Stabilize GB300 nightly tests (#35044)

2026-08-17 03:30:16 -07:00
parent 744740dbea
commit 82995a001b
13 changed files with 515 additions and 351 deletions
@@ -0,0 +1,6 @@
+"""Shared configuration for tests on the dedicated GB300 runner."""
+
+# The runner's ephemeral client-port range is 10240-65535. Keep the TCPStore
+# rendezvous below it so a recently closed client connection cannot make the
+# subsequent TCPStore bind fail with EADDRINUSE.
+GB300_NCCL_PORT = "10000"
@@ -20,7 +20,7 @@ from sglang.test.test_deterministic_utils import (
 )
 from sglang.test.test_utils import DEFAULT_URL_FOR_TEST

-register_cuda_ci(est_time=900, stage="nightly", runner_config="4-gpu-gb300")
+register_cuda_ci(est_time=360, stage="nightly", runner_config="4-gpu-b200")

 GLM_MODEL = "zai-org/GLM-4.7-Flash"

@@ -1,150 +0,0 @@
-import unittest
-
-from sglang.test.accuracy_test_runner import AccuracyTestParams
-from sglang.test.ci.ci_register import register_cuda_ci
-from sglang.test.performance_test_runner import PerformanceTestParams
-from sglang.test.run_combined_tests import run_combined_tests
-from sglang.test.test_utils import ModelLaunchSettings
-
-register_cuda_ci(est_time=7200, stage="nightly", runner_config="4-gpu-gb300")
-
-MODEL_PATH = "deepseek-ai/DeepSeek-V4-Pro"
-SERVER_LAUNCH_TIMEOUT = 3600
-
-DEEPEP_CONFIG = '{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
-
-LOW_LATENCY_ARGS = [
-    "--trust-remote-code",
-    "--moe-runner-backend",
-    "flashinfer_mxfp4",
-    "--speculative-algorithm",
-    "EAGLE",
-    "--speculative-num-steps",
-    "3",
-    "--speculative-eagle-topk",
-    "1",
-    "--speculative-num-draft-tokens",
-    "4",
-    "--chunked-prefill-size",
-    "8192",
-    "--disable-flashinfer-autotune",
-    "--swa-full-tokens-ratio",
-    "0.1",
-    "--mem-fraction-static",
-    "0.85",
-]
-
-BALANCED_ARGS = [
-    "--trust-remote-code",
-    "--dp",
-    "4",
-    "--enable-dp-attention",
-    "--moe-a2a-backend",
-    "deepep",
-    "--speculative-algorithm",
-    "EAGLE",
-    "--speculative-num-steps",
-    "1",
-    "--speculative-eagle-topk",
-    "1",
-    "--speculative-num-draft-tokens",
-    "2",
-    "--mem-fraction-static",
-    "0.85",
-    "--cuda-graph-max-bs-decode",
-    "128",
-    "--max-running-requests",
-    "256",
-    "--deepep-config",
-    DEEPEP_CONFIG,
-]
-
-HIGH_THROUGHPUT_ARGS = [
-    "--trust-remote-code",
-    "--dp",
-    "4",
-    "--enable-dp-attention",
-    "--moe-a2a-backend",
-    "megamoe",
-    "--mem-fraction-static",
-    "0.9",
-    "--cuda-graph-max-bs-decode",
-    "128",
-    "--max-running-requests",
-    "256",
-]
-
-BALANCED_ENV = {
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
-}
-
-HIGH_THROUGHPUT_ENV = {
-    "SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK": "8320",
-}
-
-PERFORMANCE_BATCH_SIZES = {
-    "low-latency": [1, 4, 16],
-    "balanced": [64],
-    "high-throughput": [128],
-}
-
-
-class TestDeepSeekV4ProFp4(unittest.TestCase):
-    """DeepSeek-V4-Pro FP4 on GB300 (4x B200 NVL4, tp=4)."""
