这次的 FlashAttention 算法的改进版本,是解决 Transformers 扩展到更长序列长度的问题,也就是增加了 Context 的长度。由于 Attention其运行时间和内存与序列长度的平方成正比增加,所以Transformers 中的注意力层成为扩展到更长序列的瓶颈。FlashAttention-2 就是通过优化工作分区和并行性来提高注意力计算的效率。 从上图...
切换模式 登录/注册 周博洋 MS做AI的 Transformer又偷着更新了,即使不支持flash attention的卡,也有替代方案,原生支持SDPA和STFT, 一行代码model.to_bettertransfomer()就可以搞定,而且是训练推理双提升 发布于 2023-12-26 20:52・IP 属地北京 ...
cd transformers-4.38.0pip install -e . 因为对源码本身做了修改,transformer 的安装会被覆盖,而且不再需要使用flash-attention 的安装了,因此推荐使用下面的安装方式和顺序 1、先以源码方式安装 data-juicer shell cd ../better_synth_challenge_baseline/data-juicerpip install -v -e . 这里需要等待执行一段时...
In the past few months, we’ve been working on the next version, FlashAttention-2, that makes FlashAttention even better. Rewritten completely from scratch to use the primitives from Nvidia’s CUTLASS 3.x and its core library CuTe, FlashAttention-2 is about 2x faster than its previous versi...
Transformer Engine release v0.11.0 adds support for FlashAttention-2 in PyTorch for improved performance. It is a known issue that FlashAttention-2 compilation is resource-intensive and requires a large amount of RAM (see bug), which may lead to out of memory errors during the installation of...
Transformer Engine release v0.11.0 adds support for FlashAttention-2 in PyTorch for improved performance. It is a known issue that FlashAttention-2 compilation is resource-intensive and requires a large amount of RAM (see bug), which may lead to out of memory errors during the installation of...
EncoderTransformerTransformerTransformerE-Branchformer DecoderTransformerTransformerTransformerTransformer Parameters74M244M769M1.55B1.55B889M101M1.02B Layers61224323224618 Hidden size51276810241280128010243841024 Attention heads81216202016616 ...
7.What is the attention mechanism? A way of determining the importance of each word in a sentence for the translation of another sentence 8.What is a transformer model? A deep learning model that uses self-attention to learn relationships between different parts of a sequence. ...
《FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning》翻译与解读 扩展Transformer的上下文长度是一个挑战—需要更长上下文的语言模型:GPT-4(32k)、MPT(65k)、Claude(100k) Just within the last year, there have been several language models with much longer context than before...
博客文章:FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning | Princeton NLP Group 时间 2023年7月17日 作者 谷歌学者Tri Dao,坦福大学计算机科学博士,Together.AI的首席科学家 扩展Transformer的上下文长度是一个挑战—需要更长上下文的语言模型:GPT-4(32k)、MPT(65k)、Claude(100k...