[core] remove beam search from the core (#9105)

benchmarks/backend_request_func.py

@@ -23,7 +23,6 @@ class RequestFuncInput:
     output_len: int
     model: str
     best_of: int = 1
-    use_beam_search: bool = False
     logprobs: Optional[int] = None
     multi_modal_content: Optional[dict] = None
     ignore_eos: bool = False
@@ -49,7 +48,6 @@ async def async_request_tgi(
     assert api_url.endswith("generate_stream")
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         params = {
             "best_of": request_func_input.best_of,
             "max_new_tokens": request_func_input.output_len,
@@ -121,7 +119,6 @@ async def async_request_trt_llm(
     assert api_url.endswith("generate_stream")
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         assert request_func_input.best_of == 1
         payload = {
             "accumulate_tokens": True,
@@ -187,7 +184,6 @@ async def async_request_deepspeed_mii(
 ) -> RequestFuncOutput:
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
         assert request_func_input.best_of == 1
-        assert not request_func_input.use_beam_search
 
         payload = {
             "prompt": request_func_input.prompt,
@@ -235,7 +231,6 @@ async def async_request_openai_completions(
     ), "OpenAI Completions API URL must end with 'completions' or 'profile'."
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         payload = {
             "model": request_func_input.model,
             "prompt": request_func_input.prompt,
@@ -317,7 +312,6 @@ async def async_request_openai_chat_completions(
     ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         content = [{"type": "text", "text": request_func_input.prompt}]
         if request_func_input.multi_modal_content:
             content.append(request_func_input.multi_modal_content)