Add regression tests for the stray-forward compile-cache reset

Covers the two codex findings fixed in this PR under the GPU-free
tests/conftest.py harness:

- the reset helper is an exported module-level symbol in
  unsloth.models._utils (it previously lived only inside the RL trainer
  template string, so every non-RL import silently no-op'd)
- _unsloth_install_pretrain_detector keeps a recorded "seen" forward on an
  idempotent reinstall with a live hook, and only resets it after teardown
- only a grad-enabled pre-train forward marks the cache poisoned
- _unsloth_reset_stray_compile_cache warns and clears seen when a stray
  forward was seen, tears the hook down even on the clean path, and walks
  the .model/.base_model/.module wrapper chain to reach a nested marker

tests/test_pretrain_compile_reset.py

@@ -0,0 +1,149 @@
+# Unsloth - 2x faster, 60% less VRAM LLM training and finetuning
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+
+"""The stray-pre-train-forward detector and its torch.compile cache reset.
+
+A grad-enabled forward/backward run before ``trainer.train()`` poisons the
+AOTAutograd backward-graph cache; the detector records it so train() can drop
+that cache. These cover the idempotent-reinstall evidence guard, the reset's
+chain-walk/teardown behaviour, and that the helper is importable at module
+scope (every non-RL training entry point imports it). Runs under the GPU-free
+``tests/conftest.py`` harness.
+"""
+
+from __future__ import annotations
+
+import warnings
+
+import unsloth  # noqa: F401  (installs the unsloth patches the functions live behind)
+
+import torch
+
+from unsloth.models._utils import (
+    _unsloth_install_pretrain_detector,
+    _unsloth_reset_stray_compile_cache,
+)
+
+
+class _Trainer:
+    """Minimal ``self`` stand-in: the reset only reads ``self.model``."""
+
+
+def test_reset_helper_is_importable_and_exported():
+    # Regression: the helper used to live only inside rl.py's RLTrainer_replacement template
+    # string (exec'd into a generated trainer module), so importing it from a real module raised
+    # ImportError and every non-RL consumer (SFT trainer.py, the plain-Trainer loop, the RL
+    # template's own delegation) silently no-op'd. Pin it as an exported module-level symbol.
+    from unsloth.models import _utils
+
+    assert callable(_utils._unsloth_reset_stray_compile_cache)
+    assert "_unsloth_reset_stray_compile_cache" in _utils.__all__
+
+
+def test_fresh_install_starts_unseen():
+    m = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(m)
+    marker = m._unsloth_pretrain_marker
+    assert marker["seen"] is False
+    assert "hook" in marker  # a live hook is registered
+
+
+def test_reinstall_with_live_hook_preserves_seen():
+    # Re-entering get_peft_model/patch_peft_model after a grad-enabled probe must NOT wipe the
+    # recorded poisoning, or train() skips the reset and the NaN/flat-loss bug returns.
+    m = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(m)
+    hook = m._unsloth_pretrain_marker["hook"]
+    m._unsloth_pretrain_marker["seen"] = True  # a probe the live hook recorded
+
+    _unsloth_install_pretrain_detector(m)  # idempotent re-install
+    marker = m._unsloth_pretrain_marker
+    assert marker["seen"] is True  # evidence kept
+    assert marker["hook"] is hook  # same hook, not double-registered
+
+
+def test_reinstall_after_teardown_resets_and_reregisters():
+    m = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(m)
+    marker = m._unsloth_pretrain_marker
+    marker["seen"] = True
+    marker.pop("hook").remove()  # simulate teardown (what the reset does)
+
+    _unsloth_install_pretrain_detector(m)  # no live hook -> fresh registration
+    assert marker["seen"] is False  # reset for the new session
+    assert "hook" in marker
+
+
+def test_grad_enabled_forward_marks_seen_no_grad_does_not():
+    m = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(m)
+    with torch.no_grad():
+        m(torch.zeros(1, 2))
+    assert m._unsloth_pretrain_marker["seen"] is False  # no backward graph -> clean
+    m(torch.zeros(1, 2))  # grad-enabled forward poisons the cache
+    assert m._unsloth_pretrain_marker["seen"] is True
+
+
+def test_reset_clears_seen_and_warns_when_a_stray_forward_was_seen():
+    m = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(m)
+    m._unsloth_pretrain_marker["seen"] = True  # a stray pre-train forward
+    trainer = _Trainer()
+    trainer.model = m
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        _unsloth_reset_stray_compile_cache(trainer)
+
+    assert any("manual forward/backward" in str(w.message) for w in caught)
+    assert "hook" not in m._unsloth_pretrain_marker  # hook torn down
+    assert m._unsloth_pretrain_marker["seen"] is False  # evidence consumed
+
+
+def test_reset_tears_down_hook_even_when_not_seen():
+    # The clean path still removes the one-shot hook so it adds no per-step cost, but must not
+    # warn or reset Dynamo (nothing was poisoned).
+    m = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(m)  # seen stays False
+    trainer = _Trainer()
+    trainer.model = m
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        _unsloth_reset_stray_compile_cache(trainer)
+
+    assert not any("manual forward/backward" in str(w.message) for w in caught)
+    assert "hook" not in m._unsloth_pretrain_marker
+    assert m._unsloth_pretrain_marker["seen"] is False
+
+
+def test_reset_walks_wrapper_chain_to_reach_a_nested_marker():
+    # The probe may have run on an inner wrapper (.model/.base_model/.module), not self.model.
+    inner = torch.nn.Linear(2, 2)
+    _unsloth_install_pretrain_detector(inner)
+    inner._unsloth_pretrain_marker["seen"] = True
+
+    class _Wrapper:  # e.g. a PEFT base_model wrapping the real module
+        pass
+
+    outer = _Wrapper()
+    outer.base_model = inner
+    trainer = _Trainer()
+    trainer.model = outer
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        _unsloth_reset_stray_compile_cache(trainer)
+
+    assert "hook" not in inner._unsloth_pretrain_marker  # found and torn down through the chain
+    assert inner._unsloth_pretrain_marker["seen"] is False

unsloth/models/_utils.py

@@ -62,6 +62,8 @@ __all__ = [
     "patch_unsloth_smart_gradient_checkpointing",
     "unpatch_unsloth_smart_gradient_checkpointing",
     "apply_unsloth_gradient_checkpointing",
+    "_unsloth_install_pretrain_detector",
+    "_unsloth_reset_stray_compile_cache",
     "patch_compiled_autograd",
     "process_vision_info",
     "unsloth_compile_transformers",
@@ -194,6 +196,109 @@ from unsloth_zoo.temporary_patches import (
 )
 
 
+def _unsloth_install_pretrain_detector(model):
+    """Attach a one-shot forward pre-hook recording whether a forward ran before
+    trainer.train(), so prepare_for_training_mode can drop a torch.compile graph cache poisoned
+    by a stray manual forward/backward. Idempotent; no-op if the model cannot take hooks."""
+    if model is None or not hasattr(model, "register_forward_pre_hook"):
+        return model
+    marker = getattr(model, "_unsloth_pretrain_marker", None)
+    if isinstance(marker, dict):
+        # A live hook is already recording: keep it (no duplicates) and DON'T clear seen -- a
+        # grad-enabled probe may have already flagged the poisoned cache, and a re-entrant
+        # get_peft_model/patch_peft_model call must not erase that before train() resets.
+        if "hook" in marker:
+            return model
+        # Marker exists but its hook was torn down -> reinstall fresh, so reset seen.
+        marker["seen"] = False
+    else:
+        marker = {"seen": False}
+        try:
+            model._unsloth_pretrain_marker = marker
+        except Exception:
+            return model
+
+    def _mark(_module, _inp):
+        # Only a grad-enabled forward poisons the AOTAutograd backward-graph cache; a no-grad
+        # probe builds no backward graph, so treat it as clean (avoids a needless dynamo reset).
+        if torch.is_grad_enabled():
+            marker["seen"] = True
+
+    try:
+        marker["hook"] = model.register_forward_pre_hook(_mark)
+    except Exception:
+        pass
+    return model
+
+
+def _unsloth_reset_stray_compile_cache(self):
+    # A manual forward/backward under torch.compile BEFORE trainer.train() (e.g. a grad-norm
+    # probe) caches a forward + AOTAutograd backward graph in a one-off context; reusing it
+    # poisons training with NaN/zero gradients. If such a forward was seen and compile is on,
+    # drop the compiled-graph cache so training recompiles cleanly. No-op on the normal path.
+    # Module-level (not just inside the RL trainer template) so the SFT auto-packing wrapper and
+    # the plain-Trainer loop can import and run it too.
+    import os
+
+    model = getattr(self, "model", None)
+    if model is None:
+        return
+    # The detector hook can sit on any wrapper in the chain, and the probe may have run on a
+    # different one than self.model, so walk the chain: detect a "seen" marker anywhere and
+    # collect every marker to tear down below.
+    markers = []
+    seen = False
+    _curr = model
+    _visited = set()
+    while _curr is not None and id(_curr) not in _visited:
+        _visited.add(id(_curr))
+        _m = getattr(_curr, "_unsloth_pretrain_marker", None)
+        if isinstance(_m, dict):
+            markers.append(_m)
+            if _m.get("seen"):
+                seen = True
+        # Follow the wrapper chain: Unsloth/HF (.model), PEFT (.base_model), DDP/FSDP (.module).
