Add Or Fix Type Checking

Add Or Fix Type Checking

Input

• <target>: module or directory to type-check (if known).
• Optional make typing or CI output showing typing failures.

Workflow

• Identify scope from the failing run:
• If you already have make typing or CI output, extract the failing file/module paths.
• If not, run:

     make typing

• Choose the narrowest target that covers the failures.

• Run ty check for the target to get a focused baseline:

   ty check --respect-ignore-files --exclude '**/*_pb*' <target>

• Triage errors by category before fixing anything:
• Wrong/missing type annotations on signatures
• Attribute access on union types (for example X | None)
• Functions returning broad unions (for example str | list | BatchEncoding)
• Mixin/protocol self-type issues
• Dynamic attributes on objects or modules
• Third-party stub gaps (missing kwargs, missing __version__, etc.)

• Apply fixes using this priority order (simplest first):

a. Narrow unions with isinstance() / if x is None / hasattr().

This is the primary tool for resolving union-type errors. ty narrows

through all of these patterns, including the negative forms:

      # Narrow X | None — use `if ...: raise`, never `assert`
      if x is None:
          raise ValueError("x must not be None")
      x.method()  # ty knows x is X here

      # Narrow str | UploadFile
      if isinstance(field, str):
          raise TypeError("Expected file upload, got string")
      await field.read()  # ty knows field is UploadFile here

      # Narrow broad union parameters early in a function body
      # (common for methods accepting e.g. list | dict | BatchEncoding)
      if isinstance(encoded_inputs, (list, tuple)):
          raise TypeError("Expected a mapping, got sequence")
      encoded_inputs.keys()  # ty sees only the dict/mapping types now

b. Use local variables to help ty track narrowing across closures.

When self.x is X | None and you need to pass it to nested functions

or closures, ty cannot track that self.x stays non-None. Copy to a

local variable and narrow the local:

      manager = self.batching_manager
      if manager is None:
          raise RuntimeError("Manager not initialized")
      # Use `manager` (not `self.batching_manager`) in nested functions

c. Split chained calls when the intermediate type is a broad union.

If func().method() fails because func() returns a union, split it:

      # BAD: ty can't narrow through chained calls
      result = func(return_dict=True).to(device)["input_ids"]

      # GOOD: split, narrow, then chain
      result = func(return_dict=True)
      if not hasattr(result, "to"):
          raise TypeError("Expected dict-like result")
      inputs = result.to(device)["input_ids"]

d. Fix incorrect type hints at the source. If a parameter is typed X | None

but can never be None when actually called, remove None from the hint.

e. Annotate untyped attributes. Add type annotations to instance variables

set in __init__ or elsewhere (for example self.foo: list[int] = []).

Declare class-level attributes that are set dynamically later

(for example _cache: Cache, _token_tensor: torch.Tensor | None).

f. Use @overload for methods with input-dependent return types.

When a method returns different types based on the input type (e.g.

__getitem__ with str vs int keys), use @overload to declare each

signature separately:

      from typing import overload

      @overload
      def __getitem__(self, item: str) -> ValueType: ...
      @overload
      def __getitem__(self, item: int) -> EncodingType: ...
      @overload
      def __getitem__(self, item: slice) -> dict[str, ValueType]: ...

      def __getitem__(self, item: int | str | slice) -> ValueType | EncodingType | dict[str, ValueType]:
          ...  # actual implementation

This eliminates cast() calls at usage sites by giving the checker

precise return types for each call pattern.

g. Make container classes generic to propagate value types.

When a class like UserDict holds values whose type changes after

transformation (e.g. lists → tensors after .to()), make the class

generic so methods can return narrowed types:

      from typing import Generic, overload
      from typing_extensions import TypeVar

      _V = TypeVar("_V", default=Any)  # default=Any keeps existing code working

      class MyDict(UserDict, Generic[_V]):
          @overload
          def __getitem__(self, item: str) -> _V: ...
          # ...

          def to(self, device) -> MyDict[torch.Tensor]:
              # after .to(), values are tensors
              ...
              return self  # type: ignore[return-value]

The default=Any (from typing_extensions) means unparameterized usage

like MyDict() stays MyDict[Any] — no existing code needs to change.

