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fix: lazy load top-level AutoModel exports #3290
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1 @@ | ||
| 1.3.20 | ||
| 1.3.21 |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,49 @@ | ||
| import os | ||
| import subprocess | ||
| import sys | ||
| from pathlib import Path | ||
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| def test_top_level_import_without_torch_succeeds(): | ||
| repo_root = Path(__file__).resolve().parents[1] | ||
| script = r""" | ||
| import importlib.abc | ||
| import sys | ||
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| class BlockTorch(importlib.abc.MetaPathFinder): | ||
| def find_spec(self, fullname, path=None, target=None): | ||
| if fullname == "torch" or fullname.startswith("torch."): | ||
| raise ModuleNotFoundError("No module named 'torch'", name="torch") | ||
| return None | ||
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| sys.meta_path.insert(0, BlockTorch()) | ||
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| import funasr | ||
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| print(funasr.__version__) | ||
| assert isinstance(funasr.get_import_errors(), dict) | ||
| try: | ||
| from funasr import AutoModel # noqa: F401 | ||
| except ModuleNotFoundError as error: | ||
| assert error.name == "torch" | ||
| assert "requires PyTorch before using AutoModel" in str(error) | ||
| else: | ||
| raise AssertionError("AutoModel import should fail clearly when torch is missing") | ||
| """ | ||
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| env = os.environ.copy() | ||
| env["PYTHONPATH"] = str(repo_root) | ||
| env.pop("FUNASR_STRICT_IMPORT", None) | ||
| result = subprocess.run( | ||
| [sys.executable, "-c", script], | ||
| cwd=repo_root, | ||
| env=env, | ||
| text=True, | ||
| stdout=subprocess.PIPE, | ||
| stderr=subprocess.PIPE, | ||
| check=False, | ||
| ) | ||
|
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| assert result.returncode == 0, result.stdout + result.stderr |
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The error message is currently hardcoded to mention
AutoModel. Since_LAZY_EXPORTSalso containsAutoFrontend, attempting to importAutoFrontendwhen PyTorch is missing will raise an error message that confusingly refers toAutoModel.We can make this dynamic by using the
nameparameter passed to__getattr__to ensure the error message is accurate for whichever component is being imported.