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

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

vllm/v1/request.py

 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:

vllm/v1/worker/gpu_model_runner.py

@@ -28,7 +28,7 @@ from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.sample.metadata import SamplingMetadata
 
 if TYPE_CHECKING:
-    from vllm.multimodal.base import PlaceholderRange
+    from vllm.multimodal.inputs import PlaceholderRange
     from vllm.v1.core.scheduler import SchedulerOutput
 
 logger = init_logger(__name__)

vllm/worker/cpu_model_runner.py

@@ -148,19 +148,29 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
             query_lens=seq_lens,
         )
 
-    def _compute_multi_modal_input(self, seq_group: SequenceGroupMetadata,
-                                   seq_data: SequenceData, computed_len: int,
-                                   mm_processor_kwargs: Dict[str, Any]):
-
+    def _compute_multi_modal_input(
+        self,
+        seq_data: SequenceData,
+        computed_len: int,
+        seq_group_metadata: SequenceGroupMetadata,
+    ):
         # NOTE: mm_data only includes the subset of multi-modal items that
         # intersect with the current prefill positions.
         mm_data, placeholder_maps = MultiModalPlaceholderMap.from_seq_group(
-            seq_group, range(computed_len, len(seq_data.get_token_ids())))
+            seq_group_metadata,
+            range(computed_len, len(seq_data.get_token_ids())),
+        )
 
         if not mm_data:
-            return
+            return None, None, None
 
-        mm_kwargs = self.multi_modal_input_mapper(mm_data, mm_processor_kwargs)
+        if self.runner.mm_registry.has_processor(self.runner.model_config):
+            mm_kwargs = mm_data
+        else:
+            mm_kwargs = self.multi_modal_input_mapper(
+                mm_data,
+                seq_group_metadata.mm_processor_kwargs,
+            )
 
         # special processing for mrope position deltas.
         mrope_positions = None
@@ -202,7 +212,7 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
 
         slot_mapping: List[int] = []
         seq_lens: List[int] = []
-        multi_model_kwargs_list: List[MultiModalKwargs] = []
+        multi_modal_kwargs_list: List[MultiModalKwargs] = []
         multi_modal_placeholder_maps: Dict[
             str,
             MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
@@ -223,11 +233,14 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
 
             mrope_positions = None
             if seq_group_metadata.multi_modal_data:
-                mm_kwargs, placeholder_maps, mrope_positions = self \
-                    ._compute_multi_modal_input(
-                        seq_group_metadata, seq_data, computed_len,
-                    seq_group_metadata.mm_processor_kwargs)
-                multi_model_kwargs_list.append(mm_kwargs)
+                (
+                    mm_kwargs,
+                    placeholder_maps,
+                    mrope_positions,
+                ) = self._compute_multi_modal_input(seq_data, computed_len,
+                                                    seq_group_metadata)
+
+                multi_modal_kwargs_list.append(mm_kwargs)
                 for modality, placeholder_map in placeholder_maps.items():
                     multi_modal_placeholder_maps[modality].extend(
                         placeholder_map)
@@ -302,7 +315,7 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
             multi_modal_placeholder_index_maps=placeholder_index_maps,
         )
 
-        multi_modal_kwargs = MultiModalKwargs.batch(multi_model_kwargs_list)
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
 
         return (input_tokens, input_positions, attn_metadata, seq_lens,
                 multi_modal_kwargs)

vllm/worker/hpu_model_runner.py

@@ -716,7 +716,7 @@ class HPUModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
         context_lens: List[int] = []
         query_lens: List[int] = []
         prefix_block_tables: List[List[int]] = []
-        multi_model_kwargs_list: List[MultiModalKwargs] = []
+        multi_modal_kwargs_list: List[MultiModalKwargs] = []
 
         if len(seq_group_metadata_list) == 0:
             return PreparePromptMetadata.empty()
@@ -777,7 +777,7 @@ class HPUModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
             mm_data = seq_group_metadata.multi_modal_data
             if mm_data:
                 mm_kwargs = self.multi_modal_input_mapper(mm_data)
-                multi_model_kwargs_list.append(mm_kwargs)
+                multi_modal_kwargs_list.append(mm_kwargs)
 
             if seq_group_metadata.block_tables is None:
                 # During memory profiling, the block tables are not initialized
@@ -876,7 +876,7 @@ class HPUModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
             multi_modal_placeholder_index_maps=
             None  # FIXME(kzawora): mutli-modality will not work here
         )
-        multi_modal_kwargs = MultiModalKwargs.batch(multi_model_kwargs_list)
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
 
         return PreparePromptMetadata(input_tokens=input_tokens,
                                      input_positions=input_positions,

vllm/worker/model_runner.py

@@ -252,7 +252,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             prompt_adapter_request: Optional[PromptAdapterRequest] = None,
 
             # Multi-modal inputs.
-            multi_model_kwargs: Optional[MultiModalKwargs] = None,
+            multi_modal_kwargs: Optional[MultiModalKwargs] = None,
             multi_modal_placeholder_maps: Optional[Dict[
                 str, MultiModalPlaceholderMap]] = None,
 
@@ -373,7 +373,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                     prompt_adapter_prompt_mapping or [])
 
             self.prompt_adapter_request = prompt_adapter_request
-            self.multi_model_kwargs = multi_model_kwargs
+            self.multi_modal_kwargs = multi_modal_kwargs
             self.multi_modal_placeholder_maps = multi_modal_placeholder_maps
             self.prefix_cache_hit = prefix_cache_hit
 
@@ -661,10 +661,15 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         if not mm_data:
             return
 
-        mm_kwargs = self.multi_modal_input_mapper(
-            mm_data,
-            mm_processor_kwargs=seq_group_metadata.mm_processor_kwargs)
-        inter_data.multi_model_kwargs = mm_kwargs
+        if self.runner.mm_registry.has_processor(self.runner.model_config):
+            mm_kwargs = mm_data
+        else:
+            mm_kwargs = self.multi_modal_input_mapper(
+                mm_data,
+                seq_group_metadata.mm_processor_kwargs,
+            )
+
+        inter_data.multi_modal_kwargs = mm_kwargs
         inter_data.multi_modal_placeholder_maps = placeholder_maps
 
         # special processing for mrope position deltas.
@@ -938,11 +943,11 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             )
 
         # Multi-modal data.
-        multi_model_kwargs_list = [
-            data.multi_model_kwargs for data in self.inter_data_list
-            if data.multi_model_kwargs is not None
+        multi_modal_kwargs_list = [
+            data.multi_modal_kwargs for data in self.inter_data_list
+            if data.multi_modal_kwargs is not None
         ]
-        multi_modal_kwargs = MultiModalKwargs.batch(multi_model_kwargs_list)
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
 
         return self.model_input_cls(
             input_tokens=input_tokens_tensor,

vllm/worker/neuron_model_runner.py

@@ -67,7 +67,8 @@ class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
         self.pin_memory = is_pin_memory_available()
 
         # Multi-modal data support
-        self.multi_modal_input_mapper = MULTIMODAL_REGISTRY \
+        self.mm_registry = MULTIMODAL_REGISTRY
+        self.multi_modal_input_mapper = self.mm_registry \
             .create_input_mapper(self.model_config)
 
         # Lazy initialization.
@@ -122,7 +123,7 @@ class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
         input_block_ids: List[int] = []
 
         seq_lens: List[int] = []
-        multi_model_kwargs_list: List[MultiModalKwargs] = []
+        multi_modal_kwargs_list: List[MultiModalKwargs] = []
         for seq_group_metadata in seq_group_metadata_list:
             assert seq_group_metadata.is_prompt
             seq_ids = list(seq_group_metadata.seq_data.keys())
@@ -144,12 +145,15 @@ class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
 
             mm_data = seq_group_metadata.multi_modal_data
             if mm_data:
-                # Process multi-modal data
-                mm_kwargs = self.multi_modal_input_mapper(
-                    mm_data,
-                    mm_processor_kwargs=seq_group_metadata.mm_processor_kwargs,
-                )
-                multi_model_kwargs_list.append(mm_kwargs)
+                if self.mm_registry.has_processor(self.model_config):
+                    mm_kwargs = mm_data
+                else:
+                    mm_kwargs = self.multi_modal_input_mapper(
+                        mm_data,
+                        seq_group_metadata.mm_processor_kwargs,
+                    )
+
+                multi_modal_kwargs_list.append(mm_kwargs)
 
         max_seq_len = max(seq_lens)
         assert max_seq_len > 0
@@ -167,7 +171,7 @@ class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
                                        dtype=torch.long,
                                        device=self.device)
 
