[None][feat] Report per-request speculative-decoding acceptance on the response

[debermudez]

Background

TensorRT-LLM already computes per-request speculative-decoding acceptance on the PyTorch backend, then discards the attribution at the HTTP boundary. The exact (accepted, drafted) totals reach the serve layer and go only to the server-side perf-metrics JSONL; the per-position vectors are consumed into the Prometheus counters, where collector.py sums them across every request. There is no surface from which an HTTP client can obtain per-request acceptance:

Surface	Why it does not work
GET /metrics	Per iteration, not per request — one iteration touches many requests via in-flight batching, so specDecodingStats is a batch rollup. Also drained on read into a deque(maxlen=1000) that evicts silently.
GET /prometheus/metrics	Cumulative over server lifetime, summed across all requests. ~32 fixed time series regardless of request count.
Response headers	build_metrics_headers() emits only timing and step breakdowns; no spec field. Server-Timing is a flat name;dur= list, so it structurally cannot carry a histogram.
trtllm.perf_metrics SSE event	Built by JSON-serializing that same headers dict, so it inherits the same limitation.
perf_metrics_output_dir JSONL	Has the data, but it is a file on the server's filesystem.

Benchmarking tools consequently cannot report acceptance length per request. This is a known gap — tests/integration/defs/perf/test_perf_sanity.py carries CI exemptions noting that aiperf does not propagate TRT-LLM's non-standard avg_decoded_tokens_per_iter field.

Summary

Two commits, reviewable independently.

1. [fix] per-position arrays truncating at 16 positions. MAX_SPEC_DECODE_POSITIONS = 16 was bounding two unrelated things: per-request buffer size (an accuracy concern) and Prometheus token_position label cardinality (an operational one). The second was emergent, not stated — MetricsCollector iterates range(len(per_pos_drafted)) with no clamp of its own, so the cardinality bound held only because the arrays happened to be length 16.

That means max_draft_len > 16, reachable with EAGLE3 dynamic-tree and Medusa, silently drops every position past 16, and the existing trtllm_spec_decode_{drafted,accepted}_tokens_total counters under-report with no indication anything was lost.

The two concerns are separated: the arrays grow on demand in the accumulator, and an explicit MAX_SPEC_DECODE_POSITION_LABELS clamp moves to MetricsCollector where cardinality actually matters. MAX_SPEC_DECODE_POSITIONS is retained as the initial allocation size, so the common case never reallocates. This commit stands alone as a bug fix and is a prerequisite for the emitted payload below being trustworthy.

2. [feat] emit acceptance per choice. Added as speculative_decoding on each response choice, beside the existing avg_decoded_tokens_per_iter — which already establishes a per-request spec-decode field in the response body.

{
  "index": 0,
  "message": {"role": "assistant", "content": "..."},
  "finish_reason": "stop",
  "avg_decoded_tokens_per_iter": 2.5,
  "speculative_decoding": {
    "acceptance_rate": 0.5,
    "total_accepted_draft_tokens": 30,
    "total_draft_tokens": 60,
    "num_spec_steps": 20,
    "acceptance_histogram": [8, 0, 6, 6],
    "num_spec_tokens": 3
  }
}

No new instrumentation. Every value is already an attribute on GenerationResultBase, which is exactly what the four OpenAI postprocess handlers already receive — so this is a serializer, not a probe.

per_pos_accepted is prefix-cumulative and therefore a survival function, so the acceptance histogram is its negative first difference. Totals come from spec_dec_totals, which the executor accumulates exactly, rather than from summing the (previously capped) vectors.

Mean acceptance length is deliberately not a field: it would duplicate avg_decoded_tokens_per_iter on the same choice and the two could drift. Consumers derive it as 1 + total_accepted_draft_tokens / num_spec_steps.

Enablement

Server-side only, via a new per_request_spec_decode_stats TorchLlmArgs field (status="prototype"), set through the YAML passed to --extra_llm_api_options. Off by default.

This deliberately differs from return_perf_metrics, which additionally requires a per-request X-TRTLLM-return-metrics header. Benchmarking clients discover this payload by shape rather than being told which engine they are talking to, so requiring a vendor-specific request header would mean a client must already know it is talking to TensorRT-LLM in order to find out. The cost stays opt-in because an operator who does not set the field pays nothing.

Kept independent of return_perf_metrics, which also mounts the Prometheus endpoint — coupling them would mean asking for acceptance numbers silently starts a metrics server.

Correctness

Three identities hold on every emitted payload, and a consumer may rely on them:

sum(acceptance_histogram)        == num_spec_steps
sum(j * acceptance_histogram[j]) == total_accepted_draft_tokens
total_accepted_draft_tokens      <= total_draft_tokens

Verified end to end against a live trtllm-serve run (20/20 requests, 0% errors) with a benchmarking client consuming the field. Summed across the run, the pooled histogram reconciles exactly:

Check	Computed	Reported
sum(pooled_histogram)	1624+207+64+42+35 = 1972	total steps = 1972
sum(j x histogram[j])	207+128+126+140 = 601	total accepted = 601
601/7813	7.6923076923%	overall acceptance rate = 7.6923076923076925
1 + 601/1972	1.304766734279919	token-weighted acceptance length = 1.304766734279919

The derived quantities match to the full float. num_spec_tokens: 4 was read correctly from speculative_config, and the histogram was sized max_draft_len + 1.