-
-    def test_deepseek_v4_pro_fp4(self):
-        variants = [
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=LOW_LATENCY_ARGS,
-                variant="low-latency",
-                launch_timeout=SERVER_LAUNCH_TIMEOUT,
-            ),
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=BALANCED_ARGS,
-                env=BALANCED_ENV,
-                variant="balanced",
-                launch_timeout=SERVER_LAUNCH_TIMEOUT,
-            ),
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=HIGH_THROUGHPUT_ARGS,
-                env=HIGH_THROUGHPUT_ENV,
-                variant="high-throughput",
-                launch_timeout=SERVER_LAUNCH_TIMEOUT,
-            ),
-        ]
-
-        failures = []
-        accuracy_params = AccuracyTestParams(
-            dataset="gsm8k",
-            baseline_accuracy=0.935,
-            temperature=1.0,
-            top_p=1.0,
-        )
-        for variant in variants:
-            try:
-                run_combined_tests(
-                    models=[variant],
-                    test_name=f"DeepSeek-V4-Pro-FP4 ({variant.variant})",
-                    accuracy_params=accuracy_params,
-                    performance_params=PerformanceTestParams(
-                        batch_sizes=PERFORMANCE_BATCH_SIZES[variant.variant],
-                        result_dir="performance_results_gb300",
-                    ),
-                )
-            except AssertionError as e:
-                failures.append(f"{variant.variant}: {e}")
-
-        if failures:
-            raise AssertionError(
-                "DeepSeek-V4-Pro-FP4 failures:\n" + "\n".join(failures)
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
@@ -0,0 +1,78 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=600, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "deepseek-ai/DeepSeek-V4-Pro"
+SERVER_LAUNCH_TIMEOUT = 3600
+DEEPEP_CONFIG = '{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
+
+BALANCED_ARGS = [
+    "--trust-remote-code",
+    "--dp",
+    "4",
+    "--enable-dp-attention",
+    "--moe-a2a-backend",
+    "deepep",
+    "--speculative-algorithm",
+    "EAGLE",
+    "--speculative-num-steps",
+    "1",
+    "--speculative-eagle-topk",
+    "1",
+    "--speculative-num-draft-tokens",
+    "2",
+    "--mem-fraction-static",
+    "0.85",
+    "--cuda-graph-max-bs-decode",
+    "128",
+    "--max-running-requests",
+    "256",
+    "--deepep-config",
+    DEEPEP_CONFIG,
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+BALANCED_ENV = {
+    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
+}
+
+
+class TestDeepSeekV4ProFp4Balanced(CustomTestCase):
+    """DeepSeek-V4-Pro FP4 balanced config on GB300."""
+
+    def test_deepseek_v4_pro_fp4_balanced(self):
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=BALANCED_ARGS,
+                    env=BALANCED_ENV,
+                    variant="balanced",
+                    launch_timeout=SERVER_LAUNCH_TIMEOUT,
+                )
+            ],
+            test_name="DeepSeek-V4-Pro-FP4 (balanced)",
+            accuracy_params=AccuracyTestParams(
+                dataset="gsm8k",
+                baseline_accuracy=0.935,
+                temperature=1.0,
+                top_p=1.0,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[64],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,67 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=600, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "deepseek-ai/DeepSeek-V4-Pro"
+SERVER_LAUNCH_TIMEOUT = 3600
+
+HIGH_THROUGHPUT_ARGS = [
+    "--trust-remote-code",
+    "--dp",
+    "4",
+    "--enable-dp-attention",
+    "--moe-a2a-backend",
+    "megamoe",
+    "--mem-fraction-static",
+    "0.9",
+    "--cuda-graph-max-bs-decode",
+    "128",
+    "--max-running-requests",
+    "256",
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+HIGH_THROUGHPUT_ENV = {
+    "SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK": "8320",
+}
+
+
+class TestDeepSeekV4ProFp4HighThroughput(CustomTestCase):
+    """DeepSeek-V4-Pro FP4 high-throughput config on GB300."""
+
+    def test_deepseek_v4_pro_fp4_high_throughput(self):
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=HIGH_THROUGHPUT_ARGS,
+                    env=HIGH_THROUGHPUT_ENV,
+                    variant="high-throughput",
+                    launch_timeout=SERVER_LAUNCH_TIMEOUT,
+                )
+            ],
+            test_name="DeepSeek-V4-Pro-FP4 (high-throughput)",
+            accuracy_params=AccuracyTestParams(
+                dataset="gsm8k",
+                baseline_accuracy=0.935,
+                temperature=1.0,
+                top_p=1.0,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[128],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,68 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=720, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "deepseek-ai/DeepSeek-V4-Pro"
+SERVER_LAUNCH_TIMEOUT = 3600
+
+LOW_LATENCY_ARGS = [
+    "--trust-remote-code",
+    "--moe-runner-backend",
+    "flashinfer_mxfp4",
+    "--speculative-algorithm",
+    "EAGLE",
+    "--speculative-num-steps",
+    "3",
+    "--speculative-eagle-topk",
+    "1",
+    "--speculative-num-draft-tokens",
+    "4",
+    "--chunked-prefill-size",
+    "8192",
+    "--disable-flashinfer-autotune",
+    "--swa-full-tokens-ratio",
+    "0.1",
+    "--mem-fraction-static",
+    "0.85",
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+
+class TestDeepSeekV4ProFp4LowLatency(CustomTestCase):
+    """DeepSeek-V4-Pro FP4 low-latency config on GB300."""
