Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/pytorch-labs/torchfix

TorchFix - a linter for PyTorch-using code with autofix support
https://github.com/pytorch-labs/torchfix

flake8 flake8-plugin linter python pytorch static-analysis static-code-analysis

Last synced: 3 months ago
JSON representation

TorchFix - a linter for PyTorch-using code with autofix support

Lists

README 

        
# TorchFix - a linter for PyTorch-using code with autofix support



[![PyPI](https://img.shields.io/pypi/v/torchfix.svg)](https://pypi.org/project/torchfix/)



TorchFix is a Python code static analysis tool - a linter with autofix capabilities -

for users of PyTorch. It can be used to find and fix issues like usage of deprecated

PyTorch functions and non-public symbols, and to adopt PyTorch best practices in general.



TorchFix is built upon https://github.com/Instagram/LibCST - a library to manipulate

Python concrete syntax trees. LibCST enables "codemods" (autofixes) in addition to

reporting issues.



TorchFix can be used as a Flake8 plugin (linting only) or as a standalone

program (with autofix available for a subset of the lint violations).



> [!WARNING]

> Currently TorchFix is in a **beta version** stage, so there are still a lot of rough

edges and many things can and will change.



## Installation



To install the latest code from GitHub, clone/download

https://github.com/pytorch-labs/torchfix and run `pip install .`

inside the directory.



To install a release version from PyPI, run `pip install torchfix`.



## Usage



After the installation, TorchFix will be available as a Flake8 plugin, so running

Flake8 normally will run the TorchFix linter.



To see only TorchFix warnings without the rest of the Flake8 linters, you can run

`flake8 --isolated --select=TOR0,TOR1,TOR2`



TorchFix can also be run as a standalone program: `torchfix .`

Add `--fix` parameter to try to autofix some of the issues (the files will be overwritten!)

To see some additional debug info, add `--show-stderr` parameter.



> [!CAUTION]

> Please keep in mind that autofix is a best-effort mechanism. Given the dynamic nature of Python,

and especially the beta version status of TorchFix, it's very difficult to have

certainty when making changes to code, even for the seemingly trivial fixes.



Warnings for issues with codes starting with TOR0, TOR1, and TOR2 are enabled by default.

Warnings with other codes may be too noisy, so not enabled by default.

To enable them, use standard flake8 configuration options for the plugin mode or use

`torchfix --select=ALL .` for the standalone mode.



## Reporting problems



If you encounter a bug or some other problem with TorchFix, please file an issue on

https://github.com/pytorch-labs/torchfix/issues.



## Rules



### TOR001 Use of removed function



#### torch.solve



This function was deprecated since PyTorch version 1.9 and is now removed.



`torch.solve` is deprecated in favor of `torch.linalg.solve`.

`torch.linalg.solve` has its arguments reversed and does not return the LU factorization.



To get the LU factorization see `torch.lu`, which can be used with `torch.lu_solve` or `torch.lu_unpack`.



`X = torch.solve(B, A).solution` should be replaced with `X = torch.linalg.solve(A, B)`.



### TOR002 Likely typo `require_grad` in assignment. Did you mean `requires_grad`?



This is a common misspelling that can lead to silent performance issues.



### TOR003 Please pass `use_reentrant` explicitly to `checkpoint`



The default value of the `use_reentrant` parameter in `torch.utils.checkpoint` is being changed

from `True` to `False`. In the meantime, the value needs to be passed explicitly.



See this [forum post](https://dev-discuss.pytorch.org/t/bc-breaking-update-to-torch-utils-checkpoint-not-passing-in-use-reentrant-flag-will-raise-an-error/1745)

for details.



### TOR101 Use of deprecated function



#### torch.nn.utils.weight_norm



This function is deprecated. Use `torch.nn.utils.parametrizations.weight_norm`

which uses the modern parametrization API. The new `weight_norm` is compatible

with `state_dict` generated from old `weight_norm`.



Migration guide:



* The magnitude (``weight_g``) and direction (``weight_v``) are now expressed

    as ``parametrizations.weight.original0`` and ``parametrizations.weight.original1``

    respectively.



* To remove the weight normalization reparametrization, use

    `torch.nn.utils.parametrize.remove_parametrizations`.



* The weight is no longer recomputed once at module forward; instead, it will

    be recomputed on every access.  To restore the old behavior, use

    `torch.nn.utils.parametrize.cached` before invoking the module

    in question.



## License

TorchFix is BSD License licensed, as found in the LICENSE file.