Disable lr_scheduler.step() in manual optimization

CHANGELOG.md

@@ -102,6 +102,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed `PyTorchProfiler` to use `torch.autograd.profiler.record_function` to record functions ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))
 
 
+- Disabled `lr_schdeulder.step()` in manual optimization  ([#6825](https://github.com/PyTorchLightning/pytorch-lightning/pull/6825))
+
+
 ### Deprecated
 
 - `period` has been deprecated in favor of `every_n_val_epochs` in the `ModelCheckpoint` callback ([#6146](https://github.com/PyTorchLightning/pytorch-lightning/pull/6146))

pytorch_lightning/trainer/connectors/optimizer_connector.py

@@ -32,7 +32,7 @@ def update_learning_rates(self, interval: str, monitor_metrics=None):
             interval: either 'epoch' or 'step'.
             monitor_metrics: dict of possible values to monitor
         """
-        if not self.trainer.lr_schedulers:
+        if not self.trainer.lr_schedulers or not self.trainer.train_loop.automatic_optimization:
             return
 
         for scheduler_idx, lr_scheduler in enumerate(self.trainer.lr_schedulers):

tests/trainer/optimization/test_manual_optimization.py

@@ -1147,3 +1147,41 @@ def dis_closure():
 @RunIf(min_gpus=2, special=True)
 def test_step_with_optimizer_closure_with_different_frequencies_ddp_with_toggle_model(tmpdir):
     train_manual_optimization(tmpdir, "ddp", model_cls=TestManualOptimizationDDPModelToggleModel)
+
+
+def test_lr_scheduler_step_not_called(tmpdir):
+    """
+    Test `lr_scheduler.step()` is not called in manual optimization.
+    """
+    class TestModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.automatic_optimization = False
+
+        def training_step(self, batch, batch_idx):
+            opt = self.optimizers()
+
+            output = self(batch)
+            loss = self.loss(batch, output)
+
+            opt.zero_grad()
+            self.manual_backward(loss)
+            opt.step()
+
+    model = TestModel()
+    model.training_step_end = None
+    model.training_epoch_end = None
+
+    trainer = Trainer(
+        max_epochs=1,
+        default_root_dir=tmpdir,
+        fast_dev_run=2,
+    )
+
+    with patch("torch.optim.lr_scheduler.StepLR.step") as lr_step:
+        trainer.fit(model)
+
+    # If a lr scheduler inherits `torch.optim.lr_scheduler._LRScheduler`,
+    # `.step()` is called once during its instantiation.
+    # Thus, the call count should be 1.
+    assert lr_step.call_count == 1