+
+    def test_deepseek_v4_pro_fp4_low_latency(self):
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=LOW_LATENCY_ARGS,
+                    variant="low-latency",
+                    launch_timeout=SERVER_LAUNCH_TIMEOUT,
+                )
+            ],
+            test_name="DeepSeek-V4-Pro-FP4 (low-latency)",
+            accuracy_params=AccuracyTestParams(
+                dataset="gsm8k",
+                baseline_accuracy=0.935,
+                temperature=1.0,
+                top_p=1.0,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[1, 4, 16],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -2,9 +2,10 @@ import unittest

 from sglang.test.accuracy_test_runner import AccuracyTestParams
 from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
 from sglang.test.performance_test_runner import PerformanceTestParams
 from sglang.test.run_combined_tests import run_combined_tests
-from sglang.test.test_utils import ModelLaunchSettings
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings

 register_cuda_ci(est_time=2280, stage="nightly", runner_config="4-gpu-gb300")

@@ -18,6 +19,8 @@ COMMON_ARGS = [
    "--moe-runner-backend=flashinfer_trtllm",
    "--mem-fraction-static=0.9",
    "--enable-metrics",
+    "--nccl-port",
+    GB300_NCCL_PORT,
 ]

 TP_MTP_ARGS = [
@@ -35,7 +38,7 @@ DP_MTP_ARGS = [
 ]


-class TestGlm52Nvfp4(unittest.TestCase):
+class TestGlm52Nvfp4(CustomTestCase):
    """GLM-5.2 NVFP4 on GB300 (4x GB300 NVL4, tp=4)."""

    def test_glm52_nvfp4(self):
@@ -1,100 +0,0 @@
-import unittest
-
-from sglang.test.accuracy_test_runner import AccuracyTestParams
-from sglang.test.ci.ci_register import register_cuda_ci
-from sglang.test.performance_test_runner import PerformanceTestParams
-from sglang.test.run_combined_tests import run_combined_tests
-from sglang.test.test_utils import ModelLaunchSettings
-
-register_cuda_ci(est_time=7200, stage="nightly", runner_config="4-gpu-gb300")
-
-MODEL_PATH = "nvidia/Kimi-K2.5-NVFP4"
-DRAFT_MODEL_PATH = "lightseekorg/kimi-k2.5-eagle3-mla"
-
-COMMON_ARGS = [
-    "--trust-remote-code",
-    "--reasoning-parser=kimi_k2",
-    "--tool-call-parser=kimi_k2",
-    "--quantization=modelopt_fp4",
-    "--attention-backend=tokenspeed_mla",
-    "--kv-cache-dtype=fp8_e4m3",
-    "--moe-runner-backend=flashinfer_trtllm",
-    "--mem-fraction-static=0.8",
-    "--enable-metrics",
-    "--speculative-algorithm=EAGLE3",
-    f"--speculative-draft-model-path={DRAFT_MODEL_PATH}",
-    "--speculative-draft-model-quantization=unquant",
-]
-
-TP_EAGLE_ARGS = [
-    "--speculative-num-steps=3",
-    "--speculative-eagle-topk=1",
-    "--speculative-num-draft-tokens=4",
-]
-
-DP_EAGLE_ARGS = [
-    "--speculative-num-steps=1",
-    "--speculative-eagle-topk=1",
-    "--speculative-num-draft-tokens=2",
-]
-
-PERFORMANCE_BATCH_SIZES = {
-    "TP4+EAGLE3": [1, 8],
-    "TP4+DP4+DPA+EAGLE3": [16],
-}
-
-
-class TestKimiK25Nvfp4(unittest.TestCase):
-    """Kimi-K2.5 NVFP4 + EAGLE3 on GB300 (4x GB300 NVL4, tp=4)."""
