diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3d685ea..6ccd138 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,6 +15,10 @@ jobs: timeout-minutes: 20 steps: - uses: actions/checkout@v7.0.1 + with: + # Historical evidence authentication checks recorded commits against + # HEAD, so the test job needs the complete ancestry graph. + fetch-depth: 0 - uses: actions/setup-python@v7.0.0 with: python-version: "3.11" diff --git a/.gitignore b/.gitignore index 41c6035..0ef6f73 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ __pycache__/ *.py[cod] *.egg-info/ .pytest_cache/ +.pytest-tmp-*/ .ruff_cache/ .coverage htmlcov/ diff --git a/README.md b/README.md index 5bb72fd..cc06886 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,10 @@

- Quickstart · + Quickstart · Storage · - Evidence · + v0.2 evidence · + Stage B · Compatibility · Reproduce

@@ -23,9 +24,16 @@ recurrent matrix states used by Qwen3.5 Gated DeltaNet layers. Pass its cache to ordinary eager Transformers model calls to keep those states as grouped INT4 or INT8 payloads between calls. -Its frozen v0.2 layout passed a 500-task held-out MBPP teacher-forced fidelity -protocol: task-macro excess NLL was 72.75% lower than uniform INT4 while the -persistent recurrent state occupied exactly 2,564,096 resident bytes. +Its frozen v0.2 layout passed a 500-task held-out MBPP teacher-forced +recurrent-state fidelity protocol. Relative to uniform INT4, task-macro excess +NLL above the matched FP32-state reference was 72.75% lower while the packed +persistent recurrent state—including payloads, FP16 scales, and precision +masks—occupied exactly 2,564,096 resident bytes. + +The experimental v0.3 RHT-CQER-32 method has now also passed its separate +32-task development gate: aligned excess NLL was 52.73% lower than CQER-32 at +the same packed-state and selector byte counts. That newer result is +development evidence, not held-out confirmation. It currently targets [`Qwen/Qwen3.5-0.8B-Base`](https://huggingface.co/Qwen/Qwen3.5-0.8B-Base). @@ -36,11 +44,13 @@ I built and maintain RecurQuant as an open research project. — [Muhammad Labeeb Aryan](https://github.com/Labeeb2339). Licensed under [Apache-2.0](LICENSE). -## 60-second setup +## Quickstart -The commands are short; the first run still needs to download the pinned model -and tokenizer. Python 3.11 and a CUDA GPU are recommended for the evaluated -path. +This installs the public v0.2 alpha from its frozen tag. The first model-backed +run downloads the pinned model and tokenizer. Python 3.11 and a CUDA GPU match +the evaluated path; `recurquant demo` uses synthetic states and does not +download a model. RHT-CQER-32 remains an experimental research path rather +than the default package policy. Windows PowerShell: @@ -199,6 +209,37 @@ Important boundaries: It does not support generated-code quality, speed, peak memory, whole-model memory, cross-model generality, or a breakthrough claim. +## Experiment 009 Stage-B development + +RHT-CQER-32 applies a deterministic right-side randomized Hadamard transform +inside each existing recurrent-state row group before the same physical Q4/Q8 +packing used by CQER-32. The transform does not change the frozen 1,976-row +precision allocation or storage contract: both methods use 2,564,096 packed +state bytes and 2,711,552 resident bytes including the query-energy selector. + +On the preregistered 32-task ranked MBPP `[32, 64)` development window, +RHT-CQER-32 passed all eight frozen advancement checks. Task-macro aligned +excess NLL fell from `0.323944` to `0.153129`, a **52.73% reduction**. +Aggregate local recurrent-state reconstruction SSE fell from `36,409.363073` +to `15,345.844948`, a **57.85% reduction**. + +![Authenticated Experiment 009 Stage-B excess-NLL and state-SSE comparison.](assets/experiment009-stage-b-overview.svg) + +RHT-CQER-32 had lower excess NLL on 27 of 32 tasks, with no ties. The paired +CQER-minus-RHT improvement was `0.170815` nats/token and its frozen +10,000-sample paired 95% bootstrap interval was `[0.116082, 0.229438]`. + +![Per-task paired CQER-32 minus RHT-CQER-32 excess-NLL differences.](assets/experiment009-stage-b-paired.svg) + +This is positive development evidence on one pinned model and task window, not +held-out confirmation for RHT-CQER-32. Randomized Hadamard quantization is +prior art, and the current Python implementation has no fused-kernel, latency, +peak-memory, cross-model, or independent external-reproduction result. See the +[full Stage-B result](research/EXPERIMENT_009_STAGE_B_RESULT.md), +[verification receipt](research/EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md), +and +[machine-readable release manifest](evidence/experiment009-rht-cqer-stage-b-result-manifest.json). + ## Scope The supported public surface is deliberately narrow: @@ -226,9 +267,12 @@ the held-out confirmation and beat all three tested same-byte random placements. That is a confirmed case study, not proof of novelty or general superiority. Q-Mamba already studies 4-bit persistent Mamba2 states, Quamba2 quantizes cached SSM states, and other mixed-precision and replay systems overlap parts of this -design space. RecurQuant currently has no fused packed kernel or measured speed -claim. I therefore do not present this release as a breakthrough, a whole-model -memory reduction, or a cross-model result. See the +design space. Experiment 009 adds a positive 32-task development result for a +known right-RHT codec composed with CQER-32, but it is not a new confirmation +result or evidence that Hadamard quantization is new. RecurQuant currently has +no fused packed kernel or measured speed claim. I therefore do not present this +release as a breakthrough, a whole-model memory reduction, or a cross-model +result. See the [claim boundary](research/CLAIM_BOUNDARY.md) and [prior-art review](research/PRIOR_ART.md) for the exact comparison. @@ -243,6 +287,13 @@ memory reduction, or a cross-model result. See the - [Frozen public evaluation protocol](research/PUBLIC_EVAL_PROTOCOL_V02.md) - [MBPP development report](research/DEVELOPMENT_002.md) - [Current research status](research/STATUS.md) +- [CORA-C2 development result](research/EXPERIMENT_008_RESULT.md) +- [Experiment 009 frozen protocol](research/EXPERIMENT_009_RHT_CQER_PROTOCOL.md) +- [Experiment 009 Stage-A result](research/EXPERIMENT_009_STAGE_A_RESULT.md) +- [Experiment 009 Stage-B identity freeze](research/EXPERIMENT_009_STAGE_B_IDENTITY.md) +- [Experiment 009 Stage-B result](research/EXPERIMENT_009_STAGE_B_RESULT.md) +- [Experiment 009 verification receipt](research/EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md) +- [Experiment 009 release manifest](evidence/experiment009-rht-cqer-stage-b-result-manifest.json) - [Claim boundary](research/CLAIM_BOUNDARY.md) and [prior-art review](research/PRIOR_ART.md) - [Failed proxy signals and empirical-sensitivity pivot](research/EXPERIMENT_001_SIGNAL_PIVOT.md) diff --git a/assets/experiment009-stage-b-overview.svg b/assets/experiment009-stage-b-overview.svg new file mode 100644 index 0000000..1711c2a --- /dev/null +++ b/assets/experiment009-stage-b-overview.svg @@ -0,0 +1,96 @@ + + + Experiment 009 Stage-B authenticated development overview + Experiment 009 Stage B compares four frozen methods on 32 MBPP development tasks. CQER-32 has task-macro aligned excess NLL 0.323944; composing it with a right randomized Hadamard transform has 0.153129, 52.73 percent lower. Aggregate local codec state SSE falls from 36409.363073 to 15345.844948, 57.85 percent lower. All eight frozen development advancement gates passed. This is not confirmation, novelty, speed, deployment, or breakthrough evidence. + {"advancement_gate_passed":true,"artifact_kind":"recurquant_rht_cqer32_stage_b_development","canonical_evidence_sha256":"2b15c732e894510f0421a22fcca9435e035dd15c4d3b50e2fcb733c0d1df58a8","chart":"experiment009-stage-b-overview","claim_boundary":"This is the frozen 32-task Experiment 009 development result for a known right-RHT codec composed with CQER-32 on one pinned model and MBPP window. 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Positive values favor right-RHT. The mean improvement is 0.170815 nats per token; the paired 10,000-sample bootstrap 95 percent interval is 0.116082 to 0.229438. This is authenticated development evidence, not confirmation. + {"advancement_gate_passed":true,"artifact_kind":"recurquant_rht_cqer32_stage_b_development","canonical_evidence_sha256":"2b15c732e894510f0421a22fcca9435e035dd15c4d3b50e2fcb733c0d1df58a8","chart":"experiment009-stage-b-paired-cqer-minus-rht","claim_boundary":"This is the frozen 32-task Experiment 009 development result for a known right-RHT codec composed with CQER-32 on one pinned model and MBPP window. 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+ + + + + + + + Paired task result · CQER-32 minus right-RHT + POSITIVE VALUES FAVOR RIGHT-RHT · FROZEN TASK ORDER + + PAIRED MEAN · 95% BOOTSTRAP CI + + + + + + +0.1708 mean · [0.1161, 0.2294] 95% CI + 27 wins · 0 ties · 5 losses + CQER − RIGHT-RHT ALIGNED EXCESS NLL (NATS / TOKEN) + + -0.1 + + +0.0 + + +0.1 + + +0.2 + + +0.3 + + +0.4 + + +0.5 + + +0.6 + + Task 725: CQER minus right-RHT delta NLL +0.015634; win + + + Task 616: CQER minus right-RHT delta NLL +0.359436; win + + + Task 686: CQER minus right-RHT delta NLL -0.069045; loss + + + Task 722: CQER minus right-RHT delta NLL +0.147465; win + + + Task 919: CQER minus right-RHT delta NLL +0.006999; win + + + Task 636: CQER minus right-RHT delta NLL +0.064591; win + + + Task 607: CQER minus right-RHT delta NLL -0.077088; loss + + + Task 950: CQER minus right-RHT delta NLL +0.009495; win + + + Task 885: CQER minus right-RHT delta NLL -0.062839; loss + + + Task 646: CQER minus right-RHT delta NLL +0.201048; win + + + Task 708: CQER minus right-RHT delta NLL +0.488130; win + + + Task 840: CQER minus right-RHT delta NLL +0.239418; win + + + Task 793: CQER minus right-RHT delta NLL +0.438036; win + + + Task 860: CQER minus right-RHT delta NLL +0.313842; win + + + Task 800: CQER minus right-RHT delta NLL +0.212743; win + + + Task 867: CQER minus right-RHT delta NLL +0.350312; win + + + Task 622: CQER minus right-RHT delta NLL +0.085935; win + + + Task 677: CQER minus right-RHT delta NLL +0.252163; win + + + Task 849: CQER minus right-RHT delta NLL +0.232114; win + + + Task 756: CQER minus right-RHT delta NLL +0.144575; win + + + Task 960: CQER minus right-RHT delta NLL -0.029710; loss + + + Task 741: CQER minus right-RHT delta NLL +0.052509; win + + + Task 763: CQER minus right-RHT delta NLL +0.213314; win + + + Task 924: CQER minus right-RHT delta NLL +0.297789; win + + + Task 839: CQER minus right-RHT delta NLL +0.192452; win + + + Task 870: CQER minus right-RHT delta NLL +0.163585; win + + + Task 862: CQER minus right-RHT delta NLL -0.029252; loss + + + Task 877: CQER minus right-RHT delta NLL +0.094759; win + + + Task 672: CQER minus right-RHT delta NLL +0.088659; win + + + Task 894: CQER minus right-RHT delta NLL +0.195419; win + + + Task 911: CQER minus right-RHT delta NLL +0.289855; win + + + Task 907: CQER minus right-RHT delta NLL +0.583748; win + + + mean +0.1708 + 725 + 616 + 686 + 722 + 919 + 636 + 607 + 950 + 885 + 646 + 708 + 840 + 793 + 860 + 800 + 867 + 622 + 677 + 849 + 756 + 960 + 741 + 763 + 924 + 839 + 870 + 862 + 877 + 672 + 894 + 911 + 907 + Task IDs follow the authenticated identity order · 10,000 paired task bootstraps · seed 2339 · two-sided equal-tailed 95% CI + 32-task MBPP development evidence only; no confirmation, novelty, speed, deployment, or breakthrough claim. + diff --git a/evidence/experiment005-adaptive-same-calibration-8task-556e527.json b/evidence/experiment005-adaptive-same-calibration-8task-556e527.json new file mode 100644 index 0000000..c106a5e --- /dev/null +++ b/evidence/experiment005-adaptive-same-calibration-8task-556e527.json @@ -0,0 +1,2915 @@ +{ + "artifact_kind": "recurquant_adaptive_row_packing_same_calibration_quality_diagnostic", + "canonical_evidence_sha256": "f07a6b852a4c427c0b9946ad44c21d023299d173eb24695462e99e3785061d61", + "evidence": { + "adaptive_policy_contracts": { + "adaptive_mse_hrr_h1_quota": { + "batch_size": 1, + "layer_quota_source": "hrr_h1 selector plan", + "resident_bytes": 2564096, + "selection": "per-update aligned INT4-to-INT8 row MSE reduction" + }, + "adaptive_mse_target_directional_fisher_quota": { + "batch_size": 1, + "layer_quota_source": "target_directional_fisher_difference_int4 selector plan", + "resident_bytes": 2564096, + "selection": "per-update aligned INT4-to-INT8 row MSE reduction" + } + }, + "aggregates": { + "adaptive_mse_hrr_h1_quota": { + "macro_cvar95_kl": 3.3163906931877136, + "macro_delta_nll": 0.7794309854507446, + "macro_mean_kl": 0.7939629852771759, + 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It is not confirmation, novelty, speed, state-of-the-art, deployment, or breakthrough evidence." +} diff --git a/research/CLAIM_BOUNDARY.md b/research/CLAIM_BOUNDARY.md index 0a2613d..285ee5f 100644 --- a/research/CLAIM_BOUNDARY.md +++ b/research/CLAIM_BOUNDARY.md @@ -1,6 +1,6 @@ # Claim boundary -Last reviewed: 2026-07-22 +Last reviewed: 2026-07-26 ## What is already established @@ -13,7 +13,13 @@ Last reviewed: 2026-07-22 fewer persistent-state writes exist in current recurrent-model systems work. - Update residuals have been used as importance signals for bounded auxiliary memory in Gated DeltaNet. +- Query magnitude combined with quantization difficulty has already been used + for mixed-precision KV-cache channel selection in + [MixKVQ](https://arxiv.org/abs/2512.19206). - Mixed precision and memory-budget allocation are broad existing ideas. +- Randomized Hadamard transforms and learned rotations are established + quantization outlier-suppression techniques. RecurQuant cannot claim to have + invented rotation-based or Hadamard-based quantization. ## Scoped v0.2 confirmation finding @@ -32,6 +38,80 @@ generated-code correctness, lower latency, lower whole-model or peak memory, or cross-model generality. Mean decay, write gate, state-update magnitude, and residual magnitude remain rejected diagnostic selectors from the earlier pilot. +## Current v0.3 research boundary + +Experiment 005 permanently failed its frozen numerical prerequisite before +holdout (`13/16 = 0.8125` sign agreement versus a required `0.95`). Its later +same-calibration adaptive-MSE postmortem was promising but achieved only a +7.93% descriptive excess-NLL reduction against the strongest static empirical +selector, with a paired 95% interval crossing zero. The ranked `[8, 16)` +holdout remained unopened. + +Experiment 006 implemented deterministic rank fusion of offline +target-directional sensitivity and causal per-write reconstruction benefit at +the same exact byte budget. Its authenticated same-calibration GPU diagnostic +rejected the frozen equal-rank primary: macro excess NLL was `0.514873` versus +`0.493302` for plain adaptive MSE, and the paired interval crossed zero. Its +3.90% point improvement over the strongest static method was below the frozen +20% requirement, again with an interval crossing zero. Better predeclared +ablations remain exploratory and cannot be relabelled as the primary. The +candidate stopped before its numerical prerequisite and the ranked `[8, 16)` +holdout remained unopened. Experiment 006 supports no improvement, +generalization, novelty, deployment, or breakthrough claim. + +Experiment 007 implemented CQER-32, a causal normalized-query-energy EMA times +exact INT4-to-INT8 reconstruction benefit for physically packed Gated DeltaNet +recurrent-state rows. Its authenticated same-calibration diagnostic improved +macro excess NLL by 6.18% over plain adaptive MSE and 13.62% over static +target-Fisher, but both paired intervals crossed zero. It failed the frozen +20% static-reduction requirement and the top-1 non-inferiority margin. The +packed state used exactly `2,564,096` bytes, while persistent selector EMA +state increased the resident total to `2,711,552` bytes. The FP64 prerequisite +was not reached and ranked `[8, 16)` remained unopened. Experiment 007 supports +no generalization, novelty, latency, deployment, state-of-the-art, or +breakthrough claim. + +Experiment 008 tested CORA-C2 on a separate frozen `[16, 32)` development +window. CORA-C2 lowered macro excess NLL by 26.17% relative to static +target-Fisher and 21.62% relative to adaptive MSE, but it was 10.54% worse than +CQER-32. Raw CORA was 3.35% worse than CQER-32. Confirmation-2 reduced +normalized committed-mask churn by 79.99%, but worsened raw-CORA NLL by 6.96%, +above the frozen 1% limit. Five advancement checks failed, the independent +verifier was not reached, and ranked `[8, 16)` remained unopened. Experiment +008 therefore supports neither improvement over CQER-32 nor a general benefit +from its observability or two-hit mechanisms. + +Observability-weighted quantization error is not a new principle. Classical +finite-wordlength state-space analysis expresses output noise using the +quantization-noise covariance and observability Gramian. CORA's diagonal +observability score times measured row distortion belongs to that same broad +mathematical family. Its Gated DeltaNet adaptation may still be a useful +engineering hypothesis, but the failed development result does not establish +it as a contribution. + +Experiment 009 freezes RHT-CQER-32, a deterministic right-side randomized +Hadamard codec composed with CQER-32. Its one-task Stage-A screen passed every +frozen gate, reducing state SSE by `59.97%` and aligned excess NLL by `58.59%` +at equal packed-state and selector bytes. + +Its separately authenticated 32-task Stage-B development run passed all eight +frozen advancement checks. Relative to CQER-32, task-macro aligned excess NLL +fell from `0.323944` to `0.153129` (`52.73%`), the paired 95% interval for +CQER-minus-RHT improvement was `[0.116082, 0.229438]`, and RHT-CQER-32 won 27 +of 32 strict task comparisons. Aggregate local recurrent-state reconstruction +SSE fell from `36,409.363073` to `15,345.844948` (`57.85%`). Macro mean KL and +CVaR95 KL were lower and top-1 agreement was higher. Both candidates retained +the exact `2,564,096` packed-state bytes and `2,711,552` selector-aware +resident bytes. + +That is a positive development result for one method, model revision, dataset +construction, and ranked MBPP window. It is not held-out confirmation for +RHT-CQER-32, cross-model evidence, generated-code correctness, or an +end-to-end systems result. The codec regenerates SHA-256-derived signs and +materializes FP32 transform workspaces in the current Python path. The result +does not establish novelty or speed because randomized Hadamard and rotation +quantizers are prior art and the implementation has no fused-kernel benchmark. + ## Claims prohibited without new evidence - "First recurrent-state quantization method." @@ -42,7 +122,7 @@ residual magnitude remain rejected diagnostic selectors from the earlier pilot. recurrent-state payloads and scales only. - "Speeds up inference" without passing the separate latency gate under the stated benchmark. -- "Breakthrough," "state of the art," or "novel" based on the v0.2 evidence. +- "Breakthrough," "state of the art," or "novel" based on the current evidence. - Authorship of Qwen3.5 or any other base model. ## What this project contributes diff --git a/research/EXPERIMENT_003_HRR_DIAGNOSTIC.md b/research/EXPERIMENT_003_HRR_DIAGNOSTIC.md new file mode 100644 index 0000000..783fc84 --- /dev/null +++ b/research/EXPERIMENT_003_HRR_DIAGNOSTIC.md @@ -0,0 +1,177 @@ +# Experiment 003: HRR same-calibration diagnostic + +Status: H32 horizon hypothesis rejected at the diagnostic stage; pivot opened + +Date: 2026-07-22 + +## Decision + +I tested whether looking farther ahead improved the row allocator before +freezing a development protocol. It did not. At the same physical byte budget, +`hrr_h32` produced higher macro excess NLL than the immediate-read `hrr_h1` +selector: + +```text +hrr_h1 macro excess NLL = 0.6218181103467941 +hrr_h32 macro excess NLL = 0.7428321987390518 +H1 - H32 = -0.12101408839225769 +paired 95% interval = [-0.21324796304106708, -0.04036934301257135] +``` + +The interval excludes zero in H1's favor, and H1 had lower excess NLL on seven +of the eight tasks. H32 also had worse macro mean KL, CVaR95 KL, and top-1 +agreement. I therefore reject the H32 horizon hypothesis at this diagnostic +stage. I will not carry H32 forward as the primary candidate or tune the +horizon on these same examples. + +## Scope and evidence boundary + +This was a same-data implementation diagnostic, not a held-out evaluation. +Both row selection and quality evaluation used the same eight MBPP `train` +calibration examples in this order: + +```text +945, 794, 657, 702, 651, 720, 903, 918 +``` + +The quality run scored 650 teacher-forced code tokens. The selector trace had +642 pre-update decode positions because each sequence of `n` code tokens +provides `n - 1` recurrent transitions. The model and data were: + +- model: `Qwen/Qwen3.5-0.8B-Base` +- model revision: `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` +- MBPP revision: `4bb6404fdc6cacfda99d4ac4205087b89d32030c` +- MBPP manifest SHA256: + `97d691a6d45ee29668f5b0151c1a0885629539ac9e7967e9baa30cfb5c97ae8b` +- selector seed: `2339` +- random-row baseline seed: `1101` +- paired bootstrap: 10,000 task-level resamples, seed `2339` + +This scope can reveal a selector or cache integration failure. It cannot show +held-out generalization, cross-domain robustness, speed, or end-to-end memory +improvement. + +## Reproducibility anchors + +The artifacts were generated from a dirty experimental worktree rooted at Git +commit `23f07cfe5f0dfd2406bbc4f85f885280e467b533`; that commit alone does not +reconstruct the uncommitted experimental implementation. + +| Record | SHA256 | +| --- | --- | +| Selector canonical evidence | `b3a2ee24d2a2dc8bbf9d2b81e9909b7d8844fe0e611cdc0dc51570b5db562b43` | +| Selector artifact file | `a0627fde2681c8085ff3667d3b7c21d7a7a882a01f62491704492f39303e30fb` | +| Quality canonical evidence | `8bac3d436597d0e6dee563c037c56fd4369597f11745dff67de98b5ebeffb9d2` | +| Quality artifact file | `fdd05416a696554c2c6450df6c7ee4f1090fed05cb3910df4ff66ad4e400b903` | + +The ignored local files are `artifacts/hrr-diagnostic-8task.json` and +`artifacts/hrr-quality-diagnostic-8task.json`. + +These hashes authenticate the two local files, not the unavailable dirty +source state that produced them. The numerical results in this document are +therefore locally verified but not release-reproducible from a clean public +commit. Before publication as reproducible evidence, rerun the diagnostic from +the clean committed implementation and publish the resulting artifacts. Until +then, the numbers remain historical exploratory observations only. + +## Physical format and selectors + +The row-format selectors used the same exact resident-state budget: + +| Component | Bytes | +| --- | ---: | +| INT4/INT8 payload | 2,485,760 | +| 36,864 FP16 row scales | 73,728 | +| One precision bit for each row | 4,608 | +| **Total** | **2,564,096** | + +Each row-format policy promoted exactly 1,976 of 36,864 rows from INT4 to +INT8. Each promotion added 64 payload bytes. The physical cache reported the +same `2,564,096` resident bytes for `hrr_h1`, `hrr_h32`, `row_mse`, and +`random_rows_s1101`. It also reported `2,564,096` bytes for the mask-free v0.2 +static layout and `2,433,024` bytes for uniform INT4. The FP32 recurrent-state +reference occupied `18,874,368` bytes. + +The evaluated selectors were: + +- `hrr_h1`: actual INT4-to-INT8 marginal read-risk reduction at the immediate + read, measured on frozen full-precision traces; +- `hrr_h32`: the same marginal score propagated across up to 32 future reads; +- `row_mse`: marginal row reconstruction-MSE reduction from INT4 to INT8; +- `random_rows_s1101`: equal-count random row promotions with fixed seed 1101; +- `v02_layer0_static`: layer 0 at INT8 and the other recurrent layers at INT4; +- `uniform_int4`: all recurrent-state rows at INT4. + +H1 and H32 selected substantially similar policies: 1,872 promoted rows +overlapped, with Jaccard similarity `0.9`. The remaining 104 promotions in +each policy were enough to produce a measurable quality difference in this +small diagnostic. + +## Numeric results + +All values are task-macro aggregates over the same eight examples and 650 +teacher-forced tokens. Lower excess NLL, mean KL, and CVaR95 KL are better; +higher top-1 agreement is better. + +| Method | Excess NLL | Mean KL | CVaR95 KL | Top-1 agreement | +| --- | ---: | ---: | ---: | ---: | +| `hrr_h1` | 0.6218181103467941 | 0.635199373587966 | 3.388882279396057 | 0.7301077470183372 | +| `hrr_h32` | 0.7428321987390518 | 0.7663818132132292 | 4.135775178670883 | 0.7108453139662743 | +| `row_mse` | 0.7827504575252533 | 0.8211609497666359 | 3.675527900457382 | 0.6954880431294441 | +| `v02_layer0_static` | 0.8490711152553558 | 0.9121971130371094 | 4.451788902282715 | 0.6625703647732735 | +| `uniform_int4` | 2.6367427557706833 | 2.796179473400116 | 7.581509709358215 | 0.3663086034357548 | +| `random_rows_s1101` | 2.683250844478607 | 2.791328117251396 | 7.672792077064514 | 0.36465270072221756 | + +The paired contrasts below are `baseline excess NLL - hrr_h32 excess NLL`. +Positive values favor H32; negative values favor the named baseline. + +| Baseline | Mean paired contrast | Paired 95% interval | +| --- | ---: | ---: | +| `hrr_h1` | -0.12101408839225769 | [-0.21324796304106708, -0.04036934301257135] | +| `row_mse` | 0.03991825878620148 | [-0.05020202696323395, 0.12860547192394733] | +| `v02_layer0_static` | 0.10623891651630402 | [0.031215629354119343, 0.20246222615242004] | +| `uniform_int4` | 1.8939105570316315 | [1.5334751740098, 2.2339482568204403] | +| `random_rows_s1101` | 1.9404186457395554 | [1.6602401442825794, 2.197948945313692] | + +H32 beat the coarse static, uniform, and single random baselines in this +same-data run. That does not rescue the horizon hypothesis: H1 was the stronger +equal-format selector, and row MSE was statistically unresolved against H32. + +## Working diagnosis, not a finding + +HRR scores one quantization error injected into a captured full-precision state +and propagates it through frozen full-precision `q`, `k`, `g`, and `beta` +values. The physical cache behaves differently: it quantizes after prefill and +after every recurrent update, so later states and activations come from an +already perturbed, repeatedly requantized trajectory. + +One possible explanation is that a 32-read score accumulates effects that do +not survive, or are replaced by new error, under repeated requantization. H1 +may align better because it asks only about the next read. This is a post-hoc +hypothesis, not a demonstrated cause. The current artifacts do not isolate +horizon length from repeated requantization, and the high H1/H32 policy overlap +makes causal interpretation especially weak. + +To test this explanation, a future diagnostic would need controlled one-shot +versus repeated-quantization ablations and direct row interventions. I will not +describe the mismatch as the reason for failure until those tests exist. + +## Pivot: empirical downstream loss sensitivity + +The next selector will target measured downstream loss rather than extending a +hand-crafted read proxy farther through a frozen trajectory. The planned path +is: + +1. Build a small single-row promotion oracle on the physical, repeatedly + quantized cache and measure the actual change in downstream excess NLL. +2. Test a directional loss or empirical-Fisher score with respect to the + recurrent state, using the real `Q8(S) - Q4(S)` promotion direction rather + than weight magnitude or a generic parameter Hessian. +3. Compare that scalable score with the intervention oracle, H1, row MSE, + static v0.2, and prespecified random seeds at the same counted byte budget. +4. Freeze a new development protocol only if the score is stable across + calibration subsets and improves on the strongest baseline without tuning + on its evaluation examples. + +This is a new hypothesis. The present diagnostic rejects H32; it does not show +that a loss/Fisher selector will succeed. diff --git a/research/EXPERIMENT_003_HRR_PROTOCOL_DRAFT.md b/research/EXPERIMENT_003_HRR_PROTOCOL_DRAFT.md new file mode 100644 index 0000000..6604457 --- /dev/null +++ b/research/EXPERIMENT_003_HRR_PROTOCOL_DRAFT.md @@ -0,0 +1,271 @@ +# Experiment 003 — quantizer-aware horizon row allocation + +Status: historical protocol draft; superseded by +[`EXPERIMENT_003_HRR_DIAGNOSTIC.md`](EXPERIMENT_003_HRR_DIAGNOSTIC.md) + +The same-data diagnostic rejected H32. This file preserves the plan that was +tested; its H32 primary, forward stages, gates, and claim ladder are not active +and are not evidence of an improvement. + +Date opened: 2026-07-22 + +## Question + +At one exact resident recurrent-state byte budget, can a static row-level +INT4/INT8 policy selected from full-precision calibration traces preserve +Qwen3.5 Gated DeltaNet predictions better than the strongest equal-byte +baselines? + +The candidate is **horizon read risk (HRR)**. It propagates the measured error +from quantizing one recurrent-state row through the frozen Gated DeltaNet +trajectory and scores its effect on future reads. This is a local sensitivity +method, not a claim that the complete quantized model remains on the same +trajectory. + +## Why this is a separate experiment + +The earlier hand-built gate, forgetting, update-norm, and residual-magnitude +proxies were weak and prompt-dependent. Their best observed absolute Spearman +correlation was about 0.41 and the sign changed across prompts. Experiment 003 +does not reinterpret that negative result. It replaces the proxy with an +empirical, quantizer-aware sensitivity calculation and requires new baselines +and new evidence. + +The v0.2 policy remains frozen. Experiment 003 cannot rewrite its calibration, +development, or confirmation artifacts. + +## Local score + +Holding the full-precision teacher trajectory fixed, the recurrent-state error +obeys + +```text +E_(t+1) = A_t E_t +A_t = exp(g_t) (I - beta_t k_t k_t^T) +``` + +For a read at future position `tau`, define + +```text +r_(tau,t) = q_tau^T A_tau ... A_t +``` + +The isolated squared contribution of source row `b` is + +```text +r_(tau,t,b)^2 ||E_t[b, :]||_2^2 +``` + +HRR averages this contribution over the next `H` available reads and then +task-macro averages the result. The implementation applies the transition to a +backward query vector, so it does not allocate one full error matrix per source +row. + +The allocator must rank the **marginal value of promotion**, not raw INT4 risk: + +```text +DeltaHRR_b = HRR_b(E_INT4) - HRR_b(E_INT8) +``` + +Negative values remain negative. They must not be clamped merely to make the +selector look stable. + +## Trace contract + +- Run the model in evaluation and inference mode with an ordinary FP32 + recurrent state and otherwise pinned BF16 model arithmetic. +- Prefill normally. During single-token teacher-forced decode, capture the + state immediately before the token update and the `q`, `k`, `g`, and `beta` + values consumed by that update. +- Normalize `q` and `k` exactly once and apply the kernel's `1/sqrt(key_dim)` + query scale exactly once. +- Quantize the captured state independently to INT4 and INT8 using the actual + grouped quantizer. Store only per-row error energies in the calibration + trace, not the full error matrices. +- Drain traces at the task boundary. No task trace may leak into another task. +- A packed evaluation must quantize and store the recurrent state after prefill + and after every recurrent update. Quantizing a completed FP32 trace once is + not an implementation test. + +## Physical format and budget + +For the batch-one Qwen3.5 layout under test: + +- 18 Gated DeltaNet layers; +- 16 heads per layer; +- 128 state rows per head; +- 128 values per row; +- 36,864 independently selectable rows in total; +- symmetric signed INT4 or INT8 payload; +- one FP16 scale per row; +- one packed precision bit per row. + +At this layout: + +| Component | Bytes | +| --- | ---: | +| All-INT4 payload | 2,359,296 | +| FP16 row scales | 73,728 | +| Precision mask | 4,608 | +| 1,976 INT4-to-INT8 promotions | 126,464 | +| **Total** | **2,564,096** | + +The static v0.2 layer policy reaches the same total with 2,048 promoted rows +and no row mask. HRR therefore receives 72 fewer INT8 rows at equal bytes. This +metadata disadvantage is part of the comparison, not something to hide. + +`2,564,096 bytes` means the sum of live recurrent-state payload, scale, and +precision-metadata tensors. It does not mean total cache, model, allocator-peak, +or whole-process memory. Any index, alignment, prefix table, or kernel metadata +introduced later must be counted. + +## Candidate and equal-byte comparators + +1. `v02_layer0_static`: model layer 0 at INT8 and the other recurrent layers at + INT4. It uses 2,048 promotions and no row mask. +2. `random_rows`: 1,976 row promotions for at least 20 prespecified seeds. +3. `row_mse`: rank the measured reduction in row reconstruction error from + INT4 to INT8. +4. `activation_weighted_row_mse`: weight the same marginal reconstruction + error by a prespecified activation statistic. Do not call this AWQ. +5. `directional_fisher`: a calibration-only directional loss score using the + actual INT4-to-INT8 error difference. +6. `hrr_h1`: immediate-read risk with the same physical row format. +7. `hrr_h32`: the primary finite-horizon candidate in this historical plan; + the subsequent diagnostic rejected it. +8. `one_row_oracle`: optional expensive single-row perturbations on a small + calibration subset only. It is a diagnostic ceiling, never a deployable + baseline. + +Every row-format method promotes exactly 1,976 rows and stores the identical +4,608-byte mask. Static v0.2 is allowed its real metadata-free layout but may +not exceed the shared total byte target. + +## Models and domains + +The intended confirmation matrix is: + +| Model | Code | Natural text | +| --- | --- | --- | +| `Qwen/Qwen3.5-0.8B-Base` | one fixed public split | one fixed public split | +| `Qwen/Qwen3.5-2B-Base` | the same protocol | the same protocol | + +The official configurations currently expose the same 24-layer hybrid layout +with 18 recurrent layers and `16 x 128 x 128` recurrent states. Testing both +sizes is scale validation, not validation on another recurrent architecture. + +MBPP training examples may be used for calibration and its validation examples +for development. The v0.2 MBPP confirmation split has already been inspected, +so it cannot serve as untouched v0.3 confirmation. Exact code and natural-text +confirmation datasets, revisions, example IDs, serialization, token windows, +and content hashes must be written here before their outputs are run. + +## Metrics + +Primary: + +- task/document-macro excess NLL against the FP32 recurrent-state reference. + +Secondary: + +- mean and worst-10% token KL; +- top-1 token flip rate; +- worst-10% task/document excess-NLL CVaR; +- packed/QDQ numerical parity; +- payload, scale, mask, padding, transient, allocated, and reserved bytes; +- decode tokens/s and p50/p95 single-token latency; +- policy overlap and rank stability across calibration subsets. + +The task or document window is the statistical unit. Use 10,000 paired +bootstrap resamples with seed 2339. Tokens from one sequence are not independent +samples. + +## Historical sequence of work + +The sequence below records the pre-diagnostic plan. Experiment 003 stopped at +the diagnostic pilot after H32 failed; the later stages were not opened. + +### A. Engineering validity + +1. Prove the analytic scorer against explicit isolated-row propagation. +2. Prove the row-energy form matches the full-error form. +3. Prove mixed packing matches explicit per-row QDQ. +4. Prove the exact byte decomposition above. +5. Prove lossless packed beam reorder and cache integration on tiny Qwen + configurations. + +### B. Diagnostic pilot + +Run a small, explicitly non-confirmatory 0.8B trace to catch normalization, +state-timing, policy-mask, and performance defects. Pilot outputs may change the +implementation but cannot be described as generalization evidence. + +### C. Freeze + +Before development, record model revisions, dataset revisions and IDs, token +manifests, the complete method matrix, horizon, averaging, seeds, byte +accounting, numerical tolerances, evaluator commit, and environment. + +### D. Development gate + +The plan would have run every method on the complete frozen development +partitions and proceeded only if the primary candidate: + +- uses exactly 2,564,096 resident recurrent-state bytes; +- agrees with explicit QDQ to absolute tolerance `1e-6`; +- reduces macro excess NLL by at least 20% versus every equal-byte comparator; +- has a paired 95% bootstrap interval above zero against the strongest + comparator; +- does not regress worst-10% excess-NLL CVaR or top-1 flips beyond frozen + margins; and +- has a positive effect in every model-by-domain development cell. + +Any failed gate is a publishable negative result. Changing the selector, +horizon, dataset, or threshold creates a new experiment version. + +### E. Untouched confirmation + +Run the frozen matrix once on the untouched confirmation partitions. No partial +preview, row sweep, prompt change, or favorable-subset filtering is allowed. + +## Claim ladder + +No claim rung in this historical plan was earned. + +Before development passes: + +> RecurQuant is testing an exact-byte row-level INT4/INT8 Gated DeltaNet cache +> and a quantizer-aware finite-horizon allocator. + +After all frozen confirmation gates pass: + +> On the two named Qwen3.5 checkpoints and two named domains, HRR reduced excess +> NLL by X% over the strongest equal-byte baseline at 2,564,096 resident +> recurrent-state bytes. + +Passing this local protocol would support a narrow research contribution, not +the word “breakthrough.” That stronger description additionally requires a +fused packed kernel, measured memory and speed improvements on at least two GPU +classes, longer-context downstream tasks, another Gated DeltaNet-family model, +and preferably independent replication. + +## Closest-work boundary + +The broad ingredients are not new: + +- [WriteSAE](https://arxiv.org/abs/2605.12770) analyzes how Gated DeltaNet state + interventions propagate to future reads and logits. +- [Q-Mamba](https://aclanthology.org/2025.findings-acl.551/) quantizes recurrent + Mamba state caches to low precision. +- [TQS-PTQ](https://arxiv.org/abs/2606.13300) uses finite-horizon trajectory + sensitivity for quantization. +- [RateQuant](https://arxiv.org/abs/2605.06675) performs quantizer-aware + exact-budget mixed-precision cache allocation. +- [MixKVQ](https://arxiv.org/abs/2512.19206) combines quantization difficulty + with future-query relevance for mixed-precision KV caches. + +The research question is whether their still-uncovered intersection—actual +INT4-to-INT8 marginal error, analytic Gated DeltaNet transitions, exact-byte +row allocation, and physical recurrent-cache packing—produces a repeatable +benefit. A literature search cannot establish a “first” claim, so the project +will not make one. diff --git a/research/EXPERIMENT_004_LOSS_SENSITIVITY_PROTOCOL_DRAFT.md b/research/EXPERIMENT_004_LOSS_SENSITIVITY_PROTOCOL_DRAFT.md new file mode 100644 index 0000000..1991987 --- /dev/null +++ b/research/EXPERIMENT_004_LOSS_SENSITIVITY_PROTOCOL_DRAFT.md @@ -0,0 +1,443 @@ +# Experiment 004: loss-sensitive row allocation + +> **Status: working protocol draft - not frozen.** +> +> This document defines the next falsifiable RecurQuant experiment. It reports +> no positive result and supports no novelty, improvement, or breakthrough +> claim. Formulas, data manifests, thresholds, and implementation commits must +> be frozen before their corresponding evaluation stage begins. + +Date opened: 2026-07-22 + +## Research question + +At one exact resident recurrent-state byte budget, can a static row-level +INT4/INT8 policy selected by downstream loss sensitivity preserve the outputs +of Qwen3.5 Gated DeltaNet layers better than H1 read risk, row reconstruction +MSE, the v0.2 static policy, and prespecified random policies? + +The primary candidate measures the signed first-order loss change along the +actual INT4-to-INT8 promotion direction on a cache that has already followed a +repeated-INT4-QDQ trajectory. A model-Fisher risk difference is the label-free +secondary candidate. Both are ranking approximations. Neither is treated as a +result until it predicts physical row interventions and then passes untouched +confirmation. + +## Why the experiment changed + +Experiment 003 rejected the longer-horizon HRR candidate in its same-data +diagnostic. H32 was worse than H1 even though the two policies overlapped +strongly. One untested hypothesis is a structural mismatch: HRR propagated one +error through a frozen full-precision trajectory, while the real cache stores +a newly quantized recurrent state after prefill and after every update. The +existing artifacts do not isolate this cause. + +I will not retrofit that failure into evidence for the new method. Experiment +004 starts a new hypothesis and tests it against direct physical +interventions. If loss sensitivity does not predict those interventions, the +experiment stops before a large confirmation run. + +## Existing exploratory artifacts + +The local `loss-sensitivity-diagnostic-8task.json` selector artifact and +`loss-sensitivity-quality-diagnostic-8task.json` quality artifact were created +before the full Stage A gate below was completed. They are exploratory +engineering diagnostics at rung 0 only: they may expose implementation failures, +but they do not satisfy Stage A, freeze a ranked policy, open Stage B or held-out +evaluation, or support a positive result. Any protocol-gated ranking must be +regenerated only after Stage A passes from a clean committed implementation. + +## Runtime and notation contract + +For recurrent layer `l`, head `h`, physical state row `r`, and storage boundary +`t`, let `u^4_(t,r)` be the row immediately before storage on the trajectory +generated with every recurrent-state row stored as INT4 after every update. +The superscript records the trajectory, not the precision of this transient +pre-storage value. + +Let `Q4` and `Q8` be the exact RecurQuant QDQ maps used by the packed +evaluator, including grouping, rounding, clipping, stored FP16 scale +conversion, padding, and dequantization. +Define + +```text +s^4_(t,r) = Q4(u^4_(t,r)) +delta_(t,r) = Q8(u^4_(t,r)) - Q4(u^4_(t,r)) +e4_(t,r) = Q4(u^4_(t,r)) - u^4_(t,r) +e8_(t,r) = Q8(u^4_(t,r)) - u^4_(t,r) +``` + +`delta` is the actual local promotion direction. It must not be replaced by a +weight norm, activation magnitude, gate mean, or an error measured on a +full-precision cache. Calibration uses the same recurrent-state timing and +quantizer implementation as packed evaluation. The cache is quantized after +prefill and after every recurrent update. + +The deployed policy is static: one precision bit per physical row, reused at +every storage boundary. It is not allowed to inspect confirmation targets or +choose precision per prompt. + +## Primary candidate: signed Taylor promotion gain + +For a teacher-forced calibration sequence, let `L_next` be the target negative +log-likelihood for the next token after a storage boundary. Evaluate its +gradient at the real repeated-INT4 trajectory: + +```text +g_(t,r) = d L_next / d s^4_(t,r) +``` + +The first-order predicted loss reduction from promoting row `r` is + +```text +T_r = E_task [ (1 / |T_task|) sum_t - ] +``` + +A positive value predicts that the INT8 move lowers loss; a negative value +predicts harm. I will retain the sign. I will not square the directional +derivative, take its absolute value, or clamp negative coordinates merely to +make rankings look stable. + +The expectation first averages scored storage boundaries within a task or +document and then gives tasks/documents equal weight. A multi-domain policy +also gives each frozen domain equal weight. This prevents long examples or one +domain from determining the policy by token count alone. + +This is deliberately an immediate-loss score: one reverse pass scores every +row, but it does not pretend to differentiate a full future suffix through +hard repeated quantization. A truncated-suffix straight-through estimator is +a separate possible method and may not silently replace this candidate after +results are visible. The primary score still ignores higher-order curvature, +changes in later quantization bins, future effects beyond the next read, and +interactions among many simultaneous row promotions. The full physical oracle +and equal-byte evaluation are therefore mandatory rather than optional +validation. + +## Label-free secondary: row-block model-Fisher risk difference + +The secondary candidate uses model-sampled pseudo-labels instead of dataset +targets. At each calibration window, sample `M` pseudo-label paths from the +model distribution at the chosen local expansion point. For sample `m`, let +`h_m` be the gradient of its negative log-probability with respect to the +flattened time-by-value block for physical row `r`. Using the corresponding +flattened INT4 and INT8 error blocks, define + +```text +F_r = (1 / (2M)) sum_m [ (h_m^T e4_r)^2 - (h_m^T e8_r)^2 ] +``` + +The two squared terms are evaluated separately before subtraction. The method +must not use `(h^T (e4 - e8))^2`, because that is the local Fisher sensitivity +of the move between endpoints, not the predicted reduction in risk relative +to the unquantized row. It must not use +`sum_i h_i^2 max(e4_i^2 - e8_i^2, 0)`, because coordinate-wise clipping changes +the diagonal-Fisher objective. + +Pseudo-labels, RNG seeds, sample count `M`, expansion-point construction, and +Monte Carlo error bars will be frozen. This score is only "label-free" in the +sense that it does not consume benchmark targets; it still depends on the +model distribution and calibration inputs. The empirical Fisher is not a +generic Hessian substitute, so this candidate remains secondary unless local +KL checks and physical interventions validate it. + +## Comparators and controls + +All row-format selectors receive the same promotion count and store the same +precision mask. + +1. `taylor_signed_next`: the primary score `T_r`. +2. `model_fisher`: the secondary score `F_r`. +3. `hrr_h1`: the Experiment 003 immediate-read marginal risk score, recomputed + on the same calibration manifest. +4. `row_mse`: the task-macro mean of + `||e4_(t,r)||_2^2 - ||e8_(t,r)||_2^2`, with negative values retained. +5. `v02_layer0_static`: layer 0 in INT8 and the remaining recurrent layers in + INT4, using its real mask-free layout. +6. `random_rows`: at least 20 row policies generated from prespecified seeds. + The full distribution is reported; the most favorable seed is not promoted + to the named baseline after results are visible. +7. `uniform_int4`: a lower-storage control, not an equal-byte competitor. +8. `fp32_state`: the quality reference, not a compressed competitor. + +The strongest equal-byte comparator is whichever of H1, row MSE, static v0.2, +and the prespecified random distribution has the lowest frozen-development +macro excess NLL. I will not compare only against the easiest baseline. + +## Stage A: analytic and finite-difference sanity + +Before ranking policies, the implementation must pass four checks on tiny +models and then on a stratified sample of real Qwen3.5 storage events. + +1. **Trajectory parity.** Captured `u^4`, `Q4(u^4)`, scales, codes, and logits + must match the packed repeated-INT4 path within frozen numerical tolerances. +2. **Directional derivative.** At one selected row and storage boundary, + inject `alpha * delta` after INT4 QDQ and evaluate the same next-token loss. + Compare `` with the central difference + `(L(+epsilon) - L(-epsilon)) / (2 epsilon)` for + `epsilon in {1/4, 1/8, 1/16, 1/32}`. +3. **Convergence.** For derivatives whose finite-difference magnitude exceeds + the frozen near-zero floor, require at least 95% sign agreement, median + relative error at most 10%, and a decreasing-error trend over the final + three epsilon values. Near-zero cases use an absolute error bound chosen + from FP32 repeat noise before the test is opened. +4. **Fisher/KL check.** On the same strata, compare the Monte Carlo model-Fisher + quadratic with the measured local KL at shrinking error scales. Report + correlation, calibration slope, and Monte Carlo confidence intervals. A + Fisher score that does not approach the local KL as scale shrinks is an + implementation or approximation failure, not evidence against Taylor + sensitivity. + +The strata cover every recurrent layer, multiple heads, early/middle/late +tokens, both domains, score signs, and quantization-error deciles. Any failed +check blocks the physical-oracle stage. Thresholds above are provisional until +the development freeze; changing them after opening test outputs creates a new +experiment version. + +## Stage B: stratified physical row oracle + +The oracle measures what each scalable score is supposed to predict. It uses a +held-out oracle partition that is not used to compute scores. + +For each sampled physical row `r`, run two complete packed trajectories: + +```text +baseline: all recurrent-state rows stored as INT4 +oracle r: row r stored as INT8 at every boundary; all other rows stored as INT4 +O_r = L_baseline - L_oracle_r +``` + +This is an actual recurrent intervention, not a QDQ replay of a completed FP32 +trace. It includes altered future states, later quantization, and the exact +row policy machinery. One-row trials all have the same incremental payload; +their purpose is causal ranking, not the final equal-byte claim. + +Rows are sampled before oracle outputs are opened. The sampling frame is +balanced across recurrent layer, head, calibration-score decile, MSE decile, +and sign, with a prespecified random seed. At least 384 unique rows per +model-by-domain development cell are evaluated unless a blinded power analysis +freezes a larger count. All sampled rows are retained, including negative and +catastrophic interventions. + +For every candidate and comparator, report: + +- Spearman rho and Kendall tau-b against `O_r`, with row-stratified bootstrap + intervals; +- oracle gain in each predicted score decile; +- precision and regret for the top 10% of sampled rows; +- results by layer, head, token-position stratum, and score sign; and +- the frequency with which an allegedly beneficial promotion has `O_r < 0`. + +The primary candidate passes this gate only if its rank association has a +95% interval strictly above zero in every model-by-domain cell, its macro +top-decile oracle gain is at least 20% higher than the strongest of H1 and row +MSE, and no cell has negative top-decile mean gain. These are diagnostic gates, +not confirmation claims. If Taylor fails, I will publish the failure and stop; +I will not select a favorable layer subset after inspecting the oracle. + +## Stage C: split-half policy stability + +Calibration task/document IDs are divided into fixed halves A and B within +each domain. I will build one balanced two-domain policy from A and another +from B for each model, without using oracle or quality-evaluation targets. + +Before the development quality run, require in every model cell: + +- all-row Spearman correlation of at least 0.70 between A and B scores; +- Jaccard overlap of at least 0.50 between the promoted row sets; and +- no more than 10 percentage points of promotion share moving into or out of + any recurrent layer. + +The final development policy uses the complete calibration partition only +after these thresholds pass. Failure means the allocator is too dependent on +the calibration sample for a static public policy. Averaging unstable halves +after seeing the failure does not count as a pass. + +## Exact physical byte budget + +For the currently targeted batch-one Qwen3.5 recurrent-state layout, each +physical row contains 128 values. INT4 stores 64 payload bytes per row, INT8 +stores 128, and one promotion therefore costs 64 payload bytes. There are +36,864 selectable rows in the 18-layer, 16-head layout. + +| Component | Row-format policy | Static v0.2 | +| --- | ---: | ---: | +| All-INT4 payload | 2,359,296 | 2,359,296 | +| 36,864 FP16 scales | 73,728 | 73,728 | +| Precision mask | 4,608 | 0 | +| INT8 promotion payload | 126,464 | 131,072 | +| Promoted rows | 1,976 | 2,048 | +| **Resident recurrent-state total** | **2,564,096** | **2,564,096** | + +Every row selector promotes exactly 1,976 rows. Static v0.2 keeps its genuine +metadata advantage and therefore promotes 72 more rows at the same total. +Uniform INT4 occupies 2,433,024 bytes; FP32 recurrent states occupy 18,874,368 +bytes for this layout. + +The evaluator will derive these counts from live tensor shapes and storage, +not accept the table as proof. Payload, stored scales, mask, indices, alignment, +padding, prefix tables, allocator-owned persistent tensors, transient bytes, +allocated bytes, and reserved bytes are recorded separately. Any new kernel +metadata is charged to the method that requires it. A model revision with a +different state layout gets a newly derived equal-byte budget before freeze. +Resident-state savings do not imply peak-memory or speed gains; those require +their own measured results. + +## Data separation and freeze sequence + +The confirmation matrix contains two base checkpoints and two domains: + +| Checkpoint | Code | Natural text | +| --- | --- | --- | +| `Qwen/Qwen3.5-0.8B-Base` | fixed public partition | fixed public partition | +| `Qwen/Qwen3.5-2B-Base` | fixed public partition | fixed public partition | + +These two checkpoints test scale, not architectural transfer. The separate +architecture gate for any later breakthrough-level claim is +[Gated DeltaNet-2](https://arxiv.org/abs/2605.22791), whose erase and write +gates are channel-wise and decoupled. Its +[official implementation](https://github.com/NVlabs/GatedDeltaNet-2) exposes +the same `128 x 128` recurrent-state geometry but is not a drop-in Transformers +cache, and its source has different license terms. A third-party checkpoint +must be pinned, audited, and covered by a separate integration protocol before +it can count as evidence. Tiny random models or a port that merely runs do not +count as cross-architecture quality validation. + +The current +[370M community checkpoint](https://huggingface.co/LLM-OS-Models/gdn2-370m-fineweb-edu-100b) +does not satisfy that gate: its pinned Hub revision contains raw `.pth` weights +but not the exact runnable model wrapper, tokenizer, config, or reference +logits, and the linked training-code repository is unavailable. The official +NVIDIA source uses noncommercial license terms and its released GPT wrapper +does not currently thread the GDN-2 cache. GDN-2 is therefore deferred from +Experiment 004 rather than treated as an easy extra result. + +Exact dataset names, immutable revisions, example IDs, serialization, +tokenization, context windows, content hashes, and exclusion rules must be +written into the freeze record before any corresponding outputs are opened. +Previously inspected RecurQuant confirmation examples are ineligible for an +"untouched" claim. + +The sequence is: + +1. **Engineering sandbox:** unit tests, tiny-model checks, profiler work, and + explicitly non-evidentiary debugging prompts. +2. **Calibration:** compute scores from fixed calibration partitions. Target + labels are allowed only for `taylor_signed_next` and are never reused as held-out + evidence. +3. **Oracle development:** open only the prespecified stratified one-row trials. +4. **Development freeze:** record formulas, normalization, data manifests, + seeds, pseudo-label count, row sample, byte formulas, metrics, thresholds, + implementation commit, dependencies, and environment before the full + development quality matrix runs. +5. **Development run:** execute every comparator. Proceed only if all gates + pass without changing the candidate. +6. **Confirmation freeze:** lock the selected formula, one policy per model, + packed format, evaluator, statistical plan, and claim language. Hash the + complete record. +7. **Untouched confirmation:** run the complete 2-by-2 matrix once. No partial + previews, policy sweeps, prompt edits, seed replacement, or favorable-subset + filtering. +8. **Independent replication:** release the frozen artifacts and require a + reproduction outside the original implementation/evaluation run before any + breakthrough wording is considered. + +Any change to the score, row budget, model, dataset, seed set, threshold, or +primary metric after its freeze opens a new numbered experiment. Negative +results remain in the repository. + +## Full-policy development and confirmation gates + +Primary metric: task/document-macro excess NLL relative to the FP32-state +reference. The statistical unit is the task or document, not the token. Use +10,000 paired bootstrap resamples with frozen seed 2339. + +Secondary metrics are mean token KL, worst-10% token KL, top-1 agreement, +worst-10% task/document excess-NLL CVaR, free-generation task metrics, +long-context retrieval when applicable, exact bytes, decode throughput, and +p50/p95 single-token latency. + +At both development and untouched confirmation, `taylor_signed_next` must: + +1. occupy exactly 2,564,096 resident recurrent-state bytes for the frozen + layout and pass packed-versus-explicit-QDQ parity; +2. reduce macro excess NLL by at least 20% relative to the strongest equal-byte + comparator; +3. have a paired 95% bootstrap interval strictly above zero for that reduction; +4. improve excess NLL in every model-by-domain cell; +5. remain non-inferior on frozen margins for CVaR, top-1 agreement, retrieval, + and free generation; and +6. disclose throughput, latency, peak allocated memory, and peak reserved + memory even when they regress. + +A development pass permits the confirmation run, not a public performance +claim. A confirmation failure is the result. It is not repaired by redefining +the primary metric or removing the failing cell. + +## Claim ladder + +Each rung permits only the wording shown below. + +| Rung | Required evidence | Permitted claim | +| --- | --- | --- | +| 0. Hypothesis | This working draft | "RecurQuant is testing signed downstream-loss sensitivity for recurrent-state row allocation." | +| 1. Implementation | Trajectory parity and finite-difference checks pass | "The implemented score matches local directional derivatives on the tested repeated-QDQ path." | +| 2. Oracle validity | The frozen stratified physical-row gate passes | "The score predicts held-out single-row promotion effects on the named development sample." | +| 3. Held-out result | Split-half, development, and untouched 2-by-2 confirmation gates pass | "At the stated byte budget, the frozen policy reduced excess NLL by X% over the strongest named comparator on the named checkpoints and domains." | +| 4. Research contribution | Rung 3, closest-method reproduction, released code/artifacts, and measured systems behavior | "RecurQuant provides a validated loss-sensitive mixed-precision recurrent-state allocation method within the evaluated scope." | +| 5. Breakthrough consideration | Independent replication, additional recurrent architecture and hardware, meaningful advantage over closest current methods, and no hidden quality or systems regression | Only then may "breakthrough" be evaluated as a community claim; it is never inferred from this project's own benchmark alone. | + +RecurQuant is currently at rung 0 for Experiment 004. Passing one checkpoint, +one domain, a same-data diagnostic, or a correlation plot cannot skip rungs. + +## Prior-work and novelty boundary + +The broad ingredients are established. [SqueezeLLM](https://arxiv.org/abs/2306.07629) +uses sensitivity information in post-training LLM quantization; +[HAWQ-V2](https://arxiv.org/abs/1911.03852) and +[HAWQ-V3](https://proceedings.mlr.press/v139/yao21a.html) develop +Hessian-aware mixed precision and hardware-aware integer inference; and +[GuidedQuant](https://proceedings.mlr.press/v267/kim25d.html) studies +gradient-guided low-bit quantization. The limitations of treating empirical +Fisher as a general curvature proxy are analyzed directly in +[On the Relationship Between the Fisher Information and the Hessian](https://arxiv.org/abs/1905.12558). + +The recurrent setting is also active prior art. [Gated DeltaNet](https://proceedings.iclr.cc/paper_files/paper/2025/hash/4904fad153f6434a7bcf04465d4be2cc-Abstract-Conference.html) +defines the gated delta recurrence used by this model family, and the +[official Transformers Qwen3.5 implementation](https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen3_5/modeling_qwen3_5.py) +is the executable architecture reference. [Q-Mamba](https://aclanthology.org/2025.findings-acl.551/) +quantizes recurrent state caches. [TQS-PTQ](https://arxiv.org/abs/2606.13300), +[RateQuant](https://arxiv.org/abs/2605.06675), and +[Block-GTQ](https://arxiv.org/abs/2606.24033) cover adjacent trajectory, +rate-allocation, and blockwise mixed-precision ideas. + +[Gated DeltaNet-2](https://arxiv.org/abs/2605.22791) is directly relevant to +the generalization boundary: it replaces the tied scalar erase/write gate with +separate channel-wise gates while retaining a fixed recurrent matrix state. +It is a strong external architecture test, not evidence for the current Qwen +implementation. RecurQuant will not claim architectural generality until a +pinned pretrained GDN-2 checkpoint passes an independently frozen protocol. + +Therefore, signed Taylor scoring, Fisher scoring, mixed bit widths, and +recurrent-cache quantization are not individually novel. The open question is +narrower: whether measuring the real INT4-to-INT8 promotion direction on a +repeatedly requantized Gated DeltaNet path yields a stable, exact-byte row +allocator that survives physical interventions and untouched evaluation. A +positive answer could support a scoped contribution. It would not establish a +"first" or a breakthrough without the higher claim rungs. + +## Required public artifacts + +If the protocol advances, I will release: + +- the frozen protocol and hashes before confirmation; +- source code for trace capture, both scores, all comparators, mixed packing, + and physical interventions; +- immutable model and dataset revisions plus exact example manifests; +- per-task metrics, all random seeds, bootstrap inputs, and negative rows; +- exact byte ledgers and memory/latency profiler outputs; +- environment and hardware records; and +- raw artifacts sufficient to recompute every table and graph. + +The README may visualize a result only after its rung is earned. A graph from +the calibration set must be labeled calibration; an oracle plot must be +labeled diagnostic; and neither may be presented as held-out performance. diff --git a/research/EXPERIMENT_005_ADAPTIVE_ROW_PACKING_PROTOCOL_DRAFT.md b/research/EXPERIMENT_005_ADAPTIVE_ROW_PACKING_PROTOCOL_DRAFT.md new file mode 100644 index 0000000..5891caa --- /dev/null +++ b/research/EXPERIMENT_005_ADAPTIVE_ROW_PACKING_PROTOCOL_DRAFT.md @@ -0,0 +1,338 @@ +# Experiment 005: quota-calibrated adaptive row packing + +> **Status: pre-holdout protocol freeze; implementation complete; holdout not +> opened.** +> +> This experiment is a new hypothesis after Experiment 004's signed-Taylor +> policy failed its same-calibration diagnostic. It makes no improvement, +> novelty, speed, or breakthrough claim. The selector artifacts, implementation +> commit, and source hashes must be frozen before the ranked calibration +> holdout is opened. + +Date opened: 2026-07-23 + +## Research question + +At the same exact resident recurrent-state byte budget, does separating the +allocation problem into: + +1. an offline, target-directional Fisher estimate of **how many** INT8 rows + each recurrent layer receives; and +2. a runtime reconstruction-error decision about **which** rows receive those + slots at each storage boundary + +preserve Qwen3.5 Gated DeltaNet outputs better than static row identities, +H1 read-risk allocation, row MSE, the v0.2 layer-0 policy, and random rows? + +The method is called **quota-calibrated adaptive row packing** in this protocol. +That is a descriptive name, not a novelty claim. + +## Why this is a separate experiment + +The first Experiment 004 pilot ranked rows by the signed local Taylor benefit + +```text +-gradient dot (Q8(raw) - Q4(raw)). +``` + +On the eight inspected calibration tasks, that complete 1,976-row policy was +worse than H1 and the properly formed squared endpoint-risk diagnostics. The +sign formula itself remains subject to an independent real-storage-boundary +finite-difference check, but a correct one-row derivative would not rescue a +poor simultaneous static policy. Experiment 005 therefore does not relabel the +failed Taylor policy or silently make another Experiment 004 score primary. + +The new hypothesis addresses the observed prompt dependence directly: layer +budgets remain frozen, while row identities may respond to the actual state +being stored. + +## Frozen candidate + +### Offline layer quotas + +Calibration follows the repeated-INT4 quantize/dequantize trajectory. For row +`r` at a scored storage boundary, with target-NLL gradient `g` and aligned +endpoint errors + +```text +e4 = Q4(raw) - raw +e8 = Q8(raw) - raw, +``` + +the row score is + +```text +D_r = task_macro_mean[(g dot e4)^2 - (g dot e8)^2]. +``` + +Negative values are retained. Globally ranking `D_r` under the exact physical +budget produces a static plan. Experiment 005 discards that plan's row +identities after using only its per-layer promotion counts `q_l`. + +This target-gradient score is a supervised calibration statistic. It is not +the label-free model Fisher and must not be described as one. + +### Runtime row choice + +For every raw recurrent-state write at layer `l`, compute each physical row's +aligned reconstruction-error reduction: + +```text +A_(t,l,r) = mean((Q4(raw_(t,l,r)) - raw_(t,l,r))^2) + - mean((Q8(raw_(t,l,r)) - raw_(t,l,r))^2). +``` + +Promote exactly the top `q_l` rows to INT8 for that write. Ties are resolved by +stable flattened `[head, key-row]` order. The selection is batch-one only in +this experiment. It cannot inspect the next token, target label, logits, or a +future query at inference time. + +The primary method name in artifacts is: + +```text +adaptive_mse_target_directional_fisher_quota +``` + +## Required ablations and controls + +Every adaptive and selector-plan static row-format policy has the same 1,976 +promotions, mask format, and exact resident bytes. The v0.2 control reaches the +same resident-byte total with 2,048 promotions because it has no precision +mask. Uniform INT4 is a lower-byte control. These differences are part of the +frozen comparison and may not be hidden by calling every policy equal-format. + +1. `adaptive_mse_target_directional_fisher_quota` — frozen primary. +2. `adaptive_mse_hrr_h1_quota` — identical runtime selector with H1-derived + per-layer quotas; isolates whether the offline quota signal matters. +3. `target_directional_fisher_difference_int4` — static row identities from + the same Fisher score; isolates whether per-update adaptation matters. +4. `hrr_h1` — strongest pre-existing static diagnostic comparator. +5. `hrr_h32` — retained negative Experiment 003 hypothesis. +6. `row_mse` — static task-macro reconstruction-error allocation. +7. `v02_layer0_static` — layer 0 INT8, remaining recurrent layers INT4, with + its genuine mask-free metadata advantage. +8. `random_rows_s1101` — prespecified random static policy. +9. `uniform_int4` — lower-byte control, not an equal-byte competitor. + +The signed-Taylor and other Experiment 004 diagnostic policies remain in the +artifact for transparency but are not eligible to replace the frozen primary +after holdout results are visible. + +## Exact storage contract + +For Qwen3.5-0.8B-Base at batch one: + +| Component | Adaptive/static row format | Static v0.2 | +| --- | ---: | ---: | +| All-INT4 payload | 2,359,296 B | 2,359,296 B | +| FP16 scales | 73,728 B | 73,728 B | +| Precision mask | 4,608 B | 0 B | +| INT8 promotion payload | 126,464 B | 131,072 B | +| Promoted rows | 1,976 | 2,048 | +| **Resident total** | **2,564,096 B** | **2,564,096 B** | + +Adaptive row identities change without changing the number or size of packed +INT4 payloads, INT8 payloads, FP16 scales, or mask bytes. Exact storage is +checked from live packed tensors after every evaluated task. This is not a +claim about model weights, full KV memory, temporary workspace, CUDA allocator +peaks, throughput, or latency. + +## Frozen local derivative validator + +Before the heldout-calibration window is opened, the independent storage-boundary +diagnostic must run in FP32 and return `passed: true`. It uses one pinned +calibration task and target transition, four rows chosen only from fixed geometry, +physical Q4 and Q8 row endpoints, and the epsilon grid +`{1/4, 1/8, 1/16, 1/32}`. At the Q4 endpoint it checks the local target-NLL +directional derivative + +```text +dL/dalpha at alpha=0 = g dot (Q8(raw) - Q4(raw)) +``` + +against central differences. Equivalently, its local predicted benefit is the +negative of that derivative. The validator checks storage timing, endpoint +construction, derivative sign, and local finite-difference consistency. It does +**not** validate the algebra or implementation of the squared quota score: + +```text +(g dot e4)^2 - (g dot e8)^2 +``` + +The squared-score implementation is covered by selector unit tests; its arrays +and derived plans are authenticated by artifact hashes. Whether the resulting +layer quotas improve quality is the separate question tested by the frozen +quality diagnostic. + +The derivative artifact passes only if every condition below holds: + +1. the model and recurrent-state calculation use FP32; +2. the maximum repeated-`alpha=0` loss difference over all rows and epsilons is + at most `1e-7`; +3. a row is informative when the absolute autograd derivative is strictly + greater than `1e-8`, and at least three of the four rows are informative; +4. central-difference sign agreement over all informative row/epsilon checks is + at least `0.95`; +5. the median absolute relative derivative error over those informative checks + is at most `0.10`; +6. at least `0.75` of informative rows converge, where convergence means the + absolute error at epsilon `1/32` is no greater than at epsilon `1/8`; and +7. for every near-zero row, the absolute central-versus-autograd derivative + error is at most `2e-7` at every epsilon. + +Failure is recorded and keeps the holdout closed. These thresholds, row strata, +epsilon grid, transition, and FP32 requirement may not be changed after seeing +the diagnostic output and called the same validation gate. + +## Freeze and data separation + +The first ranked eight rows of the pinned MBPP calibration population are the +selector partition. They were already inspected while developing Experiments +003 and 004 and can provide no held-out claim. + +The first Experiment 005 generalization check is fixed before opening it: + +```text +phase: calibration +ranked offset: 8 +task count: 8 +window: [8, 16) +``` + +Experiment 005 heldout mode is fail-closed: it requires both frozen selector +artifacts, `--calibration-offset 8`, and `--limit 8`. Any other offset or task +count is not this protocol and must be refused rather than emitted under the +Experiment 005 heldout artifact kind. + +Before tokenization or model loading, the evaluator must authenticate each +selector artifact against the first eight rows of the pinned ranked MBPP +calibration population. Ordered task IDs, canonical row-content hashes, +dataset/config/revision, source split, selection namespace, and formatter +version must match the pinned selector prefix. It must then prove that window +`[8, 16)` is disjoint from the union of all authenticated selector-artifact task +IDs. Self-consistency between two selector artifacts is not sufficient. + +The evaluator records the heldout window's canonical row-content manifest, +formatter version, token counts, model and dataset revisions, implementation +commit, command, packages, and hardware. It records an explicit map of relevant +implementation source paths and hashes both before and after the run, requires +those maps to be identical, and requires the same clean Git commit at both +boundaries. A dirty or changing implementation cannot produce a passing +heldout artifact. This remains a **heldout-calibration diagnostic**, not +development or confirmation evidence. + +The holdout may be opened only after: + +- the selector scripts and adaptive evaluator are committed; +- new selector artifacts are generated from that committed implementation; +- their canonical hashes and quantizer contracts verify; +- both selector manifests authenticate against the pinned first-eight-row + prefix; +- the real storage-boundary derivative diagnostic satisfies every numeric local + gate above and records `passed: true`; +- the evaluator starts and ends on the same clean implementation commit with + unchanged source hashes; and +- this candidate, method list, offset, task count, primary metric, and gates + are committed. + +## Metrics and gates + +Primary metric: task-macro excess next-token NLL relative to an FP32 recurrent +state, scored only on code transitions that occur after a quantized recurrent +state has been stored. The prompt-to-first-code-token prediction is excluded +because the cache policy cannot affect it. Use 10,000 paired bootstrap +resamples with seed 2339. + +The equal-byte static comparator set is frozen as `hrr_h1`, `hrr_h32`, +`row_mse`, `random_rows_s1101`, `v02_layer0_static`, +`signed_taylor_next_int4`, `target_directional_fisher_difference_int4`, +`target_diagonal_fisher_difference_int4`, and +`delta_direction_magnitude_int4`. No member may be excluded after results are +visible. The strongest comparator is selected deterministically as the member +with the lowest macro excess NLL on this frozen window. Uniform INT4 is excluded +because it uses fewer resident bytes; `adaptive_mse_hrr_h1_quota` is adaptive +and is tested separately. The primary passes this small heldout-calibration gate +only if all conditions hold: + +1. resident recurrent-state bytes equal 2,564,096 exactly on every task; +2. macro excess NLL is lower than every equal-byte static comparator; +3. the paired 95% interval versus the strongest equal-byte static comparator + is strictly above zero when expressed as comparator minus primary; +4. relative excess-NLL reduction versus that comparator is at least 20%; +5. the paired 95% interval versus `adaptive_mse_hrr_h1_quota` is also strictly + above zero when expressed as H1-adaptive minus primary, so the supervised + quota signal contributes beyond adaptation alone; +6. macro top-1 agreement is no more than `0.01` below the strongest equal-byte + static comparator; +7. macro worst-token KL CVaR95 is no more than `0.10` above that comparator; +8. on every task, the primary's excess NLL is at most `1.0` worse than that + comparator's excess NLL; +9. no metric or model output is non-finite; and +10. all code, artifact, manifest, and source hashes verify, the repository is + clean, and the start/end implementation records match. + +The evaluator must record each condition, its threshold, observed value, and +pass/fail result, plus an overall conjunction. There is no discretionary +interpretation of “materially worse” or “catastrophic” after the results are +visible. If the strongest comparator has non-positive excess NLL so that the +relative-reduction condition is not meaningful, condition 4 fails closed. + +Eight tasks are too few for a research claim. Passing advances the method to a +larger calibration split, a separately frozen development domain, free +generation, long-context retrieval, multiple rounding seeds, and a second +checkpoint. Failing is recorded and stops this candidate without changing its +formula after the fact. + +## Systems boundary + +The current Python prototype quantizes both candidate endpoints to score rows +and then physically packs the chosen endpoint. It is expected to add overhead. +No speed claim is permitted. A fused Triton/CUDA kernel is a later engineering +gate and must benchmark p50/p95 decode latency, throughput, peak allocated and +reserved memory, and kernel workspace against static packing. + +Stochastic rounding is also a separate experiment. It may not be introduced +after viewing this nearest-rounding holdout and called the same candidate. + +## Prior work and novelty boundary + +The broad ingredients are not new: + +- [Q-Mamba](https://aclanthology.org/2025.findings-acl.551/) quantizes Mamba + state caches and introduces state/channel decoupled scaling plus reconstruction. +- [OuroMamba](https://arxiv.org/abs/2503.10959) uses per-time-step dynamic + outlier-channel selection for mixed-precision Vision Mamba inference. +- [Nemotron 3 Super](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf) + reports recurrent cache-error accumulation and uses stochastic rounding for + its FP16 Mamba state cache. +- [RateQuant](https://arxiv.org/abs/2605.06675) calibrates mixed-precision KV + cache allocation with rate-distortion models. +- [TQS-PTQ](https://arxiv.org/abs/2606.13300) studies trajectory-based + sensitivity for quantized dynamical systems. + +Therefore dynamic mixed precision, state-cache quantization, calibration, +reconstruction error, Fisher/Taylor sensitivity, and layer quotas cannot be +claimed individually. The narrow open contribution is whether the particular +combination of supervised recurrent-layer quotas, exact-byte physical +INT4/INT8 matrix-row packing, and causal per-write row selection works on a +Gated DeltaNet language-model cache. Even a positive result on this protocol +would support only a scoped experimental contribution. + +## Claim ladder + +1. **Implementation:** exact packed bytes and deterministic adaptive masks pass + unit and real-model integration tests. +2. **Local validity:** the independent local target-directional derivative + checks pass; this does not validate the squared quota formula or policy + quality. +3. **Heldout-calibration signal:** the frozen eight-task gate above passes. +4. **Development result:** larger code and natural-text cells, second model, + free generation, long context, and systems measurements pass a new freeze. +5. **Research contribution:** closest-method reproductions and released raw + artifacts support a stable advantage. +6. **Breakthrough consideration:** independent replication, another recurrent + architecture and hardware, and a meaningful advantage without hidden + systems or quality regression. + +Experiment 005 is currently below rung 3. The word "breakthrough" is not +permitted in the README, release title, social post, or result figure. diff --git a/research/EXPERIMENT_005_RESULT.md b/research/EXPERIMENT_005_RESULT.md new file mode 100644 index 0000000..335dbd3 --- /dev/null +++ b/research/EXPERIMENT_005_RESULT.md @@ -0,0 +1,205 @@ +# Experiment 005 result: gate failure and same-calibration postmortem + +> **Status: permanently failed before holdout.** +> +> The frozen storage-boundary validator achieved 13/16 sign checks +> (`0.8125`) against a required minimum of `0.95`. Experiment 005 therefore +> did not open its ranked `[8, 16)` heldout-calibration window. Later results +> on the already inspected selector partition are postmortem diagnostics only; +> they do not reopen the gate or support an improvement, generalization, +> novelty, or breakthrough claim. + +Date recorded: 2026-07-23 + +## Frozen gate evidence + +The permanent gate decision is backed by: + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment005-storage-boundary-599862e.json` | +| Artifact kind | `recurquant_storage_boundary_taylor_diagnostic` | +| Clean implementation commit | `599862eef3b635f14b05c578f02393a0abd072a6` | +| File SHA-256 | `61a2936bd20679bad441921d26b556f5986eec61449c4a9743c9b0b5e0bea86d` | +| Canonical evidence SHA-256 | `b168330b4c39963b7c149230d4b1ad9fa57b20b02d6d95ee583ee9941f68b19f` | +| Created at | `2026-07-22T17:07:41.231231+00:00` | +| Model arithmetic | FP32 | +| Hardware | NVIDIA RTX 5070 Laptop GPU | +| PyTorch | `2.11.0+cu128` | +| Transformers | `5.14.1` | +| Evidence verification | valid, with no verifier errors | + +The artifact records `passed: false`. Its only failed conjunction member is +central-difference sign agreement: + +| Check | Frozen threshold | Observed | Result | +| --- | ---: | ---: | --- | +| Repeated baseline maximum difference | at most `1e-7` | `0.0` | pass | +| Informative rows | at least 3 of 4 | 4 of 4 | pass | +| Central-difference sign agreement | at least `0.95` | `13/16 = 0.8125` | **fail** | +| Median absolute relative derivative error | at most `0.10` | `0.03400520381314055` | pass | +| Convergent informative rows | at least `0.75` | `0.75` | pass | +| Near-zero-row absolute-error checks | at most `2e-7` | no near-zero rows | pass | + +The overall gate is a conjunction, so one failed condition is a failed gate. +The threshold, row strata, epsilon grid, arithmetic mode, and pass rule remain +as frozen. We will not weaken them, delete the failed row, or rerun a modified +validator and label it the Experiment 005 result. + +## Failure localization + +All three sign misses came from the fixed `early_low` row. The other three +rows produced 12/12 correct sign checks. For `early_low`, the autograd +directional derivative was + +```text ++1.534285755042659e-07 +``` + +at a base target NLL of `0.03833549842238426`. One FP32 scalar ULP at that loss +is `3.725290298461914e-09`. Across epsilon values `1/4`, `1/8`, `1/16`, and +`1/32`, the expected central-difference numerators were approximately 20.59, +10.30, 5.15, and 2.57 ULPs. The observed numerator counts were 0, 31, 0, and 0 +ULPs, yielding sign flags `false`, `true`, `false`, and `false`. The observed +endpoint benefit for this row was zero. + +This pattern is **consistent with finite-precision loss resolution** at that +row and transition. It does not prove that resolution was the sole cause, and +it does not turn the failed gate into a pass. More importantly, this validator +tested the signed local derivative + +```text +g dot (Q8(raw) - Q4(raw)), +``` + +not the actual squared endpoint score used to form the target-directional +Fisher quotas: + +```text +(g dot e4)^2 - (g dot e8)^2. +``` + +Future candidates must validate their own score algebra and packing directly. +They must not inherit this failed signed-derivative check as evidence for a +different formula. + +## Holdout remained unopened + +Experiment 005 froze the following first generalization window: + +```text +phase: calibration +ranked offset: 8 +task count: 8 +window: [8, 16) +``` + +The gate failed before tokenization or model loading for that window. No +Experiment 005 heldout artifact was produced, and the ranked `[8, 16)` rows +remain unchanged and unopened. Any later protocol that proposes to use this +window must authenticate that fact before access; it may not silently substitute +a different window after seeing data. + +## Same-calibration postmortem + +After the failure, we ran one descriptive quality diagnostic on the already +inspected selector partition. Its evidence is: + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment005-adaptive-same-calibration-8task-556e527.json` | +| Artifact kind | `recurquant_adaptive_row_packing_same_calibration_quality_diagnostic` | +| Clean implementation commit | `556e527b75f73d557e2bddf4a9973ce945cf1ab9` | +| File SHA-256 | `3495698932b43d93f387bb61492f91fc38840097020980977451be2042a02164` | +| Canonical evidence SHA-256 | `f07a6b852a4c427c0b9946ad44c21d023299d173eb24695462e99e3785061d61` | +| Created at | `2026-07-22T17:40:53.827148+00:00` | +| Selection | selector-task prefix, offset 0, limit 8, stop 8 | +| Aligned scored tokens | 642 | +| Primary method | `adaptive_mse_target_directional_fisher_quota` | +| Holdout applicability | `false` | +| Holdout pass value | `null` | + +The filename begins with `experiment006` because it was generated during later +development, but its primary method is the plain Experiment 005 adaptive-MSE +candidate. It contains no rank fusion and cannot be treated as Experiment 006 +evidence. The eight task IDs were `945`, `794`, `657`, `702`, `651`, `720`, +`903`, and `918`; all belong to the selector partition already used for method +development. + +The task-macro results were: + +| Method | Excess next-token NLL | +| --- | ---: | +| `adaptive_mse_target_directional_fisher_quota` | `0.4933023080229759` | +| `target_directional_fisher_difference_int4` | `0.5357813686132431` | +| `target_diagonal_fisher_difference_int4` | `0.5480052754282951` | +| `delta_direction_magnitude_int4` | `0.5449846908450127` | +| `hrr_h1` | `0.6301188990473747` | +| `hrr_h32` | `0.7533748596906662` | +| `adaptive_mse_hrr_h1_quota` | `0.7794309854507446` | +| `row_mse` | `0.7946401834487915` | +| `v02_layer0_static` | `0.8614744991064072` | +| `uniform_int4` | `2.6861148476600647` | +| `random_rows_s1101` | `2.729534089565277` | + +For the primary, mean KL was `0.4791950900107622`, worst-token KL CVaR95 was +`2.902877390384674`, and top-1 agreement was `0.7851893156766891`. + +Using 10,000 paired bootstrap resamples with seed 2339, comparator-minus-primary +contrasts included: + +| Comparator | Mean improvement | Paired 95% interval | +| --- | ---: | ---: | +| `adaptive_mse_hrr_h1_quota` | `0.2861286774277687` | `[0.1524614840745926, 0.44845127817243335]` | +| `target_directional_fisher_difference_int4` | `0.04247906059026718` | `[-0.026418810337781883, 0.12283947486430402]` | +| `target_diagonal_fisher_difference_int4` | `0.054702967405319214` | `[-0.025941481441259337, 0.14100585989654058]` | +| `delta_direction_magnitude_int4` | `0.05168238282203674` | `[-0.05204220674932003, 0.14878664575517173]` | +| `hrr_h1` | `0.1368165910243988` | `[0.0874815434217453, 0.1885298192501068]` | + +The strongest equal-byte static comparator by excess NLL was +`target_directional_fisher_difference_int4`. The descriptive relative +reduction against it was + +```text +(0.5357813686132431 - 0.4933023080229759) +/ 0.5357813686132431 += 0.0792843183409219 +``` + +or approximately 7.93%. That is below the frozen 20% requirement, and the +paired interval against this comparator crosses zero. The postmortem therefore +would not satisfy the frozen quality gate even if it had been held out. Its +positive interval against the H1-quota adaptive ablation is useful for forming +a new hypothesis, but it is not evidence of generalization. + +## Related selector artifacts + +Two selector artifacts were also generated at commit +`556e527b75f73d557e2bddf4a9973ce945cf1ab9`: + +| Artifact | File SHA-256 | Canonical evidence SHA-256 | +| --- | --- | --- | +| `artifacts/experiment006-hrr-selector-8task-556e527.json` | `fa02e1d468ecc13c78b7cf8e63f237e372c556d9fed0c1f4b47c9dd901a808dd` | `07e646ccb9b1df5ff9873a94f7bacb07d7a4e2b70136e3a68f40d1619814d899` | +| `artifacts/experiment006-loss-selector-8task-556e527.json` | `33bdc5939429281ba5377eeb02d59fac72a0f8da657c713bb7854d235e2fb057` | `ae92af38475720eb1ce19527f1c2de3d0d1fc045a1160a83f8da30ecde282214` | + +These are authenticated exploratory inputs, not heldout results. They predate +the Experiment 006 fusion freeze and cannot satisfy a later protocol's +implementation, score-validation, or provenance prerequisites without +regeneration from that protocol's clean committed implementation. + +## Result and claim boundary + +The durable findings are limited to: + +1. the frozen Experiment 005 numerical gate failed at `0.8125 < 0.95`; +2. the failure was localized to three checks on one low-signal row and is + consistent with, but not proven to be caused by, FP32 loss resolution; +3. the ranked `[8, 16)` holdout was not opened; +4. plain adaptive MSE was promising on the same eight tasks used to construct + its quotas, but did not clear the frozen effect-size or strongest-static + uncertainty requirements; and +5. any successor is a new candidate with a new artifact kind and an independent + validation gate. + +Experiment 005 makes no breakthrough claim. It remains below the +heldout-calibration rung of the claim ladder. diff --git a/research/EXPERIMENT_006_RANK_FUSION_PROTOCOL_DRAFT.md b/research/EXPERIMENT_006_RANK_FUSION_PROTOCOL_DRAFT.md new file mode 100644 index 0000000..88ad05e --- /dev/null +++ b/research/EXPERIMENT_006_RANK_FUSION_PROTOCOL_DRAFT.md @@ -0,0 +1,570 @@ +# Experiment 006: quota-constrained rank fusion protocol draft + +> **Status: pre-holdout protocol freeze. The ranked `[8, 16)` window is +> unopened.