huggingface / huggingface/diffusers
Potential incorrect indentation for logging in train_dreambooth.py
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Description
Description:
In the train_dreambooth.py script, the logging and progress bar updates appear to be executed on every training step, even when using gradient accumulation. This might lead to incorrect or redundant logging.
The relevant code is located around lines 1393-1395:
The global_step is only incremented when accelerator.sync_gradients is true. However, the logging calls (progress_bar.set_postfix and accelerator.log) are outside this block. This means that when gradient accumulation is used, these lines are executed for every batch, but the global_step value passed to accelerator.log does not change until an optimization step occurs. This could result in multiple log entries for the same global_step.
It seems more appropriate to move the logging logic inside the if accelerator.sync_gradients: block to ensure that logging only happens once per optimization step.
Proposed Change:
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
# ... checkpointing and validation logic ...
- logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
+ logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+ progress_bar.set_postfix(**logs)
+ accelerator.log(logs, step=global_step)
Could you please confirm if this is the intended behavior or if the indentation should be corrected? Thank you!
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Research direction
Start in examples/dreambooth/train_dreambooth.py around lines 1346-1395 and inspect how accelerator.sync_gradients controls global_step. Check whether progress_bar.set_postfix and accelerator.log run once per optimization step or on every batch; done means the intended logging behavior is confirmed and the indentation is corrected if needed.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 50/100