Only methods that narrow the value type (like .to()) declare a specific

return type. This eliminates cast() at all call sites.

h. Use self: "ProtocolType" for mixins. When a mixin accesses attributes

from its host class, define a Protocol in src/transformers/_typing.py and

annotate self on methods that need it. Apply this consistently to all methods

in the mixin. Import under TYPE_CHECKING to avoid circular imports.

i. Use TypeGuard functions for dynamic module attributes (for example

torch.npu, torch.xpu, torch.compiler). Instead of getattr(torch, "npu")

or hasattr(torch, "npu") and torch.npu.is_available(), define a type guard

function in src/transformers/_typing.py:

      def has_torch_npu(mod: ModuleType) -> TypeGuard[Any]:
          return hasattr(mod, "npu") and mod.npu.is_available()

Then use it as a narrowing check: if has_torch_npu(torch): torch.npu.device_count().

After the guard, ty treats the module as Any, allowing attribute access without

getattr() or cast(). See existing guards in _typing.py for all device backends.

Key rules for type guards:

• Use TypeGuard[Any] (not a Protocol) — this is the simplest form that works

with ty and avoids losing the original module's known attributes.

• The guard function must be called directly in an if condition for narrowing

to work. ty does NOT narrow through and conditions or if not guard: return.

• Import guards with from .._typing import has_torch_xxx (not via module

attribute _typing.has_torch_xxx) — ty only resolves TypeGuard from

direct imports.

j. Use getattr() / setattr() for dynamic model/config attributes.

For runtime-injected fields (for example config/model flags), use

getattr(obj, "field", default) for reads and setattr(obj, "field", value)

for writes. Also use getattr() for third-party packages missing type stubs

(for example getattr(safetensors, "__version__", "unknown")).

Avoid getattr(torch, "npu") style — use type guards instead (see above).

k. Use cast() as a last resort before # type: ignore.

Use when you've structurally validated the type but the checker can't see it:

pattern-matched AST nodes, known-typed dict values, or validated API responses.

      # After structural validation confirms the type:
      stmt = cast(cst.Assign, node.body[0])
      annotations = cast(list[Annotation], [])

Do not use cast() for module attribute narrowing — use type guards.

Do not use cast() when @overload or generics can solve it at the source.

l. Use # type: ignore only for third-party stub defects. This means

cases where the third-party package's type stubs are wrong or incomplete

and there is no way to narrow or cast around it. Examples:

• A kwarg that exists at runtime but is missing from the stubs
• A method that exists but isn't declared in the stubs

Always add the specific error code: # type: ignore[call-arg], not bare

# type: ignore.

• Things to never do:
• Never use assert for type narrowing. Asserts are stripped by python -O

and must not be relied on for correctness. Use if ...: raise instead.

• Never use # type: ignore as a first resort. Exhaust all approaches above first.
• Do not use getattr(torch, "backend") to access dynamic device backends

(npu, xpu, hpu, musa, mlu, neuron, compiler) — use type guards

• Do not use cast() for module attribute narrowing — use type guards
• Do not use cast() when @overload or generics can eliminate it at the source
• Do not add helper methods or abstractions just to satisfy the type checker

(especially for only 1-2 occurrences)

• Do not pollute base classes with domain-specific fields; use Protocols
• Do not add if x is not None guards for values guaranteed non-None

by the call chain; fix the annotation instead

• Do not use conditional inheritance patterns; annotate self instead

• Organization:
• Keep shared Protocols and type aliases in src/transformers/_typing.py
• Import type-only symbols under if TYPE_CHECKING: to avoid circular deps
• Use from __future__ import annotations for PEP 604 syntax (X | Y)

• Verify and close the PR loop:
• Re-run ty check on the same <target>
• Re-run make typing to confirm the type/model-rules step passes
• If working toward merge readiness, run make check-repo
• Ensure runtime behavior did not change and run relevant tests

• Update CI coverage when adding new typed areas:
• Update ty_check_dirs in Makefile to include newly type-checked directories.