** +> +> The immutable primary is equal rank fusion with `lambda = 0.5`. Values +> `lambda = 0.25` and `lambda = 0.75` are predeclared ablations only; neither +> may replace the primary after results are visible. The `lambda = 0` and +> `lambda = 1` endpoints are controls. This protocol makes no improvement, +> novelty, systems, or breakthrough claim. + +Date opened: 2026-07-23 + +## Relation to Experiment 005 + +Experiment 005 failed its frozen storage-boundary sign gate: 13 of 16 checks +agreed, or `0.8125`, below the required `0.95`. That failure is permanent and +is recorded in `EXPERIMENT_005_RESULT.md`. It stopped Experiment 005 before +the ranked `[8, 16)` heldout-calibration window was opened. + +The failed gate validated a signed local derivative, + +```text +g dot (Q8(raw) - Q4(raw)), +``` + +not the squared endpoint score that generated the layer quotas and not the +reconstruction-error score used at runtime. Experiment 006 therefore uses a +new candidate-aligned numerical and packing gate. The old finite-difference +result remains available as a labeled non-gating diagnostic; it is not erased, +rerun under looser thresholds, or inherited as evidence for this candidate. + +## Research question + +At the same exact resident recurrent-state byte budget and the same frozen +per-layer INT8 quotas, does a deterministic equal-rank combination of: + +1. offline target-directional sensitivity; and +2. causal per-write INT4-to-INT8 reconstruction-error reduction + +preserve Qwen3.5 Gated DeltaNet outputs better than either component alone, +the strongest equal-byte static policy, and the H1-quota adaptive control on a +previously unopened eight-task calibration window? + +This is a small heldout-calibration diagnostic. It is not a confirmation set. + +## Frozen candidate + +### Physical rows and layer quotas + +Each Gated DeltaNet recurrent layer contains 2,048 physical matrix rows: +16 heads by 128 key rows. A row's canonical coordinate is +`(layer_index, head_index, key_row_index)`, with flattened in-layer index + +```text +head_index * 128 + key_row_index. +``` + +The offline selector computes the frozen +`target_directional_fisher_difference_int4` score for every row. For a scored +storage boundary, with target-NLL gradient `g` and aligned quantization errors + +```text +e4 = Q4(raw) - raw +e8 = Q8(raw) - raw, +``` + +the primitive score is + +```text +(g dot e4)^2 - (g dot e8)^2. +``` + +Task-macro aggregation over the authenticated first eight selector tasks gives +the static row score `S_(l,r)`. Negative values are retained. A global stable +descending rank under the exact format budget selects 1,976 rows. The number +selected in layer `l` becomes its immutable quota `q_l`, with + +```text +sum_l q_l = 1,976. +``` + +The exact quota vector and the global selected mask must be recorded and +hashed in the regenerated selector artifact. Experiment 006 never retunes +`q_l` on the heldout window. + +### Per-write reconstruction score + +At each real recurrent-state write, the dynamic score for row `r` in layer +`l` is + +```text +M_(t,l,r) = mean((Q4(raw_(t,l,r)) - raw_(t,l,r))^2) + - mean((Q8(raw_(t,l,r)) - raw_(t,l,r))^2). +``` + +The Q4 and Q8 errors are aligned endpoints of the same source row and the same +quantizer contract. Higher values mean that promoting the row removes more +instantaneous reconstruction error. + +### Deterministic per-layer ranks + +For each layer independently: + +- `r_S(l,r)` is the zero-based descending rank of `S_(l,r)`; +- `r_M(t,l,r)` is the zero-based descending rank of `M_(t,l,r)` at that write; +- rank 0 is best; and +- exact score ties are resolved by ascending canonical flattened in-layer + coordinate. + +Ranks are permutations of the integers 0 through 2,047. No unstable GPU sort, +random tie break, epsilon tie, or rounded display value may determine a rank. +The ranking implementation must produce the same integer arrays as the +canonical CPU FP64 implementation. + +### Frozen fusion family + +`lambda` is the weight on the **static sensitivity rank**. It does not mix +directional Fisher with diagonal Fisher. Multiplying by four gives exact +integer costs and avoids floating-point tie ambiguity: + +| Role | `lambda` | Integer cost to minimize | +| --- | ---: | --- | +| Endpoint control: plain adaptive MSE | `0` | `4 * r_M` | +| Predeclared ablation | `0.25` | `3 * r_M + r_S` | +| **Frozen primary: equal rank fusion** | **`0.5`** | **`2 * r_M + 2 * r_S`** | +| Predeclared ablation | `0.75` | `r_M + 3 * r_S` | +| Endpoint control: static target-Fisher plan | `1` | `4 * r_S` | + +For every write and layer, select exactly the `q_l` rows with smallest fusion +cost. Final cost ties are resolved by ascending canonical flattened coordinate. +The physical packer stores those rows as INT8 and all other rows as INT4. + +The primary artifact method name is frozen as: + +```text +rank_fusion_l050_target_fisher_adaptive_mse +``` + +The `lambda = 0.25` and `lambda = 0.75` results must be reported regardless of +sign, but they are ablations. A better ablation cannot be promoted to primary, +used for the pass decision, or described as the prespecified result. Any later +choice of a different weight is a new experiment on new data. + +### Endpoint invariants + +Before any heldout access: + +1. `lambda = 0` must reproduce the existing + `adaptive_mse_target_directional_fisher_quota` mask exactly at every + audited write; +2. `lambda = 1` must reproduce the frozen static + `target_directional_fisher_difference_int4` mask exactly at every write; +3. each layer must select exactly `q_l` rows for every lambda value; +4. the total promotion count must be exactly 1,976; and +5. repeated runs from identical inputs must produce byte-identical ranks, + costs, masks, payloads, and hashes. + +One mismatch fails closed. The endpoints are controls, not alternative primary +methods. + +### Causality boundary + +The experiment is batch-one. Static ranks are frozen from the selector +partition. At runtime, the dynamic rank may use only the current raw state and +its current aligned Q4/Q8 endpoints before that state is stored. It may not use +the next token, future token, target label, future query, future state, or +future logits. + +## Exact storage contract + +For Qwen3.5-0.8B-Base at batch one: + +| Component | Rank-fusion/static row format | Static v0.2 | +| --- | ---: | ---: | +| All-INT4 payload | 2,359,296 B | 2,359,296 B | +| FP16 scales | 73,728 B | 73,728 B | +| Precision mask | 4,608 B | 0 B | +| INT8 promotion payload | 126,464 B | 131,072 B | +| Promoted rows | 1,976 | 2,048 | +| **Resident total** | **2,564,096 B** | **2,564,096 B** | + +The evaluator checks these values from live packed tensors after every task +for every rank-fusion method. Resident bytes exclude model weights, the full +attention cache, temporary endpoint tensors, rank workspaces, kernel +workspace, framework bookkeeping, and CUDA allocator reserved memory. + +## Candidate-aligned numerical gate + +The gate validates the score algebra actually used by this candidate and the +physical masks derived from it. All reference calculations run on CPU FP64 +from the exact source FP32 tensors saved before score reduction. The model-side +implementation may remain FP32, but it must agree with the independent +reference within the deterministic bounds below and must produce identical +ranks and masks. + +This is an implementation-validity audit, not a statistical sample. Its +geometry is fixed without reading selector scores: + +- selector-task positions `0` and `7` in authenticated order; +- the first and last affected code transitions in each selected task; +- recurrent model layers `0`, `9`, `18`, and `22`; +- value heads `0`, `7`, and `15`; and +- key rows `0`, `31`, `63`, `95`, and `127`. + +The static dot-product audit therefore contains exactly 240 primitive rows. +For the same 16 task-transition-layer writes, the dynamic audit retains the +whole `[16, 128, 128]` source state and validates all 2,048 row scores, ranks, +and the final mask. This scope is small enough to release as raw sidecar data +while covering early through late layers, heads, rows, short-horizon and +long-horizon boundaries. Expanding or replacing this set after inspecting its +result is a new gate, not an Experiment 006 retry. + +Let FP32 unit roundoff be + +```text +u = 2^-24 = 5.960464477539063e-8. +``` + +For a reduction of length 128, freeze + +```text +gamma_128 = 128*u / (1 - 128*u) + = 7.629452739355006e-6 + +gamma_3 = 3*u / (1 - 3*u) + = 1.788139663006007e-7. +``` + +### Static sensitivity score + +For every one of the 240 frozen primitive rows, independently compute for `b` +in `{4, 8}`: + +```text +d_b = sum_i float64(g_i) * float64(e_b_i) +A_b = sum_i abs(float64(g_i) * float64(e_b_i)) +B_b = gamma_128 * A_b +``` + +and + +```text +S_ref = d_4^2 - d_8^2 + +B_S = 2*abs(d_4)*B_4 + B_4^2 + + 2*abs(d_8)*B_8 + B_8^2 + + gamma_3 * ((abs(d_4) + B_4)^2 + (abs(d_8) + B_8)^2). +``` + +For all 240 finite primitive rows, the implementation score must satisfy + +```text +abs(S_impl - S_ref) <= B_S. +``` + +There is no aggregate pass percentage. Separately, the selector must retain +authenticated per-task FP64 accumulator arrays for the complete 36,864-row +production score field. Independent CPU code recomputes their task-macro mean, +stable global plan, per-layer `r_S`, `q_l`, and endpoint mask from those stored +arrays. The production artifact must match every complete-array hash, rank, +quota, coordinate, and mask exactly. The sampled primitive audit validates the +underlying dot-and-square implementation; the full-array replay validates its +aggregation and allocation without requiring tens of gigabytes of raw +gradient/error tensors. + +### Dynamic reconstruction score + +For all 2,048 rows at each of the 16 frozen writes, independently compute + +```text +m_b = (1/128) * sum_i float64(e_b_i)^2 +M_ref = m_4 - m_8 + +gamma_256 = 256*u / (1 - 256*u) + = 1.5259021896696422e-5 + +B_M = gamma_256 * (m_4 + m_8) + + 8*epsilon_FP64*max(1, m_4 + m_8). +``` + +For every finite audited row, require + +```text +abs(M_impl - M_ref) <= B_M. +``` + +The small FP64 term is only a deterministic representation guard. For each +audited write, canonical CPU FP64 values determine all 2,048 `r_M` positions; +the production ranks, integer fusion costs, masks, and packed endpoints must +match exactly. + +These bounds assume finite, normal-range arithmetic without overflow or +underflow. A non-finite source, score, metric, payload, or model output, or an +overflow/underflow condition affecting a checked reduction, fails closed and +is recorded rather than omitted. If the implementation is changed to use the +canonical FP64 scores directly, the artifact must still store the exact FP64 +array hashes and prove rank, quota, mask, and packing parity. + +### Exact tensor-byte authentication + +Every released audit source and derived array is content-addressed using its +exact logical bytes, not JSON-rendered decimal strings. The artifact records +dtype, shape, axis meaning, little-endian byte order, contiguous C-order, and +SHA-256 for: + +- the 240 sampled source FP32 `g`, `e4`, and `e8` rows; +- the 16 full audited FP32 source states and their physical Q4/Q8 endpoints; +- sampled canonical FP64 primitive scores and complete authenticated per-task + FP64 selector accumulator arrays; +- canonical FP64 per-write MSE scores for all audited rows; +- per-layer static and dynamic integer ranks; +- the integer quota vector; +- every lambda's integer fusion-cost arrays; +- selected masks and promotion coordinates; and +- packed INT4 payloads, INT8 promotion payloads, FP16 scales, and mask bytes. + +Large raw tensors may live in a deterministic `.npz` sidecar, but the JSON gate +artifact must authenticate the sidecar file and every contained logical array. +The validator recomputes task-macro aggregation, stable ties, exact layer +quotas, all 1,976 promotions, and the 2,564,096-byte resident total. Every +check must pass. The gate artifact reports each condition and an overall +conjunction and exits nonzero on failure. + +## Artifact and provenance prerequisites + +Experiment 006 uses distinct schema and artifact kinds. No Experiment 004 or +005 artifact may be renamed or relabeled as Experiment 006 evidence. Before +holdout access, the following must exist from one clean committed protocol and +implementation: + +1. a regenerated target-directional selector artifact; +2. a regenerated H1 selector artifact for the quota-control method; +3. a passing candidate-aligned numerical, rank, endpoint-parity, byte, and + packing gate artifact; and +4. a preflight record authenticating the unopened holdout and all inputs. + +Each artifact must record: + +- its schema version, artifact kind, file SHA-256, and canonical evidence + SHA-256; +- the committed SHA-256 of this protocol and every relevant source file; +- the same clean Git commit at start and end, with identical source maps; +- model `Qwen/Qwen3.5-0.8B-Base` at revision + `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68`; +- dataset `google-research-datasets/mbpp`, config `full`, source split `train`, + calibration phase, at revision + `4bb6404fdc6cacfda99d4ac4205087b89d32030c`; +- selection namespace `rq-v0.2` and formatter version + `recurquant.mbpp-prompt-code.v1`; +- the exact tokenizer identity, revision, settings, rendered text hashes, + ordered task IDs, canonical row-content hashes, and aligned token counts; +- the quantizer contract, rounding mode, scale representation, storage timing, + tensor shapes, row-coordinate convention, and recurrent-layer list; +- `q_l`, all method names, the complete lambda table, integer cost formulas, + ranks, masks, exact tensor-byte hashes, and endpoint-parity results; +- bootstrap seed 2339, random-row seed 1101, all other RNG seeds and states, + deterministic settings, and commands; +- Python, PyTorch, Transformers, CUDA, driver, package-lock, OS, CPU, GPU, and + hardware details; and +- every gate threshold, observation, pass value, exception, warning, and exit + status. + +The clean commit must contain both the frozen protocol and implementation. All +selector and gate artifacts must be generated afterward from that exact +commit. A dirty tree, a source hash change, an uncommitted protocol, or an +artifact from a predecessor commit fails preflight. + +### Existing evidence is exploratory only + +The following authenticated artifacts predate this fusion protocol: + +| Artifact | File SHA-256 | Canonical evidence SHA-256 | Classification | +| --- | --- | --- | --- | +| `experiment005-storage-boundary-599862e.json` | `61a2936bd20679bad441921d26b556f5986eec61449c4a9743c9b0b5e0bea86d` | `b168330b4c39963b7c149230d4b1ad9fa57b20b02d6d95ee583ee9941f68b19f` | frozen failed E005 gate, `passed: false` | +| `experiment006-hrr-selector-8task-599862e.json` | `7a67e159f9dbab5e92cc9a837831359d3aa180ccd4725807f1836a7d5aeba55a` | `5acbe38d575c741153cf0142bced9227a9654693f946e4d768b9b62847bb672b` | pre-freeze selector input | +| `experiment006-loss-selector-8task-599862e.json` | `b4972b40b32d67557520c0af3d3c4467f69283819765f8c3e1c9f4b92f560bf6` | `9243ec49933442c4d48f6e51321c33010d681fc5a3fffa4cca70599d52cf26d4` | pre-freeze selector input | +| `experiment006-hrr-selector-8task-556e527.json` | `fa02e1d468ecc13c78b7cf8e63f237e372c556d9fed0c1f4b47c9dd901a808dd` | `07e646ccb9b1df5ff9873a94f7bacb07d7a4e2b70136e3a68f40d1619814d899` | pre-freeze selector input | +| `experiment006-loss-selector-8task-556e527.json` | `33bdc5939429281ba5377eeb02d59fac72a0f8da657c713bb7854d235e2fb057` | `ae92af38475720eb1ce19527f1c2de3d0d1fc045a1160a83f8da30ecde282214` | pre-freeze selector input | +| `experiment006-adaptive-same-calibration-8task-556e527.json` | `3495698932b43d93f387bb61492f91fc38840097020980977451be2042a02164` | `f07a6b852a4c427c0b9946ad44c21d023299d173eb24695462e99e3785061d61` | E005 same-calibration postmortem; no fusion | + +They may be cited for history and used to test artifact readers. They cannot +satisfy Experiment 006's regenerated selector, candidate-aligned gate, +preflight, or heldout-result requirements. + +## Frozen methods and controls + +Every row-format method below uses exactly 1,976 promotions and the exact +2,564,096-byte resident recurrent-state total unless explicitly noted: + +1. `rank_fusion_l050_target_fisher_adaptive_mse` - frozen primary. +2. `rank_fusion_l025_target_fisher_adaptive_mse` - report-only ablation. +3. `rank_fusion_l075_target_fisher_adaptive_mse` - report-only ablation. +4. `adaptive_mse_target_directional_fisher_quota` - `lambda = 0` endpoint and + individual adaptive component. +5. `target_directional_fisher_difference_int4` - `lambda = 1` endpoint and + equal-byte static comparator. +6. `adaptive_mse_hrr_h1_quota` - individual adaptive quota control. +7. `hrr_h1` - static comparator. +8. `hrr_h32` - retained negative static hypothesis. +9. `row_mse` - static task-macro reconstruction-error allocation. +10. `v02_layer0_static` - equal resident bytes with 2,048 promotions and no + precision mask. +11. `random_rows_s1101` - prespecified random static policy. +12. `signed_taylor_next_int4` - retained static diagnostic. +13. `target_diagonal_fisher_difference_int4` - retained static diagnostic. +14. `delta_direction_magnitude_int4` - retained static diagnostic. +15. `uniform_int4` - lower-byte control, not an equal-byte competitor. + +The strongest individual adaptive component is selected deterministically as +the lower-macro-excess-NLL member of methods 4 and 6 on the frozen holdout. +The `lambda = 1` endpoint is static and is not eligible for that label. The +`lambda = 0.25` and `lambda = 0.75` fused ablations are not individual +components and cannot replace or qualify the primary. + +## Data separation and one-time holdout audit + +The authenticated first eight ranked MBPP calibration rows are the selector +partition. They have already been inspected and can support only development +and same-calibration diagnostics. Repository evidence at this protocol freeze +contains selector-prefix and offset-0 postmortem artifacts, but no artifact +that opens the ranked `[8, 16)` window. + +The one-time Experiment 006 window is frozen as: + +```text +phase: calibration +ranked offset: 8 +task count: 8 +window: [8, 16) +``` + +Before tokenization or model loading, the heldout evaluator must: + +1. verify both regenerated selector artifacts, the passing Experiment 006 + numerical/packing gate, this protocol hash, and the same clean commit; +2. reconstruct and authenticate the pinned ranked population independently; +3. require exact arguments `--calibration-offset 8` and `--limit 8`; +4. authenticate the first-eight selector prefix by dataset/config/revision, + source split, phase, namespace, formatter, ordered IDs, and row hashes; +5. prove `[8, 16)` is disjoint from every selector and development task ID; +6. verify that no prior artifact, log, cache manifest, command record, or + source change shows that `[8, 16)` was opened; and +7. record a passing preflight before loading any heldout text. + +If there is evidence that `[8, 16)` was previously opened, the protocol aborts. +It must not silently choose another offset under the Experiment 006 name. + +After preflight, one invocation evaluates every frozen method on the same +ordered tasks and aligned transitions. It writes a complete artifact even when +a metric or gate fails, records the failure, and exits with status 2. A crash +may be resumed only from authenticated, immutable per-task records without +rerunning, omitting, or replacing a revealed task. No lambda, quota, component, +method, threshold, seed, metric, or comparator may change after heldout access. + +The evaluator records the heldout row-content manifest, rendered-text hashes, +token counts, source maps, commands, packages, hardware, clean commit, and +start/end hashes. Start and end records must be identical. + +## Metrics and frozen quality gate + +The primary metric is task-macro excess next-token NLL relative to an FP32 +recurrent state, scored only on code transitions after a quantized recurrent +state has been stored. The prompt-to-first-code-token prediction is excluded. +Use 10,000 paired bootstrap resamples with seed 2339. + +The equal-byte static comparator set is unchanged from Experiment 005: + +```text +hrr_h1 +hrr_h32 +row_mse +random_rows_s1101 +v02_layer0_static +signed_taylor_next_int4 +target_directional_fisher_difference_int4 +target_diagonal_fisher_difference_int4 +delta_direction_magnitude_int4 +``` + +The strongest equal-byte static comparator is the member with the lowest +macro excess NLL on the frozen window. Uniform INT4 is excluded because it +uses fewer resident bytes. Adaptive methods and fused ablations are excluded +from the static set. + +The `lambda = 0.5` primary passes this small heldout-calibration gate only if +all conditions hold: + +1. resident recurrent-state bytes equal 2,564,096 exactly on every task; +2. primary macro excess NLL is lower than every equal-byte static comparator; +3. the paired 95% interval versus the strongest equal-byte static comparator + is strictly above zero when expressed as comparator minus primary; +4. relative excess-NLL reduction versus that static comparator is at least + 20%; if its excess NLL is non-positive, this condition fails closed; +5. the paired 95% interval versus `adaptive_mse_hrr_h1_quota` is strictly above + zero when expressed as H1-adaptive minus primary; +6. the paired 95% interval versus the strongest individual adaptive component + defined above is strictly above zero when expressed as component minus + primary; +7. macro top-1 agreement is no more than `0.01` below the strongest equal-byte + static comparator; +8. macro worst-token KL CVaR95 is no more than `0.10` above that comparator; +9. on every task, primary excess NLL is at most `1.0` worse than that + comparator's excess NLL; +10. no metric, source tensor, score, payload, or model output is non-finite; +11. the candidate-aligned numerical, rank, endpoint-parity, promotion-count, + and exact-byte gate passes every row and write; and +12. every code, protocol, artifact, manifest, tensor-byte, and source hash + verifies, with the same clean repository commit and source map at start and + end. + +Conditions 1 through 5 and 7 through 12 preserve Experiment 005's quality and +evidence thresholds. Condition 6 is the additional rank-fusion requirement: +the fused primary must beat its strongest individual adaptive component with +a paired lower confidence bound above zero. The evaluator records every +threshold, observed value, interval, pass value, and the overall conjunction. +There is no discretionary override. + +The two intermediate-lambda ablations are always reported but do not enter the +primary pass conjunction. They can diagnose the shape of the tradeoff; they +cannot rescue a failed `lambda = 0.5` result. + +## Systems boundary + +This prototype computes two quantized endpoints, CPU-reference audits, stable +ranks, integer fusion costs, and masks in Python. It is expected to add +overhead. Resident packed-state bytes alone do not establish lower end-to-end +memory, faster decoding, or practical deployment value. + +No latency, throughput, peak-memory, energy, kernel-efficiency, or speed claim +is permitted from this experiment. A later fused Triton or CUDA implementation +must preserve exact mask and endpoint parity and separately benchmark p50/p95 +decode latency, throughput, peak allocated and reserved memory, and workspace +against static packing on named hardware. + +## Prior-art and novelty boundary + +Rank aggregation, Fisher/Taylor sensitivity, reconstruction-error selection, +mixed-bit quantization, layer quotas, dynamic precision, and recurrent-state +cache quantization are established ideas. No one ingredient is claimed as new. + +The narrow testable contribution is the particular exact-byte combination of +frozen target-directional layer quotas, per-layer static sensitivity ranks, +causal per-write aligned Q4-to-Q8 MSE ranks, and deterministic integer rank +fusion for a Gated DeltaNet language-model recurrent state. + +A pass on eight MBPP calibration tasks would show only a scoped signal worth +further study. It would not establish architectural generality, useful speed, +state-of-the-art quality, or a breakthrough. Promotion beyond this stage +requires, at minimum: + +- a larger untouched code development set and a separately frozen test set; +- natural-text, retrieval, free-generation, and long-context evaluations; +- multiple checkpoints, model sizes, recurrent architectures, and rounding + seeds; +- closest-method reproductions and full systems benchmarks; and +- independent replication from released code and raw artifacts. + +## Decision rule + +- **Gate failure before holdout:** record the failed prerequisite; keep the + holdout closed. +- **Heldout primary failure:** publish the complete artifact and stop this + candidate. Do not tune lambda on `[8, 16)`. +- **Ablation wins but primary fails:** report the ablation result as exploratory + and open a new protocol on new data; do not relabel it primary. +- **Primary passes:** advance only to a larger preregistered development stage. + Do not use breakthrough language. + +The current state is below the heldout-calibration rung of the claim ladder. diff --git a/research/EXPERIMENT_006_RESULT.md b/research/EXPERIMENT_006_RESULT.md new file mode 100644 index 0000000..d1988d4 --- /dev/null +++ b/research/EXPERIMENT_006_RESULT.md @@ -0,0 +1,113 @@ +# Experiment 006 result: equal-rank fusion rejected before holdout + +> **Status: stopped on the inspected selector partition; holdout unopened.** +> +> The frozen `lambda = 0.5` primary did not beat either of its two strongest +> component endpoints with a positive paired confidence bound. It was worse +> than plain adaptive MSE by point estimate, improved on the strongest static +> method by only 3.90%, and its intervals against both methods crossed zero. +> The predeclared `0.25` and `0.75` ablations were better, but the protocol +> forbids promoting an ablation after seeing results. Experiment 006 therefore +> stops without opening ranked MBPP window `[8, 16)` and without an +> improvement, generalization, novelty, systems, or breakthrough claim. + +Date recorded: 2026-07-23 + +## Authenticated diagnostic + +The quality run used the same eight MBPP calibration tasks that produced the +selector inputs. It is a candidate-rejection diagnostic, not heldout evidence. + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment006-rank-fusion-same-calibration-8task-c2ad68b.json` | +| Artifact kind | `recurquant_rank_fusion_same_calibration_quality_diagnostic` | +| Clean implementation commit | `c2ad68b22433b1b077df5aafaacc667e305b2294` | +| File SHA-256 | `9824f4db5a3b8eb7de392537768e658ba7d921cc52c1ba7714d8e8433c85dbbf` | +| Canonical evidence SHA-256 | `94699c298767d5d1f1e9f2ca61f766fe541871b90bb2f054c8b2aef9bd292827` | +| Created at | `2026-07-22T18:30:13.878345+00:00` | +| Model | `Qwen/Qwen3.5-0.8B-Base` at `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` | +| Device and dtype | CUDA, bfloat16 | +| Task IDs | `945`, `794`, `657`, `702`, `651`, `720`, `903`, `918` | +| Aligned scored tokens | 642 | +| Bootstrap | 10,000 paired resamples, seed 2339 | +| Repository clean and stable | true | +| Evidence verification | valid, with no verifier errors | + +Every mixed candidate physically stored 1,976 INT8-promoted rows in exactly +2,564,096 resident recurrent-state bytes: 2,485,760 payload bytes, 73,728 +scale bytes, and 4,608 mask bytes. This verifies equal storage for the quality +diagnostic; it is not a latency or whole-model-memory result. + +## Frozen-primary result + +The primary was fixed before this run as equal ordinal-rank fusion between: + +1. offline target-directional-Fisher row sensitivity; and +2. causal per-write aligned INT4-to-INT8 reconstruction-MSE benefit. + +Within each layer, the calibrated exact-byte plan fixed the promotion quota. +Only the promoted row identities could change. + +| Method | Role | Macro excess NLL | Mean KL | CVaR95 KL | Top-1 agreement | +| --- | --- | ---: | ---: | ---: | ---: | +| `rank_fusion_l050_target_fisher_adaptive_mse` | frozen primary | `0.5148733034729958` | `0.47354681603610516` | `3.0126770064234734` | `0.7846591621637344` | +| `adaptive_mse_target_directional_fisher_quota` | dynamic endpoint | `0.4933023080229759` | `0.4791950900107622` | `2.902877390384674` | `0.7851893156766891` | +| `target_directional_fisher_difference_int4` | strongest static endpoint | `0.5357813686132431` | `0.5205607209354639` | `3.361491322517395` | `0.7908198460936546` | +| `adaptive_mse_hrr_h1_quota` | H1-quota adaptive control | `0.7794309854507446` | `0.7939629852771759` | `3.3163906931877136` | `0.6900708600878716` | + +Comparator-minus-primary paired contrasts were: + +| Comparator | Mean improvement | Paired 95% interval | Interpretation | +| --- | ---: | ---: | --- | +| Plain adaptive target-Fisher quota | `-0.021570995450019836` | `[-0.09485602006316185, 0.04348200932145117]` | primary point estimate is worse; interval crosses zero | +| Static target-directional Fisher | `0.020908065140247345` | `[-0.05002020299434662, 0.12526661902666092]` | primary point estimate is better; interval crosses zero | +| H1-quota adaptive control | `0.26455768197774887` | `[0.09466566145420074, 0.4699110761284828]` | primary is better than this weaker control | + +Against the strongest static method, the primary's descriptive excess-NLL +reduction was only 3.90%, below the frozen 20% threshold. Against plain +adaptive MSE, excess NLL increased by 4.37%. Conditions requiring a positive +paired lower bound against the strongest static and strongest individual +adaptive methods therefore would fail even before the unavailable +candidate-aligned numerical gate is considered. + +## Predeclared ablations + +The protocol required both intermediate weights to be reported but prohibited +either from replacing the primary after results were visible. + +| Method | Macro excess NLL | Relative to strongest static | Relative to plain adaptive | +| --- | ---: | ---: | ---: | +| `rank_fusion_l025_target_fisher_adaptive_mse` | `0.45971328765153885` | 14.20% lower | 6.81% lower | +| `rank_fusion_l075_target_fisher_adaptive_mse` | `0.45596349984407425` | 14.90% lower | 7.57% lower | + +Both ablations had lower excess NLL than the frozen primary on these same +eight tasks. In ablation-minus-primary terms, the paired evidence was strongest +for `lambda = 0.25`: its interval was +`[-0.1031366042792797, -0.01751011610031128]`, entirely below zero. The +`lambda = 0.75` interval was `[-0.11918644607067108, +0.010958641767501831]` and crossed zero. + +These results show that equal ordinal rank mixing was not the best point on the +inspected data. They do not authorize selecting `0.25`, `0.75`, or another +weight and calling it prespecified. Any weight-based successor would need a new +protocol and new development data. + +## Decision and next hypothesis + +Experiment 006 is rejected as a route to the existing holdout: + +- the frozen primary did not dominate either strong endpoint; +- the required candidate-aligned numerical/packing gate was not implemented; +- the ranked `[8, 16)` window was never tokenized or loaded; and +- the ablation pattern cannot rescue the primary. + +The result also localizes the remaining weakness. Instantaneous reconstruction +MSE measures which rows are hard to store, while the static score measures +average calibrated loss sensitivity. Neither directly measures which state +rows the next recurrent read will use. A successor may test a causal predictor +of row read energy while retaining the same frozen per-layer quotas and exact +resident bytes. That is a new experiment, not a repair or relabeling of +Experiment 006. + +Experiment 006 makes no breakthrough claim. diff --git a/research/EXPERIMENT_007_CQER_PROTOCOL.md b/research/EXPERIMENT_007_CQER_PROTOCOL.md new file mode 100644 index 0000000..2f9a8d9 --- /dev/null +++ b/research/EXPERIMENT_007_CQER_PROTOCOL.md @@ -0,0 +1,330 @@ +# Experiment 007: CQER-32 protocol + +> **Status: development protocol frozen before the first quality run.** +> +> CQER-32 means **Causal Query-weighted Error Reduction with a 32-token +> half-life**. Its primary formula, normalization, EMA decay, quotas, task +> partition, metrics, controls, and advancement rule are fixed below. The +> ranked MBPP window `[8, 16)` remains unopened. This protocol makes no +> improvement, novelty, speed, or breakthrough claim. + +Date opened: 2026-07-23 + +## Why Experiment 006 stopped + +Experiment 006 combined a static loss-sensitivity rank with an instantaneous +reconstruction-error rank. On the already inspected selector partition, its +frozen equal-rank primary had macro excess NLL `0.514873`, compared with +`0.493302` for plain adaptive MSE and `0.535781` for the strongest static +method. Its paired intervals against both endpoints crossed zero, and its +3.90% point improvement over the strongest static method was below the frozen +20% threshold. Better intermediate-weight ablations were visible only after +the result and cannot replace the primary. + +That result suggests a missing variable rather than a missing fusion weight. +Reconstruction MSE estimates how much a state row is damaged by low-bit +storage. It does not estimate how strongly the model will read that row. + +## Research question + +At the same frozen per-layer INT8 promotion quotas, does multiplying each row's +causal INT4-to-INT8 reconstruction benefit by a causal estimate of normalized +query energy reduce recurrent-state quantization error at model outputs? + +The candidate is motivated by the Gated DeltaNet read. Ignoring cross-row +error covariance, a row's contribution to expected squared read error scales +with both: + +1. the row's state reconstruction error; and +2. the squared query coordinate used to read that row. + +CQER-32 tests this factorization directly. The ignored covariance and the use +of past query energy to predict future energy are explicit approximations. + +## Frozen primary + +The artifact method name is: + +```text +query_ema32_weighted_mse_target_fisher_quota +``` + +The short research name is `CQER-32`. + +### Query normalization + +For query tensor `q` after Qwen3.5's convolution, head reshape, and value-head +repetition, detach and convert it to FP32, then normalize every token and head +with the pinned Gated DeltaNet formula: + +```text +q_hat = q / sqrt(sum(q^2, key_row) + 1e-6) +energy = q_hat^2 +``` + +The candidate supports only ordinary batch-one prefill, cached multi-token +continuation, and single-token decode. Packed sequences with `cu_seqlens` fail +closed in this version. + +### Causal query-energy EMA + +The half-life is frozen at 32 query tokens: + +```text +rho = 2^(-1 / 32) +``` + +For each model layer, value head, and key row: + +```text +EMA_t = rho * EMA_(t-1) + (1 - rho) * energy_t +``` + +Before the first token and after cache reset: + +```text +EMA_0 = 1 / key_row_count +``` + +For an `n`-token prefill or continuation chunk, the implementation must be +numerically equivalent to folding tokens in chronological order: + +```text +EMA_new = rho^n * EMA_old + + (1 - rho) * sum_j rho^(n-1-j) * energy_j +``` + +No future query may contribute. The current call's queries are observed only +after its Gated DeltaNet kernel succeeds. They select the recurrent state that +is stored after that call and can affect only later model outputs; they cannot +change the output already produced by the same kernel call. + +### Per-write reconstruction benefit + +For incoming recurrent state `S` and each `[head, key_row]`, compute aligned +INT4 and INT8 endpoints with the same group geometry, scale type, nearest +rounding, and seed: + +```text +benefit = mean_v((Q4(S) - S)^2) - mean_v((Q8(S) - S)^2) +``` + +The primary score is: + +```text +CQER_score = EMA_new * benefit +``` + +Within every layer, select the fixed quota with the largest score. Exact score +ties preserve canonical flattened `[head, key_row]` order. Scores, sorting, +and selection run without autograd. + +### Frozen layer quotas + +CQER-32 does not search a new layer allocation. It inherits the authenticated +target-directional-Fisher per-layer quota vector from the inspected selector +partition. Across the 18 recurrent layers the quotas sum to exactly 1,976 +INT8 rows. Every other row is INT4. + +The quota vector is fixed; only row identities may change at a state write. +This isolates the within-layer read-importance hypothesis. + +The exact `(layer_index: promoted_rows)` allocation is: + +```text +0:355, 1:380, 2:269, 4:179, 5:185, 6:105, +8:80, 9:43, 10:84, 12:30, 13:62, 14:54, +16:45, 17:27, 18:7, 20:9, 21:7, 22:55 +``` + +The evaluator must compare against this independent vector. It must not derive +both the candidate allocation and the expected gate allocation from the same +selector object. + +## Stage-and-consume causality contract + +The observer and cache use one fail-closed record per layer write: + +1. the observer identifies the exact cache object passed to the Qwen3.5 layer; +2. the Gated DeltaNet kernel receives the post-convolution query; +3. only after that kernel succeeds, the observer stages the query observation; +4. the immediately following recurrent-state write consumes it once; +5. packing success commits the new EMA and consumes the record; and +6. any exception clears an unconsumed record. + +Missing, duplicate, stale, wrong-layer, wrong-update, wrong-shape, +wrong-device, batch-greater-than-one, or non-finite observations raise. A +reference, static, adaptive-MSE, or other unregistered cache passes through +without receiving observations. A model path that bypasses the wrapped kernels +cannot silently fall back to plain MSE. + +The observer restores every wrapped model callable on context exit, including +exceptional exits. Multiple registered CQER caches remain independent and are +dispatched by object identity, not by class alone. + +## Storage contract + +The packed recurrent-state representation remains identical in byte budget to +the other exact-quota mixed methods: + +| Component | Bytes | +| --- | ---: | +| INT4/INT8 payloads | 2,485,760 | +| FP16 group scales | 73,728 | +| precision masks | 4,608 | +| **packed recurrent-state resident bytes** | **2,564,096** | + +CQER-32 also stores one persistent FP32 `[heads, key_rows]` EMA per recurrent +layer. For the pinned 18-layer, 16-head, 128-row geometry: + +```text +18 * 16 * 128 * 4 = 147,456 bytes +``` + +Therefore the selector-aware total is: + +```text +2,564,096 + 147,456 = 2,711,552 bytes +``` + +Artifacts and documentation must report all three fields separately: + +```text +packed_recurrent_state_bytes +selector_auxiliary_bytes +resident_bytes_including_selector +``` + +The temporary query, endpoint, score, and sort workspaces are not resident +cache bytes and must not be hidden if peak-memory measurements are later made. +CQER-32 is not equal-total-memory to a method that has no selector state. + +## Frozen development diagnostic + +The first run may use only the already inspected MBPP selector-task prefix: + +```text +phase: calibration +ranked offset: 0 +task count: 8 +task IDs: 945, 794, 657, 702, 651, 720, 903, 918 +``` + +Before the first quality run, the exact selector evidence identities were also +pinned to remove an ambiguity found during pre-run audit: + +```text +HRR canonical evidence SHA-256: +7970961fd88b522998189ad64f26b333aed9c88ff5f653de5449fd9e01d8cbc8 + +loss-sensitivity canonical evidence SHA-256: +bff4e33253990b8115e1f35e74516c4975c2fe4aac5066475afe968eb8a64609 +``` + +This is a protocol-precision amendment, not a response to quality results: no +Experiment 007 task had been evaluated when these identities were added. + +This data already influenced the target-Fisher quotas and prior hypotheses. A +result on it can reject an implementation or candidate but cannot establish +generalization. + +The primary metric remains task-macro excess next-token NLL relative to FP32 +recurrent state on aligned code transitions after a quantized state has been +stored. The prompt-to-first-code-token prediction is excluded. Also report +mean KL, worst-token KL CVaR95, top-1 agreement, task/token counts, all per-task +rows, and 10,000 paired bootstrap contrasts with seed 2339. + +Required controls are: + +```text +adaptive_mse_target_directional_fisher_quota +target_directional_fisher_difference_int4 +adaptive_mse_hrr_h1_quota +hrr_h1 +v02_layer0_static +uniform_int4 +``` + +The authenticated Experiment 006 rank-fusion methods may be reported for +context but are not advancement comparators. + +### Development advancement rule + +CQER-32 advances to construction of its independent numeric/packing gate only +if all conditions hold on the inspected eight-task diagnostic: + +1. every task and layer has the exact fixed promotion count; +2. packed recurrent-state bytes equal 2,564,096 and selector bytes equal + 147,456 after every completed task; +3. macro excess NLL is lower than both plain adaptive target-Fisher-quota MSE + and static target-directional Fisher; +4. relative excess-NLL reduction versus plain adaptive MSE is at least 5%; +5. relative excess-NLL reduction versus the strongest static method is at + least 20%; +6. top-1 agreement is no more than `0.01` below the better of those two + comparators; +7. CVaR95 KL is no more than `0.10` above the lower of those two comparators; +8. every value and model output is finite; +9. every staged observation is consumed exactly once; and +10. code, model, dataset, selector, source, and artifact hashes verify from one + clean stable commit. + +This is a development filter, not a heldout quality pass. A paired confidence +interval is reported but is not used to tune `rho`, the formula, or quotas. +Failure stops this frozen candidate. Changing the half-life, adding a static +term, or changing the quota is a new experiment. + +## Candidate-aligned prerequisite before holdout + +If the development rule passes, a separately committed verifier must replay +the selector independently in CPU FP64 from authenticated source tensors. It +must cover prefill, cached chunk, and recurrent decode and verify: + +- exact normalized query energies; +- exact chronological EMA recurrence and the chunk/step equivalence bound; +- aligned Q4 and Q8 row errors; +- CQER scores; +- stable ranks and masks; +- promotion counts and per-layer quotas; +- packed payload, scale, and mask bytes; and +- selector auxiliary bytes and reset/offload/prefetch behavior. + +Every checked primitive must meet a predeclared forward-error bound, and every +rank, mask, quota, logical-array hash, and physical-byte hash must match +exactly. There is no aggregate pass percentage. One mismatch fails closed. + +The verifier, raw sidecar schema, sampled coordinates, arithmetic bounds, and +pass conjunction must be frozen in a clean commit before the `[8, 16)` window +is tokenized or loaded. + +## Holdout and claim boundary + +The ranked MBPP calibration window `[8, 16)` remains reserved. CQER-32 cannot +open it unless the development rule and independent candidate-aligned +prerequisite both pass from committed, authenticated artifacts. + +Even a pass on that eight-task window would establish only a scoped signal for +the pinned model, cache geometry, and teacher-forced code metric. A defensible +research contribution would still require a larger preregistered development +and test split, natural text, retrieval, long-context and free-generation +tasks, multiple checkpoints and recurrent architectures, closest-method +comparisons, systems kernels and benchmarks, and independent reproduction. + +Query-aware mixed precision, EMA predictors, reconstruction-error selection, +and recurrent-state quantization all have prior art. The narrow candidate under +test is their exact causal combination for physical Gated DeltaNet recurrent +state rows under fixed per-layer byte quotas. Novelty is unresolved and cannot +be inferred from a quality result. + +## Decision rule + +- **Development rule fails:** authenticate the result, keep `[8, 16)` closed, + and stop CQER-32. +- **Numeric prerequisite fails:** authenticate the failure, keep `[8, 16)` + closed, and stop CQER-32. +- **Both pass:** freeze the one-time heldout evaluator before any protected + data access. +- **Heldout result fails:** publish the complete negative result; do not tune + on the protected tasks. +- **Heldout result passes:** advance to broader preregistered evaluation. Do + not use breakthrough or state-of-the-art language. diff --git a/research/EXPERIMENT_007_RESULT.md b/research/EXPERIMENT_007_RESULT.md new file mode 100644 index 0000000..35b4f2f --- /dev/null +++ b/research/EXPERIMENT_007_RESULT.md @@ -0,0 +1,104 @@ +# Experiment 007 result: CQER-32 rejected before holdout + +> **Status: development gate failed; holdout unopened.** +> +> CQER-32 lowered macro excess NLL relative to both frozen components on the +> already inspected eight-task partition, but it failed two preregistered +> requirements. Its reduction against the static target-Fisher comparator was +> `13.62%`, below the required `20%`, and its top-1 agreement disadvantage was +> `0.02690`, above the allowed `0.01`. The complete gate therefore failed. +> Ranked MBPP window `[8, 16)` remains unopened, and Experiment 007 supports no +> generalization, novelty, systems, state-of-the-art, or breakthrough claim. + +Date recorded: 2026-07-23 + +## Authenticated diagnostic + +This run used the same eight MBPP calibration tasks that informed the frozen +target-Fisher layer quotas. It can reject the candidate, but it cannot establish +generalization. + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment007-cqer32-same-calibration-8task-3a062771.json` | +| Artifact kind | `recurquant_cqer32_same_calibration_quality_diagnostic` | +| Clean implementation commit | `3a06277171684e6e6bf4aec1153c16c968fea0fe` | +| File SHA-256 | `6f1e6d22eafde9be7c231cc81dc04e7a127c8b14f47ec2865de9326a61040f32` | +| Canonical evidence SHA-256 | `2226dcc055fff3ebff4dadb9a9908166d361dfd2f61b32757d2eb9c650d1381a` | +| Created at | `2026-07-22T19:37:50.097105+00:00` | +| Model | `Qwen/Qwen3.5-0.8B-Base` at `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` | +| Device and dtype | CUDA, bfloat16 | +| Task IDs | `945`, `794`, `657`, `702`, `651`, `720`, `903`, `918` | +| Aligned scored tokens | 642 | +| Bootstrap | 10,000 paired resamples, seed 2339 | +| Repository and source files | clean and stable at start and end | +| Evidence verification | valid, with no verifier errors | + +Every CQER write consumed exactly one causal query observation. Across all +tasks and layers, the state-update and observation counts matched. All metrics +and logits were finite, every layer realized its exact frozen quota, and the +implementation stored exactly 1,976 promoted rows. + +## Frozen-primary result + +CQER-32 scores each recurrent-state row with: + +```text +32-token EMA(normalized query energy) * (INT4 row MSE - INT8 row MSE) +``` + +The per-layer target-Fisher quotas were frozen before the run. Only the row +identities selected inside each layer could change at a state write. + +| Method | Role | Macro excess NLL | Mean KL | CVaR95 KL | Top-1 agreement | +| --- | --- | ---: | ---: | ---: | ---: | +| `query_ema32_weighted_mse_target_fisher_quota` | frozen primary | `0.4627917185` | `0.4525271095` | `2.7080783099` | `0.7639170736` | +| `adaptive_mse_target_directional_fisher_quota` | error-only dynamic component | `0.4933023080` | `0.4791950900` | `2.9028773904` | `0.7851893157` | +| `target_directional_fisher_difference_int4` | frozen static component | `0.5357813686` | `0.5205607209` | `3.3614913225` | `0.7908198461` | + +Comparator-minus-CQER paired NLL contrasts were: + +| Comparator | Mean improvement | Paired 95% interval | Interpretation | +| --- | ---: | ---: | --- | +| Plain adaptive target-Fisher quota | `0.0305105895` | `[-0.0312529538, 0.0830206126]` | 6.18% descriptive reduction; interval crosses zero | +| Static target-directional Fisher | `0.0729896501` | `[-0.0110369474, 0.1781795248]` | 13.62% descriptive reduction; interval crosses zero | + +CQER had the lowest mean KL and CVaR95 KL of the three methods, but its top-1 +agreement was lower than both. The preregistered gate was conjunctive, so the +two failures cannot be offset by the passing metrics. + +## Gate decision + +| Frozen check | Required | Observed | Result | +| --- | ---: | ---: | --- | +| Lower NLL than both components | yes | `0.46279 < 0.49330 < 0.53578` | pass | +| NLL reduction vs plain adaptive | at least 5% | 6.18% | pass | +| NLL reduction vs static Fisher | at least 20% | 13.62% | **fail** | +| Top-1 disadvantage vs better comparator | at most 0.01 | 0.02690 | **fail** | +| CVaR95 disadvantage | at most 0.10 | `-0.19480` | pass | +| Exact packed and selector bytes | exact | exact | pass | +| Exact quotas and stage/consume handshake | exact | exact | pass | +| Finite values and authenticated run | required | satisfied | pass | + +The packed recurrent-state representation used `2,564,096` bytes. CQER's +persistent FP32 query-energy EMA used another `147,456` bytes, for +`2,711,552` resident bytes including selector state. This is not an equal-total- +memory result against selectors with no persistent auxiliary state, and it is +not a latency or peak-memory measurement. + +## Decision + +Experiment 007 stops here: + +- the frozen development gate failed; +- the candidate-aligned FP64 prerequisite is not reached; +- ranked MBPP window `[8, 16)` remains unopened; and +- the 32-token half-life, formula, quotas, or thresholds will not be tuned and + relabelled as Experiment 007. + +The positive point estimates are useful engineering evidence, but they are not +statistically decisive on eight reused tasks and do not meet the frozen bar. A +successor must be a new preregistered experiment with a new development split +and explicit comparison to the nearest query-aware quantization prior art. + +Experiment 007 makes no breakthrough claim. diff --git a/research/EXPERIMENT_008_CORA_C2_PROTOCOL.md b/research/EXPERIMENT_008_CORA_C2_PROTOCOL.md new file mode 100644 index 0000000..75ad1cf --- /dev/null +++ b/research/EXPERIMENT_008_CORA_C2_PROTOCOL.md @@ -0,0 +1,326 @@ +# Experiment 008: CORA-C2 protocol + +> **Status: frozen before quality evaluation; development gate later failed.** +> +> CORA-C2 means **Causal Observability Row Allocation with Confirmation-2**. +> The state-transition formula, diagonal approximation, normalization, +> confirmation rule, quotas, storage accounting, development window, +> comparators, metrics, and advancement rule are fixed below. Ranked MBPP +> window `[8, 16)` remains protected and unopened. The authenticated outcome +> is recorded in [`EXPERIMENT_008_RESULT.md`](EXPERIMENT_008_RESULT.md); the +> protocol below remains the pre-result specification. + +Protocol frozen: 2026-07-23 + +## Question + +Can a causal approximation to recurrent-state observability allocate a fixed +INT8 row budget more faithfully than static Fisher sensitivity, per-write +reconstruction MSE, and CQER-32, while a parameter-free two-hit admission rule +prevents unstable precision-mask switching? + +The frozen artifact method name is: + +```text +causal_observability_confirm2_mse_target_fisher_quota +``` + +Experiment 007 showed a useful but insufficient CQER-32 point estimate. Its +macro excess NLL improved by 6.18% over adaptive MSE and 13.62% over static +target-Fisher, but the static reduction missed the frozen 20% gate, both paired +intervals crossed zero, and top-1 agreement missed the allowed margin. A +read-only postmortem also found that final-transition mask churn correlated +negatively with CQER NLL gain, although only the last transition was recorded. +Those observations motivate this experiment; they are not confirmation data. + +## Verified transition orientation + +For one Qwen3.5 Gated DeltaNet head, the recurrent state has shape +`[key_row, value]`. With normalized key `k`, scalar write gate `beta`, and +scalar decay `a = exp(g)`, the state update is: + +```text +S_t = a_t (I - beta_t k_t k_t^T) S_(t-1) + beta_t k_t v_t^T +``` + +Define the state-error transition: + +```text +T_t = a_t (I - beta_t k_t k_t^T) +``` + +This follows the pinned Transformers 5.14.1 torch recurrence and the Gated +DeltaNet update. It does not assume that gates alone predict end-to-end loss. + +## Causal diagonal observability filter + +The exact finite-horizon observability Gramian depends on future transitions +and queries, so it is unavailable to a causal inference-time selector. +CORA uses a normalized, locally stationary Lyapunov filter instead: + +```text +P_bar_t = q*_t q*_t^T + T_t^T P_bar_(t-1) T_t +``` + +This is explicitly a predictor, not the true future Gramian. CORA stores only +the diagonal `p = diag(P_bar)`. Query and key vectors are L2-normalized with +`eps = 1e-6`, exactly matching the pinned kernel contract. If key-row width is +`d`, the read query is `q* = q_hat / sqrt(d)`. + +For one head and token, let: + +```text +u = k_hat^2 +c = sum_j(p_j * u_j) +``` + +The frozen `O(d)` diagonal recurrence is: + +```text +r_j = q*_j^2 + a^2 * ( + p_j * (1 - beta * u_j)^2 + + beta^2 * u_j * max(c - p_j * u_j, 0) +) +``` + +The `max` only suppresses negative roundoff in a mathematically non-negative +sum. After every token, trace-normalize across every head and key row in the +layer: + +```text +p = r / sum(r) +``` + +Frozen numerical and causal details: + +- initialize and reset `p` to `1 / (heads * key_rows)`; +- process multi-token chunks strictly in chronological order; +- compute normalized query, key, decay, beta, recurrence, and normalization in + FP64 workspace, then commit persistent `p` in FP32; +- use the post-convolution, repeated-head query and key passed to the Gated + DeltaNet kernel; +- stage the candidate only after the kernel succeeds and before its state write; +- commit `p` only after packing and every postcondition succeed; +- reject missing, duplicate, stale, wrong-shape, wrong-device, batch-greater- + than-one, non-finite, or non-positive-trace observations; and +- reject packed-sequence `cu_seqlens` in this experiment. + +## Row error and raw allocation + +For incoming state row `S_i`, use the exact aligned physical quantizers from +the packed cache: + +```text +benefit_i = ||S_i - Q4(S_i)||_2^2 - ||S_i - Q8(S_i)||_2^2 +score_i = p_i * benefit_i +``` + +`Q4` and `Q8` use one group per `[head, key_row]`, FP16 scales, nearest +rounding, seed 2339, epsilon `1e-12`, and the existing physical packer. The raw +mask `R_t` is the stable top quota by `score`; exact ties preserve flattened +`[head, key_row]` order. + +The per-layer quotas remain the authenticated target-directional-Fisher vector: + +```text +0:355, 1:380, 2:269, 4:179, 5:185, 6:105, +8:80, 9:43, 10:84, 12:30, 13:62, 14:54, +16:45, 17:27, 18:7, 20:9, 21:7, 22:55 +``` + +They sum to exactly 1,976 promoted INT8 rows. CORA does not search a new layer +allocation. + +## Confirmation-2 switching + +Let `R_t` be the raw CORA top-quota mask and `M_t` the committed precision +mask. The first write uses: + +```text +M_1 = R_1 +``` + +For every later state write: + +```text +eligible_t = M_(t-1) OR (R_(t-1) AND R_t) +M_t = stable_top_quota(score_t restricted to eligible_t) +``` + +Incumbents remain eligible. A new row must appear in the raw top quota on two +consecutive writes before it can replace one. Because `M_(t-1)` already +contains the full quota, the eligible set always contains at least that many +rows and the committed mask always has the exact quota. Stable score order and +then canonical flattened order resolve ties. + +Persistent `p`, the bit-packed previous raw mask, the packed state, counters, +and evidence form one transaction. Any failure rolls all of them back and +clears the staged observation. + +## Storage contract + +| Component | Bytes | +| --- | ---: | +| Packed INT4/INT8 recurrent state, scales, and committed masks | 2,564,096 | +| FP32 observability diagonal | 147,456 | +| Bit-packed previous raw masks | 4,608 | +| **Selector auxiliary tensors** | **152,064** | +| **Resident bytes including selector** | **2,716,160** | + +Temporary query, key, gate, recurrence, endpoint, score, and sort workspaces +are not resident cache bytes. This experiment makes no equal-total-memory, +peak-memory, latency, throughput, or fused-kernel claim. + +## Frozen development partition + +The new development diagnostic uses the frozen MBPP calibration ranking but a +window not used for E005-E007 quality evaluation: + +```text +dataset: google-research-datasets/mbpp, full +revision: 4bb6404fdc6cacfda99d4ac4205087b89d32030c +phase/source: calibration/train +selection namespace: rq-v0.2 +ranked offset: 16 +task count: 16 +window: [16, 32) +``` + +The exact ordered task IDs, content manifest, and tokenizer manifest may be +resolved after this protocol commit, but they must be committed in a +pre-quality identity amendment before model loading. No quality metric from +`[16, 32)` may be observed before that amendment. + +That identity amendment is now frozen in +[`EXPERIMENT_008_DEVELOPMENT_IDENTITY.md`](EXPERIMENT_008_DEVELOPMENT_IDENTITY.md): + +```text +ordered task IDs: +666, 795, 944, 653, 857, 884, 878, 822, +687, 820, 920, 771, 869, 851, 728, 704 + +content-manifest SHA-256: +21dcc6e1955918a9f6baae3d02e7ba2781600405f91fe42bbe18eac8ca6dde5e + +token-manifest SHA-256: +5a8e7b56528e3ccecc95ff83b2e59749d81dab27d0233fefafc510622a973f87 +``` + +Ranked window `[8, 16)` remains protected. It cannot be loaded, tokenized, or +evaluated unless every development and independent numerical prerequisite in +this protocol passes from committed artifacts. + +The pre-quality implementation meaning of dataset-loader access versus retained +evaluation rows is recorded in +[`EXPERIMENT_008_DATA_ACCESS_CLARIFICATION.md`](EXPERIMENT_008_DATA_ACCESS_CLARIFICATION.md). +No protected row is retained, canonicalized, formatted, tokenized, or evaluated. + +## Frozen methods and ablations + +All methods use the same model revision, quantizers, per-layer quotas, packed- +state byte contract, tasks, and teacher-forced transition alignment: + +1. static target-directional Fisher; +2. adaptive target-Fisher per-write MSE; +3. CQER-32; +4. CORA without Confirmation-2; +5. CQER-32 with Confirmation-2; and +6. **CORA-C2**, the frozen primary. + +Ablations must be reported but cannot replace the primary after results are +visible. + +## Metrics and instrumentation + +Primary metric: task-macro excess next-token NLL relative to FP32 recurrent +state on code transitions after a packed state has been stored. Exclude the +prompt-to-first-code-token transition exactly as in Experiments 005-007. + +Also record: + +- mean KL, token CVaR95 KL, maximum KL, and top-1 agreement; +- reference and candidate NLL, task/token counts, and all per-task rows; +- full-code secondary metrics; +- 10,000 paired task bootstrap contrasts with seed 2339; +- all-logit and all-metric finiteness; +- exact packed, selector, and combined resident bytes; +- per-layer raw and committed mask hashes and quotas; +- cumulative raw and committed XOR churn after the first write; +- normalized churn `total_xor / (2 * quota * transition_count)`; +- mask overlap, dwell/admission counts, cutoff score and normalized gap; +- transition observations staged/consumed and tokens processed; and +- repository commit, clean state, source hashes, artifact hashes, dataset + manifest, tokenizer manifest, model revision, and command. + +## Frozen development gate + +CORA-C2 advances only if every condition holds: + +1. exact quotas, packed bytes, selector bytes, stage/consume counts, stable + commit/source hashes, authenticated manifests, finite logits, and finite + metrics; +2. primary macro excess NLL is lower than static Fisher, adaptive MSE, and + CQER-32; +3. NLL reduction is at least 20% versus static Fisher; +4. NLL reduction is at least 5% versus adaptive MSE; +5. NLL reduction is at least 5% versus CQER-32; +6. the paired 95% improvement lower bound is above zero versus both static + Fisher and CQER-32; +7. primary top-1 agreement is at least the better of static Fisher and adaptive + MSE minus `0.01`; +8. primary top-1 agreement is not lower than CQER-32; +9. primary CVaR95 is no more than `0.10` above the better static/adaptive value; +10. raw CORA improves NLL by at least 3% versus CQER-32, isolating the + transition-aware signal; +11. C2 reduces normalized committed churn by at least 50% versus raw CORA; +12. C2 does not lower top-1 agreement versus raw CORA; and +13. C2 worsens raw-CORA NLL by no more than 1%. + +If any condition fails, authenticate the result, keep `[8, 16)` closed, and +stop Experiment 008. Thresholds and ablations cannot be changed and relabelled +after observing `[16, 32)`. + +## Independent numerical prerequisite + +Only after the development gate passes, build a verifier that does not import +the production recurrence, ranking, quantization, or packing helpers. It must: + +- replay the diagonal recurrence in CPU FP64; +- compare every step with explicit `T^T diag(p) T` on adversarial and captured + fixtures; +- verify chronological chunk/step equivalence; +- replay aligned Q4/Q8 endpoints, scores, stable ranks, raw masks, C2 masks, + quota counts, and physical bytes; +- cover zero/constant rows, half-step rounding boundaries, extreme finite + values, ties, reset, transfer, and exception rollback; and +- require exact mask/byte hashes and derived numerical error bounds rather than + an aggregate pass percentage. + +Any ambiguity or mismatch stops the experiment before holdout. + +## Prior-art and claim boundary + +The mathematical ingredients are established, and novelty is unresolved: + +- [Gated DeltaNet](https://arxiv.org/abs/2412.06464) defines the host recurrent + update; +- [WriteSAE](https://arxiv.org/abs/2605.12770) derives the same state transition + for later-logit cache interventions, while CORA applies a diagonal online + filter to physical row-precision allocation; +- [MixKVQ](https://arxiv.org/abs/2512.19206) combines query magnitude and + quantization difficulty for KV-cache channels and is the nearest query-aware + mixed-precision mechanism; +- [TQS-PTQ](https://arxiv.org/abs/2606.13300) uses trajectory sensitivity for + offline time-series weight allocation; and +- Q-Mamba, Quamba2, OuroMamba, and RateQuant already cover adjacent state-cache, + SSM, dynamic-channel, and rate-distortion quantization ideas. + +CORA-C2 differs by combining the actual Gated DeltaNet error transition, an +online diagonal observability approximation, physical Q4/Q8 recurrent-state +row error, and exact-quota two-hit admission. That distinction is a research +hypothesis, not proof of novelty. + +Experiment 008 cannot support “breakthrough,” “first,” “novel,” or +“state-of-the-art” language without passing the frozen development and +numerical gates, one-time holdout, broader models/tasks, closest-method +comparisons, fused-system measurements, and independent reproduction. diff --git a/research/EXPERIMENT_008_DATA_ACCESS_CLARIFICATION.md b/research/EXPERIMENT_008_DATA_ACCESS_CLARIFICATION.md new file mode 100644 index 0000000..13a6923 --- /dev/null +++ b/research/EXPERIMENT_008_DATA_ACCESS_CLARIFICATION.md @@ -0,0 +1,33 @@ +# Experiment 008 data-access clarification + +> **Status: committed before any Experiment 008 model load, forward pass, or +> quality metric.** + +The MBPP source is a streaming public split rather than a server-side keyed +database. Locating already-pinned task IDs therefore requires the dataset +loader to stream source records. A non-target record can transiently pass +through the loader even when its content is never retained or used. + +For Experiment 008, "protected and unopened" has this enforceable meaning: + +- ranked window `[8, 16)` is never selected, retained, canonicalized, hashed, + formatted, tokenized, passed to the model, or evaluated; +- the evaluator loads only the exact pinned selector-prefix IDs and `[16, 32)` + development IDs into application state; +- non-target stream records are inspected only for `task_id` and discarded; +- the legacy Experiment 005 route to `[8, 16)` is locked; and +- any task-ID, row-content, token-manifest, ordering, repository, or source-file + mismatch fails before model loading. + +This clarification fixes an implementation ambiguity in the frozen wording. It +does not reveal a protected prompt, token, logit, loss, or quality result and +does not change the method, tasks, thresholds, or advancement rule. + +## Metric aggregation clarification + +The protocol's `token CVaR95 KL` is computed within each task over that task's +scored tokens, then averaged with equal task weight. This is the +`macro_cvar95_kl` contract already used by Experiments 005-007 and prevents long +reference programs from dominating the task-macro evaluation. The artifact +also records the maximum KL over all task maxima as `maximum_kl`. The frozen +`0.10` CVaR margin applies to `macro_cvar95_kl`. diff --git a/research/EXPERIMENT_008_DEVELOPMENT_IDENTITY.md b/research/EXPERIMENT_008_DEVELOPMENT_IDENTITY.md new file mode 100644 index 0000000..78d721c --- /dev/null +++ b/research/EXPERIMENT_008_DEVELOPMENT_IDENTITY.md @@ -0,0 +1,70 @@ +# Experiment 008 development identity + +> **Status: exact `[16, 32)` identities pinned before model loading or quality +> evaluation.** + +Identity resolved: 2026-07-23 + +The CORA-C2 protocol was already committed at `1a7c34a` before this window was +resolved. This step loaded the pinned public MBPP rows and local tokenizer only. +It did not load model weights, run a forward pass, compute logits, or observe a +quality metric. + +## Frozen provenance + +| Field | Value | +| --- | --- | +| Dataset | `google-research-datasets/mbpp`, config `full` | +| Dataset revision | `4bb6404fdc6cacfda99d4ac4205087b89d32030c` | +| Source split | `train` | +| Selection namespace | `rq-v0.2` | +| Ranked window | `[16, 32)` | +| Ordered task IDs | `666, 795, 944, 653, 857, 884, 878, 822, 687, 820, 920, 771, 869, 851, 728, 704` | +| Content-manifest SHA-256 | `21dcc6e1955918a9f6baae3d02e7ba2781600405f91fe42bbe18eac8ca6dde5e` | +| Prompt formatter | `recurquant.mbpp-prompt-code.v1` | +| Tokenizer | `Qwen2Tokenizer`, Transformers 5.14.1 | +| Tokenizer source | `Qwen/Qwen3.5-0.8B-Base` at `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` | +| Token-manifest SHA-256 | `5a8e7b56528e3ccecc95ff83b2e59749d81dab27d0233fefafc510622a973f87` | +| Total aligned scored tokens | 798 | +| Total full-code scored tokens | 814 | + +The selector inputs remain the exact frozen pair: + +```text +HRR canonical evidence SHA-256: +7970961fd88b522998189ad64f26b333aed9c88ff5f653de5449fd9e01d8cbc8 + +loss-sensitivity canonical evidence SHA-256: +bff4e33253990b8115e1f35e74516c4975c2fe4aac5066475afe968eb8a64609 +``` + +## Ordered row and token identity + +The rank column is zero-based in the complete frozen calibration ordering. +Row hashes come from canonical MBPP content. `Aligned scored` excludes the +prompt-to-first-code-token transition. + +| Rank | Task ID | Row SHA-256 | Prompt tokens | Code tokens | Aligned scored | +| ---: | ---: | --- | ---: | ---: | ---: | +| 16 | 666 | `b4f5989005c921c3ab94ab52c8115e79f99a22390bc1d6e6235d36fd02687fb9` | 69 | 39 | 38 | +| 17 | 795 | `0da1480294ac295b6ceebf822dd2b512863c26b314e43f95ca6e5945e6ecfd82` | 290 | 35 | 34 | +| 18 | 944 | `9a7fbac2ccd13b8940f81ef75d20a4521cad8d39ccfe78714d85c49f712597d0` | 91 | 26 | 25 | +| 19 | 653 | `346aee9efc80955f531c1574db8b5c2eb25b48876c76a0aba1a35f15138800db` | 249 | 38 | 37 | +| 20 | 857 | `e9ac9acf8ca396274eef0b4479cfe2c0126ab6656222828b6198a497fa46f913` | 271 | 22 | 21 | +| 21 | 884 | `947efb4b2967163d5a5e7816e091b8b4a2a58c73e479c66ccc1b65fd7ea79cbb` | 112 | 68 | 67 | +| 22 | 878 | `88b2f4e5c166757c111f0baff74c20fa4d8f13a4fa13d8403c7cacc4f0558e9c` | 139 | 29 | 28 | +| 23 | 822 | `ba6eaa8bdd27506a5372e9e6beeb080d23bbbdc6996661d75ab26290d05fb3a4` | 71 | 132 | 131 | +| 24 | 687 | `350e5f813e96ccbba22a8db978e72840806c6e6fe4be28a06905c3eae5cb68ef` | 89 | 64 | 63 | +| 25 | 820 | `9ba789ae1cb3f3e21ec124ab7bd3447665019e54aa77fdb409c6d5a871994809` | 74 | 31 | 30 | +| 26 | 920 | `1539ae14cc92af940de077b6700a7a17ca2649aa26e165e15f6761cabde1363a` | 215 | 36 | 35 | +| 27 | 771 | `7470507a8c6c27b54bb1a56ef07e012d75c78bacbf8570aff8d0bb5f9919ac67` | 75 | 135 | 134 | +| 28 | 869 | `be0c42f832fb5afeb271845eee64ce97f919537ea266e41c881b798c9d15cabc` | 305 | 47 | 46 | +| 29 | 851 | `b1761ef5396f9ba11c5047b5cb0ff0955f3c8ad67f1c8449a4d7b394f8009076` | 95 | 36 | 35 | +| 30 | 728 | `1c0a375e35a7a918b8caaaf3314b75c36dd75e95f4ce01fa24ac80bb7c5af8cc` | 128 | 37 | 36 | +| 31 | 704 | `dfedafc5ad5c0985fcb5f3451f9434628f9d43077fe643ca1d5026106b5caccc` | 114 | 39 | 38 | + +Any task order, row hash, prompt/code token count, manifest hash, tokenizer, +formatter, selector identity, or model revision mismatch must fail before model +execution. + +Ranked window `[8, 16)` was not loaded or tokenized by this identity step. diff --git a/research/EXPERIMENT_008_RESULT.md b/research/EXPERIMENT_008_RESULT.md new file mode 100644 index 0000000..d0e04be --- /dev/null +++ b/research/EXPERIMENT_008_RESULT.md @@ -0,0 +1,128 @@ +# Experiment 008 result: CORA-C2 rejected before holdout + +> **Status: development gate failed; protected holdout unopened.** +> +> CORA-C2 beat static target-Fisher by `26.17%` and adaptive MSE by +> `21.62%` on the frozen 16-task development window. It did not beat CQER-32: +> macro excess NLL was `0.391586` for CORA-C2 and `0.354250` for CQER-32. +> Confirmation-2 reduced normalized committed-mask churn by `79.99%`, but it +> worsened raw-CORA NLL by `6.96%`, above the frozen `1%` limit. Five +> preregistered checks failed. Ranked MBPP window `[8, 16)` remains unopened. + +Date recorded: 2026-07-23 + +## Authenticated diagnostic + +The development identity, implementation, six methods, storage contract, and +13-condition advancement rule were frozen before this run. The run used the +separate ranked MBPP calibration window `[16, 32)`. + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment008-cora-c2-development-16task-69eec866.json` | +| Artifact kind | `recurquant_cora_c2_development_quality_diagnostic` | +| Clean implementation commit | `69eec866b90f2ae5386da23a8f34fba4c428d9e1` | +| File SHA-256 | `03da6dc27641e7816f6f03bd059876322974530911d28e1d9719dd992af8f9db` | +| Canonical evidence SHA-256 | `24877f73068f765ef0bae6b2bee54ff95d5204977b3c08dfe95516d651c2fb70` | +| Created at | `2026-07-22T20:51:42.559117+00:00` | +| Model | `Qwen/Qwen3.5-0.8B-Base` at `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` | +| Device and dtype | CUDA, bfloat16 | +| Task IDs | `666`, `795`, `944`, `653`, `857`, `884`, `878`, `822`, `687`, `820`, `920`, `771`, `869`, `851`, `728`, `704` | +| Aligned scored tokens | 798 | +| Bootstrap | 10,000 paired task resamples, seed 2339 | +| Repository and source files | clean and stable at start and end | +| Evidence verification | valid, with no verifier errors | + +Independent rechecks reproduced every paired bootstrap contrast, source and +selector hash, task and token identity, byte count, quota, and transition +handshake. The protected `[8, 16)` loader guards also passed after the run. +This is an authenticated method failure, not an execution failure. + +## Frozen-primary result + +| Method | Role | Macro excess NLL | Mean KL | CVaR95 KL | Top-1 agreement | +| --- | --- | ---: | ---: | ---: | ---: | +| `target_directional_fisher_difference_int4` | static Fisher | `0.5303981081` | `0.5286498712` | `3.3478351496` | `0.7586527802` | +| `adaptive_mse_target_directional_fisher_quota` | per-write row MSE | `0.4996191338` | `0.4905063435` | `3.0781028867` | `0.7698915936` | +| `query_ema32_weighted_mse_target_fisher_quota` | CQER-32 | **`0.3542503491`** | **`0.3641314693`** | **`2.4055756629`** | `0.8027831651` | +| `causal_observability_mse_target_fisher_quota` | raw CORA | `0.3661060743` | `0.3896415923` | `2.6112778820` | `0.8017726764` | +| `query_ema32_confirm2_mse_target_fisher_quota` | CQER-32+C2 | `0.3951669149` | `0.4165541902` | `2.7546808161` | **`0.8111176938`** | +| `causal_observability_confirm2_mse_target_fisher_quota` | frozen primary | `0.3915858567` | `0.4186489517` | `2.8813917115` | `0.8103738017` | + +Comparator-minus-primary paired NLL contrasts were: + +| Comparator | Mean improvement | Paired 95% interval | Interpretation | +| --- | ---: | ---: | --- | +| Static target-Fisher | `0.1388122514` | `[0.0436136126, 0.2466757520]` | 26.17% reduction; pass | +| Adaptive MSE | `0.1080332771` | `[0.0277902825, 0.1880076991]` | 21.62% reduction; descriptive pass | +| CQER-32 | `-0.0373355076` | `[-0.1060209079, 0.0192620480]` | 10.54% worse; fail | +| Raw CORA | `-0.0254797824` | `[-0.0689048139, 0.0198680538]` | C2 was 6.96% worse; fail | + +CORA's transition-aware point estimate was also `3.35%` worse than CQER-32, +so the observability recurrence did not improve the strongest causal selector. +The uncertainty interval for raw CORA versus CORA-C2 crossed zero, but the +frozen advancement rule used the prespecified relative-worsening threshold as +well as the paired comparisons. + +## Confirmation-2 result + +Confirmation-2 behaved as a strong debouncer: + +| Selector | Normalized committed churn | +| --- | ---: | +| Raw CORA | `0.0129384697` | +| CORA-C2 | `0.0025893428` | +| Relative reduction | **`79.9873%`** | + +That stability was not free. CORA-C2 raised NLL from `0.366106` to `0.391586`. +The same pattern appeared in the CQER ablation: C2 raised NLL from `0.354250` +to `0.395167`. C2 improved top-1 agreement for both selectors, but its NLL and +tail-KL cost rules it out as the frozen advancement mechanism. This experiment +supports treating two-hit admission as a switching-quality trade-off, not as a +generally beneficial selector. + +## Gate decision + +| Frozen check | Observed | Result | +| --- | ---: | --- | +| Lower NLL than static, adaptive, and CQER | CQER `0.354250` < primary `0.391586` | **fail** | +| NLL reduction vs static | 26.17%, minimum 20% | pass | +| NLL reduction vs adaptive | 21.62%, minimum 5% | pass | +| NLL reduction vs CQER | -10.54%, minimum 5% | **fail** | +| Paired lower bound vs static | `0.043614 > 0` | pass | +| Paired lower bound vs CQER | `-0.106021 <= 0` | **fail** | +| Top-1 margin vs static/adaptive | primary was higher | pass | +| Top-1 not lower than CQER | `0.810374 >= 0.802783` | pass | +| CVaR95 margin vs static/adaptive | primary was lower | pass | +| Raw CORA NLL reduction vs CQER | -3.35%, minimum 3% | **fail** | +| C2 churn reduction vs raw CORA | 79.99%, minimum 50% | pass | +| C2 top-1 not lower than raw CORA | `0.810374 >= 0.801773` | pass | +| C2 NLL worsening vs raw CORA | 6.96%, maximum 1% | **fail** | +| Exact bytes, quotas, handshakes, finiteness, identity | all exact | pass | + +The packed state used `2,564,096` bytes. CORA-C2 used another `152,064` +resident bytes for its FP32 observability diagonal and bit-packed prior raw +masks, for `2,716,160` bytes including selector state. This is not an +equal-total-memory, peak-memory, latency, throughput, or fused-kernel result. + +## Interpretation and decision + +The negative result isolates two useful facts: + +1. The frozen diagonal causal-observability proxy was not better than the much + simpler query-energy EMA on this development window. +2. Requiring two consecutive raw-mask hits removes most switching but is too + sticky for the quality objective at this token scale. + +Experiment 008 stops here. The independent numerical verifier is not built, +the protected `[8, 16)` window is not opened, and no formula, threshold, or +ablation will be tuned and relabelled as Experiment 008. + +The underlying observability-weighted-error idea also has classical finite- +wordlength state-space precedent. CORA is best understood as an experimental +Gated DeltaNet adaptation of that principle, not a newly invented principle. +Any successor must use a new protocol and development identity, replace or +substantially strengthen the failed signal, and compare against CQER-32. + +Experiment 008 makes no novelty, state-of-the-art, deployment, or breakthrough +claim. diff --git a/research/EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md b/research/EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md new file mode 100644 index 0000000..1ec0707 --- /dev/null +++ b/research/EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md @@ -0,0 +1,82 @@ +# Experiment 009 data-access clarification + +> **Status: recorded after Stage A and before any Stage-B identity resolution, +> tokenizer load, model load, forward pass, or quality metric.** +> +> This clarification narrows ambiguous transport wording. It does not change +> the method, ranked windows, task count, metrics, thresholds, or advancement +> rule. + +Recorded: 2026-07-26 + +## Why this is necessary + +The pinned MBPP source is exposed through Hugging Face streaming, not a +server-side keyed API. The transport can deserialize a complete source record +before RecurQuant's loader receives its mapping. RecurQuant can control which +fields its code inspects and which rows enter application state, but it cannot +truthfully prove that the dataset transport never deserialized a non-target +record. + +The Stage-A artifact contains the legacy field: + +```text +protected_window_8_16_loaded_tokenized_or_evaluated = false +``` + +Its word `loaded` is too broad if read as transport-level deserialization. The +authenticated artifact remains immutable. For Experiment 009, that field is +interpreted only as the enforceable application-level boundary below. + +## Enforceable protected-window boundary + +Ranked MBPP window `[8, 16)` must never be: + +- selected as a target row; +- retained as a row mapping in RecurQuant application state; +- canonicalized or content-hashed; +- formatted into a prompt/code example; +- tokenized; +- passed to a model; +- evaluated; or +- included in a quality or diagnostic artifact. + +For non-target streaming records, RecurQuant may inspect only `task_id` and +must discard the record immediately. Dataset transport may already have +deserialized other fields; those fields must not be read by RecurQuant code. +Task IDs alone may be used to compute the frozen rank ordering. + +This is the practical boundary required by a public streaming source: +protected task content and tokens remain unseen by the experiment, while +source traversal is acknowledged rather than hidden. + +## Stage-B identity algorithm + +The `[32, 64)` identity step must use two content-separated passes: + +1. Stream the pinned training split and inspect only `task_id`. +2. Rank task IDs with the frozen `rq-v0.2` SHA-256 key and retain only the 32 + target IDs at ranks `[32, 64)`. +3. Stream again through the task-ID loader. Inspect only `task_id` for + non-target records and canonicalize only the 32 target rows. +4. Verify their exact rank order, canonical row hashes, and content manifest. +5. Load the pinned tokenizer and tokenize only those 32 target rows. +6. Write and authenticate the identity artifact without loading model weights + or computing logits, losses, state errors, or any other quality metric. + +The implementation must have a fail-closed test mapping that raises if any +non-target field is read. It must also assert that selected, canonicalized, +formatted, tokenized, and evaluated task-ID sets are disjoint from ranked +window `[8, 16)`. + +## Claim correction + +The precise Stage-A statement is: + +> Task `666` was the only row selected, retained, canonicalized, formatted, +> tokenized, passed to the model, or evaluated. RecurQuant code inspected only +> `task_id` on non-target stream records. The dataset transport may have +> deserialized complete non-target records. + +No protected prompt, code, row hash, token sequence, logit, loss, or state +metric was retained or reported by Stage A. diff --git a/research/EXPERIMENT_009_RHT_CQER_PROTOCOL.md b/research/EXPERIMENT_009_RHT_CQER_PROTOCOL.md new file mode 100644 index 0000000..7ae8d10 --- /dev/null +++ b/research/EXPERIMENT_009_RHT_CQER_PROTOCOL.md @@ -0,0 +1,233 @@ +# Experiment 009: right-RHT CQER protocol + +> **Status: screening and development protocol frozen before quality +> evaluation.** +> +> This protocol defines a cheap falsification screen on one already-open task +> and, only if that screen passes, a separately authenticated 32-task +> development run. Ranked MBPP window `[8, 16)` remains protected and must not +> be selected, retained, canonicalized, formatted, tokenized, passed to the +> model, or evaluated by either stage. Public-stream traversal is defined in +> [`EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md`](EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md). + +Protocol frozen: 2026-07-26 + +## Question + +Does a deterministic orthonormal transform along each Gated DeltaNet state +row's value axis reduce absmax INT4 outlier error enough to improve CQER-32 at +the same packed recurrent-state and selector byte counts? + +The candidate method name is: + +```text +right_rht_query_ema32_weighted_mse_target_fisher_quota +``` + +The short research name is **RHT-CQER-32**. This is an experimental codec +variant, not a novelty, speed, or breakthrough claim. + +## Codec + +For recurrent row `s` with value width `d = 128`, layer `l`, and value head +`h`, define a deterministic sign diagonal `D[l,h]` and the unnormalized +Walsh-Hadamard matrix `H` satisfying `H H^T = d I`: + +```text +encode(s) = s D[l,h] H / sqrt(d) +decode(z) = z H D[l,h] / sqrt(d) +``` + +The FP32 encode/decode pair is orthonormal up to floating-point error. It +operates only within one existing `[head, key-row]` group, so it does not mix +CQER row identities or query coordinates. + +The sign schedule is frozen as follows: + +- seed: `2339`; +- domain separator: `recurquant.right-rht.signs.v1\0`; +- SHA-256 input fields: seed, model-layer index, head index, width, and block + counter, each encoded as unsigned little-endian 64-bit values; +- digest bits are consumed byte-major and least-significant-bit first; +- bit one maps to `+1`, bit zero maps to `-1`; +- the concatenated INT8 sign schedule for model layers + `0,1,2,4,5,6,8,9,10,12,13,14,16,17,18,20,21,22`, 16 heads, and width 128 + has SHA-256 + `2d5137b5ebeb325f100b34190618783b9e47bd2ce9b27b6cdf3cdc94459dabc3`. + +Signs and FP32 transform workspaces are regenerated transiently in the current +reference implementation. No sign tensor is resident in the cache. Runtime, +temporary workspace, allocator peak, and whole-model memory must be reported +separately before any systems claim. + +## Quantization and selection + +RHT-CQER-32 uses the existing physical Q4/Q8 group formats, FP16 scale per +row, nearest rounding, and one packed precision bit per row. Both CQER +endpoints are evaluated through the same codec: + +```text +e4 = s - decode(Q4(encode(s))) +e8 = s - decode(Q8(encode(s))) +benefit = mean_value(e4^2 - e8^2) +score = EMA32(normalized_query^2) * benefit +``` + +The causal query-energy EMA, stable ranking, stage/consume handshake, and +target-directional-Fisher per-layer quotas are unchanged from Experiment 007. +Confirmation-2 is forbidden. + +The quotas remain: + +```text +0:355, 1:380, 2:269, 4:179, 5:185, 6:105, +8:80, 9:43, 10:84, 12:30, 13:62, 14:54, +16:45, 17:27, 18:7, 20:9, 21:7, 22:55 +``` + +They sum to 1,976 Q8 rows. For both CQER-32 and RHT-CQER-32: + +| Component | Bytes | +| --- | ---: | +| Q4/Q8 payloads | 2,485,760 | +| FP16 scales | 73,728 | +| precision masks | 4,608 | +| **packed recurrent state** | **2,564,096** | +| FP32 query-energy EMA | 147,456 | +| **resident bytes including selector** | **2,711,552** | + +## Stage A: already-open falsification screen + +Stage A uses only task `666`, already opened and authenticated in Experiment +008: + +```text +ranked calibration index: 16 +row SHA-256: +b4f5989005c921c3ab94ab52c8115e79f99a22390bc1d6e6235d36fd02687fb9 +prompt tokens: 69 +code tokens: 39 +aligned scored tokens: 38 +``` + +The evaluator must compare reference FP32 state, frozen CQER-32, and +RHT-CQER-32 in one teacher-forced run. It must record aligned excess NLL, mean +KL, CVaR95 KL, maximum KL, top-1 agreement, per-write state error, exact +storage, quotas, observation handshakes, finiteness, source hashes, repository +identity, and artifact hashes. + +Stage A passes only if every condition holds: + +1. the repository is clean and unchanged throughout the run; +2. the row identity and token counts above match before model loading; +3. all packed states occupy exactly `2,564,096` bytes and selector-aware + totals equal `2,711,552` bytes; +4. every layer has its exact frozen quota, all observations are consumed once, + and all logits and metrics are finite; +5. deterministic production-code self-consistency gives right-RHT inverse + relative L2 below + `3e-7` and exact physical-pack versus transformed-QDQ reconstruction; +6. closed-loop aggregate per-write state SSE is at least 50% lower than + CQER-32; +7. RHT-CQER-32 aligned excess NLL is at least 10% lower than CQER-32; +8. RHT-CQER-32 mean KL is lower; and +9. RHT-CQER-32 top-1 agreement is not lower. + +Task `666` was selected before RHT quality was measured because it is the first +task in the already-authenticated Experiment 008 window and one of CORA's +recorded tail failures. Passing one exposed task authorizes only Stage B. +Failure stops this candidate without trying another sign seed, normalization, +transform axis, or threshold on the same task. + +## Stage B: new 32-task development run + +Only after Stage A passes from a committed authenticated artifact may the +ranked calibration window `[32, 64)` be resolved. The exact ordered task IDs, +row hashes, content manifest, prompt/code token counts, and tokenizer manifest +must be committed in an identity amendment before model weights are loaded or +any quality metric is observed. + +Identity resolution must first rank an ID-only stream, then canonicalize and +tokenize only the 32 selected target rows. Non-target source records may be +inspected only for `task_id` and must be discarded immediately. Dataset +transport may deserialize complete records; this transport fact is not treated +as experiment-level content access. + +Before either identity pass, the resolver must authenticate the exact Stage-A +artifact and match its Python `3.11.15`, datasets `4.8.5`, NumPy `2.4.6`, +safetensors `0.8.0`, PyTorch `2.11.0+cu128`, Transformers `5.14.1`, CUDA +availability, CUDA `12.8` runtime, and `cuda` device type. The identity records +that structured runtime contract. The evaluator must match it again before +reloading any target row or tokenizer and before opening model weights. + +The Stage B methods are frozen to: + +1. static target-directional Fisher Q4/Q8; +2. adaptive per-write MSE under target-Fisher layer quotas; +3. CQER-32; and +4. RHT-CQER-32. + +Primary metric: task-macro aligned excess next-token NLL relative to FP32 +recurrent state. Also report mean KL, task-macro CVaR95 KL, maximum KL, top-1 +agreement, full-code secondary metrics, task/token counts, and 10,000 paired +task bootstraps with seed 2339. + +Task-macro metrics are the unweighted mean of the 32 per-task summaries. The +paired bootstrap operates on the 32 aligned excess-NLL differences +`CQER-32 - RHT-CQER-32`: it resamples paired tasks with replacement using +NumPy `default_rng(2339)` and reports the equal-tailed, two-sided 95% +percentile interval from 10,000 resamples. Advancement requires its lower +endpoint to be strictly greater than zero. A task is an RHT win only when its +excess NLL is strictly lower; exact equality is recorded as a tie and is not a +win. The relative excess-NLL gate is valid only when CQER-32's macro excess +NLL is positive; a zero or negative baseline fails closed instead of producing +a relative claim. + +The state-error advancement metric is local codec reconstruction SSE: +materialized packed state minus that candidate trajectory's own pre-pack +source state. It is summed over every recurrent layer, write, and task before +the relative reduction is computed. This is a write-micro aggregate, so tasks +with more code-token writes contribute more state elements. Candidate state +versus the matched FP32 trajectory is recorded separately as a secondary +diagnostic and is not an advancement gate. + +Stage B advances only if all integrity conditions from Stage A hold and: + +1. RHT-CQER-32 lowers macro excess NLL by at least 20% versus CQER-32; +2. the paired 95% lower bound for CQER-minus-RHT excess NLL is above zero; +3. RHT-CQER-32 wins on at least 20 of 32 task-level excess-NLL comparisons; +4. macro mean KL is lower than CQER-32; +5. macro CVaR95 KL is no higher than CQER-32; +6. macro top-1 agreement is no more than `0.005` below CQER-32; +7. no task's RHT-minus-CQER excess-NLL disadvantage exceeds `0.25`; and +8. aggregate per-write state SSE is at least 50% lower than CQER-32. + +If any condition fails, authenticate the negative result and stop +RHT-CQER-32. Do not change the transform seed, thresholds, task window, or +method name after results are visible. + +## Claim and prior-art boundary + +Randomized Hadamard and learned rotation methods are established in +[QuIP#](https://arxiv.org/abs/2402.04396), +[QuaRot](https://arxiv.org/abs/2404.00456), +[SpinQuant](https://arxiv.org/abs/2405.16406), and +[TurboQuant](https://arxiv.org/abs/2504.19874). MambaQuant applies +rotation-based outlier suppression to an adjacent state-space architecture. +CQER also has close query-relevance and quantization-difficulty precedent in +MixKVQ. + +Therefore a positive result would support only this narrow engineering and +empirical contribution: a deterministic physical right-RHT codec composed +with causal row allocation for a pinned Gated DeltaNet recurrent cache under +an exact byte contract. It would not establish that rotations, Hadamard +quantization, query-aware precision, or recurrent-state quantization are new. + +The repository now includes a separately implemented dense NumPy check of the +sign derivation, Hadamard transform, FP16-scale quantizer, and decode. Before +release-level or paper-level claims, the candidate still requires external +reproduction of that verifier, another model size, natural-text and +long-context tasks, closest-method comparisons, optimized packed kernels, +end-to-end latency and peak-memory measurements, and independent reproduction +of the quality result. Ranked window `[8, 16)` stays closed until those +prerequisites are separately frozen and passed. diff --git a/research/EXPERIMENT_009_STAGE_A_AUDIT.md b/research/EXPERIMENT_009_STAGE_A_AUDIT.md new file mode 100644 index 0000000..e7c5c15 --- /dev/null +++ b/research/EXPERIMENT_009_STAGE_A_AUDIT.md @@ -0,0 +1,78 @@ +# Experiment 009 Stage-A post-result audit + +> **Status: the authenticated Stage-A result is unchanged; its verifier and +> Stage-B handoff were hardened before any Stage-B identity, tokenizer, model, +> or quality access.** + +Recorded: 2026-07-26 + +## Immutable result + +The Stage-A artifact remains byte-for-byte unchanged: + +```text +evidence/experiment009-rht-cqer-stage-a-666-5be8d48.json +file SHA-256: +98a432843dc438f2d5fde34f8704f154ebc3ee12c93ba7c469369acfedfb15b5 +canonical evidence SHA-256: +9e03a1e8cefb5801406a47a2e5e365686afb0a05e10e099a989cee616b505ed1 +``` + +The audit did not change the method, sign seed, task, thresholds, metrics, +model, tokenizer, selector artifacts, row quotas, or recorded measurements. +It rechecked the existing evidence and strengthened what future code must +recompute before accepting it. + +## Findings and corrections + +The first verifier trusted several internally recorded summaries more than it +should have. The hardened verifier now independently recomputes and +cross-checks: + +- candidate-minus-reference excess NLL and the shared reference baseline; +- KL ordering, probability bounds, NLL non-negativity, and finiteness; +- aggregate, per-write, and per-layer state SSE, MSE, element counts, layer + order, write order, and exact recurrent-state shape; +- FP32 recurrent source-state provenance; +- per-layer Q8 quotas, selected-row counts, exact selection method, and every + stage/consume handshake; +- final physical precision-mask hashes against selector diagnostics; and +- exact packed-state and selector-aware byte accounting. + +Mutation tests now demonstrate that coherent changes to these fields are +rejected instead of being accepted through a self-consistent wrapper hash. +The historical artifact still passes the hardened verifier. + +The original phrase “independent numeric evidence” was also too strong for a +production implementation checking itself. That check is now described as +deterministic production-code self-consistency. A separate dense NumPy +implementation independently derives the SHA-256 sign schedule, constructs +the Hadamard transform, applies the FP16-scale Q4/Q8 quantizer, decodes the +state, and compares its outputs with the production implementation. + +## Data-access clarification + +The public Hugging Face streaming transport may deserialize complete source +records before yielding them. The enforceable boundary is therefore +application-level: RecurQuant reads only `task_id` on non-target rows and does +not retain, canonicalize, format, tokenize, model, evaluate, or report their +content. Ranked MBPP window `[8, 16)` remained outside every protected +application content set. + +The immutable Stage-A artifact predates this wording and contains a legacy +field using the broader word `loaded`. Its exact interpretation is fixed in +[`EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md`](EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md); +the artifact was not silently rewritten. + +## Runtime precision + +The model weights and forward passes used CUDA bfloat16. The recurrent +source/reference state used for quantization and state-error measurement was +FP32. Both facts are now stated separately in the result and checked by the +handoff. + +## Decision boundary + +The one-task Stage-A pass remains a falsification-screen result. It authorizes +only the separately frozen Stage-B development run. It is not confirmation, +novelty, speed, deployment, state-of-the-art, or breakthrough evidence. diff --git a/research/EXPERIMENT_009_STAGE_A_RESULT.md b/research/EXPERIMENT_009_STAGE_A_RESULT.md new file mode 100644 index 0000000..0a280d3 --- /dev/null +++ b/research/EXPERIMENT_009_STAGE_A_RESULT.md @@ -0,0 +1,103 @@ +# Experiment 009 Stage-A result: RHT-CQER advances + +> **Status: all frozen Stage-A gates passed on the one-task falsification +> screen.** +> +> RHT-CQER-32 reduced closed-loop recurrent-state SSE by `59.97%`, aligned +> excess NLL by `58.59%`, and aligned mean KL by `31.19%` relative to CQER-32 +> at the exact same packed-state and selector byte counts. Top-1 agreement was +> unchanged. This authorizes the separately frozen 32-task development stage; +> it is not confirmation, a speed result, or a breakthrough claim. + +Date recorded: 2026-07-26 + +The immutable artifact and its strengthened post-result verification are +documented separately in the +[Stage-A audit](EXPERIMENT_009_STAGE_A_AUDIT.md). + +## Authenticated artifact + +The method, task identity, sign schedule, implementation, byte contract, and +nine-condition gate were committed before model quality was observed. + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment009-rht-cqer-stage-a-666-5be8d48.json` | +| Artifact kind | `recurquant_rht_cqer32_stage_a_screen` | +| Clean implementation commit | `5be8d48369d94081e55aa389c25f63c303c7b0dd` | +| File SHA-256 | `98a432843dc438f2d5fde34f8704f154ebc3ee12c93ba7c469369acfedfb15b5` | +| Canonical evidence SHA-256 | `9e03a1e8cefb5801406a47a2e5e365686afb0a05e10e099a989cee616b505ed1` | +| Created at | `2026-07-26T03:34:27.699967+00:00` | +| Model | `Qwen/Qwen3.5-0.8B-Base` at `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` | +| Model runtime device and weight dtype | CUDA, bfloat16 | +| Recurrent cache source/reference dtype | FP32 | +| Task identity | MBPP task `666`, row SHA-256 `b4f5989005c921c3ab94ab52c8115e79f99a22390bc1d6e6235d36fd02687fb9` | +| Token identity | 69 prompt, 39 code, 38 aligned scored tokens | +| Evidence verification | valid, with no verifier errors | + +The repository and all hashed source files were clean and stable from the +start of model loading through the atomic artifact write. Dataset identity was +authenticated before model weights were loaded. Task `666` was the only row +selected, retained, canonicalized, formatted, tokenized, passed to the model, +or evaluated. RecurQuant code inspected only `task_id` on non-target streaming +records. The dataset transport may have deserialized complete records; see the +[data-access clarification](EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md). +Ranked MBPP window `[8, 16)` remained outside application-level content and +quality access. + +## Frozen screen result + +| Metric | CQER-32 | RHT-CQER-32 | Change | +| --- | ---: | ---: | ---: | +| Closed-loop state SSE | `906.603876` | `362.875870` | **59.97% lower** | +| Aligned excess NLL | `0.329244` | `0.136350` | **58.59% lower** | +| Aligned mean KL | `0.224385` | `0.154389` | **31.19% lower** | +| Aligned CVaR95 KL | `1.276270` | `1.022663` | 19.87% lower | +| Aligned maximum KL | `1.563906` | `1.397917` | 10.61% lower | +| Aligned top-1 agreement | `0.868421` | `0.868421` | unchanged | + +The full-code secondary metrics showed the same direction: + +| Metric | CQER-32 | RHT-CQER-32 | +| --- | ---: | ---: | +| Excess NLL | `0.320802` | `0.132853` | +| Mean KL | `0.218631` | `0.150430` | +| Top-1 agreement | `0.871795` | `0.871795` | + +## Exact physical contract + +Both methods used: + +| Component | Bytes | +| --- | ---: | +| Q4/Q8 payloads | 2,485,760 | +| FP16 scales | 73,728 | +| precision masks | 4,608 | +| **packed recurrent state** | **2,564,096** | +| FP32 query-energy selector | 147,456 | +| **resident bytes including selector** | **2,711,552** | + +Every layer retained its frozen target-Fisher quota, totaling 1,976 Q8 rows. +All 39 recurrent-state writes and 107 observed query tokens completed the +stage/consume handshake exactly once. All logits and reported metrics were +finite. + +Deterministic production-code self-consistency measured a right-RHT inverse +relative L2 of `1.1468e-7`, below the frozen `3e-7` threshold. Physical +transformed packing matched the production transform-quantize-dequantize +reconstruction exactly. The sign schedule hash matched the preregistered +value. This is wiring evidence, not an independently implemented numerical +verifier. + +## Decision and limitation + +Every frozen Stage-A check passed. Experiment 009 may now resolve and commit +the new ranked MBPP `[32, 64)` development identities before any Stage-B model +quality is observed. + +This result contains one deliberately exposed task. It shows that the codec +survived a cheap falsification test; it does not estimate generalization, +statistical uncertainty, long-context behavior, another model size, peak +memory, latency, or fused-kernel performance. Randomized Hadamard and rotation +quantization are established prior art. No novelty, state-of-the-art, +deployment, or breakthrough claim follows from Stage A. diff --git a/research/EXPERIMENT_009_STAGE_B_IDENTITY.md b/research/EXPERIMENT_009_STAGE_B_IDENTITY.md new file mode 100644 index 0000000..c69f638 --- /dev/null +++ b/research/EXPERIMENT_009_STAGE_B_IDENTITY.md @@ -0,0 +1,112 @@ +# Experiment 009 Stage-B identity freeze + +> **Status: the 32-task development identity is authenticated and frozen +> before model weights or quality metrics are opened.** +> +> This artifact fixes the data, tokenizer, row-allocation plan, runtime, and +> source-code identity for the one permitted Stage-B run. It is not +> performance, novelty, state-of-the-art, or breakthrough evidence. + +Date recorded: 2026-07-26 + +## Authenticated artifact + +| Field | Value | +| --- | --- | +| Artifact | `evidence/experiment009-rht-cqer-stage-b-identity-cdc603b.json` | +| Artifact kind | `recurquant_rht_cqer32_stage_b_identity` | +| Frozen source commit | `cdc603b6b8462e42e98495c5ec0610faa9473721` | +| File SHA-256 | `f94e60adb6d9ab51b5894e3ad8b3c0064a8e69721e3df24f90a81f0b4009fe19` | +| Canonical evidence SHA-256 | `4074db6860d3f36dff1a13b7945d0c5c5d619ee208f0fdb35108defb9512c343` | +| Created at | `2026-07-26T05:12:01.884112+00:00` | +| Ordered identity SHA-256 | `3a7f9f2e1b60321680082d93ea983425085f0e26afad03c49413b41e6e4b2ddc` | +| Content manifest SHA-256 | `7a66a3d1241baf27887f43f04129b9e6d8ce46f24e22835b9888cab53d172bcb` | +| Token manifest SHA-256 | `76b73079b704c48a5e87d479887e5b88d2a0a9c1516c1187acae22e0c71618a9` | +| Row-plan SHA-256 | `b480b6483bec2f07ef56388df27f5d78402b9b9545f2c039d332495de2a9fbde` | + +The artifact loader independently recomputed its canonical hash and validated +the complete production schema. The committed copy is byte-identical to the +generated artifact. + +## Frozen evaluation identity + +The development set contains 32 ranked MBPP tasks from the preregistered +`[32, 64)` window: + +```text +725, 616, 686, 722, 919, 636, 607, 950, +885, 646, 708, 840, 793, 860, 800, 867, +622, 677, 849, 756, 960, 741, 763, 924, +839, 870, 862, 877, 672, 894, 911, 907 +``` + +| Token total | Count | +| --- | ---: | +| Prompt tokens | 4,079 | +| Code tokens | 1,988 | +| Aligned scored tokens | 1,956 | + +The model and tokenizer are fixed to +`Qwen/Qwen3.5-0.8B-Base` at revision +`dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68`. The tokenizer is +`Qwen2Tokenizer` from Transformers `5.14.1`, with the exact prompt/code +special-token settings recorded in the artifact. + +## Frozen runtime and row plan + +The identity authenticated the same runtime used for Stage A before reading +dataset content: + +| Component | Frozen value | +| --- | --- | +| Python | `3.11.15` | +| PyTorch | `2.11.0+cu128` | +| CUDA runtime | `12.8` | +| Transformers | `5.14.1` | +| Datasets | `4.8.5` | +| NumPy | `2.4.6` | +| Safetensors | `0.8.0` | + +The compact selector plan promotes 1,976 of 36,864 recurrent-state groups. +It preserves the exact 2,564,096-byte packed-state target, including the +4,608-byte precision mask. Both source selector artifacts and their canonical +evidence hashes are bound inside the identity. + +## Access and integrity boundary + +The identity run: + +- authenticated Stage A, the runtime, selector artifacts, source files, and + repository commit before target content access; +- used a task-ID-only ranking pass and retained no row mappings from it; +- retained, canonicalized, formatted, and tokenized only the 32 Stage-B tasks; +- did not run a model forward pass; +- did not load model weights, logits, or quality metrics; +- did not access the protected ranked `[8, 16)` window at the application + content, tokenization, model, or evaluation layers; and +- recorded repository-relative paths only. + +The Hugging Face streaming transport may deserialize complete source records +before yielding them. RecurQuant itself inspected only `task_id` on +non-target rows; the artifact records both transport and application-level +counters. + +## Safe runtime rejection + +The first invocation used a different local virtual environment. Its runtime +check detected that `datasets` was absent and stopped before dataset access or +artifact creation. The successful invocation used the exact Stage-A runtime +listed above. No dependency, seed, threshold, task window, selector, or source +file was changed after the rejection. + +## Historical authorization + +At the time of this freeze, the identity authorized one Stage-B development +evaluation whose result had to be preserved whether the eight advancement +gates passed or failed. No threshold, seed, method, task, or source file could +be changed after observing the result. + +That evaluation is now complete. The authenticated outcome is recorded in the +[Stage-B result](EXPERIMENT_009_STAGE_B_RESULT.md), and the immutable-artifact +audit is recorded in the +[verification receipt](EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md). diff --git a/research/EXPERIMENT_009_STAGE_B_RESULT.md b/research/EXPERIMENT_009_STAGE_B_RESULT.md new file mode 100644 index 0000000..1f98c21 --- /dev/null +++ b/research/EXPERIMENT_009_STAGE_B_RESULT.md @@ -0,0 +1,126 @@ +# Experiment 009 Stage-B result: RHT-CQER-32 advances + +> **Status: all eight frozen Stage-B development gates passed.** +> +> On the authenticated 32-task MBPP development window, RHT-CQER-32 lowered +> task-macro aligned excess NLL by `52.73%` and aggregate local +> recurrent-state reconstruction SSE by `57.85%` relative to CQER-32 at the +> exact same packed-state and selector byte counts. This is development +> evidence for one pinned model and task window. It is not confirmation, +> novelty, a speed result, state-of-the-art evidence, or a breakthrough claim. + +Date recorded: 2026-07-26 + +## Authenticated artifact + +I preserved the complete raw result as a compressed GitHub release asset +instead of adding 168 MB of repeated state-error records to Git history. The +small committed +[release manifest](../evidence/experiment009-rht-cqer-stage-b-result-manifest.json) +binds the downloadable artifact, its checksums, the frozen identity, the +evaluation commit, and the verifier receipt. + +| Field | Value | +| --- | --- | +| Artifact kind | `recurquant_rht_cqer32_stage_b_development` | +| Raw artifact | `experiment009-rht-cqer-stage-b-result-cdc603b.json` | +| Raw bytes | `167,987,192` | +| Raw file SHA-256 | `57b341d37871a52977b1ff89709864f3e6e0927154e5b2b9275b6f374953fe05` | +| Canonical evidence SHA-256 | `2b15c732e894510f0421a22fcca9435e035dd15c4d3b50e2fcb733c0d1df58a8` | +| Created at | `2026-07-26T05:46:49.671275+00:00` | +| Frozen evaluation commit | `8168c469b252bc9e707e51feaeccc3f940f190bb` | +| Corrected verifier commit | `2075154e642c39a14432adcc8ec32da679b534d3` | +| Model | `Qwen/Qwen3.5-0.8B-Base` at `dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68` | +| Development identity | ranked MBPP window `[32, 64)`, 32 tasks | +| Primary scored tokens | 1,956 aligned code tokens | + +The artifact's repository commit, source hashes, runtime, model revision, +ordered task identity, token manifest, selector artifacts, row plan, sign +schedule, methods, thresholds, storage contract, per-task results, raw state +records, aggregate metrics, bootstrap interval, integrity decision, and eight +advancement checks all passed strict semantic validation. The +[verification receipt](EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md) records +the interrupted attempts and the post-write verifier correction without +rewriting the artifact or rerunning the model. + +## Primary result + +The primary metric is the unweighted mean of each task's aligned excess +next-token NLL above its matched FP32 recurrent-state reference. + +| Metric | CQER-32 | RHT-CQER-32 | Change | +| --- | ---: | ---: | ---: | +| Task-macro aligned excess NLL | `0.323944` | `0.153129` | **52.73% lower** | +| Task-macro mean KL | `0.328890` | `0.193501` | lower | +| Task-macro CVaR95 KL | `2.046658` | `1.335989` | lower | +| Task-macro top-1 agreement | `0.826000` | `0.879499` | `+0.053498` | +| Aggregate local state SSE | `36,409.363073` | `15,345.844948` | **57.85% lower** | + +![Authenticated Stage-B excess-NLL and state-SSE comparison.](../assets/experiment009-stage-b-overview.svg) + +The paired task-macro improvement was `0.170815` nats/token. A 10,000-sample +paired task bootstrap with frozen seed `2339` produced a two-sided 95% +equal-tailed interval of `[0.116082, 0.229438]`. RHT-CQER-32 had strictly lower +excess NLL on 27 of 32 tasks, with no ties. + +![Per-task paired CQER-32 minus RHT-CQER-32 excess-NLL differences.](../assets/experiment009-stage-b-paired.svg) + +Five tasks favored CQER-32. The largest RHT-minus-CQER task disadvantage was +`0.077088` nats/token on task `607`, below the frozen `0.25` ceiling. The +paired chart keeps those negative cases visible instead of hiding them behind +the aggregate. + +## Frozen gate decision + +| Frozen advancement check | Required | Observed | Result | +| --- | ---: | ---: | :---: | +| Macro excess-NLL reduction | at least 20% | `52.7299%` | pass | +| Paired 95% lower bound | above 0 | `0.116082` | pass | +| Strict task-level wins | at least 20 of 32 | `27/32`, 0 ties | pass | +| Macro mean KL | lower | `0.328890 -> 0.193501` | pass | +| Macro CVaR95 KL | no higher | `2.046658 -> 1.335989` | pass | +| Macro top-1 disadvantage | at most 0.005 | `-0.053498` | pass | +| Maximum task NLL disadvantage | at most 0.25 | `0.077088` | pass | +| Aggregate local state-SSE reduction | at least 50% | `57.8519%` | pass | + +All eight advancement checks and every prerequisite integrity check passed. +Under the frozen protocol, RHT-CQER-32 advances beyond this development stage. +The result does not authorize retuning the transform seed, thresholds, task +window, selector, or method name. + +## Exact storage and numerical checks + +CQER-32 and RHT-CQER-32 both used the same 1,976 promoted Q8 rows: + +| Component | Bytes | +| --- | ---: | +| Q4/Q8 payloads | 2,485,760 | +| FP16 scales | 73,728 | +| precision masks | 4,608 | +| **packed recurrent state** | **2,564,096** | +| FP32 query-energy selector | 147,456 | +| **resident bytes including selector** | **2,711,552** | + +The state-SSE comparison sums local codec reconstruction error over 35,784 +matched recurrent-state records and 9,380,560,896 elements per method. It is a +write-micro reconstruction metric against each method's own pre-pack source +state, not an end-to-end peak-memory or latency measurement. + +The independent dense NumPy reference matched the production right-RHT encode +within `9.5367e-7`, matched physical packing exactly, derived the exact frozen +sign schedule, and passed its preregistered thresholds. The production inverse +relative L2 was `1.1468e-7`, below `3e-7`. + +## Decision boundary + +This 32-task result is a positive, authenticated development result. It does +not replace the separate 500-task held-out v0.2 confirmation, and it is not a +new held-out confirmation for RHT-CQER-32. Ranked MBPP window `[8, 16)` stayed +outside application-level content, tokenization, model, and evaluation access. + +Randomized Hadamard and rotation quantizers are established prior art. The +current Python implementation regenerates signs and materializes FP32 +transform workspaces; it has no fused-kernel, latency, throughput, peak-memory, +long-context, cross-model, or independent external reproduction result. I +therefore report exactly the frozen development finding and no broader +novelty, deployment, state-of-the-art, speed, or breakthrough claim. diff --git a/research/EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md b/research/EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md new file mode 100644 index 0000000..a71fee9 --- /dev/null +++ b/research/EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md @@ -0,0 +1,112 @@ +# Experiment 009 Stage-B verification receipt + +> **Status: the original Stage-B artifact is immutable and passes the +> corrected strict semantic verifier.