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stas00
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Dec 11, 2025
| # Note: DeepSpeed does NOT automatically scale loss during backward for all ZeRO stages, | ||
| # particularly ZeRO-2 where gradient partitioning can cause incorrect accumulation | ||
| # if the loss is not pre-scaled. This ensures consistent behavior across all ZeRO stages. | ||
| loss = loss / self.gradient_accumulation_steps |
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Are you sure the issue isn't somewhere else?
You can see this is already done here:
https://github.com/deepspeedai/DeepSpeed/blob/b00b75f05852e0791f1e2b9c1cc894cd690e2da4/deepspeed/runtime/engine.py#L2482
and grads are scaled here:
https://github.com/deepspeedai/DeepSpeed/blob/b00b75f05852e0791f1e2b9c1cc894cd690e2da4/deepspeed/runtime/engine.py#L2360
so I suspect the above change is likely to break things, no?
cc: @tjruwase to double check.
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yes agree! i was just wanted to test things... reverting
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What does this PR do?
This pull request updates the
backwardmethod insrc/accelerate/accelerator.pyto ensure consistent loss scaling across all distributed training backends.Distributed training consistency:
gradient_accumulation_steps, regardless of the backend, to prevent incorrect accumulation in DeepSpeed ZeRO-2 and similar scenarios. This change makes loss scaling explicit and consistent for all distributed types.Fixes #3877
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