+        _nxt = getattr(_curr, "model", None)
+        if _nxt is None:
+            _nxt = getattr(_curr, "base_model", None)
+        if _nxt is None:
+            _nxt = getattr(_curr, "module", None)
+        _curr = _nxt
+    if seen and os.environ.get("UNSLOTH_COMPILE_DISABLE", "0") != "1":
+        try:
+            import torch._dynamo as _dynamo
+            _dynamo.reset()
+        except Exception:
+            pass
+        try:
+            from unsloth_zoo.gradient_checkpointing import (
+                reset_unsloth_gradient_checkpointing_buffers,
+            )
+            reset_unsloth_gradient_checkpointing_buffers()
+        except Exception:
+            pass
+        try:
+            model.zero_grad(set_to_none = True)
+        except Exception:
+            pass
+        import warnings
+
+        warnings.warn(
+            "Unsloth: detected a manual forward/backward run before trainer.train(); "
+            "reset the torch.compile graph cache it poisoned so training starts clean. "
+            "To avoid this, run any pre-train probe under `with torch.no_grad():`."
+        )
+    # Tear down every one-shot detector hook in the chain so none adds per-step cost.
+    for _m in markers:
+        hook = _m.pop("hook", None)
+        if hook is not None:
+            try:
+                hook.remove()
+            except Exception:
+                pass
+        _m["seen"] = False
+
+
 def apply_unsloth_gradient_checkpointing(use_gradient_checkpointing, max_seq_length, dtype):
     """
     Apply gradient checkpointing with smart heuristics.

unsloth/models/llama.py

@@ -2756,6 +2756,20 @@ class FastLlamaModel:
             "is_torch_tpu_available()",
             "False",
         )
+        # Wire the stray-forward compile-cache reset into the plain Trainer path: get_peft_model
+        # arms the pre-train detector for every LoRA model, but only the TRL SFT/RL wrappers run
+        # the reset. A grad-enabled probe before a bare transformers.Trainer.train() would
+        # otherwise keep the poisoned Dynamo cache and leave the detector hook installed. Anchored
+        # on the first body statement; a no-op (and harmless) if upstream drops that line.
+        inner_training_loop = inner_training_loop.replace(
+            "self.accelerator.free_memory()",
+            "self.accelerator.free_memory()\n"
+            "    try:\n"
+            "        from unsloth.models._utils import _unsloth_reset_stray_compile_cache as _unsloth_reset_cc\n"
+            "        _unsloth_reset_cc(self)\n"
+            "    except Exception: pass",
+            1,
+        )
         exec(inner_training_loop, globals())
         Trainer._inner_training_loop = _fast_inner_training_loop
 
@@ -2938,6 +2952,9 @@ class FastLlamaModel:
             )
         if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1":
             print("Unsloth: Full finetuning is enabled, so .get_peft_model has no effect")
+            # Full finetuning still compiles, so a stray pre-train forward can poison the
+            # cache; install the detector here too (it is idempotent).
+            _unsloth_install_pretrain_detector(model)
             return model
         transformers_set_seed(random_state)
 
@@ -3016,6 +3033,9 @@ class FastLlamaModel:
                         model.get_output_embeddings(), DEVICE_TYPE_TORCH
                     )
 
