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Experimental tooling for measuring how compressing a reasoning trace before the model produces its answer affects output quality. An OpenAI-compatible sidecar splits one /v1/chat/completions request into phase 1 (think until </think>), a compression step, and phase 2 (resume with the compressed trace spliced back in). It drives vLLM's existing token-in/token-out API (/v1/chat/completions/render, /inference/v1/generate, /tokenize, /detokenize), so no vLLM source changes are required. Compression arms: identity (re-tokenization control), truncate (non-semantic floor), and self (the model under test compresses its own trace with enable_thinking=false). Both the original and compressed traces are logged to JSONL, since the sidecar is the only place both exist. Verified end-to-end on GCP-NRT with Qwen/Qwen3.8-27B-FP8 on one B200: 16/16 correct across 4 problems x (baseline + 3 arms), all mechanism checks passing. Two failure modes found and fixed during that run: a compression prompt asking to preserve the original voice made verbatim copying the easiest greedy continuation, and too tight a max_tokens cap on the compressor silently turned the self arm into the truncate arm. This is research tooling, not a vLLM contribution; it is not intended as a PR to vllm-project/vllm. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Adds verify_conditioning.py and records the phase-2 prompt provenance in the trace log (prompt_tail, original_trace_in_prompt, compressed_trace_in_prompt), plus an `inject` arm that replaces the compressed trace with caller-supplied text via cot_inject. Mechanical check: for the self arm the phase-2 prompt contains the compressed trace and not the original. Behavioural check: injecting two traces that reach the same correct answer by different routes gives perfect separation on a 501-token-trace problem -- the answer follows whichever derivation it was handed (4/4 and 0/4 both ways). Also records a caveat that matters for the experiment: the same test on a 121-token-trace problem shows no route dependence, so on easy problems the compression arms cannot degrade because the trace is not load-bearing. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Extracts the think -> compress -> resume core into a shared coroutine and adds a /v1/completions route on top of it, so harnesses that use the completions endpoint are covered too. Previously those requests fell through the passthrough route to vLLM uncompressed, producing a baseline number that looked like a result. Handles the case a raw prompt introduces: a bare few-shot prompt has no <think> scaffolding, so there is no reasoning block to compress. The sidecar detects that, returns the generation untouched, and flags no_think_block rather than force-closing a block that never opened. Caller stop strings are therefore applied to phase 1 as well as phase 2. Options the pipeline cannot honour (echo, suffix, logprobs, best_of, n > 1, stream) are rejected with 400 instead of silently ignored. Also points the conditioning route check at a hard problem, where it discriminates 4/4 vs 0/4; on the easy problem the model re-solves from the question and ignores the injected route, which made the check a permanent false failure. Verified: new /v1/completions tests pass (compression on a pre-rendered chat prompt, no_think_block on a bare prompt, batch and token-id prompts, option rejection), and the chat path reproduces its pre-refactor numbers exactly (16/16, identical token counts). Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Logging: records now go to a bounded asyncio.Queue drained by a single background task that batches them and does json.dumps plus the write syscall in asyncio.to_thread. The previous code wrote and flushed inline on the event loop, which on lustre can stall every in-flight request for milliseconds. One consumer means the file handle has a single owner, so no lock is needed at all. write_record awaits put() so a full queue applies backpressure rather than dropping experiment rows, and the lifespan drains the queue before closing the file. Pass-through fidelity: when a prompt opens no <think> block the sidecar must return what vLLM would have returned for the same request. Three bugs fixed: - phase 1 used THINK_BUDGET instead of the caller's max_tokens, so a request for 8 tokens could generate thousands - temperature/top_p were defaulted to 0.6/1.0, overriding the model's own generation_config; only params the caller actually set are forwarded now - the returned text kept the stop string and was .strip()ed, where /v1/completions removes the stop string and preserves whitespace Whether a think block exists is now decided up front from the prompt tail rather than after a full phase-1 generation, so a bare prompt costs exactly one generation. Verified: byte-identical to /v1/completions on bare prompts, on max_tokens capping, and with sampling params omitted; chat path unchanged (8/8, same token counts); 24 concurrent requests logged with no loss across a graceful shutdown. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
1. `stop` as a bare string was handed to list(), so "END" became
['E','N','D'] and generation stopped on the first letter E. The
completions route normalised it; chat did not. Both now share
normalize_stop().
