[Doc] Add missing llava family multi-image examples #19698
Conversation
Signed-off-by: Isotr0py <2037008807@qq.com>
|
👋 Hi! Thank you for contributing to the vLLM project. 💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels. Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging. To run CI, PR reviewers can either: Add 🚀 |
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Summary of Changes
Hello @Isotr0py, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request enhances the offline_inference examples by introducing dedicated functions and configurations for various LLaVA family models to support multi-image inference. This addresses a previously missing set of examples for these popular vision-language models, improving the comprehensiveness of the documentation and examples and fixing issue #19521.
Highlights
- New LLaVA Multi-Image Examples: I've added three new functions (
load_llava,load_llava_next,load_llava_onevision) toexamples/offline_inference/vision_language_multi_image.py. These functions demonstrate how to configure and use different LLaVA family models (llava-hf/llava-1.5-7b-hf,llava-hf/llava-v1.6-mistral-7b-hf,llava-hf/llava-onevision-qwen2-7b-ov-hf) for multi-image inference, including setting appropriateEngineArgsand applying chat templates. - Example Integration: The newly created LLaVA example functions have been integrated into the
MODEL_MAPdictionary within the same file. This makes them discoverable and usable within thevision_language_multi_image.pyexample script, allowing users to easily run multi-image inference with these LLaVA models.
Using Gemini Code Assist
The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.
Invoking Gemini
You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.
| Feature | Command | Description |
|---|---|---|
| Code Review | /gemini review |
Performs a code review for the current pull request in its current state. |
| Pull Request Summary | /gemini summary |
Provides a summary of the current pull request in its current state. |
| Comment | @gemini-code-assist | Responds in comments when explicitly tagged, both in issue comments and review comments. |
| Help | /gemini help |
Displays a list of available commands. |
Customization
To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.
Limitations & Feedback
Gemini Code Assist is currently in preview and may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments to provide feedback.
You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.
Footnotes
-
Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩
There was a problem hiding this comment.
Code Review
This pull request adds examples for the LLaVA family models. The core logic is sound, but refactoring the load_llava* functions to reduce code duplication will enhance maintainability. Also, grouping the LLaVA family models together in model_example_map will improve readability.
| def load_llava(question: str, image_urls: list[str]) -> ModelRequestData: | ||
| model_name = "llava-hf/llava-1.5-7b-hf" | ||
| engine_args = EngineArgs( | ||
| model=model_name, | ||
| max_num_seqs=16, | ||
| limit_mm_per_prompt={"image": len(image_urls)}, | ||
| ) | ||
|
|
||
| placeholders = [{"type": "image", "image": url} for url in image_urls] | ||
| messages = [ | ||
| { | ||
| "role": "user", | ||
| "content": [ | ||
| *placeholders, | ||
| {"type": "text", "text": question}, | ||
| ], | ||
| } | ||
| ] | ||
|
|
||
| processor = AutoProcessor.from_pretrained(model_name) | ||
|
|
||
| prompt = processor.apply_chat_template( | ||
| messages, tokenize=False, add_generation_prompt=True | ||
| ) | ||
|
|
||
| return ModelRequestData( | ||
| engine_args=engine_args, | ||
| prompt=prompt, | ||
| image_data=[fetch_image(url) for url in image_urls], | ||
| ) | ||
|
|
||
|
|
||
| def load_llava_next(question: str, image_urls: list[str]) -> ModelRequestData: | ||
| model_name = "llava-hf/llava-v1.6-mistral-7b-hf" | ||
| engine_args = EngineArgs( | ||
| model=model_name, | ||
| max_model_len=8192, | ||
| max_num_seqs=16, | ||
| limit_mm_per_prompt={"image": len(image_urls)}, | ||
| ) | ||
|
|
||
| placeholders = [{"type": "image", "image": url} for url in image_urls] | ||
