Separate two entry points: Engine and HTTP server (#2996)

Co-authored-by: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com>

docs/references/supported_models.md

@@ -91,7 +91,7 @@ Here is how you can do it:
 
 ```python
 from sglang.srt.models.registry import ModelRegistry
-from sglang.srt.server import launch_server
+from sglang.srt.entrypoints.http_server import launch_server
 
 # for a single model, you can add it to the registry
 ModelRegistry.models[model_name] = model_class

python/sglang/api.py

@@ -40,7 +40,7 @@ def Runtime(*args, **kwargs):
 
 def Engine(*args, **kwargs):
     # Avoid importing unnecessary dependency
-    from sglang.srt.server import Engine
+    from sglang.srt.entrypoints.engine import Engine
 
     return Engine(*args, **kwargs)
 

python/sglang/bench_offline_throughput.py

@@ -28,7 +28,7 @@ from sglang.bench_serving import (
     set_ulimit,
 )
 from sglang.lang.backend.runtime_endpoint import Runtime
-from sglang.srt.server import Engine
+from sglang.srt.entrypoints.engine import Engine
 from sglang.srt.server_args import ServerArgs
 
 

python/sglang/bench_one_batch.py

@@ -57,12 +57,12 @@ import torch
 import torch.distributed as dist
 
 from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.entrypoints.engine import _set_envs_and_config
 from sglang.srt.hf_transformers_utils import get_tokenizer
 from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_executor.model_runner import ModelRunner
 from sglang.srt.sampling.sampling_params import SamplingParams
-from sglang.srt.server import _set_envs_and_config
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import configure_logger, kill_process_tree, suppress_other_loggers

python/sglang/bench_one_batch_server.py

@@ -22,7 +22,7 @@ from typing import Tuple
 import numpy as np
 import requests
 
-from sglang.srt.server import launch_server
+from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import kill_process_tree
 

python/sglang/lang/backend/runtime_endpoint.py

@@ -351,7 +351,7 @@ class Runtime:
         """See the arguments in server_args.py::ServerArgs"""
         # We delay the import of any `sglang.srt` components in `sglang.lang`, so users can run
         # client code without installing SRT server and its dependency if they want.
-        from sglang.srt.server import launch_server
+        from sglang.srt.entrypoints.http_server import launch_server
         from sglang.srt.server_args import ServerArgs
         from sglang.srt.utils import is_port_available
 

python/sglang/launch_server.py

@@ -3,7 +3,7 @@
 import os
 import sys
 
-from sglang.srt.server import launch_server
+from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import prepare_server_args
 from sglang.srt.utils import kill_process_tree
 

python/sglang/srt/entrypoints/engine.py

@@ -0,0 +1,449 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+The entry point of inference server. (SRT = SGLang Runtime)
+
+This file implements python APIs for the inference engine.
+"""
+
+import asyncio
+import atexit
+import dataclasses
+import logging
+import multiprocessing as mp
+import os
+import signal
+import threading
+from typing import AsyncIterator, Dict, Iterator, List, Optional, Tuple, Union
+
+# Fix a bug of Python threading
+setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
+
+import torch
+import uvloop
+
+from sglang.srt.managers.data_parallel_controller import (
+    run_data_parallel_controller_process,
+)
+from sglang.srt.managers.detokenizer_manager import run_detokenizer_process
+from sglang.srt.managers.io_struct import (
+    EmbeddingReqInput,
+    GenerateReqInput,
+    GetWeightsByNameReqInput,
+    InitWeightsUpdateGroupReqInput,
+    ReleaseMemoryOccupationReqInput,
+    ResumeMemoryOccupationReqInput,
+    UpdateWeightsFromDistributedReqInput,
+    UpdateWeightsFromTensorReqInput,
+)
+from sglang.srt.managers.scheduler import run_scheduler_process
+from sglang.srt.managers.tokenizer_manager import TokenizerManager
+from sglang.srt.openai_api.adapter import load_chat_template_for_openai_api
+from sglang.srt.server_args import PortArgs, ServerArgs
+from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
+from sglang.srt.utils import (
+    MultiprocessingSerializer,
+    assert_pkg_version,
+    configure_logger,
+    kill_process_tree,
+    maybe_set_triton_cache_manager,
+    prepare_model_and_tokenizer,
+    set_prometheus_multiproc_dir,
+    set_ulimit,
+)
+from sglang.version import __version__
+
+logger = logging.getLogger(__name__)
+asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
+
+
+class Engine:
+    """
+    The entry point to the inference engine.
+
+    - The engine consists of three components:
+        1. TokenizerManager: Tokenizes the requests and sends them to the scheduler.
+        2. Scheduler (subprocess): Receives requests from the Tokenizer Manager, schedules batches, forwards them, and sends the output tokens to the Detokenizer Manager.
+        3. DetokenizerManager (subprocess): Detokenizes the output tokens and sends the result back to the Tokenizer Manager.
+
+    Note:
+    1. The HTTP server, Engine, and TokenizerManager both run in the main process.
+    2. Inter-process communication is done through ICP (each process uses a different port) via the ZMQ library.
+    """
+
+    def __init__(self, **kwargs):
+        """
+        The arguments of this function is the same as `sglang/srt/server_args.py::ServerArgs`.
+        Please refer to `ServerArgs` for the documentation.
+        """
+        if "server_args" in kwargs:
+            # Directly load server_args
+            server_args = kwargs["server_args"]
+        else:
+            # Construct server_args from kwargs
+            if "log_level" not in kwargs:
+                # Do not print logs by default
+                kwargs["log_level"] = "error"
+            server_args = ServerArgs(**kwargs)
+
+        # Shutdown the subprocesses automatically when the program exists
+        atexit.register(self.shutdown)
+
+        # Launch subprocesses
+        tokenizer_manager, scheduler_info = _launch_subprocesses(
+            server_args=server_args
+        )
+        self.tokenizer_manager = tokenizer_manager
+        self.scheduler_info = scheduler_info
+
+    def generate(
+        self,
+        # The input prompt. It can be a single prompt or a batch of prompts.
+        prompt: Optional[Union[List[str], str]] = None,
+        sampling_params: Optional[Union[List[Dict], Dict]] = None,
+        # The token ids for text; one can either specify text or input_ids.
+        input_ids: Optional[Union[List[List[int]], List[int]]] = None,
+        return_logprob: Optional[Union[List[bool], bool]] = False,
+        logprob_start_len: Optional[Union[List[int], int]] = None,
+        top_logprobs_num: Optional[Union[List[int], int]] = None,
+        lora_path: Optional[List[Optional[str]]] = None,
+        custom_logit_processor: Optional[Union[List[str], str]] = None,
+        stream: bool = False,
+    ) -> Union[Dict, Iterator[Dict]]:
+        """
+        The arguments of this function is the same as `sglang/srt/managers/io_struct.py::GenerateReqInput`.
+        Please refer to `GenerateReqInput` for the documentation.
+        """
+        obj = GenerateReqInput(
+            text=prompt,
+            input_ids=input_ids,
+            sampling_params=sampling_params,
+            return_logprob=return_logprob,
+            logprob_start_len=logprob_start_len,
+            top_logprobs_num=top_logprobs_num,
+            lora_path=lora_path,
+            custom_logit_processor=custom_logit_processor,
+            stream=stream,
+        )
+        loop = asyncio.get_event_loop()
+        generator = self.tokenizer_manager.generate_request(obj, None)
+
+        if stream:
+
+            def generator_wrapper():
+                while True:
+                    try:
+                        chunk = loop.run_until_complete(generator.__anext__())
+                        yield chunk
+                    except StopAsyncIteration:
+                        break
+
+            return generator_wrapper()
+        else:
+            ret = loop.run_until_complete(generator.__anext__())
+            return ret
+
+    async def async_generate(
+        self,
+        # The input prompt. It can be a single prompt or a batch of prompts.
+        prompt: Optional[Union[List[str], str]] = None,
+        sampling_params: Optional[Union[List[Dict], Dict]] = None,
+        # The token ids for text; one can either specify text or input_ids.
+        input_ids: Optional[Union[List[List[int]], List[int]]] = None,
+        return_logprob: Optional[Union[List[bool], bool]] = False,
+        logprob_start_len: Optional[Union[List[int], int]] = None,
+        top_logprobs_num: Optional[Union[List[int], int]] = None,
+        lora_path: Optional[List[Optional[str]]] = None,
+        custom_logit_processor: Optional[Union[List[str], str]] = None,
+        stream: bool = False,
+    ) -> Union[Dict, AsyncIterator[Dict]]:
+        """
+        The arguments of this function is the same as `sglang/srt/managers/io_struct.py::GenerateReqInput`.
+        Please refer to `GenerateReqInput` for the documentation.
+        """
+        obj = GenerateReqInput(
+            text=prompt,
+            input_ids=input_ids,
+            sampling_params=sampling_params,
+            return_logprob=return_logprob,
+            logprob_start_len=logprob_start_len,
+            top_logprobs_num=top_logprobs_num,
+            lora_path=lora_path,
+            stream=stream,
+            custom_logit_processor=custom_logit_processor,
+        )
+        generator = self.tokenizer_manager.generate_request(obj, None)
+
+        if stream is True:
+            return generator
+        else:
+            return await generator.__anext__()
+
+    def encode(
+        self,
+        prompt: Union[str, List[str], List[Dict], List[List[Dict]]],
+    ) -> Dict:
+        """
+        The arguments of this function is the same as `sglang/srt/managers/io_struct.py::EmbeddingReqInput`.
+        Please refer to `EmbeddingReqInput` for the documentation.
