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zihan zhou d626421fff Fix the bug of fma 
Hi, I find in scale_apply_exp2, The code comments also mentioned this issue:
https://github.com/pytorch/pytorch/issues/121558

This issue is that the ffma instruction generates some calculation errors
during the flash attention compared to fadd and fmul separated.

For fadd and fmul, the calculation is:
round_fp32(x_i * scale) - round_fp32(x_i * scale)
For max(x), this value is 0.

But For ffma, the calculation is:
x_i * scale - round_fp32(x_i * scale)
Although the accuracy of ffma calculations has actually improved,
there have been errors in the values.

We can raise this issue by changing the initialization value of q k,
and the final outs will all be 0:

q = torch.full((b, s_q, h_q, d), 133120.0)
blocked_k = torch.full((block_table.numel(), block_size, h_kv, d), 133120.0)

If we define UNFUSE_FMA, This problem has been alleviated, but it still
cannot pass the cal-diff test. I am not sure if it is an accuracy issue,
but I think it is necessary to fix the fma bug first.
2025-03-19 11:11:05 +08:00
benchmark fix(benchmark): store 'compare' and 'one' perf results in csv files and visualize them 2025-02-26 00:14:51 +08:00
csrc add missing copyright 2025-03-01 18:24:24 +08:00
flash_mla Update docstring 2025-02-25 00:11:57 +08:00
tests Style fix 2025-02-25 09:18:11 +08:00
.gitignore add gitignore for png and csv files in benchmark 2025-02-25 00:38:02 +08:00
.gitmodules Initial commit 2025-02-24 09:20:23 +08:00
LICENSE Initial commit 2025-02-24 09:20:23 +08:00
README.md add community support for [AMD] 2025-03-01 17:55:58 +08:00
setup.py Fix the bug of fma 2025-03-19 11:11:05 +08:00

README.md

FlashMLA

FlashMLA is an efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences serving.

Currently released:

  • BF16, FP16
  • Paged kvcache with block size of 64

Quick start

Install

python setup.py install

Benchmark

python tests/test_flash_mla.py

Achieving up to 3000 GB/s in memory-bound configuration and 580 TFLOPS in computation-bound configuration on H800 SXM5, using CUDA 12.8.

Usage

from flash_mla import get_mla_metadata, flash_mla_with_kvcache

tile_scheduler_metadata, num_splits = get_mla_metadata(cache_seqlens, s_q * h_q // h_kv, h_kv)

for i in range(num_layers):
    ...
    o_i, lse_i = flash_mla_with_kvcache(
        q_i, kvcache_i, block_table, cache_seqlens, dv,
        tile_scheduler_metadata, num_splits, causal=True,
    )
    ...

Requirements

  • Hopper GPUs
  • CUDA 12.3 and above
    • But we highly recommend 12.8 or above for the best performance
  • PyTorch 2.0 and above

Acknowledgement

FlashMLA is inspired by FlashAttention 2&3 and cutlass projects.

Community Support

MetaX

For MetaX GPUs, visit the official website: MetaX.

The corresponding FlashMLA version can be found at: MetaX-MACA/FlashMLA

Moore Threads

For the Moore Threads GPU, visit the official website: Moore Threads.

The corresponding FlashMLA version is available on GitHub: MooreThreads/MT-flashMLA.

Hygon DCU

For the Hygon DCU, visit the official website: Hygon Developer.

The corresponding FlashMLA version is available here: OpenDAS/MLAttention.

Intellifusion

For the Intellifusion NNP, visit the official website: Intellifusion.

The corresponding FlashMLA version is available on Gitee: Intellifusion/tyllm.

Iluvatar Corex

For Iluvatar Corex GPUs, visit the official website: Iluvatar Corex.

The corresponding FlashMLA version is available on GitHub: Deep-Spark/FlashMLA

AMD Instinct

For AMD Instinct GPUs, visit the official website: AMD Instinct.

The corresponding FlashMLA version can be found at: AITER/MLA

Citation

@misc{flashmla2025,
      title={FlashMLA: Efficient MLA decoding kernels},
      author={Jiashi Li},
      year={2025},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/deepseek-ai/FlashMLA}},
}