TensorRT-LLMs/tests/unittest/llmapi/apps/openai_server.py
2025-12-16 05:16:32 -08:00

237 lines
8.5 KiB
Python

# Adapted from
# https://github.com/vllm-project/vllm/blob/baaedfdb2d3f1d70b7dbcde08b083abfe6017a92/tests/utils.py
import os
import subprocess
import sys
import tempfile
import time
from typing import List, Optional
import openai
import requests
import yaml
from tensorrt_llm._utils import get_free_port
from tensorrt_llm.llmapi.disagg_utils import ServerRole
class RemoteOpenAIServer:
DUMMY_API_KEY = "tensorrt_llm"
MAX_SERVER_START_WAIT_S = 7200 # wait for server to start for 7200 seconds (~ 2 hours) for LLM models weight loading
def __init__(self,
model: str,
cli_args: List[str] = None,
llmapi_launch: bool = False,
port: int = None,
host: str = "localhost",
env: Optional[dict] = None,
rank: int = -1,
extra_config: Optional[dict] = None,
log_path: Optional[str] = None,
wait: bool = True,
role: Optional[ServerRole] = None) -> None:
self.host = host
self.port = port if port is not None else get_free_port()
self.rank = rank if rank != -1 else int(
os.environ.get("SLURM_PROCID", 0))
self.extra_config_file = None
self.log_path = log_path
self.log_file = None
self.role = role
args = ["--host", f"{self.host}", "--port", f"{self.port}"]
if self.role is not None:
args += ["--server_role", self.role.name]
if cli_args:
args += cli_args
if extra_config:
with tempfile.NamedTemporaryFile(mode="w+",
delete=False,
delete_on_close=False) as f:
f.write(yaml.dump(extra_config))
self.extra_config_file = f.name
args += ["--extra_llm_api_options", self.extra_config_file]
launch_cmd = ["trtllm-serve"] + [model] + args
if llmapi_launch:
# start server with llmapi-launch on multi nodes
launch_cmd = ["trtllm-llmapi-launch"] + launch_cmd
if not env:
env = os.environ.copy()
self.proc = subprocess.Popen(launch_cmd,
env=env,
stdout=self._get_output(),
stderr=self._get_output())
if wait:
self.wait_for_server(timeout=self.MAX_SERVER_START_WAIT_S)
def _get_output(self):
if self.log_file:
return self.log_file
elif self.log_path:
self.log_file = open(self.log_path, "w+")
return self.log_file
else:
return sys.stdout
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.terminate()
def terminate(self):
if self.proc is None:
return
self.proc.terminate()
try:
self.proc.wait(timeout=30)
except subprocess.TimeoutExpired as e:
self.proc.kill()
self.proc.wait(timeout=30)
try:
if self.extra_config_file:
os.remove(self.extra_config_file)
except Exception as e:
print(f"Error removing extra config file: {e}")
self.proc = None
if self.log_file:
self.log_file.close()
self.log_file = None
def wait_for_server(self, timeout: float):
self._wait_for_server(url=self.url_for("health"), timeout=timeout)
def _wait_for_server(self, *, url: str, timeout: float):
# run health check on the first rank only.
start = time.time()
while True:
try:
if self.rank == 0:
if requests.get(url).status_code == 200:
break
else:
time.sleep(0.5)
else:
time.sleep(timeout)
break
except Exception as err:
result = self.proc.poll()
if result is not None and result != 0:
raise RuntimeError("Server exited unexpectedly.") from err
time.sleep(0.5)
if time.time() - start > timeout:
# Terminate the server to avoid the process keeping running in background after timeout
self.terminate()
raise RuntimeError(
"Server failed to start in time.") from err
@property
def url_root(self) -> str:
return f"http://{self.host}:{self.port}"
def url_for(self, *parts: str) -> str:
return self.url_root + "/" + "/".join(parts)
def get_client(self):
return openai.OpenAI(
base_url=self.url_for("v1"),
api_key=self.DUMMY_API_KEY,
)
def get_async_client(self, **kwargs):
return openai.AsyncOpenAI(base_url=self.url_for("v1"),
api_key=self.DUMMY_API_KEY,
**kwargs)
class RemoteDisaggOpenAIServer(RemoteOpenAIServer):
def __init__(self,
ctx_servers: List[str],
gen_servers: List[str],
port: int = -1,
env: Optional[dict] = None,
llmapi_launch: bool = False,
disagg_config: Optional[dict] = None,
log_path: Optional[str] = None,
wait_ready: bool = True) -> None:
self.ctx_servers = ctx_servers
self.gen_servers = gen_servers
self.host = "0.0.0.0"
self.port = get_free_port() if port is None or port < 0 else port
self.rank = 0
self.disagg_config = self._get_extra_config()
if disagg_config:
self.disagg_config.update(disagg_config)
self.log_path = log_path
self.log_file = None
self.extra_config_file = os.path.join(
tempfile.gettempdir(), f"disagg_config_{self.port}.yaml")
with open(self.extra_config_file, "w+") as f:
yaml.dump(self.disagg_config, f)
launch_cmd = [
"trtllm-serve", "disaggregated", "-c", self.extra_config_file
]
print(f"launch_cmd: {launch_cmd}, extra_config: {self.disagg_config}")
if llmapi_launch:
# start server with llmapi-launch on multi nodes
launch_cmd = ["trtllm-llmapi-launch"] + launch_cmd
if not env:
env = os.environ.copy()
self.proc = subprocess.Popen(launch_cmd,
env=env,
stdout=self._get_output(),
stderr=self._get_output())
if wait_ready:
self._wait_for_server(url=self.url_for("health"),
timeout=self.MAX_SERVER_START_WAIT_S)
def _get_extra_config(self):
return {
"context_servers": {
"num_instances": len(self.ctx_servers),
"urls": self.ctx_servers
},
"generation_servers": {
"num_instances": len(self.gen_servers),
"urls": self.gen_servers
},
"port": self.port,
"hostname": self.host,
"perf_metrics_max_requests": 1000,
}
class RemoteMMEncoderServer(RemoteOpenAIServer):
"""Remote server for testing multimodal encoder endpoints."""
def __init__(self,
model: str,
cli_args: List[str] = None,
port: int = None,
log_path: Optional[str] = None) -> None:
# Reuse parent initialization but change the command
import subprocess
from tensorrt_llm._utils import get_free_port
self.host = "localhost"
self.port = port if port is not None else get_free_port()
self.rank = os.environ.get("SLURM_PROCID", 0)
self.log_path = log_path
self.log_file = None
args = ["--host", f"{self.host}", "--port", f"{self.port}"]
if cli_args:
args += cli_args
# Use mm_embedding_serve command instead of regular serve
launch_cmd = ["trtllm-serve", "mm_embedding_serve"] + [model] + args
self.proc = subprocess.Popen(launch_cmd,
stdout=self._get_output(),
stderr=self._get_output())
self._wait_for_server(url=self.url_for("health"),
timeout=self.MAX_SERVER_START_WAIT_S)