Update hooks pseudocode (#7713)

CHANGELOG.md

@@ -74,7 +74,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Refactored result handling in training loop ([#7506](https://github.com/PyTorchLightning/pytorch-lightning/pull/7506))
     * Moved attributes `hiddens` and `split_idx` to TrainLoop ([#7507](https://github.com/PyTorchLightning/pytorch-lightning/pull/7507))
     * Refactored the logic around manual and automatic optimization inside the optimizer loop ([#7526](https://github.com/PyTorchLightning/pytorch-lightning/pull/7526))
-
+    * Simplified "should run validation" logic ([#7682](https://github.com/PyTorchLightning/pytorch-lightning/pull/7682))
+    * Refactored "should run validation" logic when the trainer is signaled to stop ([#7701](https://github.com/PyTorchLightning/pytorch-lightning/pull/7701))
 
 - Moved `ignore_scalar_return_in_dp` warning suppression to the DataParallelPlugin class ([#7421](https://github.com/PyTorchLightning/pytorch-lightning/pull/7421/))
 

docs/source/common/lightning_module.rst

@@ -1064,7 +1064,9 @@ override :meth:`pytorch_lightning.core.LightningModule.tbptt_split_batch`:
 
 Hooks
 ^^^^^
-This is the pseudocode to describe how all the hooks are called during a call to ``.fit()``.
+This is the pseudocode to describe the structure of :meth:`~pytorch_lightning.trainer.Trainer.fit`.
+The inputs and outputs of each function are not represented for simplicity. Please check each function's API reference
+for more information.
 
 .. code-block:: python
 
@@ -1075,36 +1077,41 @@ This is the pseudocode to describe how all the hooks are called during a call to
 
         configure_callbacks()
 
-        on_fit_start()
-
-        for gpu/tpu in gpu/tpus:
-            train_on_device(model.copy())
-
-        on_fit_end()
+        with parallel(devices):
+            # devices can be GPUs, TPUs, ...
+            train_on_device(model)
 
     def train_on_device(model):
-        # setup is called PER DEVICE
-        setup()
+        # called PER DEVICE
+        on_fit_start()
+        setup('fit')
         configure_optimizers()
+
         on_pretrain_routine_start()
+        on_pretrain_routine_end()
+
+        # the sanity check runs here
 
+        on_train_start()
         for epoch in epochs:
             train_loop()
+        on_train_end()
 
-        teardown()
+        on_fit_end()
+        teardown('fit')
 
     def train_loop():
         on_epoch_start()
         on_train_epoch_start()
-        train_outs = []
-        for train_batch in train_dataloader():
+
+        for batch in train_dataloader():
             on_train_batch_start()
 
-            # ----- train_step methods -------
-            out = training_step(batch)
-            train_outs.append(out)
+            on_before_batch_transfer()
+            transfer_batch_to_device()
+            on_after_batch_transfer()
 
-            loss = out.loss
+            training_step()
 
             on_before_zero_grad()
             optimizer_zero_grad()
@@ -1114,38 +1121,42 @@ This is the pseudocode to describe how all the hooks are called during a call to
 
             optimizer_step()
 
-            on_train_batch_end(out)
+            on_train_batch_end()
 
             if should_check_val:
                 val_loop()
-
         # end training epoch
-        training_epoch_end(outs)
-        on_train_epoch_end(outs)
+        training_epoch_end()
+
+        on_train_epoch_end()
         on_epoch_end()
 
     def val_loop():
-        model.eval()
+        on_validation_model_eval()  # calls `model.eval()`
         torch.set_grad_enabled(False)
 
+        on_validation_start()
         on_epoch_start()
         on_validation_epoch_start()
-        val_outs = []
-        for val_batch in val_dataloader():
+
+        for batch in val_dataloader():
             on_validation_batch_start()
 
-            # -------- val step methods -------
-            out = validation_step(val_batch)
-            val_outs.append(out)
+            on_before_batch_transfer()
+            transfer_batch_to_device()
+            on_after_batch_transfer()
+
+            validation_step()
 
-            on_validation_batch_end(out)
+            on_validation_batch_end()
+        validation_epoch_end()
 
-        validation_epoch_end(val_outs)
         on_validation_epoch_end()
         on_epoch_end()
+        on_validation_end()
 
         # set up for train
-        model.train()
+        on_validation_model_train()  # calls `model.train()`
         torch.set_grad_enabled(True)
 
 backward

tests/models/test_hooks.py

@@ -255,6 +255,18 @@ def __init__(self):
             'on_validation_batch_end',
         ]
 
+    def prepare_data(self):
+        self.called.append("prepare_data")
+        return super().prepare_data()
+
+    def configure_callbacks(self):
+        self.called.append("configure_callbacks")
+        return super().configure_callbacks()
+
+    def configure_optimizers(self):
+        self.called.append("configure_optimizers")
+        return super().configure_optimizers()
+
     def training_step(self, *args, **kwargs):
         self.called.append("training_step")
         return super().training_step(*args, **kwargs)
@@ -451,7 +463,10 @@ def test_trainer_model_hook_system_fit(tmpdir):
     assert model.called == []
     trainer.fit(model)
     expected = [
+        'prepare_data',
+        'configure_callbacks',
         'setup_fit',
+        'configure_optimizers',
         'on_fit_start',
         'on_pretrain_routine_start',
         'on_pretrain_routine_end',
@@ -504,7 +519,10 @@ def test_trainer_model_hook_system_fit_no_val(tmpdir):
     assert model.called == []
     trainer.fit(model)
     expected = [
+        'prepare_data',
+        'configure_callbacks',
         'setup_fit',
+        'configure_optimizers',
         'on_fit_start',
         'on_pretrain_routine_start',
         'on_pretrain_routine_end',
@@ -535,6 +553,8 @@ def test_trainer_model_hook_system_validate(tmpdir):
     assert model.called == []
     trainer.validate(model, verbose=False)
     expected = [
+        'prepare_data',
+        'configure_callbacks',
         'setup_validate',
         'on_validation_model_eval',
         'on_validation_start',
@@ -567,6 +587,8 @@ def test_trainer_model_hook_system_test(tmpdir):
     assert model.called == []
     trainer.test(model, verbose=False)
     expected = [
+        'prepare_data',
+        'configure_callbacks',
         'setup_test',
         'on_test_model_eval',
         'on_test_start',