Support mx_tensor and enable it's test on Intel GPU

Author: xiaowangintel

test/prototype/mx_formats/test_inference_workflow.py

@@ -36,6 +36,14 @@
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
 
 
+devices = []
+if torch.cuda.is_available():
+    devices.append("cuda")
+
+if torch.xpu.is_available():
+    devices.append("xpu")
+
+
 # source: https://stackoverflow.com/a/22638709
 @pytest.fixture(autouse=True)
 def run_around_tests():
@@ -63,37 +71,50 @@ def cuda_kernel_profiler(kernel_pattern):
     result["found"] = any(kernel_pattern in name for name in kernel_names)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(
+    not (torch.cuda.is_available() or torch.xpu.is_available()),
+    reason="CUDA or XPU not available",
+)
 @pytest.mark.skipif(
     not torch_version_at_least("2.8.0"), reason="torch.compile requires PyTorch 2.8+"
 )
 @pytest.mark.parametrize("elem_dtype", [torch.float8_e4m3fn, torch.float4_e2m1fn_x2])
 @pytest.mark.parametrize("bias", [True, False])
 @pytest.mark.parametrize("compile", [True, False])
-@pytest.mark.parametrize("emulate", [True, False])
+@pytest.mark.parametrize(
+    "emulate", [True, False] if (not torch.xpu.is_available()) else [True]
+)
+@pytest.mark.parametrize("device", devices)
 @torch.no_grad()
 @skip_if_rocm(
     "ROCm float4 gemm require gfx950"
 )  # TODO(future): deploy gfx950 in ROCM CI
-def test_inference_workflow_mx(elem_dtype, bias: bool, compile: bool, emulate: bool):
+def test_inference_workflow_mx(
+    elem_dtype, bias: bool, compile: bool, emulate: bool, device
+):
     """
     Smoke test for inference compile
     """
     # TODO(future): figure out why these CUDA capability conditions are not properly
     # applied when inside `pytest.mark.skipif` for this test
-    if elem_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+    if (
+        elem_dtype in (torch.float8_e4m3fn, torch.float8_e5m2)
+    ) and torch.cuda.is_available():
         if not is_sm_at_least_89():
             pytest.skip("CUDA capability >= 8.9 required for float8 in triton")
         elif not is_sm_at_least_100() and not emulate:
             pytest.skip("CUDA capability >= 10.0 required for mxfp8 gemm")
-    elif elem_dtype == torch.float4_e2m1fn_x2:
+    elif (elem_dtype == torch.float4_e2m1fn_x2) and torch.cuda.is_available():
         if not is_sm_at_least_100() and not emulate:
             pytest.skip("CUDA capability >= 10.0 required for mxfp4 gemm")
         elif compile:
             # TODO(future PR): investigate and fix this
-            pytest.skip("mxfp4 + compile currently does not work, low SQNR")
+            pytest.skip("mxfp4 + compile currently does not work on CUDA, low SQNR")
+
+    if (elem_dtype == torch.float4_e2m1fn_x2) and torch.xpu.is_available() and compile:
+        pytest.skip("mxfp4 + compile currently does not work on XPU, low SQNR")
 
-    m = nn.Linear(32, 128, bias=bias, dtype=torch.bfloat16, device="cuda")
+    m = nn.Linear(32, 128, bias=bias, dtype=torch.bfloat16, device=device)
     m_mx = copy.deepcopy(m)
 
     if emulate:
@@ -111,7 +132,7 @@ def test_inference_workflow_mx(elem_dtype, bias: bool, compile: bool, emulate: b
     if compile:
         m_mx = torch.compile(m_mx, fullgraph=True)
 
-    x = torch.randn(128, 32, device="cuda", dtype=torch.bfloat16)
+    x = torch.randn(128, 32, device=device, dtype=torch.bfloat16)
     y_ref = m(x)
     y_mx = m_mx(x)
     sqnr = compute_error(y_ref, y_mx)