fix(pt): finetuning property/dipole/polar/dos fitting with multi-dimensional data causes error

[Chengqian-Zhang]

Fix issue #4108

If a pretrained model is labeled with energy and the out_bias is one dimension. If we want to finetune a dos/polar/dipole/property model using this pretrained model, the out_bias of finetuning model is multi-dimension(example: numb_dos = 250). An error occurs:
RuntimeError: Error(s) in loading state_dict for ModelWrapper:
size mismatch for model.Default.atomic_model.out_bias: copying a param with shape torch.Size([1, 118, 1]) from checkpoint, the shape in current model is torch.Size([1, 118, 250]).
size mismatch for model.Default.atomic_model.out_std: copying a param with shape torch.Size([1, 118, 1]) from checkpoint, the shape in current model is torch.Size([1, 118, 250]).

When using new fitting, old out_bias is useless because we will recompute the new bias in later code. So we do not need to load old out_bias when using new fitting finetune.

Summary by CodeRabbit

• New Features

  • Enhanced parameter collection for fine-tuning, refining criteria for parameter retention.
  • Introduced a model checkpoint file for saving and resuming training states, facilitating iterative development.

• Tests

  • Added a new test class to validate training and fine-tuning processes, ensuring model performance consistency across configurations.

[coderabbitai]

Walkthrough

Walkthrough

The pull request modifies the logic in the collect_single_finetune_params function within training.py, refining the criteria for parameter selection during fine-tuning. A new model checkpoint file is added to support model persistence, and a new test class is introduced to validate both training and fine-tuning processes, enhancing the testing framework.

Changes

Files	Change Summary
deepmd/pt/train/training.py	Updated collect_single_finetune_params to exclude parameters containing ".descriptor." when _new_fitting is true.
source/checkpoint	Added a new model checkpoint file model.ckpt-1.pt for saving model state during training.
source/tests/pt/test_training.py	Introduced TestPropFintuFromEnerModel class with tests for training and fine-tuning processes, including setup and teardown methods.

Possibly related PRs

• fix: bugs in uts for property fit #4120: Modifies the test suite for property fitting, potentially relating to the changes in parameter handling in the main PR's collect_single_finetune_params function.
• fix(pt/dp): share params of repinit_three_body #4139: Enhances the handling of parameters in descriptor methods, relevant to the changes in parameter collection during model training and fine-tuning.

Suggested reviewers

• njzjz
• iProzd
• wanghan-iapcm

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Commits

Files that changed from the base of the PR and between cf8bac4 and e51a4f7.

Files selected for processing (1)

• deepmd/pt/train/training.py (1 hunks)

Files skipped from review as they are similar to previous changes (1)

• deepmd/pt/train/training.py

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[coderabbitai]

Actionable comments posted: 2

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between ba9f02f and 1dac68c.

Files selected for processing (1)

• deepmd/pt/train/training.py (1 hunks)

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 83.43%. Comparing base (f5cfeab) to head (e51a4f7).
Report is 3 commits behind head on devel.

Additional details and impacted files

@@            Coverage Diff             @@
##            devel    #4145      +/-   ##
==========================================
+ Coverage   83.42%   83.43%   +0.01%     
==========================================
  Files         532      532              
  Lines       52048    52049       +1     
  Branches     3046     3046              
==========================================
+ Hits        43419    43429      +10     
+ Misses       7682     7672      -10     
- Partials      947      948       +1     

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