Support overlapping two batches (#4068)

test/srt/test_two_batch_overlap.py

@@ -0,0 +1,72 @@
+import os
+import unittest
+from types import SimpleNamespace
+
+import requests
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_MLA_MODEL_NAME_FOR_TEST,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    popen_launch_server,
+)
+
+
+class TestTwoBatchOverlap(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DEFAULT_MLA_MODEL_NAME_FOR_TEST
+        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=[
+                "--trust-remote-code",
+                "--tp",
+                "2",
+                "--dp",
+                "2",
+                "--enable-dp-attention",
+                "--enable-deepep-moe",
+                "--deepep-mode",
+                "normal",
+                "--disable-cuda-graph",  # DeepEP normal does not support CUDA Graph
+                "--enable-two-batch-overlap",
+            ],
+            env={"SGL_ENABLE_JIT_DEEPGEMM": "0", **os.environ},
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_generate_single_prompt(self):
+        response = requests.post(
+            self.base_url + "/generate",
+            # we use an uncommon start to minimise the chance that the cache is hit by chance
+            json={
+                "text": "_ 1+1=2, 1+2=3, 1+3=4, 1+4=",
+                "sampling_params": {"temperature": 0, "max_new_tokens": 8},
+            },
+        )
+        print(f"{response.json()=}")
+        self.assertEquals(response.json()["text"], "5, 1+5=6")
+
+    def test_mmlu(self):
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mmlu",
+            num_examples=64,
+            num_threads=32,
+        )
+
+        metrics = run_eval(args)
+        self.assertGreater(metrics["score"], 0.5)
+
+
+if __name__ == "__main__":
+    unittest.main()