Higher priority for user input of max_prefill_tokens & format (#540)

python/sglang/srt/models/qwen.py

 # Adapted from
 # https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/qwen.py#L1
-from typing import Any, Dict, Optional, Iterable, Tuple
+from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn

python/sglang/srt/models/qwen2.py

 # Adapted from llama2.py
 # Modify details for the adaptation of Qwen2 model.
 """Inference-only Qwen2 model compatible with HuggingFace weights."""
-from typing import Any, Dict, Optional, Tuple, Iterable
+from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn

python/sglang/srt/models/stablelm.py

@@ -2,7 +2,7 @@
 # https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/stablelm.py#L1
 """Inference-only StableLM-2 (https://huggingface.co/stabilityai/stablelm-2-1_6b)
 model compatible with HuggingFace weights."""
-from typing import Optional, Tuple, Iterable
+from typing import Iterable, Optional, Tuple
 
 import torch
 from torch import nn

python/sglang/srt/models/yivl.py

 """Inference-only Yi-VL model."""
 
-from typing import Tuple, Iterable, Optional
+from typing import Iterable, Optional, Tuple
 
 import torch
 import torch.nn as nn
 from transformers import CLIPVisionModel, LlavaConfig
 from vllm.config import CacheConfig
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.models.llava import (
     LlavaLlamaForCausalLM,
     monkey_path_clip_vision_embed_forward,

python/sglang/srt/openai_api_adapter.py

@@ -6,7 +6,7 @@ import os
 from http import HTTPStatus
 
 from fastapi import Request
-from fastapi.responses import StreamingResponse, JSONResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 
 from sglang.srt.conversation import (
     Conversation,
@@ -40,21 +40,18 @@ chat_template_name = None
 def create_error_response(
     message: str,
     err_type: str = "BadRequestError",
-    status_code: HTTPStatus = HTTPStatus.BAD_REQUEST):
-    error = ErrorResponse(message=message,
-                          type=err_type,
-                          code=status_code.value)
-    return JSONResponse(content=error.model_dump(),
-                        status_code=error.code)
+    status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
+):
+    error = ErrorResponse(message=message, type=err_type, code=status_code.value)
+    return JSONResponse(content=error.model_dump(), status_code=error.code)
 
 
 def create_streaming_error_response(
     message: str,
     err_type: str = "BadRequestError",
-    status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> str:
-    error = ErrorResponse(message=message,
-                          type=err_type,
-                          code=status_code.value)
+    status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
+) -> str:
+    error = ErrorResponse(message=message, type=err_type, code=status_code.value)
     json_str = json.dumps({"error": error.model_dump()})
     return json_str
 
@@ -125,7 +122,8 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
             n_prev_token = 0
             try:
                 async for content in tokenizer_manager.generate_request(
-                    adapted_request, raw_request):
+                    adapted_request, raw_request
+                ):
                     text = content["text"]
                     prompt_tokens = content["meta_info"]["prompt_tokens"]
                     completion_tokens = content["meta_info"]["completion_tokens"]
@@ -154,12 +152,14 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
                             decode_token_logprobs=content["meta_info"][
                                 "decode_token_logprobs"
                             ][n_prev_token:],
-                            decode_top_logprobs=content["meta_info"]["decode_top_logprobs"][
-                                n_prev_token:
-                            ],
+                            decode_top_logprobs=content["meta_info"][
+                                "decode_top_logprobs"
+                            ][n_prev_token:],
                         )
 
-                        n_prev_token = len(content["meta_info"]["decode_token_logprobs"])
+                        n_prev_token = len(
+                            content["meta_info"]["decode_token_logprobs"]
+                        )
                     else:
                         logprobs = None
 
@@ -188,13 +188,17 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
                 yield f"data: {error}\n\n"
             yield "data: [DONE]\n\n"
 
-        return StreamingResponse(generate_stream_resp(), media_type="text/event-stream",
-                                 background=tokenizer_manager.create_abort_task(adapted_request))
+        return StreamingResponse(
+            generate_stream_resp(),
+            media_type="text/event-stream",
+            background=tokenizer_manager.create_abort_task(adapted_request),
+        )
 
     # Non-streaming response.
     try:
         ret = await tokenizer_manager.generate_request(
-            adapted_request, raw_request).__anext__()
+            adapted_request, raw_request
+        ).__anext__()
     except ValueError as e:
         return create_error_response(str(e))
 
