## Summary
From #16861
This PR fixes the incorrect `ClassDef` handling of
`SemanticIndexBuilder::visit_stmt`, which fixes some of the incorrect
behavior of referencing the class itself in the class scope (a complete
fix requires a different fix, which will be done in the another PR).
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Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Catch some Instances, but raise type error for the rest of them
Fixes#16851
## Test Plan
Extend invalid.md in annotations
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Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
There can be semi-cyclic inheritance patterns (e.g. recursive generics)
that are not technically inheritance cycles, but that can cause us to
hit Salsa query cycles in evaluating a type's MRO. Add fixed-point
handling to these MRO-related queries so we don't panic on these cycles.
The details of what queries we hit in what order in this case will
change as we implement support for generics, but ultimately we will
probably need cycle handling for all queries that can re-enter type
inference, otherwise we are susceptible to small changes in query
execution order causing panics.
Fixes#14333
Further reduces the panicking set of seeds in #14737
Pulls in the latest Salsa main branch, which supports fixpoint
iteration, and uses it to handle all query cycles.
With this, we no longer need to skip any corpus files to avoid panics.
Latest perf results show a 6% incremental and 1% cold-check regression.
This is not a "no cycles" regression, as tomllib and typeshed do trigger
some definition cycles (previously handled by our old
`infer_definition_types` fallback to `Unknown`). We don't currently have
a benchmark we can use to measure the pure no-cycles regression, though
I expect there would still be some regression; the fixpoint iteration
feature in Salsa does add some overhead even for non-cyclic queries.
I think this regression is within the reasonable range for this feature.
We can do further optimization work later, but I don't think it's the
top priority right now. So going ahead and acknowledging the regression
on CodSpeed.
Mypy primer is happy, so this doesn't regress anything on our
currently-checked projects. I expect it probably unlocks adding a number
of new projects to our ecosystem check that previously would have
panicked.
Fixes#13792Fixes#14672
## Summary
This PR closes#16248.
If the return type of the function isn't assignable to the one
specified, an `invalid-return-type` error occurs.
I thought it would be better to report this as a different kind of error
than the `invalid-assignment` error, so I defined this as a new error.
## Test Plan
All type inconsistencies in the test cases have been replaced with
appropriate ones.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
* Attributes/method are now properly looked up on metaclasses, when
called on class objects
* We properly distinguish between data descriptors and non-data
descriptors (but we do not yet support them in store-context, i.e.
`obj.data_descr = …`)
* The descriptor protocol is now implemented in a single unified place
for instances, classes and dunder-calls. Unions and possibly-unbound
symbols are supported in all possible stages of the process by creating
union types as results.
* In general, the handling of "possibly-unbound" symbols has been
improved in a lot of places: meta-class attributes, attributes,
descriptors with possibly-unbound `__get__` methods, instance
attributes, …
* We keep track of type qualifiers in a lot more places. I anticipate
that this will be useful if we import e.g. `Final` symbols from other
modules (see relevant change to typing spec:
https://github.com/python/typing/pull/1937).
* Detection and special-casing of the `typing.Protocol` special form in
order to avoid lots of changes in the test suite due to new `@Todo`
types when looking up attributes on builtin types which have `Protocol`
in their MRO. We previously
looked up attributes in a wrong way, which is why this didn't come up
before.
closes#16367closes#15966
## Context
The way attribute lookup in `Type::member` worked before was simply
wrong (mostly my own fault). The whole instance-attribute lookup should
probably never have been integrated into `Type::member`. And the
`Type::static_member` function that I introduced in my last descriptor
PR was the wrong abstraction. It's kind of fascinating how far this
approach took us, but I am pretty confident that the new approach
proposed here is what we need to model this correctly.
There are three key pieces that are required to implement attribute
lookups:
- **`Type::class_member`**/**`Type::find_in_mro`**: The
`Type::find_in_mro` method that can look up attributes on class bodies
(and corresponding bases). This is a partial function on types, as it
can not be called on instance types like`Type::Instance(…)` or
`Type::IntLiteral(…)`. For this reason, we usually call it through
`Type::class_member`, which is essentially just
`type.to_meta_type().find_in_mro(…)` plus union/intersection handling.
- **`Type::instance_member`**: This new function is basically the
type-level equivalent to `obj.__dict__[name]` when called on
`Type::Instance(…)`. We use this to discover instance attributes such as
those that we see as declarations on class bodies or as (annotated)
assignments to `self.attr` in methods of a class.
- The implementation of the descriptor protocol. It works slightly
different for instances and for class objects, but it can be described
by the general framework:
- Call `type.class_member("attribute")` to look up "attribute" in the
MRO of the meta type of `type`. Call the resulting `Symbol` `meta_attr`
(even if it's unbound).
- Use `meta_attr.class_member("__get__")` to look up `__get__` on the
*meta type* of `meta_attr`. Call it with `__get__(meta_attr, self,
self.to_meta_type())`. If this fails (either the lookup or the call),
just proceed with `meta_attr`. Otherwise, replace `meta_attr` in the
following with the return type of `__get__`. In this step, we also probe
if a `__set__` or `__delete__` method exists and store it in
`meta_attr_kind` (can be either "data descriptor" or "normal attribute
or non-data descriptor").
- Compute a `fallback` type.
- For instances, we use `self.instance_member("attribute")`
- For class objects, we use `class_attr =
self.find_in_mro("attribute")`, and then try to invoke the descriptor
protocol on `class_attr`, i.e. we look up `__get__` on the meta type of
`class_attr` and call it with `__get__(class_attr, None, self)`. This
additional invocation of the descriptor protocol on the fallback type is
one major asymmetry in the otherwise universal descriptor protocol
implementation.
- Finally, we look at `meta_attr`, `meta_attr_kind` and `fallback`, and
handle various cases of (possible) unboundness of these symbols.
- If `meta_attr` is bound and a data descriptor, just return `meta_attr`
- If `meta_attr` is not a data descriptor, and `fallback` is bound, just
return `fallback`
- If `meta_attr` is not a data descriptor, and `fallback` is unbound,
return `meta_attr`
- Return unions of these three possibilities for partially-bound
symbols.
This allows us to handle class objects and instances within the same
framework. There is a minor additional detail where for instances, we do
not allow the fallback type (the instance attribute) to completely
shadow the non-data descriptor. We do this because we (currently) don't
want to pretend that we can statically infer that an instance attribute
is always set.
Dunder method calls can also be embedded into this framework. The only
thing that changes is that *there is no fallback type*. If a dunder
method is called on an instance, we do not fall back to instance
variables. If a dunder method is called on a class object, we only look
it up on the meta class, never on the class itself.
## Test Plan
New Markdown tests.
To kick off the work of supporting generics, this adds many new
(currently failing) tests, showing the behavior we plan to support.
This is still missing a lot! Not included:
- typevar tuples
- param specs
- variance
- `Self`
But it's a good start! We can add more failing tests for those once we
tackle these.
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Co-authored-by: Carl Meyer <carl@astral.sh>
