memory-efficient-attention-pytorch
flash-attention
| memory-efficient-attention-pytorch | flash-attention | |
|---|---|---|
| 2 | 27 | |
| 227 | 15,142 | |
| - | 4.9% | |
| 6.1 | 9.2 | |
| almost 2 years ago | 7 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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memory-efficient-attention-pytorch
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[Discussion] Fine tune model for long context
Check these efficient attention mechanism which are almost a drop in replacement: efficient attention flash attention
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Will Transformers Take over Artificial Intelligence?
I would recommend Routing Transformer https://github.com/lucidrains/routing-transformer but the real truth is nothing beats full attention. Luckily, someone recently figured out how to get past the memory bottleneck. https://github.com/lucidrains/memory-efficient-attention-pyt...
flash-attention
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FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-Precision
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:
https://github.com/Dao-AILab/flash-attention
3) They're comparing this implementation of Flash Attention (which is written in raw CUDA C++) to the Triton implementation of a similar algorithm (which is written in Triton): https://triton-lang.org/main/getting-started/tutorials/06-fu...
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How the Transformer Architecture Was Likely Discovered: A Step-by-Step Guide
If you're looking for an implementation, I highly recommend checking out fast attention [https://github.com/Dao-AILab/flash-attention]. It's my go-to, and far better than anything we could whip up here using just PyTorch or TensorFlow.
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Interactive Coloring with ControlNet
* Even if I bought a 3090, I would have to get a computer to go with it, along with a PSU and some cooling. Don't know where to start with that.
[1] https://github.com/Dao-AILab/flash-attention/issues/190
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Coding Self-Attention, Multi-Head Attention, Cross-Attention, Causal-Attention
highly recommend using Tri's implementation https://github.com/Dao-AILab/flash-attention rotary should be built in, and some group overseas even contributed alibi
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PSA: new ExLlamaV2 quant method makes 70Bs perform much better at low bpw quants
Doesn't seem so https://github.com/Dao-AILab/flash-attention/issues/542 No updates for a while.
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VLLM: 24x faster LLM serving than HuggingFace Transformers
I wonder how this compares to Flash Attention (https://github.com/HazyResearch/flash-attention), which is the other "memory aware" Attention project I'm aware of.
I guess Flash Attention is more about utilizing memory GPU SRam correctly, where this is more about using the OS/CPU memory better?
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Hacking Around ChatGPT’s Character Limits with the Code Interpreter
https://github.com/HazyResearch/flash-attention
- Flash Attention on Consumer
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Unlimiformer: Long-Range Transformers with Unlimited Length Input
After a very quick read, that's my understanding too: It's just KNN search. So I agree on points 1-3. When something works well, I don't care much about point 4.
I've had only mixed success with KNN search. Maybe I haven't done it right? Nothing seems to work quite as well for me as explicit token-token interactions by some form of attention, which as we all know is too costly for long sequences (O(n²)). Lately I've been playing with https://github.com/hazyresearch/safari , which uses a lot less compute and seems promising. Otherwise, for long sequences I've yet to find something better than https://github.com/HazyResearch/flash-attention for n×n interactions and https://github.com/glassroom/heinsen_routing for n×m interactions. If anyone here has other suggestions, I'd love to hear about them.
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Ask HN: Bypassing GPT-4 8k tokens limit
Longer sequence length in transformers is an active area of research (see e.g the great work from the Flash-attention team - https://github.com/HazyResearch/flash-attention), and I'm sure will improve things dramatically very soon.
What are some alternatives?
vit-pytorch - Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
Compact-Transformers - Escaping the Big Data Paradigm with Compact Transformers, 2021 (Train your Vision Transformers in 30 mins on CIFAR-10 with a single GPU!)
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
memory-efficient-attention-pyt
RWKV-LM - RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
routing-transformer - Fully featured implementation of Routing Transformer
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
x-transformers - A concise but complete full-attention transformer with a set of promising experimental features from various papers
alpaca_lora_4bit