** +> +> The correction changed only the validator's interpretation of canonical +> manifest order. It did not change the artifact, method, data, model run, +> metrics, thresholds, bootstrap, or gate decision. + +Recorded: 2026-07-26 + +## Artifact under verification + +```text +raw artifact: +experiment009-rht-cqer-stage-b-result-cdc603b.json + +raw file SHA-256: +57b341d37871a52977b1ff89709864f3e6e0927154e5b2b9275b6f374953fe05 + +canonical evidence SHA-256: +2b15c732e894510f0421a22fcca9435e035dd15c4d3b50e2fcb733c0d1df58a8 + +evaluation commit: +8168c469b252bc9e707e51feaeccc3f940f190bb + +corrected verifier commit: +2075154e642c39a14432adcc8ec32da679b534d3 +``` + +The +[release manifest](../evidence/experiment009-rht-cqer-stage-b-result-manifest.json) +binds these identifiers to the compressed public artifact and its checksum. + +## Run and interruption record + +I kept three non-result events separate from the authenticated result: + +1. An initial invocation used the wrong local Python environment. The frozen + runtime contract rejected it before dataset access, tokenizer/model loading, + or artifact creation. +2. A later shell invocation had a 14-second terminal timeout, which terminated + the partial evaluator process. I verified that no Python process remained, + no output artifact existed, and no quality metric had been exposed. I then + restarted the identical frozen command as a hidden background process. +3. The completed evaluator atomically wrote the result artifact, then its + post-write strict loader falsely rejected the valid canonical content + manifest. The artifact already existed at the raw and canonical hashes + above. I did not rerun the model or alter that artifact. + +The wrong-runtime rejection and timed-out partial process revealed no result +that could be used for tuning or retry selection. The successful model +evaluation ran against the committed identity and exact frozen source at +commit `8168c469b252bc9e707e51feaeccc3f940f190bb`. + +## Validator finding and correction + +The artifact carries two intentional orders: + +- task result records stay in the exact frozen ranked-window order; and +- the canonical content-manifest rows are sorted by integer task ID before + hashing. + +The original post-write loader incorrectly expected the canonical manifest in +ranked-window order. That contradicted the resolver's canonicalization rule +and caused a false rejection after a valid artifact had already been written. + +Commit `2075154e642c39a14432adcc8ec32da679b534d3` corrected that one validator +assumption. The loader now independently requires: + +- exact ranked-window order for task records; +- exact task-ID-sorted order for canonical manifest rows; +- exact `(task_id, row_sha256)` projection between those two representations; +- exact content-manifest, token-manifest, and ordered-identity hashes; and +- rejection of missing, duplicate, extra, reordered, or hash-mutated rows. + +The fix therefore narrows acceptance to the protocol's actual canonical form; +it does not relax identity authentication. + +## Independent audit + +The corrected verifier authenticated the immutable artifact without executing +another model forward pass. The audit: + +- recomputed the canonical wrapper hash and complete production schema; +- authenticated the Stage-A and Stage-B identity artifacts; +- compared every one of the 37 recorded source-file hashes with the exact file + bytes at evaluation commit `8168c469b252bc9e707e51feaeccc3f940f190bb`; +- authenticated all imported repository modules and the clean, stable + evaluation commit; +- revalidated the runtime, model revision, dataset access boundary, ordered + tasks, token counts, row plan, selector bindings, sign schedule, physical + storage, quotas, and state-record coverage; +- recomputed task and macro metrics, the 10,000-sample paired bootstrap, local + state-SSE aggregates, all integrity checks, and all eight advancement checks; + and +- passed the focused canonical-order and tamper test suite. + +The strict loader accepts the original raw file SHA-256 +`57b341d37871a52977b1ff89709864f3e6e0927154e5b2b9275b6f374953fe05` +and canonical evidence SHA-256 +`2b15c732e894510f0421a22fcca9435e035dd15c4d3b50e2fcb733c0d1df58a8`. +No evidence field was reconstructed, deleted, reordered, or rewritten to make +the artifact pass. + +## Verification boundary + +This receipt establishes that the published artifact matches the frozen +Experiment 009 Stage-B protocol and that its positive gate decision is +recomputable from its recorded evidence. It does not turn a 32-task +development result into confirmation, establish novelty, measure speed, or +support a state-of-the-art, deployment, or breakthrough claim. diff --git a/research/MULTIBIT_REFERENCE_DESIGN.md b/research/MULTIBIT_REFERENCE_DESIGN.md new file mode 100644 index 0000000..3e2d468 --- /dev/null +++ b/research/MULTIBIT_REFERENCE_DESIGN.md @@ -0,0 +1,115 @@ +# Exact-byte Q4/Q6/Q8 reference design + +> **Status: implemented and unit-tested as a correctness-first reference.** +> +> This is not yet connected to the Qwen3.5 cache evaluator and has no quality, +> latency, kernel, novelty, or deployment result. + +Last updated: 2026-07-26 + +## Physical format + +Every 128-value recurrent-state row uses one symmetric signed quantizer and +one FP16 scale: + +| Precision | Code range | Payload per row | +| --- | ---: | ---: | +| Q4 | `[-7, 7]` | 64 bytes | +| Q6 | `[-31, 31]` | 96 bytes | +| Q8 | `[-127, 127]` | 128 bytes | + +Q6 uses an exact little-endian bit stream: four two's-complement 6-bit codes +occupy three bytes. Separate Q4, Q6, and Q8 pools are indexed by a canonical +two-bit precision stream: + +```text +0 = Q4 +1 = Q6 +2 = Q8 +3 = invalid +``` + +The public reference object validates its complete shape, scale, code-stream, +pool, dtype, device, contiguity, and byte-count invariants when constructed. + +## Correct equal-byte accounting + +The pinned Qwen3.5 cache contains: + +```text +18 layers * 16 heads * 128 rows = 36,864 row groups +``` + +The existing Q4/Q8 design spends one precision bit per row and promotes 1,976 +rows to Q8: + +```text +payloads: 2,485,760 bytes +scales: 73,728 bytes +mask: 4,608 bytes +total: 2,564,096 bytes +``` + +A general three-width design requires two bits per row, so its precision +stream costs 9,216 bytes. At the same total state budget, it has exactly 3,808 +32-byte marginal steps: + +```text +Q4 payload baseline: 2,359,296 bytes +FP16 scales: 73,728 bytes +two-bit codes: 9,216 bytes +3,808 steps * 32: 121,856 bytes +total: 2,564,096 bytes +``` + +This is **not** 3,952 Q6 rows and not an exact two-times coverage result. A row +at Q6 consumes one step; a row at Q8 consumes two. The final counts depend on +the allocator. + +To preserve every old layer's byte count, a layer with old Q8 quota `q` gets: + +```text +K = 2q - 8 +``` + +The eight-step subtraction pays for that layer's additional 256 precision-code +bytes. The frozen per-layer step budgets sum to 3,808 and reproduce the +2,564,096-byte total exactly. + +## Exact reference allocator + +For each row, the allocator receives complete weighted distortions +`D4`, `D6`, and `D8`. It minimizes total distortion subject to: + +```text +sum(precision_code) = K +precision_code in {0, 1, 2} +``` + +The CPU reference uses dynamic programming over all complete row states. It is +globally optimal even when the Q6-to-Q8 improvement is larger than the +Q4-to-Q6 improvement, where naive independent marginal sorting can violate +precedence. Exact ties give higher precision to the earlier flattened row. + +For `N` rows and step budget `K`, the reference complexity is: + +```text +time: O(NK) +choice storage: O(NK) bytes +FP64 workspace: O(K) +``` + +This is an oracle/correctness path, not the final per-token runtime selector. +An optimized deployment path would need a GPU allocator and a packed-native +mixed dequantization/Gated DeltaNet kernel. INT6 has no native arithmetic path +on the tested GPU, so end-to-end benchmarks—not payload math—must decide +whether its additional bandwidth efficiency survives unpack cost. + +## Claim boundary + +Mixed-bit allocation, rate-distortion optimization, INT6 formats, and dynamic +precision are established ideas. RateQuant, MixKVQ, OuroMamba, and related +work are close prior art. The current contribution is limited to an auditable +physical format, exact byte parity, and a globally optimal reference allocator +for this recurrent-state geometry. A new frozen quality experiment is required +before the design can be recommended over Q4/Q8 CQER. diff --git a/research/PRIOR_ART.md b/research/PRIOR_ART.md index cd8da01..8e21f3e 100644 --- a/research/PRIOR_ART.md +++ b/research/PRIOR_ART.md @@ -1,6 +1,6 @@ # Prior-art audit -Last searched: 2026-07-22 +Last searched: 2026-07-26 This is a living claim boundary, not proof that no related work exists. Repeat the search before any paper or novelty statement. @@ -44,6 +44,28 @@ the search before any paper or novelty statement. - [AVMP](https://arxiv.org/abs/2605.22416) dynamically rebalances KV and SSM memory pools. +## Transition- and observability-weighted error + +- Classical finite-wordlength state-space analysis already propagates + quantization-noise covariance to output noise through an observability + Gramian. In particular, + [Hilaire, Menard, and Sentieys (EUSIPCO 2007)](https://www.eurasip.org/Proceedings/Eusipco/Eusipco2007/Papers/b2l-g05.pdf) + writes the state-noise contribution as `tr(Psi_X * W_o)`. With diagonal + error covariance, this is the same broad algebraic family as weighting each + row's distortion by a diagonal observability term. RecurQuant cannot claim + to have invented observability-weighted quantization error. +- [WriteSAE](https://arxiv.org/abs/2605.12770) derives the Gated DeltaNet cache- + write perturbation transition and its later-query/logit effect. It is an + interpretability method rather than a quantizer, but it precludes claiming + that the transition or downstream-influence principle is new. +- [TQS-PTQ](https://arxiv.org/abs/2606.13300) treats quantized rollout error as + a dynamical-system trajectory-sensitivity problem and uses the score for + mixed-precision allocation. It targets offline time-series model tensors, + not causal Gated DeltaNet cache rows. +- Nemotron 3 Super and Ultra propagate recurrent-cache errors through products + of state transitions. They do not use CORA's online row allocator, but they + preclude a broad "first transition-aware recurrent-cache quantizer" claim. + ## Mixed-precision cache allocation - [KVTuner](https://arxiv.org/abs/2502.04420), @@ -55,11 +77,42 @@ the search before any paper or novelty statement. [PM-KVQ](https://arxiv.org/abs/2505.18610), and [SpectrumKV](https://arxiv.org/abs/2606.08635) cover fixed-average-bit, progressive, or per-token mixed-precision KV policies. +- [MixKVQ](https://arxiv.org/abs/2512.19206) combines query relevance and + intrinsic quantization difficulty for budgeted mixed-precision key-cache + channels. It is the closest published analogue to multiplying a read- + importance signal by physical quantization benefit. +- [Kitty](https://arxiv.org/abs/2511.18643) dynamically promotes a fixed quota + of key-cache channels using runtime sensitivity and magnitude, with Triton + kernels. It targets transformer KV caches rather than recurrent matrices. +- [OuroMamba](https://arxiv.org/abs/2503.10959) updates outlier-channel choices + at every timestep and assigns higher precision dynamically for Vision-Mamba + activations. It precludes a broad "first online dynamic mixed-precision SSM + channel" claim. These works target transformer KV caches rather than Gated DeltaNet recurrent matrices, but they preclude broad claims that sensitivity-guided, per-layer, dynamic, or equal-budget cache precision allocation is new. +## Rotation and Hadamard codecs + +- [QuIP#](https://arxiv.org/abs/2402.04396) uses incoherence processing with + randomized Hadamard transforms for low-bit language-model quantization. +- [QuaRot](https://arxiv.org/abs/2404.00456) uses computational invariance and + Hadamard rotations to remove activation outliers. +- [SpinQuant](https://arxiv.org/abs/2405.16406) learns rotations for improved + LLM quantization, and + [TurboQuant](https://arxiv.org/abs/2504.19874) develops fast randomized + transforms for outlier suppression. +- [MambaQuant](https://arxiv.org/abs/2501.13484) applies rotation-based + outlier suppression to an architecture adjacent to RecurQuant's recurrent + state setting. + +These methods make the transform principle established prior art. +RHT-CQER-32's narrow hypothesis is whether a fixed, reproducible right-side +transform composes usefully with physical causal row selection for one Gated +DeltaNet cache layout. A positive result cannot support a broad first, +rotation, Hadamard, or outlier-suppression novelty claim. + ## Architecture evidence - [Gated DeltaNet-2](https://arxiv.org/abs/2605.22791) separates channel-wise @@ -73,17 +126,22 @@ dynamic, or equal-budget cache precision allocation is new. ## Audited gap, not a novelty claim -The scan did not locate a published implementation combining all three: +The scan did not locate a published implementation combining all four: 1. sub-8-bit **persistent** Gated DeltaNet cache storage; -2. Gated DeltaNet-specific read/sensitivity-based precision allocation; and -3. a packed end-to-end runtime kernel with measured quality and latency. +2. a causal transition-derived diagonal future-read score; +3. physical row-distortion allocation with confirmation-gated admissions; and +4. a packed end-to-end runtime kernel with measured quality and latency. That negative search is not proof of firstness, and the current RecurQuant -alpha does not satisfy item 3: it physically stores the cache in packed form +alpha does not satisfy item 4: it physically stores the cache in packed form but materializes one recurrent state for each layer call and has no fused recurrence kernel or latency result. +Experiment 008 also failed its frozen development gate: CORA-C2 and raw CORA +were both worse on macro excess NLL than CQER-32. Exact-combination novelty +would not rescue a method that has not demonstrated the required quality. + The frozen v0.2 protocol compares uniform INT4, one uniform INT8 reference, three same-byte random layer placements, MSE-selected placement, nearest and stochastic rounding, and the prespecified layer-0 policy. It does **not** diff --git a/research/STATUS.md b/research/STATUS.md index 957a2a6..ef9939a 100644 --- a/research/STATUS.md +++ b/research/STATUS.md @@ -1,5 +1,99 @@ # Research status +## v0.3 experimental track + +Last updated: 2026-07-26 + +Experiment 005 stopped before holdout after its frozen real-storage-boundary +sign gate achieved `13/16 = 0.8125`, below the required `0.95`. Its permanent +failure, authenticated artifact, and same-calibration postmortem are recorded +in [`EXPERIMENT_005_RESULT.md`](EXPERIMENT_005_RESULT.md). The ranked MBPP +calibration window `[8, 16)` was not opened. + +Experiment 006 tested deterministic ordinal-rank fusion of offline +target-directional sensitivity and causal per-write reconstruction benefit at +the same exact byte budget. On the already inspected eight-task selector +partition, its frozen `lambda = 0.5` primary had macro excess NLL `0.514873`, +worse by point estimate than plain adaptive MSE at `0.493302`; the paired 95% +interval crossed zero. It improved only 3.90% over the strongest static method, +also with an interval crossing zero. The better `0.25` and `0.75` ablations +cannot replace the frozen primary. The candidate was therefore stopped before +its numerical prerequisite or holdout. The ranked `[8, 16)` window remains +unopened. See [`EXPERIMENT_006_RESULT.md`](EXPERIMENT_006_RESULT.md). + +Experiment 007 tested CQER-32: a causal 32-token EMA of normalized query energy +times exact per-write INT4-to-INT8 row reconstruction benefit, with the same +frozen target-Fisher layer quotas. On the already inspected eight-task +partition it lowered macro excess NLL to `0.462792`, a 6.18% descriptive +reduction from plain adaptive MSE and 13.62% from static target-Fisher. Both +paired 95% intervals crossed zero. The frozen gate failed because the static +reduction was below 20% and top-1 agreement trailed the better comparator by +`0.02690`, above the `0.01` margin. All exact-byte, causal-handshake, finiteness, +and integrity checks passed. Experiment 007 therefore stopped before its FP64 +prerequisite or holdout; ranked `[8, 16)` remains unopened. See +[`EXPERIMENT_007_RESULT.md`](EXPERIMENT_007_RESULT.md). + +Experiment 008 tested CORA-C2 on its separately frozen 16-task `[16, 32)` +development window. CORA-C2 improved macro excess NLL by 26.17% over static +target-Fisher and 21.62% over adaptive MSE, but it was 10.54% worse than +CQER-32. Raw CORA was also 3.35% worse than CQER-32. Confirmation-2 reduced +normalized committed-mask churn by 79.99%, yet worsened raw-CORA NLL by 6.96%, +above the frozen 1% limit. Five advancement checks failed. The authenticated +result is recorded in [`EXPERIMENT_008_RESULT.md`](EXPERIMENT_008_RESULT.md). +The independent verifier was not reached and ranked `[8, 16)` remains +protected. + +Experiment 009 tested RHT-CQER-32, which composes CQER-32 with a deterministic +orthonormal right-side Hadamard codec while preserving the exact Q4/Q8 packed +state and selector byte counts. Its one-task Stage-A falsification screen on +already-open task 666 passed all nine frozen checks: closed-loop state SSE +fell `59.97%`, aligned excess NLL fell `58.59%`, and aligned mean KL fell +`31.19%` relative to CQER-32 while top-1 agreement and bytes were unchanged. +The authenticated screen is in +[`EXPERIMENT_009_STAGE_A_RESULT.md`](EXPERIMENT_009_STAGE_A_RESULT.md). + +The separately frozen 32-task Stage-B development run then passed all eight +advancement checks. Relative to CQER-32, task-macro aligned excess NLL fell +from `0.323944` to `0.153129` (`52.73%`), with 27 of 32 strict task wins and a +paired 95% bootstrap interval of `[0.116082, 0.229438]`. Aggregate local +recurrent-state reconstruction SSE fell from `36,409.363073` to +`15,345.844948` (`57.85%`). Mean KL and CVaR95 KL were lower, top-1 agreement +was higher, and the exact `2,564,096` packed-state bytes plus `147,456` +selector bytes were unchanged. + +The frozen protocol, committed identity, authenticated result, post-write +verification record, and compact release manifest are: + +- [`EXPERIMENT_009_RHT_CQER_PROTOCOL.md`](EXPERIMENT_009_RHT_CQER_PROTOCOL.md) +- [`EXPERIMENT_009_STAGE_B_IDENTITY.md`](EXPERIMENT_009_STAGE_B_IDENTITY.md) +- [`EXPERIMENT_009_STAGE_B_RESULT.md`](EXPERIMENT_009_STAGE_B_RESULT.md) +- [`EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md`](EXPERIMENT_009_STAGE_B_VERIFICATION_RECEIPT.md) +- [`../evidence/experiment009-rht-cqer-stage-b-result-manifest.json`](../evidence/experiment009-rht-cqer-stage-b-result-manifest.json) + +The public-stream application-level access boundary remains fixed in +[`EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md`](EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md). +Ranked MBPP window `[8, 16)` remained protected. + +In parallel, the repository now contains a correctness-first physical Q4/Q6/Q8 +packer and an exact dynamic-programming allocator. Its corrected two-bit +metadata contract provides 3,808 marginal precision steps—not 3,952 rows—at +the same `2,564,096` state bytes. It is not yet cache-integrated or +quality-evaluated. See +[`MULTIBIT_REFERENCE_DESIGN.md`](MULTIBIT_REFERENCE_DESIGN.md). + +The nearest known mechanism-level comparisons include MixKVQ's query-magnitude +and quantization-difficulty scoring and established randomized-Hadamard or +rotation quantizers. CQER-32 cannot be described as the first query-aware +mixed-precision method, and RHT-CQER-32 cannot be described as the first +rotation-based quantizer. + +Stage B supports a scoped positive development result for one frozen method, +pinned model, and 32-task MBPP window. It is not held-out confirmation for +RHT-CQER-32 and does not support a generalized v0.3, novelty, speed, +state-of-the-art, deployment, or breakthrough claim. + +## v0.2 confirmed release + The frozen v0.2 public-data study completed on 2026-07-22. Every preregistered quality gate passed on all 500 untouched MBPP test tasks and 30,244 scored tokens. The exact result, integrity anchors, infrastructure-resume record, and @@ -13,7 +107,7 @@ development decision is in [`DEVELOPMENT_002.md`](DEVELOPMENT_002.md). The remainder of this file preserves the diagnostic v0.1 snapshot. Do not read its historical "next action" as the current project state. -Last updated: 2026-07-22 +Snapshot date: 2026-07-22 ## Confirmed implementation diff --git a/scripts/evaluate_rht_cqer_stage_b.py b/scripts/evaluate_rht_cqer_stage_b.py new file mode 100644 index 0000000..a13601a --- /dev/null +++ b/scripts/evaluate_rht_cqer_stage_b.py @@ -0,0 +1,4165 @@ +#!/usr/bin/env python3 +"""Evaluate the frozen Experiment 009 Stage-B development window. + +This evaluator accepts only a separately committed Stage-B identity artifact. +It has no task, rank-window, seed, bootstrap-count, method, or threshold flags. +The protected ranked MBPP window [8, 16) is never resolved by this script. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib +import importlib.metadata +import json +import math +import os +import platform +import string +import subprocess +import sys +import tempfile +from collections.abc import Mapping, Sequence +from datetime import UTC, datetime +from pathlib import Path, PurePosixPath +from statistics import fmean +from typing import Any + +import numpy as np +import torch +from transformers import AutoTokenizer, DynamicCache, Qwen3_5ForCausalLM + +from recurquant.cache import iter_recurrent_states +from recurquant.evaluation import paired_bootstrap_mean_improvement +from recurquant.evidence import canonical_json_bytes, verify_evidence_artifact +from recurquant.mixed_quantization import quantize_pack_mixed +from recurquant.public_data import mbpp_manifest_content_sha256 +from recurquant.quantization import QuantizationSpec +from recurquant.query_energy import Qwen35QueryEnergyObserver +from recurquant.qwen35 import ( + create_qwen35_adaptive_exact_budget_cache, + create_qwen35_exact_budget_cache, + create_qwen35_query_ema_exact_budget_cache, + create_qwen35_right_rht_query_ema_exact_budget_cache, +) +from recurquant.rht import right_rht_encode, right_rht_signs +from recurquant.row_policy import ExactBudgetRowPlan, RowLocation + +pilot = importlib.import_module( + "scripts.pilot_evaluate_hrr" if __package__ else "pilot_evaluate_hrr" +) +screen = importlib.import_module( + "scripts.screen_rht_cqer" if __package__ else "screen_rht_cqer" +) +identity_resolver = importlib.import_module( + "scripts.resolve_rht_cqer_stage_b_identity" + if __package__ + else "resolve_rht_cqer_stage_b_identity" +) + +SEED = 2339 +BOOTSTRAP_SAMPLES = 10_000 +STAGE_B_OFFSET = 32 +STAGE_B_LIMIT = 32 +STAGE_B_STOP = 64 +PROTECTED_WINDOW = (8, 16) + +ARTIFACT_KIND = "recurquant_rht_cqer32_stage_b_development" +IDENTITY_ARTIFACT_KIND = "recurquant_rht_cqer32_stage_b_identity" +STAGE_A_ARTIFACT_KIND = "recurquant_rht_cqer32_stage_a_screen" +RESULT_CLAIM_BOUNDARY = ( + "This is the frozen 32-task Experiment 009 development result for a " + "known right-RHT codec composed with CQER-32 on one pinned model and " + "MBPP window. It is not confirmation, novelty, speed, state-of-the-art, " + "deployment, or breakthrough evidence." +) +PRIMARY_STATE_ERROR_METRIC = ( + "closed-loop local codec reconstruction SSE: materialized packed " + "state minus that candidate trajectory's own pre-pack source" +) +REFERENCE_ALIGNED_STATE_ERROR_METRIC = ( + "materialized candidate state minus matched FP32 recurrent state " + "after each write; secondary and not an advancement gate" +) +PROTECTED_EVALUATION_FIELD = ( + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated" +) +DATA_ACCESS_TRANSPORT_LIMITATION = ( + "The Hugging Face streaming transport may deserialize complete source " + "records before yielding mappings. These counters describe fields read " + "and rows retained by RecurQuant application code." +) +STAGE_A_PATH_PRIVACY_LIMITATION = ( + "the immutable Stage-A artifact predates the shareability rule and " + "contains local absolute paths internally; they are not copied here" +) + +STATIC_METHOD = "target_directional_fisher_difference_int4" +ADAPTIVE_METHOD = "adaptive_mse_target_directional_fisher_quota" +CQER_METHOD = "query_ema32_weighted_mse_target_fisher_quota" +RHT_METHOD = "right_rht_query_ema32_weighted_mse_target_fisher_quota" +METHODS = (STATIC_METHOD, ADAPTIVE_METHOD, CQER_METHOD, RHT_METHOD) +QUERY_METHODS = (CQER_METHOD, RHT_METHOD) +TARGET_FISHER_SCORE = STATIC_METHOD + +FROZEN_LAYER_QUOTAS = dict(pilot.CQER_FROZEN_LAYER_QUOTAS) +FROZEN_LINEAR_LAYERS = tuple(FROZEN_LAYER_QUOTAS) +TARGET_PROMOTED_ROWS = 1_976 +TARGET_PAYLOAD_BYTES = 2_485_760 +TARGET_SCALE_BYTES = 73_728 +TARGET_MASK_BYTES = 4_608 +TARGET_PACKED_STATE_BYTES = 2_564_096 +TARGET_SELECTOR_BYTES = 147_456 +TARGET_TOTAL_BYTES = 2_711_552 +EXPECTED_SIGN_SCHEDULE_SHA256 = screen.EXPECTED_SIGN_SCHEDULE_SHA256 +MAX_RHT_INVERSE_RELATIVE_L2 = 3e-7 +PRODUCTION_SELF_CHECK_STORAGE_BYTES = 1_645 +INDEPENDENT_ENCODE_MAX_ABS_THRESHOLD = 2e-6 +INDEPENDENT_PACK_MAX_ABS_THRESHOLD = 6e-6 +INDEPENDENT_REFERENCE_DESCRIPTION = ( + "independent dense NumPy Hadamard, SHA-256 sign derivation, " + "FP16-scale absmax quantizer, and decode" +) + +MIN_DELTA_NLL_REDUCTION = 0.20 +MIN_TASK_WINS = 20 +MAX_TOP1_DISADVANTAGE = 0.005 +MAX_TASK_NLL_DISADVANTAGE = 0.25 +MIN_STATE_SSE_REDUCTION = 0.50 + +METRIC_FIELDS = ( + "mean_kl", + "cvar95_kl", + "max_kl", + "top1_agreement", + "reference_nll", + "candidate_nll", + "delta_nll", +) + +SOURCE_FILES = identity_resolver.STAGE_B_SOURCE_FILES +RESULT_EVIDENCE_FIELDS = { + "schema_version", + "artifact_kind", + "diagnostic_only", + "claim_boundary", + "created_at_utc", + "protocol", + "prerequisite_artifacts", + "model", + "dataset", + "metric_contract", + "methods", + "storage", + "aggregates", + "aggregates_full_code_secondary", + "per_task", + "per_task_full_code_secondary", + "per_task_token_primitives", + "per_task_full_code_token_primitives", + "paired_bootstrap_cqer_minus_rht_aligned_delta_nll", + "selector_diagnostics", + "state_error", + "unit_evidence", + "integrity_inputs", + "stage_b_integrity", + "stage_b_gate", + "runtime_environment", + "environment", + "repository", + "source_files", + "command_template", +} +RESULT_IMPORTED_MODULE_PATHS = { + "pilot_evaluate_hrr": "scripts/pilot_evaluate_hrr.py", + "screen_rht_cqer": "scripts/screen_rht_cqer.py", + "stage_b_identity_resolver": "scripts/resolve_rht_cqer_stage_b_identity.py", + "recurquant_cache": "src/recurquant/cache.py", + "recurquant_evaluation": "src/recurquant/evaluation.py", + "recurquant_packed_cache": "src/recurquant/packed_cache.py", + "recurquant_qwen35": "src/recurquant/qwen35.py", + "recurquant_rht": "src/recurquant/rht.py", +} + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Run the frozen Experiment 009 RHT-CQER 32-task development evaluation." + ) + ) + parser.add_argument("--stage-a-artifact", type=Path, required=True) + parser.add_argument("--identity-artifact", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--local-files-only", action="store_true") + return parser.parse_args(argv) + + +def _strict_int(value: object, *, context: str) -> int: + if isinstance(value, bool) or not isinstance(value, int): + raise ValueError(f"{context} must be an integer") + return value + + +def _finite_float(value: object, *, context: str) -> float: + if isinstance(value, bool) or not isinstance(value, (int, float)): + raise ValueError(f"{context} must be numeric") + rendered = float(value) + if not math.isfinite(rendered): + raise ValueError(f"{context} must be finite") + return rendered + + +def _mapping(value: object, *, context: str) -> Mapping[str, Any]: + if not isinstance(value, Mapping): + raise ValueError(f"{context} must be an object") + return value + + +def _sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def _gate_check(function: Any) -> dict[str, Any]: + try: + result = function() + except (KeyError, TypeError, ValueError) as error: + return {"passed": False, "error": str(error)} + if not isinstance(result, Mapping): + return {"passed": bool(result)} + normalized = dict(result) + normalized["passed"] = normalized.get("passed") is True + return normalized + + +def _load_evidence_artifact( + path: Path, + *, + expected_kind: str, +) -> tuple[dict[str, Any], str]: + payload = path.read_bytes() + verification = verify_evidence_artifact(path) + if verification.get("valid") is not True: + raise ValueError( + f"{expected_kind} artifact failed evidence verification: " + + "; ".join(str(error) for error in verification.get("errors", [])) + ) + try: + artifact = json.loads(payload.decode("utf-8")) + except (UnicodeDecodeError, json.JSONDecodeError) as error: + raise ValueError(f"{expected_kind} artifact must be strict UTF-8 JSON") from error + if not isinstance(artifact, dict) or artifact.get("artifact_kind") != expected_kind: + raise ValueError(f"unexpected artifact kind; expected {expected_kind}") + evidence = artifact.get("evidence") + if not isinstance(evidence, dict): + raise ValueError(f"{expected_kind} evidence must be an object") + expected = artifact.get("canonical_evidence_sha256") + actual = _sha256_bytes(canonical_json_bytes(evidence)) + if expected != actual: + raise ValueError(f"{expected_kind} canonical evidence hash does not match") + return evidence, _sha256_bytes(payload) + + +def _git(*arguments: str, repository_root: Path) -> subprocess.CompletedProcess[str]: + return subprocess.run( + ["git", *arguments], + cwd=repository_root, + check=False, + capture_output=True, + text=True, + ) + + +def authenticate_committed_file(path: Path, repository_root: Path) -> dict[str, str]: + """Require a file's current bytes to be identical to the current Git tree.""" + + root = repository_root.resolve() + resolved = path.resolve() + try: + relative = resolved.relative_to(root).as_posix() + except ValueError as error: + raise ValueError("authenticated prerequisite must be inside the repository") from error + tracked = _git("ls-files", "--error-unmatch", "--", relative, repository_root=root) + if tracked.returncode != 0: + raise ValueError(f"authenticated prerequisite is not tracked by Git: {relative}") + committed = subprocess.run( + ["git", "show", f"HEAD:{relative}"], + cwd=root, + check=True, + capture_output=True, + ).stdout + current = resolved.read_bytes() + if current != committed: + raise ValueError(f"authenticated prerequisite differs from HEAD: {relative}") + return { + "path": relative, + "sha256": _sha256_bytes(current), + "git_blob_sha256": _sha256_bytes(committed), + } + + +def authenticate_imported_module_paths( + repository_root: Path, + *, + modules: Mapping[str, Any] | None = None, +) -> dict[str, str]: + """Require evaluator imports to resolve to the authenticated repository tree.""" + + identity_resolver.validate_all_imported_repository_modules_frozen( + repository_root, + modules=modules, + ) + expected = { + "pilot_evaluate_hrr": ( + pilot, + "scripts/pilot_evaluate_hrr.py", + ), + "screen_rht_cqer": ( + screen, + "scripts/screen_rht_cqer.py", + ), + "stage_b_identity_resolver": ( + identity_resolver, + "scripts/resolve_rht_cqer_stage_b_identity.py", + ), + "recurquant_cache": ( + importlib.import_module("recurquant.cache"), + "src/recurquant/cache.py", + ), + "recurquant_evaluation": ( + importlib.import_module("recurquant.evaluation"), + "src/recurquant/evaluation.py", + ), + "recurquant_packed_cache": ( + importlib.import_module("recurquant.packed_cache"), + "src/recurquant/packed_cache.py", + ), + "recurquant_qwen35": ( + importlib.import_module("recurquant.qwen35"), + "src/recurquant/qwen35.py", + ), + "recurquant_rht": ( + importlib.import_module("recurquant.rht"), + "src/recurquant/rht.py", + ), + } + root = repository_root.resolve() + authenticated: dict[str, str] = {} + for name, (module, relative) in expected.items(): + raw_path = getattr(module, "__file__", None) + if not isinstance(raw_path, str): + raise ValueError(f"{name} does not expose an import path") + actual = Path(raw_path).resolve() + wanted = (root / relative).resolve() + if actual != wanted: + raise ValueError( + f"{name} resolved outside the authenticated repository: {actual}" + ) + authenticated[name] = actual.relative_to(root).as_posix() + return authenticated + + +def _require_ancestor(commit: object, *, repository_root: Path, context: str) -> str: + if not isinstance(commit, str) or len(commit) != 40: + raise ValueError(f"{context} must record a 40-character commit") + result = _git( + "merge-base", + "--is-ancestor", + commit, + "HEAD", + repository_root=repository_root, + ) + if result.returncode != 0: + raise ValueError(f"{context} commit is not an ancestor of the evaluator commit") + return commit + + +def authenticate_stage_a_prerequisite( + path: Path, + *, + repository_root: Path, +) -> tuple[dict[str, Any], dict[str, str]]: + """Recompute and authenticate the committed Stage-A advancement result.""" + + tracked = authenticate_committed_file(path, repository_root) + evidence, file_sha256 = _load_evidence_artifact( + path, + expected_kind=STAGE_A_ARTIFACT_KIND, + ) + if tracked["sha256"] != file_sha256: + raise ValueError("Stage-A tracked-file hash does not match the artifact hash") + if evidence.get("screening_only") is not True: + raise ValueError("Stage-A prerequisite must be the frozen screening artifact") + protocol = _mapping(evidence.get("protocol"), context="Stage-A protocol") + if ( + protocol.get("method") != RHT_METHOD + or protocol.get("protected_ranked_window") != list(PROTECTED_WINDOW) + or protocol.get("protected_window_accessed") is not False + ): + raise ValueError("Stage-A protocol contract drifted") + repository = _mapping(evidence.get("repository"), context="Stage-A repository") + stage_a_commit = _require_ancestor( + repository.get("commit"), + repository_root=repository_root, + context="Stage-A repository", + ) + start = _mapping(repository.get("start"), context="Stage-A repository start") + end = _mapping(repository.get("end"), context="Stage-A repository end") + dataset = _mapping(evidence.get("dataset"), context="Stage-A dataset") + integrity = { + "repository_clean_at_start": start.get("worktree_clean") is True, + "repository_clean_at_end": end.get("worktree_clean") is True, + "repository_commit_stable": ( + start.get("commit") == end.get("commit") == stage_a_commit + ), + "source_hashes_stable": ( + _mapping( + evidence.get("source_files"), + context="Stage-A source files", + ).get("stable") + is True + ), + "identity_authenticated_before_model_weights": ( + dataset.get("identity_authenticated_before_model_weights") is True + ), + "protected_window_8_16_accessed": ( + dataset.get("protected_window_8_16_loaded_tokenized_or_evaluated") + is not False + ), + } + recomputed = screen.evaluate_stage_a_gate( + aligned_metrics=_mapping( + evidence.get("metrics_aligned"), + context="Stage-A aligned metrics", + ), + full_code_metrics=_mapping( + evidence.get("metrics_full_code_secondary"), + context="Stage-A full-code metrics", + ), + storage=_mapping( + _mapping(evidence.get("storage"), context="Stage-A storage").get( + "candidates" + ), + context="Stage-A candidate storage", + ), + selector_diagnostics=_mapping( + evidence.get("selector_diagnostics"), + context="Stage-A selector diagnostics", + ), + state_errors=_mapping( + evidence.get("state_error"), + context="Stage-A state error", + ), + unit_evidence=_mapping( + evidence.get("unit_evidence"), + context="Stage-A unit evidence", + ), + integrity=integrity, + ) + if recomputed.get("passed") is not True: + raise ValueError("Stage-A advancement gate does not pass when recomputed") + recorded_gate = _mapping(evidence.get("stage_a_gate"), context="recorded Stage-A gate") + recorded_checks = _mapping( + recorded_gate.get("checks"), + context="recorded Stage-A checks", + ) + if recorded_gate.get("passed") is not True or any( + _mapping(check, context="recorded Stage-A check").get("passed") is not True + for check in recorded_checks.values() + ): + raise ValueError("recorded Stage-A gate did not pass every frozen check") + return evidence, { + **tracked, + "artifact_sha256": file_sha256, + "canonical_evidence_sha256": _sha256_bytes(canonical_json_bytes(evidence)), + "implementation_commit": stage_a_commit, + "gate_recomputed_and_passed": "true", + } + + +def _identity_task_records( + identity: Mapping[str, Any], +) -> tuple[dict[str, Any], ...]: + dataset = _mapping(identity.get("dataset"), context="Stage-B identity dataset") + tasks = dataset.get("tasks") + if ( + isinstance(tasks, (str, bytes)) + or not isinstance(tasks, Sequence) + or len(tasks) != STAGE_B_LIMIT + ): + raise ValueError("Stage-B identity must contain exactly 32 task records") + normalized: list[dict[str, Any]] = [] + seen: set[int] = set() + expected_fields = { + "rank", + "task_id", + "row_sha256", + "prompt_tokens", + "code_tokens", + "aligned_scored_tokens", + "full_code_scored_tokens", + "prompt_text_sha256", + "code_text_sha256", + "prompt_token_ids_sha256", + "code_token_ids_sha256", + } + for offset, raw in enumerate(tasks): + record = dict(_mapping(raw, context=f"Stage-B identity task {offset}")) + if set(record) != expected_fields: + raise ValueError(f"Stage-B identity task {offset} fields drifted") + rank = _strict_int(record.get("rank"), context=f"identity task {offset} rank") + task_id = _strict_int( + record.get("task_id"), + context=f"identity task {offset} task_id", + ) + if rank != STAGE_B_OFFSET + offset: + raise ValueError("Stage-B identity ranks must be the ordered window [32, 64)") + if task_id in seen: + raise ValueError("Stage-B identity task IDs must be unique") + seen.add(task_id) + for field in ( + "row_sha256", + "prompt_text_sha256", + "code_text_sha256", + "prompt_token_ids_sha256", + "code_token_ids_sha256", + ): + value = record.get(field) + if ( + not isinstance(value, str) + or len(value) != 64 + or any(character not in "0123456789abcdef" for character in value) + ): + raise ValueError(f"identity task {task_id} {field} is not a SHA-256") + prompt_tokens = _strict_int( + record.get("prompt_tokens"), + context=f"identity task {task_id} prompt_tokens", + ) + code_tokens = _strict_int( + record.get("code_tokens"), + context=f"identity task {task_id} code_tokens", + ) + aligned = _strict_int( + record.get("aligned_scored_tokens"), + context=f"identity task {task_id} aligned_scored_tokens", + ) + full = _strict_int( + record.get("full_code_scored_tokens"), + context=f"identity task {task_id} full_code_scored_tokens", + ) + if prompt_tokens < 1 or code_tokens < 2: + raise ValueError(f"identity task {task_id} has invalid token counts") + if aligned != code_tokens - 1 or full != code_tokens: + raise ValueError(f"identity task {task_id} aligned/full token contract drifted") + normalized.append(record) + ordered_ids = dataset.get("ordered_task_ids") + if ordered_ids != [record["task_id"] for record in normalized]: + raise ValueError("Stage-B ordered task IDs differ from its task records") + if dataset.get("selection_window") != { + "offset": STAGE_B_OFFSET, + "limit": STAGE_B_LIMIT, + "stop_exclusive": STAGE_B_STOP, + }: + raise ValueError("Stage-B identity selection window must equal [32, 64)") + if dataset.get("protected_window") != { + "offset": PROTECTED_WINDOW[0], + "stop_exclusive": PROTECTED_WINDOW[1], + "content_retained_canonicalized_or_tokenized": False, + }: + raise ValueError("Stage-B identity protected window must equal [8, 16)") + return tuple(normalized) + + +def authenticate_stage_b_identity( + path: Path, + *, + repository_root: Path, +) -> tuple[dict[str, Any], dict[str, str], tuple[dict[str, Any], ...]]: + """Authenticate the committed identity amendment without opening model weights.""" + + tracked = authenticate_committed_file(path, repository_root) + identity, file_sha256 = identity_resolver.load_stage_b_identity_artifact(path) + identity_resolver.validate_stage_b_identity_evidence(identity) + if identity.get("artifact_kind") != IDENTITY_ARTIFACT_KIND: + raise ValueError("Stage-B identity evidence kind drifted") + if tracked["sha256"] != file_sha256: + raise ValueError("Stage-B identity tracked-file hash does not match its artifact") + model = _mapping(identity.get("model_contract"), context="identity model contract") + if ( + model.get("id") != identity_resolver.MODEL_ID + or model.get("revision") != identity_resolver.MODEL_REVISION + or model.get("weights_loaded") is not False + ): + raise ValueError("Stage-B identity model/no-weights contract drifted") + tokenizer = _mapping( + identity.get("tokenizer_contract"), + context="identity tokenizer contract", + ) + if ( + tokenizer.get("source_id") != model["id"] + or tokenizer.get("revision") != model["revision"] + or tokenizer.get("trust_remote_code") is not False + or tokenizer.get("prompt_add_special_tokens") is not True + or tokenizer.get("code_add_special_tokens") is not False + ): + raise ValueError("Stage-B identity tokenizer contract drifted") + tasks = _identity_task_records(identity) + dataset = _mapping(identity.get("dataset"), context="identity dataset") + manifest = _mapping(dataset.get("manifest"), context="identity content manifest") + if ( + mbpp_manifest_content_sha256(manifest) + != dataset.get("content_manifest_sha256") + ): + raise ValueError("Stage-B identity content-manifest hash does not match") + integrity = _mapping(identity.get("integrity"), context="identity integrity") + required_true = ( + "stage_a_authenticated_before_dataset_access", + "repository_clean_at_start", + "repository_clean_at_end", + "repository_commit_stable", + "source_hashes_stable", + "task_id_only_ranking_pass", + "only_stage_b_content_retained_canonicalized_and_tokenized", + ) + if any(integrity.get(field) is not True for field in required_true): + raise ValueError("Stage-B identity integrity prerequisites did not all pass") + if ( + integrity.get( + "protected_window_8_16_content_retained_canonicalized_or_tokenized" + ) + is not False + or integrity.get("model_weights_loaded") is not False + or integrity.get("model_forward_pass_run") is not False + or integrity.get("logits_or_quality_metrics_observed") is not False + ): + raise ValueError("Stage-B identity consumed forbidden data, weights, or metrics") + repository = _mapping(identity.get("repository"), context="identity repository") + identity_commit = _require_ancestor( + repository.get("commit"), + repository_root=repository_root, + context="Stage-B identity repository", + ) + return identity, { + **tracked, + "artifact_sha256": file_sha256, + "canonical_evidence_sha256": _sha256_bytes(canonical_json_bytes(identity)), + "resolver_commit": identity_commit, + }, tasks + + +def authenticate_identity_source_freeze( + identity: Mapping[str, Any], + *, + current_source_hashes: Mapping[str, str], +) -> dict[str, Any]: + """Require the identity amendment to freeze the exact Stage-B runtime bytes.""" + + source_files = _mapping( + identity.get("source_files"), + context="identity frozen source files", + ) + expected_paths = list(SOURCE_FILES) + if source_files.get("paths") != expected_paths: + raise ValueError("identity did not freeze the exact Stage-B source path set") + start = _mapping( + source_files.get("sha256_start"), + context="identity source hashes start", + ) + end = _mapping( + source_files.get("sha256_end"), + context="identity source hashes end", + ) + current = dict(current_source_hashes) + if ( + source_files.get("stable") is not True + or dict(start) != dict(end) + or dict(start) != current + or set(start) != set(expected_paths) + ): + raise ValueError( + "current Stage-B evaluator/method/test bytes differ from the identity freeze" + ) + return { + "passed": True, + "path_count": len(expected_paths), + "paths": expected_paths, + "sha256": current, + } + + +def authenticate_stage_b_runtime_environment( + stage_a: Mapping[str, Any], + identity: Mapping[str, Any], + *, + local_files_only: bool, +) -> dict[str, Any]: + """Match the live runtime to Stage A and the committed identity before data.""" + + authenticated = identity_resolver.authenticate_runtime_environment( + stage_a, + local_files_only=local_files_only, + ) + expected = _mapping( + identity.get("environment"), + context="Stage-B identity runtime environment", + ) + if authenticated != dict(expected): + raise ValueError( + "authenticated runtime environment differs from the Stage-B identity" + ) + return dict(authenticated) + + +def plan_from_identity( + identity: Mapping[str, Any], + *, + stage_a: Mapping[str, Any], +) -> tuple[ExactBudgetRowPlan, dict[str, Any]]: + """Reconstruct the sole Stage-B row plan from committed identity bytes.""" + + raw_plan = dict(_mapping(identity.get("row_plan"), context="identity row plan")) + recorded_hash = raw_plan.pop("canonical_plan_sha256", None) + if recorded_hash != _sha256_bytes(canonical_json_bytes(raw_plan)): + raise ValueError("identity row-plan canonical hash does not match") + required_fields = { + "schema", + "method", + "selector_binding", + "model", + "quantization", + "accounting", + "score_shapes", + "layer_quotas", + "high_precision_rows", + } + if set(raw_plan) != required_fields: + raise ValueError("identity row-plan fields do not match the frozen schema") + if ( + raw_plan["schema"] != "recurquant.experiment009-stage-b-row-plan.v1" + or raw_plan["method"] != TARGET_FISHER_SCORE + ): + raise ValueError("identity row-plan schema or method drifted") + + stage_a_selectors = _mapping( + stage_a.get("selector_artifacts"), + context="Stage-A selector binding", + ) + expected_selector_binding = { + "selector_file_sha256": stage_a_selectors["selector_file_sha256"], + "selector_canonical_evidence_sha256": stage_a_selectors[ + "selector_canonical_evidence_sha256" + ], + "loss_selector_file_sha256": stage_a_selectors[ + "loss_selector_file_sha256" + ], + "loss_selector_canonical_evidence_sha256": stage_a_selectors[ + "loss_selector_canonical_evidence_sha256" + ], + } + if dict(_mapping(raw_plan["selector_binding"], context="row-plan selectors")) != ( + expected_selector_binding + ): + raise ValueError("identity row plan is not bound to the exact Stage-A selectors") + model = _mapping(raw_plan["model"], context="row-plan model") + identity_model = _mapping(identity.get("model_contract"), context="identity model") + if dict(model) != { + "id": identity_model["id"], + "revision": identity_model["revision"], + }: + raise ValueError("identity row-plan model contract drifted") + quantization = _mapping(raw_plan["quantization"], context="row-plan quantization") + if dict(quantization) != { + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + }: + raise ValueError("identity row-plan quantization contract drifted") + accounting = _mapping(raw_plan["accounting"], context="row-plan accounting") + expected_accounting = { + "total_groups": 36_864, + "mask_bytes": TARGET_MASK_BYTES, + "promotion_increment_bytes": 64, + "target_resident_bytes": TARGET_PACKED_STATE_BYTES, + "resident_bytes": TARGET_PACKED_STATE_BYTES, + "promoted_group_count": TARGET_PROMOTED_ROWS, + } + if dict(accounting) != expected_accounting: + raise ValueError("identity row-plan byte accounting drifted") + expected_shapes = [ + {"layer_index": layer, "heads": 16, "rows": 128} + for layer in FROZEN_LINEAR_LAYERS + ] + if raw_plan["score_shapes"] != expected_shapes: + raise ValueError("identity row-plan geometry drifted") + expected_quotas = { + str(layer): quota for layer, quota in FROZEN_LAYER_QUOTAS.items() + } + if raw_plan["layer_quotas"] != expected_quotas: + raise ValueError("identity row-plan layer quotas drifted") + + raw_locations = raw_plan["high_precision_rows"] + if ( + isinstance(raw_locations, (str, bytes)) + or not isinstance(raw_locations, Sequence) + or len(raw_locations) != TARGET_PROMOTED_ROWS + ): + raise ValueError("identity row plan must contain exactly 1,976 promoted rows") + locations: list[RowLocation] = [] + seen: set[tuple[int, int, int]] = set() + counts = {layer: 0 for layer in FROZEN_LINEAR_LAYERS} + for index, raw_location in enumerate(raw_locations): + location = _mapping(raw_location, context=f"row-plan location {index}") + if set(location) != {"layer_index", "head_index", "row_index"}: + raise ValueError(f"row-plan location {index} fields drifted") + layer = _strict_int(location["layer_index"], context="plan layer") + head = _strict_int(location["head_index"], context="plan head") + row = _strict_int(location["row_index"], context="plan row") + key = (layer, head, row) + if layer not in counts or not 0 <= head < 16 or not 0 <= row < 128: + raise ValueError(f"row-plan location {index} is outside frozen geometry") + if key in seen: + raise ValueError(f"row-plan location {index} is duplicated") + seen.add(key) + counts[layer] += 1 + locations.append(RowLocation(layer, head, row)) + if counts != FROZEN_LAYER_QUOTAS: + raise ValueError("identity promoted rows do not realize the frozen quotas") + + plan = ExactBudgetRowPlan( + low_bits=4, + high_bits=8, + group_size=128, + scale_bits=16, + total_groups=36_864, + mask_bytes=TARGET_MASK_BYTES, + promotion_increment_bytes=64, + target_resident_bytes=TARGET_PACKED_STATE_BYTES, + resident_bytes=TARGET_PACKED_STATE_BYTES, + high_precision_rows=tuple(locations), + score_shapes=tuple((layer, 16, 128) for layer in FROZEN_LINEAR_LAYERS), + ) + if ( + plan.promoted_group_count != TARGET_PROMOTED_ROWS + or { + layer: len(plan.groups_for_layer(layer)) + for layer in FROZEN_LINEAR_LAYERS + } + != FROZEN_LAYER_QUOTAS + ): + raise ValueError("reconstructed identity row plan failed its exact quota contract") + return plan, { + "passed": True, + "canonical_plan_sha256": recorded_hash, + "method": TARGET_FISHER_SCORE, + "promoted_group_count": TARGET_PROMOTED_ROWS, + "resident_bytes": TARGET_PACKED_STATE_BYTES, + "selector_binding": expected_selector_binding, + } + + +def _normalize_metric_rows( + per_task: Mapping[str, Sequence[Mapping[str, Any]]], + *, + task_records: Sequence[Mapping[str, Any]], + token_field: str, + context: str, +) -> dict[str, list[dict[str, float | int | bool]]]: + if set(per_task) != set(METHODS): + raise ValueError(f"{context} metrics must contain exactly the four frozen methods") + expected_ids = [int(record["task_id"]) for record in task_records] + normalized: dict[str, list[dict[str, float | int | bool]]] = {} + for method in METHODS: + rows = per_task[method] + if len(rows) != STAGE_B_LIMIT: + raise ValueError(f"{context} {method} must contain exactly 32 task rows") + method_rows: list[dict[str, float | int | bool]] = [] + for index, (row, identity) in enumerate(zip(rows, task_records, strict=True)): + task_id = _strict_int( + row.get("task_id"), + context=f"{context} {method} row {index} task_id", + ) + if task_id != expected_ids[index]: + raise ValueError(f"{context} {method} task order drifted") + token_count = _strict_int( + row.get("token_count"), + context=f"{context} {method} task {task_id} token_count", + ) + if token_count != int(identity[token_field]): + raise ValueError(f"{context} {method} task {task_id} token count drifted") + if row.get("all_logits_finite") is not True: + raise ValueError(f"{context} {method} task {task_id} logits are not finite") + values = { + field: _finite_float( + row.get(field), + context=f"{context} {method} task {task_id} {field}", + ) + for field in METRIC_FIELDS + } + if ( + values["mean_kl"] < 0 + or values["cvar95_kl"] < values["mean_kl"] + or values["max_kl"] < values["cvar95_kl"] + ): + raise ValueError( + f"{context} {method} task {task_id} KL metrics are invalid or unordered" + ) + if not 0 <= values["top1_agreement"] <= 1: + raise ValueError( + f"{context} {method} task {task_id} top-1 agreement is outside [0, 1]" + ) + if values["reference_nll"] < 0 or values["candidate_nll"] < 0: + raise ValueError( + f"{context} {method} task {task_id} NLL metrics must be non-negative" + ) + arithmetic_delta = values["candidate_nll"] - values["reference_nll"] + if not math.isclose( + values["delta_nll"], + arithmetic_delta, + rel_tol=1e-7, + abs_tol=2e-6, + ): + raise ValueError( + f"{context} {method} task {task_id} delta_nll is not candidate-reference" + ) + method_rows.append( + { + "task_id": task_id, + "token_count": token_count, + "all_logits_finite": True, + **values, + } + ) + normalized[method] = method_rows + for task_index, task_id in enumerate(expected_ids): + reference_values = [ + float(normalized[method][task_index]["reference_nll"]) + for method in METHODS + ] + if any( + not math.isclose( + value, + reference_values[0], + rel_tol=0, + abs_tol=1e-7, + ) + for value in reference_values[1:] + ): + raise ValueError( + f"{context} task {task_id} methods do not share one reference NLL" + ) + return normalized + + +def _expected_aggregates( + normalized: Mapping[str, Sequence[Mapping[str, float | int | bool]]], +) -> dict[str, dict[str, float | int]]: + return { + method: { + "task_count": len(rows), + "macro_delta_nll": fmean(float(row["delta_nll"]) for row in rows), + "macro_mean_kl": fmean(float(row["mean_kl"]) for row in rows), + "macro_cvar95_kl": fmean(float(row["cvar95_kl"]) for row in rows), + "maximum_kl": max(float(row["max_kl"]) for row in rows), + "macro_top1_agreement": fmean( + float(row["top1_agreement"]) for row in rows + ), + "token_count": sum(int(row["token_count"]) for row in rows), + } + for method, rows in normalized.items() + } + + +def _accumulator_trace(accumulator: Any) -> dict[str, Any]: + """Retain compact per-token primitives so every task summary is reproducible.""" + + fields = { + "kl": torch.cat(accumulator.kl).to(torch.float32), + "reference_nll": torch.cat(accumulator.reference_nll).to(torch.float32), + "candidate_nll": torch.cat(accumulator.candidate_nll).to(torch.float32), + "top1_agreement": torch.cat(accumulator.top1_agreement).to(torch.bool), + "outputs_finite": torch.stack(accumulator.outputs_finite).to(torch.bool), + } + token_count = int(fields["kl"].numel()) + if token_count <= 0 or any(int(values.numel()) != token_count for values in fields.values()): + raise RuntimeError("token primitive accumulators have inconsistent lengths") + payload = { + "token_count": token_count, + **{ + name: ( + values.tolist() + if name not in {"top1_agreement", "outputs_finite"} + else [bool(value) for value in values.tolist()] + ) + for name, values in fields.items() + }, + } + return { + **payload, + "canonical_primitives_sha256": _sha256_bytes(canonical_json_bytes(payload)), + } + + +def _summary_from_trace(trace: Mapping[str, Any]) -> dict[str, float | int | bool]: + token_count = _strict_int(trace.get("token_count"), context="trace token_count") + if token_count <= 0: + raise ValueError("trace token_count must be positive") + numeric: dict[str, torch.Tensor] = {} + for field in ("kl", "reference_nll", "candidate_nll"): + raw = trace.get(field) + if isinstance(raw, (str, bytes)) or not isinstance(raw, Sequence): + raise ValueError(f"trace {field} must be an array") + values = torch.tensor( + [ + _finite_float(value, context=f"trace {field} value") + for value in raw + ], + dtype=torch.float32, + ) + if int(values.numel()) != token_count: + raise ValueError(f"trace {field} length differs from token_count") + numeric[field] = values + boolean: dict[str, torch.Tensor] = {} + for field in ("top1_agreement", "outputs_finite"): + raw = trace.get(field) + if ( + isinstance(raw, (str, bytes)) + or not isinstance(raw, Sequence) + or len(raw) != token_count + or any(not isinstance(value, bool) for value in raw) + ): + raise ValueError(f"trace {field} must be a token-aligned boolean array") + boolean[field] = torch.tensor(raw, dtype=torch.bool) + payload = { + "token_count": token_count, + **{ + field: list(trace[field]) + for field in ( + "kl", + "reference_nll", + "candidate_nll", + "top1_agreement", + "outputs_finite", + ) + }, + } + if trace.get("canonical_primitives_sha256") != _sha256_bytes( + canonical_json_bytes(payload) + ): + raise ValueError("trace canonical primitive hash does not match") + + kl = numeric["kl"] + reference = numeric["reference_nll"] + candidate = numeric["candidate_nll"] + tail_count = max(1, math.ceil(token_count * 0.05)) + reference_mean = reference.mean() + candidate_mean = candidate.mean() + return { + "token_count": token_count, + "mean_kl": float(kl.mean().item()), + "cvar95_kl": float(torch.topk(kl, k=tail_count).values.mean().item()), + "max_kl": float(kl.max().item()), + "top1_agreement": float( + boolean["top1_agreement"].to(torch.float32).mean().item() + ), + "reference_nll": float(reference_mean.item()), + "candidate_nll": float(candidate_mean.item()), + "delta_nll": float((candidate_mean - reference_mean).item()), + "all_logits_finite": bool(boolean["outputs_finite"].all().item()), + } + + +def audit_metric_traces( + traces: Mapping[str, Sequence[Mapping[str, Any]]], + summaries: Mapping[str, Sequence[Mapping[str, Any]]], + *, + task_records: Sequence[Mapping[str, Any]], + token_field: str, + context: str, +) -> dict[str, Any]: + """Recompute every task metric from stored token primitives and hashes.""" + + if set(traces) != set(METHODS): + raise ValueError(f"{context} traces must contain the four frozen methods") + for method in METHODS: + if len(traces[method]) != STAGE_B_LIMIT: + raise ValueError(f"{context} {method} must contain exactly 32 traces") + for task_index, identity in enumerate(task_records): + task_id = int(identity["task_id"]) + expected_tokens = int(identity[token_field]) + reference_trace: Sequence[Any] | None = None + for method in METHODS: + trace = traces[method][task_index] + if int(trace.get("task_id", -1)) != task_id: + raise ValueError(f"{context} {method} trace task order drifted") + recomputed = _summary_from_trace(trace) + if recomputed["token_count"] != expected_tokens: + raise ValueError(f"{context} {method} task {task_id} trace count drifted") + expected_summary = { + key: value + for key, value in summaries[method][task_index].items() + if key != "task_id" + } + if recomputed != expected_summary: + raise ValueError( + f"{context} {method} task {task_id} summary differs from token primitives" + ) + method_reference = trace.get("reference_nll") + if reference_trace is None: + reference_trace = method_reference + elif method_reference != reference_trace: + raise ValueError( + f"{context} task {task_id} methods do not share reference primitives" + ) + return { + "passed": True, + "task_count": STAGE_B_LIMIT, + "methods": list(METHODS), + "token_primitive_hashes_verified": True, + "summaries_recomputed": True, + } + + +def _audit_aggregate_match( + aggregates: Mapping[str, Mapping[str, Any]], + expected: Mapping[str, Mapping[str, Any]], + *, + context: str, +) -> dict[str, Any]: + if set(aggregates) != set(METHODS): + raise ValueError(f"{context} aggregates must contain the four frozen methods") + for method in METHODS: + if dict(aggregates[method]) != dict(expected[method]): + raise ValueError(f"{context} {method} aggregate differs from task-macro recomputation") + return { + "passed": True, + "methods": list(METHODS), + "task_count_per_method": STAGE_B_LIMIT, + } + + +def audit_storage(storage: Mapping[str, Mapping[str, Any]]) -> dict[str, Any]: + if set(storage) != set(METHODS): + raise ValueError("storage must contain exactly the four frozen methods") + common = { + "payload_bytes": TARGET_PAYLOAD_BYTES, + "scale_bytes": TARGET_SCALE_BYTES, + "mask_bytes": TARGET_MASK_BYTES, + "resident_bytes": TARGET_PACKED_STATE_BYTES, + "high_precision_groups": TARGET_PROMOTED_ROWS, + } + for method in METHODS: + for field, expected in common.items(): + if _strict_int( + storage[method].get(field), + context=f"{method} storage {field}", + ) != expected: + raise ValueError(f"{method} storage {field} drifted") + for method in QUERY_METHODS: + if ( + _strict_int( + storage[method].get("selector_auxiliary_bytes"), + context=f"{method} selector auxiliary bytes", + ) + != TARGET_SELECTOR_BYTES + or _strict_int( + storage[method].get("resident_bytes_including_selector"), + context=f"{method} selector-aware resident bytes", + ) + != TARGET_TOTAL_BYTES + ): + raise ValueError(f"{method} selector-aware storage drifted") + return { + "passed": True, + **common, + "query_selector_auxiliary_bytes": TARGET_SELECTOR_BYTES, + "query_resident_bytes_including_selector": TARGET_TOTAL_BYTES, + } + + +def audit_task_selector_diagnostics( + diagnostics: Mapping[str, Sequence[Mapping[str, Any]]], + *, + task_records: Sequence[Mapping[str, Any]], +) -> dict[str, Any]: + if set(diagnostics) != set(QUERY_METHODS): + raise ValueError("selector diagnostics must contain exactly CQER and RHT-CQER") + methods: dict[str, list[dict[str, Any]]] = {} + expected_selection = { + CQER_METHOD: "query_ema32_weighted_aligned_mse_reduction", + RHT_METHOD: RHT_METHOD, + } + for method in QUERY_METHODS: + task_diagnostics = diagnostics[method] + if len(task_diagnostics) != STAGE_B_LIMIT: + raise ValueError(f"{method} must report diagnostics for exactly 32 tasks") + audited_tasks: list[dict[str, Any]] = [] + for identity, task_record in zip( + task_records, + task_diagnostics, + strict=True, + ): + task_id = _strict_int(task_record.get("task_id"), context="diagnostic task_id") + if task_id != int(identity["task_id"]): + raise ValueError(f"{method} diagnostic task order drifted") + layers = task_record.get("layers") + if isinstance(layers, (str, bytes)) or not isinstance(layers, Sequence): + raise ValueError(f"{method} task {task_id} layers must be an array") + if len(layers) != len(FROZEN_LINEAR_LAYERS): + raise ValueError(f"{method} task {task_id} did not report every layer") + by_layer: dict[int, Mapping[str, Any]] = {} + for raw_layer in layers: + layer_record = _mapping( + raw_layer, + context=f"{method} task {task_id} layer", + ) + layer = _strict_int( + layer_record.get("layer_index"), + context=f"{method} task {task_id} layer_index", + ) + if layer in by_layer: + raise ValueError(f"{method} task {task_id} repeats layer {layer}") + by_layer[layer] = layer_record + if set(by_layer) != set(FROZEN_LINEAR_LAYERS): + raise ValueError(f"{method} task {task_id} layer identity drifted") + + expected_writes = int(identity["code_tokens"]) + expected_query_tokens = ( + int(identity["prompt_tokens"]) + + int(identity["aligned_scored_tokens"]) + ) + for layer in FROZEN_LINEAR_LAYERS: + record = by_layer[layer] + quota = _strict_int( + record.get("quota"), + context=f"{method} task {task_id} layer {layer} quota", + ) + selected = _strict_int( + record.get("current_selected_count"), + context=f"{method} task {task_id} layer {layer} selection count", + ) + updates = _strict_int( + record.get("state_updates"), + context=f"{method} task {task_id} layer {layer} updates", + ) + staged = _strict_int( + record.get("observations_staged"), + context=f"{method} task {task_id} layer {layer} staged", + ) + committed = _strict_int( + record.get("observations_committed"), + context=f"{method} task {task_id} layer {layer} committed", + ) + tokens = _strict_int( + record.get("tokens_observed"), + context=f"{method} task {task_id} layer {layer} tokens", + ) + if quota != FROZEN_LAYER_QUOTAS[layer] or selected != quota: + raise ValueError(f"{method} task {task_id} layer {layer} quota drifted") + if (updates, staged, committed) != ( + expected_writes, + expected_writes, + expected_writes, + ): + raise ValueError( + f"{method} task {task_id} layer {layer} handshake drifted" + ) + if tokens != expected_query_tokens: + raise ValueError( + f"{method} task {task_id} layer {layer} query-token count drifted" + ) + if ( + record.get("pending_observation") is not False + or record.get("confirmation_two") is not False + or record.get("selection_method") != expected_selection[method] + ): + raise ValueError( + f"{method} task {task_id} layer {layer} method/handshake drifted" + ) + mask = record.get("current_mask_sha256") + if ( + not isinstance(mask, str) + or len(mask) != 64 + or any(character not in "0123456789abcdef" for character in mask) + ): + raise ValueError( + f"{method} task {task_id} layer {layer} mask hash is invalid" + ) + if method == RHT_METHOD and ( + record.get("state_codec") != "right_rht_sha256_signs_v1" + or record.get("state_codec_seed") != SEED + or record.get("state_codec_axis") != "value" + or record.get("state_codec_normalization") != "orthonormal" + or record.get("state_codec_persistent_tensor_bytes") != 0 + ): + raise ValueError( + f"{method} task {task_id} layer {layer} codec contract drifted" + ) + audited_tasks.append( + { + "task_id": task_id, + "state_writes": expected_writes, + "query_tokens": expected_query_tokens, + "layers": len(FROZEN_LINEAR_LAYERS), + } + ) + methods[method] = audited_tasks + return { + "passed": True, + "quota_sum": sum(FROZEN_LAYER_QUOTAS.values()), + "task_count": STAGE_B_LIMIT, + "methods": methods, + } + + +def validate_task_state_error_coverage( + state_errors: Mapping[str, Mapping[str, Any]], + *, + expected_writes: int, +) -> dict[str, Any]: + if set(state_errors) != set(QUERY_METHODS): + raise ValueError("state-error evidence must contain exactly CQER and RHT-CQER") + if state_errors[CQER_METHOD].get("coverage") != state_errors[RHT_METHOD].get( + "coverage" + ): + raise ValueError("CQER and RHT-CQER state-error coverage differs") + expected_records = len(FROZEN_LINEAR_LAYERS) * expected_writes + for method in QUERY_METHODS: + record = state_errors[method] + if int(record.get("record_count", -1)) != expected_records: + raise ValueError(f"{method} state-error record count drifted") + per_layer = record.get("per_layer") + per_write = record.get("per_write") + if not isinstance(per_layer, Mapping) or set(per_layer) != { + str(layer) for layer in FROZEN_LINEAR_LAYERS + }: + raise ValueError(f"{method} state-error layers drifted") + if not isinstance(per_write, Mapping) or set(per_write) != { + str(write) for write in range(expected_writes) + }: + raise ValueError(f"{method} state-error writes drifted") + if any( + int(per_layer[str(layer)].get("record_count", -1)) != expected_writes + for layer in FROZEN_LINEAR_LAYERS + ): + raise ValueError(f"{method} did not record every layer on every write") + if any( + int(per_write[str(write)].get("record_count", -1)) + != len(FROZEN_LINEAR_LAYERS) + for write in range(expected_writes) + ): + raise ValueError(f"{method} did not record every layer within each write") + return { + "passed": True, + "state_writes": expected_writes, + "records_per_method": expected_records, + } + + +def recompute_local_codec_state_error_summary( + summary: Mapping[str, Any], + *, + method: str, + expected_writes: int, + final_mask_hashes: Mapping[int, str], +) -> dict[str, Any]: + """Recompute local pre-pack-to-materialized QDQ SSE from every raw record.""" + + raw_records = summary.get("records") + if ( + isinstance(raw_records, (str, bytes)) + or not isinstance(raw_records, Sequence) + or len(raw_records) != len(FROZEN_LINEAR_LAYERS) * expected_writes + ): + raise ValueError(f"{method} raw local-codec records have the wrong length") + expected_selection = ( + "query_ema32_weighted_aligned_mse_reduction" + if method == CQER_METHOD + else RHT_METHOD + ) + normalized: list[dict[str, Any]] = [] + per_layer: dict[int, dict[str, float | int]] = { + layer: {"record_count": 0, "element_count": 0, "state_sse": 0.0} + for layer in FROZEN_LINEAR_LAYERS + } + per_write: dict[int, dict[str, float | int]] = { + write: {"record_count": 0, "element_count": 0, "state_sse": 0.0} + for write in range(expected_writes) + } + expected_shape = (1, 16, 128, 128) + element_count = math.prod(expected_shape) + for update_index, raw_record in enumerate(raw_records): + record = dict(_mapping(raw_record, context=f"{method} raw state-error record")) + write = update_index // len(FROZEN_LINEAR_LAYERS) + layer = FROZEN_LINEAR_LAYERS[update_index % len(FROZEN_LINEAR_LAYERS)] + if ( + _strict_int(record.get("update_index"), context="state update_index") + != update_index + or _strict_int(record.get("write_ordinal"), context="state write_ordinal") + != write + or _strict_int(record.get("layer_index"), context="state layer_index") + != layer + or _strict_int(record.get("state_index"), context="state state_index") != 0 + ): + raise ValueError(f"{method} raw state-error write/layer order drifted") + shape = record.get("shape") + if list(shape) != list(expected_shape): + raise ValueError(f"{method} layer {layer} recurrent-state geometry drifted") + quota = FROZEN_LAYER_QUOTAS[layer] + expected_payload = 2_048 * 64 + quota * 64 + expected_scale = 2_048 * 2 + expected_mask = 2_048 // 8 + expected_resident = expected_payload + expected_scale + expected_mask + integer_contract = { + "element_count": element_count, + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + "total_groups": 2_048, + "high_precision_groups": quota, + "baseline_bytes": element_count * 4, + "payload_bytes": expected_payload, + "scale_bytes": expected_scale, + "mask_bytes": expected_mask, + "resident_bytes": expected_resident, + } + for field, expected in integer_contract.items(): + if _strict_int( + record.get(field), + context=f"{method} layer {layer} {field}", + ) != expected: + raise ValueError(f"{method} layer {layer} state-error {field} drifted") + if ( + record.get("rounding") != "nearest" + or record.get("source_dtype") != "torch.float32" + or record.get("selection_method") != expected_selection + ): + raise ValueError(f"{method} layer {layer} state-error codec contract drifted") + mask_hash = record.get("high_precision_mask_sha256") + if ( + not isinstance(mask_hash, str) + or len(mask_hash) != 64 + or any(character not in "0123456789abcdef" for character in mask_hash) + ): + raise ValueError(f"{method} layer {layer} state-error mask hash is invalid") + mse = _finite_float(record.get("mean_squared_error"), context="state MSE") + maximum = _finite_float(record.get("max_absolute_error"), context="state max error") + relative_l2 = _finite_float(record.get("relative_l2_error"), context="state relative L2") + recorded_sse = _finite_float(record.get("state_sse"), context="state SSE") + if min(mse, maximum, relative_l2, recorded_sse) < 0: + raise ValueError(f"{method} raw state-error magnitudes must be non-negative") + recomputed_sse = mse * element_count + if recorded_sse != recomputed_sse: + raise ValueError(f"{method} raw state-error SSE is not MSE times elements") + normalized.append(record) + for totals in (per_layer[layer], per_write[write]): + totals["record_count"] = int(totals["record_count"]) + 1 + totals["element_count"] = int(totals["element_count"]) + element_count + totals["state_sse"] = float(totals["state_sse"]) + recomputed_sse + + for layer in FROZEN_LINEAR_LAYERS: + final_record = normalized[ + (expected_writes - 1) * len(FROZEN_LINEAR_LAYERS) + + FROZEN_LINEAR_LAYERS.index(layer) + ] + if final_record["high_precision_mask_sha256"] != final_mask_hashes.get(layer): + raise ValueError( + f"{method} layer {layer} final raw-record mask does not match diagnostics" + ) + coverage = [ + { + "write_ordinal": int(record["write_ordinal"]), + "layer_index": int(record["layer_index"]), + "state_index": int(record["state_index"]), + "shape": list(record["shape"]), + } + for record in normalized + ] + total_elements = len(normalized) * element_count + total_sse = math.fsum(float(record["state_sse"]) for record in normalized) + recomputed = { + "record_count": len(normalized), + "element_count": total_elements, + "aggregate_state_sse": total_sse, + "aggregate_state_mse": total_sse / total_elements, + "coverage": coverage, + "per_layer": { + str(layer): { + "record_count": int(values["record_count"]), + "element_count": int(values["element_count"]), + "state_sse": float(values["state_sse"]), + } + for layer, values in per_layer.items() + }, + "per_write": { + str(write): { + "record_count": int(values["record_count"]), + "element_count": int(values["element_count"]), + "state_sse": float(values["state_sse"]), + } + for write, values in per_write.items() + }, + "records": normalized, + } + if dict(summary) != recomputed: + raise ValueError(f"{method} state-error aggregates differ from raw recomputation") + return recomputed + + +def audit_state_errors( + per_task_state_errors: Mapping[str, Sequence[Mapping[str, Any]]], + *, + task_records: Sequence[Mapping[str, Any]], + selector_diagnostics: Mapping[str, Sequence[Mapping[str, Any]]], +) -> dict[str, Any]: + if set(per_task_state_errors) != set(QUERY_METHODS): + raise ValueError("per-task state errors must contain CQER and RHT-CQER") + audits: list[dict[str, Any]] = [] + for index, identity in enumerate(task_records): + task_id = int(identity["task_id"]) + task_pair: dict[str, Mapping[str, Any]] = {} + for method in QUERY_METHODS: + records = per_task_state_errors[method] + if len(records) != STAGE_B_LIMIT: + raise ValueError(f"{method} must contain state errors for exactly 32 tasks") + task_record = records[index] + if int(task_record.get("task_id", -1)) != task_id: + raise ValueError(f"{method} state-error task order drifted") + summary = _mapping( + task_record.get("state_error"), + context=f"{method} task {task_id} state error", + ) + diagnostics = selector_diagnostics[method][index] + layers = diagnostics.get("layers") + if not isinstance(layers, Sequence): + raise ValueError(f"{method} task {task_id} diagnostics are missing") + final_masks = { + int(layer["layer_index"]): str(layer["current_mask_sha256"]) + for layer in layers + } + task_pair[method] = recompute_local_codec_state_error_summary( + summary, + method=method, + expected_writes=int(identity["code_tokens"]), + final_mask_hashes=final_masks, + ) + coverage = validate_task_state_error_coverage( + task_pair, + expected_writes=int(identity["code_tokens"]), + ) + audits.append({"task_id": task_id, **coverage}) + return {"passed": True, "task_count": STAGE_B_LIMIT, "tasks": audits} + + +def aggregate_state_errors( + per_task_state_errors: Mapping[str, Sequence[Mapping[str, Any]]], +) -> dict[str, dict[str, float | int]]: + aggregates: dict[str, dict[str, float | int]] = {} + for method in QUERY_METHODS: + records = per_task_state_errors[method] + element_count = sum( + int(_mapping(record["state_error"], context="state error").get("element_count", -1)) + for record in records + ) + state_sse = math.fsum( + _finite_float( + _mapping(record["state_error"], context="state error").get( + "aggregate_state_sse" + ), + context=f"{method} aggregate state SSE", + ) + for record in records + ) + record_count = sum( + int(_mapping(record["state_error"], context="state error").get("record_count", -1)) + for record in records + ) + if element_count <= 0 or record_count <= 0 or state_sse < 0: + raise ValueError(f"{method} aggregate state-error evidence is invalid") + aggregates[method] = { + "task_count": len(records), + "record_count": record_count, + "element_count": element_count, + "aggregate_state_sse": state_sse, + "aggregate_state_mse": state_sse / element_count, + } + return aggregates + + +def audit_reference_aligned_state_errors( + per_task_records: Mapping[str, Sequence[Mapping[str, Any]]], + *, + task_records: Sequence[Mapping[str, Any]], +) -> dict[str, Any]: + """Authenticate the FP64 candidate-versus-matched-FP32 secondary diagnostic.""" + + if set(per_task_records) != set(QUERY_METHODS): + raise ValueError("reference-aligned state errors must contain CQER and RHT-CQER") + expected_shape = [1, 16, 128, 128] + expected_elements = math.prod(expected_shape) + for method in QUERY_METHODS: + if len(per_task_records[method]) != STAGE_B_LIMIT: + raise ValueError(f"{method} reference-aligned evidence must contain 32 tasks") + for identity, task_wrapper in zip( + task_records, + per_task_records[method], + strict=True, + ): + task_id = int(identity["task_id"]) + if int(task_wrapper.get("task_id", -1)) != task_id: + raise ValueError(f"{method} reference-aligned task order drifted") + summary = _mapping( + task_wrapper.get("state_error"), + context=f"{method} task {task_id} reference-aligned summary", + ) + writes = summary.get("writes") + expected_writes = int(identity["code_tokens"]) + if ( + summary.get("metric") + != "candidate_materialized_state_minus_matched_fp32_state" + or not isinstance(writes, Sequence) + or len(writes) != expected_writes + ): + raise ValueError(f"{method} task {task_id} reference-aligned contract drifted") + total_elements = 0 + total_sse_values: list[float] = [] + for write_ordinal, write in enumerate(writes): + record = _mapping( + write, + context=f"{method} task {task_id} reference-aligned write", + ) + layers = record.get("layers") + if ( + int(record.get("write_ordinal", -1)) != write_ordinal + or not isinstance(layers, Sequence) + or len(layers) != len(FROZEN_LINEAR_LAYERS) + ): + raise ValueError( + f"{method} task {task_id} reference-aligned write coverage drifted" + ) + write_sse_values: list[float] = [] + for expected_layer, layer_record in zip( + FROZEN_LINEAR_LAYERS, + layers, + strict=True, + ): + layer = _mapping( + layer_record, + context=f"{method} reference-aligned layer", + ) + if ( + int(layer.get("layer_index", -1)) != expected_layer + or int(layer.get("state_index", -1)) != 0 + or layer.get("shape") != expected_shape + or int(layer.get("element_count", -1)) != expected_elements + ): + raise ValueError( + f"{method} task {task_id} reference-aligned geometry drifted" + ) + sse = _finite_float(layer.get("state_sse"), context="reference SSE") + mse = _finite_float(layer.get("state_mse"), context="reference MSE") + relative = _finite_float( + layer.get("relative_l2_error"), + context="reference relative L2", + ) + maximum = _finite_float( + layer.get("max_absolute_error"), + context="reference maximum error", + ) + if min(sse, mse, relative, maximum) < 0 or mse != sse / expected_elements: + raise ValueError( + f"{method} task {task_id} reference-aligned metrics drifted" + ) + write_sse_values.append(sse) + write_sse = math.fsum(write_sse_values) + write_elements = len(FROZEN_LINEAR_LAYERS) * expected_elements + if ( + int(record.get("record_count", -1)) != len(FROZEN_LINEAR_LAYERS) + or int(record.get("element_count", -1)) != write_elements + or _finite_float( + record.get("state_sse"), + context="reference write SSE", + ) + != write_sse + ): + raise ValueError( + f"{method} task {task_id} reference-aligned write totals drifted" + ) + total_elements += write_elements + total_sse_values.append(write_sse) + total_sse = math.fsum(total_sse_values) + if ( + int(summary.get("write_count", -1)) != expected_writes + or int(summary.get("record_count", -1)) + != expected_writes * len(FROZEN_LINEAR_LAYERS) + or int(summary.get("element_count", -1)) != total_elements + or _finite_float( + summary.get("aggregate_state_sse"), + context="reference aggregate SSE", + ) + != total_sse + or _finite_float( + summary.get("aggregate_state_mse"), + context="reference aggregate MSE", + ) + != total_sse / total_elements + ): + raise ValueError( + f"{method} task {task_id} reference-aligned aggregate drifted" + ) + return { + "passed": True, + "task_count": STAGE_B_LIMIT, + "methods": list(QUERY_METHODS), + "accumulation_dtype": "torch.float64", + "advancement_gate_metric": False, + } + + +def _dense_reference_signs(layer: int, heads: int, width: int) -> np.ndarray: + signs = np.empty((1, heads, 1, width), dtype=np.float32) + domain = b"recurquant.right-rht.signs.v1\0" + for head in range(heads): + values: list[float] = [] + counter = 0 + while len(values) < width: + message = b"".join( + ( + domain, + SEED.to_bytes(8, "little"), + layer.to_bytes(8, "little"), + head.to_bytes(8, "little"), + width.to_bytes(8, "little"), + counter.to_bytes(8, "little"), + ) + ) + for byte in hashlib.sha256(message).digest(): + for bit in range(8): + values.append(1.0 if byte & (1 << bit) else -1.0) + if len(values) == width: + break + if len(values) == width: + break + counter += 1 + signs[0, head, 0] = np.asarray(values, dtype=np.float32) + return signs + + +def _dense_hadamard(width: int) -> np.ndarray: + matrix = np.ones((1, 1), dtype=np.float32) + while matrix.shape[0] < width: + matrix = np.block([[matrix, matrix], [matrix, -matrix]]) + if matrix.shape != (width, width): + raise ValueError("dense Hadamard reference requires a power-of-two width") + return matrix + + +def _dense_reference_encode(state: np.ndarray, *, layer: int) -> np.ndarray: + width = state.shape[-1] + signed = state.astype(np.float32) * _dense_reference_signs( + layer, + state.shape[1], + width, + ) + return np.matmul(signed, _dense_hadamard(width)) / math.sqrt(width) + + +def _dense_reference_decode(encoded: np.ndarray, *, layer: int) -> np.ndarray: + width = encoded.shape[-1] + decoded = np.matmul(encoded.astype(np.float32), _dense_hadamard(width)) + decoded /= math.sqrt(width) + return decoded * _dense_reference_signs(layer, encoded.shape[1], width) + + +def _dense_reference_mixed_qdq( + state: np.ndarray, + mask: np.ndarray, + *, + layer: int, +) -> np.ndarray: + encoded = _dense_reference_encode(state, layer=layer) + groups = encoded.reshape(-1, 128) + qmax = np.where(mask.reshape(-1), 127.0, 7.0).reshape(-1, 1) + ideal_scales = np.max(np.abs(groups), axis=1, keepdims=True) / qmax + ideal_scales = np.where(ideal_scales > 1e-8, ideal_scales, 1.0) + scales = np.clip( + ideal_scales, + 2.0**-24, + np.finfo(np.float16).max, + ).astype(np.float16).astype(np.float32) + codes = np.rint(groups / scales) + codes = np.maximum(np.minimum(codes, qmax), -qmax) + quantized = (codes * scales).reshape(encoded.shape) + return _dense_reference_decode(quantized, layer=layer) + + +def compute_independent_dense_rht_evidence() -> dict[str, Any]: + """Compare production RHT/packing with dense NumPy reference implementations.""" + + encode_state = torch.randn( + (1, 2, 3, 128), + generator=torch.Generator().manual_seed(811), + dtype=torch.float32, + ) + reference_signs = _dense_reference_signs(7, 2, 128) + production_signs = right_rht_signs( + layer_index=7, + expected_heads=2, + width=128, + device="cpu", + ).numpy() + reference_encoded = _dense_reference_encode(encode_state.numpy(), layer=7) + production_encoded = right_rht_encode( + encode_state, + layer_index=7, + expected_heads=2, + ).numpy() + encode_max_abs = float(np.max(np.abs(production_encoded - reference_encoded))) + + pack_state = torch.randn( + (1, 2, 4, 128), + generator=torch.Generator().manual_seed(823), + dtype=torch.float32, + ) + mask = torch.tensor( + [[False, True, False, True], [True, False, False, True]], + dtype=torch.bool, + ) + packed = quantize_pack_mixed( + pack_state, + mask, + low_spec=QuantizationSpec(bits=4, group_size=128, flatten_last_dims=2), + high_spec=QuantizationSpec(bits=8, group_size=128, flatten_last_dims=2), + right_rht_layer_index=9, + right_rht_expected_heads=2, + ) + reference_packed = _dense_reference_mixed_qdq( + pack_state.numpy(), + mask.numpy(), + layer=9, + ) + pack_max_abs = float( + np.max(np.abs(packed.dequantize().numpy() - reference_packed)) + ) + return { + "reference": INDEPENDENT_REFERENCE_DESCRIPTION, + "signs_exact": bool(np.array_equal(production_signs, reference_signs)), + "encode_max_abs_difference": encode_max_abs, + "encode_max_abs_threshold": INDEPENDENT_ENCODE_MAX_ABS_THRESHOLD, + "physical_pack_max_abs_difference": pack_max_abs, + "physical_pack_max_abs_threshold": INDEPENDENT_PACK_MAX_ABS_THRESHOLD, + "passed": ( + np.array_equal(production_signs, reference_signs) + and encode_max_abs <= INDEPENDENT_ENCODE_MAX_ABS_THRESHOLD + and pack_max_abs <= INDEPENDENT_PACK_MAX_ABS_THRESHOLD + ), + } + + +def validate_unit_evidence_schema( + unit_evidence: Mapping[str, Any], +) -> dict[str, Any]: + """Validate exact production schemas and recompute every derived unit flag.""" + + _require_exact_fields( + unit_evidence, + {"production_self_check", "independent_dense_reference"}, + context="Stage-B unit evidence", + ) + production = _mapping( + unit_evidence.get("production_self_check"), + context="production RHT self-check", + ) + _require_exact_fields( + production, + { + "inverse_relative_l2", + "inverse_relative_l2_threshold", + "physical_pack_matches_transformed_qdq", + "physical_pack_max_abs_difference", + "physical_pack_storage_bytes", + "sign_schedule_sha256", + "expected_sign_schedule_sha256", + "seed", + "device", + "dtype", + }, + context="production RHT self-check", + ) + independent = _mapping( + unit_evidence.get("independent_dense_reference"), + context="independent dense RHT reference", + ) + _require_exact_fields( + independent, + { + "reference", + "signs_exact", + "encode_max_abs_difference", + "encode_max_abs_threshold", + "physical_pack_max_abs_difference", + "physical_pack_max_abs_threshold", + "passed", + }, + context="independent dense RHT reference", + ) + + inverse = _finite_float( + production.get("inverse_relative_l2"), + context="right-RHT inverse relative L2", + ) + pack_difference = _finite_float( + production.get("physical_pack_max_abs_difference"), + context="right-RHT physical-pack maximum difference", + ) + if inverse < 0 or pack_difference < 0: + raise ValueError("production RHT self-check errors must be non-negative") + if ( + production.get("inverse_relative_l2_threshold") + != MAX_RHT_INVERSE_RELATIVE_L2 + or production.get("physical_pack_storage_bytes") + != PRODUCTION_SELF_CHECK_STORAGE_BYTES + or production.get("expected_sign_schedule_sha256") + != EXPECTED_SIGN_SCHEDULE_SHA256 + or production.get("seed") != SEED + or production.get("device") != "cpu" + or production.get("dtype") != "torch.float32" + ): + raise ValueError("production RHT self-check frozen contract drifted") + _require_sha256( + production.get("sign_schedule_sha256"), + context="production RHT sign schedule", + ) + _require_sha256( + production.get("expected_sign_schedule_sha256"), + context="expected RHT sign schedule", + ) + production_passed = ( + inverse < MAX_RHT_INVERSE_RELATIVE_L2 + and production.get("physical_pack_matches_transformed_qdq") is True + and pack_difference == 0.0 + and production.get("sign_schedule_sha256") == EXPECTED_SIGN_SCHEDULE_SHA256 + ) + + signs_exact = independent.get("signs_exact") + recorded_passed = independent.get("passed") + if not isinstance(signs_exact, bool) or not isinstance(recorded_passed, bool): + raise ValueError("independent dense RHT boolean fields drifted") + encode_difference = _finite_float( + independent.get("encode_max_abs_difference"), + context="independent encode maximum difference", + ) + independent_pack_difference = _finite_float( + independent.get("physical_pack_max_abs_difference"), + context="independent pack maximum difference", + ) + if ( + encode_difference < 0 + or independent_pack_difference < 0 + or independent.get("reference") != INDEPENDENT_REFERENCE_DESCRIPTION + or independent.get("encode_max_abs_threshold") + != INDEPENDENT_ENCODE_MAX_ABS_THRESHOLD + or independent.get("physical_pack_max_abs_threshold") + != INDEPENDENT_PACK_MAX_ABS_THRESHOLD + ): + raise ValueError("independent dense RHT reference contract drifted") + independent_passed = ( + signs_exact + and encode_difference <= INDEPENDENT_ENCODE_MAX_ABS_THRESHOLD + and independent_pack_difference <= INDEPENDENT_PACK_MAX_ABS_THRESHOLD + ) + if recorded_passed is not independent_passed: + raise ValueError( + "independent dense RHT passed flag contradicts its component conditions" + ) + return { + "production_passed": production_passed, + "independent_passed": independent_passed, + "inverse_relative_l2": inverse, + "encode_max_abs_difference": encode_difference, + "physical_pack_max_abs_difference": independent_pack_difference, + } + + +def audit_unit_evidence(unit_evidence: Mapping[str, Any]) -> dict[str, Any]: + audit = validate_unit_evidence_schema(unit_evidence) + if audit["production_passed"] is not True: + raise ValueError("right-RHT production self-check did not pass") + if audit["independent_passed"] is not True: + raise ValueError("independent dense RHT reference did not pass") + return { + "passed": True, + "inverse_relative_l2": audit["inverse_relative_l2"], + "maximum_inverse_relative_l2": MAX_RHT_INVERSE_RELATIVE_L2, + "physical_pack_matches_transformed_qdq": True, + "sign_schedule_sha256": EXPECTED_SIGN_SCHEDULE_SHA256, + "independent_dense_reference_passed": True, + "independent_encode_max_abs_difference": audit[ + "encode_max_abs_difference" + ], + "independent_pack_max_abs_difference": audit[ + "physical_pack_max_abs_difference" + ], + } + + +def evaluate_stage_b_integrity( + *, + per_task: Mapping[str, Sequence[Mapping[str, Any]]], + per_task_full_code: Mapping[str, Sequence[Mapping[str, Any]]], + per_task_token_traces: Mapping[str, Sequence[Mapping[str, Any]]], + per_task_full_code_token_traces: Mapping[ + str, + Sequence[Mapping[str, Any]], + ], + aggregates: Mapping[str, Mapping[str, Any]], + aggregates_full_code: Mapping[str, Mapping[str, Any]], + storage: Mapping[str, Mapping[str, Any]], + selector_diagnostics: Mapping[str, Sequence[Mapping[str, Any]]], + per_task_state_errors: Mapping[str, Sequence[Mapping[str, Any]]], + per_task_reference_aligned_state_errors: Mapping[ + str, + Sequence[Mapping[str, Any]], + ], + task_records: Sequence[Mapping[str, Any]], + unit_evidence: Mapping[str, Any], + integrity: Mapping[str, Any], +) -> dict[str, Any]: + """Machine-check all Stage-A integrity conditions on the 32-task run.""" + + normalized_aligned: dict[str, list[dict[str, float | int | bool]]] | None = None + normalized_full: dict[str, list[dict[str, float | int | bool]]] | None = None + + def provenance_check() -> dict[str, Any]: + required_true = ( + "repository_clean_at_start", + "repository_clean_at_end", + "repository_commit_stable", + "source_hashes_stable", + "stage_a_artifact_committed_authenticated_and_passed", + "identity_artifact_committed_and_authenticated", + "identity_authenticated_before_model_weights", + "identity_row_plan_authenticated", + "identity_source_freeze_matches_current_bytes", + "imported_modules_resolved_to_authenticated_repository", + "runtime_environment_authenticated_before_dataset_access", + ) + if any(integrity.get(field) is not True for field in required_true): + raise ValueError("Stage-B provenance prerequisites did not all pass") + if ( + integrity.get( + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated" + ) + is not False + ): + raise ValueError( + "protected ranked window [8, 16) entered an application content set" + ) + return { + "passed": True, + **{field: True for field in required_true}, + ( + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated" + ): False, + } + + def metric_check() -> dict[str, Any]: + nonlocal normalized_aligned, normalized_full + normalized_aligned = _normalize_metric_rows( + per_task, + task_records=task_records, + token_field="aligned_scored_tokens", + context="aligned", + ) + normalized_full = _normalize_metric_rows( + per_task_full_code, + task_records=task_records, + token_field="full_code_scored_tokens", + context="full-code", + ) + expected_aligned = _expected_aggregates(normalized_aligned) + expected_full = _expected_aggregates(normalized_full) + _audit_aggregate_match(aggregates, expected_aligned, context="aligned") + _audit_aggregate_match( + aggregates_full_code, + expected_full, + context="full-code", + ) + aligned_trace_audit = audit_metric_traces( + per_task_token_traces, + per_task, + task_records=task_records, + token_field="aligned_scored_tokens", + context="aligned", + ) + full_trace_audit = audit_metric_traces( + per_task_full_code_token_traces, + per_task_full_code, + task_records=task_records, + token_field="full_code_scored_tokens", + context="full-code", + ) + return { + "passed": True, + "task_count": STAGE_B_LIMIT, + "methods": list(METHODS), + "all_logits_and_metrics_finite": True, + "task_macro_aggregates_recomputed": True, + "aligned_token_trace_audit": aligned_trace_audit, + "full_code_token_trace_audit": full_trace_audit, + } + + checks = { + "committed_clean_stable_provenance": _gate_check(provenance_check), + "finite_exact_task_macro_metrics": _gate_check(metric_check), + "exact_physical_storage": _gate_check(lambda: audit_storage(storage)), + "exact_quotas_and_query_handshakes": _gate_check( + lambda: audit_task_selector_diagnostics( + selector_diagnostics, + task_records=task_records, + ) + ), + "complete_matched_state_error_coverage": _gate_check( + lambda: audit_state_errors( + per_task_state_errors, + task_records=task_records, + selector_diagnostics=selector_diagnostics, + ) + ), + "authenticated_reference_aligned_state_secondary": _gate_check( + lambda: audit_reference_aligned_state_errors( + per_task_reference_aligned_state_errors, + task_records=task_records, + ) + ), + "deterministic_rht_codec_self_check": _gate_check( + lambda: audit_unit_evidence(unit_evidence) + ), + } + return { + "schema": "recurquant.experiment009-stage-b-integrity.v1", + "passed": all(check.get("passed") is True for check in checks.values()), + "checks": checks, + } + + +def _validate_bootstrap( + bootstrap: Mapping[str, Any], + *, + cqer_values: Sequence[float], + rht_values: Sequence[float], +) -> dict[str, Any]: + recomputed = paired_bootstrap_mean_improvement( + list(cqer_values), + list(rht_values), + samples=BOOTSTRAP_SAMPLES, + seed=SEED, + ) + if dict(bootstrap) != recomputed: + raise ValueError("paired bootstrap differs from the frozen recomputation") + if ( + bootstrap.get("paired_examples") != STAGE_B_LIMIT + or bootstrap.get("bootstrap_samples") != BOOTSTRAP_SAMPLES + or bootstrap.get("seed") != SEED + or bootstrap.get("confidence") != 0.95 + ): + raise ValueError("paired bootstrap contract drifted") + interval = bootstrap.get("confidence_interval") + if not isinstance(interval, list) or len(interval) != 2: + raise ValueError("paired bootstrap confidence interval is invalid") + lower = _finite_float(interval[0], context="paired bootstrap lower bound") + upper = _finite_float(interval[1], context="paired bootstrap upper bound") + return { + "passed": lower > 0, + "lower": lower, + "upper": upper, + "confidence": 0.95, + "bootstrap_samples": BOOTSTRAP_SAMPLES, + "seed": SEED, + } + + +def evaluate_stage_b_gate( + *, + aggregates: Mapping[str, Mapping[str, Any]], + per_task: Mapping[str, Sequence[Mapping[str, Any]]], + paired_bootstrap: Mapping[str, Any], + aggregate_state_error: Mapping[str, Mapping[str, Any]], + per_task_state_errors: Mapping[str, Sequence[Mapping[str, Any]]], + selector_diagnostics: Mapping[str, Sequence[Mapping[str, Any]]], + task_records: Sequence[Mapping[str, Any]], + integrity_gate: Mapping[str, Any], +) -> dict[str, Any]: + """Recompute and evaluate exactly the eight frozen Stage-B checks.""" + + if set(aggregates) != set(METHODS) or set(per_task) != set(METHODS): + raise ValueError("Stage-B gate requires exactly the four frozen methods") + if len(per_task[CQER_METHOD]) != STAGE_B_LIMIT or len(per_task[RHT_METHOD]) != STAGE_B_LIMIT: + raise ValueError("Stage-B gate requires exactly 32 paired task rows") + + normalized = _normalize_metric_rows( + per_task, + task_records=task_records, + token_field="aligned_scored_tokens", + context="Stage-B advancement", + ) + expected_aggregates = _expected_aggregates(normalized) + _audit_aggregate_match( + aggregates, + expected_aggregates, + context="Stage-B advancement", + ) + audit_state_errors( + per_task_state_errors, + task_records=task_records, + selector_diagnostics=selector_diagnostics, + ) + expected_state_error = aggregate_state_errors(per_task_state_errors) + if { + method: dict(_mapping(record, context=f"{method} state aggregate")) + for method, record in aggregate_state_error.items() + } != expected_state_error: + raise ValueError( + "recorded aggregate state error differs from raw state-error recomputation" + ) + + cqer = _mapping(aggregates[CQER_METHOD], context="CQER aggregate") + rht = _mapping(aggregates[RHT_METHOD], context="RHT aggregate") + cqer_values = [ + _finite_float(row.get("delta_nll"), context="CQER task delta NLL") + for row in per_task[CQER_METHOD] + ] + rht_values = [ + _finite_float(row.get("delta_nll"), context="RHT task delta NLL") + for row in per_task[RHT_METHOD] + ] + + def nll_reduction_check() -> dict[str, Any]: + baseline = _finite_float(cqer.get("macro_delta_nll"), context="CQER macro excess NLL") + candidate = _finite_float(rht.get("macro_delta_nll"), context="RHT macro excess NLL") + if baseline <= 0: + raise ValueError("CQER macro excess NLL must be positive for the relative gate") + reduction = (baseline - candidate) / baseline + return { + "passed": reduction >= MIN_DELTA_NLL_REDUCTION, + "cqer_macro_delta_nll": baseline, + "rht_macro_delta_nll": candidate, + "relative_reduction": reduction, + "minimum_relative_reduction": MIN_DELTA_NLL_REDUCTION, + } + + def win_check() -> dict[str, Any]: + wins = sum( + candidate < baseline + for baseline, candidate in zip(cqer_values, rht_values, strict=True) + ) + ties = sum( + candidate == baseline + for baseline, candidate in zip(cqer_values, rht_values, strict=True) + ) + return { + "passed": wins >= MIN_TASK_WINS, + "rht_wins": wins, + "ties": ties, + "task_count": STAGE_B_LIMIT, + "minimum_wins": MIN_TASK_WINS, + } + + def bootstrap_check() -> dict[str, Any]: + cqer_macro = _finite_float( + cqer.get("macro_delta_nll"), + context="CQER macro excess NLL for paired bootstrap", + ) + if cqer_macro <= 0: + raise ValueError( + "CQER macro excess NLL must be positive for the paired advancement gate" + ) + return _validate_bootstrap( + paired_bootstrap, + cqer_values=cqer_values, + rht_values=rht_values, + ) + + def mean_kl_check() -> dict[str, Any]: + baseline = _finite_float(cqer.get("macro_mean_kl"), context="CQER macro mean KL") + candidate = _finite_float(rht.get("macro_mean_kl"), context="RHT macro mean KL") + return { + "passed": candidate < baseline, + "cqer_macro_mean_kl": baseline, + "rht_macro_mean_kl": candidate, + } + + def cvar_check() -> dict[str, Any]: + baseline = _finite_float(cqer.get("macro_cvar95_kl"), context="CQER macro CVaR95 KL") + candidate = _finite_float(rht.get("macro_cvar95_kl"), context="RHT macro CVaR95 KL") + return { + "passed": candidate <= baseline, + "cqer_macro_cvar95_kl": baseline, + "rht_macro_cvar95_kl": candidate, + } + + def top1_check() -> dict[str, Any]: + baseline = _finite_float( + cqer.get("macro_top1_agreement"), + context="CQER macro top-1 agreement", + ) + candidate = _finite_float( + rht.get("macro_top1_agreement"), + context="RHT macro top-1 agreement", + ) + disadvantage = baseline - candidate + return { + "passed": disadvantage <= MAX_TOP1_DISADVANTAGE, + "cqer_macro_top1_agreement": baseline, + "rht_macro_top1_agreement": candidate, + "observed_disadvantage": disadvantage, + "maximum_disadvantage": MAX_TOP1_DISADVANTAGE, + } + + def tail_task_check() -> dict[str, Any]: + disadvantages = [ + { + "task_id": int(cqer_row["task_id"]), + "rht_minus_cqer_delta_nll": candidate - baseline, + } + for cqer_row, baseline, candidate in zip( + per_task[CQER_METHOD], + cqer_values, + rht_values, + strict=True, + ) + ] + worst = max(disadvantages, key=lambda record: record["rht_minus_cqer_delta_nll"]) + return { + "passed": worst["rht_minus_cqer_delta_nll"] <= MAX_TASK_NLL_DISADVANTAGE, + "worst_task_id": worst["task_id"], + "maximum_observed_disadvantage": worst["rht_minus_cqer_delta_nll"], + "maximum_allowed_disadvantage": MAX_TASK_NLL_DISADVANTAGE, + "per_task": disadvantages, + } + + def state_sse_check() -> dict[str, Any]: + if set(aggregate_state_error) != set(QUERY_METHODS): + raise ValueError("aggregate state error must contain CQER and RHT-CQER") + baseline = _finite_float( + aggregate_state_error[CQER_METHOD].get("aggregate_state_sse"), + context="CQER aggregate state SSE", + ) + candidate = _finite_float( + aggregate_state_error[RHT_METHOD].get("aggregate_state_sse"), + context="RHT aggregate state SSE", + ) + if baseline <= 0 or candidate < 0: + raise ValueError("state SSE baseline must be positive and candidate non-negative") + reduction = (baseline - candidate) / baseline + return { + "passed": reduction >= MIN_STATE_SSE_REDUCTION, + "cqer_state_sse": baseline, + "rht_state_sse": candidate, + "relative_reduction": reduction, + "minimum_relative_reduction": MIN_STATE_SSE_REDUCTION, + } + + advancement_checks = { + "macro_excess_nll_relative_reduction": _gate_check(nll_reduction_check), + "paired_95pct_lower_bound_above_zero": _gate_check(bootstrap_check), + "at_least_20_task_level_excess_nll_wins": _gate_check(win_check), + "lower_macro_mean_kl": _gate_check(mean_kl_check), + "macro_cvar95_kl_not_higher": _gate_check(cvar_check), + "macro_top1_disadvantage_at_most_0_005": _gate_check(top1_check), + "maximum_task_excess_nll_disadvantage_at_most_0_25": _gate_check( + tail_task_check + ), + "aggregate_state_sse_relative_reduction": _gate_check(state_sse_check), + } + if len(advancement_checks) != 8: + raise AssertionError("Experiment 009 Stage-B must have exactly eight advancement checks") + integrity_passed = integrity_gate.get("passed") is True + return { + "schema": "recurquant.experiment009-stage-b-gate.v1", + "applicable": True, + "passed": ( + integrity_passed + and all(check.get("passed") is True for check in advancement_checks.values()) + ), + "integrity_passed": integrity_passed, + "advancement_checks": advancement_checks, + "thresholds": { + "minimum_macro_excess_nll_relative_reduction": MIN_DELTA_NLL_REDUCTION, + "paired_confidence": 0.95, + "paired_lower_bound_strictly_positive": True, + "minimum_task_wins": MIN_TASK_WINS, + "macro_mean_kl_strictly_lower": True, + "macro_cvar95_kl_not_higher": True, + "maximum_macro_top1_disadvantage": MAX_TOP1_DISADVANTAGE, + "maximum_per_task_excess_nll_disadvantage": MAX_TASK_NLL_DISADVANTAGE, + "minimum_aggregate_state_sse_relative_reduction": MIN_STATE_SSE_REDUCTION, + "bootstrap_samples": BOOTSTRAP_SAMPLES, + "seed": SEED, + }, + } + + +def _require_exact_fields( + value: Mapping[str, Any], + expected: set[str], + *, + context: str, +) -> None: + if set(value) != expected: + raise ValueError(f"{context} fields drifted") + + +def _require_sha256(value: object, *, context: str) -> str: + if ( + not isinstance(value, str) + or len(value) != 64 + or value != value.lower() + or any(character not in string.hexdigits for character in value) + ): + raise ValueError(f"{context} must be a lowercase SHA-256") + return value + + +def _require_git_sha(value: object, *, context: str) -> str: + if ( + not isinstance(value, str) + or len(value) != 40 + or value != value.lower() + or any(character not in string.hexdigits for character in value) + ): + raise ValueError(f"{context} must be a full lowercase Git SHA") + return value + + +def _require_safe_repository_path(value: object, *, context: str) -> str: + if not isinstance(value, str) or not value or "\\" in value: + raise ValueError(f"{context} must be a non-empty POSIX repository path") + path = PurePosixPath(value) + if ( + path.is_absolute() + or str(path) != value + or any(part in {"", ".", ".."