Support initialization with dict configuration

deepspeed/runtime/engine.py

@@ -435,9 +435,9 @@ def _set_distributed_vars(self):
     # Configure based on command line arguments
     def _configure_with_arguments(self, args, mpu):
         self.local_rank = args.local_rank if hasattr(args, 'local_rank') else 0
-        self._config = DeepSpeedConfig(args.deepspeed_config,
-                                       mpu,
-                                       param_dict=self.config_params)
+        config_file = args.deepspeed_config if hasattr(args,
+                                                       'deepspeed_config') else None
+        self._config = DeepSpeedConfig(config_file, mpu, param_dict=self.config_params)
 
     # Validate command line arguments
     def _do_args_sanity_check(self, args):

tests/unit/simple_model.py

@@ -161,16 +161,21 @@ def create_config_from_dict(tmpdir, config_dict):
     return config_path
 
 
-def args_from_dict(tmpdir, config_dict):
-    config_path = create_config_from_dict(tmpdir, config_dict)
+def create_deepspeed_args():
     parser = argparse.ArgumentParser()
     args = parser.parse_args(args='')
     args.deepspeed = True
-    args.deepspeed_config = config_path
     if torch.distributed.is_initialized():
         # We assume up to one full node executing unit tests
         assert torch.distributed.get_world_size() <= torch.cuda.device_count()
         args.local_rank = torch.distributed.get_rank()
     else:
         args.local_rank = 0
     return args
+
+
+def args_from_dict(tmpdir, config_dict):
+    args = create_deepspeed_args()
+    config_path = create_config_from_dict(tmpdir, config_dict)
+    args.deepspeed_config = config_path
+    return args

tests/unit/test_fp16.py

@@ -6,7 +6,7 @@
 import os
 from deepspeed.ops.adam import FusedAdam
 from common import distributed_test
-from simple_model import SimpleModel, SimpleOptimizer, random_dataloader, args_from_dict
+from simple_model import SimpleModel, SimpleOptimizer, random_dataloader, args_from_dict, create_deepspeed_args
 
 try:
     from apex import amp
@@ -194,6 +194,41 @@ def _test_adamw_fp16_basic(args, model, hidden_dim):
     _test_adamw_fp16_basic(args=args, model=model, hidden_dim=hidden_dim)
 
 
+def test_dict_config_adamw_fp16_basic():
+    config_dict = {
+        "train_batch_size": 1,
+        "steps_per_print": 1,
+        "fp16": {
+            "enabled": True
+        }
+    }
+    args = create_deepspeed_args()
+    hidden_dim = 10
+
+    model = SimpleModel(hidden_dim, empty_grad=False)
+
+    @distributed_test(world_size=[1])
+    def _test_adamw_fp16_basic(args, model, hidden_dim, config_dict):
+        optimizer = torch.optim.AdamW(params=model.parameters())
+        model, _, _,_ = deepspeed.initialize(args=args,
+                                             model=model,
+                                             optimizer=optimizer,
+                                             config_params=config_dict)
+        data_loader = random_dataloader(model=model,
+                                        total_samples=50,
+                                        hidden_dim=hidden_dim,
+                                        device=model.device)
+        for n, batch in enumerate(data_loader):
+            loss = model(batch[0], batch[1])
+            model.backward(loss)
+            model.step()
+
+    _test_adamw_fp16_basic(args=args,
+                           model=model,
+                           hidden_dim=hidden_dim,
+                           config_dict=config_dict)
+
+
 def test_adamw_fp16_empty_grad(tmpdir):
     config_dict = {
         "train_batch_size": 1,