Ignore last batches when calculating final train loss

Visual change. This only changes what the trainer reports as the final
training loss.

Not quite sure if the value before was accurate anyway, since gradient
accumulation would not let the optimizer step every batch anyway.

For a big enough dataset, this should not have any impact at all.

The final loss value will be reported based on the last calculation of
the loss, correctly taking into consideration gradient accumulation as
well.

optimus/trainer.py

@@ -155,10 +155,6 @@ class Trainer():
                                         f"~{self.ms_per_batch:.2f} ms/batch | " \
                                         f" lr: {lr:.7f}"
 
-        # account for last batches when computing average train loss
-        self.train_loss = total_loss / (len(self.dl.train) % est_interval - 1)
-        self.train_ppl = math.exp(self.train_loss)
-
         pb.on_iter_end()
 
     def _do_epoch_validate(self):