+        """
+
+        obj = EmbeddingReqInput(text=prompt)
+        loop = asyncio.get_event_loop()
+        generator = self.tokenizer_manager.generate_request(obj, None)
+        ret = loop.run_until_complete(generator.__anext__())
+        return ret
+
+    def shutdown(self):
+        """Shutdown the engine"""
+        kill_process_tree(os.getpid(), include_parent=False)
+
+    def start_profile(self):
+        self.tokenizer_manager.start_profile()
+
+    def stop_profile(self):
+        self.tokenizer_manager.stop_profile()
+
+    def get_server_info(self):
+        return {
+            **dataclasses.asdict(self.tokenizer_manager.server_args),  # server args
+            **self.scheduler_info,
+            "version": __version__,
+        }
+
+    def init_weights_update_group(
+        self,
+        master_address: str,
+        master_port: int,
+        rank_offset: int,
+        world_size: int,
+        group_name: str,
+        backend: str = "nccl",
+    ):
+        """Initialize parameter update group."""
+        obj = InitWeightsUpdateGroupReqInput(
+            master_address=master_address,
+            master_port=master_port,
+            rank_offset=rank_offset,
+            world_size=world_size,
+            group_name=group_name,
+            backend=backend,
+        )
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.init_weights_update_group(obj, None)
+        )
+
+    def update_weights_from_distributed(self, name: str, dtype, shape):
+        """Update weights from distributed source."""
+        obj = UpdateWeightsFromDistributedReqInput(
+            name=name,
+            dtype=dtype,
+            shape=shape,
+        )
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.update_weights_from_distributed(obj, None)
+        )
+
+    def update_weights_from_tensor(self, named_tensors: List[Tuple[str, torch.Tensor]]):
+        """Update weights from distributed source."""
+        obj = UpdateWeightsFromTensorReqInput(
+            serialized_named_tensors=MultiprocessingSerializer.serialize(named_tensors)
+        )
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.update_weights_from_tensor(obj, None)
+        )
+
+    def get_weights_by_name(self, name: str, truncate_size: int = 100):
+        """Get weights by parameter name."""
+        obj = GetWeightsByNameReqInput(name=name, truncate_size=truncate_size)
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.get_weights_by_name(obj, None)
+        )
+
+    def release_memory_occupation(self):
+        """Release GPU occupation temporarily."""
+        obj = ReleaseMemoryOccupationReqInput()
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.release_memory_occupation(obj, None)
+        )
+
+    def resume_memory_occupation(self):
+        """Resume GPU occupation."""
+        obj = ResumeMemoryOccupationReqInput()
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.resume_memory_occupation(obj, None)
+        )
+
+
+def _set_envs_and_config(server_args: ServerArgs):
+    # Set global environments
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+    os.environ["NCCL_CUMEM_ENABLE"] = "0"
+    os.environ["NCCL_NVLS_ENABLE"] = "0"
+    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+    os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "4"
+
+    # Set prometheus env vars
+    if server_args.enable_metrics:
+        set_prometheus_multiproc_dir()
+
+    # Set ulimit
+    set_ulimit()
+
+    # Fix triton bugs
+    if server_args.tp_size * server_args.dp_size > 1:
+        # FIXME: remove this after https://github.com/triton-lang/triton/pull/4295 is used as a dependency.
+        maybe_set_triton_cache_manager()
+
+    # Check flashinfer version
+    if server_args.attention_backend == "flashinfer":
+        assert_pkg_version(
+            "flashinfer",
+            "0.1.6",
+            "Please uninstall the old version and "
+            "reinstall the latest version by following the instructions "
+            "at https://docs.flashinfer.ai/installation.html.",
+        )
+
+    # Register the signal handler.
+    # The child processes will send SIGQUIT to this process when any error happens
+    # This process then clean up the whole process tree
+    def sigquit_handler(signum, frame):
+        logger.error(
+            "Received sigquit from a child proces. It usually means the child failed."
+        )
+        kill_process_tree(os.getpid())
+
+    signal.signal(signal.SIGQUIT, sigquit_handler)
+
+    # Set mp start method
+    mp.set_start_method("spawn", force=True)
+
+
+def _launch_subprocesses(server_args: ServerArgs) -> Tuple[TokenizerManager, Dict]:
+    """
+    Launch the TokenizerManager in the main process, the Scheduler in a subprocess, and the DetokenizerManager in another subprocess.
+    """
+    # Configure global environment
+    configure_logger(server_args)
+    server_args.check_server_args()
+    _set_envs_and_config(server_args)
+
+    # Allocate ports for inter-process communications
+    port_args = PortArgs.init_new(server_args)
+    logger.info(f"{server_args=}")
+
+    # If using model from www.modelscope.cn, first download the model.
+    server_args.model_path, server_args.tokenizer_path = prepare_model_and_tokenizer(
+        server_args.model_path, server_args.tokenizer_path
+    )
+
+    scheduler_procs = []
+    if server_args.dp_size == 1:
+        # Launch tensor parallel scheduler processes
+        memory_saver_adapter = TorchMemorySaverAdapter.create(
+            enable=server_args.enable_memory_saver
+        )
+
+        scheduler_pipe_readers = []
+        tp_size_per_node = server_args.tp_size // server_args.nnodes
+        tp_rank_range = range(
+            tp_size_per_node * server_args.node_rank,
+            tp_size_per_node * (server_args.node_rank + 1),
+        )
+        for tp_rank in tp_rank_range:
+            reader, writer = mp.Pipe(duplex=False)
+            gpu_id = server_args.base_gpu_id + tp_rank % tp_size_per_node
+            proc = mp.Process(
+                target=run_scheduler_process,
+                args=(server_args, port_args, gpu_id, tp_rank, None, writer),
+            )
+            with memory_saver_adapter.configure_subprocess():
+                proc.start()
+            scheduler_procs.append(proc)
+            scheduler_pipe_readers.append(reader)
+    else:
+        # Launch the data parallel controller
+        reader, writer = mp.Pipe(duplex=False)
+        scheduler_pipe_readers = [reader]
+        proc = mp.Process(
+            target=run_data_parallel_controller_process,
+            args=(server_args, port_args, writer),
+        )
+        proc.start()
+        scheduler_procs.append(proc)
+
+    if server_args.node_rank >= 1:
+        # In multi-node cases, non-zero rank nodes do not need to run tokenizer or detokenizer,
+        # so they can just wait here.
+
+        for reader in scheduler_pipe_readers:
+            data = reader.recv()
+            assert data["status"] == "ready"
+
+        if os.getenv("SGLANG_BLOCK_NONZERO_RANK_CHILDREN") == "0":
+            # When using `Engine` as a Python API, we don't want to block here.
+            return
+
+        for proc in scheduler_procs:
+            proc.join()
+            logger.error(
+                f"Scheduler or DataParallelController {proc.pid} terminated with {proc.exitcode}"
+            )
+        return
+
+    # Launch detokenizer process
+    detoken_proc = mp.Process(
+        target=run_detokenizer_process,
+        args=(
+            server_args,
+            port_args,
+        ),
+    )
+    detoken_proc.start()
+
+    # Launch tokenizer process
+    tokenizer_manager = TokenizerManager(server_args, port_args)
+    if server_args.chat_template:
+        load_chat_template_for_openai_api(tokenizer_manager, server_args.chat_template)
+
+    # Wait for the model to finish loading
+    scheduler_infos = []
+    for i in range(len(scheduler_pipe_readers)):
+        try:
+            data = scheduler_pipe_readers[i].recv()
+        except EOFError:
+            logger.error(
+                f"Rank {i} scheduler is dead. Please check if there are relevant logs."
+            )
+            scheduler_procs[i].join()
+            logger.error(f"Exit code: {scheduler_procs[i].exitcode}")
+            raise
+
+        if data["status"] != "ready":
+            raise RuntimeError(
+                "Initialization failed. Please see the error messages above."
+            )
+        scheduler_infos.append(data)
+
+    # Assume all schedulers have the same scheduler_info
+    scheduler_info = scheduler_infos[0]
+    tokenizer_manager.max_req_input_len = scheduler_info["max_req_input_len"]
+    return tokenizer_manager, scheduler_info

python/sglang/srt/entrypoints/http_server.py

@@ -0,0 +1,579 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+The entry point of inference server. (SRT = SGLang Runtime)
+
+This file implements HTTP APIs for the inferenc engine via fastapi.
+"""
+
+import asyncio
+import dataclasses
+import logging
+import multiprocessing as multiprocessing
+import os
+import threading
+import time
+from http import HTTPStatus
+from typing import AsyncIterator, Dict, Optional
+
+# Fix a bug of Python threading
+setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
+
+import orjson
+import requests
+import uvicorn
+import uvloop
+from fastapi import FastAPI, File, Form, Request, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import ORJSONResponse, Response, StreamingResponse
+
+from sglang.srt.entrypoints.engine import _launch_subprocesses
+from sglang.srt.managers.io_struct import (
+    CloseSessionReqInput,
+    ConfigureLoggingReq,
+    EmbeddingReqInput,
+    GenerateReqInput,
+    GetWeightsByNameReqInput,
+    InitWeightsUpdateGroupReqInput,
+    OpenSessionReqInput,
+    ReleaseMemoryOccupationReqInput,
+    ResumeMemoryOccupationReqInput,
+    UpdateWeightFromDiskReqInput,
+    UpdateWeightsFromDistributedReqInput,
+)
+from sglang.srt.managers.tokenizer_manager import TokenizerManager
+from sglang.srt.metrics.func_timer import enable_func_timer
+from sglang.srt.openai_api.adapter import (
+    v1_batches,
+    v1_cancel_batch,
+    v1_chat_completions,
+    v1_completions,
+    v1_delete_file,
+    v1_embeddings,
+    v1_files_create,
+    v1_retrieve_batch,
+    v1_retrieve_file,
+    v1_retrieve_file_content,
+)
+from sglang.srt.openai_api.protocol import ModelCard, ModelList
+from sglang.srt.server_args import ServerArgs
+from sglang.srt.utils import (
+    add_api_key_middleware,
+    add_prometheus_middleware,
+    delete_directory,
+    kill_process_tree,
+    set_uvicorn_logging_configs,
+)
+from sglang.utils import get_exception_traceback
+from sglang.version import __version__
+
+logger = logging.getLogger(__name__)
+asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
+
+# Fast API
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# Store global states
+@dataclasses.dataclass
+class _GlobalState:
+    tokenizer_manager: TokenizerManager
+    scheduler_info: Dict
+
+
+_global_state: Optional[_GlobalState] = None
+
+
+def set_global_state(global_state: _GlobalState):
+    global _global_state
+    _global_state = global_state
+
+
+##### Native API endpoints #####
+
+
+@app.get("/health")
+async def health() -> Response:
+    """Check the health of the http server."""
