modify-shaped-array-dsl

Verwenden, wenn Pyrefly eine falsche Tensorform berechnet (oder eine fehlt, die nicht in einer Stub-Signatur ausgedrückt werden kann) und Sie eine Shape-DSL-Regel hinzufügen oder korrigieren müssen.

npx skills add https://github.com/facebook/pyrefly --skill modify-shaped-array-dsl

You are modifying Pyrefly's tensor-shape DSL — the logic that computes the output shape of a torch op from its input shapes.

This skill points at code; it does not duplicate it. Read the files below to learn the details. What follows is only the map and the invariant you must uphold (add a unit test).

How the DSL works (the 30-second version)

A shape rule is a Python function in tensor-shapes/pyrefly-torch-stubs/torch-stubs/_shapes.pyi, decorated with @type_shape_dsl_function, that computes a type-level value using a restricted Python subset. Public stubs call the function directly in return annotations, for example Tensor[reshape(Shape, Target)]. The checker validates and evaluates these calls; CPython treats the decorator as a runtime no-op.

There are two kinds of change. A stub-only change edits _shapes.pyi and the public return annotation to compose existing operations. A DSL-kernel change edits the Rust validator or evaluator to add a genuinely new operation; reach for it only when the rule cannot be expressed by composing the existing DSL.

For a stub parameter that accepts either an integer tuple or list, use IntTupleOrList[Values] with Values: IntTuple. Direct, unstarred list literals bind their values; existing and starred lists remain gradual, while direct literals containing non-integers are rejected. This is a stub-signature feature, not a reason to add list handling to a DSL kernel.

The type-level DSL implementation lives primarily in crates/pyrefly_types/src/type_level_dsl.rs, with separate modules for type system operations such as MapIntTuples. The symbolic dimension algebra it uses lives in crates/pyrefly_types/src/dimension.rs.

Preserve tensor types in numeric formulas

Integer/float arithmetic overloads can sometimes cause a tensor expression to lose type information during overload selection. In tensor code, make formulas explicitly floating-point when the result is intended to remain a tensor. For example, multiply an exponent by 1.0, or use a floating-point base such as 2.0 instead of 2. These equivalent forms steer overload selection toward floating-point tensor arithmetic.

Spell gradual shapes canonically

Use int for a gradual dimension, IntTuple for a gradual whole shape, and bare Int for a gradual shape integer. For example, prefer Tensor[[int, 3]] to Tensor[[Any, 3]], Tensor[IntTuple] to Tensor[Any], and Int to Int[Any]. The Any spellings remain legal for compatibility, but use them only when a test specifically exercises Any propagation.

Test the layer you change

For a stub-only change in _shapes.pyi that composes existing DSL operations, add a focused test to that library's static shape corpus and a runtime cross-check where possible. Do not duplicate the stub rule in pyrefly/lib/test/shape_dsl.rs; such a test does not exercise the implementation that changed.

For a DSL-kernel change, add a targeted test in pyrefly/lib/test/shape_dsl.rs. An end-to-end example alone does not pin the kernel behavior, so explicitly cover the relevant algebra and edge cases. Read nearby type-level DSL tests before adding one. Use assert_type when the expected type is expressible and inline # E: ... markers for diagnostics. Tests for the retained V1 kernel compatibility path are isolated in the legacy module and should not be used as templates for new rules.

Run a kernel test with:

  • buck: buck test fbcode//pyrefly:test-library -- <test_name>
  • cargo: cargo test <test_name>

After a DSL-kernel (Rust) change you must rebuild before the checker sees it: buck build fbcode//pyrefly:pyrefly (or cargo build). Stub-only _shapes.pyi edits need no rebuild.

For any DSL-kernel or broader Pyrefly core change that modifies shape manipulation semantics (as opposed to only editing torch/numpy stubs), the default verification gate is:

tensor-shapes/run_all_shape_tests.py

This gate runs the shape-relevant Rust unit tests plus the non-runtime tensor-shape corpus tests, and defaults to cargo with automatic buck fallback. Use --mode buck or --mode cargo when you need to pin the backend, and add --include-runtime-tests only when runtime coverage is relevant.

Contributing the change

  • fbsource: land as a diff.
  • clone: open a PR against the stubs / Rust source in place.

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