[aaroncheng00]

Overview

Implements beam search in the Rust frontend for /v1/completions and /v1/chat/completions (use_beam_search, beam width n, length_penalty), mirroring Python's online beam search. Part of the Rust frontend parity roadmap #44280 ("Beam search via the existing use_beam_search request parameter, including length_penalty").

• Scoring follows Python after [Bugfix][Frontend] Normalize beam search scores by generated length #62928 (normalize by generated length).
• Related: [Rust Frontend] Support OpenAI n choices #58914 (general OpenAI n choices). This PR does not depend on it; beam output assembles its own choices, so it can be rebased onto [Rust Frontend] Support OpenAI n choices #58914's merging later.
• Prior work: [Rust Frontend] Beam search support for completions and chat completions #47705 by @yangyang-cs95 implements the same Python algorithm. It predates [Bugfix][Frontend] Normalize beam search scores by generated length #62928 (score by generated length) and [Bugfix][Frontend] Stop beam search on generation config EOS tokens #62072 (generation-config EOS), and builds requests outside the text pipeline. This PR is written on current main; I'm happy to close it if @yangyang-cs95 prefers to continue there.

Claims

• use_beam_search=true with n ≥ 1 and length_penalty now works on both endpoints, replacing the previous use_beam_search is not supported. / length_penalty is not supported. 400s.
• Non-streaming output matches the Python frontend (beam texts, order, finish_reason, stop_reason, usage) on Qwen2.5-0.5B-Instruct.
• Requests that Python accepts but silently ignores under beam search are rejected with a 400 that names the parameter (see Details).
• Dropping the client connection aborts in-flight step requests.

Validation

Model comparison, Python vs Rust frontend. Qwen/Qwen2.5-0.5B-Instruct, float32, CPU (Apple M2, 16 GB), n=3, temperature=0, max_tokens=24. Each frontend served the same model in turn; responses were reduced to index, text/content, finish_reason, stop_reason and usage, then diffed.

Endpoint	length_penalty	Result
/v1/completions	1.0	identical
/v1/completions	0.5	identical
/v1/chat/completions	1.0	identical
/v1/chat/completions	0.5	identical (top two beams swap vs 1.0, same in both frontends)

The only diff was usage.prompt_tokens_details: null from Rust, which #65374 has since removed on main.

Reproduction

VLLM_CPU_KVCACHE_SPACE=2 vllm serve Qwen/Qwen2.5-0.5B-Instruct --dtype float32 --max-model-len 2048 --port 8000
# then, with VLLM_USE_RUST_FRONTEND=1 for the Rust run

M=Qwen/Qwen2.5-0.5B-Instruct
for lp in 1.0 0.5; do
  curl -s localhost:8000/v1/completions -H 'Content-Type: application/json' -d "{
    \"model\":\"$M\",\"prompt\":\"The capital of France is\",\"max_tokens\":24,
    \"n\":3,\"use_beam_search\":true,\"temperature\":0,\"length_penalty\":$lp}" \
  | jq -c '[.choices[] | {index,text,finish_reason,stop_reason}], .usage'
  curl -s localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d "{
    \"model\":\"$M\",\"messages\":[{\"role\":\"user\",\"content\":\"Name three fruits.\"}],
    \"max_tokens\":24,\"n\":3,\"use_beam_search\":true,\"temperature\":0,\"length_penalty\":$lp}" \
  | jq -c '[.choices[] | {index,content:.message.content,finish_reason,stop_reason}], .usage'
done

Edge cases, Python vs Rust (same setup, full responses diffed):

Case	Result
n=1; max_tokens=1; ignore_eos; echo; temperature=0.7; length_penalty 0, −1, 2	identical
chat, include_stop_str_in_output; skip_special_tokens=false	identical (Rust's annotations: null in this run was removed on main by #65374)
completions logprobs=2; chat top_logprobs=2	same tokens and logprobs; top-candidate sets and order differ (see Logprobs in Details)
n=11 (logprobs=22 > default --max-logprobs 20)	400 in both; message wording differs, as for any over-limit logprobs on main
n=0	400 beam_width must be at least 1, got 0. in both
stop, top_p	Python ignores them (200); Rust rejects them (400, intended)
chat stream	Python streams all beams after the search; Rust rejects it (400, intended)

