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Update roadmap

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Tri Dao 2023-05-17 08:32:26 -07:00
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@ -37,6 +37,10 @@ As Triton is a higher-level language than CUDA, it might be easier to understand
and experiment with. The notations in the Triton implementation are also closer and experiment with. The notations in the Triton implementation are also closer
to what's used in our paper. to what's used in our paper.
We also have an experimental implementation in Triton that support attention
bias (e.g. ALiBi):
https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attn_triton.py
## Installation and features ## Installation and features
@ -76,10 +80,6 @@ Our tentative roadmap:
6. ~~[Jul 2022] Support head dimension 128~~[Done]. 6. ~~[Jul 2022] Support head dimension 128~~[Done].
7. ~~[Aug 2022] Fuse rotary embedding~~[Done]. 7. ~~[Aug 2022] Fuse rotary embedding~~[Done].
8. ~~[Mar 2023] Support SM90 GPUs (H100)~~[Done]. 8. ~~[Mar 2023] Support SM90 GPUs (H100)~~[Done].
9. [Apr 2023] Refactor to use Cutlass 3.x.
10. [May 2023] Support attention bias (e.g. ALiBi, relative positional encoding).
11. [Jun 2023] Support SM70 GPUs (V100).
12. [Jun 2023] Support fp8 (H100).
## How to use FlashAttention ## How to use FlashAttention