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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 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 | # 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
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import AcceleratorState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def extract_model_from_parallel(model, keep_fp32_wrapper: bool = False):
"""
Extract a model from its distributed containers.
Args:
model (`torch.nn.Module`):
The model to extract.
keep_fp32_wrapper (`bool`, *optional*):
Whether to remove mixed precision hooks from the model.
Returns:
`torch.nn.Module`: The extracted model.
"""
options = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(model, options):
model = model.module
if not keep_fp32_wrapper:
forward = getattr(model, "forward")
original_forward = model.__dict__.pop("_original_forward", None)
if original_forward is not None:
while hasattr(forward, "__wrapped__"):
forward = forward.__wrapped__
if forward == original_forward:
break
model.forward = forward
return model
def wait_for_everyone():
"""
Introduces a blocking point in the script, making sure all processes have reached this point before continuing.
<Tip warning={true}>
Make sure all processes will reach this instruction otherwise one of your processes will hang forever.
</Tip>
"""
if (
AcceleratorState().distributed_type == DistributedType.MULTI_GPU
or AcceleratorState().distributed_type == DistributedType.MULTI_CPU
or AcceleratorState().distributed_type == DistributedType.DEEPSPEED
or AcceleratorState().distributed_type == DistributedType.FSDP
):
torch.distributed.barrier()
elif AcceleratorState().distributed_type == DistributedType.TPU:
xm.rendezvous("accelerate.utils.wait_for_everyone")
def save(obj, f):
"""
Save the data to disk. Use in place of `torch.save()`.
Args:
obj: The data to save
f: The file (or file-like object) to use to save the data
"""
if AcceleratorState().distributed_type == DistributedType.TPU:
xm.save(obj, f)
elif AcceleratorState().local_process_index == 0:
torch.save(obj, f)
@contextmanager
def patch_environment(**kwargs):
"""
A context manager that will add each keyword argument passed to `os.environ` and remove them when exiting.
Will convert the values in `kwargs` to strings and upper-case all the keys.
"""
for key, value in kwargs.items():
os.environ[key.upper()] = str(value)
yield
for key in kwargs:
del os.environ[key.upper()]
def get_pretty_name(obj):
"""
Gets a pretty name from `obj`.
"""
if not hasattr(obj, "__qualname__") and not hasattr(obj, "__name__"):
obj = getattr(obj, "__class__", obj)
if hasattr(obj, "__qualname__"):
return obj.__qualname__
if hasattr(obj, "__name__"):
return obj.__name__
return str(obj)
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