[1/N] Initial prototype for multi-modal processor (#10044)

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

vllm/v1/request.py

@@ -1,7 +1,7 @@
 import enum
-from typing import TYPE_CHECKING, List, Optional, Union
+from typing import List, Optional, Union
 
-from vllm.inputs.data import DecoderOnlyInputs
+from vllm.inputs import DecoderOnlyInputs, SingletonInputsAdapter, token_inputs
 from vllm.lora.request import LoRARequest
 from vllm.multimodal import MultiModalKwargs
 from vllm.sampling_params import SamplingParams
@@ -9,23 +9,20 @@ from vllm.sequence import RequestMetrics
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.utils import ConstantList
 
-if TYPE_CHECKING:
-    from vllm.inputs import DecoderOnlyInputs
-
 
 class Request:
 
     def __init__(
         self,
         request_id: str,
-        inputs: "DecoderOnlyInputs",
+        inputs: DecoderOnlyInputs,
         sampling_params: SamplingParams,
         eos_token_id: Optional[int],
         arrival_time: float,
         lora_request: Optional[LoRARequest] = None,
     ) -> None:
         self.request_id = request_id
-        self.inputs = inputs
+        self.inputs = SingletonInputsAdapter(inputs)
         self.sampling_params = sampling_params
         # Because of LoRA, the eos token id can be different for each request.
         self.eos_token_id = eos_token_id
@@ -41,17 +38,17 @@ class Request:
         assert sampling_params.max_tokens is not None
         self.max_tokens = sampling_params.max_tokens
 
-        self.prompt = inputs.get("prompt")
-        self.prompt_token_ids = inputs["prompt_token_ids"]
+        self.prompt = self.inputs.prompt
+        self.prompt_token_ids = self.inputs.prompt_token_ids
         self.num_prompt_tokens = len(self.prompt_token_ids)
         self._output_token_ids: List[int] = []
         self._all_token_ids: List[int] = self.prompt_token_ids.copy()
         self.num_computed_tokens = 0
 
         # Raw multimodal data before the mm input mapper (e.g., PIL images).
-        self.mm_data = inputs.get("multi_modal_data")
-        self.mm_processor_kwargs = inputs.get("mm_processor_kwargs")
-        mm_positions = inputs.get("multi_modal_placeholders")
+        self.mm_data = self.inputs.multi_modal_data
+        self.mm_processor_kwargs = self.inputs.mm_processor_kwargs
+        mm_positions = self.inputs.multi_modal_placeholders
         if mm_positions:
             # FIXME(woosuk): Support other modalities.
             self.mm_positions = mm_positions.get("image", [])
@@ -64,8 +61,7 @@ class Request:
     def from_engine_core_request(cls, request: EngineCoreRequest) -> "Request":
         return cls(
             request_id=request.request_id,
-            inputs=DecoderOnlyInputs(
-                type="token",
+            inputs=token_inputs(
                 prompt_token_ids=request.prompt_token_ids,
                 prompt=request.prompt,
                 multi_modal_data=request.mm_data,
@@ -114,7 +110,7 @@ class Request:
         return RequestStatus.get_finished_reason(self.status)
 
     def has_encoder_inputs(self) -> bool:
-        return self.mm_data is not None
+        return len(self.mm_data) > 0
 
     @property
     def num_encoder_inputs(self) -> int: