[V0 Deprecation] Remove Prompt Adapters (#20588)

Signed-off-by: mgoin <mgoin64@gmail.com>

vllm/v1/engine/processor.py

@@ -16,7 +16,6 @@ from vllm.multimodal.inputs import PlaceholderRange
 from vllm.multimodal.processing import EncDecMultiModalProcessor
 from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
 from vllm.pooling_params import PoolingParams
-from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 from vllm.v1.engine import EngineCoreRequest
@@ -226,7 +225,6 @@ class Processor:
         lora_request: Optional[LoRARequest] = None,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
-        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
         data_parallel_rank: Optional[int] = None,
     ) -> tuple[Optional[str], EngineCoreRequest]:
@@ -237,8 +235,6 @@ class Processor:
         self._validate_params(params, lora_request)
         if trace_headers is not None:
             raise ValueError("V1 does not support tracing yet.")
-        if prompt_adapter_request is not None:
-            raise ValueError("V1 does not support prompt_adapter_request.")
 
         data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
         if data_parallel_rank is not None and not (0 <= data_parallel_rank <
@@ -253,12 +249,10 @@ class Processor:
         # 1. Tokenize text prompt, with LoRA request if one exists.
         # 2. For multimodal models with a merged preprocessor, preprocess
         #   multimodal data and expand prompt token ids accordingly.
-        # 3. Apply prompt adapter to prompt token ids if one exists.
         processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
             prompt,
             tokenization_kwargs=tokenization_kwargs,
             lora_request=lora_request,
-            prompt_adapter_request=prompt_adapter_request,
             return_mm_hashes=self.use_hash,
         )
         from vllm.platforms import current_platform