fix: deeptensor output, add dipole stat UT

[anyangml]

Summary by CodeRabbit

• New Features

  • Introduced model_dipole for enhanced dipole model configurations.
  • Added support for "global_dipole" data in finetuning tests.

• Improvements

  • Updated return type of the eval method to ensure consistency and clarity in the output.
  • Improved energy calculation logic in finetuning tests by considering additional data types.

• Testing

  • Enhanced test coverage for dipole and DOS models with updated test cases and configurations.

[coderabbitai]

Walkthrough

The recent changes involve modifications to the return type of the eval method in deepmd/infer/deep_tensor.py, converting it from a single np.ndarray to Tuple[np.ndarray]. Additionally, the test_permutation.py and test_finetune.py test files have been updated to incorporate a new model_dipole dictionary configuration, modify energy calculation requirements, and add new data requirements for "global_dipole."

Changes

Files	Change Summary
deepmd/infer/deep_tensor.py	Changed the return type of eval method from np.ndarray to Tuple[np.ndarray], returning values wrapped in tuples.
source/tests/pt/model/test_permutation.py	Added model_dipole dictionary with configurations for a dipole model.
source/tests/pt/test_finetune.py	Added model_dipole, new data requirements for "global_dipole," and modified energy calculations and class declarations.

Sequence Diagram(s)

sequenceDiagram
    participant Tester as Test Suite
    participant DT as DeepTensor
    participant ModelConfig as Model Configuration

    rect rgb(191, 223, 255)
    note over Tester, DT: Interaction for evaluating models
    Tester ->> DT: Call eval()
    DT -->> Tester: Return tuple of np.ndarray
    end

    rect rgb(245, 224, 177)
    note over Tester, ModelConfig: Interaction for configuring models in tests
    Tester ->> ModelConfig: Load model_dipole configuration
    ModelConfig -->> Tester: Configurations returned
    end

    rect rgb(255, 191, 191)
    note over Tester: Testing with new data requirements
    Tester ->> Tester: Test with global_dipole
    Tester ->> Tester: Adjust energy calculations
    end

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[codecov]

Codecov Report

Attention: Patch coverage is 0% with 2 lines in your changes missing coverage. Please review.

Project coverage is 34.83%. Comparing base (1c3e099) to head (aa5c20d).
Report is 114 commits behind head on devel.

Files with missing lines	Patch %	Lines
deepmd/infer/deep_tensor.py	0.00%	2 Missing ⚠️

❗ There is a different number of reports uploaded between BASE (1c3e099) and HEAD (aa5c20d). Click for more details.

HEAD has 24 uploads less than BASE

Flag	BASE (1c3e099)	HEAD (aa5c20d)
	26	2

Additional details and impacted files

@@             Coverage Diff             @@
##            devel    #3948       +/-   ##
===========================================
- Coverage   82.84%   34.83%   -48.02%     
===========================================
  Files         520      520               
  Lines       50827    50795       -32     
  Branches     3015     3015               
===========================================
- Hits        42108    17692    -24416     
- Misses       7785    32495    +24710     
+ Partials      934      608      -326     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[anyangml]

I may close this PR if ndarry is the expected output type.

[wanghan-iapcm]

Could you please explain the reason of using Tuple[np.ndarray] instead of np.ndarray as returned type of DeepTensor?

[anyangml]

Could you please explain the reason of using Tuple[np.ndarray] instead of np.ndarray as returned type of DeepTensor?

It seems the other DeepModels all return a tuple object, I thought they should be consistent. When adding the new UT, DeepDipole eval needs special handling if a ndarray is returned. Although the UT is not as important, since dipole model does not apply bias, just want to check the changes made in #3945.