Known limits

• PyTorch backend only. The C++/TRT path populates spec metrics via updateNumTokensPerIteration and has no per-position vectors, so the field is simply absent there.
• Not wired for /v1/responses, whose schema has no choices array to attach to.
• num_spec_tokens is null under draft_len_schedule, where the per-step bound varies by batch size — the honest answer rather than a guess.
• Tree drafting: total_draft_tokens counts paths, not tree nodes, mirroring the getMaxDraftPathLen clamp in updateNumTokensPerIteration. The histogram is tree-agnostic by construction — it records output lengths per step and encodes no parent/child structure.
• Disaggregated serving is untested.

Test coverage

• tests/unittest/executor/test_spec_decode_stats_payload.py (new) — histogram derivation, the three identities, padding to the configured budget, adaptive drafting, deep drafting past the initial capacity, and every omission path. Registered in l0_a10.yml.
• tests/unittest/executor/test_spec_dec_stats_pairing.py — cases for growth past the initial capacity and for the common case not reallocating.
• tests/unittest/metrics/test_collector.py — label cardinality stays capped when the arrays run deep.
• TorchLlmArgs change reflected in the API-stability reference and the golden manifest.

API stability

Additive with a default, so api-compatible. Touches tests/unittest/api_stability/references/llm.yaml and tensorrt_llm/usage/llm_args_golden_manifest.json; please confirm the manifest matches a fresh run of scripts/generate_llm_args_golden_manifest.py in CI.

🤖 Generated with Claude Code

Dev Engineer Review

• Adds opt-in per-request speculative-decoding statistics to PyTorch chat and completion response choices.
• Grows executor position arrays on demand and caps Prometheus position labels at 16.
• Omits statistics when the opt-in is disabled or the required per-request data is unavailable. Variable draft-length schedules report no fixed num_spec_tokens bound.
• Adds the TorchLlmArgs option and updates its compatibility references.
• The latest working-tree diff shows import-formatting changes in py_executor.py and llm_args.py; it does not show additional behavior changes.

QA Engineer Review

• Tests cover position-array pairing and growth, statistics derivation and invariants, drafting modes, omission conditions, server option propagation, serialization, and metric label cardinality.
• test_spec_dec_stats_pairing.py, test_spec_decode_stats_payload.py, and test_spec_decode_serialization.py are included in the A10 PyTorch CI list.
• The supplied A10 list excerpt does not show the metrics collector or API-stability tests.
• Test execution results were not supplied.
• Coverage verdict: sufficient for the described behavior; test execution remains unconfirmed.

Per-File QA Perspective

• tensorrt_llm/_torch/pyexecutor/py_executor.py: Verify that position arrays grow when observed drafting depth exceeds their current capacity, without unnecessary growth within capacity.
• tensorrt_llm/executor/postproc_worker.py: Verify that the new controls default to disabled and support unspecified fixed draft length.
• tensorrt_llm/llmapi/llm_args.py: Verify that per_request_spec_decode_stats defaults to False. The latest working-tree changes shown are import formatting only.
• tensorrt_llm/metrics/collector.py: Verify that per-position metric labels stop at positions 0–15 when arrays contain more positions.
• tensorrt_llm/serve/openai_protocol.py: Verify statistics serialization for chat and completion choices, including streaming choices, and omission when absent.
• tensorrt_llm/serve/openai_server.py: Verify opt-in propagation and fixed-bound resolution, including None for variable draft schedules.
• tensorrt_llm/serve/postprocess_handlers.py: Verify the eligibility checks, aggregate statistics, and histogram derivation for completed responses.
• tensorrt_llm/usage/llm_args_golden_manifest.json: Verify the new argument is represented in the golden manifest.
• tests/integration/test_lists/test-db/l0_a10.yml: Adds the pairing, payload, and serialization tests to the A10 PyTorch CI list.
• tests/unittest/api_stability/references/llm.yaml: Verify the prototype option and its default in the API reference.
• tests/unittest/executor/test_spec_dec_stats_pairing.py: Covers verified-token pairing, deep drafting, and buffer capacity; listed in the A10 CI list.
• tests/unittest/executor/test_spec_decode_stats_payload.py: Covers histogram derivation, drafting bounds, omission cases, and server propagation; listed in the A10 CI list.
• tests/unittest/executor/test_spec_decode_serialization.py: Covers absent and present statistics across response-choice serialization modes; listed in the A10 CI list.
• tests/unittest/metrics/test_collector.py: Covers speculative-decoding metric behavior and the position-label limit; the supplied A10 list excerpt does not show this test.

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Walkthrough

The change adds opt-in speculative-decoding statistics to chat and completion responses. It preserves per-position data beyond the initial capacity and limits per-position metric labels to 16 positions.

