[AMD] skip deterministic inference for MLA FP8 test (#23382)

Co-authored-by: YC Yen-Ching Tseng <yctseng@amd.com>

test/registered/mla/test_mla_fp8.py

@@ -8,6 +8,7 @@ from sglang.test.test_utils import (
     DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
     DEFAULT_URL_FOR_TEST,
     CustomTestCase,
+    is_in_amd_ci,
     popen_launch_server,
 )
 
@@ -23,23 +24,26 @@ class TestMLA(CustomTestCase, MGSMEnMixin):
     def setUpClass(cls):
         cls.model = DEFAULT_MLA_FP8_MODEL_NAME_FOR_TEST
         cls.base_url = DEFAULT_URL_FOR_TEST
+        other_args = [
+            "--trust-remote-code",
+            "--kv-cache-dtype",
+            "fp8_e5m2",
+            # Pin MoE expert dispatch and kernel reduction order so MGSM
+            # scores don't drift across runs. The eval already uses greedy
+            # decoding, but FP8 dequant + non-deterministic MoE top-k
+            # tie-breaks produce ~1–3 point swings without this flag and
+            # straddle the 0.8 threshold. With deterministic inference,
+            # the score becomes a fixed function of (model, weights, CUDA
+            # stack), so threshold-edge flakes stop being random noise.
+        ]
+        if not is_in_amd_ci():
+            # On AMD, the default attention backend (aiter) is not in the deterministic-inference allowlist, so the server fails to start, disable it.
+            other_args.append("--enable-deterministic-inference")
         cls.process = popen_launch_server(
             cls.model,
             cls.base_url,
             timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            other_args=[
+            other_args=other_args,
-                "--trust-remote-code",
-                "--kv-cache-dtype",
-                "fp8_e5m2",
-                # Pin MoE expert dispatch and kernel reduction order so MGSM
-                # scores don't drift across runs. The eval already uses greedy
-                # decoding, but FP8 dequant + non-deterministic MoE top-k
-                # tie-breaks produce ~1–3 point swings without this flag and
-                # straddle the 0.8 threshold. With deterministic inference,
-                # the score becomes a fixed function of (model, weights, CUDA
-                # stack), so threshold-edge flakes stop being random noise.
-                "--enable-deterministic-inference",
-            ],
         )
 
     @classmethod