54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
import torch
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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
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import torch.distributed as dist
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from torch.nn.modules import Module
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class DistributedDataParallel(Module):
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def __init__(self, module):
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super(DistributedDataParallel, self).__init__()
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self.warn_on_half = True#$ True if dist._backend == dist.dist_backend.GLOO else False
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self.module = module
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for p in self.module.state_dict().values():
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if torch.is_tensor(p):
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dist.broadcast(p, 0)
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def allreduce_params():
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if(self.needs_reduction):
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self.needs_reduction = False
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buckets = {}
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for param in self.module.parameters():
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if param.requires_grad and param.grad is not None:
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tp = type(param.data)
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if tp not in buckets:
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buckets[tp] = []
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buckets[tp].append(param)
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if self.warn_on_half:
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if torch.cuda.HalfTensor in buckets:
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print("WARNING: gloo dist backend for half parameters may be extremely slow." +
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" It is recommended to use the NCCL backend in this case.")
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self.warn_on_half = False
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for tp in buckets:
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bucket = buckets[tp]
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grads = [param.grad.data for param in bucket]
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coalesced = _flatten_dense_tensors(grads)
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dist.all_reduce(coalesced)
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coalesced /= dist.get_world_size()
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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
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buf.copy_(synced)
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for param in list(self.module.parameters()):
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if param.requires_grad:
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def allreduce_hook(*unused):
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param._execution_engine.queue_callback(allreduce_params)
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param.register_hook(allreduce_hook)
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def forward(self, *inputs, **kwargs):
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self.needs_reduction = True
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return self.module(*inputs, **kwargs)
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