subclass zoo

This repository contains a number of examples of Tensor subclasses in PyTorch, specifically using __torch_dispatch__ to integrate deeply into PyTorch's existing subsystems (there's also some use of modes as well). We're still working out good APIs for working with Tensor subclasses, and this repository is here to tell you about what we've figured out so far! To run these examples, you will want a recent nightly of PyTorch.

Here's what's in the repo so far:

• inner_autograd_tensor.py shows how to override autograd from __torch_dispatch__, by deferring autograd to the inner tensor on a subclass.
• negative_tensor.py is a reimplementation of negative tensor views as implemented in PyTorch core (https://github.com/pytorch/pytorch/pull/56058)
• python_meta_tensor.py is a demonstration of how to extend an existing tensor (meta tensor) with some extra behavior (in this case, implementations of meta functions for operations that don't support it natively)
• sparse_output.py
• tracer_tensor.py
• trivial_tensors.py is a comparison for two ways how to "wrap" tensors, one using inheritance (is-a) and one using composition (has-a) (so called wrapper tensors)
• verifier_tensor.py

There are also some utility files:

• base_tensor.py contains a common superclass that most of our tensors inherit from, that fixes up some problems with directly inheriting from torch.Tensor. We intend to upstream these changes so that this superclass is not necessary.
• utils.py contains some handy utility functions that we found ourselves repeatedly using in our implementations.

We're still working on the APIs in questions, so sometimes there will be bugs. bug_zoo.py contains repros for known bugs we're tracking in PyTorch proper.

TODO

• CUDA sanitizer in Python (hard cuz no event hooks)
• Sparse gradients / outputs per Christian (using modes; gradients hard cuz need torch function mode)
• SSD tensor
• Reimplement functionalization tensor
• Nested tensor
• Custom allocator mode (albanD)
• Lazy tensor
• Immutable tensor
• Various ways of writing FX passes https://gist.github.com/1c640ea30fd7451b08e90e34461459c1

Work plan

• TODO: merge BaseTensor into Tensor

  • TODO: torch function disable https://github.com/pytorch/pytorch/pull/73942

• Get rid of fill_defaults

• Compositionality

  • TODO: suppress elem in init

Developer notes

• This repo is formatted with ufmt and autoflakes. Use ./format.sh to reformat all the files in this repository.