[Refactor] Relocate tests from tests/v1/entrypoints/ to tests/entrypoints/ (#37500)
Signed-off-by: sfeng33 <4florafeng@gmail.com>
This commit is contained in:
Flora Feng
692
tests/entrypoints/openai/completion/test_completion.py
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692
tests/entrypoints/openai/completion/test_completion.py
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@@ -0,0 +1,692 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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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from openai import BadRequestError
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from tests.utils import RemoteOpenAIServer
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from vllm.tokenizers import get_tokenizer
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# any model with a chat template should work here
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MODEL_NAME = "facebook/opt-125m"
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@pytest.fixture(scope="module")
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def default_server_args():
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return [
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"--dtype",
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"float32",
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"--max-model-len",
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"2048",
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"--max-num-seqs",
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"128",
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"--enforce-eager",
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"--enable-prompt-tokens-details",
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"--no-enable-prefix-caching",
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]
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@pytest.fixture(scope="module")
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def server(default_server_args):
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with RemoteOpenAIServer(MODEL_NAME, default_server_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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"model_name",
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[MODEL_NAME],
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)
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async def test_single_completion(client: openai.AsyncOpenAI, model_name: str) -> None:
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completion = await client.completions.create(
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model=model_name, prompt="Hello, my name is", max_tokens=5, temperature=0.0
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)
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assert completion.id is not None
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assert completion.choices is not None and len(completion.choices) == 1
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choice = completion.choices[0]
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assert len(choice.text) >= 5
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assert choice.finish_reason == "length"
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assert completion.usage == openai.types.CompletionUsage(
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completion_tokens=5, prompt_tokens=6, total_tokens=11
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)
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# test using token IDs
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completion = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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)
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assert len(completion.choices[0].text) >= 1
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assert completion.choices[0].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_no_logprobs(client: openai.AsyncOpenAI, model_name: str):
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# test using token IDs
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completion = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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logprobs=None,
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)
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choice = 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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"model_name",
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[MODEL_NAME],
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)
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async def test_zero_logprobs(client: openai.AsyncOpenAI, model_name: str):
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# test using token IDs
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completion = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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logprobs=0,
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)
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choice = completion.choices[0]
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assert choice.logprobs is not None
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assert choice.logprobs.token_logprobs is not None
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assert choice.logprobs.top_logprobs is not None
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assert len(choice.logprobs.top_logprobs[0]) == 1
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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_some_logprobs(client: openai.AsyncOpenAI, model_name: str):
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# test using token IDs
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completion = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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logprobs=5,
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)
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choice = completion.choices[0]
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assert choice.logprobs is not None
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assert choice.logprobs.token_logprobs is not None
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assert choice.logprobs.top_logprobs is not None
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assert 5 <= len(choice.logprobs.top_logprobs[0]) <= 6
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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_too_many_completion_logprobs(
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client: openai.AsyncOpenAI, model_name: str
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) -> None:
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with pytest.raises(
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(openai.BadRequestError, openai.APIError)
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): # test using token IDs
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await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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# vLLM has higher default max_logprobs (20 instead of 5) to support
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# both Completion API and Chat Completion API
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logprobs=21,
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)
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...
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with pytest.raises(
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(openai.BadRequestError, openai.APIError)
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): # test using token IDs
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stream = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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# vLLM has higher default max_logprobs (20 instead of 5) to support
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# both Completion API and Chat Completion API
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logprobs=30,
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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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# the server should still work afterwards
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completion = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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)
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assert len(completion.choices[0].text) >= 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_completion(
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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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"prompt": ["A robot may not injure another robot", "My name is"],
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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.completions.create(**params)
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else:
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completion = await client.completions.create(**params)
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if prompt_logprobs is not None:
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assert completion.choices[0].prompt_logprobs is not None
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assert len(completion.choices[0].prompt_logprobs) > 0
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assert completion.choices[1].prompt_logprobs is not None
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assert len(completion.choices[1].prompt_logprobs) > 0
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else:
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assert completion.choices[0].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_completion_streaming(
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client: openai.AsyncOpenAI, model_name: str
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) -> None:
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prompt = "What is an LLM?"
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single_completion = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=5,
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temperature=0.0,
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)
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single_output = single_completion.choices[0].text
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stream = await client.completions.create(
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model=model_name, prompt=prompt, max_tokens=5, temperature=0.0, 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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chunks.append(chunk.choices[0].text)
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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 == "length"
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assert chunk.choices[0].text
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assert "".join(chunks) == single_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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[MODEL_NAME],
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)
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async def test_parallel_no_streaming(client: openai.AsyncOpenAI, model_name: str):
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"""Parallel sampling without streaming.
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A single request output contains a list of completions.
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"""
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prompt = "What is an LLM?"
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n = 3
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max_tokens = 50 # we want some to finish earlier than others
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# High temperature to maximize chance of unique completions.
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completion = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=max_tokens,
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n=n,
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temperature=1.0,
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stream=False,
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logprobs=0,
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seed=42,
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)
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# Assert `n` completions
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num_completions = len(completion.choices)
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assert num_completions == n, f"Num completions {num_completions} but expected {n}."
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completion_repeats: dict[str, int] = {}
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output_token_lengths = set()
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for idx, choice in enumerate(completion.choices):
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# Assert correct completion index & some finish reason.
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assert choice.index == idx, f"Index {choice.index} but expected {idx}."
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assert choice.finish_reason is not None, "None finish_reason is invalid."
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text = choice.text
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completion_repeats[text] = completion_repeats.get(text, 0) + 1
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output_token_lengths.add(len(choice.logprobs.tokens))
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# Assert subrequests finished at different times
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assert len(output_token_lengths) > 1
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# Assert `n` unique completions
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num_unique = len(completion_repeats)
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if num_unique != n:
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repeats = {txt: num for (txt, num) in completion_repeats.items() if num > 1}
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raise AssertionError(
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f"Expected {n} unique completions, got {num_unique}; repeats: {repeats}."
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)
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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_parallel_streaming(client: openai.AsyncOpenAI, model_name: str):
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"""Streaming for parallel sampling.
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The tokens from multiple samples, are flattened into a single stream,
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with an index to indicate which sample the token belongs to.
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"""
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prompt = "What is an LLM?"
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n = 3
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max_tokens = 50 # we want some to finish earlier than others
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stream = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=max_tokens,
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n=n,
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temperature=1.0,
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stream=True,
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seed=42,
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)
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chunks: list[list[str]] = [[] for _ in range(n)]
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finish_reason_count = 0
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async for chunk in stream:
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index = chunk.choices[0].index
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text = chunk.choices[0].text
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chunks[index].append(text)
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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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# Assert `n` completions with correct finish reasons
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assert finish_reason_count == n, (
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f"Expected {n} completions with valid indices and finish_reason."
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)
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completion_repeats: dict[str, int] = {}
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chunk_lengths = set()
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for chunk in chunks:
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chunk_len = len(chunk)
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# Assert correct number of completion tokens
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chunk_lengths.add(chunk_len)
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assert chunk_len <= max_tokens, (
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f"max_tokens={max_tokens} but chunk len is {chunk_len}."
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)
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text = "".join(chunk)
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completion_repeats[text] = completion_repeats.get(text, 0) + 1
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print(text)
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# Assert subrequests finished at different times
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assert len(chunk_lengths) > 1
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# Assert `n` unique completions
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num_unique = len(completion_repeats)
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if num_unique != n:
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repeats = {txt: num for (txt, num) in completion_repeats.items() if num > 1}
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raise AssertionError(
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f"{num_unique} unique completions, expected {n}; repeats: {repeats}"
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)
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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_completion_stream_options(client: openai.AsyncOpenAI, model_name: str):
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prompt = "What is the capital of France?"
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# Test stream=True, stream_options=
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# {"include_usage": False, "continuous_usage_stats": False}
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stream = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=5,
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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": False,
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"continuous_usage_stats": False,
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},
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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=
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# {"include_usage": False, "continuous_usage_stats": True}
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stream = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=5,
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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": False,
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"continuous_usage_stats": True,
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},
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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=
|
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# {"include_usage": True, "continuous_usage_stats": False}
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stream = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=5,
