[Misc] DeepSeek Decode Optimizations

[varun-sundar-rabindranath]

Purpose

Combining:
neuralmagic#97 - 3d quant
#19806 - fp8 dispatch
#19721 - dynamic shape silu mul

Test Plan

Test Result

(Optional) Documentation Update

[github-actions]

👋 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.

🚀

[gemini-code-assist]

Summary of Changes

Hello @varun-sundar-rabindranath, 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 for DeepSeek models within vLLM, primarily by enhancing the handling of dynamic shapes and enabling efficient FP8 quantization and dispatch. It integrates new batched activation and 3D FP8 quantization kernels, specifically designed for Mixture-of-Experts (MoE) layers, to process only valid tokens and reduce computational overhead.

Highlights

• Batched Activation Kernel: A new CUDA kernel (batched_act_and_mul_kernel) and its Python binding (batched_silu_and_mul) are introduced to perform SiLU and multiply operations efficiently on batched inputs with varying numbers of valid tokens, avoiding computation on padded data.
• 3D FP8 Quantization: A new Triton kernel (_per_token_group_quant_fp8_3d) is implemented for FP8 quantization of 3D tensors (Experts, Tokens, Hidden_size), enabling dynamic token-per-expert processing for improved efficiency in MoE layers.
• FP8 Dispatch Activation: Conditional logic is added to enable FP8 dispatch for DeepEP (DeepSeek) models, optimizing data movement and computation when specific quantization criteria are met.
• MoE Layer Integration: The newly developed masked activation and 3D FP8 quantization techniques are integrated into the BatchedDeepGemmExperts and FusedBatchedMoE layers, applying these optimizations directly to Mixture-of-Experts architectures.
• Centralized Masked Kernels: A new masked_kernels.py module is created to house Triton kernels that handle dynamically shaped (masked) inputs, improving code organization and reusability for sparse operations.

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. ↩

[gemini-code-assist]

Code Review

This pull request introduces significant optimizations for MoE layers, focusing on batched/masked activation and FP8 quantization. Key changes include new CUDA and Triton kernels for efficient processing of variable-length token batches, masked FP8 quantization kernels, and their integration into existing MoE expert implementations. Conditional FP8 dispatch for DeepEP low-latency kernels is also enabled. The changes are well-tested and appear to effectively address the goal of optimizing DeepSeek decoding performance. A minor cleanup in a CUDA kernel and a suggestion for increased flexibility in an FP8 quantization utility function are noted.

[gemini-code-assist]

This semicolon is unnecessary and can be removed.

  const int64_t batch_idx = blockIdx.x;

[gemini-code-assist]

Consider making fp8_dtype a parameter that can be passed to quant_fp8_3d rather than hardcoding torch.float8_e4m3fn. This would offer more flexibility if other FP8 variants (e.g., e5m2) are needed in the future, though e4m3fn is standard for activations.

[varun-sundar-rabindranath]

Retire in favor of https://github.com/vllm-project/vllm/tree/ll_deepgemm_opt