flashattention加速了LLM的training,缩短了时间,并且可以训练更长句子长度的LLM。在LLM训练领域是一个非常优秀的作品,现在已经广泛用在了各个project中。 引言/动机 FlashAttention是继standard Attention和Memory-efficient Attention(论文:《Self-attention Does Not Need O(n2) Memory》后,当前主流的Attention优化方法。
通过打包 Flash Attention 来提升 Hugging Face 训练效率 简单概述现在，在 Hugging Face 中，使用打包的指令调整示例 (无需填充) 进行训练已与 Flash Attention 2 兼容，这要归功于一个最近的 PR以及新的DataCollatorWithFlattening。最近的 PRhttps://github.com/huggingface/transformers/pull/31629DataCollatorWith...
简单概述现在,在 Hugging Face 中,使用打包的指令调整示例 (无需填充) 进行训练已与 Flash Attention 2 兼容,这要归功于一个 最近的 PR 以及新的 DataCollatorWithFlattening。它可以在保持收敛质量的同时,将…
如果你正在使用TRL中的 Hugging FaceSFTTrainer配合DataCollatorForCompletionOnlyLM,那么所需的两个步骤是: 使用Flash Attention 2 实例化模型 在调用DataCollatorForCompletionOnlyLM时设置padding_free=True,如下所示: collator = DataCollatorForCompletionOnlyLM(response_template_ids, tokenizer=tokenizer, padding_free=...
现在,在 Hugging Face 中,使用打包的指令调整示例 (无需填充) 进行训练已与 Flash Attention 2 兼容,这要归功于一个最近的 PR以及新的DataCollatorWithFlattening。 最近的 PR:https://github.com/huggingface/transformers/pull/31629 DataCollatorWithFlattening:https://hf.co/docs/transformers/main/en/main_clas...
使用Flash Attention 2 实例化模型 使用新的DataCollatorWithFlattening 如果你正在使用TRL中的 Hugging FaceSFTTrainer配合DataCollatorForCompletionOnlyLM,那么所需的两个步骤是: 使用Flash Attention 2 实例化模型 在调用DataCollatorForCompletionOnlyLM时设置padding_free=True,如下所示: ...
使用chinese-alpaca-2-7b模型在两块H800进行SFT训练,开启Flash Attention加速,训练报错,请帮忙看一下,谢谢。 信息如下: [INFO|trainer.py:1812] 2024-03-14 12:07:36,974 >> *** Running training *** [INFO|trainer.py:1813] 2024-03-14 12:07:36,974 >> Num examples = 48,818 [INFO|trainer....
We also include a training script to train GPT2 on Openwebtext and GPT3 on The Pile. Triton implementation of FlashAttention Phil Tillet (OpenAI) has an experimental implementation of FlashAttention in Triton: https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py ...
A year ago, we released FlashAttention, a new algorithm to speed up attention and reduce its memory footprint—without any approximation. We’ve been very happy to see FlashAttention being adopted by many organizations and research labs to speed up their training & inference (see this page fo...
Hugging Face SFT trainer has always offered the option to use packing to combine multiple training examples, allowing for maximal utilization of GPU resources. However, up till now, it did not offer proper masking of each packed training example. This capability has been added to Hugging Face ...