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 ...
#当Q,K,V已堆叠为一个张量时,使用flash_attn_qkvpacked_func out=flash_attn_qkvpacked_func(qkv,dropout_p=0.0,softmax_scale=None,causal=False,window_size=(-1,-1),alibi_slopes=None,deterministic=False)# 直接使用Q,K,V时,使用flash_attn_func out=flash_attn_func(q,k,v,dropout_p=0.0,soft...
.py", line 12, in <module> from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func File "/usr/local/lib/python3.10/dist-packages/flash_attn/__init__.py", line 3, in <module> from flash_attn.flash_attn_interface import ( ...
flash_attn_unpadded_kvpacked_func -> flash_attn_varlen_kvpacked_func If the inputs have the same sequence lengths in the same batch, it is simpler and faster to use these functions: flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False) flash_attn_func(q...
基于安全考虑,Gitee 建议配置并使用私人令牌替代登录密码进行克隆、推送等操作 Username for 'https://gitee.com': userName Password for 'https://userName@gitee.com':#私人令牌 main 分支(1) 管理 管理 main LongBench / llama_flash_attn_monkey_patch.py Loading......
feat = flash_attn.flash_attn_varlen_qkvpacked_func( AttributeError: module 'flash_attn' has no attribute 'flash_attn_varlen_qkvpacked_func'
flash_attn_unpadded_kvpacked_func -> flash_attn_varlen_kvpacked_func If the inputs have the same sequence lengths in the same batch, it is simpler and faster to use these functions: flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False) flash_attn_func(q,...
flash_attn_unpadded_kvpacked_func->flash_attn_varlen_kvpacked_func If the inputs have the same sequence lengths in the same batch, it is simpler and faster to use these functions: flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False) ...
class FlashAttnVarlenKVPackedFunc(torch.autograd.Function): @@ -716,8 +719,9 @@ def forward( alibi_slopes, deterministic, return_softmax, is_grad_enabled, ): is_grad = torch.is_grad_enabled() and any( is_grad = is_grad_enabled and any( x.requires_grad for x in [q, kv] ) if...
Hi! First of all, thank you for your incredible work on this repository. I'm wondering, if there is a way to use flash_attn_varlen_qkvpacked_func with window_size, so that first/last K tokens (CLS tokens) were performing regular (global)...