[Frontend] optimize beam_search code

[zhanggzh]

Purpose

In the source code of vllm's beam_search, a two-fold loop is used that will splice the generated tokens and compute the cumulative probabilities This splicing operation takes essentially the same order of magnitude of time as each execution of generate.

Code:

Time-Consuming:

Optimize the above logic into the following code, using sorting and then splicing to reduce the time complexity.

Test Plan

Set request parameters:

payload = {
    "model": MODEL,
    "messages": [
      {
        "role": "user",
        "content": prompt
      }
    ],
    "use_beam_search": True,
    "n": 30,
    "max_tokens": 8
}

Test Result

[gemini-code-assist]

Hello @zhanggzh, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

Summary of Changes

Hello! Gemini here, providing a summary of this pull request to help everyone get up to speed quickly.

This PR, titled "[Frontend] optimize beam_search code", aims to improve the performance of the beam search implementation within vLLM. According to the author, the previous approach involved a two-fold loop for processing generated tokens and computing cumulative probabilities, which included splicing operations that were computationally expensive, sometimes taking as much time as the generation step itself. The goal is to optimize this logic using sorting and splicing with NumPy to reduce time complexity.

The core changes involve refactoring the token processing loop within the beam_search function in vllm/engine/protocol.py. Instead of iterating through each beam and its generated tokens individually to build a new list of potential beams, the updated code collects all potential next tokens and their cumulative log probabilities into NumPy arrays. It then handles EOS tokens separately by identifying them using NumPy indexing and adding completed beams. For non-EOS tokens, it uses np.argpartition to efficiently find the indices corresponding to the top beam_width cumulative probabilities across all candidates, and then constructs the next set of beams from these top candidates. This replaces the previous approach of appending to a list and then sorting the entire list.

Highlights

• Performance Optimization: The primary goal of this PR is to optimize the beam search algorithm's performance by improving the efficiency of processing generated tokens and calculating cumulative probabilities.
• Beam Search Logic Refactor: The logic for selecting the top beams in each step of the beam search has been refactored to use NumPy operations (np.array, np.where, np.argpartition) for collecting, filtering (EOS), and selecting the top candidates based on cumulative log probabilities.
• Reduced Time Complexity: The author states that the previous two-fold loop with splicing was time-consuming and that the new approach using sorting/partitioning is intended to reduce the time complexity of this step.

Changelog

• vllm/engine/protocol.py
  • Added import for the numpy library.
  • Introduced a variable logprobs_num to explicitly store the required number of log probabilities (2 * beam_width).
  • Replaced the nested loop structure for processing beam outputs with a new logic that collects all potential next tokens and their cumulative log probabilities into NumPy arrays.
  • Implemented separate handling for End-of-Sentence (EOS) tokens using np.where to identify and finalize completed beams.
  • Used np.argpartition to efficiently find the indices of the top beam_width non-EOS candidates based on cumulative log probabilities.
  • Constructed the next set of active beams (new_beams) by iterating through the indices of the top candidates identified by np.argpartition.

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[gemini-code-assist]

Code Review

The PR aims to optimize the beam search implementation, and the performance gains shown are promising. However, there are a couple of critical concerns regarding the correctness of the refactored logic for beam pruning and indexing that need to be addressed. Specifically, the handling of variable numbers of logprobs per beam and the change in criteria for pruning active beams (not using length penalty) could lead to different results compared to the original implementation.

Summary of Findings

• Correctness of Beam Indexing: The method for mapping flattened indices back to original beam indices (idx // logprobs_num) might be incorrect if beams contribute a variable number of logprobs or if some beams have no logprobs. This could lead to incorrect beam propagation.
• Beam Pruning Criterion Changed: The new beam pruning logic uses raw cumulative log probabilities, whereas the original implementation used a length-penalized score (sort_beams_key). This change in criterion can alter the set of active beams and potentially the final output of the beam search.
• Comment Style: Minor inconsistency in comment style (double hash ## vs single hash #).

Merge Readiness

The optimization approach is promising for performance, but the identified critical issues regarding the correctness of beam indexing and the change in beam pruning criteria need to be addressed before this PR can be merged. I am unable to approve this pull request in its current state. Please ensure these points are resolved, and further testing might be beneficial to confirm behavioral equivalence with the original logic under various conditions (especially different length_penalty values).

[gemini-code-assist]

The indexing idx // logprobs_num to retrieve current_beam and result assumes that each beam processed in the loop from line 151 consistently contributes logprobs_num items to all_beams_token_id and all_beams_logprob.

However, if result.outputs[0].logprobs is None (handled by the if on line 153) or if the logprobs dictionary contains fewer than logprobs_num items for some beams, the flattened arrays will not have a uniform structure. This would make idx // logprobs_num an incorrect way to map back to the original beam index for beams that appear after an irregularly sized or skipped beam.

Could you clarify if len(logprobs.keys()) is guaranteed to be logprobs_num for all beams that have logprobs? If not, this indexing could lead to incorrect beam assignments.

A more robust approach might involve storing the original beam index alongside each token and logprob when creating the flattened arrays, and then using that stored index. For example:

# When populating the lists:
all_beams_original_indices = []
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]
        num_candidates_for_this_beam = len(logprobs.keys())
        all_beams_token_id.extend(list(logprobs.keys()))
        all_beams_logprob.extend([...])
        all_beams_original_indices.extend([i] * num_candidates_for_this_beam)

# Then, when retrieving:
original_beam_idx = all_beams_original_indices[idx]
current_beam = all_beams[original_beam_idx]
result = output[original_beam_idx]

[gemini-code-assist]

The beam pruning logic has changed. The original code (lines 176-177 in the 'before' view) used sorted(new_beams, key=sort_beams_key, reverse=True) to select the top beam_width beams for the next iteration. The sort_beams_key incorporates a length_penalty.

The new logic (line 183) uses np.argpartition(-all_beams_logprob, beam_width) to select the top beams. However, all_beams_logprob (calculated on line 156) contains raw cumulative log probabilities (current_beam.cum_logprob + logprob_obj.logprob) and does not include the length penalty.

This means that active beams are now pruned based on a different criterion (raw log probability) than what sort_beams_key (length-penalized score) would use. This is a significant change in the beam search behavior and could lead to different outcomes, especially if length_penalty is not 1.0.

Was this change in pruning criteria intentional? If the goal is to maintain the same beam search behavior while optimizing, the np.argpartition should operate on scores calculated using the length_penalty, similar to how sort_beams_key works.

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