[Misc] Set default torch num threads for input processing (#31879)

Signed-off-by: Roger Wang <hey@rogerw.io>

vllm/model_executor/models/internvl.py

@@ -7,7 +7,6 @@
 # Copyright (c) 2023 OpenGVLab
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
-import os
 from abc import ABC, abstractmethod
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Any, Literal, TypeAlias, TypeVar
@@ -52,7 +51,6 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.tokenizers import TokenizerLike
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
-from vllm.utils.torch_utils import set_default_torch_num_threads
 
 from .interfaces import (
     MultiModalEmbeddings,
@@ -143,19 +141,7 @@ def build_transform(input_size: int):
             T.Normalize(mean=MEAN, std=STD),
         ]
     )
-    # Image transformation operations (which include tensor computations
-    # on the CPU) can occupy a substantial number of CPU cores, introducing
-    # overhead due to CPU contention. This issue becomes particularly
-    # noticeable when deploying multiple vLLM instances on a single machine.
-    # Therefore, it is necessary to limit the number of threads allocated to
-    # image transformation tasks.
-    num_threads = int(os.environ.get("OMP_NUM_THREADS", "1"))
-
-    def apply(img):
-        with set_default_torch_num_threads(num_threads):
-            return transform(img)
-
-    return apply
+    return transform
 
 
 # adapted from https://huggingface.co/OpenGVLab/InternVL2-1B

vllm/v1/engine/input_processor.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import os
 import time
 from collections.abc import Mapping
 from typing import Any, Literal, cast
@@ -23,6 +24,7 @@ from vllm.sampling_params import _SAMPLING_EPS, SamplingParams
 from vllm.tokenizers import TokenizerLike
 from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.utils import length_from_prompt_token_ids_or_embeds, random_uuid
+from vllm.utils.torch_utils import set_default_torch_num_threads
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.metrics.stats import MultiModalCacheStats
 from vllm.v1.structured_output.backend_guidance import (
@@ -493,7 +495,15 @@ class InputProcessor:
         # 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.
-        with set_request_id(request_id):
+        num_threads = int(os.environ.get("OMP_NUM_THREADS", "1"))
+        if "OMP_NUM_THREADS" not in os.environ:
+            logger.debug_once(
+                "OMP_NUM_THREADS is not set; defaulting Torch threads to %d for "
+                "input preprocessing.",
+                num_threads,
+            )
+
+        with set_request_id(request_id), set_default_torch_num_threads(num_threads):
             processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
                 prompt,
                 tokenization_kwargs=tokenization_kwargs,