[Misc] Remove dangling references to SamplingType.BEAM (#13402)

vllm/model_executor/layers/sampler.py

@@ -68,7 +68,6 @@ class SampleResultArgsType:
     sample_results_dict: SampleResultsDictType
     sampling_metadata: SamplingMetadata
     greedy_samples: Optional[torch.Tensor]
-    beam_search_logprobs: Optional[torch.Tensor]
 
 
 # Union of non-deferred (single-step scheduling)
@@ -510,74 +509,6 @@ def _random_sample(
     return results
 
 
-def _beam_search_sample(
-    selected_seq_groups: List[SequenceGroupToSample],
-    logprobs: torch.Tensor,
-) -> SampleResultType:
-    """Run beam sampling on a given samples.
-
-    Args:
-        selected_seq_groups: A list of sequence groups batched.
-        logprobs: (num_selected_samples, vocab_size,) A tensor of logprob
-        on selected sample indices.
-    Returns:
-        Tuple of (next_token_ids, parent_ids). The length of returned list is
-        same as the length of selected_seq_groups. If the corresponding
-        seq_group has do_sample=False, tuple contains ([], [])
-    """
-    # We sample 2 * beam_width candidates to make sure that with high
-    # probability we can get `beam_width` candidates in addition to
-    # the finished sequences for the next iteration. See
-    # https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
-    # for details. See also HF reference:
-    # https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
-    #
-    # NOTE: Beam search is not vectorized, so its speed can be slower than
-    # other sampling methods.
-    sample_idx = 0
-    results: SampleResultType = []
-    for seq_group in selected_seq_groups:
-        if not seq_group.do_sample:
-            results.append(([], []))
-            continue
-
-        is_prompt = seq_group.is_prompt
-        seq_ids, sampling_params = seq_group.seq_ids, seq_group.sampling_params
-        num_parent_seqs = len(seq_ids)
-        beam_width = sampling_params.n
-        seq_group_logprobs = logprobs[sample_idx:sample_idx + num_parent_seqs]
-        if is_prompt:
-            # Prompt phase.
-            assert num_parent_seqs == 1, (
-                "Prompt input should have only one seq.")
-            parent_ids = [0] * (2 * beam_width)
-            _, next_token_ids = torch.topk(seq_group_logprobs[0],
-                                           2 * beam_width)
-            next_token_ids = next_token_ids.tolist()
-        else:
-            # Generation phase.
-            cumulative_logprobs: List[float] = [
-                seq_group.seq_data[seq_id].cumulative_logprob
-                for seq_id in seq_ids
-            ]
-            cumulative_logprobs_tensor = torch.tensor(
-                cumulative_logprobs,
-                dtype=torch.float,
-                device=seq_group_logprobs.device)
-            seq_group_logprobs = (seq_group_logprobs +
-                                  cumulative_logprobs_tensor.unsqueeze(dim=1))
-            _, topk_ids = torch.topk(seq_group_logprobs.flatten(),
-                                     2 * beam_width)
-            topk_ids = topk_ids.tolist()
-            vocab_size = seq_group_logprobs.size(-1)
-            parent_ids = [i // vocab_size for i in topk_ids]
-            next_token_ids = [i % vocab_size for i in topk_ids]
-        results.append((next_token_ids, parent_ids))
-        sample_idx += num_parent_seqs
-    assert sample_idx == logprobs.size(0)
-    return results
-
-
 # torch.multinomial forces a GPU<->CPU sync.
 # Therefore, we use an optimized implementation instead.
 # Note that we always sample with replacement.
@@ -666,14 +597,12 @@ def get_pythonized_sample_results(
         sampling_metadata,
         greedy_samples,
         multinomial_samples,
-        beam_search_logprobs,
         sample_results_dict,
     ) = (
         sample_result_args.sample_metadata,
         sample_result_args.sampling_metadata,
         sample_result_args.greedy_samples,
         sample_result_args.multinomial_samples,
-        sample_result_args.beam_search_logprobs,
         sample_result_args.sample_results_dict,
     )
 
@@ -686,9 +615,6 @@ def get_pythonized_sample_results(
         elif sampling_type in (SamplingType.RANDOM, SamplingType.RANDOM_SEED):
             sample_results = _random_sample(seq_groups,
                                             multinomial_samples[sampling_type])
-        elif sampling_type == SamplingType.BEAM:
-            sample_results = _beam_search_sample(seq_groups,
-                                                 beam_search_logprobs)
         sample_results_dict.update(zip(seq_group_id, sample_results))
 
     return [
@@ -731,7 +657,6 @@ def _sample_with_torch(
     sample_metadata: SampleMetadataType = {}
     multinomial_samples: MultinomialSamplesType = {}
     greedy_samples: Optional[torch.Tensor] = None
-    beam_search_logprobs: Optional[torch.Tensor] = None
 
     # Create output tensor for sampled token ids.
     if include_gpu_probs_tensor:
@@ -800,8 +725,6 @@ def _sample_with_torch(
                 sampled_token_ids_tensor[long_sample_indices] = \
                     multinomial_samples[sampling_type].to(torch.long)
 
-        elif sampling_type == SamplingType.BEAM:
-            beam_search_logprobs = logprobs[sample_indices]
         else:
             raise ValueError(f"Unsupported sampling type: {sampling_type}")
 
@@ -812,7 +735,6 @@ def _sample_with_torch(
         sample_metadata=sample_metadata,
         multinomial_samples=multinomial_samples,
         greedy_samples=greedy_samples,
-        beam_search_logprobs=beam_search_logprobs,
         sample_results_dict=sample_results_dict)
 
     if not sampling_metadata.skip_sampler_cpu_output: