optimize fused_sigmoid_gateing_recurrent

[Todobe]

Optimize fused_sigmoid_gateing_recurrent op：
Optimize the core strategy by placing two heads into one core for cyclic computation to increase parallelism.
Adjust the order of load and store operations so that the store from the previous cycle and the load of the current cycle can be performed in parallel.

[gemini-code-assist]

Summary of Changes

Hello @Todobe, 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 introduces significant performance optimizations to the fused_sigmoid_gating_recurrent operation. The changes focus on enhancing computational parallelism by processing multiple attention heads within a single core and improving memory access efficiency through reordered load and store operations, ultimately leading to faster execution of this recurrent neural network component.

Highlights

• Increased Parallelism: The core strategy has been optimized to place two heads into a single core for cyclic computation, significantly increasing parallelism within the fused_sigmoid_gating_recurrent operation.
• Optimized Memory Access: The order of load and store operations has been adjusted to allow the store from the previous cycle and the load of the current cycle to execute in parallel, improving memory access efficiency.
• Kernel Parameterization: A new constant BHV (Block Head Vector) has been introduced and passed to the kernel to manage the new head-per-core processing strategy.
• Grid Reconfiguration: The kernel launch grid has been reconfigured to accommodate the new parallelism strategy, changing from (NK, NV, N * HV) to (NV, N, NHV) where NHV = HV // BHV.

🧠 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 aims to optimize the fused_sigmoid_gating_recurrent kernel by increasing parallelism. The changes introduce a new loop to process multiple heads within one core and adjust the kernel's grid configuration. While the optimization strategy is sound, I've found a critical correctness issue in the implementation. The recurrent state update logic is broken because the hidden state is re-initialized at every time step within the main loop. This prevents the state from being passed correctly through time. I have provided a detailed comment with a code suggestion to restructure the kernel and fix this bug, which also addresses some other minor issues like missing other parameters in tl.load and the use of potentially unsafe data-dependent control flow.