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clean avail. imports & enums (#5256) 

* clean avail. imports

* enums

* fix missing
This commit is contained in:
Jirka Borovec 2020-12-29 19:02:18 +01:00 committed by GitHub
parent 7a615b5651
commit ae9956f997
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5 changed files with 160 additions and 108 deletions
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123
pytorch_lightning/utilities/__init__.py
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@ -12,14 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""General utilities"""
import importlib
import platform
from distutils.version import LooseVersion
from enum import Enum
from typing import Union
import numpy
import torch
from pytorch_lightning.utilities.apply_func import move_data_to_device # noqa: F401
from pytorch_lightning.utilities.distributed import ( # noqa: F401
@ -28,106 +22,33 @@ from pytorch_lightning.utilities.distributed import ( # noqa: F401
rank_zero_only,
rank_zero_warn,
)
from pytorch_lightning.utilities.enums import ( # noqa: F401
LightningEnum,
AMPType,
DistributedType,
DeviceType,
)
from pytorch_lightning.utilities.imports import ( # noqa: F401
_APEX_AVAILABLE,
_NATIVE_AMP_AVAILABLE,
_XLA_AVAILABLE,
_OMEGACONF_AVAILABLE,
_HYDRA_AVAILABLE,
_HOROVOD_AVAILABLE,
_TORCHTEXT_AVAILABLE,
_FAIRSCALE_AVAILABLE,
_RPC_AVAILABLE,
_GROUP_AVAILABLE,
_FAIRSCALE_PIPE_AVAILABLE,
_BOLTS_AVAILABLE,
_module_available,
)
from pytorch_lightning.utilities.parsing import AttributeDict, flatten_dict, is_picklable # noqa: F401
from pytorch_lightning.utilities.xla_device import _XLA_AVAILABLE, XLADeviceUtils # noqa: F401
from pytorch_lightning.utilities.xla_device import XLADeviceUtils # noqa: F401
def _module_available(module_path: str) -> bool:
"""Testing if given module is avalaible in your env
>>> _module_available('os')
True
>>> _module_available('bla.bla')
False
"""
# todo: find a better way than try / except
try:
mods = module_path.split('.')
assert mods, 'nothing given to test'
# it has to be tested as per partets
for i in range(len(mods)):
module_path = '.'.join(mods[:i + 1])
if importlib.util.find_spec(module_path) is None:
return False
return True
except AttributeError:
return False
_APEX_AVAILABLE = _module_available("apex.amp")
_NATIVE_AMP_AVAILABLE = _module_available("torch.cuda.amp") and hasattr(torch.cuda.amp, "autocast")
_OMEGACONF_AVAILABLE = _module_available("omegaconf")
_HYDRA_AVAILABLE = _module_available("hydra")
_HOROVOD_AVAILABLE = _module_available("horovod.torch")
_TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
_FAIRSCALE_AVAILABLE = platform.system() != 'Windows' and _module_available('fairscale.nn.data_parallel')
_RPC_AVAILABLE = platform.system() != 'Windows' and _module_available('torch.distributed.rpc')
_GROUP_AVAILABLE = platform.system() != 'Windows' and _module_available('torch.distributed.group')
_FAIRSCALE_PIPE_AVAILABLE = _FAIRSCALE_AVAILABLE and LooseVersion(torch.__version__) >= LooseVersion("1.6.0")
_BOLTS_AVAILABLE = _module_available('pl_bolts')
FLOAT16_EPSILON = numpy.finfo(numpy.float16).eps
FLOAT32_EPSILON = numpy.finfo(numpy.float32).eps
FLOAT64_EPSILON = numpy.finfo(numpy.float64).eps
class LightningEnum(str, Enum):
""" Type of any enumerator with allowed comparison to string invariant to cases. """
@classmethod
def from_str(cls, value: str) -> 'LightningEnum':
statuses = [status for status in dir(cls) if not status.startswith('_')]
for st in statuses:
if st.lower() == value.lower():
return getattr(cls, st)
return None
def __eq__(self, other: Union[str, Enum]) -> bool:
other = other.value if isinstance(other, Enum) else str(other)
return self.value.lower() == other.lower()
class AMPType(LightningEnum):
"""Type of Automatic Mixed Precission used for training.
>>> # you can math the type with string
>>> AMPType.APEX == 'apex'
True
"""
APEX = 'apex'
NATIVE = 'native'
class DistributedType(LightningEnum):
""" Define type of ditributed computing.
>>> # you can math the type with string
>>> DistributedType.DDP == 'ddp'
True
>>> # which is case invariant
>>> DistributedType.DDP2 == 'DDP2'
True
"""
DP = 'dp'
DDP = 'ddp'
DDP2 = 'ddp2'
DDP_SPAWN = 'ddp_spawn'
HOROVOD = 'horovod'
class DeviceType(LightningEnum):
""" Define Device type byt its nature - acceleatrors.
>>> DeviceType.CPU == DeviceType.from_str('cpu')
True
>>> # you can math the type with string
>>> DeviceType.GPU == 'GPU'
True
>>> # which is case invariant
>>> DeviceType.TPU == 'tpu'
True
"""
CPU = 'CPU'
GPU = 'GPU'
TPU = 'TPU'

