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[Frontend] add add_request_id
middleware
#9594
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👋 Hi! Thank you for contributing to the vLLM project. 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:
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It's a little vague where and how unit test should be written, suggestions are welcome. |
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This makes sense to me given it's industry standard. It would be great to add comment about this as well. Thanks!
@simon-mo Just have added an example using custom headers in the |
This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: cjackal <[email protected]>
Signed-off-by: cjackal <[email protected]>
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Looks good then, sorry for the delay!
Signed-off-by: cjackal <[email protected]> Signed-off-by: OmerD <[email protected]>
Signed-off-by: cjackal <[email protected]> Signed-off-by: Loc Huynh <[email protected]>
Signed-off-by: cjackal <[email protected]> Signed-off-by: Jee Jee Li <[email protected]>
Signed-off-by: cjackal <[email protected]>
Signed-off-by: cjackal <[email protected]> Signed-off-by: Sumit Dubey <[email protected]>
This PR made a starlette bug manifest itself in all use-cases of deploying the vLLM OpenAI compatible API server. The bug causes requests cancelled by the client to not be aborted from the LLM engine. Discussed here |
FIX #9593
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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.
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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:
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For changes on the vLLM frontend (e.g., OpenAI API server,LLM
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format.sh
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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.
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.torch.libary.opcheck()
to test the function registration and meta-function for any registered ops. Seetests/kernels
for examples.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!