Fixed configure optimizer from dict without "scheduler" key (Lightning-AI#1443)

* `configure_optimizer` from dict with only "optimizer" key. bug fixed

* autopep8

* pep8speaks suggested fixes

* CHANGELOG.md upd

Author: alexeykarnachev

CHANGELOG.md

@@ -12,6 +12,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed optimizer configuration when `configure_optimizers` returns dict without `lr_scheduler` ([#1443](https://github.com/PyTorchLightning/pytorch-lightning/pull/1443))
 - Fixed default `DistributedSampler` for DDP training ([#1425](https://github.com/PyTorchLightning/pytorch-lightning/pull/1425))
 - Fixed workers warning not on windows ([#1430](https://github.com/PyTorchLightning/pytorch-lightning/pull/1430))
 - Fixed returning tuple from `run_training_batch` ([#1431](https://github.com/PyTorchLightning/pytorch-lightning/pull/1431))

pytorch_lightning/trainer/optimizers.py

@@ -39,6 +39,8 @@ def init_optimizers(
             lr_scheduler = optim_conf.get("lr_scheduler", [])
             if lr_scheduler:
                 lr_schedulers = self.configure_schedulers([lr_scheduler])
+            else:
+                lr_schedulers = []
             return [optimizer], lr_schedulers, []
 
         # multiple dictionaries

pytorch_lightning/trainer/supporters.py

@@ -20,6 +20,7 @@ class TensorRunningAccum(object):
         >>> accum.last(), accum.mean(), accum.min(), accum.max()
         (tensor(12.), tensor(10.), tensor(8.), tensor(12.))
     """
+
     def __init__(self, window_length: int):
         self.window_length = window_length
         self.memory = torch.Tensor(self.window_length)

pytorch_lightning/trainer/trainer.py

@@ -554,7 +554,8 @@ def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
                                             if at[0] not in depr_arg_names):
             for allowed_type in (at for at in allowed_types if at in arg_types):
                 if isinstance(allowed_type, bool):
-                    allowed_type = lambda x: bool(distutils.util.strtobool(x))
+                    def allowed_type(x):
+                        return bool(distutils.util.strtobool(x))
                 parser.add_argument(
                     f'--{arg}',
                     default=arg_default,

tests/trainer/test_optimizers.py

@@ -275,3 +275,26 @@ class CurrentTestModel(
 
     # verify training completed
     assert result == 1
+
+
+def test_configure_optimizer_from_dict(tmpdir):
+    """Tests if `configure_optimizer` method could return a dictionary with
+    `optimizer` field only.
+    """
+
+    class CurrentTestModel(LightTrainDataloader, TestModelBase):
+        def configure_optimizers(self):
+            config = {
+                'optimizer': torch.optim.SGD(params=self.parameters(), lr=1e-03)
+            }
+            return config
+
+    hparams = tutils.get_default_hparams()
+    model = CurrentTestModel(hparams)
+
+    trainer_options = dict(default_save_path=tmpdir, max_epochs=1)
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+    assert result == 1