.. image:: https://cdn.rawgit.com/pymc-devs/pytensor/main/doc/images/PyTensor_RGB.svg :height: 100px :alt: PyTensor logo :align: center

|Tests Status| |Coverage|

|Project Name| is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It provides the computational backend for PyMC <https://github.com/pymc-devs/pymc>__.

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 <https://github.com/google/jax>, and Numba <https://github.com/numba/numba>
• Contrary to PyTorch and TensorFlow, PyTensor maintains a static graph which can be modified in-place to allow for advanced optimizations

Getting started

.. code-block:: python

import pytensor
from pytensor import tensor as pt

# Declare two symbolic floating-point scalars
a = pt.dscalar("a")
b = pt.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 = pytensor.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 = pytensor.grad(c, a)

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

assert f_dc(1.5, 2.5) == 1.0

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

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

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

pytensor.dprint(d)
#  Add [id A]
#  ├─ ExpandDims{axis=0} [id B]
#  │  └─ True_div [id C]
#  │     ├─ a [id D]
#  │     └─ a [id D]
#  └─ dot [id E]
#     ├─ Add [id F]
#     │  ├─ M [id G]
#     │  └─ ExpandDims{axes=[0, 1]} [id H]
#     │     └─ a [id D]
#     └─ v [id I]

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

# `a/a` -> `1` and the dot product is replaced with a BLAS function
# (i.e. CGemv)
pytensor.dprint(f_d)
# Add [id A] 5
#  ├─ [1.] [id B]
#  └─ CGemv{inplace} [id C] 4
#     ├─ AllocEmpty{dtype='float64'} [id D] 3
#     │  └─ Shape_i{0} [id E] 2
#     │     └─ M [id F]
#     ├─ 1.0 [id G]
#     ├─ Add [id H] 1
#     │  ├─ M [id F]
#     │  └─ ExpandDims{axes=[0, 1]} [id I] 0
#     │     └─ a [id J]
#     ├─ v [id K]
#     └─ 0.0 [id L]

See the PyTensor documentation <https://pytensor.readthedocs.io/en/latest/>__ for in-depth tutorials.

Installation

The latest release of |Project Name| can be installed from PyPI using pip:

::

pip install pytensor

Or via conda-forge:

::

conda install -c conda-forge pytensor

The current development branch of |Project Name| can be installed from GitHub, also using pip:

::

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

Background

PyTensor is a fork of Aesara <https://github.com/aesara-devs/aesara>, which is a fork of Theano <https://github.com/Theano/Theano>.

Contributing

We welcome bug reports and fixes and improvements to the documentation.

For more information on contributing, please see the contributing guide <https://pytensor.readthedocs.io/en/latest/dev_start_guide.html>__.

A good place to start contributing is by looking through the issues here <https://github.com/pymc-devs/pytensor/issues>__.

.. |Project Name| replace:: PyTensor .. |Tests Status| image:: https://github.com/pymc-devs/pytensor/workflows/Tests/badge.svg?branch=main :target: https://github.com/pymc-devs/pytensor/actions?query=workflow%3ATests+branch%3Amain .. |Coverage| image:: https://codecov.io/gh/pymc-devs/pytensor/branch/main/graph/badge.svg?token=WVwr8nZYmc :target: https://codecov.io/gh/pymc-devs/pytensor