Merge branch 'master' into fix/deepspeed_logging_per_gpu

CHANGELOG.md

@@ -136,6 +136,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed deprecated `CheckpointConnector.hpc_load` property in favor of `CheckpointConnector.restore` ([#10525](https://github.com/PyTorchLightning/pytorch-lightning/pull/10525))
 
 
+- Removed deprecated `reload_dataloaders_every_epoch` from `Trainer` in favour of `reload_dataloaders_every_n_epochs` ([#10481](https://github.com/PyTorchLightning/pytorch-lightning/pull/10481))
+
+
 
 ### Fixed
 
@@ -157,6 +160,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Squeeze the early stopping monitor to remove empty tensor dimensions ([#10461](https://github.com/PyTorchLightning/pytorch-lightning/issues/10461))
 
 
+- Fixed sampler replacement logic with `overfit_batches` to only replace the sample when `SequentialSampler` is not used ([#10486](https://github.com/PyTorchLightning/pytorch-lightning/issues/10486))
+
+
+-
+
+
 -
 
 ## [1.5.1] - 2021-11-09

docs/source/_templates/layout.html

@@ -4,7 +4,7 @@
 {% block footer %}
 {{ super() }}
 <script script type="text/javascript">
-  var collapsedSections = ['Best practices', 'Lightning API', 'Optional extensions', 'Tutorials', 'API References', 'Bolts', 'Examples', 'Partner Domain Frameworks', 'Community'];
+  var collapsedSections = ['Best practices', 'Optional extensions', 'Tutorials', 'API References', 'Bolts', 'Examples', 'Partner Domain Frameworks', 'Community'];
 </script>
 
 {% endblock %}

pl_examples/loop_examples/kfold.py

@@ -205,7 +205,7 @@ def on_run_end(self) -> None:
         voting_model = EnsembleVotingModel(type(self.trainer.lightning_module), checkpoint_paths)
         voting_model.trainer = self.trainer
         # This requires to connect the new model and move it the right device.
-        self.trainer.accelerator.connect(voting_model)
+        self.trainer.training_type_plugin.connect(voting_model)
         self.trainer.training_type_plugin.model_to_device()
         self.trainer.test_loop.run()
 

pl_examples/loop_examples/yielding_training_step.py

@@ -86,7 +86,7 @@ def _training_step(self, generator):
         # Here, instead of calling `lightning_module.training_step()`
         # we call next() on the generator!
         training_step_output = next(generator)
-        self.trainer.accelerator.post_training_step()
+        self.trainer.training_type_plugin.post_training_step()
 
         training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
 

pytorch_lightning/trainer/connectors/data_connector.py

@@ -64,7 +64,6 @@ def on_trainer_init(
         self,
         check_val_every_n_epoch: int,
         reload_dataloaders_every_n_epochs: int,
-        reload_dataloaders_every_epoch: bool,
         prepare_data_per_node: Optional[bool] = None,
     ) -> None:
         self.trainer.datamodule = None
@@ -83,13 +82,6 @@ def on_trainer_init(
 
         self.trainer.check_val_every_n_epoch = check_val_every_n_epoch
 
-        if reload_dataloaders_every_epoch:
-            reload_dataloaders_every_n_epochs = int(reload_dataloaders_every_epoch)
-            rank_zero_deprecation(
-                "`reload_dataloaders_every_epoch` is deprecated in v1.4 and will be removed in v1.6."
-                " Please use `reload_dataloaders_every_n_epochs` in Trainer."
-            )
-
         if not isinstance(reload_dataloaders_every_n_epochs, int) or (reload_dataloaders_every_n_epochs < 0):
             raise MisconfigurationException(
                 f"`reload_dataloaders_every_n_epochs` should be an int >= 0, got {reload_dataloaders_every_n_epochs}."

pytorch_lightning/trainer/data_loading.py

@@ -438,8 +438,7 @@ def _reset_eval_dataloader(
         for loader_i in range(len(dataloaders)):
             loader = dataloaders[loader_i]
 
-            if hasattr(loader, "sampler") and isinstance(loader.sampler, RandomSampler):
-
+            if hasattr(loader, "sampler") and not isinstance(loader.sampler, SequentialSampler):
                 # when overfitting, the dataloader should not have sampler
                 if self.overfit_batches > 0 and mode.evaluating:
                     rank_zero_warn(
@@ -591,16 +590,17 @@ def _add_sampler_metadata_collate(dataloader: DataLoader) -> None:
 
     @staticmethod
     def _resolve_overfit_batches(dataloader: Collection[DataLoader]) -> Collection[DataLoader]:
-        has_random_sampler = False
+        all_have_sequential_sampler = True
 
-        def resolve_had_random_sampler(dataloader: DataLoader):
-            nonlocal has_random_sampler
-            if not has_random_sampler:
-                has_random_sampler = isinstance(dataloader.sampler, RandomSampler)
+        def resolve_has_no_sequential_sampler(dataloader: DataLoader):
+            nonlocal all_have_sequential_sampler
+            all_have_sequential_sampler = all_have_sequential_sampler & isinstance(
+                dataloader.sampler, SequentialSampler
+            )
 
-        apply_to_collection(dataloader, DataLoader, resolve_had_random_sampler)
+        apply_to_collection(dataloader, DataLoader, resolve_has_no_sequential_sampler)
 
-        if has_random_sampler:
+        if not all_have_sequential_sampler:
             rank_zero_warn(
                 "You requested to overfit but enabled training dataloader shuffling."
                 " We are turning off the training dataloader shuffling for you."

pytorch_lightning/trainer/trainer.py

@@ -162,7 +162,6 @@ def __init__(
         benchmark: bool = False,
         deterministic: bool = False,
         reload_dataloaders_every_n_epochs: int = 0,
-        reload_dataloaders_every_epoch: bool = False,
         auto_lr_find: Union[bool, str] = False,
         replace_sampler_ddp: bool = True,
         detect_anomaly: bool = False,
@@ -341,12 +340,6 @@ def __init__(
 
             reload_dataloaders_every_n_epochs: Set to a non-negative integer to reload dataloaders every n epochs.
 
-            reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch.
-
-                .. deprecated:: v1.4
-                    ``reload_dataloaders_every_epoch`` has been deprecated in v1.4 and will be removed in v1.6.
-                    Please use ``reload_dataloaders_every_n_epochs``.
-
             replace_sampler_ddp: Explicitly enables or disables sampler replacement. If not specified this
                 will toggled automatically when DDP is used. By default it will add ``shuffle=True`` for
                 train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it,
@@ -515,7 +508,6 @@ def __init__(
         self._data_connector.on_trainer_init(
             check_val_every_n_epoch,
             reload_dataloaders_every_n_epochs,
-            reload_dataloaders_every_epoch,
             prepare_data_per_node,
         )
 

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_hooks.py

@@ -866,7 +866,7 @@ def call(hook, fn, *args, **kwargs):
         limit_predict_batches=batches,
         enable_progress_bar=False,
         enable_model_summary=False,
-        reload_dataloaders_every_epoch=True,
+        reload_dataloaders_every_n_epochs=True,
     )
 
     called = []

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):