Fix self.log(on_epoch=True) on_batch_start

CHANGELOG.md

@@ -527,11 +527,15 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `broadcast` in `DDPPlugin` and ``DDPSpawnPlugin` to respect the `src` input  ([#9691](https://github.com/PyTorchLightning/pytorch-lightning/pull/9691))
 
 
+- Fixed `self.log(on_epoch=True)` for the `on_batch_start` and `on_train_batch_start` hooks ([#9780](https://github.com/PyTorchLightning/pytorch-lightning/pull/9780))
+
+
 - Fixed restoring training state during `trainer.fit` only ([#9413](https://github.com/PyTorchLightning/pytorch-lightning/pull/9413))
 
 
 - Fixed DeepSpeed and Lightning both calling the scheduler ([#9788](https://github.com/PyTorchLightning/pytorch-lightning/pull/9788))
 
+
 - Fixed missing arguments when saving hyperparameters from the parent class but not from the child class ([#9800](https://github.com/PyTorchLightning/pytorch-lightning/pull/9800))
 
 

pytorch_lightning/loops/batch/training_batch_loop.py

@@ -23,9 +23,6 @@
 from pytorch_lightning.loops.optimization.optimizer_loop import OptimizerLoop
 from pytorch_lightning.loops.utilities import _get_active_optimizers
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
-from pytorch_lightning.utilities import AttributeDict
-from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
-from pytorch_lightning.utilities.warnings import WarningCache
 
 _OUTPUTS_TYPE = List[Union[_OPTIMIZER_LOOP_OUTPUTS_TYPE, _MANUAL_LOOP_OUTPUTS_TYPE]]
 
@@ -43,7 +40,6 @@ def __init__(self) -> None:
         self.manual_loop = ManualOptimization()
 
         self._outputs: _OUTPUTS_TYPE = []
-        self._warning_cache: WarningCache = WarningCache()
         self._remaining_splits: Optional[List[Any]] = None
 
     @property
@@ -59,42 +55,6 @@ def connect(
         if manual_loop is not None:
             self.manual_loop = manual_loop
 
-    def run(self, batch: Any, batch_idx: int) -> AttributeDict:
-        """Runs all the data splits and the ``on_batch_start`` and ``on_train_batch_start`` hooks.
-
-        Args:
-            batch: the current batch to run the train step on
-            batch_idx: the index of the current batch
-        """
-        if batch is None:
-            self._warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...")
-            return AttributeDict(signal=0, outputs=[])
-
-        # hook
-        self.trainer.logger_connector.on_batch_start()
-        response = self.trainer.call_hook("on_batch_start")
-        if response == -1:
-            return AttributeDict(signal=-1)
-
-        # hook
-        # TODO: Update this in v1.7 (deprecation: #9816)
-        model_fx = self.trainer.lightning_module.on_train_batch_start
-        extra_kwargs = (
-            {"dataloader_idx": 0}
-            if callable(model_fx) and is_param_in_hook_signature(model_fx, "dataloader_idx", explicit=True)
-            else {}
-        )
-        response = self.trainer.call_hook("on_train_batch_start", batch, batch_idx, **extra_kwargs)
-        if response == -1:
-            return AttributeDict(signal=-1)
-
-        self.trainer.fit_loop.epoch_loop.batch_progress.increment_started()
-
-        super().run(batch, batch_idx)
-
-        output, self._outputs = AttributeDict(signal=0, outputs=self._outputs), None  # free memory
-        return output
-
     def reset(self) -> None:
         """Resets the loop state."""
         self._outputs = []
@@ -117,11 +77,10 @@ def advance(self, batch, batch_idx):
             batch_idx: the index of the current batch
         """
         void(batch)
-        split_idx, split_batch = self._remaining_splits.pop(0)
-        self.split_idx = split_idx
+        self.split_idx, split_batch = self._remaining_splits.pop(0)
 
         # let logger connector extract current batch size
-        self.trainer.logger_connector.on_train_split_start(batch_idx, split_idx, split_batch)
+        self.trainer.logger_connector.on_train_split_start(self.split_idx, split_batch)
 
         # choose which loop will run the optimization
         if self.trainer.lightning_module.automatic_optimization:
@@ -135,10 +94,12 @@ def advance(self, batch, batch_idx):
             # then `advance` doesn't finish and an empty dict is returned
             self._outputs.append(outputs)
 
-    def on_run_end(self) -> None:
+    def on_run_end(self) -> _OUTPUTS_TYPE:
         self.optimizer_loop._hiddens = None
         # this is not necessary as the manual loop runs for only 1 iteration, but just in case
         self.manual_loop._hiddens = None
+        output, self._outputs = self._outputs, None  # free memory
+        return output
 
     def teardown(self) -> None:
         # release memory

pytorch_lightning/loops/epoch/evaluation_epoch_loop.py

@@ -233,10 +233,10 @@ def _on_evaluation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx:
         Raises:
             AssertionError: If the number of dataloaders is None (has not yet been set).
         """
-        self.trainer.logger_connector.on_batch_start()
+        self.trainer.logger_connector.on_batch_start(batch_idx)
 
         assert self._num_dataloaders is not None
-        self.trainer.logger_connector.on_evaluation_batch_start(batch, batch_idx, dataloader_idx, self._num_dataloaders)
+        self.trainer.logger_connector.on_evaluation_batch_start(batch, dataloader_idx, self._num_dataloaders)
 
         if self.trainer.testing:
             self.trainer.call_hook("on_test_batch_start", batch, batch_idx, dataloader_idx)

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -28,6 +28,7 @@
 from pytorch_lightning.utilities.fetching import AbstractDataFetcher
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
+from pytorch_lightning.utilities.warnings import WarningCache
 
 _OUTPUTS_TYPE = List[_BATCH_OUTPUTS_TYPE]
 
@@ -57,6 +58,7 @@ def __init__(self, min_steps: int, max_steps: int):
 
         self._results = ResultCollection(training=True)
         self._outputs: _OUTPUTS_TYPE = []
+        self._warning_cache = WarningCache()
         self._dataloader_iter: Optional[Iterator] = None
         # caches the loaded dataloader state until dataloader objects are available
         self._dataloader_state_dict: Dict[str, Any] = {}
@@ -151,14 +153,37 @@ def advance(self, *args: Any, **kwargs: Any) -> None:
 
         self.batch_progress.increment_ready()
 
-        with self.trainer.profiler.profile("run_training_batch"):
-            batch_output = self.batch_loop.run(batch, batch_idx)
+        if batch is None:
+            self._warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...")
+            batch_output = []
+        else:
+            # hook
+            self.trainer.logger_connector.on_batch_start(batch_idx)
+            response = self.trainer.call_hook("on_batch_start")
+            if response == -1:
+                self.batch_progress.increment_processed()
+                raise StopIteration
+
+            # TODO: Update this in v1.7 (deprecation: #9816)
+            model_fx = self.trainer.lightning_module.on_train_batch_start
+            extra_kwargs = (
+                {"dataloader_idx": 0}
+                if callable(model_fx) and is_param_in_hook_signature(model_fx, "dataloader_idx", explicit=True)
+                else {}
+            )
 
-        self.batch_progress.increment_processed()
+            # hook
+            response = self.trainer.call_hook("on_train_batch_start", batch, batch_idx, **extra_kwargs)
+            if response == -1:
+                self.batch_progress.increment_processed()
+                raise StopIteration
 
-        # when returning -1 from train_step, we end epoch early
-        if batch_output.signal == -1:
-            raise StopIteration
+            self.batch_progress.increment_started()
+
+            with self.trainer.profiler.profile("run_training_batch"):
+                batch_output = self.batch_loop.run(batch, batch_idx)
+
+        self.batch_progress.increment_processed()
 
