[bug] [docs] Clearer optimizer_step override instructions

pytorch_lightning/accelerators/accelerator.py

@@ -113,11 +113,12 @@ def optimizer_step(self, optimizer, batch_idx, opt_idx, lambda_closure):
 
         # model hook
         model_ref.optimizer_step(
-            self.trainer.current_epoch,
-            batch_idx,
-            optimizer,
-            opt_idx,
-            lambda_closure,
+            epoch=self.trainer.current_epoch,
+            batch_idx=batch_idx,
+            optimizer=optimizer,
+            optimizer_idx=opt_idx,
+            optimizer_closure=lambda_closure,
+            on_tpu=False,  # TPUAccelerator class sets this as True
             using_native_amp=native_amp,
             using_lbfgs=is_lbfgs
         )

pytorch_lightning/accelerators/tpu_accelerator.py

@@ -242,11 +242,13 @@ def optimizer_step(self, optimizer, batch_idx, opt_idx, lambda_closure):
 
         # model hook
         model_ref.optimizer_step(
-            self.trainer.current_epoch,
-            batch_idx, optimizer,
-            opt_idx,
-            lambda_closure,
+            epoch=self.trainer.current_epoch,
+            batch_idx=batch_idx,
+            optimizer=optimizer,
+            optimizer_idx=opt_idx,
+            optimizer_closure=lambda_closure,
             on_tpu=True,
+            using_native_amp=False,
             using_lbfgs=is_lbfgs
         )
 

pytorch_lightning/core/lightning.py

@@ -1143,17 +1143,23 @@ def optimizer_step(
         batch_idx: int,
         optimizer: Optimizer,
         optimizer_idx: int,
-        optimizer_closure: Optional[Callable] = None,
-        on_tpu: bool = False,
-        using_native_amp: bool = False,
-        using_lbfgs: bool = False,
+        optimizer_closure: Optional[Callable],
+        on_tpu: bool,
+        using_native_amp: bool,
+        using_lbfgs: bool,
     ) -> None:
         r"""
         Override this method to adjust the default way the
         :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.
         By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example
         once per optimizer.
 
+        Warning:
+            If you are overriding this method, make sure that you pass the ``optimizer_closure`` parameter
+            to ``optimizer.step()`` function as shown in the examples. This ensures that
+            ``train_step_and_backward_closure`` is called within
+            :meth:`~pytorch_lightning.trainer.training_loop.TrainLoop.run_training_batch`.
+
         Args:
             epoch: Current epoch
             batch_idx: Index of current batch
@@ -1168,23 +1174,23 @@ def optimizer_step(
             .. code-block:: python
 
                 # DEFAULT
-                def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
+                def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
                                    optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
-                    optimizer.step()
+                    optimizer.step(closure=optimizer_closure)
 
                 # Alternating schedule for optimizer steps (i.e.: GANs)
-                def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
+                def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
                                    optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
                     # update generator opt every 2 steps
                     if optimizer_idx == 0:
                         if batch_idx % 2 == 0 :
-                            optimizer.step()
+                            optimizer.step(closure=optimizer_closure)
                             optimizer.zero_grad()
 
                     # update discriminator opt every 4 steps
                     if optimizer_idx == 1:
                         if batch_idx % 4 == 0 :
-                            optimizer.step()
+                            optimizer.step(closure=optimizer_closure)
                             optimizer.zero_grad()
 
                     # ...
@@ -1197,16 +1203,16 @@ def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
             .. code-block:: python
 
                 # learning rate warm-up
-                def optimizer_step(self, current_epoch, batch_idx, optimizer,
-                                    optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
+                def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
+                                   optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
                     # warm up lr
                     if self.trainer.global_step < 500:
                         lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
                         for pg in optimizer.param_groups:
                             pg['lr'] = lr_scale * self.learning_rate
 
                     # update params
-                    optimizer.step()
+                    optimizer.step(closure=optimizer_closure)
                     optimizer.zero_grad()
 
         Note: