Skip strategy=ddp_spawn, accelerator=cpu, python>=3.9 tests (Lightning-AI#10550)

Author: carmocca

tests/callbacks/test_early_stopping.py

@@ -381,16 +381,16 @@ def on_train_end(self) -> None:
 
 _ES_CHECK = dict(check_on_train_epoch_end=True)
 _ES_CHECK_P3 = dict(patience=3, check_on_train_epoch_end=True)
-_NO_WIN = dict(marks=RunIf(skip_windows=True))
+_SPAWN_MARK = dict(marks=RunIf(skip_windows=True, skip_49370=True))
 
 
 @pytest.mark.parametrize(
     "callbacks, expected_stop_epoch, check_on_train_epoch_end, strategy, num_processes",
     [
         ([EarlyStopping("abc"), EarlyStopping("cba", patience=3)], 3, False, None, 1),
         ([EarlyStopping("cba", patience=3), EarlyStopping("abc")], 3, False, None, 1),
-        pytest.param([EarlyStopping("abc"), EarlyStopping("cba", patience=3)], 3, False, "ddp_spawn", 2, **_NO_WIN),
-        pytest.param([EarlyStopping("cba", patience=3), EarlyStopping("abc")], 3, False, "ddp_spawn", 2, **_NO_WIN),
+        pytest.param([EarlyStopping("abc"), EarlyStopping("cba", patience=3)], 3, False, "ddp_spawn", 2, **_SPAWN_MARK),
+        pytest.param([EarlyStopping("cba", patience=3), EarlyStopping("abc")], 3, False, "ddp_spawn", 2, **_SPAWN_MARK),
         ([EarlyStopping("abc", **_ES_CHECK), EarlyStopping("cba", **_ES_CHECK_P3)], 3, True, None, 1),
         ([EarlyStopping("cba", **_ES_CHECK_P3), EarlyStopping("abc", **_ES_CHECK)], 3, True, None, 1),
         pytest.param(
@@ -399,15 +399,15 @@ def on_train_end(self) -> None:
             True,
             "ddp_spawn",
             2,
-            **_NO_WIN,
+            **_SPAWN_MARK,
         ),
         pytest.param(
             [EarlyStopping("cba", **_ES_CHECK_P3), EarlyStopping("abc", **_ES_CHECK)],
             3,
             True,
             "ddp_spawn",
             2,
-            **_NO_WIN,
+            **_SPAWN_MARK,
         ),
     ],
 )

tests/callbacks/test_pruning.py

@@ -187,7 +187,7 @@ def test_pruning_callback_ddp_spawn(tmpdir):
     train_with_pruning_callback(tmpdir, use_global_unstructured=True, strategy="ddp_spawn", gpus=2)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_pruning_callback_ddp_cpu(tmpdir):
     train_with_pruning_callback(tmpdir, parameters_to_prune=True, strategy="ddp_spawn", num_processes=2)
 

tests/callbacks/test_stochastic_weight_avg.py

@@ -148,7 +148,7 @@ def test_swa_callback_ddp_spawn(tmpdir):
     train_with_swa(tmpdir, strategy="ddp_spawn", gpus=2)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_swa_callback_ddp_cpu(tmpdir):
     train_with_swa(tmpdir, strategy="ddp_spawn", num_processes=2)
 

tests/checkpointing/test_model_checkpoint.py

@@ -385,7 +385,7 @@ def on_train_end(self, trainer, pl_module):
             assert torch.save.call_count == 0
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_model_checkpoint_no_extraneous_invocations(tmpdir):
     """Test to ensure that the model callback saves the checkpoints only once in distributed mode."""
     model = LogInTwoMethods()

tests/checkpointing/test_torch_saving.py

@@ -34,7 +34,7 @@ def test_model_torch_save(tmpdir):
     trainer = torch.load(temp_path)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_model_torch_save_ddp_cpu(tmpdir):
     """Test to ensure torch save does not fail for model and trainer using cpu ddp."""
     model = BoringModel()

tests/deprecated_api/test_remove_1-7.py

@@ -245,7 +245,7 @@ def get_from_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
         return super().get_from_queue(queue)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_v1_7_0_deprecate_add_get_queue(tmpdir):
     model = BoringCallbackDDPSpawnModel()
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, num_processes=2, strategy="ddp_spawn")

tests/helpers/runif.py

@@ -70,6 +70,7 @@ def __new__(
         fairscale_fully_sharded: bool = False,
         deepspeed: bool = False,
         rich: bool = False,
+        skip_49370: bool = False,
         **kwargs,
     ):
         """
@@ -91,6 +92,7 @@ def __new__(
             fairscale_fully_sharded: if `fairscale` fully sharded module is required to run the test
             deepspeed: if `deepspeed` module is required to run the test
             rich: if `rich` module is required to run the test
+            skip_49370: Skip the test as it's impacted by https://github.com/pytorch/pytorch/issues/49370.
             kwargs: native pytest.mark.skipif keyword arguments
         """
         conditions = []
@@ -165,6 +167,15 @@ def __new__(
             conditions.append(not _RICH_AVAILABLE)
             reasons.append("Rich")
 
+        if skip_49370:
+            # strategy=ddp_spawn, accelerator=cpu, python>=3.9, torch<1.8 does not work
+            py_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
+            ge_3_9 = Version(py_version) >= Version("3.9")
+            torch_version = get_distribution("torch").version
+            old_torch = Version(torch_version) < Version("1.8")
+            conditions.append(ge_3_9 and old_torch)
+            reasons.append("Impacted by https://github.com/pytorch/pytorch/issues/49370")
+
         reasons = [rs for cond, rs in zip(conditions, reasons) if cond]
         return pytest.mark.skipif(
             *args, condition=any(conditions), reason=f"Requires: [{' + '.join(reasons)}]", **kwargs

tests/loggers/test_all.py

@@ -321,8 +321,8 @@ def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
             assert pl_module.logger.experiment.something(foo="bar") is None
 
 
+@RunIf(skip_windows=True, skip_49370=True)
 @pytest.mark.parametrize("logger_class", [CometLogger, CSVLogger, MLFlowLogger, TensorBoardLogger, TestTubeLogger])
-@RunIf(skip_windows=True)
 def test_logger_created_on_rank_zero_only(tmpdir, monkeypatch, logger_class):
     """Test that loggers get replaced by dummy loggers on global rank > 0."""
     _patch_comet_atexit(monkeypatch)

tests/models/test_cpu.py

@@ -122,7 +122,7 @@ def validation_step(self, *args, **kwargs):
     model.unfreeze()
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_multi_cpu_model_ddp(tmpdir):
     """Make sure DDP works."""
     tutils.set_random_main_port()

tests/models/test_horovod.py

@@ -66,7 +66,7 @@ def _run_horovod(trainer_options, on_gpu=False):
     assert exit_code == 0
 
 
-@RunIf(skip_windows=True, horovod=True)
+@RunIf(skip_windows=True, horovod=True, skip_49370=True)
 def test_horovod_cpu(tmpdir):
     """Test Horovod running multi-process on CPU."""
     trainer_options = dict(
@@ -82,7 +82,7 @@ def test_horovod_cpu(tmpdir):
     _run_horovod(trainer_options)
 
 
-@RunIf(skip_windows=True, horovod=True)
+@RunIf(skip_windows=True, horovod=True, skip_49370=True)
 def test_horovod_cpu_clip_grad_by_value(tmpdir):
     """Test Horovod running multi-process on CPU."""
     trainer_options = dict(
@@ -99,7 +99,7 @@ def test_horovod_cpu_clip_grad_by_value(tmpdir):
     _run_horovod(trainer_options)
 
 
-@RunIf(skip_windows=True, horovod=True)
+@RunIf(skip_windows=True, horovod=True, skip_49370=True)
 def test_horovod_cpu_implicit(tmpdir):
     """Test Horovod without specifying a backend, inferring from env set by `horovodrun`."""
     trainer_options = dict(

tests/plugins/test_ddp_spawn_plugin.py

@@ -46,7 +46,7 @@ def get_from_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
         return super().get_from_queue(queue)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_ddp_cpu():
     """Tests if device is set correctly when training for DDPSpawnPlugin."""
     trainer = Trainer(num_processes=2, fast_dev_run=True)
@@ -91,7 +91,7 @@ def get_from_queue(self, trainer: Trainer, queue: torch.multiprocessing.SimpleQu
         return super().get_from_queue(trainer, queue)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_ddp_spawn_add_get_queue(tmpdir):
     """Tests add_to_queue/get_from_queue with DDPSpawnPlugin."""
 
