Open Source Ecosystems

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

A fast and hackable meta-tensor library in Python

Features • Get Started • Install • Get help • Contribute

Roadmap • Docs

[!IMPORTANT] This project is no longer actively maintained. See https://github.com/aesara-devs/aesara/discussions/1521#discussioncomment-7687831 and https://x.com/BrandonTWillard/status/1729350499793588249 for more details.

Features

A hackable, pure-Python codebase
Extensible graph framework suitable for rapid development of custom operators and symbolic optimizations
Implements an extensible graph transpilation framework that currently provides
compilation via C, JAX, and Numba.
Based on one of the most widely-used Python tensor libraries: Theano.

Get started

import aesara
from aesara import tensor as at

# Declare two symbolic floating-point scalars
a = at.dscalar("a")
b = at.dscalar("b")

# Create a simple example expression
c = a + b

# Convert the expression into a callable object that takes `(a, b)`
# values as input and computes the value of `c`.
f_c = aesara.function([a, b], c)

assert f_c(1.5, 2.5) == 4.0

# Compute the gradient of the example expression with respect to `a`
dc = aesara.grad(c, a)

f_dc = aesara.function([a, b], dc)

assert f_dc(1.5, 2.5) == 1.0

# Compiling functions with `aesara.function` also optimizes
# expression graphs by removing unnecessary operations and
# replacing computations with more efficient ones.

v = at.vector("v")
M = at.matrix("M")

d = a/a + (M + a).dot(v)

aesara.dprint(d)
# Elemwise{add,no_inplace} [id A] ''
#  |InplaceDimShuffle{x} [id B] ''
#  | |Elemwise{true_divide,no_inplace} [id C] ''
#  |   |a [id D]
#  |   |a [id D]
#  |dot [id E] ''
#    |Elemwise{add,no_inplace} [id F] ''
#    | |M [id G]
#    | |InplaceDimShuffle{x,x} [id H] ''
#    |   |a [id D]
#    |v [id I]

f_d = aesara.function([a, v, M], d)

# `a/a` -> `1` and the dot product is replaced with a BLAS function
# (i.e. CGemv)
aesara.dprint(f_d)
# Elemwise{Add}[(0, 1)] [id A] ''   5
#  |TensorConstant{(1,) of 1.0} [id B]
#  |CGemv{inplace} [id C] ''   4
#    |AllocEmpty{dtype='float64'} [id D] ''   3
#    | |Shape_i{0} [id E] ''   2
#    |   |M [id F]
#    |TensorConstant{1.0} [id G]
#    |Elemwise{add,no_inplace} [id H] ''   1
#    | |M [id F]
#    | |InplaceDimShuffle{x,x} [id I] ''   0
#    |   |a [id J]
#    |v [id K]
#    |TensorConstant{0.0} [id L]

See the Aesara documentation for in-depth tutorials.

Install

The latest release of Aesara can be installed from PyPI using pip:

pip install aesara

Or via conda-forge:

conda install -c conda-forge aesara

The current development branch of Aesara can be installed from GitHub, also using pip:

pip install git+https://github.com/aesara-devs/aesara

Get help

Report bugs by opening an issue. If you have a question regarding the usage of Aesara, start a discussion. For real-time feedback or more general chat about Aesara use our Discord server, or Gitter.