[Frontend] Optimize beam search loop by sorting and then splicing (#19347)

Signed-off-by: zhangguozhu <zhangguozhu@360.cn>
Signed-off-by: mgoin <mgoin64@gmail.com>
Co-authored-by: zhangguozhu <zhangguozhu@360.cn>
Co-authored-by: mgoin <mgoin64@gmail.com>

vllm/entrypoints/openai/serving_engine.py

@@ -10,6 +10,7 @@ from concurrent.futures import ThreadPoolExecutor
 from http import HTTPStatus
 from typing import Any, ClassVar, Generic, TypeAlias, TypeVar
 
+import numpy as np
 import torch
 from fastapi import Request
 from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
@@ -389,8 +390,9 @@ class OpenAIServing:
 
         sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty)
 
+        logprobs_num = 2 * beam_width
         beam_search_params = SamplingParams(
-            logprobs=2 * beam_width,
+            logprobs=logprobs_num,
             max_tokens=1,
             temperature=temperature,
         )
@@ -443,40 +445,75 @@ class OpenAIServing:
             output = [x[0] for x in await asyncio.gather(*tasks)]
 
             new_beams = []
-            for i, current_beam in enumerate(all_beams):
-                result = output[i]
-
+            # Store all new tokens generated by beam
+            all_beams_token_id = []
+            # Store the cumulative probability of all tokens
+            # generated by beam search
+            all_beams_logprob = []
+            # Iterate through all beam inference results
+            for i, result in enumerate(output):
+                current_beam = all_beams[i]
                 if result.outputs[0].logprobs is not None:
                     logprobs = result.outputs[0].logprobs[0]
-                    for token_id, logprob_obj in logprobs.items():
-                        if token_id == eos_token_id and not ignore_eos:
-                            completed.append(
-                                BeamSearchSequence(
-                                    tokens=current_beam.tokens + [token_id]
-                                    if include_stop_str_in_output
-                                    else current_beam.tokens,
-                                    logprobs=current_beam.logprobs + [logprobs],
-                                    cum_logprob=current_beam.cum_logprob
-                                    + logprob_obj.logprob,
-                                    finish_reason="stop",
-                                    stop_reason=eos_token_id,
-                                )
-                            )
-                        else:
-                            new_beams.append(
-                                BeamSearchSequence(
-                                    tokens=current_beam.tokens + [token_id],
-                                    logprobs=current_beam.logprobs + [logprobs],
-                                    lora_request=current_beam.lora_request,
-                                    cum_logprob=current_beam.cum_logprob
-                                    + logprob_obj.logprob,
-                                    multi_modal_data=current_beam.multi_modal_data,
-                                    mm_processor_kwargs=current_beam.mm_processor_kwargs,
-                                )
-                            )
-
-            sorted_beams = sorted(new_beams, key=sort_beams_key, reverse=True)
-            all_beams = sorted_beams[:beam_width]
+                    all_beams_token_id.extend(list(logprobs.keys()))
+                    all_beams_logprob.extend(
+                        [
+                            current_beam.cum_logprob + obj.logprob
+                            for obj in logprobs.values()
+                        ]
+                    )
+
+            # Handle the token for the end of sentence (EOS)
+            all_beams_token_id = np.array(all_beams_token_id)
+            all_beams_logprob = np.array(all_beams_logprob)
+
+            if not ignore_eos:
+                # Get the index position of eos token in all generated results
+                eos_idx = np.where(all_beams_token_id == eos_token_id)[0]
+                for idx in eos_idx:
+                    current_beam = all_beams[idx // logprobs_num]
+                    result = output[idx // logprobs_num]
+                    assert result.outputs[0].logprobs is not None
+                    logprobs_entry = result.outputs[0].logprobs[0]
+                    completed.append(
+                        BeamSearchSequence(
+                            tokens=current_beam.tokens + [eos_token_id]
+                            if include_stop_str_in_output
+                            else current_beam.tokens,
+                            logprobs=current_beam.logprobs + [logprobs_entry],
+                            cum_logprob=float(all_beams_logprob[idx]),
+                            finish_reason="stop",
+                            stop_reason=eos_token_id,
+                        )
+                    )
+                # After processing, set the log probability of the eos condition
+                # to negative infinity.
+                all_beams_logprob[eos_idx] = -np.inf
+
+            # Processing non-EOS tokens
+            # Get indices of the top beam_width probabilities
+            topn_idx = np.argpartition(np.negative(all_beams_logprob), beam_width)[
+                :beam_width
+            ]
+
+            for idx in topn_idx:
+                current_beam = all_beams[idx // logprobs_num]
+                result = output[idx // logprobs_num]
+                token_id = int(all_beams_token_id[idx])
+                assert result.outputs[0].logprobs is not None
+                logprobs_entry = result.outputs[0].logprobs[0]
+                new_beams.append(
+                    BeamSearchSequence(
+                        tokens=current_beam.tokens + [token_id],
+                        logprobs=current_beam.logprobs + [logprobs_entry],
+                        lora_request=current_beam.lora_request,
+                        cum_logprob=float(all_beams_logprob[idx]),
+                        multi_modal_data=current_beam.multi_modal_data,
+                        mm_processor_kwargs=current_beam.mm_processor_kwargs,
+                    )
+                )
+
+            all_beams = new_beams
 
         completed.extend(all_beams)
         sorted_completed = sorted(completed, key=sort_beams_key, reverse=True)