Implementation of Flash-Attention (both forward and backward) with PyTorch, CUDA, and Triton - Flash-Attention-Implementation/flashattn at main · liangyuwang/Flash-Attention-Implementation
Implementation of Flash-Attention (both forward and backward) with PyTorch, CUDA, and Triton - liangyuwang/Flash-Attention-Implementation
针对你提出的“flash_attn is not installed. using pytorch native attention implementation.”问题,我将按照提供的tips进行回答: 确认flash_attn库是否已安装: 你可以通过运行pip show flash_attn来检查flash_attn库是否已安装在你的环境中。如果返回了库的详细信息,则说明已安装;如果提示未找到该库,则需要安装。
FlashMLA on MXMACA We provide the implementation of FlashMLA from FlashAttention-2(version 2.6.3), based on MACA toolkit and C500 chips. FlashAttention-2 currently supports: Datatype fp16 and bf16. Multi-Token Parallelism = 1 Paged kvcache with block size equal to 2^n (n >= 0) How ...
Previus work: llama.cpp#778 Previously, the initiative to implement Flash Attention to improve inference performance in llama.cpp had already been introduced. However, it was assumed that this appr...
flash_attention.py - Implementation of the general formulation of FlashAttention which takes in Q, K, V and a mask. The code includes both the forward and backward algorithms and a simple test of equivalence of the forward pass with normal attention as well. flash_attention_causal.py - The...
feat: Update Qwen2-VL-Model to support flash_attention_2 implementation Verified 8d81161 Merge pull request#1from LaureatePoet/dev… Verified e4968ad XprobeBotadded thefeaturelabelSep 12, 2024 XprobeBotadded this to thev0.15milestoneSep 12, 2024 ...
Thank you for your work on flash-attention. I noticed numerical differences between flash_attn_varlen_kvpacked_func and vanilla implementation of x-attention below. In autoregressive normalizing flows, this difference is large enough to produce high invertibility error when computing invertibility tests...
There are some arithmetic errors with the current implementation. The reason for them is probably that flash attention will return bf16 value for each block, so we cannot accumluate the values with the original fp32 ones. And also because we need to save extra fp32 buffer during computation...
We used thenanoT5implementation as the base for our work. We worked on optimizing the core component of the model, which is the attention part. We used the Flash Attention (v2) that optimize both the memory usage and the efficient use of Tensor Cores. ...