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

vllm/outputs.py

@@ -142,10 +142,6 @@ class RequestOutput:
         else:
             # Get the top-n sequences.
             n = sampling_params.n
-            if sampling_params.use_beam_search:
-                sorting_key = lambda seq: seq.get_beam_search_score(
-                    sampling_params.length_penalty)
-            else:
             sorting_key = lambda seq: seq.get_cumulative_logprob()
             sorted_seqs = sorted(seqs, key=sorting_key, reverse=True)
             top_n_seqs = sorted_seqs[:n]

vllm/sampling_params.py

@@ -10,7 +10,6 @@ import torch
 from pydantic import BaseModel
 from typing_extensions import Annotated
 
-import vllm.envs as envs
 from vllm.logger import init_logger
 
 logger = init_logger(__name__)
@@ -23,7 +22,6 @@ class SamplingType(IntEnum):
     GREEDY = 0
     RANDOM = 1
     RANDOM_SEED = 2
-    BEAM = 3
 
 
 LogitsProcessor = Union[Callable[[List[int], torch.Tensor], torch.Tensor],
@@ -134,16 +132,6 @@ class SamplingParams(
             considered, relative to the probability of the most likely token.
             Must be in [0, 1]. Set to 0 to disable this.
         seed: Random seed to use for the generation.
-        use_beam_search: Whether to use beam search instead of sampling.
-        length_penalty: Float that penalizes sequences based on their length.
-            Used in beam search.
-        early_stopping: Controls the stopping condition for beam search. It
-            accepts the following values: `True`, where the generation stops as
-            soon as there are `best_of` complete candidates; `False`, where an
-            heuristic is applied and the generation stops when is it very
-            unlikely to find better candidates; `"never"`, where the beam search
-            procedure only stops when there cannot be better candidates
-            (canonical beam search algorithm).
         stop: List of strings that stop the generation when they are generated.
             The returned output will not contain the stop strings.
         stop_token_ids: List of tokens that stop the generation when they are
@@ -193,9 +181,6 @@ class SamplingParams(
     top_k: int = -1
     min_p: float = 0.0
     seed: Optional[int] = None
-    use_beam_search: bool = False
-    length_penalty: float = 1.0
-    early_stopping: Union[bool, str] = False
     stop: Optional[Union[str, List[str]]] = None
     stop_token_ids: Optional[List[int]] = None
     ignore_eos: bool = False
@@ -238,9 +223,6 @@ class SamplingParams(
         top_k: int = -1,
         min_p: float = 0.0,
         seed: Optional[int] = None,
-        use_beam_search: bool = False,
-        length_penalty: float = 1.0,
-        early_stopping: Union[bool, str] = False,
         stop: Optional[Union[str, List[str]]] = None,
         stop_token_ids: Optional[List[int]] = None,
         include_stop_str_in_output: bool = False,
@@ -280,9 +262,6 @@ class SamplingParams(
             top_k=top_k,
             min_p=min_p,
             seed=seed,
-            use_beam_search=use_beam_search,
-            length_penalty=length_penalty,
-            early_stopping=early_stopping,
             stop=stop,
             stop_token_ids=stop_token_ids,
             include_stop_str_in_output=include_stop_str_in_output,
@@ -334,14 +313,7 @@ class SamplingParams(
             self.output_text_buffer_length = max(len(s) for s in self.stop) - 1
 
         self._verify_args()
-        if self.use_beam_search:
-            if not envs.VLLM_ALLOW_DEPRECATED_BEAM_SEARCH:
-                raise ValueError(
-                    "Using beam search as a sampling parameter is deprecated, and will be removed in the future release. Please use the `vllm.LLM.use_beam_search` method for dedicated beam search instead, or set the environment variable `VLLM_ALLOW_DEPRECATED_BEAM_SEARCH=1` to suppress this error. For more details, see https://github.com/vllm-project/vllm/issues/8306 ."  # noqa
-                )
-            self._verify_beam_search()
-        else:
-            self._verify_non_beam_search()
+
         if self.temperature < _SAMPLING_EPS:
             # Zero temperature means greedy sampling.
             self.top_p = 1.0
@@ -417,31 +389,6 @@ class SamplingParams(
                 RequestOutputKind.DELTA):
             raise ValueError("best_of must equal n to use output_kind=DELTA")
 
