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[Frontend] Integer token IDs for generate output logprobs (GenerateLogProbs) - #58181
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Documentation preview: https://vllm--58181.org.readthedocs.build/en/58181/ |
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LGTM — the conversion is correct and the U+FFFD context handling matches the engine's contract. One sequencing heads-up: this rewrites the same |
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Thanks for the review! Agreed on the order: #55029 first. I've already prepared the follow-up locally. It is #55029 merged onto this branch: the streaming paths keep your per-chunk resolution and carried context ( |
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@yinli-systems decode_token_id still has a bug which I raised in #59241. It goes through convert_ids_to_tokens → convert_tokens_to_string which drops the SentencePiece leading space the engine has restored since #48674. So ▁true comes back as 'true' instead of ' true' and top_logprobs keys collide on /v1/completions/derender.
Since you're already touching this function, could you switch it to convert_ids_list_to_tokens(tokenizer, [token_id])[0]? It's just one line.
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@hickeyma Done in 5c83af2a0: |
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Beyond the inline NaN-logprob finding, I also checked whether the now-strict GenerateLogProbs/GenerateTokensChoice schema (protocol.py:404) would break /inference/v1/generate parsing during a mixed-version disaggregated rollout (old generate server emitting the old ChatCompletionLogProbs shape against a new derender, or vice versa) — that is exactly the breaking change the PR's release note already documents, not a new defect introduced here, so I'm not flagging it separately.
Extended reasoning...
Verified the confirmed NaN-logprob finding independently: _create_tokens_logprobs (serving.py) computes max(step_token.logprob, -9999.0) with the possibly-NaN value as the first argument, and Python's max() keeps the first operand when a comparison against NaN is involved, so the sentinel never applies and a literal NaN reaches the JSON response; the PR's own new test only asserts rank is None, not the logprob value, so this slipped through. Checked git history: the two findings from my prior review (Rust O(n^2) dedup, rank=0 on NaN) were addressed in commit 871a7cd, whose message claims the logprob was "already clamped" — that belief is what left this new bug unaddressed. The mixed-version 422 candidate was examined and ruled out as the documented breaking-change behavior itself, not a separate bug.
max(nan, -9999.0) is nan in Python, so the clamps in _create_tokens_logprobs and the text-mode builder let a NaN logprob through: JSONResponse rejected it (500, non-streaming) and streaming wrote "logprob": null, which derender then refused as logprob is required. The Rust frontend already gets -9999.0 from f32::max. Add _clamp_logprob (NaN -> -9999.0, else max(x, -9999.0)) and use it at every call site, tokens and text mode. Tests assert -9999.0 for the content and top_logprobs entries and that the result serializes as strict JSON. Signed-off-by: Yin Li <kevinli.ai.work@gmail.com>
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Move the flat per-token sampled logprobs from a separate choice field (token_logprobs) into GenerateLogProbs.sampled, the shape agreed in vllm-project#57574 now that GenerateLogProbs is on main (vllm-project#58181). In the sampled-only mode (return_token_logprobs with logprobs=0) content is None and no content entries are built; with logprobs > 0 content is returned alongside. return_token_logprobs is tokens-mode only (400 otherwise), since text mode returns ChatCompletionLogProbs; logprobs.sampled is omitted from responses that did not ask for it. Docs describe sampled and the content: null state. Signed-off-by: Yin Li <kevinli.ai.work@gmail.com>
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Non-streaming output_mode="tokens" /inference/v1/generate requests with top-k sample logprobs now keep them as engine rows (ArrayLogprobs, via the private SamplingParams._array_logprobs switch) and render the GenerateLogProbs JSON straight from the rows: the same bytes as the per-entry pydantic path (_create_tokens_logprobs + model_dump + JSONResponse), without a Python object per (position, slot). Large bodies are built off the event loop: from 2^15 entries in a worker thread, from 2^20 entries in a pool of two daemon threads. Streaming, output_mode="text", logprobs=-1 and requests without sample logprobs are unchanged. Rows that do not map directly to the per-entry shape (irregular widths, repeated top-k ids, a sampled id not in slot 0, values JSON cannot represent) fall back to the per-entry path for that choice; a storage failure fails the request with a 500. Ported from aoshen02#111 (round 24) to the GenerateLogProbs shape of vllm-project#58181 ({token_id, logprob, rank, top_logprobs: [{token_id, logprob, rank}]}, rank 0 sent as null) and to output_mode (vllm-project#58588). The VLLM_GENERATE_ARRAY_LOGPROBS switch is dropped. Co-authored-by: aoshen02 <aoshen02@users.noreply.github.com> Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: hao-aaron <aaronhao@inferact.ai>
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Non-streaming output_mode="tokens" /inference/v1/generate requests with top-k sample logprobs now keep them as engine rows (ArrayLogprobs, via the private SamplingParams._array_logprobs switch) and render the GenerateLogProbs JSON straight from the rows: the same bytes as the per-entry pydantic path (_create_tokens_logprobs + model_dump + JSONResponse), without a Python object per (position, slot). Large bodies are built off the event loop: from 2^15 entries in a worker thread, from 2^20 entries in a pool of two daemon threads. Streaming, output_mode="text", logprobs=-1 and requests without sample logprobs are unchanged. Rows that do not map directly to the per-entry shape (irregular widths, repeated top-k ids, a sampled id not in slot 0, values JSON cannot represent) fall back to the per-entry path for that choice; a storage failure fails the request with a 500. Ported from aoshen02#111 (round 24) to the GenerateLogProbs shape of vllm-project#58181 ({token_id, logprob, rank, top_logprobs: [{token_id, logprob, rank}]}, rank 0 sent as null) and to output_mode (vllm-project#58588). The VLLM_GENERATE_ARRAY_LOGPROBS switch is dropped. Co-authored-by: aoshen02 <aoshen02@users.noreply.github.com> Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: hao-aaron <aaronhao@inferact.ai>
