flash-attention VS EfficientZero

Compare flash-attention vs EfficientZero and see what are their differences.

flash-attention

Fast and memory-efficient exact attention (by Dao-AILab)

EfficientZero

Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021. (by YeWR)
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flash-attention EfficientZero
25 9
10,263 823
8.8% -
9.4 0.0
3 days ago 3 months ago
Python Python
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

flash-attention

Posts with mentions or reviews of flash-attention. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-10.

EfficientZero

Posts with mentions or reviews of EfficientZero. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-19.

What are some alternatives?

When comparing flash-attention and EfficientZero you can also consider the following projects:

xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.

TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

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"

RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

alpaca_lora_4bit

XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

StableLM - StableLM: Stability AI Language Models

RWKV-v2-RNN-Pile - RWKV-v2-RNN trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.

quality

kernl - Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.