Support vLLM 0.10.2 #278
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Support vLLM 0.10.2 #278
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Summary of Changes
Hello @pan-x-c, 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 primarily focuses on modernizing the project's infrastructure by upgrading its core dependencies, most notably moving to vLLM version 0.10.2. This upgrade necessitated adjustments to the Docker build environment, updates to other related Python packages like Ray, and modifications to the vLLM engine's configuration to accommodate API changes, such as the deprecation of the V0 engine and changes in logging parameters. These changes ensure continued compatibility and leverage the latest improvements from the vLLM ecosystem.
Highlights
- vLLM Version Upgrade: The
vllmdependency has been updated to support versions up to0.10.2, enabling compatibility with the latest features and fixes. - Dependency Updates: The
ray[default]dependency has been upgraded to>=2.48.0, and the strict version constraint ontransformershas been removed. - Docker Environment Update: The Docker base image has been upgraded to
nvcr.io/nvidia/cuda:12.6.3-cudnn-devel-ubuntu22.04to align with newer CUDA versions. - vLLM Engine Configuration Alignment: The
use_v1parameter, related to the V0 engine, has been removed from vLLM configurations and tests, reflecting its deprecation in vLLM 0.10.2. - Dynamic Logging Configuration: Implemented conditional logic to handle changes in vLLM's logging parameter (
disable_log_requestsvs.enable_log_requests) based on the installed vLLM version.
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Contributor
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Code Review
This pull request updates dependencies to support vllm version 0.10.2, including ray and transformers, and updates the CUDA version in the Dockerfile accordingly. The code changes primarily focus on removing the now-obsolete use_v1 configuration and adapting to a parameter rename in vllm.AsyncEngineArgs. My review has identified a critical issue in trinity/common/models/vllm_model.py related to this adaptation. The current implementation for conditionally setting an engine argument is likely to cause a runtime AttributeError. I have provided a detailed explanation and a suggested refactoring to resolve this potential bug. The other changes appear correct and consistent with the goal of the pull request.
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hiyuchang
approved these changes
Sep 16, 2025
yaochaorui
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Sep 19, 2025
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Description
As the title says
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