[Chore] update tvm-ffi to 0.1.8

[DarkSharpness]

Motivation

https://github.com/apache/tvm-ffi/releases/tag/v0.1.8

We may hopefully land this feature apache/tvm-ffi#374

Modifications

Accuracy Tests

Benchmarking and Profiling

Checklist

• Format your code according to the Format code with pre-commit.
• Add unit tests according to the Run and add unit tests.
• Update documentation according to Write documentations.
• Provide accuracy and speed benchmark results according to Test the accuracy and Benchmark the speed.
• Follow the SGLang code style guidance.

Review Process

1. Ping Merge Oncalls to start the PR flow. See the PR Merge Process.
2. Get approvals from CODEOWNERS and other reviewers.
3. Trigger CI tests with comments or contact authorized users to do so.
   • /tag-run-ci-label, /rerun-failed-ci, /tag-and-rerun-ci
4. After green CI and required approvals, ask Merge Oncalls to merge.

[DarkSharpness]

/tag-and-rerun-ci

[gemini-code-assist]

Summary of Changes

Hello @DarkSharpness, 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 performs a routine maintenance update by bumping the apache-tvm-ffi dependency to version 0.1.8 across the project's various pyproject.toml configurations. This ensures the project benefits from the latest improvements and bug fixes in the tvm-ffi library, potentially enabling new functionalities or enhancing existing ones, as indicated by the motivation to integrate recent upstream features.

Highlights

• Dependency Update: The apache-tvm-ffi dependency has been updated across all relevant pyproject.toml files to version 0.1.8.
• Version Pinning: The dependency is now pinned to apache-tvm-ffi>=0.1.8,<0.2, ensuring compatibility while allowing for future patch releases.
• Potential Feature Enablement: This update is motivated by the potential to land new features from the apache/tvm-ffi project, specifically referencing a pull request related to tvm-ffi/pull/374.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

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 by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

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 pull request 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. ↩

[gemini-code-assist]

Code Review

This pull request updates the tvm-ffi dependency to version 0.1.8 by adding apache-tvm-ffi>=0.1.8,<0.2 to the dependency lists in pyproject.toml, pyproject_cpu.toml, pyproject_other.toml, and pyproject_xpu.toml. The change is correctly implemented across all relevant configuration files, with appropriate version pinning and correct alphabetical placement. My main feedback is a suggestion to improve maintainability by reducing the duplication of dependencies across these multiple pyproject.toml files. Consolidating into a single pyproject.toml using optional dependencies (extras) would simplify future dependency management. I've left a specific comment with more details on this suggestion.

[gemini-code-assist]

This dependency is also added to pyproject_cpu.toml, pyproject_other.toml, and pyproject_xpu.toml. This duplication across multiple pyproject.toml files can make dependency management difficult and error-prone.

To improve maintainability, consider consolidating dependencies. One approach is to use a single pyproject.toml and define environment-specific dependencies using optional-dependencies (extras). For example:

[project.optional-dependencies]
core = [
  "aiohttp",
  "anthropic>=0.20.0",
  # ... other common dependencies
]
cuda = [
  "sglang[core]",
  "cuda-python==12.9",
  # ... other cuda-specific dependencies
]
cpu = [
  "sglang[core]",
  # ... other cpu-specific dependencies
]

This would centralize your dependency list and make updates like this one simpler and safer. This is a larger refactoring that could be addressed in a separate ticket, but it's worth considering for the long-term health of the project.