Cleanup qkv_bias cat in beit model so it can be traced

tests/test_models.py

@@ -407,7 +407,6 @@ def test_model_backward_fx(model_name, batch_size):
 
 # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
 EXCLUDE_FX_JIT_FILTERS = [
-    'beit_*',
     'deit_*_distilled_patch16_224',
     'levit*',
     'pit_*_distilled_224',

timm/models/beit.py

@@ -86,9 +86,11 @@ class Attention(nn.Module):
         self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
         if qkv_bias:
             self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False)
             self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
         else:
             self.q_bias = None
+            self.k_bias = None
             self.v_bias = None
 
         if window_size:
@@ -127,13 +129,7 @@ class Attention(nn.Module):
 
     def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
         B, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            if torch.jit.is_scripting():
-                # FIXME requires_grad breaks w/ torchscript
-                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias), self.v_bias))
-            else:
-                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
         qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
         qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)