-
-    def test_kimi_k25_nvfp4(self):
-        variants = [
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=COMMON_ARGS + TP_EAGLE_ARGS,
-                variant="TP4+EAGLE3",
-            ),
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=COMMON_ARGS
-                + ["--dp-size=4", "--enable-dp-attention"]
-                + DP_EAGLE_ARGS,
-                variant="TP4+DP4+DPA+EAGLE3",
-            ),
-        ]
-
-        failures = []
-        # Pinned to what `ns eval --benchmarks=mmmu-pro:1` sent implicitly --
-        # its `:1` suffix means temperature 0.7, not greedy -- so the baseline
-        # carries over unchanged. Do not "simplify" these away.
-        accuracy_params = AccuracyTestParams(
-            dataset="mmmu_pro_vision",
-            baseline_accuracy=0.69,
-            repeat=1,
-            max_tokens=32768,
-            temperature=0.7,
-            seed=0,
-            sgl_eval_thinking=False,
-        )
-        for variant in variants:
-            try:
-                run_combined_tests(
-                    models=[variant],
-                    test_name=f"Kimi-K2.5-NVFP4 ({variant.variant})",
-                    accuracy_params=accuracy_params,
-                    performance_params=PerformanceTestParams(
-                        batch_sizes=PERFORMANCE_BATCH_SIZES[variant.variant],
-                        result_dir="performance_results_gb300",
-                    ),
-                )
-            except AssertionError as e:
-                failures.append(f"{variant.variant}: {e}")
-
-        if failures:
-            raise AssertionError("Kimi-K2.5-NVFP4 failures:\n" + "\n".join(failures))
-
-
-if __name__ == "__main__":
-    unittest.main()
@@ -0,0 +1,75 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=7200, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "nvidia/Kimi-K2.5-NVFP4"
+DRAFT_MODEL_PATH = "lightseekorg/kimi-k2.5-eagle3-mla"
+
+COMMON_ARGS = [
+    "--trust-remote-code",
+    "--reasoning-parser=kimi_k2",
+    "--tool-call-parser=kimi_k2",
+    "--quantization=modelopt_fp4",
+    "--attention-backend=tokenspeed_mla",
+    "--kv-cache-dtype=fp8_e4m3",
+    "--moe-runner-backend=flashinfer_trtllm",
+    "--mem-fraction-static=0.8",
+    "--enable-metrics",
+    "--speculative-algorithm=EAGLE3",
+    f"--speculative-draft-model-path={DRAFT_MODEL_PATH}",
+    "--speculative-draft-model-quantization=unquant",
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+DP_EAGLE_ARGS = [
+    "--speculative-num-steps=1",
+    "--speculative-eagle-topk=1",
+    "--speculative-num-draft-tokens=2",
+]
+
+
+class TestKimiK25Nvfp4Dp(CustomTestCase):
+    """Kimi-K2.5 NVFP4 DP4+DPA+EAGLE3 on GB300 (4x GB300 NVL4)."""
+
+    def test_kimi_k25_nvfp4_dp(self):
+        # Pinned to what `ns eval --benchmarks=mmmu-pro:1` sent implicitly --
+        # its `:1` suffix means temperature 0.7, not greedy -- so the baseline
+        # carries over unchanged. Do not "simplify" these away.
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=COMMON_ARGS
+                    + ["--dp-size=4", "--enable-dp-attention"]
+                    + DP_EAGLE_ARGS,
+                    variant="TP4+DP4+DPA+EAGLE3",
+                )
+            ],
+            test_name="Kimi-K2.5-NVFP4 (TP4+DP4+DPA+EAGLE3)",
+            accuracy_params=AccuracyTestParams(
+                dataset="mmmu_pro_vision",
+                baseline_accuracy=0.69,
+                repeat=1,
+                max_tokens=32768,
+                temperature=0.7,
+                seed=0,
+                sgl_eval_thinking=False,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[16],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,73 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=4800, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "nvidia/Kimi-K2.5-NVFP4"
+DRAFT_MODEL_PATH = "lightseekorg/kimi-k2.5-eagle3-mla"
+
+COMMON_ARGS = [
+    "--trust-remote-code",
+    "--reasoning-parser=kimi_k2",
+    "--tool-call-parser=kimi_k2",
+    "--quantization=modelopt_fp4",
+    "--attention-backend=tokenspeed_mla",
+    "--kv-cache-dtype=fp8_e4m3",
+    "--moe-runner-backend=flashinfer_trtllm",
+    "--mem-fraction-static=0.8",
+    "--enable-metrics",
+    "--speculative-algorithm=EAGLE3",
+    f"--speculative-draft-model-path={DRAFT_MODEL_PATH}",
+    "--speculative-draft-model-quantization=unquant",
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+TP_EAGLE_ARGS = [
+    "--speculative-num-steps=3",
+    "--speculative-eagle-topk=1",
+    "--speculative-num-draft-tokens=4",
+]
+
+
+class TestKimiK25Nvfp4Tp(CustomTestCase):
+    """Kimi-K2.5 NVFP4 TP4+EAGLE3 on GB300 (4x GB300 NVL4)."""
