[pl_examples] default warmup steps=0 (#5316)

examples/lightning_base.py

@@ -122,12 +122,9 @@ class BaseTransformer(pl.LightningModule):
         else:
             optimizer.step()
         optimizer.zero_grad()
-        self.lr_scheduler.step()
-
-    def get_tqdm_dict(self):
-        avg_loss = getattr(self.trainer, "avg_loss", 0.0)
-        tqdm_dict = {"loss": "{:.3f}".format(avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]}
-        return tqdm_dict
+        self.lr_scheduler.step()  # By default, PL will only step every epoch.
+        lrs = {f"lr_group_{i}": lr for i, lr in enumerate(self.lr_scheduler.get_lr())}
+        self.logger.log_metrics(lrs)
 
     def test_step(self, batch, batch_nb):
         return self.validation_step(batch, batch_nb)
@@ -202,7 +199,7 @@ class BaseTransformer(pl.LightningModule):
         parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
         parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
         parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-        parser.add_argument("--warmup_steps", default=500, type=int, help="Linear warmup over warmup_steps.")
+        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
         parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
         parser.add_argument(
             "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."