flash-attn-jax 0.4.1

Flash Attention port for JAX

FlashAttention JAX

This repository provides a jax binding to https://github.com/Dao-AILab/flash-attention. To avoid depending on pytorch, since torch and jax installations often conflict, this is a fork of the official repo.

Please see Tri Dao's repo for more information about flash attention. Also check there for how to cite the authors if you used flash attention in your work.

FlashAttention and FlashAttention-2 are free to use and modify (see LICENSE). Please cite (see below) and credit FlashAttention if you use it.

Installation

Requirements:

• CUDA 12.3 and above.
• Linux. Same story as with the pytorch repo. I haven't tested compilation of the jax bindings on windows.
• JAX >= 0.5.*. The custom call api changed in this version.

To install: pip install flash-attn-jax will get the latest release from pypi. This gives you the cuda 12.8 build. CUDA 11 isn't supported any more (since jax stopped supporting it).

Installing from source

Flash attention takes a long time to compile unless you have a powerful machine. But if you want to compile from source, I use cibuildwheel to compile the releases. You could do the same. Something like (for python 3.12):

git clone https://github.com/nshepperd/flash-attn-jax
cd flash-attn-jax
cibuildwheel --only cp312-manylinux_x86_64 # I think cibuildwheel needs superuser privileges on some systems because of docker reasons?

This will create a wheel in the wheelhouse directory. You can then install it with pip install wheelhouse/flash_attn_jax_*.whl. Or you could build it without docker using uv build --wheel. You need cuda installed in that case.

Usage

Interface: src/flash_attn_jax/flash.py

from flash_attn_jax import flash_mha

# flash_mha : [n, l, h, d] x [n, lk, hk, d] x [n, lk, hk, d] -> [n, l, h, d]
flash_mha(q,k,v,softmax_scale=None, is_causal=False, window_size=(-1,-1))

This supports multi-query and grouped-query attention (when hk != h). The softmax_scale is the multiplier for the softmax, defaulting to 1/sqrt(d). Set window_size to positive values for sliding window attention.

Now Supports Ring Attention

Use jax.Array and shard your tensors along the length dimension, and flash_mha will automatically use the ring attention algorithm (forward and backward).

os.environ["XLA_FLAGS"] = '--xla_gpu_enable_latency_hiding_scheduler=true'
#...
with Mesh(devices, axis_names=('len',)) as mesh:
        sharding = NamedSharding(mesh, P(None,'len')) # n l
        tokens = jax.device_put(tokens, sharding)
        # invoke your jax.jit'd transformer.forward

The latency hiding seems to be reliable now that some bugs have been fixed, as long as you enable the latency hiding scheduler as above.

GPU support

FlashAttention-2 currently supports:

1. Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100). Support for Turing GPUs (T4, RTX 2080) is coming soon, please use FlashAttention 1.x for Turing GPUs for now.
2. Datatype fp16 and bf16 (bf16 requires Ampere, Ada, or Hopper GPUs).
3. All head dimensions up to 256. Head dim > 192 backward requires A100/A800 or H100/H800. Head dim 256 backward now works on consumer GPUs (if there's no dropout) as of flash-attn 2.5.5.