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# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import os
from typing import Union
import torch
import torch.distributed
from ...models.utils.list import list_mean, list_sum
__all__ = [
"dist_init",
"is_dist_initialized",
"get_dist_rank",
"get_dist_size",
"is_master",
"dist_barrier",
"get_dist_local_rank",
"sync_tensor",
]
def dist_init() -> None:
if is_dist_initialized():
return
try:
torch.distributed.init_process_group(backend="nccl")
assert torch.distributed.is_initialized()
except Exception:
os.environ["RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
os.environ["LOCAL_RANK"] = "0"
print("warning: dist not init")
def is_dist_initialized() -> bool:
return torch.distributed.is_initialized()
def get_dist_rank() -> int:
return int(os.environ["RANK"])
def get_dist_size() -> int:
return int(os.environ["WORLD_SIZE"])
def is_master() -> bool:
return get_dist_rank() == 0
def dist_barrier() -> None:
if is_dist_initialized():
torch.distributed.barrier()
def get_dist_local_rank() -> int:
return int(os.environ["LOCAL_RANK"])
def sync_tensor(tensor: Union[torch.Tensor, float], reduce="mean") -> Union[torch.Tensor, list[torch.Tensor]]:
if not is_dist_initialized():
return tensor
if not isinstance(tensor, torch.Tensor):
tensor = torch.Tensor(1).fill_(tensor).cuda()
tensor_list = [torch.empty_like(tensor) for _ in range(get_dist_size())]
torch.distributed.all_gather(tensor_list, tensor.contiguous(), async_op=False)
if reduce == "mean":
return list_mean(tensor_list)
elif reduce == "sum":
return list_sum(tensor_list)
elif reduce == "cat":
return torch.cat(tensor_list, dim=0)
elif reduce == "root":
return tensor_list[0]
else:
return tensor_list
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