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,softmax_scale=None,causal=False,window_size=(-1,-1),alibi_slop...
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 ...
feat = flash_attn.flash_attn_varlen_qkvpacked_func( AttributeError: module 'flash_attn' has no attribute 'flash_attn_varlen_qkvpacked_func'
fromflash_attn.flash_attn_interfaceimportflash_attn_varlen_qkvpacked_func fromflash_attn.bert_paddingimportunpad_input,pad_input defforward( self, hidden_states:torch.Tensor, attention_mask:Optional[torch.Tensor]=None, position_ids:Optional[torch.Tensor]=None, ...
flash_attn_unpadded_qkvpacked_func except:# v2 fromflash_attn.flash_attn_interfaceimportflash_attn_varlen_qkvpacked_funcasflash_attn_unpadded_qkvpacked_func fromflash_attn.bert_paddingimportpad_input,unpad_input classFlashAttention(nn.Module): ...
.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 ( ...
10 changes: 3 additions & 7 deletions 10 tests/test_flash_attn.py Original file line numberDiff line numberDiff line change @@ -12,7 +12,7 @@ flash_attn_varlen_kvpacked_func, flash_attn_varlen_qkvpacked_func, ) from flash_attn.bert_padding import index_first_axis, pad_input, un...
fromflash_attn.flash_attn_interfaceimport\ flash_attn_unpadded_qkvpacked_func except:# v2 fromflash_attn.flash_attn_interfaceimportflash_attn_varlen_qkvpacked_funcasflash_attn_unpadded_qkvpacked_func fromflash_attn.bert_paddingimportpad_input,unpad_input ...
flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False, window_size=(-1, -1), alibi_slopes=None, deterministic=False): """dropout_p should be set to 0.0 during evaluation If Q, K, V are already stacked into 1 tensor, this function will be faster than cal...
the varlen versions only support passing onecu_seqlens. The main idea is to use thesoftmax_lseoutput from the flash attention kernels. The current performance on 8xH800 is (benchmark/benchmark_qkvpacked_func.py): GPUtheoretic flash_attnring_attnzigzag_ringstripe_attn ...