feat(pt): support disp_training and time_training in pt

[iProzd]

Summary by CodeRabbit

• New Features
  • Introduced options to display training progress and log training times, enhancing user visibility and tracking capabilities during model training sessions.

[coderabbitai]

Walkthrough

Walkthrough

The updates focus on enhancing the training functionality by introducing new parameters to control display and timing aspects during training sessions. These changes allow for more detailed monitoring and performance analysis, including the ability to log and calculate average training times per batch, thereby improving the utility and diagnostic capabilities of the training process.

Changes

File Path	Change Summary
deepmd/pt/train/...	Added display_in_training and timing_in_training parameters. Modified conditions in fake_model() and updated log_loss_valid() to handle new timing logs and calculate average training time per batch. Initialized total_train_time.

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Recent Review Details

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Review profile: CHILL

Commits

Files that changed from the base of the PR and between 18cdbf0 and e9ef748.

Files selected for processing (1)

• deepmd/pt/train/training.py (5 hunks)

Additional comments not posted (3)

deepmd/pt/train/training.py (3)

142-143: Initialization of display_in_training and timing_in_training parameters is correctly handled with default values.

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Line range hint 816-927: The usage of display_in_training and timing_in_training in the run method is correctly implemented to control the logging of training progress and timing information.

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1006-1023: The implementation of average training time logging is accurate and thoughtful, excluding the first batch to provide more reliable metrics.

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[coderabbitai]

Actionable comments posted: 0

Out of diff range and nitpick comments (1)

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

142-143: The model resuming and finetuning logic is robust, handling various scenarios and potential issues with missing keys. Consider adding more detailed comments to explain the conditions and operations for future maintainability.

[codecov]

Codecov Report

Attention: Patch coverage is 90.90909% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 82.49%. Comparing base (18cdbf0) to head (e9ef748).

Files	Patch %	Lines
deepmd/pt/train/training.py	90.90%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##            devel    #3775   +/-   ##
=======================================
  Coverage   82.49%   82.49%           
=======================================
  Files         515      515           
  Lines       48633    48642    +9     
  Branches     2979     2979           
=======================================
+ Hits        40118    40126    +8     
- Misses       7604     7605    +1     
  Partials      911      911           

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