+    return Response(status_code=200)
+
+
+@app.get("/health_generate")
+async def health_generate(request: Request) -> Response:
+    """Check the health of the inference server by generating one token."""
+
+    sampling_params = {"max_new_tokens": 1, "temperature": 0.7}
+
+    if _global_state.tokenizer_manager.is_generation:
+        gri = GenerateReqInput(
+            input_ids=[0], sampling_params=sampling_params, log_metrics=False
+        )
+    else:
+        gri = EmbeddingReqInput(
+            input_ids=[0], sampling_params=sampling_params, log_metrics=False
+        )
+
+    try:
+        async for _ in _global_state.tokenizer_manager.generate_request(gri, request):
+            break
+        return Response(status_code=200)
+    except Exception as e:
+        logger.exception(e)
+        return Response(status_code=503)
+
+
+@app.get("/get_model_info")
+async def get_model_info():
+    """Get the model information."""
+    result = {
+        "model_path": _global_state.tokenizer_manager.model_path,
+        "tokenizer_path": _global_state.tokenizer_manager.server_args.tokenizer_path,
+        "is_generation": _global_state.tokenizer_manager.is_generation,
+    }
+    return result
+
+
+@app.get("/get_server_info")
+async def get_server_info():
+    return {
+        **dataclasses.asdict(_global_state.tokenizer_manager.server_args),
+        **_global_state.scheduler_info,
+        "version": __version__,
+    }
+
+
+# fastapi implicitly converts json in the request to obj (dataclass)
+@app.api_route("/generate", methods=["POST", "PUT"])
+async def generate_request(obj: GenerateReqInput, request: Request):
+    """Handle a generate request."""
+    if obj.stream:
+
+        async def stream_results() -> AsyncIterator[bytes]:
+            try:
+                async for out in _global_state.tokenizer_manager.generate_request(
+                    obj, request
+                ):
+                    yield b"data: " + orjson.dumps(
+                        out, option=orjson.OPT_NON_STR_KEYS
+                    ) + b"\n\n"
+            except ValueError as e:
+                out = {"error": {"message": str(e)}}
+                yield b"data: " + orjson.dumps(
+                    out, option=orjson.OPT_NON_STR_KEYS
+                ) + b"\n\n"
+            yield b"data: [DONE]\n\n"
+
+        return StreamingResponse(
+            stream_results(),
+            media_type="text/event-stream",
+            background=_global_state.tokenizer_manager.create_abort_task(obj),
+        )
+    else:
+        try:
+            ret = await _global_state.tokenizer_manager.generate_request(
+                obj, request
+            ).__anext__()
+            return ret
+        except ValueError as e:
+            logger.error(f"Error: {e}")
+            return _create_error_response(e)
+
+
+@app.api_route("/encode", methods=["POST", "PUT"])
+async def encode_request(obj: EmbeddingReqInput, request: Request):
+    """Handle an embedding request."""
+    try:
+        ret = await _global_state.tokenizer_manager.generate_request(
+            obj, request
+        ).__anext__()
+        return ret
+    except ValueError as e:
+        return _create_error_response(e)
+
+
+@app.api_route("/classify", methods=["POST", "PUT"])
+async def classify_request(obj: EmbeddingReqInput, request: Request):
+    """Handle a reward model request. Now the arguments and return values are the same as embedding models."""
+    try:
+        ret = await _global_state.tokenizer_manager.generate_request(
+            obj, request
+        ).__anext__()
+        return ret
+    except ValueError as e:
+        return _create_error_response(e)
+
+
+@app.post("/flush_cache")
+async def flush_cache():
+    """Flush the radix cache."""
+    _global_state.tokenizer_manager.flush_cache()
+    return Response(
+        content="Cache flushed.\nPlease check backend logs for more details. "
+        "(When there are running or waiting requests, the operation will not be performed.)\n",
+        status_code=200,
+    )
+
+
+@app.api_route("/start_profile", methods=["GET", "POST"])
+async def start_profile_async():
+    """Start profiling."""
+    _global_state.tokenizer_manager.start_profile()
+    return Response(
+        content="Start profiling.\n",
+        status_code=200,
+    )
+
+
+@app.api_route("/stop_profile", methods=["GET", "POST"])
+async def stop_profile_async():
+    """Stop profiling."""
+    _global_state.tokenizer_manager.stop_profile()
+    return Response(
+        content="Stop profiling. This will take some time.\n",
+        status_code=200,
+    )
+
+
+@app.post("/update_weights_from_disk")
+async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: Request):
+    """Update the weights from disk in-place without re-launching the server."""
+    success, message = await _global_state.tokenizer_manager.update_weights_from_disk(
+        obj, request
+    )
+    content = {"success": success, "message": message}
+    if success:
+        return ORJSONResponse(
+            content,
+            status_code=HTTPStatus.OK,
+        )
+    else:
+        return ORJSONResponse(
+            content,
+            status_code=HTTPStatus.BAD_REQUEST,
+        )
+
+
+@app.post("/init_weights_update_group")
+async def init_weights_update_group(
+    obj: InitWeightsUpdateGroupReqInput, request: Request
+):
+    """Initialize the parameter update group."""
+    success, message = await _global_state.tokenizer_manager.init_weights_update_group(
+        obj, request
+    )
+    content = {"success": success, "message": message}
+    if success:
+        return ORJSONResponse(content, status_code=200)
+    else:
+        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)
+
+
+@app.post("/update_weights_from_distributed")
+async def update_weights_from_distributed(
+    obj: UpdateWeightsFromDistributedReqInput, request: Request
+):
+    """Update model parameter from distributed online."""
+    success, message = (
+        await _global_state.tokenizer_manager.update_weights_from_distributed(
+            obj, request
+        )
+    )
+    content = {"success": success, "message": message}
+    if success:
+        return ORJSONResponse(content, status_code=200)
+    else:
+        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)
+
+
+@app.api_route("/get_weights_by_name", methods=["GET", "POST"])
+async def get_weights_by_name(obj: GetWeightsByNameReqInput, request: Request):
+    """Get model parameter by name."""
+    try:
+        ret = await _global_state.tokenizer_manager.get_weights_by_name(obj, request)
+        if ret is None:
+            return _create_error_response("Get parameter by name failed")
+        else:
+            return ORJSONResponse(ret, status_code=200)
+    except Exception as e:
+        return _create_error_response(e)
+
+
+@app.api_route("/release_memory_occupation", methods=["GET", "POST"])
+async def release_memory_occupation(
+    obj: ReleaseMemoryOccupationReqInput, request: Request
+):
+    """Release GPU occupation temporarily"""
+    try:
+        await _global_state.tokenizer_manager.release_memory_occupation(obj, request)
+    except Exception as e:
+        return _create_error_response(e)
+
+
+@app.api_route("/resume_memory_occupation", methods=["GET", "POST"])
+async def resume_memory_occupation(
+    obj: ResumeMemoryOccupationReqInput, request: Request
+):
+    """Resume GPU occupation"""
+    try:
+        await _global_state.tokenizer_manager.resume_memory_occupation(obj, request)
+    except Exception as e:
+        return _create_error_response(e)
+
+
+@app.api_route("/open_session", methods=["GET", "POST"])
+async def open_session(obj: OpenSessionReqInput, request: Request):
+    """Open a session, and return its unique session id."""
+    try:
+        session_id = await _global_state.tokenizer_manager.open_session(obj, request)
+        if session_id is None:
+            raise Exception(
+                "Failed to open the session. Check if a session with the same id is still open."
+            )
+        return session_id
+    except Exception as e:
+        return _create_error_response(e)
+
+
+@app.api_route("/close_session", methods=["GET", "POST"])
+async def close_session(obj: CloseSessionReqInput, request: Request):
+    """Close the session"""
+    try:
+        await _global_state.tokenizer_manager.close_session(obj, request)
+        return Response(status_code=200)
+    except Exception as e:
+        return _create_error_response(e)
+
+
+@app.api_route("/configure_logging", methods=["GET", "POST"])
+async def configure_logging(obj: ConfigureLoggingReq, request: Request):
+    """Close the session"""
+    _global_state.tokenizer_manager.configure_logging(obj)
+    return Response(status_code=200)
+
+
+##### OpenAI-compatible API endpoints #####
+
+
+@app.post("/v1/completions")
+async def openai_v1_completions(raw_request: Request):
+    return await v1_completions(_global_state.tokenizer_manager, raw_request)
+
+
+@app.post("/v1/chat/completions")
+async def openai_v1_chat_completions(raw_request: Request):
+    return await v1_chat_completions(_global_state.tokenizer_manager, raw_request)
+
+
+@app.post("/v1/embeddings", response_class=ORJSONResponse)
+async def openai_v1_embeddings(raw_request: Request):
+    response = await v1_embeddings(_global_state.tokenizer_manager, raw_request)
+    return response
+
+
+@app.get("/v1/models", response_class=ORJSONResponse)
+def available_models():
+    """Show available models."""
+    served_model_names = [_global_state.tokenizer_manager.served_model_name]
+    model_cards = []
+    for served_model_name in served_model_names:
+        model_cards.append(ModelCard(id=served_model_name, root=served_model_name))
+    return ModelList(data=model_cards)
+
+
+@app.post("/v1/files")
+async def openai_v1_files(file: UploadFile = File(...), purpose: str = Form("batch")):
+    return await v1_files_create(
+        file, purpose, _global_state.tokenizer_manager.server_args.file_storage_pth
+    )
+
+
+@app.delete("/v1/files/{file_id}")
+async def delete_file(file_id: str):
+    # https://platform.openai.com/docs/api-reference/files/delete
+    return await v1_delete_file(file_id)
+
+
+@app.post("/v1/batches")
+async def openai_v1_batches(raw_request: Request):
+    return await v1_batches(_global_state.tokenizer_manager, raw_request)
+
+
+@app.post("/v1/batches/{batch_id}/cancel")
+async def cancel_batches(batch_id: str):
+    # https://platform.openai.com/docs/api-reference/batch/cancel
+    return await v1_cancel_batch(_global_state.tokenizer_manager, batch_id)
+
+
+@app.get("/v1/batches/{batch_id}")
+async def retrieve_batch(batch_id: str):
+    return await v1_retrieve_batch(batch_id)
+
+
+@app.get("/v1/files/{file_id}")
+async def retrieve_file(file_id: str):
+    # https://platform.openai.com/docs/api-reference/files/retrieve
+    return await v1_retrieve_file(file_id)
+
+
+@app.get("/v1/files/{file_id}/content")
+async def retrieve_file_content(file_id: str):
+    # https://platform.openai.com/docs/api-reference/files/retrieve-contents
+    return await v1_retrieve_file_content(file_id)
+
+
+def _create_error_response(e):
+    return ORJSONResponse(
+        {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST
+    )
+
+
+def launch_server(
+    server_args: ServerArgs,
+    pipe_finish_writer: Optional[multiprocessing.connection.Connection] = None,
+):
+    """
+    Launch SRT (SGLang Runtime) Server.
+
+    The SRT server consists of an HTTP server and an SRT engine.
+
+    - HTTP server: A FastAPI server that routes requests to the engine.
+    - The engine consists of three components:
+        1. TokenizerManager: Tokenizes the requests and sends them to the scheduler.
+        2. Scheduler (subprocess): Receives requests from the Tokenizer Manager, schedules batches, forwards them, and sends the output tokens to the Detokenizer Manager.
+        3. DetokenizerManager (subprocess): Detokenizes the output tokens and sends the result back to the Tokenizer Manager.
+
+    Note:
+    1. The HTTP server, Engine, and TokenizerManager both run in the main process.
+    2. Inter-process communication is done through ICP (each process uses a different port) via the ZMQ library.
+    """
+    tokenizer_manager, scheduler_info = _launch_subprocesses(server_args=server_args)
+    set_global_state(
+        _GlobalState(
+            tokenizer_manager=tokenizer_manager,
+            scheduler_info=scheduler_info,
+        )
+    )
+
+    # Add api key authorization
+    if server_args.api_key:
+        add_api_key_middleware(app, server_args.api_key)
+
+    # Add prometheus middleware
+    if server_args.enable_metrics:
+        add_prometheus_middleware(app)
+        enable_func_timer()
+
+    # Send a warmup request
+    t = threading.Thread(
+        target=_wait_and_warmup,
+        args=(
+            server_args,
+            pipe_finish_writer,
+            _global_state.tokenizer_manager.image_token_id,
+        ),
+    )
+    t.start()
+
+    try:
+        # Update logging configs
+        set_uvicorn_logging_configs()
+
+        # Listen for HTTP requests
+        uvicorn.run(
+            app,
+            host=server_args.host,
+            port=server_args.port,
+            log_level=server_args.log_level_http or server_args.log_level,
+            timeout_keep_alive=5,
+            loop="uvloop",
+        )
+    finally:
+        t.join()
+
+
+def _wait_and_warmup(server_args, pipe_finish_writer, image_token_text):
+    headers = {}
+    url = server_args.url()
+    if server_args.api_key:
+        headers["Authorization"] = f"Bearer {server_args.api_key}"
+
+    # Wait until the server is launched
+    success = False
+    for _ in range(120):
+        time.sleep(1)
+        try:
+            res = requests.get(url + "/get_model_info", timeout=5, headers=headers)
+            assert res.status_code == 200, f"{res=}, {res.text=}"
+            success = True
+            break
+        except (AssertionError, requests.exceptions.RequestException):
+            last_traceback = get_exception_traceback()
+            pass
+
+    if not success:
+        if pipe_finish_writer is not None:
+            pipe_finish_writer.send(last_traceback)
+        logger.error(f"Initialization failed. warmup error: {last_traceback}")
+        kill_process_tree(os.getpid())
+        return
+
+    model_info = res.json()
+
+    # Send a warmup request
+    request_name = "/generate" if model_info["is_generation"] else "/encode"
+    max_new_tokens = 8 if model_info["is_generation"] else 1
+    json_data = {
+        "sampling_params": {
+            "temperature": 0,
+            "max_new_tokens": max_new_tokens,
+        },
+    }
+    if server_args.skip_tokenizer_init:
+        json_data["input_ids"] = [10, 11, 12]
+    else:
+        json_data["text"] = "The capital city of France is"
+
+    try:
+        for _ in range(server_args.dp_size):
+            res = requests.post(
+                url + request_name,
+                json=json_data,
+                headers=headers,
+                timeout=600,
+            )
+            assert res.status_code == 200, f"{res}"
+    except Exception:
+        last_traceback = get_exception_traceback()
+        if pipe_finish_writer is not None:
+            pipe_finish_writer.send(last_traceback)
+        logger.error(f"Initialization failed. warmup error: {last_traceback}")
+        kill_process_tree(os.getpid())
+        return
+
+    # Debug print
+    # logger.info(f"{res.json()=}")
+
+    logger.info("The server is fired up and ready to roll!")
+    if pipe_finish_writer is not None:
+        pipe_finish_writer.send("ready")
+
+    if server_args.delete_ckpt_after_loading:
+        delete_directory(server_args.model_path)

python/sglang/srt/managers/io_struct.py

@@ -22,7 +22,6 @@ from enum import Enum
 from typing import Dict, List, Optional, Union
 
 from sglang.srt.managers.schedule_batch import BaseFinishReason
-from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
 from sglang.srt.sampling.sampling_params import SamplingParams
 
 

python/sglang/srt/managers/tokenizer_manager.py

@@ -176,7 +176,7 @@ class TokenizerManager:
                 )
 
         # Store states
-        self.to_create_loop = True
+        self.no_create_loop = False
         self.rid_to_state: Dict[str, ReqState] = {}
         self.dump_requests_folder = ""  # By default do not dump
         self.dump_requests_threshold = 1000
@@ -684,7 +684,6 @@ class TokenizerManager:
     async def close_session(
         self, obj: CloseSessionReqInput, request: Optional[fastapi.Request] = None
     ):
-        assert not self.to_create_loop, "close session should not be the first request"
         await self.send_to_scheduler.send_pyobj(obj)
 
     def configure_logging(self, obj: ConfigureLoggingReq):
@@ -713,10 +712,10 @@ class TokenizerManager:
         return background_tasks
 
     def auto_create_handle_loop(self):
-        if not self.to_create_loop:
+        if self.no_create_loop:
             return
 
-        self.to_create_loop = False
+        self.no_create_loop = True
         loop = asyncio.get_event_loop()
         self.asyncio_tasks.add(
             loop.create_task(print_exception_wrapper(self.handle_loop))

python/sglang/srt/server.py

@@ -11,949 +11,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""
-The entry point of inference server.
-SRT = SGLang Runtime.
-"""
 
-import asyncio
+# Some shortcuts for backward compatbility.
-import atexit
+# They will be removed in new versions.
-import dataclasses
+from sglang.srt.entrypoints.engine import Engine
-import json
+from sglang.srt.entrypoints.http_server import launch_server
-import logging
-import multiprocessing as mp
-import os
-import signal
-import threading
-import time
-from http import HTTPStatus
-from typing import AsyncIterator, Dict, List, Optional, Tuple, Union
-
-import torch
-
-from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
-
-# Fix a bug of Python threading
-setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
-
-import aiohttp
-import orjson
-import requests
-import uvicorn
-import uvloop
-from fastapi import FastAPI, File, Form, Request, UploadFile
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import ORJSONResponse, Response, StreamingResponse
-
-from sglang.srt.managers.data_parallel_controller import (
-    run_data_parallel_controller_process,
-)
-from sglang.srt.managers.detokenizer_manager import run_detokenizer_process
-from sglang.srt.managers.io_struct import (
-    CloseSessionReqInput,
-    ConfigureLoggingReq,
-    EmbeddingReqInput,
-    GenerateReqInput,
-    GetWeightsByNameReqInput,
-    InitWeightsUpdateGroupReqInput,
-    OpenSessionReqInput,
-    ReleaseMemoryOccupationReqInput,
-    ResumeMemoryOccupationReqInput,
-    UpdateWeightFromDiskReqInput,
-    UpdateWeightsFromDistributedReqInput,
-    UpdateWeightsFromTensorReqInput,
-)
-from sglang.srt.managers.scheduler import run_scheduler_process
-from sglang.srt.managers.tokenizer_manager import TokenizerManager
-from sglang.srt.metrics.func_timer import enable_func_timer, time_func_latency
-from sglang.srt.openai_api.adapter import (
-    load_chat_template_for_openai_api,
-    v1_batches,
-    v1_cancel_batch,
-    v1_chat_completions,
-    v1_completions,
-    v1_delete_file,
-    v1_embeddings,
-    v1_files_create,
-    v1_retrieve_batch,
-    v1_retrieve_file,
-    v1_retrieve_file_content,
-)
-from sglang.srt.openai_api.protocol import ModelCard, ModelList
-from sglang.srt.server_args import PortArgs, ServerArgs
-from sglang.srt.utils import (
-    MultiprocessingSerializer,
-    add_api_key_middleware,
-    add_prometheus_middleware,
-    assert_pkg_version,
-    configure_logger,
-    delete_directory,
-    kill_process_tree,
-    maybe_set_triton_cache_manager,
-    prepare_model_and_tokenizer,
-    set_prometheus_multiproc_dir,
-    set_ulimit,
-    set_uvicorn_logging_configs,
-)
-from sglang.utils import get_exception_traceback
-from sglang.version import __version__
-
-logger = logging.getLogger(__name__)
-
-asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
-
-# Fast API
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-tokenizer_manager: TokenizerManager = None
-scheduler_info: Dict = None
-
-
-##### Native API endpoints #####
-
-
-@app.get("/health")
-async def health() -> Response:
-    """Check the health of the http server."""
