Skip to content

Splitting attention _fwd_grouped_kernel_stage1 to improve occupancy #19774

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

Open
wants to merge 1 commit into
base: main
Choose a base branch
from

Conversation

ekuznetsov139
Copy link
Contributor

@ekuznetsov139 ekuznetsov139 commented Jun 17, 2025

This splits _fwd_grouped_kernel_stage1 operation in two parts, using a temporary buffer.

The rationale is as follows. _fwd_grouped_kernel_stage1 becomes a significant bottleneck in DeepSeek-V3 with large context lengths. It is slow, because it severely under-utilizes the GPU (it is launched with a very small grid size). This patch breaks it into _fwd_grouped_kernel_stage1_part1 (launched using a larger grid, writes into a temporary buffer) and _fwd_grouped_kernel_stage1_part2 (a small grid but much lighter).

Performance (context length vs. generation throughput in token/s, 4x AMD Instinct MI325, deepseek-ai/DeepSeek-V3-0324, --max_seq_len_to_capture 131072):

| chars   | baseline | PR    |
|---------|----------|-------|
| 0       | 41.7     | 41.7  |
| 50k     | 11.6     | 37.7  |
| 100k    | 6.9      | 34.9  |
| 200k    | 3.7      | 30.8  |

Copy link

👋 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 fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

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 ready label to the PR or enable auto-merge.

🚀

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 @ekuznetsov139, 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 a significant optimization for attention computation, particularly beneficial for models like DeepSeek-V3 handling very long context lengths. By splitting a critical Triton kernel into two stages and leveraging a temporary buffer, the changes aim to improve GPU occupancy and overall generation throughput, addressing a key performance bottleneck.

Highlights

  • Performance Optimization: The core _fwd_grouped_kernel_stage1 Triton kernel, identified as a bottleneck for large context lengths, has been split into two new kernels: _fwd_grouped_kernel_stage1_part1 and _fwd_grouped_kernel_stage1_part2. This refactoring aims to improve GPU occupancy and generation throughput, especially for DeepSeek-V3 with context lengths exceeding 100k characters.
  • Temporary Buffer for Intermediate Results: A temporary buffer (attn_buf) is introduced to store intermediate attention calculation results between the two new kernel stages. This buffer is managed by a global cache (attn_buf_cache) in vllm/attention/backends/triton_mla.py to optimize memory allocation.
  • Kernel Execution Re-orchestration: The _fwd_grouped_kernel_stage1_part1 kernel is designed to be launched with a larger grid size, writing partial attention sums and logsumexp values to the temporary buffer. The _fwd_grouped_kernel_stage1_part2 kernel then aggregates these partial results from the buffer to compute the final attention output, allowing for more efficient GPU utilization.
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

  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 performance optimization by splitting the _fwd_grouped_kernel_stage1 Triton kernel into two parts to improve GPU occupancy, which is beneficial for large context lengths. The core logic for the kernel split and the use of a temporary buffer (attn_buf) appears to be implemented.

Key areas for review include:

  • The management of the global attn_buf_cache, which could lead to memory issues without proper eviction or lifecycle management.
  • The use of magic numbers (e.g., 32 for temp_page_count) that should be defined as constants for clarity.
  • Minor points on code clarity within the Triton kernels, such as an unused variable and a potentially misleading comment.
  • A check for potential division by zero in the second stage of the new kernel.
  • Removal of development artifacts like an if True: block.

Addressing these points will enhance the robustness and maintainability of the changes.

Comment on lines 484 to 486
tl.store(Att_Out + offs_mid_o + offs_dv,
acc / e_sum,
mask=mask_dv)
Copy link
Contributor

Choose a reason for hiding this comment

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

medium

There's a potential division by zero if e_sum is 0.0. This can occur if all attention scores (qk values) for a particular head and split were masked to -inf, leading to p (from tl.exp(qk - n_e_max[:, None])) being all zeros, and thus tl.sum(p,1) being zero.

If e_sum is zero, acc / e_sum will result in NaN or Inf. Is this scenario handled by upstream logic, or should a check be added here? For example, tl.where(e_sum == 0, 0.0, acc / e_sum) if the output should be zero in such cases.

@ekuznetsov139 ekuznetsov139 force-pushed the split_triton_mla branch 2 times, most recently from feb0741 to f8a7416 Compare June 20, 2025 13:39
Signed-off-by: Eugene Kuznetsov <eugene.kuznetsov@amd.com>
@ekuznetsov139 ekuznetsov139 changed the title [WIP] Splitting attention _fwd_grouped_kernel_stage1 to improve occupancy Splitting attention _fwd_grouped_kernel_stage1 to improve occupancy Jul 3, 2025
@ekuznetsov139
Copy link
Contributor Author

Bump

@sunway513
Copy link

@gshtras can we get this PR reviewed? thanks

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.

2 participants