Support piecewise CUDA graph with NSA (#23351)

2026-05-22 14:39:50 -07:00
parent 2df9e8b4b3
commit cadfa2d025
12 changed files with 317 additions and 58 deletions
@@ -0,0 +1,71 @@
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+)
+
+register_cuda_ci(est_time=900, stage="base-c", runner_config="4-gpu-b200")
+
+GLM5_FP4_MODEL = "nvidia/GLM-5-NVFP4"
+
+
+class TestPCGGlm5Fp4(CustomTestCase):
+    """PCG prefill on GLM-5-NVFP4 (DSA model, TP=4, B200).
+
+    GLM-5 uses GlmMoeDsaForCausalLM (DSA attention). This test verifies that
+    piecewise CUDA graph works correctly after the DSA indexer was updated to
+    cache k_fp8/k_scale for PCG-compatible prefill.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = GLM5_FP4_MODEL
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tp-size",
+                "4",
+                "--trust-remote-code",
+                "--reasoning-parser",
+                "glm45",
+                "--tool-call-parser",
+                "glm47",
+                "--quantization",
+                "modelopt_fp4",
+                "--disable-flashinfer-autotune",
+                "--enforce-piecewise-cuda-graph",
+                "--model-loader-extra-config",
+                '{"enable_multithread_load": true, "num_threads": 64}',
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="gsm8k",
+            num_examples=200,
+            num_threads=200,
+            max_tokens=4096,
+        )
+        metrics = run_eval(args)
+        print(f"{metrics=}")
+        self.assertGreater(metrics["score"], 0.92)
+
+
+if __name__ == "__main__":
+    unittest.main()