fix all SDPA dropouts

timm/models/beit.py

@@ -155,7 +155,7 @@ class Attention(nn.Module):
             x = F.scaled_dot_product_attention(
                 q, k, v,
                 attn_mask=rel_pos_bias,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/cait.py

@@ -50,7 +50,7 @@ class ClassAttn(nn.Module):
         if self.fused_attn:
             x_cls = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/eva.py

@@ -126,7 +126,7 @@ class EvaAttention(nn.Module):
             x = F.scaled_dot_product_attention(
                 q, k, v,
                 attn_mask=attn_mask,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/fastvit.py

@@ -514,7 +514,7 @@ class Attention(nn.Module):
         if self.fused_attn:
             x = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p if self.training else 0.0,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/maxxvit.py

@@ -190,7 +190,7 @@ class Attention2d(nn.Module):
                 k.transpose(-1, -2).contiguous(),
                 v.transpose(-1, -2).contiguous(),
                 attn_mask=attn_bias,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             ).transpose(-1, -2).reshape(B, -1, H, W)
         else:
             q = q * self.scale
@@ -259,7 +259,7 @@ class AttentionCl(nn.Module):
             x = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
                 attn_mask=attn_bias,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/metaformer.py

@@ -198,7 +198,7 @@ class Attention(nn.Module):
         if self.fused_attn:
             x = F.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             attn = (q @ k.transpose(-2, -1)) * self.scale

timm/models/nest.py

@@ -59,14 +59,14 @@ class Attention(nn.Module):
     def forward(self, x):
         """
         x is shape: B (batch_size), T (image blocks), N (seq length per image block), C (embed dim)
-        """ 
+        """
         B, T, N, C = x.shape
         # result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
         qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).permute(3, 0, 4, 1, 2, 5)
         q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
 
         if self.fused_attn:
-            x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p)
+            x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.)
         else:
             q = q * self.scale
             attn = q @ k.transpose(-2, -1) # (B, H, T, N, N)
@@ -330,7 +330,7 @@ class Nest(nn.Module):
         # Hint: (img_size // patch_size) gives number of patches along edge of image. sqrt(self.num_blocks[0]) is the
         #  number of blocks along edge of image
         self.block_size = int((img_size // patch_size) // math.sqrt(self.num_blocks[0]))
-        
+
         # Patch embedding
         self.patch_embed = PatchEmbed(
             img_size=img_size,

timm/models/pvt_v2.py

@@ -130,7 +130,7 @@ class Attention(nn.Module):
         k, v = kv.unbind(0)
 
         if self.fused_attn:
-            x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p)
+            x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.)
         else:
             q = q * self.scale
             attn = q @ k.transpose(-2, -1)

timm/models/swin_transformer.py

@@ -164,7 +164,7 @@ class WindowAttention(nn.Module):
             x = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
                 attn_mask=attn_mask,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/twins.py

@@ -75,7 +75,7 @@ class LocallyGroupedAttn(nn.Module):
         if self.fused_attn:
             x = F.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale
@@ -172,7 +172,7 @@ class GlobalSubSampleAttn(nn.Module):
         if self.fused_attn:
             x = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/visformer.py

@@ -95,7 +95,7 @@ class Attention(nn.Module):
         if self.fused_attn:
             x = torch.nn.functional.scaled_dot_product_attention(
                 q.contiguous(), k.contiguous(), v.contiguous(),
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             attn = (q @ k.transpose(-2, -1)) * self.scale

timm/models/vision_transformer.py

@@ -85,7 +85,7 @@ class Attention(nn.Module):
         if self.fused_attn:
             x = F.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale
@@ -285,7 +285,7 @@ class ParallelScalingBlock(nn.Module):
         if self.fused_attn:
             x_attn = F.scaled_dot_product_attention(
                 q, k, v,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale
@@ -1151,7 +1151,7 @@ default_cfgs = generate_default_cfgs({
         url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
         hf_hub_id='timm/',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    
+
     # DINOv2 pretrained - https://arxiv.org/abs/2304.07193 (no classifier head, for fine-tune/features only)
     'vit_small_patch14_dinov2.lvd142m': _cfg(
         url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth',
@@ -1471,7 +1471,7 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         license='cc-by-nc-4.0',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    
+
     'vit_huge_patch14_224_ijepa.in1k': _cfg(
         url='https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.14-300e.pth.tar',
         # hf_hub_id='timm/',
@@ -2080,7 +2080,7 @@ def vit_giant_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     # With SwiGLUPacked, we need to set hidden_features = 2 * 4096 = 8192
 
     model_args = dict(
-        patch_size=14, embed_dim=1536, depth=40, num_heads=24, init_values=1e-5, 
+        patch_size=14, embed_dim=1536, depth=40, num_heads=24, init_values=1e-5,
         mlp_ratio=2.66667 * 2, mlp_layer=SwiGLUPacked, img_size=518, act_layer=nn.SiLU
     )
     model = _create_vision_transformer(

timm/models/vision_transformer_relpos.py

@@ -71,7 +71,7 @@ class RelPosAttention(nn.Module):
             x = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
                 attn_mask=attn_bias,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale

timm/models/vision_transformer_sam.py

@@ -168,7 +168,7 @@ class Attention(nn.Module):
             x = torch.nn.functional.scaled_dot_product_attention(
                 q, k, v,
                 attn_mask=attn_bias,
-                dropout_p=self.attn_drop.p,
+                dropout_p=self.attn_drop.p if self.training else 0.,
             )
         else:
             q = q * self.scale