perf: Create default sampling params only once during initialization (#1294)

examples/multimodal/components/processor.py

@@ -59,6 +59,7 @@ class Processor(ProcessMixIn):
         class_name = self.__class__.__name__
         self.engine_args = parse_vllm_args(class_name, "")
         self.model_config = self.engine_args.create_model_config()
+        self.default_sampling_params = self.model_config.get_diff_sampling_param()
         self.tokenizer = self._create_tokenizer(self.engine_args)
         self.chat_processor = ChatProcessor(self.tokenizer, self.model_config)
         self.completions_processor = CompletionsProcessor(

examples/multimodal/utils/chat_processor.py

@@ -29,6 +29,7 @@ from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_engine import RequestPrompt
 from vllm.inputs.data import TokensPrompt
+from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 
@@ -38,6 +39,7 @@ class ProcessMixInRequired(Protocol):
     chat_processor: "ChatProcessor | None"
     completions_processor: "CompletionsProcessor | None"
     model_config: ModelConfig
+    default_sampling_params: SamplingParams
 
 
 class ProcessMixIn(ProcessMixInRequired):
@@ -50,6 +52,7 @@ class ProcessMixIn(ProcessMixInRequired):
     chat_processor: "ChatProcessor | None"
     completions_processor: "CompletionsProcessor | None"
     model_config: ModelConfig
+    default_sampling_params: SamplingParams
 
     def __init__(self):
         pass
@@ -76,11 +79,11 @@ class ProcessMixIn(ProcessMixInRequired):
         default_max_tokens = self.model_config.max_model_len - len(
             preprocess_result.engine_prompt["prompt_token_ids"]
         )
-        default_sampling_params = self.model_config.get_diff_sampling_param()
+
         sampling_params = request.to_sampling_params(
             default_max_tokens,
             self.model_config.logits_processor_pattern,
-            default_sampling_params,
+            self.default_sampling_params,
         )
         return (
             request,