[VLM] Simplify post-processing of replacement info (#12269)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/blip2.py

@@ -14,12 +14,12 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
-                                    MultiModalInputs, MultiModalKwargs,
-                                    NestedTensors, PlaceholderRange)
+from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
+                                    NestedTensors)
 from vllm.multimodal.parse import MultiModalDataItems
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, PromptReplacement)
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptReplacementDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
@@ -481,30 +481,13 @@ class Blip2MultiModalProcessor(BaseMultiModalProcessor[Blip2ProcessingInfo]):
             PromptReplacement(
                 modality="image",
                 target="</s>",
-                replacement="<image>" * num_image_tokens + "</s>",
+                replacement=PromptReplacementDetails(
+                    full="<image>" * num_image_tokens + "</s>",
+                    features="<image>" * num_image_tokens,
+                ),
             )
         ]
 
-    def apply(
-        self,
-        prompt: Union[str, list[int]],
-        mm_data: MultiModalDataDict,
-        hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> MultiModalInputs:
-        result = super().apply(prompt, mm_data, hf_processor_mm_kwargs)
-
-        # Only <image> tokens should be considered as placeholders,
-        # so we ignore the trailing bos_token
-        result["mm_placeholders"] = {
-            modality: [
-                PlaceholderRange(offset=p["offset"], length=p["length"] - 1)
-                for p in ps
-            ]
-            for modality, ps in result["mm_placeholders"].items()
-        }
-
-        return result
-
 
 @MULTIMODAL_REGISTRY.register_processor(Blip2MultiModalProcessor,
                                         info=Blip2ProcessingInfo,