facebook 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.
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.
You MUST unit-test the DSL logic, not just an example
An end-to-end example (tensor-shapes/pyrefly-torch-stubs/examples) exercises an op but does
not pin the algebra — off-by-one, ceiling-vs-floor, and zero/negative-dim
edge cases slip through. Add a targeted test that asserts the computed shape.
Tests live in pyrefly/lib/test/shape_dsl.rs. 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 it:
- buck:
buck test pyrefly:pyrefly_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.