Added error when sequence length is bigger than max_position_embeddin…

…gs (#32156)

* Added error when sequence length is bigger than max_position_embeddings

* Fixed formatting

* Fixed bug

* Changed copies to match

* Fixed bug

* Applied suggestions

* Removed redundant code

* Fixed bugs

* Bug fix

* Bug fix

* Added requested Changes

* Fixed bug

* Fixed unwanted change

* Fixed unwanated changes

* Fixed formatting

src/transformers/models/blip/modeling_blip.py

@@ -309,6 +309,13 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
         seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]

src/transformers/models/clip/modeling_clip.py

@@ -277,6 +277,13 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
         seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]

src/transformers/models/clipseg/modeling_clipseg.py

@@ -244,6 +244,13 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
         seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]

src/transformers/models/groupvit/modeling_groupvit.py

@@ -446,6 +446,13 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
         seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]

src/transformers/models/siglip/modeling_siglip.py

@@ -340,6 +340,13 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
         seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]

src/transformers/models/x_clip/modeling_x_clip.py

@@ -203,6 +203,13 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
         seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]