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cleanup
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@@ -198,7 +198,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
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# We set step_scheduler_with_optimizer=False to prevent accelerate from adjusting the lr_scheduler steps based on the num_processes
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# We set find_unused_parameters=True to handle models with conditional computation
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if accelerator is None:
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ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
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# Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
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# Force the device to be CPU when the active config's device is set to CPU (works for both policy and reward model training).
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