#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
lengths_per_sample is not None 577 + else inference_params.sequence_len_offset 578 + ) 579 + return flash_attn_with_kvcache( 580 + q, 581 + kv_cache[:, :, 0], 582 + kv_cache[:, :, 1], 583 + kv[:, :, 0], 584 + kv[:, :, 1], 585 + cache_seqlens=...
PR Category CINN PR Types Others Description flash_attn_qkvpacked, flash_attn_varlen_qkvpacked调用相同infermeta函数,因此为被调用函数增加了infer symbolic函数 flash_attn_with_sparse_mask调用已有infer symbolic函数,无需进行修改
if key_padding_mask is None: qkv = rearrange(qkv, 'b s ... -> (b s) ...') max_s = seqlen cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, device=qkv.device) output = flash_attn_unpadded_qkvpacked_func( ...
exportPYTHONPATH=$PWDpytest -q -s test_flash_attn.py Once the package is installed, you can import it as follows: importflash_attn_interface flash_attn_interface.flash_attn_func() Installation and features Requirements: CUDA toolkit or ROCm toolkit ...
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 ...
and output linear layers are not included): ```python from flash_attn.flash_attention import FlashAttention ``` flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False): """dropout_p should be set to 0.0 during evaluation If Q, K, V are already stacked into ...
indices,batch_size,seqlen), 'b s (h d) -> b s h d',h=nheads) else: assertmax_sisnotNone output=flash_attn_unpadded_qkvpacked_func( qkv,cu_seqlens,max_s,self.dropout_pifself.trainingelse0.0, softmax_scale=self.softmax_scale,causal=causal ...
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 calling...
feat = flash_attn.flash_attn_varlen_qkvpacked_func( AttributeError: module 'flash_attn' has no attribute 'flash_attn_varlen_qkvpacked_func'