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distributed.py: replacing to avoid distributed error
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@@ -1,6 +1,7 @@
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import torch
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import torch.distributed as dist
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from torch.nn.modules import Module
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from torch.autograd import Variable
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def _flatten_dense_tensors(tensors):
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"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
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@@ -161,7 +162,7 @@ def apply_gradient_allreduce(module):
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for param in list(module.parameters()):
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def allreduce_hook(*unused):
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param._execution_engine.queue_callback(allreduce_params)
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Variable._execution_engine.queue_callback(allreduce_params)
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if param.requires_grad:
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param.register_hook(allreduce_hook)
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