modify-shaped-array-dsl
Sử dụng khi Pyrefly tính toán sai hình dạng tensor (hoặc thiếu hình dạng không thể biểu diễn trong chữ ký stub) và bạn cần thêm hoặc sửa quy tắc shape-DSL.
npx skills add https://github.com/facebook/pyrefly --skill modify-shaped-array-dslYou 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 has two pieces. An IR function is a Python function in
tensor-shapes/pyrefly-torch-stubs/torch-stubs/_shapes.pyi, decorated @shape_dsl_function, that
computes shapes using a restricted Python subset (arithmetic + - * // %,
comprehensions, if, a few builtins, ShapedArray). It is traced, not
executed by CPython. A library stub attaches it to an op with
@uses_shape_dsl(ir_fn) (e.g. tensor-shapes/pyrefly-torch-stubs/torch-stubs/linalg.pyi); the
stub's declared return is a "fixture" (gives the base Tensor/tuple structure)
and the IR function fills in the actual dims.
There are two kinds of change. A stub-only change edits _shapes.pyi to add
or fix an IR function composing existing arithmetic — no rebuild needed, and it
covers the large majority of cases. A DSL-kernel change edits the Rust
evaluator to add a genuinely new primitive operation; reach for it only when the
arithmetic you need cannot be expressed by composing what _shapes.pyi already
has.
How the decorator is traced into the checker (follow this chain if you need to
touch the wiring): uses_shape_dsl/shape_dsl_function are recognized in
pyrefly/lib/export/special.rs; the binding step extracts the IR name in
pyrefly/lib/binding/function.rs; the solve step resolves it to a
ShapeTransform in pyrefly/lib/alt/function.rs; it's applied at call sites via
alt/callable.rs (evaluate). The Rust evaluator and all arithmetic primitives
live in one file, crates/pyrefly_types/src/meta_shape_dsl.rs (the binop
arithmetic is eval_binop); the symbolic dim algebra it calls
(SizeExpr::add/sub/mul/floor_div) is 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 it before adding one —
shape_dsl_env() defines IR functions in a synthetic my_shapes.pyi and
consumers in my_lib.pyi, and testcase! blocks assert results with
assert_type(fn(args), Literal[n]). Copy an existing case
(test_uses_shape_dsl_cross_function_call is a good template). For pure
arithmetic, an int -> int IR function with assert_type(..., Literal[n])
tests the primitives directly without needing ShapedArray fixtures. Use inline
# E: ... markers to assert compile-time DSL diagnostics.
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.