add some examples for other benchmark scripts #19893
Conversation
|
👋 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 @reidliu41, 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 benchmarks/README.md file by adding a variety of new example commands for several existing benchmark scripts. The additions aim to provide clearer guidance and ready-to-use configurations for users looking to test structured output generation, long document QA throughput, prefix caching, and request prioritization within the system.
Highlights
- Structured Output Benchmarking Examples: Added comprehensive examples for
benchmark_serving_structured_output.py, covering various structured generation types such as JSON schema, grammar-based, regex-based, choice-based, and XGrammar datasets. This includes the necessary server setup command. - Long Document QA Throughput Benchmarking Examples: Included new examples for
benchmark_long_document_qa_throughput.py, demonstrating how to benchmark long document question-answering with prefix caching, including basic tests and different prompt repetition modes (random, tile, interleave). - Prefix Caching Benchmarking Examples: Provided examples for
benchmark_prefix_caching.pyto illustrate the efficiency of automatic prefix caching, with scenarios for fixed prompts and usage with the ShareGPT dataset. - Request Prioritization Benchmarking Examples: Added examples for
benchmark_prioritization.py, showcasing how to benchmark request prioritization within vLLM, covering basic prioritization tests and scenarios involving multiple sequences per prompt.
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 valuable examples for several benchmark scripts to the benchmarks/README.md file, covering structured output, long document QA, prefix caching, and request prioritization. The documentation is generally clear and well-structured, making it easier for users to understand how to run these benchmarks.
One minor correction is needed for an incorrect script path in one of the examples.
Additionally, the PR description checklist is largely unfilled. While this PR primarily adds documentation, completing relevant sections of the checklist (like confirming no further documentation updates are needed beyond this PR) can be helpful for maintainers.
benchmarks/README.md
Outdated
| ### Multiple Sequences per Prompt | ||
|
|
||
| ```bash | ||
| python3 benchmark_prioritization.py \ |
There was a problem hiding this comment.
The path to the benchmark_prioritization.py script seems to be missing the benchmarks/ directory prefix. Users copying this command will likely encounter a 'file not found' error.
All other benchmark scripts in this README are invoked with python3 benchmarks/<script_name>.py.
| python3 benchmark_prioritization.py \ | |
| python3 benchmarks/benchmark_prioritization.py \ |
Signed-off-by: reidliu41 <[email protected]>
reidliu41
force-pushed
the
add-benchmark-examples
branch
from
c3bed8b to
2a9bae3
Compare
|
hi @houseroad @ywang96 could you please help to take a look if you have time? thanks a lot |
|
hi team, can someone help to take a look? thanks |
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
Test Plan
Test Result
(Optional) Documentation Update