} for part in path.parts) + or (path.parts and ":" in path.parts[0]) + ): + raise ValueError(f"{context} must be a normalized repository-relative path") + return value + + +def _validate_result_protocol(evidence: Mapping[str, Any]) -> None: + protocol = _mapping(evidence.get("protocol"), context="Stage-B result protocol") + expected = { + "name": "Experiment 009 Stage B", + "ranked_window": [STAGE_B_OFFSET, STAGE_B_STOP], + "protected_ranked_window": list(PROTECTED_WINDOW), + "protected_window_application_content_intersection": False, + "methods_locked": True, + "thresholds_locked": True, + "bootstrap_samples": BOOTSTRAP_SAMPLES, + "bootstrap_seed": SEED, + } + if dict(protocol) != expected: + raise ValueError("Stage-B result protocol contract drifted") + + metric_contract = _mapping( + evidence.get("metric_contract"), + context="Stage-B result metric contract", + ) + if dict(metric_contract) != { + "primary": "task-macro aligned excess next-token NLL versus FP32 state", + "aligned_excludes": "prompt-to-first-code-token prediction", + "secondary": "task-macro full-code metrics", + "paired_bootstrap_samples": BOOTSTRAP_SAMPLES, + "paired_bootstrap_seed": SEED, + }: + raise ValueError("Stage-B result metric contract drifted") + + +def _validate_result_prerequisites( + evidence: Mapping[str, Any], +) -> dict[str, str]: + prerequisites = _mapping( + evidence.get("prerequisite_artifacts"), + context="Stage-B result prerequisites", + ) + _require_exact_fields( + prerequisites, + { + "stage_a", + "stage_b_identity", + "committed_row_plan", + "identity_source_freeze", + }, + context="Stage-B result prerequisites", + ) + + stage_a = _mapping(prerequisites["stage_a"], context="Stage-A prerequisite") + _require_exact_fields( + stage_a, + { + "path", + "sha256", + "git_blob_sha256", + "artifact_sha256", + "canonical_evidence_sha256", + "implementation_commit", + "gate_recomputed_and_passed", + "artifact_kind", + "historical_path_privacy_limitation", + }, + context="Stage-A prerequisite", + ) + if ( + _require_safe_repository_path( + stage_a.get("path"), + context="Stage-A prerequisite path", + ) + != identity_resolver.STAGE_A_ARTIFACT_RELATIVE_PATH + or stage_a.get("artifact_kind") != STAGE_A_ARTIFACT_KIND + or stage_a.get("sha256") != identity_resolver.STAGE_A_FILE_SHA256 + or stage_a.get("git_blob_sha256") != identity_resolver.STAGE_A_FILE_SHA256 + or stage_a.get("artifact_sha256") != identity_resolver.STAGE_A_FILE_SHA256 + or stage_a.get("canonical_evidence_sha256") + != identity_resolver.STAGE_A_CANONICAL_EVIDENCE_SHA256 + or stage_a.get("implementation_commit") + != identity_resolver.STAGE_A_IMPLEMENTATION_COMMIT + or stage_a.get("gate_recomputed_and_passed") != "true" + or stage_a.get("historical_path_privacy_limitation") + != STAGE_A_PATH_PRIVACY_LIMITATION + ): + raise ValueError("Stage-A prerequisite contract drifted") + + identity = _mapping( + prerequisites["stage_b_identity"], + context="Stage-B identity prerequisite", + ) + _require_exact_fields( + identity, + { + "path", + "sha256", + "git_blob_sha256", + "artifact_sha256", + "canonical_evidence_sha256", + "resolver_commit", + "artifact_kind", + }, + context="Stage-B identity prerequisite", + ) + identity_path = _require_safe_repository_path( + identity.get("path"), + context="Stage-B identity prerequisite path", + ) + identity_file_sha = _require_sha256( + identity.get("sha256"), + context="Stage-B identity file", + ) + if ( + not identity_path.endswith(".json") + or identity.get("artifact_kind") != IDENTITY_ARTIFACT_KIND + or identity.get("git_blob_sha256") != identity_file_sha + or identity.get("artifact_sha256") != identity_file_sha + ): + raise ValueError("Stage-B identity prerequisite contract drifted") + _require_sha256( + identity.get("canonical_evidence_sha256"), + context="Stage-B identity canonical evidence", + ) + _require_git_sha( + identity.get("resolver_commit"), + context="Stage-B identity resolver commit", + ) + + row_plan = _mapping( + prerequisites["committed_row_plan"], + context="Stage-B committed row-plan authentication", + ) + _require_exact_fields( + row_plan, + { + "passed", + "canonical_plan_sha256", + "method", + "promoted_group_count", + "resident_bytes", + "selector_binding", + }, + context="Stage-B committed row-plan authentication", + ) + selectors = _mapping( + row_plan.get("selector_binding"), + context="Stage-B row-plan selector binding", + ) + expected_selectors = { + "selector_file_sha256": identity_resolver.SELECTOR_FILE_SHA256, + "selector_canonical_evidence_sha256": ( + identity_resolver.SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": identity_resolver.LOSS_SELECTOR_FILE_SHA256, + "loss_selector_canonical_evidence_sha256": ( + identity_resolver.LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + } + if ( + row_plan.get("passed") is not True + or row_plan.get("method") != TARGET_FISHER_SCORE + or row_plan.get("promoted_group_count") != TARGET_PROMOTED_ROWS + or row_plan.get("resident_bytes") != TARGET_PACKED_STATE_BYTES + or dict(selectors) != expected_selectors + ): + raise ValueError("Stage-B committed row-plan contract drifted") + _require_sha256( + row_plan.get("canonical_plan_sha256"), + context="Stage-B committed row plan", + ) + + source_freeze = _mapping( + prerequisites["identity_source_freeze"], + context="Stage-B identity source freeze", + ) + _require_exact_fields( + source_freeze, + {"passed", "path_count", "paths", "sha256"}, + context="Stage-B identity source freeze", + ) + paths = list(SOURCE_FILES) + hashes = _mapping( + source_freeze.get("sha256"), + context="Stage-B identity source-freeze hashes", + ) + if ( + source_freeze.get("passed") is not True + or source_freeze.get("path_count") != len(paths) + or source_freeze.get("paths") != paths + or set(hashes) != set(paths) + ): + raise ValueError("Stage-B identity source-freeze contract drifted") + for path in paths: + _require_safe_repository_path(path, context="Stage-B frozen source path") + _require_sha256( + hashes[path], + context=f"Stage-B frozen source {path}", + ) + return dict(hashes) + + +def _validate_identity_data_access( + value: object, + *, + ordered_ids: list[int], + source_rows: int, +) -> dict[str, Any]: + access = _mapping(value, context="Stage-B identity data access") + _require_exact_fields( + access, + { + "transport_limitation", + "ranking_pass", + "target_load_pass", + "application_task_id_sets", + "protected_window_intersection", + }, + context="Stage-B identity data access", + ) + if access.get("transport_limitation") != DATA_ACCESS_TRANSPORT_LIMITATION: + raise ValueError("Stage-B identity transport limitation drifted") + ranking = _mapping( + access.get("ranking_pass"), + context="Stage-B identity ranking access", + ) + if dict(ranking) != { + "transport_records_yielded": source_rows, + "task_id_fields_inspected": source_rows, + "non_task_id_fields_read_by_recurquant": 0, + "row_mappings_retained": 0, + }: + raise ValueError("Stage-B identity ranking access drifted") + target = _mapping( + access.get("target_load_pass"), + context="Stage-B identity target-load access", + ) + target_yielded = _strict_int( + target.get("transport_records_yielded"), + context="Stage-B identity target-load records", + ) + if target_yielded < STAGE_B_LIMIT or dict(target) != { + "transport_records_yielded": target_yielded, + "task_id_fields_inspected": target_yielded, + "non_target_content_fields_read_by_recurquant": 0, + "target_rows_retained_and_canonicalized": STAGE_B_LIMIT, + }: + raise ValueError("Stage-B identity target-load access drifted") + application_keys = { + "selected", + "retained", + "canonicalized", + "formatted", + "tokenized", + "passed_to_model", + "evaluated", + } + expected_sets = { + "selected": ordered_ids, + "retained": ordered_ids, + "canonicalized": ordered_ids, + "formatted": ordered_ids, + "tokenized": ordered_ids, + "passed_to_model": [], + "evaluated": [], + } + application_sets = _mapping( + access.get("application_task_id_sets"), + context="Stage-B identity application task sets", + ) + if set(application_sets) != application_keys or dict(application_sets) != expected_sets: + raise ValueError("Stage-B identity application task sets drifted") + protected = _mapping( + access.get("protected_window_intersection"), + context="Stage-B identity protected-window intersection", + ) + if set(protected) != application_keys or any( + item is not False for item in protected.values() + ): + raise ValueError("protected ranked window entered the identity access sets") + return dict(access) + + +def _validate_result_identity_dataset( + identity_dataset: Mapping[str, Any], +) -> tuple[tuple[dict[str, Any], ...], dict[str, Any]]: + expected_fields = { + "id", + "config", + "revision", + "phase", + "source_split", + "selection_namespace", + "formatter_version", + "selection_mode", + "selection_window", + "protected_window", + "ordered_task_ids", + "manifest", + "content_manifest_sha256", + "token_manifest_sha256", + "ordered_identity_sha256", + "tasks", + "totals", + "data_access", + } + _require_exact_fields( + identity_dataset, + expected_fields, + context="Stage-B result identity dataset", + ) + expected_contract = { + "id": identity_resolver.MBPP_DATASET_ID, + "config": identity_resolver.MBPP_CONFIG, + "revision": identity_resolver.MBPP_REVISION, + "phase": "calibration", + "source_split": identity_resolver.mbpp_source_split("calibration"), + "selection_namespace": identity_resolver.MBPP_SELECTION_NAMESPACE, + "formatter_version": identity_resolver.MBPP_FORMATTER_VERSION, + "selection_mode": "task_id_ranking_then_exact_task_id_stream", + } + if any(identity_dataset.get(key) != value for key, value in expected_contract.items()): + raise ValueError("Stage-B result identity dataset contract drifted") + task_records = _identity_task_records({"dataset": identity_dataset}) + ordered_ids = [int(record["task_id"]) for record in task_records] + + manifest = _mapping( + identity_dataset.get("manifest"), + context="Stage-B result identity content manifest", + ) + manifest_fields = { + "schema", + "dataset_id", + "config", + "revision", + "phase", + "source_split", + "selection_namespace", + "formatter_version", + "row_count", + "rows", + } + _require_exact_fields( + manifest, + manifest_fields, + context="Stage-B result identity content manifest", + ) + expected_manifest_contract = { + "schema": identity_resolver.MBPP_MANIFEST_SCHEMA, + "dataset_id": identity_resolver.MBPP_DATASET_ID, + "config": identity_resolver.MBPP_CONFIG, + "revision": identity_resolver.MBPP_REVISION, + "phase": "calibration", + "source_split": identity_resolver.mbpp_source_split("calibration"), + "selection_namespace": identity_resolver.MBPP_SELECTION_NAMESPACE, + "formatter_version": identity_resolver.MBPP_FORMATTER_VERSION, + "row_count": STAGE_B_LIMIT, + } + if any(manifest.get(key) != value for key, value in expected_manifest_contract.items()): + raise ValueError("Stage-B result identity manifest contract drifted") + rows = manifest.get("rows") + if not isinstance(rows, list) or len(rows) != STAGE_B_LIMIT: + raise ValueError("Stage-B result identity manifest rows drifted") + normalized_rows: list[dict[str, Any]] = [] + seen_manifest_ids: set[int] = set() + previous_task_id: int | None = None + for offset, raw_row in enumerate(rows): + row = _mapping( + raw_row, + context=f"Stage-B result identity manifest row {offset}", + ) + _require_exact_fields( + row, + {"task_id", "sha256"}, + context=f"Stage-B result identity manifest row {offset}", + ) + task_id = _strict_int( + row.get("task_id"), + context=f"Stage-B result identity manifest row {offset} task_id", + ) + row_sha256 = _require_sha256( + row.get("sha256"), + context=f"Stage-B result identity manifest row {offset}", + ) + if task_id in seen_manifest_ids: + raise ValueError("Stage-B result identity manifest task IDs are duplicated") + if previous_task_id is not None and task_id <= previous_task_id: + raise ValueError( + "Stage-B result identity manifest rows are not in canonical task-ID order" + ) + seen_manifest_ids.add(task_id) + previous_task_id = task_id + normalized_rows.append({"task_id": task_id, "sha256": row_sha256}) + expected_rows = sorted( + ( + { + "task_id": int(record["task_id"]), + "sha256": str(record["row_sha256"]), + } + for record in task_records + ), + key=lambda row: row["task_id"], + ) + if normalized_rows != expected_rows: + raise ValueError("Stage-B result identity manifest rows do not match tasks") + content_hash = _require_sha256( + identity_dataset.get("content_manifest_sha256"), + context="Stage-B result identity content manifest", + ) + if mbpp_manifest_content_sha256(manifest) != content_hash: + raise ValueError("Stage-B result identity content-manifest hash drifted") + token_hash = _require_sha256( + identity_dataset.get("token_manifest_sha256"), + context="Stage-B result identity token manifest", + ) + if identity_resolver.token_manifest_sha256(task_records) != token_hash: + raise ValueError("Stage-B result identity token-manifest hash drifted") + ordered_hash = _require_sha256( + identity_dataset.get("ordered_identity_sha256"), + context="Stage-B result ordered identity", + ) + if ( + identity_resolver.ordered_identity_sha256( + content_manifest_sha256=content_hash, + task_records=task_records, + ) + != ordered_hash + ): + raise ValueError("Stage-B result ordered-identity hash drifted") + + totals = _mapping( + identity_dataset.get("totals"), + context="Stage-B result identity totals", + ) + source_rows = _strict_int( + totals.get("source_train_rows_seen_by_task_id_only"), + context="Stage-B result identity source row count", + ) + if source_rows < identity_resolver.MBPP_CALIBRATION_SIZE or dict(totals) != { + "source_train_rows_seen_by_task_id_only": source_rows, + "retained_rows": STAGE_B_LIMIT, + "prompt_tokens": sum(int(record["prompt_tokens"]) for record in task_records), + "code_tokens": sum(int(record["code_tokens"]) for record in task_records), + "aligned_scored_tokens": sum( + int(record["aligned_scored_tokens"]) for record in task_records + ), + "full_code_scored_tokens": sum( + int(record["full_code_scored_tokens"]) for record in task_records + ), + }: + raise ValueError("Stage-B result identity totals drifted") + access = _validate_identity_data_access( + identity_dataset.get("data_access"), + ordered_ids=ordered_ids, + source_rows=source_rows, + ) + return task_records, access + + +def _validate_result_dataset( + evidence: Mapping[str, Any], +) -> tuple[dict[str, Any], ...]: + dataset = _mapping(evidence.get("dataset"), context="Stage-B result dataset") + protected_field = PROTECTED_EVALUATION_FIELD + _require_exact_fields( + dataset, + { + "phase", + "selection_mode", + "identity", + "identity_validation", + "data_access", + "identity_authenticated_before_model_weights", + protected_field, + }, + context="Stage-B result dataset", + ) + if ( + dataset.get("phase") != "calibration" + or dataset.get("selection_mode") + != "committed_exact_task_ids_from_ranked_window_32_64" + or dataset.get("identity_authenticated_before_model_weights") is not True + or dataset.get(protected_field) is not False + ): + raise ValueError("Stage-B result dataset contract drifted") + identity_dataset = _mapping( + dataset.get("identity"), + context="Stage-B result identity dataset", + ) + task_records, identity_access = _validate_result_identity_dataset(identity_dataset) + ordered_ids = [int(record["task_id"]) for record in task_records] + + identity_validation = _mapping( + dataset.get("identity_validation"), + context="Stage-B result runtime identity validation", + ) + expected_validation = { + "passed": True, + "authenticated_before_model_weights": True, + "ordered_task_ids": ordered_ids, + "content_manifest_sha256": identity_dataset["content_manifest_sha256"], + "token_manifest_sha256": identity_dataset["token_manifest_sha256"], + "ordered_identity_sha256": identity_dataset["ordered_identity_sha256"], + } + if dict(identity_validation) != expected_validation: + raise ValueError("Stage-B result runtime identity validation drifted") + + access = _mapping( + dataset.get("data_access"), + context="Stage-B result evaluation data access", + ) + _require_exact_fields( + access, + { + "transport_limitation", + "identity_resolution", + "evaluator_target_load", + "evaluator_application_task_id_sets", + "protected_window_intersection", + "non_target_source_records", + }, + context="Stage-B result evaluation data access", + ) + if ( + access.get("transport_limitation") != DATA_ACCESS_TRANSPORT_LIMITATION + or access.get("identity_resolution") != identity_access + ): + raise ValueError("Stage-B result evaluation data-access binding drifted") + target = _mapping( + access.get("evaluator_target_load"), + context="Stage-B result evaluator target load", + ) + target_yielded = _strict_int( + target.get("transport_records_yielded"), + context="Stage-B result evaluator target-load records", + ) + if target_yielded < STAGE_B_LIMIT or dict(target) != { + "transport_records_yielded": target_yielded, + "task_id_fields_inspected": target_yielded, + "non_target_content_fields_read_by_recurquant": 0, + "target_rows_retained_and_canonicalized": STAGE_B_LIMIT, + }: + raise ValueError("Stage-B result evaluator target-load access drifted") + application_keys = { + "selected", + "retained", + "canonicalized", + "formatted", + "tokenized", + "passed_to_model", + "evaluated", + } + expected_sets = {key: ordered_ids for key in application_keys} + application_sets = _mapping( + access.get("evaluator_application_task_id_sets"), + context="Stage-B result evaluator application task sets", + ) + if set(application_sets) != application_keys or dict(application_sets) != expected_sets: + raise ValueError("Stage-B result evaluator application task sets drifted") + protected = _mapping( + access.get("protected_window_intersection"), + context="Stage-B result protected-window intersection", + ) + if set(protected) != application_keys or any( + item is not False for item in protected.values() + ): + raise ValueError("protected ranked window entered evaluator application sets") + if access.get("non_target_source_records") != { + "recurquant_fields_inspected": ["task_id"], + "content_retained_canonicalized_formatted_tokenized_or_evaluated": False, + }: + raise ValueError("Stage-B result non-target access contract drifted") + return task_records + + +def _validate_result_runtime_and_model(evidence: Mapping[str, Any]) -> None: + runtime = _mapping( + evidence.get("runtime_environment"), + context="Stage-B result authenticated runtime", + ) + local_files_only = runtime.get("local_files_only") + if not isinstance(local_files_only, bool): + raise ValueError("Stage-B result local_files_only must be boolean") + python_version = ".".join( + str(component) for component in identity_resolver.STAGE_A_PYTHON_VERSION + ) + expected_runtime = { + "schema": identity_resolver.RUNTIME_ENVIRONMENT_SCHEMA, + "stage_a_binding": { + "artifact_kind": STAGE_A_ARTIFACT_KIND, + "file_sha256": identity_resolver.STAGE_A_FILE_SHA256, + "canonical_evidence_sha256": ( + identity_resolver.STAGE_A_CANONICAL_EVIDENCE_SHA256 + ), + }, + "python": { + "major": identity_resolver.STAGE_A_PYTHON_VERSION[0], + "minor": identity_resolver.STAGE_A_PYTHON_VERSION[1], + "micro": identity_resolver.STAGE_A_PYTHON_VERSION[2], + "version": python_version, + }, + "packages": dict(identity_resolver.STAGE_A_PACKAGE_VERSIONS), + "cuda": dict(identity_resolver.STAGE_A_CUDA_CONTRACT), + "runtime_matches_stage_a": True, + "local_files_only": local_files_only, + } + if dict(runtime) != expected_runtime: + raise ValueError("Stage-B result authenticated runtime contract drifted") + + model = _mapping(evidence.get("model"), context="Stage-B result model") + if dict(model) != { + "id": identity_resolver.MODEL_ID, + "revision": identity_resolver.MODEL_REVISION, + "dtype": "torch.bfloat16", + "device": "cuda", + }: + raise ValueError("Stage-B result model contract drifted") + + environment = _mapping( + evidence.get("environment"), + context="Stage-B result observed environment", + ) + _require_exact_fields( + environment, + { + "python", + "platform", + "packages", + "cuda_available", + "cuda_runtime", + "gpu", + }, + context="Stage-B result observed environment", + ) + observed_python = environment.get("python") + observed_platform = environment.get("platform") + if ( + not isinstance(observed_python, str) + or observed_python.split(maxsplit=1)[0] != python_version + or not isinstance(observed_platform, str) + or not observed_platform + or environment.get("packages") != expected_runtime["packages"] + or environment.get("cuda_available") is not True + or environment.get("cuda_runtime") + != expected_runtime["cuda"]["runtime_version"] + or not isinstance(environment.get("gpu"), str) + or not environment.get("gpu") + ): + raise ValueError("Stage-B result observed environment drifted") + + +def _validate_result_repository_and_sources( + evidence: Mapping[str, Any], + *, + frozen_source_hashes: Mapping[str, str], +) -> None: + repository = _mapping( + evidence.get("repository"), + context="Stage-B result repository", + ) + _require_exact_fields( + repository, + {"commit", "start", "end", "stable_commit"}, + context="Stage-B result repository", + ) + commit = _require_git_sha( + repository.get("commit"), + context="Stage-B result repository commit", + ) + for name in ("start", "end"): + snapshot = _mapping( + repository.get(name), + context=f"Stage-B result repository {name}", + ) + if dict(snapshot) != { + "commit": commit, + "worktree_clean": True, + "status": [], + }: + raise ValueError("Stage-B result repository was not clean and stable") + if repository.get("stable_commit") is not True: + raise ValueError("Stage-B result repository commit was not stable") + + sources = _mapping( + evidence.get("source_files"), + context="Stage-B result source files", + ) + _require_exact_fields( + sources, + { + "paths", + "sha256_start", + "sha256_end", + "stable", + "imported_module_paths", + }, + context="Stage-B result source files", + ) + paths = list(SOURCE_FILES) + start = _mapping( + sources.get("sha256_start"), + context="Stage-B result source hashes start", + ) + end = _mapping( + sources.get("sha256_end"), + context="Stage-B result source hashes end", + ) + imported = _mapping( + sources.get("imported_module_paths"), + context="Stage-B result imported module paths", + ) + if ( + sources.get("paths") != paths + or sources.get("stable") is not True + or dict(start) != dict(end) + or dict(start) != dict(frozen_source_hashes) + or set(start) != set(paths) + or dict(imported) != RESULT_IMPORTED_MODULE_PATHS + ): + raise ValueError("Stage-B result source-file authentication drifted") + for path in paths: + _require_safe_repository_path(path, context="Stage-B result source path") + _require_sha256(start[path], context=f"Stage-B result source {path}") + for name, path in imported.items(): + _require_safe_repository_path( + path, + context=f"Stage-B result imported module {name}", + ) + if path not in SOURCE_FILES: + raise ValueError("Stage-B result imported module is outside source freeze") + + +def _validate_result_command(evidence: Mapping[str, Any]) -> None: + command = evidence.get("command_template") + if not isinstance(command, list): + raise ValueError("Stage-B result command template must be an array") + expected_prefix = [ + "python", + "scripts/evaluate_rht_cqer_stage_b.py", + "--stage-a-artifact", + "", + "--identity-artifact", + "", + "--output", + "", + "--device", + ] + if ( + command[: len(expected_prefix)] != expected_prefix + or len(command) not in {len(expected_prefix) + 1, len(expected_prefix) + 2} + or command[len(expected_prefix)] not in {"auto", "cuda"} + ): + raise ValueError("Stage-B result command template drifted") + suffix = command[len(expected_prefix) + 1 :] + runtime = _mapping( + evidence.get("runtime_environment"), + context="Stage-B result authenticated runtime", + ) + expected_suffix = ["--local-files-only"] if runtime["local_files_only"] else [] + if suffix != expected_suffix: + raise ValueError("Stage-B result command/runtime binding drifted") + + +def validate_stage_b_result_evidence( + evidence: Mapping[str, Any], +) -> dict[str, Any]: + """Recompute every semantic Stage-B result section from one evidence object.""" + + _require_exact_fields( + evidence, + RESULT_EVIDENCE_FIELDS, + context="Stage-B result evidence", + ) + if ( + evidence.get("schema_version") != 1 + or evidence.get("artifact_kind") != ARTIFACT_KIND + or evidence.get("diagnostic_only") is not True + or evidence.get("claim_boundary") != RESULT_CLAIM_BOUNDARY + or evidence.get("methods") != list(METHODS) + ): + raise ValueError("Stage-B result schema or claim boundary drifted") + created_at = evidence.get("created_at_utc") + if not isinstance(created_at, str): + raise ValueError("Stage-B result creation time must be ISO-8601") + try: + parsed_created_at = datetime.fromisoformat(created_at) + except ValueError as error: + raise ValueError("Stage-B result creation time is not valid ISO-8601") from error + if ( + parsed_created_at.tzinfo is None + or parsed_created_at.utcoffset() is None + or parsed_created_at.utcoffset().total_seconds() != 0 + ): + raise ValueError("Stage-B result creation time must be UTC") + + _validate_result_protocol(evidence) + frozen_source_hashes = _validate_result_prerequisites(evidence) + task_records = _validate_result_dataset(evidence) + _validate_result_runtime_and_model(evidence) + _validate_result_repository_and_sources( + evidence, + frozen_source_hashes=frozen_source_hashes, + ) + _validate_result_command(evidence) + + per_task = _mapping( + evidence.get("per_task"), + context="Stage-B result aligned per-task metrics", + ) + per_task_full_code = _mapping( + evidence.get("per_task_full_code_secondary"), + context="Stage-B result full-code per-task metrics", + ) + per_task_token_traces = _mapping( + evidence.get("per_task_token_primitives"), + context="Stage-B result aligned token primitives", + ) + per_task_full_code_token_traces = _mapping( + evidence.get("per_task_full_code_token_primitives"), + context="Stage-B result full-code token primitives", + ) + aggregates = _mapping( + evidence.get("aggregates"), + context="Stage-B result aligned aggregates", + ) + aggregates_full_code = _mapping( + evidence.get("aggregates_full_code_secondary"), + context="Stage-B result full-code aggregates", + ) + storage_section = _mapping( + evidence.get("storage"), + context="Stage-B result storage", + ) + _require_exact_fields( + storage_section, + {"fp32_reference_recurrent_state_bytes", "candidates"}, + context="Stage-B result storage", + ) + if ( + _strict_int( + storage_section.get("fp32_reference_recurrent_state_bytes"), + context="Stage-B FP32 recurrent-state bytes", + ) + <= 0 + ): + raise ValueError("Stage-B FP32 recurrent-state bytes must be positive") + storage = _mapping( + storage_section.get("candidates"), + context="Stage-B result candidate storage", + ) + selector_diagnostics = _mapping( + evidence.get("selector_diagnostics"), + context="Stage-B result selector diagnostics", + ) + state_error = _mapping( + evidence.get("state_error"), + context="Stage-B result state error", + ) + _require_exact_fields( + state_error, + { + "primary_gate_metric", + "aggregates", + "per_task", + "reference_aligned_secondary", + }, + context="Stage-B result state error", + ) + if state_error.get("primary_gate_metric") != PRIMARY_STATE_ERROR_METRIC: + raise ValueError("Stage-B primary state-error metric label drifted") + aggregate_state_error = _mapping( + state_error.get("aggregates"), + context="Stage-B result state-error aggregates", + ) + per_task_state_errors = _mapping( + state_error.get("per_task"), + context="Stage-B result per-task state errors", + ) + reference_aligned = _mapping( + state_error.get("reference_aligned_secondary"), + context="Stage-B result reference-aligned state error", + ) + _require_exact_fields( + reference_aligned, + {"metric", "per_task"}, + context="Stage-B result reference-aligned state error", + ) + if ( + reference_aligned.get("metric") + != REFERENCE_ALIGNED_STATE_ERROR_METRIC + ): + raise ValueError("Stage-B reference-aligned state-error metric label drifted") + per_task_reference_aligned = _mapping( + reference_aligned.get("per_task"), + context="Stage-B result per-task reference-aligned state error", + ) + unit_evidence = _mapping( + evidence.get("unit_evidence"), + context="Stage-B result unit evidence", + ) + validate_unit_evidence_schema(unit_evidence) + integrity_inputs = _mapping( + evidence.get("integrity_inputs"), + context="Stage-B result integrity inputs", + ) + expected_integrity_input_fields = { + "repository_clean_at_start", + "repository_clean_at_end", + "repository_commit_stable", + "source_hashes_stable", + "stage_a_artifact_committed_authenticated_and_passed", + "identity_artifact_committed_and_authenticated", + "identity_authenticated_before_model_weights", + "identity_row_plan_authenticated", + "identity_source_freeze_matches_current_bytes", + "imported_modules_resolved_to_authenticated_repository", + "runtime_environment_authenticated_before_dataset_access", + PROTECTED_EVALUATION_FIELD, + } + if set(integrity_inputs) != expected_integrity_input_fields: + raise ValueError("Stage-B result integrity-input fields drifted") + + recomputed_integrity = evaluate_stage_b_integrity( + per_task=per_task, + per_task_full_code=per_task_full_code, + per_task_token_traces=per_task_token_traces, + per_task_full_code_token_traces=per_task_full_code_token_traces, + aggregates=aggregates, + aggregates_full_code=aggregates_full_code, + storage=storage, + selector_diagnostics=selector_diagnostics, + per_task_state_errors=per_task_state_errors, + per_task_reference_aligned_state_errors=per_task_reference_aligned, + task_records=task_records, + unit_evidence=unit_evidence, + integrity=integrity_inputs, + ) + recorded_integrity = _mapping( + evidence.get("stage_b_integrity"), + context="recorded Stage-B integrity", + ) + if dict(recorded_integrity) != recomputed_integrity: + raise ValueError("recorded Stage-B integrity differs from recomputation") + + bootstrap = _mapping( + evidence.get("paired_bootstrap_cqer_minus_rht_aligned_delta_nll"), + context="Stage-B result paired bootstrap", + ) + recomputed_gate = evaluate_stage_b_gate( + aggregates=aggregates, + per_task=per_task, + paired_bootstrap=bootstrap, + aggregate_state_error=aggregate_state_error, + per_task_state_errors=per_task_state_errors, + selector_diagnostics=selector_diagnostics, + task_records=task_records, + integrity_gate=recomputed_integrity, + ) + recorded_gate = _mapping( + evidence.get("stage_b_gate"), + context="recorded Stage-B gate", + ) + if dict(recorded_gate) != recomputed_gate: + raise ValueError("recorded Stage-B gate differs from recomputation") + if len(recomputed_gate.get("advancement_checks", {})) != 8: + raise ValueError("Stage-B result does not contain exactly eight advancement gates") + return { + "passed": True, + "integrity_passed": recomputed_integrity.get("passed") is True, + "advancement_passed": recomputed_gate.get("passed") is True, + "task_count": STAGE_B_LIMIT, + "bootstrap_samples": BOOTSTRAP_SAMPLES, + "bootstrap_seed": SEED, + "advancement_check_count": 8, + } + + +def load_and_validate_stage_b_result_artifact( + path: str | Path, +) -> tuple[dict[str, Any], dict[str, Any]]: + """Load a canonical Stage-B artifact and re-run its complete semantic audit.""" + + artifact_path = Path(path) + raw = artifact_path.read_bytes() + verification = verify_evidence_artifact(artifact_path) + if verification.get("valid") is not True: + raise ValueError( + "Stage-B result artifact failed canonical verification: " + + "; ".join(str(error) for error in verification.get("errors", [])) + ) + try: + document = json.loads(raw.decode("utf-8")) + except (UnicodeDecodeError, json.JSONDecodeError) as error: + raise ValueError("Stage-B result artifact must be strict UTF-8 JSON") from error + if ( + not isinstance(document, dict) + or set(document) + != { + "schema_version", + "artifact_kind", + "canonical_evidence_sha256", + "evidence", + } + or document.get("schema_version") != 1 + or document.get("artifact_kind") != ARTIFACT_KIND + or not isinstance(document.get("evidence"), dict) + ): + raise ValueError("Stage-B result artifact wrapper schema drifted") + if raw != canonical_json_bytes(document): + raise ValueError("Stage-B result artifact is not canonically serialized") + evidence = dict(document["evidence"]) + semantic = validate_stage_b_result_evidence(evidence) + return evidence, { + **semantic, + "artifact_sha256": _sha256_bytes(raw), + "canonical_evidence_sha256": document["canonical_evidence_sha256"], + "canonical_round_trip": True, + } + + +def _record_reference_aligned_state_error( + reference_cache: object, + candidates: Mapping[str, object], + *, + write_ordinal: int, + records: dict[str, list[dict[str, Any]]], +) -> None: + """Record candidate-state divergence from the matched FP32 cache trajectory.""" + + reference = { + (state.layer_index, state.state_index): state.tensor.detach() + for state in iter_recurrent_states(reference_cache) + } + expected_keys = {(layer, 0) for layer in FROZEN_LINEAR_LAYERS} + if set(reference) != expected_keys: + raise RuntimeError("FP32 recurrent-state geometry drifted") + for method, cache in candidates.items(): + candidate = { + (state.layer_index, state.state_index): state.tensor.detach() + for state in iter_recurrent_states(cache) + } + if set(candidate) != expected_keys: + raise RuntimeError(f"{method} recurrent-state geometry differs from FP32") + layer_records: list[dict[str, Any]] = [] + for layer in FROZEN_LINEAR_LAYERS: + key = (layer, 0) + reference_tensor = reference[key].to(torch.float64) + candidate_tensor = candidate[key].to( + device=reference_tensor.device, + dtype=torch.float64, + ) + if candidate_tensor.shape != reference_tensor.shape: + raise RuntimeError(f"{method} layer {layer} recurrent-state shape drifted") + error = candidate_tensor - reference_tensor + state_sse = float(error.square().sum().item()) + element_count = error.numel() + relative_l2 = torch.linalg.vector_norm(error) / torch.linalg.vector_norm( + reference_tensor + ).clamp_min(1e-12) + layer_records.append( + { + "layer_index": layer, + "state_index": 0, + "shape": list(error.shape), + "element_count": element_count, + "state_sse": state_sse, + "state_mse": state_sse / element_count, + "relative_l2_error": float(relative_l2.item()), + "max_absolute_error": float(error.abs().max().item()), + } + ) + records[method].append( + { + "write_ordinal": write_ordinal, + "record_count": len(layer_records), + "element_count": sum( + int(record["element_count"]) for record in layer_records + ), + "state_sse": math.fsum( + float(record["state_sse"]) for record in layer_records + ), + "layers": layer_records, + } + ) + + +def _aggregate_reference_aligned_state_error( + records: Mapping[str, Sequence[Mapping[str, Any]]], +) -> dict[str, dict[str, Any]]: + result: dict[str, dict[str, Any]] = {} + for method in QUERY_METHODS: + writes = records[method] + total_elements = sum(int(record["element_count"]) for record in writes) + total_sse = math.fsum(float(record["state_sse"]) for record in writes) + result[method] = { + "metric": "candidate_materialized_state_minus_matched_fp32_state", + "write_count": len(writes), + "record_count": sum(int(record["record_count"]) for record in writes), + "element_count": total_elements, + "aggregate_state_sse": total_sse, + "aggregate_state_mse": total_sse / total_elements, + "writes": list(writes), + } + return result + + +def evaluate_task( + model: Qwen3_5ForCausalLM, + *, + prompt_ids: torch.Tensor, + code_ids: torch.Tensor, + plan: ExactBudgetRowPlan, +) -> dict[str, Any]: + """Evaluate FP32 reference and all four frozen methods in one teacher-forced loop.""" + + reference_cache = DynamicCache(config=model.config) + static = create_qwen35_exact_budget_cache(model, plan=plan) + adaptive = create_qwen35_adaptive_exact_budget_cache(model, plan=plan) + cqer = create_qwen35_query_ema_exact_budget_cache( + model, + plan=plan, + record_evidence=True, + ) + rht = create_qwen35_right_rht_query_ema_exact_budget_cache( + model, + plan=plan, + record_evidence=True, + ) + caches = { + STATIC_METHOD: static, + ADAPTIVE_METHOD: adaptive, + CQER_METHOD: cqer, + RHT_METHOD: rht, + } + aligned = {name: pilot._TokenAccumulator.empty() for name in METHODS} # noqa: SLF001 + full = {name: pilot._TokenAccumulator.empty() for name in METHODS} # noqa: SLF001 + observer = Qwen35QueryEnergyObserver(model, caches=[cqer, rht]) + reference_aligned_records = {method: [] for method in QUERY_METHODS} + + with observer: + reference_output = model( + prompt_ids, + past_key_values=reference_cache, + use_cache=True, + logits_to_keep=1, + ) + candidate_outputs = { + name: model( + prompt_ids, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + for name, cache in caches.items() + } + _record_reference_aligned_state_error( + reference_cache, + {CQER_METHOD: cqer, RHT_METHOD: rht}, + write_ordinal=0, + records=reference_aligned_records, + ) + pilot._append_metrics( # noqa: SLF001 + full, + reference_output.logits, + {name: output.logits for name, output in candidate_outputs.items()}, + code_ids[:, :1], + ) + + for token_index in range(code_ids.shape[1] - 1): + input_token = code_ids[:, token_index : token_index + 1] + target_token = code_ids[:, token_index + 1 : token_index + 2] + reference_output = model( + input_token, + past_key_values=reference_cache, + use_cache=True, + logits_to_keep=1, + ) + candidate_outputs = { + name: model( + input_token, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + for name, cache in caches.items() + } + _record_reference_aligned_state_error( + reference_cache, + {CQER_METHOD: cqer, RHT_METHOD: rht}, + write_ordinal=token_index + 1, + records=reference_aligned_records, + ) + logits = {name: output.logits for name, output in candidate_outputs.items()} + pilot._append_metrics( # noqa: SLF001 + aligned, + reference_output.logits, + logits, + target_token, + ) + pilot._append_metrics( # noqa: SLF001 + full, + reference_output.logits, + logits, + target_token, + ) + + reference_bytes = sum( + state.tensor.numel() * state.tensor.element_size() + for state in iter_recurrent_states(reference_cache) + ) + return { + "aligned_metrics": {name: values.summary() for name, values in aligned.items()}, + "full_code_metrics": {name: values.summary() for name, values in full.items()}, + "aligned_token_traces": { + name: _accumulator_trace(values) for name, values in aligned.items() + }, + "full_code_token_traces": { + name: _accumulator_trace(values) for name, values in full.items() + }, + "storage": {name: cache.storage_summary() for name, cache in caches.items()}, + "selector_diagnostics": { + CQER_METHOD: cqer.query_ema_diagnostics(), + RHT_METHOD: rht.query_ema_diagnostics(), + }, + "state_errors": { + CQER_METHOD: screen.aggregate_state_error_evidence(cqer.update_evidence), + RHT_METHOD: screen.aggregate_state_error_evidence(rht.update_evidence), + }, + "reference_aligned_state_errors": _aggregate_reference_aligned_state_error( + reference_aligned_records + ), + "reference_recurrent_state_bytes": reference_bytes, + } + + +def _package_versions() -> dict[str, str | None]: + versions: dict[str, str | None] = {} + for name in ("datasets", "numpy", "safetensors", "torch", "transformers"): + try: + versions[name] = importlib.metadata.version(name) + except importlib.metadata.PackageNotFoundError: + versions[name] = None + return versions + + +def _atomic_write(path: Path, payload: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + prefix=f".{path.name}.", + suffix=".tmp", + dir=path.parent, + ) + temporary = Path(temporary_name) + try: + with os.fdopen(descriptor, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + if temporary.exists(): + temporary.unlink() + + +def _evaluation_data_access_record( + identity: Mapping[str, Any], + runtime_identity: Any, +) -> dict[str, Any]: + """Record transport limits and exact application-level Stage-B content sets.""" + + selected = [int(task_id) for task_id in runtime_identity.ordered_task_ids] + if selected != list(identity["dataset"]["ordered_task_ids"]): + raise ValueError("runtime selected task IDs differ from the committed identity") + if runtime_identity.access_audit is None: + raise ValueError("runtime identity lacks task-ID stream access instrumentation") + runtime_before_weights = runtime_identity.access_audit.as_dict( + selected_task_ids=selected + ) + protected = runtime_before_weights["protected_window_intersection"] + if any(value is not False for value in protected.values()): + raise ValueError("protected ranked window intersects a runtime application set") + return { + "transport_limitation": runtime_before_weights["transport_limitation"], + "identity_resolution": identity["dataset"]["data_access"], + "evaluator_target_load": runtime_before_weights["target_load_pass"], + "evaluator_application_task_id_sets": { + "selected": selected, + "retained": selected, + "canonicalized": selected, + "formatted": selected, + "tokenized": selected, + "passed_to_model": selected, + "evaluated": selected, + }, + "protected_window_intersection": { + "selected": False, + "retained": False, + "canonicalized": False, + "formatted": False, + "tokenized": False, + "passed_to_model": False, + "evaluated": False, + }, + "non_target_source_records": { + "recurquant_fields_inspected": ["task_id"], + "content_retained_canonicalized_formatted_tokenized_or_evaluated": False, + }, + } + + +def main(argv: Sequence[str] | None = None) -> int: + args = parse_args(argv) + repository_root = Path(__file__).resolve().parents[1] + repository_start = pilot.git_state() + pilot.validate_cora_development_repository_start(repository_start) + pilot.validate_heldout_output_path(args.output, repository_root) + source_hashes_start = pilot.source_file_hashes(repository_root, SOURCE_FILES) + imported_module_paths = authenticate_imported_module_paths(repository_root) + + # Every prerequisite is authenticated before any dataset or tokenizer access. + stage_a, stage_a_hashes = authenticate_stage_a_prerequisite( + args.stage_a_artifact, + repository_root=repository_root, + ) + identity, identity_hashes, task_records = authenticate_stage_b_identity( + args.identity_artifact, + repository_root=repository_root, + ) + identity_source_freeze = authenticate_identity_source_freeze( + identity, + current_source_hashes=source_hashes_start, + ) + plan, row_plan_authentication = plan_from_identity(identity, stage_a=stage_a) + authorization = _mapping( + identity.get("authorization"), + context="Stage-B identity authorization", + ) + if ( + authorization.get("stage_a_file_sha256") + != stage_a_hashes["artifact_sha256"] + or authorization.get("stage_a_canonical_evidence_sha256") + != stage_a_hashes["canonical_evidence_sha256"] + or authorization.get("stage_a_gate_passed") is not True + ): + raise ValueError("Stage-B identity does not authorize the authenticated Stage-A pass") + runtime_environment = authenticate_stage_b_runtime_environment( + stage_a, + identity, + local_files_only=bool(args.local_files_only), + ) + # Exact committed IDs are the only application-level content selector. + authenticated_rows = identity_resolver.load_authenticated_stage_b_rows(identity) + task_ids = authenticated_rows.ordered_task_ids + if task_ids != tuple(int(record["task_id"]) for record in task_records): + raise RuntimeError("Stage-B task-ID loader returned an unauthorized identity") + + device = pilot.select_device(args.device) + dtype = torch.bfloat16 if device.type == "cuda" else torch.float32 + stage_a_model = _mapping(stage_a.get("model"), context="Stage-A model") + if stage_a_model.get("dtype") != str(dtype): + raise ValueError( + f"Stage-B dtype {dtype} differs from Stage-A dtype {stage_a_model.get('dtype')}" + ) + model_id = str(identity["model_contract"]["id"]) + revision = str(identity["model_contract"]["revision"]) + tokenizer = AutoTokenizer.from_pretrained( + model_id, + revision=revision, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + runtime_identity = identity_resolver.authenticate_stage_b_runtime_identity( + identity, + authenticated_rows, + tokenizer, + ) + if runtime_identity.ordered_task_ids != task_ids: + raise RuntimeError("runtime token identity changed the committed task order") + encoded_tasks = tuple( + ( + task.row, + torch.tensor([task.prompt_token_ids], dtype=torch.int64), + torch.tensor([task.code_token_ids], dtype=torch.int64), + ) + for task in runtime_identity.tasks + ) + identity_validation = { + "passed": True, + "authenticated_before_model_weights": True, + "ordered_task_ids": list(runtime_identity.ordered_task_ids), + "content_manifest_sha256": runtime_identity.content_manifest_sha256, + "token_manifest_sha256": runtime_identity.token_manifest_sha256, + "ordered_identity_sha256": runtime_identity.ordered_identity_sha256, + } + data_access = _evaluation_data_access_record(identity, runtime_identity) + + # Identity, text, tokenizer output, and all hashes now match before weights. + unit_evidence = { + "production_self_check": screen.compute_unit_evidence(), + "independent_dense_reference": compute_independent_dense_rht_evidence(), + } + model = Qwen3_5ForCausalLM.from_pretrained( + model_id, + revision=revision, + dtype=dtype, + attn_implementation="eager", + low_cpu_mem_usage=True, + use_safetensors=True, + local_files_only=args.local_files_only, + ).to(device) + model.eval() + + per_task = {method: [] for method in METHODS} + per_task_full_code = {method: [] for method in METHODS} + per_task_token_traces = {method: [] for method in METHODS} + per_task_full_code_token_traces = {method: [] for method in METHODS} + selector_diagnostics = {method: [] for method in QUERY_METHODS} + per_task_state_errors = {method: [] for method in QUERY_METHODS} + per_task_reference_aligned_state_errors = { + method: [] for method in QUERY_METHODS + } + storage_anchor: dict[str, dict[str, Any]] | None = None + reference_state_bytes: int | None = None + + with torch.inference_mode(): + for task_number, ((_row, prompt_cpu, code_cpu), identity_task) in enumerate( + zip(encoded_tasks, task_records, strict=True), + start=1, + ): + torch.manual_seed(SEED) + result = evaluate_task( + model, + prompt_ids=prompt_cpu.to(device), + code_ids=code_cpu.to(device), + plan=plan, + ) + storage = result["storage"] + task_reference_bytes = int(result["reference_recurrent_state_bytes"]) + if storage_anchor is None: + storage_anchor = storage + reference_state_bytes = task_reference_bytes + elif storage != storage_anchor or task_reference_bytes != reference_state_bytes: + raise RuntimeError("resident state storage changed between Stage-B tasks") + task_id = int(identity_task["task_id"]) + for method in METHODS: + per_task[method].append( + {"task_id": task_id, **result["aligned_metrics"][method]} + ) + per_task_full_code[method].append( + {"task_id": task_id, **result["full_code_metrics"][method]} + ) + per_task_token_traces[method].append( + {"task_id": task_id, **result["aligned_token_traces"][method]} + ) + per_task_full_code_token_traces[method].append( + { + "task_id": task_id, + **result["full_code_token_traces"][method], + } + ) + for method in QUERY_METHODS: + selector_diagnostics[method].append( + { + "task_id": task_id, + "layers": result["selector_diagnostics"][method], + } + ) + per_task_state_errors[method].append( + { + "task_id": task_id, + "state_error": result["state_errors"][method], + } + ) + per_task_reference_aligned_state_errors[method].append( + { + "task_id": task_id, + "state_error": result["reference_aligned_state_errors"][ + method + ], + } + ) + print( + f"[{task_number}/{STAGE_B_LIMIT}] task={task_id} " + f"code_tokens={code_cpu.shape[1]}", + flush=True, + ) + + assert storage_anchor is not None + assert reference_state_bytes is not None + aggregates = pilot.aggregate_task_rows(per_task) + aggregates_full_code = pilot.aggregate_task_rows(per_task_full_code) + cqer_values = [float(row["delta_nll"]) for row in per_task[CQER_METHOD]] + rht_values = [float(row["delta_nll"]) for row in per_task[RHT_METHOD]] + bootstrap = paired_bootstrap_mean_improvement( + cqer_values, + rht_values, + samples=BOOTSTRAP_SAMPLES, + seed=SEED, + ) + state_error_aggregates = aggregate_state_errors(per_task_state_errors) + + repository_end = pilot.git_state() + source_hashes_end = pilot.source_file_hashes(repository_root, SOURCE_FILES) + pilot.validate_cora_development_repository_end( + start_repository=repository_start, + end_repository=repository_end, + start_source_hashes=source_hashes_start, + end_source_hashes=source_hashes_end, + ) + integrity = { + "repository_clean_at_start": repository_start["worktree_clean"] is True, + "repository_clean_at_end": repository_end["worktree_clean"] is True, + "repository_commit_stable": repository_start["commit"] == repository_end["commit"], + "source_hashes_stable": source_hashes_start == source_hashes_end, + "stage_a_artifact_committed_authenticated_and_passed": True, + "identity_artifact_committed_and_authenticated": True, + "identity_authenticated_before_model_weights": ( + identity_validation.get("passed") is True + ), + "identity_row_plan_authenticated": row_plan_authentication["passed"] is True, + "identity_source_freeze_matches_current_bytes": ( + identity_source_freeze["passed"] is True + ), + "imported_modules_resolved_to_authenticated_repository": True, + "runtime_environment_authenticated_before_dataset_access": True, + ( + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated" + ): False, + } + integrity_gate = evaluate_stage_b_integrity( + per_task=per_task, + per_task_full_code=per_task_full_code, + per_task_token_traces=per_task_token_traces, + per_task_full_code_token_traces=per_task_full_code_token_traces, + aggregates=aggregates, + aggregates_full_code=aggregates_full_code, + storage=storage_anchor, + selector_diagnostics=selector_diagnostics, + per_task_state_errors=per_task_state_errors, + per_task_reference_aligned_state_errors=( + per_task_reference_aligned_state_errors + ), + task_records=task_records, + unit_evidence=unit_evidence, + integrity=integrity, + ) + gate = evaluate_stage_b_gate( + aggregates=aggregates, + per_task=per_task, + paired_bootstrap=bootstrap, + aggregate_state_error=state_error_aggregates, + per_task_state_errors=per_task_state_errors, + selector_diagnostics=selector_diagnostics, + task_records=task_records, + integrity_gate=integrity_gate, + ) + + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "diagnostic_only": True, + "claim_boundary": RESULT_CLAIM_BOUNDARY, + "created_at_utc": datetime.now(UTC).isoformat(), + "protocol": { + "name": "Experiment 009 Stage B", + "ranked_window": [STAGE_B_OFFSET, STAGE_B_STOP], + "protected_ranked_window": list(PROTECTED_WINDOW), + "protected_window_application_content_intersection": False, + "methods_locked": True, + "thresholds_locked": True, + "bootstrap_samples": BOOTSTRAP_SAMPLES, + "bootstrap_seed": SEED, + }, + "prerequisite_artifacts": { + "stage_a": { + **stage_a_hashes, + "artifact_kind": stage_a["artifact_kind"], + "historical_path_privacy_limitation": STAGE_A_PATH_PRIVACY_LIMITATION, + }, + "stage_b_identity": { + **identity_hashes, + "artifact_kind": IDENTITY_ARTIFACT_KIND, + }, + "committed_row_plan": row_plan_authentication, + "identity_source_freeze": identity_source_freeze, + }, + "model": { + "id": model_id, + "revision": revision, + "dtype": str(dtype), + "device": str(device), + }, + "dataset": { + "phase": "calibration", + "selection_mode": "committed_exact_task_ids_from_ranked_window_32_64", + "identity": identity["dataset"], + "identity_validation": identity_validation, + "data_access": data_access, + "identity_authenticated_before_model_weights": True, + ( + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated" + ): False, + }, + "metric_contract": { + "primary": "task-macro aligned excess next-token NLL versus FP32 state", + "aligned_excludes": "prompt-to-first-code-token prediction", + "secondary": "task-macro full-code metrics", + "paired_bootstrap_samples": BOOTSTRAP_SAMPLES, + "paired_bootstrap_seed": SEED, + }, + "methods": list(METHODS), + "storage": { + "fp32_reference_recurrent_state_bytes": reference_state_bytes, + "candidates": storage_anchor, + }, + "aggregates": aggregates, + "aggregates_full_code_secondary": aggregates_full_code, + "per_task": per_task, + "per_task_full_code_secondary": per_task_full_code, + "per_task_token_primitives": per_task_token_traces, + "per_task_full_code_token_primitives": per_task_full_code_token_traces, + "paired_bootstrap_cqer_minus_rht_aligned_delta_nll": bootstrap, + "selector_diagnostics": selector_diagnostics, + "state_error": { + "primary_gate_metric": PRIMARY_STATE_ERROR_METRIC, + "aggregates": state_error_aggregates, + "per_task": per_task_state_errors, + "reference_aligned_secondary": { + "metric": REFERENCE_ALIGNED_STATE_ERROR_METRIC, + "per_task": per_task_reference_aligned_state_errors, + }, + }, + "unit_evidence": unit_evidence, + "integrity_inputs": integrity, + "stage_b_integrity": integrity_gate, + "stage_b_gate": gate, + "runtime_environment": runtime_environment, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "packages": _package_versions(), + "cuda_available": torch.cuda.is_available(), + "cuda_runtime": torch.version.cuda, + "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else None, + }, + "repository": { + "commit": repository_end["commit"], + "start": repository_start, + "end": repository_end, + "stable_commit": repository_start["commit"] == repository_end["commit"], + }, + "source_files": { + "paths": list(SOURCE_FILES), + "sha256_start": source_hashes_start, + "sha256_end": source_hashes_end, + "stable": source_hashes_start == source_hashes_end, + "imported_module_paths": imported_module_paths, + }, + "command_template": [ + "python", + "scripts/evaluate_rht_cqer_stage_b.py", + "--stage-a-artifact", + "", + "--identity-artifact", + "", + "--output", + "", + "--device", + args.device, + *(["--local-files-only"] if args.local_files_only else []), + ], + } + artifact = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "canonical_evidence_sha256": _sha256_bytes(canonical_json_bytes(evidence)), + "evidence": evidence, + } + payload = canonical_json_bytes(artifact) + _atomic_write(args.output, payload) + round_trip_evidence, semantic_verification = ( + load_and_validate_stage_b_result_artifact(args.output) + ) + if round_trip_evidence != evidence: + raise RuntimeError("written Stage-B evidence changed during canonical round-trip") + + post_write_repository = pilot.git_state() + post_write_hashes = pilot.source_file_hashes(repository_root, SOURCE_FILES) + pilot.validate_cora_development_repository_end( + start_repository=repository_start, + end_repository=post_write_repository, + start_source_hashes=source_hashes_start, + end_source_hashes=post_write_hashes, + ) + print( + json.dumps( + { + "output": str(args.output.resolve()), + "artifact_sha256": _sha256_bytes(payload), + "canonical_evidence_sha256": artifact["canonical_evidence_sha256"], + "stage_b_integrity": integrity_gate, + "stage_b_gate": gate, + "semantic_verification": semantic_verification, + "post_write_repository_authenticated": True, + }, + indent=2, + sort_keys=True, + ) + ) + return 0 if gate["passed"] is True else 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/generate_readme_assets.py b/scripts/generate_readme_assets.py index c1042ea..e0c4d95 100644 --- a/scripts/generate_readme_assets.py +++ b/scripts/generate_readme_assets.py @@ -1,4 +1,4 @@ -"""Generate deterministic README charts from committed RecurQuant evidence. +"""Generate deterministic README charts from authenticated RecurQuant evidence. The script deliberately parses the source records instead of duplicating their numbers. Run it without arguments to update the SVG files, or with ``--check`` @@ -14,6 +14,7 @@ import hashlib import html import json +import math import re import sys import xml.etree.ElementTree as ET @@ -24,8 +25,33 @@ SOURCE = ROOT / "research" / "EXPERIMENT_002_SCALE_CORRECTION.md" DEVELOPMENT_SOURCE = ROOT / "evidence" / "mbpp-v02-development.json" CONFIRMATION_SOURCE = ROOT / "evidence" / "mbpp-v02-confirmation.json" +STAGE_B_SOURCE = ROOT / "artifacts" / "experiment009-rht-cqer-stage-b-result-cdc603b.json" +STAGE_B_MANIFEST = ROOT / "evidence" / "experiment009-rht-cqer-stage-b-result-manifest.json" ASSETS = ROOT / "assets" +STAGE_B_ARTIFACT_KIND = "recurquant_rht_cqer32_stage_b_development" +STAGE_B_ARTIFACT_SHA256 = "57b341d37871a52977b1ff89709864f3e6e0927154e5b2b9275b6f374953fe05" +STAGE_B_CANONICAL_SHA256 = "2b15c732e894510f0421a22fcca9435e035dd15c4d3b50e2fcb733c0d1df58a8" +STAGE_B_RESULT_COMMIT = "8168c469b252bc9e707e51feaeccc3f940f190bb" +STAGE_B_VERIFIER_FIX_COMMIT = "2075154e642c39a14432adcc8ec32da679b534d3" +STAGE_B_RELEASE_DOWNLOAD = ( + "https://github.com/Labeeb2339/recurquant/releases/download/" + "experiment009-stage-b-cdc603b/" + "experiment009-rht-cqer-stage-b-result-cdc603b.json.zip" +) +STAGE_B_GRAPH_ASSETS = { + "assets/experiment009-stage-b-overview.svg": ("experiment009-stage-b-overview"), + "assets/experiment009-stage-b-paired.svg": ("experiment009-stage-b-paired-cqer-minus-rht"), +} +STAGE_B_METHODS = ( + "target_directional_fisher_difference_int4", + "adaptive_mse_target_directional_fisher_quota", + "query_ema32_weighted_mse_target_fisher_quota", + "right_rht_query_ema32_weighted_mse_target_fisher_quota", +) +STAGE_B_CQER_METHOD = "query_ema32_weighted_mse_target_fisher_quota" +STAGE_B_RHT_METHOD = "right_rht_query_ema32_weighted_mse_target_fisher_quota" + def _capture(source: str, pattern: str, label: str) -> tuple[str, ...]: match = re.search(pattern, source, flags=re.MULTILINE) @@ -802,7 +828,650 @@ def _confirmation_svg(data: dict[str, object]) -> str: """ -def _outputs() -> dict[Path, str]: +def _load_validated_stage_b_result( + path: Path, +) -> tuple[dict[str, object], dict[str, object]]: + """Load Stage B through its strict canonical and semantic verifier.""" + + root_string = str(ROOT) + if root_string not in sys.path: + sys.path.insert(0, root_string) + from scripts.evaluate_rht_cqer_stage_b import ( # noqa: PLC0415 + load_and_validate_stage_b_result_artifact, + ) + + return load_and_validate_stage_b_result_artifact(path) + + +def _stage_b_data() -> dict[str, object]: + if not STAGE_B_SOURCE.is_file(): + raise FileNotFoundError( + "The authenticated Experiment 009 Stage-B raw result is required to " + "generate its README assets. Download " + f"{STAGE_B_RELEASE_DOWNLOAD}, verify SHA256 " + f"{STAGE_B_ARTIFACT_SHA256}, and extract the JSON to " + f"{STAGE_B_SOURCE.relative_to(ROOT)}. A clean clone can still run " + "`python scripts/generate_readme_assets.py --check` against the " + "tracked release manifest." + ) + # This call is intentionally first: no chart field is read until the complete + # canonical and semantic audit has succeeded. + evidence, verification = _load_validated_stage_b_result(STAGE_B_SOURCE) + + expected_verification = { + "passed": True, + "integrity_passed": True, + "advancement_passed": True, + "task_count": 32, + "bootstrap_samples": 10_000, + "bootstrap_seed": 2339, + "advancement_check_count": 8, + "artifact_sha256": STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": STAGE_B_CANONICAL_SHA256, + "canonical_round_trip": True, + } + if verification != expected_verification: + raise ValueError("Experiment 009 Stage-B verification record drifted") + if evidence.get("artifact_kind") != STAGE_B_ARTIFACT_KIND: + raise ValueError("Unexpected Experiment 009 Stage-B artifact kind") + if evidence.get("methods") != list(STAGE_B_METHODS): + raise ValueError("Experiment 009 Stage-B method order drifted") + + repository = evidence.get("repository") + if not isinstance(repository, dict): + raise ValueError("Experiment 009 Stage-B repository record is missing") + if ( + repository.get("commit") != STAGE_B_RESULT_COMMIT + or repository.get("stable_commit") is not True + ): + raise ValueError("Experiment 009 Stage-B result commit drifted") + + integrity = evidence.get("stage_b_integrity") + gate = evidence.get("stage_b_gate") + if not isinstance(integrity, dict) or integrity.get("passed") is not True: + raise ValueError("Experiment 009 Stage-B integrity did not pass") + if not isinstance(gate, dict) or gate.get("passed") is not True: + raise ValueError("Experiment 009 Stage-B advancement gate did not pass") + + dataset = evidence.get("dataset") + if not isinstance(dataset, dict) or not isinstance(dataset.get("identity"), dict): + raise ValueError("Experiment 009 Stage-B identity is missing") + ordered_task_ids = dataset["identity"].get("ordered_task_ids") + if ( + not isinstance(ordered_task_ids, list) + or len(ordered_task_ids) != 32 + or any(type(task_id) is not int for task_id in ordered_task_ids) + ): + raise ValueError("Experiment 009 Stage-B frozen task order drifted") + + aggregates = evidence.get("aggregates") + per_task = evidence.get("per_task") + if ( + not isinstance(aggregates, dict) + or set(aggregates) != set(STAGE_B_METHODS) + or not isinstance(per_task, dict) + or set(per_task) != set(STAGE_B_METHODS) + ): + raise ValueError("Experiment 009 Stage-B metrics do not match frozen methods") + + macro_delta_nll: dict[str, float] = {} + for method in STAGE_B_METHODS: + aggregate = aggregates[method] + rows = per_task[method] + if not isinstance(aggregate, dict) or not isinstance(rows, list): + raise ValueError(f"Experiment 009 Stage-B {method} metrics are malformed") + if [row.get("task_id") for row in rows] != ordered_task_ids: + raise ValueError(f"Experiment 009 Stage-B {method} task order drifted") + value = aggregate.get("macro_delta_nll") + if type(value) not in {float, int} or not math.isfinite(float(value)): + raise ValueError(f"Experiment 009 Stage-B {method} macro NLL is invalid") + macro_delta_nll[method] = float(value) + + state_error = evidence.get("state_error") + if not isinstance(state_error, dict) or not isinstance( + state_error.get("aggregates"), + dict, + ): + raise ValueError("Experiment 009 Stage-B state-error aggregates are missing") + state_aggregates = state_error["aggregates"] + if set(state_aggregates) != {STAGE_B_CQER_METHOD, STAGE_B_RHT_METHOD}: + raise ValueError("Experiment 009 Stage-B state-error method set drifted") + state_sse: dict[str, float] = {} + for method in (STAGE_B_CQER_METHOD, STAGE_B_RHT_METHOD): + record = state_aggregates[method] + if not isinstance(record, dict): + raise ValueError(f"Experiment 009 Stage-B {method} state error is malformed") + value = record.get("aggregate_state_sse") + if type(value) not in {float, int} or not math.isfinite(float(value)): + raise ValueError(f"Experiment 009 Stage-B {method} state SSE is invalid") + state_sse[method] = float(value) + + cqer_rows = per_task[STAGE_B_CQER_METHOD] + rht_rows = per_task[STAGE_B_RHT_METHOD] + paired = [] + for task_id, cqer_row, rht_row in zip( + ordered_task_ids, + cqer_rows, + rht_rows, + strict=True, + ): + cqer_value = float(cqer_row["delta_nll"]) + rht_value = float(rht_row["delta_nll"]) + improvement = cqer_value - rht_value + paired.append( + { + "task_id": task_id, + "cqer_delta_nll": cqer_value, + "rht_delta_nll": rht_value, + "cqer_minus_rht_delta_nll": improvement, + "outcome": ("win" if improvement > 0 else "loss" if improvement < 0 else "tie"), + } + ) + + wins = sum(point["outcome"] == "win" for point in paired) + ties = sum(point["outcome"] == "tie" for point in paired) + losses = sum(point["outcome"] == "loss" for point in paired) + bootstrap = evidence.get("paired_bootstrap_cqer_minus_rht_aligned_delta_nll") + if not isinstance(bootstrap, dict): + raise ValueError("Experiment 009 Stage-B paired bootstrap is missing") + mean_improvement = math.fsum( + float(point["cqer_minus_rht_delta_nll"]) for point in paired + ) / len(paired) + if ( + bootstrap.get("paired_examples") != 32 + or bootstrap.get("bootstrap_samples") != 10_000 + or bootstrap.get("seed") != 2339 + or bootstrap.get("confidence") != 0.95 + or not math.isclose( + float(bootstrap.get("mean_improvement", math.nan)), + mean_improvement, + rel_tol=0, + abs_tol=1e-15, + ) + ): + raise ValueError("Experiment 009 Stage-B paired bootstrap drifted") + confidence_interval = bootstrap.get("confidence_interval") + if ( + not isinstance(confidence_interval, list) + or len(confidence_interval) != 2 + or not all( + type(value) in {float, int} and math.isfinite(float(value)) + for value in confidence_interval + ) + ): + raise ValueError("Experiment 009 Stage-B paired interval is invalid") + + win_check = gate.get("advancement_checks", {}).get( + "at_least_20_task_level_excess_nll_wins", + {}, + ) + if ( + win_check.get("rht_wins") != wins + or win_check.get("ties") != ties + or wins + ties + losses != 32 + ): + raise ValueError("Experiment 009 Stage-B paired outcomes drifted") + + return { + "artifact_kind": STAGE_B_ARTIFACT_KIND, + "artifact_sha256": STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": STAGE_B_CANONICAL_SHA256, + "result_commit": STAGE_B_RESULT_COMMIT, + "verifier_fix_commit": STAGE_B_VERIFIER_FIX_COMMIT, + "verification": verification, + "integrity_passed": True, + "gate_passed": True, + "claim_boundary": evidence["claim_boundary"], + "ordered_task_ids": ordered_task_ids, + "macro_delta_nll": macro_delta_nll, + "state_sse": state_sse, + "paired": paired, + "wins": wins, + "ties": ties, + "losses": losses, + "bootstrap": bootstrap, + } + + +def _stage_b_metadata( + data: dict[str, object], + *, + chart: str, + values: object, +) -> str: + return _metadata( + { + "chart": chart, + "artifact_kind": data["artifact_kind"], + "source_artifact_sha256": data["artifact_sha256"], + "canonical_evidence_sha256": data["canonical_evidence_sha256"], + "original_result_commit": data["result_commit"], + "verifier_fix_commit": data["verifier_fix_commit"], + "integrity_passed": data["integrity_passed"], + "advancement_gate_passed": data["gate_passed"], + "semantic_verification": data["verification"], + "claim_boundary": data["claim_boundary"], + "values": values, + }, + source_document=("artifacts/experiment009-rht-cqer-stage-b-result-cdc603b.json"), + ) + + +def _stage_b_overview_svg(data: dict[str, object]) -> str: + macro = data["macro_delta_nll"] + state_sse = data["state_sse"] + assert isinstance(macro, dict) + assert isinstance(state_sse, dict) + + method_rows = ( + ("Directional Fisher INT4", STAGE_B_METHODS[0], "baseline"), + ("Adaptive MSE quota", STAGE_B_METHODS[1], "baseline"), + ("CQER-32", STAGE_B_CQER_METHOD, "cqer"), + ("right-RHT + CQER-32", STAGE_B_RHT_METHOD, "rht"), + ) + macro_axis_max = Decimal("0.5") + macro_x = Decimal(215) + macro_width = Decimal(330) + macro_rows = [] + for index, (label, method, css_class) in enumerate(method_rows): + value = Decimal(str(macro[method])) + y = Decimal(132 + index * 66) + width = value / macro_axis_max * macro_width + macro_rows.append( + f''' + {html.escape(label)} + + + {html.escape(label)}: {value:.6f} task-macro aligned excess NLL + + {value:.4f} + ''' + ) + + cqer_sse = Decimal(str(state_sse[STAGE_B_CQER_METHOD])) + rht_sse = Decimal(str(state_sse[STAGE_B_RHT_METHOD])) + state_axis_max = Decimal("40000") + state_x = Decimal(665) + state_width = Decimal(240) + state_rows = [] + for label, value, css_class, y in ( + ("CQER-32", cqer_sse, "cqer", Decimal(160)), + ("right-RHT + CQER-32", rht_sse, "rht", Decimal(250)), + ): + width = value / state_axis_max * state_width + state_rows.append( + f''' + {html.escape(label)} + + + {html.escape(label)}: {value:.6f} aggregate local codec state SSE + + {value:,.0f} + ''' + ) + + macro_reduction = ( + Decimal(1) + - Decimal(str(macro[STAGE_B_RHT_METHOD])) / Decimal(str(macro[STAGE_B_CQER_METHOD])) + ) * Decimal(100) + state_reduction = (Decimal(1) - rht_sse / cqer_sse) * Decimal(100) + values = { + "macro_delta_nll": macro, + "state_sse": state_sse, + "cqer_to_rht_macro_delta_nll_reduction": float(macro_reduction / Decimal(100)), + "cqer_to_rht_state_sse_reduction": float(state_reduction / Decimal(100)), + } + metadata = _stage_b_metadata( + data, + chart="experiment009-stage-b-overview", + values=values, + ) + description = ( + "Experiment 009 Stage B compares four frozen methods on 32 MBPP development " + f"tasks. CQER-32 has task-macro aligned excess NLL " + f"{Decimal(str(macro[STAGE_B_CQER_METHOD])):.6f}; composing it with a " + f"right randomized Hadamard transform has {Decimal(str(macro[STAGE_B_RHT_METHOD])):.6f}, " + f"{macro_reduction:.2f} percent lower. Aggregate local codec state SSE falls " + f"from {cqer_sse:.6f} to {rht_sse:.6f}, {state_reduction:.2f} percent lower. " + "All eight frozen development advancement gates passed. This is not " + "confirmation, novelty, speed, deployment, or breakthrough evidence." + ) + return f""" + + Experiment 009 Stage-B authenticated development overview + {html.escape(description)} + {metadata} + + + + + + + + + + + + + + Experiment 009 · Stage B development + AUTHENTICATED 32-TASK RESULT · LOWER IS BETTER + + Task-macro aligned excess NLL + nats / token + Local codec state SSE + aggregate + +{chr(10).join(macro_rows)} +{chr(10).join(state_rows)} + {macro_reduction:.2f}% lower macro excess NLL + {state_reduction:.2f}% lower state SSE + + Frozen MBPP development window · 32 tasks · 1,956 aligned reference tokens · 8/8 advancement gates passed + Diagnostic development evidence only; no confirmation, novelty, speed, deployment, or breakthrough claim. + +""" + + +def _stage_b_paired_svg(data: dict[str, object]) -> str: + points = data["paired"] + bootstrap = data["bootstrap"] + assert isinstance(points, list) + assert isinstance(bootstrap, dict) + + plot_x = Decimal(62) + plot_width = Decimal(854) + plot_top = Decimal(164) + plot_height = Decimal(286) + domain_min = Decimal("-0.1") + domain_max = Decimal("0.6") + + def y_position(value: Decimal) -> Decimal: + return plot_top + (domain_max - value) / (domain_max - domain_min) * plot_height + + zero_y = y_position(Decimal(0)) + step = plot_width / Decimal(len(points)) + bar_width = Decimal(15) + bars = [] + labels = [] + for index, point in enumerate(points): + assert isinstance(point, dict) + value = Decimal(str(point["cqer_minus_rht_delta_nll"])) + center = plot_x + (Decimal(index) + Decimal("0.5")) * step + value_y = y_position(value) + rect_y = min(value_y, zero_y) + height = abs(zero_y - value_y) + if height == 0: + height = Decimal("1") + outcome = str(point["outcome"]) + bars.append( + f''' + Task {point["task_id"]}: CQER minus right-RHT delta NLL {value:+.6f}; {outcome} + ''' + ) + labels.append( + f""" {point["task_id"]}""" + ) + + grid = [] + for tick_tenths in range(-1, 7): + value = Decimal(tick_tenths) / Decimal(10) + y = y_position(value) + css_class = "zero" if tick_tenths == 0 else "grid" + grid.append( + f''' + {value:+.1f}''' + ) + + mean = Decimal(str(bootstrap["mean_improvement"])) + ci_low, ci_high = (Decimal(str(value)) for value in bootstrap["confidence_interval"]) + mean_y = y_position(mean) + ci_axis_x = Decimal(62) + ci_axis_width = Decimal(300) + ci_axis_max = Decimal("0.25") + + def ci_x(value: Decimal) -> Decimal: + return ci_axis_x + value / ci_axis_max * ci_axis_width + + values = { + "ordered_points": points, + "wins": data["wins"], + "ties": data["ties"], + "losses": data["losses"], + "paired_bootstrap": bootstrap, + } + metadata = _stage_b_metadata( + data, + chart="experiment009-stage-b-paired-cqer-minus-rht", + values=values, + ) + description = ( + "In frozen task order, the paired CQER-32 minus right-RHT plus CQER-32 " + f"aligned excess NLL difference is positive on {data['wins']} tasks, zero " + f"on {data['ties']}, and negative on {data['losses']}. Positive values favor " + f"right-RHT. The mean improvement is {mean:.6f} nats per token; the paired " + f"10,000-sample bootstrap 95 percent interval is {ci_low:.6f} to " + f"{ci_high:.6f}. This is authenticated development evidence, not confirmation." + ) + return f""" + + Experiment 009 Stage-B paired CQER minus right-RHT excess NLL by frozen task order + {html.escape(description)} + {metadata} + + + + + + + + Paired task result · CQER-32 minus right-RHT + POSITIVE VALUES FAVOR RIGHT-RHT · FROZEN TASK ORDER + + PAIRED MEAN · 95% BOOTSTRAP CI + + + + + + {mean:+.4f} mean · [{ci_low:.4f}, {ci_high:.4f}] 95% CI + {data["wins"]} wins · {data["ties"]} ties · {data["losses"]} losses + CQER − RIGHT-RHT ALIGNED EXCESS NLL (NATS / TOKEN) +{chr(10).join(grid)} +{chr(10).join(bars)} + + mean {mean:+.4f} +{chr(10).join(labels)} + Task IDs follow the authenticated identity order · 10,000 paired task bootstraps · seed 2339 · two-sided equal-tailed 95% CI + 32-task MBPP development evidence only; no confirmation, novelty, speed, deployment, or breakthrough claim. + +""" + + +def _stage_b_graph_metadata(content: str, *, path: Path) -> dict[str, object]: + _validate_svg(content, path) + root = ET.fromstring(content) + metadata = root.find("{http://www.w3.org/2000/svg}metadata") + if metadata is None or metadata.text is None: + raise ValueError(f"{path.name} has no RecurQuant provenance metadata") + try: + record = json.loads(metadata.text, parse_constant=_reject_non_finite) + except json.JSONDecodeError as error: + raise ValueError(f"{path.name} has invalid provenance JSON") from error + if not isinstance(record, dict): + raise ValueError(f"{path.name} provenance must be a JSON object") + return record + + +def _validate_stage_b_graph_assets_from_manifest() -> None: + """Validate tracked Stage-B SVG receipts when the raw release asset is absent.""" + + try: + manifest_raw = STAGE_B_MANIFEST.read_bytes() + except FileNotFoundError as error: + raise ValueError( + f"tracked Stage-B release manifest is missing: {STAGE_B_MANIFEST.relative_to(ROOT)}" + ) from error + try: + manifest = json.loads( + manifest_raw.decode("utf-8"), + parse_constant=_reject_non_finite, + ) + except (UnicodeDecodeError, json.JSONDecodeError) as error: + raise ValueError("tracked Stage-B release manifest is not strict UTF-8 JSON") from error + if not isinstance(manifest, dict): + raise ValueError("tracked Stage-B release manifest must be a JSON object") + + result = manifest.get("result") + verification = manifest.get("verification") + graph_assets = manifest.get("graph_assets") + if ( + manifest.get("artifact_kind") != "recurquant_experiment009_stage_b_release_manifest" + or manifest.get("schema_version") != 1 + or not isinstance(result, dict) + or not isinstance(verification, dict) + or not isinstance(graph_assets, dict) + ): + raise ValueError("tracked Stage-B release manifest schema drifted") + + if ( + result.get("artifact_kind") != STAGE_B_ARTIFACT_KIND + or result.get("raw_filename") != STAGE_B_SOURCE.name + or result.get("raw_sha256") != STAGE_B_ARTIFACT_SHA256 + or result.get("canonical_evidence_sha256") != STAGE_B_CANONICAL_SHA256 + or result.get("evaluation_commit") != STAGE_B_RESULT_COMMIT + or result.get("verifier_fix_commit") != STAGE_B_VERIFIER_FIX_COMMIT + ): + raise ValueError("tracked Stage-B result provenance drifted") + if ( + verification.get("strict_loader_passed") is not True + or verification.get("canonical_round_trip") is not True + or verification.get("integrity_passed") is not True + or verification.get("advancement_passed") is not True + or verification.get("advancement_check_count") != 8 + or verification.get("task_count") != 32 + or verification.get("bootstrap_samples") != 10_000 + or verification.get("bootstrap_seed") != 2339 + ): + raise ValueError("tracked Stage-B verification receipt drifted") + + expected_graph_provenance = { + "source_artifact_sha256": STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": STAGE_B_CANONICAL_SHA256, + "original_result_commit": STAGE_B_RESULT_COMMIT, + "verifier_fix_commit": STAGE_B_VERIFIER_FIX_COMMIT, + "artifact_kind": STAGE_B_ARTIFACT_KIND, + "integrity_passed": True, + "advancement_gate_passed": True, + } + if { + key: graph_assets.get(key) for key in expected_graph_provenance + } != expected_graph_provenance: + raise ValueError("tracked Stage-B graph provenance drifted") + + asset_receipts = graph_assets.get("assets") + if not isinstance(asset_receipts, dict) or set(asset_receipts) != set(STAGE_B_GRAPH_ASSETS): + raise ValueError("tracked Stage-B graph asset set drifted") + + expected_semantic_verification = { + "passed": True, + "integrity_passed": True, + "advancement_passed": True, + "task_count": 32, + "bootstrap_samples": 10_000, + "bootstrap_seed": 2339, + "advancement_check_count": 8, + "artifact_sha256": STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": STAGE_B_CANONICAL_SHA256, + "canonical_round_trip": True, + } + for relative_path, chart in STAGE_B_GRAPH_ASSETS.items(): + receipt = asset_receipts[relative_path] + if not isinstance(receipt, dict) or set(receipt) != { + "bytes", + "sha256", + "chart", + }: + raise ValueError(f"tracked graph receipt schema drifted for {relative_path}") + if receipt.get("chart") != chart: + raise ValueError(f"tracked graph chart identity drifted for {relative_path}") + expected_bytes = receipt.get("bytes") + expected_sha256 = receipt.get("sha256") + if ( + type(expected_bytes) is not int + or expected_bytes <= 0 + or not isinstance(expected_sha256, str) + or re.fullmatch(r"[0-9a-f]{64}", expected_sha256) is None + ): + raise ValueError(f"tracked graph byte receipt is invalid for {relative_path}") + + path = ROOT / Path(relative_path) + try: + raw = path.read_bytes() + except FileNotFoundError as error: + raise ValueError(f"tracked Stage-B graph is missing: {relative_path}") from error + actual_sha256 = hashlib.sha256(raw).hexdigest() + if len(raw) != expected_bytes or actual_sha256 != expected_sha256: + raise ValueError( + f"tracked Stage-B graph bytes drifted for {relative_path}: " + f"expected {expected_bytes} bytes sha256={expected_sha256}, " + f"got {len(raw)} bytes sha256={actual_sha256}" + ) + try: + content = raw.decode("utf-8") + except UnicodeDecodeError as error: + raise ValueError(f"{relative_path} is not UTF-8 SVG") from error + metadata = _stage_b_graph_metadata(content, path=path) + expected_metadata = { + "generated_by": "scripts/generate_readme_assets.py", + "source_document": ("artifacts/experiment009-rht-cqer-stage-b-result-cdc603b.json"), + "chart": chart, + **expected_graph_provenance, + } + if {key: metadata.get(key) for key in expected_metadata} != expected_metadata: + raise ValueError(f"embedded graph provenance drifted for {relative_path}") + if metadata.get("semantic_verification") != expected_semantic_verification: + raise ValueError(f"embedded semantic verification drifted for {relative_path}") + if "values" not in metadata or "claim_boundary" not in metadata: + raise ValueError(f"embedded graph evidence is incomplete for {relative_path}") + + +def _legacy_outputs() -> dict[Path, str]: data = _source_data() development_data = _development_data() confirmation_data = _confirmation_data() @@ -814,6 +1483,18 @@ def _outputs() -> dict[Path, str]: } +def _outputs() -> dict[Path, str]: + outputs = _legacy_outputs() + stage_b_data = _stage_b_data() + outputs.update( + { + ASSETS / "experiment009-stage-b-overview.svg": _stage_b_overview_svg(stage_b_data), + ASSETS / "experiment009-stage-b-paired.svg": _stage_b_paired_svg(stage_b_data), + } + ) + return outputs + + def _validate_svg(content: str, path: Path) -> None: try: ET.fromstring(content) @@ -830,7 +1511,16 @@ def main() -> int: ) args = parser.parse_args() - outputs = _outputs() + stage_b_source_available = STAGE_B_SOURCE.is_file() + if not args.check and not stage_b_source_available: + try: + _stage_b_data() + except FileNotFoundError as error: + print(str(error), file=sys.stderr) + return 2 + raise AssertionError("missing Stage-B source check did not fail") + + outputs = _outputs() if stage_b_source_available else _legacy_outputs() stale = [] for path, content in outputs.items(): _validate_svg(content, path) @@ -843,12 +1533,28 @@ def main() -> int: digest = hashlib.sha256(content.encode("utf-8")).hexdigest() print(f"wrote {path.relative_to(ROOT)} sha256={digest}") + manifest_error = None + if args.check and not stage_b_source_available: + try: + _validate_stage_b_graph_assets_from_manifest() + except ValueError as error: + manifest_error = str(error) + if stale: for path in stale: print(f"stale: {path}", file=sys.stderr) + if manifest_error is not None: + print(f"invalid Stage-B graph receipt: {manifest_error}", file=sys.stderr) + if stale or manifest_error is not None: return 1 if args.check: - print("README assets are up to date and valid XML.") + if stage_b_source_available: + print("README assets are up to date and valid XML.") + else: + print( + "README assets are up to date; Stage-B SVG bytes and provenance " + "match the tracked release manifest." + ) return 0 diff --git a/scripts/pilot_evaluate_hrr.py b/scripts/pilot_evaluate_hrr.py new file mode 100644 index 0000000..81c5d95 --- /dev/null +++ b/scripts/pilot_evaluate_hrr.py @@ -0,0 +1,3662 @@ +#!/usr/bin/env python3 +"""Evaluate frozen row-selector plans on calibration rows as a diagnostic. + +Offset zero checks the selector-task prefix. Experiment 008 has one separately +frozen development window at offset 16. Other positive offsets are reserved for +the Experiment 005 holdout contract. No mode is confirmation evidence. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import math +import platform +import subprocess +import sys +from collections.abc import Mapping, Sequence +from contextlib import nullcontext +from dataclasses import dataclass +from datetime import UTC, datetime +from pathlib import Path +from statistics import fmean +from typing import Any + +import torch +from transformers import AutoTokenizer, DynamicCache, Qwen3_5ForCausalLM + +from recurquant.cache import iter_recurrent_states +from recurquant.evaluation import ( + TokenFidelity, + fidelity_summary, + paired_bootstrap_mean_improvement, + token_fidelity, +) +from recurquant.evidence import canonical_json_bytes, verify_evidence_artifact +from recurquant.packed_cache import ( + AdaptiveMixedPackedRecurrentStateCache, + CoraMixedPackedRecurrentStateCache, + MixedPackedRecurrentStateCache, + PackedRecurrentStateCache, + QueryEmaMixedPackedRecurrentStateCache, + RankFusedMixedPackedRecurrentStateCache, +) +from recurquant.public_data import ( + MBPP_CALIBRATION_SIZE, + format_mbpp_example, + load_mbpp_rows, + load_mbpp_rows_by_task_ids, + mbpp_manifest, + mbpp_manifest_content_sha256, + mbpp_manifest_sha256, +) +from recurquant.query_energy import Qwen35QueryEnergyObserver +from recurquant.qwen35 import ( + create_qwen35_adaptive_exact_budget_cache, + create_qwen35_cora_exact_budget_cache, + create_qwen35_exact_budget_cache, + create_qwen35_packed_cache, + create_qwen35_query_ema_exact_budget_cache, + create_qwen35_rank_fused_exact_budget_cache, + create_qwen35_v02_mixed_cache, +) +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from recurquant.transition_observer import Qwen35TransitionObserver + +SEED = 2339 +RANDOM_ROW_SEED = 1101 +HRR_ARTIFACT_KIND = "recurquant_hrr_calibration_diagnostic" +LOSS_ARTIFACT_KIND = "recurquant_loss_sensitivity_calibration_diagnostic" +STORAGE_BOUNDARY_ARTIFACT_KIND = "recurquant_storage_boundary_taylor_diagnostic" +LOSS_SELECTOR_PRIMARY = "signed_taylor_next_int4" +ADAPTIVE_H1 = "adaptive_mse_hrr_h1_quota" +ADAPTIVE_TARGET_FISHER = "adaptive_mse_target_directional_fisher_quota" +RANK_FUSION_METHODS = ( + ("rank_fusion_l025_target_fisher_adaptive_mse", 0.25), + ("rank_fusion_l050_target_fisher_adaptive_mse", 0.50), + ("rank_fusion_l075_target_fisher_adaptive_mse", 0.75), +) +RANK_FUSION_PRIMARY = "rank_fusion_l050_target_fisher_adaptive_mse" +QUERY_EMA_PRIMARY = "query_ema32_weighted_mse_target_fisher_quota" +QUERY_EMA_HALF_LIFE = 32 +CORA_C2_PRIMARY = "causal_observability_confirm2_mse_target_fisher_quota" +CORA_RAW = "causal_observability_mse_target_fisher_quota" +QUERY_EMA_C2 = "query_ema32_confirm2_mse_target_fisher_quota" +LOSS_SCORE_NAMES = ( + LOSS_SELECTOR_PRIMARY, + "target_directional_fisher_difference_int4", + "target_diagonal_fisher_difference_int4", + "delta_direction_magnitude_int4", +) +QUANTIZER_FIELDS = ( + "bits", + "group_size", + "scale_bits", + "flatten_last_dims", + "rounding", + "seed", + "epsilon", +) +FROZEN_HOLDOUT_OFFSET = 8 +FROZEN_HOLDOUT_LIMIT = 8 +FROZEN_HRR_HORIZON = 32 +FROZEN_BOOTSTRAP_SAMPLES = 10_000 +CQER_DEVELOPMENT_LIMIT = 8 +CQER_DEVELOPMENT_TASK_IDS = (945, 794, 657, 702, 651, 720, 903, 918) +CQER_FROZEN_SELECTOR_CANONICAL_SHA256S = ( + "7970961fd88b522998189ad64f26b333aed9c88ff5f653de5449fd9e01d8cbc8", + "bff4e33253990b8115e1f35e74516c4975c2fe4aac5066475afe968eb8a64609", +) +CQER_FROZEN_LAYER_QUOTAS = ( + (0, 355), + (1, 380), + (2, 269), + (4, 179), + (5, 185), + (6, 105), + (8, 80), + (9, 43), + (10, 84), + (12, 30), + (13, 62), + (14, 54), + (16, 45), + (17, 27), + (18, 7), + (20, 9), + (21, 7), + (22, 55), +) +E008_DEVELOPMENT_OFFSET = 16 +E008_DEVELOPMENT_LIMIT = 16 +E008_DEVELOPMENT_TASK_IDS = ( + 666, + 795, + 944, + 653, + 857, + 884, + 878, + 822, + 687, + 820, + 920, + 771, + 869, + 851, + 728, + 704, +) +E008_DEVELOPMENT_CONTENT_MANIFEST_SHA256 = ( + "21dcc6e1955918a9f6baae3d02e7ba2781600405f91fe42bbe18eac8ca6dde5e" +) +E008_DEVELOPMENT_TOKEN_MANIFEST_SHA256 = ( + "5a8e7b56528e3ccecc95ff83b2e59749d81dab27d0233fefafc510622a973f87" +) +TARGET_RESIDENT_BYTES = 2_564_096 +QUERY_SELECTOR_AUXILIARY_BYTES = 147_456 +C2_PREVIOUS_RAW_MASK_BYTES = 4_608 +CORA_SELECTOR_AUXILIARY_BYTES = 152_064 +MIN_RELATIVE_NLL_REDUCTION = 0.20 +CQER_MIN_RELATIVE_NLL_REDUCTION_VS_ADAPTIVE = 0.05 +CORA_MIN_RELATIVE_NLL_REDUCTION_VS_ADAPTIVE = 0.05 +CORA_MIN_RELATIVE_NLL_REDUCTION_VS_CQER = 0.05 +CORA_RAW_MIN_RELATIVE_NLL_REDUCTION_VS_CQER = 0.03 +CORA_C2_MIN_NORMALIZED_CHURN_REDUCTION = 0.50 +CORA_C2_MAX_RELATIVE_NLL_WORSENING_VS_RAW = 0.01 +TOP1_DISADVANTAGE_MARGIN = 0.01 +CVAR95_DISADVANTAGE_MARGIN = 0.10 +MAX_PER_TASK_NLL_DISADVANTAGE = 1.0 +FROZEN_STATIC_COMPARATORS = ( + "v02_layer0_static", + "hrr_h1", + "hrr_h32", + "row_mse", + "random_rows_s1101", + *LOSS_SCORE_NAMES, +) +EVALUATOR_SOURCE_FILES = ( + "scripts/pilot_evaluate_hrr.py", + "src/recurquant/cache.py", + "src/recurquant/evaluation.py", + "src/recurquant/evidence.py", + "src/recurquant/metrics.py", + "src/recurquant/mixed_quantization.py", + "src/recurquant/packed_cache.py", + "src/recurquant/public_data.py", + "src/recurquant/query_energy.py", + "src/recurquant/transition_observer.py", + "src/recurquant/quantization.py", + "src/recurquant/qwen35.py", + "src/recurquant/row_policy.py", +) + +RankFusionCacheSpec = tuple[ + ExactBudgetRowPlan, + Mapping[int, torch.Tensor], + float, +] +CoraCacheSpec = tuple[ExactBudgetRowPlan, bool] + + +@dataclass(slots=True) +class _TokenAccumulator: + kl: list[torch.Tensor] + reference_nll: list[torch.Tensor] + candidate_nll: list[torch.Tensor] + top1_agreement: list[torch.Tensor] + outputs_finite: list[torch.Tensor] + + @classmethod + def empty(cls) -> _TokenAccumulator: + return cls([], [], [], [], []) + + def append(self, values: TokenFidelity, *, outputs_finite: torch.Tensor) -> None: + cpu = values.to_cpu() + self.kl.append(cpu.kl.reshape(-1)) + self.reference_nll.append(cpu.reference_nll.reshape(-1)) + self.candidate_nll.append(cpu.candidate_nll.reshape(-1)) + self.top1_agreement.append(cpu.top1_agreement.reshape(-1)) + self.outputs_finite.append(outputs_finite.detach().to("cpu").reshape(())) + + def summary(self) -> dict[str, float | int]: + summary = fidelity_summary( + TokenFidelity( + kl=torch.cat(self.kl), + reference_nll=torch.cat(self.reference_nll), + candidate_nll=torch.cat(self.candidate_nll), + top1_agreement=torch.cat(self.top1_agreement), + ) + ) + summary["all_logits_finite"] = bool(torch.stack(self.outputs_finite).all().item()) + return summary + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Selector-prefix or heldout-calibration quality diagnostic; " + "never confirmation evidence." + ) + ) + parser.add_argument("--selector-artifact", type=Path, required=True) + parser.add_argument("--loss-selector-artifact", type=Path) + parser.add_argument("--storage-boundary-artifact", type=Path) + parser.add_argument( + "--rank-fusion", + action="store_true", + help=( + "Run the frozen Experiment 006 same-calibration rank-fusion primary " + "and its predeclared 0.25/0.75 ablations. Positive offsets remain " + "disabled until the separate Experiment 006 prerequisite is implemented." + ), + ) + parser.add_argument( + "--query-ema", + action="store_true", + help=( + "Run the frozen Experiment 007 CQER-32 same-calibration primary. " + "Positive offsets remain disabled until the candidate-aligned " + "Experiment 007 prerequisite is implemented and passes." + ), + ) + parser.add_argument( + "--cora-c2", + action="store_true", + help=( + "Run the frozen Experiment 008 CORA-C2 development diagnostic on " + "the exact ranked calibration window [16, 32). The protected " + "window [8, 16) remains disabled unless every prerequisite passes." + ), + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--limit", type=int) + parser.add_argument( + "--calibration-offset", + type=int, + default=0, + help=( + "Start index in the frozen ranked calibration population. Positive offsets " + "must select rows disjoint from every selector artifact." + ), + ) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--bootstrap-samples", type=int, default=10_000) + return parser.parse_args() + + +def sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def select_device(requested: str) -> torch.device: + if requested == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + return torch.device("cuda") + if requested == "cpu": + return torch.device("cpu") + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def load_selector_artifact( + path: Path, + *, + expected_kind: str, +) -> tuple[dict[str, Any], str]: + payload = path.read_bytes() + verification = verify_evidence_artifact(path) + if not verification["valid"]: + raise ValueError( + "selector artifact failed evidence verification: " + "; ".join(verification["errors"]) + ) + try: + artifact = json.loads(payload.decode("utf-8")) + except (UnicodeDecodeError, json.JSONDecodeError) as error: + raise ValueError("selector artifact must be strict UTF-8 JSON") from error + if not isinstance(artifact, dict): + raise ValueError("selector artifact root must be an object") + if artifact.get("artifact_kind") != expected_kind: + raise ValueError("unexpected selector artifact kind") + evidence = artifact.get("evidence") + if not isinstance(evidence, dict) or evidence.get("diagnostic_only") is not True: + raise ValueError("selector artifact must be a calibration diagnostic") + expected = artifact.get("canonical_evidence_sha256") + actual = sha256_bytes(canonical_json_bytes(evidence)) + if expected != actual: + raise ValueError("selector canonical evidence hash does not match") + return evidence, sha256_bytes(payload) + + +def _require_mapping(value: object, *, context: str) -> Mapping[str, Any]: + if not isinstance(value, Mapping): + raise ValueError(f"{context} must be an object") + return value + + +def _selector_token_contract(evidence: Mapping[str, Any]) -> tuple[dict[str, int], ...]: + """Normalize and validate the selector's ordered token manifest.""" + + dataset = _require_mapping(evidence.get("dataset"), context="selector dataset") + tasks = dataset.get("tasks") + if not isinstance(tasks, list) or not tasks: + raise ValueError("selector dataset tasks must be a non-empty array") + kind = evidence.get("artifact_kind") + if kind == HRR_ARTIFACT_KIND: + affected_field = "captured_decode_tokens" + elif kind == LOSS_ARTIFACT_KIND: + affected_field = "scored_transitions" + else: + raise ValueError("unexpected selector artifact kind") + + normalized: list[dict[str, int]] = [] + seen: set[int] = set() + for index, raw_record in enumerate(tasks): + record = _require_mapping(raw_record, context=f"selector task {index}") + try: + task_id = int(record["task_id"]) + prompt_tokens = int(record["prompt_tokens"]) + code_tokens = int(record["code_tokens"]) + affected_tokens = int(record[affected_field]) + except (KeyError, TypeError, ValueError) as error: + raise ValueError(f"selector task {index} has invalid token metadata") from error + if task_id in seen: + raise ValueError(f"selector token manifest repeats task_id {task_id}") + if prompt_tokens < 1 or code_tokens < 2: + raise ValueError(f"selector task {task_id} has invalid token counts") + if affected_tokens != code_tokens - 1: + raise ValueError( + f"selector task {task_id} affected-token count must equal code_tokens - 1" + ) + seen.add(task_id) + normalized.append( + { + "task_id": task_id, + "prompt_tokens": prompt_tokens, + "code_tokens": code_tokens, + "affected_tokens": affected_tokens, + } + ) + return tuple(normalized) + + +def _selector_quantizer_contract(evidence: Mapping[str, Any]) -> dict[str, Any]: + """Return a strict, explicit row-quantizer contract from a selector.""" + + raw_contract = _require_mapping( + evidence.get("quantizers"), + context="selector quantizers", + ) + axis_contract = raw_contract.get("axis_contract") + if axis_contract != "one independent group per recurrent [head, key-row]": + raise ValueError("selector quantizer axis contract is missing or unsupported") + + normalized: dict[str, Any] = {"axis_contract": axis_contract} + for name, bits in (("int4", 4), ("int8", 8)): + raw_spec = _require_mapping( + raw_contract.get(name), + context=f"selector {name} quantizer", + ) + missing = [field for field in QUANTIZER_FIELDS if field not in raw_spec] + if missing: + raise ValueError(f"selector {name} quantizer lacks fields: {', '.join(missing)}") + spec = {field: raw_spec[field] for field in QUANTIZER_FIELDS} + if spec["bits"] != bits: + raise ValueError(f"selector {name} quantizer must use bits={bits}") + if spec["rounding"] not in ("nearest", "stochastic"): + raise ValueError(f"selector {name} quantizer has unsupported rounding") + if int(spec["group_size"]) <= 0 or int(spec["flatten_last_dims"]) <= 0: + raise ValueError(f"selector {name} quantizer has invalid grouping") + if int(spec["scale_bits"]) not in (16, 32): + raise ValueError(f"selector {name} quantizer has invalid scale_bits") + if float(spec["epsilon"]) <= 0: + raise ValueError(f"selector {name} quantizer has invalid epsilon") + normalized[name] = spec + + matched_fields = tuple(field for field in QUANTIZER_FIELDS if field != "bits") + mismatches = [ + field for field in matched_fields if normalized["int4"][field] != normalized["int8"][field] + ] + if mismatches: + raise ValueError("selector INT4/INT8 quantizers disagree on: " + ", ".join(mismatches)) + return normalized + + +def validate_selector_contract(evidence: dict[str, Any]) -> None: + """Validate one selector's self-contained provenance and quantizer contract.""" + + model = _require_mapping(evidence.get("model"), context="selector model") + for field in ("id", "revision", "dtype"): + if not isinstance(model.get(field), str) or not model[field]: + raise ValueError(f"selector model {field} must be a non-empty string") + layers = model.get("linear_attention_layers") + if ( + not isinstance(layers, list) + or not layers + or any( + isinstance(value, bool) or not isinstance(value, int) or value < 0 for value in layers + ) + ): + raise ValueError("selector model linear_attention_layers must be non-negative integers") + if len(set(layers)) != len(layers): + raise ValueError("selector model linear_attention_layers must be unique") + + dataset = _require_mapping(evidence.get("dataset"), context="selector dataset") + manifest = _require_mapping(dataset.get("manifest"), context="selector dataset manifest") + recorded_manifest_hash = dataset.get("manifest_sha256") + actual_manifest_hash = mbpp_manifest_content_sha256(manifest) + if recorded_manifest_hash != actual_manifest_hash: + raise ValueError("selector dataset manifest hash does not match its content") + if manifest.get("formatter_version") != "recurquant.mbpp-prompt-code.v1": + raise ValueError("selector formatter version is unsupported") + token_contract = _selector_token_contract(evidence) + manifest_ids = {int(record["task_id"]) for record in manifest.get("rows", [])} + if manifest_ids != {record["task_id"] for record in token_contract}: + raise ValueError("selector token manifest and content manifest task IDs do not match") + + try: + seed = int(evidence["seed"]) + except (KeyError, TypeError, ValueError) as error: + raise ValueError("selector seed must be an integer") from error + quantizers = _selector_quantizer_contract(evidence) + budget = _require_mapping(evidence.get("byte_budget"), context="selector byte budget") + for name in ("int4", "int8"): + spec = quantizers[name] + if int(spec["seed"]) != seed: + raise ValueError(f"selector {name} seed does not match the artifact seed") + if int(spec["group_size"]) != int(budget.get("group_size", -1)): + raise ValueError(f"selector {name} group_size does not match the byte budget") + if int(spec["scale_bits"]) != int(budget.get("scale_bits", -1)): + raise ValueError(f"selector {name} scale_bits does not match the byte budget") + + if evidence.get("artifact_kind") == HRR_ARTIFACT_KIND: + method = _require_mapping(evidence.get("method"), context="selector method") + try: + normalization_epsilon = float(method["normalization_epsilon"]) + except (KeyError, TypeError, ValueError) as error: + raise ValueError("HRR selector lacks a valid normalization_epsilon") from error + if normalization_epsilon <= 0: + raise ValueError("HRR selector normalization_epsilon must be positive") + + +def validate_compatible_selector( + reference: dict[str, Any], + candidate: dict[str, Any], +) -> None: + """Require two calibration selectors to describe the same frozen inputs.""" + + validate_selector_contract(reference) + validate_selector_contract(candidate) + for field in ("id", "revision", "dtype", "linear_attention_layers"): + if candidate["model"][field] != reference["model"][field]: + raise ValueError(f"selector model {field} values do not match") + if candidate["dataset"]["manifest_sha256"] != reference["dataset"]["manifest_sha256"]: + raise ValueError("selector calibration manifest hashes do not match") + if candidate["dataset"]["manifest"] != reference["dataset"]["manifest"]: + raise ValueError("selector calibration content manifests do not