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930 lines
29 KiB
Python
930 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# imports for structured outputs tests
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import json
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import jsonschema
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import openai # use the official client for correctness check
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import pytest
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import pytest_asyncio
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import regex as re
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import requests
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import torch
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from openai import BadRequestError
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from ...utils import RemoteOpenAIServer
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# any model with a chat template should work here
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MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
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@pytest.fixture(scope="module")
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def server(zephyr_lora_files): # noqa: F811
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args = [
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# use half precision for speed and memory savings in CI environment
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"--dtype",
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"bfloat16",
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"--max-model-len",
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"8192",
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"--enforce-eager",
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# lora config below
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"--enable-lora",
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"--lora-modules",
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f"zephyr-lora={zephyr_lora_files}",
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"--max-lora-rank",
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"64",
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"--max-cpu-loras",
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"2",
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"--max-num-seqs",
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"128",
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]
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with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
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yield remote_server
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@pytest_asyncio.fixture
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async def client(server):
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async with server.get_async_client() as async_client:
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yield async_client
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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# first test base model, then test loras
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"model_name",
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[MODEL_NAME, "zephyr-lora"],
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)
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async def test_no_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{"role": "user", "content": "what is 1+1?"},
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]
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=5,
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temperature=0.0,
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logprobs=False,
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)
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choice = chat_completion.choices[0]
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assert choice.logprobs is None
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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# just test 1 lora hereafter
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"model_name",
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[MODEL_NAME, "zephyr-lora"],
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)
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async def test_zero_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{"role": "user", "content": "what is 1+1?"},
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]
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=5,
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temperature=0.0,
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logprobs=True,
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top_logprobs=0,
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)
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choice = chat_completion.choices[0]
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assert choice.logprobs is not None
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assert choice.logprobs.content is not None
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assert len(choice.logprobs.content[0].top_logprobs) == 0
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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[MODEL_NAME, "zephyr-lora"],
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)
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async def test_some_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{"role": "user", "content": "what is 1+1?"},
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]
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=5,
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temperature=0.0,
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logprobs=True,
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top_logprobs=5,
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)
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choice = chat_completion.choices[0]
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assert choice.logprobs is not None
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assert choice.logprobs.content is not None
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assert len(choice.logprobs.content[0].top_logprobs) == 5
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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[MODEL_NAME, "zephyr-lora"],
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)
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async def test_too_many_chat_logprobs(client: openai.AsyncOpenAI, model_name: str):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{"role": "user", "content": "what is 1+1?"},
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]
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# Default max_logprobs is 20, so this should raise an error
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with pytest.raises((openai.BadRequestError, openai.APIError)):
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stream = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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logprobs=True,
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top_logprobs=21,
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stream=True,
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)
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async for chunk in stream:
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...
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with pytest.raises(openai.BadRequestError):
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await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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logprobs=True,
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top_logprobs=30,
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stream=False,
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)
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# the server should still work afterwards
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chat_completion = await client.chat.completions.create(
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model=model_name, messages=messages, max_completion_tokens=10, stream=False
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)
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 0
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name, prompt_logprobs",
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[(MODEL_NAME, 1), (MODEL_NAME, 0), (MODEL_NAME, -1), (MODEL_NAME, None)],
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)
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async def test_prompt_logprobs_chat(
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client: openai.AsyncOpenAI, model_name: str, prompt_logprobs: int | None
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):
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params: dict = {
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Who won the world series in 2020?"},
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{
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"role": "assistant",
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"content": "The Los Angeles Dodgers won the World Series in 2020.",
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},
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{"role": "user", "content": "Where was it played?"},
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],
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"model": model_name,
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}
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if prompt_logprobs is not None:
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params["extra_body"] = {"prompt_logprobs": prompt_logprobs}
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if prompt_logprobs is not None and prompt_logprobs < 0:
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with pytest.raises(BadRequestError):
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await client.chat.completions.create(**params)
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else:
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completion = await client.chat.completions.create(**params)
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if prompt_logprobs is not None:
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assert completion.prompt_logprobs is not None
