-
-
Notifications
You must be signed in to change notification settings - Fork 11.5k
[CI/Build] Fix FlashInfer double build in Dockerfile #20651
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] Fix FlashInfer double build in Dockerfile #20651
Conversation
Signed-off-by: mgoin <[email protected]>
Signed-off-by: mgoin <[email protected]>
|
👋 Hi! Thank you for contributing to the vLLM project. 💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels. Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run 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 either: Add 🚀 |
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.
Summary of Changes
Hello @mgoin, 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 addresses a critical build configuration issue in the Dockerfile related to FlashInfer. It streamlines the FlashInfer installation process by eliminating redundant build steps and ensuring that the correct CUDA architectures are targeted based on the specific CUDA version being used. This fix aims to prevent potential JIT performance anomalies observed in previous releases by resolving a 'double build' scenario.
Highlights
- FlashInfer Build Consolidation: Consolidated the FlashInfer build process within the
Dockerfile, removing a redundant build step that was causing conflictingTORCH_CUDA_ARCH_LISTsettings and potential performance issues. - CUDA Architecture Targeting Logic: Refined the logic for setting the
TORCH_CUDA_ARCH_LISTvariable, nowFI_TORCH_CUDA_ARCH_LIST, to ensure FlashInfer is built with the correct CUDA architectures (e.g., 7.5, 8.0, 8.9, 9.0a, 10.0a, 12.0) based on the specificCUDA_VERSION(11.x, 12.0-12.7, or 12.8+). - Dockerfile Streamlining: Cleaned up the
Dockerfileby removing unnecessaryexport TORCH_CUDA_ARCH_LISTand duplicategit clonecommands, making the FlashInfer installation process more efficient and less error-prone.
Using Gemini Code Assist
The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.
Invoking Gemini
You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.
| Feature | Command | Description |
|---|---|---|
| Code Review | /gemini review |
Performs a code review for the current pull request in its current state. |
| Pull Request Summary | /gemini summary |
Provides a summary of the current pull request in its current state. |
| Comment | @gemini-code-assist | Responds in comments when explicitly tagged, both in issue comments and review comments. |
| Help | /gemini help |
Displays a list of available commands. |
Customization
To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.
Limitations & Feedback
Gemini Code Assist is currently in preview and may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments to provide feedback.
You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.
Footnotes
-
Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩
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.
Code Review
This pull request fixes a double build issue for FlashInfer in the Dockerfile by removing the redundant build process and refining the logic for CUDA architecture selection. A comment was clarified for better maintainability.
tlrmchlsmth
left a comment
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.
one small comment otherwise LGTM, thanks!
) Signed-off-by: mgoin <[email protected]>
) Signed-off-by: mgoin <[email protected]>
) Signed-off-by: mgoin <[email protected]> Signed-off-by: Jinzhen Lin <[email protected]>
) Signed-off-by: mgoin <[email protected]> Signed-off-by: Diego-Castan <[email protected]>
) Signed-off-by: mgoin <[email protected]>
Purpose
It seems there was a bad merge between #18064 and #20136 that resulted in FlashInfer being built "both ways" with both sets of TORCH_CUDA_ARCH_LIST. I'm not sure how the CUDA 11.8 build was working with this in place, but this may have resulted in some of the JIT performance weirdness observed in the last release.
Test Plan
Test Result