Add accuracy test to CI: MMLU (#882)

test/srt/test_eval_accuracy.py

+import json
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_child_process
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import MODEL_NAME_FOR_TEST, popen_launch_server
+
+
+class TestAccuracy(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        port = 30000
+
+        cls.model = MODEL_NAME_FOR_TEST
+        cls.base_url = f"http://localhost:{port}"
+        cls.process = popen_launch_server(cls.model, port, timeout=300)
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_child_process(cls.process.pid)
+
+    def test_mmlu(self):
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mmlu",
+            num_examples=20,
+            num_threads=20,
+        )
+
+        metrics = run_eval(args)
+        assert metrics["score"] >= 0.5
+
+
+if __name__ == "__main__":
+    unittest.main(warnings="ignore")
+
+    # t = TestAccuracy()
+    # t.setUpClass()
+    # t.test_mmlu()
+    # t.tearDownClass()

test/srt/test_openai_server.py

 import json
-import subprocess
-import time
 import unittest
 
 import openai
-import requests
 
 from sglang.srt.utils import kill_child_process
+from sglang.test.test_utils import MODEL_NAME_FOR_TEST, popen_launch_server
 
 
 class TestOpenAIServer(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        model = "meta-llama/Meta-Llama-3.1-8B-Instruct"
         port = 30000
-        timeout = 300
-
-        command = [
-            "python3",
-            "-m",
-            "sglang.launch_server",
-            "--model-path",
-            model,
-            "--host",
-            "localhost",
-            "--port",
-            str(port),
-        ]
-        cls.process = subprocess.Popen(command, stdout=None, stderr=None)
+
+        cls.model = MODEL_NAME_FOR_TEST
         cls.base_url = f"http://localhost:{port}/v1"
-        cls.model = model
-
-        start_time = time.time()
-        while time.time() - start_time < timeout:
-            try:
-                response = requests.get(f"{cls.base_url}/models")
-                if response.status_code == 200:
-                    return
-            except requests.RequestException:
-                pass
-            time.sleep(10)
-        raise TimeoutError("Server failed to start within the timeout period.")
+        cls.process = popen_launch_server(cls.model, port, timeout=300)
 
     @classmethod
     def tearDownClass(cls):
@@ -178,8 +152,6 @@ class TestOpenAIServer(unittest.TestCase):
 
         is_first = True
         for response in generator:
-            print(response)
-
             data = response.choices[0].delta
             if is_first:
                 data.role == "assistant"

test/srt/test_srt_endpoint.py

+import json
+import unittest
+
+import requests
+
+from sglang.srt.utils import kill_child_process
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import MODEL_NAME_FOR_TEST, popen_launch_server
+
+
+class TestSRTEndpoint(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        port = 30000
+
+        cls.model = MODEL_NAME_FOR_TEST
+        cls.base_url = f"http://localhost:{port}"
+        cls.process = popen_launch_server(cls.model, port, timeout=300)
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_child_process(cls.process.pid)
+
+    def run_decode(
+        self, return_logprob=False, top_logprobs_num=0, return_text=False, n=1
+    ):
+        response = requests.post(
+            self.base_url + "/generate",
+            json={
+                "text": "The capital of France is",
+                "sampling_params": {
+                    "temperature": 0 if n == 1 else 0.5,
+                    "max_new_tokens": 32,
+                    "n": n,
+                },
+                "stream": False,
+                "return_logprob": return_logprob,
+                "top_logprobs_num": top_logprobs_num,
+                "return_text_in_logprobs": return_text,
+                "logprob_start_len": 0,
+            },
+        )
+        print(json.dumps(response.json()))
+        print("=" * 100)
+
+    def test_simple_decode(self):
+        self.run_decode()
+
+    def test_parallel_sample(self):
+        self.run_decode(n=3)
+
+    def test_logprob(self):
+        for top_logprobs_num in [0, 3]:
+            for return_text in [True, False]:
+                self.run_decode(
+                    return_logprob=True,
+                    top_logprobs_num=top_logprobs_num,
+                    return_text=return_text,
+                )
+
+
+if __name__ == "__main__":
+    unittest.main(warnings="ignore")