self.dropout = dropout# flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0self.flash =hasattr(torch.nn.functional,'scaled_dot_product_attention')ifnotself.flash:print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")# causal mask to ensure that ...
论文首先提出目前部分Memory-efficient attention工作缺少分布式扩展方法,将其与TP或者PP进行结合会带来大量的通信代价。部分SP方法,如Ring Self-Attention,Ring Attention缺少高效的Attention实现,如FlashAttention。 论文中进行分布式扩展FlashAttention,以达到高GPU利用率与低通信代价。 2. 论文方法介绍 以三个挑战作为方法提...
Why: FlashAttention的效果- FlashAttention和块稀疏FlashAttention使Transformer能够处理更长的上下文,从而产生更高质量的模型(GPT-2的困惑度提高了0.7,长文档分类的提升了6.4个百分点),并且具有全新的能力:首个在Path-X挑战(序列长度16K,准确率61.4%)和Path-256(序列长度64K,准确率63.1%)上表现优于随机性能的Transform...
1196 + TORCH_CHECK(false, "[_efficient_attention_forward] Unsupported mask type on ROCM, for now"); 1197 + } 1159 1198 1160 - const auto softmax_scale = sdp::calculate_scale(query, scale).expect_float(); 1161 1199 1162 - using aotriton::v2::flash::attn_fwd; 1163 - using...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [ROCm] CK Memory-Efficient Attention (attention bias support) · pytorch/pytorch@6774212
This work proposes SWattention, a highly efficient method for computing the exact attention on the SW26010pro processor. To fully utilize the 6 core groups (CG) and 64 cores per CG on the processor, we design a two-level parallel task partition strategy. Asynchronous memory access is employed...
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length
memoryefficientattention.zipHe**er 上传10.99 KB 文件格式 zip Memory Efficient Attention是一种用于Jax和PyTorch的注意力机制,其计算复杂度仅为O(sqrt(n))。这一技术对于处理大规模序列数据具有重要意义,因为它可以显著减少内存占用。在很多NLP和CV任务中,序列长度通常非常大,传统的注意力机制需要更多的内存来存储...
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length
FlashAttention (Dao, 2023) effectively reduces the quadratic peak memory usage to linear in training transformer-based large language models (LLMs) on a single GPU. In this paper, we introduce DISTFLASHATTN, a distributed memory-efficient attention mechanism optimized for long-context LLMs training...