F.sdpa for visformer fails w/o contiguous on qkv, make experimental

timm/models/visformer.py

@@ -80,7 +80,7 @@ class Attention(nn.Module):
         head_dim = round(dim // num_heads * head_dim_ratio)
         self.head_dim = head_dim
         self.scale = head_dim ** -0.5
-        self.fused_attn = use_fused_attn()
+        self.fused_attn = use_fused_attn(experimental=True)
 
         self.qkv = nn.Conv2d(dim, head_dim * num_heads * 3, 1, stride=1, padding=0, bias=False)
         self.attn_drop = nn.Dropout(attn_drop)
@@ -94,7 +94,7 @@ class Attention(nn.Module):
 
         if self.fused_attn:
             x = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v,
+                q.contiguous(), k.contiguous(), v.contiguous(),
                 dropout_p=self.attn_drop.p,
             )
         else: