pytensor

Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

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Project links

Project description

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

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

Getting started

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 PyTensor 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 PyTensor can be installed from GitHub, also using pip:

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

Contributing

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

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

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

Project details

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Release history Release notifications | RSS feed

2.26.2

Nov 11, 2024

2.26.1

Nov 11, 2024

2.26.0

Nov 8, 2024

2.25.5

Oct 3, 2024

2.25.4

Aug 24, 2024

2.25.3

Aug 21, 2024

2.25.2

Jul 15, 2024

2.25.1

Jul 10, 2024

2.25.0

Jul 9, 2024

2.24.2

Jul 8, 2024

2.24.1

Jul 4, 2024

2.24.0

Jul 3, 2024

2.23.0

Jun 6, 2024

2.22.1

May 28, 2024

2.22.0

May 22, 2024

2.21.0

May 13, 2024

2.20.0

Mar 28, 2024

2.19.0

Mar 9, 2024

2.18.6

Jan 12, 2024

2.18.5

Jan 2, 2024

2.18.4

Dec 15, 2023

2.18.3

Dec 11, 2023

2.18.2

Dec 10, 2023

2.18.1

Nov 24, 2023

2.18.0

Nov 21, 2023

2.17.4

Nov 12, 2023

2.17.3

Oct 17, 2023

2.17.2

Oct 12, 2023

2.17.1

Oct 4, 2023

2.17.0

Sep 29, 2023

2.16.3

Sep 25, 2023

2.16.2

Sep 21, 2023

This version

2.16.1

Sep 12, 2023

2.16.0

Sep 7, 2023

2.15.0

Aug 25, 2023

2.14.2

Aug 4, 2023

2.14.1

Jul 27, 2023

2.14.0

Jul 26, 2023

2.13.1

Jul 15, 2023

2.13.0

Jul 14, 2023

2.12.3

Jun 17, 2023

2.12.2

Jun 13, 2023

2.12.1

May 24, 2023

2.12.0

May 22, 2023

2.11.3

May 10, 2023

2.11.2

Apr 25, 2023

2.11.1

Apr 13, 2023

2.11.0

Apr 7, 2023

2.10.1

Feb 20, 2023

2.10.0

Feb 15, 2023

2.9.1

Jan 10, 2023

2.8.11

Dec 14, 2022

2.8.10

Nov 23, 2022

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pytensor 2.16.1

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