Remove the Colossal AI integration (#19528)

.azure/gpu-tests-pytorch.yml

@@ -105,16 +105,9 @@ jobs:
           done
         displayName: "Adjust dependencies"
 
-      - bash: |
-          pip install -q -r .actions/requirements.txt
-          python .actions/assistant.py requirements_prune_pkgs \
-            --packages="[lightning-colossalai]" \
-            --req_files="[requirements/_integrations/strategies.txt]"
-        displayName: "Prune packages" # these have installation issues
-
       - bash: |
           extra=$(python -c "print({'lightning': 'pytorch-'}.get('$(PACKAGE_NAME)', ''))")
-          pip install -e ".[${extra}dev]" -r requirements/_integrations/strategies.txt pytest-timeout -U --find-links="${TORCH_URL}"
+          pip install -e ".[${extra}dev]" pytest-timeout -U --find-links="${TORCH_URL}"
         displayName: "Install package & dependencies"
 
       - bash: pip uninstall -y lightning

requirements/_integrations/strategies.txt

@@ -1,4 +0,0 @@
-# NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
-#  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
-
-lightning-colossalai >=0.1.0

src/lightning/pytorch/callbacks/gradient_accumulation_scheduler.py

@@ -27,7 +27,6 @@ from typing_extensions import override
 import lightning.pytorch as pl
 from lightning.pytorch.callbacks.callback import Callback
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
-from lightning.pytorch.utilities.imports import _LIGHTNING_COLOSSALAI_AVAILABLE
 from lightning.pytorch.utilities.model_helpers import is_overridden
 from lightning.pytorch.utilities.rank_zero import rank_zero_warn
 
@@ -125,13 +124,7 @@ class GradientAccumulationScheduler(Callback):
         # local import to avoid circular import
         from lightning.pytorch.strategies import DeepSpeedStrategy
 
-        unsupported_strategies = [DeepSpeedStrategy]
-        if _LIGHTNING_COLOSSALAI_AVAILABLE:
-            from lightning_colossalai import ColossalAIStrategy
-
-            unsupported_strategies.append(ColossalAIStrategy)
-
-        if isinstance(trainer.strategy, tuple(unsupported_strategies)):
+        if isinstance(trainer.strategy, DeepSpeedStrategy):
             raise RuntimeError(
                 f"The `{type(trainer.strategy).__name__}` does not support `accumulate_grad_batches` changing"
                 " between epochs."

src/lightning/pytorch/trainer/connectors/accelerator_connector.py

@@ -62,10 +62,7 @@ from lightning.pytorch.strategies import (
 )
 from lightning.pytorch.strategies.ddp import _DDP_FORK_ALIASES
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
-from lightning.pytorch.utilities.imports import (
-    _LIGHTNING_COLOSSALAI_AVAILABLE,
-    _habana_available_and_importable,
-)
+from lightning.pytorch.utilities.imports import _habana_available_and_importable
 from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
 
 log = logging.getLogger(__name__)
@@ -191,9 +188,6 @@ class _AcceleratorConnector:
 
         self._strategy_flag = strategy
 
-        if strategy == "colossalai" and not _LIGHTNING_COLOSSALAI_AVAILABLE:
-            raise ModuleNotFoundError(str(_LIGHTNING_COLOSSALAI_AVAILABLE))
-
         if strategy != "auto" and strategy not in self._registered_strategies and not isinstance(strategy, Strategy):
             raise ValueError(
                 f"You selected an invalid strategy name: `strategy={strategy!r}`."
@@ -490,12 +484,6 @@ class _AcceleratorConnector:
             if isinstance(self.accelerator, HPUAccelerator):
                 return HPUPrecisionPlugin(self._precision_flag)
 
