Abstract your array operations.
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autoray is a lightweight python AUTOmatic-arRAY library for
abstracting your tensor operations. Primarily it provides an
automatic dispatch mechanism
that means you can write backend agnostic code that works for:
- numpy
- pytorch
- jax
- cupy
- dask
- autograd
- tensorflow
- sparse
- mars
- ... and indeed any library that provides a numpy-ish api, even if it
knows nothing about
autoray.
Beyond that, abstracting the array interface allows you to:
- swap custom versions of functions for specific backends
- trace through computations lazily without actually running them
- automatically share intermediates and fold constants in computations
- compile functions with a unified interface for different backends
Basic usage
The main function of autoray is
do,
which takes a function
name followed by *args and **kwargs, and automatically looks up (and
caches) the correct function to match the equivalent numpy call:
from autoray as ar
def noised_svd(x):
# automatic dispatch based on supplied array
U, s, VH = ar.do('linalg.svd', x)
# automatic dispatch based on different array
sn = s + 0.1 * ar.do('random.normal', size=ar.shape(s), like=s)
# automatic dispatch for multiple arrays for certain functions
return ar.do('einsum', 'ij,j,jk->ik', U, sn, VH)
# explicit backend given by string
x = ar.do('random.uniform', size=(100, 100), like="torch")
# this function now works for any backend
y = noised_svd(x)
# explicit inference of backend from array
ar.infer_backend(y)
# 'torch'
If you don't like the explicit do syntax, or simply want a
drop-in replacement for existing code, you can also import the autoray.numpy
module:
from autoray import numpy as np
# set a temporary default backend
with ar.backend_like('cupy'):
z = np.ones((3, 4), dtype='float32')
np.exp(z)
# array([[2.7182817, 2.7182817, 2.7182817, 2.7182817],
# [2.7182817, 2.7182817, 2.7182817, 2.7182817],
# [2.7182817, 2.7182817, 2.7182817, 2.7182817]], dtype=float32)
Custom backends and functions can be dynamically registered with:
The main documentation is available at autoray.readthedocs.io.
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