add pytorch skips

tests/tests_pytorch/callbacks/test_spike.py

@@ -3,6 +3,7 @@
 
 import pytest
 import torch
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from lightning.fabric.utilities.spike import _TORCHMETRICS_GREATER_EQUAL_1_0_0, TrainingSpikeException
 from lightning.pytorch import LightningModule, Trainer
 from lightning.pytorch.callbacks.spike import SpikeDetection
@@ -46,6 +47,11 @@ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
             super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)
 
 
+@pytest.mark.xfail(
+    # https://github.com/pytorch/pytorch/issues/116056
+    sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+    reason="Windows + DDP issue in PyTorch 2.2",
+)
 @pytest.mark.flaky(max_runs=3)
 @pytest.mark.parametrize(
     ("global_rank_spike", "num_devices", "spike_value", "finite_only"),

tests/tests_pytorch/loops/test_prediction_loop.py

@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
+import sys
 
 import pytest
 from lightning.pytorch import LightningModule, Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from torch.utils.data import DataLoader, DistributedSampler, SequentialSampler
 
 
@@ -50,6 +52,11 @@ def predict_step(self, batch, batch_idx):
     assert trainer.predict_loop.predictions == []
 
 
+@pytest.mark.xfail(
+    # https://github.com/pytorch/pytorch/issues/116056
+    sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+    reason="Windows + DDP issue in PyTorch 2.2",
+)
 @pytest.mark.parametrize("use_distributed_sampler", [False, True])
 def test_prediction_loop_batch_sampler_set_epoch_called(tmp_path, use_distributed_sampler):
     """Tests that set_epoch is called on the dataloader's batch sampler (if any) during prediction."""

tests/tests_pytorch/models/test_amp.py

@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import sys
 from unittest import mock
 
 import pytest
 import torch
 from lightning.fabric.plugins.environments import SLURMEnvironment
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from torch.utils.data import DataLoader
@@ -53,7 +55,11 @@ def _assert_autocast_enabled(self):
     [
         ("single_device", "16-mixed", 1),
         ("single_device", "bf16-mixed", 1),
-        ("ddp_spawn", "16-mixed", 2),
+        pytest.param("ddp_spawn", "16-mixed", 2, marks=pytest.mark.xfail(
+            # https://github.com/pytorch/pytorch/issues/116056
+            sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+            reason="Windows + DDP issue in PyTorch 2.2",
+        )),
         pytest.param("ddp_spawn", "bf16-mixed", 2, marks=RunIf(skip_windows=True)),
     ],
 )

tests/tests_pytorch/serve/test_servable_module_validator.py

@@ -1,10 +1,12 @@
+import sys
 from typing import Dict
 
 import pytest
 import torch
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.serve.servable_module_validator import ServableModule, ServableModuleValidator
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from torch import Tensor
 
 
@@ -36,6 +38,11 @@ def test_servable_module_validator():
     callback.on_train_start(Trainer(accelerator="cpu"), model)
 
 
+@pytest.mark.xfail(
+    # https://github.com/pytorch/pytorch/issues/116056
+    sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+    reason="Windows + DDP issue in PyTorch 2.2",
+)
 @pytest.mark.flaky(reruns=3)
 def test_servable_module_validator_with_trainer(tmpdir):
     callback = ServableModuleValidator()

tests/tests_pytorch/strategies/launchers/test_multiprocessing.py

@@ -12,13 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import sys
 from multiprocessing import Process
 from unittest import mock
 from unittest.mock import ANY, Mock, call, patch
 
 import pytest
 import torch
 from lightning.fabric.plugins import ClusterEnvironment
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.strategies import DDPStrategy
@@ -194,6 +196,11 @@ def on_fit_start(self) -> None:
         assert torch.equal(self.layer.weight.data, self.tied_layer.weight.data)
 
 
+@pytest.mark.xfail(
+    # https://github.com/pytorch/pytorch/issues/116056
+    sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+    reason="Windows + DDP issue in PyTorch 2.2",
+)
 def test_memory_sharing_disabled():
     """Test that the multiprocessing launcher disables memory sharing on model parameters and buffers to avoid race
     conditions on model updates."""
@@ -214,6 +221,11 @@ def test_check_for_missing_main_guard():
         launcher.launch(function=Mock())
 
 
+@pytest.mark.xfail(
+    # https://github.com/pytorch/pytorch/issues/116056
+    sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+    reason="Windows + DDP issue in PyTorch 2.2",
+)
 def test_fit_twice_raises():
     model = BoringModel()
     trainer = Trainer(

tests/tests_pytorch/trainer/connectors/test_data_connector.py

@@ -11,6 +11,7 @@
 # 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 sys
 from re import escape
 from typing import Sized
 from unittest import mock
@@ -19,6 +20,7 @@
 import lightning.fabric
 import pytest
 from lightning.fabric.utilities.distributed import DistributedSamplerWrapper
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from lightning.fabric.utilities.warnings import PossibleUserWarning
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel, RandomDataset
@@ -123,6 +125,11 @@ def on_train_end(self):
             self.ctx.__exit__(None, None, None)
 
 
+@pytest.mark.xfail(
+    # https://github.com/pytorch/pytorch/issues/116056
+    sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+    reason="Windows + DDP issue in PyTorch 2.2",
+)
 @pytest.mark.parametrize("num_workers", [0, 1, 2])
 def test_dataloader_persistent_workers_performance_warning(num_workers, tmp_path):
     """Test that when the multiprocessing start-method is 'spawn', we recommend setting `persistent_workers=True`."""

tests/tests_pytorch/trainer/logging_/test_train_loop_logging.py

@@ -15,6 +15,7 @@
 
 import collections
 import itertools
+import sys
 from re import escape
 from unittest import mock
 from unittest.mock import call
@@ -30,6 +31,7 @@
 from lightning.pytorch.trainer.states import RunningStage
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 from lightning.pytorch.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_0_11 as _TM_GE_0_11
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
 from lightning_utilities.test.warning import no_warning_call
 from torch import Tensor
 from torch.utils.data import DataLoader
@@ -346,7 +348,11 @@ def validation_step(self, batch, batch_idx):
     ("devices", "accelerator"),
     [
         (1, "cpu"),
-        (2, "cpu"),
+        pytest.param(2, "cpu", marks=pytest.mark.xfail(
+            # https://github.com/pytorch/pytorch/issues/116056
+            sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2,
+            reason="Windows + DDP issue in PyTorch 2.2",
+    )),
         pytest.param(2, "gpu", marks=RunIf(min_cuda_gpus=2)),
     ],
 )