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Debugging tool to print information (especially sharding) about jax arrays

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Lukas Winkler a80cbafe1a add typing		2024-08-02 17:02:39 +02:00
jax_array_info	add typing	2024-08-02 17:02:39 +02:00
tests	change output format slightly and add shard_map example	2024-07-29 15:29:03 +02:00
.gitignore	initial version	2023-09-04 15:34:05 +02:00
LICENSE	initial version	2023-09-04 15:34:05 +02:00
pyproject.toml	initial version	2023-09-04 15:34:05 +02:00
README.md	document print_array_stats and add dtype to it	2024-06-21 12:18:31 +02:00

README.md

jax-array-info

This package contains two functions for debugging jax Arrays:

pip install git+https://github.com/Findus23/jax-array-info.git

from jax_array_info import sharding_info, sharding_vis, print_array_stats

`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)

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")

╭────────────────── 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() that also supports arrays with more than 2 dimensions (by ignoring non-sharded dimensions in the visualisation until reaching 2 dimensions)

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)

─────────── 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 │
│       │       │       │       │       │       │       │       │
│       │       │       │       │       │       │       │       │
│       │       │       │       │       │       │       │       │
│       │       │       │       │       │       │       │       │
└───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┘

`print_array_stats()`

Shows a nice overview over the all currently allocated arrays ordered by size.

Disclaimer: This uses jax.live_arrays() to get its information. There might be allocated arrays that are missing in this view. Also

arr = jax.numpy.zeros(shape=(16, 16, 16))
arr2 = jax.device_put(jax.numpy.zeros(shape=(2, 16, 4)), NamedSharding(mesh, P(None, "gpus")))

print_array_stats()

             allocated jax arrays              
┏━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ size     ┃ shape        ┃      sharded      ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ 16.0 KiB │ (16, 16, 16) │                   │
│ 64.0 B   │ (2, 16, 4)   │ ✔ (512.0 B total) │
├──────────┼──────────────┼───────────────────┤
│ 16.1 KiB │              │                   │
└──────────┴──────────────┴───────────────────┘

Examples

See tests/