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assert len(completion.prompt_logprobs) > 0
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else:
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assert completion.prompt_logprobs is None
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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[MODEL_NAME],
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)
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async def test_more_than_one_prompt_logprobs_chat(
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client: openai.AsyncOpenAI, model_name: str
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):
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params: dict = {
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Who won the world series in 2020?"},
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{
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"role": "assistant",
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"content": "The Los Angeles Dodgers won the World Series in 2020.",
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},
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{"role": "user", "content": "Where was it played?"},
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],
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"model": model_name,
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"extra_body": {"prompt_logprobs": 1},
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}
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completion_1 = await client.chat.completions.create(**params)
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params["extra_body"] = {"prompt_logprobs": 2}
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completion_2 = await client.chat.completions.create(**params)
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assert len(completion_1.prompt_logprobs[3]) == 1
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assert len(completion_2.prompt_logprobs[3]) == 2
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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[MODEL_NAME, "zephyr-lora"],
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)
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async def test_single_chat_session(client: openai.AsyncOpenAI, model_name: str):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{"role": "user", "content": "what is 1+1?"},
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]
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# test single completion
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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logprobs=True,
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top_logprobs=5,
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)
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assert chat_completion.id is not None
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assert len(chat_completion.choices) == 1
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choice = chat_completion.choices[0]
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assert choice.finish_reason == "length"
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assert chat_completion.usage == openai.types.CompletionUsage(
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completion_tokens=10, prompt_tokens=37, total_tokens=47
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)
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message = choice.message
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assert message.content is not None and len(message.content) >= 10
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assert message.role == "assistant"
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messages.append({"role": "assistant", "content": message.content})
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# test multi-turn dialogue
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messages.append({"role": "user", "content": "express your result in json"})
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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)
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 0
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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# just test 1 lora hereafter
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"model_name",
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[MODEL_NAME, "zephyr-lora"],
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)
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async def test_chat_streaming(client: openai.AsyncOpenAI, model_name: str):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{"role": "user", "content": "what is 1+1?"},
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]
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# test single completion
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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)
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output = chat_completion.choices[0].message.content
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stop_reason = chat_completion.choices[0].finish_reason
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# test streaming
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stream = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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stream=True,
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)
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chunks: list[str] = []
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finish_reason_count = 0
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async for chunk in stream:
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delta = chunk.choices[0].delta
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if delta.role:
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assert delta.role == "assistant"
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if delta.content:
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chunks.append(delta.content)
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if chunk.choices[0].finish_reason is not None:
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finish_reason_count += 1
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# finish reason should only return in last block
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assert finish_reason_count == 1
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assert chunk.choices[0].finish_reason == stop_reason
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assert delta.content
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assert "".join(chunks) == output
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"],
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)
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async def test_chat_completion_stream_options(
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client: openai.AsyncOpenAI, model_name: str
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):
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "What is the capital of France?"},
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]
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# Test stream=True, stream_options={"include_usage": False}
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stream = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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stream=True,
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stream_options={"include_usage": False},
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)
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async for chunk in stream:
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assert chunk.usage is None
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# Test stream=True, stream_options={"include_usage": True,
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# "continuous_usage_stats": False}}
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stream = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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stream=True,
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stream_options={"include_usage": True, "continuous_usage_stats": False},
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)
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async for chunk in stream:
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if chunk.choices[0].finish_reason is None:
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assert chunk.usage is None
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else:
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assert chunk.usage is None
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final_chunk = await anext(stream)
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assert final_chunk.usage is not None
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assert final_chunk.usage.prompt_tokens > 0
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assert final_chunk.usage.completion_tokens > 0
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assert final_chunk.usage.total_tokens == (
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final_chunk.usage.prompt_tokens + final_chunk.usage.completion_tokens
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)
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assert final_chunk.choices == []
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# Test stream=False, stream_options={"include_usage": None}
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with pytest.raises(BadRequestError):
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await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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stream=False,
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stream_options={"include_usage": None},
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)
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# Test stream=False, stream_options={"include_usage": True}
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with pytest.raises(BadRequestError):
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await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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stream=False,
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stream_options={"include_usage": True},
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)
