Fix val_loop run on restart

CHANGELOG.md

@@ -454,6 +454,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Disbled sampler replacement when using `IterableDataset` ([#11507](https://github.com/PyTorchLightning/pytorch-lightning/pull/11507))
 
 
+- Fixed an issue to avoid validation loop run on restart ([#11552](https://github.com/PyTorchLightning/pytorch-lightning/pull/11552))
+
+
 ## [1.5.8] - 2022-01-05
 
 ### Fixed

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -483,6 +483,11 @@ def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool) -> bool:
 
         # TODO(@awaelchli): let training/eval loop handle logic around limit_*_batches and val_check_batch
         is_val_check_batch = is_last_batch
+
+        # while restarting with no fault-tolerant, batch_progress.current.ready is -1
+        if batch_idx == -1:
+            return False
+
         if isinstance(self.trainer.limit_train_batches, int) and is_infinite_dataset:
             is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0
         elif self.trainer.val_check_batch != float("inf"):

tests/loops/epoch/test_training_epoch_loop.py

@@ -14,6 +14,8 @@
 import pytest
 
 from pytorch_lightning.loops import TrainingEpochLoop
+from pytorch_lightning.trainer.trainer import Trainer
+from tests.helpers.boring_model import BoringModel
 
 _out00 = {"loss": 0.0}
 _out01 = {"loss": 0.1}
@@ -141,3 +143,17 @@ def test_prepare_outputs_training_batch_end_manual(batch_end_outputs, expected):
         num_optimizers=-1,  # does not matter for manual optimization
     )
     assert prepared == expected
+
+
+def test_no_val_on_train_epoch_loop_restart(tmpdir):
+    """Test that training validation loop doesn't get triggered at the beginning of a restart."""
+    trainer = Trainer()
+    model = BoringModel()
+    trainer.strategy.connect(model)
+    trainer._data_connector.attach_data(model)
+    trainer.reset_train_dataloader()
+    training_epoch_loop = trainer.fit_loop.epoch_loop
+    training_epoch_loop.restarting = True
+    assert not training_epoch_loop._should_check_val_fx(
+        training_epoch_loop.batch_idx, training_epoch_loop.batch_progress.is_last_batch
+    )