Fix Plan

The fix involves modifying the get_scheduler function in optimization.py to properly handle the timesteps argument when the scheduler name is explicitly checked.

Code Changes

To fix the issue, update the get_scheduler function as follows:

def get_scheduler(
    name: str,
    optimizer: torch.optim.Optimizer,
    num_warmup_steps: Optional[int] = None,
    num_training_steps: Optional[int] = None,
    num_cycles: Optional[int] = None,
    last_epoch: int = -1,
    timesteps: Optional[str] = None,  # Add timesteps argument
):
    # ... (rest of the function remains the same)

    if name == "inverse_sqrt":
        if timesteps == "random":
            # Implement random timesteps logic here
            num_training_steps = random.randint(1, 1000)  # Example random timesteps
        elif timesteps is not None:
            # Implement custom timesteps logic here
            num_training_steps = int(timesteps)
        # ... (rest of the function remains the same)

Verification

To verify the fix, trigger a learning job with "inverse_sqrt" and set timesteps to "random" or a custom value. The timesteps argument should now take effect, and the scheduler should behave accordingly.

Extra Tips

• Make sure to update the get_scheduler function in the correct file (optimization.py) and commit the changes.
• Test the fix with different timesteps values to ensure it works as expected.
• Consider adding additional error handling and logging to ensure the timesteps argument is properly validated and handled.