feat(pt): add trainable to property fitting

[ChiahsinChu]

Add keyword trainable to property fitting.

Summary by CodeRabbit

• New Features

  • Introduced a configurable "trainable" option that allows users to control whether network parameters are updated individually or collectively.

• Documentation

  • Enhanced descriptions to clearly explain how the new trainability setting works.

[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request adds a new parameter, trainable, to control the trainability of the fitting network parameters. In the PropertyFittingNet class constructor, the parameter is defined as Union[bool, list[bool]] with a default value of True and is passed to the superclass. A similar change is made in the fitting_property function in the argcheck module, along with corresponding documentation updates and the necessary import adjustments.

Changes

File(s)	Change Summary
deepmd/pt/.../property.py deepmd/utils/argcheck.py	Added new trainable parameter (type: Union[bool, list[bool]], default: True) to both the PropertyFittingNet constructor and the fitting_property function. Updated documentation to clarify its behavior, especially when provided as a list, and added necessary import for Union.

Possibly related PRs

• fix(pt): finetuning property/dipole/polar/dos fitting with multi-dimensional data causes error #4145: Introduces similar trainable parameter modifications to enhance configurability of fitting parameters.

Suggested reviewers

• njzjz
• iProzd
• wanghan-iapcm

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📜 Recent review details

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📥 Commits

Reviewing files that changed from the base of the PR and between 176c746 and 46de84c.

📒 Files selected for processing (2)

• deepmd/pt/model/task/property.py (3 hunks)
• deepmd/utils/argcheck.py (2 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (20)

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🔇 Additional comments (2)

deepmd/pt/model/task/property.py (1)

92-92: LGTM! The trainable parameter is properly implemented.

The new trainable parameter is well-defined with proper typing and a sensible default value. The parameter is correctly passed to the superclass.

Also applies to: 112-112

deepmd/utils/argcheck.py (1)

1583-1585: LGTM! The argument definition and documentation are well-implemented.

The trainable parameter is properly defined with:

• Clear documentation explaining both boolean and list options
• Correct typing as [list[bool], bool]
• Appropriate default value matching the implementation

Also applies to: 1622-1628

✨ Finishing Touches

• 📝 Generate Docstrings (Beta)

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 84.57%. Comparing base (176c746) to head (46de84c).
Report is 1 commits behind head on devel.

Additional details and impacted files

@@            Coverage Diff             @@
##            devel    #4599      +/-   ##
==========================================
- Coverage   84.58%   84.57%   -0.01%     
==========================================
  Files         680      680              
  Lines       64509    64509              
  Branches     3540     3539       -1     
==========================================
- Hits        54562    54561       -1     
+ Misses       8807     8806       -1     
- Partials     1140     1142       +2     

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