         # update non-plateau LR schedulers
         # update epoch-interval ones only when we are at the end of training epoch
@@ -167,7 +192,7 @@ def advance(self, *args: Any, **kwargs: Any) -> None:
             self.update_lr_schedulers("epoch", update_plateau_schedulers=False)
 
         batch_end_outputs = self._prepare_outputs_training_batch_end(
-            batch_output.outputs,
+            batch_output,
             automatic=self.trainer.lightning_module.trainer.lightning_module.automatic_optimization,
             num_optimizers=len(self.trainer.optimizers),
         )
@@ -186,7 +211,7 @@ def advance(self, *args: Any, **kwargs: Any) -> None:
         self.batch_progress.increment_completed()
 
         if is_overridden("training_epoch_end", self.trainer.lightning_module):
-            self._outputs.append(batch_output.outputs)
+            self._outputs.append(batch_output)
 
         # -----------------------------------------
         # SAVE METRICS TO LOGGERS AND PROGRESS_BAR

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -138,15 +138,14 @@ def _increment_eval_log_step(self) -> None:
         elif self.trainer.state.stage is RunningStage.TESTING:
             self._test_log_step += 1
 
-    def on_evaluation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int, num_dataloaders: int) -> None:
+    def on_evaluation_batch_start(self, batch: Any, dataloader_idx: int, num_dataloaders: int) -> None:
         model = self.trainer.lightning_module
         # set dataloader_idx only if multiple ones
         model._current_dataloader_idx = dataloader_idx if num_dataloaders > 1 else None
 
         # track batch_size
         assert self.trainer._results is not None
         self.trainer._results.extract_batch_size(batch)
-        self._batch_idx = batch_idx
 
     def update_eval_step_metrics(self) -> None:
         if self.trainer.sanity_checking:
@@ -213,14 +212,12 @@ def update_eval_epoch_metrics(self) -> List[_OUT_DICT]:
     Train metric updates
     """
 
-    def on_train_split_start(self, batch_idx: int, split_idx: int, split_batch: Any) -> None:
+    def on_train_split_start(self, split_idx: int, split_batch: Any) -> None:
         assert self.trainer._results is not None
         # when the user requests `dataloader_iter`, we can't track the batch_size
         # and this is left to user responsibility.
         if isinstance(split_batch, pl.utilities.fetching.DataLoaderIterDataFetcher):
             self.trainer._results.extract_batch_size(split_batch)
-
-        self._batch_idx = batch_idx
         self._split_idx = split_idx
 
     def update_train_step_metrics(self) -> None:
@@ -267,7 +264,8 @@ def _log_gpus_metrics(self) -> None:
     def on_epoch_start(self) -> None:
         self._epoch_end_reached = False
 
-    def on_batch_start(self) -> None:
+    def on_batch_start(self, batch_idx: int) -> None:
+        self._batch_idx = batch_idx
         self._epoch_end_reached = False
 
     def epoch_end_reached(self) -> None:

tests/loops/test_evaluation_loop_flow.py

@@ -64,10 +64,8 @@ def backward(self, loss, optimizer, optimizer_idx):
     # simulate training manually
     trainer.state.stage = RunningStage.TRAINING
     batch_idx, batch = 0, next(iter(model.train_dataloader()))
-    out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
-    assert out.signal == 0
+    train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
 
-    train_step_out = out.outputs
     assert len(train_step_out) == 1
     train_step_out = train_step_out[0][0]
     assert isinstance(train_step_out["loss"], torch.Tensor)
@@ -129,10 +127,8 @@ def backward(self, loss, optimizer, optimizer_idx):
     trainer.state.stage = RunningStage.TRAINING
     # make sure training outputs what is expected
     batch_idx, batch = 0, next(iter(model.train_dataloader()))
-    out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
-    assert out.signal == 0
+    train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
 
-    train_step_out = out.outputs
     assert len(train_step_out) == 1
     train_step_out = train_step_out[0][0]
     assert isinstance(train_step_out["loss"], torch.Tensor)

tests/loops/test_training_loop_flow_scalar.py

@@ -147,10 +147,8 @@ def backward(self, loss, optimizer, optimizer_idx):
     trainer.state.stage = RunningStage.TRAINING
     # make sure training outputs what is expected
     batch_idx, batch = 0, next(iter(model.train_dataloader()))
-    out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
-    assert out.signal == 0
+    train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
 
-    train_step_out = out.outputs
     assert len(train_step_out) == 1
     train_step_out = train_step_out[0][0]
     assert isinstance(train_step_out["loss"], torch.Tensor)
@@ -221,10 +219,8 @@ def backward(self, loss, optimizer, optimizer_idx):
     trainer.state.stage = RunningStage.TRAINING
     # make sure training outputs what is expected
     batch_idx, batch = 0, next(iter(model.train_dataloader()))
-    out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
-    assert out.signal == 0
+    train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
 
-    train_step_out = out.outputs
     assert len(train_step_out) == 1
     train_step_out = train_step_out[0][0]
     assert isinstance(train_step_out["loss"], torch.Tensor)
@@ -311,8 +307,7 @@ def training_step(self, batch, batch_idx):
     for batch_idx, batch in enumerate(model.train_dataloader()):
         out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
         if not batch_idx % 2:
-            assert out.outputs == []
-        assert out.signal == 0
+            assert out == []
 
 
 def test_training_step_none_batches(tmpdir):
@@ -321,7 +316,6 @@ def test_training_step_none_batches(tmpdir):
     class TestModel(BoringModel):
         def __init__(self):
             super().__init__()
-
             self.counter = 0
 
         def collate_none_when_even(self, batch):
@@ -333,12 +327,17 @@ def collate_none_when_even(self, batch):
             return result
 
         def train_dataloader(self):
-            return DataLoader(RandomDataset(32, 64), collate_fn=self.collate_none_when_even)
+            return DataLoader(RandomDataset(32, 4), collate_fn=self.collate_none_when_even)
+
+        def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+            if batch_idx % 2 == 0:
+                assert outputs == []
+            else:
+                assert outputs
 
     model = TestModel()
     trainer = Trainer(
         default_root_dir=tmpdir,
-        limit_train_batches=4,
         limit_val_batches=1,
         max_epochs=4,
         enable_model_summary=False,
@@ -348,12 +347,3 @@ def train_dataloader(self):
 
     with pytest.warns(UserWarning, match=r".*train_dataloader yielded None.*"):
         trainer.fit(model)
-
-    trainer.state.stage = RunningStage.TRAINING
-
-    # manually check a few batches
-    for batch_idx, batch in enumerate(model.train_dataloader()):
-        out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
-        if not batch_idx % 2:
-            assert out.outputs == []
-        assert out.signal == 0

tests/trainer/logging_/test_train_loop_logging.py

@@ -276,11 +276,21 @@ def on_train_epoch_start(self, _, pl_module):
                 pl_module, "on_train_epoch_start", on_steps=self.choices, on_epochs=[True], prob_bars=self.choices
             )
 
+        def on_batch_start(self, _, pl_module, *__):
+            self.make_logging(
+                pl_module, "on_batch_start", on_steps=self.choices, on_epochs=self.choices, prob_bars=self.choices
+            )
+
         def on_batch_end(self, _, pl_module):
             self.make_logging(
                 pl_module, "on_batch_end", on_steps=self.choices, on_epochs=self.choices, prob_bars=self.choices
             )
 
+        def on_train_batch_start(self, _, pl_module, *__):
+            self.make_logging(
+                pl_module, "on_train_batch_start", on_steps=self.choices, on_epochs=self.choices, prob_bars=self.choices
+            )
+
         def on_train_batch_end(self, _, pl_module, *__):
             self.make_logging(
                 pl_module, "on_train_batch_end", on_steps=self.choices, on_epochs=self.choices, prob_bars=self.choices
@@ -323,7 +333,9 @@ def training_step(self, batch, batch_idx):
         "on_train_start": 1,
         "on_epoch_start": 1,
         "on_train_epoch_start": 1,
+        "on_train_batch_start": 2,
         "on_train_batch_end": 2,
+        "on_batch_start": 2,
         "on_batch_end": 2,
         "on_train_epoch_end": 1,
         "on_epoch_end": 1,