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Move the flat per-token sampled logprobs from a separate choice field (token_logprobs) into GenerateLogProbs.sampled, the shape agreed in vllm-project#57574 now that GenerateLogProbs is on main (vllm-project#58181). In the sampled-only mode (return_token_logprobs with logprobs=0) content is None and no content entries are built; with logprobs > 0 content is returned alongside. return_token_logprobs is tokens-mode only (400 otherwise), since text mode returns ChatCompletionLogProbs; logprobs.sampled is omitted from responses that did not ask for it. Docs describe sampled and the content: null state. Signed-off-by: Yin Li <kevinli.ai.work@gmail.com>
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…gProbs) (vllm-project#58181) Signed-off-by: Yin Li <kevinli.ai.work@gmail.com> Co-authored-by: Yin Li <kevinli.ai.work@gmail.com> Co-authored-by: Claude <noreply@anthropic.com>
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Purpose
Fixes #57574
Fixes #59241
Implements the
GenerateLogProbsshape agreed in #57574./inference/v1/generateis a vLLM-defined token-in/token-out API, but its output logprobs used the OpenAI shape: the generate server has no tokenizer, so it wrote every token as a"token_id:N"placeholder string that derender parsed back out, and the two frontends disagreed onbytes(Python left it unset, the Rust frontend set it to the UTF-8 bytes of the placeholder itself).Scope is exactly the split @hickeyma described in #57574 (comment): this PR is
contentonly. #57442 stacksGenerateLogProbs.sampledon top afterwards.Shape
top_logprobsis a list, not a dict: JSON turns dict keys into strings and the order would be implicit. It keeps the engine's order: the sampled token first, then the remaining candidates in rank order. With non-greedy sampling the sampled token can be outside the top k (e.g. ranks[5, 1, 2]); it then takes one of the k slots and rank k is left out, as on the OpenAI endpoints and the main engine path.rankis kept: the engine already computes it and the Rust frontend's gRPCCandidateTokenInfocarries it. It was being dropped on the floor before.bytes, no stringtoken.logprobis required, as in [Feature]: Integer token IDs for logprobs in/inference/v1/generateresponses (GenerateLogProbs) #57574 and the Rust frontend. When the sampled token is absent from the engine's top-k map the server passes-9999.0explicitly, the value the OpenAI shapes already put on the wire for that case, so it is unchanged.What changed
scale_out/token_in_token_out):_create_tokens_logprobsbuilds the new type;GenerateResponseChoice.logprobsandGenerateResponseStreamChoice.logprobsareGenerateLogProbs | None, streaming and non-streaming.routes/inference/generate): same field swap,position_to_generate_logprobs_contentemits ids + rank instead offormat_token_id._resolve_logprobsconvertsGenerateLogProbs→ChatCompletionLogProbs, fillingtoken/bytesfrom the tokenizer, and_convert_chat_logprobs_to_completion_logprobsthen producesCompletionLogProbsas before. The U+FFFD byte-fallback correction (_correct_decoded_token) is untouched — it needs the preceding sampled token ids as context, and the integer shape now carries those directly instead of reconstructing them from placeholder strings._parse_token_id_placeholderandresolve_token_id_placeholderare gone; the id-taking core is nowdecode_token_idinentrypoints/generate/base/serving.py.prompt_logprobs(already integer-keyed) andreturn_tokens_as_token_idson/v1/chat/completions//v1/completions, which is a user-facing OpenAI option that usestoken_id:Non purpose.Release note
Documented in
docs/serving/online_serving/renderer.md(new "Generate Output Logprobs" section, with the breaking-change note) andderenderer.md.Test Plan
tests/entrypoints/scale_out/derender/test_generate_logprobs_conversion.py(new): unit tests over_resolve_logprobswith a stub tokenizer — decoding + bytes + rank-order preservation, the U+FFFD byte-fallback path where the second half of a two-byte character is only decodable with the preceding sampled id as context, andcontent=None.tests/entrypoints/scale_out/token_in_token_out/test_tokens_logprobs.py: sampled entry carriestoken_id/rank, per-candidate ids,logprobs=0still emits the sampled token, sampled token missing from the top-k map keeps the-9999.0sentinel.tests/entrypoints/scale_out/derender/test_derender.py,test_derender_parity.py: derender chat/completion logprob tests and the coupled-vs-derender parity test now feed the generate-shaped payload; parity assertions ontoken/bytesare unchanged.tests/entrypoints/scale_out/token_in_token_out/test_generate_stream.py,test_serving_tokens.py: streaming chunks and the sampling-mask test readtoken_id.routes::tests::non_stream_raw_generate_returns_token_output_envelopeandstream_raw_generate_returns_sse_chunks_and_usageassert integertoken_id+rankand a nulltoken/byteson the wire.Test Result
Rust, locally:
The two updated assertions run through the mock engine and confirm the wire shape end to end (
"token_id": 33, "rank": 1,token/bytesabsent), streaming and non-streaming.Python, locally (CPU-only box, no GPU):
ruff check/ruff format --checkclean on every touched file.Caveat on the local Python run: the box has no GPU, so the interpreter is a vLLM 0.29.0 install with this branch's source on
PYTHONPATH. That is enough for the pure-Python paths above, but the server-backed entrypoints tests do not run in it.tests/entrypoints/scale_out/token_in_token_out/test_generate_stream.pyfails there — identically (29 failed, 1 passed) on unmodifiedmainin the same setup, so those failures are the environment, not this change. CI is still the first real execution of the server-backed tests; happy to fix up promptly if anything falls over.Essential Elements