Is FlashAttention simply a drop-in replacement for the attention operation in an LLM? Can it be used anywhere that an "attention" operation is used? Or does a LLM need to be trained specially to use FA?
How does FA relate to attention strategies like GQA (grouped query attention) or sliding-window attention? Are they orthogonal concepts? Or you need a specific FA implementation for each strategy?
Recently llama.cpp added flash attention support - does this just mean they started consuming a flash attention-provided CUDA kernel or something?
lastly, in this post, they compare FlashAttention to Triton. I thought Triton was like an abstraction layer? Couldn't FA be implemented in Triton? I just don't really get what it means to say "FlashAttention vs. Triton".
1) Pretty much, it's mathematically equivalent. The only software issues are things like managing dependency versions and data formats in-memory, but Flash Attention 2 is already built into HuggingFace and other popular libraries. Flash Attention 3 probably will be soon, although it requires an H100 GPU to run
2) Flash Attention 2 added support for GQA in past version updates:
> Is FlashAttention simply a drop-in replacement for the attention operation in an LLM? Can it be used anywhere that an "attention" operation is used? Or does a LLM need to be trained specially to use FA?
Yes
> How does FA relate to attention strategies like GQA (grouped query attention) or sliding-window attention? Are they orthogonal concepts? Or you need a specific FA implementation for each strategy?
Flash Attention is a way of calculating the Softmax(QK^T)V part of attention, whereas GQA is a way of calculating the Q, K, and V matricies. Sliding window attention (less sure about this, there are a bunch of windowed attention techniques) change the attention mask (the thing that controls which queries can attend to which keys).
> Recently llama.cpp added flash attention support - does this just mean they started consuming a flash attention-provided CUDA kernel or something?
I don't use llama.cpp but that sounds about right.
> lastly, in this post, they compare FlashAttention to Triton. I thought Triton was like an abstraction layer? Couldn't FA be implemented in Triton? I just don't really get what it means to say "FlashAttention vs. Triton".
They're talking about a previous Flash Attention implementation written in Triton.
Is FlashAttention simply a drop-in replacement for the attention operation in an LLM? Can it be used anywhere that an "attention" operation is used? Or does a LLM need to be trained specially to use FA?
How does FA relate to attention strategies like GQA (grouped query attention) or sliding-window attention? Are they orthogonal concepts? Or you need a specific FA implementation for each strategy?
Recently llama.cpp added flash attention support - does this just mean they started consuming a flash attention-provided CUDA kernel or something?
lastly, in this post, they compare FlashAttention to Triton. I thought Triton was like an abstraction layer? Couldn't FA be implemented in Triton? I just don't really get what it means to say "FlashAttention vs. Triton".