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# Test stream=True, stream_options={"include_usage": True,
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# "continuous_usage_stats": True}
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stream = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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extra_body=dict(min_tokens=10),
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temperature=0.0,
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stream=True,
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stream_options={
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"include_usage": True,
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"continuous_usage_stats": True,
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},
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)
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last_completion_tokens = 0
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async for chunk in stream:
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assert chunk.usage.prompt_tokens >= 0
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assert (
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last_completion_tokens == 0
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or chunk.usage.completion_tokens > last_completion_tokens
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or (
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not chunk.choices
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and chunk.usage.completion_tokens == last_completion_tokens
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)
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)
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assert chunk.usage.total_tokens == (
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chunk.usage.prompt_tokens + chunk.usage.completion_tokens
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)
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last_completion_tokens = chunk.usage.completion_tokens
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assert last_completion_tokens == 10
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@pytest.mark.asyncio
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async def test_structured_outputs_choice_chat(
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client: openai.AsyncOpenAI,
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sample_structured_outputs_choices,
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):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{
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"role": "user",
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"content": "The best language for type-safe systems programming is ",
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},
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]
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chat_completion = await client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.7,
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extra_body=dict(
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structured_outputs={"choice": sample_structured_outputs_choices}
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),
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)
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choice1 = chat_completion.choices[0].message.content
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assert choice1 in sample_structured_outputs_choices
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messages.append({"role": "assistant", "content": choice1})
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messages.append({"role": "user", "content": "I disagree, pick another one"})
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chat_completion = await client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.7,
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extra_body=dict(
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structured_outputs={"choice": sample_structured_outputs_choices}
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),
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)
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choice2 = chat_completion.choices[0].message.content
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assert choice2 in sample_structured_outputs_choices
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assert choice1 != choice2
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@pytest.mark.asyncio
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async def test_structured_outputs_json_chat(
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client: openai.AsyncOpenAI,
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sample_json_schema,
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):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
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{
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"role": "user",
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"content": f"Give an example JSON for an employee profile that "
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f"fits this schema: {sample_json_schema}",
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},
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]
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chat_completion = await client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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max_completion_tokens=1000,
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extra_body=dict(structured_outputs={"json": sample_json_schema}),
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)
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message = chat_completion.choices[0].message
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assert message.content is not None
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json1 = json.loads(message.content)
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jsonschema.validate(instance=json1, schema=sample_json_schema)
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messages.append({"role": "assistant", "content": message.content})
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messages.append(
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{"role": "user", "content": "Give me another one with a different name and age"}
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)
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chat_completion = await client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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max_completion_tokens=1000,
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extra_body=dict(structured_outputs={"json": sample_json_schema}),
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)
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message = chat_completion.choices[0].message
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assert message.content is not None
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json2 = json.loads(message.content)
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jsonschema.validate(instance=json2, schema=sample_json_schema)
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assert json1["name"] != json2["name"]
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assert json1["age"] != json2["age"]
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@pytest.mark.asyncio
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async def test_structured_outputs_regex_chat(
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client: openai.AsyncOpenAI,
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sample_regex,
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):
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messages = [
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{"role": "system", "content": "you are a helpful assistant"},
|
|
{
|
|
"role": "user",
|
|
"content": f"Give an example IP address with this regex: {sample_regex}",
|
|
},
|
|
]
|
|
chat_completion = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=20,
|
|
extra_body=dict(structured_outputs={"regex": sample_regex}),
|
|
)
|
|
ip1 = chat_completion.choices[0].message.content
|
|
assert ip1 is not None
|
|
assert re.fullmatch(sample_regex, ip1) is not None
|
|
|
|
messages.append({"role": "assistant", "content": ip1})
|
|
messages.append({"role": "user", "content": "Give me a different one"})
|
|
chat_completion = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=20,
|
|
extra_body=dict(structured_outputs={"regex": sample_regex}),
|
|
)
|
|
ip2 = chat_completion.choices[0].message.content
|
|
assert ip2 is not None
|
|
assert re.fullmatch(sample_regex, ip2) is not None
|
|
assert ip1 != ip2
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_structured_outputs_type_error(client: openai.AsyncOpenAI):
|
|
messages = [
|
|
{"role": "system", "content": "you are a helpful assistant"},
|
|
{
|
|
"role": "user",
|
|
"content": "The best language for type-safe systems programming is ",
|
|
},
|
|
]
|
|
|
|
with pytest.raises(openai.BadRequestError):
|
|
_ = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
extra_body=dict(structured_outputs={"regex": {1: "Python", 2: "C++"}}),
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_structured_outputs_choice_chat_logprobs(
|
|
client: openai.AsyncOpenAI, sample_structured_outputs_choices
|
|
):
|
|
messages = [
|
|
{"role": "system", "content": "you are a helpful assistant"},
|
|
{
|
|
"role": "user",
|
|
"content": "The best language for type-safe systems programming is ",
|
|
},
|
|
]
|
|
chat_completion = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=10,
|
|
logprobs=True,
|
|
top_logprobs=5,
|
|
extra_body=dict(
|
|
structured_outputs={"choice": sample_structured_outputs_choices}
|
|
),
|
|
)
|
|
|
|
assert chat_completion.choices[0].logprobs is not None
|
|
assert chat_completion.choices[0].logprobs.content is not None
|
|
top_logprobs = chat_completion.choices[0].logprobs.content[0].top_logprobs
|
|
|
|
# -9999.0 is the minimum logprob returned by OpenAI
|
|
for item in top_logprobs:
|
|
assert item.logprob >= -9999.0, f"Failed (top_logprobs={top_logprobs})"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_named_tool_use(
|
|
client: openai.AsyncOpenAI,
|
|
sample_json_schema,
|
|
):
|
|
messages = [
|
|
{"role": "system", "content": "you are a helpful assistant"},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
"Give an example JSON for an employee profile using the specified tool."
