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7b208bc35c
* Fix misleading 'only for image models' error for Qwen3-VL when torchvision is missing transformers >= 5.4 hard-requires torchvision for VLM image/video processors and no longer falls back to a slow processor. Without torchvision the processor load raises ImportError, unsloth degrades to a text-only tokenizer, and the vision data collator later fails with 'UnslothVisionDataCollator is only for image models!'. Detect this case at load time and raise a clear, actionable error pointing at the missing torchvision dependency instead. Fixes unslothai/unsloth#4202 * Apply kwarg-spacing format hook to vision torchvision guard (pre-commit) * Make torchvision-missing detection precise: check availability first, match specific error text * Tighten code comments (no logic change) * Make missing-torchvision VLM error version-agnostic The raise also fires on transformers 4.57.x for VLMs with a video processor (Qwen2.5-VL, Qwen3-VL), where AutoVideoProcessor requires torchvision. The old message claimed 'transformers >= 5.4 requires torchvision', which is inaccurate on 4.57.x. Reword to state torchvision is required for this model's vision processors without a version-specific claim. --------- Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
"""Regression test for unsloth#4202: detect missing torchvision so the loader can
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surface the real cause instead of a misleading collator error."""
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import importlib.util
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from unittest import mock
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from unsloth.models.vision import _missing_torchvision_error
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def test_error_text_mentions_torchvision_is_detected():
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err = ImportError("Qwen3VLVideoProcessor requires the Torchvision library but ...")
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assert _missing_torchvision_error(err) is True
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def test_torchvision_missing_is_detected_without_error():
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with mock.patch.object(importlib.util, "find_spec", return_value = None):
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assert _missing_torchvision_error(None) is True
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def test_torchvision_present_unrelated_error_is_not_flagged():
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sentinel = object()
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with mock.patch.object(importlib.util, "find_spec", return_value = sentinel):
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assert _missing_torchvision_error(ValueError("unrelated")) is False
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assert _missing_torchvision_error(None) is False
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def test_matches_real_environment():
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# find_spec is the source of truth when no error is supplied.
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expected = importlib.util.find_spec("torchvision") is None
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assert _missing_torchvision_error(None) is expected
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