| messages = [ | ||
| { | ||
| "role": "user", | ||
| "content": [ | ||
| *placeholders, | ||
| {"type": "text", "text": question}, | ||
| ], | ||
| } | ||
| ] | ||
|
|
||
| processor = AutoProcessor.from_pretrained(model_name) | ||
|
|
||
| prompt = processor.apply_chat_template( | ||
| messages, tokenize=False, add_generation_prompt=True | ||
| ) | ||
|
|
||
| return ModelRequestData( | ||
| engine_args=engine_args, | ||
| prompt=prompt, | ||
| image_data=[fetch_image(url) for url in image_urls], | ||
| ) | ||
|
|
||
|
|
||
| def load_llava_onevision(question: str, image_urls: list[str]) -> ModelRequestData: | ||
| model_name = "llava-hf/llava-onevision-qwen2-7b-ov-hf" | ||
| engine_args = EngineArgs( | ||
| model=model_name, | ||
| max_model_len=16384, | ||
| max_num_seqs=16, | ||
| limit_mm_per_prompt={"image": len(image_urls)}, | ||
| ) | ||
|
|
||
| placeholders = [{"type": "image", "image": url} for url in image_urls] | ||
| messages = [ | ||
| { | ||
| "role": "user", | ||
| "content": [ | ||
| *placeholders, | ||
| {"type": "text", "text": question}, | ||
| ], | ||
| } | ||
| ] | ||
|
|
||
| processor = AutoProcessor.from_pretrained(model_name) | ||
|
|
||
| prompt = processor.apply_chat_template( | ||
| messages, tokenize=False, add_generation_prompt=True | ||
| ) | ||
|
|
||
| return ModelRequestData( | ||
| engine_args=engine_args, | ||
| prompt=prompt, | ||
| image_data=[fetch_image(url) for url in image_urls], | ||
| ) |
There was a problem hiding this comment.
The functions load_llava, load_llava_next, and load_llava_onevision share a lot of code. Consider refactoring the common logic into a private helper function (e.g., _load_llava_family_model). This helper function can take the model_name and engine_args overrides as parameters.
The public functions would then become simple wrappers calling this helper with their specific configurations. Add docstrings for the public-facing functions to enhance clarity.
def _load_llava_family_model(
question: str,
image_urls: list[str],
model_name: str,
engine_args_overrides: Optional[dict] = None,
) -> ModelRequestData:
"""Helper function to load LLaVA family models."""
if engine_args_overrides is None:
engine_args_overrides = {}
engine_args_dict = {
"model": model_name,
"max_num_seqs": 16,
"limit_mm_per_prompt": {"image": len(image_urls)},
**engine_args_overrides,
}
engine_args = EngineArgs(**engine_args_dict)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_llava(question: str, image_urls: list[str]) -> ModelRequestData:
"""Loads data for the llava-hf/llava-1.5-7b-hf model."""
return _load_llava_family_model(
question,
image_urls,
model_name="llava-hf/llava-1.5-7b-hf",
)
def load_llava_next(question: str, image_urls: list[str]) -> ModelRequestData:
"""Loads data for the llava-hf/llava-v1.6-mistral-7b-hf model."""
return _load_llava_family_model(
question,
image_urls,
model_name="llava-hf/llava-v1.6-mistral-7b-hf",
engine_args_overrides={"max_model_len": 8192},
)
def load_llava_onevision(question: str, image_urls: list[str]) -> ModelRequestData:
"""Loads data for the llava-hf/llava-onevision-qwen2-7b-ov-hf model."""
return _load_llava_family_model(
question,
image_urls,
model_name="llava-hf/llava-onevision-qwen2-7b-ov-hf",
engine_args_overrides={"max_model_len": 16384},
)| "llava": load_llava, | ||
| "llava-next": load_llava_next, | ||
| "llava-onevision": load_llava_onevision, |
There was a problem hiding this comment.
Consider grouping the LLaVA family models together for better readability.
"kimi_vl": load_kimi_vl,
"llava": load_llava,
"llava-next": load_llava_next,
"llava-onevision": load_llava_onevision,
"llama4": load_llama4,Signed-off-by: Isotr0py <2037008807@qq.com>
github-actions
bot
added
the
ready
Signed-off-by: Isotr0py <2037008807@qq.com> Signed-off-by: Linkun <github@lkchen.net>
Signed-off-by: Isotr0py <2037008807@qq.com>
Signed-off-by: Isotr0py <2037008807@qq.com> Signed-off-by: minpeter <kali2005611@gmail.com>
Signed-off-by: Isotr0py <2037008807@qq.com> Signed-off-by: Yang Wang <elainewy@meta.com>
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
Test Plan
Test Result
(Optional) Documentation Update
examples/offline_inference/vision_language_multi_image.py