Support tokenization_kwargs override (#29794)

Signed-off-by: piood <2477084691@qq.com>

tests/conftest.py

@@ -405,6 +405,7 @@ class HfRunner:
         images: PromptImageInput | None = None,
         videos: PromptVideoInput | None = None,
         audios: PromptAudioInput | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> list[BatchFeature | BatchEncoding | dict[str, torch.Tensor]]:
         if images is not None:
             assert len(prompts) == len(images)
@@ -418,10 +419,18 @@ class HfRunner:
         all_inputs: list[BatchFeature | BatchEncoding | dict[str, torch.Tensor]] = []
         for i, prompt in enumerate(prompts):
             if isinstance(prompt, str):
-                processor_kwargs: dict[str, Any] = {
+                # Create a copy to avoid modifying the original dict
+                processor_kwargs = (
+                    tokenization_kwargs.copy()
+                    if tokenization_kwargs is not None
+                    else {}
+                )
+                processor_kwargs.update(
+                    {
                         "text": prompt,
                         "return_tensors": "pt",
                     }
+                )
                 if images is not None and (image := images[i]) is not None:
                     processor_kwargs["images"] = image
                 if videos is not None and (video := videos[i]) is not None:

tests/models/multimodal/pooling/test_siglip.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from typing import Any
+
 import pytest
 from transformers import SiglipModel
 
@@ -35,7 +37,11 @@ def _run_test(
     model: str,
     *,
     dtype: str,
+    tokenization_kwargs: dict[str, Any] | None = None,
 ) -> None:
+    if tokenization_kwargs is None:
+        tokenization_kwargs = {}
+
     with vllm_runner(
         model,
         runner="pooling",
@@ -44,10 +50,14 @@ def _run_test(
         max_model_len=64,
         gpu_memory_utilization=0.7,
     ) as vllm_model:
-        vllm_outputs = vllm_model.embed(input_texts, images=input_images)
+        vllm_outputs = vllm_model.embed(
+            input_texts, images=input_images, tokenization_kwargs=tokenization_kwargs
+        )
 
     with hf_runner(model, dtype=dtype, auto_cls=SiglipModel) as hf_model:
-        all_inputs = hf_model.get_inputs(input_texts, images=input_images)
+        all_inputs = hf_model.get_inputs(
+            input_texts, images=input_images, tokenization_kwargs=tokenization_kwargs
+        )
 
         all_outputs = []
         for inputs in all_inputs:
@@ -94,6 +104,10 @@ def test_models_text(
         input_images,  # type: ignore
         model,
         dtype=dtype,
+        tokenization_kwargs={
+            "padding": "max_length",
+            "max_length": 64,
+        },  # siglip2 was trained with this padding setting.
     )
 
 

vllm/entrypoints/llm.py

@@ -1076,6 +1076,7 @@ class LLM:
             params=pooling_params,
             use_tqdm=use_tqdm,
             lora_request=lora_request,
+            tokenization_kwargs=tokenization_kwargs,
         )
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
@@ -1113,6 +1114,7 @@ class LLM:
         use_tqdm: bool | Callable[..., tqdm] = True,
         pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
         lora_request: list[LoRARequest] | LoRARequest | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> list[EmbeddingRequestOutput]:
         """
         Generate an embedding vector for each prompt.
@@ -1150,6 +1152,7 @@ class LLM:
             pooling_params=pooling_params,
             lora_request=lora_request,
             pooling_task="embed",
+            tokenization_kwargs=tokenization_kwargs,
         )
 
         return [EmbeddingRequestOutput.from_base(item) for item in items]
@@ -1161,6 +1164,7 @@ class LLM:
         use_tqdm: bool | Callable[..., tqdm] = True,
         pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
         lora_request: list[LoRARequest] | LoRARequest | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> list[ClassificationRequestOutput]:
         """
         Generate class logits for each prompt.
@@ -1196,6 +1200,7 @@ class LLM:
             pooling_params=pooling_params,
             lora_request=lora_request,
             pooling_task="classify",
+            tokenization_kwargs=tokenization_kwargs,
         )
 