@@ -299,7 +303,9 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
 
             stream_buffer = ""
             try:
-                async for content in tokenizer_manager.generate_request(adapted_request, raw_request):
+                async for content in tokenizer_manager.generate_request(
+                    adapted_request, raw_request
+                ):
                     if is_first:
                         # First chunk with role
                         is_first = False
@@ -334,13 +340,17 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
                 yield f"data: {error}\n\n"
             yield "data: [DONE]\n\n"
 
-        return StreamingResponse(generate_stream_resp(), media_type="text/event-stream",
-                                 background=tokenizer_manager.create_abort_task(adapted_request))
+        return StreamingResponse(
+            generate_stream_resp(),
+            media_type="text/event-stream",
+            background=tokenizer_manager.create_abort_task(adapted_request),
+        )
 
     # Non-streaming response.
     try:
         ret = await tokenizer_manager.generate_request(
-            adapted_request, raw_request).__anext__()
+            adapted_request, raw_request
+        ).__anext__()
     except ValueError as e:
         return create_error_response(str(e))
 

python/sglang/srt/server.py

@@ -13,7 +13,7 @@ import sys
 import threading
 import time
 from http import HTTPStatus
-from typing import Optional, Dict
+from typing import Dict, Optional
 
 # Fix a bug of Python threading
 setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
@@ -29,10 +29,14 @@ from fastapi.responses import JSONResponse, Response, StreamingResponse
 from sglang.backend.runtime_endpoint import RuntimeEndpoint
 from sglang.srt.constrained import disable_cache
 from sglang.srt.hf_transformers_utils import get_tokenizer
+from sglang.srt.managers.controller.manager_multi import (
+    start_controller_process as start_controller_process_multi,
+)
+from sglang.srt.managers.controller.manager_single import (
+    start_controller_process as start_controller_process_single,
+)
 from sglang.srt.managers.detokenizer_manager import start_detokenizer_process
 from sglang.srt.managers.io_struct import GenerateReqInput
-from sglang.srt.managers.controller.manager_single import start_controller_process as start_controller_process_single
-from sglang.srt.managers.controller.manager_multi import start_controller_process as start_controller_process_multi
 from sglang.srt.managers.tokenizer_manager import TokenizerManager
 from sglang.srt.openai_api_adapter import (
     load_chat_template_for_openai_api,
@@ -97,8 +101,11 @@ async def generate_request(obj: GenerateReqInput, request: Request):
                 yield f"data: {json.dumps(out, ensure_ascii=False)}\n\n"
             yield "data: [DONE]\n\n"
 
-        return StreamingResponse(stream_results(), media_type="text/event-stream",
-                                 background=tokenizer_manager.create_abort_task(obj))
+        return StreamingResponse(
+            stream_results(),
+            media_type="text/event-stream",
+            background=tokenizer_manager.create_abort_task(obj),
+        )
     else:
         try:
             ret = await tokenizer_manager.generate_request(obj, request).__anext__()

python/sglang/srt/utils.py

 """Common utilities."""
 
 import base64
-import multiprocessing
 import logging
+import multiprocessing
 import os
 import random
 import socket
@@ -17,12 +17,11 @@ import requests
 import rpyc
 import torch
 import triton
-from rpyc.utils.server import ThreadedServer
 from fastapi.responses import JSONResponse
 from packaging import version as pkg_version
+from rpyc.utils.server import ThreadedServer
 from starlette.middleware.base import BaseHTTPMiddleware
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -377,7 +376,7 @@ def init_rpyc_service(service: rpyc.Service, port: int):
         protocol_config={
             "allow_public_attrs": True,
             "allow_pickle": True,
-            "sync_request_timeout": 3600
+            "sync_request_timeout": 3600,
         },
     )
     t.logger.setLevel(logging.WARN)
@@ -396,7 +395,7 @@ def connect_to_rpyc_service(port, host="localhost"):
                 config={
                     "allow_public_attrs": True,
                     "allow_pickle": True,
-                    "sync_request_timeout": 3600
+                    "sync_request_timeout": 3600,
                 },
             )
             break
@@ -423,7 +422,9 @@ def suppress_other_loggers():
 
     vllm_default_logger.setLevel(logging.WARN)
     logging.getLogger("vllm.config").setLevel(logging.ERROR)
-    logging.getLogger("vllm.distributed.device_communicators.pynccl").setLevel(logging.WARN)
+    logging.getLogger("vllm.distributed.device_communicators.pynccl").setLevel(
+        logging.WARN
+    )
     logging.getLogger("vllm.selector").setLevel(logging.WARN)
     logging.getLogger("vllm.utils").setLevel(logging.WARN)
 