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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": False,
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},
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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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|
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# Test stream=True, stream_options=
|
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# {"include_usage": True, "continuous_usage_stats": True}
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stream = await client.completions.create(
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model=model_name,
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prompt=prompt,
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max_tokens=5,
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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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async for chunk in stream:
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assert chunk.usage is not None
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assert chunk.usage.prompt_tokens > 0
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assert chunk.usage.completion_tokens > 0
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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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if chunk.choices[0].finish_reason is not 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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|
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# Test stream=True, stream_options={}
|
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stream = await client.completions.create(
|
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model=model_name,
|
||||
prompt=prompt,
|
||||
max_tokens=5,
|
||||
temperature=0.0,
|
||||
stream=True,
|
||||
stream_options={},
|
||||
)
|
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async for chunk in stream:
|
||||
assert chunk.usage is None
|
||||
|
||||
# Test stream=False, stream_options=
|
||||
# {"include_usage": None}
|
||||
with pytest.raises(BadRequestError):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
max_tokens=5,
|
||||
temperature=0.0,
|
||||
stream=False,
|
||||
stream_options={"include_usage": None},
|
||||
)
|
||||
|
||||
# Test stream=False, stream_options=
|
||||
# {"include_usage": True}
|
||||
with pytest.raises(BadRequestError):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
max_tokens=5,
|
||||
temperature=0.0,
|
||||
stream=False,
|
||||
stream_options={"include_usage": True},
|
||||
)
|
||||
|
||||
# Test stream=False, stream_options=
|
||||
# {"continuous_usage_stats": None}
|
||||
with pytest.raises(BadRequestError):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
max_tokens=5,
|
||||
temperature=0.0,
|
||||
stream=False,
|
||||
stream_options={"continuous_usage_stats": None},
|
||||
)
|
||||
|
||||
# Test stream=False, stream_options=
|
||||
# {"continuous_usage_stats": True}
|
||||
with pytest.raises(BadRequestError):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
max_tokens=5,
|
||||
temperature=0.0,
|
||||
stream=False,
|
||||
stream_options={"continuous_usage_stats": True},
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[MODEL_NAME],
|
||||
)
|
||||
async def test_batch_completions(client: openai.AsyncOpenAI, model_name: str):
|
||||
# test both text and token IDs
|
||||
for prompts in (["Hello, my name is"] * 2, [[0, 0, 0, 0, 0]] * 2):
|
||||
# test simple list
|
||||
batch = await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompts,
|
||||
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=prompts,
|
||||
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=prompts,
|
||||
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]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[MODEL_NAME],
|
||||
)
|
||||
@pytest.mark.parametrize("logprobs_arg", [1, 0])
|
||||
async def test_echo_logprob_completion(
|
||||
client: openai.AsyncOpenAI, model_name: str, logprobs_arg: int
|
||||
):
|
||||
tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
|
||||
# test using text and token IDs
|
||||
for prompt in ("Hello, my name is", [0, 0, 0, 0, 0]):
|
||||
completion = await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
max_tokens=5,
|
||||
temperature=0.0,
|
||||
echo=True,
|
||||
logprobs=logprobs_arg,
|
||||
)
|
||||
|
||||
prompt_text = tokenizer.decode(prompt) if isinstance(prompt, list) else prompt
|
||||
assert re.search(r"^" + prompt_text, completion.choices[0].text)
|
||||
logprobs = completion.choices[0].logprobs
|
||||
assert logprobs is not None
|
||||
assert len(logprobs.text_offset) > 5
|
||||
assert len(logprobs.token_logprobs) > 5 and logprobs.token_logprobs[0] is None
|
||||
assert len(logprobs.top_logprobs) > 5 and logprobs.top_logprobs[0] is None
|
||||
for top_logprobs in logprobs.top_logprobs[1:]:
|
||||
assert max(logprobs_arg, 1) <= len(top_logprobs) <= logprobs_arg + 1
|
||||
assert len(logprobs.tokens) > 5
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[MODEL_NAME],
|
||||
)
|
||||
async def test_invalid_json_schema(client: openai.AsyncOpenAI, model_name: str) -> None:
|
||||
invalid_json_schema = {
|
||||
"$defs": {
|
||||
"CarType": {
|
||||
"enum": ["sedan", "SUV", "Truck", "Coupe"],
|
||||
"title": "CarType",
|
||||
"type": "string",
|
||||
}
|
||||
},
|
||||
"properties": {
|
||||
"brand": {"title": "Brand", "type": "string"},
|
||||
"model": {"title": "Model", "type": "string"},
|
||||
"car_type": {"$ref": "#/$defs/CarType"},
|
||||
"foo": "bar",
|
||||
},
|
||||
"required": ["brand", "model", "car_type"],
|
||||
"title": "CarDescription",
|
||||
"type": "object",
|
||||
}
|
||||
prompt = (
|
||||
"Generate a JSON with the brand, model and car_type of"
|
||||
"the most iconic car from the 90's"
|
||||
)
|
||||
with pytest.raises((openai.BadRequestError, openai.APIError)):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
extra_body={"structured_outputs": {"json": invalid_json_schema}},
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[MODEL_NAME],
|
||||
)
|
||||
async def test_invalid_regex(client: openai.AsyncOpenAI, model_name: str):
|
||||
prompt = (
|
||||
"Generate an email address for Alan Turing, who works in Enigma."
|
||||
"End in .com and new line. Example result:"
|
||||
"alan.turing@enigma.com\n"
|
||||
)
|
||||
|
||||
with pytest.raises((openai.BadRequestError, openai.APIError)):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
extra_body={"structured_outputs": {"regex": r"[.*"}, "stop": ["\n"]},
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[MODEL_NAME],
|
||||
)
|
||||
async def test_invalid_grammar(client: openai.AsyncOpenAI, model_name: str):
|
||||
invalid_simplified_sql_grammar = """
|
||||
root ::= select_statementinvalidsyntax
|
||||
|
||||
select_statement ::= "SELECT " column " from " table " where " condition
|
||||
|
||||
column ::= "col_1 " | "col_2 "
|
||||
|
||||
table ::= "table_1 " | "table_2 "
|
||||
|
||||
condition ::= column "= " number
|
||||
|
||||
number ::= "1 " | "2 "
|
||||
"""
|
||||
|
||||
prompt = (
|
||||
"Generate an SQL query to show the 'username' and 'email'"
|
||||
"from the 'users' table."
|
||||
)
|
||||
with pytest.raises((openai.BadRequestError, openai.APIError)):
|
||||
await client.completions.create(
|
||||
model=model_name,
|
||||
prompt=prompt,
|
||||
extra_body={
|
||||
"structured_outputs": {"grammar": invalid_simplified_sql_grammar}
|
||||
},
|
||||
)
|
||||
Reference in New Issue
Block a user