-    return Response(status_code=200)
-
-
-@app.get("/health_generate")
-async def health_generate(request: Request) -> Response:
-    """Check the health of the inference server by generating one token."""
-
-    sampling_params = {"max_new_tokens": 1, "temperature": 0.7}
-
-    if tokenizer_manager.is_generation:
-        gri = GenerateReqInput(
-            input_ids=[0], sampling_params=sampling_params, log_metrics=False
-        )
-    else:
-        gri = EmbeddingReqInput(
-            input_ids=[0], sampling_params=sampling_params, log_metrics=False
-        )
-
-    try:
-        async for _ in tokenizer_manager.generate_request(gri, request):
-            break
-        return Response(status_code=200)
-    except Exception as e:
-        logger.exception(e)
-        return Response(status_code=503)
-
-
-@app.get("/get_model_info")
-async def get_model_info():
-    """Get the model information."""
-    result = {
-        "model_path": tokenizer_manager.model_path,
-        "tokenizer_path": tokenizer_manager.server_args.tokenizer_path,
-        "is_generation": tokenizer_manager.is_generation,
-    }
-    return result
-
-
-@app.get("/get_server_info")
-async def get_server_info():
-    return {
-        **dataclasses.asdict(tokenizer_manager.server_args),
-        **scheduler_info,
-        "version": __version__,
-    }
-
-
-# fastapi implicitly converts json in the request to obj (dataclass)
-@app.api_route("/generate", methods=["POST", "PUT"])
-@time_func_latency
-async def generate_request(obj: GenerateReqInput, request: Request):
-    """Handle a generate request."""
-    if obj.stream:
-
-        async def stream_results() -> AsyncIterator[bytes]:
-            try:
-                async for out in tokenizer_manager.generate_request(obj, request):
-                    yield b"data: " + orjson.dumps(
-                        out, option=orjson.OPT_NON_STR_KEYS
-                    ) + b"\n\n"
-            except ValueError as e:
-                out = {"error": {"message": str(e)}}
-                yield b"data: " + orjson.dumps(
-                    out, option=orjson.OPT_NON_STR_KEYS
-                ) + b"\n\n"
-            yield b"data: [DONE]\n\n"
-
-        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__()
-            return ret
-        except ValueError as e:
-            logger.error(f"Error: {e}")
-            return _create_error_response(e)
-
-
-@app.api_route("/encode", methods=["POST", "PUT"])
-@time_func_latency
-async def encode_request(obj: EmbeddingReqInput, request: Request):
-    """Handle an embedding request."""
-    try:
-        ret = await tokenizer_manager.generate_request(obj, request).__anext__()
-        return ret
-    except ValueError as e:
-        return _create_error_response(e)
-
-
-@app.api_route("/classify", methods=["POST", "PUT"])
-@time_func_latency
-async def classify_request(obj: EmbeddingReqInput, request: Request):
-    """Handle a reward model request. Now the arguments and return values are the same as embedding models."""
-    try:
-        ret = await tokenizer_manager.generate_request(obj, request).__anext__()
-        return ret
-    except ValueError as e:
-        return _create_error_response(e)
-
-
-@app.post("/flush_cache")
-async def flush_cache():
-    """Flush the radix cache."""
-    tokenizer_manager.flush_cache()
-    return Response(
-        content="Cache flushed.\nPlease check backend logs for more details. "
-        "(When there are running or waiting requests, the operation will not be performed.)\n",
-        status_code=200,
-    )
-
-
-@app.api_route("/start_profile", methods=["GET", "POST"])
-async def start_profile_async():
-    """Start profiling."""
-    tokenizer_manager.start_profile()
-    return Response(
-        content="Start profiling.\n",
-        status_code=200,
-    )
-
-
-@app.api_route("/stop_profile", methods=["GET", "POST"])
-async def stop_profile_async():
-    """Stop profiling."""
-    tokenizer_manager.stop_profile()
-    return Response(
-        content="Stop profiling. This will take some time.\n",
-        status_code=200,
-    )
-
-
-@app.post("/update_weights_from_disk")
-@time_func_latency
-async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: Request):
-    """Update the weights from disk in-place without re-launching the server."""
-    success, message = await tokenizer_manager.update_weights_from_disk(obj, request)
-    content = {"success": success, "message": message}
-    if success:
-        return ORJSONResponse(
-            content,
-            status_code=HTTPStatus.OK,
-        )
-    else:
-        return ORJSONResponse(
-            content,
-            status_code=HTTPStatus.BAD_REQUEST,
-        )
-
-
-@app.post("/init_weights_update_group")
-async def init_weights_update_group(
-    obj: InitWeightsUpdateGroupReqInput, request: Request
-):
-    """Initialize the parameter update group."""
-    success, message = await tokenizer_manager.init_weights_update_group(obj, request)
-    content = {"success": success, "message": message}
-    if success:
-        return ORJSONResponse(content, status_code=200)
-    else:
-        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)
-
-
-@app.post("/update_weights_from_distributed")
-async def update_weights_from_distributed(
-    obj: UpdateWeightsFromDistributedReqInput, request: Request
-):
-    """Update model parameter from distributed online."""
-    success, message = await tokenizer_manager.update_weights_from_distributed(
-        obj, request
-    )
-    content = {"success": success, "message": message}
-    if success:
-        return ORJSONResponse(content, status_code=200)
-    else:
-        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)
-
-
-@app.api_route("/get_weights_by_name", methods=["GET", "POST"])
-async def get_weights_by_name(obj: GetWeightsByNameReqInput, request: Request):
-    """Get model parameter by name."""
-    try:
-        ret = await tokenizer_manager.get_weights_by_name(obj, request)
-        if ret is None:
-            return _create_error_response("Get parameter by name failed")
-        else:
-            return ORJSONResponse(ret, status_code=200)
-    except Exception as e:
-        return _create_error_response(e)
-
-
-@app.api_route("/release_memory_occupation", methods=["GET", "POST"])
-async def release_memory_occupation(
-    obj: ReleaseMemoryOccupationReqInput, request: Request
-):
-    """Release GPU occupation temporarily"""
-    try:
-        await tokenizer_manager.release_memory_occupation(obj, request)
-    except Exception as e:
-        return _create_error_response(e)
-
-
-@app.api_route("/resume_memory_occupation", methods=["GET", "POST"])
-async def resume_memory_occupation(
-    obj: ResumeMemoryOccupationReqInput, request: Request
-):
-    """Resume GPU occupation"""
-    try:
-        await tokenizer_manager.resume_memory_occupation(obj, request)
-    except Exception as e:
-        return _create_error_response(e)
-
-
-@app.api_route("/open_session", methods=["GET", "POST"])
-async def open_session(obj: OpenSessionReqInput, request: Request):
-    """Open a session, and return its unique session id."""
-    try:
-        session_id = await tokenizer_manager.open_session(obj, request)
-        if session_id is None:
-            raise Exception(
-                "Failed to open the session. Check if a session with the same id is still open."
-            )
-        return session_id
-    except Exception as e:
-        return _create_error_response(e)
-
-
-@app.api_route("/close_session", methods=["GET", "POST"])
-async def close_session(obj: CloseSessionReqInput, request: Request):
-    """Close the session"""
-    try:
-        await tokenizer_manager.close_session(obj, request)
-        return Response(status_code=200)
-    except Exception as e:
-        return _create_error_response(e)
-
-
-@app.api_route("/configure_logging", methods=["GET", "POST"])
-async def configure_logging(obj: ConfigureLoggingReq, request: Request):
-    """Close the session"""
-    tokenizer_manager.configure_logging(obj)
-    return Response(status_code=200)
-
-
-##### OpenAI-compatible API endpoints #####
-
-
-@app.post("/v1/completions")
-@time_func_latency
-async def openai_v1_completions(raw_request: Request):
-    return await v1_completions(tokenizer_manager, raw_request)
-
-
-@app.post("/v1/chat/completions")
-@time_func_latency
-async def openai_v1_chat_completions(raw_request: Request):
-    return await v1_chat_completions(tokenizer_manager, raw_request)
-
-
-@app.post("/v1/embeddings", response_class=ORJSONResponse)
-@time_func_latency
-async def openai_v1_embeddings(raw_request: Request):
-    response = await v1_embeddings(tokenizer_manager, raw_request)
-    return response
-
-
-@app.get("/v1/models", response_class=ORJSONResponse)
-def available_models():
-    """Show available models."""
-    served_model_names = [tokenizer_manager.served_model_name]
-    model_cards = []
-    for served_model_name in served_model_names:
-        model_cards.append(ModelCard(id=served_model_name, root=served_model_name))
-    return ModelList(data=model_cards)
-
-
-@app.post("/v1/files")
-async def openai_v1_files(file: UploadFile = File(...), purpose: str = Form("batch")):
-    return await v1_files_create(
-        file, purpose, tokenizer_manager.server_args.file_storage_pth
-    )
-
-
-@app.delete("/v1/files/{file_id}")
-async def delete_file(file_id: str):
-    # https://platform.openai.com/docs/api-reference/files/delete
-    return await v1_delete_file(file_id)
-
-
-@app.post("/v1/batches")
-async def openai_v1_batches(raw_request: Request):
-    return await v1_batches(tokenizer_manager, raw_request)
-
-
-@app.post("/v1/batches/{batch_id}/cancel")
-async def cancel_batches(batch_id: str):
-    # https://platform.openai.com/docs/api-reference/batch/cancel
-    return await v1_cancel_batch(tokenizer_manager, batch_id)
-
-
-@app.get("/v1/batches/{batch_id}")
-async def retrieve_batch(batch_id: str):
-    return await v1_retrieve_batch(batch_id)
-
-
-@app.get("/v1/files/{file_id}")
-async def retrieve_file(file_id: str):
-    # https://platform.openai.com/docs/api-reference/files/retrieve
-    return await v1_retrieve_file(file_id)
-
-
-@app.get("/v1/files/{file_id}/content")
-async def retrieve_file_content(file_id: str):
-    # https://platform.openai.com/docs/api-reference/files/retrieve-contents
-    return await v1_retrieve_file_content(file_id)
-
-
-def _create_error_response(e):
-    return ORJSONResponse(
-        {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST
-    )
-
-
-def launch_engine(
-    server_args: ServerArgs,
-):
-    """
-    Launch the TokenizerManager in the main process, the Scheduler in a subprocess, and the DetokenizerManager in another subprocess.
-    """
-
-    global tokenizer_manager
-    global scheduler_info
-
-    # Configure global environment
-    configure_logger(server_args)
-    server_args.check_server_args()
-    _set_envs_and_config(server_args)
-
-    # Allocate ports for inter-process communications
-    port_args = PortArgs.init_new(server_args)
-    logger.info(f"{server_args=}")
-
-    # If using model from www.modelscope.cn, first download the model.
-    server_args.model_path, server_args.tokenizer_path = prepare_model_and_tokenizer(
-        server_args.model_path, server_args.tokenizer_path
-    )
-
-    scheduler_procs = []
-    if server_args.dp_size == 1:
-        # Launch tensor parallel scheduler processes
-        memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=server_args.enable_memory_saver
-        )
-
-        scheduler_pipe_readers = []
-        tp_size_per_node = server_args.tp_size // server_args.nnodes
-        tp_rank_range = range(
-            tp_size_per_node * server_args.node_rank,
-            tp_size_per_node * (server_args.node_rank + 1),
-        )
-        for tp_rank in tp_rank_range:
-            reader, writer = mp.Pipe(duplex=False)
-            gpu_id = server_args.base_gpu_id + tp_rank % tp_size_per_node
-            proc = mp.Process(
-                target=run_scheduler_process,
-                args=(server_args, port_args, gpu_id, tp_rank, None, writer),
-            )
-            with memory_saver_adapter.configure_subprocess():
-                proc.start()
-            scheduler_procs.append(proc)
-            scheduler_pipe_readers.append(reader)
-    else:
-        # Launch the data parallel controller
-        reader, writer = mp.Pipe(duplex=False)
-        scheduler_pipe_readers = [reader]
-        proc = mp.Process(
-            target=run_data_parallel_controller_process,
-            args=(server_args, port_args, writer),
-        )
-        proc.start()
-        scheduler_procs.append(proc)
-
-    if server_args.node_rank >= 1:
-        # In multi-node cases, non-zero rank nodes do not need to run tokenizer or detokenizer,
-        # so they can just wait here.
-
-        for reader in scheduler_pipe_readers:
-            data = reader.recv()
-            assert data["status"] == "ready"
-
-        if os.getenv("SGLANG_BLOCK_NONZERO_RANK_CHILDREN") == "0":
-            # When using `Engine` as a Python API, we don't want to block here.
-            return
-
-        for proc in scheduler_procs:
-            proc.join()
-            logger.error(
-                f"Scheduler or DataParallelController {proc.pid} terminated with {proc.exitcode}"
-            )
-        return
-
-    # Launch detokenizer process
-    detoken_proc = mp.Process(
-        target=run_detokenizer_process,
-        args=(
-            server_args,
-            port_args,
-        ),
-    )
-    detoken_proc.start()
-
-    # Launch tokenizer process
-    tokenizer_manager = TokenizerManager(server_args, port_args)
-    if server_args.chat_template:
-        load_chat_template_for_openai_api(tokenizer_manager, server_args.chat_template)
-
-    # Wait for model to finish loading
-    scheduler_infos = []
-    for i in range(len(scheduler_pipe_readers)):
-        try:
-            data = scheduler_pipe_readers[i].recv()
-        except EOFError as e:
-            logger.exception(e)
-            logger.error(
-                f"Rank {i} scheduler is dead. Please check if there are relevant logs."
-            )
-            scheduler_procs[i].join()
-            logger.error(f"Exit code: {scheduler_procs[i].exitcode}")
-            raise
-
-        if data["status"] != "ready":
-            raise RuntimeError(
-                "Initialization failed. Please see the error messages above."
-            )
-        scheduler_infos.append(data)
-
-    # Assume all schedulers have same scheduler_info
-    scheduler_info = scheduler_infos[0]
-    tokenizer_manager.max_req_input_len = scheduler_info["max_req_input_len"]
-
-
-def launch_server(
-    server_args: ServerArgs,
-    pipe_finish_writer: Optional[mp.connection.Connection] = None,
-):
-    """
-    Launch SRT (SGLang Runtime) Server
-
-    The SRT server consists of an HTTP server and the SRT engine.
-
-    1. HTTP server: A FastAPI server that routes requests to the engine.
-    2. SRT engine:
-        1. TokenizerManager: Tokenizes the requests and sends them to the scheduler.
-        2. Scheduler (subprocess): Receives requests from the Tokenizer Manager, schedules batches, forwards them, and sends the output tokens to the Detokenizer Manager.
-        3. DetokenizerManager (subprocess): Detokenizes the output tokens and sends the result back to the Tokenizer Manager.
-
-    Note:
-    1. The HTTP server and TokenizerManager both run in the main process.
-    2. Inter-process communication is done through ICP (each process uses a different port) via the ZMQ library.
-    """
-    launch_engine(server_args=server_args)
-
-    # Add api key authorization
-    if server_args.api_key:
-        add_api_key_middleware(app, server_args.api_key)
-
-    # Add prometheus middleware
-    if server_args.enable_metrics:
-        add_prometheus_middleware(app)
-        enable_func_timer()
-
-    # Send a warmup request
-    t = threading.Thread(
-        target=_wait_and_warmup,
-        args=(
-            server_args,
-            pipe_finish_writer,
-            tokenizer_manager.image_token_id,
-        ),
-    )
-    t.start()
-
-    try:
-        # Update logging configs
-        set_uvicorn_logging_configs()
-
-        # Listen for HTTP requests
-        uvicorn.run(
-            app,
-            host=server_args.host,
-            port=server_args.port,
-            log_level=server_args.log_level_http or server_args.log_level,
-            timeout_keep_alive=5,
-            loop="uvloop",
-        )
-    finally:
-        t.join()
-
-
-def _set_envs_and_config(server_args: ServerArgs):
-    # Set global environments
-    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-    os.environ["NCCL_CUMEM_ENABLE"] = "0"
-    os.environ["NCCL_NVLS_ENABLE"] = "0"
-    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
-    os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "4"
-
-    # Set prometheus env vars
-    if server_args.enable_metrics:
-        set_prometheus_multiproc_dir()
-
-    # Set ulimit
-    set_ulimit()
-
-    # Fix triton bugs
-    if server_args.tp_size * server_args.dp_size > 1:
-        # FIXME: remove this after https://github.com/triton-lang/triton/pull/4295 is used as a dependency.
-        maybe_set_triton_cache_manager()
-
-    # Check flashinfer version
-    if server_args.attention_backend == "flashinfer":
-        assert_pkg_version(
-            "flashinfer",
-            "0.1.6",
-            "Please uninstall the old version and "
-            "reinstall the latest version by following the instructions "
-            "at https://docs.flashinfer.ai/installation.html.",
-        )
-
-    # Register the signal handler.
-    # The child processes will send SIGQUIT to this process when any error happens
-    # This process then clean up the whole process tree
-    def sigquit_handler(signum, frame):
-        logger.error(
-            "Received sigquit from a child proces. It usually means the child failed."
