[Frontend] API support for beam search

benchmarks/benchmark_throughput.py

@@ -15,6 +15,7 @@
 from vllm.entrypoints.openai.api_server import (
     build_async_engine_client_from_engine_args)
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser, merge_async_iterators
 
 
@@ -145,10 +146,13 @@ def run_vllm(
         for prompt, input_len, _output_len in requests:
             assert _output_len == output_len
         start = time.perf_counter()
-        llm.beam_search(prompts,
-                        beam_width=n,
-                        max_tokens=output_len,
-                        ignore_eos=True)
+        llm.beam_search(
+            prompts,
+            BeamSearchParams(
+                beam_width=n,
+                max_tokens=output_len,
+                ignore_eos=True,
+            ))
         end = time.perf_counter()
     return end - start
 

tests/conftest.py

@@ -35,6 +35,7 @@
                          to_enc_dec_tuple_list, zip_enc_dec_prompts)
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
+from vllm.sampling_params import BeamSearchParams
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, cuda_device_count_stateless,
                         identity, is_cpu)
 
@@ -812,7 +813,9 @@ def generate_beam_search_new(
         beam_width: int,
         max_tokens: int,
     ) -> List[Tuple[List[List[int]], List[str]]]:
-        outputs = self.model.beam_search(prompts, beam_width, max_tokens)
+        outputs = self.model.beam_search(
+            prompts,
+            BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens))
         returned_outputs = []
         for output in outputs:
             token_ids = [x.tokens for x in output.sequences]

tests/entrypoints/openai/test_completion.py

@@ -495,25 +495,30 @@ async def test_batch_completions(client: openai.AsyncOpenAI, model_name: str):
         assert len(batch.choices) == 2
         assert batch.choices[0].text == batch.choices[1].text
 
-        # test n = 2
-        batch = await client.completions.create(
-            model=model_name,
-            prompt=prompts,
-            n=2,
-            max_tokens=5,
-            temperature=0.0,
-            extra_body=dict(
-                # NOTE: this has to be true for n > 1 in vLLM, but not necessary
-                # for official client.
-                use_beam_search=True),
-        )
-        assert len(batch.choices) == 4
-        assert batch.choices[0].text != batch.choices[
-            1].text, "beam search should be different"
-        assert batch.choices[0].text == batch.choices[
-            2].text, "two copies of the same prompt should be the same"
-        assert batch.choices[1].text == batch.choices[
-            3].text, "two copies of the same prompt should be the same"
+        try:
+            # test n = 2
+            batch = await client.completions.create(
+                model=model_name,
+                prompt=prompts,
+                n=2,
+                max_tokens=5,
+                temperature=0.0,
+                extra_body=dict(
+                    # NOTE: this has to be true for n > 1 in vLLM, but
+                    # not necessary for official client.
+                    use_beam_search=True),
+            )
+            assert len(batch.choices) == 4
+            assert batch.choices[0].text != batch.choices[
+                1].text, "beam search should be different"
+            assert batch.choices[0].text == batch.choices[
+                2].text, "two copies of the same prompt should be the same"
+            assert batch.choices[1].text == batch.choices[
+                3].text, "two copies of the same prompt should be the same"
+        except BadRequestError as e:
+            # the only allowed exception is when beam search is not supported
+            # in the default mqllmengine
+            assert "--disable-frontend-multiprocessing" in str(e)
 
         # test streaming
         batch = await client.completions.create(

vllm/engine/async_llm_engine.py

@@ -14,23 +14,26 @@
 from vllm.engine.async_timeout import asyncio_timeout
 from vllm.engine.llm_engine import LLMEngine, SchedulerOutputState
 from vllm.engine.metrics_types import StatLoggerBase
+from vllm.entrypoints.llm import BeamSearchSequence
 from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.gpu_executor import GPUExecutorAsync
 from vllm.executor.ray_utils import initialize_ray_cluster
-from vllm.inputs import PromptType
+from vllm.inputs import PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding import (
     get_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.outputs import (CompletionOutput, EmbeddingRequestOutput,
+                          RequestOutput)
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.sequence import ExecuteModelRequest
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import deprecate_kwargs, weak_bind
+from vllm.utils import (collect_from_async_generator, deprecate_kwargs,
+                        random_uuid, weak_bind)
 
 logger = init_logger(__name__)
 ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
@@ -1036,6 +1039,102 @@ async def generate(
         ):
             yield LLMEngine.validate_output(output, RequestOutput)
 
