[Bugfix] Fix dtype mismatch in PaliGemma (#6367)

tests/models/test_paligemma.py

@@ -129,7 +129,7 @@ def run_test(
         [0.25, 0.5, 1.0],
     ],
 )
-@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("dtype", ["float", "half"])
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
 def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,

vllm/model_executor/models/gemma.py

@@ -277,6 +277,7 @@ class GemmaModel(nn.Module):
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if inputs_embeds is not None:

vllm/model_executor/models/paligemma.py

@@ -19,7 +19,7 @@ from vllm.model_executor.models.gemma import GemmaModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import cached_get_tokenizer
-from vllm.sequence import SamplerOutput, SequenceData
+from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData
 
 from .interfaces import SupportsVision
 from .utils import merge_vision_embeddings
@@ -111,7 +111,7 @@ def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):
     orig_prompt = llm_inputs.get("prompt")
     orig_prompt_ids = llm_inputs.get("prompt_token_ids")
 
-    if image_token_str in orig_prompt:
+    if orig_prompt is not None and image_token_str in orig_prompt:
         logger.warning(
             "The image token '%s' was detected in the prompt and "
             "will be removed. Please follow the proper prompt format"
@@ -214,7 +214,9 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsVision):
     def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
                                   pixel_values: torch.Tensor) -> torch.Tensor:
 
-        image_outputs = vision_tower(pixel_values, output_hidden_states=True)
+        target_dtype = vision_tower.get_input_embeddings().weight.dtype
+        image_outputs = vision_tower(pixel_values.to(dtype=target_dtype),
+                                     output_hidden_states=True)
 
         selected_image_features = image_outputs.last_hidden_state
 
@@ -236,9 +238,12 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsVision):
 
         return self.multi_modal_projector(image_features)
 
-    def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
                 kv_caches: List[torch.Tensor],
                 attn_metadata: AttentionMetadata,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
                 **kwargs: object) -> SamplerOutput:
 
         parsed_image_input = self._parse_and_validate_image_input(**kwargs)
@@ -263,6 +268,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsVision):
                                             positions,
                                             kv_caches,
                                             attn_metadata,
+                                            None,
                                             inputs_embeds=inputs_embeds)
 
         return hidden_states