[Misc] Clean up Kimi-VL (#16833)

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

vllm/model_executor/models/kimi_vl.py

@@ -56,7 +56,6 @@ from transformers.activations import GELUActivation
 from vllm.config import VllmConfig
 from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
-from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
@@ -70,22 +69,20 @@ from vllm.model_executor.models.moonvit import MoonVitPretrainedModel
 from vllm.model_executor.models.utils import merge_multimodal_embeddings
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
-                                    NestedTensors)
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, PromptReplacement,
                                         PromptUpdate)
-from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs import KimiVLConfig, MoonViTConfig
 from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
 
 from .utils import is_pp_missing_parameter, maybe_prefix
 
-logger = init_logger(__name__)
-
 
 # For dummy input only
 @dataclass
@@ -143,6 +140,9 @@ class KimiVLProcessingInfo(BaseProcessingInfo):
     def get_hf_config(self):
         return self.ctx.get_hf_config(KimiVLConfig)
 
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
     def get_num_image_tokens(
         self,
         *,
@@ -180,23 +180,6 @@ class KimiVLProcessingInfo(BaseProcessingInfo):
         token_width = (width + pad_width) // (kernel_size[1] * patch_size)
         return int(token_height * token_width)
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
-        # None means unlimited
-        return {"image": None}
-
-    def get_mm_max_tokens_per_item(
-        self,
-        seq_len: int,
-        mm_counts: Mapping[str, int],
-    ) -> Mapping[str, int]:
-        return {
-            "image":
-            self.get_num_image_tokens(
-                image_width=MaxImageTokenMeta.width,
-                image_height=MaxImageTokenMeta.height,
-            ),
-        }
-
     @property
     def image_token_id(self) -> int:
         return self.get_hf_config().media_placeholder_token_id
@@ -204,34 +187,28 @@ class KimiVLProcessingInfo(BaseProcessingInfo):
 
 class KimiVLDummyInputsBuilder(BaseDummyInputsBuilder[KimiVLProcessingInfo]):
 
-    def __init__(self, info: KimiVLProcessingInfo) -> None:
-        super().__init__(info)
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
 
-        self.image_token_id = self.info.image_token_id
-        self.image_token = self.info.get_tokenizer().decode(
-            self.image_token_id)
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
 
-    def get_dummy_processor_inputs(
+        return image_token * num_images
+
+    def get_dummy_mm_data(
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-    ) -> ProcessorInputs:
+    ) -> MultiModalDataDict:
         num_images = mm_counts.get("image", 0)
 
-        width = MaxImageTokenMeta.width
-        height = MaxImageTokenMeta.height
-        mm_data = {
+        return {
             "image":
-            self._get_dummy_images(width=width,
-                                   height=height,
+            self._get_dummy_images(width=MaxImageTokenMeta.width,
+                                   height=MaxImageTokenMeta.height,
                                    num_images=num_images)
         }
 
-        return ProcessorInputs(
-            prompt_text=self.image_token * num_images,
-            mm_data=mm_data,
-        )
-
 
 class KimiVLMultiModalProcessor(BaseMultiModalProcessor[KimiVLProcessingInfo]):