File size: 3,300 Bytes
c13737d | 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 | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.
import os
import sys
import torch
from ..utils import is_torch_version
from .dataclasses import DistributedType
def get_launch_prefix():
"""
Grabs the correct launcher for starting a distributed command, such as either `torchrun`, `python -m
torch.distributed.run`, etc
"""
if is_torch_version(">=", "1.10.0"):
cmd = ["torchrun"]
elif is_torch_version(">=", "1.9.0"):
cmd = [sys.executable, "-m", "torch.distributed.run"]
else:
cmd = [sys.executable, "-m", "torch.distributed.launch", "--use_env"]
return cmd
def _filter_args(args):
"""
Filters out all `accelerate` specific args
"""
if is_torch_version(">=", "1.9.1"):
import torch.distributed.run as distrib_run
distrib_args = distrib_run.get_args_parser()
new_args, _ = distrib_args.parse_known_args()
for key, value in vars(args).items():
if key in vars(new_args).keys():
setattr(new_args, key, value)
return new_args
def env_var_path_add(env_var_name, path_to_add):
"""
Extends a path-based environment variable's value with a new path and returns the updated value. It's up to the
caller to set it in os.environ.
"""
paths = [p for p in os.environ.get(env_var_name, "").split(":") if len(p) > 0]
paths.append(str(path_to_add))
return ":".join(paths)
class PrepareForLaunch:
"""
Prepare a function that will launched in a distributed setup.
Args:
launcher (`Callable`):
The function to launch.
distributed_type ([`~state.DistributedType`]):
The distributed type to prepare for.
debug (`bool`, *optional*, defaults to `False`):
Whether or not this is a debug launch.
"""
def __init__(self, launcher, distributed_type="NO", debug=False):
self.launcher = launcher
self.distributed_type = DistributedType(distributed_type)
self.debug = debug
def __call__(self, index, *args):
if self.debug:
world_size = int(os.environ.get("WORLD_SIZE"))
rdv_file = os.environ.get("ACCELERATE_DEBUG_RDV_FILE")
torch.distributed.init_process_group(
"gloo",
rank=index,
store=torch.distributed.FileStore(rdv_file, world_size),
world_size=world_size,
)
elif self.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
# Prepare the environment for torch.distributed
os.environ["LOCAL_RANK"] = str(index)
os.environ["RANK"] = str(index)
os.environ["FORK_LAUNCHED"] = str(1)
self.launcher(*args)
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