vllm icon indicating copy to clipboard operation
vllm copied to clipboard

[Misc] Compute query_start_loc on CPU

Open zhengy001 opened this issue 1 year ago • 2 comments

FILL IN THE PR DESCRIPTION HERE

Avoid creating intermediate tensor query_lens_tensor and compute query_start_loc on CPU and allow h2d async.

BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE


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:

  • We adhere to Google Python style guide and Google C++ style guide.
  • Pass all linter checks. Please use format.sh to format your code.
  • The code need to be well-documented to ensure future contributors can easily understand the code.
  • Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.
  • Please add documentation to docs/source/ if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.

Adding or changing kernels

Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.

  • Make sure custom ops are registered following PyTorch guidelines: Custom C++ and CUDA Operators and The Custom Operators Manual
  • Custom operations that return Tensors require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.
  • Use torch.libary.opcheck() to test the function registration and meta-function for any registered ops. See tests/kernels for examples.
  • When changing the C++ signature of an existing op, the schema must be updated to reflect the changes.
  • If a new custom type is needed, see the following document: Custom Class Support in PT2.

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:

  • After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.
  • After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.
  • After the review, the reviewer will put an 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.
  • Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.

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!

zhengy001 avatar Oct 17 '24 05:10 zhengy001

👋 Hi! Thank you for contributing to the vLLM project. Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

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 do one of these:

  • Add ready label to the PR
  • Enable auto-merge.

🚀

github-actions[bot] avatar Oct 17 '24 05:10 github-actions[bot]

Can someone explain what does buildkite/fastcheck/pr/docker-build-image test do?

zhengy001 avatar Oct 18 '24 12:10 zhengy001

Can someone explain what does buildkite/fastcheck/pr/docker-build-image test do?

Sorry for the late reply, it builds the docker image which is used to run all of the tests. I have retried this step just now.

DarkLight1337 avatar Oct 22 '24 06:10 DarkLight1337

This pull request has merge conflicts that must be resolved before it can be merged. @zhengy001 please rebase it. https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

mergify[bot] avatar Nov 02 '24 07:11 mergify[bot]

@zhengy001 Can you run a benchmark for this PR? LGTM once the performance is determined to be still reasonable. (I don't have idle device to run with FA2 backend right now)

You can run benchmark with following commands:

python3 benchmark_throughput.py \
    --model meta-llama/Meta-Llama-3.1-8B-Instruct \
    --backend vllm \
    --input-len 292 \
    --output-len 579 \
    --num-prompts 1000

Isotr0py avatar Nov 04 '24 03:11 Isotr0py

@zhengy001 Can you run a benchmark for this PR? LGTM once the performance is determined to be still reasonable. (I don't have idle device to run with FA2 backend right now)

You can run benchmark with following commands:

python3 benchmark_throughput.py \
    --model meta-llama/Meta-Llama-3.1-8B-Instruct \
    --backend vllm \
    --input-len 292 \
    --output-len 579 \
    --num-prompts 1000

I use Llama2-7B

Before: Throughput: 7.48 requests/s, 6512.49 total tokens/s, 4329.20 output tokens/s

After: Throughput: 7.48 requests/s, 6511.69 total tokens/s, 4328.67 output tokens/s

zhengy001 avatar Nov 04 '24 06:11 zhengy001

LGTM! The benchmark result looks reasonable!

Thanks, @Isotr0py, do you know the failed buildkite/fastcheck/pr/tpu-test error? image

Seems unrelated, do you know how to retry CI?

zhengy001 avatar Nov 04 '24 06:11 zhengy001

You can sync this PR branch with the main branch to re-run the CI from new commit.

Isotr0py avatar Nov 04 '24 06:11 Isotr0py

You can sync this PR branch with the main branch to re-run the CI from new commit.

Okay, thanks.

zhengy001 avatar Nov 04 '24 06:11 zhengy001