Replace hardcoded CUDA device with get_device() for XPU support (#13599)

Co-authored-by: Ma Mingfei <mingfei.ma@intel.com>

test/registered/quant/test_fp8_kernel.py

@@ -11,6 +11,13 @@ from sglang.test.test_utils import CustomTestCase
 
 register_cuda_ci(est_time=10, suite="stage-b-test-1-gpu-large")
 
+from sglang.srt.utils import get_device, is_cuda, is_xpu
+
+_is_cuda = is_cuda()
+_is_xpu = is_xpu()
+
+device = get_device()
+
 
 class TestFP8Base(CustomTestCase):
     @classmethod
@@ -26,7 +33,7 @@ class TestFP8Base(CustomTestCase):
     @staticmethod
     def _make_A(M, K, group_size, out_dtype):
         quant_A = torch.rand(
-            M, K // group_size, group_size, dtype=torch.float32, device="cuda"
+            M, K // group_size, group_size, dtype=torch.float32, device=device
         )
         # -1 ~ 1
         quant_A = quant_A * 2 - 1
@@ -38,7 +45,7 @@ class TestFP8Base(CustomTestCase):
         quant_A = quant_A.to(out_dtype).to(torch.float32)
 
         # create scale and A
-        scale = torch.rand(M, K // group_size, dtype=torch.float32, device="cuda")
+        scale = torch.rand(M, K // group_size, dtype=torch.float32, device=device)
         scale /= fmax
         A = quant_A * scale[..., None]
 
@@ -60,7 +67,7 @@ class TestFP8Base(CustomTestCase):
             N_aligned // group_size,
             group_size,
             dtype=torch.float32,
-            device="cuda",
+            device=device,
         )
         quant_B = quant_B * 2 - 1
 
@@ -77,7 +84,7 @@ class TestFP8Base(CustomTestCase):
             N_aligned // group_size,
             1,
             dtype=torch.float32,
-            device="cuda",
+            device=device,
         )
         scale /= fmax
 
@@ -91,8 +98,9 @@ class TestFP8Base(CustomTestCase):
 
 class TestPerTokenGroupQuantFP8(TestFP8Base):
     def test_per_token_group_quant_fp8(self):
-        if torch.cuda.get_device_capability()[0] < 9:
+        if _is_cuda and torch.cuda.get_device_capability()[0] < 9:
             return
+
         A, A_quant_gt, scale_gt = self._make_A(
             M=self.M, K=self.K, group_size=self.group_size, out_dtype=self.quant_type
         )
@@ -107,8 +115,14 @@ class TestPerTokenGroupQuantFP8(TestFP8Base):
 
 class TestW8A8BlockFP8Matmul(TestFP8Base):
     def test_w8a8_block_fp8_matmul(self):
-        if torch.cuda.get_device_capability()[0] < 9:
+        if _is_cuda and torch.cuda.get_device_capability()[0] < 9:
             return
+        elif _is_xpu:
+            # XPU doesn't provide traditional capability info like CUDA
+            pass
+        else:
+            return
+
         A, A_quant_gt, A_scale_gt = self._make_A(
             M=self.M, K=self.K, group_size=self.group_size, out_dtype=self.quant_type
         )

test/registered/vlm/test_vlm_input_format.py

@@ -35,6 +35,7 @@ if not hasattr(_hf_activations, "PytorchGELUTanh"):
 from sglang import Engine
 from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest
 from sglang.srt.parser.conversation import generate_chat_conv
+from sglang.srt.utils.common import is_cuda, is_xpu
 from sglang.srt.utils.hf_transformers_utils import _fix_added_tokens_encoding
 
 register_cuda_ci(est_time=747, suite="stage-b-test-1-gpu-large")
@@ -42,6 +43,9 @@ register_cuda_ci(est_time=747, suite="stage-b-test-1-gpu-large")
 IMAGE_MAN_IRONING_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/man_ironing_on_back_of_suv.png"
 IMAGE_SGL_LOGO_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/sgl_logo.png"
 
+_is_cuda = is_cuda()
+_is_xpu = is_xpu()
+
 
 class VLMInputTestBase:
     model_path = None
@@ -53,12 +57,20 @@ class VLMInputTestBase:
     def setUpClass(cls):
         assert cls.model_path is not None, "Set model_path in subclass"
         assert cls.chat_template is not None, "Set chat_template in subclass"
+
         cls.image_urls = [IMAGE_MAN_IRONING_URL, IMAGE_SGL_LOGO_URL]
-        cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        if _is_cuda:
+            cls.device = torch.device("cuda")
+        elif _is_xpu:
+            cls.device = torch.device("xpu")
+        else:
+            cls.device = torch.device("cpu")
+
         cls.main_image = []
         for image_url in cls.image_urls:
             response = requests.get(image_url)
             cls.main_image.append(Image.open(BytesIO(response.content)))
+
         cls.processor = AutoProcessor.from_pretrained(
             cls.model_path, trust_remote_code=True, use_fast=True
         )