jaxtyc 0.7.2

Static array shape checking for JAX powered by eval_shape

jaxtyc

Static array shape checking for JAX powered by jax.eval_shape.

Reads jaxtyping annotations and verifies shapes at analysis time -- no runtime cost, no FLOPs. Each named dimension is assigned a unique prime number, making shape mismatches unambiguous.

Watch the demo video

Features

• Zero runtime cost -- jax.eval_shape only; no arrays allocated, no computation executed
• Prime-based symbolic shapes -- each dimension name maps to a unique prime (>= 101), so d_in != d_out is guaranteed
• 10 diagnostic rules -- shape/rank mismatch, cross-function propagation, parameter consistency, tuple return checking, trace errors
• Inline suppressions -- # jaxtyc: ignore and # jaxtyc: ignore[rule-name]
• LSP server -- diagnostics, hover, CodeLens, go-to-definition, references, rename, code actions, completion, semantic tokens, inlay hints, signature help, linked editing, folding, call hierarchy
• LSP multiplexer -- jaxtyc mux runs ty/pyright + jaxtyc behind a single stdio pipe
• CLI with 4 output formats -- full, concise, json, github (inline PR annotations)
• Flax NNX + Equinox support -- traces bound methods on module instances
• Configurable via pyproject.toml -- severity threshold, rule ignoring, file exclusion, einops preferences

Installation

pip install jaxtyc
# or
uv add jaxtyc

Extras:

Extra	Installs	Use case
jaxtyc[watch]	watchfiles	jaxtyc watch -- re-check on file save
jaxtyc[flax]	flax >=0.10	Flax NNX module tracing
jaxtyc[equinox]	equinox >=0.11	Equinox module tracing
jaxtyc[einops]	einops >=0.8	einops-style fix suggestions + inlay hints with pattern dim names
jaxtyc[all]	All of the above	Everything

Quick Start

# model.py
import jax.numpy as jnp
from jaxtyping import Array, Float

def linear(
    x: Float[Array, "batch seq d_in"],
    w: Float[Array, "d_in d_out"],
) -> Float[Array, "batch seq d_out"]:
    return jnp.matmul(x, w.T)  # Bug: .T swaps dims, produces (batch, seq, d_in)

$ jaxtyc check model.py
model.py:8:0: error[shape-mismatch]
  Shape mismatch in return of `linear`
    Expected: (batch, seq, d_out)
    Got:      (batch, seq, d_in)

Found 1 error(s) in 1 function(s) checked (0.03s)

Editor Integration

VS Code

Install the jaxtyc extension:

cd editors/vscode && npm install && npm run bundle
npx @vscode/vsce package --allow-missing-repository
code --install-extension jaxtyc-*.vsix

Or use the justfile: just vscode-update

The extension auto-discovers your Python environment (.venv, VIRTUAL_ENV, or jaxtyc on PATH) and starts the LSP server automatically. Supports multi-root workspaces with per-folder LSP clients. Includes jaxtyping snippets, a trace visualization webview, and a status bar quick pick menu.

Other Editors

jaxtyc works in any editor that supports LSP (Neovim, Helix, etc.). See the editor setup docs for configuration.

CLI

jaxtyc check <paths>...          # Shape-check files or directories
jaxtyc trace <file.py::func>     # Trace intermediate shapes through a function
jaxtyc watch <paths>...          # Watch and re-check on change
jaxtyc lsp                       # Start the LSP server (stdio)
jaxtyc mux                       # Start the LSP multiplexer (ty/pyright + jaxtyc)
jaxtyc version                   # Print version

Documentation

Full docs at beegass.github.io/jaxtyc.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

MIT