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@ -33,14 +33,19 @@ class PipelineSharedModuleGradientHandler(BaseGradientHandler): |
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# Pack the buckets. |
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# Pack the buckets. |
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for param in self._model.parameters(): |
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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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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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tp = param.data.type() |
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buckets[group][tp].append(param) |
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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 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 group, group_buckets in buckets.items(): |
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for tp, bucket in group_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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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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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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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)): |
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