test(version-compat): keep GRPO fake-run logits finite on CPU (#7247)

* test(version-compat): keep GRPO fake-run logits finite on CPU

The GRPO fake-run test samples completions from a tiny untrained model on
CPU. Such a model can emit non-finite logits, so torch.multinomial inside
generate() intermittently raises "probability tensor contains either inf,
nan or element < 0" -- a nondeterministic sampling failure, not a regression
(the Trainer already fixes the seed, but CPU reduction order is not
bit-reproducible). Add a forward hook that sanitizes the LM head logits to a
finite bounded range before sampling, so the fake run reliably exercises the
whole train loop; the test checks the loop runs, not the numerics.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* test(version-compat): drop redundant nan_to_num bounds (clamp handles them)

* test(version-compat): scope GRPO finite-logits guard to the GRPO test

Only test_grpo_trains_on_cpu autoregressively samples completions, so it is
the only canary that can hit the non-finite-logits torch.multinomial crash.
Move the _guard_finite_logits hook out of the shared _load_plain() and into
test_grpo_trains_on_cpu so the SFT and DPO canaries keep asserting against the
model's true, unclamped logits.

---------

Co-authored-by: Daniel Han <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

tests/version_compat/test_trl_fake_train_cpu.py

@@ -158,6 +158,37 @@ except Exception:
 _MODEL = "hf-internal-testing/tiny-random-LlamaForCausalLM"
 
 
+def _guard_finite_logits(model):
+    """Keep the LM head logits finite so GRPO sampling can't crash.
+
+    ``test_grpo_trains_on_cpu`` samples completions from a tiny, *untrained*
+    random model on CPU. Driven autoregressively -- and nudged by the fake
+    reward's optimizer step between the two train steps -- such a model can emit
+    non-finite logits, so ``torch.multinomial`` inside ``generate()``
+    intermittently raises "probability tensor contains either `inf`, `nan` or
+    element < 0". That is a well-known nondeterministic sampling failure, not an
+    Unsloth/TRL regression: the Trainer already fixes the seed, but CPU reduction
+    order is not bit-reproducible, so the blow-up still surfaces every so often.
+
+    Sanitize the logits to a finite, bounded range (out of place, so autograd
+    stays valid) before they reach the sampler. This test asserts the train loop
+    runs end to end, not the (deliberately meaningless) numerics, so bounding the
+    logits changes nothing it checks while making the run reliable.
+    """
+
+    def _finite_logits_hook(_module, _inputs, output):
+        logits = getattr(output, "logits", None)
+        if logits is None:
+            return output
+        # nan_to_num maps nan -> 0 and the infinities to large finite values;
+        # clamp then bounds everything to [-30, 30].
+        output.logits = torch.nan_to_num(logits).clamp(-30.0, 30.0)
+        return output
+
+    model.register_forward_hook(_finite_logits_hook)
+    return model
+
+
 def _load_plain():
     """Tiny plain HF model + tokenizer on CPU. Skips (not fails) if the model
     cannot be fetched -- that is a network/hub issue, not an unsloth regression."""
@@ -233,6 +264,11 @@ def test_grpo_trains_on_cpu(tmp_path):
 
     assert GRPOTrainer.__name__ == "UnslothGRPOTrainer", "GRPO patch did not apply"
     model, tok = _load_plain()
+    # GRPO is the only canary that autoregressively samples completions, so it is
+    # the only one that can hit the non-finite-logits multinomial crash. Install
+    # the guard here (not in _load_plain) so the SFT/DPO canaries keep asserting
+    # against the model's true, unclamped outputs.
+    _guard_finite_logits(model)
     ds = Dataset.from_list([{"prompt": "hi there"}] * 4)
     cfg = GRPOConfig(
         output_dir = str(tmp_path / "ci_grpo"),