[Frontend] API support for beam search (#9087)

Co-authored-by: youkaichao <youkaichao@126.com>

vllm/entrypoints/llm.py

@@ -22,8 +22,8 @@ from vllm.model_executor.guided_decoding.guided_fields import (
 from vllm.outputs import EmbeddingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sampling_params import (GuidedDecodingParams, RequestOutputKind,
-                                  SamplingParams)
+from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
+                                  RequestOutputKind, SamplingParams)
 from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
                                                get_cached_tokenizer)
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
@@ -394,10 +394,7 @@ class LLM:
     def beam_search(
         self,
         prompts: List[Union[str, List[int]]],
-        beam_width: int,
-        max_tokens: int,
-        ignore_eos: bool = False,
-        temperature: float = 0.0,
+        params: BeamSearchParams,
     ) -> List[BeamSearchOutput]:
         """
         Generate sequences using beam search.
@@ -405,14 +402,17 @@ class LLM:
         Args:
             prompts: A list of prompts. Each prompt can be a string or a list
                 of token IDs.
-            beam_width: The number of beams to keep at each step.
-            max_tokens: The max number of tokens to generate for each prompt.
-            temperature: The temperature to use for generation.
-        
+            params: The beam search parameters.
+
         TODO: how does beam search work together with length penalty, frequency
         penalty, and stopping criteria, etc.?
         """
 
+        beam_width = params.beam_width
+        max_tokens = params.max_tokens
+        temperature = params.temperature
+        ignore_eos = params.ignore_eos
+
         tokenizer = self.get_tokenizer()
         # generate 2 * beam_width candidates at each step
         # following the huggingface transformers implementation