Xpu/weekly simple model enablement 2026 08 30 (#37193)

Co-authored-by: dayanandav <dayananda.vasantha.kumar@intel.com>
Co-authored-by: Girijala, Pavan Sivaram <pavan.sivaram.girijala@intel.com>
Co-authored-by: Cui, Lily <lily.cui@intel.com>
Co-authored-by: Juan Muneton <juan.muneton.gallego@intel.com>
Co-authored-by: Gao, Pengfei <pengfei.gao@intel.com>

test/registered/xpu/test_layernorm_xpu.py

@@ -0,0 +1,52 @@
+"""Tests for Gemma4RMSNorm forward_xpu dispatch."""
+
+import unittest
+
+import torch
+
+from sglang.test.ci.ci_register import register_xpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_xpu_ci(est_time=30, suite="stage-b-test-1-gpu-xpu")
+
+
+class TestGemma4RMSNormXPU(CustomTestCase):
+    def setUp(self):
+        if not torch.xpu.is_available():
+            self.skipTest("XPU not available")
+        torch.manual_seed(42)
+        from sglang.srt.layers.layernorm import Gemma4RMSNorm
+
+        self.norm = Gemma4RMSNorm(128, eps=1e-6, scale_shift=1.0).to("xpu")
+
+    def test_2d_input(self):
+        x = torch.randn(4, 128, dtype=torch.bfloat16, device="xpu")
+        out = self.norm.forward_xpu(x)
+        self.assertEqual(out.shape, (4, 128))
+        ref = self.norm.forward_native(x)
+        torch.testing.assert_close(out, ref, rtol=1e-2, atol=1e-3)
+
+    def test_3d_input(self):
+        x = torch.randn(4, 8, 128, dtype=torch.bfloat16, device="xpu")
+        out = self.norm.forward_xpu(x)
+        self.assertEqual(out.shape, (4, 8, 128))
+        ref = self.norm.forward_native(x)
+        torch.testing.assert_close(out, ref, rtol=1e-2, atol=1e-3)
+
+    def test_scale_shift_zero(self):
+        from sglang.srt.layers.layernorm import Gemma4RMSNorm
+
+        norm0 = Gemma4RMSNorm(128, eps=1e-6, scale_shift=0.0).to("xpu")
+        x = torch.randn(4, 128, dtype=torch.bfloat16, device="xpu")
+        out = norm0.forward_xpu(x)
+        ref = norm0.forward_native(x)
+        torch.testing.assert_close(out, ref, rtol=1e-2, atol=1e-3)
+
+    def test_empty_tensor(self):
+        x = torch.empty(0, 128, dtype=torch.bfloat16, device="xpu")
+        out = self.norm.forward_xpu(x)
+        self.assertEqual(out.shape, (0, 128))
+
+
+if __name__ == "__main__":
+    unittest.main()