Spaces:
Sleeping
Sleeping
File size: 5,453 Bytes
82fea12 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
# 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 socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def is_compiled_module(module):
"""
Check whether the module was compiled with torch.compile()
"""
if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"):
return False
return isinstance(module, torch._dynamo.eval_frame.OptimizedModule)
def extract_model_from_parallel(model, keep_fp32_wrapper: bool = True):
"""
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)
is_compiled = is_compiled_module(model)
if is_compiled:
compiled_model = model
model = model._orig_mod
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
if getattr(model, "_converted_to_transformer_engine", False):
convert_model(model, to_transformer_engine=False)
if is_compiled:
compiled_model._orig_mod = model
model = compiled_model
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>
"""
PartialState().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 PartialState().distributed_type == DistributedType.TPU:
xm.save(obj, f)
elif PartialState().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.
Example:
```python
>>> import os
>>> from accelerate.utils import patch_environment
>>> with patch_environment(FOO="bar"):
... print(os.environ["FOO"]) # prints "bar"
>>> print(os.environ["FOO"]) # raises KeyError
```
"""
for key, value in kwargs.items():
os.environ[key.upper()] = str(value)
yield
for key in kwargs:
if key.upper() in os.environ:
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)
def merge_dicts(source, destination):
"""
Recursively merges two dictionaries.
Args:
source (`dict`): The dictionary to merge into `destination`.
destination (`dict`): The dictionary to merge `source` into.
"""
for key, value in source.items():
if isinstance(value, dict):
node = destination.setdefault(key, {})
merge_dicts(value, node)
else:
destination[key] = value
return destination
def is_port_in_use(port: int = None) -> bool:
"""
Checks if a port is in use on `localhost`. Useful for checking if multiple `accelerate launch` commands have been
run and need to see if the port is already in use.
"""
if port is None:
port = 29500
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
return s.connect_ex(("localhost", port)) == 0
|