-
-
Notifications
You must be signed in to change notification settings - Fork 5.3k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[CI/Build] Add nightly benchmarking for tgi, tensorrt-llm and lmdeploy #5362
Conversation
I have finished an initial implementation on |
…ot overlap with performance benchmark
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
This looks good. One thing I couldn't find a single place to find all the workload description (i.e. where is the parameters for benchmark serving which should be identical for all backends).
Hi @KuntaiDu Nice work! May we consider adding benchmarks for glm-4-9b-chat and Qwen2-72B-Instruct? They are currently the SOTA models in CJK native support. If OK and needed, I am happy to help. Thanks. cc @simon-mo |
Sorry for the late reply (github somehow did not remind me of this message). Feel free to raise a new PR to do this! |
Following PR #5073, this PR aims to compare
vllm
and alternatives (like tgi, tensorrt-llm and lmdeploy --- feel free to comment if you feel there are also other alternatives we need to benchmark) ON THE SAME WORKLOAD (the same as PR #5073) USING THE SAME BENCHMARKING SCRIPT (benchmark_serving.py
).For fair comparison, we will run vllm and alternatives in their corresponding official docker image.
This will be a nightly benchmark as running all alternatives on all workloads can be pretty time-consuming.
TODO lists:
nightly-tests.json
PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
[Bugfix]
for bug fixes.[CI/Build]
for build or continuous integration improvements.[Doc]
for documentation fixes and improvements.[Model]
for adding a new model or improving an existing model. Model name should appear in the title.[Frontend]
For changes on the vLLM frontend (e.g., OpenAI API server,LLM
class, etc.)[Kernel]
for changes affecting CUDA kernels or other compute kernels.[Core]
for changes in the core vLLM logic (e.g.,LLMEngine
,AsyncLLMEngine
,Scheduler
, etc.)[Hardware][Vendor]
for hardware-specific changes. Vendor name should appear in the prefix (e.g.,[Hardware][AMD]
).[Misc]
for PRs that do not fit the above categories. Please use this sparingly.Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
The PR need to meet the following code quality standards:
format.sh
to format your code.docs/source/
if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with
rfc-required
and might not go through the PR.What to Expect for the Reviews
The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:
action-required
label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!