Add regression tests for the stray-forward compile-cache reset (#6569)
* Add regression tests for the stray-forward compile-cache reset Follow-up to #6511, which fixed the bug but whose squash merge did not include the tests. These cover the two issues that fix addressed, under the GPU-free tests/conftest.py harness: - _unsloth_reset_stray_compile_cache 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 - the reset 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 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Pin UNSLOTH_COMPILE_DISABLE in the warn-path reset tests The reset only warns and resets Dynamo when UNSLOTH_COMPILE_DISABLE != "1". A GPU-free CI env that sets it to "1" would make the warn assertion in test_reset_clears_seen_and_warns_when_a_stray_forward_was_seen flaky. monkeypatch it to "0" in both warn-path tests so the warn / no-warn assertions are deterministic and test the seen flag, not the env. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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tests/test_pretrain_compile_reset.py
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tests/test_pretrain_compile_reset.py
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# Unsloth - 2x faster, 60% less VRAM LLM training and finetuning
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# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Lesser General Public License for more details.
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"""The stray-pre-train-forward detector and its torch.compile cache reset.
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A grad-enabled forward/backward run before ``trainer.train()`` poisons the
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AOTAutograd backward-graph cache; the detector records it so train() can drop
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that cache. These cover the idempotent-reinstall evidence guard, the reset's
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chain-walk/teardown behaviour, and that the helper is importable at module
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scope (every non-RL training entry point imports it). Runs under the GPU-free
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``tests/conftest.py`` harness.
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"""
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from __future__ import annotations
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import warnings
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import unsloth # noqa: F401 (installs the unsloth patches the functions live behind)
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import torch
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from unsloth.models._utils import (
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_unsloth_install_pretrain_detector,
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_unsloth_reset_stray_compile_cache,
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)
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class _Trainer:
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"""Minimal ``self`` stand-in: the reset only reads ``self.model``."""
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def test_reset_helper_is_importable_and_exported():
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# Regression: the helper used to live only inside rl.py's RLTrainer_replacement template
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# string (exec'd into a generated trainer module), so importing it from a real module raised
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# ImportError and every non-RL consumer (SFT trainer.py, the plain-Trainer loop, the RL
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# template's own delegation) silently no-op'd. Pin it as an exported module-level symbol.
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from unsloth.models import _utils
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assert callable(_utils._unsloth_reset_stray_compile_cache)
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assert "_unsloth_reset_stray_compile_cache" in _utils.__all__
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def test_fresh_install_starts_unseen():
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m = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(m)
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marker = m._unsloth_pretrain_marker
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assert marker["seen"] is False
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assert "hook" in marker # a live hook is registered
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def test_reinstall_with_live_hook_preserves_seen():
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# Re-entering get_peft_model/patch_peft_model after a grad-enabled probe must NOT wipe the
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# recorded poisoning, or train() skips the reset and the NaN/flat-loss bug returns.
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m = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(m)
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hook = m._unsloth_pretrain_marker["hook"]
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m._unsloth_pretrain_marker["seen"] = True # a probe the live hook recorded
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_unsloth_install_pretrain_detector(m) # idempotent re-install
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marker = m._unsloth_pretrain_marker
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assert marker["seen"] is True # evidence kept
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assert marker["hook"] is hook # same hook, not double-registered
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def test_reinstall_after_teardown_resets_and_reregisters():
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m = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(m)
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marker = m._unsloth_pretrain_marker
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marker["seen"] = True
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marker.pop("hook").remove() # simulate teardown (what the reset does)
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_unsloth_install_pretrain_detector(m) # no live hook -> fresh registration
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assert marker["seen"] is False # reset for the new session
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assert "hook" in marker
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def test_grad_enabled_forward_marks_seen_no_grad_does_not():
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m = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(m)
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with torch.no_grad():
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m(torch.zeros(1, 2))
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assert m._unsloth_pretrain_marker["seen"] is False # no backward graph -> clean
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m(torch.zeros(1, 2)) # grad-enabled forward poisons the cache
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assert m._unsloth_pretrain_marker["seen"] is True
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def test_reset_clears_seen_and_warns_when_a_stray_forward_was_seen(monkeypatch):
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# Pin compile on: the reset only warns/resets when UNSLOTH_COMPILE_DISABLE != "1", which a
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# GPU-free CI env may set, so force it here to make the warn assertion deterministic.
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monkeypatch.setenv("UNSLOTH_COMPILE_DISABLE", "0")
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m = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(m)
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m._unsloth_pretrain_marker["seen"] = True # a stray pre-train forward
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trainer = _Trainer()
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trainer.model = m
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with warnings.catch_warnings(record = True) as caught:
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warnings.simplefilter("always")
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_unsloth_reset_stray_compile_cache(trainer)
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assert any("manual forward/backward" in str(w.message) for w in caught)
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assert "hook" not in m._unsloth_pretrain_marker # hook torn down
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assert m._unsloth_pretrain_marker["seen"] is False # evidence consumed
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def test_reset_tears_down_hook_even_when_not_seen(monkeypatch):
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# The clean path still removes the one-shot hook so it adds no per-step cost, but must not
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# warn or reset Dynamo (nothing was poisoned). Pin compile on so the absent warning proves
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# seen==False is the reason, not a disabled-compile short circuit.
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monkeypatch.setenv("UNSLOTH_COMPILE_DISABLE", "0")
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m = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(m) # seen stays False
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trainer = _Trainer()
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trainer.model = m
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with warnings.catch_warnings(record = True) as caught:
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warnings.simplefilter("always")
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_unsloth_reset_stray_compile_cache(trainer)
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assert not any("manual forward/backward" in str(w.message) for w in caught)
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assert "hook" not in m._unsloth_pretrain_marker
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assert m._unsloth_pretrain_marker["seen"] is False
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def test_reset_walks_wrapper_chain_to_reach_a_nested_marker():
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# The probe may have run on an inner wrapper (.model/.base_model/.module), not self.model.
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inner = torch.nn.Linear(2, 2)
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_unsloth_install_pretrain_detector(inner)
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inner._unsloth_pretrain_marker["seen"] = True
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class _Wrapper: # e.g. a PEFT base_model wrapping the real module
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pass
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outer = _Wrapper()
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outer.base_model = inner
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trainer = _Trainer()
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trainer.model = outer
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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_unsloth_reset_stray_compile_cache(trainer)
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assert "hook" not in inner._unsloth_pretrain_marker # found and torn down through the chain
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assert inner._unsloth_pretrain_marker["seen"] is False
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