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[Frontend][Misc] Enforce Pixel Values as Input Type for VLMs in API Server #5374
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DarkLight1337
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* upstream/main: (126 commits) [Bugfix][Frontend] Cleanup "fix chat logprobs" (vllm-project#5026) [Bugfix] OpenAI entrypoint limits logprobs while ignoring server defined --max-logprobs (vllm-project#5312) [Misc] Various simplifications and typing fixes (vllm-project#5368) [ci] Fix Buildkite agent path (vllm-project#5392) [Doc] Add documentation for FP8 W8A8 (vllm-project#5388) Bump version to v0.5.0 (vllm-project#5384) [Docs] Alphabetically sort sponsors (vllm-project#5386) [Docs] Add Docs on Limitations of VLM Support (vllm-project#5383) [ci] Mount buildkite agent on Docker container to upload benchmark results (vllm-project#5330) [ci] Use small_cpu_queue for doc build (vllm-project#5331) [Bugfix] Fix LLaVA-NeXT (vllm-project#5380) [Feature][Frontend]: Continued `stream_options` implementation also in CompletionRequest (vllm-project#5319) [Model] Initial support for LLaVA-NeXT (vllm-project#4199) [Misc] Improve error message when LoRA parsing fails (vllm-project#5194) [misc][typo] fix typo (vllm-project#5372) [Frontend][Misc] Enforce Pixel Values as Input Type for VLMs in API Server (vllm-project#5374) [Misc] Update to comply with the new `compressed-tensors` config (vllm-project#5350) [Bugfix] Fix KeyError: 1 When Using LoRA adapters (vllm-project#5164) [Kernel][Misc] Use TORCH_LIBRARY instead of PYBIND11_MODULE for custom ops (vllm-project#5047) [mis][ci/test] fix flaky test in test_sharded_state_loader.py (vllm-project#5361) ...
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This PR adds a check on the image input type when launching vLLM API server for serving vision language models. Eventually we shouldn't need this check when we can get rid of
VisionLanguageConfig
after the developer facing refactoring.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!