Rejections on the live Rust server:

Request	Response
completions, beam search + stream	400 Streaming is not currently supported with beam search (Python's message)
completions, beam search + top_p: 0.5	400 `top_p` is not supported with beam search. (param: top_p)
completions, n: 2 without beam search	400 Only n=1 is supported without use_beam_search.
chat, beam search + stream	400 Streaming is not supported with beam search. (param: stream)

Tests:

cd rust
cargo test -p vllm-text -p vllm-chat -p vllm-server   # 1260/1261 passed; the failure, `audio_pipeline_attributes_stage_timings_to_request`, is timing-flaky without this PR too
cargo test -p vllm-text beam_search                    # 9 unit tests
cargo test -p vllm-server beam                         # 8 route tests; disconnect test also run 60x, 60/60
cargo clippy --workspace --all-targets --all-features --locked -- -D warnings   # pinned 1.95 toolchain, as in CI: clean
cargo fmt --all --check                                                         # clean

The pre-commit hooks passed on each commit, except actionlint, which was skipped locally because it needs Go >= 1.25.

• Unit tests (vllm-text, beam_search.rs) cover:

  • length-penalty scoring and the final ranking
  • a worse first step that wins later
  • EOS completion, include_stop_str_in_output and ignore_eos
  • the chosen token leading its logprobs position
  • tie order
  • the unsupported-parameter list (snapshot)

• End-to-end tests (test_beam_search_completion in completion/test_completion.py, test_beam_search_chat_completion in chat_completion/test_chat_completion.py) check n distinct beams by token IDs, choice indices, and usage counting the prompt once. Both the Python entrypoints CI and the Rust frontend GPU CI (rust_frontend.yaml) already select these files, so they run on GPU with both frontends. Locally, both pass with both frontends on CPU (Apple M2), using each file's CI model and server flags:

  export VLLM_CPU_KVCACHE_SPACE=2
  for fe in 0 1; do
    VLLM_USE_RUST_FRONTEND=$fe python -m pytest -v tests/entrypoints/openai/completion/test_completion.py -k test_beam_search_completion            # passed
    VLLM_USE_RUST_FRONTEND=$fe python -m pytest -v tests/entrypoints/openai/chat_completion/test_chat_completion.py -k test_beam_search_chat_completion  # passed
  done

• Route tests (routes/tests.rs, mock engine) cover:

  • beam order and usage on completions
  • one message per beam on chat
  • a step error failing the request
  • aborting in-flight steps when the client disconnects
  • every rejection message (snapshot)
  • tools with tool_choice auto/none vs required, and a strict tool under auto
  • an EOS-only beam returning empty logprobs
  • beam fields at their default values being accepted without beam search

Details

Design. Same algorithm as vllm/entrypoints/generate/beam_search/online.py:

• Each step sends one engine request per live beam, with max_tokens=1 and logprobs=2·n.
• EOS candidates move to the completed set with stop_reason=<eos id>.
• Live candidates are sorted by cum_logprob / max(len,1)**length_penalty and the top n kept. len counts generated tokens, including EOS ([Bugfix][Frontend] Normalize beam search scores by generated length #62928).
• At the end, completed and live beams are sorted by score and the top n returned.

Ownership by crate:

• vllm-text: TextLlm::beam_search runs the steps and builds one synthetic terminal output per beam. That output goes through the existing decoded_text_event_stream, so detokenization, stop handling and logprobs reuse the normal path.
• vllm-chat: ChatLlm::beam_search pairs each beam with its own chat output processor, so reasoning and tool parsing run per choice. Commit 1 is a mechanical refactor that lets prepare create several processors.
• vllm-server: validation, and assembling the multi-choice response with merged usage.

