Remove deprecated `DistributedType` and `DeviceType` enum classes (#14045)
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Carlos Mocholí
GitHub
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76836a33cd
commit
aaeff90254
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@ -30,7 +30,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Deprecated `amp_level` from `Trainer` in favour of passing it explictly via precision plugin ([#13898](https://github.com/Lightning-AI/lightning/pull/13898))
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-
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- Deprecated the calls to `pytorch_lightning.utiltiies.meta` functions in favor of built-in https://github.com/pytorch/torchdistx support ([#13868](https://github.com/Lightning-AI/lightning/pull/13868))
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### Removed
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@ -44,6 +44,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Removed the deprecated `DDP2Strategy` ([#14026](https://github.com/Lightning-AI/lightning/pull/14026))
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- Removed the deprecated `DistributedType` and `DeviceType` enum classes ([#14045](https://github.com/Lightning-AI/lightning/pull/14045))
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- Removed the experimental `pytorch_lightning.utiltiies.meta` functions in favor of built-in https://github.com/pytorch/torchdistx support ([#13868](https://github.com/Lightning-AI/lightning/pull/13868))
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### Fixed
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- Casted only floating point tensors to fp16 with IPUs ([#13983](https://github.com/Lightning-AI/lightning/pull/13983))
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@ -21,7 +21,6 @@ from pytorch_lightning.utilities.enums import ( # noqa: F401
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_AcceleratorType,
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_StrategyType,
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AMPType,
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DistributedType,
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GradClipAlgorithmType,
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LightningEnum,
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)
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@ -15,11 +15,9 @@
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from __future__ import annotations
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import os
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from enum import Enum, EnumMeta
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from typing import Any
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from enum import Enum
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.warnings import rank_zero_deprecation
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class LightningEnum(str, Enum):
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@ -43,37 +41,6 @@ class LightningEnum(str, Enum):
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return hash(self.value.lower())
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class _DeprecatedEnumMeta(EnumMeta):
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"""Enum that calls `deprecate()` whenever a member is accessed.
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Adapted from: https://stackoverflow.com/a/62309159/208880
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"""
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def __getattribute__(cls, name: str) -> Any:
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obj = super().__getattribute__(name)
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# ignore __dunder__ names -- prevents potential recursion errors
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if not (name.startswith("__") and name.endswith("__")) and isinstance(obj, Enum):
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obj.deprecate()
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return obj
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def __getitem__(cls, name: str) -> Any:
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member: _DeprecatedEnumMeta = super().__getitem__(name)
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member.deprecate()
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return member
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def __call__(cls, *args: Any, **kwargs: Any) -> Any:
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obj = super().__call__(*args, **kwargs)
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if isinstance(obj, Enum):
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obj.deprecate()
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return obj
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class _DeprecatedEnum(LightningEnum, metaclass=_DeprecatedEnumMeta):
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"""_DeprecatedEnum calls an enum's `deprecate()` method on member access."""
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pass
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class AMPType(LightningEnum):
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"""Type of Automatic Mixed Precission used for training.
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@ -110,66 +77,6 @@ class PrecisionType(LightningEnum):
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return [x.value for x in PrecisionType]
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class DistributedType(_DeprecatedEnum):
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"""Define type of training strategy.
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Deprecated since v1.6.0 and will be removed in v1.8.0.
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Use `_StrategyType` instead.
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"""
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DP = "dp"
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DDP = "ddp"
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DDP_SPAWN = "ddp_spawn"
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TPU_SPAWN = "tpu_spawn"
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DEEPSPEED = "deepspeed"
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HOROVOD = "horovod"
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DDP_SHARDED = "ddp_sharded"
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DDP_SHARDED_SPAWN = "ddp_sharded_spawn"
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DDP_FULLY_SHARDED = "ddp_fully_sharded"
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HPU_PARALLEL = "hpu_parallel"
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@staticmethod
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def interactive_compatible_types() -> list[DistributedType]:
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"""Returns a list containing interactive compatible DistributeTypes."""
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return [
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DistributedType.DP,
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DistributedType.DDP_SPAWN,
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DistributedType.DDP_SHARDED_SPAWN,
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DistributedType.TPU_SPAWN,
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]
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def is_interactive_compatible(self) -> bool:
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"""Returns whether self is interactive compatible."""
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return self in DistributedType.interactive_compatible_types()
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def deprecate(self) -> None:
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rank_zero_deprecation(
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"`DistributedType` Enum has been deprecated in v1.6 and will be removed in v1.8."
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f" Use the string value `{self.value!r}` instead."
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)
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class DeviceType(_DeprecatedEnum):
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"""Define Device type by its nature - accelerators.
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Deprecated since v1.6.0 and will be removed in v1.8.0.
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Use `_AcceleratorType` instead.
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"""
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CPU = "CPU"
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GPU = "GPU"
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IPU = "IPU"
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TPU = "TPU"
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def deprecate(self) -> None:
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rank_zero_deprecation(
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"`DeviceType` Enum has been deprecated in v1.6 and will be removed in v1.8."
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f" Use the string value `{self.value!r}` instead."
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)
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class GradClipAlgorithmType(LightningEnum):
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"""Define gradient_clip_algorithm types - training-tricks.
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NORM type means "clipping gradients by norm". This computed over all model parameters together.
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@ -36,7 +36,6 @@ from pytorch_lightning.trainer.configuration_validator import _check_datamodule_
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from pytorch_lightning.trainer.states import RunningStage
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from pytorch_lightning.utilities import device_parser
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from pytorch_lightning.utilities.apply_func import move_data_to_device
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from pytorch_lightning.utilities.enums import DeviceType, DistributedType
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from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
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from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
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from tests_pytorch.deprecated_api import no_deprecated_call
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@ -44,18 +43,6 @@ from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
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def test_v1_8_0_deprecated_distributed_type_enum():
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with pytest.deprecated_call(match="has been deprecated in v1.6 and will be removed in v1.8."):
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_ = DistributedType.DDP
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def test_v1_8_0_deprecated_device_type_enum():
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with pytest.deprecated_call(match="has been deprecated in v1.6 and will be removed in v1.8."):
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_ = DeviceType.CPU
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@pytest.mark.skipif(not _TORCHTEXT_LEGACY, reason="torchtext.legacy is deprecated.")
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def test_v1_8_0_deprecated_torchtext_batch():
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