[DLLM] Implement initial dynamic batching for diffusion LLM (#14883)

2026-01-17 16:48:15 +08:00
parent 737a1183d6
commit d2c863878c
8 changed files with 380 additions and 87 deletions
@@ -0,0 +1,71 @@
+from sglang.test.ci.ci_register import register_cuda_ci
+
+register_cuda_ci(est_time=500, suite="stage-b-test-large-1-gpu")
+
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+)
+
+"""
+Test dLLM batching capability on CUDA GPUs.
+
+As current dLLM batching performance is suboptimal to BS=1, this test only verifies correctness.
+The test will be removed once dLLM batching performance improves.
+"""
+
+
+class TestBatching(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "inclusionAI/LLaDA2.0-mini"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+
+        other_args = [
+            "--trust-remote-code",
+            "--mem-fraction-static",
+            "0.9",
+            "--max-running-requests",
+            "4",
+            "--attention-backend",
+            "flashinfer",
+            "--dllm-algorithm",
+            "LowConfidence",
+        ]
+
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval_few_shot_gsm8k(args)
+        print(f"{metrics=}")
+
+        self.assertGreater(metrics["accuracy"], 0.88)
+
+
+if __name__ == "__main__":
+    unittest.main()