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

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.

Rule Code Assignment Policy

New rule codes are assigned incrementally across the following categories:

• TOR0XX, TOR1XX: General-purpose torch functionality.
• TOR2XX: Domain-specific rules, such as TorchVision.
• TOR4XX: Noisy rules that are disabled by default.
• TOR9XX: Internal rules specific for pytorch/pytorch repo, other users should not use these.

TOR0, TOR1 and TOR2 are enabled by default.

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).

torch.symeig

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

torch.symeig is deprecated in favor of torch.linalg.eigh.

The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.

L, _ = torch.symeig(A, upper=upper)

should be replaced with

L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')

and

L, V = torch.symeig(A, eigenvectors=True)

should be replaced with

L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L')

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 for details.

TOR004 Import of removed function

See TOR001.

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.

torch.backends.cuda.sdp_kernel

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.

torch.chain_matmul

This function is deprecated in favor of torch.linalg.multi_dot.

Migration guide: multi_dot accepts a list of two or more tensors whereas chain_matmul accepted multiple tensors as input arguments. For migration, convert the multiple tensors in argument of chain_matmul into a list of two or more tensors for multi_dot.

Example: Replace torch.chain_matmul(a, b, c) with torch.linalg.multi_dot([a, b, c]).

torch.cholesky

torch.cholesky() is deprecated in favor of torch.linalg.cholesky().

Migration guide:

• L = torch.cholesky(A) should be replaced with L = torch.linalg.cholesky(A).
• L = torch.cholesky(A, upper=True) should be replaced with L = torch.linalg.cholesky(A).mH

torch.qr

torch.qr() is deprecated in favor of torch.linalg.qr().

Migration guide:

• The usage Q, R = torch.qr(A) should be replaced with Q, R = torch.linalg.qr(A).
• The boolean parameter some of torch.qr is replaced with a string parameter mode in torch.linalg.qr. The corresponding change in usage is from Q, R = torch.qr(A, some=False) to Q, R = torch.linalg.qr(A, mode="complete").

torch.range

The function torch.range() is deprecated as its usage is incompatible with Python's builtin range. Instead, use torch.arange() as it produces values in [start, end).

Migration guide:

• torch.range(start, end) produces values in the range of [start, end]. But torch.arange(start, end) produces values in [start, end). For step size of 1, migrate usage from torch.range(start, end, 1) to torch.arange(start, end+1, 1).

TOR102 torch.load without weights_only parameter is unsafe.

Explicitly set weights_only to False only if you trust the data you load and full pickle functionality is needed, otherwise set weights_only=True.

TOR103 Import of deprecated function

See TOR101.

License

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