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other.py
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# 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
from types import MethodType
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .constants import FSDP_PYTORCH_VERSION
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_safetensors_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
if is_safetensors_available():
from safetensors.torch import save_file as safe_save_file
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():
from deepspeed import DeepSpeedEngine
options += (DeepSpeedEngine,)
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
options += (FSDP,)
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 = MethodType(forward, model)
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, safe_serialization=False):
"""
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
safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save `obj` using `safetensors`
"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(obj, f)
elif PartialState().local_process_index == 0:
if safe_serialization:
safe_save_file(obj, f, metadata={"format": "pt"})
else:
torch.save(obj, f)
@contextmanager
def clear_environment():
"""
A context manager that will cache origin `os.environ` and replace it with a empty dictionary in this context.
When this context exits, the cached `os.environ` will be back.
Example:
```python
>>> import os
>>> from accelerate.utils import clear_environment
>>> os.environ["FOO"] = "bar"
>>> with clear_environment():
... print(os.environ)
... os.environ["FOO"] = "new_bar"
... print(os.environ["FOO"])
{}
new_bar
>>> print(os.environ["FOO"])
bar
```
"""
_old_os_environ = os.environ
os.environ = dict()
yield
os.environ = _old_os_environ
@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
```
"""
existing_vars = {}
for key, value in kwargs.items():
key = key.upper()
if key in os.environ:
existing_vars[key] = os.environ[key]
os.environ[key] = str(value)
yield
for key in kwargs:
key = key.upper()
if key in existing_vars:
# restore previous value
os.environ[key] = existing_vars[key]
else:
os.environ.pop(key, None)
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
def convert_bytes(size):
"Converts `size` from bytes to the largest possible unit"
for x in ["bytes", "KB", "MB", "GB", "TB"]:
if size < 1024.0:
return f"{round(size, 2)} {x}"
size /= 1024.0
return f"{round(size, 2)} PB"