@@ -464,6 +465,7 @@ def monkey_patch_vllm_p2p_access_check(gpu_id: int):
     device_name = torch.cuda.get_device_name(gpu_id)
     if "RTX 40" not in device_name:
         import vllm.distributed.device_communicators.custom_all_reduce_utils as tgt
+
         setattr(tgt, "gpu_p2p_access_check", lambda *arg, **kwargs: True)
 
 
@@ -485,4 +487,3 @@ class APIKeyValidatorMiddleware(BaseHTTPMiddleware):
             )
         response = await call_next(request)
         return response
-

python/sglang/test/test_programs.py

@@ -356,16 +356,25 @@ def test_completion_speculative():
         s += "Construct a character within the following format:\n"
         s += "Name: Steve Jobs.\nBirthday: February 24, 1955.\nJob: Apple CEO.\n"
         s += "\nPlease generate new Name, Birthday and Job.\n"
-        s += "Name:" + sgl.gen("name", stop="\n") + "\nBirthday:" + sgl.gen("birthday", stop="\n")
+        s += (
+            "Name:"
+            + sgl.gen("name", stop="\n")
+            + "\nBirthday:"
+            + sgl.gen("birthday", stop="\n")
+        )
         s += "\nJob:" + sgl.gen("job", stop="\n") + "\n"
 
-
     @sgl.function
     def gen_character_no_spec(s):
         s += "Construct a character within the following format:\n"
         s += "Name: Steve Jobs.\nBirthday: February 24, 1955.\nJob: Apple CEO.\n"
         s += "\nPlease generate new Name, Birthday and Job.\n"
-        s += "Name:" + sgl.gen("name", stop="\n") + "\nBirthday:" + sgl.gen("birthday", stop="\n")
+        s += (
+            "Name:"
+            + sgl.gen("name", stop="\n")
+            + "\nBirthday:"
+            + sgl.gen("birthday", stop="\n")
+        )
         s += "\nJob:" + sgl.gen("job", stop="\n") + "\n"
 
     token_usage = sgl.global_config.default_backend.token_usage
@@ -378,7 +387,9 @@ def test_completion_speculative():
     gen_character_no_spec().sync()
     usage_with_no_spec = token_usage.prompt_tokens
 
-    assert usage_with_spec < usage_with_no_spec, f"{usage_with_spec} vs {usage_with_no_spec}"
+    assert (
+        usage_with_spec < usage_with_no_spec
+    ), f"{usage_with_spec} vs {usage_with_no_spec}"
 
 
 def test_chat_completion_speculative():
@@ -386,8 +397,17 @@ def test_chat_completion_speculative():
     def gen_character_spec(s):
         s += sgl.system("You are a helpful assistant.")
         s += sgl.user("Construct a character within the following format:")
-        s += sgl.assistant("Name: Steve Jobs.\nBirthday: February 24, 1955.\nJob: Apple CEO.\n")
+        s += sgl.assistant(
+            "Name: Steve Jobs.\nBirthday: February 24, 1955.\nJob: Apple CEO.\n"
+        )
         s += sgl.user("Please generate new Name, Birthday and Job.\n")
-        s += sgl.assistant("Name:" + sgl.gen("name", stop="\n") + "\nBirthday:" + sgl.gen("birthday", stop="\n") + "\nJob:" + sgl.gen("job", stop="\n"))
+        s += sgl.assistant(
+            "Name:"
+            + sgl.gen("name", stop="\n")
+            + "\nBirthday:"
+            + sgl.gen("birthday", stop="\n")
+            + "\nJob:"
+            + sgl.gen("job", stop="\n")
+        )
 
     gen_character_spec().sync()

python/sglang/utils.py

@@ -15,7 +15,6 @@ from json import dumps
 import numpy as np
 import requests
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -255,7 +254,9 @@ def run_with_timeout(func, args=(), kwargs=None, timeout=None):
 
 def graceful_registry(sub_module_name):
     def graceful_shutdown(signum, frame):
-        logger.info(f"{sub_module_name} Received signal to shutdown. Performing graceful shutdown...")
+        logger.info(
+            f"{sub_module_name} Received signal to shutdown. Performing graceful shutdown..."
+        )
         if signum == signal.SIGTERM:
             logger.info(f"{sub_module_name} recive sigterm")
 

test/lang/test_openai_backend.py

@@ -2,6 +2,8 @@ import unittest
 
 from sglang import OpenAI, set_default_backend
 from sglang.test.test_programs import (
+    test_chat_completion_speculative,
+    test_completion_speculative,
     test_decode_int,
     test_decode_json,
     test_expert_answer,
@@ -14,8 +16,6 @@ from sglang.test.test_programs import (
     test_select,
     test_stream,
     test_tool_use,
-    test_completion_speculative,
-    test_chat_completion_speculative
 )