ViperEkura
164be9708b
- Replace hand-rolled BaseConfig (from_dict/to_dict/_coerce/_unwrap_optional) with pydantic.dataclasses
- from_dict now uses cls(**d), to_dict uses dataclasses.asdict + json.dumps filter
- TrainConfig: required fields are now truly required (no default=None), delete manual validate()/__post_init__
- Remove dead required() helper and metadata={'help': ...} annotations
- Fix gradient_checkpointing_modules type from List[str] to List[type]
- Add pydantic>=2.0 as direct dependency in pyproject.toml
- Add numpy-style Parameters docstrings to all config classes
- Enable use_attribute_docstrings in BaseConfig for schema generation
- LoRAConfig also migrated to pydantic dataclass
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import json
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from dataclasses import asdict
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from pathlib import Path
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from typing import Any, Dict, Self, Union
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from pydantic import ConfigDict
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from pydantic.dataclasses import dataclass
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@dataclass(config=ConfigDict(use_attribute_docstrings=True))
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class BaseConfig:
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def to_dict(self) -> Dict[str, Any]:
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result = {}
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for k, v in asdict(self).items():
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if isinstance(v, tuple):
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v = list(v)
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try:
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json.dumps(v)
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result[k] = v
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except (TypeError, ValueError):
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# Skip non-serializable runtime objects (e.g. model_fn, dataset).
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# TrainConfig mixes hyperparams with callables/datasets; only the
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# JSON-serializable subset is written to checkpoint meta.
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pass
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return result
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@classmethod
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def from_dict(cls, d: Dict[str, Any]) -> Self:
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return cls(**d)
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@classmethod
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def from_file(cls, path: Union[str, Path]) -> Self:
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with open(path, "r", encoding="utf-8") as f:
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return cls.from_dict(json.load(f))
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def to_file(self, path: Union[str, Path]):
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with open(path, "w", encoding="utf-8") as f:
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json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)
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