Skip to content

Conversation

fzyzcjy
Copy link
Collaborator

@fzyzcjy fzyzcjy commented Sep 9, 2025

📌 Description

useful for ncu

🔍 Related Issues

🚀 Pull Request Checklist

Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete.

✅ Pre-commit Checks

  • I have installed pre-commit by running pip install pre-commit (or used your preferred method).
  • I have installed the hooks with pre-commit install.
  • I have run the hooks manually with pre-commit run --all-files and fixed any reported issues.

If you are unsure about how to set up pre-commit, see the pre-commit documentation.

🧪 Tests

  • Tests have been added or updated as needed.
  • All tests are passing (unittest, etc.).

Reviewer Notes

This reverts commit a2e1b7e.
Copy link
Contributor

@gemini-code-assist gemini-code-assist bot left a 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 @fzyzcjy, 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 provides a small but impactful update to the JIT compilation setup, enabling the inclusion of line information in compiled CUDA kernels. This enhancement is specifically designed to aid in profiling and debugging efforts, particularly when using tools like NVIDIA Nsight Compute (ncu), by providing more granular detail for performance analysis.

Highlights

  • JIT Compilation Flags: Introduced a new option to include line information during CUDA JIT compilation. This is controlled by the FLASHINFER_JIT_LINEINFO environment variable.
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 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. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

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

  1. 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.

Copy link
Contributor

@gemini-code-assist gemini-code-assist bot left a 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 introduces a way to enable CUDA line information for JIT compilation via the FLASHINFER_JIT_LINEINFO environment variable, which is useful for profiling with tools like ncu. My review identifies a potential issue in the implementation where the flag might be enabled by default unintentionally and could also be duplicated. I've provided a suggestion to correct this behavior.

Comment on lines +201 to +202
if bool(os.environ.get("FLASHINFER_JIT_LINEINFO", "0")):
cuda_cflags += ["-lineinfo"]
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

high

This logic has two potential issues:

  1. The boolean check is incorrect: bool("0") is True, so os.environ.get("FLASHINFER_JIT_LINEINFO", "0") will cause this condition to be met by default. The check should be against "1", similar to FLASHINFER_JIT_VERBOSE.
  2. The -lineinfo flag may be added twice, as it's already included on line 191 when verbose mode is active.

The suggested change below corrects the check and prevents adding a duplicate flag.

Suggested change
if bool(os.environ.get("FLASHINFER_JIT_LINEINFO", "0")):
cuda_cflags += ["-lineinfo"]
if os.environ.get("FLASHINFER_JIT_LINEINFO", "0") == "1" and "-lineinfo" not in cuda_cflags:
cuda_cflags += ["-lineinfo"]

@zhyncs zhyncs enabled auto-merge (squash) September 9, 2025 09:22
@zhyncs zhyncs merged commit a69a8bf into flashinfer-ai:main Sep 9, 2025
2 checks passed
cuda_cflags += ["-DNDEBUG"]

# useful for ncu
if bool(os.environ.get("FLASHINFER_JIT_LINEINFO", "0")):
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

it's covered in FLASHINFER_JIT_VERBOSE

Copy link
Collaborator Author

@fzyzcjy fzyzcjy Sep 9, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

good point, btw it seems ptxas's register-usage-level (enabled in the verbose path) is:

--register-usage-level <0..10>                      (-regUsageLevel)            
        Controls the aggressiveness of optimizations that affect register usage.
        ([0..10], default = 5) Higher values aggressively optimize the source program,
        trading off additional register usage for potential improvements in the generated
        code. Lower values inhibit optimizations that aggressively increase register
        usage. This option can work in conjunction with -maxrregcount and CUDA launch
        bounds. This is a BETA feature for advanced users and there is no guarantee
        that the implementation stays consistent between ptxas releases.
        Default value:  5.

thus I am not very sure whether it changes program behavior

cuda_cflags += ["-DNDEBUG"]

# useful for ncu
if bool(os.environ.get("FLASHINFER_JIT_LINEINFO", "0")):
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

bool("0") evaluates to True, so bool(os.environ.get("FLASHINFER_JIT_LINEINFO", "0")) will be True if this environment variable is not set.

This bug is fixed in #1872 , note that -lineinfo will greatly increase binary size, see my comment in #1872 (comment)

cc @fzyzcjy @zhyncs for viz

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Good point, I should have written bool(int(...))...

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants