# FlashAttention adoption We've been very happy to see FlashAttention being adopted by many organizations and research labs to speed up their training / inference (within 6 months after FlashAttention's release, at the time of writing). This page contains a partial list of places where FlashAttention is being used. If you'd like to add links to your organization / product / codebase, please open a PR or email us. We'd very much like to hear from you! ## Integrated into machine learning frameworks - Pytorch: [integrated](https://github.com/pytorch/pytorch/pull/81434) into core Pytorch in nn.Transformer. - Huggingface's [transformers](https://github.com/huggingface/transformers) library. [On-going](https://github.com/huggingface/transformers/pull/18439), blogpost coming soon. - Microsoft's [DeepSpeed](https://github.com/microsoft/DeepSpeed): FlashAttention is [integrated](https://github.com/microsoft/DeepSpeed/blob/ec13da6ba7cabc44bb4745a64a208b8580792954/deepspeed/ops/transformer/inference/triton_ops.py) into DeepSpeed's inference engine. - Nvidia's [Megatron-LM](https://github.com/NVIDIA/Megatron-LM/pull/267). This library is a popular framework on training large transformer language models at scale. - MosaicML [Composer](https://github.com/mosaicml/composer) [library](https://www.mosaicml.com/blog/gpt-3-quality-for-500k). Composer is a library for efficient neural network training. - EleutherAI's [GPT-NeoX](https://github.com/EleutherAI/gpt-neox/pull/725). This is a research library for training large language transformer models at scale based on NVIDIA's Megatron-LM and Microsoft's DeepSpeed. - PaddlePaddle: integrated into the framework with [API](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/nn/functional/flash_attention.py) `paddle.nn.functional.flash_attention`. ## MLPerf benchmarks [MLPerf](https://mlcommons.org/en/) is a competitive machine learning performance benchmark. FlashAttention yields the fastest BERT training on cloud instances in MLPerf training 2.0 (June 2022) and MLPerf training 2.1 (November 2022). - MLPerf 2.0: [IEEE Spectrum](https://spectrum.ieee.org/mlperf-rankings-2022) and [Forbes](ttps://www.forbes.com/sites/moorinsights/2022/07/12/google-dethrones-nvidia-in-latest-artificial-intelligence-benchmarking-tests/) articles about our submission to the MLPerf 2.0 benchmark using FlashAttention. - MLPerf 2.1 - collaboration between [Azure and Hazy Research](https://techcommunity.microsoft.com/t5/azure-high-performance-computing/azure-collaborates-with-hazy-research-and-nvidia-to-achieve/ba-p/3667511): for the first time, we can train MLPerf BERT in under 2 minutes on 16 nodes. - MLPerf 2.1 - [Nvidia](https://developer.nvidia.com/blog/leading-mlperf-training-2-1-with-full-stack-optimizations-for-ai/): Nvidia uses techniques from FlashAttention to make their (already extremely optimized) BERT implementation go even faster. - MLPerf 2.1 - [MosaicML](https://www.mosaicml.com/blog/mlperf-nlp-nov2022): FlashAttention helps train BERT 2.7x faster in the open division. ## Language model training & inference - [PubMedGPT 2.7B](https://crfm.stanford.edu/2022/12/15/pubmedgpt.html), a domain-specific LLM for biomedicine, by Stanford CRFM, trained on [MosaicML](https://www.mosaicml.com/blog/introducing-pubmed-gpt) Cloud. Just using FlashAttention nearly halves the total training time. - Meta's [AITemplate](https://ai.facebook.com/blog/gpu-inference-engine-nvidia-amd-open-source/) uses FlashAttention as part of their approach to speed up Transformer inference (up to 5.3x on BERT). - Nvidia's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) is a state-of-the-art Transformer inference library. As of version [5.2](https://github.com/NVIDIA/FasterTransformer/commit/b672f49e256ba7a2d4fc9691d270b60b7fc1a2ff), FlashAttention is used as a component of FasterTransformer to speed up GPT inference. - [Kernl](https://github.com/ELS-RD/kernl) is a library for fast Transformer inference. They use FlashAttention as part of their [approach](https://twitter.com/pommedeterre33/status/1585284221014245377) to speed up Transformers by up to 12x. ## Diffusion model training and inference - Huggingface's [diffusers](https://github.com/huggingface/diffusers) library for diffusion models. FlashAttention is integrated into [diffusers v0.7.0](https://github.com/huggingface/diffusers/releases/tag/v0.7.0). Up to 2x faster inference and lower memory usage. - Colossal-AI's [implementation](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion) of Stable Diffusion: with FlashAttention as one of its components, it speeds up pretraining by up to 6.5x, and reduces the hardware cost of fine-tuning by 7x. - Meta's [AITemplate](https://ai.facebook.com/blog/gpu-inference-engine-nvidia-amd-open-source/) with FlashAttention one of the components, is currently the [fastest](https://twitter.com/bing_xu_/status/1590447334055632897) Stable Diffusion inference engine that we know of. - Stable Diffusion inference from [Labml.ai](https://twitter.com/labmlai/status/1573634095732490240): 50% speedup. - Our own Stable Diffusion [fork](https://twitter.com/realDanFu/status/1580641495991754752) uses FlashAttention to get 3-4x speedup compared to the original version. ## Other models - [Uni-Fold](https://github.com/dptech-corp/Uni-Fold): Uni-Fold is an open-source platform for developing protein models beyond AlphaFold. With FlashAttention, Uni-Fold is 2.6x [faster](https://twitter.com/guolin_ke/status/1580532071901995008) than AlphaFold. - [OpenFold](https://github.com/aqlaboratory/openfold): a trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2. With FlashAttention as one of its [components](https://twitter.com/gahdritz/status/1595420944880779266), it is up to 3x faster than AlphaFold2 to run inference on short sequences, and can predict 2x longer structures. ## Different implementations - [Triton](https://github.com/openai/triton): an [implementation](https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py) of FlashAttention in Triton by Phil Tillet from OpenAI. Triton is a Python-based language and compiler for parallel programming. - [xformers](https://github.com/facebookresearch/xformers): The xformers team has implemented [memory-efficient attention](https://twitter.com/fvsmassa/status/1580229170629849089) in a similar spirit to FlashAttention. xformers dynamically dispatches to whichever implementation is available / faster. - [Jax](https://github.com/google/jax): an [implementation](https://github.com/lucidrains/flash-attention-jax) in Jax by [lucidrains](https://github.com/lucidrains/). - [Metal](https://developer.apple.com/metal): an [implementation](https://github.com/philipturner/metal-flash-attention) in Metal by Philip Turner. This ports FlashAttention to mobile GPU architectures such as Apple silicon.