fix(config): annotate PreTrainedConfig.dtype as Any to fix pydantic schema generation (#45070)

[IgnazioDS]

Problem

Fixes #45070. PreTrainedConfig.dtype was annotated as Union[str, "torch.dtype"] | None. Since torch is only imported under TYPE_CHECKING, pydantic's schema builder encounters the "torch.dtype" forward reference at runtime and fails with:

PydanticUndefinedAnnotation: name 'torch' is not defined

This breaks any user code that includes PreTrainedConfig as a field in a pydantic model (e.g. vLLM speculator configs, config validators).

The repro from the issue:

from pydantic import BaseModel, ConfigDict, Field
from transformers import PretrainedConfig

class MyModelConfig(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)
    sub_config: PretrainedConfig = Field(...)

MyModelConfig.model_rebuild(force=True)  # PydanticUndefinedAnnotation

Fix

Change dtype: Union[str, "torch.dtype"] | None = None to dtype: Any = None as suggested by @zucchini-nlp in the issue.

Any is semantically correct for pydantic's purposes — the field accepts string or torch.dtype values at runtime. The forward reference is now only needed for static type checkers (which still see the correct type through TYPE_CHECKING-gated imports in user code). The runtime behaviour is unchanged.

Test Plan

• MyModelConfig.model_rebuild(force=True) no longer raises PydanticUndefinedAnnotation
• PreTrainedConfig can be used as a pydantic field with arbitrary_types_allowed=True
• Existing dtype handling in __post_init__ still works (assigns torch.dtype objects at runtime)
• Existing model loading tests pass

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