mirror of https://github.com/coqui-ai/TTS.git
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
# edited from https://github.com/fastai/imagenet-fast/blob/master/imagenet_nv/distributed.py
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import torch
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import torch.distributed as dist
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def reduce_tensor(tensor, num_gpus):
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rt = tensor.clone()
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dist.all_reduce(rt, op=dist.reduce_op.SUM)
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rt /= num_gpus
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return rt
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def init_distributed(rank, num_gpus, group_name, dist_backend, dist_url):
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assert torch.cuda.is_available(), "Distributed mode requires CUDA."
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# Set cuda device so everything is done on the right GPU.
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torch.cuda.set_device(rank % torch.cuda.device_count())
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# Initialize distributed communication
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dist.init_process_group(dist_backend, init_method=dist_url, world_size=num_gpus, rank=rank, group_name=group_name)
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def apply_gradient_allreduce(module):
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# sync model parameters
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for p in module.state_dict().values():
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if not torch.is_tensor(p):
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continue
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dist.broadcast(p, 0)
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def allreduce_params():
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if module.needs_reduction:
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module.needs_reduction = False
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# bucketing params based on value types
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buckets = {}
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for param in 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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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, op=dist.reduce_op.SUM)
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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(module.parameters()):
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def allreduce_hook(*_):
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Variable._execution_engine.queue_callback(allreduce_params) # pylint: disable=protected-access
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if param.requires_grad:
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param.register_hook(allreduce_hook)
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def set_needs_reduction(self, *_):
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self.needs_reduction = True
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module.register_forward_hook(set_needs_reduction)
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return module
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