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Fix flash_attn import in siglip_vit #34

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Fix flash_attn import in siglip_vit #34

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8 changes: 6 additions & 2 deletions deepseek_vl2/models/siglip_vit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,11 +12,15 @@
AttentionPoolLatent, PatchDropout, resample_abs_pos_embed, LayerType
)
from timm.models._manipulate import named_apply, checkpoint_seq, adapt_input_conv
from flash_attn import flash_attn_qkvpacked_func
from transformers.modeling_utils import is_flash_attn_2_available
from xformers.ops import memory_efficient_attention
from functools import partial


if is_flash_attn_2_available():
from flash_attn import flash_attn_qkvpacked_func


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
Expand Down Expand Up @@ -134,7 +138,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)

if not self.qk_norm:
if self.head_dim % 32 == 0:
if self.head_dim % 32 == 0 and is_flash_attn_2_available():
# flashattn的head_dim必须是32的倍数，SigLIP-SO400M无法使用flashattn
x = flash_attn_qkvpacked_func(qkv, dropout_p=self.attn_drop.p if self.training else 0.,
deterministic=self.deterministic)
Expand Down