feat(sampling): reproducible deterministic sampling + validator replay (Stage-1), non-enforcing shadow path

[bonujel]

What this is

The vLLM half of the two-stage inference-validation design in gonka-ai/gonka#1199: reproducible deterministic sampling on the executor, plus a validator-side replay/verification path.

This PR is:

• Draft
• Non-enforcing
• Disabled by default behind VLLM_DETERMINISTIC_SAMPLING
• Limited to code and tests, with no dev notes or design docs

How it works

The design has two stages:

1. Stage 1 — sampling replay: Re-derives each position's token from the signed logprobs and seed, then checks that it matches the reported token.
2. Stage 2 — distance: Recomputes logprobs and checks the distribution distance.

For Stage 1 to accept honest artifacts, the executor and validators—Python here and Go on-chain—must reduce the same logprobs to the same token, bit for bit, in both languages.

Scope of this PR. Stage 1 (sampling replay) is complete and verified end to end. Stage 2 contributes the distance metric only (mae_distance / Go MAEDistance); its threshold and any enforcement are policy and out of scope (see Still open, Decision D). Signature verification of the artifact (Decision A) is also out of scope. In short: this makes honest executor output replayable and provably consistent, not yet enforceable.

What changed

Executor sampling

v1/sample/ops/topk_topp_sampler.deterministic_sample

• Computes weights through the decimal pipeline over the raw logprobs, with an exact 2^16 total.
• Samples in canonical token-ID string order.
• Uses a per-position seed in the form base|pos.
• Replaces the previous float-based (probs * 2^16).round() pipeline, numeric token order, and single-stream sampling path.
• Uses the same computation that the validator replays.

Python validator

validation_sampling.py

• Adds verify_sequence, which performs sequence-level replay and returns a three-valued verdict: Honest, Fraud, or Inconclusive.
• Adds mae_distance, which calculates Stage 2 distance as MAE over the signed top-K support.
• Returns Inconclusive for unbounded support, seed-domain mismatch, and temperature 0 / greedy sampling.
• Updates serving_chat._perform_validation to use the new validator.
• Retires the legacy recompute path in validation_logic.py from serving.

Primitives

v1/sample/deterministic_utils.py

• Updates sample_categorical_weights to raise on zero-weight input instead of silently falling back to the last index, matching the behavior of the Go validator.

Cross-language parity

• Extends tests/v1/validation/conformance_vectors.json to cover the sequence layer and distance metric, not only the primitives.
• The companion gonka detsample Go validator consumes the same file and asserts byte-identical results.

Evidence

GPU experiments were run with GPT-2 and Qwen2.5 on an RTX 4090 (isolated environment, SDPA backend).

Question	Result
Float executor vs. decimal validator false-reject rate	14–77%; decimal pipeline: 0/128
E1 live in a real vLLM engine, rather than an HF proxy	70% divergence (float weights + numeric order combined; higher than the offline 14–77% because the live path also samples in numeric token order)
Which executor fix is sufficient?	E1 only: 39–59%; order only: 32–57%; both together: 0%
Full flow in real vLLM: inference → record → validate	Current artifact: FRAUD; after the fix: HONEST in 24/24 positions
Can the validator recompute logprobs instead of replaying?	No; batch composition alone causes 0–8% flips
Signed support size	Fixed top-256 distorts 7–33%; sign the filter's retained set
Stage 2 metric	MAE/KL over top-K separates honest and wrong-model outputs by approximately 40–65×; sampled-token delta overlaps
Quantized same model	Precision ladder: int4 is caught; int8 remains a gray zone

Headline: The executor fix reduces executor-versus-validator divergence from approximately 70% to 0%, verified end to end in a real vLLM engine. An honest deterministic inference now produces an artifact that the contract validator confirms as HONEST. (Measured on GPT-2, one sampling config; not yet a whole-fleet guarantee — see Still open.)

Testing

pytest tests/v1/validation tests/v1/sample passes with 45 tests on a CUDA GPU. Coverage includes the replay flow, three-valued verdicts, MAE distance, token-order convergence, chain-bound seed domain, and the sequence and distance conformance vectors. The end-to-end inference → validate loop was also run in a real vLLM engine, as described in the Evidence section.

Still open — do not merge for enforcement

1. Decision A — signed-artifact contract and signature check. Replay currently trusts the supplied logprobs; there is no responseHash binding or signature verification.
2. Chain-bound seed wiring. derive_chain_bound_seed exists and is tested, but the executor and validator still build the seed from request.seed. This requires transporting inference_id into vLLM (Decision D10) and having the validator derive the seed instead of trusting the payload.
3. Signed support size. DETERMINISTIC_SUPPORT_K=64 is a placeholder under Decision D2; the executor and validator must agree on a pinned value.
4. Stage 2 threshold. The metric is settled as MAE over the support; the threshold value, number of epochs, and cross-architecture CI gate remain policy decisions under Decision D.
5. Untested areas. Cross-architecture determinism (current evidence is a single RTX 4090 architecture), real production model and backend (current testing uses GPT-2 with SDPA/eager), tokenizer drift across MLNode versions, and production batch-size performance.

Branch base / rebase

This branch was cut from an earlier main snapshot and has not yet been rebased onto the current main, so GitHub currently reports conflicts. Rebasing is a ready-for-review step and has been deferred while the PR remains a draft.

Companion PR

The gonka-side implementation—the Go detsample validator and shared conformance vectors—is tracked separately in gonka-ai/gonka#1448.

Refs: gonka-ai/gonka#1199

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