diff --git a/test/distributed/test_c10d_common.py b/test/distributed/test_c10d_common.py
index 903df26bba9f6..d3cb65f7befb1 100644
--- a/test/distributed/test_c10d_common.py
+++ b/test/distributed/test_c10d_common.py
@@ -31,6 +31,7 @@
 from torch.testing._internal.common_distributed import (
     MultiProcessTestCase,
     skip_if_lt_x_gpu,
+    get_device_count,
 )
 from torch.testing._internal.common_utils import (
     instantiate_parametrized_tests,
@@ -60,17 +61,13 @@
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-def gpus_for_rank(world_size):
+def gpus_for_rank(world_size, backend):
     """Multigpu tests are designed to simulate the multi nodes with multi
     GPUs on each node. Nccl backend requires equal #GPUs in each process.
     On a single node, all visible GPUs are evenly
     divided to subsets, each process only uses a subset.
     """
-    device_count = (
-        torch.xpu.device_count()
-        if torch.xpu.is_available()
-        else torch.cuda.device_count()
-    )
+    device_count = get_device_count(backend)
     visible_devices = list(range(device_count))
     gpus_per_process = device_count // world_size
     gpus_for_rank = []
@@ -833,7 +830,7 @@ def update_parameters(model):
     def _gpu_model_with_ddp_comm_hook(
         self, process_group, hook=None, gradient_as_bucket_view=False, state=None
     ):
-        device_id = gpus_for_rank(self.world_size)[self.rank][0]
+        device_id = gpus_for_rank(self.world_size, process_group.name())[self.rank][0]
         gpu_model = DistributedDataParallel(
             ModuleForDdpCommHook().to(device_id),
             device_ids=[device_id],
@@ -850,7 +847,7 @@ def _gpu_model_with_ddp_comm_hook(
     def _gpu_model_with_builtin_ddp_comm_hook(
         self, process_group, hook=None, gradient_as_bucket_view=False
     ):
-        device_id = gpus_for_rank(self.world_size)[self.rank][0]
+        device_id = gpus_for_rank(self.world_size, process_group.name())[self.rank][0]
         gpu_model = DistributedDataParallel(
             ModuleForDdpCommHook().to(device_id),
             device_ids=[device_id],
diff --git a/torch/testing/_internal/common_distributed.py b/torch/testing/_internal/common_distributed.py
index 9ec38c9ca671c..3e1664690b713 100644
--- a/torch/testing/_internal/common_distributed.py
+++ b/torch/testing/_internal/common_distributed.py
@@ -93,8 +93,9 @@ class DistTestCases:
 
     # Sets showing that something is implemented
     backend_feature = {}
-    backend_feature["gpu"] = {"nccl", "gloo", "ucc"}
+    backend_feature["gpu"] = {"nccl", "gloo", "ucc", "xccl"}
     backend_feature["cuda"] = {"nccl", "gloo", "ucc"}
+    backend_feature["cuda"] = {"xccl"}
     backend_feature["ddp"] = {"nccl", "gloo", "ucc"}
     backend_feature["subgroup"] = {"nccl", "gloo", "ucc"}
     backend_feature["plugin"] = set()
@@ -462,6 +463,15 @@ def compute_sum(fn, world_size: int):
         ]
     ]
 
+# Returns the number of GPUs, currently only for CUDA and XPU.
+def get_device_count(backend: str):
+    assert c10d.is_backend_available(backend)
+    if backend in backend_feature.get("cuda", set()):
+        return torch.cuda.device_count()
+    elif backend in backend_feature.get("xpu", set()):
+        return torch.xpu.device_count()
+    else:
+        raise ValueError(f"Unsupported backend: {backend}")
 
 # HELPER FOR MULTIGPU TESTS
 def init_multigpu_helper(world_size: int, backend: str):
@@ -470,7 +480,7 @@ def init_multigpu_helper(world_size: int, backend: str):
     On a single node, all visible GPUs are evenly
     divided to subsets, each process only uses a subset.
     """
-    nGPUs = torch.xpu.device_count() if torch.xpu.is_available() else torch.cuda.device_count()
+    nGPUs = get_device_count(backend)
     visible_devices = range(nGPUs)
 
     # If rank is less than or equal to number of available GPU's