         return [ClassificationRequestOutput.from_base(item) for item in items]
@@ -1209,6 +1214,7 @@ class LLM:
         use_tqdm: bool | Callable[..., tqdm] = True,
         pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
         lora_request: list[LoRARequest] | LoRARequest | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> list[PoolingRequestOutput]:
         """
         Generate rewards for each prompt.
@@ -1236,6 +1242,7 @@ class LLM:
             pooling_params=pooling_params,
             truncate_prompt_tokens=truncate_prompt_tokens,
             pooling_task="token_classify",
+            tokenization_kwargs=tokenization_kwargs,
         )
 
     def _embedding_score(
@@ -1247,6 +1254,7 @@ class LLM:
         use_tqdm: bool | Callable[..., tqdm] = True,
         pooling_params: PoolingParams | None = None,
         lora_request: list[LoRARequest] | LoRARequest | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> list[ScoringRequestOutput]:
         encoded_output: list[PoolingRequestOutput] = self.encode(
             text_1 + text_2,
@@ -1255,6 +1263,7 @@ class LLM:
             lora_request=lora_request,
             pooling_params=pooling_params,
             pooling_task="embed",
+            tokenization_kwargs=tokenization_kwargs,
         )
 
         encoded_output_1: list[PoolingRequestOutput] = encoded_output[0 : len(text_1)]
@@ -1279,6 +1288,7 @@ class LLM:
         use_tqdm: bool | Callable[..., tqdm] = True,
         pooling_params: PoolingParams | None = None,
         lora_request: list[LoRARequest] | LoRARequest | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> list[ScoringRequestOutput]:
         model_config = self.model_config
 
@@ -1294,7 +1304,8 @@ class LLM:
         pooling_params.verify("score", model_config)
         pooling_params_list = list[PoolingParams]()
 
-        tokenization_kwargs: dict[str, Any] = {}
+        local_kwargs = tokenization_kwargs or {}
+        tokenization_kwargs = local_kwargs.copy()
 
         _validate_truncation_size(
             model_config.max_model_len, truncate_prompt_tokens, tokenization_kwargs
@@ -1557,6 +1568,7 @@ class LLM:
         use_tqdm: bool | Callable[..., tqdm] = True,
         lora_request: Sequence[LoRARequest] | LoRARequest | None,
         priority: list[int] | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> None:
         if isinstance(prompts, (str, dict)):
             # Convert a single prompt to a list.
@@ -1602,6 +1614,7 @@ class LLM:
                     if isinstance(lora_request, Sequence)
                     else lora_request,
                     priority=priority[i] if priority else 0,
+                    tokenization_kwargs=tokenization_kwargs,
                 )
                 added_request_ids.append(request_id)
         except Exception as e:
@@ -1665,9 +1678,12 @@ class LLM:
         *,
         lora_request: LoRARequest | None,
         priority: int,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> tuple[EngineCoreRequest, dict[str, Any]]:
         """Use the Processor to process inputs for LLMEngine."""
-        tokenization_kwargs: dict[str, Any] = {}
+
+        local_kwargs = tokenization_kwargs or {}
+        tokenization_kwargs = local_kwargs.copy()
         _validate_truncation_size(
             self.model_config.max_model_len,
             params.truncate_prompt_tokens,
@@ -1690,6 +1706,7 @@ class LLM:
         params: SamplingParams | PoolingParams,
         lora_request: LoRARequest | None = None,
         priority: int = 0,
+        tokenization_kwargs: dict[str, Any] | None = None,
     ) -> str:
         prompt_text, _, _ = get_prompt_components(prompt)
         request_id = str(next(self.request_counter))
@@ -1700,6 +1717,7 @@ class LLM:
             params,
             lora_request=lora_request,
             priority=priority,
+            tokenization_kwargs=tokenization_kwargs,
         )
 
         self.llm_engine.add_request(