Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
- 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
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_div,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.
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
We welcome bug reports and fixes and improvements to the documentation.
For more information on contributing, please see the contributing guide and the Aesara Mission Statement.
A good place to start contributing is by looking through the issues here.
Special thanks to Bram Timmer for the logo.