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Assume accumulate_grad=2, log_every_n_steps=50, val_check_interval=8000; tensorboard is the self.log. The self.global_step will add one when model.forward is done, but tensorboard step will add one when loss.step() is done. So my model will valid when tensorboard step = 4000, because this time the self.global_step is 8000.
I think this logic should be unified.
What version are you seeing the problem on?
v2.4
How to reproduce the bug
No response
Error messages and logs
# Error messages and logs here please
Environment
Current environment
#- PyTorch Lightning Version (e.g., 2.4.0):
#- PyTorch Version (e.g., 2.4):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
More info
No response
The text was updated successfully, but these errors were encountered:
Bug description
Assume accumulate_grad=2, log_every_n_steps=50, val_check_interval=8000; tensorboard is the self.log. The self.global_step will add one when model.forward is done, but tensorboard step will add one when loss.step() is done. So my model will valid when tensorboard step = 4000, because this time the self.global_step is 8000.
I think this logic should be unified.
What version are you seeing the problem on?
v2.4
How to reproduce the bug
No response
Error messages and logs
Environment
Current environment
More info
No response
The text was updated successfully, but these errors were encountered: