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import os
import subprocess
import time
import sys
import pytest
import requests
import ray # using Ray for overall ease of process management, parallel requests, and debugging.
import openai # use the official client for correctness check
MAX_SERVER_START_WAIT_S = 600 # wait for server to start for 60 seconds
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" # any model with a chat template should work here
pytestmark = pytest.mark.asyncio
@ray.remote(num_gpus=1)
class ServerRunner:
def __init__(self, args):
env = os.environ.copy()
env["PYTHONUNBUFFERED"] = "1"
self.proc = subprocess.Popen(
["python3", "-m", "vllm.entrypoints.openai.api_server"] + args,
env=env,
stdout=sys.stdout,
stderr=sys.stderr,
)
self._wait_for_server()
def ready(self):
return True
def _wait_for_server(self):
# run health check
start = time.time()
while True:
try:
if requests.get(
"http://localhost:8000/health").status_code == 200:
break
except Exception as err:
if self.proc.poll() is not None:
raise RuntimeError("Server exited unexpectedly.") from err
time.sleep(0.5)
if time.time() - start > MAX_SERVER_START_WAIT_S:
raise RuntimeError(
"Server failed to start in time.") from err
def __del__(self):
if hasattr(self, "proc"):
self.proc.terminate()
@pytest.fixture(scope="session")
def server():
ray.init()
server_runner = ServerRunner.remote([
"--model",
MODEL_NAME,
"--dtype",
"bfloat16", # use half precision for speed and memory savings in CI environment
"--max-model-len",
"8192",
"--enforce-eager",
])
ray.get(server_runner.ready.remote())
yield server_runner
ray.shutdown()
@pytest.fixture(scope="session")
def client():
client = openai.AsyncOpenAI(
base_url="http://localhost:8000/v1",
api_key="token-abc123",
)
yield client
async def test_single_completion(server, client: openai.AsyncOpenAI):
completion = await client.completions.create(model=MODEL_NAME,
prompt="Hello, my name is",
max_tokens=5,
temperature=0.0)
assert completion.id is not None
assert completion.choices is not None and len(completion.choices) == 1
assert completion.choices[0].text is not None and len(
completion.choices[0].text) >= 5
assert completion.choices[0].finish_reason == "length"
assert completion.usage == openai.types.CompletionUsage(
completion_tokens=5, prompt_tokens=6, total_tokens=11)
# test using token IDs
completion = await client.completions.create(
model=MODEL_NAME,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
)
assert completion.choices[0].text is not None and len(
completion.choices[0].text) >= 5
async def test_single_chat_session(server, client: openai.AsyncOpenAI):
messages = [{
"role": "system",
"content": "you are a helpful assistant"
}, {
"role": "user",
"content": "what is 1+1?"
}]
# test single completion
chat_completion = await client.chat.completions.create(
model=MODEL_NAME,
messages=messages,
max_tokens=10,
)
assert chat_completion.id is not None
assert chat_completion.choices is not None and len(
chat_completion.choices) == 1
assert chat_completion.choices[0].message is not None
message = chat_completion.choices[0].message
assert message.content is not None and len(message.content) >= 10
assert message.role == "assistant"
messages.append({"role": "assistant", "content": message.content})
# test multi-turn dialogue
messages.append({"role": "user", "content": "express your result in json"})
chat_completion = await client.chat.completions.create(
model=MODEL_NAME,
messages=messages,
max_tokens=10,
)
message = chat_completion.choices[0].message
assert message.content is not None and len(message.content) >= 0
async def test_completion_streaming(server, client: openai.AsyncOpenAI):
prompt = "What is an LLM?"
single_completion = await client.completions.create(
model=MODEL_NAME,
prompt=prompt,
max_tokens=5,
temperature=0.0,
)
single_output = single_completion.choices[0].text
single_usage = single_completion.usage
stream = await client.completions.create(
model=MODEL_NAME,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
)
chunks = []
async for chunk in stream:
chunks.append(chunk.choices[0].text)
assert chunk.choices[0].finish_reason == "length"
assert chunk.usage == single_usage
assert "".join(chunks) == single_output
async def test_chat_streaming(server, client: openai.AsyncOpenAI):
messages = [{
"role": "system",
"content": "you are a helpful assistant"
}, {
"role": "user",
"content": "what is 1+1?"
}]
# test single completion
chat_completion = await client.chat.completions.create(
model=MODEL_NAME,
messages=messages,
max_tokens=10,
temperature=0.0,
)
output = chat_completion.choices[0].message.content
stop_reason = chat_completion.choices[0].finish_reason
# test streaming
stream = await client.chat.completions.create(
model=MODEL_NAME,
messages=messages,
max_tokens=10,
temperature=0.0,
stream=True,
)
chunks = []
async for chunk in stream:
delta = chunk.choices[0].delta
if delta.role:
assert delta.role == "assistant"
if delta.content:
chunks.append(delta.content)
assert chunk.choices[0].finish_reason == stop_reason
assert "".join(chunks) == output
async def test_batch_completions(server, client: openai.AsyncOpenAI):
# test simple list
batch = await client.completions.create(
model=MODEL_NAME,
prompt=["Hello, my name is", "Hello, my name is"],
max_tokens=5,
temperature=0.0,
)
assert len(batch.choices) == 2
assert batch.choices[0].text == batch.choices[1].text
# test n = 2
batch = await client.completions.create(
model=MODEL_NAME,
prompt=["Hello, my name is", "Hello, my name is"],
n=2,
max_tokens=5,
temperature=0.0,
extra_body=dict(
# NOTE: this has to be true for n > 1 in vLLM, but not necessary for official client.
use_beam_search=True),
)
assert len(batch.choices) == 4
assert batch.choices[0].text != batch.choices[
1].text, "beam search should be different"
assert batch.choices[0].text == batch.choices[
2].text, "two copies of the same prompt should be the same"
assert batch.choices[1].text == batch.choices[
3].text, "two copies of the same prompt should be the same"
# test streaming
batch = await client.completions.create(
model=MODEL_NAME,
prompt=["Hello, my name is", "Hello, my name is"],
max_tokens=5,
temperature=0.0,
stream=True,
)
texts = [""] * 2
async for chunk in batch:
assert len(chunk.choices) == 1
choice = chunk.choices[0]
texts[choice.index] += choice.text
assert texts[0] == texts[1]
if __name__ == "__main__":
pytest.main([__file__])
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