Rebase 2026/02/23

Update Dockerfile for vllm wheel version and enhance input processing in Omni models. Added decoding for byte messages in async requests, included new metrics in benchmarks, and improved pause state handling in the scheduler. Refined input and output processing methods for better multimodal support and error handling.

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

vllm_omni/engine/input_processor.py

@@ -10,25 +10,13 @@ from vllm.inputs.parse import split_enc_dec_inputs
 from vllm.logger import init_logger
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.multimodal.inputs import MultiModalFeatureSpec
-
-try:
-    from vllm.multimodal.processing import set_request_id
-except ImportError:  # vllm without set_request_id (older releases)
-    from contextlib import contextmanager
-
-    @contextmanager
-    def set_request_id(_request_id: str):
-        yield
-
-
 from vllm.multimodal.utils import argsort_mm_positions
 from vllm.pooling_params import PoolingParams
 from vllm.renderers import BaseRenderer
-from vllm.renderers.inputs import DictPrompt, TokPrompt
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.utils import length_from_prompt_token_ids_or_embeds
-from vllm.utils.torch_utils import set_default_torch_num_threads
+from vllm.utils.jsontree import json_iter_leaves
 from vllm.v1.engine.input_processor import InputProcessor
 
 from vllm_omni.engine import (
@@ -131,7 +119,7 @@ class OmniInputProcessor(InputProcessor):
     def process_inputs(
         self,
         request_id: str,
-        prompt: PromptType | DictPrompt | TokPrompt,
+        prompt: PromptType | ProcessorInputs,
         params: SamplingParams | PoolingParams,
         arrival_time: float | None = None,
         lora_request: LoRARequest | None = None,
@@ -192,11 +180,10 @@ class OmniInputProcessor(InputProcessor):
             if arrival_time is None:
                 arrival_time = time.time()
 
-            with set_request_id(request_id), set_default_torch_num_threads():
-                processed_inputs = self.input_preprocessor.preprocess(
-                    prompt,
-                    tokenization_kwargs=tokenization_kwargs,
-                )
+            processed_inputs = self.input_preprocessor.preprocess(
+                prompt,
+                tokenization_kwargs=tokenization_kwargs,
+            )
 
         self._platform_validate_request(processed_inputs, params)
 
@@ -236,6 +223,13 @@ class OmniInputProcessor(InputProcessor):
             decoder_mm_positions = decoder_inputs["mm_placeholders"]
             decoder_mm_hashes = decoder_inputs["mm_hashes"]
 
+            if not all(isinstance(leaf, str) for leaf in json_iter_leaves(decoder_mm_hashes)):
+                raise ValueError(
+                    f"mm_hashes must contain only strings, got: {decoder_mm_hashes}. "
+                    "This is likely due to an incorrect custom implementation of "
+                    "MultiModalProcessor.apply method."
+                )
+
             # Merge and flatten multimodal placeholders, hashes and inputs
             # from dictionaries to lists, and sort them by each item's position
             # in the input sequence.