Init distributed torch only if needed

deepspeed/__init__.py

@@ -32,7 +32,7 @@ def initialize(args,
                training_data=None,
                lr_scheduler=None,
                mpu=None,
-               dist_init_required=True,
+               dist_init_required=None,
                collate_fn=None):
     r"""Initialize the DeepSpeed Engine.
 
@@ -56,7 +56,8 @@ def initialize(args,
         mpu: Optional: A model parallelism unit object that implements
             get_{model,data}_parallel_{rank,group,world_size}()
 
-        dist_init_required: Optional: Initializes torch.distributed
+        dist_init_required: Optional: None will auto-initialize torch.distributed if needed,
+            otherwise the user can force it to be initialized or not via boolean.
 
         collate_fn: Optional: Merges a list of samples to form a
             mini-batch of Tensor(s).  Used when using batched loading from a

deepspeed/pt/deepspeed_light.py

@@ -97,7 +97,7 @@ def __init__(self,
                  training_data=None,
                  lr_scheduler=None,
                  mpu=None,
-                 dist_init_required=True,
+                 dist_init_required=None,
                  collate_fn=None):
         super(DeepSpeedLight, self).__init__()
 
@@ -119,8 +119,19 @@ def __init__(self,
         self.gradient_average = True
         self.warn_unscaled_loss = True
 
+        if dist_init_required is None:
+            dist_init_required = not dist.is_initialized()
+
+        self.dist_backend = "nccl"
         if dist_init_required:
-            dist.init_process_group(backend="nccl")
+            if not dist.is_initialized():
+                logging.info("Initializing torch distributed with backend: {}".format(
+                    self.dist_backend))
+                dist.init_process_group(backend=self.dist_backend)
+            else:
+                logging.warning(
+                    "Was given dist_init_required=True but detected that torch"
+                    "distributed was already initialized, cannot initialize twice.")
 
         self._do_args_sanity_check(args)
         self._configure_with_arguments(args, mpu)

tests/unit/test_config.py

@@ -142,8 +142,7 @@ def test_deprecated_deepscale_config(tmpdir):
     def _test_deprecated_deepscale_config(args, model, hidden_dim):
         model, _, _,_ = deepspeed.initialize(args=args,
                                              model=model,
-                                             model_parameters=model.parameters(),
-                                             dist_init_required=False)
+                                             model_parameters=model.parameters())
         data_loader = random_dataloader(model=model,
                                         total_samples=5,
                                         hidden_dim=hidden_dim,
@@ -154,3 +153,45 @@ def _test_deprecated_deepscale_config(args, model, hidden_dim):
             model.step()
 
     _test_deprecated_deepscale_config(args=args, model=model, hidden_dim=hidden_dim)
+
+
+def test_dist_init_true(tmpdir):
+    config_dict = {
+        "train_batch_size": 1,
+        "optimizer": {
+            "type": "Adam",
+            "params": {
+                "lr": 0.00015
+            }
+        },
+        "fp16": {
+            "enabled": True
+        }
+    }
+
+    config_path = create_config_from_dict(tmpdir, config_dict)
+    parser = argparse.ArgumentParser()
+    args = parser.parse_args(args='')
+    args.deepscale_config = config_path
+    args.local_rank = 0
+
+    hidden_dim = 10
+
+    model = SimpleModel(hidden_dim)
+
+    @distributed_test(world_size=[1])
+    def _test_dist_init_true(args, model, hidden_dim):
+        model, _, _,_ = deepspeed.initialize(args=args,
+                                             model=model,
+                                             model_parameters=model.parameters(),
+                                             dist_init_required=True)
+        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])
+            model.backward(loss)
+            model.step()
+
+    _test_dist_init_true(args=args, model=model, hidden_dim=hidden_dim)

tests/unit/test_fp16.py

@@ -32,8 +32,7 @@ def test_lamb_fp16_basic(tmpdir):
     def _test_lamb_fp16_basic(args, model, hidden_dim):
         model, _, _,_ = deepspeed.initialize(args=args,
                                              model=model,
-                                             model_parameters=model.parameters(),
-                                             dist_init_required=False)
+                                             model_parameters=model.parameters())
         data_loader = random_dataloader(model=model,
                                         total_samples=50,
                                         hidden_dim=hidden_dim,
@@ -70,8 +69,7 @@ def test_lamb_fp16_empty_grad(tmpdir):
     def _test_lamb_fp16_empty_grad(args, model, hidden_dim):
         model, _, _,_ = deepspeed.initialize(args=args,
                                              model=model,
-                                             model_parameters=model.parameters(),
-                                             dist_init_required=False)
+                                             model_parameters=model.parameters())
         data_loader = random_dataloader(model=model,
                                         total_samples=50,
                                         hidden_dim=hidden_dim,
@@ -102,8 +100,7 @@ def _test_adamw_fp16_basic(args, model, hidden_dim):
         optimizer = torch.optim.AdamW(params=model.parameters())
         model, _, _,_ = deepspeed.initialize(args=args,
                                              model=model,
-                                             optimizer=optimizer,
-                                             dist_init_required=False)
+                                             optimizer=optimizer)
         data_loader = random_dataloader(model=model,
                                         total_samples=50,
                                         hidden_dim=hidden_dim,
@@ -134,8 +131,7 @@ def _test_adamw_fp16_empty_grad(args, model, hidden_dim):
         optimizer = torch.optim.AdamW(params=model.parameters())
         model, _, _,_ = deepspeed.initialize(args=args,
                                              model=model,
-                                             optimizer=optimizer,
-                                             dist_init_required=False)
+                                             optimizer=optimizer)
         data_loader = random_dataloader(model=model,
                                         total_samples=50,
                                         hidden_dim=hidden_dim,