Move src/pytorch_lightning/lite to src/lightning_lite (#14735)

examples/convert_from_pt_to_pl/image_classifier_2_lite.py

@@ -38,10 +38,10 @@ import torchvision.transforms as T
 from torch.optim.lr_scheduler import StepLR
 from torchmetrics.classification import Accuracy
 
+from lightning_lite.lite import LightningLite  # import LightningLite
 from pytorch_lightning import seed_everything
 from pytorch_lightning.demos.boring_classes import Net
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.lite import LightningLite  # import LightningLite
 
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")
 

examples/convert_from_pt_to_pl/image_classifier_3_lite_to_lightning_module.py

@@ -34,10 +34,10 @@ import torchvision.transforms as T
 from torch.optim.lr_scheduler import StepLR
 from torchmetrics import Accuracy
 
+from lightning_lite.lite import LightningLite
 from pytorch_lightning import seed_everything
 from pytorch_lightning.demos.boring_classes import Net
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.lite import LightningLite
 
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")
 

examples/pl_loops/mnist_lite.py

@@ -22,10 +22,10 @@ import torchvision.transforms as T
 from torch.optim.lr_scheduler import StepLR
 from torchmetrics import Accuracy
 
+from lightning_lite.lite import LightningLite
 from pytorch_lightning import seed_everything
 from pytorch_lightning.demos.boring_classes import Net
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.lite import LightningLite
 from pytorch_lightning.loops import Loop
 
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")

src/lightning_lite/__init__.py

@@ -12,15 +12,11 @@ if not _root_logger.hasHandlers():
     _logger.addHandler(logging.StreamHandler())
     _logger.propagate = False
 
-# TODO(lite): Re-enable this import
+
-# from lightning_lite.lite import LightningLite
+from lightning_lite.lite import LightningLite  # noqa: E402
 from lightning_lite.utilities.seed import seed_everything  # noqa: E402
 
-__all__ = [
+__all__ = ["LightningLite", "seed_everything"]
-    # TODO(lite): Re-enable this import
-    # "LightningLite",
-    "seed_everything",
-]
 
 # for compatibility with namespace packages
 __import__("pkg_resources").declare_namespace(__name__)

src/lightning_lite/lite.py

@@ -40,7 +40,7 @@ from lightning_lite.utilities.data import (
 )
 from lightning_lite.utilities.distributed import DistributedSamplerWrapper
 from lightning_lite.utilities.seed import seed_everything
-from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
+from lightning_lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
 
 
 class LightningLite(ABC):

src/lightning_lite/utilities/rank_zero.py

@@ -34,7 +34,7 @@ rank_zero_module.log = logging.getLogger(__name__)
 
 
 def _get_rank(
-    strategy: Optional["lightning_lite.strategies.Strategy"] = None,  # type: ignore[name-defined]
+    strategy: Optional["lightning_lite.strategies.Strategy"] = None,
 ) -> Optional[int]:
     if strategy is not None:
         return strategy.global_rank

src/pytorch_lightning/callbacks/early_stopping.py

@@ -261,7 +261,7 @@ class EarlyStopping(Callback):
 
     @staticmethod
     def _log_info(trainer: Optional["pl.Trainer"], message: str, log_rank_zero_only: bool) -> None:
-        rank = _get_rank(strategy=(trainer.strategy if trainer is not None else None))
+        rank = _get_rank(strategy=(trainer.strategy if trainer is not None else None))  # type: ignore[arg-type]
         if trainer is not None and trainer.world_size <= 1:
             rank = None
         message = rank_prefixed_message(message, rank)

src/pytorch_lightning/lite/__init__.py

@@ -1,17 +0,0 @@
-# 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.
-
-from pytorch_lightning.lite.lite import LightningLite
-
-__all__ = ["LightningLite"]

tests/tests_lite/helpers/runif.py

@@ -23,7 +23,7 @@ from pkg_resources import get_distribution
 from lightning_lite.accelerators.mps import MPSAccelerator
 from lightning_lite.strategies.deepspeed import _DEEPSPEED_AVAILABLE
 from lightning_lite.strategies.fairscale import _FAIRSCALE_AVAILABLE
-from lightning_lite.utilities.imports import _TPU_AVAILABLE
+from lightning_lite.utilities.imports import _TORCH_GREATER_EQUAL_1_10, _TPU_AVAILABLE
 
 
 class RunIf:
@@ -42,6 +42,7 @@ class RunIf:
         min_torch: Optional[str] = None,
         max_torch: Optional[str] = None,
         min_python: Optional[str] = None,
+        bf16_cuda: bool = False,
         tpu: bool = False,
         mps: Optional[bool] = None,
         skip_windows: bool = False,
@@ -57,6 +58,7 @@ class RunIf:
             min_torch: Require that PyTorch is greater or equal than this version.
             max_torch: Require that PyTorch is less than this version.
             min_python: Require that Python is greater or equal than this version.
+            bf16_cuda: Require that CUDA device supports bf16.
             tpu: Require that TPU is available.
             mps: If True: Require that MPS (Apple Silicon) is available,
                 if False: Explicitly Require that MPS is not available
@@ -91,6 +93,20 @@ class RunIf:
             conditions.append(Version(py_version) < Version(min_python))
             reasons.append(f"python>={min_python}")
 
+        if bf16_cuda:
+            try:
+                cond = not (torch.cuda.is_available() and _TORCH_GREATER_EQUAL_1_10 and torch.cuda.is_bf16_supported())
+            except (AssertionError, RuntimeError) as e:
+                # AssertionError: Torch not compiled with CUDA enabled
+                # RuntimeError: Found no NVIDIA driver on your system.
+                is_unrelated = "Found no NVIDIA driver" not in str(e) or "Torch not compiled with CUDA" not in str(e)
+                if is_unrelated:
+                    raise e
+                cond = True
+
+            conditions.append(cond)
+            reasons.append("CUDA device bf16")
+
         if skip_windows:
             conditions.append(sys.platform == "win32")
             reasons.append("unimplemented on Windows")

tests/tests_lite/test_lite.py

@@ -20,17 +20,17 @@ import pytest
 import torch
 import torch.distributed
 import torch.nn.functional
+from tests_lite.helpers.runif import RunIf
 from torch import nn
 from torch.utils.data import DataLoader, DistributedSampler, Sampler
 
+from lightning_lite.lite import LightningLite
 from lightning_lite.plugins import Precision
 from lightning_lite.strategies import DeepSpeedStrategy, Strategy
 from lightning_lite.utilities import _StrategyType
 from lightning_lite.utilities.exceptions import MisconfigurationException
 from lightning_lite.utilities.seed import pl_worker_init_function
-from pytorch_lightning.lite import LightningLite
+from lightning_lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
-from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
-from tests_pytorch.helpers.runif import RunIf
 
 
 class EmptyLite(LightningLite):
@@ -165,7 +165,7 @@ def test_setup_dataloaders_return_type():
     assert lite_dataloader1.dataset is dataset1
 
 
-@mock.patch("pytorch_lightning.lite.lite._replace_dunder_methods")
+@mock.patch("lightning_lite.lite._replace_dunder_methods")
 def test_setup_dataloaders_captures_dataloader_arguments(ctx_manager):
     """Test that Lite intercepts the DataLoader constructor arguments with a context manager in its run method."""
 
@@ -210,7 +210,7 @@ def test_setup_dataloaders_twice_fails():
 
 
 @mock.patch(
-    "pytorch_lightning.lite.lite.LightningLite.device",
+    "lightning_lite.lite.LightningLite.device",
     new_callable=PropertyMock,
     return_value=torch.device("cuda", 1),
 )

tests/tests_lite/test_parity.py

@@ -23,18 +23,18 @@ import torch.distributed
 import torch.multiprocessing as mp
 import torch.nn.functional
 from lightning_utilities.core.apply_func import apply_to_collection
+from tests_lite.helpers.runif import RunIf
 from torch import nn
 from torch.nn.parallel.distributed import DistributedDataParallel
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
+from lightning_lite.lite import LightningLite
 from lightning_lite.plugins.environments.lightning_environment import find_free_network_port
+from lightning_lite.strategies.ddp_spawn import DDPSpawnStrategy
 from lightning_lite.utilities.apply_func import move_data_to_device
 from lightning_lite.utilities.cloud_io import atomic_save
 from pytorch_lightning.demos.boring_classes import RandomDataset
-from pytorch_lightning.lite import LightningLite
-from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
-from tests_pytorch.helpers.runif import RunIf
 
 
 class BoringModel(nn.Module):

tests/tests_lite/test_wrappers.py

@@ -15,12 +15,12 @@ from unittest.mock import Mock
 
 import pytest
 import torch
+from tests_lite.helpers.runif import RunIf
 from torch.utils.data.dataloader import DataLoader
 
+from lightning_lite.lite import LightningLite
 from lightning_lite.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
-from pytorch_lightning.lite import LightningLite
+from lightning_lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
-from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
-from tests_pytorch.helpers.runif import RunIf
 
 
 class EmptyLite(LightningLite):