-    def _verify_beam_search(self) -> None:
-        if self.best_of == 1:
-            raise ValueError("best_of must be greater than 1 when using beam "
-                             f"search. Got {self.best_of}.")
-        if self.temperature > _SAMPLING_EPS:
-            raise ValueError("temperature must be 0 when using beam search.")
-        if self.top_p < 1.0 - _SAMPLING_EPS:
-            raise ValueError("top_p must be 1 when using beam search.")
-        if self.top_k != -1:
-            raise ValueError("top_k must be -1 when using beam search.")
-        if self.early_stopping not in [True, False, "never"]:
-            raise ValueError(
-                f"early_stopping must be True, False, or 'never', "
-                f"got {self.early_stopping}.")
-
-    def _verify_non_beam_search(self) -> None:
-        if self.early_stopping is not False:
-            raise ValueError("early_stopping is not effective and must be "
-                             "False when not using beam search.")
-        if (self.length_penalty < 1.0 - _SAMPLING_EPS
-                or self.length_penalty > 1.0 + _SAMPLING_EPS):
-            raise ValueError(
-                "length_penalty is not effective and must be the "
-                "default value of 1.0 when not using beam search.")
-
     def _verify_greedy_sampling(self) -> None:
         assert isinstance(self.best_of, int)
         if self.best_of > 1:
@@ -476,8 +423,6 @@ class SamplingParams(
 
     @cached_property
     def sampling_type(self) -> SamplingType:
-        if self.use_beam_search:
-            return SamplingType.BEAM
         if self.temperature < _SAMPLING_EPS:
             return SamplingType.GREEDY
         if self.seed is not None:
@@ -514,9 +459,6 @@ class SamplingParams(
             f"top_k={self.top_k}, "
             f"min_p={self.min_p}, "
             f"seed={self.seed}, "
-            f"use_beam_search={self.use_beam_search}, "
-            f"length_penalty={self.length_penalty}, "
-            f"early_stopping={self.early_stopping}, "
             f"stop={self.stop}, "
             f"stop_token_ids={self.stop_token_ids}, "
             f"include_stop_str_in_output={self.include_stop_str_in_output}, "
@@ -542,3 +484,4 @@ class BeamSearchParams(
     max_tokens: int
     ignore_eos: bool = False
     temperature: float = 0.0
+    length_penalty: float = 1.0

vllm/sequence.py

@@ -577,25 +577,6 @@ class Sequence:
     def get_cumulative_logprob(self) -> float:
         return self.data.cumulative_logprob
 
-    def get_beam_search_score(self,
-                              length_penalty: float = 1.0,
-                              seq_len: Optional[int] = None,
-                              eos_token_id: Optional[int] = None) -> float:
-        """Calculate the beam search score with length penalty.
-
-        Adapted from
-
-        https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938
-        """
-        if seq_len is None:
-            seq_len = self.get_len()
-            # NOTE: HF implementation does not count the EOS token
-            # towards the length, we align with that here for testing.
-            if (eos_token_id is not None
-                    and self.get_last_token_id() == eos_token_id):
-                seq_len -= 1
-        return self.get_cumulative_logprob() / (seq_len**length_penalty)
-
     def is_finished(self) -> bool:
         return SequenceStatus.is_finished(self.status)
 
@@ -809,13 +790,6 @@ class SequenceGroup:
     def get_max_num_running_seqs(self) -> int:
         """The maximum number of sequences running in parallel in the remaining
         lifetime of the request."""
-        if self.sampling_params and self.sampling_params.use_beam_search:
-            # For beam search, maximally there will always be `best_of` beam
-            # candidates running in the future.
-            best_of = self.sampling_params.best_of
-            assert isinstance(best_of, int)
-            return best_of
-        else:
         if self.sampling_params:
             best_of = self.sampling_params.best_of
             assert isinstance(best_of, int)

vllm/utils.py

@@ -1361,3 +1361,22 @@ class AtomicCounter:
     @property
     def value(self):
         return self._value
+
+
+def get_beam_search_score(
+    tokens: List[int],
+    cumulative_logprob: float,
+    eos_token_id: int,
+    length_penalty: float = 1.0,
+) -> float:
+    """Calculate the beam search score with length penalty.
+
+    Adapted from
+
+    https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938
+    """
+    seq_len = len(tokens)
+    if tokens[-1] == eos_token_id:
+        seq_len -= 1
+
+    return cumulative_logprob / (seq_len**length_penalty)

vllm/worker/tpu_model_runner.py

@@ -453,9 +453,6 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
                     f"Best of > {_MAX_NUM_SAMPLES} is not supported by the TPU "
                     "backend.")
             best_of.append(sampling_params.best_of)
-            if sampling_params.use_beam_search:
-                raise NotImplementedError(
-                    "Beam search is not supported by the TPU backend.")
             if sampling_params.logprobs is not None:
                 raise NotImplementedError(
                     "logprobs is not currently supported by the TPU backend.")