@@ -128,7 +128,7 @@ def on_predict_start(self) -> None:
         assert isinstance(self.trainer.model, LightningModule)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_ddp_spawn_configure_ddp(tmpdir):
     """Tests with ddp spawn plugin."""
     trainer = Trainer(default_root_dir=tmpdir, num_processes=2, strategy="ddp_spawn", fast_dev_run=True)

tests/profiler/test_profiler.py

@@ -161,7 +161,7 @@ def test_simple_profiler_with_nonexisting_dirpath(tmpdir):
     assert nonexisting_tmpdir.join("fit-profiler.txt").exists()
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_simple_profiler_distributed_files(tmpdir):
     """Ensure the proper files are saved in distributed."""
     profiler = SimpleProfiler(dirpath=tmpdir, filename="profiler")
@@ -226,6 +226,7 @@ def test_advanced_profiler_iterable_durations(advanced_profiler, action: str, ex
     np.testing.assert_allclose(recored_total_duration, expected_total_duration, rtol=0.2)
 
 
+@pytest.mark.flaky(reruns=3)
 def test_advanced_profiler_overhead(advanced_profiler, n_iter=5):
     """ensure that the profiler doesn't introduce too much overhead during training."""
     for _ in range(n_iter):

tests/trainer/logging_/test_distributed_logging.py

@@ -59,7 +59,7 @@ def on_train_end(self):
         assert self.log_name.format(rank=self.local_rank) in self.logger.logs, "Expected rank to be logged"
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_all_rank_logging_ddp_cpu(tmpdir):
     """Check that all ranks can be logged from."""
     model = TestModel()

tests/trainer/logging_/test_train_loop_logging.py

@@ -395,7 +395,7 @@ def validation_step(self, batch, batch_idx):
         return super().validation_step(batch, batch_idx)
 
 
-@pytest.mark.parametrize("devices", [1, pytest.param(2, marks=RunIf(skip_windows=True))])
+@pytest.mark.parametrize("devices", [1, pytest.param(2, marks=RunIf(skip_windows=True, skip_49370=True))])
 def test_logging_sync_dist_true(tmpdir, devices):
     """Tests to ensure that the sync_dist flag works (should just return the original value)"""
     fake_result = 1

tests/trainer/properties/test_get_model.py

@@ -37,7 +37,7 @@ def test_get_model(tmpdir):
     trainer.fit(model)
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_get_model_ddp_cpu(tmpdir):
     """Tests that `trainer.lightning_module` extracts the model correctly when using ddp on cpu."""
 

tests/trainer/test_data_loading.py

@@ -133,7 +133,7 @@ def _get_warning_msg():
             assert warn_str in msg
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 @pytest.mark.parametrize("num_workers", [0, 1])
 def test_dataloader_warnings(tmpdir, num_workers):
     trainer = Trainer(default_root_dir=tmpdir, strategy="ddp_spawn", num_processes=2, fast_dev_run=4)

tests/trainer/test_trainer.py

@@ -1809,7 +1809,7 @@ def on_predict_start(self) -> None:
 
 
 @pytest.mark.parametrize(
-    "strategy,num_processes", [(None, 1), pytest.param("ddp_spawn", 2, marks=RunIf(skip_windows=True))]
+    "strategy,num_processes", [(None, 1), pytest.param("ddp_spawn", 2, marks=RunIf(skip_windows=True, skip_49370=True))]
 )
 def test_model_in_correct_mode_during_stages(tmpdir, strategy, num_processes):
     model = TrainerStagesModel()
@@ -1830,7 +1830,7 @@ def validation_epoch_end(self, outputs) -> None:
         pass
 
 
-@RunIf(skip_windows=True)
+@RunIf(skip_windows=True, skip_49370=True)
 def test_fit_test_synchronization(tmpdir):
     """Test that the trainer synchronizes processes before returning control back to the caller."""
     tutils.set_random_main_port()

tests/utilities/test_all_gather_grad.py

@@ -41,8 +41,8 @@ def _test_all_gather_ddp(rank, world_size):
     assert torch.allclose(grad2, tensor2.grad)
 
 
-@RunIf(skip_windows=True)
-def test_all_gather_ddp():
+@RunIf(skip_windows=True, skip_49370=True)
+def test_all_gather_ddp_spawn():
     world_size = 3
     torch.multiprocessing.spawn(_test_all_gather_ddp, args=(world_size,), nprocs=world_size)