Fix repo consistency

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -927,11 +927,7 @@ def forward(
         hidden_states = outputs[0]
         logits = self.score(hidden_states)
 
-        if input_ids is not None:
-            batch_size, sequence_length = input_ids.shape[:2]
-        else:
-            batch_size, sequence_length = inputs_embeds.shape[:2]
-
+        batch_size = logits.shape[0]
         if self.config.pad_token_id is None and batch_size != 1:
             raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
         if self.config.pad_token_id is None:

src/transformers/models/gpt_neox/modular_gpt_neox.py

@@ -625,29 +625,24 @@ def forward(
         hidden_states = outputs[0]
         logits = self.score(hidden_states)
 
-        if input_ids is not None:
-            batch_size, sequence_length = input_ids.shape[:2]
-        else:
-            batch_size, sequence_length = inputs_embeds.shape[:2]
-
+        batch_size = logits.shape[0]
         if self.config.pad_token_id is None and batch_size != 1:
             raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
         if self.config.pad_token_id is None:
-            sequence_lengths = -1
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
         else:
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
-                logger.warning_once(
-                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-                )
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+            )
 
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
 
         loss = None
         if labels is not None:

src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py

@@ -1261,7 +1261,7 @@ def _update_causal_mask(
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None
-            and attention_mask.device.type == "cuda"
+            and attention_mask.device.type in ["cuda", "xpu"]
             and not output_attentions
         ):
             # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when

src/transformers/models/zamba2/modeling_zamba2.py

@@ -1557,7 +1557,7 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None
-            and attention_mask.device.type == "cuda"
+            and attention_mask.device.type in ["cuda", "xpu"]
         ):
             # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
             # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.