diff --git a/test/test_distributed.py b/test/test_distributed.py
index 2665459e4191..e25da8591a73 100644
--- a/test/test_distributed.py
+++ b/test/test_distributed.py
@@ -20,6 +20,15 @@ from common_utils import TestCase, run_tests
 from torch._utils_internal import TEST_MASTER_ADDR as MASTER_ADDR
 from torch._utils_internal import TEST_MASTER_PORT as MASTER_PORT
 
+try:
+    import torchvision
+    HAS_TORCHVISION = True
+except ImportError:
+    HAS_TORCHVISION = False
+
+
+skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")
+
 BACKEND = os.environ["BACKEND"]
 TEMP_DIR = os.environ["TEMP_DIR"]
 INIT_METHOD = os.getenv("INIT_METHOD", "env://")
@@ -1528,6 +1537,19 @@ class _DistTestBase(object):
         gpus = list(map(lambda i: torch.device('cuda:' + str(i)), gpus))
         self._test_DistributedDataParallel_SyncBatchNorm(gpu_subset=gpus, rank=rank, output_device=torch.device('cuda'))
 
+    @skipIfNoTorchVision
+    def test_SyncBatchNorm_process_group(self):
+        # When adopting `convert_sync_batchnorm` to convert a `nn.modules`,
+        # it need to recursively pass the `process_group` in the module when the `SyncBatchNorm`
+        # is nested in a sub-module or sub-sub-module (e.g. resnet50 in torchvision.models).
+
+        process_ids = 0
+        process_group = torch.distributed.new_group([process_ids])
+        res50_model = torchvision.models.resnet50()
+        res50_model_sync = nn.SyncBatchNorm.convert_sync_batchnorm(copy.deepcopy(res50_model), process_group)
+        process_group_sync = res50_model_sync.layer1[0].bn1.process_group
+        self.assertEqual(process_group_sync, process_group)
+
 if BACKEND == "gloo" or BACKEND == "nccl":
     WORLD_SIZE = os.environ["WORLD_SIZE"]
 
diff --git a/torch/nn/modules/batchnorm.py b/torch/nn/modules/batchnorm.py
index cf360bfe90d9..0428ffeeeac3 100644
--- a/torch/nn/modules/batchnorm.py
+++ b/torch/nn/modules/batchnorm.py
@@ -505,6 +505,6 @@ class SyncBatchNorm(_BatchNorm):
             module_output.running_var = module.running_var
             module_output.num_batches_tracked = module.num_batches_tracked
         for name, child in module.named_children():
-            module_output.add_module(name, cls.convert_sync_batchnorm(child))
+            module_output.add_module(name, cls.convert_sync_batchnorm(child, process_group))
         del module
         return module_output