# jax-array-info This package contains two functions for debugging jax `Array`s: ```python from jax_array_info import sharding_info, sharding_vis ``` ## `sharding_info(arr)` `sharding_info(arr)` prints general information about a jax or numpy array with special focus on sharding ( supporting `SingleDeviceSharding`, `GSPMDSharding`, `PositionalSharding`, `NamedSharding` and `PmapSharding`) ```python array = jax.numpy.zeros(shape=(N, N, N), dtype=jax.numpy.float32) array = jax.device_put(array, NamedSharding(mesh, P(None, "gpus"))) sharding_info(array, "some_array") ``` ```text ╭────────────────── some_array ───────────────────╮ │ shape: (128, 128, 128) │ │ dtype: float32 │ │ size: 8.0 MiB │ │ NamedSharding: P(None, 'gpus') │ │ axis 1 is sharded: CPU 0 contains 0:16 (of 128) │ ╰─────────────────────────────────────────────────╯ ``` ## `sharding_vis(arr)` A modified version of [`jax.debug.visualize_array_sharding()`](https://jax.readthedocs.io/en/latest/_autosummary/jax.debug.visualize_array_sharding.html) that also supports arrays with more than 2 dimensions (by ignoring non-sharded dimensions in the visualisation until reaching 2 dimensions) ```python array = jax.numpy.zeros(shape=(N, N, N), dtype=jax.numpy.float32) array = jax.device_put(array, NamedSharding(mesh, P(None, "gpus"))) sharding_vis(array) ``` ```text ─────────── showing dims [0, 1] from original shape (128, 128, 128) ──────────── ┌───────┬───────┬───────┬───────┬───────┬───────┬───────┬───────┐ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ CPU 0 │ CPU 1 │ CPU 2 │ CPU 3 │ CPU 4 │ CPU 5 │ CPU 6 │ CPU 7 │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┘ ``` ### Examples See [`tests/`](./tests/jaxtest.py)