flash-attention
DeepSpeed
| flash-attention | DeepSpeed | |
|---|---|---|
| 27 | 52 | |
| 15,061 | 36,284 | |
| 4.3% | 2.0% | |
| 9.2 | 9.7 | |
| 6 days ago | 3 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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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.
DeepSpeed
- DeepSpeed-Domino: Communication-Free LLM Training Engine
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
DeepSpeed can handle parallelism concerns, and even offload data/model to RAM, or even NVMe (!?) . I'm surprised I don't see this project used more.
- [P][D] A100 is much slower than expected at low batch size for text generation
- DeepSpeed-FastGen: High-Throughput for LLMs via MII and DeepSpeed-Inference
- DeepSpeed-FastGen: High-Throughput Text Generation for LLMs
- Why async gradient update doesn't get popular in LLM community?
- DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models (r/MachineLearning)
- [P] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
- A comprehensive guide to running Llama 2 locally
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
And https://github.com/microsoft/deepspeed
What are some alternatives?
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
ColossalAI - Making large AI models cheaper, faster and more accessible
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
unsloth - Finetune Llama 3.3, Mistral, Phi-4, Qwen 2.5 & Gemma LLMs 2-5x faster with 70% less memory
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.
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
memory-efficient-attention-pytorch - Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"
Megatron-LM - Ongoing research training transformer models at scale
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
alpaca_lora_4bit
fairscale - PyTorch extensions for high performance and large scale training.