diff --git a/examples/grouped_gemm.py b/examples/grouped_gemm.py
index d89623844..0a510cc4b 100644
--- a/examples/grouped_gemm.py
+++ b/examples/grouped_gemm.py
@@ -118,7 +118,13 @@ def grouped_gemm_jagged_persistent(
     """
     # Set worker count to match GPU streaming multiprocessor count
     device = A_packed.device
-    num_workers = torch.cuda.get_device_properties(device).multi_processor_count  # type: ignore[arg-type]
+    if device.type == "xpu":
+        # TODO(EikanWang): gpu_subslice_count is an out-of-date term. we will update it to XeCore number.
+        num_workers = torch.xpu.get_device_properties(device.index).gpu_subslice_count
+    else:
+        num_workers = torch.cuda.get_device_properties(
+            device.index
+        ).multi_processor_count
 
     # Define tunable block sizes for M, N dimensions (auto-tuned at runtime)
     BLOCK_M = hl.register_block_size(32, 128)
@@ -280,7 +286,7 @@ def _reference_grouped_gemm(
 # ---------------------------
 def main() -> None:
     torch.manual_seed(0)  # Ensure reproducible test results
-    device = "cuda"
+    device = "xpu" if torch.xpu.is_available() else "cuda"
     dtype = torch.bfloat16
     G = 4  # Number of groups to test
     K, N = 256, 128  # Shared dimensions: K (reduction), N (output columns)
diff --git a/helion/_compiler/type_propagation.py b/helion/_compiler/type_propagation.py
index a48cd9130..6e0ad4dd4 100644
--- a/helion/_compiler/type_propagation.py
+++ b/helion/_compiler/type_propagation.py
@@ -271,19 +271,27 @@ def from_example(cls, value: object, origin: Origin) -> TypeInfo:
             # This allows zip to work in list comprehensions
             zipped_tuples = tuple(tuple(items) for items in value)
             return cls.from_example(zipped_tuples, origin)
-        if isinstance(value, torch.cuda._CudaDeviceProperties):
+        if isinstance(
+            value, (torch.cuda._CudaDeviceProperties, torch.xpu._XpuDeviceProperties)
+        ):
             attrs = {}
             env = CompileEnvironment.current()
 
+            compute_unit_literal = (
+                "gpu_subslice_count"
+                if torch.xpu.is_available()
+                else "multi_processor_count"
+            )
+
             # Only `multi_processor_count` attribute is supported for now
             # TODO(yf225): support other torch.cuda._CudaDeviceProperties attributes
-            attr_origin = AttributeOrigin(origin, "multi_processor_count")
+            attr_origin = AttributeOrigin(origin, compute_unit_literal)
             # Create a symbolic integer that can be passed as kernel argument
             sym = env.create_unbacked_symint()
             HostFunction.current().expr_to_origin[sym._sympy_()] = SymbolOrigin(
                 origin=attr_origin
             )
-            attrs["multi_processor_count"] = SymIntType(attr_origin, sym)
+            attrs[compute_unit_literal] = SymIntType(attr_origin, sym)
 
             return ClassType(origin, attrs)
         raise exc.UnsupportedPythonType(type(value).__name__)
diff --git a/test/test_examples.expected b/test/test_examples.expected
index 2c004def2..3e2bc61f0 100644
--- a/test/test_examples.expected
+++ b/test/test_examples.expected
@@ -1318,7 +1318,10 @@ def grouped_gemm_jagged_persistent(A_packed: torch.Tensor, B: torch.Tensor, grou
         Output tensor of shape ``[sum(M_i), N]``.
     """
     device = A_packed.device
-    num_workers = torch.cuda.get_device_properties(device).multi_processor_count
+    if device.type == 'xpu':
+        num_workers = torch.xpu.get_device_properties(device.index).gpu_subslice_count
+    else:
+        num_workers = torch.cuda.get_device_properties(device.index).multi_processor_count
     total_M, K = A_packed.shape
     K2, N = B.shape
     assert K == K2