Re-styling in seq2seq attention (#11613)

src/transformers/models/bart/modeling_bart.py

@@ -210,28 +210,26 @@ class BartAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -249,17 +247,14 @@ class BartAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -211,28 +211,26 @@ class BlenderbotAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -250,17 +248,14 @@ class BlenderbotAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -209,28 +209,26 @@ class BlenderbotSmallAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -248,17 +246,14 @@ class BlenderbotSmallAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/m2m_100/modeling_m2m_100.py

@@ -280,28 +280,26 @@ class M2M100Attention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -319,17 +317,14 @@ class M2M100Attention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/marian/modeling_marian.py

@@ -226,28 +226,26 @@ class MarianAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -265,17 +263,14 @@ class MarianAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/mbart/modeling_mbart.py

@@ -217,28 +217,26 @@ class MBartAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -256,17 +254,14 @@ class MBartAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/pegasus/modeling_pegasus.py

@@ -226,28 +226,26 @@ class PegasusAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -265,17 +263,14 @@ class PegasusAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -293,28 +293,26 @@ class Speech2TextAttention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -332,17 +330,14 @@ class Speech2TextAttention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -356,28 +356,26 @@ class Wav2Vec2Attention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -395,17 +393,14 @@ class Wav2Vec2Attention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -1721,28 +1721,26 @@ class {{cookiecutter.camelcase_modelname}}Attention(nn.Module):
         src_len = key_states.size(1)
         attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
 
-        assert attn_weights.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            src_len,
-        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
 
         if attention_mask is not None:
-            assert attention_mask.size() == (
-                bsz,
-                1,
-                tgt_len,
-                src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+                )
             attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
@@ -1760,17 +1758,14 @@ class {{cookiecutter.camelcase_modelname}}Attention(nn.Module):
 
         attn_output = torch.bmm(attn_probs, value_states)
 
-        assert attn_output.size() == (
-            bsz * self.num_heads,
-            tgt_len,
-            self.head_dim,
-        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
+            )
 
-        attn_output = (
-            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-            .transpose(1, 2)
-            .reshape(bsz, tgt_len, embed_dim)
-        )
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
 
         attn_output = self.out_proj(attn_output)