use the enabled arg in GradScaler

train.py

@@ -173,11 +173,8 @@ if block_size < model.config.block_size:
     model.crop_block_size(block_size)
 model.to(device)
 
-# initialize a GradScaler if data type is float16
-scaler = None
-if dtype == 'float16':
-    print(f"Initializing Gradient Scaler to account for dtype: {dtype}")
-    scaler = torch.cuda.amp.GradScaler()
+# initialize a GradScaler. If enabled=False scaler is a no-op
+scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))
 
 # optimizer
 optimizer = model.configure_optimizers(weight_decay, learning_rate, (beta1, beta2))
@@ -283,17 +280,14 @@ while True:
         with ctx:
             logits, loss = model(X, Y)
         # backward pass, with gradient scaling if training in fp16
-        scaler.scale(loss).backward() if scaler else loss.backward()
+        scaler.scale(loss).backward()
     # clip the gradient
     if grad_clip != 0.0:
-        scaler.unscale_(optimizer) if scaler else None
+        scaler.unscale_(optimizer)
         torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
-    # step the optimizer
-    if scaler:
+    # step the optimizer and scaler if training in fp16
     scaler.step(optimizer)
     scaler.update()
-    else:
-        optimizer.step()
     # flush the gradients as soon as we can, no need for this memory anymore
     optimizer.zero_grad(set_to_none=True)