Upgrade torch to 2.4.0 in pyproject.toml #382
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Signed-off-by: Tsz Wai Ko <[email protected]>
…taining and using element offsets
…ction including both
…the update of sympy
Signed-off-by: Tsz Wai Ko <[email protected]>
…s and Simulations using the M3GNet Universal Potential.ipynb
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Tsz Wai Ko <[email protected]>
Signed-off-by: Tsz Wai Ko <[email protected]>
WalkthroughThe changes in this pull request primarily involve updates to the Changes
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🧹 Outside diff range and nitpick comments (1)
src/matgl/layers/_core.py (1)
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85-89: Typographical error in the docstringIn the docstring for the
__init__method ofMLP_norm, the parameter description foractivationis duplicated. Please remove the redundant word.Apply this diff to fix the typo:
Args: dims: Dimensions of each layer of MLP. - activation: activation: Activation function. + activation: Activation function. activate_last: Whether to apply activation to last layer. use_bias: Whether to use bias.
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src/matgl/layers/_core.py (2)
5-5: Importing 'cast' is appropriate for type safetyThe inclusion of
castfrom thetypingmodule is necessary for the explicit type casting used later in the code.
133-133: Ensuring type safety with explicitcastUsing
cast(dict, norm_kwargs)ensures thatnorm_kwargsis recognized as a dictionary by static type checkers. This is appropriate and helps maintain type safety.
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pyproject.toml (1)
53-56: Summary of dependency changes and suggested next stepsThis PR makes two key changes to dependencies:
- Downgrades the maximum allowed version of
dglfrom 2.4.0 to 2.2.1- Upgrades the maximum allowed version of
torchfrom 2.2.1 to 2.4.0While the
torchupgrade aligns with the PR objectives, thedgldowngrade is unexpected. To ensure these changes are appropriate and don't introduce issues:
- Clarify the rationale behind the
dglversion constraint change.- Verify compatibility between the new
torchversion and the constraineddglversion.- Run the full test suite to catch any regressions or compatibility issues.
- Update the PR description to explain both changes, not just the
torchupgrade.- Consider updating any documentation that might reference these dependency versions.
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pyproject.toml (2)
56-56: LGTM! Verify compatibility with existing codebaseThe upgrade of the
torchdependency to version<=2.4.0aligns with the PR objectives. This change could potentially introduce new features or performance improvements.To ensure a smooth transition:
- Verify that all existing torch-dependent code in the project is compatible with torch 2.4.0.
- Update any torch-specific code that might leverage new features or changed APIs in version 2.4.0.
- Run the test suite to catch any potential regressions.
To help identify potential areas that might need attention, you can run:
#!/bin/bash # Search for torch imports and usages in the codebase rg --type python "import torch|from torch" -C 5 # Look for any torch version-specific code or comments rg --type python "torch.*2\.[0-9]" -C 5
53-53: Verify the intention behind downgrading dgl version constraintThe
dgldependency version constraint has been tightened from<=2.4.0to<=2.2.1. This change restricts the project to use an older version ofdgl, which seems to contradict the PR's objective of upgrading dependencies.Could you please clarify:
- The reason for downgrading the
dglversion constraint?- Whether this change is related to addressing the issues with the united test for numpy version 2.0.1 mentioned in the PR description?
- If this change has been tested for compatibility with other dependencies, especially the upgraded
torchversion?To check for potential compatibility issues, you can run:
Summary
Upgrade torch to 2.4.0 in pyproject.toml to see if the united test for numpy 2.0.1 can be fixed
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ruff.mypy.duecredit@due.dcitedecorators to reference relevant papers by DOI (example)Tip: Install
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