Allow DeepSpeed models to be initialized with optimizer=None (#469)

Allow DeepSpeed models to be initialized with optimizer=None
Co-authored-by: Shaden Smith <Shaden.Smith@microsoft.com>

deepspeed/runtime/engine.py

@@ -466,10 +466,9 @@ class DeepSpeedEngine(Module):
     # Validate configuration based on command line arguments
     def _do_sanity_check(self):
         if not self.client_optimizer:
+            if self.optimizer_name() is not None:
                 assert self._is_supported_optimizer(self.optimizer_name()), \
                     '{} is not a supported DeepSpeed Optimizer'.format(self.optimizer_name())
-            assert self.client_model_parameters, \
-                'DeepSpeed {} optimizer requires parameters in initialize() call'.format(self.optimizer_name())
 
         if self.optimizer_name() == LAMB_OPTIMIZER:
             assert self.dynamic_loss_scale(), \
@@ -1289,7 +1288,7 @@ class DeepSpeedEngine(Module):
 
         self.load_module_state_dict(state_dict=checkpoint['module'],
                                     strict=load_module_strict)
-        if not self.zero_optimization():
+        if self.optimizer is not None and not self.zero_optimization():
             if self.fp16_enabled():
                 self.optimizer.load_state_dict(
                     checkpoint['optimizer'],

tests/unit/test_config.py

@@ -195,3 +195,34 @@ def test_dist_init_true(tmpdir):
             model.step()
 
     _test_dist_init_true(args=args, model=model, hidden_dim=hidden_dim)
+
+
+def test_init_no_optimizer(tmpdir):
+
+    config_dict = {"train_batch_size": 1, "fp16": {"enabled": True}}
+    config_path = create_config_from_dict(tmpdir, config_dict)
+
+    @distributed_test(world_size=1)
+    def _helper():
+        parser = argparse.ArgumentParser()
+        args = parser.parse_args(args='')
+        args.deepscale_config = config_path
+        args.local_rank = 0
+
+        hidden_dim = 10
+
+        model = SimpleModel(hidden_dim=hidden_dim)
+
+        model, _, _, _ = deepspeed.initialize(args=args, model=model)
+        data_loader = random_dataloader(model=model,
+                                        total_samples=5,
+                                        hidden_dim=hidden_dim,
+                                        device=model.device)
+        for n, batch in enumerate(data_loader):
+            loss = model(batch[0], batch[1])
+            with pytest.raises(AssertionError):
+                model.backward(loss)
+            with pytest.raises(AssertionError):
+                model.step()
+
+    _helper()