vllm - 💡(How to fix) Fix [Feature]: Helion Kernels for MLA (Decode) [1 participants]

TL;DR

Implementing custom pytorch-helion kernels for MultiHead Latent Attention with fp8 quantization may require studying existing MLA implementations and utilizing Helion's autotuner for optimal configuration.

Guidance

• Study FlashMLA and other MLA implementations to understand their architecture and quantization strategies.
• Utilize Helion's autotuner to find optimal configurations for the custom kernels on different hardware.
• Implement MLA Decode with dynamic fp8 quantization per token and per block to achieve the desired functionality.
• Provide autotuned configs for different hardware to ensure compatibility and performance.

Notes

The implementation progress suggests that the custom kernel development is still in progress, and the autotuner will play a crucial role in optimizing the kernel performance.

Recommendation

Apply workaround by utilizing Helion's autotuner to find optimal configurations for the custom kernels, as it guarantees to compile down to a single triton kernel, resulting in a low-effort, high-reward scenario.