pymc-devs/pytensor: PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

.. 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 __.

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

• Contrary to PyTorch and TensorFlow, PyTensor maintains a static graph which can be modified in-place to

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 __ for in-depth tutorials.

Installation ============

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

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

PyTensor is a fork of Aesara __, which is a fork of Theano __.

For more information on contributing, please see the contributing guide __.

A good place to start contributing is by looking through the issues here `__.

.. |Project Name| replace:: PyTensor .. |Tests Status| image:: https://github.com/pymc-devs/pytensor/workflows/Tests/badge.svg :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