-        )
-        kill_process_tree(os.getpid())
-
-    signal.signal(signal.SIGQUIT, sigquit_handler)
-
-    # Set mp start method
-    mp.set_start_method("spawn", force=True)
-
-
-def _wait_and_warmup(server_args, pipe_finish_writer, image_token_text):
-    headers = {}
-    url = server_args.url()
-    if server_args.api_key:
-        headers["Authorization"] = f"Bearer {server_args.api_key}"
-
-    # Wait until the server is launched
-    success = False
-    for _ in range(120):
-        time.sleep(1)
-        try:
-            res = requests.get(url + "/get_model_info", timeout=5, headers=headers)
-            assert res.status_code == 200, f"{res=}, {res.text=}"
-            success = True
-            break
-        except (AssertionError, requests.exceptions.RequestException):
-            last_traceback = get_exception_traceback()
-            pass
-
-    if not success:
-        if pipe_finish_writer is not None:
-            pipe_finish_writer.send(last_traceback)
-        logger.error(f"Initialization failed. warmup error: {last_traceback}")
-        kill_process_tree(os.getpid())
-        return
-
-    model_info = res.json()
-
-    # Send a warmup request
-    request_name = "/generate" if model_info["is_generation"] else "/encode"
-    max_new_tokens = 8 if model_info["is_generation"] else 1
-    json_data = {
-        "sampling_params": {
-            "temperature": 0,
-            "max_new_tokens": max_new_tokens,
-        },
-    }
-    if server_args.skip_tokenizer_init:
-        json_data["input_ids"] = [10, 11, 12]
-    else:
-        json_data["text"] = "The capital city of France is"
-
-    try:
-        for _ in range(server_args.dp_size):
-            res = requests.post(
-                url + request_name,
-                json=json_data,
-                headers=headers,
-                timeout=600,
-            )
-            assert res.status_code == 200, f"{res}"
-    except Exception:
-        last_traceback = get_exception_traceback()
-        if pipe_finish_writer is not None:
-            pipe_finish_writer.send(last_traceback)
-        logger.error(f"Initialization failed. warmup error: {last_traceback}")
-        kill_process_tree(os.getpid())
-        return
-
-    # Debug print
-    # logger.info(f"{res.json()=}")
-
-    logger.info("The server is fired up and ready to roll!")
-    if pipe_finish_writer is not None:
-        pipe_finish_writer.send("ready")
-
-    if server_args.delete_ckpt_after_loading:
-        delete_directory(server_args.model_path)
-
-
-STREAM_END_SYMBOL = b"data: [DONE]"
-STREAM_CHUNK_START_SYMBOL = b"data:"
-
-
-class Engine:
-    """
-    SRT Engine without an HTTP server layer.
-
-    This class provides a direct inference engine without the need for an HTTP server. It is designed for use cases where
-    launching the HTTP server adds unnecessary complexity or overhead,
-    """
-
-    def __init__(self, log_level: str = "error", *args, **kwargs):
-        """See the arguments in server_args.py::ServerArgs"""
-
-        # before python program terminates, call shutdown implicitly. Therefore, users don't have to explicitly call .shutdown()
-        atexit.register(self.shutdown)
-
-        server_args = ServerArgs(*args, log_level=log_level, **kwargs)
-        launch_engine(server_args=server_args)
-
-    def generate(
-        self,
-        # The input prompt. It can be a single prompt or a batch of prompts.
-        prompt: Optional[Union[List[str], str]] = None,
-        sampling_params: Optional[Union[List[Dict], Dict]] = None,
-        # The token ids for text; one can either specify text or input_ids.
-        input_ids: Optional[Union[List[List[int]], List[int]]] = None,
-        return_logprob: Optional[Union[List[bool], bool]] = False,
-        logprob_start_len: Optional[Union[List[int], int]] = None,
-        top_logprobs_num: Optional[Union[List[int], int]] = None,
-        lora_path: Optional[List[Optional[str]]] = None,
-        custom_logit_processor: Optional[Union[List[str], str]] = None,
-        stream: bool = False,
-    ):
-        obj = GenerateReqInput(
-            text=prompt,
-            input_ids=input_ids,
-            sampling_params=sampling_params,
-            return_logprob=return_logprob,
-            logprob_start_len=logprob_start_len,
-            top_logprobs_num=top_logprobs_num,
-            lora_path=lora_path,
-            stream=stream,
-            custom_logit_processor=custom_logit_processor,
-        )
-
-        # get the current event loop
-        loop = asyncio.get_event_loop()
-        ret = loop.run_until_complete(generate_request(obj, None))
-
-        if stream is True:
-
-            def generator_wrapper():
-                offset = 0
-                loop = asyncio.get_event_loop()
-                generator = ret.body_iterator
-                while True:
-                    chunk = loop.run_until_complete(generator.__anext__())
-
-                    if chunk.startswith(STREAM_END_SYMBOL):
-                        break
-                    else:
-                        data = json.loads(chunk[len(STREAM_CHUNK_START_SYMBOL) :])
-                        data["text"] = data["text"][offset:]
-                        offset += len(data["text"])
-                        yield data
-
-            # we cannot yield in the scope of generate() because python does not allow yield + return in the same function
-            # however, it allows to wrap the generator as a subfunction and return
-            return generator_wrapper()
-        else:
-            return ret
-
-    async def async_generate(
-        self,
-        # The input prompt. It can be a single prompt or a batch of prompts.
-        prompt: Optional[Union[List[str], str]] = None,
-        sampling_params: Optional[Dict] = None,
-        # The token ids for text; one can either specify text or input_ids.
-        input_ids: Optional[Union[List[List[int]], List[int]]] = None,
-        return_logprob: Optional[Union[List[bool], bool]] = False,
-        logprob_start_len: Optional[Union[List[int], int]] = None,
-        top_logprobs_num: Optional[Union[List[int], int]] = None,
-        lora_path: Optional[List[Optional[str]]] = None,
-        custom_logit_processor: Optional[Union[str, List[str]]] = None,
-        stream: bool = False,
-    ):
-        obj = GenerateReqInput(
-            text=prompt,
-            input_ids=input_ids,
-            sampling_params=sampling_params,
-            return_logprob=return_logprob,
-            logprob_start_len=logprob_start_len,
-            top_logprobs_num=top_logprobs_num,
-            lora_path=lora_path,
-            stream=stream,
-            custom_logit_processor=custom_logit_processor,
-        )
-
-        ret = await generate_request(obj, None)
-
-        if stream is True:
-            generator = ret.body_iterator
-
-            async def generator_wrapper():
-                offset = 0
-
-                while True:
-                    chunk = await generator.__anext__()
-
-                    if chunk.startswith(STREAM_END_SYMBOL):
-                        break
-                    else:
-                        data = json.loads(chunk[len(STREAM_CHUNK_START_SYMBOL) :])
-                        data["text"] = data["text"][offset:]
-                        offset += len(data["text"])
-                        yield data
-
-            return generator_wrapper()
-        else:
-            return ret
-
-    def shutdown(self):
-        kill_process_tree(os.getpid(), include_parent=False)
-
-    def get_tokenizer(self):
-        global tokenizer_manager
-
-        if tokenizer_manager is None:
-            raise ReferenceError("Tokenizer Manager is not initialized.")
-        else:
-            return tokenizer_manager.tokenizer
-
-    def encode(
-        self,
-        prompt: Union[str, List[str], List[Dict], List[List[Dict]]],
-    ):
-        obj = EmbeddingReqInput(text=prompt)
-
-        # get the current event loop
-        loop = asyncio.get_event_loop()
-        return loop.run_until_complete(encode_request(obj, None))
-
-    def start_profile(self):
-        tokenizer_manager.start_profile()
-
-    def stop_profile(self):
-        tokenizer_manager.stop_profile()
-
-    def get_server_info(self):
-        return {
-            **dataclasses.asdict(tokenizer_manager.server_args),  # server args
-            **scheduler_info,
-            "version": __version__,
-        }
-
-    def init_weights_update_group(
-        self,
-        master_address: str,
-        master_port: int,
-        rank_offset: int,
-        world_size: int,
-        group_name: str,
-        backend: str = "nccl",
-    ):
-        """Initialize parameter update group."""
-        obj = InitWeightsUpdateGroupReqInput(
-            master_address=master_address,
-            master_port=master_port,
-            rank_offset=rank_offset,
-            world_size=world_size,
-            group_name=group_name,
-            backend=backend,
-        )
-        loop = asyncio.get_event_loop()
-        return loop.run_until_complete(
-            tokenizer_manager.init_weights_update_group(obj, None)
-        )
-
-    def update_weights_from_distributed(self, name, dtype, shape):
-        """Update weights from distributed source."""
-        obj = UpdateWeightsFromDistributedReqInput(
-            name=name,
-            dtype=dtype,
-            shape=shape,
-        )
-        loop = asyncio.get_event_loop()
-        return loop.run_until_complete(
-            tokenizer_manager.update_weights_from_distributed(obj, None)
-        )
-
-    def update_weights_from_tensor(self, named_tensors: List[Tuple[str, torch.Tensor]]):
-        """Update weights from distributed source."""
-        obj = UpdateWeightsFromTensorReqInput(
-            serialized_named_tensors=MultiprocessingSerializer.serialize(named_tensors)
-        )
-        loop = asyncio.get_event_loop()
-        return loop.run_until_complete(
-            tokenizer_manager.update_weights_from_tensor(obj, None)
-        )
-
-    def get_weights_by_name(self, name, truncate_size=100):
-        """Get weights by parameter name."""
