[doc] Update the order of zero_grad and backward

docs/source/common/lightning_module.rst

@@ -178,12 +178,14 @@ Under the hood, Lightning does the following (pseudocode):
         loss = training_step(batch)
         losses.append(loss.detach())
 
+        # clear gradients
+        optimizer.zero_grad()
+
         # backward
         loss.backward()
 
-        # apply and clear grads
+        # update parameters
         optimizer.step()
-        optimizer.zero_grad()
 
 
 Training epoch-level metrics
@@ -212,12 +214,14 @@ Here's the pseudocode of what it does under the hood:
         # forward
         out = training_step(val_batch)
 
+        # clear gradients
+        optimizer.zero_grad()
+
         # backward
         loss.backward()
 
-        # apply and clear grads
+        # update parameters
         optimizer.step()
-        optimizer.zero_grad()
 
     epoch_metric = torch.mean(torch.stack([x['train_loss'] for x in outs]))
 
@@ -247,12 +251,14 @@ The matching pseudocode is:
         # forward
         out = training_step(val_batch)
 
+        # clear gradients
+        optimizer.zero_grad()
+
         # backward
         loss.backward()
 
-        # apply and clear grads
+        # update parameters
         optimizer.step()
-        optimizer.zero_grad()
 
     training_epoch_end(outs)
 
@@ -946,9 +952,9 @@ When set to ``False``, Lightning does not automate the optimization process. Thi
         opt = self.optimizers(use_pl_optimizer=True)
 
         loss = ...
+        opt.zero_grad()
         self.manual_backward(loss)
         opt.step()
-        opt.zero_grad()
 
 This is recommended only if using 2+ optimizers AND if you know how to perform the optimization procedure properly. Note that automatic optimization can still be used with multiple optimizers by relying on the ``optimizer_idx`` parameter. Manual optimization is most useful for research topics like reinforcement learning, sparse coding, and GAN research.
 
@@ -1048,11 +1054,13 @@ This is the pseudocode to describe how all the hooks are called during a call to
 
             loss = out.loss
 
+            on_before_zero_grad()
+            optimizer_zero_grad()
+
             backward()
             on_after_backward()
+
             optimizer_step()
-            on_before_zero_grad()
-            optimizer_zero_grad()
 
             on_train_batch_end(out)
 

docs/source/common/trainer.rst

@@ -75,12 +75,14 @@ Here's the pseudocode for what the trainer does under the hood (showing the trai
         # train step
         loss = training_step(batch)
 
+        # clear gradients
+        optimizer.zero_grad()
+
         # backward
         loss.backward()
 
-        # apply and clear grads
+        # update parameters
         optimizer.step()
-        optimizer.zero_grad()
 
         losses.append(loss)