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Use when Pyrefly computes a wrong tensor shape (or is missing one that can't be expressed in a stub signature) and you need to add or fix a shape-DSL rule. Requires a Pyrefly checkout (fbsource or a clone); not usable from a pip/site-packages install.
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| name | modify-shaped-array-dsl |
| description | Use when Pyrefly computes a wrong tensor shape (or is missing one that can't be expressed in a stub signature) and you need to add or fix a shape-DSL rule. Requires a Pyrefly checkout (fbsource or a clone); not usable from a pip/site-packages install. |
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).
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/ are recognized in
; the binding step extracts the IR name in
; the solve step resolves it to a
in ; it's applied at call sites via
(). The Rust evaluator and all arithmetic primitives
live in , (the binop
arithmetic is ); the symbolic dim algebra it calls
() is in .
shape_dsl_functionpyrefly/lib/export/special.rspyrefly/lib/binding/function.rsShapeTransformpyrefly/lib/alt/function.rsalt/callable.rsevaluatecrates/pyrefly_types/src/meta_shape_dsl.rseval_binopSizeExpr::add/sub/mul/floor_divcrates/pyrefly_types/src/dimension.rsInteger/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.
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 test pyrefly:pyrefly_library -- <test_name>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.