[Bugfix][Frontend] Count reasoning tokens for the Muse-Glimmer parser - #54091
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The muse_glimmer reasoning parser inherited the base count_reasoning_tokens() that returns 0, so usage.completion_tokens_details.reasoning_tokens was always 0. Classify the decoded output with the parser's own logic, including the turn re-open after tool calls, and count the reasoning text. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Meghana Madhyastha <meghana.madhyastha@parasail.io>
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I think that #54238 is better approach :) |
MuseGlimmerReasoningParser inherited the base count_reasoning_tokens() that returns 0, so usage.completion_tokens_details.reasoning_tokens was always 0 for --reasoning-parser muse_glimmer. The ATEM framing markers are not guaranteed to be single vocab tokens (e.g. " to=self" is two tokens), so tokens cannot be classified by id. Instead, decode each token once (cached per request), record its character offset, locate to=self bodies in the decoded text, and count the tokens starting inside those spans with bisect. This counts the original tokens exactly rather than re-encoding the reasoning text. The body-end rule (<|eom|> / <|eot|> / next channel header / EOF) is factored into _body_end() and shared with _classify_bodies(), so the streamed reasoning text and the reported count cut bodies identically. Related: vllm-project#54091 takes the decode -> classify -> re-encode approach for the same bug; this variant is exact at span boundaries and ~12x cheaper per call (247us vs 2984us at 4k reasoning tokens with the real tokenizer), which matters because chat streaming re-counts the full token list on every delta. Signed-off-by: Donghee Na <donghee.na92@gmail.com>
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The muse_glimmer reasoning parser inherited the base count_reasoning_tokens() that returns 0, so usage.completion_tokens_details.reasoning_tokens was always 0. Classify the decoded output with the parser's own logic, including the turn re-open after tool calls, and count the reasoning text.
Purpose
With
--reasoning-parser muse_glimmer, chat completions always reportreasoning_tokens: 0inusage.completion_tokens_details, because the parser inherits the basecount_reasoning_tokens()that returns 0. Clients that rely on the count (OpenRouter's reasoning checks, billing) see no reasoning even when the model produced a long reasoning body.This implements
count_reasoning_tokens()for the Muse-Glimmer parser: decode the generated ids, classify the bodies with the parser's existing logic, and re-encode the reasoning text to count it. Classification is done without anchoring to the current assistant turn, because after a tool call the model re-opens the turn (<|eom|><|start|>assistant to=user<|message|>) and a turn-anchored scan would drop the reasoning emitted before it.Note: developed with AI assistance (Claude); the code and tests were reviewed and validated end-to-end by the author.
Test Plan
New unit tests in
tests/reasoning/test_muse_glimmer_count_reasoning_tokens.py(reasoning only, reasoning + content, no reasoning, tool-call turn re-open, empty input).Test Result
Before:
reasoning_tokens: 0for every request.After: e.g.
reasoning_tokens: 43for a request with an 83-character reasoning body,0when the model emits none; tool-call turns count the reasoning emitted before the re-open. Unit tests pass.Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.