+                # Pre-wrapped PEFT model passes through here; still arm the detector so an RL
+                # trainer can reset a compile cache poisoned by a pre-train forward.
+                _unsloth_install_pretrain_detector(model)
                 return model
             else:
                 raise TypeError(
@@ -3368,6 +3388,9 @@ class FastLlamaModel:
             m.for_training = functools.partial(FastBaseModel.for_training, m)
             m.for_inference = functools.partial(FastBaseModel.for_inference, m)
             m = m.model
+        # Detect a stray pre-train forward so train() can drop the torch.compile
+        # graph cache it would otherwise poison (see prepare_for_training_mode).
+        _unsloth_install_pretrain_detector(model)
         return model
 
     @staticmethod
@@ -3585,6 +3608,9 @@ class FastLlamaModel:
             m.for_training = functools.partial(FastBaseModel.for_training, m)
             m.for_inference = functools.partial(FastBaseModel.for_inference, m)
             m = m.model
+        # Detect a stray pre-train forward so train() can drop the torch.compile
+        # graph cache it would otherwise poison (see prepare_for_training_mode).
+        _unsloth_install_pretrain_detector(model)
         return model
 
     @staticmethod

unsloth/models/rl.py

@@ -390,9 +390,20 @@ try:
     from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers
 except:
     def reset_unsloth_gradient_checkpointing_buffers(): pass
+# Canonical reset lives in unsloth.models._utils so the SFT auto-packing wrapper and the plain
+# Trainer loop can import the same helper; fall back to a no-op only if it can't be imported.
+try:
+    from unsloth.models._utils import _unsloth_reset_stray_compile_cache
+except Exception:
+    def _unsloth_reset_stray_compile_cache(self): pass
 def prepare_for_training_mode(f):
     @functools.wraps(f)
     def wrapper(self, *args, **kwargs):
+        # Drop any torch.compile graph cache poisoned by a stray pre-train forward.
+        try:
+            _unsloth_reset_stray_compile_cache(self)
+        except Exception:
+            pass
         # Finish the previous W&B run if this is a subsequent train() call.
         # We do this at the START of train() (not the end) so that
         # evaluate() / log() still work after train() completes.

unsloth/models/vision.py

@@ -1380,6 +1380,9 @@ class FastBaseModel:
     ):
         if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1":
             print("Unsloth: Full finetuning is enabled, so .get_peft_model has no effect")
+            # Full finetuning still compiles, so a stray pre-train forward can poison the
+            # cache; install the detector here too (it is idempotent).
+            _unsloth_install_pretrain_detector(model)
             return model
         transformers_set_seed(random_state)
 
@@ -1577,6 +1580,9 @@ class FastBaseModel:
             m.for_training = functools.partial(FastBaseModel.for_training, m)
             m.for_inference = functools.partial(FastBaseModel.for_inference, m)
             m = m.model
+        # Detect a stray pre-train forward so train() can drop the torch.compile
+        # graph cache it would otherwise poison (see prepare_for_training_mode).
+        _unsloth_install_pretrain_detector(model)
         return model
 
     @staticmethod

unsloth/trainer.py

@@ -596,6 +596,30 @@ def _patch_sft_trainer_auto_packing(trl_module):
             )
             print(message)
 
+        # get_peft_model installs a pre-train forward detector for plain LoRA/vision models,
+        # but only RL trainers run the reset via prepare_for_training_mode. Wire it into the
+        # SFT train() path too, else a grad-enabled probe before train() leaves the poisoned
+        # Dynamo cache in place and the detector hook installed on every training forward.
+        # (For UnslothSFTTrainer the later prepare_for_training_mode assignment supersedes this.)
+        if not getattr(self, "_unsloth_train_reset_wrapped", False):
+            try:
+                from unsloth.models._utils import _unsloth_reset_stray_compile_cache
+
+                _orig_train = self.train
+
+                @wraps(_orig_train)
+                def _train_with_reset(*train_args, **train_kwargs):
+                    try:
+                        _unsloth_reset_stray_compile_cache(self)
+                    except Exception:
+                        pass
+                    return _orig_train(*train_args, **train_kwargs)
+
+                self.train = _train_with_reset
+                self._unsloth_train_reset_wrapped = True
+            except Exception:
+                pass
+
     sft_trainer.__init__ = new_init
     sft_trainer._unsloth_auto_packing_wrapped = True