+    async def beam_search(
+        self,
+        prompt: Union[PromptType, List[int]],
+        request_id: str,
+        params: BeamSearchParams,
+    ) -> AsyncGenerator[RequestOutput, None]:
+
+        beam_width = params.beam_width
+        max_tokens = params.max_tokens
+        ignore_eos = params.ignore_eos
+        temperature = params.temperature
+
+        tokenizer = await self.get_tokenizer()
+        tokenizedPrompt = prompt if isinstance(
+            prompt, list) else tokenizer.encode(prompt)
+        tokenizedLength = len(tokenizedPrompt)
+
+        beam_search_params = SamplingParams(logprobs=2 * beam_width,
+                                            max_tokens=1,
+                                            temperature=temperature)
+        all_beams = [BeamSearchSequence(tokens=tokenizedPrompt, cum_logprob=0)]
+        completed = []
+
+        for _ in range(max_tokens):
+            prompts_batch = [
+                TokensPrompt(prompt_token_ids=beam.tokens)
+                for beam in all_beams
+            ]
+
+            tasks = []
+
+            request_id = f"beam_search-{random_uuid()}"
+            for i, individual_prompt in enumerate(prompts_batch):
+                request_id_item = f"{request_id}-{i}"
+                task = asyncio.create_task(
+                    collect_from_async_generator(
+                        self.generate(individual_prompt, beam_search_params,
+                                      request_id_item)))
+                tasks.append(task)
+
+            output = await asyncio.gather(*tasks)
+
+            output = [x[0] for x in output]
+
+            logger.info(output)
+
+            new_beams = []
+            for i, current_beam in enumerate(all_beams):
+                result = output[i]
+
+                if result.outputs[0].logprobs is not None:
+                    logprobs = result.outputs[0].logprobs[0]
+                    for token_id, logprob_obj in logprobs.items():
+                        new_beam = BeamSearchSequence(
+                            tokens=current_beam.tokens + [token_id],
+                            cum_logprob=current_beam.cum_logprob +
+                            logprob_obj.logprob)
+
+                        if token_id == tokenizer.eos_token_id and \
+                            not ignore_eos:
+                            completed.append(new_beam)
+                        else:
+                            new_beams.append(new_beam)
+
+            sorted_beams = sorted(new_beams,
+                                  key=lambda x: x.cum_logprob,
+                                  reverse=True)
+            all_beams = sorted_beams[:beam_width]
+
+        completed.extend(all_beams)
+        sorted_completed = sorted(completed,
+                                  key=lambda x: x.cum_logprob,
+                                  reverse=True)
+        best_beams = sorted_completed[:beam_width]
+
+        for beam in best_beams:
+            beam.text = tokenizer.decode(beam.tokens[tokenizedLength:])
+
+        beam_search_output = RequestOutput(
+            request_id=request_id,
+            prompt=prompt,
+            outputs=[
+                CompletionOutput(
+                    text=beam.text,
+                    cumulative_logprob=beam.cum_logprob,
+                    token_ids=beam.tokens,
+                    index=i,
+                    logprobs=beam.cum_logprob,
+                ) for (i, beam) in enumerate(best_beams)
+            ],
+            finished=True,
+            prompt_token_ids=tokenizedPrompt,
+            prompt_logprobs=None)
+
+        yield LLMEngine.validate_output(beam_search_output, RequestOutput)
+
     async def encode(
         self,
         prompt: PromptType,

vllm/entrypoints/llm.py

@@ -22,8 +22,8 @@
 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
@@ -394,25 +394,25 @@ def generate(
     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.
 
         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

vllm/entrypoints/logger.py

@@ -4,7 +4,7 @@
 from vllm.lora.request import LoRARequest
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import BeamSearchParams, SamplingParams
 
 logger = init_logger(__name__)
 
@@ -21,7 +21,8 @@ def log_inputs(
         request_id: str,
         prompt: Optional[str],
         prompt_token_ids: Optional[List[int]],
-        params: Optional[Union[SamplingParams, PoolingParams]],
+        params: Optional[Union[SamplingParams, PoolingParams,
+                               BeamSearchParams]],
         lora_request: Optional[LoRARequest],
         prompt_adapter_request: Optional[PromptAdapterRequest],
     ) -> None:

vllm/entrypoints/openai/protocol.py

@@ -11,8 +11,8 @@
 
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.pooling_params import PoolingParams
-from vllm.sampling_params import (GuidedDecodingParams, RequestOutputKind,
-                                  SamplingParams)
+from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
+                                  RequestOutputKind, SamplingParams)
 from vllm.sequence import Logprob
 from vllm.utils import random_uuid
 
@@ -288,6 +288,22 @@ class ChatCompletionRequest(OpenAIBaseModel):
 
     # doc: end-chat-completion-extra-params
 
+    def to_beam_search_params(self,
+                              default_max_tokens: int) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        n = self.n if self.n is not None else 1
+        temperature = self.temperature if self.temperature is not None else 0.0
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+        )
+
     def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
         max_tokens = self.max_tokens
         if max_tokens is None:
@@ -567,6 +583,22 @@ class CompletionRequest(OpenAIBaseModel):
 
     # doc: end-completion-extra-params
 
+    def to_beam_search_params(self,
+                              default_max_tokens: int) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        n = self.n if self.n is not None else 1
+        temperature = self.temperature if self.temperature is not None else 0.0
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+        )
+
     def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
         max_tokens = self.max_tokens
         if max_tokens is None: