Source code for pytorch_lightning.utilities.argparse
# Copyright The PyTorch Lightning team.
#
# 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 inspect
import os
from abc import ABC
from argparse import _ArgumentGroup, ArgumentParser, Namespace
from contextlib import suppress
from typing import Any, Callable, Dict, List, Tuple, Type, Union
import pytorch_lightning as pl
from pytorch_lightning.utilities.parsing import str_to_bool, str_to_bool_or_int, str_to_bool_or_str
[docs]class ParseArgparserDataType(ABC):
def __init__(self, *_: Any, **__: Any) -> None:
pass
@classmethod
def parse_argparser(cls, args: "ArgumentParser") -> Any:
pass
[docs]def from_argparse_args(
cls: Type[ParseArgparserDataType], args: Union[Namespace, ArgumentParser], **kwargs: Any
) -> ParseArgparserDataType:
"""Create an instance from CLI arguments. Eventually use varibles from OS environement which are defined as
"PL_<CLASS-NAME>_<CLASS_ARUMENT_NAME>".
Args:
cls: Lightning class
args: The parser or namespace to take arguments from. Only known arguments will be
parsed and passed to the :class:`Trainer`.
**kwargs: Additional keyword arguments that may override ones in the parser or namespace.
These must be valid Trainer arguments.
Example:
>>> from pytorch_lightning import Trainer
>>> parser = ArgumentParser(add_help=False)
>>> parser = Trainer.add_argparse_args(parser)
>>> parser.add_argument('--my_custom_arg', default='something') # doctest: +SKIP
>>> args = Trainer.parse_argparser(parser.parse_args(""))
>>> trainer = Trainer.from_argparse_args(args, logger=False)
"""
if isinstance(args, ArgumentParser):
args = cls.parse_argparser(args)
params = vars(args)
# we only want to pass in valid Trainer args, the rest may be user specific
valid_kwargs = inspect.signature(cls.__init__).parameters
trainer_kwargs = {name: params[name] for name in valid_kwargs if name in params}
trainer_kwargs.update(**kwargs)
return cls(**trainer_kwargs)
[docs]def parse_argparser(cls: Type["pl.Trainer"], arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:
"""Parse CLI arguments, required for custom bool types."""
args = arg_parser.parse_args() if isinstance(arg_parser, ArgumentParser) else arg_parser
types_default = {arg: (arg_types, arg_default) for arg, arg_types, arg_default in get_init_arguments_and_types(cls)}
modified_args = {}
for k, v in vars(args).items():
if k in types_default and v is None:
# We need to figure out if the None is due to using nargs="?" or if it comes from the default value
arg_types, arg_default = types_default[k]
if bool in arg_types and isinstance(arg_default, bool):
# Value has been passed as a flag => It is currently None, so we need to set it to True
# We always set to True, regardless of the default value.
# Users must pass False directly, but when passing nothing True is assumed.
# i.e. the only way to disable something that defaults to True is to use the long form:
# "--a_default_true_arg False" becomes False, while "--a_default_false_arg" becomes None,
# which then becomes True here.
v = True
modified_args[k] = v
return Namespace(**modified_args)
[docs]def parse_env_variables(cls: Type["pl.Trainer"], template: str = "PL_%(cls_name)s_%(cls_argument)s") -> Namespace:
"""Parse environment arguments if they are defined.
Example:
>>> from pytorch_lightning import Trainer
>>> parse_env_variables(Trainer)
Namespace()
>>> import os
>>> os.environ["PL_TRAINER_GPUS"] = '42'
>>> os.environ["PL_TRAINER_BLABLABLA"] = '1.23'
>>> parse_env_variables(Trainer)
Namespace(gpus=42)
>>> del os.environ["PL_TRAINER_GPUS"]
"""
cls_arg_defaults = get_init_arguments_and_types(cls)
env_args = {}
for arg_name, _, _ in cls_arg_defaults:
env = template % {"cls_name": cls.__name__.upper(), "cls_argument": arg_name.upper()}
val = os.environ.get(env)
if not (val is None or val == ""):
# todo: specify the possible exception
with suppress(Exception):
# converting to native types like int/float/bool
val = eval(val)
env_args[arg_name] = val
return Namespace(**env_args)
[docs]def get_init_arguments_and_types(cls: Any) -> List[Tuple[str, Tuple, Any]]:
r"""Scans the class signature and returns argument names, types and default values.
Returns:
List with tuples of 3 values:
(argument name, set with argument types, argument default value).
Examples:
>>> from pytorch_lightning import Trainer
>>> args = get_init_arguments_and_types(Trainer)
"""
cls_default_params = inspect.signature(cls).parameters
name_type_default = []
for arg in cls_default_params:
arg_type = cls_default_params[arg].annotation
arg_default = cls_default_params[arg].default
try:
arg_types = tuple(arg_type.__args__)
except (AttributeError, TypeError):
arg_types = (arg_type,)
name_type_default.append((arg, arg_types, arg_default))
return name_type_default
def _get_abbrev_qualified_cls_name(cls: Any) -> str:
assert isinstance(cls, type), repr(cls)
if cls.__module__.startswith("pytorch_lightning."):
# Abbreviate.
return f"pl.{cls.__name__}"
# Fully qualified.
return f"{cls.__module__}.{cls.__qualname__}"
[docs]def add_argparse_args(
cls: Type["pl.Trainer"], parent_parser: ArgumentParser, *, use_argument_group: bool = True
) -> Union[_ArgumentGroup, ArgumentParser]:
r"""Extends existing argparse by default attributes for ``cls``.
Args:
cls: Lightning class
parent_parser:
The custom cli arguments parser, which will be extended by
the class's default arguments.
use_argument_group:
By default, this is True, and uses ``add_argument_group`` to add
a new group.
If False, this will use old behavior.
Returns:
If use_argument_group is True, returns ``parent_parser`` to keep old
workflows. If False, will return the new ArgumentParser object.
Only arguments of the allowed types (str, float, int, bool) will
extend the ``parent_parser``.
Raises:
RuntimeError:
If ``parent_parser`` is not an ``ArgumentParser`` instance
Examples:
# Option 1: Default usage.
>>> import argparse
>>> from pytorch_lightning import Trainer
>>> parser = argparse.ArgumentParser()
>>> parser = Trainer.add_argparse_args(parser)
>>> args = parser.parse_args([])
# Option 2: Disable use_argument_group (old behavior).
>>> import argparse
>>> from pytorch_lightning import Trainer
>>> parser = argparse.ArgumentParser()
>>> parser = Trainer.add_argparse_args(parser, use_argument_group=False)
>>> args = parser.parse_args([])
"""
if isinstance(parent_parser, _ArgumentGroup):
raise RuntimeError("Please only pass an ArgumentParser instance.")
if use_argument_group:
group_name = _get_abbrev_qualified_cls_name(cls)
parser: Union[_ArgumentGroup, ArgumentParser] = parent_parser.add_argument_group(group_name)
else:
parser = ArgumentParser(parents=[parent_parser], add_help=False)
ignore_arg_names = ["self", "args", "kwargs"]
if hasattr(cls, "get_deprecated_arg_names"):
ignore_arg_names += cls.get_deprecated_arg_names()
allowed_types = (str, int, float, bool)
# Get symbols from cls or init function.
for symbol in (cls, cls.__init__):
args_and_types = get_init_arguments_and_types(symbol)
args_and_types = [x for x in args_and_types if x[0] not in ignore_arg_names]
if len(args_and_types) > 0:
break
args_help = _parse_args_from_docstring(cls.__init__.__doc__ or cls.__doc__ or "")
for arg, arg_types, arg_default in args_and_types:
arg_types = tuple(at for at in allowed_types if at in arg_types)
if not arg_types:
# skip argument with not supported type
continue
arg_kwargs: Dict[str, Any] = {}
if bool in arg_types:
arg_kwargs.update(nargs="?", const=True)
# if the only arg type is bool
if len(arg_types) == 1:
use_type: Callable[[str], Union[bool, int, float, str]] = str_to_bool
elif int in arg_types:
use_type = str_to_bool_or_int
elif str in arg_types:
use_type = str_to_bool_or_str
else:
# filter out the bool as we need to use more general
use_type = [at for at in arg_types if at is not bool][0]
else:
use_type = arg_types[0]
if arg == "gpus" or arg == "tpu_cores":
use_type = _gpus_allowed_type
# hack for types in (int, float)
if len(arg_types) == 2 and int in set(arg_types) and float in set(arg_types):
use_type = _int_or_float_type
# hack for track_grad_norm
if arg == "track_grad_norm":
use_type = float
# hack for precision
if arg == "precision":
use_type = _precision_allowed_type
parser.add_argument(
f"--{arg}", dest=arg, default=arg_default, type=use_type, help=args_help.get(arg), **arg_kwargs
)
if use_argument_group:
return parent_parser
return parser
def _parse_args_from_docstring(docstring: str) -> Dict[str, str]:
arg_block_indent = None
current_arg = ""
parsed = {}
for line in docstring.split("\n"):
stripped = line.lstrip()
if not stripped:
continue
line_indent = len(line) - len(stripped)
if stripped.startswith(("Args:", "Arguments:", "Parameters:")):
arg_block_indent = line_indent + 4
elif arg_block_indent is None:
continue
elif line_indent < arg_block_indent:
break
elif line_indent == arg_block_indent:
current_arg, arg_description = stripped.split(":", maxsplit=1)
parsed[current_arg] = arg_description.lstrip()
elif line_indent > arg_block_indent:
parsed[current_arg] += f" {stripped}"
return parsed
def _gpus_allowed_type(x: str) -> Union[int, str]:
if "," in x:
return str(x)
return int(x)
def _int_or_float_type(x: Union[int, float, str]) -> Union[int, float]:
if "." in str(x):
return float(x)
return int(x)
def _precision_allowed_type(x: Union[int, str]) -> Union[int, str]:
"""
>>> _precision_allowed_type("32")
32
>>> _precision_allowed_type("bf16")
'bf16'
"""
try:
return int(x)
except ValueError:
return x