|
|
),
|
|
},
|
|
]
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "dummy_function_name",
|
|
"description": "This is a dummy function",
|
|
"parameters": sample_json_schema,
|
|
},
|
|
}
|
|
]
|
|
tool_choice = {"type": "function", "function": {"name": "dummy_function_name"}}
|
|
|
|
# non-streaming
|
|
|
|
chat_completion = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=1000,
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
)
|
|
message = chat_completion.choices[0].message
|
|
assert len(message.content) == 0
|
|
json_string = message.tool_calls[0].function.arguments
|
|
json1 = json.loads(json_string)
|
|
jsonschema.validate(instance=json1, schema=sample_json_schema)
|
|
|
|
messages.append({"role": "assistant", "content": json_string})
|
|
messages.append(
|
|
{"role": "user", "content": "Give me another one with a different name and age"}
|
|
)
|
|
|
|
# streaming
|
|
|
|
stream = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=1000,
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
stream=True,
|
|
)
|
|
|
|
output = []
|
|
finish_reason_count = 0
|
|
async for chunk in stream:
|
|
delta = chunk.choices[0].delta
|
|
if delta.role:
|
|
assert delta.role == "assistant"
|
|
assert delta.content is None or len(delta.content) == 0
|
|
if delta.tool_calls:
|
|
output.append(delta.tool_calls[0].function.arguments)
|
|
if chunk.choices[0].finish_reason is not None:
|
|
finish_reason_count += 1
|
|
# finish reason should only return in last block
|
|
assert finish_reason_count == 1
|
|
json2 = json.loads("".join(output))
|
|
jsonschema.validate(instance=json2, schema=sample_json_schema)
|
|
assert json1["name"] != json2["name"]
|
|
assert json1["age"] != json2["age"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_inconsistent_tool_choice_and_tools(
|
|
client: openai.AsyncOpenAI, sample_json_schema
|
|
):
|
|
messages = [
|
|
{"role": "system", "content": "you are a helpful assistant"},
|
|
{
|
|
"role": "user",
|
|
"content": f"Give an example JSON for an employee profile that "
|
|
f"fits this schema: {sample_json_schema}",
|
|
},
|
|
]
|
|
|
|
with pytest.raises(openai.BadRequestError):
|
|
await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=1000,
|
|
tool_choice={
|
|
"type": "function",
|
|
"function": {"name": "dummy_function_name"},
|
|
},
|
|
)
|
|
|
|
with pytest.raises(openai.BadRequestError):
|
|
await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=1000,
|
|
tools=[
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "dummy_function_name",
|
|
"description": "This is a dummy function",
|
|
"parameters": sample_json_schema,
|
|
},
|
|
}
|
|
],
|
|
tool_choice={
|
|
"type": "function",
|
|
"function": {"name": "nondefined_function_name"},
|
|
},
|
|
)
|
|
with pytest.raises(openai.BadRequestError):
|
|
await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=messages,
|
|
max_completion_tokens=1000,
|
|
tools=[
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "dummy_function_name",
|
|
"description": "This is a dummy function",
|
|
"parameters": sample_json_schema,
|
|
},
|
|
}
|
|
],
|
|
tool_choice={},
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_response_format_json_object(client: openai.AsyncOpenAI):
|
|
for _ in range(2):
|
|
resp = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
"what is 1+1? please respond with a JSON object, "
|
|
'the format is {"result": 2}'
|
|
),
|
|
}
|
|
],
|
|
response_format={"type": "json_object"},
|
|
)
|
|
|
|
content = resp.choices[0].message.content
|
|
assert content is not None
|
|
|
|
loaded = json.loads(content)
|
|
assert loaded == {"result": 2}, loaded
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_response_format_json_schema(client: openai.AsyncOpenAI):
|
|
prompt = 'what is 1+1? The format is "result": 2'
|
|
# Check that this prompt cannot lead to a valid JSON without json_schema
|
|
