TL;DR
MyTorch is a small, educational autograd implementation written in Python that mirrors the PyTorch API and relies on NumPy for numerical operations. It implements graph-based reverse-mode automatic differentiation, supports both backward and grad interfaces, and can compute higher-order derivatives without the extra create_graph step used by PyTorch.
What happened
A GitHub repository named mytorch presents a minimalist automatic-differentiation engine implemented in roughly 450 lines of Python. The code models its design on PyTorch's graph-based reverse-mode autodiff while delegating numerical work to NumPy. The README includes runnable examples showing scalar and non-scalar use: one example computes first and second derivatives for a scalar expression; another demonstrates broadcasting and gradient accumulation for tensors. The project exposes the familiar torch.autograd.backward and torch.autograd.grad interfaces and, according to its notes, allows arbitrarily high derivatives without requiring PyTorch’s create_graph=True. The repository is licensed under the Unlicense, is written entirely in Python, and shows modest community attention (9 stars, no forks, no published releases). The author suggests the code is easy to extend and could be adapted to add modules or GPU support, but those are presented as possibilities rather than completed features.
Why it matters
- Provides a compact, readable implementation for learning how reverse-mode autodiff works.
- Reuses the PyTorch-style API, lowering the barrier for developers familiar with PyTorch to inspect internals.
- Supports arbitrarily high-order derivatives without PyTorch’s create_graph=True step, simplifying some workflows.
- Serves as a starting point for experimentation or extension (e.g., layer APIs or alternative backends).
Key facts
- Project name: mytorch (hosted on GitHub under obround/mytorch).
- Implemented in Python and uses NumPy for core numerical operations.
- Design follows graph-based reverse-mode automatic differentiation similar to PyTorch.
- Repository claims implementation fits in about 450 lines of Python.
- Exposes torch.autograd.backward and torch.autograd.grad interfaces.
- Demonstrated support for arbitrarily high derivatives on both scalars and non-scalars.
- README includes examples showing scalar higher-order derivatives and broadcasting with gradient accumulation.
- Repository metadata: Unlicense, 9 stars, 0 forks, 0 watchers, no published releases, language: Python (100%).
What to watch next
- Addition of a torch.nn-compatible module layer set — not confirmed in the source.
- Adaptation to GPU backends using libraries like CuPy or Numba — not confirmed in the source.
- Potential rewrite in a lower-level language using BLAS for performance, as suggested by the author — not confirmed in the source.
Quick glossary
- Autograd: Automatic differentiation: a method for computing derivatives of functions defined by computer programs, commonly used to train machine learning models.
- Reverse-mode automatic differentiation: A differentiation technique that computes gradients efficiently for scalar-valued functions by propagating derivatives backward through a computational graph.
- Broadcasting: A set of rules that allow array operations between arrays of different shapes by virtually expanding one array to match the shape of the other.
- NumPy: A widely used Python library for numerical computing that provides array objects and routines for fast mathematical operations.
- PyTorch API: A popular machine learning library's programming interface; here it refers to compatible naming and autograd primitives such as tensor, backward, and grad.
Reader FAQ
Is MyTorch production-ready?
not confirmed in the source
Can it compute higher-order derivatives?
Yes. The repository demonstrates arbitrarily high derivatives for scalars and non-scalars and supports torch.autograd.backward and torch.autograd.grad; it notes you don't need PyTorch's create_graph=True to compute higher orders.
Does MyTorch run on GPU out of the box?
not confirmed in the source
What is the project license and where is the code hosted?
The code is hosted on GitHub at obround/mytorch and is released under the Unlicense.
mytorch Easily extensible autograd implemented python with pytorch API. Uses numpy to do the heavy-lifting. Implementation is very similar to pytorch (graph-based reverse-mode autodiff). It wouldn't be too tough to…
Sources
- MyTorch – Minimalist autograd in 450 lines of Python
- The Fundamentals of Autograd
- Implement Autograd with PyTorch – by Sakshi Bhatia
- adaline-with-autograd.ipynb – stat453-deep-learning-ss20
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