e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
351 lines
10 KiB
Python
351 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Dict, List
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import openai
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import pytest
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import pytest_asyncio
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from vllm.multimodal.utils import encode_video_base64, fetch_video
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from ...utils import RemoteOpenAIServer
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MODEL_NAME = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
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MAXIMUM_VIDEOS = 4
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TEST_VIDEO_URLS = [
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"http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4",
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"http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ElephantsDream.mp4",
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"http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerBlazes.mp4",
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"http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4",
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]
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@pytest.fixture(scope="module")
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def server():
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args = [
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"--task",
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"generate",
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"--dtype",
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"bfloat16",
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"--max-model-len",
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"32768",
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"--max-num-seqs",
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"2",
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"--enforce-eager",
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"--trust-remote-code",
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"--limit-mm-per-prompt",
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f"video={MAXIMUM_VIDEOS}",
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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.fixture(scope="session")
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def base64_encoded_video() -> Dict[str, str]:
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return {
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video_url: encode_video_base64(fetch_video(video_url))
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for video_url in TEST_VIDEO_URLS
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}
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("video_url", TEST_VIDEO_URLS)
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async def test_single_chat_session_video(client: openai.AsyncOpenAI,
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model_name: str, video_url: str):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "video_url",
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"video_url": {
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"url": video_url
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}
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},
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{
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"type": "text",
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"text": "What's in this video?"
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},
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],
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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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temperature=0.0,
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top_logprobs=5)
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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=6299, total_tokens=6309)
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message = choice.message
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message = chat_completion.choices[0].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("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("video_url", TEST_VIDEO_URLS)
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async def test_single_chat_session_video_beamsearch(client: openai.AsyncOpenAI,
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model_name: str,
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video_url: str):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "video_url",
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"video_url": {
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"url": video_url
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}
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},
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{
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"type": "text",
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"text": "What's in this video?"
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},
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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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n=2,
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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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extra_body=dict(use_beam_search=True))
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assert len(chat_completion.choices) == 2
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assert chat_completion.choices[
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0].message.content != chat_completion.choices[1].message.content
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("video_url", TEST_VIDEO_URLS)
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async def test_single_chat_session_video_base64encoded(
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client: openai.AsyncOpenAI, model_name: str, video_url: str,
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base64_encoded_video: Dict[str, str]):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "video_url",
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"video_url": {
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"url":
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f"data:video/jpeg;base64,{base64_encoded_video[video_url]}"
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}
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},
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{
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"type": "text",
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"text": "What's in this video?"
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},
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],
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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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temperature=0.0,
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top_logprobs=5)
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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=6299, total_tokens=6309)
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message = choice.message
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message = chat_completion.choices[0].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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temperature=0.0,
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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("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("video_url", TEST_VIDEO_URLS)
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async def test_single_chat_session_video_base64encoded_beamsearch(
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client: openai.AsyncOpenAI, model_name: str, video_url: str,
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base64_encoded_video: Dict[str, str]):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "video_url",
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"video_url": {
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"url":
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f"data:video/jpeg;base64,{base64_encoded_video[video_url]}"
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}
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},
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{
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"type": "text",
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"text": "What's in this video?"
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},
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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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n=2,
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max_completion_tokens=10,
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extra_body=dict(use_beam_search=True))
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assert len(chat_completion.choices) == 2
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assert chat_completion.choices[
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0].message.content != chat_completion.choices[1].message.content
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("video_url", TEST_VIDEO_URLS)
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async def test_chat_streaming_video(client: openai.AsyncOpenAI,
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model_name: str, video_url: str):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "video_url",
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"video_url": {
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"url": video_url
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}
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},
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{
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"type": "text",
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"text": "What's in this video?"
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},
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],
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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("model_name", [MODEL_NAME])
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@pytest.mark.parametrize(
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"video_urls",
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[TEST_VIDEO_URLS[:i] for i in range(2, len(TEST_VIDEO_URLS))])
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async def test_multi_video_input(client: openai.AsyncOpenAI, model_name: str,
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video_urls: List[str]):
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messages = [{
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"role":
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"user",
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"content": [
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*({
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"type": "video_url",
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"video_url": {
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"url": video_url
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}
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} for video_url in video_urls),
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{
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"type": "text",
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"text": "What's in this video?"
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},
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],
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}]
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if len(video_urls) > MAXIMUM_VIDEOS:
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with pytest.raises(openai.BadRequestError): # test multi-video input
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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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)
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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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completion = completion.choices[0].text
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assert completion is not None and len(completion) >= 0
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else:
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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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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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