coderabbitai[bot]
commented
1 week ago Walkthrough
## Walkthrough The recent changes introduce new functionality for handling the initialization of model branches in a deep learning model, specifically when fine-tuning with randomly chosen branches. The updates also ensure correct initialization of the fitting net based on predefined rules, and provide clearer documentation for these processes. Tests are updated accordingly to ensure these new functionalities are correctly implemented and verified. ## Changes | File Path | Change Summary | |----------------------------------------|--------------------------------------------------------------------------------------------------------| | `deepmd/pt/utils/finetune.py` | Added logic to handle `RANDOM` model branch for fine-tuning and updated conditions for model initialization. | | `source/tests/pt/test_training.py` | Included tests for random fitting initialization and updated trainer setups for new fine-tuning logic. | | `deepmd/pt/entrypoints/main.py` | Ensured `RANDOM` model name isn't used in multi-task mode and included it in the model display list. | | `doc/train/finetuning.md` | Documented the new fine-tuning processes and clarified handling of fitting net weights and model branches. | | `source/tests/pt/test_dp_show.py` | Added `'RANDOM'` model branch to available branches in the checkpoint output test case. | ## Sequence Diagram(s) ```mermaid sequenceDiagram participant User participant Trainer participant Model participant FittingNet User->>Trainer: Configure fine-tuning with `--model-branch RANDOM` Trainer->>Model: Initialize Model->>FittingNet: Check `model_branch_from` alt model_branch_from is RANDOM FittingNet-->>FittingNet: Initialize with random weights else model_branch_from is pre-trained FittingNet-->>FittingNet: Initialize with pre-trained weights end Model->>Trainer: Initialization complete Trainer->>User: Fine-tuning setup complete ```Recent review details
**Configuration used: CodeRabbit UI** **Review profile: CHILL**Commits
Files that changed from the base of the PR and between 913141ac445b75be7d5b895dca6eb5cfefe73d33 and 4b58c91229fd3b737fa914c7f724bdb39cf6557a.Files selected for processing (2)
* deepmd/pt/entrypoints/main.py (2 hunks) * source/tests/pt/test_dp_show.py (1 hunks)Files skipped from review as they are similar to previous changes (1)
* source/tests/pt/test_dp_show.pyAdditional context used
Ruff
deepmd/pt/entrypoints/main.py
117-117: Local variable `f` is assigned to but never used Remove assignment to unused variable `f` (F841) --- 382-385: Use ternary operator `FLAGS = parse_args(args=args) if not isinstance(args, argparse.Namespace) else args` instead of `if`-`else`-block Replace `if`-`else`-block with `FLAGS = parse_args(args=args) if not isinstance(args, argparse.Namespace) else args` (SIM108)
Additional comments not posted (2)
deepmd/pt/entrypoints/main.py (2)
`232-235`: **Validation for model name in multitask mode is correctly implemented.** The addition of this validation is important to prevent the use of 'RANDOM' as a model name in multitask mode, which could lead to configuration conflicts. The implementation using an assert statement is appropriate here. --- `344-348`: **Properly updated model branches list to include 'RANDOM'.** This update ensures that users are aware of the 'RANDOM' option for initializing a fitting net, enhancing the flexibility of model configuration. The log message is clear and informative.
codecov[bot]
Support fine-tuning from random fitting in single-from-single fine-tuning.
Summary by CodeRabbit
New Features
Documentation
--model-branch RANDOMparameter.Tests