2. The chat route assumed a think block is always open. With
chat_template_kwargs={"enable_thinking": false} the template renders a
*closed* empty block, so there is nothing to compress -- yet the route ran
the full compression path and ignored the caller's max_tokens. It now runs
the same prompt_opens_think() probe as the completions route, and honours
max_tokens on the pass-through path while keeping the fixed ANSWER_BUDGET
on the compression path (arms must share a budget or the comparison is
invalid).
3/4. n>1 and logprobs were silently ignored on chat while the completions
route rejected them. A pass@k harness would have received one choice and
mis-scored. Both routes now share reject_unsupported().
Adds test_edge_cases.py covering all four. Verified: bare-string and
list-form stop produce byte-identical output; enable_thinking=false yields
no_think_block with 6 of 12 permitted tokens; n/logprobs/stream all 400.
Completions fidelity and chat arms unchanged (8/8, same token counts).
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Phase 1 now always arms the </think> stop and reasoning is detected by asking whether phase 1 ended there, rather than deciding up front from the prompt whether a think block exists. A stop condition that never fires cannot change the output, so arming it costs nothing on prompts that never reason -- pass-through stays byte-identical to vLLM. What it buys is the case the prompt-based decision missed: the model opening a think block on its own. "Write a long essay about clouds." opens no think block in the prompt, but the model emits one, and that trace is now compressed 322 -> 89 tokens instead of being returned uncompressed. The prompt probe survives for two narrower jobs: choosing the phase-1 budget (THINK_BUDGET when the prompt already reasons, the caller's max_tokens otherwise, so a bare completion does not burn the think budget to find out), and deciding whether phase 2 must re-insert the opening <think>. That replaces the per-model case_b branch with a per-request fact, which is what it should have been -- case_b could not describe a model that opens a think block for some prompts and not others. Verified: pass-through still byte-identical on all three fidelity cases including max_tokens capping; spontaneous traces now compressed; chat arms and edge cases unchanged (8/8, same token counts). Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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The terminal 1/2/3 framing read as a requirement. Both vLLM and the sidecar are servers that block their shell, so they are two processes; one shell with & is fine, which is what run_gcp_nrt.sbatch already does. Also records why the sidecar stays out of the vLLM process: a sidecar restart is ~12s against ~320s to reload the model, and separate processes let several arms share one GPU. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Adds a How it works section: the broker call sequence, the fact that one inbound request becomes three independent vLLM generations, and the two distinct roles </think> plays (stop condition in GEN 1, prompt text in GEN 3). Records why step 1 returns token IDs (GEN 3 splices inside the assistant turn, which messages cannot express; plus no prompt drift and a guaranteed prefix cache hit), and why the trace is detokenized before GEN 2 rather than concatenating token IDs (possible and marginally more robust, but saves ~0.1% of CPU-side time and the text is needed for the log anyway). Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Every arm is now a token-IDs -> token-IDs function, so the trace never round-trips through text on the way to the compressor. - the compression prompt prefix is pre-tokenized once at startup, by rendering with a sentinel and splitting on it (template-agnostic; verified against the rendered token IDs, with a per-request render as fallback). This is why the budget moved after the trace: a per-request number in the prefix would make the prefix dynamic. - GEN 2 takes C_PREFIX + raw reasoning_ids + a small per-request tail - c_ids comes straight from GEN 2 instead of re-tokenizing its text - splice is compressed_ids + a pre-tokenized close tag - provenance is an exact token-subsequence test, not string containment - prompt_opens scans token IDs instead of detokenizing a tail - renders are LRU cached, so arms and seeds over the same messages share one - the three log-only detokenizes run concurrently with a generation Measured: 8 upstream calls per self-arm request, 3 of them GPU, critical path 5. identity is now exactly ratio 1.0 (a true no-op; it was 0.9917 from an rstrip artifact) and truncate lands at 0.297-0.299 instead of 0.289, both because the arms no longer pass through text. retok_identical is null by default since drift is impossible by construction; MEASURE_RETOK=1 restores it for characterising a new tokenizer. Trace schema bumped to 3 -- ratios shift slightly, so records are not comparable across the change. Verified: 16/16 with all mechanism checks; completions fidelity byte-identical including spontaneous-think capture; edge cases and conditioning unchanged (route dependence still 4/4 vs 0/4); MEASURE_RETOK=1 reports True on every record. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