+
+    def test_kimi_k25_nvfp4_tp(self):
+        # Pinned to what `ns eval --benchmarks=mmmu-pro:1` sent implicitly --
+        # its `:1` suffix means temperature 0.7, not greedy -- so the baseline
+        # carries over unchanged. Do not "simplify" these away.
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=COMMON_ARGS + TP_EAGLE_ARGS,
+                    variant="TP4+EAGLE3",
+                )
+            ],
+            test_name="Kimi-K2.5-NVFP4 (TP4+EAGLE3)",
+            accuracy_params=AccuracyTestParams(
+                dataset="mmmu_pro_vision",
+                baseline_accuracy=0.69,
+                repeat=1,
+                max_tokens=32768,
+                temperature=0.7,
+                seed=0,
+                sgl_eval_thinking=False,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[1, 8],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -1,98 +0,0 @@
-import unittest
-
-from sglang.test.accuracy_test_runner import AccuracyTestParams
-from sglang.test.ci.ci_register import register_cuda_ci
-from sglang.test.performance_test_runner import PerformanceTestParams
-from sglang.test.run_combined_tests import run_combined_tests
-from sglang.test.test_utils import ModelLaunchSettings
-
-register_cuda_ci(est_time=7200, stage="nightly", runner_config="4-gpu-gb300")
-
-MODEL_PATH = "Qwen/Qwen3.5-397B-A17B-FP8"
-
-COMMON_ARGS = [
-    "--trust-remote-code",
-    "--reasoning-parser=qwen3",
-    "--tool-call-parser=qwen3_coder",
-    "--enable-flashinfer-allreduce-fusion",
-    "--attention-backend=trtllm_mha",
-    "--mem-fraction-static=0.8",
-    "--mamba-scheduler-strategy=extra_buffer",
-    "--enable-multimodal",
-    "--enable-metrics",
-]
-
-TP_MTP_ARGS = [
-    "--speculative-algorithm=EAGLE",
-    "--speculative-num-steps=3",
-    "--speculative-eagle-topk=1",
-    "--speculative-num-draft-tokens=4",
-]
-
-DP_MTP_ARGS = [
-    "--speculative-algorithm=EAGLE",
-    "--speculative-num-steps=1",
-    "--speculative-eagle-topk=1",
-    "--speculative-num-draft-tokens=2",
-]
-
-PERFORMANCE_BATCH_SIZES = {
-    "TP4+MTP": [1, 4],
-    "TP4+DP4+DPA+MTP": [16],
-}
-
-
-class TestQwen35Fp8(unittest.TestCase):
-    """Qwen3.5-397B FP8 on GB300 (4x GB300 NVL4, tp=4)."""
-
-    def test_qwen35_fp8(self):
-        variants = [
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=COMMON_ARGS + TP_MTP_ARGS,
-                variant="TP4+MTP",
-            ),
-            ModelLaunchSettings(
-                MODEL_PATH,
-                tp_size=4,
-                extra_args=COMMON_ARGS
-                + ["--dp-size=4", "--enable-dp-attention"]
-                + DP_MTP_ARGS,
-                variant="TP4+DP4+DPA+MTP",
-            ),
-        ]
-
-        failures = []
-        # Pinned to what `ns eval --benchmarks=mmmu-pro:1` sent implicitly --
-        # its `:1` suffix means temperature 0.7, not greedy -- so the baseline
-        # carries over unchanged. Do not "simplify" these away.
