Fix checkpointed state for lr_schedulers with step interval

CHANGELOG.md

@@ -260,6 +260,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Fixed
 
 
+- Fixed `lr_scheduler` checkpointed state by calling `update_lr_schedulers` before saving checkpoints ([#7877](https://github.com/PyTorchLightning/pytorch-lightning/pull/7877))
+
+
 - Fixed ambiguous warning when both overfit and train dataloader shuffling are enabled ([#7685](https://github.com/PyTorchLightning/pytorch-lightning/pull/7685))
 
 

pytorch_lightning/loops/fit_loop.py

@@ -219,7 +219,7 @@ def on_advance_end(self) -> None:
         if self.training_loop.batches_seen == 0:
             return
 
-        self.training_loop.update_lr_schedulers('epoch')
+        self.training_loop.update_lr_schedulers('epoch', update_plateau_schedulers=True)
 
         did_train_only = self.trainer.disable_validation or self.trainer.evaluation_loop.skip
         if did_train_only:

pytorch_lightning/loops/training_epoch_loop.py

@@ -115,6 +115,12 @@ def advance(self, dataloader_iter: Iterator, **kwargs: Any) -> None:
         if batch_output.signal == -1:
             raise StopIteration
 
+        # update non-plateau LR schedulers
+        # update epoch-interval ones only when we are at the end of training epoch
+        self.update_lr_schedulers('step', update_plateau_schedulers=False)
+        if self._num_training_batches_reached(is_last):
+            self.update_lr_schedulers('epoch', update_plateau_schedulers=False)
+
         batch_end_outputs = [opt_idx_out for opt_idx_out in batch_output.training_step_output if len(opt_idx_out)]
         processed_batch_end_outputs = self._prepare_outputs(batch_end_outputs, batch_mode=True)
 
@@ -153,8 +159,8 @@ def on_advance_end(self):
         # -----------------------------------------
         self.save_loggers_on_train_batch_end()
 
-        # update LR schedulers
-        self.update_lr_schedulers('step')
+        # update plateau LR scheduler after metrics are logged
+        self.update_lr_schedulers('step', update_plateau_schedulers=True)
         self.trainer.checkpoint_connector.has_trained = True
 
         self.total_batch_idx += 1
@@ -351,15 +357,13 @@ def _prepare_outputs(
             processed_outputs = processed_outputs[0]
         return processed_outputs
 
-    def update_lr_schedulers(self, interval: str) -> None:
+    def update_lr_schedulers(self, interval: str, update_plateau_schedulers: bool) -> None:
         """updates the lr schedulers based on the given interval"""
-        if interval == "step":
-            finished_accumulation = self.batch_loop._accumulated_batches_reached()
-            finished_epoch = self._num_training_batches_reached()
-            if not finished_accumulation and not finished_epoch:
-                return
+        if interval == "step" and self.batch_loop.should_accumulate():
+            return
         self.trainer.optimizer_connector.update_learning_rates(
             interval=interval,
+            update_plateau_schedulers=update_plateau_schedulers,
             opt_indices=[opt_idx for opt_idx, _ in self.batch_loop.get_active_optimizers(self.total_batch_idx)],
         )
 

pytorch_lightning/trainer/connectors/optimizer_connector.py

@@ -29,11 +29,17 @@ def on_trainer_init(self) -> None:
         self.trainer.optimizers = []
         self.trainer.optimizer_frequencies = []
 
-    def update_learning_rates(self, interval: str, opt_indices: Optional[List[int]] = None) -> None:
+    def update_learning_rates(
+        self, interval: str, update_plateau_schedulers: bool, opt_indices: Optional[List[int]] = None
+    ) -> None:
         """Update learning rates.
 
         Args:
             interval: either 'epoch' or 'step'.
+            update_plateau_schedulers: control whether ``ReduceLROnPlateau`` or non-plateau schedulers get updated.
+                This is used so non-plateau schedulers can be updated before running validation. Checkpoints are
+                commonly saved during validation, however, on-plateau schedulers might monitor a validation metric
+                so they have to be updated separately.
             opt_indices: indices of the optimizers to update.
         """
         if not self.trainer.lr_schedulers or not self.trainer.lightning_module.automatic_optimization:
@@ -46,6 +52,9 @@ def update_learning_rates(self, interval: str, opt_indices: Optional[List[int]]
             if isinstance(lr_scheduler['opt_idx'], int) and lr_scheduler['opt_idx'] not in opt_indices:
                 continue
 
+            if update_plateau_schedulers ^ lr_scheduler["reduce_on_plateau"]:
+                continue
+
             current_idx = self.trainer.train_loop.batch_idx if interval == 'step' else self.trainer.current_epoch
             current_idx += 1  # account for both batch and epoch starts from 0
             # Take step if call to update_learning_rates matches the interval key and

tests/callbacks/test_finetuning_callback.py

@@ -27,7 +27,8 @@
 
 class TestBackboneFinetuningCallback(BackboneFinetuning):
 