match") + if _selector_token_contract(candidate) != _selector_token_contract(reference): + raise ValueError("selector calibration token manifests do not match") + if int(candidate["seed"]) != int(reference["seed"]): + raise ValueError("selector seeds do not match") + if _selector_quantizer_contract(candidate) != _selector_quantizer_contract(reference): + raise ValueError("selector quantizer contracts do not match") + budget_fields = ( + "target_resident_bytes", + "group_size", + "scale_bits", + "precision_mask_bits_per_group", + ) + for field in budget_fields: + if int(candidate["byte_budget"][field]) != int(reference["byte_budget"][field]): + raise ValueError(f"selector byte-budget {field} values do not match") + + +def validate_calibration_prefix( + selector: Mapping[str, Any], + *, + actual_manifest: Mapping[str, Any], + expected_task_ids: Sequence[int], +) -> None: + """Verify a selected prefix against authenticated per-row content hashes.""" + + dataset = _require_mapping(selector.get("dataset"), context="selector dataset") + full_manifest = _require_mapping( + dataset.get("manifest"), + context="selector dataset manifest", + ) + expected_id_set = set(expected_task_ids) + if len(expected_id_set) != len(expected_task_ids): + raise ValueError("selector prefix task IDs must be unique") + full_rows = full_manifest.get("rows") + if not isinstance(full_rows, list): + raise ValueError("selector dataset manifest rows must be an array") + selected_rows = [record for record in full_rows if int(record["task_id"]) in expected_id_set] + if len(selected_rows) != len(expected_task_ids): + raise ValueError("selector prefix is not fully represented in the content manifest") + expected_manifest = dict(full_manifest) + expected_manifest["row_count"] = len(selected_rows) + expected_manifest["rows"] = selected_rows + if dict(actual_manifest) != expected_manifest: + raise ValueError("calibration prefix content manifest does not match the selector artifact") + + +def selector_task_ids(selectors: Sequence[Mapping[str, Any]]) -> frozenset[int]: + """Return the union of authenticated task IDs used by selector artifacts.""" + + if not selectors: + raise ValueError("at least one selector artifact is required") + return frozenset( + record["task_id"] for selector in selectors for record in _selector_token_contract(selector) + ) + + +def select_calibration_window( + ranked_rows: Sequence[Mapping[str, Any]], + *, + offset: int, + limit: int, + selectors: Sequence[Mapping[str, Any]], +) -> tuple[Mapping[str, Any], ...]: + """Slice ranked calibration rows and enforce selector disjointness for holdouts.""" + + if isinstance(offset, bool) or not isinstance(offset, int) or offset < 0: + raise ValueError("calibration offset must be a non-negative integer") + if isinstance(limit, bool) or not isinstance(limit, int) or limit < 1: + raise ValueError("calibration limit must be a positive integer") + stop = offset + limit + if stop > len(ranked_rows): + raise ValueError( + f"calibration window [{offset}:{stop}] exceeds {len(ranked_rows)} ranked rows" + ) + selected = tuple(ranked_rows[offset:stop]) + if len(selected) != limit: + raise RuntimeError("calibration window slicing returned an unexpected row count") + if offset > 0: + evaluation_ids = {int(row["task_id"]) for row in selected} + overlap = evaluation_ids & selector_task_ids(selectors) + if overlap: + rendered = ", ".join(str(task_id) for task_id in sorted(overlap)) + raise ValueError( + "heldout-calibration rows overlap selector artifact tasks: " + rendered + ) + return selected + + +def validate_frozen_holdout_request( + *, + offset: int, + limit: int, + bootstrap_samples: int, + selectors: Sequence[Mapping[str, Any]], + loss_selector_present: bool, + storage_boundary_present: bool, + rank_fusion_enabled: bool = False, + query_ema_enabled: bool = False, + cora_c2_enabled: bool = False, +) -> None: + """Refuse unauthorized positive offsets, including E008's protected window.""" + + if offset == 0: + return + if cora_c2_enabled: + if ( + offset == E008_DEVELOPMENT_OFFSET + and limit == E008_DEVELOPMENT_LIMIT + and bootstrap_samples == FROZEN_BOOTSTRAP_SAMPLES + ): + return + raise ValueError( + "Experiment 008 CORA-C2 may bypass the heldout contract only for its " + "exact development window [16, 32); protected window [8, 16) remains closed" + ) + if rank_fusion_enabled: + raise ValueError( + "Experiment 006 rank-fusion holdout remains closed until its separate " + "candidate-aligned numeric prerequisite and frozen gate are implemented" + ) + if query_ema_enabled: + raise ValueError( + "Experiment 007 CQER-32 holdout remains closed until its development " + "rule and candidate-aligned numeric prerequisite pass" + ) + if offset != FROZEN_HOLDOUT_OFFSET or limit != FROZEN_HOLDOUT_LIMIT: + raise ValueError( + "Experiment 005 heldout-calibration requires the exact ranked window " + f"[{FROZEN_HOLDOUT_OFFSET}, " + f"{FROZEN_HOLDOUT_OFFSET + FROZEN_HOLDOUT_LIMIT})" + ) + if bootstrap_samples != FROZEN_BOOTSTRAP_SAMPLES: + raise ValueError( + "Experiment 005 heldout-calibration requires exactly " + f"{FROZEN_BOOTSTRAP_SAMPLES} bootstrap samples" + ) + if not loss_selector_present or len(selectors) != 2: + raise ValueError("Experiment 005 heldout-calibration requires both HRR and loss selectors") + if not storage_boundary_present: + raise ValueError( + "Experiment 005 heldout-calibration requires a passing storage-boundary artifact" + ) + kinds = [selector.get("artifact_kind") for selector in selectors] + if kinds != [HRR_ARTIFACT_KIND, LOSS_ARTIFACT_KIND]: + raise ValueError("Experiment 005 requires HRR then loss selector artifacts") + try: + horizon = int(selectors[0]["method"]["horizon"]) + except (KeyError, TypeError, ValueError) as error: + raise ValueError("Experiment 005 HRR selector lacks a valid horizon") from error + if horizon != FROZEN_HRR_HORIZON: + raise ValueError( + f"Experiment 005 heldout-calibration requires HRR horizon {FROZEN_HRR_HORIZON}" + ) + for selector in selectors: + task_count = len(_selector_token_contract(selector)) + if task_count != FROZEN_HOLDOUT_LIMIT: + raise ValueError( + "each Experiment 005 selector must contain exactly " + f"{FROZEN_HOLDOUT_LIMIT} tasks; found {task_count}" + ) + raise ValueError( + "ranked window [8, 16) is now protected by Experiment 008 and cannot be " + "loaded or evaluated through the legacy Experiment 005 path" + ) + + +def validate_cqer_development_request( + *, + enabled: bool, + offset: int, + limit: int, + bootstrap_samples: int, +) -> None: + """Freeze Experiment 007 to the complete inspected selector partition.""" + + if not enabled: + return + if ( + offset != 0 + or limit != CQER_DEVELOPMENT_LIMIT + or bootstrap_samples != FROZEN_BOOTSTRAP_SAMPLES + ): + raise ValueError( + "Experiment 007 CQER-32 development requires offset 0, exactly 8 tasks, " + "and exactly 10000 bootstrap samples" + ) + + +def validate_cqer_selector_artifacts( + *, + enabled: bool, + selectors: Sequence[Mapping[str, Any]], +) -> None: + """Require CQER selectors to be the exact preregistered evidence pair.""" + + if not enabled: + return + if len(selectors) != 2: + raise ValueError( + "Experiment 007 CQER-32 requires exactly two authenticated " + "eight-task selector artifacts" + ) + kinds = tuple(selector.get("artifact_kind") for selector in selectors) + if kinds != (HRR_ARTIFACT_KIND, LOSS_ARTIFACT_KIND): + raise ValueError("Experiment 007 CQER-32 requires HRR then loss selectors") + token_contracts = tuple(_selector_token_contract(selector) for selector in selectors) + if any(len(contract) != CQER_DEVELOPMENT_LIMIT for contract in token_contracts): + raise ValueError( + "Experiment 007 CQER-32 requires exactly two authenticated " + "eight-task selector artifacts" + ) + task_id_orders = tuple( + tuple(int(record["task_id"]) for record in contract) + for contract in token_contracts + ) + if any(task_ids != CQER_DEVELOPMENT_TASK_IDS for task_ids in task_id_orders): + raise ValueError( + "Experiment 007 CQER-32 selector task IDs do not match the frozen order" + ) + canonical_hashes = tuple( + sha256_bytes(canonical_json_bytes(selector)) for selector in selectors + ) + if canonical_hashes != CQER_FROZEN_SELECTOR_CANONICAL_SHA256S: + raise ValueError( + "Experiment 007 CQER-32 selector canonical hashes do not match the " + "frozen artifacts" + ) + + +def validate_cqer_development_task_ids( + *, + enabled: bool, + task_ids: Sequence[int], +) -> None: + """Pin the exact ordered development tasks before model execution.""" + + if enabled and tuple(int(task_id) for task_id in task_ids) != CQER_DEVELOPMENT_TASK_IDS: + raise ValueError( + "Experiment 007 CQER-32 task IDs do not match the frozen ordered prefix" + ) + + +def validate_cqer_layer_quotas( + *, + enabled: bool, + quotas: Mapping[int, int], +) -> None: + """Pin the preregistered target-Fisher layer allocation.""" + + if not enabled: + return + normalized = tuple(sorted((int(layer), int(quota)) for layer, quota in quotas.items())) + expected = tuple(sorted(CQER_FROZEN_LAYER_QUOTAS)) + if normalized != expected or sum(quota for _, quota in normalized) != 1_976: + raise ValueError( + "Experiment 007 CQER-32 layer quotas do not match the frozen " + "target-Fisher allocation" + ) + + +def validate_cora_development_request( + *, + enabled: bool, + offset: int, + limit: int, + bootstrap_samples: int, +) -> None: + """Freeze Experiment 008 to its predeclared development window.""" + + if not enabled: + return + if ( + offset != E008_DEVELOPMENT_OFFSET + or limit != E008_DEVELOPMENT_LIMIT + or bootstrap_samples != FROZEN_BOOTSTRAP_SAMPLES + ): + raise ValueError( + "Experiment 008 CORA-C2 development requires ranked window [16, 32), " + "exactly 16 tasks, and exactly 10000 bootstrap samples; protected " + "window [8, 16) remains closed" + ) + + +def validate_cora_selector_artifacts( + *, + enabled: bool, + selectors: Sequence[Mapping[str, Any]], +) -> None: + """Authenticate the exact selector pair frozen before Experiment 008.""" + + if not enabled: + return + if len(selectors) != 2: + raise ValueError( + "Experiment 008 CORA-C2 requires exactly two authenticated selector artifacts" + ) + kinds = tuple(selector.get("artifact_kind") for selector in selectors) + if kinds != (HRR_ARTIFACT_KIND, LOSS_ARTIFACT_KIND): + raise ValueError("Experiment 008 CORA-C2 requires HRR then loss selectors") + token_contracts = tuple(_selector_token_contract(selector) for selector in selectors) + if any(len(contract) != CQER_DEVELOPMENT_LIMIT for contract in token_contracts): + raise ValueError( + "Experiment 008 CORA-C2 requires the exact authenticated eight-task " + "selector pair" + ) + task_id_orders = tuple( + tuple(int(record["task_id"]) for record in contract) + for contract in token_contracts + ) + if any(task_ids != CQER_DEVELOPMENT_TASK_IDS for task_ids in task_id_orders): + raise ValueError( + "Experiment 008 CORA-C2 selector task IDs do not match the frozen order" + ) + canonical_hashes = tuple( + sha256_bytes(canonical_json_bytes(selector)) for selector in selectors + ) + if canonical_hashes != CQER_FROZEN_SELECTOR_CANONICAL_SHA256S: + raise ValueError( + "Experiment 008 CORA-C2 selector canonical hashes do not match the " + "frozen artifacts" + ) + + +def validate_cora_development_identity( + *, + enabled: bool, + task_ids: Sequence[int], + content_manifest_sha256: str, + token_manifest_sha256: str | None = None, +) -> None: + """Fail closed on any change to Experiment 008's pinned data identity.""" + + if not enabled: + return + if tuple(int(task_id) for task_id in task_ids) != E008_DEVELOPMENT_TASK_IDS: + raise ValueError( + "Experiment 008 CORA-C2 task IDs do not match the frozen [16, 32) order" + ) + if content_manifest_sha256 != E008_DEVELOPMENT_CONTENT_MANIFEST_SHA256: + raise ValueError( + "Experiment 008 CORA-C2 content manifest does not match the frozen identity" + ) + if ( + token_manifest_sha256 is not None + and token_manifest_sha256 != E008_DEVELOPMENT_TOKEN_MANIFEST_SHA256 + ): + raise ValueError( + "Experiment 008 CORA-C2 token manifest does not match the frozen identity" + ) + + +def validate_cora_layer_quotas( + *, + enabled: bool, + quotas: Mapping[int, int], +) -> None: + """Pin CORA to the authenticated target-Fisher layer allocation.""" + + if not enabled: + return + normalized = tuple(sorted((int(layer), int(quota)) for layer, quota in quotas.items())) + expected = tuple(sorted(CQER_FROZEN_LAYER_QUOTAS)) + if normalized != expected or sum(quota for _, quota in normalized) != 1_976: + raise ValueError( + "Experiment 008 CORA-C2 layer quotas do not match the frozen " + "target-Fisher allocation" + ) + + +def authenticate_selector_prefix( + selector: Mapping[str, Any], + *, + ranked_prefix_manifest: Mapping[str, Any], + ranked_prefix_task_ids: Sequence[int], +) -> None: + """Authenticate a selector against the pinned ranked MBPP selector prefix.""" + + token_contract = _selector_token_contract(selector) + if len(token_contract) != FROZEN_HOLDOUT_LIMIT: + raise ValueError( + f"selector must contain exactly {FROZEN_HOLDOUT_LIMIT} frozen prefix tasks" + ) + recorded_ids = [record["task_id"] for record in token_contract] + if recorded_ids != list(ranked_prefix_task_ids): + raise ValueError("selector ordered task IDs do not match the pinned ranked prefix") + validate_calibration_prefix( + selector, + actual_manifest=ranked_prefix_manifest, + expected_task_ids=ranked_prefix_task_ids, + ) + + +def validate_actual_token_manifest( + selector: Mapping[str, Any], + actual_records: Sequence[Mapping[str, int]], +) -> None: + """Verify tokenizer output against the selector's ordered token manifest.""" + + expected = _selector_token_contract(selector) + normalized_actual = tuple( + { + "task_id": int(record["task_id"]), + "prompt_tokens": int(record["prompt_tokens"]), + "code_tokens": int(record["code_tokens"]), + "affected_tokens": int(record["aligned_scored_tokens"]), + } + for record in actual_records + ) + if normalized_actual != expected[: len(normalized_actual)]: + raise ValueError("actual formatter/token manifest does not match the selector prefix") + + +def encode_task_rows( + tokenizer: Any, + rows: Sequence[Mapping[str, Any]], +) -> tuple[ + list[tuple[Mapping[str, Any], torch.Tensor, torch.Tensor]], + list[dict[str, int]], +]: + """Tokenize rows and build the exact aligned/full-code token manifest.""" + + encoded: list[tuple[Mapping[str, Any], torch.Tensor, torch.Tensor]] = [] + manifest: list[dict[str, int]] = [] + for row in rows: + formatted = format_mbpp_example(row) + prompt_ids = tokenizer( + formatted.prompt, + add_special_tokens=True, + return_tensors="pt", + )["input_ids"] + code_ids = tokenizer( + formatted.code, + add_special_tokens=False, + return_tensors="pt", + )["input_ids"] + code_tokens = int(code_ids.shape[1]) + if code_tokens < 2: + raise RuntimeError(f"MBPP task {row['task_id']} has fewer than two code tokens") + manifest.append( + { + "task_id": int(row["task_id"]), + "prompt_tokens": int(prompt_ids.shape[1]), + "code_tokens": code_tokens, + "aligned_scored_tokens": code_tokens - 1, + "full_code_scored_tokens": code_tokens, + } + ) + encoded.append((row, prompt_ids, code_ids)) + return encoded, manifest + + +def paired_contrast( + baseline_values: list[float], + candidate_values: list[float], + *, + samples: int, + seed: int, +) -> dict[str, Any]: + """Return bootstrap uncertainty only when at least two pairs exist.""" + + if len(baseline_values) != len(candidate_values): + raise ValueError("paired contrast values must have equal length") + if not baseline_values: + raise ValueError("paired contrast requires at least one example") + if len(baseline_values) == 1: + return { + "paired_examples": 1, + "mean_improvement": float(baseline_values[0] - candidate_values[0]), + "confidence": None, + "confidence_interval": None, + "bootstrap_samples": 0, + "seed": None, + "note": "descriptive only; uncertainty is not estimable from one paired task", + } + return paired_bootstrap_mean_improvement( + baseline_values, + candidate_values, + samples=samples, + seed=seed, + ) + + +def aggregate_task_rows( + per_task: Mapping[str, Sequence[Mapping[str, float | int]]], +) -> dict[str, dict[str, float | int]]: + """Compute task-macro summaries while retaining the exact token count.""" + + return { + name: { + "task_count": len(task_rows), + "macro_delta_nll": fmean(float(row["delta_nll"]) for row in task_rows), + "macro_mean_kl": fmean(float(row["mean_kl"]) for row in task_rows), + "macro_cvar95_kl": fmean(float(row["cvar95_kl"]) for row in task_rows), + "maximum_kl": max(float(row["max_kl"]) for row in task_rows), + "macro_top1_agreement": fmean(float(row["top1_agreement"]) for row in task_rows), + "token_count": sum(int(row["token_count"]) for row in task_rows), + } + for name, task_rows in per_task.items() + } + + +def _finite_float(value: object, *, context: str) -> float: + if isinstance(value, bool) or not isinstance(value, (int, float)): + raise ValueError(f"{context} must be numeric") + rendered = float(value) + if not math.isfinite(rendered): + raise ValueError(f"{context} must be finite") + return rendered + + +def _paired_lower_bound( + contrasts: Mapping[str, Mapping[str, Any]], + name: str, +) -> float | None: + record = contrasts.get(name) + if not isinstance(record, Mapping): + return None + interval = record.get("confidence_interval") + if not isinstance(interval, list) or len(interval) != 2: + return None + try: + lower = _finite_float(interval[0], context=f"{name} paired lower bound") + except ValueError: + return None + return lower + + +def evaluate_frozen_holdout_gate( + *, + aggregates: Mapping[str, Mapping[str, float | int]], + per_task: Mapping[str, Sequence[Mapping[str, float | int]]], + contrasts: Mapping[str, Mapping[str, Any]], + storage: Mapping[str, Mapping[str, int | float | bool]], + primary_name: str, +) -> dict[str, Any]: + """Evaluate the machine-checkable Experiment 005 heldout-calibration gate.""" + + if primary_name != ADAPTIVE_TARGET_FISHER: + raise ValueError( + f"frozen heldout primary must be {ADAPTIVE_TARGET_FISHER}, got {primary_name}" + ) + required = (primary_name, ADAPTIVE_H1) + for name in required: + if name not in aggregates or name not in per_task or name not in storage: + raise ValueError(f"heldout gate lacks required method {name}") + + expected_methods = { + "uniform_int4", + *FROZEN_STATIC_COMPARATORS, + ADAPTIVE_H1, + ADAPTIVE_TARGET_FISHER, + } + if set(aggregates) != expected_methods: + raise ValueError("heldout methods do not match the frozen Experiment 005 method set") + all_static_names = list(FROZEN_STATIC_COMPARATORS) + static_names = [ + name + for name in all_static_names + if name in storage and int(storage[name].get("resident_bytes", -1)) == TARGET_RESIDENT_BYTES + ] + if not static_names: + raise ValueError("heldout gate requires at least one equal-byte static comparator") + strongest_static = min( + static_names, + key=lambda name: _finite_float( + aggregates[name]["macro_delta_nll"], + context=f"{name} macro_delta_nll", + ), + ) + + primary = aggregates[primary_name] + static = aggregates[strongest_static] + adaptive_h1 = aggregates[ADAPTIVE_H1] + primary_nll = _finite_float(primary["macro_delta_nll"], context="primary macro_delta_nll") + static_nll = _finite_float( + static["macro_delta_nll"], + context="strongest static macro_delta_nll", + ) + adaptive_h1_nll = _finite_float( + adaptive_h1["macro_delta_nll"], + context="adaptive H1 macro_delta_nll", + ) + static_nlls = { + name: _finite_float( + aggregates[name]["macro_delta_nll"], + context=f"{name} macro_delta_nll", + ) + for name in static_names + } + relative_reduction = (static_nll - primary_nll) / static_nll if static_nll > 0 else None + + primary_top1 = _finite_float( + primary["macro_top1_agreement"], + context="primary macro_top1_agreement", + ) + static_top1 = _finite_float( + static["macro_top1_agreement"], + context="strongest static macro_top1_agreement", + ) + primary_cvar = _finite_float( + primary["macro_cvar95_kl"], + context="primary macro_cvar95_kl", + ) + static_cvar = _finite_float( + static["macro_cvar95_kl"], + context="strongest static macro_cvar95_kl", + ) + + primary_tasks = { + int(row["task_id"]): _finite_float( + row["delta_nll"], + context=f"primary task {row['task_id']} delta_nll", + ) + for row in per_task[primary_name] + } + static_tasks = { + int(row["task_id"]): _finite_float( + row["delta_nll"], + context=f"static task {row['task_id']} delta_nll", + ) + for row in per_task[strongest_static] + } + if not primary_tasks or primary_tasks.keys() != static_tasks.keys(): + raise ValueError("primary and strongest static per-task IDs must match") + per_task_disadvantages = { + str(task_id): primary_tasks[task_id] - static_tasks[task_id] + for task_id in sorted(primary_tasks) + } + worst_task_id = max(per_task_disadvantages, key=per_task_disadvantages.__getitem__) + worst_task_disadvantage = per_task_disadvantages[worst_task_id] + + exact_storage_observed = { + name: int(summary.get("resident_bytes", -1)) + for name, summary in storage.items() + if name != "uniform_int4" + } + exact_storage_passed = bool(exact_storage_observed) and all( + value == TARGET_RESIDENT_BYTES for value in exact_storage_observed.values() + ) + static_lower_bound = _paired_lower_bound(contrasts, strongest_static) + adaptive_h1_lower_bound = _paired_lower_bound(contrasts, ADAPTIVE_H1) + + checks: dict[str, dict[str, Any]] = { + "exact_resident_bytes": { + "passed": exact_storage_passed, + "required_bytes": TARGET_RESIDENT_BYTES, + "observed": exact_storage_observed, + }, + "lower_nll_than_every_equal_byte_static": { + "passed": all(primary_nll < value for value in static_nlls.values()), + "primary": primary_nll, + "static_comparators": static_nlls, + }, + "relative_nll_reduction_vs_strongest_static": { + "passed": ( + relative_reduction is not None and relative_reduction >= MIN_RELATIVE_NLL_REDUCTION + ), + "observed": relative_reduction, + "minimum": MIN_RELATIVE_NLL_REDUCTION, + "denominator_contract": "strongest static excess NLL must be positive", + }, + "lower_nll_than_adaptive_h1": { + "passed": primary_nll < adaptive_h1_nll, + "primary": primary_nll, + "adaptive_h1": adaptive_h1_nll, + }, + "paired_lower_ci_vs_strongest_static": { + "passed": static_lower_bound is not None and static_lower_bound > 0, + "observed_lower_bound": static_lower_bound, + "required": "strictly greater than zero", + }, + "paired_lower_ci_vs_adaptive_h1": { + "passed": adaptive_h1_lower_bound is not None and adaptive_h1_lower_bound > 0, + "observed_lower_bound": adaptive_h1_lower_bound, + "required": "strictly greater than zero", + }, + "top1_disadvantage_margin_vs_strongest_static": { + "passed": primary_top1 >= static_top1 - TOP1_DISADVANTAGE_MARGIN, + "observed_disadvantage": static_top1 - primary_top1, + "maximum": TOP1_DISADVANTAGE_MARGIN, + }, + "cvar95_disadvantage_margin_vs_strongest_static": { + "passed": primary_cvar <= static_cvar + CVAR95_DISADVANTAGE_MARGIN, + "observed_disadvantage": primary_cvar - static_cvar, + "maximum": CVAR95_DISADVANTAGE_MARGIN, + }, + "maximum_per_task_nll_disadvantage_vs_strongest_static": { + "passed": worst_task_disadvantage <= MAX_PER_TASK_NLL_DISADVANTAGE, + "observed": worst_task_disadvantage, + "maximum": MAX_PER_TASK_NLL_DISADVANTAGE, + "worst_task_id": int(worst_task_id), + "per_task": per_task_disadvantages, + }, + } + return { + "schema": "recurquant.experiment005-heldout-gate.v1", + "applicable": True, + "passed": all(check["passed"] is True for check in checks.values()), + "primary": primary_name, + "strongest_equal_byte_static": strongest_static, + "adaptive_control": ADAPTIVE_H1, + "thresholds": { + "minimum_relative_nll_reduction": MIN_RELATIVE_NLL_REDUCTION, + "maximum_top1_disadvantage": TOP1_DISADVANTAGE_MARGIN, + "maximum_cvar95_kl_disadvantage": CVAR95_DISADVANTAGE_MARGIN, + "maximum_per_task_nll_disadvantage": MAX_PER_TASK_NLL_DISADVANTAGE, + "paired_lower_ci_must_be_strictly_positive": True, + }, + "checks": checks, + } + + +def evaluate_cqer_development_gate( + *, + aggregates: Mapping[str, Mapping[str, float | int]], + per_task: Mapping[str, Sequence[Mapping[str, float | int]]], + per_task_full_code: Mapping[str, Sequence[Mapping[str, float | int]]], + storage: Mapping[str, Mapping[str, int | float | bool]], + query_ema_diagnostics: Mapping[str, Sequence[Mapping[str, object]]], + expected_quotas: Mapping[int, int], + expected_packed_bytes: int, + expected_selector_auxiliary_bytes: int, +) -> dict[str, Any]: + """Evaluate Experiment 007's frozen same-calibration advancement filter.""" + + required = ( + QUERY_EMA_PRIMARY, + ADAPTIVE_TARGET_FISHER, + "target_directional_fisher_difference_int4", + ) + for name in required: + if ( + name not in aggregates + or name not in per_task + or name not in per_task_full_code + or name not in storage + ): + raise ValueError(f"CQER development gate lacks required method {name}") + diagnostic_tasks = query_ema_diagnostics.get(QUERY_EMA_PRIMARY) + if ( + not isinstance(diagnostic_tasks, Sequence) + or len(diagnostic_tasks) != CQER_DEVELOPMENT_LIMIT + ): + raise ValueError( + "CQER development gate requires query-EMA diagnostics for exactly " + f"{CQER_DEVELOPMENT_LIMIT} tasks" + ) + for name in required: + if ( + len(per_task[name]) != CQER_DEVELOPMENT_LIMIT + or len(per_task_full_code[name]) != CQER_DEVELOPMENT_LIMIT + ): + raise ValueError( + "CQER development gate requires aligned and full-code metrics for " + f"exactly {CQER_DEVELOPMENT_LIMIT} tasks per required method" + ) + + primary = aggregates[QUERY_EMA_PRIMARY] + adaptive = aggregates[ADAPTIVE_TARGET_FISHER] + static_name = "target_directional_fisher_difference_int4" + static = aggregates[static_name] + primary_nll = _finite_float(primary["macro_delta_nll"], context="CQER macro_delta_nll") + adaptive_nll = _finite_float( + adaptive["macro_delta_nll"], + context="adaptive target-Fisher macro_delta_nll", + ) + static_nll = _finite_float( + static["macro_delta_nll"], + context="static target-Fisher macro_delta_nll", + ) + relative_vs_adaptive = ( + (adaptive_nll - primary_nll) / adaptive_nll if adaptive_nll > 0 else None + ) + relative_vs_static = ( + (static_nll - primary_nll) / static_nll if static_nll > 0 else None + ) + + primary_top1 = _finite_float( + primary["macro_top1_agreement"], + context="CQER macro_top1_agreement", + ) + better_comparator_top1 = max( + _finite_float( + adaptive["macro_top1_agreement"], + context="adaptive target-Fisher macro_top1_agreement", + ), + _finite_float( + static["macro_top1_agreement"], + context="static target-Fisher macro_top1_agreement", + ), + ) + primary_cvar = _finite_float( + primary["macro_cvar95_kl"], + context="CQER macro_cvar95_kl", + ) + lower_comparator_cvar = min( + _finite_float( + adaptive["macro_cvar95_kl"], + context="adaptive target-Fisher macro_cvar95_kl", + ), + _finite_float( + static["macro_cvar95_kl"], + context="static target-Fisher macro_cvar95_kl", + ), + ) + + expected_layers = dict(sorted(expected_quotas.items())) + layer_audit: list[dict[str, object]] = [] + selector_contract_passed = True + finite_diagnostics = True + for task_record in diagnostic_tasks: + task_id = int(task_record.get("task_id", -1)) + raw_layers = task_record.get("layers") + if not isinstance(raw_layers, list): + selector_contract_passed = False + layer_audit.append({"task_id": task_id, "error": "layers must be an array"}) + continue + observed_layers: dict[int, Mapping[str, object]] = {} + for raw_layer in raw_layers: + if not isinstance(raw_layer, Mapping): + selector_contract_passed = False + continue + layer_index = int(raw_layer.get("layer_index", -1)) + if layer_index in observed_layers: + selector_contract_passed = False + continue + observed_layers[layer_index] = raw_layer + task_passed = set(observed_layers) == set(expected_layers) + observations = 0 + state_updates = 0 + for layer_index, quota in expected_layers.items(): + record = observed_layers.get(layer_index) + if record is None: + task_passed = False + continue + observed_quota = int(record.get("quota", -1)) + selected = int(record.get("current_selected_count", -1)) + committed = int(record.get("observations_committed", -1)) + updates = int(record.get("state_updates", -2)) + auxiliary = int(record.get("selector_auxiliary_bytes", -1)) + mask_sha256 = record.get("current_mask_sha256") + pending = record.get("pending_observation") + cutoff = record.get("last_cutoff_score_margin") + if cutoff is not None: + try: + _finite_float(cutoff, context="CQER cutoff score margin") + except ValueError: + finite_diagnostics = False + observations += max(committed, 0) + state_updates += max(updates, 0) + task_passed = task_passed and all( + ( + observed_quota == quota, + selected == quota, + committed == updates, + committed > 0, + auxiliary > 0, + isinstance(mask_sha256, str) and len(mask_sha256) == 64, + pending is False, + ) + ) + selector_contract_passed = selector_contract_passed and task_passed + layer_audit.append( + { + "task_id": task_id, + "passed": task_passed, + "observations_committed": observations, + "state_updates": state_updates, + } + ) + + summary = storage[QUERY_EMA_PRIMARY] + observed_packed_bytes = int(summary.get("resident_bytes", -1)) + observed_auxiliary_bytes = int(summary.get("selector_auxiliary_bytes", -1)) + observed_total_bytes = int(summary.get("resident_bytes_including_selector", -1)) + observed_promotions = int(summary.get("high_precision_groups", -1)) + expected_promotions = sum(expected_layers.values()) + + all_metrics_finite = finite_diagnostics + for metric_partition in (per_task, per_task_full_code): + for method_rows in metric_partition.values(): + for row in method_rows: + logits_finite = row.get("all_logits_finite") + if logits_finite is not True: + all_metrics_finite = False + for key, value in row.items(): + if key in ("task_id", "all_logits_finite"): + continue + try: + _finite_float(value, context=f"per-task metric {key}") + except ValueError: + all_metrics_finite = False + + checks: dict[str, dict[str, Any]] = { + "exact_per_layer_quotas": { + "passed": selector_contract_passed and observed_promotions == expected_promotions, + "expected_quotas": {str(key): value for key, value in expected_layers.items()}, + "expected_total_promotions": expected_promotions, + "observed_total_promotions": observed_promotions, + "task_audit": layer_audit, + }, + "exact_packed_and_selector_bytes": { + "passed": ( + observed_packed_bytes == expected_packed_bytes + and observed_auxiliary_bytes == expected_selector_auxiliary_bytes + and observed_total_bytes + == expected_packed_bytes + expected_selector_auxiliary_bytes + ), + "expected_packed_bytes": expected_packed_bytes, + "observed_packed_bytes": observed_packed_bytes, + "expected_selector_auxiliary_bytes": expected_selector_auxiliary_bytes, + "observed_selector_auxiliary_bytes": observed_auxiliary_bytes, + "expected_total_bytes": ( + expected_packed_bytes + expected_selector_auxiliary_bytes + ), + "observed_total_bytes": observed_total_bytes, + }, + "lower_nll_than_both_components": { + "passed": primary_nll < adaptive_nll and primary_nll < static_nll, + "primary": primary_nll, + "adaptive_component": adaptive_nll, + "static_component": static_nll, + }, + "relative_nll_reduction_vs_plain_adaptive": { + "passed": ( + relative_vs_adaptive is not None + and relative_vs_adaptive >= CQER_MIN_RELATIVE_NLL_REDUCTION_VS_ADAPTIVE + ), + "observed": relative_vs_adaptive, + "minimum": CQER_MIN_RELATIVE_NLL_REDUCTION_VS_ADAPTIVE, + }, + "relative_nll_reduction_vs_strongest_static": { + "passed": ( + relative_vs_static is not None + and relative_vs_static >= MIN_RELATIVE_NLL_REDUCTION + ), + "observed": relative_vs_static, + "minimum": MIN_RELATIVE_NLL_REDUCTION, + }, + "top1_disadvantage_margin": { + "passed": primary_top1 >= better_comparator_top1 - TOP1_DISADVANTAGE_MARGIN, + "observed_disadvantage": better_comparator_top1 - primary_top1, + "maximum": TOP1_DISADVANTAGE_MARGIN, + }, + "cvar95_disadvantage_margin": { + "passed": primary_cvar <= lower_comparator_cvar + CVAR95_DISADVANTAGE_MARGIN, + "observed_disadvantage": primary_cvar - lower_comparator_cvar, + "maximum": CVAR95_DISADVANTAGE_MARGIN, + }, + "all_values_finite": { + "passed": all_metrics_finite, + }, + "exact_stage_consume_handshake": { + "passed": selector_contract_passed, + "task_audit": layer_audit, + }, + } + return { + "schema": "recurquant.experiment007-cqer32-development-gate.v1", + "applicable": True, + "passed": all(check["passed"] is True for check in checks.values()), + "primary": QUERY_EMA_PRIMARY, + "comparators": { + "plain_adaptive": ADAPTIVE_TARGET_FISHER, + "static": static_name, + }, + "checks": checks, + } + + +def _diagnostic_int( + record: Mapping[str, object], + *names: str, + default: int = -1, +) -> int: + """Read one non-boolean diagnostic integer using compatible field aliases.""" + + for name in names: + value = record.get(name) + if isinstance(value, int) and not isinstance(value, bool): + return value + return default + + +def _mask_hash_is_valid(value: object) -> bool: + return ( + isinstance(value, str) + and len(value) == 64 + and all(character in "0123456789abcdef" for character in value) + ) + + +def _audit_e008_selector_diagnostics( + *, + selector_diagnostics: Mapping[str, Sequence[Mapping[str, object]]], + expected_quotas: Mapping[int, int], + token_manifest: Sequence[Mapping[str, int]], +) -> dict[str, Any]: + """Audit exact-quota handshakes and aggregate frozen churn diagnostics.""" + + selector_methods = ( + QUERY_EMA_PRIMARY, + CORA_RAW, + QUERY_EMA_C2, + CORA_C2_PRIMARY, + ) + expected_layers = dict(sorted((int(k), int(v)) for k, v in expected_quotas.items())) + if len(token_manifest) != E008_DEVELOPMENT_LIMIT: + raise ValueError( + "CORA-C2 diagnostic audit requires the exact 16-task token manifest" + ) + expected_task_contract: dict[int, tuple[int, int]] = {} + for index, record in enumerate(token_manifest): + task_id = int(record["task_id"]) + prompt_tokens = int(record["prompt_tokens"]) + code_tokens = int(record["code_tokens"]) + aligned_tokens = int(record["aligned_scored_tokens"]) + if ( + task_id != E008_DEVELOPMENT_TASK_IDS[index] + or prompt_tokens < 1 + or code_tokens < 2 + or aligned_tokens != code_tokens - 1 + or task_id in expected_task_contract + ): + raise ValueError("CORA-C2 token manifest does not match the frozen task contract") + expected_task_contract[task_id] = ( + code_tokens, + prompt_tokens + aligned_tokens, + ) + quota_contract = True + handshake_contract = True + finite_diagnostics = True + method_audits: dict[str, list[dict[str, object]]] = {} + churn: dict[str, dict[str, float | int | None]] = {} + + for method in selector_methods: + tasks = selector_diagnostics.get(method) + if not isinstance(tasks, Sequence) or len(tasks) != E008_DEVELOPMENT_LIMIT: + quota_contract = False + handshake_contract = False + method_audits[method] = [ + { + "error": ( + "diagnostics must contain exactly " + f"{E008_DEVELOPMENT_LIMIT} task records" + ) + } + ] + continue + + task_audits: list[dict[str, object]] = [] + churn_numerator = 0 + churn_denominator = 0 + for task_index, task_record in enumerate(tasks): + task_id = _diagnostic_int(task_record, "task_id") + task_identity_passed = task_id == E008_DEVELOPMENT_TASK_IDS[task_index] + expected_updates, expected_tokens = expected_task_contract.get(task_id, (-1, -1)) + quota_contract = quota_contract and task_identity_passed + handshake_contract = handshake_contract and task_identity_passed + raw_layers = task_record.get("layers") + if not isinstance(raw_layers, list): + quota_contract = False + handshake_contract = False + task_audits.append({"task_id": task_id, "error": "layers must be an array"}) + continue + observed_layers: dict[int, Mapping[str, object]] = {} + for raw_layer in raw_layers: + if not isinstance(raw_layer, Mapping): + quota_contract = False + handshake_contract = False + continue + layer_index = _diagnostic_int(raw_layer, "layer_index") + if layer_index in observed_layers: + quota_contract = False + handshake_contract = False + continue + observed_layers[layer_index] = raw_layer + + task_quota_passed = ( + task_identity_passed and set(observed_layers) == set(expected_layers) + ) + task_handshake_passed = task_quota_passed + task_updates = 0 + task_tokens = 0 + task_committed_churn = 0 + task_transition_count = 0 + for layer_index, quota in expected_layers.items(): + record = observed_layers.get(layer_index) + if record is None: + task_quota_passed = False + task_handshake_passed = False + continue + observed_quota = _diagnostic_int(record, "quota") + selected = _diagnostic_int(record, "current_selected_count") + updates = _diagnostic_int(record, "state_updates") + committed = _diagnostic_int( + record, + "observations_committed", + "transition_observations_committed", + ) + staged = _diagnostic_int( + record, + "observations_staged", + "transition_observations_staged", + ) + tokens = _diagnostic_int( + record, + "tokens_observed", + "transition_tokens_observed", + ) + pending = record.get("pending_observation") + current_hash = record.get( + "committed_mask_sha256", + record.get("current_mask_sha256"), + ) + expected_confirmation_two = method in (QUERY_EMA_C2, CORA_C2_PRIMARY) + task_quota_passed = task_quota_passed and all( + ( + observed_quota == quota, + selected == quota, + _mask_hash_is_valid(current_hash), + record.get("confirmation_two") is expected_confirmation_two, + ) + ) + if method in (CORA_RAW, CORA_C2_PRIMARY): + task_quota_passed = task_quota_passed and ( + record.get("selection_method") == method + ) + task_handshake_passed = task_handshake_passed and all( + ( + staged == committed, + committed == updates, + committed == expected_updates, + tokens == expected_tokens, + pending is False, + ) + ) + task_updates += max(updates, 0) + task_tokens += max(tokens, 0) + + for key, value in record.items(): + if ( + isinstance(value, (int, float)) + and not isinstance(value, bool) + and not math.isfinite(float(value)) + ): + finite_diagnostics = False + if key in { + "last_raw_cutoff_score", + "last_raw_score_gap", + "last_raw_normalized_gap", + "last_committed_cutoff_score", + "last_committed_score_gap", + "last_committed_normalized_gap", + "last_cutoff_score_margin", + "raw_normalized_churn", + "committed_normalized_churn", + "observability_trace", + "observability_min", + "observability_max", + } and value is not None: + try: + _finite_float(value, context=f"{method} {key}") + except ValueError: + finite_diagnostics = False + + try: + l2norm_eps = _finite_float( + record.get("l2norm_eps"), + context=f"{method} l2norm_eps", + ) + except ValueError: + l2norm_eps = -1.0 + finite_diagnostics = False + if l2norm_eps != 1e-6: + task_handshake_passed = False + if method in (QUERY_EMA_PRIMARY, QUERY_EMA_C2): + try: + ema_decay = _finite_float( + record.get("ema_decay"), + context=f"{method} EMA decay", + ) + except ValueError: + ema_decay = -1.0 + finite_diagnostics = False + if not math.isclose( + ema_decay, + 2.0 ** (-1.0 / QUERY_EMA_HALF_LIFE), + rel_tol=0.0, + abs_tol=1e-15, + ): + task_handshake_passed = False + + if method in (CORA_RAW, QUERY_EMA_C2, CORA_C2_PRIMARY): + raw_hash = record.get("raw_mask_sha256") + if not _mask_hash_is_valid(raw_hash): + task_quota_passed = False + if method in (CORA_RAW, CORA_C2_PRIMARY): + try: + observability_trace = _finite_float( + record.get("observability_trace"), + context=f"{method} observability trace", + ) + observability_min = _finite_float( + record.get("observability_min"), + context=f"{method} observability minimum", + ) + observability_max = _finite_float( + record.get("observability_max"), + context=f"{method} observability maximum", + ) + except ValueError: + observability_trace = -1.0 + observability_min = -1.0 + observability_max = -1.0 + finite_diagnostics = False + if not all( + ( + math.isclose( + observability_trace, + 1.0, + rel_tol=0.0, + abs_tol=5e-6, + ), + 0.0 <= observability_min <= observability_max <= 1.0, + record.get("observability_dtype") == "torch.float32", + _diagnostic_int(record, "observability_diagonal_bytes") > 0, + _diagnostic_int(record, "previous_raw_mask_bytes") > 0, + _diagnostic_int(record, "selector_auxiliary_bytes") > 0, + ) + ): + task_handshake_passed = False + transitions = _diagnostic_int( + record, + "mask_transition_count", + "selection_transitions", + "transition_count", + ) + raw_churn = _diagnostic_int( + record, + "raw_xor_churn_total", + "cumulative_raw_xor_churn", + "total_raw_mask_churn", + ) + committed_churn = _diagnostic_int( + record, + "committed_xor_churn_total", + "cumulative_committed_xor_churn", + "total_committed_mask_churn", + ) + admissions = _diagnostic_int(record, "admissions_total") + dwell = _diagnostic_int(record, "dwell_total") + denominator = 2 * quota * max(transitions, 0) + expected_raw_normalized = ( + raw_churn / denominator if denominator > 0 else None + ) + expected_committed_normalized = ( + committed_churn / denominator if denominator > 0 else None + ) + try: + observed_raw_normalized = _finite_float( + record.get("raw_normalized_churn"), + context=f"{method} raw normalized churn", + ) + observed_committed_normalized = _finite_float( + record.get("committed_normalized_churn"), + context=f"{method} committed normalized churn", + ) + except ValueError: + observed_raw_normalized = None + observed_committed_normalized = None + finite_diagnostics = False + if ( + transitions != max(updates - 1, 0) + or raw_churn < 0 + or committed_churn < 0 + or raw_churn > 2 * quota * transitions + or committed_churn > 2 * quota * transitions + or admissions < 0 + or dwell < 0 + or admissions * 2 != committed_churn + or dwell + admissions != quota * transitions + or expected_raw_normalized is None + or expected_committed_normalized is None + or observed_raw_normalized is None + or observed_committed_normalized is None + or not math.isclose( + observed_raw_normalized, + expected_raw_normalized, + rel_tol=0.0, + abs_tol=1e-12, + ) + or not math.isclose( + observed_committed_normalized, + expected_committed_normalized, + rel_tol=0.0, + abs_tol=1e-12, + ) + or (method == CORA_RAW and raw_churn != committed_churn) + ): + task_handshake_passed = False + task_committed_churn += max(committed_churn, 0) + task_transition_count += max(transitions, 0) + churn_denominator += 2 * quota * max(transitions, 0) + + quota_contract = quota_contract and task_quota_passed + handshake_contract = handshake_contract and task_handshake_passed + churn_numerator += task_committed_churn + task_audits.append( + { + "task_id": task_id, + "quota_passed": task_quota_passed, + "handshake_passed": task_handshake_passed, + "state_updates": task_updates, + "tokens_observed": task_tokens, + "committed_xor_churn": task_committed_churn, + "mask_transition_count": task_transition_count, + } + ) + method_audits[method] = task_audits + if method in (CORA_RAW, CORA_C2_PRIMARY): + normalized = ( + churn_numerator / churn_denominator if churn_denominator > 0 else None + ) + churn[method] = { + "committed_xor_churn_total": churn_numerator, + "normalization_denominator": churn_denominator, + "normalized_committed_churn": normalized, + } + + return { + "quota_contract_passed": quota_contract, + "handshake_contract_passed": handshake_contract, + "finite_diagnostics": finite_diagnostics, + "method_audits": method_audits, + "churn": churn, + } + + +def evaluate_cora_c2_development_gate( + *, + aggregates: Mapping[str, Mapping[str, float | int]], + aggregates_full_code: Mapping[str, Mapping[str, float | int]], + per_task: Mapping[str, Sequence[Mapping[str, float | int]]], + per_task_full_code: Mapping[str, Sequence[Mapping[str, float | int]]], + storage: Mapping[str, Mapping[str, int | float | bool]], + selector_diagnostics: Mapping[str, Sequence[Mapping[str, object]]], + contrasts: Mapping[str, Mapping[str, Any]], + token_manifest: Sequence[Mapping[str, int]], + expected_quotas: Mapping[int, int], + expected_packed_bytes: int, + expected_query_auxiliary_bytes: int, + expected_cora_auxiliary_bytes: int, +) -> dict[str, Any]: + """Evaluate the complete frozen Experiment 008 development conjunction.""" + + static_name = "target_directional_fisher_difference_int4" + required = ( + static_name, + ADAPTIVE_TARGET_FISHER, + QUERY_EMA_PRIMARY, + CORA_RAW, + QUERY_EMA_C2, + CORA_C2_PRIMARY, + ) + expected_method_set = set(required) + for partition_name, partition in ( + ("aggregates", aggregates), + ("aggregates_full_code", aggregates_full_code), + ("per_task", per_task), + ("per_task_full_code", per_task_full_code), + ("storage", storage), + ): + if set(partition) != expected_method_set: + raise ValueError( + "CORA-C2 development " + f"{partition_name} methods do not match the frozen six-method set" + ) + expected_selector_methods = { + QUERY_EMA_PRIMARY, + CORA_RAW, + QUERY_EMA_C2, + CORA_C2_PRIMARY, + } + if set(selector_diagnostics) != expected_selector_methods: + raise ValueError( + "CORA-C2 selector diagnostics do not match the frozen four dynamic methods" + ) + for name in required: + if any( + name not in partition + for partition in (aggregates, per_task, per_task_full_code, storage) + ): + raise ValueError(f"CORA-C2 development gate lacks required method {name}") + if ( + len(per_task[name]) != E008_DEVELOPMENT_LIMIT + or len(per_task_full_code[name]) != E008_DEVELOPMENT_LIMIT + ): + raise ValueError( + "CORA-C2 development gate requires exactly " + f"{E008_DEVELOPMENT_LIMIT} aligned tasks per required method" + ) + + expected_ids = tuple(int(record["task_id"]) for record in token_manifest) + aligned_tokens_by_id = { + int(record["task_id"]): int(record["aligned_scored_tokens"]) + for record in token_manifest + } + full_tokens_by_id = { + int(record["task_id"]): int(record["full_code_scored_tokens"]) + for record in token_manifest + } + expected_aligned_total = sum(aligned_tokens_by_id.values()) + expected_full_total = sum(full_tokens_by_id.values()) + for name in required: + aligned_rows = per_task[name] + full_rows = per_task_full_code[name] + if tuple(int(row["task_id"]) for row in aligned_rows) != expected_ids: + raise ValueError(f"{name} aligned task IDs do not match the token manifest") + if tuple(int(row["task_id"]) for row in full_rows) != expected_ids: + raise ValueError(f"{name} full-code task IDs do not match the token manifest") + if any( + int(row["token_count"]) != aligned_tokens_by_id[int(row["task_id"])] + for row in aligned_rows + ): + raise ValueError(f"{name} aligned token counts do not match the token manifest") + if any( + int(row["token_count"]) != full_tokens_by_id[int(row["task_id"])] + for row in full_rows + ): + raise ValueError(f"{name} full-code token counts do not match the token manifest") + if ( + int(aggregates[name].get("task_count", -1)) != E008_DEVELOPMENT_LIMIT + or int(aggregates[name].get("token_count", -1)) != expected_aligned_total + or int(aggregates_full_code[name].get("task_count", -1)) + != E008_DEVELOPMENT_LIMIT + or int(aggregates_full_code[name].get("token_count", -1)) + != expected_full_total + ): + raise ValueError(f"{name} aggregate task/token counts do not match the manifest") + + audit = _audit_e008_selector_diagnostics( + selector_diagnostics=selector_diagnostics, + expected_quotas=expected_quotas, + token_manifest=token_manifest, + ) + expected_promotions = sum(int(value) for value in expected_quotas.values()) + expected_auxiliary = { + QUERY_EMA_PRIMARY: expected_query_auxiliary_bytes, + CORA_RAW: expected_cora_auxiliary_bytes, + QUERY_EMA_C2: expected_cora_auxiliary_bytes, + CORA_C2_PRIMARY: expected_cora_auxiliary_bytes, + } + storage_audit: dict[str, dict[str, int]] = {} + exact_storage = True + exact_promotions = True + for method, auxiliary_bytes in expected_auxiliary.items(): + summary = storage[method] + observed_packed = int(summary.get("resident_bytes", -1)) + observed_auxiliary = int(summary.get("selector_auxiliary_bytes", -1)) + observed_total = int(summary.get("resident_bytes_including_selector", -1)) + observed_promotions = int(summary.get("high_precision_groups", -1)) + exact_storage = exact_storage and all( + ( + observed_packed == expected_packed_bytes, + observed_auxiliary == auxiliary_bytes, + observed_total == expected_packed_bytes + auxiliary_bytes, + ) + ) + exact_promotions = exact_promotions and observed_promotions == expected_promotions + storage_audit[method] = { + "packed_bytes": observed_packed, + "selector_auxiliary_bytes": observed_auxiliary, + "resident_bytes_including_selector": observed_total, + "high_precision_groups": observed_promotions, + } + + all_values_finite = bool(audit["finite_diagnostics"]) + for name in required: + for aggregate_partition in (aggregates, aggregates_full_code): + for key, value in aggregate_partition[name].items(): + try: + _finite_float(value, context=f"{name} aggregate {key}") + except ValueError: + all_values_finite = False + for partition in (per_task, per_task_full_code): + for row in partition[name]: + if row.get("all_logits_finite") is not True: + all_values_finite = False + for key, value in row.items(): + if key in ("task_id", "all_logits_finite"): + continue + try: + _finite_float(value, context=f"{name} per-task {key}") + except ValueError: + all_values_finite = False + + primary = aggregates[CORA_C2_PRIMARY] + static = aggregates[static_name] + adaptive = aggregates[ADAPTIVE_TARGET_FISHER] + cqer = aggregates[QUERY_EMA_PRIMARY] + raw = aggregates[CORA_RAW] + primary_nll = _finite_float(primary["macro_delta_nll"], context="CORA-C2 NLL") + static_nll = _finite_float(static["macro_delta_nll"], context="static NLL") + adaptive_nll = _finite_float(adaptive["macro_delta_nll"], context="adaptive NLL") + cqer_nll = _finite_float(cqer["macro_delta_nll"], context="CQER-32 NLL") + raw_nll = _finite_float(raw["macro_delta_nll"], context="raw CORA NLL") + + def reduction(baseline: float, candidate: float) -> float | None: + return (baseline - candidate) / baseline if baseline > 0 else None + + relative_static = reduction(static_nll, primary_nll) + relative_adaptive = reduction(adaptive_nll, primary_nll) + relative_cqer = reduction(cqer_nll, primary_nll) + raw_relative_cqer = reduction(cqer_nll, raw_nll) + c2_worsening_raw = ( + (primary_nll - raw_nll) / raw_nll if raw_nll > 0 else None + ) + + primary_top1 = _finite_float( + primary["macro_top1_agreement"], context="CORA-C2 top1" + ) + static_top1 = _finite_float(static["macro_top1_agreement"], context="static top1") + adaptive_top1 = _finite_float( + adaptive["macro_top1_agreement"], context="adaptive top1" + ) + cqer_top1 = _finite_float(cqer["macro_top1_agreement"], context="CQER-32 top1") + raw_top1 = _finite_float(raw["macro_top1_agreement"], context="raw CORA top1") + primary_cvar = _finite_float(primary["macro_cvar95_kl"], context="CORA-C2 CVaR95") + static_cvar = _finite_float(static["macro_cvar95_kl"], context="static CVaR95") + adaptive_cvar = _finite_float( + adaptive["macro_cvar95_kl"], context="adaptive CVaR95" + ) + static_lower = _paired_lower_bound(contrasts, static_name) + cqer_lower = _paired_lower_bound(contrasts, QUERY_EMA_PRIMARY) + + churn = audit["churn"] + raw_churn_record = churn.get(CORA_RAW, {}) + c2_churn_record = churn.get(CORA_C2_PRIMARY, {}) + raw_normalized_churn = raw_churn_record.get("normalized_committed_churn") + c2_normalized_churn = c2_churn_record.get("normalized_committed_churn") + churn_reduction = ( + (float(raw_normalized_churn) - float(c2_normalized_churn)) + / float(raw_normalized_churn) + if isinstance(raw_normalized_churn, (int, float)) + and not isinstance(raw_normalized_churn, bool) + and isinstance(c2_normalized_churn, (int, float)) + and not isinstance(c2_normalized_churn, bool) + and float(raw_normalized_churn) > 0 + else None + ) + + checks: dict[str, dict[str, Any]] = { + "exact_per_layer_quotas": { + "passed": bool(audit["quota_contract_passed"]) and exact_promotions, + "expected_quotas": {str(key): value for key, value in sorted(expected_quotas.items())}, + "expected_total_promotions": expected_promotions, + "method_audits": audit["method_audits"], + }, + "exact_packed_and_selector_bytes": { + "passed": exact_storage, + "expected_packed_bytes": expected_packed_bytes, + "expected_query_auxiliary_bytes": expected_query_auxiliary_bytes, + "expected_cora_auxiliary_bytes": expected_cora_auxiliary_bytes, + "observed": storage_audit, + }, + "exact_stage_consume_handshake": { + "passed": bool(audit["handshake_contract_passed"]), + "method_audits": audit["method_audits"], + }, + "all_values_finite": {"passed": all_values_finite}, + "lower_nll_than_primary_comparators": { + "passed": primary_nll < min(static_nll, adaptive_nll, cqer_nll), + "primary": primary_nll, + "static": static_nll, + "adaptive": adaptive_nll, + "cqer": cqer_nll, + }, + "relative_nll_reduction_vs_static": { + "passed": relative_static is not None and relative_static >= MIN_RELATIVE_NLL_REDUCTION, + "observed": relative_static, + "minimum": MIN_RELATIVE_NLL_REDUCTION, + }, + "relative_nll_reduction_vs_adaptive": { + "passed": relative_adaptive is not None + and relative_adaptive >= CORA_MIN_RELATIVE_NLL_REDUCTION_VS_ADAPTIVE, + "observed": relative_adaptive, + "minimum": CORA_MIN_RELATIVE_NLL_REDUCTION_VS_ADAPTIVE, + }, + "relative_nll_reduction_vs_cqer": { + "passed": relative_cqer is not None + and relative_cqer >= CORA_MIN_RELATIVE_NLL_REDUCTION_VS_CQER, + "observed": relative_cqer, + "minimum": CORA_MIN_RELATIVE_NLL_REDUCTION_VS_CQER, + }, + "paired_lower_ci_vs_static": { + "passed": static_lower is not None and static_lower > 0, + "observed_lower_bound": static_lower, + "required": "strictly greater than zero", + }, + "paired_lower_ci_vs_cqer": { + "passed": cqer_lower is not None and cqer_lower > 0, + "observed_lower_bound": cqer_lower, + "required": "strictly greater than zero", + }, + "top1_margin_vs_static_adaptive": { + "passed": primary_top1 >= max(static_top1, adaptive_top1) - TOP1_DISADVANTAGE_MARGIN, + "observed_disadvantage": max(static_top1, adaptive_top1) - primary_top1, + "maximum": TOP1_DISADVANTAGE_MARGIN, + }, + "top1_not_lower_than_cqer": { + "passed": primary_top1 >= cqer_top1, + "primary": primary_top1, + "cqer": cqer_top1, + }, + "cvar95_margin_vs_static_adaptive": { + "passed": primary_cvar <= min(static_cvar, adaptive_cvar) + CVAR95_DISADVANTAGE_MARGIN, + "observed_disadvantage": primary_cvar - min(static_cvar, adaptive_cvar), + "maximum": CVAR95_DISADVANTAGE_MARGIN, + }, + "raw_cora_relative_nll_reduction_vs_cqer": { + "passed": raw_relative_cqer is not None + and raw_relative_cqer >= CORA_RAW_MIN_RELATIVE_NLL_REDUCTION_VS_CQER, + "observed": raw_relative_cqer, + "minimum": CORA_RAW_MIN_RELATIVE_NLL_REDUCTION_VS_CQER, + }, + "c2_churn_reduction_vs_raw": { + "passed": churn_reduction is not None + and churn_reduction >= CORA_C2_MIN_NORMALIZED_CHURN_REDUCTION, + "observed": churn_reduction, + "minimum": CORA_C2_MIN_NORMALIZED_CHURN_REDUCTION, + "raw": raw_churn_record, + "c2": c2_churn_record, + }, + "c2_top1_not_lower_than_raw": { + "passed": primary_top1 >= raw_top1, + "primary": primary_top1, + "raw": raw_top1, + }, + "c2_nll_worsening_vs_raw": { + "passed": c2_worsening_raw is not None + and c2_worsening_raw <= CORA_C2_MAX_RELATIVE_NLL_WORSENING_VS_RAW, + "observed": c2_worsening_raw, + "maximum": CORA_C2_MAX_RELATIVE_NLL_WORSENING_VS_RAW, + }, + } + return { + "schema": "recurquant.experiment008-cora-c2-development-gate.v1", + "applicable": True, + "passed": all(check["passed"] is True for check in checks.values()), + "primary": CORA_C2_PRIMARY, + "comparators": { + "static": static_name, + "adaptive": ADAPTIVE_TARGET_FISHER, + "cqer": QUERY_EMA_PRIMARY, + "raw_cora": CORA_RAW, + "cqer_c2_ablation": QUERY_EMA_C2, + }, + "checks": checks, + } + + +def primary_claim_text(primary_name: str) -> str: + """Describe the actual primary without implying a missing loss selector.""" + + if primary_name == CORA_C2_PRIMARY: + return ( + "The actual primary uses a causal diagonal observability predictor " + "derived from the Gated DeltaNet state transition, multiplies it by " + "aligned INT4-to-INT8 row squared-error reduction, and applies the frozen " + "Confirmation-2 admission rule under target-Fisher layer quotas." + ) + if primary_name == CORA_RAW: + return ( + "This ablation uses the same causal diagonal observability predictor " + "and row squared-error benefit as CORA-C2 without Confirmation-2." + ) + if primary_name == QUERY_EMA_C2: + return ( + "This ablation applies Confirmation-2 to the CQER-32 normalized-query-" + "energy EMA selector under the same fixed layer quotas." + ) + if primary_name == QUERY_EMA_PRIMARY: + return ( + "The actual primary uses target-directional-Fisher per-layer quotas " + "and causally weights each per-write aligned INT4-to-INT8 row-MSE " + "reduction by a normalized-query-energy EMA with a 32-token half-life." + ) + if primary_name == RANK_FUSION_PRIMARY: + return ( + "The actual primary uses target-directional-Fisher per-layer quotas " + "and equal-weight ordinal rank fusion between calibrated static row " + "sensitivity and causal per-write INT4-to-INT8 MSE reduction." + ) + if primary_name == ADAPTIVE_TARGET_FISHER: + return ( + "The actual primary uses target-directional-Fisher per-layer quotas " + "with causal per-write row-MSE selection." + ) + return ( + f"The actual primary is {primary_name}, a static HRR row policy; no " + "loss-selector primary is present." + ) + + +def diagnostic_exit_code(*, heldout_calibration: bool, gate_passed: object) -> int: + """Return the prespecified non-zero status for a failed frozen holdout gate.""" + + return 2 if heldout_calibration and gate_passed is not True else 0 + + +def scores_from_artifact(evidence: dict[str, Any], name: str) -> dict[int, torch.Tensor]: + try: + score_record = evidence["scores"][name] + arrays = score_record["arrays"] + expected_arrays_sha256 = score_record["canonical_arrays_sha256"] + except (KeyError, TypeError) as error: + raise ValueError(f"selector artifact lacks score arrays for {name}") from error + if not isinstance(arrays, dict): + raise ValueError(f"selector score arrays for {name} must be an object") + actual_arrays_sha256 = sha256_bytes(canonical_json_bytes(arrays)) + if expected_arrays_sha256 != actual_arrays_sha256: + raise ValueError(f"selector score array hash does not match for {name}") + scores: dict[int, torch.Tensor] = {} + for raw_layer, values in arrays.items(): + try: + layer_index = int(raw_layer) + tensor = torch.tensor(values, dtype=torch.float64) + except (TypeError, ValueError) as error: + raise ValueError(f"invalid selector scores for layer {raw_layer!r}") from error + if str(layer_index) != raw_layer or layer_index < 0 or layer_index in scores: + raise ValueError(f"selector score layer key {raw_layer!r} is not canonical and unique") + if tensor.ndim != 2 or not torch.isfinite(tensor).all().item(): + raise ValueError(f"selector layer {layer_index} scores must be a finite matrix") + scores[layer_index] = tensor + if not scores: + raise ValueError(f"selector score arrays for {name} must not be empty") + return scores + + +def plan_from_artifact(evidence: dict[str, Any], name: str) -> ExactBudgetRowPlan: + scores = scores_from_artifact(evidence, name) + budget = evidence["byte_budget"] + plan = select_rows_exact_budget( + scores, + target_resident_bytes=int(budget["target_resident_bytes"]), + group_size=int(budget["group_size"]), + scale_bits=int(budget["scale_bits"]), + ) + recorded = evidence["plans"][name] + expected_locations = tuple( + ( + int(location["layer_index"]), + int(location["head_index"]), + int(location["row_index"]), + ) + for location in recorded["locations"] + ) + actual_locations = tuple( + (location.layer_index, location.head_index, location.row_index) + for location in plan.high_precision_rows + ) + if expected_locations != actual_locations: + raise ValueError(f"recomputed {name} plan does not match the selector artifact") + return plan + + +def random_plan_like(template: ExactBudgetRowPlan) -> ExactBudgetRowPlan: + generator = torch.Generator().manual_seed(RANDOM_ROW_SEED) + scores = { + layer_index: torch.rand((heads, rows), generator=generator) + for layer_index, heads, rows in template.score_shapes + } + return select_rows_exact_budget( + scores, + target_resident_bytes=template.target_resident_bytes, + low_bits=template.low_bits, + high_bits=template.high_bits, + group_size=template.group_size, + scale_bits=template.scale_bits, + ) + + +def make_caches( + model: Qwen3_5ForCausalLM, + *, + plans: dict[str, ExactBudgetRowPlan], + adaptive_plans: dict[str, ExactBudgetRowPlan], + rank_fusion_specs: Mapping[str, RankFusionCacheSpec] | None = None, + query_ema_plans: Mapping[str, ExactBudgetRowPlan] | None = None, + query_ema_c2_plans: Mapping[str, ExactBudgetRowPlan] | None = None, + cora_specs: Mapping[str, CoraCacheSpec] | None = None, + include_default_baselines: bool = True, +) -> dict[ + str, + PackedRecurrentStateCache + | MixedPackedRecurrentStateCache + | AdaptiveMixedPackedRecurrentStateCache + | CoraMixedPackedRecurrentStateCache + | QueryEmaMixedPackedRecurrentStateCache + | RankFusedMixedPackedRecurrentStateCache, +]: + caches: dict[ + str, + PackedRecurrentStateCache + | MixedPackedRecurrentStateCache + | AdaptiveMixedPackedRecurrentStateCache + | CoraMixedPackedRecurrentStateCache + | QueryEmaMixedPackedRecurrentStateCache + | RankFusedMixedPackedRecurrentStateCache, + ] = ( + { + "uniform_int4": create_qwen35_packed_cache(model, bits=4), + "v02_layer0_static": create_qwen35_v02_mixed_cache(model), + } + if include_default_baselines + else {} + ) + for name, plan in plans.items(): + caches[name] = create_qwen35_exact_budget_cache(model, plan=plan) + for name, plan in adaptive_plans.items(): + if name in caches: + raise ValueError(f"adaptive cache name duplicates another method: {name}") + caches[name] = create_qwen35_adaptive_exact_budget_cache(model, plan=plan) + score_device = next(model.parameters()).device + for name, (plan, static_scores, static_rank_weight) in (rank_fusion_specs or {}).items(): + if name in caches: + raise ValueError(f"rank-fusion cache name duplicates another method: {name}") + scores_on_device = { + layer_index: scores.to(score_device) + for layer_index, scores in static_scores.items() + } + caches[name] = create_qwen35_rank_fused_exact_budget_cache( + model, + plan=plan, + static_scores_by_layer=scores_on_device, + static_rank_weight=static_rank_weight, + ) + for name, plan in (query_ema_plans or {}).items(): + if name in caches: + raise ValueError(f"query-EMA cache name duplicates another method: {name}") + caches[name] = create_qwen35_query_ema_exact_budget_cache(model, plan=plan) + for name, plan in (query_ema_c2_plans or {}).items(): + if name in caches: + raise ValueError(f"query-EMA C2 cache name duplicates another method: {name}") + caches[name] = create_qwen35_query_ema_exact_budget_cache( + model, + plan=plan, + confirmation_two=True, + ) + for name, (plan, confirmation_two) in (cora_specs or {}).items(): + if name in caches: + raise ValueError(f"CORA cache name duplicates another method: {name}") + caches[name] = create_qwen35_cora_exact_budget_cache( + model, + plan=plan, + confirmation_two=confirmation_two, + ) + return caches + + +def _append_metrics( + accumulators: dict[str, _TokenAccumulator], + reference_logits: torch.Tensor, + candidate_logits: dict[str, torch.Tensor], + target: torch.Tensor, +) -> None: + reference_finite = torch.isfinite(reference_logits).all() + for name, logits in candidate_logits.items(): + accumulators[name].append( + token_fidelity(reference_logits, logits, target), + outputs_finite=reference_finite & torch.isfinite(logits).all(), + ) + + +def evaluate_task( + model: Qwen3_5ForCausalLM, + *, + prompt_ids: torch.Tensor, + code_ids: torch.Tensor, + plans: dict[str, ExactBudgetRowPlan], + adaptive_plans: dict[str, ExactBudgetRowPlan], + rank_fusion_specs: Mapping[str, RankFusionCacheSpec] | None = None, + query_ema_plans: Mapping[str, ExactBudgetRowPlan] | None = None, + query_ema_c2_plans: Mapping[str, ExactBudgetRowPlan] | None = None, + cora_specs: Mapping[str, CoraCacheSpec] | None = None, + include_default_baselines: bool = True, +) -> tuple[ + dict[str, dict[str, float | int]], + dict[str, dict[str, float | int]], + dict[str, dict[str, int | float | bool]], + dict[str, object], + int, +]: + reference_cache = DynamicCache(config=model.config) + caches = make_caches( + model, + plans=plans, + adaptive_plans=adaptive_plans, + rank_fusion_specs=rank_fusion_specs, + query_ema_plans=query_ema_plans, + query_ema_c2_plans=query_ema_c2_plans, + cora_specs=cora_specs, + include_default_baselines=include_default_baselines, + ) + aligned_accumulators = {name: _TokenAccumulator.empty() for name in caches} + full_code_accumulators = {name: _TokenAccumulator.empty() for name in caches} + + query_ema_caches = { + name: cache + for name, cache in caches.items() + if isinstance(cache, QueryEmaMixedPackedRecurrentStateCache) + } + cora_caches = { + name: cache + for name, cache in caches.items() + if isinstance(cache, CoraMixedPackedRecurrentStateCache) + } + query_observer_context = ( + Qwen35QueryEnergyObserver(model, caches=list(query_ema_caches.values())) + if query_ema_caches + else nullcontext() + ) + transition_observer_context = ( + Qwen35TransitionObserver(model, caches=list(cora_caches.values())) + if cora_caches + else nullcontext() + ) + with query_observer_context, transition_observer_context: + reference_output = model( + prompt_ids, + past_key_values=reference_cache, + use_cache=True, + logits_to_keep=1, + ) + candidate_outputs = { + name: model( + prompt_ids, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + for name, cache in caches.items() + } + _append_metrics( + full_code_accumulators, + reference_output.logits, + {name: output.logits for name, output in candidate_outputs.items()}, + code_ids[:, :1], + ) + + for token_index in range(code_ids.shape[1] - 1): + input_token = code_ids[:, token_index : token_index + 1] + target_token = code_ids[:, token_index + 1 : token_index + 2] + reference_output = model( + input_token, + past_key_values=reference_cache, + use_cache=True, + logits_to_keep=1, + ) + candidate_outputs = { + name: model( + input_token, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + for name, cache in caches.items() + } + _append_metrics( + aligned_accumulators, + reference_output.logits, + {name: output.logits for name, output in candidate_outputs.items()}, + target_token, + ) + _append_metrics( + full_code_accumulators, + reference_output.logits, + {name: output.logits for name, output in candidate_outputs.items()}, + target_token, + ) + + reference_bytes = sum( + state.tensor.numel() * state.tensor.element_size() + for state in iter_recurrent_states(reference_cache) + ) + return ( + {name: accumulator.summary() for name, accumulator in aligned_accumulators.items()}, + {name: accumulator.summary() for name, accumulator in full_code_accumulators.items()}, + {name: cache.storage_summary() for name, cache in caches.items()}, + { + **{ + name: cache.query_ema_diagnostics() + for name, cache in query_ema_caches.items() + }, + **{ + name: cache.observability_diagnostics() + for name, cache in cora_caches.items() + }, + }, + reference_bytes, + ) + + +def git_state() -> dict[str, object]: + repository_root = Path(__file__).resolve().parents[1] + commit = subprocess.run( + ["git", "rev-parse", "HEAD"], + check=True, + capture_output=True, + text=True, + cwd=repository_root, + ).stdout.strip() + status = subprocess.run( + ["git", "status", "--short"], + check=True, + capture_output=True, + text=True, + cwd=repository_root, + ).stdout.splitlines() + return {"commit": commit, "worktree_clean": not status, "status": status} + + +def source_file_hashes( + repository_root: Path, + relative_paths: Sequence[str] = EVALUATOR_SOURCE_FILES, +) -> dict[str, str]: + """Hash the frozen evaluator implementation files in stable path order.""" + + resolved_root = repository_root.resolve() + hashes: dict[str, str] = {} + for raw_path in relative_paths: + path = (resolved_root / raw_path).resolve() + try: + relative = path.relative_to(resolved_root).as_posix() + except ValueError as error: + raise ValueError(f"source path escapes repository root: {raw_path}") from error + if not path.is_file(): + raise ValueError(f"required evaluator source file is missing: {relative}") + hashes[relative] = sha256_bytes(path.read_bytes()) + return hashes + + +def validate_heldout_repository_start( + repository: Mapping[str, object], + selectors: Sequence[Mapping[str, Any]], +) -> None: + """Require a clean frozen commit shared by evaluator and selector artifacts.""" + + commit = repository.get("commit") + if not isinstance(commit, str) or not commit: + raise ValueError("heldout repository commit is missing") + if repository.get("worktree_clean") is not True or repository.get("status") != []: + raise ValueError("heldout-calibration requires a clean worktree at start") + for selector in selectors: + selector_repository = _require_mapping( + selector.get("repository"), + context="selector repository", + ) + if selector_repository.get("commit") != commit: + raise ValueError("selector artifact commit does not match heldout evaluator commit") + if ( + selector_repository.get("worktree_clean") is not True + or selector_repository.get("status") != [] + ): + raise ValueError("selector artifact was not generated from a clean worktree") + + +def validate_cora_development_repository_start( + repository: Mapping[str, object], +) -> None: + """Require committed, clean E008 code before spending its frozen window.""" + + commit = repository.get("commit") + if not isinstance(commit, str) or len(commit) != 40: + raise ValueError("Experiment 008 requires a resolved 40-character Git commit") + if repository.get("worktree_clean") is not True or repository.get("status") != []: + raise ValueError( + "Experiment 008 development requires a clean committed worktree before " + "dataset loading, tokenization, or model execution" + ) + + +def validate_heldout_output_path(output: Path, repository_root: Path) -> None: + """Require heldout output to stay outside Git state or under an ignore rule.""" + + resolved_root = repository_root.resolve() + resolved_output = output.resolve() + try: + relative = resolved_output.relative_to(resolved_root) + except ValueError: + return + ignored = subprocess.run( + ["git", "check-ignore", "--quiet", "--", relative.as_posix()], + cwd=resolved_root, + check=False, + ) + if ignored.returncode != 0: + raise ValueError( + "heldout output inside the repository must be Git-ignored so the worktree remains clean" + ) + + +def validate_heldout_repository_end( + *, + start_repository: Mapping[str, object], + end_repository: Mapping[str, object], + start_source_hashes: Mapping[str, str], + end_source_hashes: Mapping[str, str], +) -> None: + """Refuse heldout evidence if commit, worktree, or source files changed mid-run.""" + + if end_repository.get("commit") != start_repository.get("commit"): + raise RuntimeError("repository commit changed during heldout-calibration") + if end_repository.get("worktree_clean") is not True or end_repository.get("status") != []: + raise RuntimeError("worktree changed during heldout-calibration") + if dict(end_source_hashes) != dict(start_source_hashes): + raise RuntimeError("evaluator source files changed during heldout-calibration") + + +def validate_cora_development_repository_end( + *, + start_repository: Mapping[str, object], + end_repository: Mapping[str, object], + start_source_hashes: Mapping[str, str], + end_source_hashes: Mapping[str, str], +) -> None: + """Abort E008 evidence if its committed implementation changes mid-run.""" + + if end_repository.get("commit") != start_repository.get("commit"): + raise RuntimeError("Experiment 008 repository commit changed during evaluation") + if end_repository.get("worktree_clean") is not True or end_repository.get("status") != []: + raise RuntimeError("Experiment 008 worktree changed during evaluation") + if dict(end_source_hashes) != dict(start_source_hashes): + raise RuntimeError("Experiment 008 evaluator sources changed during evaluation") + + +def validate_storage_boundary_prerequisite( + evidence: Mapping[str, Any], + *, + expected_commit: str, + expected_model: Mapping[str, Any], +) -> None: + """Authenticate and independently check the frozen local derivative gate summary.""" + + if evidence.get("artifact_kind") != STORAGE_BOUNDARY_ARTIFACT_KIND: + raise ValueError("unexpected storage-boundary artifact kind") + model = _require_mapping(evidence.get("model"), context="storage-boundary model") + if model.get("id") != expected_model.get("id") or model.get("revision") != expected_model.get( + "revision" + ): + raise ValueError("storage-boundary model does not match selector model") + if model.get("dtype") != "torch.float32": + raise ValueError("storage-boundary gate must use torch.float32") + + gate = _require_mapping( + evidence.get("derivative_gate"), + context="storage-boundary derivative gate", + ) + if gate.get("passed") is not True or gate.get("failures") != []: + raise ValueError("storage-boundary derivative gate did not pass") + thresholds = _require_mapping(gate.get("thresholds"), context="derivative thresholds") + expected_thresholds = { + "model_dtype": "torch.float32", + "baseline_repeat_absolute_tolerance": 1e-7, + "derivative_informative_floor": 1e-8, + "near_zero_absolute_tolerance": 2e-7, + "minimum_informative_rows": 3, + "minimum_sign_agreement": 0.95, + "maximum_median_relative_error": 0.10, + "minimum_converged_row_fraction": 0.75, + } + if dict(thresholds) != expected_thresholds: + raise ValueError("storage-boundary derivative thresholds do not match the freeze") + observed = _require_mapping(gate.get("observed"), context="derivative observations") + numeric_passed = ( + int(observed.get("rows", -1)) == 4 + and int(observed.get("informative_rows", -1)) >= 3 + and _finite_float( + observed.get("maximum_baseline_repeat_absolute_error"), + context="maximum baseline repeat error", + ) + <= 1e-7 + and _finite_float(observed.get("sign_agreement"), context="sign agreement") >= 0.95 + and _finite_float( + observed.get("median_relative_error"), + context="median relative error", + ) + <= 0.10 + and _finite_float( + observed.get("converged_row_fraction"), + context="converged row fraction", + ) + >= 0.75 + and int(observed.get("near_zero_checks_passed", -1)) + == int(observed.get("near_zero_checks", -2)) + ) + if not numeric_passed: + raise ValueError("storage-boundary numeric observations do not pass the frozen gate") + + implementation = _require_mapping( + evidence.get("implementation"), + context="storage-boundary implementation", + ) + source_start = _require_mapping( + implementation.get("source_hashes_start"), + context="storage-boundary source hashes start", + ) + source_end = _require_mapping( + implementation.get("source_hashes_end"), + context="storage-boundary source hashes end", + ) + if ( + not source_start + or dict(source_start) != dict(source_end) + or implementation.get("unchanged_during_run") is not True + ): + raise ValueError("storage-boundary implementation source hashes are not stable") + repository = _require_mapping( + evidence.get("repository"), + context="storage-boundary repository", + ) + for phase in ("start", "end"): + state = _require_mapping( + repository.get(phase), + context=f"storage-boundary repository {phase}", + ) + if state.get("commit") != expected_commit: + raise ValueError("storage-boundary commit does not match heldout evaluator") + if state.get("worktree_clean") is not True or state.get("status") != []: + raise ValueError("storage-boundary artifact was not generated cleanly") + + +def _package_versions() -> dict[str, str]: + names = ("datasets", "numpy", "safetensors", "torch", "transformers") + return {name: importlib.metadata.version(name) for name in names} + + +def main() -> int: + args = parse_args() + if args.limit is not None and not 1 <= args.limit <= 16: + raise ValueError("--limit must be between 1 and 16") + if args.calibration_offset < 0: + raise ValueError("--calibration-offset must be non-negative") + if args.bootstrap_samples <= 0: + raise ValueError("--bootstrap-samples must be positive") + if sum(bool(value) for value in (args.rank_fusion, args.query_ema, args.cora_c2)) > 1: + raise ValueError("--rank-fusion, --query-ema, and --cora-c2 are mutually exclusive") + cora_development = bool(args.cora_c2) + heldout_calibration = args.calibration_offset > 0 and not cora_development + repository_root = Path(__file__).resolve().parents[1] + repository_start = git_state() + source_hashes_start = source_file_hashes(repository_root) + + selector, selector_sha256 = load_selector_artifact( + args.selector_artifact, + expected_kind=HRR_ARTIFACT_KIND, + ) + validate_selector_contract(selector) + loss_selector: dict[str, Any] | None = None + loss_selector_sha256: str | None = None + if args.loss_selector_artifact is not None: + loss_selector, loss_selector_sha256 = load_selector_artifact( + args.loss_selector_artifact, + expected_kind=LOSS_ARTIFACT_KIND, + ) + validate_compatible_selector(selector, loss_selector) + if args.rank_fusion and loss_selector is None: + raise ValueError("--rank-fusion requires --loss-selector-artifact") + if (args.query_ema or args.cora_c2) and loss_selector is None: + raise ValueError("--query-ema and --cora-c2 require --loss-selector-artifact") + storage_boundary: dict[str, Any] | None = None + storage_boundary_sha256: str | None = None + if args.storage_boundary_artifact is not None: + storage_boundary, storage_boundary_sha256 = load_selector_artifact( + args.storage_boundary_artifact, + expected_kind=STORAGE_BOUNDARY_ARTIFACT_KIND, + ) + selectors: list[dict[str, Any]] = [selector] + if loss_selector is not None: + selectors.append(loss_selector) + validate_cqer_selector_artifacts(enabled=args.query_ema, selectors=selectors) + validate_cora_selector_artifacts(enabled=args.cora_c2, selectors=selectors) + all_selector_task_ids = selector_task_ids(selectors) + task_records = selector["dataset"]["tasks"] + available_tasks = len(task_records) + limit = ( + E008_DEVELOPMENT_LIMIT + if args.cora_c2 and args.limit is None + else available_tasks if args.limit is None else args.limit + ) + validate_cqer_development_request( + enabled=args.query_ema, + offset=args.calibration_offset, + limit=limit, + bootstrap_samples=args.bootstrap_samples, + ) + validate_cora_development_request( + enabled=args.cora_c2, + offset=args.calibration_offset, + limit=limit, + bootstrap_samples=args.bootstrap_samples, + ) + validate_frozen_holdout_request( + offset=args.calibration_offset, + limit=limit, + bootstrap_samples=args.bootstrap_samples, + selectors=selectors, + loss_selector_present=loss_selector is not None, + storage_boundary_present=storage_boundary is not None, + rank_fusion_enabled=args.rank_fusion, + query_ema_enabled=args.query_ema, + cora_c2_enabled=args.cora_c2, + ) + if heldout_calibration: + validate_heldout_repository_start(repository_start, selectors) + validate_heldout_output_path(args.output, repository_root) + assert storage_boundary is not None + validate_storage_boundary_prerequisite( + storage_boundary, + expected_commit=str(repository_start["commit"]), + expected_model=selector["model"], + ) + if cora_development: + validate_cora_development_repository_start(repository_start) + validate_heldout_output_path(args.output, repository_root) + if args.calibration_offset == 0 and limit > available_tasks: + raise ValueError( + f"--limit={limit} exceeds the selector's {available_tasks} calibration tasks" + ) + window_stop = args.calibration_offset + limit + if window_stop > MBPP_CALIBRATION_SIZE: + raise ValueError( + "calibration window exceeds the frozen ranked calibration population: " + f"{window_stop} > {MBPP_CALIBRATION_SIZE}" + ) + if cora_development: + targeted_rows = load_mbpp_rows_by_task_ids( + "calibration", + task_ids=(*CQER_DEVELOPMENT_TASK_IDS, *E008_DEVELOPMENT_TASK_IDS), + ) + selector_prefix_rows = tuple(targeted_rows[:FROZEN_HOLDOUT_LIMIT]) + rows = tuple(targeted_rows[FROZEN_HOLDOUT_LIMIT:]) + else: + ranked_rows = load_mbpp_rows("calibration", limit=window_stop) + selector_prefix_rows = tuple(ranked_rows[:FROZEN_HOLDOUT_LIMIT]) + rows = select_calibration_window( + ranked_rows, + offset=args.calibration_offset, + limit=limit, + selectors=selectors, + ) + if heldout_calibration or args.query_ema or args.cora_c2: + selector_prefix_ids = [int(row["task_id"]) for row in selector_prefix_rows] + selector_prefix_manifest = mbpp_manifest(selector_prefix_rows, phase="calibration") + for selector_evidence in selectors: + authenticate_selector_prefix( + selector_evidence, + ranked_prefix_manifest=selector_prefix_manifest, + ranked_prefix_task_ids=selector_prefix_ids, + ) + actual_ids = [row["task_id"] for row in rows] + validate_cqer_development_task_ids(enabled=args.query_ema, task_ids=actual_ids) + if args.calibration_offset == 0: + expected_ids = [int(record["task_id"]) for record in task_records[:limit]] + if actual_ids != expected_ids: + raise ValueError("calibration task IDs do not match the selector artifact prefix") + actual_manifest = mbpp_manifest(rows, phase="calibration") + if args.calibration_offset == 0: + validate_calibration_prefix( + selector, + actual_manifest=actual_manifest, + expected_task_ids=actual_ids, + ) + actual_manifest_sha256 = mbpp_manifest_sha256(rows, phase="calibration") + if actual_manifest_sha256 != mbpp_manifest_content_sha256(actual_manifest): + raise RuntimeError("calibration manifest helpers produced inconsistent hashes") + validate_cora_development_identity( + enabled=args.cora_c2, + task_ids=actual_ids, + content_manifest_sha256=actual_manifest_sha256, + ) + + horizon = int(selector["method"]["horizon"]) + hrr_primary_name = f"hrr_h{horizon}" + h1_plan = plan_from_artifact(selector, "hrr_h1") + hrr_primary_plan = plan_from_artifact(selector, hrr_primary_name) + mse_plan = plan_from_artifact(selector, "row_mse") + plans = { + "hrr_h1": h1_plan, + hrr_primary_name: hrr_primary_plan, + "row_mse": mse_plan, + "random_rows_s1101": random_plan_like(hrr_primary_plan), + } + adaptive_plans = {ADAPTIVE_H1: h1_plan} + rank_fusion_specs: dict[str, RankFusionCacheSpec] = {} + query_ema_plans: dict[str, ExactBudgetRowPlan] = {} + query_ema_c2_plans: dict[str, ExactBudgetRowPlan] = {} + cora_specs: dict[str, CoraCacheSpec] = {} + primary_name = hrr_primary_name + primary_plan = hrr_primary_plan + if loss_selector is not None: + plans.update({name: plan_from_artifact(loss_selector, name) for name in LOSS_SCORE_NAMES}) + adaptive_plans[ADAPTIVE_TARGET_FISHER] = plans["target_directional_fisher_difference_int4"] + primary_name = ADAPTIVE_TARGET_FISHER + primary_plan = adaptive_plans[primary_name] + if args.rank_fusion: + target_fisher_scores = scores_from_artifact( + loss_selector, + "target_directional_fisher_difference_int4", + ) + rank_fusion_specs = { + name: (primary_plan, target_fisher_scores, weight) + for name, weight in RANK_FUSION_METHODS + } + primary_name = RANK_FUSION_PRIMARY + if args.query_ema: + query_ema_plans = {QUERY_EMA_PRIMARY: primary_plan} + primary_name = QUERY_EMA_PRIMARY + if args.cora_c2: + # Experiment 008 freezes exactly six methods. Do not spend its + # preregistered window on inherited exploratory baselines. + plans = { + "target_directional_fisher_difference_int4": primary_plan, + } + adaptive_plans = {ADAPTIVE_TARGET_FISHER: primary_plan} + query_ema_plans = {QUERY_EMA_PRIMARY: primary_plan} + query_ema_c2_plans = {QUERY_EMA_C2: primary_plan} + cora_specs = { + CORA_RAW: (primary_plan, False), + CORA_C2_PRIMARY: (primary_plan, True), + } + primary_name = CORA_C2_PRIMARY + cqer_plan_quotas = { + layer_index: len(primary_plan.groups_for_layer(layer_index)) + for layer_index, _, _ in primary_plan.score_shapes + } + validate_cqer_layer_quotas(enabled=args.query_ema, quotas=cqer_plan_quotas) + validate_cora_layer_quotas(enabled=args.cora_c2, quotas=cqer_plan_quotas) + + torch.manual_seed(SEED) + device = select_device(args.device) + dtype = torch.bfloat16 if device.type == "cuda" else torch.float32 + model_record = selector["model"] + if model_record["dtype"] != str(dtype): + raise ValueError( + "evaluation dtype does not match selector calibration dtype: " + f"{dtype} != {model_record['dtype']}" + ) + quantizer_contract = _selector_quantizer_contract(selector) + for name in ("int4", "int8"): + spec = quantizer_contract[name] + if ( + spec["rounding"] != "nearest" + or int(spec["seed"]) != SEED + or float(spec["epsilon"]) != 1e-12 + or int(spec["flatten_last_dims"]) != 1 + ): + raise ValueError( + f"selector {name} quantizer does not match the physical evaluator contract" + ) + model_id = str(model_record["id"]) + revision = str(model_record["revision"]) + tokenizer = AutoTokenizer.from_pretrained( + model_id, + revision=revision, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + + encoded_tasks, token_manifest = encode_task_rows(tokenizer, rows) + token_manifest_sha256 = sha256_bytes(canonical_json_bytes(token_manifest)) + validate_cora_development_identity( + enabled=args.cora_c2, + task_ids=actual_ids, + content_manifest_sha256=actual_manifest_sha256, + token_manifest_sha256=token_manifest_sha256, + ) + authenticated_selector_prefix: dict[str, object] | None = None + if heldout_calibration or args.query_ema or args.cora_c2: + _, selector_prefix_token_manifest = encode_task_rows(tokenizer, selector_prefix_rows) + for selector_evidence in selectors: + validate_actual_token_manifest( + selector_evidence, + selector_prefix_token_manifest, + ) + authenticated_selector_prefix = { + "manifest": selector_prefix_manifest, + "manifest_sha256": mbpp_manifest_content_sha256(selector_prefix_manifest), + "ordered_task_ids": selector_prefix_ids, + "token_manifest": selector_prefix_token_manifest, + "selector_count": len(selectors), + "all_selectors_matched": True, + } + else: + validate_actual_token_manifest(selector, token_manifest) + if loss_selector is not None: + validate_actual_token_manifest(loss_selector, token_manifest) + + model = Qwen3_5ForCausalLM.from_pretrained( + model_id, + revision=revision, + dtype=dtype, + attn_implementation="eager", + low_cpu_mem_usage=True, + use_safetensors=True, + local_files_only=args.local_files_only, + ).to(device) + model.eval() + + per_task: dict[str, list[dict[str, float | int]]] = {} + per_task_full_code: dict[str, list[dict[str, float | int]]] = {} + per_task_selector_diagnostics: dict[str, list[dict[str, object]]] = {} + storage_anchor: dict[str, dict[str, int | float | bool]] | None = None + reference_state_bytes: int | None = None + with torch.inference_mode(): + for task_number, (row, prompt_cpu, code_cpu) in enumerate(encoded_tasks, start=1): + prompt_ids = prompt_cpu.to(device) + code_ids = code_cpu.to(device) + ( + summaries, + full_code_summaries, + storage, + selector_diagnostics, + task_reference_bytes, + ) = evaluate_task( + model, + prompt_ids=prompt_ids, + code_ids=code_ids, + plans=plans, + adaptive_plans=adaptive_plans, + rank_fusion_specs=rank_fusion_specs, + query_ema_plans=query_ema_plans, + query_ema_c2_plans=query_ema_c2_plans, + cora_specs=cora_specs, + include_default_baselines=not args.cora_c2, + ) + if storage_anchor is None: + storage_anchor = storage + reference_state_bytes = task_reference_bytes + elif storage != storage_anchor or task_reference_bytes != reference_state_bytes: + raise RuntimeError("resident state storage changed between calibration tasks") + for name, summary in summaries.items(): + per_task.setdefault(name, []).append({"task_id": row["task_id"], **summary}) + for name, summary in full_code_summaries.items(): + per_task_full_code.setdefault(name, []).append( + {"task_id": row["task_id"], **summary} + ) + for name, diagnostics in selector_diagnostics.items(): + per_task_selector_diagnostics.setdefault(name, []).append( + {"task_id": row["task_id"], "layers": diagnostics} + ) + print( + f"[{task_number}/{len(rows)}] task={row['task_id']} " + f"code_tokens={code_ids.shape[1]}", + flush=True, + ) + + assert storage_anchor is not None + assert reference_state_bytes is not None + for name in plans: + summary = storage_anchor[name] + if summary["resident_bytes"] != plans[name].resident_bytes: + raise RuntimeError(f"{name} did not realize its exact resident-byte plan") + for name in adaptive_plans: + summary = storage_anchor[name] + if summary["resident_bytes"] != adaptive_plans[name].resident_bytes: + raise RuntimeError(f"{name} did not realize its exact resident-byte plan") + for name, (plan, _, _) in rank_fusion_specs.items(): + summary = storage_anchor[name] + if summary["resident_bytes"] != plan.resident_bytes: + raise RuntimeError(f"{name} did not realize its exact resident-byte plan") + expected_selector_auxiliary_bytes = sum( + heads * rows * torch.empty((), dtype=torch.float32).element_size() + for _, heads, rows in primary_plan.score_shapes + ) + for name, plan in query_ema_plans.items(): + summary = storage_anchor[name] + if summary["resident_bytes"] != plan.resident_bytes: + raise RuntimeError(f"{name} did not realize its exact packed-state byte plan") + if summary["selector_auxiliary_bytes"] != expected_selector_auxiliary_bytes: + raise RuntimeError(f"{name} did not realize the frozen selector auxiliary bytes") + if summary["resident_bytes_including_selector"] != ( + plan.resident_bytes + expected_selector_auxiliary_bytes + ): + raise RuntimeError(f"{name} selector-aware resident byte total is inconsistent") + expected_previous_raw_mask_bytes = sum( + (heads * rows + 7) // 8 for _, heads, rows in primary_plan.score_shapes + ) + if expected_selector_auxiliary_bytes != QUERY_SELECTOR_AUXILIARY_BYTES: + raise RuntimeError("query selector auxiliary bytes drifted from the frozen contract") + if expected_previous_raw_mask_bytes != C2_PREVIOUS_RAW_MASK_BYTES: + raise RuntimeError("Confirmation-2 mask bytes drifted from the frozen contract") + if ( + expected_selector_auxiliary_bytes + expected_previous_raw_mask_bytes + != CORA_SELECTOR_AUXILIARY_BYTES + ): + raise RuntimeError("CORA-C2 selector bytes drifted from the frozen contract") + for name, plan in query_ema_c2_plans.items(): + summary = storage_anchor[name] + expected_total_auxiliary = ( + expected_selector_auxiliary_bytes + expected_previous_raw_mask_bytes + ) + if summary["resident_bytes"] != plan.resident_bytes: + raise RuntimeError(f"{name} did not realize its exact packed-state byte plan") + if summary["selector_auxiliary_bytes"] != expected_total_auxiliary: + raise RuntimeError(f"{name} did not realize its frozen selector bytes") + if summary["resident_bytes_including_selector"] != ( + plan.resident_bytes + expected_total_auxiliary + ): + raise RuntimeError(f"{name} selector-aware resident byte total is inconsistent") + for name, (plan, _confirmation_two) in cora_specs.items(): + summary = storage_anchor[name] + # Raw CORA also retains its previous raw mask to report the frozen + # cumulative raw/committed churn ablation honestly. + expected_total_auxiliary = ( + expected_selector_auxiliary_bytes + expected_previous_raw_mask_bytes + ) + if summary["resident_bytes"] != plan.resident_bytes: + raise RuntimeError(f"{name} did not realize its exact packed-state byte plan") + if summary["selector_auxiliary_bytes"] != expected_total_auxiliary: + raise RuntimeError(f"{name} did not realize its frozen selector bytes") + if summary["resident_bytes_including_selector"] != ( + plan.resident_bytes + expected_total_auxiliary + ): + raise RuntimeError(f"{name} selector-aware resident byte total is inconsistent") + if ( + "v02_layer0_static" in storage_anchor + and storage_anchor["v02_layer0_static"]["resident_bytes"] + != primary_plan.resident_bytes + ): + raise RuntimeError("v0.2 static and the primary row plan are not equal-byte") + + aggregates = aggregate_task_rows(per_task) + aggregates_full_code = aggregate_task_rows(per_task_full_code) + + primary_values = [float(row["delta_nll"]) for row in per_task[primary_name]] + contrasts = { + name: paired_contrast( + [float(row["delta_nll"]) for row in task_rows], + primary_values, + samples=args.bootstrap_samples, + seed=SEED, + ) + for name, task_rows in per_task.items() + if name != primary_name + } + repository_end = git_state() + source_hashes_end = source_file_hashes(repository_root) + if cora_development: + validate_cora_development_repository_end( + start_repository=repository_start, + end_repository=repository_end, + start_source_hashes=source_hashes_start, + end_source_hashes=source_hashes_end, + ) + development_gate: dict[str, Any] | None = None + if args.query_ema: + expected_quotas = dict(CQER_FROZEN_LAYER_QUOTAS) + development_gate = evaluate_cqer_development_gate( + aggregates=aggregates, + per_task=per_task, + per_task_full_code=per_task_full_code, + storage=storage_anchor, + query_ema_diagnostics=per_task_selector_diagnostics, + expected_quotas=expected_quotas, + expected_packed_bytes=primary_plan.resident_bytes, + expected_selector_auxiliary_bytes=expected_selector_auxiliary_bytes, + ) + development_gate["checks"]["authenticated_repository_sources_and_manifests"] = { + "passed": ( + repository_start["commit"] == repository_end["commit"] + and repository_start["worktree_clean"] is True + and repository_end["worktree_clean"] is True + and source_hashes_start == source_hashes_end + ), + "repository_commit_stable": ( + repository_start["commit"] == repository_end["commit"] + ), + "worktree_clean_at_start_and_end": ( + repository_start["worktree_clean"] is True + and repository_end["worktree_clean"] is True + ), + "source_hashes_stable": source_hashes_start == source_hashes_end, + "selector_artifacts_authenticated": True, + "evaluation_manifest_authenticated": True, + } + development_gate["passed"] = all( + check["passed"] is True + for check in development_gate["checks"].values() + ) + if args.cora_c2: + development_gate = evaluate_cora_c2_development_gate( + aggregates=aggregates, + aggregates_full_code=aggregates_full_code, + per_task=per_task, + per_task_full_code=per_task_full_code, + storage=storage_anchor, + selector_diagnostics=per_task_selector_diagnostics, + contrasts=contrasts, + token_manifest=token_manifest, + expected_quotas=dict(CQER_FROZEN_LAYER_QUOTAS), + expected_packed_bytes=primary_plan.resident_bytes, + expected_query_auxiliary_bytes=expected_selector_auxiliary_bytes, + expected_cora_auxiliary_bytes=( + expected_selector_auxiliary_bytes + expected_previous_raw_mask_bytes + ), + ) + development_gate["checks"]["authenticated_repository_sources_and_manifests"] = { + "passed": ( + repository_start["commit"] == repository_end["commit"] + and repository_start["worktree_clean"] is True + and repository_end["worktree_clean"] is True + and source_hashes_start == source_hashes_end + and actual_ids == list(E008_DEVELOPMENT_TASK_IDS) + and actual_manifest_sha256 + == E008_DEVELOPMENT_CONTENT_MANIFEST_SHA256 + and token_manifest_sha256 == E008_DEVELOPMENT_TOKEN_MANIFEST_SHA256 + ), + "repository_commit_stable": ( + repository_start["commit"] == repository_end["commit"] + ), + "worktree_clean_at_start_and_end": ( + repository_start["worktree_clean"] is True + and repository_end["worktree_clean"] is True + ), + "source_hashes_stable": source_hashes_start == source_hashes_end, + "selector_artifacts_authenticated": True, + "development_content_manifest_authenticated": ( + actual_manifest_sha256 == E008_DEVELOPMENT_CONTENT_MANIFEST_SHA256 + ), + "development_token_manifest_authenticated": ( + token_manifest_sha256 == E008_DEVELOPMENT_TOKEN_MANIFEST_SHA256 + ), + "protected_window_8_16_evaluated": False, + } + development_gate["passed"] = all( + check["passed"] is True for check in development_gate["checks"].values() + ) + if heldout_calibration: + validate_heldout_repository_end( + start_repository=repository_start, + end_repository=repository_end, + start_source_hashes=source_hashes_start, + end_source_hashes=source_hashes_end, + ) + heldout_gate = evaluate_frozen_holdout_gate( + aggregates=aggregates, + per_task=per_task, + contrasts=contrasts, + storage=storage_anchor, + primary_name=primary_name, + ) + heldout_gate["checks"]["authenticated_repository_sources_and_manifests"] = { + "passed": True, + "repository_commit_stable": True, + "worktree_clean_at_start_and_end": True, + "source_hashes_stable": True, + "selector_prefixes_authenticated": True, + "evaluation_manifest_authenticated": True, + "storage_boundary_prerequisite_passed": True, + } + heldout_gate["passed"] = all( + check["passed"] is True for check in heldout_gate["checks"].values() + ) + else: + heldout_gate = { + "schema": ( + "recurquant.experiment008-heldout-gate.v1" + if args.cora_c2 + else ( + "recurquant.experiment007-heldout-gate.v1" + if args.query_ema + else ( + "recurquant.experiment006-heldout-gate.v1" + if args.rank_fusion + else "recurquant.experiment005-heldout-gate.v1" + ) + ) + ), + "applicable": False, + "passed": None, + "reason": ( + "the frozen Experiment 008 development diagnostic cannot satisfy its " + "independent numerical or protected holdout prerequisites; ranked " + "window [8, 16) remains closed" + if args.cora_c2 + else ( + "same-calibration diagnostics cannot satisfy the frozen Experiment 007 " + "holdout gate; CQER-32 positive offsets remain disabled" + if args.query_ema + else ( + "same-calibration diagnostics cannot satisfy the frozen Experiment 006 " + "holdout gate; rank-fusion positive offsets remain disabled" + if args.rank_fusion + else ( + "same-calibration diagnostics cannot satisfy the frozen holdout gate" + ) + ) + ) + ), + } + selector_artifacts = { + "hrr": { + "path": str(args.selector_artifact.resolve()), + "sha256": selector_sha256, + "canonical_evidence_sha256": sha256_bytes(canonical_json_bytes(selector)), + } + } + quality_artifact_kind = ( + "recurquant_hrr_heldout_calibration_quality_diagnostic" + if heldout_calibration + else "recurquant_hrr_same_calibration_quality_diagnostic" + ) + if loss_selector is not None: + assert args.loss_selector_artifact is not None + assert loss_selector_sha256 is not None + selector_artifacts["loss_sensitivity"] = { + "path": str(args.loss_selector_artifact.resolve()), + "sha256": loss_selector_sha256, + "canonical_evidence_sha256": sha256_bytes(canonical_json_bytes(loss_selector)), + } + quality_artifact_kind = ( + "recurquant_adaptive_row_packing_heldout_calibration_quality_diagnostic" + if heldout_calibration + else "recurquant_adaptive_row_packing_same_calibration_quality_diagnostic" + ) + if args.rank_fusion: + quality_artifact_kind = "recurquant_rank_fusion_same_calibration_quality_diagnostic" + if args.query_ema: + quality_artifact_kind = "recurquant_cqer32_same_calibration_quality_diagnostic" + if args.cora_c2: + quality_artifact_kind = "recurquant_cora_c2_development_quality_diagnostic" + prerequisite_artifacts: dict[str, dict[str, Any]] = {} + if storage_boundary is not None: + assert args.storage_boundary_artifact is not None + assert storage_boundary_sha256 is not None + prerequisite_artifacts["storage_boundary"] = { + "path": str(args.storage_boundary_artifact.resolve()), + "sha256": storage_boundary_sha256, + "canonical_evidence_sha256": sha256_bytes(canonical_json_bytes(storage_boundary)), + "artifact_kind": storage_boundary["artifact_kind"], + "derivative_gate_passed": storage_boundary["derivative_gate"]["passed"], + } + primary_claim = primary_claim_text(primary_name) + if heldout_calibration: + claim_boundary = ( + "This is a heldout-calibration diagnostic: every evaluation task is a " + "ranked calibration row disjoint from every selector artifact task, and " + "all static plans and adaptive layer quotas come unchanged from those " + "selector artifacts. It is not " + "development or confirmation evidence and cannot establish final " + "generalization, novelty, speed, or a breakthrough. " + f"{primary_claim} The primary metric " + "excludes the prompt-to-first-code-token prediction because no stored " + "quantized recurrent state can affect that output." + ) + elif args.cora_c2: + claim_boundary = ( + "This is the frozen Experiment 008 development diagnostic on ranked " + "calibration window [16, 32), disjoint from the authenticated selector " + "prefix and from prior quality windows. Ranked window [8, 16) remains " + "protected and was not tokenized or evaluated. This result cannot establish " + "confirmation generalization, novelty, speed, state of the art, or a " + f"breakthrough. {primary_claim} The primary metric excludes the " + "prompt-to-first-code-token prediction because no stored quantized " + "recurrent state can affect that output." + ) + else: + claim_boundary = ( + "The selector and quality diagnostic use the same MBPP calibration tasks. " + "This can catch implementation failures but cannot establish held-out " + "generalization, novelty, speed, or a breakthrough. " + f"{primary_claim} The primary metric " + "excludes the prompt-to-first-code-token prediction because no stored " + "quantized recurrent state can affect that output." + ) + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": quality_artifact_kind, + "diagnostic_only": True, + "claim_boundary": claim_boundary, + "created_at_utc": datetime.now(UTC).isoformat(), + "selector_artifacts": selector_artifacts, + "prerequisite_artifacts": prerequisite_artifacts, + "model": { + "id": model_id, + "revision": revision, + "dtype": str(dtype), + "device": str(device), + }, + "dataset": { + "phase": "calibration", + "manifest_sha256": actual_manifest_sha256, + "content_manifest_sha256": actual_manifest_sha256, + "token_manifest_sha256": token_manifest_sha256, + "manifest": actual_manifest, + "task_count": len(rows), + "tasks": token_manifest, + "selection_mode": ( + "experiment008_development" + if args.cora_c2 + else "heldout_calibration" if heldout_calibration else "selector_task_prefix" + ), + "selection_window": { + "calibration_offset": args.calibration_offset, + "limit": limit, + "stop_exclusive": window_stop, + "resolved_before_tokenization_and_model_load": True, + }, + "evaluation_task_ids": actual_ids, + "selector_task_ids": sorted(all_selector_task_ids), + "selector_task_prefix": ( + False + if args.cora_c2 + else not heldout_calibration and limit < available_tasks + ), + "disjoint_from_all_selector_artifacts": ( + True if heldout_calibration or args.cora_c2 else None + ), + "development_identity": ( + { + "ordered_task_ids": list(E008_DEVELOPMENT_TASK_IDS), + "content_manifest_sha256": E008_DEVELOPMENT_CONTENT_MANIFEST_SHA256, + "token_manifest_sha256": E008_DEVELOPMENT_TOKEN_MANIFEST_SHA256, + "actual_token_manifest_sha256": token_manifest_sha256, + "identity_authenticated_before_model_load": True, + "protected_window": [8, 16], + "protected_window_tokenized_or_evaluated": False, + } + if args.cora_c2 + else None + ), + "authenticated_selector_prefix": authenticated_selector_prefix, + }, + "metric_contract": { + "primary": "calibration-aligned code transitions after recurrent-state storage", + "primary_tokens_per_task": "code_tokens - 1", + "excluded_from_primary": "prompt-to-first-code-token prediction", + "secondary": "full reference-code tokens, including the unaffected first token", + "contrasts": "paired task-macro baseline delta NLL minus primary delta NLL", + "cvar95_kl": ( + "per-task token CVaR95 KL, followed by an equal-weight task macro mean" + ), + "maximum_kl": "maximum token KL across every evaluated task", + }, + "methods": list(per_task), + "primary": primary_name, + "adaptive_policy_contracts": { + **( + { + ADAPTIVE_H1: { + "selection": "per-update aligned INT4-to-INT8 row MSE reduction", + "layer_quota_source": "hrr_h1 selector plan", + "batch_size": 1, + "resident_bytes": adaptive_plans[ADAPTIVE_H1].resident_bytes, + } + } + if ADAPTIVE_H1 in adaptive_plans + else {} + ), + **( + { + ADAPTIVE_TARGET_FISHER: { + "selection": "per-update aligned INT4-to-INT8 row MSE reduction", + "layer_quota_source": ( + "target_directional_fisher_difference_int4 selector plan" + ), + "batch_size": 1, + "resident_bytes": adaptive_plans[ADAPTIVE_TARGET_FISHER].resident_bytes, + } + } + if loss_selector is not None + else {} + ), + **{ + name: { + "selection": ( + "per-layer ordinal rank fusion of calibrated target-directional-" + "Fisher scores and per-write aligned INT4-to-INT8 row MSE reduction" + ), + "static_rank_weight": weight, + "dynamic_rank_weight": 1.0 - weight, + "rank_normalization": ( + "zero-best ordinal positions, stable flattened row ties" + ), + "layer_quota_source": ( + "target_directional_fisher_difference_int4 selector plan" + ), + "batch_size": 1, + "resident_bytes": plan.resident_bytes, + } + for name, (plan, _, weight) in rank_fusion_specs.items() + }, + **{ + name: { + "selection": ( + "causal normalized-query-energy EMA multiplied by per-write " + "aligned INT4-to-INT8 row-MSE reduction" + ), + "query_normalization": "q / sqrt(sum(q^2) + 1e-6), computed in FP32", + "query_energy_half_life_tokens": QUERY_EMA_HALF_LIFE, + "query_energy_decay": 2.0 ** (-1.0 / QUERY_EMA_HALF_LIFE), + "initial_query_energy": "uniform 1 / key-row count", + "layer_quota_source": ( + "target_directional_fisher_difference_int4 selector plan" + ), + "batch_size": 1, + "packed_recurrent_state_bytes": plan.resident_bytes, + "selector_auxiliary_bytes": expected_selector_auxiliary_bytes, + "resident_bytes_including_selector": ( + plan.resident_bytes + expected_selector_auxiliary_bytes + ), + } + for name, plan in query_ema_plans.items() + }, + **{ + name: { + "selection": ( + "causal normalized-query-energy EMA multiplied by per-write " + "aligned INT4-to-INT8 row-MSE reduction with Confirmation-2" + ), + "query_normalization": "q / sqrt(sum(q^2) + 1e-6), computed in FP32", + "query_energy_half_life_tokens": QUERY_EMA_HALF_LIFE, + "confirmation_two": True, + "confirmation_rule": ( + "incumbents remain eligible; new rows require consecutive raw " + "top-quota membership on two state writes" + ), + "layer_quota_source": ( + "target_directional_fisher_difference_int4 selector plan" + ), + "batch_size": 1, + "packed_recurrent_state_bytes": plan.resident_bytes, + "selector_auxiliary_bytes": ( + expected_selector_auxiliary_bytes + + expected_previous_raw_mask_bytes + ), + "resident_bytes_including_selector": ( + plan.resident_bytes + + expected_selector_auxiliary_bytes + + expected_previous_raw_mask_bytes + ), + } + for name, plan in query_ema_c2_plans.items() + }, + **{ + name: { + "selection": ( + "causal trace-normalized diagonal Gated DeltaNet observability " + "predictor multiplied by aligned per-write INT4-to-INT8 row " + "squared-error reduction" + ), + "observability_recurrence": ( + "diag(q*q^T + T^T diag(p) T), T=exp(g)(I-beta kk^T)" + ), + "query_key_l2norm_epsilon": 1e-6, + "read_query_scale": "1 / sqrt(key_row_width)", + "workspace_dtype": "torch.float64", + "persistent_observability_dtype": "torch.float32", + "trace_normalization": "across all heads and key rows per layer/token", + "confirmation_two": confirmation_two, + "layer_quota_source": ( + "target_directional_fisher_difference_int4 selector plan" + ), + "batch_size": 1, + "packed_recurrent_state_bytes": plan.resident_bytes, + "observability_diagonal_bytes": expected_selector_auxiliary_bytes, + "previous_raw_mask_bytes": ( + expected_previous_raw_mask_bytes + ), + "selector_auxiliary_bytes": ( + expected_selector_auxiliary_bytes + + expected_previous_raw_mask_bytes + ), + "resident_bytes_including_selector": ( + plan.resident_bytes + + expected_selector_auxiliary_bytes + + expected_previous_raw_mask_bytes + ), + } + for name, (plan, confirmation_two) in cora_specs.items() + }, + }, + "storage": { + "fp32_reference_recurrent_state_bytes": reference_state_bytes, + "candidates": storage_anchor, + }, + "aggregates": aggregates, + "aggregates_full_code_secondary": aggregates_full_code, + "contrasts_baseline_minus_primary_aligned_delta_nll": contrasts, + "heldout_gate": heldout_gate, + "development_gate": development_gate, + "per_task": per_task, + "per_task_full_code_secondary": per_task_full_code, + "selector_diagnostics": per_task_selector_diagnostics, + "query_ema_diagnostics": { + name: records + for name, records in per_task_selector_diagnostics.items() + if name in {QUERY_EMA_PRIMARY, QUERY_EMA_C2} + }, + "observability_diagnostics": { + name: records + for name, records in per_task_selector_diagnostics.items() + if name in {CORA_RAW, CORA_C2_PRIMARY} + }, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "packages": _package_versions(), + "cuda_available": torch.cuda.is_available(), + "cuda_runtime": torch.version.cuda, + "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else None, + }, + "repository": { + "commit": repository_end["commit"], + "worktree_clean": repository_end["worktree_clean"], + "status": repository_end["status"], + "start": repository_start, + "end": repository_end, + "stable_commit": repository_start["commit"] == repository_end["commit"], + }, + "source_files": { + "paths": list(EVALUATOR_SOURCE_FILES), + "sha256_start": source_hashes_start, + "sha256_end": source_hashes_end, + "stable": source_hashes_start == source_hashes_end, + }, + "command": [sys.executable, *sys.argv], + } + canonical_evidence = canonical_json_bytes(evidence) + artifact = { + "schema_version": 1, + "artifact_kind": quality_artifact_kind, + "canonical_evidence_sha256": sha256_bytes(canonical_evidence), + "evidence": evidence, + } + payload = canonical_json_bytes(artifact) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_bytes(payload) + print( + json.dumps( + { + "output": str(args.output.resolve()), + "artifact_sha256": sha256_bytes(payload), + "primary": primary_name, + "heldout_gate": heldout_gate, + "development_gate": development_gate, + "aggregates": aggregates, + "contrasts": contrasts, + }, + indent=2, + sort_keys=True, + ) + ) + return diagnostic_exit_code( + heldout_calibration=heldout_calibration, + gate_passed=heldout_gate["passed"], + ) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pilot_hrr_rows.py b/scripts/pilot_hrr_rows.py new file mode 100644 index 0000000..e2edbe0 --- /dev/null +++ b/scripts/pilot_hrr_rows.py @@ -0,0 +1,449 @@ +#!