为了填补这一空白,我们引入了一个可扩展的推理解决方案:Easy and Efficient Transformer (EET),包括算法和实现层面的一系列 Transformer 推理优化。 首先,我们为长输入和大隐藏尺寸设计了高度优化的内核。 其次,我们提出了一个灵活的 CUDA 内存管理器,以减少部署大型模型时的内存占用。与最先进的 Transformer 推理库(F...
EET(Easy and Efficient Transformer) is a friendly Pytorch inference plugin focus on Transformer-based models to make mega-size model affordable. Features New🔥: Support Baichuan, LLaMA and other LLMs. New🔥: Support int8 quantization.
Easy and Efficient Quantization for Transformers. Contribute to twaka/EETQ development by creating an account on GitHub.
[图片] 网易伏羲实验室开源的超大模型推理引擎Easy and Efficient Transformer 在性能上和其他引擎(如NVIDIA的Fas…显示全部 关注者5 被浏览212 关注问题写回答 邀请回答 好问题 添加评论 分享 暂时还没有回答,开始写第一个回答...
对于文本部分的输入,我们和CLIP一样采用12层Text Transformer作为文本侧Encoder,通过BPE分词器做文本预处理,并限定文本词长小于77。这两个电商场景的CLIP模型参数配置如下表: 如上表所述,电商CLIP模型包含了两个不同的图像Encoder架构,为表中的pai-clip-commercial-base-en和pai-clip-commercial-large-en模型,分别...
Efficient and accurate prediction of protein structure using RoseTTAFold2. Preprint at bioRxiv https://doi.org/10.1101/2023.05.24.542179 (2023). Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017). Google Scholar Humphreys, I. R. et ...
Efficient and accurate prediction of protein structure using RoseTTAFold2. Preprint at bioRxiv https://doi.org/10.1101/2023.05.24.542179 (2023). Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017). Google Scholar Humphreys, I. R. et ...
Nanostructured ruthenium on γ-Al2O3 catalysts for the efficient hydrogenation of aromatic compounds Free and trioctylamine (TOA)-stabilized ruthenium nanoparticles have been prepared by decomposition of the metal precursor Ru(η 6-cycloocta-1,3,5-triene)(... G Marconi,P Pertici,C Evangelisti,.....
Large-scale transformer-based deep learning models trained on large amounts of data have shown great results in recent years in several cognitive tasks and are behind new products and features that augment human capabilities. Azure Machine Learning (Azur
DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infini...