mirror of https://github.com/coqui-ai/TTS.git
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
# edited from https://github.com/fastai/imagenet-fast/blob/master/imagenet_nv/distributed.py
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
|
|
from torch.autograd import Variable
|
|
|
|
|
|
def reduce_tensor(tensor, num_gpus):
|
|
rt = tensor.clone()
|
|
dist.all_reduce(rt, op=dist.reduce_op.SUM)
|
|
rt /= num_gpus
|
|
return rt
|
|
|
|
|
|
def init_distributed(rank, num_gpus, group_name, dist_backend, dist_url):
|
|
assert torch.cuda.is_available(), "Distributed mode requires CUDA."
|
|
|
|
# Set cuda device so everything is done on the right GPU.
|
|
torch.cuda.set_device(rank % torch.cuda.device_count())
|
|
|
|
# Initialize distributed communication
|
|
dist.init_process_group(dist_backend, init_method=dist_url, world_size=num_gpus, rank=rank, group_name=group_name)
|
|
|
|
|
|
def apply_gradient_allreduce(module):
|
|
|
|
# sync model parameters
|
|
for p in module.state_dict().values():
|
|
if not torch.is_tensor(p):
|
|
continue
|
|
dist.broadcast(p, 0)
|
|
|
|
def allreduce_params():
|
|
if module.needs_reduction:
|
|
module.needs_reduction = False
|
|
# bucketing params based on value types
|
|
buckets = {}
|
|
for param in module.parameters():
|
|
if param.requires_grad and param.grad is not None:
|
|
tp = type(param.data)
|
|
if tp not in buckets:
|
|
buckets[tp] = []
|
|
buckets[tp].append(param)
|
|
for tp in buckets:
|
|
bucket = buckets[tp]
|
|
grads = [param.grad.data for param in bucket]
|
|
coalesced = _flatten_dense_tensors(grads)
|
|
dist.all_reduce(coalesced, op=dist.reduce_op.SUM)
|
|
coalesced /= dist.get_world_size()
|
|
for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
|
|
buf.copy_(synced)
|
|
|
|
for param in list(module.parameters()):
|
|
|
|
def allreduce_hook(*_):
|
|
Variable._execution_engine.queue_callback(allreduce_params) # pylint: disable=protected-access
|
|
|
|
if param.requires_grad:
|
|
param.register_hook(allreduce_hook)
|
|
|
|
def set_needs_reduction(self, *_):
|
|
self.needs_reduction = True
|
|
|
|
module.register_forward_hook(set_needs_reduction)
|
|
return module
|