/usr/bin/env python3 +"""Run a calibration-only diagnostic pilot for HRR row selection. + +This script never opens MBPP development or confirmation data. Its output is a +debug artifact for validating trace timing, score construction, and exact-byte +row-plan generation before a protocol is frozen. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import platform +import subprocess +import sys +from collections.abc import Mapping +from dataclasses import asdict +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +import torch +from transformers import AutoTokenizer, DynamicCache, Qwen3_5ForCausalLM + +from recurquant.evidence import canonical_json_bytes +from recurquant.horizon_calibration import ( + GDNHorizonCalibrationRecorder, + TaskMacroHorizonAccumulator, +) +from recurquant.public_data import ( + format_mbpp_example, + load_mbpp_rows, + mbpp_manifest, + mbpp_manifest_sha256, +) +from recurquant.quantization import QuantizationSpec +from recurquant.row_policy import ExactBudgetRowPlan, RowLocation, select_rows_exact_budget + +MODEL_ID = "Qwen/Qwen3.5-0.8B-Base" +MODEL_REVISION = "dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68" +SEED = 2339 +TARGET_RESIDENT_BYTES = 2_564_096 +HORIZON_EPSILON = 1e-6 + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Calibration-only HRR row-selector diagnostic; not a benchmark run." + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--limit", type=int, default=1) + parser.add_argument("--horizon", type=int, default=32) + parser.add_argument("--max-code-tokens", type=int, default=512) + parser.add_argument("--group-size", type=int, default=128) + parser.add_argument("--target-resident-bytes", type=int, default=TARGET_RESIDENT_BYTES) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--model-id", default=MODEL_ID) + parser.add_argument("--revision", default=MODEL_REVISION) + return parser.parse_args() + + +def select_device(requested: str) -> torch.device: + if requested == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + return torch.device("cuda") + if requested == "cpu": + return torch.device("cpu") + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def git_state() -> dict[str, object]: + commit = subprocess.run( + ["git", "rev-parse", "HEAD"], + check=True, + capture_output=True, + text=True, + ).stdout.strip() + status = subprocess.run( + ["git", "status", "--short"], + check=True, + capture_output=True, + text=True, + ).stdout.splitlines() + return {"commit": commit, "worktree_clean": not status, "status": status} + + +def _score_dict( + summaries: Mapping[int, object], +) -> dict[int, torch.Tensor]: + return { + layer_index: summary.int4_minus_int8.to(torch.float32) + for layer_index, summary in summaries.items() + } + + +def _plan_counts(plan: ExactBudgetRowPlan) -> dict[str, int]: + counts = {str(layer_index): 0 for layer_index, _, _ in plan.score_shapes} + for location in plan.high_precision_rows: + counts[str(location.layer_index)] += 1 + return counts + + +def _plan_locations(plan: ExactBudgetRowPlan) -> list[dict[str, int]]: + return [ + { + "layer_index": location.layer_index, + "head_index": location.head_index, + "row_index": location.row_index, + } + for location in plan.high_precision_rows + ] + + +def _score_payload(scores: Mapping[int, torch.Tensor]) -> dict[str, object]: + arrays = { + str(layer_index): tensor.detach().to(torch.float32).cpu().tolist() + for layer_index, tensor in sorted(scores.items()) + } + encoded = canonical_json_bytes(arrays) + flattened = torch.cat([scores[index].reshape(-1).cpu() for index in sorted(scores)]) + return { + "arrays": arrays, + "canonical_arrays_sha256": sha256_bytes(encoded), + "minimum": float(flattened.min().item()), + "maximum": float(flattened.max().item()), + "mean": float(flattened.mean().item()), + "negative_rows": int((flattened < 0).sum().item()), + } + + +def _overlap(left: ExactBudgetRowPlan, right: ExactBudgetRowPlan) -> dict[str, float | int]: + left_set = set(left.high_precision_rows) + right_set = set(right.high_precision_rows) + intersection = len(left_set & right_set) + union = len(left_set | right_set) + return { + "intersection": intersection, + "union": union, + "jaccard": intersection / union if union else 1.0, + } + + +def _top_rows( + scores: Mapping[int, torch.Tensor], + *, + count: int = 20, +) -> list[dict[str, float | int]]: + candidates: list[tuple[float, RowLocation]] = [] + for layer_index, layer_scores in scores.items(): + values = layer_scores.detach().to(device="cpu", dtype=torch.float64) + for head_index in range(values.shape[0]): + for row_index in range(values.shape[1]): + candidates.append( + ( + float(values[head_index, row_index].item()), + RowLocation(layer_index, head_index, row_index), + ) + ) + ordered = sorted(candidates, key=lambda item: (-item[0], item[1]))[:count] + return [ + { + "score": score, + "layer_index": location.layer_index, + "head_index": location.head_index, + "row_index": location.row_index, + } + for score, location in ordered + ] + + +def _package_versions() -> dict[str, str]: + names = ("datasets", "numpy", "safetensors", "torch", "transformers") + return {name: importlib.metadata.version(name) for name in names} + + +def main() -> int: + args = parse_args() + if not 1 <= args.limit <= 16: + raise ValueError("--limit must be between 1 and 16 for a diagnostic pilot") + if args.horizon <= 0: + raise ValueError("--horizon must be positive") + if args.max_code_tokens < 2: + raise ValueError("--max-code-tokens must be at least 2") + + torch.manual_seed(SEED) + device = select_device(args.device) + dtype = torch.bfloat16 if device.type == "cuda" else torch.float32 + rows = load_mbpp_rows("calibration", limit=args.limit) + dataset_manifest = mbpp_manifest(rows, phase="calibration") + + tokenizer = AutoTokenizer.from_pretrained( + args.model_id, + revision=args.revision, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + model = Qwen3_5ForCausalLM.from_pretrained( + args.model_id, + revision=args.revision, + dtype=dtype, + attn_implementation="eager", + low_cpu_mem_usage=True, + use_safetensors=True, + local_files_only=args.local_files_only, + ).to(device) + model.eval() + + layer_indices = tuple( + index + for index, layer_type in enumerate(model.config.layer_types) + if layer_type == "linear_attention" + ) + if len(layer_indices) != 18: + raise RuntimeError(f"Expected 18 GDN layers, found {len(layer_indices)}") + + int4_spec = QuantizationSpec( + bits=4, + group_size=args.group_size, + scale_bits=16, + flatten_last_dims=1, + rounding="nearest", + seed=SEED, + ) + int8_spec = QuantizationSpec( + bits=8, + group_size=args.group_size, + scale_bits=16, + flatten_last_dims=1, + rounding="nearest", + seed=SEED, + ) + recorder = GDNHorizonCalibrationRecorder( + model, + layer_indices=layer_indices, + max_tokens_per_layer=args.max_code_tokens - 1, + int4_spec=int4_spec, + int8_spec=int8_spec, + epsilon=HORIZON_EPSILON, + ) + h1_accumulator = TaskMacroHorizonAccumulator(horizon=1, epsilon=HORIZON_EPSILON) + hrr_accumulator = TaskMacroHorizonAccumulator( + horizon=args.horizon, + epsilon=HORIZON_EPSILON, + ) + mse_sums: dict[int, torch.Tensor] = {} + task_records: list[dict[str, int]] = [] + + with torch.inference_mode(), recorder: + for row_number, row in enumerate(rows, start=1): + formatted = format_mbpp_example(row) + prompt_ids = tokenizer( + formatted.prompt, + add_special_tokens=True, + return_tensors="pt", + )["input_ids"].to(device) + code_ids = tokenizer( + formatted.code, + add_special_tokens=False, + return_tensors="pt", + )["input_ids"].to(device) + code_tokens = int(code_ids.shape[1]) + if code_tokens < 2: + raise RuntimeError(f"MBPP task {row['task_id']} has fewer than two code tokens") + if code_tokens > args.max_code_tokens: + raise RuntimeError( + f"MBPP task {row['task_id']} has {code_tokens} code tokens, exceeding " + f"--max-code-tokens={args.max_code_tokens}; no truncation is allowed" + ) + + cache = DynamicCache(config=model.config) + recorder.enabled = False + model( + prompt_ids, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + recorder.enabled = True + for token_index in range(code_tokens - 1): + model( + code_ids[:, token_index : token_index + 1], + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + recorder.enabled = False + + traces = recorder.drain_traces() + if set(traces) != set(layer_indices): + raise RuntimeError("captured trace layers do not match the model GDN layers") + h1_accumulator.add_task(traces) + hrr_accumulator.add_task(traces) + for layer_index, trace in traces.items(): + task_marginal_mse = ( + trace.int4_row_error_energies - trace.int8_row_error_energies + ).mean(dim=(0, 1), dtype=torch.float64) + if layer_index in mse_sums: + mse_sums[layer_index] += task_marginal_mse + else: + mse_sums[layer_index] = task_marginal_mse.clone() + retained_trace_bytes = sum(trace.retained_bytes for trace in traces.values()) + task_records.append( + { + "task_id": row["task_id"], + "prompt_tokens": int(prompt_ids.shape[1]), + "code_tokens": code_tokens, + "captured_decode_tokens": code_tokens - 1, + "retained_trace_bytes": retained_trace_bytes, + } + ) + print( + f"[{row_number}/{len(rows)}] task={row['task_id']} " + f"code_tokens={code_tokens} trace_bytes={retained_trace_bytes}", + flush=True, + ) + + h1_scores = _score_dict(h1_accumulator.summaries()) + hrr_scores = _score_dict(hrr_accumulator.summaries()) + mse_scores = {layer_index: score / len(rows) for layer_index, score in mse_sums.items()} + plans = { + "hrr_h1": select_rows_exact_budget( + h1_scores, + target_resident_bytes=args.target_resident_bytes, + group_size=args.group_size, + ), + f"hrr_h{args.horizon}": select_rows_exact_budget( + hrr_scores, + target_resident_bytes=args.target_resident_bytes, + group_size=args.group_size, + ), + "row_mse": select_rows_exact_budget( + mse_scores, + target_resident_bytes=args.target_resident_bytes, + group_size=args.group_size, + ), + } + + plan_payload = { + name: { + "evidence": plan.evidence_dict(), + "promotions_by_layer": _plan_counts(plan), + "locations": _plan_locations(plan), + } + for name, plan in plans.items() + } + score_payload = { + "hrr_h1": _score_payload(h1_scores), + f"hrr_h{args.horizon}": _score_payload(hrr_scores), + "row_mse": _score_payload(mse_scores), + } + primary_name = f"hrr_h{args.horizon}" + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": "recurquant_hrr_calibration_diagnostic", + "diagnostic_only": True, + "claim_boundary": ( + "Calibration-only selector diagnostic. It does not measure language-model " + "quality, generalization, memory peaks, latency, novelty, or a breakthrough." + ), + "created_at_utc": datetime.now(UTC).isoformat(), + "seed": SEED, + "model": { + "id": args.model_id, + "revision": args.revision, + "dtype": str(dtype), + "device": str(device), + "linear_attention_layers": list(layer_indices), + }, + "dataset": { + "manifest": dataset_manifest, + "manifest_sha256": mbpp_manifest_sha256(rows, phase="calibration"), + "tasks": task_records, + }, + "method": { + "horizon": args.horizon, + "normalization_epsilon": HORIZON_EPSILON, + "score": "finite-horizon INT4 read risk minus INT8 read risk", + "task_averaging": "mean writes within task, then equal-weight task mean", + "normalization": "kernel-compatible q/k L2 normalization; query scale once", + "state_timing": "FP32 recurrent state immediately before each decode token update", + }, + "quantizers": { + "axis_contract": "one independent group per recurrent [head, key-row]", + "int4": asdict(int4_spec), + "int8": asdict(int8_spec), + }, + "byte_budget": { + "target_resident_bytes": args.target_resident_bytes, + "group_size": args.group_size, + "scale_bits": 16, + "precision_mask_bits_per_group": 1, + }, + "scores": score_payload, + "plans": plan_payload, + "primary_top_rows": _top_rows(hrr_scores), + "policy_overlap": { + "primary_vs_h1": _overlap(plans[primary_name], plans["hrr_h1"]), + "primary_vs_row_mse": _overlap(plans[primary_name], plans["row_mse"]), + }, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "packages": _package_versions(), + "cuda_available": torch.cuda.is_available(), + "cuda_runtime": torch.version.cuda, + "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else None, + }, + "repository": git_state(), + "command": [sys.executable, *sys.argv], + } + canonical_evidence = canonical_json_bytes(evidence) + artifact = { + "schema_version": 1, + "artifact_kind": "recurquant_hrr_calibration_diagnostic", + "canonical_evidence_sha256": sha256_bytes(canonical_evidence), + "evidence": evidence, + } + payload = canonical_json_bytes(artifact) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_bytes(payload) + print( + json.dumps( + { + "output": str(args.output.resolve()), + "artifact_sha256": sha256_bytes(payload), + "canonical_evidence_sha256": artifact["canonical_evidence_sha256"], + "primary_plan": primary_name, + "resident_bytes": plans[primary_name].resident_bytes, + "promoted_rows": plans[primary_name].promoted_group_count, + "promotions_by_layer": _plan_counts(plans[primary_name]), + "overlap": evidence["policy_overlap"], + }, + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pilot_loss_sensitivity_rows.py b/scripts/pilot_loss_sensitivity_rows.py new file mode 100644 index 0000000..8072cb7 --- /dev/null +++ b/scripts/pilot_loss_sensitivity_rows.py @@ -0,0 +1,481 @@ +#!/usr/bin/env python3 +"""Build exact-byte row plans from one-step loss sensitivity. + +This script opens only the pinned MBPP calibration partition. It follows a +repeated-QDQ all-INT4 recurrent-state trajectory, differentiates the next-token +target loss once per transition, and task-macro averages the resulting row +scores. Its output is an implementation diagnostic, not held-out evidence. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import platform +import subprocess +import sys +from dataclasses import asdict +from datetime import UTC, datetime +from pathlib import Path +from statistics import fmean +from typing import Any + +import torch +from transformers import AutoTokenizer, DynamicCache, Qwen3_5ForCausalLM + +from recurquant.evidence import canonical_json_bytes +from recurquant.fisher_sensitivity import ( + GDNInt4TrajectorySensitivityCalibrator, + TaskMacroSensitivityAccumulator, +) +from recurquant.public_data import ( + format_mbpp_example, + load_mbpp_rows, + mbpp_manifest, + mbpp_manifest_sha256, +) +from recurquant.quantization import QuantizationSpec +from recurquant.row_policy import ExactBudgetRowPlan, RowLocation, select_rows_exact_budget + +SEED = 2339 +MODEL_ID = "Qwen/Qwen3.5-0.8B-Base" +MODEL_REVISION = "dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68" +TARGET_RESIDENT_BYTES = 2_564_096 +ARTIFACT_KIND = "recurquant_loss_sensitivity_calibration_diagnostic" +PRIMARY_SCORE = "signed_taylor_next_int4" +SCORE_FIELDS = { + PRIMARY_SCORE: "taylor_benefit", + "target_directional_fisher_difference_int4": "directional_fisher_difference", + "target_diagonal_fisher_difference_int4": "diagonal_fisher_difference", + "delta_direction_magnitude_int4": "delta_direction_fisher_magnitude", +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Calibration-only repeated-INT4 loss-sensitivity selector diagnostic; " + "not a benchmark or confirmation run." + ) + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--limit", type=int, default=1) + parser.add_argument("--max-code-tokens", type=int, default=512) + parser.add_argument("--group-size", type=int, default=128) + parser.add_argument( + "--target-resident-bytes", + type=int, + default=TARGET_RESIDENT_BYTES, + ) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--model-id", default=MODEL_ID) + parser.add_argument("--revision", default=MODEL_REVISION) + return parser.parse_args() + + +def select_device(requested: str) -> torch.device: + if requested == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + return torch.device("cuda") + if requested == "cpu": + return torch.device("cpu") + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def git_state() -> dict[str, object]: + commit = subprocess.run( + ["git", "rev-parse", "HEAD"], + check=True, + capture_output=True, + text=True, + ).stdout.strip() + status = subprocess.run( + ["git", "status", "--short"], + check=True, + capture_output=True, + text=True, + ).stdout.splitlines() + return { + "commit": commit, + "worktree_clean": not status, + "status": status, + } + + +def _package_versions() -> dict[str, str]: + names = ("datasets", "numpy", "safetensors", "torch", "transformers") + return {name: importlib.metadata.version(name) for name in names} + + +def _plan_counts(plan: ExactBudgetRowPlan) -> dict[str, int]: + counts = {str(layer_index): 0 for layer_index, _, _ in plan.score_shapes} + for location in plan.high_precision_rows: + counts[str(location.layer_index)] += 1 + return counts + + +def _plan_locations(plan: ExactBudgetRowPlan) -> list[dict[str, int]]: + return [ + { + "layer_index": location.layer_index, + "head_index": location.head_index, + "row_index": location.row_index, + } + for location in plan.high_precision_rows + ] + + +def _score_payload(scores: dict[int, torch.Tensor]) -> dict[str, object]: + arrays = { + str(layer_index): tensor.detach().to(torch.float64).cpu().tolist() + for layer_index, tensor in sorted(scores.items()) + } + flattened = torch.cat( + [ + scores[layer_index].detach().to(torch.float64).reshape(-1).cpu() + for layer_index in sorted(scores) + ] + ) + encoded = canonical_json_bytes(arrays) + return { + "arrays": arrays, + "canonical_arrays_sha256": sha256_bytes(encoded), + "row_count": int(flattened.numel()), + "minimum": float(flattened.min().item()), + "maximum": float(flattened.max().item()), + "mean": float(flattened.mean().item()), + "positive_rows": int((flattened > 0).sum().item()), + "zero_rows": int((flattened == 0).sum().item()), + "negative_rows": int((flattened < 0).sum().item()), + } + + +def _overlap(left: ExactBudgetRowPlan, right: ExactBudgetRowPlan) -> dict[str, float | int]: + left_rows = set(left.high_precision_rows) + right_rows = set(right.high_precision_rows) + intersection = len(left_rows & right_rows) + union = len(left_rows | right_rows) + return { + "intersection": intersection, + "union": union, + "jaccard": intersection / union if union else 1.0, + } + + +def _top_rows( + scores: dict[int, torch.Tensor], + *, + count: int = 20, +) -> list[dict[str, float | int]]: + candidates: list[tuple[float, RowLocation]] = [] + for layer_index, layer_scores in scores.items(): + values = layer_scores.detach().to(device="cpu", dtype=torch.float64) + for head_index in range(values.shape[0]): + for row_index in range(values.shape[1]): + candidates.append( + ( + float(values[head_index, row_index].item()), + RowLocation(layer_index, head_index, row_index), + ) + ) + ordered = sorted(candidates, key=lambda item: (-item[0], item[1]))[:count] + return [ + { + "score": score, + "layer_index": location.layer_index, + "head_index": location.head_index, + "row_index": location.row_index, + } + for score, location in ordered + ] + + +def _scores_from_accumulator( + accumulator: TaskMacroSensitivityAccumulator, + layer_indices: tuple[int, ...], +) -> dict[str, dict[int, torch.Tensor]]: + by_name: dict[str, dict[int, torch.Tensor]] = {name: {} for name in SCORE_FIELDS} + for layer_index in layer_indices: + summary = accumulator.summary(layer_index) + if summary.trajectory != "int4": + raise RuntimeError("loss-sensitivity accumulator did not retain the INT4 trajectory") + for public_name, field_name in SCORE_FIELDS.items(): + value = getattr(summary, field_name) + if not isinstance(value, torch.Tensor): + raise TypeError(f"sensitivity summary field {field_name} must be a tensor") + by_name[public_name][layer_index] = value + return by_name + + +def main() -> int: + args = parse_args() + if not 1 <= args.limit <= 16: + raise ValueError("--limit must be between 1 and 16 for a diagnostic pilot") + if args.max_code_tokens < 2: + raise ValueError("--max-code-tokens must be at least 2") + if args.group_size <= 0: + raise ValueError("--group-size must be positive") + + torch.manual_seed(SEED) + device = select_device(args.device) + dtype = torch.bfloat16 if device.type == "cuda" else torch.float32 + rows = load_mbpp_rows("calibration", limit=args.limit) + dataset_manifest = mbpp_manifest(rows, phase="calibration") + + tokenizer = AutoTokenizer.from_pretrained( + args.model_id, + revision=args.revision, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + model = Qwen3_5ForCausalLM.from_pretrained( + args.model_id, + revision=args.revision, + dtype=dtype, + attn_implementation="eager", + low_cpu_mem_usage=True, + use_safetensors=True, + local_files_only=args.local_files_only, + ).to(device) + model.eval() + + layer_indices = tuple( + index + for index, layer_type in enumerate(model.config.layer_types) + if layer_type == "linear_attention" + ) + if len(layer_indices) != 18: + raise RuntimeError(f"Expected 18 GDN layers, found {len(layer_indices)}") + if args.group_size != int(model.config.linear_value_head_dim): + raise ValueError( + "--group-size must equal model.config.linear_value_head_dim " + f"({args.group_size} != {model.config.linear_value_head_dim})" + ) + + int4_spec = QuantizationSpec( + bits=4, + group_size=args.group_size, + scale_bits=16, + flatten_last_dims=1, + rounding="nearest", + seed=SEED, + ) + int8_spec = QuantizationSpec( + bits=8, + group_size=args.group_size, + scale_bits=16, + flatten_last_dims=1, + rounding="nearest", + seed=SEED, + ) + accumulator = TaskMacroSensitivityAccumulator() + task_records: list[dict[str, float | int]] = [] + + for row_number, row in enumerate(rows, start=1): + formatted = format_mbpp_example(row) + prompt_ids = tokenizer( + formatted.prompt, + add_special_tokens=True, + return_tensors="pt", + )["input_ids"].to(device) + code_ids = tokenizer( + formatted.code, + add_special_tokens=False, + return_tensors="pt", + )["input_ids"].to(device) + code_tokens = int(code_ids.shape[1]) + if code_tokens < 2: + raise RuntimeError(f"MBPP task {row['task_id']} has fewer than two code tokens") + if code_tokens > args.max_code_tokens: + raise RuntimeError( + f"MBPP task {row['task_id']} has {code_tokens} code tokens, exceeding " + f"--max-code-tokens={args.max_code_tokens}; no truncation is allowed" + ) + + cache = DynamicCache(config=model.config) + with torch.no_grad(): + model( + prompt_ids, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + calibrator = GDNInt4TrajectorySensitivityCalibrator( + model, + cache, + int4_spec=int4_spec, + int8_spec=int8_spec, + ) + steps = [] + for token_index in range(code_tokens - 1): + steps.append( + calibrator.step( + code_ids[:, token_index : token_index + 1], + code_ids[:, token_index + 1 : token_index + 2], + forward_kwargs={"logits_to_keep": 1}, + ) + ) + if any( + tuple(score.layer_index for score in step.layers) != layer_indices for step in steps + ): + raise RuntimeError("sensitivity step layers do not match the model GDN layers") + accumulator.add_task(steps) + mean_nll = fmean(step.mean_nll for step in steps) + task_records.append( + { + "task_id": row["task_id"], + "prompt_tokens": int(prompt_ids.shape[1]), + "code_tokens": code_tokens, + "scored_transitions": code_tokens - 1, + "mean_target_nll_on_int4_trajectory": mean_nll, + } + ) + print( + f"[{row_number}/{len(rows)}] task={row['task_id']} " + f"code_tokens={code_tokens} transitions={code_tokens - 1} " + f"mean_nll={mean_nll:.6f}", + flush=True, + ) + + scores = _scores_from_accumulator(accumulator, layer_indices) + plans = { + name: select_rows_exact_budget( + layer_scores, + target_resident_bytes=args.target_resident_bytes, + group_size=args.group_size, + ) + for name, layer_scores in scores.items() + } + primary_plan = plans[PRIMARY_SCORE] + for name, plan in plans.items(): + if plan.resident_bytes != args.target_resident_bytes: + raise RuntimeError(f"{name} did not realize the exact target byte budget") + if plan.promoted_group_count != primary_plan.promoted_group_count: + raise RuntimeError(f"{name} did not use the common promotion count") + + score_payload = {name: _score_payload(layer_scores) for name, layer_scores in scores.items()} + plan_payload = { + name: { + "evidence": plan.evidence_dict(), + "promotions_by_layer": _plan_counts(plan), + "locations": _plan_locations(plan), + } + for name, plan in plans.items() + } + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "diagnostic_only": True, + "claim_boundary": ( + "Calibration-only, target-dependent, one-step selector diagnostic. It does " + "not establish held-out quality, free-generation behavior, novelty, speed, " + "peak memory, model-Fisher validity, or a breakthrough." + ), + "created_at_utc": datetime.now(UTC).isoformat(), + "seed": SEED, + "model": { + "id": args.model_id, + "revision": args.revision, + "dtype": str(dtype), + "device": str(device), + "linear_attention_layers": list(layer_indices), + }, + "dataset": { + "manifest": dataset_manifest, + "manifest_sha256": mbpp_manifest_sha256(rows, phase="calibration"), + "tasks": task_records, + }, + "method": { + "primary": PRIMARY_SCORE, + "trajectory": "uniform INT4 QDQ after prefill and after every recurrent update", + "loss": "teacher-forced next-token target NLL; one reverse pass per transition", + "state_timing": "stored Q4 state before the next token update", + "promotion_direction": "Q8(raw pre-storage update) minus Q4(raw pre-storage update)", + "task_averaging": "mean transitions within task, then equal-weight task mean", + "score_definitions": { + PRIMARY_SCORE: "-gradient dot (Q8 - Q4); signed predicted next-token NLL reduction", + "target_directional_fisher_difference_int4": ( + "(gradient dot e4)^2 - (gradient dot e8)^2 using observed targets" + ), + "target_diagonal_fisher_difference_int4": ( + "sum gradient^2 * (e4^2 - e8^2), without coordinate clamping" + ), + "delta_direction_magnitude_int4": ( + "(gradient dot (e4 - e8))^2; non-benefit sensitivity diagnostic" + ), + }, + "label_free_model_fisher": ( + "not implemented in this artifact; it requires model-sampled pseudo-label probes" + ), + "suffix_gradient": "not implemented; this artifact scores only the next token", + }, + "quantizers": { + "axis_contract": "one independent group per recurrent [head, key-row]", + "int4": asdict(int4_spec), + "int8": asdict(int8_spec), + }, + "byte_budget": { + "target_resident_bytes": args.target_resident_bytes, + "group_size": args.group_size, + "scale_bits": 16, + "precision_mask_bits_per_group": 1, + "promoted_rows": primary_plan.promoted_group_count, + }, + "scores": score_payload, + "plans": plan_payload, + "primary_top_rows": _top_rows(scores[PRIMARY_SCORE]), + "policy_overlap_with_primary": { + name: _overlap(primary_plan, plan) + for name, plan in plans.items() + if name != PRIMARY_SCORE + }, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "packages": _package_versions(), + "cuda_available": torch.cuda.is_available(), + "cuda_runtime": torch.version.cuda, + "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else None, + }, + "repository": git_state(), + "command": [sys.executable, *sys.argv], + } + canonical_evidence = canonical_json_bytes(evidence) + artifact = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "canonical_evidence_sha256": sha256_bytes(canonical_evidence), + "evidence": evidence, + } + payload = canonical_json_bytes(artifact) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_bytes(payload) + print( + json.dumps( + { + "output": str(args.output.resolve()), + "artifact_sha256": sha256_bytes(payload), + "canonical_evidence_sha256": artifact["canonical_evidence_sha256"], + "primary_plan": PRIMARY_SCORE, + "resident_bytes": primary_plan.resident_bytes, + "promoted_rows": primary_plan.promoted_group_count, + "promotions_by_layer": _plan_counts(primary_plan), + "overlap": evidence["policy_overlap_with_primary"], + }, + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pilot_validate_storage_boundary.py b/scripts/pilot_validate_storage_boundary.py new file mode 100644 index 0000000..c55145b --- /dev/null +++ b/scripts/pilot_validate_storage_boundary.py @@ -0,0 +1,865 @@ +#!/usr/bin/env python3 +"""Validate one-row Taylor directions at real Qwen3.5 storage boundaries. + +This bounded diagnostic reads only the pinned MBPP calibration partition. It +uses four geometry-stratified rows fixed in source before any benefit is +measured and the fixed central-difference epsilon grid {1/4, 1/8, 1/16, 1/32}. +It is an implementation check, not a policy search or quality benchmark. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import math +import platform +import statistics +import subprocess +import sys +from dataclasses import asdict +from datetime import UTC, datetime +from pathlib import Path +from typing import Any, Final + +import torch +from transformers import AutoTokenizer, DynamicCache, Qwen3_5ForCausalLM + +from recurquant.evidence import canonical_json_bytes +from recurquant.public_data import ( + format_mbpp_example, + load_mbpp_rows, + mbpp_manifest, + mbpp_manifest_sha256, +) +from recurquant.quantization import QuantizationSpec, quantize_pack +from recurquant.storage_boundary_validation import ( + StorageBoundaryRowValidation, + StorageRowLocation, + advance_uniform_int4_trajectory, + validate_qwen_storage_boundary_row, +) + +SEED: Final = 2339 +MODEL_ID: Final = "Qwen/Qwen3.5-0.8B-Base" +MODEL_REVISION: Final = "dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68" +ARTIFACT_KIND: Final = "recurquant_storage_boundary_taylor_diagnostic" +TASK_PREFIX_LIMIT: Final = 1 +TASK_INDEX: Final = 0 +TRANSITION_INDEX: Final = 0 +EPSILONS: Final = (1 / 4, 1 / 8, 1 / 16, 1 / 32) +BASELINE_REPEAT_ABSOLUTE_TOLERANCE: Final = 1e-7 +DERIVATIVE_INFORMATIVE_FLOOR: Final = 1e-8 +NEAR_ZERO_ABSOLUTE_TOLERANCE: Final = 2e-7 +MINIMUM_INFORMATIVE_ROWS: Final = 3 +MINIMUM_SIGN_AGREEMENT: Final = 0.95 +MAXIMUM_MEDIAN_RELATIVE_ERROR: Final = 0.10 +MINIMUM_CONVERGED_ROW_FRACTION: Final = 0.75 + +# (label, recurrent-layer ordinal, head index, key-row index). These four +# coordinates span early/late layers and low/high head/row indices. They are +# intentionally unrelated to any selector score or measured loss benefit. +ROW_STRATA: Final = ( + ("early_low", 0, 0, 0), + ("early_mid", 5, 5, 42), + ("late_mid", 11, 10, 85), + ("late_high", 17, 15, 127), +) + +CACHE_FINGERPRINT_CACHE_METADATA: Final = ( + "offloading", + "layer_class_to_replicate", +) +CACHE_FINGERPRINT_LAYER_METADATA: Final = ( + "number_of_states", + "is_conv_states_initialized", + "is_recurrent_states_initialized", + "has_previous_state", + "conv_kernel_size", + "is_initialized", + "cumulative_length", + "sliding_window", + "max_cache_len", + "device", + "dtype", + "record_past", +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Pinned calibration-only one-row storage-boundary derivative diagnostic; " + "not a quality benchmark." + ) + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument( + "--phase", + default="calibration", + help="Must remain 'calibration'; development and confirmation are refused.", + ) + return parser.parse_args() + + +def _sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def _select_device(requested: str) -> torch.device: + if requested == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + return torch.device("cuda") + if requested == "cpu": + return torch.device("cpu") + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def _git_state() -> dict[str, object]: + commit = subprocess.run( + ["git", "rev-parse", "HEAD"], + check=True, + capture_output=True, + text=True, + ).stdout.strip() + status = subprocess.run( + ["git", "status", "--short"], + check=True, + capture_output=True, + text=True, + ).stdout.splitlines() + tracked_diff = subprocess.run( + ["git", "diff", "--binary", "HEAD"], + check=True, + capture_output=True, + ).stdout + return { + "commit": commit, + "status": status, + "worktree_clean": not status, + "tracked_diff_sha256": _sha256_bytes(tracked_diff), + } + + +def _package_versions() -> dict[str, str]: + names = ("datasets", "numpy", "safetensors", "torch", "transformers") + return {name: importlib.metadata.version(name) for name in names} + + +def _implementation_paths() -> tuple[Path, ...]: + root = Path(__file__).resolve().parents[1] + return ( + Path(__file__).resolve(), + root / "src" / "recurquant" / "storage_boundary_validation.py", + root / "src" / "recurquant" / "quantization.py", + root / "src" / "recurquant" / "evidence.py", + root / "src" / "recurquant" / "public_data.py", + ) + + +def _source_hashes(paths: tuple[Path, ...]) -> dict[str, str]: + root = Path(__file__).resolve().parents[1] + hashes: dict[str, str] = {} + for path in paths: + relative = path.resolve().relative_to(root).as_posix() + hashes[relative] = _sha256_bytes(path.read_bytes()) + return hashes + + +def _tensor_bytes_sha256(tensor: torch.Tensor) -> str: + value = tensor.detach().contiguous().cpu() + return _sha256_bytes(value.view(torch.uint8).numpy().tobytes()) + + +def _directional_dot_float64( + gradient_row: torch.Tensor, + direction: torch.Tensor, +) -> float: + """Recompute the recorded FP32 row dot on CPU with FP64 accumulation.""" + + if gradient_row.shape != direction.shape: + raise ValueError("gradient row and direction must have the same shape") + if gradient_row.dtype != torch.float32 or direction.dtype != torch.float32: + raise TypeError("gradient row and direction must use float32 before FP64 accumulation") + gradient64 = gradient_row.detach().to(device="cpu", dtype=torch.float64) + direction64 = direction.detach().to(device="cpu", dtype=torch.float64) + value = float((gradient64 * direction64).sum(dtype=torch.float64).item()) + if not math.isfinite(value): + raise RuntimeError("directional dot product became non-finite") + return value + + +def _tensor_record(tensor: torch.Tensor, *, include_values: bool) -> dict[str, object]: + value = tensor.detach().to(device="cpu", dtype=torch.float64).contiguous() + if value.numel() == 0 or not torch.isfinite(value).all().item(): + raise ValueError("artifact tensors must be non-empty and finite") + values = value.tolist() + canonical = canonical_json_bytes( + { + "dtype": str(tensor.dtype), + "shape": list(tensor.shape), + "values": values, + } + ) + record: dict[str, object] = { + "dtype": str(tensor.dtype), + "shape": list(tensor.shape), + "canonical_values_sha256": _sha256_bytes(canonical), + "raw_bytes_sha256": _tensor_bytes_sha256(tensor), + "minimum": float(value.min().item()), + "maximum": float(value.max().item()), + "l2_norm": float(torch.linalg.vector_norm(value).item()), + } + if include_values: + record["values_float64"] = values + return record + + +def _cache_metadata_value(value: object) -> object: + if value is None or isinstance(value, (bool, int, float, str)): + return value + if isinstance(value, (torch.device, torch.dtype)): + return str(value) + if isinstance(value, type): + return f"{value.__module__}.{value.__qualname__}" + if isinstance(value, dict): + return { + str(key): _cache_metadata_value(item) + for key, item in sorted(value.items(), key=lambda pair: str(pair[0])) + } + if isinstance(value, (list, tuple)): + return [_cache_metadata_value(item) for item in value] + raise TypeError(f"unsupported cache fingerprint metadata type: {type(value).__name__}") + + +def _cache_fingerprint(cache: DynamicCache) -> str: + records: list[dict[str, object]] = [] + records.append( + { + "scope": "cache", + "class": f"{cache.__class__.__module__}.{cache.__class__.__qualname__}", + "layer_count": len(cache.layers), + "metadata": { + name: { + "present": hasattr(cache, name), + "value": ( + _cache_metadata_value(getattr(cache, name)) + if hasattr(cache, name) + else None + ), + } + for name in CACHE_FINGERPRINT_CACHE_METADATA + }, + } + ) + for layer_index, layer in enumerate(cache.layers): + records.append( + { + "scope": "layer", + "layer_index": layer_index, + "class": f"{layer.__class__.__module__}.{layer.__class__.__qualname__}", + "metadata": { + name: { + "present": hasattr(layer, name), + "value": ( + _cache_metadata_value(getattr(layer, name)) + if hasattr(layer, name) + else None + ), + } + for name in CACHE_FINGERPRINT_LAYER_METADATA + }, + } + ) + for name in ("keys", "values"): + value = getattr(layer, name, None) + if isinstance(value, torch.Tensor): + records.append( + { + "scope": "tensor", + "layer_index": layer_index, + "attribute": name, + "state_index": None, + "dtype": str(value.dtype), + "shape": list(value.shape), + "raw_bytes_sha256": _tensor_bytes_sha256(value), + } + ) + for name in ("conv_states", "recurrent_states"): + states = getattr(layer, name, None) + if not isinstance(states, dict): + continue + for state_index, value in sorted(states.items()): + if isinstance(value, torch.Tensor): + records.append( + { + "scope": "tensor", + "layer_index": layer_index, + "attribute": name, + "state_index": int(state_index), + "dtype": str(value.dtype), + "shape": list(value.shape), + "raw_bytes_sha256": _tensor_bytes_sha256(value), + } + ) + return _sha256_bytes(canonical_json_bytes(records)) + + +def _resolve_strata( + recurrent_layers: tuple[int, ...], + *, + heads: int, + rows: int, +) -> tuple[tuple[str, StorageRowLocation], ...]: + resolved: list[tuple[str, StorageRowLocation]] = [] + for label, layer_ordinal, head_index, row_index in ROW_STRATA: + if layer_ordinal >= len(recurrent_layers): + raise ValueError(f"row stratum {label} exceeds recurrent-layer geometry") + if head_index >= heads or row_index >= rows: + raise ValueError(f"row stratum {label} exceeds recurrent-state geometry") + resolved.append( + ( + label, + StorageRowLocation( + layer_index=recurrent_layers[layer_ordinal], + head_index=head_index, + row_index=row_index, + ), + ) + ) + return tuple(resolved) + + +def _physical_endpoint_record( + raw_row: torch.Tensor, + endpoint_row: torch.Tensor, + spec: QuantizationSpec, +) -> dict[str, object]: + shaped = raw_row.reshape(1, 1, 1, -1) + packed = quantize_pack(shaped, spec) + dequantized = packed.dequantize().reshape(-1).cpu() + if not torch.equal(dequantized, endpoint_row): + raise RuntimeError(f"INT{spec.bits} row endpoint does not match physical packing") + return { + "bits": spec.bits, + "payload_bytes": packed.payload.numel() * packed.payload.element_size(), + "scale_bytes": packed.scales.numel() * packed.scales.element_size(), + "resident_bytes": packed.storage_bytes, + "payload": _tensor_record(packed.payload, include_values=False), + "scales": _tensor_record(packed.scales, include_values=True), + } + + +def _row_tensors_record(result: StorageBoundaryRowValidation) -> dict[str, object]: + if not torch.equal(result.direction, result.int8_row - result.int4_row): + raise RuntimeError("recorded row direction does not equal INT8 minus INT4") + return { + "raw": _tensor_record(result.raw_row, include_values=True), + "int4": _tensor_record(result.int4_row, include_values=True), + "int8": _tensor_record(result.int8_row, include_values=True), + "direction_int8_minus_int4": _tensor_record(result.direction, include_values=True), + "loss_gradient_at_int4": _tensor_record(result.gradient_row, include_values=True), + } + + +def _assert_same_row_result( + reference: StorageBoundaryRowValidation, + candidate: StorageBoundaryRowValidation, +) -> None: + fields = ("raw_row", "int4_row", "int8_row", "direction", "gradient_row") + if any(not torch.equal(getattr(reference, name), getattr(candidate, name)) for name in fields): + raise RuntimeError("row tensors changed across the fixed epsilon grid") + scalar_fields = ( + "loss_at_zero", + "repeated_loss_at_zero", + "loss_at_int8_endpoint", + "autograd_directional_derivative", + "predicted_benefit_autograd", + "measured_endpoint_benefit", + ) + if any( + getattr(reference.comparison, name) != getattr(candidate.comparison, name) + for name in scalar_fields + ): + raise RuntimeError("shared endpoint or autograd results changed across epsilons") + + +def _evaluate_derivative_gate(row_records: list[dict[str, object]]) -> dict[str, object]: + """Apply the source-frozen FP32 derivative gate to detached row records.""" + + if len(row_records) != len(ROW_STRATA): + raise ValueError("derivative gate requires every frozen row stratum") + expected_labels = tuple(label for label, *_ in ROW_STRATA) + observed_labels = tuple(str(record.get("stratum")) for record in row_records) + if observed_labels != expected_labels: + raise ValueError("derivative gate requires frozen row strata in source order") + informative_rows = 0 + near_zero_rows = 0 + sign_checks: list[bool] = [] + relative_errors: list[float] = [] + converged_rows: list[bool] = [] + baseline_repeat_errors: list[float] = [] + near_zero_checks: list[bool] = [] + failures: list[str] = [] + + for row_record in row_records: + label = str(row_record["stratum"]) + raw_results = row_record["epsilon_results"] + if not isinstance(raw_results, list) or len(raw_results) != len(EPSILONS): + raise ValueError(f"row {label} lacks the frozen epsilon grid") + results = [dict(value) for value in raw_results] + observed_epsilons = tuple(float(value["epsilon"]) for value in results) + if any(not math.isfinite(value) for value in observed_epsilons): + raise ValueError(f"row {label} epsilon grid must be finite") + if observed_epsilons != EPSILONS: + raise ValueError(f"row {label} epsilon grid changed") + + autograd_values = [ + float(value["autograd_directional_derivative"]) for value in results + ] + central_values = [ + float(value["central_directional_derivative"]) for value in results + ] + current_baseline_errors = [ + float(value["baseline_repeat_absolute_error"]) for value in results + ] + if any( + not math.isfinite(value) + for value in (*autograd_values, *central_values, *current_baseline_errors) + ): + raise ValueError(f"row {label} derivative-gate values must be finite") + if any(value < 0 for value in current_baseline_errors): + raise ValueError(f"row {label} baseline repeat errors must be non-negative") + unique_autograd_values = set(autograd_values) + if len(unique_autograd_values) != 1: + raise ValueError(f"row {label} autograd derivative changed across epsilons") + autograd = unique_autograd_values.pop() + absolute_errors = [abs(central - autograd) for central in central_values] + baseline_repeat_errors.extend(current_baseline_errors) + + if abs(autograd) <= DERIVATIVE_INFORMATIVE_FLOOR: + near_zero_rows += 1 + checks = [error <= NEAR_ZERO_ABSOLUTE_TOLERANCE for error in absolute_errors] + near_zero_checks.extend(checks) + if not all(checks): + failures.append( + f"{label}: near-zero absolute derivative error exceeded tolerance" + ) + continue + + informative_rows += 1 + row_signs = [ + (central > 0) == (autograd > 0) and central != 0 + for central in central_values + ] + sign_checks.extend(row_signs) + relative_errors.extend(error / abs(autograd) for error in absolute_errors) + # The final three grid points are 1/8, 1/16, and 1/32. Numerical noise + # need not make them strictly monotone, but the smallest step must not + # be worse than the first of those three. + converged_rows.append(absolute_errors[-1] <= absolute_errors[1]) + + maximum_baseline_repeat_error = max(baseline_repeat_errors, default=math.inf) + sign_agreement = sum(sign_checks) / len(sign_checks) if sign_checks else None + median_relative_error = ( + statistics.median(relative_errors) if relative_errors else None + ) + converged_fraction = ( + sum(converged_rows) / len(converged_rows) if converged_rows else None + ) + if informative_rows < MINIMUM_INFORMATIVE_ROWS: + failures.append( + f"only {informative_rows} informative rows; require {MINIMUM_INFORMATIVE_ROWS}" + ) + if maximum_baseline_repeat_error > BASELINE_REPEAT_ABSOLUTE_TOLERANCE: + failures.append("repeated alpha=0 baseline exceeded absolute tolerance") + if sign_agreement is None or sign_agreement < MINIMUM_SIGN_AGREEMENT: + failures.append("central-difference sign agreement fell below threshold") + if ( + median_relative_error is None + or median_relative_error > MAXIMUM_MEDIAN_RELATIVE_ERROR + ): + failures.append("median central-difference relative error exceeded threshold") + if converged_fraction is None or converged_fraction < MINIMUM_CONVERGED_ROW_FRACTION: + failures.append("too few informative rows improved over the final epsilon range") + + return { + "passed": not failures, + "failures": failures, + "thresholds": { + "model_dtype": "torch.float32", + "baseline_repeat_absolute_tolerance": BASELINE_REPEAT_ABSOLUTE_TOLERANCE, + "derivative_informative_floor": DERIVATIVE_INFORMATIVE_FLOOR, + "near_zero_absolute_tolerance": NEAR_ZERO_ABSOLUTE_TOLERANCE, + "minimum_informative_rows": MINIMUM_INFORMATIVE_ROWS, + "minimum_sign_agreement": MINIMUM_SIGN_AGREEMENT, + "maximum_median_relative_error": MAXIMUM_MEDIAN_RELATIVE_ERROR, + "minimum_converged_row_fraction": MINIMUM_CONVERGED_ROW_FRACTION, + }, + "observed": { + "rows": len(row_records), + "informative_rows": informative_rows, + "near_zero_rows": near_zero_rows, + "sign_checks": len(sign_checks), + "sign_agreement": sign_agreement, + "median_relative_error": median_relative_error, + "converged_row_fraction": converged_fraction, + "maximum_baseline_repeat_absolute_error": maximum_baseline_repeat_error, + "near_zero_checks": len(near_zero_checks), + "near_zero_checks_passed": sum(near_zero_checks), + }, + } + + +def main() -> int: + args = parse_args() + if args.phase != "calibration": + raise ValueError( + "This implementation diagnostic refuses development and confirmation data; " + "--phase must be calibration." + ) + + torch.manual_seed(SEED) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(SEED) + device = _select_device(args.device) + # The frozen gate uses FP32 weights and FP32 NLL so central differences do + # not collapse merely because a BF16 matmul rounds away a small alpha move. + dtype = torch.float32 + implementation_paths = _implementation_paths() + source_hashes_start = _source_hashes(implementation_paths) + repository_start = _git_state() + + rows = load_mbpp_rows("calibration", limit=TASK_PREFIX_LIMIT) + if len(rows) != TASK_PREFIX_LIMIT: + raise RuntimeError("pinned calibration loader returned an unexpected task count") + row = rows[TASK_INDEX] + formatted = format_mbpp_example(row) + + tokenizer = AutoTokenizer.from_pretrained( + MODEL_ID, + revision=MODEL_REVISION, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + prompt_ids = tokenizer( + formatted.prompt, + add_special_tokens=True, + return_tensors="pt", + )["input_ids"].to(device) + code_ids = tokenizer( + formatted.code, + add_special_tokens=False, + return_tensors="pt", + )["input_ids"].to(device) + if code_ids.shape[1] < TRANSITION_INDEX + 2: + raise RuntimeError("frozen MBPP task does not contain the required target transition") + + model = Qwen3_5ForCausalLM.from_pretrained( + MODEL_ID, + revision=MODEL_REVISION, + dtype=dtype, + attn_implementation="eager", + low_cpu_mem_usage=True, + use_safetensors=True, + local_files_only=args.local_files_only, + ).to(device) + model.eval() + recurrent_layers = tuple( + index + for index, layer_type in enumerate(model.config.layer_types) + if layer_type == "linear_attention" + ) + heads = int(model.config.linear_num_value_heads) + key_rows = int(model.config.linear_key_head_dim) + value_dim = int(model.config.linear_value_head_dim) + if (len(recurrent_layers), heads, key_rows, value_dim) != (18, 16, 128, 128): + raise RuntimeError( + "Pinned Qwen3.5-0.8B recurrent geometry changed; refusing to adapt fixed strata" + ) + strata = _resolve_strata(recurrent_layers, heads=heads, rows=key_rows) + + int4_spec = QuantizationSpec( + bits=4, + group_size=value_dim, + scale_bits=16, + flatten_last_dims=1, + rounding="nearest", + seed=SEED, + ) + int8_spec = QuantizationSpec( + bits=8, + group_size=value_dim, + scale_bits=16, + flatten_last_dims=1, + rounding="nearest", + seed=SEED, + ) + + raw_cache = DynamicCache(config=model.config) + with torch.no_grad(): + model( + prompt_ids, + past_key_values=raw_cache, + use_cache=True, + logits_to_keep=1, + ) + previous_ids = code_ids[:, :TRANSITION_INDEX] + advance_uniform_int4_trajectory( + model, + raw_cache, + previous_ids, + int4_spec=int4_spec, + forward_kwargs={"logits_to_keep": 1}, + ) + input_ids = code_ids[:, TRANSITION_INDEX : TRANSITION_INDEX + 1] + target_ids = code_ids[:, TRANSITION_INDEX + 1 : TRANSITION_INDEX + 2] + raw_cache_fingerprint = _cache_fingerprint(raw_cache) + + row_records: list[dict[str, object]] = [] + for row_number, (label, location) in enumerate(strata, start=1): + reference: StorageBoundaryRowValidation | None = None + epsilon_records: list[dict[str, object]] = [] + for epsilon_number, epsilon in enumerate(EPSILONS, start=1): + before = _cache_fingerprint(raw_cache) + if before != raw_cache_fingerprint: + raise RuntimeError("raw boundary cache changed before validation") + result = validate_qwen_storage_boundary_row( + model, + raw_cache, + input_ids, + target_ids, + location=location, + int4_spec=int4_spec, + int8_spec=int8_spec, + epsilon=epsilon, + sign_floor=DERIVATIVE_INFORMATIVE_FLOOR, + forward_kwargs={"logits_to_keep": 1}, + ) + after = _cache_fingerprint(raw_cache) + if after != before: + raise RuntimeError("validation mutated the raw boundary cache") + if reference is None: + reference = result + else: + _assert_same_row_result(reference, result) + epsilon_records.append(asdict(result.comparison)) + print( + f"row {row_number}/{len(strata)} {label} " + f"epsilon {epsilon_number}/{len(EPSILONS)}={epsilon:.5f} " + f"predicted={result.comparison.predicted_benefit_autograd:.9g} " + f"central={result.comparison.predicted_benefit_central:.9g} " + f"endpoint={result.comparison.measured_endpoint_benefit:.9g}", + flush=True, + ) + + assert reference is not None + dot_product = _directional_dot_float64( + reference.gradient_row, + reference.direction, + ) + if not math.isclose( + -dot_product, + reference.comparison.predicted_benefit_autograd, + rel_tol=0, + abs_tol=1e-12, + ): + raise RuntimeError("stored gradient and direction do not reproduce Taylor benefit") + row_records.append( + { + "stratum": label, + "location": asdict(location), + "selection_basis": "fixed geometry only; no selector scores or benefits read", + "tensors": _row_tensors_record(reference), + "physical_endpoints": { + "int4": _physical_endpoint_record( + reference.raw_row, + reference.int4_row, + int4_spec, + ), + "int8": _physical_endpoint_record( + reference.raw_row, + reference.int8_row, + int8_spec, + ), + }, + "negative_gradient_dot_direction": -dot_product, + "epsilon_results": epsilon_records, + } + ) + + if _cache_fingerprint(raw_cache) != raw_cache_fingerprint: + raise RuntimeError("raw boundary cache changed during the diagnostic") + derivative_gate = _evaluate_derivative_gate(row_records) + source_hashes_end = _source_hashes(implementation_paths) + if source_hashes_end != source_hashes_start: + raise RuntimeError("implementation source changed while the diagnostic was running") + repository_end = _git_state() + + config_payload = model.config.to_dict() + prompt_token_ids = prompt_ids.detach().cpu().reshape(-1).tolist() + code_token_ids = code_ids.detach().cpu().reshape(-1).tolist() + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "diagnostic_only": True, + "claim_boundary": ( + "One pinned calibration task, one target transition, four rows fixed by geometry, " + "and four finite-difference step sizes. This checks implementation sign and local " + "derivative consistency only. It does not establish policy quality, held-out " + "generalization, free generation, statistical significance, novelty, speed, " + "memory improvement, or a breakthrough." + ), + "created_at_utc": datetime.now(UTC).isoformat(), + "seed": SEED, + "model": { + "id": MODEL_ID, + "revision": MODEL_REVISION, + "class": model.__class__.__name__, + "dtype": str(dtype), + "device": str(device), + "attention_implementation": "eager", + "parameter_count": sum(parameter.numel() for parameter in model.parameters()), + "config_canonical_sha256": _sha256_bytes(canonical_json_bytes(config_payload)), + "recurrent_geometry": { + "model_layer_indices": list(recurrent_layers), + "value_heads": heads, + "key_rows": key_rows, + "value_dim": value_dim, + }, + }, + "dataset": { + "phase": "calibration", + "development_or_confirmation_loaded": False, + "manifest": mbpp_manifest(rows, phase="calibration"), + "manifest_sha256": mbpp_manifest_sha256(rows, phase="calibration"), + "task_prefix_limit": TASK_PREFIX_LIMIT, + "task_index": TASK_INDEX, + "task_id": int(row["task_id"]), + "prompt_utf8_sha256": _sha256_bytes(formatted.prompt.encode("utf-8")), + "code_utf8_sha256": _sha256_bytes(formatted.code.encode("utf-8")), + "prompt_tokens": len(prompt_token_ids), + "code_tokens": len(code_token_ids), + "prompt_token_ids_canonical_sha256": _sha256_bytes( + canonical_json_bytes(prompt_token_ids) + ), + "code_token_ids_canonical_sha256": _sha256_bytes( + canonical_json_bytes(code_token_ids) + ), + "transition_index": TRANSITION_INDEX, + "input_token_id": int(input_ids.item()), + "target_token_id": int(target_ids.item()), + }, + "method": { + "quantity_checked": "-gradient dot (Q8(raw row) - Q4(raw row))", + "loss": "teacher-forced next-token target NLL", + "trajectory": ( + "uniform physical-INT4 pack/dequantize storage before every prior token; " + "raw update retained at the measured boundary" + ), + "storage_path": "Q4(raw) + alpha * (Q8(raw) - Q4(raw)) for one row", + "deployable_alphas": [0, 1], + "negative_alpha_note": ( + "-epsilon is controlled extrapolation used only for central difference" + ), + "epsilons": list(EPSILONS), + "forward_count": len(ROW_STRATA) * len(EPSILONS) * 5, + "cache_isolation": { + "procedure": ( + "The raw cache fingerprint is checked before and after every " + "row-epsilon validation. Each alpha forward deep-copies an identical " + "physical-INT4 boundary cache." + ), + "fingerprint_scope": ( + "Tensor shapes, dtypes, and bytes for keys, values, convolution states, " + "and recurrent states; cache/layer classes; layer count; and the listed " + "cache-history, initialization, geometry, dtype, and device metadata." + ), + "cache_metadata_attributes": list(CACHE_FINGERPRINT_CACHE_METADATA), + "layer_metadata_attributes": list(CACHE_FINGERPRINT_LAYER_METADATA), + "claim_limit": ( + "This fingerprint does not claim to serialize arbitrary unlisted Python " + "attributes or external model state." + ), + }, + "row_selection": { + "frozen_in_source": True, + "uses_measured_benefit": False, + "strata_source_coordinates": [ + { + "label": label, + "recurrent_layer_ordinal": ordinal, + "head_index": head, + "row_index": key_row, + } + for label, ordinal, head, key_row in ROW_STRATA + ], + }, + }, + "quantizers": { + "int4": asdict(int4_spec), + "int8": asdict(int8_spec), + "physical_endpoint_note": ( + "Q4 and Q8 endpoint rows are decoded from actual packed payloads and FP16 scales; " + "intermediate alphas are floating-point interventions" + ), + }, + "raw_boundary_cache_fingerprint_sha256": raw_cache_fingerprint, + "rows": row_records, + "derivative_gate": derivative_gate, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "packages": _package_versions(), + "cuda_available": torch.cuda.is_available(), + "cuda_runtime": torch.version.cuda, + "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else None, + }, + "implementation": { + "source_hashes_start": source_hashes_start, + "source_hashes_end": source_hashes_end, + "unchanged_during_run": source_hashes_start == source_hashes_end, + }, + "repository": { + "start": repository_start, + "end": repository_end, + }, + "command": [sys.executable, *sys.argv], + } + artifact = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "canonical_evidence_sha256": _sha256_bytes(canonical_json_bytes(evidence)), + "evidence": evidence, + } + payload = canonical_json_bytes(artifact) + args.output.parent.mkdir(parents=True, exist_ok=True) + temporary = args.output.with_name(args.output.name + ".tmp") + temporary.write_bytes(payload) + temporary.replace(args.output) + print( + json.dumps( + { + "output": str(args.output.resolve()), + "artifact_sha256": _sha256_bytes(payload), + "canonical_evidence_sha256": artifact["canonical_evidence_sha256"], + "task_id": int(row["task_id"]), + "transition_index": TRANSITION_INDEX, + "rows": len(row_records), + "epsilons": list(EPSILONS), + "passed": derivative_gate["passed"], + "failures": derivative_gate["failures"], + }, + indent=2, + sort_keys=True, + ) + ) + return 0 if derivative_gate["passed"] else 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/resolve_rht_cqer_stage_b_identity.py b/scripts/resolve_rht_cqer_stage_b_identity.py new file mode 100644 index 0000000..41a42bd --- /dev/null +++ b/scripts/resolve_rht_cqer_stage_b_identity.py @@ -0,0 +1,2148 @@ +#!/usr/bin/env python3 +"""Resolve the frozen Experiment 009 Stage-B identity without model weights. + +The only content retained, canonicalized, or tokenized by this program belongs +to ranked MBPP calibration window ``[32, 64)``. The first streaming pass reads +only ``task_id`` so the frozen ranking can be reconstructed. A second +task-ID-only streaming pass retains exactly the 32 authorized rows. + +This module deliberately does not load model weights, run a forward pass, or +compute a quality metric. Its authenticated output is an identity amendment, +not experimental evidence. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import os +import string +import subprocess +import sys +import tempfile +from collections.abc import Callable, Iterable, Mapping, Sequence +from dataclasses import dataclass +from datetime import UTC, datetime +from numbers import Integral +from pathlib import Path +from typing import Any, Final + +from recurquant.evidence import canonical_json_bytes, verify_evidence_artifact +from recurquant.public_data import ( + MBPP_CALIBRATION_SIZE, + MBPP_CONFIG, + MBPP_DATASET_ID, + MBPP_FORMATTER_VERSION, + MBPP_MANIFEST_SCHEMA, + MBPP_REVISION, + MBPP_SELECTION_NAMESPACE, + canonical_mbpp_row, + format_mbpp_example, + mbpp_calibration_key, + mbpp_manifest, + mbpp_manifest_content_sha256, + mbpp_row_sha256, + mbpp_source_split, +) + +ARTIFACT_KIND: Final = "recurquant_rht_cqer32_stage_b_identity" +IDENTITY_SCHEMA: Final = "recurquant.experiment009-stage-b-identity.v1" +TOKEN_MANIFEST_SCHEMA: Final = "recurquant.experiment009-stage-b-token-manifest.v1" +ORDERED_IDENTITY_SCHEMA: Final = "recurquant.experiment009-stage-b-ordered-identity.v1" +CLAIM_BOUNDARY: Final = ( + "This artifact fixes the Experiment 009 Stage-B data and tokenizer " + "identity before model weights or quality metrics are opened. It is " + "not performance, novelty, state-of-the-art, or breakthrough evidence." +) + +STAGE_B_OFFSET: Final = 32 +STAGE_B_LIMIT: Final = 32 +STAGE_B_STOP: Final = STAGE_B_OFFSET + STAGE_B_LIMIT +PROTECTED_WINDOW: Final = (8, 16) + +MODEL_ID: Final = "Qwen/Qwen3.5-0.8B-Base" +MODEL_REVISION: Final = "dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68" +TOKENIZER_CLASS: Final = "Qwen2Tokenizer" +TRANSFORMERS_VERSION: Final = "5.14.1" +TOKEN_ID_HASH_SERIALIZATION: Final = "recurquant.canonical-json.v1" +TEXT_HASH_ENCODING: Final = "utf-8" +RUNTIME_ENVIRONMENT_SCHEMA: Final = ( + "recurquant.experiment009-stage-b-runtime-environment.v1" +) +STAGE_A_PYTHON_VERSION: Final = (3, 11, 15) +STAGE_A_PACKAGE_VERSIONS: Final = { + "datasets": "4.8.5", + "numpy": "2.4.6", + "safetensors": "0.8.0", + "torch": "2.11.0+cu128", + "transformers": TRANSFORMERS_VERSION, +} +STAGE_A_CUDA_CONTRACT: Final = { + "available": True, + "runtime_version": "12.8", + "device_type": "cuda", +} + +STAGE_A_ARTIFACT_RELATIVE_PATH: Final = ( + "evidence/experiment009-rht-cqer-stage-a-666-5be8d48.json" +) +STAGE_A_ARTIFACT_KIND: Final = "recurquant_rht_cqer32_stage_a_screen" +STAGE_A_FILE_SHA256: Final = ( + "98a432843dc438f2d5fde34f8704f154ebc3ee12c93ba7c469369acfedfb15b5" +) +STAGE_A_CANONICAL_EVIDENCE_SHA256: Final = ( + "9e03a1e8cefb5801406a47a2e5e365686afb0a05e10e099a989cee616b505ed1" +) +STAGE_A_IMPLEMENTATION_COMMIT: Final = ( + "5be8d48369d94081e55aa389c25f63c303c7b0dd" +) +STAGE_A_RESULT_COMMIT: Final = "1cbc20f6c493e79f771d047fc63e96a7464eacf4" +ROW_PLAN_SCHEMA: Final = "recurquant.experiment009-stage-b-row-plan.v1" +ROW_PLAN_METHOD: Final = "target_directional_fisher_difference_int4" +SELECTOR_ARTIFACT_RELATIVE_PATH: Final = ( + "artifacts/experiment006-hrr-selector-8task-c2ad68b.json" +) +LOSS_SELECTOR_ARTIFACT_RELATIVE_PATH: Final = ( + "artifacts/experiment006-loss-selector-8task-c2ad68b.json" +) +SELECTOR_ARTIFACT_KIND: Final = "recurquant_hrr_calibration_diagnostic" +LOSS_SELECTOR_ARTIFACT_KIND: Final = ( + "recurquant_loss_sensitivity_calibration_diagnostic" +) +SELECTOR_FILE_SHA256: Final = ( + "d0c4267095ee3f5068627b189a1fd9f58cb02f6e25672d9b89dd0990e5b09330" +) +SELECTOR_CANONICAL_EVIDENCE_SHA256: Final = ( + "7970961fd88b522998189ad64f26b333aed9c88ff5f653de5449fd9e01d8cbc8" +) +LOSS_SELECTOR_FILE_SHA256: Final = ( + "95c16656edb32efbc985f2fea59e229634dd558f4f4bf04819b8efc37783a1d6" +) +LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256: Final = ( + "bff4e33253990b8115e1f35e74516c4975c2fe4aac5066475afe968eb8a64609" +) +FROZEN_LINEAR_LAYERS: Final = ( + 0, + 1, + 2, + 4, + 5, + 6, + 8, + 9, + 10, + 12, + 13, + 14, + 16, + 17, + 18, + 20, + 21, + 22, +) +FROZEN_LAYER_QUOTAS: Final = { + 0: 355, + 1: 380, + 2: 269, + 4: 179, + 5: 185, + 6: 105, + 8: 80, + 9: 43, + 10: 84, + 12: 30, + 13: 62, + 14: 54, + 16: 45, + 17: 27, + 18: 7, + 20: 9, + 21: 7, + 22: 55, +} + +STAGE_B_SOURCE_FILES: Final = ( + "research/EXPERIMENT_009_RHT_CQER_PROTOCOL.md", + "research/EXPERIMENT_009_DATA_ACCESS_CLARIFICATION.md", + "research/EXPERIMENT_009_STAGE_A_AUDIT.md", + "research/EXPERIMENT_009_STAGE_A_RESULT.md", + "scripts/evaluate_rht_cqer_stage_b.py", + "scripts/resolve_rht_cqer_stage_b_identity.py", + "scripts/screen_rht_cqer.py", + "scripts/pilot_evaluate_hrr.py", + "src/recurquant/__init__.py", + "src/recurquant/cache.py", + "src/recurquant/evaluation.py", + "src/recurquant/evidence.py", + "src/recurquant/finite_difference.py", + "src/recurquant/fisher_sensitivity.py", + "src/recurquant/horizon.py", + "src/recurquant/horizon_calibration.py", + "src/recurquant/intervention.py", + "src/recurquant/metrics.py", + "src/recurquant/mixed_quantization.py", + "src/recurquant/model_fisher.py", + "src/recurquant/multibit_policy.py", + "src/recurquant/multibit_quantization.py", + "src/recurquant/packed_cache.py", + "src/recurquant/public_data.py", + "src/recurquant/quantization.py", + "src/recurquant/query_energy.py", + "src/recurquant/qwen35.py", + "src/recurquant/rht.py", + "src/recurquant/row_policy.py", + "src/recurquant/transition_observer.py", + "tests/test_evaluate_rht_cqer_stage_b.py", + "tests/test_resolve_rht_cqer_stage_b_identity.py", + "tests/test_rht.py", + "tests/test_rht_independent_reference.py", + "tests/test_rht_mixed_quantization.py", + "tests/test_right_rht_query_ema_cache.py", + "tests/test_screen_rht_cqer.py", +) +SOURCE_FILES: Final = STAGE_B_SOURCE_FILES +IMPORTED_MODULE_PATHS: Final = { + "recurquant": "src/recurquant/__init__.py", + "recurquant.evidence": "src/recurquant/evidence.py", + "recurquant.public_data": "src/recurquant/public_data.py", +} + +LoadDataset = Callable[..., Iterable[Mapping[str, Any]]] + + +@dataclass(frozen=True, slots=True) +class EncodedIdentityTask: + """One authenticated Stage-B row and its exact tokenizer outputs.""" + + row: Mapping[str, Any] + prompt_token_ids: tuple[int, ...] + code_token_ids: tuple[int, ...] + record: Mapping[str, Any] + + +@dataclass(frozen=True, slots=True) +class StageBDataAccessAudit: + """Application-level access counters separated from transport behavior.""" + + ranking_transport_records_yielded: int + ranking_task_id_fields_inspected: int + target_transport_records_yielded: int + target_task_id_fields_inspected: int + target_rows_retained_and_canonicalized: int + + def as_dict(self, *, selected_task_ids: Sequence[int]) -> dict[str, Any]: + selected = [int(task_id) for task_id in selected_task_ids] + return { + "transport_limitation": ( + "The Hugging Face streaming transport may deserialize complete " + "source records before yielding mappings. These counters describe " + "fields read and rows retained by RecurQuant application code." + ), + "ranking_pass": { + "transport_records_yielded": self.ranking_transport_records_yielded, + "task_id_fields_inspected": self.ranking_task_id_fields_inspected, + "non_task_id_fields_read_by_recurquant": 0, + "row_mappings_retained": 0, + }, + "target_load_pass": { + "transport_records_yielded": self.target_transport_records_yielded, + "task_id_fields_inspected": self.target_task_id_fields_inspected, + "non_target_content_fields_read_by_recurquant": 0, + "target_rows_retained_and_canonicalized": ( + self.target_rows_retained_and_canonicalized + ), + }, + "application_task_id_sets": { + "selected": selected, + "retained": selected, + "canonicalized": selected, + "formatted": selected, + "tokenized": selected, + "passed_to_model": [], + "evaluated": [], + }, + "protected_window_intersection": { + "selected": False, + "retained": False, + "canonicalized": False, + "formatted": False, + "tokenized": False, + "passed_to_model": False, + "evaluated": False, + }, + } + + +@dataclass(frozen=True, slots=True) +class AuthenticatedStageBRows: + """Exact Stage-B rows plus application-level stream-access evidence.""" + + rows: tuple[Mapping[str, Any], ...] + ordered_task_ids: tuple[int, ...] + access_audit: StageBDataAccessAudit + + +@dataclass(frozen=True, slots=True) +class RuntimeIdentityAuthentication: + """Tokenized runtime identity and the hashes authenticated before weights.""" + + tasks: tuple[EncodedIdentityTask, ...] + ordered_task_ids: tuple[int, ...] + content_manifest_sha256: str + token_manifest_sha256: str + ordered_identity_sha256: str + access_audit: StageBDataAccessAudit | None + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Resolve only the frozen Experiment 009 ranked MBPP [32, 64) " + "development identity. No model weights or quality metrics are opened." + ) + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--local-files-only", action="store_true") + return parser.parse_args() + + +def _sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def _valid_sha256(value: object) -> bool: + return ( + isinstance(value, str) + and len(value) == 64 + and all(character in string.hexdigits for character in value) + and value == value.lower() + ) + + +def _strict_int(value: object, *, context: str) -> int: + if isinstance(value, bool) or not isinstance(value, Integral): + raise ValueError(f"{context} must be an integer") + return int(value) + + +def _require_mapping(value: object, *, context: str) -> Mapping[str, Any]: + if not isinstance(value, Mapping): + raise ValueError(f"{context} must be an object") + return value + + +def _require_list(value: object, *, context: str) -> list[Any]: + if not isinstance(value, list): + raise ValueError(f"{context} must be an array") + return value + + +def _python_triplet_from_stage_a(value: object) -> tuple[int, int, int]: + if not isinstance(value, str): + raise ValueError("Stage-A Python identity must be a string") + version_token = value.split(maxsplit=1)[0] + components = version_token.split(".") + if ( + len(components) != 3 + or any(not component.isdigit() for component in components) + ): + raise ValueError("Stage-A Python identity lacks an exact major.minor.micro") + triplet = tuple(int(component) for component in components) + if ".".join(str(component) for component in triplet) != version_token: + raise ValueError("Stage-A Python identity is not canonical") + return triplet[0], triplet[1], triplet[2] + + +def derive_stage_a_runtime_environment( + stage_a_evidence: Mapping[str, Any], +) -> dict[str, Any]: + """Derive the frozen Stage-B runtime from authenticated Stage-A evidence.""" + + environment = _require_mapping( + stage_a_evidence.get("environment"), + context="Stage-A environment", + ) + if set(environment) != { + "cuda_available", + "cuda_runtime", + "gpu", + "packages", + "platform", + "python", + }: + raise ValueError("Stage-A environment fields drifted") + packages = _require_mapping( + environment.get("packages"), + context="Stage-A environment packages", + ) + if set(packages) != set(STAGE_A_PACKAGE_VERSIONS) or any( + not isinstance(value, str) or not value for value in packages.values() + ): + raise ValueError("Stage-A environment package identity drifted") + model = _require_mapping(stage_a_evidence.get("model"), context="Stage-A model") + python_version = _python_triplet_from_stage_a(environment.get("python")) + cuda_available = environment.get("cuda_available") + cuda_runtime = environment.get("cuda_runtime") + device_type = model.get("device") + if ( + not isinstance(cuda_available, bool) + or not isinstance(cuda_runtime, str) + or not cuda_runtime + or not isinstance(device_type, str) + or not device_type + or not isinstance(environment.get("gpu"), str) + or not environment.get("gpu") + or not isinstance(environment.get("platform"), str) + or not environment.get("platform") + ): + raise ValueError("Stage-A accelerator environment identity is incomplete") + return { + "python": { + "major": python_version[0], + "minor": python_version[1], + "micro": python_version[2], + "version": ".".join(str(component) for component in python_version), + }, + "packages": dict(packages), + "cuda": { + "available": cuda_available, + "runtime_version": cuda_runtime, + "device_type": device_type, + }, + } + + +def _frozen_stage_a_runtime_environment() -> dict[str, Any]: + return { + "python": { + "major": STAGE_A_PYTHON_VERSION[0], + "minor": STAGE_A_PYTHON_VERSION[1], + "micro": STAGE_A_PYTHON_VERSION[2], + "version": ".".join( + str(component) for component in STAGE_A_PYTHON_VERSION + ), + }, + "packages": dict(STAGE_A_PACKAGE_VERSIONS), + "cuda": dict(STAGE_A_CUDA_CONTRACT), + } + + +def inspect_runtime_environment() -> dict[str, Any]: + """Inspect only version and accelerator metadata; do not open model weights.""" + + packages: dict[str, str] = {} + for name in STAGE_A_PACKAGE_VERSIONS: + try: + packages[name] = importlib.metadata.version(name) + except importlib.metadata.PackageNotFoundError as error: + raise ValueError(f"required runtime package is missing: {name}") from error + + import torch + + cuda_available = bool(torch.cuda.is_available()) + cuda_runtime = torch.version.cuda + if cuda_runtime is not None and not isinstance(cuda_runtime, str): + raise ValueError("runtime CUDA version must be a string or null") + version = sys.version_info + return { + "python": { + "major": version.major, + "minor": version.minor, + "micro": version.micro, + "version": f"{version.major}.{version.minor}.{version.micro}", + }, + "packages": packages, + "cuda": { + "available": cuda_available, + "runtime_version": cuda_runtime, + "device_type": "cuda" if cuda_available else "cpu", + }, + } + + +def authenticate_runtime_environment( + stage_a_evidence: Mapping[str, Any], + *, + local_files_only: bool, + runtime_environment: Mapping[str, Any] | None = None, +) -> dict[str, Any]: + """Require an exact Stage-A runtime match before Stage-B dataset access.""" + + if not isinstance(local_files_only, bool): + raise ValueError("local_files_only must be boolean") + derived = derive_stage_a_runtime_environment(stage_a_evidence) + frozen = _frozen_stage_a_runtime_environment() + if derived != frozen: + raise ValueError("authenticated Stage-A runtime contract drifted") + observed = ( + dict(runtime_environment) + if runtime_environment is not None + else inspect_runtime_environment() + ) + if observed != derived: + mismatches = [ + field + for field in ("python", "packages", "cuda") + if observed.get(field) != derived[field] + ] + extra = sorted(set(observed) - set(derived)) + if extra: + mismatches.append("unexpected_fields") + raise ValueError( + "Stage-B runtime does not exactly match authenticated Stage A: " + + ", ".join(mismatches) + ) + return { + "schema": RUNTIME_ENVIRONMENT_SCHEMA, + "stage_a_binding": { + "artifact_kind": STAGE_A_ARTIFACT_KIND, + "file_sha256": STAGE_A_FILE_SHA256, + "canonical_evidence_sha256": STAGE_A_CANONICAL_EVIDENCE_SHA256, + }, + **derived, + "runtime_matches_stage_a": True, + "local_files_only": local_files_only, + } + + +def _load_exact_evidence_artifact( + path: Path, + *, + expected_kind: str, + expected_file_sha256: str, + expected_canonical_evidence_sha256: str, +) -> dict[str, Any]: + verification = verify_evidence_artifact( + path, + expected_file_sha256=expected_file_sha256, + expected_canonical_evidence_sha256=expected_canonical_evidence_sha256, + ) + if verification["valid"] is not True: + raise ValueError( + f"{expected_kind} artifact failed authentication: " + + "; ".join(verification["errors"]) + ) + document = json.loads(path.read_bytes().decode("utf-8")) + evidence = _require_mapping(document.get("evidence"), context=expected_kind) + if evidence.get("artifact_kind") != expected_kind: + raise ValueError(f"{expected_kind} artifact kind drifted") + model = _require_mapping(evidence.get("model"), context=f"{expected_kind} model") + if ( + model.get("id") != MODEL_ID + or model.get("revision") != MODEL_REVISION + or model.get("dtype") != "torch.bfloat16" + ): + raise ValueError(f"{expected_kind} model contract drifted") + return dict(evidence) + + +def _normalize_high_precision_rows( + value: object, + *, + context: str, +) -> tuple[dict[str, int], ...]: + rows = _require_list(value, context=context) + normalized: list[dict[str, int]] = [] + seen: set[tuple[int, int, int]] = set() + for index, raw in enumerate(rows): + record = _require_mapping(raw, context=f"{context} row {index}") + if set(record) != {"layer_index", "head_index", "row_index"}: + raise ValueError(f"{context} row {index} fields drifted") + layer = _strict_int( + record.get("layer_index"), + context=f"{context} row {index} layer", + ) + head = _strict_int( + record.get("head_index"), + context=f"{context} row {index} head", + ) + row = _strict_int( + record.get("row_index"), + context=f"{context} row {index} row", + ) + identity = (layer, head, row) + if ( + layer not in FROZEN_LAYER_QUOTAS + or not 0 <= head < 16 + or not 0 <= row < 128 + or identity in seen + ): + raise ValueError(f"{context} row {index} identity is invalid or repeated") + seen.add(identity) + normalized.append( + { + "layer_index": layer, + "head_index": head, + "row_index": row, + } + ) + if tuple( + (record["layer_index"], record["head_index"], record["row_index"]) + for record in normalized + ) != tuple( + sorted( + ( + record["layer_index"], + record["head_index"], + record["row_index"], + ) + for record in normalized + ) + ): + raise ValueError(f"{context} rows are not in canonical order") + return tuple(normalized) + + +def validate_compact_row_plan(plan: Mapping[str, Any]) -> None: + """Authenticate the complete public row plan embedded in the identity.""" + + expected_fields = { + "schema", + "method", + "selector_binding", + "model", + "quantization", + "accounting", + "score_shapes", + "layer_quotas", + "high_precision_rows", + "canonical_plan_sha256", + } + if set(plan) != expected_fields: + raise ValueError("Stage-B compact row-plan fields drifted") + if plan.get("schema") != ROW_PLAN_SCHEMA or plan.get("method") != ROW_PLAN_METHOD: + raise ValueError("Stage-B compact row-plan identity drifted") + if dict( + _require_mapping(plan.get("selector_binding"), context="selector binding") + ) != { + "selector_file_sha256": SELECTOR_FILE_SHA256, + "selector_canonical_evidence_sha256": ( + SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": LOSS_SELECTOR_FILE_SHA256, + "loss_selector_canonical_evidence_sha256": ( + LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + }: + raise ValueError("Stage-B selector binding drifted") + if dict(_require_mapping(plan.get("model"), context="row-plan model")) != { + "id": MODEL_ID, + "revision": MODEL_REVISION, + }: + raise ValueError("Stage-B row-plan model drifted") + if dict( + _require_mapping(plan.get("quantization"), context="row-plan quantization") + ) != { + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + }: + raise ValueError("Stage-B row-plan quantization drifted") + if dict(_require_mapping(plan.get("accounting"), context="row-plan accounting")) != { + "total_groups": 36_864, + "mask_bytes": 4_608, + "promotion_increment_bytes": 64, + "target_resident_bytes": 2_564_096, + "resident_bytes": 2_564_096, + "promoted_group_count": 1_976, + }: + raise ValueError("Stage-B row-plan accounting drifted") + expected_shapes = [ + {"layer_index": layer, "heads": 16, "rows": 128} + for layer in FROZEN_LINEAR_LAYERS + ] + if plan.get("score_shapes") != expected_shapes: + raise ValueError("Stage-B row-plan score shapes drifted") + if dict(_require_mapping(plan.get("layer_quotas"), context="layer quotas")) != { + str(layer): quota for layer, quota in FROZEN_LAYER_QUOTAS.items() + }: + raise ValueError("Stage-B row-plan layer quotas drifted") + rows = _normalize_high_precision_rows( + plan.get("high_precision_rows"), + context="Stage-B row plan", + ) + if len(rows) != 1_976: + raise ValueError("Stage-B row plan must contain exactly 1,976 promotions") + observed_quotas = { + layer: sum(record["layer_index"] == layer for record in rows) + for layer in FROZEN_LINEAR_LAYERS + } + if observed_quotas != FROZEN_LAYER_QUOTAS: + raise ValueError("Stage-B row-plan promotion counts drifted") + recorded_hash = plan.get("canonical_plan_sha256") + if not _valid_sha256(recorded_hash): + raise ValueError("Stage-B row-plan canonical hash is invalid") + hash_payload = dict(plan) + del hash_payload["canonical_plan_sha256"] + if _sha256_bytes(canonical_json_bytes(hash_payload)) != recorded_hash: + raise ValueError("Stage-B row-plan canonical hash does not match its contents") + + +def build_compact_row_plan( + selector_path: Path, + loss_selector_path: Path, +) -> dict[str, Any]: + """Authenticate frozen selectors and extract their complete public row plan.""" + + selector = _load_exact_evidence_artifact( + selector_path, + expected_kind=SELECTOR_ARTIFACT_KIND, + expected_file_sha256=SELECTOR_FILE_SHA256, + expected_canonical_evidence_sha256=SELECTOR_CANONICAL_EVIDENCE_SHA256, + ) + loss_selector = _load_exact_evidence_artifact( + loss_selector_path, + expected_kind=LOSS_SELECTOR_ARTIFACT_KIND, + expected_file_sha256=LOSS_SELECTOR_FILE_SHA256, + expected_canonical_evidence_sha256=LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256, + ) + selector_dataset = _require_mapping( + selector.get("dataset"), + context="selector dataset", + ) + loss_dataset = _require_mapping( + loss_selector.get("dataset"), + context="loss-selector dataset", + ) + if ( + selector_dataset.get("manifest") != loss_dataset.get("manifest") + or selector_dataset.get("manifest_sha256") + != loss_dataset.get("manifest_sha256") + ): + raise ValueError("frozen selector dataset identities differ") + plan_record = _require_mapping( + _require_mapping(loss_selector.get("plans"), context="loss-selector plans").get( + ROW_PLAN_METHOD + ), + context="target-Fisher plan", + ) + if set(plan_record) != {"evidence", "locations", "promotions_by_layer"}: + raise ValueError("target-Fisher plan record fields drifted") + plan_evidence = _require_mapping( + plan_record.get("evidence"), + context="target-Fisher plan evidence", + ) + rows = _normalize_high_precision_rows( + plan_record.get("locations"), + context="target-Fisher locations", + ) + evidence_rows = _normalize_high_precision_rows( + plan_evidence.get("high_precision_rows"), + context="target-Fisher evidence rows", + ) + if rows != evidence_rows: + raise ValueError("target-Fisher plan locations disagree with its evidence") + expected_evidence_without_rows = { + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + "total_groups": 36_864, + "mask_bytes": 4_608, + "promotion_increment_bytes": 64, + "target_resident_bytes": 2_564_096, + "resident_bytes": 2_564_096, + "promoted_group_count": 1_976, + "score_shapes": [ + [layer, 16, 128] for layer in FROZEN_LINEAR_LAYERS + ], + } + if { + key: plan_evidence.get(key) for key in expected_evidence_without_rows + } != expected_evidence_without_rows: + raise ValueError("target-Fisher recorded plan accounting drifted") + observed_promotions = { + str(layer): sum(record["layer_index"] == layer for record in rows) + for layer in FROZEN_LINEAR_LAYERS + } + if observed_promotions != { + str(layer): quota for layer, quota in FROZEN_LAYER_QUOTAS.items() + }: + raise ValueError("target-Fisher recorded layer quotas drifted") + promotions_by_layer = _require_mapping( + plan_record.get("promotions_by_layer"), + context="target-Fisher promotions by layer", + ) + if { + str(key): _strict_int(value, context="promotions_by_layer value") + for key, value in promotions_by_layer.items() + } != observed_promotions: + raise ValueError("target-Fisher promotion summary drifted") + plan: dict[str, Any] = { + "schema": ROW_PLAN_SCHEMA, + "method": ROW_PLAN_METHOD, + "selector_binding": { + "selector_file_sha256": SELECTOR_FILE_SHA256, + "selector_canonical_evidence_sha256": ( + SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": LOSS_SELECTOR_FILE_SHA256, + "loss_selector_canonical_evidence_sha256": ( + LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + }, + "model": { + "id": MODEL_ID, + "revision": MODEL_REVISION, + }, + "quantization": { + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + }, + "accounting": { + "total_groups": 36_864, + "mask_bytes": 4_608, + "promotion_increment_bytes": 64, + "target_resident_bytes": 2_564_096, + "resident_bytes": 2_564_096, + "promoted_group_count": 1_976, + }, + "score_shapes": [ + {"layer_index": layer, "heads": 16, "rows": 128} + for layer in FROZEN_LINEAR_LAYERS + ], + "layer_quotas": { + str(layer): quota for layer, quota in FROZEN_LAYER_QUOTAS.items() + }, + "high_precision_rows": list(rows), + } + plan["canonical_plan_sha256"] = _sha256_bytes(canonical_json_bytes(plan)) + validate_compact_row_plan(plan) + return plan + + +def _load_dataset_lazily(*args: Any, **kwargs: Any) -> Iterable[Mapping[str, Any]]: + try: + from datasets import load_dataset + except ModuleNotFoundError as error: + raise RuntimeError( + "Stage-B identity resolution requires the optional evaluation " + "dependencies; install recurquant[eval]" + ) from error + return load_dataset(*args, **kwargs) + + +def _resolve_ranked_id_windows( + *, + load_dataset_fn: LoadDataset | None = None, +) -> tuple[tuple[int, ...], frozenset[int], int]: + """Resolve target/protected ranks while reading only source task IDs.""" + + loader = load_dataset_fn or _load_dataset_lazily + loaded = loader( + MBPP_DATASET_ID, + MBPP_CONFIG, + revision=MBPP_REVISION, + split=mbpp_source_split("calibration"), + streaming=True, + ) + task_ids: list[int] = [] + seen: set[int] = set() + for source_index, raw_row in enumerate(loaded): + if not isinstance(raw_row, Mapping): + raise TypeError(f"MBPP source row {source_index} must be a mapping") + task_id = _strict_int( + raw_row.get("task_id"), + context=f"MBPP source row {source_index} task_id", + ) + if task_id in seen: + raise ValueError(f"duplicate MBPP task_id {task_id} in source split") + seen.add(task_id) + task_ids.append(task_id) + + if len(task_ids) < MBPP_CALIBRATION_SIZE: + raise ValueError( + "MBPP train split is too small for the frozen calibration population: " + f"{len(task_ids)} < {MBPP_CALIBRATION_SIZE}" + ) + ranked = sorted(task_ids, key=lambda task_id: (mbpp_calibration_key(task_id), task_id)) + selected = tuple(ranked[STAGE_B_OFFSET:STAGE_B_STOP]) + protected = frozenset(ranked[PROTECTED_WINDOW[0] : PROTECTED_WINDOW[1]]) + if len(selected) != STAGE_B_LIMIT or len(set(selected)) != STAGE_B_LIMIT: + raise RuntimeError("Stage-B ranking did not resolve exactly 32 unique task IDs") + if set(selected) & protected: + raise RuntimeError("Stage-B target IDs overlap the protected ranked window") + return selected, protected, len(task_ids) + + +def resolve_stage_b_task_ids( + *, + load_dataset_fn: LoadDataset | None = None, +) -> tuple[tuple[int, ...], int]: + """Resolve ranks 32-63 while reading only task IDs from the source stream.""" + + selected, _protected, source_row_count = _resolve_ranked_id_windows( + load_dataset_fn=load_dataset_fn, + ) + return selected, source_row_count + + +def _load_exact_rows_with_audit( + *, + task_ids: Sequence[int], + protected_task_ids: frozenset[int], + load_dataset_fn: LoadDataset | None, +) -> tuple[tuple[Mapping[str, Any], ...], int]: + """Canonicalize targets only; non-target mappings are read for task_id only.""" + + ordered_ids = tuple(_strict_int(task_id, context="target task_id") for task_id in task_ids) + if ( + len(ordered_ids) != STAGE_B_LIMIT + or len(set(ordered_ids)) != STAGE_B_LIMIT + ): + raise ValueError("target task IDs must contain exactly 32 unique integers") + if set(ordered_ids) & protected_task_ids: + raise ValueError("target task IDs overlap the protected ranked window") + + loader = load_dataset_fn or _load_dataset_lazily + loaded = loader( + MBPP_DATASET_ID, + MBPP_CONFIG, + revision=MBPP_REVISION, + split=mbpp_source_split("calibration"), + streaming=True, + ) + targets = set(ordered_ids) + selected: dict[int, Mapping[str, Any]] = {} + transport_rows = 0 + for source_index, raw_row in enumerate(loaded): + transport_rows += 1 + if not isinstance(raw_row, Mapping): + raise TypeError(f"MBPP source row {source_index} must be a mapping") + task_id = _strict_int( + raw_row.get("task_id"), + context=f"MBPP source row {source_index} task_id", + ) + if task_id not in targets: + continue + if task_id in protected_task_ids: + raise RuntimeError("protected ranked row reached target canonicalization") + if task_id in selected: + raise ValueError(f"duplicate requested MBPP task_id {task_id} in source split") + selected[task_id] = canonical_mbpp_row(raw_row) + if len(selected) == len(targets): + break + + missing = [task_id for task_id in ordered_ids if task_id not in selected] + if missing: + rendered = ", ".join(str(task_id) for task_id in missing) + raise ValueError(f"requested MBPP task IDs are missing from source split: {rendered}") + return tuple(selected[task_id] for task_id in ordered_ids), transport_rows + + +def resolve_stage_b_rows( + *, + load_dataset_fn: LoadDataset | None = None, +) -> AuthenticatedStageBRows: + """Retain exactly the rows in the frozen ranked calibration window.""" + + task_ids, protected_ids, source_row_count = _resolve_ranked_id_windows( + load_dataset_fn=load_dataset_fn, + ) + rows, target_transport_rows = _load_exact_rows_with_audit( + task_ids=task_ids, + protected_task_ids=protected_ids, + load_dataset_fn=load_dataset_fn, + ) + actual_ids = tuple(_strict_int(row.get("task_id"), context="selected task_id") for row in rows) + if actual_ids != task_ids: + raise RuntimeError("task-ID loader changed the frozen Stage-B order") + return AuthenticatedStageBRows( + rows=tuple(rows), + ordered_task_ids=task_ids, + access_audit=StageBDataAccessAudit( + ranking_transport_records_yielded=source_row_count, + ranking_task_id_fields_inspected=source_row_count, + target_transport_records_yielded=target_transport_rows, + target_task_id_fields_inspected=target_transport_rows, + target_rows_retained_and_canonicalized=len(rows), + ), + ) + + +def _normalize_token_ids(value: object, *, context: str) -> tuple[int, ...]: + if isinstance(value, (str, bytes)) or not isinstance(value, Sequence): + raise ValueError(f"{context} must be a sequence of token IDs") + normalized = tuple(_strict_int(token, context=f"{context} token") for token in value) + if not normalized: + raise ValueError(f"{context} must not be empty") + if any(token < 0 for token in normalized): + raise ValueError(f"{context} must contain non-negative token IDs") + return normalized + + +def _tokenize_text(tokenizer: Any, text: str, *, add_special_tokens: bool) -> tuple[int, ...]: + encoded = tokenizer( + text, + add_special_tokens=add_special_tokens, + return_attention_mask=False, + return_token_type_ids=False, + ) + if not isinstance(encoded, Mapping) or "input_ids" not in encoded: + raise ValueError("tokenizer output must contain input_ids") + return _normalize_token_ids(encoded["input_ids"], context="tokenizer input_ids") + + +def tokenize_stage_b_rows( + tokenizer: Any, + rows: Sequence[Mapping[str, Any]], +) -> tuple[tuple[EncodedIdentityTask, ...], tuple[dict[str, Any], ...]]: + """Tokenize and fingerprint only the exact ordered Stage-B rows.""" + + if len(rows) != STAGE_B_LIMIT: + raise ValueError(f"Stage-B identity requires exactly {STAGE_B_LIMIT} rows") + + encoded_tasks: list[EncodedIdentityTask] = [] + records: list[dict[str, Any]] = [] + seen: set[int] = set() + for index, row in enumerate(rows): + rank = STAGE_B_OFFSET + index + task_id = _strict_int(row.get("task_id"), context=f"rank {rank} task_id") + if task_id in seen: + raise ValueError(f"Stage-B rows repeat task_id {task_id}") + seen.add(task_id) + + formatted = format_mbpp_example(row) + prompt_ids = _tokenize_text( + tokenizer, + formatted.prompt, + add_special_tokens=True, + ) + code_ids = _tokenize_text( + tokenizer, + formatted.code, + add_special_tokens=False, + ) + if len(code_ids) < 2: + raise ValueError(f"MBPP task {task_id} has fewer than two code tokens") + + record = { + "rank": rank, + "task_id": task_id, + "row_sha256": mbpp_row_sha256(row), + "prompt_tokens": len(prompt_ids), + "code_tokens": len(code_ids), + "aligned_scored_tokens": len(code_ids) - 1, + "full_code_scored_tokens": len(code_ids), + "prompt_text_sha256": _sha256_bytes(formatted.prompt.encode("utf-8")), + "code_text_sha256": _sha256_bytes(formatted.code.encode("utf-8")), + "prompt_token_ids_sha256": _sha256_bytes(canonical_json_bytes(prompt_ids)), + "code_token_ids_sha256": _sha256_bytes(canonical_json_bytes(code_ids)), + } + records.append(record) + encoded_tasks.append( + EncodedIdentityTask( + row=row, + prompt_token_ids=prompt_ids, + code_token_ids=code_ids, + record=record, + ) + ) + return tuple(encoded_tasks), tuple(records) + + +def token_manifest_sha256(records: Sequence[Mapping[str, Any]]) -> str: + """Hash the exact ordered task/token records under a named schema.""" + + payload = { + "schema": TOKEN_MANIFEST_SCHEMA, + "tasks": list(records), + } + return _sha256_bytes(canonical_json_bytes(payload)) + + +def ordered_identity_sha256( + *, + content_manifest_sha256: str, + task_records: Sequence[Mapping[str, Any]], +) -> str: + """Bind data, model, tokenizer, row order, and token identity together.""" + + payload = { + "schema": ORDERED_IDENTITY_SCHEMA, + "dataset": { + "id": MBPP_DATASET_ID, + "config": MBPP_CONFIG, + "revision": MBPP_REVISION, + "phase": "calibration", + "source_split": mbpp_source_split("calibration"), + "selection_namespace": MBPP_SELECTION_NAMESPACE, + "ranked_window": [STAGE_B_OFFSET, STAGE_B_STOP], + "content_manifest_sha256": content_manifest_sha256, + }, + "model": { + "id": MODEL_ID, + "revision": MODEL_REVISION, + }, + "tokenizer": { + "source_id": MODEL_ID, + "revision": MODEL_REVISION, + "class": TOKENIZER_CLASS, + "transformers_version": TRANSFORMERS_VERSION, + "prompt_add_special_tokens": True, + "code_add_special_tokens": False, + "formatter_version": MBPP_FORMATTER_VERSION, + "token_id_hash_serialization": TOKEN_ID_HASH_SERIALIZATION, + "text_hash_encoding": TEXT_HASH_ENCODING, + }, + "tasks": list(task_records), + } + return _sha256_bytes(canonical_json_bytes(payload)) + + +def _validate_task_records(records: object) -> tuple[Mapping[str, Any], ...]: + raw_records = _require_list(records, context="dataset tasks") + if len(raw_records) != STAGE_B_LIMIT: + raise ValueError(f"identity must contain exactly {STAGE_B_LIMIT} task records") + + normalized: list[Mapping[str, Any]] = [] + seen: set[int] = set() + hash_fields = ( + "row_sha256", + "prompt_text_sha256", + "code_text_sha256", + "prompt_token_ids_sha256", + "code_token_ids_sha256", + ) + required_fields = { + "rank", + "task_id", + "row_sha256", + "prompt_tokens", + "code_tokens", + "aligned_scored_tokens", + "full_code_scored_tokens", + "prompt_text_sha256", + "code_text_sha256", + "prompt_token_ids_sha256", + "code_token_ids_sha256", + } + for index, raw_record in enumerate(raw_records): + record = _require_mapping(raw_record, context=f"dataset task {index}") + if set(record) != required_fields: + raise ValueError(f"dataset task {index} fields do not match the frozen schema") + rank = _strict_int(record.get("rank"), context=f"dataset task {index} rank") + task_id = _strict_int(record.get("task_id"), context=f"dataset task {index} task_id") + prompt_tokens = _strict_int( + record.get("prompt_tokens"), + context=f"dataset task {index} prompt_tokens", + ) + code_tokens = _strict_int( + record.get("code_tokens"), + context=f"dataset task {index} code_tokens", + ) + aligned_tokens = _strict_int( + record.get("aligned_scored_tokens"), + context=f"dataset task {index} aligned_scored_tokens", + ) + full_tokens = _strict_int( + record.get("full_code_scored_tokens"), + context=f"dataset task {index} full_code_scored_tokens", + ) + if rank != STAGE_B_OFFSET + index: + raise ValueError("Stage-B rank order drifted") + if task_id in seen: + raise ValueError(f"identity repeats task_id {task_id}") + if prompt_tokens < 1 or code_tokens < 2: + raise ValueError(f"task {task_id} has invalid token counts") + if aligned_tokens != code_tokens - 1 or full_tokens != code_tokens: + raise ValueError(f"task {task_id} scoring counts are misaligned") + for field in hash_fields: + if not _valid_sha256(record.get(field)): + raise ValueError(f"task {task_id} {field} is not a canonical SHA-256") + seen.add(task_id) + normalized.append(record) + return tuple(normalized) + + +def validate_stage_b_identity_evidence(evidence: Mapping[str, Any]) -> None: + """Fail closed on any internal or frozen-contract drift in an identity.""" + + expected_evidence_fields = { + "schema_version", + "artifact_kind", + "identity_schema", + "identity_only", + "claim_boundary", + "created_at_utc", + "authorization", + "row_plan", + "model_contract", + "tokenizer_contract", + "dataset", + "integrity", + "repository", + "source_files", + "environment", + } + if set(evidence) != expected_evidence_fields: + raise ValueError("Stage-B identity top-level fields drifted") + if evidence.get("schema_version") != 1: + raise ValueError("Stage-B identity schema_version must be 1") + if evidence.get("artifact_kind") != ARTIFACT_KIND: + raise ValueError("unexpected Stage-B identity artifact kind") + if evidence.get("identity_schema") != IDENTITY_SCHEMA: + raise ValueError("unexpected Stage-B identity schema") + if evidence.get("identity_only") is not True: + raise ValueError("Stage-B artifact must be identity-only") + if evidence.get("claim_boundary") != CLAIM_BOUNDARY: + raise ValueError("Stage-B claim boundary drifted") + created_at = evidence.get("created_at_utc") + if not isinstance(created_at, str): + raise ValueError("Stage-B creation time must be an ISO-8601 string") + try: + parsed_created_at = datetime.fromisoformat(created_at) + except ValueError as error: + raise ValueError("Stage-B creation time is not valid ISO-8601") from error + if parsed_created_at.tzinfo is None: + raise ValueError("Stage-B creation time must include a timezone") + + authorization = _require_mapping( + evidence.get("authorization"), + context="authorization", + ) + authorization_expected = { + "stage_a_artifact_kind": STAGE_A_ARTIFACT_KIND, + "stage_a_file_sha256": STAGE_A_FILE_SHA256, + "stage_a_canonical_evidence_sha256": STAGE_A_CANONICAL_EVIDENCE_SHA256, + "stage_a_implementation_commit": STAGE_A_IMPLEMENTATION_COMMIT, + "stage_a_result_commit": STAGE_A_RESULT_COMMIT, + "stage_a_gate_passed": True, + "verified_before_dataset_access": True, + } + if dict(authorization) != authorization_expected: + raise ValueError("Stage-A authorization contract drifted") + + row_plan = _require_mapping(evidence.get("row_plan"), context="row plan") + validate_compact_row_plan(row_plan) + + model = _require_mapping(evidence.get("model_contract"), context="model contract") + if dict(model) != { + "id": MODEL_ID, + "revision": MODEL_REVISION, + "weights_loaded": False, + }: + raise ValueError("Stage-B model contract drifted") + + tokenizer = _require_mapping( + evidence.get("tokenizer_contract"), + context="tokenizer contract", + ) + if dict(tokenizer) != { + "source_id": MODEL_ID, + "revision": MODEL_REVISION, + "class": TOKENIZER_CLASS, + "transformers_version": TRANSFORMERS_VERSION, + "trust_remote_code": False, + "prompt_add_special_tokens": True, + "code_add_special_tokens": False, + "formatter_version": MBPP_FORMATTER_VERSION, + "token_id_hash_serialization": TOKEN_ID_HASH_SERIALIZATION, + "text_hash_encoding": TEXT_HASH_ENCODING, + }: + raise ValueError("Stage-B tokenizer contract drifted") + + dataset = _require_mapping(evidence.get("dataset"), context="dataset") + expected_dataset_fields = { + "id", + "config", + "revision", + "phase", + "source_split", + "selection_namespace", + "formatter_version", + "selection_mode", + "selection_window", + "protected_window", + "ordered_task_ids", + "manifest", + "content_manifest_sha256", + "token_manifest_sha256", + "ordered_identity_sha256", + "tasks", + "totals", + "data_access", + } + if set(dataset) != expected_dataset_fields: + raise ValueError("Stage-B dataset fields drifted") + if dataset.get("phase") != "calibration": + raise ValueError("Stage-B identity must use calibration phase") + expected_dataset_contract = { + "id": MBPP_DATASET_ID, + "config": MBPP_CONFIG, + "revision": MBPP_REVISION, + "source_split": mbpp_source_split("calibration"), + "selection_namespace": MBPP_SELECTION_NAMESPACE, + "formatter_version": MBPP_FORMATTER_VERSION, + } + for field, expected in expected_dataset_contract.items(): + if dataset.get(field) != expected: + raise ValueError(f"Stage-B dataset {field} drifted") + if dataset.get("selection_mode") != "task_id_ranking_then_exact_task_id_stream": + raise ValueError("Stage-B selection mode drifted") + if dataset.get("selection_window") != { + "offset": STAGE_B_OFFSET, + "limit": STAGE_B_LIMIT, + "stop_exclusive": STAGE_B_STOP, + }: + raise ValueError("Stage-B ranked window drifted") + if dataset.get("protected_window") != { + "offset": PROTECTED_WINDOW[0], + "stop_exclusive": PROTECTED_WINDOW[1], + "content_retained_canonicalized_or_tokenized": False, + }: + raise ValueError("protected-window contract drifted") + + records = _validate_task_records(dataset.get("tasks")) + ordered_ids = [_strict_int(value, context="ordered task_id") for value in _require_list( + dataset.get("ordered_task_ids"), + context="ordered task IDs", + )] + record_ids = [int(record["task_id"]) for record in records] + if ordered_ids != record_ids: + raise ValueError("ordered task IDs do not match task records") + + manifest = _require_mapping(dataset.get("manifest"), context="content manifest") + expected_manifest_fields = { + "schema", + "dataset_id", + "config", + "revision", + "phase", + "source_split", + "selection_namespace", + "formatter_version", + "row_count", + "rows", + } + if set(manifest) != expected_manifest_fields: + raise ValueError("content manifest fields drifted") + for field, expected in { + "schema": MBPP_MANIFEST_SCHEMA, + "dataset_id": MBPP_DATASET_ID, + "config": MBPP_CONFIG, + "revision": MBPP_REVISION, + "phase": "calibration", + "source_split": mbpp_source_split("calibration"), + "selection_namespace": MBPP_SELECTION_NAMESPACE, + "formatter_version": MBPP_FORMATTER_VERSION, + }.items(): + if manifest.get(field) != expected: + raise ValueError(f"content manifest {field} drifted") + content_hash = dataset.get("content_manifest_sha256") + if not _valid_sha256(content_hash): + raise ValueError("content manifest SHA-256 is invalid") + if mbpp_manifest_content_sha256(manifest) != content_hash: + raise ValueError("content manifest hash does not match embedded content") + if manifest.get("row_count") != STAGE_B_LIMIT: + raise ValueError("content manifest must contain exactly 32 rows") + manifest_rows = _require_list(manifest.get("rows"), context="content manifest rows") + record_hashes = { + int(record["task_id"]): str(record["row_sha256"]) for record in records + } + normalized_manifest_rows = tuple( + _require_mapping(item, context="content manifest row") for item in manifest_rows + ) + if any(set(record) != {"task_id", "sha256"} for record in normalized_manifest_rows): + raise ValueError("content manifest row fields drifted") + manifest_hashes = { + _strict_int(record.get("task_id"), context="manifest task_id"): record.get("sha256") + for record in normalized_manifest_rows + } + if manifest_hashes != record_hashes: + raise ValueError("content manifest rows do not match ordered row identities") + + expected_token_hash = token_manifest_sha256(records) + if dataset.get("token_manifest_sha256") != expected_token_hash: + raise ValueError("token manifest hash drifted") + expected_ordered_hash = ordered_identity_sha256( + content_manifest_sha256=str(content_hash), + task_records=records, + ) + if dataset.get("ordered_identity_sha256") != expected_ordered_hash: + raise ValueError("ordered identity hash drifted") + + totals = _require_mapping(dataset.get("totals"), context="dataset totals") + expected_totals = { + "source_train_rows_seen_by_task_id_only": _strict_int( + totals.get("source_train_rows_seen_by_task_id_only"), + context="source row count", + ), + "retained_rows": STAGE_B_LIMIT, + "prompt_tokens": sum(int(record["prompt_tokens"]) for record in records), + "code_tokens": sum(int(record["code_tokens"]) for record in records), + "aligned_scored_tokens": sum( + int(record["aligned_scored_tokens"]) for record in records + ), + "full_code_scored_tokens": sum( + int(record["full_code_scored_tokens"]) for record in records + ), + } + if expected_totals["source_train_rows_seen_by_task_id_only"] < MBPP_CALIBRATION_SIZE: + raise ValueError("source row count is too small for frozen MBPP ranking") + if dict(totals) != expected_totals: + raise ValueError("Stage-B aggregate token totals drifted") + + data_access = _require_mapping(dataset.get("data_access"), context="data access") + limitation = data_access.get("transport_limitation") + if ( + not isinstance(limitation, str) + or "may deserialize complete source records" not in limitation + or "RecurQuant application code" not in limitation + ): + raise ValueError("data-access transport limitation is missing") + ranking_pass = _require_mapping( + data_access.get("ranking_pass"), + context="ranking-pass access", + ) + ranking_yielded = _strict_int( + ranking_pass.get("transport_records_yielded"), + context="ranking transport records", + ) + if dict(ranking_pass) != { + "transport_records_yielded": ranking_yielded, + "task_id_fields_inspected": ranking_yielded, + "non_task_id_fields_read_by_recurquant": 0, + "row_mappings_retained": 0, + }: + raise ValueError("ranking-pass access counters drifted") + if ranking_yielded != expected_totals["source_train_rows_seen_by_task_id_only"]: + raise ValueError("ranking-pass access count does not match dataset totals") + target_pass = _require_mapping( + data_access.get("target_load_pass"), + context="target-load access", + ) + target_yielded = _strict_int( + target_pass.get("transport_records_yielded"), + context="target-load transport records", + ) + if target_yielded < STAGE_B_LIMIT or dict(target_pass) != { + "transport_records_yielded": target_yielded, + "task_id_fields_inspected": target_yielded, + "non_target_content_fields_read_by_recurquant": 0, + "target_rows_retained_and_canonicalized": STAGE_B_LIMIT, + }: + raise ValueError("target-load access counters drifted") + application_sets = _require_mapping( + data_access.get("application_task_id_sets"), + context="application task-ID sets", + ) + expected_application_sets = { + "selected": ordered_ids, + "retained": ordered_ids, + "canonicalized": ordered_ids, + "formatted": ordered_ids, + "tokenized": ordered_ids, + "passed_to_model": [], + "evaluated": [], + } + if dict(application_sets) != expected_application_sets: + raise ValueError("application task-ID access sets drifted") + protected_intersection = _require_mapping( + data_access.get("protected_window_intersection"), + context="protected-window intersection", + ) + if set(protected_intersection) != set(expected_application_sets) or any( + value is not False for value in protected_intersection.values() + ): + raise ValueError("protected ranked window intersects an application access set") + + integrity = _require_mapping(evidence.get("integrity"), context="integrity") + required_true = ( + "stage_a_authenticated_before_dataset_access", + "runtime_environment_authenticated_before_dataset_access", + "selector_artifacts_authenticated_before_dataset_access", + "repository_clean_at_start", + "repository_clean_at_end", + "repository_commit_stable", + "source_hashes_stable", + "task_id_only_ranking_pass", + "only_stage_b_content_retained_canonicalized_and_tokenized", + "imported_modules_resolved_to_authenticated_repository", + "output_path_external_or_git_ignored", + ) + if any(integrity.get(field) is not True for field in required_true): + raise ValueError("Stage-B integrity contract contains a failed check") + required_false = ( + "protected_window_8_16_content_retained_canonicalized_or_tokenized", + "model_weights_loaded", + "model_forward_pass_run", + "logits_or_quality_metrics_observed", + ) + if any(integrity.get(field) is not False for field in required_false): + raise ValueError("Stage-B identity observed forbidden model/data state") + if set(integrity) != set(required_true) | set(required_false): + raise ValueError("Stage-B integrity fields drifted") + + repository = _require_mapping(evidence.get("repository"), context="repository") + if set(repository) != {"commit", "start", "end", "stable_commit"}: + raise ValueError("repository fields drifted") + start = _require_mapping(repository.get("start"), context="repository start") + end = _require_mapping(repository.get("end"), context="repository end") + if set(start) != {"commit", "worktree_clean", "status"} or set(end) != { + "commit", + "worktree_clean", + "status", + }: + raise ValueError("repository snapshot fields drifted") + commit = repository.get("commit") + if ( + not isinstance(commit, str) + or len(commit) != 40 + or commit != commit.lower() + or not all(character in string.hexdigits for character in commit) + ): + raise ValueError("repository commit must be a full lowercase Git SHA") + if ( + start.get("commit") != commit + or end.get("commit") != commit + or start.get("worktree_clean") is not True + or end.get("worktree_clean") is not True + or start.get("status") != [] + or end.get("status") != [] + or repository.get("stable_commit") is not True + ): + raise ValueError("repository identity was not clean and stable") + + sources = _require_mapping(evidence.get("source_files"), context="source files") + if set(sources) != { + "paths", + "sha256_start", + "sha256_end", + "stable", + "imported_modules", + }: + raise ValueError("source-file fields drifted") + if sources.get("paths") != list(SOURCE_FILES) or sources.get("stable") is not True: + raise ValueError("source-file path contract drifted") + start_hashes = _require_mapping(sources.get("sha256_start"), context="source hashes start") + end_hashes = _require_mapping(sources.get("sha256_end"), context="source hashes end") + if dict(start_hashes) != dict(end_hashes) or set(start_hashes) != set(SOURCE_FILES): + raise ValueError("source-file hashes were not complete and stable") + if any(not _valid_sha256(value) for value in start_hashes.values()): + raise ValueError("source-file map contains an invalid SHA-256") + imported_modules = _require_mapping( + sources.get("imported_modules"), + context="imported modules", + ) + if dict(imported_modules) != IMPORTED_MODULE_PATHS: + raise ValueError("imported-module source paths drifted") + + environment = _require_mapping(evidence.get("environment"), context="environment") + if not isinstance(environment.get("local_files_only"), bool): + raise ValueError("local_files_only must be boolean") + expected_environment = { + "schema": RUNTIME_ENVIRONMENT_SCHEMA, + "stage_a_binding": { + "artifact_kind": STAGE_A_ARTIFACT_KIND, + "file_sha256": STAGE_A_FILE_SHA256, + "canonical_evidence_sha256": STAGE_A_CANONICAL_EVIDENCE_SHA256, + }, + **_frozen_stage_a_runtime_environment(), + "runtime_matches_stage_a": True, + "local_files_only": environment["local_files_only"], + } + if dict(environment) != expected_environment: + raise ValueError("Stage-B runtime environment contract drifted") + + +def load_stage_b_identity_artifact(path: str | Path) -> tuple[dict[str, Any], str]: + """Load and authenticate an Experiment 009 Stage-B identity artifact.""" + + artifact_path = Path(path) + verification = verify_evidence_artifact(artifact_path) + if verification["valid"] is not True: + raise ValueError( + "Stage-B identity artifact failed canonical verification: " + + "; ".join(verification["errors"]) + ) + raw = artifact_path.read_bytes() + try: + document = json.loads(raw.decode("utf-8")) + except (UnicodeDecodeError, json.JSONDecodeError) as error: + raise ValueError("Stage-B identity artifact must be strict UTF-8 JSON") from error + if ( + not isinstance(document, dict) + or set(document) != {"canonical_evidence_sha256", "evidence"} + or not isinstance(document.get("evidence"), dict) + ): + raise ValueError("Stage-B identity artifact must contain an evidence object") + evidence = document["evidence"] + validate_stage_b_identity_evidence(evidence) + return evidence, _sha256_bytes(raw) + + +def load_authenticated_stage_b_rows( + evidence: Mapping[str, Any], + *, + load_dataset_fn: LoadDataset | None = None, +) -> AuthenticatedStageBRows: + """Independently re-rank IDs, then load only authenticated Stage-B content.""" + + validate_stage_b_identity_evidence(evidence) + dataset = _require_mapping(evidence.get("dataset"), context="dataset") + task_ids = tuple( + _strict_int(value, context="ordered task_id") + for value in _require_list(dataset.get("ordered_task_ids"), context="ordered task IDs") + ) + expected_task_ids, protected_task_ids, source_row_count = ( + _resolve_ranked_id_windows(load_dataset_fn=load_dataset_fn) + ) + if task_ids != expected_task_ids: + raise ValueError( + "identity task IDs are not the independently resolved ranked window [32, 64)" + ) + rows, transport_rows = _load_exact_rows_with_audit( + task_ids=task_ids, + protected_task_ids=protected_task_ids, + load_dataset_fn=load_dataset_fn, + ) + actual_manifest = mbpp_manifest(rows, phase="calibration") + if actual_manifest != dataset.get("manifest"): + raise ValueError("runtime Stage-B row content does not match the identity manifest") + if mbpp_manifest_content_sha256(actual_manifest) != dataset.get( + "content_manifest_sha256" + ): + raise ValueError("runtime Stage-B content manifest hash drifted") + actual_ids = tuple(int(row["task_id"]) for row in rows) + if actual_ids != task_ids: + raise ValueError("runtime Stage-B row order drifted") + return AuthenticatedStageBRows( + rows=tuple(rows), + ordered_task_ids=task_ids, + access_audit=StageBDataAccessAudit( + ranking_transport_records_yielded=source_row_count, + ranking_task_id_fields_inspected=source_row_count, + target_transport_records_yielded=transport_rows, + target_task_id_fields_inspected=transport_rows, + target_rows_retained_and_canonicalized=len(rows), + ), + ) + + +def authenticate_stage_b_runtime_identity( + evidence: Mapping[str, Any], + rows: AuthenticatedStageBRows | Sequence[Mapping[str, Any]], + tokenizer: Any, +) -> RuntimeIdentityAuthentication: + """Re-tokenize and authenticate the complete identity before model loading.""" + + validate_stage_b_identity_evidence(evidence) + if tokenizer.__class__.__name__ != TOKENIZER_CLASS: + raise ValueError( + f"tokenizer class drifted: {tokenizer.__class__.__name__} != {TOKENIZER_CLASS}" + ) + try: + actual_transformers_version = importlib.metadata.version("transformers") + except importlib.metadata.PackageNotFoundError as error: + raise RuntimeError("transformers is required to authenticate token identity") from error + if actual_transformers_version != TRANSFORMERS_VERSION: + raise ValueError( + "transformers version drifted: " + f"{actual_transformers_version} != {TRANSFORMERS_VERSION}" + ) + + dataset = _require_mapping(evidence.get("dataset"), context="dataset") + authenticated_rows = rows if isinstance(rows, AuthenticatedStageBRows) else None + row_sequence = authenticated_rows.rows if authenticated_rows is not None else tuple(rows) + actual_manifest = mbpp_manifest(row_sequence, phase="calibration") + if actual_manifest != dataset.get("manifest"): + raise ValueError("runtime Stage-B content manifest drifted before tokenization") + encoded, records = tokenize_stage_b_rows(tokenizer, row_sequence) + if list(records) != dataset.get("tasks"): + raise ValueError("runtime Stage-B token records drifted") + if token_manifest_sha256(records) != dataset.get("token_manifest_sha256"): + raise ValueError("runtime Stage-B token manifest hash drifted") + if ordered_identity_sha256( + content_manifest_sha256=str(dataset["content_manifest_sha256"]), + task_records=records, + ) != dataset.get("ordered_identity_sha256"): + raise ValueError("runtime Stage-B ordered identity hash drifted") + return RuntimeIdentityAuthentication( + tasks=encoded, + ordered_task_ids=tuple(int(record["task_id"]) for record in records), + content_manifest_sha256=str(dataset["content_manifest_sha256"]), + token_manifest_sha256=str(dataset["token_manifest_sha256"]), + ordered_identity_sha256=str(dataset["ordered_identity_sha256"]), + access_audit=( + authenticated_rows.access_audit if authenticated_rows is not None else None + ), + ) + + +def authenticate_stage_a_artifact(path: str | Path) -> tuple[dict[str, Any], str]: + """Require the exact committed Stage-A pass before any Stage-B data access.""" + + artifact_path = Path(path) + verification = verify_evidence_artifact( + artifact_path, + expected_file_sha256=STAGE_A_FILE_SHA256, + expected_canonical_evidence_sha256=STAGE_A_CANONICAL_EVIDENCE_SHA256, + ) + if verification["valid"] is not True: + raise ValueError( + "Stage-A authorization artifact failed authentication: " + + "; ".join(verification["errors"]) + ) + raw = artifact_path.read_bytes() + document = json.loads(raw.decode("utf-8")) + evidence = _require_mapping(document.get("evidence"), context="Stage-A evidence") + if evidence.get("artifact_kind") != STAGE_A_ARTIFACT_KIND: + raise ValueError("Stage-A artifact kind drifted") + gate = _require_mapping(evidence.get("stage_a_gate"), context="Stage-A gate") + checks = _require_mapping(gate.get("checks"), context="Stage-A gate checks") + if ( + gate.get("schema") != "recurquant.experiment009-stage-a-gate.v1" + or gate.get("passed") is not True + or not checks + or any( + _require_mapping(check, context="Stage-A gate check").get("passed") is not True + for check in checks.values() + ) + ): + raise ValueError("Stage-A gate did not pass every frozen check") + model = _require_mapping(evidence.get("model"), context="Stage-A model") + if model.get("id") != MODEL_ID or model.get("revision") != MODEL_REVISION: + raise ValueError("Stage-A model identity drifted") + selectors = _require_mapping( + evidence.get("selector_artifacts"), + context="Stage-A selector artifacts", + ) + expected_selector_hashes = { + "selector_file_sha256": SELECTOR_FILE_SHA256, + "selector_canonical_evidence_sha256": ( + SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": LOSS_SELECTOR_FILE_SHA256, + "loss_selector_canonical_evidence_sha256": ( + LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + } + if any( + selectors.get(field) != expected + for field, expected in expected_selector_hashes.items() + ): + raise ValueError("Stage-A selector artifact hashes drifted") + repository = _require_mapping(evidence.get("repository"), context="Stage-A repository") + if ( + repository.get("commit") != STAGE_A_IMPLEMENTATION_COMMIT + or repository.get("stable_commit") is not True + ): + raise ValueError("Stage-A implementation commit drifted") + stage_a_dataset = _require_mapping( + evidence.get("dataset"), + context="Stage-A dataset", + ) + identity_gate = _require_mapping( + checks.get("frozen_task_identity_before_model_weights"), + context="Stage-A identity gate", + ) + if ( + stage_a_dataset.get("identity_authenticated_before_model_weights") is not True + or stage_a_dataset.get( + "protected_window_8_16_loaded_tokenized_or_evaluated" + ) + is not False + or identity_gate.get("identity_authenticated_before_model_weights") is not True + or identity_gate.get("protected_window_8_16_accessed") is not False + ): + raise ValueError("Stage-A data-access integrity drifted") + return dict(evidence), _sha256_bytes(raw) + + +def git_state(repository_root: Path) -> dict[str, Any]: + """Return a strict full-SHA and porcelain worktree snapshot.""" + + commit_result = subprocess.run( + ("git", "rev-parse", "HEAD"), + cwd=repository_root, + check=True, + capture_output=True, + text=True, + ) + status_result = subprocess.run( + ("git", "status", "--porcelain=v1", "--untracked-files=all"), + cwd=repository_root, + check=True, + capture_output=True, + text=True, + ) + commit = commit_result.stdout.strip() + status = status_result.stdout.splitlines() + if ( + len(commit) != 40 + or commit != commit.lower() + or not all(character in string.hexdigits for character in commit) + ): + raise RuntimeError("Git did not return a full lowercase commit SHA") + return { + "commit": commit, + "worktree_clean": not status, + "status": status, + } + + +def validate_repository_start(repository_root: Path, repository: Mapping[str, Any]) -> None: + """Require clean committed code containing the authenticated Stage-A result.""" + + if repository.get("worktree_clean") is not True or repository.get("status") != []: + raise ValueError("Stage-B identity resolution requires a clean worktree") + ancestor = subprocess.run( + ("git", "merge-base", "--is-ancestor", STAGE_A_RESULT_COMMIT, "HEAD"), + cwd=repository_root, + check=False, + capture_output=True, + text=True, + ) + if ancestor.returncode != 0: + raise ValueError("current HEAD does not contain the authenticated Stage-A result") + + +def validate_output_path(output: Path, repository_root: Path) -> None: + """Require external or Git-ignored output so publication cannot dirty the run.""" + + resolved_output = output.resolve() + resolved_root = repository_root.resolve() + try: + relative = resolved_output.relative_to(resolved_root) + except ValueError: + return + ignored = subprocess.run( + ("git", "check-ignore", "--quiet", "--", str(relative)), + cwd=resolved_root, + check=False, + capture_output=True, + text=True, + ) + if ignored.returncode != 0: + raise ValueError( + "Stage-B identity output inside the repository must be Git-ignored" + ) + + +def source_file_hashes(repository_root: Path) -> dict[str, str]: + """Hash the exact implementation, protocol, and tests used by the resolver.""" + + hashes: dict[str, str] = {} + resolved_root = repository_root.resolve() + for relative in SOURCE_FILES: + path = (resolved_root / relative).resolve() + try: + path.relative_to(resolved_root) + except ValueError as error: + raise ValueError(f"source path escapes repository root: {relative}") from error + if not path.is_file(): + raise FileNotFoundError(f"required source file is missing: {relative}") + hashes[relative] = _sha256_bytes(path.read_bytes()) + return hashes + + +def validate_imported_module_paths( + repository_root: Path, + *, + modules: Mapping[str, Any] | None = None, +) -> dict[str, str]: + """Bind imported RecurQuant modules to the source files being authenticated.""" + + active_modules = sys.modules if modules is None else modules + validate_all_imported_repository_modules_frozen( + repository_root, + modules=active_modules, + ) + resolved_root = repository_root.resolve() + authenticated: dict[str, str] = {} + for module_name, relative_path in IMPORTED_MODULE_PATHS.items(): + module = active_modules.get(module_name) + raw_path = getattr(module, "__file__", None) + if not isinstance(raw_path, str): + raise ValueError(f"imported module {module_name} has no source file") + actual_path = Path(raw_path).resolve() + expected_path = (resolved_root / relative_path).resolve() + if actual_path != expected_path: + raise ValueError( + f"imported module {module_name} did not resolve to authenticated " + f"repository source {relative_path}" + ) + authenticated[module_name] = relative_path + return authenticated + + +def validate_all_imported_repository_modules_frozen( + repository_root: Path, + *, + modules: Mapping[str, Any] | None = None, +) -> dict[str, str]: + """Reject any imported project script or package module outside the source freeze.""" + + resolved_root = repository_root.resolve() + frozen = set(STAGE_B_SOURCE_FILES) + imported: dict[str, str] = {} + active_modules = sys.modules if modules is None else modules + for module_name, module in sorted(active_modules.items()): + raw_path = getattr(module, "__file__", None) + if not isinstance(raw_path, str): + continue + actual_path = Path(raw_path).resolve() + try: + relative_path = actual_path.relative_to(resolved_root).as_posix() + except ValueError: + continue + if not relative_path.startswith(("scripts/", "src/recurquant/")): + continue + if not actual_path.is_file(): + raise ValueError( + f"imported repository module {module_name} has no source file: " + f"{relative_path}" + ) + if relative_path not in frozen: + raise ValueError( + f"imported repository module {module_name} is outside the " + f"Stage-B source freeze: {relative_path}" + ) + imported[module_name] = relative_path + return imported + + +def _atomic_write(path: Path, payload: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + prefix=f".{path.name}.", + suffix=".tmp", + dir=path.parent, + ) + temporary = Path(temporary_name) + try: + with os.fdopen(descriptor, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + if temporary.exists(): + temporary.unlink() + + +def main() -> int: + args = parse_args() + repository_root = Path(__file__).resolve().parents[1] + repository_start = git_state(repository_root) + validate_repository_start(repository_root, repository_start) + validate_output_path(args.output, repository_root) + source_hashes_start = source_file_hashes(repository_root) + imported_module_paths = validate_imported_module_paths(repository_root) + + stage_a_path = repository_root / STAGE_A_ARTIFACT_RELATIVE_PATH + stage_a_evidence, stage_a_file_sha = authenticate_stage_a_artifact(stage_a_path) + runtime_environment = authenticate_runtime_environment( + stage_a_evidence, + local_files_only=bool(args.local_files_only), + ) + row_plan = build_compact_row_plan( + repository_root / SELECTOR_ARTIFACT_RELATIVE_PATH, + repository_root / LOSS_SELECTOR_ARTIFACT_RELATIVE_PATH, + ) + + resolved_rows = resolve_stage_b_rows() + rows = resolved_rows.rows + task_ids = resolved_rows.ordered_task_ids + source_row_count = resolved_rows.access_audit.ranking_transport_records_yielded + content_manifest = mbpp_manifest(rows, phase="calibration") + content_hash = mbpp_manifest_content_sha256(content_manifest) + + actual_transformers_version = importlib.metadata.version("transformers") + if actual_transformers_version != TRANSFORMERS_VERSION: + raise ValueError( + "transformers version drifted before tokenization: " + f"{actual_transformers_version} != {TRANSFORMERS_VERSION}" + ) + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained( + MODEL_ID, + revision=MODEL_REVISION, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + if tokenizer.__class__.__name__ != TOKENIZER_CLASS: + raise ValueError( + f"tokenizer class drifted: {tokenizer.__class__.__name__} != {TOKENIZER_CLASS}" + ) + _encoded_tasks, task_records = tokenize_stage_b_rows(tokenizer, rows) + token_hash = token_manifest_sha256(task_records) + ordered_hash = ordered_identity_sha256( + content_manifest_sha256=content_hash, + task_records=task_records, + ) + + repository_end = git_state(repository_root) + source_hashes_end = source_file_hashes(repository_root) + if repository_end != repository_start: + raise RuntimeError("repository state changed during Stage-B identity resolution") + if source_hashes_end != source_hashes_start: + raise RuntimeError("source files changed during Stage-B identity resolution") + + totals = { + "source_train_rows_seen_by_task_id_only": source_row_count, + "retained_rows": len(rows), + "prompt_tokens": sum(int(record["prompt_tokens"]) for record in task_records), + "code_tokens": sum(int(record["code_tokens"]) for record in task_records), + "aligned_scored_tokens": sum( + int(record["aligned_scored_tokens"]) for record in task_records + ), + "full_code_scored_tokens": sum( + int(record["full_code_scored_tokens"]) for record in task_records + ), + } + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "identity_schema": IDENTITY_SCHEMA, + "identity_only": True, + "claim_boundary": CLAIM_BOUNDARY, + "created_at_utc": datetime.now(UTC).isoformat(), + "authorization": { + "stage_a_artifact_kind": STAGE_A_ARTIFACT_KIND, + "stage_a_file_sha256": stage_a_file_sha, + "stage_a_canonical_evidence_sha256": STAGE_A_CANONICAL_EVIDENCE_SHA256, + "stage_a_implementation_commit": STAGE_A_IMPLEMENTATION_COMMIT, + "stage_a_result_commit": STAGE_A_RESULT_COMMIT, + "stage_a_gate_passed": True, + "verified_before_dataset_access": True, + }, + "row_plan": row_plan, + "model_contract": { + "id": MODEL_ID, + "revision": MODEL_REVISION, + "weights_loaded": False, + }, + "tokenizer_contract": { + "source_id": MODEL_ID, + "revision": MODEL_REVISION, + "class": tokenizer.__class__.__name__, + "transformers_version": actual_transformers_version, + "trust_remote_code": False, + "prompt_add_special_tokens": True, + "code_add_special_tokens": False, + "formatter_version": MBPP_FORMATTER_VERSION, + "token_id_hash_serialization": TOKEN_ID_HASH_SERIALIZATION, + "text_hash_encoding": TEXT_HASH_ENCODING, + }, + "dataset": { + "id": MBPP_DATASET_ID, + "config": MBPP_CONFIG, + "revision": MBPP_REVISION, + "phase": "calibration", + "source_split": mbpp_source_split("calibration"), + "selection_namespace": MBPP_SELECTION_NAMESPACE, + "formatter_version": MBPP_FORMATTER_VERSION, + "selection_mode": "task_id_ranking_then_exact_task_id_stream", + "selection_window": { + "offset": STAGE_B_OFFSET, + "limit": STAGE_B_LIMIT, + "stop_exclusive": STAGE_B_STOP, + }, + "protected_window": { + "offset": PROTECTED_WINDOW[0], + "stop_exclusive": PROTECTED_WINDOW[1], + "content_retained_canonicalized_or_tokenized": False, + }, + "ordered_task_ids": list(task_ids), + "manifest": content_manifest, + "content_manifest_sha256": content_hash, + "token_manifest_sha256": token_hash, + "ordered_identity_sha256": ordered_hash, + "tasks": list(task_records), + "totals": totals, + "data_access": resolved_rows.access_audit.as_dict( + selected_task_ids=task_ids, + ), + }, + "integrity": { + "stage_a_authenticated_before_dataset_access": True, + "runtime_environment_authenticated_before_dataset_access": True, + "selector_artifacts_authenticated_before_dataset_access": True, + "repository_clean_at_start": True, + "repository_clean_at_end": True, + "repository_commit_stable": True, + "source_hashes_stable": True, + "task_id_only_ranking_pass": True, + "only_stage_b_content_retained_canonicalized_and_tokenized": True, + "imported_modules_resolved_to_authenticated_repository": True, + "protected_window_8_16_content_retained_canonicalized_or_tokenized": False, + "model_weights_loaded": False, + "model_forward_pass_run": False, + "logits_or_quality_metrics_observed": False, + "output_path_external_or_git_ignored": True, + }, + "repository": { + "commit": repository_end["commit"], + "start": repository_start, + "end": repository_end, + "stable_commit": True, + }, + "source_files": { + "paths": list(SOURCE_FILES), + "sha256_start": source_hashes_start, + "sha256_end": source_hashes_end, + "stable": True, + "imported_modules": imported_module_paths, + }, + "environment": runtime_environment, + } + validate_stage_b_identity_evidence(evidence) + + artifact = { + "canonical_evidence_sha256": _sha256_bytes(canonical_json_bytes(evidence)), + "evidence": evidence, + } + _atomic_write(args.output, canonical_json_bytes(artifact)) + verification = verify_evidence_artifact(args.output) + if verification["valid"] is not True: + raise RuntimeError( + "written Stage-B identity artifact failed verification: " + + "; ".join(verification["errors"]) + ) + + post_write_repository = git_state(repository_root) + post_write_source_hashes = source_file_hashes(repository_root) + if post_write_repository != repository_start: + raise RuntimeError("repository state changed while writing Stage-B identity") + if post_write_source_hashes != source_hashes_start: + raise RuntimeError("source files changed while writing Stage-B identity") + loaded_evidence, file_sha = load_stage_b_identity_artifact(args.output) + if loaded_evidence != evidence: + raise RuntimeError("written Stage-B identity did not round-trip exactly") + + print( + json.dumps( + { + "artifact": str(args.output.resolve()), + "file_sha256": file_sha, + "canonical_evidence_sha256": artifact["canonical_evidence_sha256"], + "content_manifest_sha256": content_hash, + "token_manifest_sha256": token_hash, + "ordered_identity_sha256": ordered_hash, + "ordered_task_ids": list(task_ids), + "prompt_tokens": totals["prompt_tokens"], + "code_tokens": totals["code_tokens"], + "aligned_scored_tokens": totals["aligned_scored_tokens"], + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated": False, + "model_weights_loaded": False, + "valid": True, + }, + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/screen_rht_cqer.py b/scripts/screen_rht_cqer.py new file mode 100644 index 0000000..3a8d7b5 --- /dev/null +++ b/scripts/screen_rht_cqer.py @@ -0,0 +1,1413 @@ +#!/usr/bin/env python3 +"""Run the frozen Experiment 009 Stage-A right-RHT CQER screen. + +This evaluator has deliberately narrow data access: it can load only the +already-open MBPP task 666. It has no task, seed, threshold, or window flags. +Passing this one-task screen authorizes only the separately frozen Stage-B +development identity; it is not confirmation, novelty, or speed evidence. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib +import importlib.metadata +import json +import math +import os +import platform +import sys +import tempfile +from collections import defaultdict +from collections.abc import Mapping, Sequence +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +import torch +from transformers import AutoTokenizer, DynamicCache, Qwen3_5ForCausalLM + +from recurquant.cache import iter_recurrent_states +from recurquant.evidence import canonical_json_bytes, verify_evidence_artifact +from recurquant.mixed_quantization import quantize_pack_mixed +from recurquant.packed_cache import ( + QueryEmaMixedPackedRecurrentStateCache, + RightRhtQueryEmaMixedPackedRecurrentStateCache, +) +from recurquant.public_data import ( + load_mbpp_rows_by_task_ids, + mbpp_manifest, + mbpp_manifest_sha256, + mbpp_row_sha256, +) +from recurquant.quantization import QuantizationSpec, quantize_dequantize +from recurquant.query_energy import Qwen35QueryEnergyObserver +from recurquant.qwen35 import ( + create_qwen35_query_ema_exact_budget_cache, + create_qwen35_right_rht_query_ema_exact_budget_cache, +) +from recurquant.rht import ( + right_rht_decode, + right_rht_encode, + right_rht_signs, +) +from recurquant.row_policy import ExactBudgetRowPlan + +pilot = importlib.import_module( + "scripts.pilot_evaluate_hrr" if __package__ else "pilot_evaluate_hrr" +) + +SEED = 2339 +ARTIFACT_KIND = "recurquant_rht_cqer32_stage_a_screen" +CQER_METHOD = "query_ema32_weighted_mse_target_fisher_quota" +RHT_METHOD = "right_rht_query_ema32_weighted_mse_target_fisher_quota" +METHODS = (CQER_METHOD, RHT_METHOD) +TARGET_FISHER_SCORE = "target_directional_fisher_difference_int4" + +TASK_ID = 666 +TASK_ROW_SHA256 = "b4f5989005c921c3ab94ab52c8115e79f99a22390bc1d6e6235d36fd02687fb9" +PROMPT_TOKENS = 69 +CODE_TOKENS = 39 +ALIGNED_TOKENS = 38 +EXPECTED_STATE_WRITES = 39 +EXPECTED_QUERY_TOKENS = PROMPT_TOKENS + ALIGNED_TOKENS + +TARGET_PACKED_STATE_BYTES = 2_564_096 +TARGET_SELECTOR_BYTES = 147_456 +TARGET_TOTAL_BYTES = 2_711_552 +TARGET_PAYLOAD_BYTES = 2_485_760 +TARGET_SCALE_BYTES = 73_728 +TARGET_MASK_BYTES = 4_608 +TARGET_PROMOTED_ROWS = 1_976 + +MIN_STATE_SSE_REDUCTION = 0.50 +MIN_DELTA_NLL_REDUCTION = 0.10 +MAX_RHT_INVERSE_RELATIVE_L2 = 3e-7 +EXPECTED_SIGN_SCHEDULE_SHA256 = ( + "2d5137b5ebeb325f100b34190618783b9e47bd2ce9b27b6cdf3cdc94459dabc3" +) + +FROZEN_LAYER_QUOTAS = dict(pilot.CQER_FROZEN_LAYER_QUOTAS) +FROZEN_LINEAR_LAYERS = tuple(FROZEN_LAYER_QUOTAS) + +SOURCE_FILES = ( + "research/EXPERIMENT_009_RHT_CQER_PROTOCOL.md", + "scripts/screen_rht_cqer.py", + "scripts/pilot_evaluate_hrr.py", + "src/recurquant/__init__.py", + "src/recurquant/cache.py", + "src/recurquant/evaluation.py", + "src/recurquant/evidence.py", + "src/recurquant/metrics.py", + "src/recurquant/mixed_quantization.py", + "src/recurquant/packed_cache.py", + "src/recurquant/public_data.py", + "src/recurquant/quantization.py", + "src/recurquant/query_energy.py", + "src/recurquant/qwen35.py", + "src/recurquant/rht.py", + "src/recurquant/row_policy.py", + "tests/test_rht.py", + "tests/test_rht_mixed_quantization.py", + "tests/test_right_rht_query_ema_cache.py", + "tests/test_screen_rht_cqer.py", +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Frozen Experiment 009 Stage-A screen on already-open MBPP task 666. " + "No other task or threshold can be selected." + ) + ) + parser.add_argument( + "--selector", + "--selector-artifact", + dest="selector", + type=Path, + required=True, + help="Authenticated frozen Experiment 007 HRR selector artifact.", + ) + parser.add_argument( + "--loss-selector", + "--loss-selector-artifact", + dest="loss_selector", + type=Path, + required=True, + help="Authenticated frozen target-Fisher selector artifact.", + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--local-files-only", action="store_true") + return parser.parse_args() + + +def _strict_int(value: object, *, context: str) -> int: + if isinstance(value, bool) or not isinstance(value, int): + raise ValueError(f"{context} must be an integer") + return value + + +def _finite_float(value: object, *, context: str) -> float: + if isinstance(value, bool) or not isinstance(value, (int, float)): + raise ValueError(f"{context} must be numeric") + result = float(value) + if not math.isfinite(result): + raise ValueError(f"{context} must be finite") + return result + + +def validate_stage_a_identity( + *, + task_id: object, + row_sha256: object, + prompt_tokens: object, + code_tokens: object, + aligned_scored_tokens: object, +) -> dict[str, int | str | bool]: + """Authenticate the immutable Stage-A row and token identity.""" + + actual_task_id = _strict_int(task_id, context="task_id") + actual_prompt = _strict_int(prompt_tokens, context="prompt_tokens") + actual_code = _strict_int(code_tokens, context="code_tokens") + actual_aligned = _strict_int( + aligned_scored_tokens, + context="aligned_scored_tokens", + ) + if actual_task_id != TASK_ID: + raise ValueError(f"Stage A is locked to task_id {TASK_ID}") + if not isinstance(row_sha256, str) or row_sha256 != TASK_ROW_SHA256: + raise ValueError("Stage-A MBPP row SHA-256 does not match the frozen identity") + if (actual_prompt, actual_code, actual_aligned) != ( + PROMPT_TOKENS, + CODE_TOKENS, + ALIGNED_TOKENS, + ): + raise ValueError( + "Stage-A token identity must be exactly " + f"{PROMPT_TOKENS}/{CODE_TOKENS}/{ALIGNED_TOKENS}" + ) + if actual_aligned != actual_code - 1: + raise ValueError("aligned token count must equal code_tokens - 1") + return { + "task_id": actual_task_id, + "row_sha256": row_sha256, + "prompt_tokens": actual_prompt, + "code_tokens": actual_code, + "aligned_scored_tokens": actual_aligned, + "authenticated_before_model_weights": True, + } + + +def _evidence_mapping(record: object, *, index: int) -> dict[str, Any]: + if isinstance(record, Mapping): + return dict(record) + evidence_dict = getattr(record, "evidence_dict", None) + if not callable(evidence_dict): + raise ValueError(f"state-error record {index} must be evidence or a mapping") + converted = evidence_dict() + if not isinstance(converted, Mapping): + raise ValueError(f"state-error record {index} evidence must be an object") + return dict(converted) + + +def aggregate_state_error_evidence(records: Sequence[object]) -> dict[str, Any]: + """Aggregate record_evidence MSE into exact per-write state SSE. + + Cache evidence stores MSE, while the frozen gate is defined on SSE. This + function authenticates each shape and computes ``MSE * element_count``. + It also assigns a causal write ordinal independently within each layer so + the two codecs can be checked for identical closed-loop coverage. + """ + + if isinstance(records, (str, bytes)) or not isinstance(records, Sequence): + raise TypeError("state-error evidence must be a sequence") + if not records: + raise ValueError("state-error evidence must not be empty") + + normalized: list[dict[str, Any]] = [] + layer_occurrences: defaultdict[tuple[int, int], int] = defaultdict(int) + seen_update_indices: set[int] = set() + layer_totals: defaultdict[int, dict[str, float | int]] = defaultdict( + lambda: {"record_count": 0, "element_count": 0, "state_sse": 0.0} + ) + write_totals: defaultdict[int, dict[str, float | int]] = defaultdict( + lambda: {"record_count": 0, "element_count": 0, "state_sse": 0.0} + ) + + for record_index, raw_record in enumerate(records): + record = _evidence_mapping(raw_record, index=record_index) + update_index = _strict_int( + record.get("update_index"), + context=f"state-error record {record_index} update_index", + ) + layer_index = _strict_int( + record.get("layer_index"), + context=f"state-error record {record_index} layer_index", + ) + state_index = _strict_int( + record.get("state_index"), + context=f"state-error record {record_index} state_index", + ) + if update_index < 0 or layer_index < 0 or state_index < 0: + raise ValueError("state-error indices must be non-negative") + if update_index in seen_update_indices: + raise ValueError(f"duplicate state-error update_index {update_index}") + seen_update_indices.add(update_index) + + raw_shape = record.get("shape") + if ( + isinstance(raw_shape, (str, bytes)) + or not isinstance(raw_shape, Sequence) + or not raw_shape + ): + raise ValueError(f"state-error record {record_index} shape is invalid") + shape = tuple( + _strict_int(dimension, context=f"state-error record {record_index} shape") + for dimension in raw_shape + ) + if any(dimension <= 0 for dimension in shape): + raise ValueError("state-error shape dimensions must be positive") + element_count = math.prod(shape) + mse = _finite_float( + record.get("mean_squared_error"), + context=f"state-error record {record_index} mean_squared_error", + ) + maximum = _finite_float( + record.get("max_absolute_error"), + context=f"state-error record {record_index} max_absolute_error", + ) + relative_l2 = _finite_float( + record.get("relative_l2_error"), + context=f"state-error record {record_index} relative_l2_error", + ) + if mse < 0 or maximum < 0 or relative_l2 < 0: + raise ValueError("state-error magnitudes must be non-negative") + state_sse = mse * element_count + if not math.isfinite(state_sse): + raise ValueError("state-error SSE must be finite") + + occurrence_key = (layer_index, state_index) + write_ordinal = layer_occurrences[occurrence_key] + layer_occurrences[occurrence_key] += 1 + if "element_count" in record and _strict_int( + record["element_count"], + context=f"state-error record {record_index} element_count", + ) != element_count: + raise ValueError("state-error element_count does not match its shape") + if "write_ordinal" in record and _strict_int( + record["write_ordinal"], + context=f"state-error record {record_index} write_ordinal", + ) != write_ordinal: + raise ValueError("state-error write_ordinal does not match causal order") + if "state_sse" in record and not math.isclose( + _finite_float( + record["state_sse"], + context=f"state-error record {record_index} state_sse", + ), + state_sse, + rel_tol=1e-12, + abs_tol=1e-12, + ): + raise ValueError("state-error state_sse does not match MSE times elements") + normalized_record = { + **record, + "shape": list(shape), + "element_count": element_count, + "state_sse": state_sse, + "write_ordinal": write_ordinal, + } + normalized.append(normalized_record) + + for totals in (layer_totals[layer_index], write_totals[write_ordinal]): + totals["record_count"] = int(totals["record_count"]) + 1 + totals["element_count"] = int(totals["element_count"]) + element_count + totals["state_sse"] = float(totals["state_sse"]) + state_sse + + normalized.sort(key=lambda record: int(record["update_index"])) + coverage = [ + { + "write_ordinal": int(record["write_ordinal"]), + "layer_index": int(record["layer_index"]), + "state_index": int(record["state_index"]), + "shape": list(record["shape"]), + } + for record in normalized + ] + total_elements = sum(int(record["element_count"]) for record in normalized) + total_sse = math.fsum(float(record["state_sse"]) for record in normalized) + return { + "record_count": len(normalized), + "element_count": total_elements, + "aggregate_state_sse": total_sse, + "aggregate_state_mse": total_sse / total_elements, + "coverage": coverage, + "per_layer": { + str(layer): { + "record_count": int(values["record_count"]), + "element_count": int(values["element_count"]), + "state_sse": float(values["state_sse"]), + } + for layer, values in sorted(layer_totals.items()) + }, + "per_write": { + str(write): { + "record_count": int(values["record_count"]), + "element_count": int(values["element_count"]), + "state_sse": float(values["state_sse"]), + } + for write, values in sorted(write_totals.items()) + }, + "records": normalized, + } + + +def validate_state_error_coverage( + state_errors: Mapping[str, Mapping[str, Any]], +) -> dict[str, Any]: + """Recompute and authenticate exact layers, writes, states, shapes, and totals.""" + + if set(state_errors) != set(METHODS): + raise ValueError("state-error evidence must contain exactly CQER and RHT-CQER") + expected_records = len(FROZEN_LINEAR_LAYERS) * EXPECTED_STATE_WRITES + expected_coverage = [ + { + "write_ordinal": write, + "layer_index": layer, + "state_index": 0, + "shape": [1, 16, 128, 128], + } + for write in range(EXPECTED_STATE_WRITES) + for layer in FROZEN_LINEAR_LAYERS + ] + final_masks: dict[str, dict[int, str]] = {} + for method, record in state_errors.items(): + raw_records = record.get("records") + if isinstance(raw_records, (str, bytes)) or not isinstance( + raw_records, Sequence + ): + raise ValueError(f"{method} must retain raw per-write state-error records") + recomputed = aggregate_state_error_evidence(raw_records) + if int(record.get("record_count", -1)) != expected_records: + raise ValueError( + f"{method} must contain exactly {expected_records} state-error records" + ) + if recomputed["record_count"] != expected_records: + raise ValueError(f"{method} raw state-error record count drifted") + if recomputed["coverage"] != expected_coverage: + raise ValueError(f"{method} raw state-error geometry or causal order drifted") + if record.get("coverage") != expected_coverage: + raise ValueError(f"{method} reported state-error coverage drifted") + if _strict_int( + record.get("element_count"), + context=f"{method} aggregate element_count", + ) != recomputed["element_count"]: + raise ValueError(f"{method} aggregate element count was not recomputed") + for field in ("aggregate_state_sse", "aggregate_state_mse"): + if not math.isclose( + _finite_float(record.get(field), context=f"{method} {field}"), + float(recomputed[field]), + rel_tol=1e-12, + abs_tol=1e-12, + ): + raise ValueError(f"{method} {field} does not match raw records") + per_layer = record.get("per_layer") + per_write = record.get("per_write") + if not isinstance(per_layer, Mapping) or set(per_layer) != { + str(layer) for layer in FROZEN_LINEAR_LAYERS + }: + raise ValueError(f"{method} state-error layers do not match the frozen geometry") + if not isinstance(per_write, Mapping) or set(per_write) != { + str(write) for write in range(EXPECTED_STATE_WRITES) + }: + raise ValueError(f"{method} state-error writes do not match the frozen run") + if any( + int(per_layer[str(layer)].get("record_count", -1)) != EXPECTED_STATE_WRITES + for layer in FROZEN_LINEAR_LAYERS + ): + raise ValueError(f"{method} did not record every layer on every write") + if any( + int(per_write[str(write)].get("record_count", -1)) + != len(FROZEN_LINEAR_LAYERS) + for write in range(EXPECTED_STATE_WRITES) + ): + raise ValueError(f"{method} did not record every layer within every write") + for grouping_name, reported, expected in ( + ("per-layer", per_layer, recomputed["per_layer"]), + ("per-write", per_write, recomputed["per_write"]), + ): + for key, expected_values in expected.items(): + reported_values = reported[key] + for field in ("record_count", "element_count"): + if _strict_int( + reported_values.get(field), + context=f"{method} {grouping_name} {key} {field}", + ) != int(expected_values[field]): + raise ValueError( + f"{method} {grouping_name} {key} {field} drifted" + ) + if not math.isclose( + _finite_float( + reported_values.get("state_sse"), + context=f"{method} {grouping_name} {key} state_sse", + ), + float(expected_values["state_sse"]), + rel_tol=1e-12, + abs_tol=1e-12, + ): + raise ValueError( + f"{method} {grouping_name} {key} state_sse drifted" + ) + expected_selection_method = ( + "query_ema32_weighted_aligned_mse_reduction" + if method == CQER_METHOD + else RHT_METHOD + ) + method_final_masks: dict[int, str] = {} + for index, raw_record in enumerate(recomputed["records"]): + layer = FROZEN_LINEAR_LAYERS[index % len(FROZEN_LINEAR_LAYERS)] + write = index // len(FROZEN_LINEAR_LAYERS) + if ( + raw_record.get("layer_index") != layer + or raw_record.get("state_index") != 0 + or raw_record.get("shape") != [1, 16, 128, 128] + or raw_record.get("update_index") != index + or raw_record.get("write_ordinal") != write + or raw_record.get("source_dtype") != "torch.float32" + or raw_record.get("selection_method") != expected_selection_method + or raw_record.get("total_groups") != 2048 + or raw_record.get("high_precision_groups") + != FROZEN_LAYER_QUOTAS[layer] + ): + raise ValueError( + f"{method} raw state-error record {index} contract drifted" + ) + mask_hash = raw_record.get("high_precision_mask_sha256") + if ( + not isinstance(mask_hash, str) + or len(mask_hash) != 64 + or any(character not in "0123456789abcdef" for character in mask_hash) + ): + raise ValueError(f"{method} raw state-error mask hash is invalid") + if write == EXPECTED_STATE_WRITES - 1: + method_final_masks[layer] = mask_hash + final_masks[method] = method_final_masks + if state_errors[CQER_METHOD].get("coverage") != state_errors[RHT_METHOD].get( + "coverage" + ): + raise ValueError("CQER and RHT-CQER state-error coverage differs") + return { + "passed": True, + "record_count_per_method": expected_records, + "state_writes": EXPECTED_STATE_WRITES, + "linear_layers": list(FROZEN_LINEAR_LAYERS), + "final_mask_sha256s": { + method: {str(layer): value for layer, value in masks.items()} + for method, masks in final_masks.items() + }, + } + + +def audit_selector_diagnostics( + diagnostics: Mapping[str, Sequence[Mapping[str, Any]]], +) -> dict[str, Any]: + """Authenticate exact quotas and one-stage/one-consume query handshakes.""" + + if set(diagnostics) != set(METHODS): + raise ValueError("selector diagnostics must contain exactly CQER and RHT-CQER") + expected_selection_methods = { + CQER_METHOD: "query_ema32_weighted_aligned_mse_reduction", + RHT_METHOD: RHT_METHOD, + } + audited: dict[str, list[dict[str, Any]]] = {} + for method in METHODS: + records = diagnostics[method] + if len(records) != len(FROZEN_LINEAR_LAYERS): + raise ValueError(f"{method} must report every frozen recurrent layer") + by_layer: dict[int, Mapping[str, Any]] = {} + for record in records: + layer = _strict_int(record.get("layer_index"), context="diagnostic layer_index") + if layer in by_layer: + raise ValueError(f"{method} repeats diagnostic layer {layer}") + by_layer[layer] = record + if set(by_layer) != set(FROZEN_LINEAR_LAYERS): + raise ValueError(f"{method} diagnostic layer identity drifted") + + method_rows: list[dict[str, Any]] = [] + for layer in FROZEN_LINEAR_LAYERS: + record = by_layer[layer] + quota = _strict_int(record.get("quota"), context=f"{method} layer {layer} quota") + selected = _strict_int( + record.get("current_selected_count"), + context=f"{method} layer {layer} current_selected_count", + ) + updates = _strict_int( + record.get("state_updates"), + context=f"{method} layer {layer} state_updates", + ) + staged = _strict_int( + record.get("observations_staged"), + context=f"{method} layer {layer} observations_staged", + ) + committed = _strict_int( + record.get("observations_committed"), + context=f"{method} layer {layer} observations_committed", + ) + observed = _strict_int( + record.get("tokens_observed"), + context=f"{method} layer {layer} tokens_observed", + ) + if quota != FROZEN_LAYER_QUOTAS[layer] or selected != quota: + raise ValueError(f"{method} layer {layer} did not realize its frozen quota") + if (updates, staged, committed) != ( + EXPECTED_STATE_WRITES, + EXPECTED_STATE_WRITES, + EXPECTED_STATE_WRITES, + ): + raise ValueError( + f"{method} layer {layer} query observation handshake is incomplete" + ) + if observed != EXPECTED_QUERY_TOKENS: + raise ValueError(f"{method} layer {layer} query token count drifted") + if record.get("pending_observation") is not False: + raise ValueError(f"{method} layer {layer} retains a pending observation") + if record.get("confirmation_two") is not False: + raise ValueError(f"{method} layer {layer} unexpectedly enabled Confirmation-2") + if record.get("selection_method") != expected_selection_methods[method]: + raise ValueError(f"{method} layer {layer} selection method drifted") + if method == RHT_METHOD and ( + record.get("state_codec") != "right_rht_sha256_signs_v1" + or record.get("state_codec_seed") != SEED + or record.get("state_codec_axis") != "value" + or record.get("state_codec_normalization") != "orthonormal" + or record.get("state_codec_persistent_tensor_bytes") != 0 + ): + raise ValueError(f"{method} layer {layer} codec contract drifted") + mask_hash = record.get("current_mask_sha256") + if ( + not isinstance(mask_hash, str) + or len(mask_hash) != 64 + or any(character not in "0123456789abcdef" for character in mask_hash) + ): + raise ValueError(f"{method} layer {layer} mask hash is invalid") + method_rows.append( + { + "layer_index": layer, + "quota": quota, + "state_updates": updates, + "observations_staged": staged, + "observations_committed": committed, + "tokens_observed": observed, + "selection_method": expected_selection_methods[method], + "current_mask_sha256": mask_hash, + } + ) + audited[method] = method_rows + return { + "passed": True, + "quota_sum": sum(FROZEN_LAYER_QUOTAS.values()), + "state_writes": EXPECTED_STATE_WRITES, + "query_tokens": EXPECTED_QUERY_TOKENS, + "methods": audited, + } + + +def _metric_summary( + metrics: Mapping[str, Mapping[str, Any]], + *, + expected_tokens: int, + context: str, +) -> dict[str, dict[str, float | int | bool]]: + if set(metrics) != set(METHODS): + raise ValueError(f"{context} metrics must contain exactly CQER and RHT-CQER") + normalized: dict[str, dict[str, float | int | bool]] = {} + fields = ( + "mean_kl", + "cvar95_kl", + "max_kl", + "top1_agreement", + "reference_nll", + "candidate_nll", + "delta_nll", + ) + for method in METHODS: + record = metrics[method] + token_count = _strict_int( + record.get("token_count"), + context=f"{context} {method} token_count", + ) + if token_count != expected_tokens: + raise ValueError( + f"{context} {method} must score exactly {expected_tokens} tokens" + ) + if record.get("all_logits_finite") is not True: + raise ValueError(f"{context} {method} logits are not all finite") + normalized[method] = { + "token_count": token_count, + "all_logits_finite": True, + **{ + field: _finite_float( + record.get(field), + context=f"{context} {method} {field}", + ) + for field in fields + }, + } + values = normalized[method] + mean_kl = float(values["mean_kl"]) + cvar95_kl = float(values["cvar95_kl"]) + max_kl = float(values["max_kl"]) + top1 = float(values["top1_agreement"]) + reference_nll = float(values["reference_nll"]) + candidate_nll = float(values["candidate_nll"]) + delta_nll = float(values["delta_nll"]) + if mean_kl < 0 or cvar95_kl < 0 or max_kl < 0: + raise ValueError(f"{context} {method} KL metrics must be non-negative") + if mean_kl > cvar95_kl or cvar95_kl > max_kl: + raise ValueError(f"{context} {method} KL summary ordering is invalid") + if not 0.0 <= top1 <= 1.0: + raise ValueError(f"{context} {method} top1_agreement must be in [0, 1]") + if reference_nll < 0 or candidate_nll < 0: + raise ValueError(f"{context} {method} NLL metrics must be non-negative") + if not math.isclose( + delta_nll, + candidate_nll - reference_nll, + rel_tol=1e-12, + abs_tol=1e-12, + ): + raise ValueError( + f"{context} {method} delta_nll does not equal candidate minus reference" + ) + reference_values = [ + float(normalized[method]["reference_nll"]) for method in METHODS + ] + if any( + not math.isclose( + value, + reference_values[0], + rel_tol=1e-12, + abs_tol=1e-12, + ) + for value in reference_values[1:] + ): + raise ValueError(f"{context} methods do not share one reference NLL") + return normalized + + +def _gate_check(function: Any) -> dict[str, Any]: + try: + result = function() + except (KeyError, TypeError, ValueError) as error: + return {"passed": False, "error": str(error)} + if isinstance(result, Mapping): + record = dict(result) + record["passed"] = record.get("passed") is True + return record + return {"passed": bool(result)} + + +def evaluate_stage_a_gate( + *, + aligned_metrics: Mapping[str, Mapping[str, Any]], + full_code_metrics: Mapping[str, Mapping[str, Any]], + storage: Mapping[str, Mapping[str, Any]], + selector_diagnostics: Mapping[str, Sequence[Mapping[str, Any]]], + state_errors: Mapping[str, Mapping[str, Any]], + unit_evidence: Mapping[str, Any], + integrity: Mapping[str, Any], +) -> dict[str, Any]: + """Evaluate only the frozen conjunction in the Experiment 009 protocol.""" + + aligned: dict[str, dict[str, float | int | bool]] | None = None + full: dict[str, dict[str, float | int | bool]] | None = None + + def repository_check() -> dict[str, Any]: + fields = ( + "repository_clean_at_start", + "repository_clean_at_end", + "repository_commit_stable", + "source_hashes_stable", + ) + if any(integrity.get(field) is not True for field in fields): + raise ValueError("repository cleanliness, commit, or source hashes were not stable") + return {"passed": True, **{field: True for field in fields}} + + def identity_check() -> dict[str, Any]: + if integrity.get("identity_authenticated_before_model_weights") is not True: + raise ValueError("row and token identity was not authenticated before model weights") + if integrity.get("protected_window_8_16_accessed") is not False: + raise ValueError("protected ranked window [8, 16) was accessed") + return { + "passed": True, + "identity_authenticated_before_model_weights": True, + "protected_window_8_16_accessed": False, + } + + def storage_check() -> dict[str, Any]: + if set(storage) != set(METHODS): + raise ValueError("storage must contain exactly CQER and RHT-CQER") + expected = { + "payload_bytes": TARGET_PAYLOAD_BYTES, + "scale_bytes": TARGET_SCALE_BYTES, + "mask_bytes": TARGET_MASK_BYTES, + "resident_bytes": TARGET_PACKED_STATE_BYTES, + "selector_auxiliary_bytes": TARGET_SELECTOR_BYTES, + "resident_bytes_including_selector": TARGET_TOTAL_BYTES, + "high_precision_groups": TARGET_PROMOTED_ROWS, + } + for method in METHODS: + for field, value in expected.items(): + if _strict_int( + storage[method].get(field), + context=f"{method} storage {field}", + ) != value: + raise ValueError(f"{method} storage {field} drifted") + return {"passed": True, **expected} + + def finite_and_handshake_check() -> dict[str, Any]: + nonlocal aligned, full + aligned = _metric_summary( + aligned_metrics, + expected_tokens=ALIGNED_TOKENS, + context="aligned", + ) + full = _metric_summary( + full_code_metrics, + expected_tokens=CODE_TOKENS, + context="full-code", + ) + selector_audit = audit_selector_diagnostics(selector_diagnostics) + coverage = validate_state_error_coverage(state_errors) + for method in METHODS: + diagnostic_masks = { + str(record["layer_index"]): record["current_mask_sha256"] + for record in selector_audit["methods"][method] + } + if diagnostic_masks != coverage["final_mask_sha256s"][method]: + raise ValueError( + f"{method} final state-error masks do not match selector diagnostics" + ) + return { + "passed": True, + "all_logits_and_metrics_finite": True, + "selector_audit": selector_audit, + "state_error_coverage": coverage, + } + + def unit_check() -> dict[str, Any]: + inverse = _finite_float( + unit_evidence.get("inverse_relative_l2"), + context="unit inverse_relative_l2", + ) + if not inverse < MAX_RHT_INVERSE_RELATIVE_L2: + raise ValueError("right-RHT inverse relative L2 did not meet the frozen bound") + if unit_evidence.get("physical_pack_matches_transformed_qdq") is not True: + raise ValueError("physical RHT pack did not exactly match transformed QDQ") + if unit_evidence.get("physical_pack_max_abs_difference") != 0.0: + raise ValueError("physical RHT pack reconstruction was not bit-exact") + if unit_evidence.get("sign_schedule_sha256") != EXPECTED_SIGN_SCHEDULE_SHA256: + raise ValueError("right-RHT sign schedule hash drifted") + return { + "passed": True, + "inverse_relative_l2": inverse, + "maximum_inverse_relative_l2": MAX_RHT_INVERSE_RELATIVE_L2, + "physical_pack_matches_transformed_qdq": True, + "sign_schedule_sha256": EXPECTED_SIGN_SCHEDULE_SHA256, + } + + def state_sse_check() -> dict[str, Any]: + baseline = _finite_float( + state_errors[CQER_METHOD].get("aggregate_state_sse"), + context="CQER aggregate state SSE", + ) + candidate = _finite_float( + state_errors[RHT_METHOD].get("aggregate_state_sse"), + context="RHT aggregate state SSE", + ) + if baseline <= 0 or candidate < 0: + raise ValueError("state SSE baseline must be positive and candidate non-negative") + reduction = (baseline - candidate) / baseline + return { + "passed": reduction >= MIN_STATE_SSE_REDUCTION, + "cqer_state_sse": baseline, + "rht_state_sse": candidate, + "relative_reduction": reduction, + "minimum_relative_reduction": MIN_STATE_SSE_REDUCTION, + } + + def delta_nll_check() -> dict[str, Any]: + normalized = aligned or _metric_summary( + aligned_metrics, + expected_tokens=ALIGNED_TOKENS, + context="aligned", + ) + baseline = float(normalized[CQER_METHOD]["delta_nll"]) + candidate = float(normalized[RHT_METHOD]["delta_nll"]) + if baseline <= 0: + raise ValueError("CQER aligned excess NLL must be positive for a relative gate") + reduction = (baseline - candidate) / baseline + return { + "passed": reduction >= MIN_DELTA_NLL_REDUCTION, + "cqer_delta_nll": baseline, + "rht_delta_nll": candidate, + "relative_reduction": reduction, + "minimum_relative_reduction": MIN_DELTA_NLL_REDUCTION, + } + + def mean_kl_check() -> dict[str, Any]: + normalized = aligned or _metric_summary( + aligned_metrics, + expected_tokens=ALIGNED_TOKENS, + context="aligned", + ) + baseline = float(normalized[CQER_METHOD]["mean_kl"]) + candidate = float(normalized[RHT_METHOD]["mean_kl"]) + return { + "passed": candidate < baseline, + "cqer_mean_kl": baseline, + "rht_mean_kl": candidate, + } + + def top1_check() -> dict[str, Any]: + normalized = aligned or _metric_summary( + aligned_metrics, + expected_tokens=ALIGNED_TOKENS, + context="aligned", + ) + baseline = float(normalized[CQER_METHOD]["top1_agreement"]) + candidate = float(normalized[RHT_METHOD]["top1_agreement"]) + return { + "passed": candidate >= baseline, + "cqer_top1_agreement": baseline, + "rht_top1_agreement": candidate, + } + + checks = { + "clean_stable_repository": _gate_check(repository_check), + "frozen_task_identity_before_model_weights": _gate_check(identity_check), + "exact_equal_storage": _gate_check(storage_check), + "finite_metrics_exact_quotas_and_handshakes": _gate_check( + finite_and_handshake_check + ), + "independent_rht_numeric_evidence": _gate_check(unit_check), + "state_sse_relative_reduction": _gate_check(state_sse_check), + "aligned_excess_nll_relative_reduction": _gate_check(delta_nll_check), + "lower_aligned_mean_kl": _gate_check(mean_kl_check), + "aligned_top1_not_lower": _gate_check(top1_check), + } + return { + "schema": "recurquant.experiment009-stage-a-gate.v1", + "applicable": True, + "passed": all(check["passed"] is True for check in checks.values()), + "checks": checks, + } + + +def compute_unit_evidence() -> dict[str, Any]: + """Recompute deterministic production-path self-consistency evidence on CPU.""" + + generator = torch.Generator().manual_seed(SEED) + state = torch.randn((1, 3, 7, 128), generator=generator, dtype=torch.float32) + encoded = right_rht_encode(state, layer_index=5, expected_heads=3) + restored = right_rht_decode(encoded, layer_index=5, expected_heads=3) + inverse_relative_l2 = float( + ( + torch.linalg.vector_norm(restored - state) + / torch.linalg.vector_norm(state).clamp_min(1e-12) + ).item() + ) + + mask = torch.zeros((3, 7), dtype=torch.bool) + mask[0, 2] = True + mask[2, 4:] = True + low = QuantizationSpec(bits=4, group_size=128, flatten_last_dims=2) + high = QuantizationSpec(bits=8, group_size=128, flatten_last_dims=2) + packed = quantize_pack_mixed( + state, + mask, + low_spec=low, + high_spec=high, + right_rht_layer_index=5, + right_rht_expected_heads=3, + ) + low_qdq = quantize_dequantize(encoded, low).tensor + high_qdq = quantize_dequantize(encoded, high).tensor + transformed_qdq = torch.where( + mask.reshape(1, 3, 7, 1), + high_qdq, + low_qdq, + ) + expected = right_rht_decode( + transformed_qdq, + layer_index=5, + expected_heads=3, + output_dtype=state.dtype, + ) + materialized = packed.dequantize() + maximum_difference = float((materialized - expected).abs().max().item()) + + schedule = torch.cat( + [ + right_rht_signs( + layer_index=layer, + expected_heads=16, + width=128, + device="cpu", + ).to(torch.int8).reshape(-1) + for layer in FROZEN_LINEAR_LAYERS + ] + ) + sign_sha256 = hashlib.sha256(schedule.numpy().tobytes()).hexdigest() + return { + "inverse_relative_l2": inverse_relative_l2, + "inverse_relative_l2_threshold": MAX_RHT_INVERSE_RELATIVE_L2, + "physical_pack_matches_transformed_qdq": torch.equal(materialized, expected), + "physical_pack_max_abs_difference": maximum_difference, + "physical_pack_storage_bytes": packed.storage_bytes, + "sign_schedule_sha256": sign_sha256, + "expected_sign_schedule_sha256": EXPECTED_SIGN_SCHEDULE_SHA256, + "seed": SEED, + "device": "cpu", + "dtype": "torch.float32", + } + + +def _append_metrics( + accumulators: Mapping[str, Any], + reference_logits: torch.Tensor, + candidate_outputs: Mapping[str, Any], + target: torch.Tensor, +) -> None: + pilot._append_metrics( # noqa: SLF001 - shared evaluator math is intentional. + accumulators, + reference_logits, + {name: output.logits for name, output in candidate_outputs.items()}, + target, + ) + + +def evaluate_screen_task( + model: Qwen3_5ForCausalLM, + *, + prompt_ids: torch.Tensor, + code_ids: torch.Tensor, + plan: ExactBudgetRowPlan, +) -> dict[str, Any]: + """Evaluate reference, CQER, and RHT-CQER in one teacher-forced loop.""" + + reference_cache = DynamicCache(config=model.config) + cqer = create_qwen35_query_ema_exact_budget_cache( + model, + plan=plan, + record_evidence=True, + ) + rht = create_qwen35_right_rht_query_ema_exact_budget_cache( + model, + plan=plan, + record_evidence=True, + ) + caches: dict[ + str, + QueryEmaMixedPackedRecurrentStateCache + | RightRhtQueryEmaMixedPackedRecurrentStateCache, + ] = { + CQER_METHOD: cqer, + RHT_METHOD: rht, + } + aligned = {name: pilot._TokenAccumulator.empty() for name in METHODS} # noqa: SLF001 + full = {name: pilot._TokenAccumulator.empty() for name in METHODS} # noqa: SLF001 + + with Qwen35QueryEnergyObserver(model, caches=list(caches.values())): + reference_output = model( + prompt_ids, + past_key_values=reference_cache, + use_cache=True, + logits_to_keep=1, + ) + candidate_outputs = { + name: model( + prompt_ids, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + for name, cache in caches.items() + } + _append_metrics(full, reference_output.logits, candidate_outputs, code_ids[:, :1]) + + for token_index in range(code_ids.shape[1] - 1): + input_token = code_ids[:, token_index : token_index + 1] + target_token = code_ids[:, token_index + 1 : token_index + 2] + reference_output = model( + input_token, + past_key_values=reference_cache, + use_cache=True, + logits_to_keep=1, + ) + candidate_outputs = { + name: model( + input_token, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + for name, cache in caches.items() + } + _append_metrics( + aligned, + reference_output.logits, + candidate_outputs, + target_token, + ) + _append_metrics( + full, + reference_output.logits, + candidate_outputs, + target_token, + ) + + reference_bytes = sum( + state.tensor.numel() * state.tensor.element_size() + for state in iter_recurrent_states(reference_cache) + ) + state_errors = { + name: aggregate_state_error_evidence(cache.update_evidence) + for name, cache in caches.items() + } + return { + "aligned_metrics": { + name: accumulator.summary() for name, accumulator in aligned.items() + }, + "full_code_metrics": { + name: accumulator.summary() for name, accumulator in full.items() + }, + "storage": {name: cache.storage_summary() for name, cache in caches.items()}, + "selector_diagnostics": { + name: cache.query_ema_diagnostics() for name, cache in caches.items() + }, + "state_errors": state_errors, + "reference_recurrent_state_bytes": reference_bytes, + } + + +def _authenticate_selectors( + selector_path: Path, + loss_selector_path: Path, +) -> tuple[dict[str, Any], dict[str, Any], dict[str, str], ExactBudgetRowPlan]: + selector, selector_file_sha = pilot.load_selector_artifact( + selector_path, + expected_kind=pilot.HRR_ARTIFACT_KIND, + ) + loss_selector, loss_file_sha = pilot.load_selector_artifact( + loss_selector_path, + expected_kind=pilot.LOSS_ARTIFACT_KIND, + ) + pilot.validate_compatible_selector(selector, loss_selector) + pilot.validate_cqer_selector_artifacts( + enabled=True, + selectors=(selector, loss_selector), + ) + plan = pilot.plan_from_artifact(loss_selector, TARGET_FISHER_SCORE) + quotas = { + layer: len(plan.groups_for_layer(layer)) + for layer, _heads, _rows in plan.score_shapes + } + pilot.validate_cqer_layer_quotas(enabled=True, quotas=quotas) + if quotas != FROZEN_LAYER_QUOTAS: + raise ValueError("target-Fisher quota vector drifted from Experiment 009") + if plan.resident_bytes != TARGET_PACKED_STATE_BYTES: + raise ValueError("target-Fisher plan does not realize the frozen byte budget") + return ( + selector, + loss_selector, + { + "selector_file_sha256": selector_file_sha, + "loss_selector_file_sha256": loss_file_sha, + "selector_canonical_evidence_sha256": pilot.sha256_bytes( + canonical_json_bytes(selector) + ), + "loss_selector_canonical_evidence_sha256": pilot.sha256_bytes( + canonical_json_bytes(loss_selector) + ), + }, + plan, + ) + + +def _package_versions() -> dict[str, str | None]: + versions: dict[str, str | None] = {} + for name in ("datasets", "numpy", "safetensors", "torch", "transformers"): + try: + versions[name] = importlib.metadata.version(name) + except importlib.metadata.PackageNotFoundError: + versions[name] = None + return versions + + +def _atomic_write(path: Path, payload: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + prefix=f".{path.name}.", + suffix=".tmp", + dir=path.parent, + ) + temporary = Path(temporary_name) + try: + with os.fdopen(descriptor, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + if temporary.exists(): + temporary.unlink() + + +def main() -> int: + args = parse_args() + repository_root = Path(__file__).resolve().parents[1] + repository_start = pilot.git_state() + pilot.validate_cora_development_repository_start(repository_start) + pilot.validate_heldout_output_path(args.output, repository_root) + source_hashes_start = pilot.source_file_hashes(repository_root, SOURCE_FILES) + + # Selector authentication precedes all dataset access. + selector, loss_selector, selector_hashes, plan = _authenticate_selectors( + args.selector, + args.loss_selector, + ) + + # The task-ID loader retains only task 666. It cannot select the protected + # ranked [8, 16) window or any other calibration identity. + rows = load_mbpp_rows_by_task_ids("calibration", task_ids=(TASK_ID,)) + if len(rows) != 1 or int(rows[0]["task_id"]) != TASK_ID: + raise RuntimeError("Stage-A task-ID loader returned an unauthorized identity") + row = rows[0] + row_sha = mbpp_row_sha256(row) + if row_sha != TASK_ROW_SHA256: + raise ValueError("Stage-A row hash drifted before tokenization") + manifest = mbpp_manifest(rows, phase="calibration") + manifest_sha = mbpp_manifest_sha256(rows, phase="calibration") + + device = pilot.select_device(args.device) + dtype = torch.bfloat16 if device.type == "cuda" else torch.float32 + model_record = selector["model"] + if model_record["dtype"] != str(dtype): + raise ValueError( + "screen dtype does not match the frozen selector dtype: " + f"{dtype} != {model_record['dtype']}" + ) + model_id = str(model_record["id"]) + revision = str(model_record["revision"]) + + # Tokenizer metadata is authenticated before model weights are opened. + tokenizer = AutoTokenizer.from_pretrained( + model_id, + revision=revision, + local_files_only=args.local_files_only, + trust_remote_code=False, + ) + encoded_tasks, token_manifest = pilot.encode_task_rows(tokenizer, rows) + if len(encoded_tasks) != 1 or len(token_manifest) != 1: + raise RuntimeError("Stage-A encoding returned an unexpected task count") + identity = validate_stage_a_identity( + task_id=token_manifest[0]["task_id"], + row_sha256=row_sha, + prompt_tokens=token_manifest[0]["prompt_tokens"], + code_tokens=token_manifest[0]["code_tokens"], + aligned_scored_tokens=token_manifest[0]["aligned_scored_tokens"], + ) + + unit_evidence = compute_unit_evidence() + model = Qwen3_5ForCausalLM.from_pretrained( + model_id, + revision=revision, + dtype=dtype, + attn_implementation="eager", + low_cpu_mem_usage=True, + use_safetensors=True, + local_files_only=args.local_files_only, + ).to(device) + model.eval() + + _row, prompt_cpu, code_cpu = encoded_tasks[0] + torch.manual_seed(SEED) + with torch.inference_mode(): + result = evaluate_screen_task( + model, + prompt_ids=prompt_cpu.to(device), + code_ids=code_cpu.to(device), + plan=plan, + ) + + repository_end = pilot.git_state() + source_hashes_end = pilot.source_file_hashes(repository_root, SOURCE_FILES) + pilot.validate_cora_development_repository_end( + start_repository=repository_start, + end_repository=repository_end, + start_source_hashes=source_hashes_start, + end_source_hashes=source_hashes_end, + ) + integrity = { + "repository_clean_at_start": repository_start["worktree_clean"] is True, + "repository_clean_at_end": repository_end["worktree_clean"] is True, + "repository_commit_stable": ( + repository_start["commit"] == repository_end["commit"] + ), + "source_hashes_stable": source_hashes_start == source_hashes_end, + "identity_authenticated_before_model_weights": ( + identity["authenticated_before_model_weights"] is True + ), + "protected_window_8_16_accessed": False, + } + gate = evaluate_stage_a_gate( + aligned_metrics=result["aligned_metrics"], + full_code_metrics=result["full_code_metrics"], + storage=result["storage"], + selector_diagnostics=result["selector_diagnostics"], + state_errors=result["state_errors"], + unit_evidence=unit_evidence, + integrity=integrity, + ) + + evidence: dict[str, Any] = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "screening_only": True, + "claim_boundary": ( + "This is a one-task, already-open falsification screen for a known " + "right-RHT codec composed with CQER-32. Passing authorizes only the " + "separately frozen 32-task development run. It is not novelty, speed, " + "confirmation, state-of-the-art, or breakthrough evidence." + ), + "created_at_utc": datetime.now(UTC).isoformat(), + "protocol": { + "name": "Experiment 009 Stage A", + "method": RHT_METHOD, + "task_locked": True, + "thresholds_locked": True, + "protected_ranked_window": [8, 16], + "protected_window_accessed": False, + }, + "selector_artifacts": { + **selector_hashes, + "selector_path": str(args.selector.resolve()), + "loss_selector_path": str(args.loss_selector.resolve()), + "frozen_canonical_evidence_sha256s": list( + pilot.CQER_FROZEN_SELECTOR_CANONICAL_SHA256S + ), + "target_fisher_score": TARGET_FISHER_SCORE, + "target_fisher_layer_quotas": { + str(layer): quota for layer, quota in FROZEN_LAYER_QUOTAS.items() + }, + "quota_sum": sum(FROZEN_LAYER_QUOTAS.values()), + "authenticated": True, + }, + "model": { + "id": model_id, + "revision": revision, + "dtype": str(dtype), + "device": str(device), + }, + "dataset": { + "phase": "calibration", + "selection_mode": "exact_already_open_task_id", + "identity": identity, + "manifest": manifest, + "manifest_sha256": manifest_sha, + "token_manifest": token_manifest, + "identity_authenticated_before_model_weights": True, + "protected_window_8_16_loaded_tokenized_or_evaluated": False, + }, + "metric_contract": { + "aligned_primary": "code transitions after recurrent-state storage", + "aligned_token_count": ALIGNED_TOKENS, + "full_code_secondary_token_count": CODE_TOKENS, + "excluded_from_aligned": "prompt-to-first-code-token prediction", + }, + "methods": list(METHODS), + "metrics_aligned": result["aligned_metrics"], + "metrics_full_code_secondary": result["full_code_metrics"], + "storage": { + "fp32_reference_recurrent_state_bytes": result[ + "reference_recurrent_state_bytes" + ], + "candidates": result["storage"], + }, + "selector_diagnostics": result["selector_diagnostics"], + "state_error": result["state_errors"], + "unit_evidence": unit_evidence, + "stage_a_gate": gate, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "packages": _package_versions(), + "cuda_available": torch.cuda.is_available(), + "cuda_runtime": torch.version.cuda, + "gpu": ( + torch.cuda.get_device_name(device) if device.type == "cuda" else None + ), + }, + "repository": { + "commit": repository_end["commit"], + "start": repository_start, + "end": repository_end, + "stable_commit": repository_start["commit"] == repository_end["commit"], + }, + "source_files": { + "paths": list(SOURCE_FILES), + "sha256_start": source_hashes_start, + "sha256_end": source_hashes_end, + "stable": source_hashes_start == source_hashes_end, + }, + "command": [sys.executable, *sys.argv], + } + artifact = { + "schema_version": 1, + "artifact_kind": ARTIFACT_KIND, + "canonical_evidence_sha256": hashlib.sha256( + canonical_json_bytes(evidence) + ).hexdigest(), + "evidence": evidence, + } + payload = canonical_json_bytes(artifact) + _atomic_write(args.output, payload) + verification = verify_evidence_artifact(args.output) + if verification["valid"] is not True: + raise RuntimeError( + "written Stage-A artifact failed verification: " + + "; ".join(verification["errors"]) + ) + + # Output is constrained to Git-ignored or external storage. Recheck that + # even artifact publication did not alter the authenticated repository. + post_write_repository = pilot.git_state() + post_write_hashes = pilot.source_file_hashes(repository_root, SOURCE_FILES) + pilot.validate_cora_development_repository_end( + start_repository=repository_start, + end_repository=post_write_repository, + start_source_hashes=source_hashes_start, + end_source_hashes=post_write_hashes, + ) + print( + json.dumps( + { + "output": str(args.output.resolve()), + "artifact_sha256": hashlib.sha256(payload).hexdigest(), + "canonical_evidence_sha256": artifact[ + "canonical_evidence_sha256" + ], + "stage_a_gate": gate, + "post_write_repository_authenticated": True, + }, + indent=2, + sort_keys=True, + ) + ) + return 0 if gate["passed"] is True else 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/recurquant/__init__.py b/src/recurquant/__init__.py index f7f4704..720b25a 100644 --- a/src/recurquant/__init__.py +++ b/src/recurquant/__init__.py @@ -1,7 +1,71 @@ """RecurQuant recurrent-state quantization primitives.""" from .evidence import verify_evidence_artifact -from .packed_cache import PackedRecurrentStateCache +from .finite_difference import ( + DirectionalDerivativeCheck, + FiniteDifferencePoint, + check_directional_derivative, +) +from .fisher_sensitivity import ( + GDNInt4TrajectorySensitivityCalibrator, + GDNOneStepSensitivityCalibrator, + RowPromotionSensitivityScores, + SensitivityStepResult, + TaskMacroSensitivityAccumulator, + TaskMacroSensitivitySummary, + row_promotion_scores_from_errors, + row_promotion_sensitivity_scores, +) +from .horizon import ( + HorizonReadRisk, + finite_horizon_row_read_risk, + finite_horizon_row_read_risk_from_energies, +) +from .horizon_calibration import ( + BitwidthHorizonReadRisk, + GDNCalibrationTrace, + GDNHorizonCalibrationRecorder, + TaskMacroHorizonAccumulator, + TaskMacroHorizonSummary, + row_quantization_error_energies, + score_gdn_calibration_trace, +) +from .intervention import ( + PhysicalMetricRun, + PhysicalRowPromotionOracleResult, + RowPromotionMeasurement, + evaluate_physical_row_promotions, + target_nll_values, +) +from .mixed_quantization import PackedMixedQuantizedTensor, quantize_pack_mixed +from .model_fisher import ( + RowBlockModelFisherRisk, + row_block_model_fisher_risk, + sample_model_pseudo_labels, +) +from .multibit_policy import ( + allocate_exact_multibit_codes, + frozen_qwen35_multibit_step_budgets, +) +from .multibit_quantization import ( + INT4_PRECISION_CODE, + INT6_PRECISION_CODE, + INT8_PRECISION_CODE, + PackedMultiBitQuantizedTensor, + quantize_pack_multibit, +) +from .packed_cache import ( + AdaptiveMixedPackedRecurrentStateCache, + CoraMixedPackedLinearAttentionLayer, + CoraMixedPackedRecurrentStateCache, + MixedPackedRecurrentStateCache, + PackedRecurrentStateCache, + QueryEmaMixedPackedLinearAttentionLayer, + QueryEmaMixedPackedRecurrentStateCache, + RankFusedMixedPackedRecurrentStateCache, + RightRhtQueryEmaMixedPackedLinearAttentionLayer, + RightRhtQueryEmaMixedPackedRecurrentStateCache, +) from .quantization import ( PackedQuantizedTensor, QuantizationResult, @@ -9,17 +73,92 @@ quantize_dequantize, quantize_pack, ) -from .qwen35 import create_qwen35_packed_cache, create_qwen35_v02_mixed_cache +from .query_energy import Qwen35QueryEnergyObserver +from .qwen35 import ( + create_qwen35_adaptive_exact_budget_cache, + create_qwen35_cora_exact_budget_cache, + create_qwen35_exact_budget_cache, + create_qwen35_packed_cache, + create_qwen35_query_ema_exact_budget_cache, + create_qwen35_rank_fused_exact_budget_cache, + create_qwen35_right_rht_query_ema_exact_budget_cache, + create_qwen35_v02_mixed_cache, +) +from .rht import RHT_SEED, fwht_unnormalized, right_rht_decode, right_rht_encode +from .row_policy import ExactBudgetRowPlan, RowLocation, select_rows_exact_budget +from .transition_observer import Qwen35TransitionObserver __all__ = [ + "AdaptiveMixedPackedRecurrentStateCache", + "BitwidthHorizonReadRisk", + "CoraMixedPackedLinearAttentionLayer", + "CoraMixedPackedRecurrentStateCache", + "DirectionalDerivativeCheck", + "ExactBudgetRowPlan", + "FiniteDifferencePoint", + "GDNCalibrationTrace", + "GDNHorizonCalibrationRecorder", + "GDNInt4TrajectorySensitivityCalibrator", + "GDNOneStepSensitivityCalibrator", + "HorizonReadRisk", + "MixedPackedRecurrentStateCache", + "PackedMixedQuantizedTensor", + "PackedMultiBitQuantizedTensor", "PackedQuantizedTensor", "PackedRecurrentStateCache", + "QueryEmaMixedPackedLinearAttentionLayer", + "QueryEmaMixedPackedRecurrentStateCache", + "Qwen35QueryEnergyObserver", + "Qwen35TransitionObserver", + "RankFusedMixedPackedRecurrentStateCache", + "RightRhtQueryEmaMixedPackedLinearAttentionLayer", + "RightRhtQueryEmaMixedPackedRecurrentStateCache", + "RHT_SEED", + "PhysicalMetricRun", + "PhysicalRowPromotionOracleResult", "QuantizationResult", "QuantizationSpec", + "INT4_PRECISION_CODE", + "INT6_PRECISION_CODE", + "INT8_PRECISION_CODE", + "RowBlockModelFisherRisk", + "RowLocation", + "RowPromotionMeasurement", + "RowPromotionSensitivityScores", + "SensitivityStepResult", + "TaskMacroHorizonAccumulator", + "TaskMacroHorizonSummary", + "TaskMacroSensitivityAccumulator", + "TaskMacroSensitivitySummary", + "check_directional_derivative", + "allocate_exact_multibit_codes", + "create_qwen35_adaptive_exact_budget_cache", + "create_qwen35_cora_exact_budget_cache", + "create_qwen35_exact_budget_cache", "create_qwen35_packed_cache", + "create_qwen35_query_ema_exact_budget_cache", + "create_qwen35_rank_fused_exact_budget_cache", + "create_qwen35_right_rht_query_ema_exact_budget_cache", "create_qwen35_v02_mixed_cache", + "finite_horizon_row_read_risk", + "finite_horizon_row_read_risk_from_energies", + "frozen_qwen35_multibit_step_budgets", + "evaluate_physical_row_promotions", "quantize_dequantize", "quantize_pack", + "quantize_pack_mixed", + "quantize_pack_multibit", + "right_rht_decode", + "right_rht_encode", + "fwht_unnormalized", + "row_block_model_fisher_risk", + "row_promotion_scores_from_errors", + "row_promotion_sensitivity_scores", + "row_quantization_error_energies", + "sample_model_pseudo_labels", + "score_gdn_calibration_trace", + "select_rows_exact_budget", + "target_nll_values", "verify_evidence_artifact", ] diff --git a/src/recurquant/finite_difference.py b/src/recurquant/finite_difference.py new file mode 100644 index 0000000..4f1bc4a --- /dev/null +++ b/src/recurquant/finite_difference.py @@ -0,0 +1,169 @@ +"""Local finite-difference checks for recurrent-state promotion directions.""" + +from __future__ import annotations + +import math +from collections.abc import Callable, Sequence +from dataclasses import dataclass +from statistics import median + +import torch + +ScalarLoss = Callable[[torch.Tensor], torch.Tensor] + + +@dataclass(frozen=True, slots=True) +class FiniteDifferencePoint: + """One central-difference comparison at a fixed interpolation scale.""" + + epsilon: float + plus_loss: float + minus_loss: float + central_derivative: float + absolute_error: float + relative_error: float + near_zero: bool + sign_agreement: bool | None + + +@dataclass(frozen=True, slots=True) +class DirectionalDerivativeCheck: + """Autograd derivative and its central finite-difference convergence trace.""" + + base_loss: float + autograd_derivative: float + gradient_l2_norm: float + direction_l2_norm: float + near_zero_floor: float + points: tuple[FiniteDifferencePoint, ...] + + @property + def median_relative_error(self) -> float: + return float(median(point.relative_error for point in self.points)) + + @property + def nonzero_sign_agreement_rate(self) -> float | None: + agreements = [ + point.sign_agreement for point in self.points if point.sign_agreement is not None + ] + if not agreements: + return None + return sum(agreements) / len(agreements) + + +def _scalar_loss(loss_fn: ScalarLoss, state: torch.Tensor, *, name: str) -> torch.Tensor: + loss = loss_fn(state) + if not isinstance(loss, torch.Tensor): + raise TypeError(f"{name} loss_fn output must be a torch.Tensor") + if loss.numel() != 1 or not loss.is_floating_point(): + raise ValueError(f"{name} loss_fn output must be one floating-point scalar") + if not torch.isfinite(loss).item(): + raise ValueError(f"{name} loss_fn output must be finite") + return loss.reshape(()) + + +def check_directional_derivative( + loss_fn: ScalarLoss, + state: torch.Tensor, + direction: torch.Tensor, + *, + epsilons: Sequence[float] = (0.25, 0.125, 0.0625, 0.03125), + near_zero_floor: float = 1e-8, +) -> DirectionalDerivativeCheck: + """Compare ``grad(loss) dot direction`` with central differences. + + This checks the local differentiable loss supplied by ``loss_fn``. It does + not validate a hard quantizer, a repeated-QDQ suffix, or a complete packed + policy unless the caller explicitly builds those semantics into the loss + function. Near-zero comparisons retain their absolute/relative errors and + use ``sign_agreement=None`` instead of manufacturing a pass. + """ + + if not callable(loss_fn): + raise TypeError("loss_fn must be callable") + if not isinstance(state, torch.Tensor) or not isinstance(direction, torch.Tensor): + raise TypeError("state and direction must be torch.Tensor values") + if state.shape != direction.shape: + raise ValueError("state and direction must have the same shape") + if state.numel() == 0: + raise ValueError("state and direction must not be empty") + if not state.is_floating_point() or not direction.is_floating_point(): + raise TypeError("state and direction must use floating-point dtypes") + if state.dtype != direction.dtype or state.device != direction.device: + raise ValueError("state and direction must share dtype and device") + if state.device.type == "meta": + raise ValueError("state and direction must be materialized") + if not torch.isfinite(state).all().item() or not torch.isfinite(direction).all().item(): + raise ValueError("state and direction must contain only finite values") + if not math.isfinite(near_zero_floor) or near_zero_floor <= 0: + raise ValueError("near_zero_floor must be a positive finite number") + + rendered_epsilons = tuple(float(value) for value in epsilons) + if not rendered_epsilons: + raise ValueError("epsilons must contain at least one value") + if any(not math.isfinite(value) or value <= 0 for value in rendered_epsilons): + raise ValueError("epsilons must contain only positive finite values") + if len(set(rendered_epsilons)) != len(rendered_epsilons): + raise ValueError("epsilons must not contain duplicates") + + leaf = state.detach().clone().requires_grad_(True) + base = _scalar_loss(loss_fn, leaf, name="base") + (gradient,) = torch.autograd.grad( + base, + leaf, + retain_graph=False, + create_graph=False, + allow_unused=False, + ) + if not torch.isfinite(gradient).all().item(): + raise RuntimeError("loss gradient became non-finite") + autograd_value = float((gradient * direction).sum().detach().item()) + + points: list[FiniteDifferencePoint] = [] + with torch.no_grad(): + for epsilon in rendered_epsilons: + plus = _scalar_loss( + loss_fn, + state.detach() + epsilon * direction.detach(), + name="plus", + ) + minus = _scalar_loss( + loss_fn, + state.detach() - epsilon * direction.detach(), + name="minus", + ) + plus_value = float(plus.item()) + minus_value = float(minus.item()) + central = (plus_value - minus_value) / (2.0 * epsilon) + absolute_error = abs(central - autograd_value) + scale = max(abs(central), abs(autograd_value), near_zero_floor) + near_zero = max(abs(central), abs(autograd_value)) <= near_zero_floor + if near_zero: + sign_agreement = None + elif central == 0.0 or autograd_value == 0.0: + sign_agreement = False + else: + sign_agreement = math.copysign(1.0, central) == math.copysign( + 1.0, autograd_value + ) + points.append( + FiniteDifferencePoint( + epsilon=epsilon, + plus_loss=plus_value, + minus_loss=minus_value, + central_derivative=central, + absolute_error=absolute_error, + relative_error=absolute_error / scale, + near_zero=near_zero, + sign_agreement=sign_agreement, + ) + ) + + return DirectionalDerivativeCheck( + base_loss=float(base.detach().item()), + autograd_derivative=autograd_value, + gradient_l2_norm=float(torch.linalg.vector_norm(gradient).item()), + direction_l2_norm=float(torch.linalg.vector_norm(direction).item()), + near_zero_floor=near_zero_floor, + points=tuple(points), + ) diff --git a/src/recurquant/fisher_sensitivity.py b/src/recurquant/fisher_sensitivity.py new file mode 100644 index 0000000..bb770df --- /dev/null +++ b/src/recurquant/fisher_sensitivity.py @@ -0,0 +1,855 @@ +"""One-step loss sensitivity for Qwen3.5 recurrent-state rows. + +This module measures how the next-token loss responds to recurrent states in +an ordinary Transformers ``DynamicCache``. It supports either the untouched +FP32 trajectory or a repeated-QDQ INT4 trajectory. Both are deliberately +bounded calibration primitives: one teacher-forced token, one reverse-mode +traversal, and no retained graph after the step. + +The returned scores estimate the benefit of replacing an INT4 row by its INT8 +version. They are calibration signals, not evidence of downstream accuracy, +memory, or latency improvements. +""" + +from __future__ import annotations + +from collections.abc import Collection, Mapping, Sequence +from dataclasses import dataclass, field +from types import MethodType +from typing import Any, Literal + +import torch +import torch.nn.functional as F +from transformers import DynamicCache + +from .quantization import ( + QuantizationSpec, + quantize_dequantize, + scheduled_quantization_spec, +) + + +@dataclass(frozen=True, slots=True) +class RowPromotionSensitivityScores: + """Signed INT4-to-INT8 sensitivity scores for one recurrent-state layer. + + Every tensor has shape ``[heads, key_dim]`` and is the batch mean of + per-example row scores. ``taylor_benefit`` is the primary first-order + estimate of loss reduction, so positive values favor promotion to INT8. + The Fisher fields are signed differences, not guaranteed improvements. + ``delta_direction_fisher_magnitude`` preserves a non-negative local + perturbation diagnostic but must not be interpreted as promotion benefit. + """ + + layer_index: int + taylor_benefit: torch.Tensor + directional_fisher_difference: torch.Tensor + diagonal_fisher_difference: torch.Tensor + delta_direction_fisher_magnitude: torch.Tensor + + @property + def shape(self) -> torch.Size: + return self.taylor_benefit.shape + + +@dataclass(frozen=True, slots=True) +class FisherStepResult: + """Detached result of one teacher-forced sensitivity step.""" + + mean_nll: float + batch_size: int + trajectory: Literal["fp32", "int4"] + layers: tuple[RowPromotionSensitivityScores, ...] + + def layer(self, layer_index: int) -> RowPromotionSensitivityScores: + for scores in self.layers: + if scores.layer_index == layer_index: + return scores + raise KeyError(f"step has no layer {layer_index}") + + +@dataclass(frozen=True, slots=True) +class TaskMacroSensitivitySummary: + """Equal-task mean of token-mean promotion-sensitivity scores.""" + + layer_index: int + tasks: int + steps: int + mean_nll: float + trajectory: Literal["fp32", "int4"] + taylor_benefit: torch.Tensor + directional_fisher_difference: torch.Tensor + diagonal_fisher_difference: torch.Tensor + delta_direction_fisher_magnitude: torch.Tensor + + +def _validate_specs(int4_spec: QuantizationSpec, int8_spec: QuantizationSpec) -> None: + if int4_spec.bits != 4: + raise ValueError("int4_spec must use bits=4") + if int8_spec.bits != 8: + raise ValueError("int8_spec must use bits=8") + if int4_spec.flatten_last_dims != 1 or int8_spec.flatten_last_dims != 1: + raise ValueError( + "INT4 and INT8 specs must use flatten_last_dims=1 so each state row " + "is quantized independently" + ) + matched_fields = ( + "group_size", + "scale_bits", + "flatten_last_dims", + "rounding", + "seed", + "epsilon", + ) + mismatched = [ + name for name in matched_fields if getattr(int4_spec, name) != getattr(int8_spec, name) + ] + if mismatched: + raise ValueError( + f"INT4 and INT8 specs may differ only in bit width; mismatched fields: {mismatched}" + ) + + +def row_promotion_scores_from_errors( + gradient: torch.Tensor, + int4_error: torch.Tensor, + int8_error: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute signed promotion scores from aligned state errors and a loss gradient. + + Inputs must have shape ``[batch, heads, key_dim, value_dim]``. With + ``delta = Q8(S) - Q4(S) = int8_error - int4_error``, the primary score is + ``-sum(gradient * delta)``. This estimates ``L(Q4(S)) - L(Q8(S))`` by a + first-order Taylor expansion around the trajectory where ``gradient`` was + measured. Positive values therefore favor INT8 under that local model. + + The two signed curvature diagnostics are + ``(g dot e4)**2 - (g dot e8)**2`` and + ``sum(g**2 * (e4**2 - e8**2))``. The final returned tensor is the old + non-negative ``(g dot (e4-e8))**2`` perturbation magnitude; it is retained + only as a diagnostic and is not an estimated benefit. + """ + + tensors = { + "gradient": gradient, + "int4_error": int4_error, + "int8_error": int8_error, + } + if gradient.ndim != 4: + raise ValueError("gradient must have shape [batch, heads, key_dim, value_dim]") + if int4_error.shape != gradient.shape or int8_error.shape != gradient.shape: + raise ValueError("gradient, int4_error, and int8_error must have the same shape") + if not all(tensor.is_floating_point() for tensor in tensors.values()): + raise TypeError("gradient, int4_error, and int8_error must be floating-point") + if any(tensor.dtype != torch.float32 for tensor in tensors.values()): + raise TypeError("gradient, int4_error, and int8_error must use torch.float32") + if len({tensor.device for tensor in tensors.values()}) != 1: + raise ValueError("gradient, int4_error, and int8_error must be on the same device") + if gradient.device.type == "meta": + raise ValueError("gradient and errors must be materialized") + if not all(torch.isfinite(tensor).all().item() for tensor in tensors.values()): + raise ValueError("gradient and errors must contain only finite values") + + grad = gradient.detach() + error4 = int4_error.detach() + error8 = int8_error.detach() + delta = error8 - error4 + taylor_per_example = -(grad * delta).sum(dim=-1) + projection4 = (grad * error4).sum(dim=-1) + projection8 = (grad * error8).sum(dim=-1) + directional_difference_per_example = projection4.square() - projection8.square() + diagonal_difference_per_example = (grad.square() * (error4.square() - error8.square())).sum( + dim=-1 + ) + delta_direction_magnitude_per_example = (grad * (error4 - error8)).sum(dim=-1).square() + + outputs = ( + taylor_per_example.mean(dim=0), + directional_difference_per_example.mean(dim=0), + diagonal_difference_per_example.mean(dim=0), + delta_direction_magnitude_per_example.mean(dim=0), + ) + if not all(torch.isfinite(value).all().item() for value in outputs): + raise RuntimeError("promotion sensitivity scores became non-finite") + return ( + outputs[0].detach().cpu(), + outputs[1].detach().cpu(), + outputs[2].detach().cpu(), + outputs[3].detach().cpu(), + ) + + +def row_promotion_sensitivity_scores( + state: torch.Tensor, + gradient: torch.Tensor, + *, + int4_spec: QuantizationSpec, + int8_spec: QuantizationSpec, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Quantize an FP32 state and compute its local row-promotion scores. + + ``state`` and ``gradient`` must have shape + ``[batch, heads, key_dim, value_dim]``. The quantizer group size must equal + ``value_dim``; this makes each physical quantization group exactly one + recurrent-state row. Each returned score tensor has shape + ``[heads, key_dim]``. The gradient is interpreted at ``state``; callers + measuring gradients on a quantized trajectory should instead call + :func:`row_promotion_scores_from_errors` with errors relative to the raw + pre-quantization update. + """ + + _validate_specs(int4_spec, int8_spec) + if state.ndim != 4: + raise ValueError("state must have shape [batch, heads, key_dim, value_dim]") + if gradient.shape != state.shape: + raise ValueError("gradient must have the same shape as state") + if not state.is_floating_point() or not gradient.is_floating_point(): + raise TypeError("state and gradient must use floating-point dtypes") + if state.device != gradient.device: + raise ValueError("state and gradient must be on the same device") + if state.device.type == "meta": + raise ValueError("state and gradient must be materialized") + if state.dtype != torch.float32 or gradient.dtype != torch.float32: + raise TypeError("state and gradient must use torch.float32") + if int4_spec.group_size != state.shape[-1]: + raise ValueError( + "quantizer group_size must equal recurrent-state value_dim so each " + f"row owns one group ({int4_spec.group_size} != {state.shape[-1]})" + ) + if not torch.isfinite(state).all().item() or not torch.isfinite(gradient).all().item(): + raise ValueError("state and gradient must contain only finite values") + + source = state.detach() + int4 = quantize_dequantize(source, int4_spec).tensor.to(torch.float32) + int8 = quantize_dequantize(source, int8_spec).tensor.to(torch.float32) + error4 = int4 - source + error8 = int8 - source + return row_promotion_scores_from_errors(gradient, error4, error8) + + +def _cache_state(cache: DynamicCache, layer_index: int) -> torch.Tensor: + if layer_index >= len(cache.layers): + raise ValueError(f"cache has no layer {layer_index}") + layer = cache.layers[layer_index] + recurrent_states = getattr(layer, "recurrent_states", None) + initialized = getattr(layer, "is_recurrent_states_initialized", None) + previous = getattr(layer, "has_previous_state", None) + if not isinstance(recurrent_states, dict) or not isinstance(initialized, dict): + raise ValueError(f"cache layer {layer_index} is not a linear-attention cache layer") + if not initialized.get(0, False) or not isinstance(recurrent_states.get(0), torch.Tensor): + raise ValueError( + f"cache layer {layer_index} has no recurrent state; run a no-grad prefill first" + ) + if not isinstance(previous, dict) or not previous.get(0, False): + raise ValueError( + f"cache layer {layer_index} has no previous state; run a no-grad prefill first" + ) + state = recurrent_states[0] + if state.dtype != torch.float32: + raise TypeError( + f"cache layer {layer_index} recurrent state must use torch.float32, got {state.dtype}" + ) + if state.ndim != 4: + raise ValueError( + f"cache layer {layer_index} recurrent state must have shape " + "[batch, heads, key_dim, value_dim]" + ) + if state.device.type == "meta" or not torch.isfinite(state).all().item(): + raise ValueError( + f"cache layer {layer_index} recurrent state must be materialized and finite" + ) + return state + + +def _detach_cache_tensors(cache: DynamicCache) -> None: + """Detach tensors that a differentiable decode may have stored in the cache.""" + + for layer in cache.layers: + for name in ("keys", "values"): + value = getattr(layer, name, None) + if isinstance(value, torch.Tensor): + setattr(layer, name, value.detach()) + for name in ("conv_states", "recurrent_states"): + values = getattr(layer, name, None) + if isinstance(values, dict): + for state_index, value in tuple(values.items()): + if isinstance(value, torch.Tensor): + values[state_index] = value.detach() + + +_CACHE_SNAPSHOT_ATTRIBUTES = ( + "keys", + "values", + "conv_states", + "recurrent_states", + "is_conv_states_initialized", + "is_recurrent_states_initialized", + "has_previous_state", + "conv_kernel_size", + "is_initialized", + "device", + "dtype", + "record_past", +) + + +def _snapshot_cache(cache: DynamicCache) -> list[dict[str, tuple[bool, object]]]: + """Capture enough warm DynamicCache state to roll back one failed token.""" + + snapshots: list[dict[str, tuple[bool, object]]] = [] + for layer in cache.layers: + snapshot: dict[str, tuple[bool, object]] = {} + for name in _CACHE_SNAPSHOT_ATTRIBUTES: + if not hasattr(layer, name): + snapshot[name] = (False, None) + continue + value = getattr(layer, name) + if name in ("conv_states", "recurrent_states") and isinstance(value, dict): + copied = { + state_index: state.detach().clone() + if isinstance(state, torch.Tensor) + else state + for state_index, state in value.items() + } + elif name in ("keys", "values") and isinstance(value, torch.Tensor): + # Dynamic attention updates assign a concatenated tensor rather + # than mutating the old one, so retaining the detached object is + # enough and avoids copying an arbitrarily long KV history. + copied = value.detach() + elif isinstance(value, dict): + copied = dict(value) + else: + copied = value + snapshot[name] = (True, copied) + snapshots.append(snapshot) + return snapshots + + +def _restore_cache( + cache: DynamicCache, + snapshots: Sequence[dict[str, tuple[bool, object]]], +) -> None: + if len(cache.layers) != len(snapshots): + raise RuntimeError("cannot roll back a DynamicCache whose layer count changed") + for layer, snapshot in zip(cache.layers, snapshots, strict=True): + for name, (existed, value) in snapshot.items(): + if existed: + setattr(layer, name, value) + elif hasattr(layer, name): + delattr(layer, name) + + +class GDNOneStepFisherCalibrator: + """Run bounded next-token sensitivity calibration on a warm Qwen3.5 cache. + + The cache must be an ordinary, non-offloaded ``DynamicCache`` populated by + a no-grad prefill. For each call to :meth:`step`, selected recurrent states + are detached and reintroduced as FP32 leaves. Model parameters are frozen + only for that call and their original ``requires_grad`` flags are restored. + Updated cache tensors are detached in ``finally`` so the next token keeps + the numerical trajectory without retaining the autograd graph. The + default ``fp32`` trajectory evaluates quantization errors around the FP32 + state. Use :class:`GDNInt4TrajectoryFisherCalibrator` when the gradient + itself must be measured on a repeated-QDQ all-INT4 trajectory. + """ + + def __init__( + self, + model: torch.nn.Module, + cache: DynamicCache, + *, + layer_indices: Collection[int], + int4_spec: QuantizationSpec | None = None, + int8_spec: QuantizationSpec | None = None, + trajectory: Literal["fp32", "int4"] = "fp32", + ) -> None: + if not isinstance(cache, DynamicCache): + raise TypeError("cache must be an ordinary transformers.DynamicCache") + if getattr(cache, "offloading", False): + raise ValueError("offloaded DynamicCache calibration is not supported") + if trajectory not in ("fp32", "int4"): + raise ValueError("trajectory must be 'fp32' or 'int4'") + selected = tuple(sorted(set(layer_indices))) + if not selected: + raise ValueError("layer_indices must select at least one GDN layer") + if any( + isinstance(index, bool) or not isinstance(index, int) or index < 0 for index in selected + ): + raise ValueError("layer_indices must contain non-negative integers") + + resolved_int4 = int4_spec or QuantizationSpec( + bits=4, + group_size=128, + flatten_last_dims=1, + ) + resolved_int8 = int8_spec or QuantizationSpec( + bits=8, + group_size=128, + flatten_last_dims=1, + ) + _validate_specs(resolved_int4, resolved_int8) + + modules = { + int(module.layer_idx): module + for module in model.modules() + if module.