small nit fixes

ghstack-source-id: 37a3f34
Pull Request resolved: #1694

Author: bobrenjc93

torchtitan/train.py

@@ -451,7 +451,7 @@ def forward_backward_step(
                 with self.maybe_enable_amp:
                     pred = model_parts[0](inputs, eos_id=self.tokenizer.eos_id)
                     loss = self.loss_fn(pred, labels)
-                # need to free to before bwd to avoid peaking memory
+                # need to free pred before bwd to avoid peaking memory
                 del pred
                 loss.backward()
 
@@ -471,7 +471,7 @@ def train_step(
         accumulated_losses = []
         # If data runs out during gradient accumulation, that
         # entire step will not be executed.
-        for microbatch in range(self.gradient_accumulation_steps):
+        for _microbatch in range(self.gradient_accumulation_steps):
             input_dict, labels = next(data_iterator)
             loss = self.forward_backward_step(input_dict, labels)
             accumulated_losses.append(loss.detach())