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Update train_ep.py
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@ -126,7 +126,6 @@ def main():
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backward(loss, **kwargs)
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if not self.sync_gradients or edp_size == 1:
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return
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return
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for p in expert_params:
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g = p.grad if p.grad is not None else torch.zeros_like(p)
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dist.all_reduce(g, op=dist.ReduceOp.AVG, group=edp_group)
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@ -145,7 +144,7 @@ def main():
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if local_rank == 0:
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trainer.save_model(ckpt_path)
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tokenizer.save_pretrained(ckpt_path)
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elif 0 < local_rank < ep_size:
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elif local_rank < ep_size:
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model.save_pretrained(ckpt_path)
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print("Training complete")
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