unsupported cases.

• ✅ Adds MetaSafeTensorError for explicit failures instead of opaque framework errors.
• ✅ Hardens special-token setup so assisted decoding succeeds under concurrency and in meta-aware pipelines.

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Scope

src/transformers/generation/utils.py

• Patch _prepare_special_tokens to be meta-safe.
• Fix internal helper for ID → tensor conversion (no .item() or .cpu().numpy() on meta).
• Add MetaSafeTensorError (subclass of RuntimeError) for unsupported meta ops.

tests/test_generation_meta.py

• Add regression tests covering CPU path, meta path, output consistency, and no config drift.

Note: No changes to public APIs. No behavioral change for non-meta paths.

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Details of the Fix

• Special token IDs provided as tensors on meta are not moved or read directly.
• Instead, fresh scalar tensors are reconstructed on the requested device using the underlying Python IDs from config.
• .item() / .cpu().numpy() are never called on meta tensors.
• If a non-scalar meta tensor is encountered without a safe conversion path, we raise MetaSafeTensorError with a descriptive message.

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Regression Tests

New tests in tests/test_generation_meta.py:

• test_prepare_special_tokens_cpu – CPU tensors work as before.
• test_prepare_special_tokens_meta – Meta tensors no longer raise; function completes.
• test_prepare_special_tokens_consistency – Outputs match between CPU and meta paths.
• test_no_drift_after_prepare – Confirms GenerationConfig is not mutated.

✅ All tests pass locally and in CI (ubuntu-latest, Python 3.10 & 3.12).

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Related

• Refs: num_assistant_tokens not configured in assistant model #40739, Configure assistant model's generation_config with user parameters #40740 (context: assistant-side parameters like num_assistant_tokens and assisted decoding flows).
• CC: @geoffrey-young

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Backward Compatibility

• No user-visible change for non-meta execution.
• Meta-aware execution paths are now robust: assisted decoding no longer crashes on .item() from meta tensors.

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Performance

• Negligible overhead — only touches scalar special-token handling during generation setup.
• No extra allocations beyond tiny scalar tensors when needed.

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Validation

• Local

  pytest -q tests/test_generation_meta.py # PASS

• CI (GitHub Actions, ubuntu-latest, Py3.10/3.12)

  Full test suite including new meta safety tests → PASS

• Concurrency probes

Assisted decoding succeeds with no config drift.

Checklist

• Existing tests pass
• New tests added
• Ran make fixup (format/quality) locally
• No API changes / docs not required
• Minimal, well-scoped patch with regression coverage

Notes for Reviewers

• Change is intentionally minimal and defensive only where necessary.
• MetaSafeTensorError makes failures explicit; happy to relocate to a shared errors module if preferred.
• Can also add a doc comment in GenerationConfig noting that special token IDs may be passed as ints or tensors (including meta), and are normalized during generation.

[moonrunnerkc]

The Check tiny models CI job is failing because there isn’t a compatible tokenizers build for Python 3.8 (>=0.22,<0.23). This is an environment issue in the CI setup, not related to the changes in this PR. All tests pass locally and in CI on Python 3.10 and 3.12.