Deviations from Python (per rust/AGENTS.md, unimplemented behavior is rejected instead of silently ignored):

• Ignored sampling params are rejected. Under beam search, Python silently drops top_p, top_k, min_p, seed, min_tokens, the penalties, stop, stop_token_ids, logit_bias, bad_words, structured_outputs, prompt logprobs, kv_transfer_params, ec_transfer_params and similar fields. Rust returns 400 for any non-default value. This is the main behavior change to review.
• Chat streaming is rejected (param: stream). Python finishes the search and then streams every beam at once as SSE chunks; no tokens stream during the search. The Rust chat SSE path currently writes a single choice (index: 0), so this needs multi-choice streaming. A possible follow-up builds on [Rust Frontend] Support OpenAI n choices #58914's n > 1 streaming: ChatLlm::beam_search already returns one finished ChatEventStream per beam, which can feed that writer, and then the 400 can be removed. Completions streaming is rejected in both frontends.
• allowed_token_ids is rejected (TODO). Python scores allowlisted beams with exact-ID logprobs, which needs Model Runner V2 support.
• Tool calling that implies an output grammar is rejected: tool_choice required or a named function, strict tools or --tool-strict-level, or a tool parser that always constrains output. Python's online beam search ignores the grammar. Its offline LLM.beam_search does enforce it per beam, by running a structured output backend in the frontend process and replaying each beam's tokens. The engine only advances a grammar over tokens it generates itself, so Rust would need its own grammar matcher or an engine protocol change. auto and none work unless strict tools or the model's tool parser impose a grammar.
• Completions with max_tokens=0 is rejected (TODO). Python returns the bare prompt as a single choice.
• Logprobs. Each position reports the chosen token, then the top k candidates, deduplicated, as Rust's non-beam responses do. Python's beam search reports the first entries of the step's 2·n candidate dict instead. So Python can return k+1 alternatives, can leave out the chosen token, and lists the candidates in rank order rather than chosen-first. The token and logprob of each position are identical.
• Ties are broken deterministically: candidates keep engine order and the sort is stable.
• Child request IDs follow Python (<parent>-<uuid>-beam-<i>), so /abort_requests with the parent ID does not reach in-flight steps. Client disconnect does abort them.
• metrics on the response is null, because there's no single engine request to report.

Shared limit: n > 10 needs --max-logprobs ≥ 2·n, because each step asks for 2·n logprobs. Both frontends reject it with the default of 20.

Not validated locally:

• GPU: I only had CPU. The new end-to-end tests cover it in CI.
• Chat with a reasoning or tool parser on a real model: covered by route tests.
• Multimodal prompts: features are forwarded to every step request, but this wasn't run on a multimodal model.

rust_frontend.yaml also updates a comment that said the Rust frontend has no beam search. test_batch_completions stays excluded, because Rust still rejects batched prompts.

Docs: the rendered docs have no Rust frontend parameter page, and recent [Rust Frontend] parity PRs haven't touched docs/. I'm happy to add a short note under "Extra Parameters" in docs/serving/online_serving/openai_compatible_server.md if reviewers want one.

Size: about 800 implementation lines and 870 test lines across 20 files. Most of the implementation is in rust/src/text/src/beam_search.rs.

Updates after review:

• An EOS-only beam with logprobs no longer returns 500 on chat; it returns empty logprobs, like Python.
• Grammar rejections from strict tools or --tool-strict-level now name those causes instead of tool_choice.
• kv_transfer_params/ec_transfer_params are rejected under their own names instead of vllm_xargs.

---

Pull Request Checklist

• I used vLLM's /pr-checklist skill. (Mandatory for agents, optional for humans).

• AI assistance was used during the creation of this PR.

• Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.

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• Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.

AI assistance (Claude Code) was used to write this change. I reviewed every changed line and ran the tests and model comparison above.

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[liulanze]

An EOS-only beam causes a 500 with logprobs=true: the chat collector converts empty logprobs to None. Please keep empty logprobs valid.

[aaroncheng00]

Thanks, good catch. Fixed in 3566d2e. Updated chat collector so empty logprobs are preserved instead of mapped to None for logprobs=true and an EOS-only beam. Added beam_search_eos_only_beam_has_empty_logprobs test which verifies EOS-only beam for both chat and completion endpoint. Confirmed the test returned 500 on chat before the fix.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @aaroncheng00.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[aaroncheng00]

@BugenZhao do you mind taking a pass on this PR when you get the chance?

Regarding testing, test_vision.py already has beam-search tests (-k beamsearch, Phi-3.5-vision) that would cover multimodal + logprobs on GPU, but the Rust CI job doesn't run that file. Happy to add it to rust_frontend.yaml in this PR if you'd like. I couldn't run that model locally.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @aaroncheng00.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork