.. 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
: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
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