-        obj = GetWeightsByNameReqInput(name=name, truncate_size=truncate_size)
-        loop = asyncio.get_event_loop()
-        return loop.run_until_complete(tokenizer_manager.get_weights_by_name(obj, None))
-
-    def release_memory_occupation(self):
-        """Release GPU occupation temporarily"""
-        obj = ReleaseMemoryOccupationReqInput()
-        loop = asyncio.get_event_loop()
-        loop.run_until_complete(tokenizer_manager.release_memory_occupation(obj, None))
-
-    def resume_memory_occupation(self):
-        """Resume GPU occupation"""
-        obj = ResumeMemoryOccupationReqInput()
-        loop = asyncio.get_event_loop()
-        loop.run_until_complete(tokenizer_manager.resume_memory_occupation(obj, None))

python/sglang/test/runners.py

@@ -12,7 +12,6 @@
 # limitations under the License.
 # ==============================================================================
 
-import json
 import multiprocessing as mp
 import os
 from dataclasses import dataclass
@@ -22,8 +21,8 @@ import torch
 import torch.nn.functional as F
 from transformers import AutoModelForCausalLM
 
+from sglang.srt.entrypoints.engine import Engine
 from sglang.srt.hf_transformers_utils import get_tokenizer
-from sglang.srt.server import Engine
 from sglang.test.test_utils import DEFAULT_PORT_FOR_SRT_TEST_RUNNER
 
 DEFAULT_PROMPTS = [

sgl-router/py_src/sglang_router/launch_server.py

@@ -13,7 +13,7 @@ import requests
 from setproctitle import setproctitle
 from sglang_router.launch_router import RouterArgs, launch_router
 
-from sglang.srt.server import launch_server
+from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import is_port_available
 

test/srt/test_metrics.py

@@ -56,7 +56,6 @@ class TestEnableMetrics(unittest.TestCase):
                 "sglang:gen_throughput",
                 "sglang:num_queue_reqs",
                 "sglang:cache_hit_rate",
-                "sglang:func_latency_seconds",
                 "sglang:prompt_tokens_total",
                 "sglang:generation_tokens_total",
                 "sglang:num_requests_total",

test/srt/test_nightly_gsm8k_eval.py

@@ -45,7 +45,7 @@ def parse_models(model_string):
     return [model.strip() for model in model_string.split(",") if model.strip()]
 
 
-def launch_server(base_url, model, is_fp8, is_tp2):
+def popen_launch_server_wrapper(base_url, model, is_fp8, is_tp2):
     other_args = ["--log-level-http", "warning", "--trust-remote-code"]
     if is_fp8:
         if "Llama-3" in model or "gemma-2" in model:
@@ -148,7 +148,9 @@ class TestNightlyGsm8KEval(unittest.TestCase):
         for model_group, is_fp8, is_tp2 in self.model_groups:
             for model in model_group:
                 with self.subTest(model=model):
-                    process = launch_server(self.base_url, model, is_fp8, is_tp2)
+                    process = popen_launch_server_wrapper(
+                        self.base_url, model, is_fp8, is_tp2
+                    )
 
                     args = SimpleNamespace(
                         base_url=self.base_url,

test/srt/test_nightly_human_eval.py

@@ -4,7 +4,7 @@ import signal
 import subprocess
 import unittest
 
-from test_nightly_gsm8k_eval import launch_server, parse_models
+from test_nightly_gsm8k_eval import parse_models, popen_launch_server_wrapper
 
 from sglang.srt.utils import kill_process_tree
 from sglang.test.test_utils import (
@@ -93,7 +93,7 @@ class TestNightlyHumanEval(unittest.TestCase):
                 # NOTE: only Llama for now
                 if "Llama" in model:
                     with self.subTest(model=model):
-                        self.process = launch_server(
+                        self.process = popen_launch_server_wrapper(
                             self.base_url, model, is_fp8, is_tp2
                         )
                         self.run_evalplus(model)

test/srt/test_srt_engine.py

@@ -1,6 +1,6 @@
 """
 Usage:
-python3 -m unittest test_srt_engine.TestSRTEngine.test_3_sync_streaming_combination
+python3 -m unittest test_srt_engine.TestSRTEngine.test_4_sync_async_stream_combination
 """
 
 import asyncio
@@ -44,83 +44,29 @@ class TestSRTEngine(unittest.TestCase):
         print(out2)
         self.assertEqual(out1, out2)
 
-    def test_2_engine_multiple_generate(self):
+    def test_2_engine_runtime_encode_consistency(self):
+        prompt = "Today is a sunny day and I like"
+        model_path = DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST
+
+        engine = sgl.Engine(model_path=model_path, is_embedding=True, random_seed=42)
+        out1 = torch.tensor(engine.encode(prompt)["embedding"])
+        engine.shutdown()
+
+        runtime = sgl.Runtime(model_path=model_path, is_embedding=True, random_seed=42)
+        out2 = torch.tensor(json.loads(runtime.encode(prompt))["embedding"])
+        runtime.shutdown()
+
+        self.assertTrue(torch.allclose(out1, out2, atol=1e-5, rtol=1e-3))
+
+    def test_3_engine_token_ids_consistency(self):
         # just to ensure there is no issue running multiple generate calls
         prompt = "Today is a sunny day and I like"
         model_path = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
-
         sampling_params = {"temperature": 0, "max_new_tokens": 8}
 
-        engine = sgl.Engine(model_path=model_path, random_seed=42)
-        engine.generate(prompt, sampling_params)
-        engine.generate(prompt, sampling_params)
-        engine.shutdown()
-
-    def test_3_sync_streaming_combination(self):
-
-        prompt = "AI safety is..."
-        sampling_params = {"temperature": 0.8, "top_p": 0.95}
-
-        async def async_streaming(engine):
-
-            generator = await engine.async_generate(
-                prompt, sampling_params, stream=True
-            )
-
-            async for output in generator:
-                print(output["text"], end="", flush=True)
-            print()
-
-        # Create an LLM.
-        llm = sgl.Engine(
-            model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
-        )
-
-        # 1. sync + non streaming
-        print("\n\n==== 1. sync + non streaming ====")
-        output = llm.generate(prompt, sampling_params)
-
-        print(output["text"])
-
-        # 2. sync + streaming
-        print("\n\n==== 2. sync + streaming ====")
-        output_generator = llm.generate(prompt, sampling_params, stream=True)
-        for output in output_generator:
-            print(output["text"], end="", flush=True)
-        print()
-
-        loop = asyncio.get_event_loop()
-        # 3. async + non_streaming
-        print("\n\n==== 3. async + non streaming ====")
-        output = loop.run_until_complete(llm.async_generate(prompt, sampling_params))
-        print(output["text"])
-
-        # 4. async + streaming
-        print("\n\n==== 4. async + streaming ====")
-        loop.run_until_complete(async_streaming(llm))
-
-        llm.shutdown()
-
-    def test_4_gsm8k(self):
-
-        args = SimpleNamespace(
-            model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
-            local_data_path=None,
-            num_shots=5,
-            num_questions=200,
-        )
-
-        metrics = run_eval(args)
-        self.assertGreater(metrics["accuracy"], 0.3)
-
-    def test_5_prompt_input_ids_consistency(self):
-        prompt = "The capital of UK is"
-
-        model_path = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
         engine = sgl.Engine(
             model_path=model_path, random_seed=42, disable_radix_cache=True
         )
-        sampling_params = {"temperature": 0, "max_new_tokens": 8}
         out1 = engine.generate(prompt, sampling_params)["text"]
 
         tokenizer = get_tokenizer(model_path)
@@ -138,21 +84,69 @@ class TestSRTEngine(unittest.TestCase):
         print(out2)
         self.assertEqual(out1, out2)
 
-    def test_6_engine_runtime_encode_consistency(self):
+    def test_4_sync_async_stream_combination(self):
-        prompt = "Today is a sunny day and I like"
+        prompt = "AI safety is"
-        model_path = DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST
+        sampling_params = {"temperature": 0.8, "top_p": 0.95}
 
-        engine = sgl.Engine(model_path=model_path, is_embedding=True, random_seed=42)
+        # Create an LLM.
-        out1 = torch.tensor(engine.encode(prompt)["embedding"])
+        llm = sgl.Engine(
-        engine.shutdown()
+            model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
+        )
 
-        runtime = sgl.Runtime(model_path=model_path, is_embedding=True, random_seed=42)
+        if True:
-        out2 = torch.tensor(json.loads(runtime.encode(prompt))["embedding"])
+            # 1. sync + non streaming
-        runtime.shutdown()
+            print("\n\n==== 1. sync + non streaming ====")
+            output = llm.generate(prompt, sampling_params)
+            print(output["text"])
 
-        self.assertTrue(torch.allclose(out1, out2, atol=1e-5, rtol=1e-3))
+            # 2. sync + streaming
+            print("\n\n==== 2. sync + streaming ====")
+            output_generator = llm.generate(prompt, sampling_params, stream=True)
+            offset = 0
+            for output in output_generator:
+                print(output["text"][offset:], end="", flush=True)
+                offset = len(output["text"])
+            print()
 
-    def test_7_engine_cpu_offload(self):
+        if True:
+            loop = asyncio.get_event_loop()
+            # 3. async + non_streaming
+            print("\n\n==== 3. async + non streaming ====")
+            output = loop.run_until_complete(
+                llm.async_generate(prompt, sampling_params)
+            )
+            print(output["text"])
+
+            # 4. async + streaming
+            async def async_streaming(engine):
+                generator = await engine.async_generate(
+                    prompt, sampling_params, stream=True
+                )
+
+                offset = 0
+                async for output in generator:
+                    print(output["text"][offset:], end="", flush=True)
+                    offset = len(output["text"])
+                print()
+
+            print("\n\n==== 4. async + streaming ====")
+            loop.run_until_complete(async_streaming(llm))
+
+        llm.shutdown()
+
+    def test_5_gsm8k(self):
+
+        args = SimpleNamespace(
+            model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
+            local_data_path=None,
+            num_shots=5,
+            num_questions=200,
+        )
+
+        metrics = run_eval(args)
+        self.assertGreater(metrics["accuracy"], 0.3)
+
+    def test_6_engine_cpu_offload(self):
         prompt = "Today is a sunny day and I like"
         model_path = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
 
@@ -182,7 +176,7 @@ class TestSRTEngine(unittest.TestCase):
         print(out2)
         self.assertEqual(out1, out2)
 
-    def test_8_engine_offline_throughput(self):
+    def test_7_engine_offline_throughput(self):
         server_args = ServerArgs(
             model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
         )