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@ -33,14 +33,19 @@ class PipelineSharedModuleGradientHandler(BaseGradientHandler):
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# Pack the buckets.
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for param in self._model.parameters():
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group = getattr(param, 'pipeline_shared_module_pg', None)
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if param.requires_grad and param.grad is not None and group is not None:
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if param.requires_grad and group is not None and (
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(hasattr(param, 'colo_attr') and not param.colo_attr.saved_grad.is_null())
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or param.grad is not None):
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tp = param.data.type()
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buckets[group][tp].append(param)
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# For each bucket, all-reduce and copy all-reduced grads.
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for group, group_buckets in buckets.items():
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for tp, bucket in group_buckets.items():
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grads = [param.grad.data for param in bucket]
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grads = [
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param.colo_attr.grad_payload if hasattr(param, 'colo_attr') else param.grad.data
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for param in bucket
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]
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coalesced = _flatten_dense_tensors(grads).to(torch.cuda.current_device())
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dist.all_reduce(coalesced, op=dist.ReduceOp.SUM, group=group)
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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
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