[soaring0616]

Summary

Pyrefly reports a false positive when a model is constructed with a value that the field's custom schema accepts, e.g. CustomModel(dtype=">i4"). The call is valid at runtime. The error appears in strict mode, and in lax mode for types that are in the lax conversion table (e.g. int, which becomes LaxInt).

This PR adds a pydantic_has_custom_schema flag to DataclassFieldKeywords. It is set when an Annotated metadata item has a __get_pydantic_core_schema__ hook that is defined outside Pydantic (or by Pydantic's BeforeValidator, PlainValidator or WrapValidator). Only get_dataclass_init reads it, to widen the synthesized __init__ parameter.

The widening applies in both strict and lax mode.

Fixes #2586

• I am a human, and I fully understand the code I am submitting.

What changed

• keywords.rs: adds the pydantic_has_custom_schema field.
• pydantic.rs: sets the flag when a metadata item's __get_pydantic_core_schema__ hook is defined outside Pydantic. Hooks defined by Pydantic are skipped because they generally build on the declared type's own validation (for example, UuidVersion adds a constraint to it), so the accepted input type does not change. The exceptions are BeforeValidator, PlainValidator and WrapValidator, which receive the raw input. Metadata that is not a class (such as Any, which is what Field(gt=0) is inferred as) never counts.
• dataclass.rs: get_dataclass_init reads the flag. When it is set, the local variable converter_param becomes Any, so only the synthesized __init__ parameter is widened.
• class_field.rs: on the AssignedInBody path (fields with a default value), only strict was copied from the annotation flags, so the flag was dropped and the __init__ parameter kept the declared type. The flag is now carried at two places: when building annotation_flags (the filter no longer discards annotations that only set the flag, and the flag is copied), and when merging them into the flags of a right-hand-side Field(...) (f.pydantic_has_custom_schema |= annotation_flags.pydantic_has_custom_schema).

Why a separate flag

An earlier attempt (#3798) set the stored converter_param to Any. That field is read in three places: get_dataclass_init, get_dataclass_replace, and attribute assignment (resolve_class_attr_setter). Widening it would also widen attribute assignment, so m.dtype = ">i4" would be wrongly accepted when it should be rejected.

The new flag is read only by get_dataclass_init, so only the __init__ parameter is widened. test_annotated_custom_schema_widens_init_param covers this: it expects m.dtype = ">i4" to still be an error.

Test Plan

• test_annotated_custom_schema_widens_init_param: in strict mode, __init__ accepts a value for a custom-schema field, while m.dtype = ">i4" is still rejected. It also checks that a field using Pydantic's UuidVersion is not widened.
• test_annotated_custom_schema_with_default: the same for a field with a default value (the AssignedInBody path).
• test_annotated_custom_schema_inherited: a subclass that inherits the field from its parent (strict=True) also gets the widened __init__ parameter.
• test_annotated_custom_schema_widens_lax_conversion_table_type: in lax mode, a custom-schema field gets Any, while a normal int field still gets LaxInt.
• test_annotated_metadata_without_custom_schema: metadata without __get_pydantic_core_schema__, including Field(gt=0), does not widen the parameter.

I ran cargo test -p pyrefly --lib pydantic::field; all 40 tests pass.
The full cargo test aborts on deep_phi_chain_term_expr with a stack overflow. This also happens on main, so I skipped that test with --skip.

Not included

• __replace__ is not handled here.
• BeforeValidator, PlainValidator and WrapValidator are not covered by a test yet: the Pydantic copy in the test environment does not include functional_validators.py.

AI disclosure

• I used Claude (Sonnet 5.5) to study the codebase, assist with implementation and debugging, and polish my English.
• I built and verified the change on my local machine, and ran a runtime experiment to confirm that assigning to the attribute does not convert the value.

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[rchen152]

Thanks for the PR! I left a couple of small suggestions to rename the init_param_any field and exclude classes defined in Pydantic itself from this behavior. Once that's done, I think this will be good to merge!

[rchen152]

Pydantic itself defines some classes that use __get_pydantic_core_schema__, but the only ones that should get __init__ parameter widening are BeforeValidator, PlainValidator, and WrapValidator. Other than those three, classes defined in Pydantic should be excluded. I think something like this should do the trick (caveat: I haven't compiled this, so consider it pseucode):

let cls = match self.expr_infer(metadata_item, &ignore_errors) {
    Type::ClassType(cls) => cls.class_object().clone(),
    Type::ClassDef(cls) => cls,
    _ => return false,
};
self.get_class_member_with_defining_class(&cls, &GET_PYDANTIC_CORE_SCHEMA)
    .is_some_and(|member| {
        let owner = &member.defining_class;
        owner.module_name().first_component() != "pydantic"
        || matches!(
            owner.name().as_str(),
            "BeforeValidator" | "PlainValidator" | "WrapValidator"
        )
    })

[meta-codesync]

This pull request has been imported. If you are a Meta employee, you can view this in D128513616. (Because this pull request was imported automatically, there will not be any future comments.)

[soaring0616]

Thanks for the review! I pushed another commit (1f07f21) with some changes:

• Renamed init_param_any to pydantic_has_custom_schema.
• A hook now counts as a custom schema only if the class that defines it is outside Pydantic, or is one of BeforeValidator, PlainValidator and WrapValidator. (Thanks for your suggestion!)
• I added a test with Pydantic’s UuidVersion to check that it is not widened.

One question about testing, related to the second comment (Claude suggests me to ask): the Pydantic copy under pyrefly/lib/test/pydantic/third-party/pydantic/ has no functional_validators.py, so I can’t test those three exceptions. Should I add a minimal stub for them, or is there a better way?

Also, I accidentally resolved one of the review threads, sorry about that, and I couldn’t find it to reopen it myself. If it doesn’t matter, please feel free to ignore this. But if I overlooked something, could you please reopen it? Thanks!

[github-actions]

According to mypy_primer, this change doesn't affect type check results on a corpus of open source code. ✅

[rchen152]

Looks good to me, thank you!

It looks like you already addressed the review thread you resolved, so all good :)

To test classes in functional_validators, your suggestion to add minimal stubs is the right one, but I think the test coverage is already fine and it's okay to omit them.

[stroxler]

Review automatically exported from Phabricator review in Meta.