Test the case when max_new_tokens is very large (#1038)

python/sglang/srt/managers/detokenizer_manager.py

@@ -32,7 +32,7 @@ from sglang.srt.managers.io_struct import (
 )
 from sglang.srt.managers.schedule_batch import FINISH_MATCHED_STR
 from sglang.srt.server_args import PortArgs, ServerArgs
-from sglang.utils import find_printable_text, get_exception_traceback, graceful_registry
+from sglang.utils import find_printable_text, get_exception_traceback
 
 asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
 
@@ -164,8 +164,6 @@ def start_detokenizer_process(
     port_args: PortArgs,
     pipe_writer,
 ):
-    graceful_registry(inspect.currentframe().f_code.co_name)
-
     try:
         manager = DetokenizerManager(server_args, port_args)
     except Exception:

python/sglang/srt/managers/policy_scheduler.py

@@ -15,6 +15,7 @@ limitations under the License.
 
 """Request policy scheduler"""
 
+import os
 import random
 from collections import defaultdict
 from contextlib import contextmanager
@@ -24,9 +25,11 @@ from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.radix_cache import TreeNode
 
-# Clip the max new tokens for the request whose max_new_tokens is very large.
+# Clip the estimation of max_new_tokens for the request whose max_new_tokens is very large.
 # This can prevent the server from being too conservative.
-CLIP_MAX_NEW_TOKENS = 4096
+# Note that this only clips the estimation in the scheduler but does not change the stop
+# condition. The request can still generate tokens until it hits the unclipped max_new_tokens.
+CLIP_MAX_NEW_TOKENS = int(os.environ.get("SGLANG_CLIP_MAX_NEW_TOKENS", "4096"))
 
 
 class PolicyScheduler:

python/sglang/srt/openai_api/adapter.py

@@ -77,7 +77,7 @@ class FileMetadata:
 batch_storage: Dict[str, BatchResponse] = {}
 file_id_request: Dict[str, FileMetadata] = {}
 file_id_response: Dict[str, FileResponse] = {}
-# map file id to file path in SGlang backend
+# map file id to file path in SGLang backend
 file_id_storage: Dict[str, str] = {}
 
 
@@ -335,7 +335,7 @@ async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRe
         }
 
     except Exception as e:
-        print("error in SGlang:", e)
+        print("error in SGLang:", e)
         # Update batch status to "failed"
         retrieve_batch = batch_storage[batch_id]
         retrieve_batch.status = "failed"

python/sglang/srt/server_args.py

@@ -64,7 +64,7 @@ class ServerArgs:
 
     # Other
     api_key: Optional[str] = None
-    file_storage_pth: str = "SGlang_storage"
+    file_storage_pth: str = "SGLang_storage"
 
     # Data parallelism
     dp_size: int = 1

python/sglang/test/test_utils.py

@@ -398,6 +398,8 @@ def popen_launch_server(
     timeout: float,
     api_key: Optional[str] = None,
     other_args: tuple = (),
+    env: Optional[dict] = None,
+    return_stdout_stderr: bool = False,
 ):
     _, host, port = base_url.split(":")
     host = host[2:]
@@ -417,7 +419,16 @@ def popen_launch_server(
     if api_key:
         command += ["--api-key", api_key]
 
-    process = subprocess.Popen(command, stdout=None, stderr=None)
+    if return_stdout_stderr:
+        process = subprocess.Popen(
+            command,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env,
+            text=True,
+        )
+    else:
+        process = subprocess.Popen(command, stdout=None, stderr=None, env=env)
 
     start_time = time.time()
     while time.time() - start_time < timeout:

test/srt/run_suite.py

@@ -5,13 +5,15 @@ from sglang.test.test_utils import run_unittest_files
 
 suites = {
     "minimal": [
-        "test_eval_accuracy.py",
-        "test_openai_server.py",
-        "test_vision_openai_server.py",
-        "test_embedding_openai_server.py",
         "test_chunked_prefill.py",
+        "test_embedding_openai_server.py",
+        "test_eval_accuracy.py",
+        "test_large_max_new_tokens.py",
+        "test_openai_server.py",
+        "test_skip_tokenizer_init.py",
         "test_torch_compile.py",
-        "test_models_from_modelscope.py",
+        "test_vision_openai_server.py",
+        "test_large_max_new_tokens.py",
         "models/test_generation_models.py",
         "models/test_embedding_models.py",
         "sampling/penaltylib",

test/srt/test_large_max_new_tokens.py

@@ -0,0 +1,72 @@
+import json
+import os
+import time
+import unittest
+from concurrent.futures import ThreadPoolExecutor
+
+import openai
+
+from sglang.srt.hf_transformers_utils import get_tokenizer
+from sglang.srt.utils import kill_child_process
+from sglang.test.test_utils import DEFAULT_MODEL_NAME_FOR_TEST, popen_launch_server
+
+
+class TestOpenAIServer(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DEFAULT_MODEL_NAME_FOR_TEST
+        cls.base_url = "http://127.0.0.1:8157"
+        cls.api_key = "sk-123456"
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=300,
+            api_key=cls.api_key,
+            other_args=("--max-total-token", "1024"),
+            env={"SGLANG_CLIP_MAX_NEW_TOKENS": "256", **os.environ},
+            return_stdout_stderr=True,
+        )
+        cls.base_url += "/v1"
+        cls.tokenizer = get_tokenizer(DEFAULT_MODEL_NAME_FOR_TEST)
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_child_process(cls.process.pid)
+
+    def run_chat_completion(self):
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=[
+                {"role": "system", "content": "You are a helpful AI assistant"},
+                {
+                    "role": "user",
+                    "content": "Please repeat the world 'hello' for 10000 times.",
+                },
+            ],
+            temperature=0,
+        )
+        return response
+
+    def test_chat_completion(self):
+        num_requests = 4
+
+        futures = []
+        with ThreadPoolExecutor(16) as executor:
+            for i in range(num_requests):
+                futures.append(executor.submit(self.run_chat_completion))
+
+            all_requests_running = False
+            for line in iter(self.process.stderr.readline, ""):
+                line = str(line)
+                print(line, end="")
+                if f"#running-req: {num_requests}" in line:
+                    all_requests_running = True
+                    break
+
+        assert all_requests_running
+
+
+if __name__ == "__main__":
+    unittest.main()