[TEST] Skip test_host_tensor_descriptor_load

Author: whitneywhtsang

python/test/unit/language/test_tensor_descriptor.py

@@ -1608,7 +1608,9 @@ def alloc_fn(size: int, align: int, stream: Optional[int]):
 @pytest.mark.parametrize("M_BLOCK,N_BLOCK", [(2, 16), (8, 16), (8, 32), (8, 128)])
 def test_host_tensor_descriptor_load(dtype_str, num_ctas, M_BLOCK, N_BLOCK, device):
     if num_ctas == 2 and (not is_cuda() or torch.cuda.get_device_capability(0)[0] not in (9, 10)):
-        pytest.skip("CTAs is unsupported for these cards")
+        pytest.xfail("CTAs is unsupported for these cards")
+    if is_xpu():
+        pytest.skip("FIXME: issue #4289")
 
     @triton.jit(debug=True)
     def kernel(out_ptr, desc, M, N, M_BLOCK: tl.constexpr, N_BLOCK: tl.constexpr):
@@ -1668,10 +1670,12 @@ def matmul_kernel_host_tensor_descriptor(a_desc, b_desc, c_desc):
 ])
 def test_host_tensor_descriptor_matmul(num_stages, num_ctas, BLOCK_M, BLOCK_N, BLOCK_K, device):
     if num_ctas == 2 and (not is_cuda() or torch.cuda.get_device_capability(0)[0] not in (9, 10)):
-        pytest.skip("CTAs is unsupported for these cards")
+        pytest.xfail("CTAs is unsupported for these cards")
 
     if is_hip() and (BLOCK_M, BLOCK_N, BLOCK_K, num_stages) == (256, 128, 32, 4):
         pytest.skip("Insufficient shared memory on HIP devices")
+    if is_xpu():
+        pytest.skip("FIXME: issue #4289")
 
     if is_interpreter():
         M, N, K = BLOCK_M, BLOCK_N, BLOCK_K