mirror of https://github.com/InternLM/InternLM
avoid allreduce for dense model in pp
parent
5539f9db50
commit
4e6db4af0f
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@ -445,7 +445,9 @@ class PipelineScheduler(BaseScheduler):
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comm.send_forward(output_obj, scatter_gather_tensors=self.scatter_gather_tensors)
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output, label = pack_return_tensors(return_tensors) if len(return_tensors) > 0 else (None, None)
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dist.all_reduce(accum_moe_loss, group=gpc.get_group(ParallelMode.PIPELINE))
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if hasattr(gpc.config.model, "num_experts"):
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dist.all_reduce(accum_moe_loss, group=gpc.get_group(ParallelMode.PIPELINE))
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if accum_loss is not None:
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accum_loss += accum_moe_loss
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@ -647,7 +649,9 @@ class PipelineScheduler(BaseScheduler):
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comm.send_backward(input_obj_grad, scatter_gather_tensors=self.scatter_gather_tensors)
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output, label = pack_return_tensors(return_tensors) if len(return_tensors) > 0 else (None, None)
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dist.all_reduce(accum_moe_loss, group=gpc.get_group(ParallelMode.PIPELINE))
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if hasattr(gpc.config.model, "num_experts"):
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dist.all_reduce(accum_moe_loss, group=gpc.get_group(ParallelMode.PIPELINE))
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if accum_loss is not None:
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accum_loss += accum_moe_loss
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@ -1387,7 +1391,8 @@ class InterleavedPipelineScheduler(PipelineScheduler):
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else:
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output, label = (None, None)
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dist.all_reduce(self._accum_moe_loss, group=gpc.get_group(ParallelMode.PIPELINE))
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if hasattr(gpc.config.model, "num_experts"):
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dist.all_reduce(self._accum_moe_loss, group=gpc.get_group(ParallelMode.PIPELINE))
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accum_moe_loss = self._accum_moe_loss
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accum_loss = self._accum_loss
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