ihh/flash-attention-jax

Implementation of Flash Attention in Jax

Flash Attention - Jax

Implementation of Flash Attention in Jax. It will likely not be as performant as with the official CUDA version, given lack of ability for fine memory management. But just for educational purposes as well as to see how clever XLA compiler is (or is not).

Install

$ pip install flash-attention-jax

Usage

from jax import random
from flash_attention_jax import flash_attention

rng_key = random.PRNGKey(42)

q = random.normal(rng_key, (1, 2, 131072, 512))  # (batch, heads, seq, dim)
k = random.normal(rng_key, (1, 2, 131072, 512))
v = random.normal(rng_key, (1, 2, 131072, 512))
mask = random.randint(rng_key, (1, 131072,), 0, 2) # (batch, seq)

out, _ = flash_attention(q, k, v, mask)

out.shape  # (1, 2, 131072, 512) - (batch, heads, seq, dim)

Quick sanity check

from flash_attention_jax import plain_attention, flash_attention, value_and_grad_difference

diff, (dq_diff, dk_diff, dv_diff) = value_and_grad_difference(
    plain_attention,
    flash_attention,
    seed = 42
)

print('shows differences between normal and flash attention for output, dq, dk, dv')
print(f'o: {diff}')       # < 1e-4
print(f'dq: {dq_diff}')   # < 1e-6
print(f'dk: {dk_diff}')   # < 1e-6
print(f'dv: {dv_diff}')   # < 1e-6

Autoregressive Flash Attention - GPT-like decoder attention

from jax import random
from flash_attention_jax import causal_flash_attention

rng_key = random.PRNGKey(42)

q = random.normal(rng_key, (131072, 512))
k = random.normal(rng_key, (131072, 512))
v = random.normal(rng_key, (131072, 512))

out, _ = causal_flash_attention(q, k, v)

out.shape  # (131072, 512)

Todo

• leading dimensions for causal flash attention variant

• figure out issue with jit and static argnums

• comment with references to paper algorithms and explanations

• make sure it can work one-headed key / values, as in PaLM

Citations

@article{Dao2022FlashAttentionFA,
    title   = {FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness},
    author  = {Tri Dao and Daniel Y. Fu and Stefano Ermon and Atri Rudra and Christopher R'e},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2205.14135}
}

@article{Rabe2021SelfattentionDN,
    title   = {Self-attention Does Not Need O(n2) Memory},
    author  = {Markus N. Rabe and Charles Staats},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2112.05682}
}