微软(中国)有限公司 员工 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 写下你的评论... ...
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...
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...
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It is a known issue that FlashAttention-2 compilation is resource-intensive and requires a large amount of RAM (seebug), which may lead to out of memory errors during the installation of Transformer Engine. Please try settingMAX_JOBS=1in the environment to circumvent the issue. ...
promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress onstructured state space models, with an efficient hardware-aware design and implementation in the spirit ofFlashAttention. ...
《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...
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《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...
2️⃣FlashAttention,也是LLM时代的重要加速基建 3️⃣torch.compile,吞吐量+10% 4️⃣训练目标:还是MLM,但mask比例从15%增加到30%,同样的计算量和数据量下有效监督信号更多 🧪实验效果:如p4,在预训练-微调的设定下,在IR(向量检索)和Code下游任务上远超RoBERTa和DeBERTv3,在NLU上和DeBERTv3接近 ...