materialsvirtuallab / matgl

Graph deep learning library for materials
BSD 3-Clause "New" or "Revised" License
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Optimize the speed of _compute_3body implementation #283

Closed kenko911 closed 4 months ago

kenko911 commented 4 months ago

Summary

The new implementation of _compute_3body accelerates the calculation of counting 3-body indices. Based on my preliminary benchmarking, the new implementation for simulating thousands of atoms improves by around 15-20% speed compared to the current implementation.

Checklist

Tip: Install pre-commit hooks to auto-check types and linting before every commit:

pip install -U pre-commit
pre-commit install
coderabbitai[bot] commented 4 months ago

[!WARNING]

Review failed

The pull request is closed.

Walkthrough

The _compute_3body function in src/matgl/graph/compute.py has been significantly optimized for performance. Key improvements include the efficient counting of bonds per atom using np.bincount and streamlined generation of triple_bond_indices with numpy operations. The management of three_body_id and max_three_body_id has also been revised for efficiency. Additionally, the return value has been simplified to return only l_g.

Changes

File Change Summary
src/matgl/graph/compute.py Optimized _compute_3body function, removed unnecessary parameters, streamlined return type.

Sequence Diagram(s)

sequenceDiagram
    participant Caller
    participant Compute as compute.py

    Caller->>Compute: _compute_3body(g)
    Compute->>Compute: Count bonds per atom using np.bincount
    Compute->>Compute: Generate triple_bond_indices via numpy operations
    Compute->>Compute: Handle three_body_id and max_three_body_id efficiently
    Compute-->>Caller: Return l_g

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