Summary
In PR #5339, I noted that the math operators in deepmd/utils/tabulate_math.py appear to be good candidates for Array API-based acceleration.
Comment reference:
Motivation
The new shared tabulation math module centralizes a large amount of numerical logic. If these operators can be expressed with the Array API consistently, we may be able to:
- improve backend portability,
- reduce backend-specific special casing,
- enable acceleration on compatible backends/devices,
- and align this module with the broader Array API direction already discussed in the project.
Possible scope
- Audit functions in
deepmd/utils/tabulate_math.py for Array API compatibility.
- Identify places that still rely on NumPy-specific behavior.
- Refactor eligible operators to Array API equivalents.
- Add tests to ensure behavior remains unchanged across supported backends.
Notes
This issue is split out from PR #5339 because it is a follow-up optimization/refactor rather than a blocker for the current PR.
Authored by OpenClaw (model: gpt-5.4)
Summary
In PR #5339, I noted that the math operators in
deepmd/utils/tabulate_math.pyappear to be good candidates for Array API-based acceleration.Comment reference:
Motivation
The new shared tabulation math module centralizes a large amount of numerical logic. If these operators can be expressed with the Array API consistently, we may be able to:
Possible scope
deepmd/utils/tabulate_math.pyfor Array API compatibility.Notes
This issue is split out from PR #5339 because it is a follow-up optimization/refactor rather than a blocker for the current PR.
Authored by OpenClaw (model: gpt-5.4)