-        multi_modal_kwargs = MultiModalKwargs.batch(multi_model_kwargs_list)
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
 
         return (input_tokens, input_positions, input_block_ids, seq_lens,
                 multi_modal_kwargs)

vllm/worker/openvino_model_runner.py

@@ -70,7 +70,8 @@ class OpenVINOModelRunner(ModelRunnerBase):
         )
 
         # Multi-modal data support
-        self.multi_modal_input_mapper = MULTIMODAL_REGISTRY \
+        self.mm_registry = MULTIMODAL_REGISTRY
+        self.multi_modal_input_mapper = self.mm_registry \
             .create_input_mapper(self.model_config)
 
         # Lazy initialization.
@@ -102,7 +103,7 @@ class OpenVINOModelRunner(ModelRunnerBase):
         seq_lens: List[int] = []
         past_lens: List[int] = []
         query_lens: List[int] = []
-        multi_model_kwargs_list: List[MultiModalKwargs] = []
+        multi_modal_kwargs_list: List[MultiModalKwargs] = []
         multi_modal_placeholder_maps: Dict[
             str,
             MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
@@ -222,11 +223,15 @@ class OpenVINOModelRunner(ModelRunnerBase):
                     mm_data, placeholder_maps = MultiModalPlaceholderMap \
                         .from_seq_group(seq_group_metadata, positions_range)
 
-                    mm_kwargs = self.multi_modal_input_mapper(
-                        mm_data,
-                        mm_processor_kwargs=seq_group_metadata.
-                        mm_processor_kwargs)
-                    multi_model_kwargs_list.append(mm_kwargs)
+                    if self.mm_registry.has_processor(self.model_config):
+                        mm_kwargs = mm_data
+                    else:
+                        mm_kwargs = self.multi_modal_input_mapper(
+                            mm_data,
+                            seq_group_metadata.mm_processor_kwargs,
+                        )
+
+                    multi_modal_kwargs_list.append(mm_kwargs)
 
                     for modality, placeholder_map in placeholder_maps.items():
                         multi_modal_placeholder_maps[modality].extend(
@@ -275,7 +280,7 @@ class OpenVINOModelRunner(ModelRunnerBase):
             multi_modal_placeholder_index_maps=placeholder_index_maps,
         )
 
-        multi_modal_kwargs = MultiModalKwargs.batch(multi_model_kwargs_list)
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
 
         return ModelInput(
             input_tokens,

vllm/worker/xpu_model_runner.py

@@ -160,7 +160,7 @@ class ModelInputForXPUBuilder(ModelRunnerInputBuilderBase[ModelInputForXPU]):
         input_positions: List[int] = []
         slot_mapping: List[int] = []
         seq_lens: List[int] = []
-        multi_model_kwargs_list: List[MultiModalKwargs] = []
+        multi_modal_kwargs_list: List[MultiModalKwargs] = []
         multi_modal_placeholder_maps: Dict[
             str,
             MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
@@ -191,8 +191,16 @@ class ModelInputForXPUBuilder(ModelRunnerInputBuilderBase[ModelInputForXPU]):
                 mm_data, placeholder_maps = MultiModalPlaceholderMap \
                     .from_seq_group(seq_group_metadata, positions_range)
 
-                mm_kwargs = self.runner.multi_modal_input_mapper(mm_data)
-                multi_model_kwargs_list.append(mm_kwargs)
+                if self.runner.mm_registry.has_processor(
+                        self.runner.model_config):
+                    mm_kwargs = mm_data
+                else:
+                    mm_kwargs = self.runner.multi_modal_input_mapper(
+                        mm_data,
+                        seq_group_metadata.mm_processor_kwargs,
+                    )
+
+                multi_modal_kwargs_list.append(mm_kwargs)
 
                 for modality, placeholder_map in placeholder_maps.items():
                     multi_modal_placeholder_maps[modality].extend(
@@ -264,7 +272,7 @@ class ModelInputForXPUBuilder(ModelRunnerInputBuilderBase[ModelInputForXPU]):
             block_tables=torch.tensor([], device=self.device, dtype=torch.int),
         )
 
-        multi_modal_kwargs = MultiModalKwargs.batch(multi_model_kwargs_list)
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
 
         return (input_tokens, input_positions, attn_metadata, seq_lens,
                 multi_modal_kwargs)