Fix support for logging within callbacks returned from `LightningModu…

…le` (#10991)

Co-authored-by: thomas chaton <[email protected]>
Co-authored-by: Carlos Mocholi <[email protected]>

CHANGELOG.md

@@ -9,7 +9,19 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Fixed
 
 - Fixed a bug where the DeepSpeedPlugin arguments `cpu_checkpointing` and `contiguous_memory_optimization` were not being forwarded to deepspeed correctly ([#10874](https://github.com/PyTorchLightning/pytorch-lightning/issues/10874))
+
+
 - Fixed an issue with `NeptuneLogger` causing checkpoints to be uploaded with a duplicated file extension ([#11015](https://github.com/PyTorchLightning/pytorch-lightning/issues/11015))
+=======
+
+
+- Fixed support for logging within callbacks returned from `LightningModule` ([#10991](https://github.com/PyTorchLightning/pytorch-lightning/pull/10991))
+
+
+-
+
+
+-
 
 
 ## [1.5.5] - 2021-12-07

pytorch_lightning/trainer/configuration_validator.py

@@ -19,7 +19,7 @@
 from pytorch_lightning.utilities.warnings import rank_zero_deprecation, rank_zero_warn
 
 
-def verify_loop_configurations(trainer: "pl.Trainer", model: "pl.LightningModule") -> None:
+def verify_loop_configurations(trainer: "pl.Trainer") -> None:
     r"""
     Checks that the model is configured correctly before the run is started.
 
@@ -28,6 +28,10 @@ def verify_loop_configurations(trainer: "pl.Trainer", model: "pl.LightningModule
         model: The model to check the configuration.
 
     """
+    model = trainer.lightning_module
+
+    if trainer.state.fn is None:
+        raise ValueError("Unexpected: Trainer state fn must be set before validating loop configuration.")
     if trainer.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
         __verify_train_val_loop_configuration(trainer, model)
         __verify_manual_optimization_support(trainer, model)

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -249,10 +249,11 @@ def _configure_timer_callback(self, max_time: Optional[Union[str, timedelta, Dic
     def _trainer_has_checkpoint_callbacks(self):
         return len(self.trainer.checkpoint_callbacks) > 0
 
-    def attach_model_logging_functions(self, model):
+    def _attach_model_logging_functions(self):
+        lightning_module = self.trainer.lightning_module
         for callback in self.trainer.callbacks:
-            callback.log = model.log
-            callback.log_dict = model.log_dict
+            callback.log = lightning_module.log
+            callback.log_dict = lightning_module.log_dict
 
     def _attach_model_callbacks(self) -> None:
         """Attaches the callbacks defined in the model.

pytorch_lightning/trainer/trainer.py

@@ -1114,17 +1114,16 @@ def _run(
         if hasattr(model, "hparams"):
             parsing.clean_namespace(model.hparams)
 
-        verify_loop_configurations(self, model)
-
-        # attach model log function to callback
-        self._callback_connector.attach_model_logging_functions(model)
-
         # attach model to the training type plugin
         self.training_type_plugin.connect(model)
 
+        self._callback_connector._attach_model_callbacks()
+        self._callback_connector._attach_model_logging_functions()
+
+        verify_loop_configurations(self)
+
         # hook
         self._data_connector.prepare_data()
-        self._callback_connector._attach_model_callbacks()
 
         # ----------------------------
         # SET UP TRAINING

tests/models/test_hooks.py

@@ -499,8 +499,8 @@ def training_step(self, batch, batch_idx):
     expected = [
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
-        dict(name="prepare_data"),
         dict(name="configure_callbacks"),
+        dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
         # DeepSpeed needs the batch size to figure out throughput logging
         *([dict(name="train_dataloader")] if kwargs.get("strategy") == "deepspeed" else []),
@@ -618,8 +618,8 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
     expected = [
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
-        dict(name="prepare_data"),
         dict(name="configure_callbacks"),
+        dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
         dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage="fit")),
         dict(name="setup", kwargs=dict(stage="fit")),
@@ -716,8 +716,8 @@ def test_trainer_model_hook_system_eval(tmpdir, batches, verb, noun, dataloader,
     expected = [
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
-        dict(name="prepare_data"),
         dict(name="configure_callbacks"),
+        dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
         dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage=verb)),
         dict(name="setup", kwargs=dict(stage=verb)),
@@ -748,8 +748,8 @@ def test_trainer_model_hook_system_predict(tmpdir):
     expected = [
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
-        dict(name="prepare_data"),
         dict(name="configure_callbacks"),
+        dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
         dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage="predict")),
         dict(name="setup", kwargs=dict(stage="predict")),