Changes

Speculative decoding statistics

Layer / File(s)	Summary
Statistics configuration and response contract tensorrt_llm/llmapi/llm_args.py, tensorrt_llm/executor/postproc_worker.py, tensorrt_llm/serve/openai_protocol.py, tensorrt_llm/usage/llm_args_golden_manifest.json, tests/unittest/api_stability/references/llm.yaml, tests/unittest/executor/test_spec_decode_serialization.py	Configuration and response models define optional speculative-decoding statistics for streaming and non-streaming chat and completion choices. Serialization omits absent statistics and retains unrelated optional fields.
Dynamic per-position accumulation tensorrt_llm/_torch/pyexecutor/py_executor.py, tests/unittest/executor/test_spec_dec_stats_pairing.py	Per-position draft and accepted-token arrays grow for deeper drafts. Tests cover expansion and reuse within the initial capacity.
Server wiring and response construction tensorrt_llm/serve/openai_server.py, tensorrt_llm/serve/postprocess_handlers.py, tests/unittest/executor/test_spec_decode_stats_payload.py, tests/integration/test_lists/test-db/l0_a10.yml	The server propagates the opt-in and draft-token bound. Postprocessing derives statistics and attaches them to eligible responses. Tests cover derivation, omission conditions, and server propagation.
Bounded metric label export tensorrt_llm/metrics/collector.py, tests/unittest/metrics/test_collector.py	Per-position metric labels are limited to positions 0 through 15. Tests verify that deeper input arrays do not create additional series.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant OpenAIServer
  participant PostprocArgs
  participant PostprocessHandlers
  participant Executor
  participant OpenAIResponse
  OpenAIServer->>PostprocArgs: enable speculative-decoding statistics and set draft-token bound
  PostprocessHandlers->>Executor: read totals and per-position vectors
  PostprocessHandlers->>OpenAIResponse: attach SpeculativeDecodingStats

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Merge Risk: ⚪ Minimal · up to 6042f

The opt-in statistics feature has targeted coverage for its response contract, accumulation behavior, and server wiring. No actionable current-head risk remains.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 23.64% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 55 functions across 11 files. (1 skipped:…	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title follows the repository format and clearly summarizes the main change: reporting per-request speculative-decoding acceptance in responses.
Description check	✅ Passed	The description explains the problem, solution, enablement, limitations, and API impact. It also lists relevant test coverage and reported validation.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

Docstring coverage is 23.64% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 55 functions across 11 files. (1 skipped: 1 unsupported.)

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Inline comments:
In `@tensorrt_llm/serve/openai_server.py`:
- Line 3158: Update handle_non_streaming_response and handle_streaming_response
to pass speculative_decoding into ChatCompletionResponseChoice and terminal
ChatCompletionResponseStreamChoice objects, preserving the opt-in behavior from
_apply_spec_decode_stats_opt_in. Add tests covering non-streaming and terminal
streaming GPT-OSS responses that assert the speculative-decoding payload.

In `@tensorrt_llm/serve/postprocess_handlers.py`:
- Around line 753-754: Configure the response choice models so only the
speculative_decoding field is excluded when its value is None, without applying
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paths using model_dump or model_dump_json, including
completion_stream_post_processor and non-streaming chat/completion responses,
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non-streaming modes asserting the key is absent when _build_spec_decode_stats
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Inline comments:
In `@tensorrt_llm/serve/openai_server.py`:
- Around line 1961-1965: Add regression tests for OpenAIServer chat and
completion request wiring by capturing _postproc_params.postproc_args passed to
generate_async. Verify disabled _per_request_spec_decode_stats leaves both
spec-decode fields unchanged, while enabled configuration sets
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and adaptive draft bounds; cover the chat and completion call paths separately.

In `@tests/unittest/executor/test_spec_dec_stats_pairing.py`:
- Around line 137-138: Strengthen the test around _accumulate by saving the
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accumulation, then assert both request attributes retain object identity
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tests/unittest/executor/test_spec_decode_stats_payload.py (1)

71-79: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Add a regression case for a smaller configured budget.

Coverage summary: TestHistogramDerivation covers histogram identities, padding, adaptive sizing, and deep drafting. TestOmission, TestNumSpecTokensResolution, and TestServerOptIn cover omission, resolution, and opt-in paths. The file is wired into l0_a10.yml. Coverage verdict: needs follow-up.

_build_spec_decode_stats uses max(deepest, num_spec_tokens) to preserve observed acceptance depth. Existing cases do not exercise num_spec_tokens < deepest. Add param([3, 3, 1], 3, (7, 9), 1, [0, 0, 2, 1], id="observed_depth_exceeds_budget"). This valid survival input would detect regression to histogram truncation.

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In `@tests/unittest/executor/test_spec_decode_stats_payload.py` around lines 71 -
79, Add a parametrized regression case to the test covering
_build_spec_decode_stats, using survival [3, 3, 1], steps 3, totals (7, 9),
num_spec_tokens 1, and expected histogram [0, 0, 2, 1], with the identifier
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In `@tests/unittest/executor/test_spec_decode_stats_payload.py`:
- Around line 71-79: Add a parametrized regression case to the test covering
_build_spec_decode_stats, using survival [3, 3, 1], steps 3, totals (7, 9),
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