As Vision Transformers (ViTs) increasingly set new benchmarks in computer vision, their practical deployment on inference engines is often hindered by their significant memory bandwidth and (on-chip) memory footprint requirements. This paper addresses this memory limitation by introducing an activation-...
FlashAttention 将 Transformers 扩展到更长的序列,从而提高它们的质量并启用新功能。我们观察到 GPT-2 的困惑度提高了 0.7,在长文档分类上对较长序列进行建模得到了 6.4 个提升点。 FlashAttention 使第一个 Transformer 能够在 Path-X挑战中实现优于机会的性能,仅通过使用更长的序列长度 (16K)。块稀疏 FlashAtten...
Accelerated Transformers 我们可以通过调用新的scaled_dot_product_attention()函数直接使用缩放点积注意力(SPDA)内核。以前我们想要加速训练,要使用第三方库,比如Flash Attention、xFormers等,现在都被原生支持到框架中了,具体的是在 torch.nn.MultiheadAttention 和 TransformerEncoderLayer 中。 下一节我们使用上下文管理...
训练速度:FlashAttention超越了MLPerf 1.1的BERT训练速度记录,提高了15%,并将GPT-2的速度提高了3倍,超过了HuggingFace和Megatron的标准Transformers。FlashAttention将长距离竞技场(LRA)基准测试的速度提高了2.4倍。 质量:FlashAttention将Transformers扩展到更长的序列,从而提高了质量。FlashAttention训练的GPT-2(上下文长度4K...
32 times longer context window than vanilla Transformers and up to 4 times longer than memory efficient Transformers. - GitHub - kyegomez/Blockwise-Parallel-Transformer: 32 times longer context window than vanilla Transformers and up to 4 times longer th
这篇论文介绍了一个新的模型家族,叫做EfficientViT,目的是提升Vision Transformers的计算速度和内存效率。通过使用一个新设计的“三明治”构建块和引入级联分组注意力(Cascaded Group Attention)机制,成功减少了计算冗余并提升了模型性能。实验结果证明,EfficientViT在速度和准确性上都超越了现有的高效模型。 Introduction 这...
official implementation of the paper: Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers (CVPR 2023) - Ugness/MeBT
June 2023 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Related File Download BibTex Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, ...
June 2023 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Related File Download BibTex Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation cost...
Recently, efficient Vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally, they use 4x4 patch embeddings and a 4-stage structure at the macro level, while utilizing sophisticated attention with multi-head configuration at the micro level. This ...