-        if _LIGHTNING_COLOSSALAI_AVAILABLE:
-            from lightning_colossalai import ColossalAIPrecisionPlugin, ColossalAIStrategy
-
-            if isinstance(self.strategy, ColossalAIStrategy):
-                return ColossalAIPrecisionPlugin(self._precision_flag)
-
         if isinstance(self.strategy, (SingleDeviceXLAStrategy, XLAStrategy)):
             return XLAPrecision(self._precision_flag)  # type: ignore
         if isinstance(self.strategy, DeepSpeedStrategy):
@@ -648,13 +636,6 @@ def _set_torch_flags(
 
 def _register_external_accelerators_and_strategies() -> None:
     """Registers all known strategies in other packages."""
-    if _LIGHTNING_COLOSSALAI_AVAILABLE:
-        from lightning_colossalai import ColossalAIStrategy
-
-        # TODO: Prevent registering multiple times
-        if "colossalai" not in StrategyRegistry:
-            ColossalAIStrategy.register_strategies(StrategyRegistry)
-
     if _habana_available_and_importable():
         from lightning_habana import HPUAccelerator, HPUParallelStrategy, SingleHPUStrategy
 

src/lightning/pytorch/utilities/imports.py

@@ -28,7 +28,6 @@ _TORCHMETRICS_GREATER_EQUAL_1_0_0 = RequirementCache("torchmetrics>=1.0.0")
 
 _OMEGACONF_AVAILABLE = package_available("omegaconf")
 _TORCHVISION_AVAILABLE = RequirementCache("torchvision")
-_LIGHTNING_COLOSSALAI_AVAILABLE = RequirementCache("lightning-colossalai")
 
 
 @functools.lru_cache(maxsize=128)

tests/tests_pytorch/callbacks/test_gradient_accumulation_scheduler.py

@@ -20,12 +20,6 @@ from lightning.pytorch.callbacks import GradientAccumulationScheduler
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.strategies import DeepSpeedStrategy
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
-from lightning.pytorch.utilities.imports import _LIGHTNING_COLOSSALAI_AVAILABLE
-
-if _LIGHTNING_COLOSSALAI_AVAILABLE:
-    from lightning_colossalai import ColossalAIStrategy
-else:
-    ColossalAIStrategy = None
 
 
 @pytest.mark.parametrize("accumulate_grad_batches", [1, 2, 3])
@@ -94,16 +88,7 @@ def test_invalid_values_for_grad_accum_scheduler(scheduling):
         _ = GradientAccumulationScheduler(scheduling=scheduling)
 
 
-@pytest.mark.parametrize(
-    "strategy_class",
-    [
-        pytest.param(
-            ColossalAIStrategy,
-            marks=pytest.mark.skipif(not _LIGHTNING_COLOSSALAI_AVAILABLE, reason="Requires ColossalAI strategy"),
-        ),
-        DeepSpeedStrategy,
-    ],
-)
+@pytest.mark.parametrize("strategy_class", [DeepSpeedStrategy])
 def test_unsupported_strategies(strategy_class):
     """Test that an error is raised for strategies that require the gradient accumulation factor to be fixed."""
     scheduler = GradientAccumulationScheduler({1: 2})

tests/tests_pytorch/trainer/connectors/test_accelerator_connector.py

@@ -59,7 +59,6 @@ from lightning.pytorch.utilities.exceptions import MisconfigurationException
 from lightning.pytorch.utilities.imports import (
     _LIGHTNING_HABANA_AVAILABLE,
 )
-from lightning_utilities.core.imports import package_available
 
 from tests_pytorch.conftest import mock_cuda_count, mock_mps_count, mock_tpu_available, mock_xla_available
 from tests_pytorch.helpers.runif import RunIf
@@ -845,21 +844,6 @@ def test_connector_defaults_match_trainer_defaults():
         assert connector_default == trainer_defaults[name]
 
 
-@RunIf(min_cuda_gpus=1)  # trigger this test on our GPU pipeline, because we don't install the package on the CPU suite
-@pytest.mark.xfail(raises=ImportError, reason="Not updated to latest API")
-@pytest.mark.skipif(not package_available("lightning_colossalai"), reason="Requires Colossal AI Strategy")
-def test_colossalai_external_strategy(monkeypatch):
-    with mock.patch(
-        "lightning.pytorch.trainer.connectors.accelerator_connector._LIGHTNING_COLOSSALAI_AVAILABLE", False
-    ), pytest.raises(ModuleNotFoundError):
-        Trainer(strategy="colossalai")
-
-    from lightning_colossalai import ColossalAIStrategy
-
-    trainer = Trainer(strategy="colossalai", precision="16-mixed")
-    assert isinstance(trainer.strategy, ColossalAIStrategy)
-
-
 class DeviceMock(Mock):
     def __instancecheck__(self, instance):
         return True