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b910c09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | # MIT License Copyright (c) 2022 joh-schb
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import os
import torch
from torch import distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data.distributed import DistributedSampler
def is_distributed():
"""
Check if the current process is part of a distributed setup.
"""
return "RANK" in os.environ and "WORLD_SIZE" in os.environ
def init_process_group(*args, **kwargs):
if not is_distributed():
return
dist.init_process_group(*args, **kwargs)
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def destroy_process_group():
if is_dist_avail_and_initialized():
dist.destroy_process_group()
def cleanup():
destroy_process_group()
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def get_device():
if torch.cuda.is_available():
return torch.device(f"cuda:{get_rank()}")
return torch.device("cpu")
def is_primary():
return get_rank() == 0
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
# data loading stuff
def data_sampler(dataset, distributed, shuffle):
if distributed:
return DistributedSampler(dataset, shuffle=shuffle)
return None
# model wrapping
def prepare_ddp_model(model, device_ids, *args, **kwargs):
if get_world_size() > 1:
model = DistributedDataParallel(model, device_ids=device_ids, *args, **kwargs)
return model
# synchronization functions
def all_reduce(tensor, op="sum"):
world_size = get_world_size()
if world_size == 1:
return tensor
if op == "sum":
dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
elif op == "avg":
dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
tensor /= get_world_size()
else:
raise ValueError(f'"{op}" is an invalid reduce operation!')
return tensor
def reduce(tensor, op=dist.ReduceOp.SUM):
world_size = get_world_size()
if world_size == 1:
return tensor
dist.reduce(tensor, dst=0, op=op)
return tensor
def gather(data, *args, **kwargs):
world_size = get_world_size()
if world_size == 1:
return [data]
output_list = [torch.zeros_like(data) for _ in range(world_size)]
if is_primary():
dist.gather(data, gather_list=output_list, *args, **kwargs)
else:
dist.gather(data, *args, **kwargs)
return output_list
def sync_params(params):
"""
Synchronize a sequence of Tensors across ranks from rank 0.
"""
if is_dist_avail_and_initialized():
for p in params:
with torch.no_grad():
dist.broadcast(p, 0)
def barrier(*args, **kwargs):
world_size = get_world_size()
if world_size == 1:
return
dist.barrier(*args, **kwargs)
# wrapper with same functionality but better readability as barrier
def wait_for_everyone(*args, **kwargs):
barrier(*args, **kwargs)
def print_primary(*args, **kwargs):
if is_primary():
print(*args, **kwargs)
def print0(*args, **kwargs):
print_primary(*args, **kwargs)
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