-    def on_train_epoch_end(self, trainer, pl_module):
+    def on_train_epoch_start(self, trainer, pl_module):
+        super().on_train_epoch_start(trainer, pl_module)
         epoch = trainer.current_epoch
         if self.unfreeze_backbone_at_epoch <= epoch:
             optimizer = trainer.optimizers[0]

tests/checkpointing/test_model_checkpoint.py

@@ -162,10 +162,9 @@ def on_validation_epoch_end(self):
         if not reduce_lr_on_plateau:
             actual_step_count = chk['lr_schedulers'][0]['_step_count']
             actual_lr = chk['lr_schedulers'][0]['_last_lr'][0]
-            # if validation_step_none, the checkpoint gets saved after the learning rate update
-            # so we need to increase the count by one
-            assert actual_step_count == epoch + 1 + validation_step_none
-            assert actual_lr == lr * gamma**(epoch + validation_step_none)
+            # checkpoint is saved after updating lr_scheduler states
+            assert actual_step_count == epoch + 2  # step_count starts at 1
+            assert actual_lr == lr * gamma**(epoch + 1)
 
         assert lr_scheduler_debug[epoch]['monitor_val'] == (score if reduce_lr_on_plateau else None)
         assert lr_scheduler_debug[epoch]['monitor_key'] == (monitor if reduce_lr_on_plateau else None)
@@ -262,6 +261,11 @@ def _make_assertions(epoch, ix, version=''):
         global_ix = ix + per_epoch_val_checks * epoch
         duplicated = bool(version)
 
+        # checkpoint saved at the end of training epoch will have updated lr_scheduler states
+        epoch_end_checkpoint = duplicated
+        if epoch_aligned:
+            epoch_end_checkpoint = ix == (per_epoch_val_checks - 1)
+
         score = model.scores[global_ix]
         expected_score = getattr(model, f'{monitor}s')[global_ix].mean().item()
         expected_filename = f'{monitor}={score:.4f}-epoch={epoch}{version}.ckpt'
@@ -281,8 +285,8 @@ def _make_assertions(epoch, ix, version=''):
         if not reduce_lr_on_plateau:
             actual_step_count = chk['lr_schedulers'][0]['_step_count']
             actual_lr = chk['lr_schedulers'][0]['_last_lr'][0]
-            assert actual_step_count == epoch + 1 + duplicated
-            assert actual_lr == lr * gamma**(epoch + duplicated)
+            assert actual_step_count == epoch + 1 + epoch_end_checkpoint
+            assert actual_lr == lr * gamma**(epoch + epoch_end_checkpoint)
 
         return score
 

tests/trainer/optimization/test_optimizers.py

@@ -18,6 +18,7 @@
 from torch import optim
 
 from pytorch_lightning import Callback, Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
 from tests.helpers.boring_model import BoringModel
@@ -620,3 +621,87 @@ def test_lr_scheduler_epoch_step_frequency(mocked_sched, check_val_every_n_epoch
     )
     trainer.fit(model)
     assert mocked_sched.call_count == expected_steps
+
+
+@pytest.mark.parametrize('every_n_train_steps, epoch_interval', [(None, True), (2, False), (2, True)])
+def test_lr_scheduler_state_updated_before_saving(tmpdir, every_n_train_steps, epoch_interval):
+    batches = 2
+    max_epochs = 1
+    lr, gamma = 1, 10
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
+        logger=False,
+        max_epochs=max_epochs,
+        limit_train_batches=batches,
+        limit_val_batches=1,
+        callbacks=[ModelCheckpoint(dirpath=tmpdir, every_n_train_steps=every_n_train_steps)]
+    )
+
+    class TestModel(BoringModel):
+
+        def configure_optimizers(self):
+            optimizer = torch.optim.SGD(self.parameters(), lr=lr)
+            lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=gamma)
+            lr_dict = {'scheduler': lr_scheduler}
+            if not epoch_interval:
+                lr_dict['interval'] = 'step'
+            return [optimizer], [lr_dict]
+
+        def on_save_checkpoint(self, checkpoint):
+            lr_dict = checkpoint['lr_schedulers'][0]
+            # 2 batches ran. since the lr_dict interval is `step`, the step count should be 2
+            assert self.trainer.global_step + 1 == batches  # the global step hasn't been increased yet
+            compare_to = max_epochs if epoch_interval else batches
+            assert lr_dict['_step_count'] - 1 == compare_to  # step count starts at 1
+            assert lr_dict['_last_lr'] == [lr * gamma**compare_to]
+            self.on_save_checkpoint_called = True
+
+    model = TestModel()
+    trainer.fit(model)
+    assert model.on_save_checkpoint_called
+
+
+def test_plateau_scheduler_lr_step_interval_updated_after_saving(tmpdir):
+    batches = 4
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
+        logger=False,
+        max_epochs=1,
+        limit_train_batches=batches,
+        limit_val_batches=1,
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)]
+    )
+
+    class TestModel(BoringModel):
+
+        def training_step(self, batch, batch_idx, optimizer_idx):
+            self.log("foo", batch_idx)
+            return super().training_step(batch, batch_idx)
+
+        def configure_optimizers(self):
+            optimizer_1 = torch.optim.Adam(self.parameters())
+            optimizer_2 = torch.optim.Adam(self.parameters())
+
+            lr_scheduler1 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_1)
+            lr_dict_1 = {'scheduler': lr_scheduler1, 'interval': 'step', 'monitor': 'foo'}
+
+            lr_scheduler2 = torch.optim.lr_scheduler.StepLR(optimizer_2, step_size=1)
+            lr_dict_2 = {'scheduler': lr_scheduler2, 'interval': 'step'}
+            return [optimizer_1, optimizer_2], [lr_dict_1, lr_dict_2]
+
+        def on_save_checkpoint(self, checkpoint):
+            lr_dict_1 = checkpoint['lr_schedulers'][0]
+            # since plateau schedulers are updated after saving checkpoint, last_epoch should be 3
+            assert lr_dict_1['last_epoch'] == batches - 1  # last epoch starts at 0
+
+            lr_dict_2 = checkpoint['lr_schedulers'][1]
+            assert lr_dict_2['_step_count'] - 1 == batches  # step count starts at 1
+
+            self.on_save_checkpoint_called = True
+
+    model = TestModel()
+    model.training_epoch_end = None
+    trainer.fit(model)
+    assert model.on_save_checkpoint_called