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.
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.
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.
If you encounter a bug or some other problem with TorchFix, please file an issue on https://github.com/pytorch-labs/torchfix/issues.
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).
This is a common misspelling that can lead to silent performance issues.
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 for details.
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 asparametrizations.weight.original0andparametrizations.weight.original1respectively. -
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.cachedbefore invoking the module in question.
This function is deprecated. Use the torch.nn.attention.sdpa_kernel context manager instead.
Migration guide:
Each boolean input parameter (defaulting to true unless specified) of sdp_kernel corresponds to a SDPBackened. If the input parameter is true, the corresponding backend should be added to the input list of sdpa_kernel.
TorchFix is BSD License licensed, as found in the LICENSE file.
