FlashMLA on MXMACA

We provide the implementation of FlashMLA from FlashAttention-2(version 2.6.3), based on MACA toolkit and C500 and C600 chips.

FlashAttention-2 currently supports:

1. Datatype fp16 and bf16.
2. Multi-Token Prediction greater or equal to 1.
3. Paged kvcache with block size equal to 2^n (n >= 0).

How to run on MXMACA Device

Installation

Requirements:

• MXMACA GPUs.
• MACA development toolkit.
• Mctlass source code.
• Pytorch2.1 and Triton2.1 from maca toolkit wheel package and above.

To install in conda env:

1. Make sure that maca pyTorch2.1 and Triton2.1 are installed.
2. Download mctlass source code from: https://sw-download.metax-tech.com/

Set environment variables

export MACA_PATH=/your/maca/path
export CUDA_PATH=$MACA_PATH/tools/cu-bridge
export MACA_CLANG_PATH=$MACA_PATH/mxgpu_llvm/bin
export LD_LIBRARY_PATH=$MACA_PATH/lib:$MACA_PATH/mxgpu_llvm/lib:$MACA_PATH/ompi/lib:$LD_LIBRARY_PATH

Install

python setup.py install

Benchmark

python tests/test_flash_mla.py

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

• MXMACA 2.27 and above
• PyTorch 2.0 and above

Acknowledgement

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

Citation

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