Paligemma- fix devices and dtype assignments (#31008)

* fix devices and dtype assignments

* [run-slow]paligemma

src/transformers/models/paligemma/modeling_paligemma.py

@@ -301,14 +301,15 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
         pad_mask = input_ids == self.pad_token_id
 
         # expand masks to match embedding dimension
-        text_mask_expanded = text_mask.unsqueeze(-1).expand(-1, -1, embed_dim)
-        pad_mask_expanded = pad_mask.unsqueeze(-1).expand(-1, -1, embed_dim)
+        text_mask_expanded = text_mask.unsqueeze(-1).expand(-1, -1, embed_dim).to(inputs_embeds.device)
+        pad_mask_expanded = pad_mask.unsqueeze(-1).expand(-1, -1, embed_dim).to(inputs_embeds.device)
         # insert padding and text token embeddings
         final_embedding = torch.where(text_mask_expanded, inputs_embeds, final_embedding)
         final_embedding = torch.where(pad_mask_expanded, torch.zeros_like(final_embedding), final_embedding)
         # insert image embeddings - the image mask is always less or equal to the sentence in length
         final_embedding = final_embedding.masked_scatter(
-            image_mask.unsqueeze(-1).expand_as(final_embedding), scaled_image_features
+            image_mask.unsqueeze(-1).expand_as(final_embedding).to(device=final_embedding.device),
+            scaled_image_features.to(device=final_embedding.device, dtype=final_embedding.dtype),
         )
         final_embedding = torch.where(pad_mask_expanded, torch.zeros_like(final_embedding), final_embedding)
         if attention_mask is not None:
@@ -329,10 +330,12 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
             if attention_mask is not None:
                 causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                 mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+                    causal_mask.device
+                )
                 # unmask the prefill
                 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    token_type_ids[:, None, None, :] == 0, 0
+                    token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
                 )
                 padding_mask = padding_mask == 0
                 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
@@ -484,7 +487,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
                 # we use the input attention mask to shift the logits and labels, because it is 2D.
                 shift_attention_mask = input_attention_mask[..., 1:]
                 shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
-                shift_labels = shift_labels[shift_attention_mask.to(logits.device) != 0].contiguous()
+                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
             else:
                 shift_logits = shift_logits.contiguous()
                 shift_labels = shift_labels.contiguous()