-        accuracy_params = AccuracyTestParams(
-            dataset="mmmu_pro_vision",
-            baseline_accuracy=0.76,
-            repeat=1,
-            max_tokens=32768,
-            temperature=0.7,
-            seed=0,
-            sgl_eval_thinking=False,
-        )
-        for variant in variants:
-            try:
-                run_combined_tests(
-                    models=[variant],
-                    test_name=f"Qwen3.5-397B-FP8 ({variant.variant})",
-                    accuracy_params=accuracy_params,
-                    performance_params=PerformanceTestParams(
-                        batch_sizes=PERFORMANCE_BATCH_SIZES[variant.variant],
-                        result_dir="performance_results_gb300",
-                    ),
-                )
-            except AssertionError as e:
-                failures.append(f"{variant.variant}: {e}")
-
-        if failures:
-            raise AssertionError("Qwen3.5-397B-FP8 failures:\n" + "\n".join(failures))
-
-
-if __name__ == "__main__":
-    unittest.main()
@@ -0,0 +1,72 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=7200, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "Qwen/Qwen3.5-397B-A17B-FP8"
+
+COMMON_ARGS = [
+    "--trust-remote-code",
+    "--reasoning-parser=qwen3",
+    "--tool-call-parser=qwen3_coder",
+    "--enable-flashinfer-allreduce-fusion",
+    "--attention-backend=trtllm_mha",
+    "--mem-fraction-static=0.8",
+    "--mamba-scheduler-strategy=extra_buffer",
+    "--enable-multimodal",
+    "--enable-metrics",
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+DP_MTP_ARGS = [
+    "--speculative-algorithm=EAGLE",
+    "--speculative-num-steps=1",
+    "--speculative-eagle-topk=1",
+    "--speculative-num-draft-tokens=2",
+]
+
+
+class TestQwen35Fp8Dp(CustomTestCase):
+    """Qwen3.5-397B FP8 DP4+DPA+MTP on GB300 (4x GB300 NVL4)."""
+
+    def test_qwen35_fp8_dp(self):
+        # Pinned to what `ns eval --benchmarks=mmmu-pro:1` sent implicitly --
+        # its `:1` suffix means temperature 0.7, not greedy -- so the baseline
+        # carries over unchanged. Do not "simplify" these away.
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=COMMON_ARGS
+                    + ["--dp-size=4", "--enable-dp-attention"]
+                    + DP_MTP_ARGS,
+                    variant="TP4+DP4+DPA+MTP",
+                )
+            ],
+            test_name="Qwen3.5-397B-FP8 (TP4+DP4+DPA+MTP)",
+            accuracy_params=AccuracyTestParams(
+                dataset="mmmu_pro_vision",
+                baseline_accuracy=0.76,
+                repeat=1,
+                max_tokens=32768,
+                temperature=0.7,
+                seed=0,
+                sgl_eval_thinking=False,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[16],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,70 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.gb300_utils import GB300_NCCL_PORT
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import CustomTestCase, ModelLaunchSettings
+
+register_cuda_ci(est_time=4800, stage="nightly", runner_config="4-gpu-gb300")
+
+MODEL_PATH = "Qwen/Qwen3.5-397B-A17B-FP8"
+
+COMMON_ARGS = [
+    "--trust-remote-code",
+    "--reasoning-parser=qwen3",
+    "--tool-call-parser=qwen3_coder",
+    "--enable-flashinfer-allreduce-fusion",
+    "--attention-backend=trtllm_mha",
+    "--mem-fraction-static=0.8",
+    "--mamba-scheduler-strategy=extra_buffer",
+    "--enable-multimodal",
+    "--enable-metrics",
+    "--nccl-port",
+    GB300_NCCL_PORT,
+]
+
+TP_MTP_ARGS = [
+    "--speculative-algorithm=EAGLE",
+    "--speculative-num-steps=3",
+    "--speculative-eagle-topk=1",
+    "--speculative-num-draft-tokens=4",
+]
+
+
+class TestQwen35Fp8Tp(CustomTestCase):
+    """Qwen3.5-397B FP8 TP4+MTP on GB300 (4x GB300 NVL4)."""
+
+    def test_qwen35_fp8_tp(self):
+        # Pinned to what `ns eval --benchmarks=mmmu-pro:1` sent implicitly --
+        # its `:1` suffix means temperature 0.7, not greedy -- so the baseline
+        # carries over unchanged. Do not "simplify" these away.
+        run_combined_tests(
+            models=[
+                ModelLaunchSettings(
+                    MODEL_PATH,
+                    tp_size=4,
+                    extra_args=COMMON_ARGS + TP_MTP_ARGS,
+                    variant="TP4+MTP",
+                )
+            ],
+            test_name="Qwen3.5-397B-FP8 (TP4+MTP)",
+            accuracy_params=AccuracyTestParams(
+                dataset="mmmu_pro_vision",
+                baseline_accuracy=0.76,
+                repeat=1,
+                max_tokens=32768,
+                temperature=0.7,
+                seed=0,
+                sgl_eval_thinking=False,
+            ),
+            performance_params=PerformanceTestParams(
+                batch_sizes=[1, 8],
+                result_dir="performance_results_gb300",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()