The previous version described the design before implementation. Replaces it with: what exists and where, the cluster workflow including the srun --overlap iteration loop, the architecture and the reasoning behind each design choice, the ten bugs found and fixed, and the experimental caveats that matter more than the code — chiefly that the current toy problems cannot show degradation because the trace is not load-bearing on them. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
…ire log Introduces five settings, all inert at their defaults so this commit changes no behaviour on its own: MAX_MODEL_LEN / CTX_SAFETY room arithmetic for the compressor request RC_MAX_CHARS optional ceiling on reasoning_content, 0 = off WIRE_LOG / WIRE_BODY_CHARS raw request/response log, "" = off The three following commits use them. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
GEN 2 sends the whole trace as prompt and asks for 3*target tokens back, so it needs 1.9n + 240 tokens at RATIO=0.3. Past n ~= 137844 that exceeds a 262144 window and vLLM rejects the request outright -- zero tokens generated, nothing partial to salvage, and compress_ids() then falls back to the FULL uncompressed trace, leaving that row silently not compressed at all. Clamping max_tokens to the remaining room turns that hard rejection into an ordinary finish_reason="length", whose partial compression is still spliced into GEN 3. Records compressor_room and compressor_cap_clamped so the affected rows are identifiable at analysis time. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
If the prompt opened a think block and phase 1 ran out of budget without emitting </think>, the generation is unfinished REASONING, not an answer. Previously it was returned as message.content, so a grader received the entire trace as the model's answer. Measured on an uncompressed xhigh HLE baseline, vLLM's --reasoning-parser puts such a generation in reasoning_content and leaves content empty: every 245760-token non-terminating sample came back with generation="" and the whole trace in reasoning_content, so the grader saw an empty answer and marked it wrong. Returning it as the answer instead shipped ~196k tokens to GPT-4o and tripped ContextWindowExceededError, killing the eval mid-run. This is not something the reasoning interceptor can undo: it strips <think> tags inside content and never inspects reasoning_content, and the sidecar has already removed those tags by the time it responds. answer_ids stays = gen so usage.completion_tokens still reports the tokens actually generated, as vLLM does. The bare-completion branch is untouched -- there the generation really is the answer, and test_completions.py asserts byte-exact pass-through. RC_MAX_CHARS is wired up here as an escape hatch only; it defaults to 0 (unlimited) because graders read content, not reasoning_content -- the same baseline judged 905 rows carrying >128k chars of reasoning_content without trouble. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
TRACE_LOG only records requests that went through the cot pipeline, so an empty trace log is ambiguous between "the harness never called us" and "it called us but nothing was pipelined". That ambiguity cost several debugging rounds on a resumed eval: the answer turned out to be that the only inbound request for the entire run was /health, because the harness was serving from its own local store and never reached the sidecar at all. A middleware covers every route, including the catch-all passthrough. Bodies are summarised rather than dumped -- one response here can be 600k chars -- keeping path, status, sizes, latency, the request's sampling knobs and the response's content/reasoning lengths and usage. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
…haviour Adds MAX_MODEL_LEN, CTX_SAFETY, RC_MAX_CHARS, WIRE_LOG and WIRE_BODY_CHARS to the configuration table, and a section explaining what the sidecar returns when phase 1 never reaches </think> -- empty content, trace in reasoning_content -- and why putting it anywhere else breaks a judge-scored eval. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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supported_models.mdandexamplesfor a new model.