10
pytorch_lightning/utilities/apply_func.py
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@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
from abc import ABC
from collections.abc import Mapping, Sequence
from copy import copy
@ -20,8 +19,9 @@ from typing import Any, Callable, Union
import torch
TORCHTEXT_AVAILABLE = importlib.util.find_spec("torchtext") is not None
if TORCHTEXT_AVAILABLE:
from pytorch_lightning.utilities.imports import _TORCHTEXT_AVAILABLE
if _TORCHTEXT_AVAILABLE:
from torchtext.data import Batch
else:
Batch = type(None)
@ -109,7 +109,7 @@ def move_data_to_device(batch: Any, device: torch.device):
def batch_to(data):
# try to move torchtext data first
if TORCHTEXT_AVAILABLE and isinstance(data, Batch):
if _TORCHTEXT_AVAILABLE and isinstance(data, Batch):
# Shallow copy because each Batch has a reference to Dataset which contains all examples
device_data = copy(data)
@ -123,5 +123,5 @@ def move_data_to_device(batch: Any, device: torch.device):
kwargs = dict(non_blocking=True) if isinstance(data, torch.Tensor) else {}
return data.to(device, **kwargs)
dtype = (TransferableDataType, Batch) if TORCHTEXT_AVAILABLE else TransferableDataType
dtype = (TransferableDataType, Batch) if _TORCHTEXT_AVAILABLE else TransferableDataType
return apply_to_collection(batch, dtype=dtype, function=batch_to)

77
pytorch_lightning/utilities/enums.py Normal file
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@ -0,0 +1,77 @@
# 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.
"""Enumerated utilities"""
from enum import Enum
from typing import Union
class LightningEnum(str, Enum):
""" Type of any enumerator with allowed comparison to string invariant to cases. """
@classmethod
def from_str(cls, value: str) -> 'LightningEnum':
statuses = [status for status in dir(cls) if not status.startswith('_')]
for st in statuses:
if st.lower() == value.lower():
return getattr(cls, st)
return None
def __eq__(self, other: Union[str, Enum]) -> bool:
other = other.value if isinstance(other, Enum) else str(other)
return self.value.lower() == other.lower()
class AMPType(LightningEnum):
"""Type of Automatic Mixed Precission used for training.
>>> # you can math the type with string
>>> AMPType.APEX == 'apex'
True
"""
APEX = 'apex'
NATIVE = 'native'
class DistributedType(LightningEnum):
""" Define type of ditributed computing.
>>> # you can math the type with string
>>> DistributedType.DDP == 'ddp'
True
>>> # which is case invariant
>>> DistributedType.DDP2 == 'DDP2'
True
"""
DP = 'dp'
DDP = 'ddp'
DDP2 = 'ddp2'
DDP_SPAWN = 'ddp_spawn'
HOROVOD = 'horovod'
class DeviceType(LightningEnum):
""" Define Device type byt its nature - acceleatrors.
>>> DeviceType.CPU == DeviceType.from_str('cpu')
True
>>> # you can math the type with string
>>> DeviceType.GPU == 'GPU'
True
>>> # which is case invariant
>>> DeviceType.TPU == 'tpu'
True
"""
CPU = 'CPU'
GPU = 'GPU'
TPU = 'TPU'

55
pytorch_lightning/utilities/imports.py Normal file
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@ -0,0 +1,55 @@
# 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.
"""General utilities"""
import importlib
import platform
from distutils.version import LooseVersion
import torch
def _module_available(module_path: str) -> bool:
"""Testing if given module is avalaible in your env
>>> _module_available('os')
True
>>> _module_available('bla.bla')
False
"""
# todo: find a better way than try / except
try:
mods = module_path.split('.')
assert mods, 'nothing given to test'
# it has to be tested as per partets
for i in range(len(mods)):
module_path = '.'.join(mods[:i + 1])
if importlib.util.find_spec(module_path) is None:
return False
return True
except AttributeError:
return False
_APEX_AVAILABLE = _module_available("apex.amp")
_NATIVE_AMP_AVAILABLE = _module_available("torch.cuda.amp") and hasattr(torch.cuda.amp, "autocast")
_OMEGACONF_AVAILABLE = _module_available("omegaconf")
_HYDRA_AVAILABLE = _module_available("hydra")
_HOROVOD_AVAILABLE = _module_available("horovod.torch")
_TORCHTEXT_AVAILABLE = _module_available("torchtext")
_XLA_AVAILABLE = _module_available("torch_xla")
_FAIRSCALE_AVAILABLE = platform.system() != 'Windows' and _module_available('fairscale.nn.data_parallel')
_RPC_AVAILABLE = platform.system() != 'Windows' and _module_available('torch.distributed.rpc')
_GROUP_AVAILABLE = platform.system() != 'Windows' and _module_available('torch.distributed.group')
_FAIRSCALE_PIPE_AVAILABLE = _FAIRSCALE_AVAILABLE and LooseVersion(torch.__version__) >= LooseVersion("1.6.0")
_BOLTS_AVAILABLE = _module_available('pl_bolts')

3
pytorch_lightning/utilities/xla_device.py
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@ -12,14 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import importlib
import queue as q
import traceback
from multiprocessing import Process, Queue
import torch
_XLA_AVAILABLE = importlib.util.find_spec("torch_xla") is not None
from pytorch_lightning.utilities.imports import _XLA_AVAILABLE
if _XLA_AVAILABLE:
import torch_xla.core.xla_model as xm