for _ in range(2):
|
|
resp = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[{"role": "user", "content": prompt}],
|
|
)
|
|
content = resp.choices[0].message.content
|
|
assert content is not None
|
|
with pytest.raises((json.JSONDecodeError, AssertionError)):
|
|
loaded = json.loads(content)
|
|
assert loaded == {"result": 2}, loaded
|
|
|
|
for _ in range(2):
|
|
resp = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[{"role": "user", "content": prompt}],
|
|
response_format={
|
|
"type": "json_schema",
|
|
"json_schema": {
|
|
"name": "foo_test",
|
|
"schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"result": {"type": "integer"},
|
|
},
|
|
},
|
|
},
|
|
},
|
|
)
|
|
|
|
content = resp.choices[0].message.content
|
|
assert content is not None
|
|
|
|
loaded = json.loads(content)
|
|
assert loaded == {"result": 2}, loaded
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_extra_fields_allowed(client: openai.AsyncOpenAI):
|
|
resp = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": "what is 1+1?",
|
|
"extra_field": "0",
|
|
}
|
|
], # type: ignore
|
|
temperature=0,
|
|
seed=0,
|
|
)
|
|
|
|
content = resp.choices[0].message.content
|
|
assert content is not None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_complex_message_content(client: openai.AsyncOpenAI):
|
|
content = [
|
|
{
|
|
"type": "text",
|
|
"text": "what is 1+1? please provide the result without any other text.",
|
|
}
|
|
]
|
|
resp = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": content,
|
|
}
|
|
],
|
|
temperature=0,
|
|
seed=0,
|
|
)
|
|
content = resp.choices[0].message.content
|
|
assert content == "2"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_custom_role(client: openai.AsyncOpenAI):
|
|
# Not sure how the model handles custom roles so we just check that
|
|
# both string and complex message content are handled in the same way
|
|
|
|
resp1 = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[
|
|
{
|
|
"role": "my-custom-role",
|
|
"content": "what is 1+1?",
|
|
}
|
|
], # type: ignore
|
|
temperature=0,
|
|
seed=0,
|
|
)
|
|
|
|
resp2 = await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[
|
|
{
|
|
"role": "my-custom-role",
|
|
"content": [{"type": "text", "text": "what is 1+1?"}],
|
|
}
|
|
], # type: ignore
|
|
temperature=0,
|
|
seed=0,
|
|
)
|
|
|
|
content1 = resp1.choices[0].message.content
|
|
content2 = resp2.choices[0].message.content
|
|
assert content1 == content2
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_long_seed(client: openai.AsyncOpenAI):
|
|
for seed in [torch.iinfo(torch.long).min - 1, torch.iinfo(torch.long).max + 1]:
|
|
with pytest.raises(BadRequestError) as exc_info:
|
|
await client.chat.completions.create(
|
|
model=MODEL_NAME,
|
|
messages=[
|
|
{
|
|
"role": "system",
|
|
"content": "You are a helpful assistant.",
|
|
}
|
|
],
|
|
temperature=0,
|
|
seed=seed,
|
|
)
|
|
|
|
assert (
|
|
"greater_than_equal" in exc_info.value.message
|
|
or "less_than_equal" in exc_info.value.message
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_invocations(server: RemoteOpenAIServer, client: openai.AsyncOpenAI):
|
|
messages = [
|
|
{"role": "system", "content": "you are a helpful assistant"},
|
|
{"role": "user", "content": "what is 1+1?"},
|
|
]
|
|
|
|
request_args = {
|
|
"model": MODEL_NAME,
|
|
"messages": messages,
|
|
"max_completion_tokens": 5,
|
|
"temperature": 0.0,
|
|
"logprobs": False,
|
|
}
|
|
|
|
chat_completion = await client.chat.completions.create(**request_args)
|
|
|
|
invocation_response = requests.post(
|
|
server.url_for("invocations"), json=request_args
|
|
)
|
|
invocation_response.raise_for_status()
|
|
|
|
chat_output = chat_completion.model_dump()
|
|
invocation_output = invocation_response.json()
|
|
|
|
assert chat_output.keys() == invocation_output.keys()
|
|
assert chat_output["choices"] == invocation_output["choices"]
|