From 172d262adf2fb6c7b5ecbd5fcdc1eb4364ca1ee2 Mon Sep 17 00:00:00 2001 From: Merve Noyan Date: Tue, 22 Aug 2023 17:53:26 +0300 Subject: [PATCH] Update docs/source/conceptual/flash_attention.md Co-authored-by: Omar Sanseviero --- docs/source/conceptual/flash_attention.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conceptual/flash_attention.md b/docs/source/conceptual/flash_attention.md index fd6f5d9f..5717bcfa 100644 --- a/docs/source/conceptual/flash_attention.md +++ b/docs/source/conceptual/flash_attention.md @@ -2,6 +2,6 @@ Scaling the transformer architecture is heavily bottlenecked by the self-attention mechanism, which has quadratic time and memory complexity. Recent developments in accelerator hardware mainly focus on enhancing compute capacities and not memory and transferring data between hardware. This results in attention operation having a memory bottleneck, also called memory-bound. **Flash Attention** is an attention algorithm used to reduce this problem and scale transformer-based models more efficiently, enabling faster training and inference. In the standard attention implementation, the cost of loading and writing keys, queries, and values from High Bandwidth Memory (HBM) is high. It loads key, query, and value from HBM to GPU on-chip SRAM, performs a single step of the attention mechanism, writes it back to HBM, and repeats this for every single attention step. Instead, Flash Attention loads keys, queries, and values once, fuses the operations of the attention mechanism, and writes them back. -It is implemented for models with custom kernels, you can check out the full list of models that support Flash Attention [here](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models). +It is implemented for models with custom kernels. You can check out the complete list of models that support Flash Attention [here](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models). You can learn more about Flash Attention by reading the paper in this [link](https://arxiv.org/abs/2205.14135).