[bugfix] DeepSpeed with no schedulers (#8580)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

CHANGELOG.md

@@ -236,6 +236,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `BackboneFinetuning` restoration ([#8501](https://github.com/PyTorchLightning/pytorch-lightning/pull/8501))
 - Fixed `lr_scheduler` with metric (e.g. `torch.optim.lr_scheduler.ReduceLROnPlateau`) when using `automatic_optimization = False` ([#7643](https://github.com/PyTorchLightning/pytorch-lightning/pull/7643))
 
+- Fixed `DeepSpeed` breaking with no schedulers ([#8580](https://github.com/PyTorchLightning/pytorch-lightning/pull/8580))
+
+
 
 ## [1.3.8] - 2021-07-01
 

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -397,7 +397,7 @@ def _init_optimizers(self) -> Tuple[Optimizer, Optional[Union[LRSchedulerTypeTup
             )
         return (
             optimizers[0],
-            schedulers[0] if schedulers else None,
+            schedulers[0] if schedulers else _get_default_scheduler_config(),
             optimizer_frequencies[0] if optimizer_frequencies else None,
         )
 
@@ -414,6 +414,7 @@ def _initialize_deepspeed_train(self, model):
                 "Using `configure_optimizers` to define optimizer and scheduler."
             )
             optimizer, lr_scheduler, _ = self._init_optimizers()
+
         scheduler = lr_scheduler["scheduler"]
 
         model_parameters = filter(lambda p: p.requires_grad, self.model.parameters())
@@ -430,8 +431,9 @@ def _initialize_deepspeed_train(self, model):
 
         # although we set these here, deepspeed manages the specific optimizer logic
         self.lightning_module.trainer.optimizers = [deepspeed_optimizer]
-        lr_scheduler["scheduler"] = deepspeed_scheduler
-        self.lightning_module.trainer.lr_schedulers = [lr_scheduler]
+        if deepspeed_scheduler is not None:
+            lr_scheduler["scheduler"] = deepspeed_scheduler
+            self.lightning_module.trainer.lr_schedulers = [lr_scheduler]
         self.model = model
 
     @contextlib.contextmanager

tests/plugins/test_deepspeed_plugin.py

@@ -33,6 +33,11 @@ def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         self.configure_sharded_model()
 
 
+class ModelParallelBoringModelNoSchedulers(ModelParallelBoringModel):
+    def configure_optimizers(self):
+        return torch.optim.SGD(self.layer.parameters(), lr=0.1)
+
+
 class ModelParallelBoringModelManualOptim(BoringModel):
     def __init__(self):
         super().__init__()
@@ -687,3 +692,17 @@ def _assert_save_model_is_equal(model, tmpdir, trainer, cls=BoringModel):
         # Assert model parameters are identical after loading
         for orig_param, trained_model_param in zip(model.parameters(), saved_model.parameters()):
             assert torch.equal(orig_param, trained_model_param)
+
+
+@RunIf(min_gpus=2, deepspeed=True, special=True)
+def test_deepspeed_multigpu_no_schedulers(tmpdir):
+    """
+    Test to ensure ZeRO Stage 3 works with a parallel model and no schedulers.
+    """
+    model = ModelParallelBoringModelNoSchedulers()
+    trainer = Trainer(
+        default_root_dir=tmpdir, plugins=[DeepSpeedPlugin(stage=3)], gpus=2, fast_dev_run=True, precision=16
+    )
+    trainer.fit(model)
+
+    _assert_save_model_is_equal(model, tmpdir, trainer, cls=ModelParallelBoringModelNoSchedulers)