pygradproject 0.0.1

Lightweight Automatic Differentiation in Python.

pygrad: A lightweight differentiation engine written in Python.

Documentation: https://baubels.github.io/pygrad/.

This is a lightweight (<300kB) automatic differentiation engine based on NumPy, Numba, and opt_einsum. Included is a differentiable Tensor class, layers such as Dropout/Linear/Attention, loss functions such as BCE/CCE, optimizers such as SGD/RMSProp/Adam, and an example DNN/CNN/Transformer architecture. This library is a good alternative if you want to do backpropagation on simple and small functions or networks, without much overhead.

The main component is the Tensor class supporting many math operations. Tensors have .value and .grad attributes, gradients being populated by calling .backward() on either self or any of its children. They can be used standalone, or for constructing more complex architectures such as a vanilla Transformer.

Installation

git clone https://github.com/baubels/pygrad.git
pip install . (or .[examples] or .[dev])

Usage

Tensors accept the same input value as a NumPy array. Create them with Tensor(value) or tensor.array(value). Run backprop on them with .backward().

A simple usage example:

from pygrad.tensor import Tensor
x = Tensor(1)
(((x**3 + x**2 + x + 1) - 1)**2).backward()
x.value, x.grad  # 1.0, 36.0

Since Tensor store their value in .value and their gradient in .grad, it's easy to perform gradient descent.

for _ in range(100):
    (((x**3 + x**2 + x + 1) - 1)**2).backward()     # gradients are automatically reset when called
    x.value = x.value - 0.01*x.grad

Tensors can also be operated on with broadcast-friendly NumPy arrays or other Tensors whose value is broadcast friendly. Internally, a Tensor will always cast it's set value to a NumPy array.

import numpy as np
x  = Tensor(np.ones((10,20)))
y  = Tensor(np.ones((20,10)))
z1 = x@y
z2 = [e-mail]((20,10))       
np.all(z1.value == z2.value)  # True

There are enough expressions defined to be able to create many different models. For example usage and in-depth descriptions of each component of pygrad, check out the docs.

Citation/Contribution

If you find this project helpful in your research or work, I kindly ask that you cite it: View Citation. Thank you!

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