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Running
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Zero
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import torch.distributed as dist
from enum import Enum
class ReduceOp(Enum):
SUM = 0,
PRODUCT = 1,
MIN = 2,
MAX = 3
def ToDistOp(self):
return {
self.SUM: dist.ReduceOp.SUM,
self.PRODUCT: dist.ReduceOp.PRODUCT,
self.MIN: dist.ReduceOp.MIN,
self.MAX: dist.ReduceOp.MAX
}[self]
def is_available():
return dist.is_available()
def get_rank():
if is_available():
return _get_rank()
else:
return 0
def get_world_size():
if is_available():
return _get_world_size()
else:
return 1
def barrier():
if is_available():
return _barrier()
#else: do nothing
def all_gather(tensor_list, tensor):
if is_available():
return _all_gather(tensor_list, tensor)
else:
tensor_list[0] = tensor
def all_reduce(tensor, op=ReduceOp.SUM):
if is_available():
return _all_reduce(tensor, op)
#else: do nothing
def reduce(tensor, dst, op=ReduceOp.SUM):
if is_available():
return _reduce(tensor, dst, op)
#else: do nothing
def broadcast(tensor, src):
if is_available():
return _broadcast(tensor, src)
#else: do nothing
def init_process_group(backend, init_method):
if is_available():
return _init_process_group(backend, init_method)
#else: do nothing
def _get_rank():
return dist.get_rank()
def _barrier():
return dist.barrier()
def _get_world_size():
return dist.get_world_size()
def _all_gather(tensor_list, tensor):
return dist.all_gather(tensor_list, tensor)
def _all_reduce(tensor, op):
return dist.all_reduce(tensor, op.ToDistOp())
def _reduce(tensor, dst, op):
return dist.reduce(tensor, dst, op.ToDistOp())
def _broadcast(tensor, src):
return dist.broadcast(tensor, src)
def _init_process_group(backend, init_method):
return dist.init_process_group(backend, init_method) |