pd: fix learning rate setting when resume

[HydrogenSulfate]

"When resuming training, there is no need to add self.start_step to the step count because Paddle uses lr_sche.last_epoch as the input for step, which already records the start_step steps."

learning rate are correct after fixing

Summary by CodeRabbit

• New Features

  • Enhanced training process with improved optimizer configuration and learning rate adjustments.
  • Refined logging of training and validation results for clarity.
  • Improved model saving logic to preserve the latest state during interruptions.
  • Enhanced tensorboard logging for detailed tracking of training metrics.

• Bug Fixes

  • Corrected lambda function for learning rate scheduler to reference warmup steps accurately.

• Chores

  • Streamlined data loading and handling for efficient training across different tasks.

[coderabbitai]

📝 Walkthrough

Walkthrough

The pull request introduces modifications to the Trainer class in the deepmd/pd/train/training.py file, focusing on improving the training process and optimizer configuration. The changes primarily enhance the robustness of training, particularly for multi-task scenarios and checkpoint resumption. Key updates include refining the learning rate scheduler, improving logging and model saving mechanisms, and streamlining data handling during training.

Changes

File	Change Summary
deepmd/pd/train/training.py	- Updated __init__ method to include optimizer state dictionary parameter - Modified learning rate scheduler lambda function - Refined logging and model saving logic - Improved tensorboard logging - Enhanced checkpoint resumption handling

Sequence Diagram

sequenceDiagram
    participant Trainer
    participant Optimizer
    participant LRScheduler
    participant ModelSaver
    participant Logger

    Trainer->>Optimizer: Initialize with optional state dict
    Trainer->>LRScheduler: Configure warmup steps
    loop Training Iteration
        Trainer->>Trainer: Prepare batch data
        Trainer->>Optimizer: Compute gradients
        Optimizer->>LRScheduler: Adjust learning rate
        Trainer->>ModelSaver: Save checkpoint periodically
        Trainer->>Logger: Log training metrics
    end
    Trainer->>ModelSaver: Save final model state

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📥 Commits

Reviewing files that changed from the base of the PR and between c0914e1 and 5fb1509.

📒 Files selected for processing (1)

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

🔇 Additional comments (2)

deepmd/pd/train/training.py (2)

591-591: Ensure correct alignment of warmup scheduling logic.

The new lambda function for the learning rate warmup appears logically consistent. However, please confirm that the “warmup to exponential” transition happens at the desired boundary and that no off-by-one issues occur if you resume from mid-training steps.

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598-598: Double-check the off-by-one effect when resuming.

Subtracting 1 from self.scheduler.last_epoch might shift the learning rate schedule by one step. If your intent is to align the scheduler precisely with the current training step, consider setting last_epoch to self.start_step - 1 or verifying that the shift doesn’t cause discrepancies.

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[codecov]

Codecov Report

Attention: Patch coverage is 0% with 1 line in your changes missing coverage. Please review.

Project coverage is 84.41%. Comparing base (c0914e1) to head (5fb1509).
Report is 1 commits behind head on devel.

Files with missing lines	Patch %	Lines
deepmd/pd/train/training.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##            devel    #4480      +/-   ##
==========================================
- Coverage   84.41%   84.41%   -0.01%     
==========================================
  Files         670      670              
  Lines       62147    62149       +2     
  Branches     3487     3486       -1     
==========================================
+ Hits        52464    52465       +1     
- Misses       8556     8558       +2     
+ Partials     1127     1126       -1     

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