__class__.__name__ == "Qwen3_5GatedDeltaNet" + and isinstance(getattr(module, "layer_idx", None), int) + } + missing = set(selected) - set(modules) + if missing: + raise ValueError( + f"model does not contain selected Qwen3.5 GDN layers {sorted(missing)}" + ) + expected_state_shapes: dict[int, tuple[int, int, int]] = {} + for layer_index in selected: + module = modules[layer_index] + geometry = ( + getattr(module, "num_v_heads", None), + getattr(module, "head_k_dim", None), + getattr(module, "head_v_dim", None), + ) + if any( + isinstance(size, bool) or not isinstance(size, int) or size <= 0 + for size in geometry + ): + raise ValueError(f"Qwen3.5 GDN layer {layer_index} has invalid state geometry") + expected_state_shapes[layer_index] = geometry + + self.model = model + self.cache = cache + self.layer_indices = selected + self.int4_spec = resolved_int4 + self.int8_spec = resolved_int8 + self._expected_state_shapes = expected_state_shapes + self.trajectory = trajectory + self._int4_error_bases: dict[int, tuple[torch.Tensor, torch.Tensor]] = {} + self._layer_update_counts = dict.fromkeys(selected, 0) + self._active = False + if trajectory == "int4": + boundary_states: dict[int, torch.Tensor] = {} + for layer_index in selected: + state = _cache_state(cache, layer_index) + self._validate_state_geometry(layer_index, state) + boundary_states[layer_index] = state + for layer_index, state in boundary_states.items(): + self._store_int4_boundary(layer_index, state) + + def _validate_state_geometry(self, layer_index: int, state: torch.Tensor) -> None: + if tuple(state.shape[1:]) != self._expected_state_shapes[layer_index]: + raise ValueError( + f"cache layer {layer_index} state shape {tuple(state.shape[1:])} does not " + f"match Qwen3.5 GDN geometry {self._expected_state_shapes[layer_index]}" + ) + if self.int4_spec.group_size != state.shape[-1]: + raise ValueError( + "quantizer group_size must equal recurrent-state value_dim at " + f"layer {layer_index} ({self.int4_spec.group_size} != {state.shape[-1]})" + ) + + def _store_int4_boundary(self, layer_index: int, raw_state: torch.Tensor) -> None: + """QDQ one raw update, store Q4, and retain the aligned promotion errors.""" + + update_index = self._layer_update_counts[layer_index] + int4_spec = scheduled_quantization_spec( + self.int4_spec, + layer_index=layer_index, + layer_update_index=update_index, + ) + int8_spec = scheduled_quantization_spec( + self.int8_spec, + layer_index=layer_index, + layer_update_index=update_index, + ) + source = raw_state.detach().to(torch.float32) + int4 = quantize_dequantize(source, int4_spec).tensor.to(torch.float32).detach() + int8 = quantize_dequantize(source, int8_spec).tensor.to(torch.float32).detach() + self.cache.layers[layer_index].recurrent_states[0] = int4 + self._int4_error_bases[layer_index] = ( + (int4 - source).detach(), + (int8 - source).detach(), + ) + self._layer_update_counts[layer_index] = update_index + 1 + + def step( + self, + input_ids: torch.Tensor, + target_token_ids: torch.Tensor, + *, + forward_kwargs: Mapping[str, Any] | None = None, + ) -> FisherStepResult: + """Score one teacher-forced token and advance the selected trajectory.""" + + if self._active: + raise RuntimeError("calibrator.step is not reentrant") + if self.model.training: + raise ValueError("model must be in evaluation mode") + if input_ids.ndim != 2 or input_ids.shape[1] != 1: + raise ValueError("input_ids must have shape [batch, 1]") + if input_ids.dtype not in (torch.int32, torch.int64): + raise TypeError("input_ids must use an integer dtype") + targets = target_token_ids + if targets.ndim == 2 and targets.shape[1] == 1: + targets = targets[:, 0] + if targets.ndim != 1 or targets.shape[0] != input_ids.shape[0]: + raise ValueError("target_token_ids must have shape [batch] or [batch, 1]") + if targets.dtype not in (torch.int32, torch.int64): + raise TypeError("target_token_ids must use an integer dtype") + if targets.device != input_ids.device: + raise ValueError("input_ids and target_token_ids must be on the same device") + + kwargs = dict(forward_kwargs or {}) + conflicts = {"input_ids", "past_key_values", "use_cache", "labels"} & set(kwargs) + if conflicts: + raise ValueError(f"forward_kwargs contains managed arguments: {sorted(conflicts)}") + + states = {index: _cache_state(self.cache, index) for index in self.layer_indices} + batch_sizes = {state.shape[0] for state in states.values()} + if batch_sizes != {input_ids.shape[0]}: + raise ValueError( + "input batch must match every selected recurrent-state batch; " + f"input={input_ids.shape[0]}, states={sorted(batch_sizes)}" + ) + for layer_index, state in states.items(): + self._validate_state_geometry(layer_index, state) + if state.device != input_ids.device: + raise ValueError( + f"input_ids and cache layer {layer_index} state must be on the same device" + ) + if self.trajectory == "int4": + error4, error8 = self._int4_error_bases[layer_index] + if error4.shape != state.shape or error8.shape != state.shape: + raise RuntimeError( + f"INT4 trajectory error basis at layer {layer_index} is misaligned" + ) + + parameters = tuple(self.model.parameters()) + if any(parameter.grad is not None for parameter in parameters): + raise ValueError("model parameters must not have pre-existing gradients") + original_requires_grad = tuple(parameter.requires_grad for parameter in parameters) + original_update_attribute = self.cache.__dict__.get("update_recurrent_state") + had_update_attribute = "update_recurrent_state" in self.cache.__dict__ + original_update = self.cache.update_recurrent_state + cache_snapshot = _snapshot_cache(self.cache) + error_bases_snapshot = dict(self._int4_error_bases) + update_counts_snapshot = dict(self._layer_update_counts) + leaves: dict[int, torch.Tensor] = {} + raw_updates: dict[int, torch.Tensor] = {} + step_succeeded = False + self._active = True + + def differentiable_update( + cache: DynamicCache, + recurrent_states: torch.Tensor, + layer_idx: int, + state_idx: int = 0, + **update_kwargs: Any, + ) -> torch.Tensor: + if layer_idx not in leaves: + return original_update( + recurrent_states, + layer_idx, + state_idx=state_idx, + **update_kwargs, + ) + if state_idx != 0: + raise ValueError("Qwen3.5 Fisher calibration supports recurrent state_idx=0 only") + layer = cache.layers[layer_idx] + layer.recurrent_states[state_idx] = recurrent_states + layer.is_recurrent_states_initialized[state_idx] = True + raw_updates[layer_idx] = recurrent_states + return recurrent_states + + try: + for parameter in parameters: + parameter.requires_grad_(False) + for layer_index, state in states.items(): + leaf = state.detach().to(torch.float32).requires_grad_(True) + self.cache.layers[layer_index].recurrent_states[0] = leaf + leaves[layer_index] = leaf + self.cache.update_recurrent_state = MethodType( # type: ignore[method-assign] + differentiable_update, + self.cache, + ) + + with torch.enable_grad(): + outputs = self.model( + input_ids=input_ids, + past_key_values=self.cache, + use_cache=True, + **kwargs, + ) + logits = getattr(outputs, "logits", None) + if not isinstance(logits, torch.Tensor) or logits.ndim != 3: + raise TypeError( + "model output must expose logits with shape [batch, sequence, vocab]" + ) + if logits.shape[:2] != input_ids.shape: + raise ValueError("model logits must align with the single-token input") + if not torch.isfinite(logits).all().item(): + raise RuntimeError("model logits became non-finite") + per_example_nll = F.cross_entropy( + logits[:, -1, :].to(torch.float32), + targets.to(torch.long), + reduction="none", + ) + if not torch.isfinite(per_example_nll).all().item(): + raise RuntimeError("target-token NLL became non-finite") + gradients = torch.autograd.grad( + # Sum keeps each independent batch item's gradient unscaled; + # row scores and reported NLL are averaged across the batch. + per_example_nll.sum(), + tuple(leaves[index] for index in self.layer_indices), + retain_graph=False, + create_graph=False, + allow_unused=False, + ) + + layer_scores: list[RowPromotionSensitivityScores] = [] + for layer_index, gradient in zip(self.layer_indices, gradients, strict=True): + if gradient.dtype != torch.float32: + gradient = gradient.to(torch.float32) + if self.trajectory == "fp32": + scores = row_promotion_sensitivity_scores( + leaves[layer_index], + gradient, + int4_spec=self.int4_spec, + int8_spec=self.int8_spec, + ) + else: + error4, error8 = self._int4_error_bases[layer_index] + scores = row_promotion_scores_from_errors(gradient, error4, error8) + layer_scores.append( + RowPromotionSensitivityScores( + layer_index=layer_index, + taylor_benefit=scores[0], + directional_fisher_difference=scores[1], + diagonal_fisher_difference=scores[2], + delta_direction_fisher_magnitude=scores[3], + ) + ) + updated = self.cache.layers[layer_index].recurrent_states[0] + if updated is leaves[layer_index]: + raise RuntimeError( + f"cache layer {layer_index} was not advanced by the model forward" + ) + if updated.dtype != torch.float32 or not torch.isfinite(updated).all().item(): + raise RuntimeError( + f"cache layer {layer_index} produced a non-finite or " + "non-FP32 recurrent state" + ) + + if any(parameter.grad is not None for parameter in parameters): + raise RuntimeError("model parameter gradients were populated during calibration") + result = FisherStepResult( + mean_nll=float(per_example_nll.detach().mean().item()), + batch_size=input_ids.shape[0], + trajectory=self.trajectory, + layers=tuple(layer_scores), + ) + step_succeeded = True + return result + finally: + try: + try: + if step_succeeded: + try: + if self.trajectory == "int4": + for layer_index in self.layer_indices: + self._store_int4_boundary( + layer_index, + raw_updates[layer_index], + ) + except BaseException: + _restore_cache(self.cache, cache_snapshot) + self._int4_error_bases = error_bases_snapshot + self._layer_update_counts = update_counts_snapshot + raise + else: + _restore_cache(self.cache, cache_snapshot) + self._int4_error_bases = error_bases_snapshot + self._layer_update_counts = update_counts_snapshot + finally: + _detach_cache_tensors(self.cache) + finally: + if had_update_attribute: + self.cache.__dict__["update_recurrent_state"] = original_update_attribute + else: + self.cache.__dict__.pop("update_recurrent_state", None) + for parameter, requires_grad in zip( + parameters, + original_requires_grad, + strict=True, + ): + parameter.requires_grad_(requires_grad) + self._active = False + + +class GDNInt4TrajectoryFisherCalibrator(GDNOneStepFisherCalibrator): + """Measure loss gradients on a repeated-QDQ all-INT4 GDN trajectory. + + Construction is the explicit prefill boundary: every raw GDN recurrent + state in the warm ordinary ``DynamicCache`` is quantized, Q4 is stored for + the next token, and ``Q8(raw)-Q4(raw)`` is retained as the next promotion + direction. After each measured token, the raw updated state is captured, + Q4 is stored again, and the next aligned error basis replaces the old one. + All Qwen3.5 GDN layers must participate so this mode represents a uniform + all-INT4 recurrent-state trajectory. + """ + + def __init__( + self, + model: torch.nn.Module, + cache: DynamicCache, + *, + int4_spec: QuantizationSpec | None = None, + int8_spec: QuantizationSpec | None = None, + ) -> None: + layer_indices = { + int(module.layer_idx) + for module in model.modules() + if module.__class__.__name__ == "Qwen3_5GatedDeltaNet" + and isinstance(getattr(module, "layer_idx", None), int) + } + if not layer_indices: + raise ValueError("model has no Qwen3.5 GDN layers") + super().__init__( + model, + cache, + layer_indices=layer_indices, + int4_spec=int4_spec, + int8_spec=int8_spec, + trajectory="int4", + ) + + +_SCORE_FIELDS = ( + "taylor_benefit", + "directional_fisher_difference", + "diagonal_fisher_difference", + "delta_direction_fisher_magnitude", +) + + +@dataclass(slots=True) +class TaskMacroSensitivityAccumulator: + """Average token scores within each task, then give every task equal weight.""" + + _tasks: int = 0 + _steps: int = 0 + _trajectory: Literal["fp32", "int4"] | None = None + _layer_shapes: dict[int, tuple[int, ...]] | None = None + _score_sums: dict[str, dict[int, torch.Tensor]] = field( + default_factory=lambda: {name: {} for name in _SCORE_FIELDS} + ) + _nll_sum: float = 0.0 + + @property + def tasks(self) -> int: + return self._tasks + + @property + def steps(self) -> int: + return self._steps + + def add_task(self, steps: Sequence[FisherStepResult]) -> None: + """Consume all token steps for one task without retaining token traces.""" + + if not steps: + raise ValueError("task must contain at least one sensitivity step") + trajectory = steps[0].trajectory + if trajectory not in ("fp32", "int4"): + raise ValueError("step trajectory must be 'fp32' or 'int4'") + if self._trajectory is not None and trajectory != self._trajectory: + raise ValueError("trajectory changed across tasks") + first_layers = {scores.layer_index: scores for scores in steps[0].layers} + if len(first_layers) != len(steps[0].layers) or not first_layers: + raise ValueError("each step must contain a non-empty unique layer set") + shapes = { + layer_index: tuple(scores.taylor_benefit.shape) + for layer_index, scores in first_layers.items() + } + if self._layer_shapes is not None and shapes != self._layer_shapes: + raise ValueError("task layer set or score shapes changed") + + task_sums = { + name: { + layer_index: torch.zeros(shape, dtype=torch.float64) + for layer_index, shape in shapes.items() + } + for name in _SCORE_FIELDS + } + task_nll = 0.0 + for step in steps: + if step.trajectory != trajectory: + raise ValueError("trajectory changed within task") + current = {scores.layer_index: scores for scores in step.layers} + if len(current) != len(step.layers) or not current: + raise ValueError("each step must contain a non-empty unique layer set") + current_shapes = { + layer_index: tuple(scores.taylor_benefit.shape) + for layer_index, scores in current.items() + } + if current_shapes != shapes: + raise ValueError("layer set or score shapes changed within task") + if not torch.isfinite(torch.tensor(step.mean_nll)).item(): + raise ValueError("step mean_nll must be finite") + task_nll += step.mean_nll + for layer_index, scores in current.items(): + values = {name: getattr(scores, name) for name in _SCORE_FIELDS} + if any(tuple(value.shape) != shapes[layer_index] for value in values.values()): + raise ValueError("all promotion score shapes must match") + if any(not value.is_floating_point() for value in values.values()): + raise TypeError("promotion scores must be floating-point") + if any(value.device.type == "meta" for value in values.values()): + raise ValueError("promotion scores must be materialized") + for name, value in values.items(): + detached = value.detach().cpu().to(torch.float64) + if not torch.isfinite(detached).all().item(): + raise ValueError("promotion scores must be finite") + task_sums[name][layer_index] += detached + + divisor = float(len(steps)) + for name in _SCORE_FIELDS: + for layer_index in shapes: + task_sums[name][layer_index] /= divisor + if layer_index not in self._score_sums[name]: + self._score_sums[name][layer_index] = torch.zeros_like( + task_sums[name][layer_index] + ) + self._score_sums[name][layer_index] += task_sums[name][layer_index] + if self._layer_shapes is None: + self._layer_shapes = shapes + if self._trajectory is None: + self._trajectory = trajectory + self._nll_sum += task_nll / divisor + self._tasks += 1 + self._steps += len(steps) + + def summary(self, layer_index: int) -> TaskMacroSensitivitySummary: + if self._tasks == 0 or layer_index not in self._score_sums["taylor_benefit"]: + raise ValueError(f"no accumulated sensitivity scores for layer {layer_index}") + assert self._trajectory is not None + return TaskMacroSensitivitySummary( + layer_index=layer_index, + tasks=self._tasks, + steps=self._steps, + mean_nll=self._nll_sum / self._tasks, + trajectory=self._trajectory, + taylor_benefit=self._score_sums["taylor_benefit"][layer_index] / self._tasks, + directional_fisher_difference=( + self._score_sums["directional_fisher_difference"][layer_index] / self._tasks + ), + diagonal_fisher_difference=( + self._score_sums["diagonal_fisher_difference"][layer_index] / self._tasks + ), + delta_direction_fisher_magnitude=( + self._score_sums["delta_direction_fisher_magnitude"][layer_index] / self._tasks + ), + ) + + +# Public names reflect that signed Taylor benefit is the primary output. Keep +# the original experimental class names as aliases for local artifact replay. +SensitivityStepResult = FisherStepResult +GDNOneStepSensitivityCalibrator = GDNOneStepFisherCalibrator +GDNInt4TrajectorySensitivityCalibrator = GDNInt4TrajectoryFisherCalibrator diff --git a/src/recurquant/horizon.py b/src/recurquant/horizon.py new file mode 100644 index 0000000..42dfc08 --- /dev/null +++ b/src/recurquant/horizon.py @@ -0,0 +1,212 @@ +"""Finite-horizon sensitivity for Gated DeltaNet recurrent-state rows. + +The score follows the linearized error dynamics of the recurrent update while +holding the full-precision q/k/g/beta trajectory fixed. It is a calibration +primitive, not evidence that a selected policy improves language-model quality. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + + +@dataclass(frozen=True, slots=True) +class HorizonReadRisk: + """Per-row finite-horizon read-risk scores and their aligned components.""" + + horizon: int + scores: torch.Tensor + per_state_scores: torch.Tensor + future_read_counts: torch.Tensor + + +def _l2_normalize(value: torch.Tensor, *, epsilon: float) -> torch.Tensor: + return value * torch.rsqrt(value.square().sum(dim=-1, keepdim=True) + epsilon) + + +def _validate_energy_inputs( + row_error_energies: torch.Tensor, + queries: torch.Tensor, + keys: torch.Tensor, + log_decays: torch.Tensor, + betas: torch.Tensor, + *, + horizon: int, + epsilon: float, +) -> None: + if isinstance(horizon, bool) or not isinstance(horizon, int) or horizon <= 0: + raise ValueError("horizon must be a positive integer") + if epsilon <= 0: + raise ValueError("epsilon must be positive") + if row_error_energies.ndim != 4: + raise ValueError("row_error_energies must have shape [time, batch, heads, key_dim]") + if queries.ndim != 4 or keys.ndim != 4: + raise ValueError("queries and keys must have shape [time, batch, heads, key_dim]") + if log_decays.ndim != 3 or betas.ndim != 3: + raise ValueError("log_decays and betas must have shape [time, batch, heads]") + if queries.shape != row_error_energies.shape or keys.shape != row_error_energies.shape: + raise ValueError( + "query/key dimensions must match row_error_energies time, batch, heads, and key_dim" + ) + expected_scalar_shape = row_error_energies.shape[:3] + if log_decays.shape != expected_scalar_shape or betas.shape != expected_scalar_shape: + raise ValueError( + "log_decay/beta dimensions must match row_error_energies time, batch, and heads" + ) + if row_error_energies.shape[0] == 0: + raise ValueError("the trace must contain at least one token") + tensors = (row_error_energies, queries, keys, log_decays, betas) + if not all(tensor.is_floating_point() for tensor in tensors): + raise TypeError("all horizon inputs must use floating-point dtypes") + if len({tensor.device for tensor in tensors}) != 1: + raise ValueError("all horizon inputs must be on the same device") + if not all(torch.isfinite(tensor).all().item() for tensor in tensors): + raise ValueError("all horizon inputs must be finite") + if (row_error_energies < 0).any().item(): + raise ValueError("row_error_energies must be non-negative") + + +def _validate_full_error_inputs(state_errors: torch.Tensor) -> None: + if state_errors.ndim != 5: + raise ValueError("state_errors must have shape [time, batch, heads, key_dim, value_dim]") + if not state_errors.is_floating_point(): + raise TypeError("all horizon inputs must use floating-point dtypes") + + +def _right_apply_transition( + row_vector: torch.Tensor, + *, + key: torch.Tensor, + log_decay: torch.Tensor, + beta: torch.Tensor, +) -> torch.Tensor: + """Apply ``g * (I - beta * k k^T)`` to a row vector from the right.""" + + projected = (row_vector * key).sum(dim=-1, keepdim=True) + corrected = row_vector - beta.unsqueeze(-1) * projected * key + return log_decay.exp().unsqueeze(-1) * corrected + + +def finite_horizon_row_read_risk_from_energies( + row_error_energies: torch.Tensor, + queries: torch.Tensor, + keys: torch.Tensor, + log_decays: torch.Tensor, + betas: torch.Tensor, + *, + horizon: int = 32, + normalize_qk: bool = True, + epsilon: float = 1e-6, +) -> HorizonReadRisk: + """Score per-row error energies over future Gated DeltaNet reads. + + ``row_error_energies[t, b, h, r]`` is the squared L2 norm of row ``r`` in + the recurrent-state quantization error just before token ``t``. This is the + sufficient error statistic for isolated-row scoring: it avoids retaining a + ``value_dim``-wide error matrix for every token while producing the same + result as :func:`finite_horizon_row_read_risk`. + + For each future read, the function propagates the query backwards through + the analytic Gated DeltaNet error transition + + ``E' = exp(g) * (I - beta * k k^T) * E``. + + The returned ``scores`` have shape ``[heads, key_dim]`` and average the + isolated squared read contribution over trace positions and batch items. + Cross-row cancellation is intentionally excluded so rows can be ranked. + """ + + _validate_energy_inputs( + row_error_energies, + queries, + keys, + log_decays, + betas, + horizon=horizon, + epsilon=epsilon, + ) + working_energies = row_error_energies.to(torch.float32) + working_queries = queries.to(torch.float32) + working_keys = keys.to(torch.float32) + working_decays = log_decays.to(torch.float32) + working_betas = betas.to(torch.float32) + if normalize_qk: + working_queries = _l2_normalize(working_queries, epsilon=epsilon) + working_keys = _l2_normalize(working_keys, epsilon=epsilon) + + time_steps, batch_size, heads, key_dim = working_energies.shape + query_scale = key_dim**-0.5 + influence = torch.zeros( + (time_steps, batch_size, heads, key_dim), + dtype=torch.float32, + device=working_energies.device, + ) + future_read_counts = torch.zeros( + time_steps, + dtype=torch.int64, + device=working_energies.device, + ) + + for read_index in range(time_steps): + propagated_query = working_queries[read_index] * query_scale + earliest_start = max(0, read_index - horizon + 1) + for start_index in range(read_index, earliest_start - 1, -1): + propagated_query = _right_apply_transition( + propagated_query, + key=working_keys[start_index], + log_decay=working_decays[start_index], + beta=working_betas[start_index], + ) + influence[start_index] += propagated_query.square() + future_read_counts[start_index] += 1 + + normalized_influence = influence / future_read_counts.clamp_min(1).view(-1, 1, 1, 1) + per_state_scores = normalized_influence * working_energies + scores = per_state_scores.mean(dim=(0, 1)) + return HorizonReadRisk( + horizon=min(horizon, time_steps), + scores=scores, + per_state_scores=per_state_scores, + future_read_counts=future_read_counts, + ) + + +def finite_horizon_row_read_risk( + state_errors: torch.Tensor, + queries: torch.Tensor, + keys: torch.Tensor, + log_decays: torch.Tensor, + betas: torch.Tensor, + *, + horizon: int = 32, + normalize_qk: bool = True, + epsilon: float = 1e-6, +) -> HorizonReadRisk: + """Score isolated recurrent-state row errors over future GDN reads. + + ``state_errors[t]`` is the quantization error in the recurrent state just + before token ``t``. For each future read, the function propagates the query + backwards through the analytic Gated DeltaNet error transition + + ``E' = exp(g) * (I - beta * k k^T) * E``. + + This avoids expanding a separate full error matrix for every source row. + The returned ``scores`` have shape ``[heads, key_dim]`` and average the + isolated squared read contribution over trace positions and batch items. + Cross-row cancellation is intentionally excluded so rows can be ranked. + """ + + _validate_full_error_inputs(state_errors) + row_error_energies = state_errors.to(torch.float32).square().sum(dim=-1) + return finite_horizon_row_read_risk_from_energies( + row_error_energies, + queries, + keys, + log_decays, + betas, + horizon=horizon, + normalize_qk=normalize_qk, + epsilon=epsilon, + ) diff --git a/src/recurquant/horizon_calibration.py b/src/recurquant/horizon_calibration.py new file mode 100644 index 0000000..e2d184d --- /dev/null +++ b/src/recurquant/horizon_calibration.py @@ -0,0 +1,582 @@ +"""Bounded calibration capture for finite-horizon Gated DeltaNet scoring. + +The recorder observes selected Qwen3.5 single-token recurrent calls without +changing their arguments or return values. It keeps only normalized q/k, +log-decay, beta, and per-row quantization-error energies for the current task. +Full recurrent-state error matrices are temporary and never retained. + +These APIs are experimental calibration primitives. They do not establish an +accuracy, memory, or latency improvement without a separate held-out test. +""" + +from __future__ import annotations + +from collections.abc import Collection, Mapping +from dataclasses import dataclass, field +from types import TracebackType +from typing import Any + +import torch + +from .horizon import HorizonReadRisk, finite_horizon_row_read_risk_from_energies +from .quantization import QuantizationSpec, quantize_dequantize + + +@dataclass(frozen=True, slots=True) +class GDNCalibrationTrace: + """One bounded task trace for one Gated DeltaNet layer. + + All tensors are FP32. Vector tensors have shape + ``[time, batch, heads, key_dim]`` and scalar tensors have shape + ``[time, batch, heads]``. The q/k vectors are already normalized exactly as + the tested Qwen3.5 PyTorch kernel normalizes them. At index ``t``, the row + energies describe the recurrent state immediately before the same token's + q/k/g/beta transition. + """ + + layer_index: int + queries: torch.Tensor + keys: torch.Tensor + log_decays: torch.Tensor + betas: torch.Tensor + int4_row_error_energies: torch.Tensor + int8_row_error_energies: torch.Tensor + dropped_calls: int = 0 + missing_initial_state_calls: int = 0 + + @property + def tokens(self) -> int: + return int(self.queries.shape[0]) + + @property + def retained_bytes(self) -> int: + tensors = ( + self.queries, + self.keys, + self.log_decays, + self.betas, + self.int4_row_error_energies, + self.int8_row_error_energies, + ) + return sum(tensor.numel() * tensor.element_size() for tensor in tensors) + + @property + def complete(self) -> bool: + return self.dropped_calls == 0 and self.missing_initial_state_calls == 0 + + +@dataclass(frozen=True, slots=True) +class BitwidthHorizonReadRisk: + """Aligned INT4 and INT8 finite-horizon row scores for one layer/task.""" + + layer_index: int + int4: HorizonReadRisk + int8: HorizonReadRisk + + @property + def int4_minus_int8(self) -> torch.Tensor: + """Return the modeled read-risk difference, without clamping its sign.""" + + return self.int4.scores - self.int8.scores + + +@dataclass(frozen=True, slots=True) +class TaskMacroHorizonSummary: + """Task-macro mean row scores retained by the streaming accumulator.""" + + layer_index: int + horizon: int + tasks: int + int4_scores: torch.Tensor + int8_scores: torch.Tensor + + @property + def int4_minus_int8(self) -> torch.Tensor: + return self.int4_scores - self.int8_scores + + +def _validate_row_spec(spec: QuantizationSpec, *, bits: int, name: str) -> None: + if spec.bits != bits: + raise ValueError(f"{name} must use bits={bits}") + if spec.flatten_last_dims != 1: + raise ValueError( + f"{name} must use flatten_last_dims=1 so recurrent-state rows are " + "quantized independently" + ) + + +def row_quantization_error_energies( + initial_state: torch.Tensor, + spec: QuantizationSpec, +) -> torch.Tensor: + """Return FP32 squared quantization-error norms for each state row. + + ``initial_state`` must have shape ``[batch, heads, key_dim, value_dim]``. + The source is explicitly converted to FP32 before quantization so the + energy measures integer quantization rather than a lower-precision input + cast. Only the returned ``[batch, heads, key_dim]`` tensor is persistent. + """ + + if initial_state.ndim != 4: + raise ValueError("initial_state must have shape [batch, heads, key_dim, value_dim]") + if not initial_state.is_floating_point(): + raise TypeError("initial_state must use a floating-point dtype") + if initial_state.device.type == "meta": + raise ValueError("initial_state must be materialized, not on the meta device") + if not torch.isfinite(initial_state).all().item(): + raise ValueError("initial_state must contain only finite values") + if spec.flatten_last_dims != 1: + raise ValueError( + "spec must use flatten_last_dims=1 so recurrent-state rows are quantized independently" + ) + + source = initial_state.detach().to(torch.float32) + quantized = quantize_dequantize(source, spec).tensor.to(torch.float32) + return (quantized - source).square().sum(dim=-1) + + +def score_gdn_calibration_trace( + trace: GDNCalibrationTrace, + *, + horizon: int = 32, + epsilon: float = 1e-6, +) -> BitwidthHorizonReadRisk: + """Score an already-normalized calibration trace at INT4 and INT8.""" + + if not trace.complete: + raise ValueError( + "cannot score an incomplete calibration trace: " + f"dropped_calls={trace.dropped_calls}, " + f"missing_initial_state_calls={trace.missing_initial_state_calls}" + ) + + int4 = finite_horizon_row_read_risk_from_energies( + trace.int4_row_error_energies, + trace.queries, + trace.keys, + trace.log_decays, + trace.betas, + horizon=horizon, + normalize_qk=False, + epsilon=epsilon, + ) + int8 = finite_horizon_row_read_risk_from_energies( + trace.int8_row_error_energies, + trace.queries, + trace.keys, + trace.log_decays, + trace.betas, + horizon=horizon, + normalize_qk=False, + epsilon=epsilon, + ) + return BitwidthHorizonReadRisk(layer_index=trace.layer_index, int4=int4, int8=int8) + + +@dataclass(slots=True) +class _LayerTraceBuffer: + queries: list[torch.Tensor] = field(default_factory=list) + keys: list[torch.Tensor] = field(default_factory=list) + log_decays: list[torch.Tensor] = field(default_factory=list) + betas: list[torch.Tensor] = field(default_factory=list) + int4_energies: list[torch.Tensor] = field(default_factory=list) + int8_energies: list[torch.Tensor] = field(default_factory=list) + expected_vector_shape: tuple[int, ...] | None = None + dropped_calls: int = 0 + missing_initial_state_calls: int = 0 + + @property + def tokens(self) -> int: + return len(self.queries) + + +def _argument( + args: tuple[object, ...], + kwargs: dict[str, object], + name: str, + position: int, +) -> object | None: + if name in kwargs: + return kwargs[name] + return args[position] if len(args) > position else None + + +def _normalize_qk(value: torch.Tensor, *, epsilon: float) -> torch.Tensor: + working = value.detach() + normalized = working * torch.rsqrt(working.square().sum(dim=-1, keepdim=True) + epsilon) + return normalized.to(torch.float32) + + +def _validate_single_token_call( + query: object, + key: object, + log_decay: object, + beta: object, + initial_state: object, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + named = { + "query": query, + "key": key, + "g": log_decay, + "beta": beta, + "initial_state": initial_state, + } + if not all(isinstance(value, torch.Tensor) for value in named.values()): + missing = [name for name, value in named.items() if not isinstance(value, torch.Tensor)] + raise TypeError(f"Gated DeltaNet call must expose tensor {', '.join(missing)}") + + query_tensor = query + key_tensor = key + decay_tensor = log_decay + beta_tensor = beta + state_tensor = initial_state + if query_tensor.ndim != 4 or key_tensor.ndim != 4: + raise ValueError("query and key must have shape [batch, 1, heads, key_dim]") + if query_tensor.shape != key_tensor.shape or query_tensor.shape[1] != 1: + raise ValueError( + "recorder accepts only aligned single-token query/key tensors with " + "shape [batch, 1, heads, key_dim]" + ) + expected_scalars = query_tensor.shape[:3] + if decay_tensor.shape != expected_scalars or beta_tensor.shape != expected_scalars: + raise ValueError("g and beta must have shape [batch, 1, heads]") + expected_state = ( + query_tensor.shape[0], + query_tensor.shape[2], + query_tensor.shape[3], + ) + if state_tensor.ndim != 4 or state_tensor.shape[:3] != expected_state: + raise ValueError( + "initial_state must have shape [batch, heads, key_dim, value_dim] " + "aligned with query/key" + ) + tensors = (query_tensor, key_tensor, decay_tensor, beta_tensor, state_tensor) + if not all(tensor.is_floating_point() for tensor in tensors): + raise TypeError("all captured Gated DeltaNet inputs must use floating-point dtypes") + if state_tensor.dtype != torch.float32: + raise TypeError( + "initial_state must use torch.float32 so calibration starts from the " + "full-precision recurrent-state reference" + ) + if len({tensor.device for tensor in tensors}) != 1: + raise ValueError("all captured Gated DeltaNet inputs must be on the same device") + if query_tensor.device.type == "meta": + raise ValueError("captured Gated DeltaNet inputs must be materialized") + if not all(torch.isfinite(tensor).all().item() for tensor in tensors): + raise ValueError("all captured Gated DeltaNet inputs must be finite") + return query_tensor, key_tensor, decay_tensor, beta_tensor, state_tensor + + +class GDNHorizonCalibrationRecorder: + """Capture bounded single-token traces from selected Qwen3.5 GDN layers. + + The caller must name at least one layer and choose a positive per-layer token + bound. Once a layer reaches that bound, further calls pass through unchanged + and are counted as dropped. ``drain_traces`` transfers the current task's + compact traces and clears recorder storage before the next task. + """ + + def __init__( + self, + model: torch.nn.Module, + *, + layer_indices: Collection[int], + max_tokens_per_layer: int, + int4_spec: QuantizationSpec | None = None, + int8_spec: QuantizationSpec | None = None, + storage_device: torch.device | str = "cpu", + epsilon: float = 1e-6, + ) -> None: + selected = tuple(sorted(set(layer_indices))) + if not selected: + raise ValueError("layer_indices must select at least one layer") + if any( + isinstance(index, bool) or not isinstance(index, int) or index < 0 for index in selected + ): + raise ValueError("layer_indices must contain non-negative integers") + if ( + isinstance(max_tokens_per_layer, bool) + or not isinstance(max_tokens_per_layer, int) + or max_tokens_per_layer <= 0 + ): + raise ValueError("max_tokens_per_layer must be a positive integer") + if epsilon <= 0: + raise ValueError("epsilon must be positive") + + resolved_int4 = int4_spec or QuantizationSpec( + bits=4, + group_size=128, + flatten_last_dims=1, + ) + resolved_int8 = int8_spec or QuantizationSpec( + bits=8, + group_size=128, + flatten_last_dims=1, + ) + _validate_row_spec(resolved_int4, bits=4, name="int4_spec") + _validate_row_spec(resolved_int8, bits=8, name="int8_spec") + resolved_storage = torch.device(storage_device) + if resolved_storage.type == "meta": + raise ValueError("storage_device must retain materialized tensors") + + self.model = model + self.layer_indices = selected + self.max_tokens_per_layer = max_tokens_per_layer + self.int4_spec = resolved_int4 + self.int8_spec = resolved_int8 + self.storage_device = resolved_storage + self.epsilon = epsilon + self.enabled = True + self._buffers = {index: _LayerTraceBuffer() for index in selected} + self._installed: list[tuple[object, str, object]] = [] + + @property + def captured_tokens(self) -> dict[int, int]: + return {index: buffer.tokens for index, buffer in self._buffers.items()} + + @property + def dropped_calls(self) -> dict[int, int]: + return {index: buffer.dropped_calls for index, buffer in self._buffers.items()} + + @property + def missing_initial_state_calls(self) -> dict[int, int]: + return { + index: buffer.missing_initial_state_calls for index, buffer in self._buffers.items() + } + + def _selected_modules(self) -> dict[int, torch.nn.Module]: + selected: dict[int, torch.nn.Module] = {} + for module in self.model.modules(): + if module.__class__.__name__ != "Qwen3_5GatedDeltaNet": + continue + layer_index = getattr(module, "layer_idx", None) + if layer_index not in self._buffers: + continue + if layer_index in selected: + raise ValueError(f"model contains duplicate GDN layer index {layer_index}") + selected[layer_index] = module + missing = set(self.layer_indices) - set(selected) + if missing: + raise ValueError(f"model does not contain selected GDN layers {sorted(missing)}") + return selected + + def _store(self, value: torch.Tensor) -> torch.Tensor: + return ( + value.detach().to(device=self.storage_device, dtype=torch.float32).clone().contiguous() + ) + + def _capture_call( + self, + *, + layer_index: int, + query: object, + key: object, + log_decay: object, + beta: object, + initial_state: object, + use_qk_l2norm_in_kernel: object, + ) -> None: + buffer = self._buffers[layer_index] + if initial_state is None: + buffer.missing_initial_state_calls += 1 + return + if buffer.tokens >= self.max_tokens_per_layer: + buffer.dropped_calls += 1 + return + if use_qk_l2norm_in_kernel is not True: + raise ValueError( + "calibration requires use_qk_l2norm_in_kernel=True to match the " + "tested Qwen3.5 recurrence" + ) + query_tensor, key_tensor, decay_tensor, beta_tensor, state_tensor = ( + _validate_single_token_call(query, key, log_decay, beta, initial_state) + ) + vector_shape = tuple(query_tensor[:, 0].shape) + if buffer.expected_vector_shape is None: + buffer.expected_vector_shape = vector_shape + elif buffer.expected_vector_shape != vector_shape: + raise ValueError( + "captured batch/head/key dimensions changed within one task; " + "drain_traces() before recording the next task" + ) + + normalized_query = _normalize_qk(query_tensor[:, 0], epsilon=self.epsilon) + normalized_key = _normalize_qk(key_tensor[:, 0], epsilon=self.epsilon) + state_float = state_tensor.detach().to(torch.float32) + int4_energy = row_quantization_error_energies(state_float, self.int4_spec) + int8_energy = row_quantization_error_energies(state_float, self.int8_spec) + + buffer.queries.append(self._store(normalized_query)) + buffer.keys.append(self._store(normalized_key)) + buffer.log_decays.append(self._store(decay_tensor[:, 0])) + buffer.betas.append(self._store(beta_tensor[:, 0])) + buffer.int4_energies.append(self._store(int4_energy)) + buffer.int8_energies.append(self._store(int8_energy)) + + def _make_wrapper(self, module: torch.nn.Module, original: Any): + layer_index = int(module.layer_idx) + + def wrapped(*args: object, **kwargs: object): + if self.enabled: + self._capture_call( + layer_index=layer_index, + query=_argument(args, kwargs, "query", 0), + key=_argument(args, kwargs, "key", 1), + log_decay=_argument(args, kwargs, "g", 3), + beta=_argument(args, kwargs, "beta", 4), + initial_state=_argument(args, kwargs, "initial_state", 5), + use_qk_l2norm_in_kernel=_argument( + args, + kwargs, + "use_qk_l2norm_in_kernel", + 7, + ), + ) + return original(*args, **kwargs) + + return wrapped + + def install(self) -> None: + if self._installed: + raise RuntimeError("recorder is already installed") + modules = self._selected_modules() + try: + for layer_index in self.layer_indices: + module = modules[layer_index] + attribute = "recurrent_gated_delta_rule" + original = getattr(module, attribute) + setattr(module, attribute, self._make_wrapper(module, original)) + self._installed.append((module, attribute, original)) + except BaseException: + self.remove() + raise + + def remove(self) -> None: + while self._installed: + module, attribute, original = self._installed.pop() + setattr(module, attribute, original) + + def clear(self) -> None: + self._buffers = {index: _LayerTraceBuffer() for index in self.layer_indices} + + def drain_traces( + self, + *, + require_complete: bool = True, + ) -> dict[int, GDNCalibrationTrace]: + """Return current task traces and clear storage after validation. + + The default rejects missing or truncated calls instead of silently + turning a token bound into a shorter calibration example. Set + ``require_complete=False`` only for recorder diagnostics; incomplete + traces are marked and cannot be scored. + """ + + if require_complete: + incomplete = { + layer_index: { + "captured_tokens": buffer.tokens, + "dropped_calls": buffer.dropped_calls, + "missing_initial_state_calls": buffer.missing_initial_state_calls, + } + for layer_index, buffer in self._buffers.items() + if buffer.tokens == 0 or buffer.dropped_calls or buffer.missing_initial_state_calls + } + if incomplete: + raise RuntimeError(f"calibration task trace is incomplete: {incomplete}") + + traces: dict[int, GDNCalibrationTrace] = {} + for layer_index, buffer in self._buffers.items(): + if not buffer.queries: + continue + traces[layer_index] = GDNCalibrationTrace( + layer_index=layer_index, + queries=torch.stack(buffer.queries), + keys=torch.stack(buffer.keys), + log_decays=torch.stack(buffer.log_decays), + betas=torch.stack(buffer.betas), + int4_row_error_energies=torch.stack(buffer.int4_energies), + int8_row_error_energies=torch.stack(buffer.int8_energies), + dropped_calls=buffer.dropped_calls, + missing_initial_state_calls=buffer.missing_initial_state_calls, + ) + self.clear() + return traces + + def __enter__(self) -> GDNHorizonCalibrationRecorder: + self.install() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + self.remove() + + +class TaskMacroHorizonAccumulator: + """Aggregate task-level layer scores without retaining task traces.""" + + def __init__(self, *, horizon: int = 32, epsilon: float = 1e-6) -> None: + if isinstance(horizon, bool) or not isinstance(horizon, int) or horizon <= 0: + raise ValueError("horizon must be a positive integer") + if epsilon <= 0: + raise ValueError("epsilon must be positive") + self.horizon = horizon + self.epsilon = epsilon + self._int4_sums: dict[int, torch.Tensor] = {} + self._int8_sums: dict[int, torch.Tensor] = {} + self._task_counts: dict[int, int] = {} + self._expected_layer_indices: frozenset[int] | None = None + + def add_task(self, traces: Mapping[int, GDNCalibrationTrace]) -> None: + """Score one task and retain only one score matrix per bitwidth/layer.""" + + if not traces: + raise ValueError("traces must contain at least one layer") + layer_indices = frozenset(traces) + if self._expected_layer_indices is None: + self._expected_layer_indices = layer_indices + elif layer_indices != self._expected_layer_indices: + raise ValueError( + "trace layer set changed across tasks: " + f"expected={sorted(self._expected_layer_indices)}, " + f"actual={sorted(layer_indices)}" + ) + for layer_index, trace in traces.items(): + if layer_index != trace.layer_index: + raise ValueError("trace mapping key must match trace.layer_index") + result = score_gdn_calibration_trace( + trace, + horizon=self.horizon, + epsilon=self.epsilon, + ) + int4_score = result.int4.scores.detach().to(device="cpu", dtype=torch.float64) + int8_score = result.int8.scores.detach().to(device="cpu", dtype=torch.float64) + if layer_index in self._int4_sums: + if self._int4_sums[layer_index].shape != int4_score.shape: + raise ValueError(f"layer {layer_index} score shape changed across tasks") + self._int4_sums[layer_index] += int4_score + self._int8_sums[layer_index] += int8_score + else: + self._int4_sums[layer_index] = int4_score.clone() + self._int8_sums[layer_index] = int8_score.clone() + self._task_counts[layer_index] = 0 + self._task_counts[layer_index] += 1 + + def summary(self, layer_index: int) -> TaskMacroHorizonSummary: + if layer_index not in self._task_counts: + raise KeyError(f"layer {layer_index} has no accumulated tasks") + tasks = self._task_counts[layer_index] + return TaskMacroHorizonSummary( + layer_index=layer_index, + horizon=self.horizon, + tasks=tasks, + int4_scores=(self._int4_sums[layer_index] / tasks).clone(), + int8_scores=(self._int8_sums[layer_index] / tasks).clone(), + ) + + def summaries(self) -> dict[int, TaskMacroHorizonSummary]: + return {layer_index: self.summary(layer_index) for layer_index in self._task_counts} diff --git a/src/recurquant/intervention.py b/src/recurquant/intervention.py new file mode 100644 index 0000000..b51ca41 --- /dev/null +++ b/src/recurquant/intervention.py @@ -0,0 +1,458 @@ +"""Bounded physical-cache interventions for validating row rankings. + +This module deliberately implements an expensive analysis oracle, not a +deployable selector. It reruns a fixed teacher-forced sequence once for an +all-INT4/background control and once per candidate row. Every candidate run +uses a fresh :class:`MixedPackedRecurrentStateCache`, so the measured metric +includes the real repeated INT4/INT8 packing and dequantization path. + +Candidate metrics use target tokens and can therefore overfit calibration +data. The results are appropriate for auditing a scalable sensitivity proxy +on a small, predeclared candidate set; they are not an inference-time policy or +confirmation evidence by themselves. +""" + +from __future__ import annotations + +import math +from collections.abc import Callable, Sequence +from dataclasses import dataclass + +import torch + +from .qwen35 import create_qwen35_exact_budget_cache +from .row_policy import ExactBudgetRowPlan, RowLocation, select_rows_exact_budget + +TokenMetric = Callable[[torch.Tensor, torch.Tensor], torch.Tensor] + +_MAX_CANDIDATE_CAP = 256 + + +@dataclass(frozen=True, slots=True) +class PhysicalMetricRun: + """One fixed-sequence measurement from a physical mixed cache.""" + + mean_metric: float + token_count: int + resident_bytes: int + high_precision_groups: int + cache_update_count: int + + +@dataclass(frozen=True, slots=True) +class RowPromotionMeasurement: + """The measured marginal effect of promoting one candidate row.""" + + location: RowLocation + run: PhysicalMetricRun + metric_delta: float + improvement: float + + +@dataclass(frozen=True, slots=True) +class PhysicalRowPromotionOracleResult: + """Results from a bounded, post-hoc physical intervention audit.""" + + metric_name: str + lower_is_better: bool + baseline: PhysicalMetricRun + measurements: tuple[RowPromotionMeasurement, ...] + background_rows: tuple[RowLocation, ...] + baseline_plan_bytes: int + intervention_plan_bytes: int + promotion_increment_bytes: int + + def ranked_measurements(self) -> tuple[RowPromotionMeasurement, ...]: + """Return candidates from largest measured improvement to smallest.""" + + return tuple( + sorted( + self.measurements, + key=lambda measurement: (-measurement.improvement, measurement.location), + ) + ) + + +def target_nll_values(logits: torch.Tensor, target_ids: torch.Tensor) -> torch.Tensor: + """Return one teacher-forced negative log-likelihood value per target token.""" + + log_probs = torch.log_softmax(logits.to(torch.float32), dim=-1) + return -log_probs.gather(-1, target_ids.to(torch.int64).unsqueeze(-1)).squeeze(-1) + + +def _validate_token_inputs( + model: torch.nn.Module, + prompt_ids: torch.Tensor, + continuation_ids: torch.Tensor, +) -> None: + for name, value in (("prompt_ids", prompt_ids), ("continuation_ids", continuation_ids)): + if not isinstance(value, torch.Tensor): + raise TypeError(f"{name} must be a torch.Tensor") + if value.ndim != 2 or value.shape[0] <= 0 or value.shape[1] <= 0: + raise ValueError(f"{name} must have non-empty shape [batch, tokens]") + if value.dtype not in (torch.int32, torch.int64): + raise TypeError(f"{name} must use torch.int32 or torch.int64") + if prompt_ids.shape[0] != continuation_ids.shape[0]: + raise ValueError("prompt_ids and continuation_ids must have the same batch size") + if prompt_ids.device != continuation_ids.device: + raise ValueError("prompt_ids and continuation_ids must be on the same device") + + parameter = next(model.parameters(), None) + if parameter is not None and parameter.device != prompt_ids.device: + raise ValueError( + "token tensors must be on the model device: " + f"tokens={prompt_ids.device}, model={parameter.device}" + ) + + vocab_size = getattr(getattr(model, "config", None), "vocab_size", None) + if isinstance(vocab_size, bool) or not isinstance(vocab_size, int) or vocab_size <= 0: + raise ValueError("model.config.vocab_size must be a positive integer") + for name, value in (("prompt_ids", prompt_ids), ("continuation_ids", continuation_ids)): + if value.min().item() < 0 or value.max().item() >= vocab_size: + raise ValueError(f"{name} contains a token outside [0, {vocab_size})") + + +def _qwen_row_geometry(model: torch.nn.Module) -> tuple[tuple[int, int, int], ...]: + config = getattr(model, "config", None) + layer_types = getattr(config, "layer_types", None) + head_count = getattr(config, "linear_num_value_heads", None) + row_count = getattr(config, "linear_key_head_dim", None) + if not isinstance(layer_types, (list, tuple)): + raise ValueError("model.config.layer_types must be a list or tuple") + if any( + isinstance(value, bool) or not isinstance(value, int) or value <= 0 + for value in (head_count, row_count) + ): + raise ValueError("model config has invalid recurrent-state row geometry") + geometry = tuple( + (layer_index, head_count, row_count) + for layer_index, layer_type in enumerate(layer_types) + if layer_type == "linear_attention" + ) + if not geometry: + raise ValueError("model config has no linear_attention layers") + return geometry + + +def _validated_locations( + locations: Sequence[RowLocation], + *, + name: str, + shapes: tuple[tuple[int, int, int], ...], +) -> tuple[RowLocation, ...]: + rendered = tuple(locations) + if any(not isinstance(location, RowLocation) for location in rendered): + raise TypeError(f"{name} must contain only RowLocation values") + if len(set(rendered)) != len(rendered): + raise ValueError(f"{name} must not contain duplicates") + + geometry = {layer_index: (heads, rows) for layer_index, heads, rows in shapes} + for location in rendered: + values = (location.layer_index, location.head_index, location.row_index) + if any(isinstance(value, bool) or not isinstance(value, int) for value in values): + raise ValueError(f"{name} indices must be integers") + layer_shape = geometry.get(location.layer_index) + if layer_shape is None: + raise ValueError(f"{name} references non-recurrent layer {location.layer_index}") + heads, rows = layer_shape + if not (0 <= location.head_index < heads and 0 <= location.row_index < rows): + raise ValueError(f"{name} contains an out-of-range location: {location}") + return tuple(sorted(rendered)) + + +def _resident_bytes( + *, + total_groups: int, + promoted_groups: int, + group_size: int, + scale_bits: int, +) -> tuple[int, int]: + low_payload_bytes = math.ceil(4 * group_size / 8) + high_payload_bytes = math.ceil(8 * group_size / 8) + increment = high_payload_bytes - low_payload_bytes + minimum = total_groups * (low_payload_bytes + scale_bits // 8) + math.ceil(total_groups / 8) + return minimum + promoted_groups * increment, increment + + +def _plan_for_rows( + *, + shapes: tuple[tuple[int, int, int], ...], + promoted_rows: tuple[RowLocation, ...], + target_resident_bytes: int, + group_size: int, + scale_bits: int, +) -> ExactBudgetRowPlan: + scores = { + layer_index: torch.zeros((heads, rows), dtype=torch.float64) + for layer_index, heads, rows in shapes + } + for location in promoted_rows: + scores[location.layer_index][location.head_index, location.row_index] = 1.0 + plan = select_rows_exact_budget( + scores, + target_resident_bytes=target_resident_bytes, + group_size=group_size, + scale_bits=scale_bits, + ) + if plan.high_precision_rows != tuple(sorted(promoted_rows)): + raise RuntimeError("exact-byte selector did not realize the requested intervention rows") + return plan + + +def _metric_values( + metric: TokenMetric, + logits: torch.Tensor, + target_ids: torch.Tensor, +) -> torch.Tensor: + values = metric(logits, target_ids) + if not isinstance(values, torch.Tensor): + raise TypeError("metric must return a torch.Tensor") + if values.shape != target_ids.shape: + raise ValueError( + "metric must return one value per target with shape " + f"{tuple(target_ids.shape)}, got {tuple(values.shape)}" + ) + if not values.is_floating_point(): + raise TypeError("metric values must use a floating-point dtype") + if not torch.isfinite(values).all().item(): + raise ValueError("metric returned a non-finite value") + return values.detach().to(device="cpu", dtype=torch.float64).reshape(-1) + + +def _run_plan( + model: torch.nn.Module, + *, + plan: ExactBudgetRowPlan, + prompt_ids: torch.Tensor, + continuation_ids: torch.Tensor, + metric: TokenMetric, + seed: int, +) -> PhysicalMetricRun: + cache = create_qwen35_exact_budget_cache( + model, + plan=plan, + rounding="nearest", + seed=seed, + record_evidence=True, + ) + values: list[torch.Tensor] = [] + with torch.inference_mode(): + output = model( + prompt_ids, + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + values.append(_metric_values(metric, output.logits, continuation_ids[:, :1])) + + for token_index in range(continuation_ids.shape[1] - 1): + output = model( + continuation_ids[:, token_index : token_index + 1], + past_key_values=cache, + use_cache=True, + logits_to_keep=1, + ) + target = continuation_ids[:, token_index + 1 : token_index + 2] + values.append(_metric_values(metric, output.logits, target)) + + summary = cache.storage_summary() + batch_size = prompt_ids.shape[0] + expected_resident = batch_size * plan.resident_bytes + expected_high_precision = batch_size * plan.promoted_group_count + resident = int(summary["resident_bytes"]) + high_precision = int(summary["high_precision_groups"]) + if resident != expected_resident or high_precision != expected_high_precision: + raise RuntimeError( + "physical cache did not realize the intervention plan: " + f"resident={resident}/{expected_resident}, " + f"high_precision_groups={high_precision}/{expected_high_precision}" + ) + + recurrent_layer_indices = tuple( + layer_index for layer_index, _, _ in plan.score_shapes + ) + forward_count = 1 + (continuation_ids.shape[1] - 1) + expected_update_count = len(recurrent_layer_indices) * forward_count + actual_update_count = len(cache.update_evidence) + update_counts = { + (layer_index, state_index): sum( + evidence.layer_index == layer_index and evidence.state_index == state_index + for evidence in cache.update_evidence + ) + for layer_index in recurrent_layer_indices + for state_index in (0,) + } + expected_update_counts = { + (layer_index, 0): forward_count for layer_index in recurrent_layer_indices + } + if ( + actual_update_count != expected_update_count + or update_counts != expected_update_counts + ): + raise RuntimeError( + "physical cache update trace did not cover one recurrent state per layer " + "for every model forward: " + f"updates={actual_update_count}/{expected_update_count}, " + f"per_layer_state={update_counts}/{expected_update_counts}" + ) + + combined = torch.cat(values) + return PhysicalMetricRun( + mean_metric=float(combined.mean().item()), + token_count=combined.numel(), + resident_bytes=resident, + high_precision_groups=high_precision, + cache_update_count=actual_update_count, + ) + + +def evaluate_physical_row_promotions( + model: torch.nn.Module, + *, + prompt_ids: torch.Tensor, + continuation_ids: torch.Tensor, + candidate_rows: Sequence[RowLocation], + background_rows: Sequence[RowLocation] = (), + intervention_resident_bytes: int | None = None, + scale_bits: int = 16, + seed: int = 2339, + max_candidates: int = 32, + metric: TokenMetric = target_nll_values, + metric_name: str = "target_nll", + lower_is_better: bool = True, +) -> PhysicalRowPromotionOracleResult: + """Measure marginal row promotions through the physical recurrent cache. + + A fresh cache first runs ``background_rows`` as the control. Each candidate + then runs in a separate cache with exactly ``background_rows + candidate``. + Thus all candidate interventions have the same exact resident-byte budget, + while the control is smaller by one INT4-to-INT8 payload increment. The + sequence, targets, nearest-rounding rule, and quantizer seed are held fixed. + + The work scales linearly with ``len(candidate_rows)`` and is intentionally + capped. Because the targets directly define the default NLL metric, use + held-out data for claims and never describe this post-hoc audit as a + deployable oracle. + """ + + if not isinstance(model, torch.nn.Module): + raise TypeError("model must be a torch.nn.Module") + if isinstance(max_candidates, bool) or not isinstance(max_candidates, int): + raise TypeError("max_candidates must be an integer") + if not 1 <= max_candidates <= _MAX_CANDIDATE_CAP: + raise ValueError(f"max_candidates must be between 1 and {_MAX_CANDIDATE_CAP}") + if isinstance(scale_bits, bool) or scale_bits not in (16, 32): + raise ValueError("scale_bits must be 16 or 32") + if isinstance(seed, bool) or not isinstance(seed, int) or seed < 0: + raise ValueError("seed must be a nonnegative integer") + if not callable(metric): + raise TypeError("metric must be callable") + if not isinstance(metric_name, str) or not metric_name.strip(): + raise ValueError("metric_name must be a non-empty string") + if not isinstance(lower_is_better, bool): + raise TypeError("lower_is_better must be a bool") + + _validate_token_inputs(model, prompt_ids, continuation_ids) + shapes = _qwen_row_geometry(model) + candidates = _validated_locations(candidate_rows, name="candidate_rows", shapes=shapes) + background = _validated_locations(background_rows, name="background_rows", shapes=shapes) + if not candidates: + raise ValueError("candidate_rows must not be empty") + if len(candidates) > max_candidates: + raise ValueError( + "candidate_rows has " + f"{len(candidates)} entries, exceeding max_candidates={max_candidates}" + ) + overlap = sorted(set(candidates) & set(background)) + if overlap: + raise ValueError(f"candidate_rows must not overlap background_rows: {overlap}") + + group_size = getattr(model.config, "linear_value_head_dim", None) + if isinstance(group_size, bool) or not isinstance(group_size, int) or group_size <= 0: + raise ValueError("model config has invalid recurrent-state group size") + total_groups = sum(heads * rows for _, heads, rows in shapes) + if len(background) + 1 > total_groups: + raise ValueError("background_rows leaves no row available for an intervention") + + baseline_bytes, promotion_increment = _resident_bytes( + total_groups=total_groups, + promoted_groups=len(background), + group_size=group_size, + scale_bits=scale_bits, + ) + expected_intervention_bytes, _ = _resident_bytes( + total_groups=total_groups, + promoted_groups=len(background) + 1, + group_size=group_size, + scale_bits=scale_bits, + ) + if intervention_resident_bytes is None: + intervention_bytes = expected_intervention_bytes + else: + if isinstance(intervention_resident_bytes, bool) or not isinstance( + intervention_resident_bytes, int + ): + raise TypeError("intervention_resident_bytes must be an integer") + if intervention_resident_bytes != expected_intervention_bytes: + raise ValueError( + "intervention_resident_bytes must exactly encode background_rows plus one " + f"promotion: expected {expected_intervention_bytes}, got " + f"{intervention_resident_bytes}" + ) + intervention_bytes = intervention_resident_bytes + + baseline_plan = _plan_for_rows( + shapes=shapes, + promoted_rows=background, + target_resident_bytes=baseline_bytes, + group_size=group_size, + scale_bits=scale_bits, + ) + baseline = _run_plan( + model, + plan=baseline_plan, + prompt_ids=prompt_ids, + continuation_ids=continuation_ids, + metric=metric, + seed=seed, + ) + + measurements: list[RowPromotionMeasurement] = [] + for candidate in candidates: + promoted = tuple(sorted((*background, candidate))) + plan = _plan_for_rows( + shapes=shapes, + promoted_rows=promoted, + target_resident_bytes=intervention_bytes, + group_size=group_size, + scale_bits=scale_bits, + ) + run = _run_plan( + model, + plan=plan, + prompt_ids=prompt_ids, + continuation_ids=continuation_ids, + metric=metric, + seed=seed, + ) + metric_delta = run.mean_metric - baseline.mean_metric + improvement = -metric_delta if lower_is_better else metric_delta + measurements.append( + RowPromotionMeasurement( + location=candidate, + run=run, + metric_delta=metric_delta, + improvement=improvement, + ) + ) + + return PhysicalRowPromotionOracleResult( + metric_name=metric_name.strip(), + lower_is_better=lower_is_better, + baseline=baseline, + measurements=tuple(measurements), + background_rows=background, + baseline_plan_bytes=baseline_bytes, + intervention_plan_bytes=intervention_bytes, + promotion_increment_bytes=promotion_increment, + ) diff --git a/src/recurquant/mixed_quantization.py b/src/recurquant/mixed_quantization.py new file mode 100644 index 0000000..7f8c5f0 --- /dev/null +++ b/src/recurquant/mixed_quantization.py @@ -0,0 +1,428 @@ +"""Physical mixed-group INT4/INT8 packing for recurrent states. + +The precision decision is stored as one packed bit per quantization group. Each +group owns one stored scale and either an INT4 or INT8 payload. ``storage_bytes`` +counts the resident tensor bytes exactly; Python metadata is not device-resident +model state and is intentionally excluded, matching ``PackedQuantizedTensor``. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +import torch + +from .quantization import ( + QuantizationSpec, + _group_tensor, + _scale_dtype, + _stochastic_round, +) +from .rht import right_rht_decode, right_rht_encode + + +def _validate_mixed_specs( + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, +) -> None: + if low_spec.bits != 4 or high_spec.bits != 8: + raise ValueError("mixed physical packing requires low INT4 and high INT8 specs") + + shared_fields = ( + "group_size", + "scale_bits", + "flatten_last_dims", + "rounding", + "seed", + "epsilon", + ) + mismatched = [ + field for field in shared_fields if getattr(low_spec, field) != getattr(high_spec, field) + ] + if mismatched: + raise ValueError( + "low_spec and high_spec must differ only in bits; mismatched fields: " + + ", ".join(mismatched) + ) + + +def _pack_precision_mask(mask: torch.Tensor) -> torch.Tensor: + flat = mask.reshape(-1).to(torch.uint8) + padding = (-flat.numel()) % 8 + if padding: + flat = torch.nn.functional.pad(flat, (0, padding)) + chunks = flat.reshape(-1, 8).to(torch.int16) + shifts = torch.arange(8, dtype=torch.int16, device=flat.device) + weights = torch.bitwise_left_shift(torch.ones_like(shifts), shifts) + return (chunks * weights).sum(dim=1).to(torch.uint8).contiguous() + + +def _unpack_precision_mask(packed: torch.Tensor, total_groups: int) -> torch.Tensor: + shifts = torch.arange(8, dtype=torch.int16, device=packed.device) + expanded = torch.bitwise_right_shift(packed.to(torch.int16).unsqueeze(1), shifts) + return torch.bitwise_and(expanded, 1).reshape(-1)[:total_groups].to(torch.bool) + + +def _pack_int4_groups(codes: torch.Tensor) -> torch.Tensor: + """Pack signed INT4 codes while preserving a byte boundary per group.""" + + nibbles = torch.bitwise_and(codes.to(torch.int16), 0x0F).to(torch.uint8) + if nibbles.shape[1] % 2: + nibbles = torch.nn.functional.pad(nibbles, (0, 1)) + return torch.bitwise_or( + nibbles[:, 0::2], + torch.bitwise_left_shift(nibbles[:, 1::2], 4), + ).contiguous() + + +def _unpack_int4_groups(payload: torch.Tensor, group_size: int) -> torch.Tensor: + low = torch.bitwise_and(payload, 0x0F).to(torch.int16) + high = torch.bitwise_right_shift(payload, 4).to(torch.int16) + low = torch.where(low >= 8, low - 16, low) + high = torch.where(high >= 8, high - 16, high) + codes = torch.empty( + (payload.shape[0], payload.shape[1] * 2), + dtype=torch.int16, + device=payload.device, + ) + codes[:, 0::2] = low + codes[:, 1::2] = high + return codes[:, :group_size] + + +def _quantize_mixed_groups( + grouped: torch.Tensor, + high_precision_mask: torch.Tensor, + low_spec: QuantizationSpec, +) -> tuple[torch.Tensor, torch.Tensor]: + qmax = torch.where( + high_precision_mask.unsqueeze(1), + torch.tensor(127.0, device=grouped.device), + torch.tensor(7.0, device=grouped.device), + ) + absmax = grouped.abs().amax(dim=-1, keepdim=True) + ideal_scales = torch.where( + absmax > low_spec.epsilon, + absmax / qmax, + torch.ones_like(absmax), + ) + scale_dtype = _scale_dtype(low_spec.scale_bits) + if scale_dtype == torch.float16: + ideal_scales = ideal_scales.clamp( + min=2.0**-24, + max=torch.finfo(torch.float16).max, + ) + stored_scales = ideal_scales.to(scale_dtype) + normalized = grouped / stored_scales.to(torch.float32) + if low_spec.rounding == "nearest": + quantized = torch.round(normalized) + else: + quantized = _stochastic_round(normalized, seed=low_spec.seed) + quantized = torch.minimum(torch.maximum(quantized, -qmax), qmax) + return quantized.to(torch.int16), stored_scales.squeeze(1).contiguous() + + +def _validate_mask_shape( + mask: torch.Tensor, + *, + rows: int, + groups_per_row: int, +) -> None: + if mask.dtype != torch.bool: + raise TypeError("high_precision_mask must use torch.bool") + expected_flat = (rows * groups_per_row,) + expected_grouped = (rows, groups_per_row) + if tuple(mask.shape) not in (expected_flat, expected_grouped): + raise ValueError( + "high_precision_mask must have shape " + f"{expected_flat} or {expected_grouped}, got {tuple(mask.shape)}" + ) + + +@dataclass(frozen=True, slots=True) +class PackedMixedQuantizedTensor: + """A tensor with physically separate INT4 and INT8 group payloads.""" + + low_payload: torch.Tensor + high_payload: torch.Tensor + scales: torch.Tensor + precision_mask: torch.Tensor + low_spec: QuantizationSpec + high_spec: QuantizationSpec + original_shape: tuple[int, ...] + original_dtype: torch.dtype + flattened_size: int + padded_size: int + rows: int + groups_per_row: int + right_rht_layer_index: int | None = None + right_rht_expected_heads: int | None = None + + def __post_init__(self) -> None: + configured = ( + self.right_rht_layer_index is not None, + self.right_rht_expected_heads is not None, + ) + if configured[0] != configured[1]: + raise ValueError( + "right-RHT layer index and expected head count must be configured together" + ) + if configured[0]: + assert self.right_rht_layer_index is not None + assert self.right_rht_expected_heads is not None + if self.right_rht_layer_index < 0: + raise ValueError("right-RHT layer index must be non-negative") + if self.right_rht_expected_heads <= 0: + raise ValueError("right-RHT expected head count must be positive") + if len(self.original_shape) != 4: + raise ValueError("right-RHT packed tensors require rank-four source state") + if self.original_shape[1] != self.right_rht_expected_heads: + raise ValueError( + "right-RHT packed tensor head metadata does not match original shape" + ) + if self.low_spec.flatten_last_dims != 2: + raise ValueError("right-RHT packed tensors require flatten_last_dims=2") + + @property + def elements(self) -> int: + return math.prod(self.original_shape) + + @property + def total_groups(self) -> int: + return self.rows * self.groups_per_row + + @property + def low_precision_groups(self) -> int: + return self.low_payload.shape[0] + + @property + def high_precision_groups(self) -> int: + return self.high_payload.shape[0] + + @property + def payload_bytes(self) -> int: + return self.low_payload.numel() * self.low_payload.element_size() + ( + self.high_payload.numel() * self.high_payload.element_size() + ) + + @property + def scale_bytes(self) -> int: + return self.scales.numel() * self.scales.element_size() + + @property + def mask_bytes(self) -> int: + return self.precision_mask.numel() * self.precision_mask.element_size() + + @property + def storage_bytes(self) -> int: + return self.payload_bytes + self.scale_bytes + self.mask_bytes + + def high_precision_mask(self) -> torch.Tensor: + """Return the unpacked mask in ``[rows, groups_per_row]`` order.""" + + return _unpack_precision_mask(self.precision_mask, self.total_groups).reshape( + self.rows, + self.groups_per_row, + ) + + def _integer_groups(self) -> torch.Tensor: + mask = self.high_precision_mask().reshape(-1) + codes = torch.empty( + (self.total_groups, self.low_spec.group_size), + dtype=torch.int16, + device=self.scales.device, + ) + codes[~mask] = _unpack_int4_groups( + self.low_payload, + self.low_spec.group_size, + ) + codes[mask] = self.high_payload.to(torch.int16) + return codes + + def dequantize(self) -> torch.Tensor: + """Materialize the mixed packed tensor in its original dtype and shape.""" + + restored_groups = self._integer_groups().to(torch.float32) * self.scales.to( + torch.float32 + ).unsqueeze(1) + restored = restored_groups.reshape(self.rows, self.padded_size)[:, : self.flattened_size] + restored = restored.reshape(self.original_shape) + if self.right_rht_layer_index is not None: + assert self.right_rht_expected_heads is not None + return right_rht_decode( + restored, + layer_index=self.right_rht_layer_index, + expected_heads=self.right_rht_expected_heads, + output_dtype=self.original_dtype, + ) + return restored.to(self.original_dtype) + + def to(self, device: torch.device | str) -> PackedMixedQuantizedTensor: + """Move all resident tensors without materializing or requantizing.""" + + return PackedMixedQuantizedTensor( + low_payload=self.low_payload.to(device), + high_payload=self.high_payload.to(device), + scales=self.scales.to(device), + precision_mask=self.precision_mask.to(device), + low_spec=self.low_spec, + high_spec=self.high_spec, + original_shape=self.original_shape, + original_dtype=self.original_dtype, + flattened_size=self.flattened_size, + padded_size=self.padded_size, + rows=self.rows, + groups_per_row=self.groups_per_row, + right_rht_layer_index=self.right_rht_layer_index, + right_rht_expected_heads=self.right_rht_expected_heads, + ) + + def reorder_batch(self, beam_idx: torch.LongTensor) -> PackedMixedQuantizedTensor: + """Select packed batch entries without another quantization round trip.""" + + if len(self.original_shape) <= self.low_spec.flatten_last_dims: + raise RuntimeError( + "Cannot reorder mixed groups when quantization includes the batch dimension" + ) + if beam_idx.ndim != 1 or beam_idx.dtype not in (torch.int32, torch.int64): + raise TypeError("beam_idx must be a one-dimensional int32 or int64 tensor") + if self.right_rht_layer_index is not None and beam_idx.numel() == 0: + raise ValueError("right-RHT packed tensors cannot reorder to an empty batch") + + batch_size = self.original_shape[0] + if self.rows % batch_size: + raise RuntimeError("mixed row metadata is inconsistent with its batch dimension") + if beam_idx.numel() and (beam_idx.min().item() < 0 or beam_idx.max().item() >= batch_size): + raise IndexError("beam_idx contains an out-of-range batch index") + + groups_per_batch = self.total_groups // batch_size + selected = beam_idx.to(self.scales.device) + selected_batch_size = selected.numel() + codes = self._integer_groups().reshape( + batch_size, + groups_per_batch, + self.low_spec.group_size, + ) + scales = self.scales.reshape(batch_size, groups_per_batch) + mask = self.high_precision_mask().reshape(batch_size, groups_per_batch) + + reordered_codes = codes.index_select(0, selected).reshape( + selected_batch_size * groups_per_batch, + self.low_spec.group_size, + ) + reordered_scales = scales.index_select(0, selected).reshape(-1).contiguous() + reordered_mask = mask.index_select(0, selected).reshape(-1) + return _from_integer_groups( + reordered_codes, + reordered_scales, + reordered_mask, + low_spec=self.low_spec, + high_spec=self.high_spec, + original_shape=(selected_batch_size, *self.original_shape[1:]), + original_dtype=self.original_dtype, + flattened_size=self.flattened_size, + padded_size=self.padded_size, + rows=selected_batch_size * (self.rows // batch_size), + groups_per_row=self.groups_per_row, + right_rht_layer_index=self.right_rht_layer_index, + right_rht_expected_heads=self.right_rht_expected_heads, + ) + + +def _from_integer_groups( + codes: torch.Tensor, + scales: torch.Tensor, + high_precision_mask: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + original_shape: tuple[int, ...], + original_dtype: torch.dtype, + flattened_size: int, + padded_size: int, + rows: int, + groups_per_row: int, + right_rht_layer_index: int | None = None, + right_rht_expected_heads: int | None = None, +) -> PackedMixedQuantizedTensor: + flat_mask = high_precision_mask.reshape(-1) + return PackedMixedQuantizedTensor( + low_payload=_pack_int4_groups(codes[~flat_mask]), + high_payload=codes[flat_mask].to(torch.int8).contiguous(), + scales=scales.contiguous(), + precision_mask=_pack_precision_mask(flat_mask), + low_spec=low_spec, + high_spec=high_spec, + original_shape=original_shape, + original_dtype=original_dtype, + flattened_size=flattened_size, + padded_size=padded_size, + rows=rows, + groups_per_row=groups_per_row, + right_rht_layer_index=right_rht_layer_index, + right_rht_expected_heads=right_rht_expected_heads, + ) + + +def quantize_pack_mixed( + tensor: torch.Tensor, + high_precision_mask: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + right_rht_layer_index: int | None = None, + right_rht_expected_heads: int | None = None, +) -> PackedMixedQuantizedTensor: + """Quantize groups into physically separate INT4 and INT8 payload pools. + + ``high_precision_mask`` may be flat or shaped ``[rows, groups_per_row]`` in + the exact group order produced by the shared grouped quantizer. + """ + + _validate_mixed_specs(low_spec, high_spec) + configured = ( + right_rht_layer_index is not None, + right_rht_expected_heads is not None, + ) + if configured[0] != configured[1]: + raise ValueError( + "right-RHT layer index and expected head count must be configured together" + ) + source = tensor + if right_rht_layer_index is not None: + assert right_rht_expected_heads is not None + source = right_rht_encode( + tensor, + layer_index=right_rht_layer_index, + expected_heads=right_rht_expected_heads, + output_dtype=torch.float32, + ) + working, grouped_dtype, flattened_size, padded_size, groups_per_row, grouped = _group_tensor( + source, low_spec + ) + original_dtype = tensor.dtype if configured[0] else grouped_dtype + rows = grouped.shape[0] + _validate_mask_shape( + high_precision_mask, + rows=rows, + groups_per_row=groups_per_row, + ) + flat_mask = high_precision_mask.detach().to(grouped.device).reshape(-1) + flat_groups = grouped.reshape(-1, low_spec.group_size) + codes, scales = _quantize_mixed_groups(flat_groups, flat_mask, low_spec) + return _from_integer_groups( + codes, + scales, + flat_mask, + low_spec=low_spec, + high_spec=high_spec, + original_shape=tuple(working.shape), + original_dtype=original_dtype, + flattened_size=flattened_size, + padded_size=padded_size, + rows=rows, + groups_per_row=groups_per_row, + right_rht_layer_index=right_rht_layer_index, + right_rht_expected_heads=right_rht_expected_heads, + ) diff --git a/src/recurquant/model_fisher.py b/src/recurquant/model_fisher.py new file mode 100644 index 0000000..6b966d5 --- /dev/null +++ b/src/recurquant/model_fisher.py @@ -0,0 +1,194 @@ +"""Label-free model-Fisher risk for recurrent-state row blocks. + +For a model-sampled pseudo-label ``y`` and a recurrent state ``S``, define the +score gradient ``h = grad_S log p(y | context)``. The local model-Fisher risk +of an endpoint error ``e`` is ``0.5 * E_y[(h dot e)^2]``. This module compares +the aligned errors of two quantized endpoints row by row: + +``0.5 * mean[(h dot e4)^2 - (h dot e8)^2]``. + +The dot product is taken over the complete value dimension of each state row +before it is squared. Consequently, interactions between coordinates within +a row are retained. Cross-row interactions are deliberately excluded because +the result is a row-allocation score, not the full-state quadratic form. + +The scoring function is pure and assumes its gradients came from pseudo-labels +sampled from the model distribution. :func:`sample_model_pseudo_labels` +provides reproducible samples when passed an explicitly seeded generator. +Computing the corresponding per-example gradients is left to the model adapter +so this primitive does not mutate or retain a live Transformers cache. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + + +@dataclass(frozen=True, slots=True) +class RowBlockModelFisherRisk: + """Model-Fisher endpoint risks and signed INT4-to-INT8 risk reduction. + + Each tensor has shape ``[heads, key_dim]``. Positive + ``risk_difference`` values favor promoting a row from INT4 to INT8; + negative values are valid and are intentionally not clamped. + """ + + int4_risk: torch.Tensor + int8_risk: torch.Tensor + risk_difference: torch.Tensor + probes: int + batch_size: int + + @property + def shape(self) -> torch.Size: + """Return the shared row-score shape.""" + + return self.risk_difference.shape + + +def _generator_device(generator: torch.Generator) -> torch.device: + return torch.device(generator.device) + + +def sample_model_pseudo_labels( + logits: torch.Tensor, + *, + probes: int, + generator: torch.Generator, +) -> torch.Tensor: + """Sample categorical pseudo-labels from the unmodified model distribution. + + ``logits`` must have shape ``[batch, vocabulary]``. The returned labels + have shape ``[probes, batch]``. No temperature, top-k, or top-p transform + is applied because those would define a different Fisher distribution. + + Reproducibility is explicit: callers should construct a generator on the + same device, seed it, and record both its initial seed and the sampled label + tensor. Reusing a generator advances its state; two independently seeded + generators with the same seed produce the same samples on the same PyTorch + backend and version. + """ + + if logits.ndim != 2: + raise ValueError("logits must have shape [batch, vocabulary]") + if logits.shape[0] == 0 or logits.shape[1] == 0: + raise ValueError("logits must contain at least one batch item and vocabulary entry") + if isinstance(probes, bool) or not isinstance(probes, int) or probes <= 0: + raise ValueError("probes must be a positive integer") + if not isinstance(generator, torch.Generator): + raise TypeError("generator must be an explicitly seeded torch.Generator") + if not logits.is_floating_point(): + raise TypeError("logits must use a floating-point dtype") + if logits.device.type == "meta": + raise ValueError("logits must be materialized") + if _generator_device(generator) != logits.device: + raise ValueError("generator and logits must be on the same device") + if not torch.isfinite(logits).all().item(): + raise ValueError("logits must contain only finite values") + + probability_dtype = torch.float64 if logits.dtype == torch.float64 else torch.float32 + probabilities = torch.softmax(logits.detach().to(probability_dtype), dim=-1) + labels = torch.multinomial( + probabilities, + num_samples=probes, + replacement=True, + generator=generator, + ) + return labels.transpose(0, 1).contiguous().detach() + + +def _validate_row_risk_inputs( + pseudo_label_score_gradients: torch.Tensor, + int4_errors: torch.Tensor, + int8_errors: torch.Tensor, +) -> None: + if pseudo_label_score_gradients.ndim != 5: + raise ValueError( + "pseudo_label_score_gradients must have shape " + "[probes, batch, heads, key_dim, value_dim]" + ) + if int4_errors.ndim != 4 or int8_errors.ndim != 4: + raise ValueError( + "int4_errors and int8_errors must have shape [batch, heads, key_dim, value_dim]" + ) + if pseudo_label_score_gradients.shape[1:] != int4_errors.shape: + raise ValueError("gradient dimensions after probes must match the aligned INT4 error shape") + if int8_errors.shape != int4_errors.shape: + raise ValueError("INT4 and INT8 errors must have the same shape") + if any(size == 0 for size in pseudo_label_score_gradients.shape): + raise ValueError("probe, batch, head, key, and value dimensions must be non-empty") + + tensors = ( + pseudo_label_score_gradients, + int4_errors, + int8_errors, + ) + if not all(tensor.is_floating_point() for tensor in tensors): + raise TypeError("gradients and endpoint errors must use floating-point dtypes") + if any(tensor.dtype not in (torch.float32, torch.float64) for tensor in tensors): + raise TypeError("gradients and endpoint errors must use torch.float32 or torch.float64") + if len({tensor.dtype for tensor in tensors}) != 1: + raise TypeError("gradients and endpoint errors must use the same dtype") + if len({tensor.device for tensor in tensors}) != 1: + raise ValueError("gradients and endpoint errors must be on the same device") + if pseudo_label_score_gradients.device.type == "meta": + raise ValueError("gradients and endpoint errors must be materialized") + if not all(torch.isfinite(tensor).all().item() for tensor in tensors): + raise ValueError("gradients and endpoint errors must contain only finite values") + + +def row_block_model_fisher_risk( + pseudo_label_score_gradients: torch.Tensor, + int4_errors: torch.Tensor, + int8_errors: torch.Tensor, +) -> RowBlockModelFisherRisk: + """Compute signed label-free model-Fisher risk reduction per state row. + + ``pseudo_label_score_gradients`` has shape + ``[probes, batch, heads, key_dim, value_dim]``. Probe ``p`` must be the + score gradient for a label independently sampled from the model predictive + distribution, not a dataset target. The sign of the score gradient is + immaterial after squaring, so gradients of pseudo-label NLL are equivalent. + + ``int4_errors`` and ``int8_errors`` have shape + ``[batch, heads, key_dim, value_dim]`` and must be aligned errors relative + to the same full-precision endpoint, for example ``Q4(S) - S`` and + ``Q8(S) - S``. Errors are shared across probes because every pseudo-label + is drawn for the same endpoint. + + The result averages equally over probes and batch items. It does not use a + gold target, square ``h dot (e4 - e8)``, replace the row-block quadratic by + a coordinate-diagonal approximation, or clamp negative differences. + """ + + _validate_row_risk_inputs( + pseudo_label_score_gradients, + int4_errors, + int8_errors, + ) + gradients = pseudo_label_score_gradients.detach() + error4 = int4_errors.detach().unsqueeze(0) + error8 = int8_errors.detach().unsqueeze(0) + + projection4 = (gradients * error4).sum(dim=-1) + projection8 = (gradients * error8).sum(dim=-1) + per_probe_int4_risk = 0.5 * projection4.square() + per_probe_int8_risk = 0.5 * projection8.square() + per_probe_difference = 0.5 * (projection4.square() - projection8.square()) + + int4_risk = per_probe_int4_risk.mean(dim=(0, 1)) + int8_risk = per_probe_int8_risk.mean(dim=(0, 1)) + risk_difference = per_probe_difference.mean(dim=(0, 1)) + outputs = (int4_risk, int8_risk, risk_difference) + if not all(torch.isfinite(tensor).all().item() for tensor in outputs): + raise RuntimeError("model-Fisher row risk became non-finite") + + return RowBlockModelFisherRisk( + int4_risk=int4_risk.detach(), + int8_risk=int8_risk.detach(), + risk_difference=risk_difference.detach(), + probes=pseudo_label_score_gradients.shape[0], + batch_size=pseudo_label_score_gradients.shape[1], + ) diff --git a/src/recurquant/multibit_policy.py b/src/recurquant/multibit_policy.py new file mode 100644 index 0000000..8cd307d --- /dev/null +++ b/src/recurquant/multibit_policy.py @@ -0,0 +1,256 @@ +"""Correctness-first exact-budget policy for row-wise INT4/INT6/INT8. + +The allocator in this module is deliberately a CPU reference implementation. +For ``N`` rows and an exact marginal-step budget ``K``, it uses ``O(NK)`` time, +``O(NK)`` bytes for backtracking choices, and ``O(K)`` floating-point working +memory. It makes no runtime or deployment claim. + +Each output code consumes zero, one, or two 2-bit marginal steps: + +``0 -> INT4``, ``1 -> INT6``, and ``2 -> INT8``. + +The dynamic program chooses among all three complete row states. It therefore +remains globally optimal when a row's INT6-to-INT8 benefit exceeds its +INT4-to-INT6 benefit, where sorting individual marginal gains would violate the +required precedence. +""" + +from __future__ import annotations + +import math +from types import MappingProxyType + +import numpy as np +import torch + +FROZEN_QWEN35_INT8_ROW_QUOTAS = MappingProxyType( + { + 0: 355, + 1: 380, + 2: 269, + 4: 179, + 5: 185, + 6: 105, + 8: 80, + 9: 43, + 10: 84, + 12: 30, + 13: 62, + 14: 54, + 16: 45, + 17: 27, + 18: 7, + 20: 9, + 21: 7, + 22: 55, + } +) + +QWEN35_ROWS_PER_LINEAR_LAYER = 16 * 128 +QWEN35_VALUE_DIM = 128 +QWEN35_SCALE_BYTES_PER_ROW = 2 +QWEN35_OLD_PRECISION_BITS_PER_ROW = 1 +QWEN35_MULTIBIT_PRECISION_BITS_PER_ROW = 2 +QWEN35_MARGINAL_STEP_BYTES = 32 +QWEN35_FROZEN_TOTAL_MARGINAL_STEPS = 3_808 +QWEN35_FROZEN_TOTAL_STATE_BYTES = 2_564_096 + + +def _validate_distortion_tensor( + tensor: torch.Tensor, + *, + name: str, + expected_shape: tuple[int, int] | None, +) -> tuple[int, int]: + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"{name} must be a torch.Tensor") + if tensor.ndim != 2 or tensor.numel() == 0: + raise ValueError(f"{name} must have non-empty shape [heads, rows]") + if not tensor.is_floating_point(): + raise TypeError(f"{name} must use a floating-point dtype") + shape = (tensor.shape[0], tensor.shape[1]) + if expected_shape is not None and shape != expected_shape: + raise ValueError( + f"D4, D6, and D8 must have identical shapes; {name} has {shape}, " + f"expected {expected_shape}" + ) + if not torch.isfinite(tensor).all().item(): + raise ValueError(f"{name} must contain only finite values") + if (tensor < 0).any().item(): + raise ValueError(f"{name} must contain only nonnegative values") + return shape + + +def allocate_exact_multibit_codes( + d4: torch.Tensor, + d6: torch.Tensor, + d8: torch.Tensor, + *, + marginal_steps: int, +) -> torch.Tensor: + """Minimize exact total distortion under a 0/1/2 step budget. + + ``d4``, ``d6``, and ``d8`` contain the complete weighted distortion for + representing each row at INT4, INT6, and INT8. All tensors must have the + same non-empty ``[heads, rows]`` shape and contain finite, nonnegative + floating-point values. + + The returned CPU ``torch.uint8`` tensor has the input shape, uses codes + ``0``/``1``/``2`` for INT4/INT6/INT8, and has an exact code sum equal to + ``marginal_steps``. Among solutions with exactly equal CPU-FP64 objective, + the lexicographically greatest flattened code vector is chosen: lower + flattened row indices receive higher precision first. + + This is a correctness/reference path. Its complexity is ``O(NK)`` time, + ``O(NK)`` choice bytes, and ``O(K)`` FP64 workspace for ``N`` rows and + budget ``K``. + """ + + shape = _validate_distortion_tensor(d4, name="D4", expected_shape=None) + _validate_distortion_tensor(d6, name="D6", expected_shape=shape) + _validate_distortion_tensor(d8, name="D8", expected_shape=shape) + if isinstance(marginal_steps, bool) or not isinstance(marginal_steps, int): + raise TypeError("marginal_steps must be an integer") + + total_rows = math.prod(shape) + if not 0 <= marginal_steps <= 2 * total_rows: + raise ValueError( + "marginal_steps must be in the exact representable range " + f"[0, {2 * total_rows}]" + ) + + if marginal_steps == 0: + return torch.zeros(shape, dtype=torch.uint8) + if marginal_steps == 2 * total_rows: + return torch.full(shape, 2, dtype=torch.uint8) + + flat_distortions = np.stack( + [ + tensor.detach().to(device="cpu", dtype=torch.float64).reshape(-1).numpy() + for tensor in (d4, d6, d8) + ], + axis=1, + ) + + # Removing a row-wise constant leaves every allocation objective difference + # unchanged. Scaling the remaining nonnegative costs prevents overflow while + # retaining the exact discrete optimization problem in CPU FP64. + flat_distortions -= flat_distortions.min(axis=1, keepdims=True) + maximum = float(flat_distortions.max()) + if maximum > 0.0: + flat_distortions /= maximum + + unreachable = np.inf + next_cost = np.full(marginal_steps + 1, unreachable, dtype=np.float64) + next_cost[0] = 0.0 + choices = np.full( + (total_rows, marginal_steps + 1), + np.iinfo(np.uint8).max, + dtype=np.uint8, + ) + + # Build suffix optima. Iterating rows backwards makes an equal-cost choice + # of the larger current code exactly equivalent to lexicographic priority + # for lower flattened row indices. + for row_index in range(total_rows - 1, -1, -1): + suffix_rows = total_rows - row_index - 1 + next_maximum_steps = min(marginal_steps, 2 * suffix_rows) + current_cost = np.full(marginal_steps + 1, unreachable, dtype=np.float64) + current_choice = choices[row_index] + + for code in (0, 1, 2): + available = min(next_maximum_steps, marginal_steps - code) + if available < 0: + continue + destination = slice(code, code + available + 1) + candidate = ( + next_cost[: available + 1] + flat_distortions[row_index, code] + ) + incumbent = current_cost[destination] + incumbent_code = current_choice[destination] + update = np.isfinite(candidate) & ( + (candidate < incumbent) + | ((candidate == incumbent) & (code > incumbent_code)) + ) + incumbent[update] = candidate[update] + incumbent_code[update] = code + + next_cost = current_cost + + if not np.isfinite(next_cost[marginal_steps]): + raise RuntimeError("exact multibit dynamic program did not reach the requested budget") + + flat_codes = np.empty(total_rows, dtype=np.uint8) + remaining = marginal_steps + invalid_choice = np.iinfo(np.uint8).max + for row_index in range(total_rows): + code = int(choices[row_index, remaining]) + if code == invalid_choice: + raise RuntimeError("exact multibit dynamic-program backtracking is incomplete") + flat_codes[row_index] = code + remaining -= code + if remaining != 0: + raise RuntimeError("exact multibit dynamic-program backtracking changed the budget") + + return torch.from_numpy(flat_codes.reshape(shape).copy()) + + +def frozen_qwen35_multibit_step_budgets() -> dict[int, int]: + """Return the exact per-layer 2-bit-step budgets matching frozen Q4/Q8 bytes. + + A Qwen3.5-0.8B Gated DeltaNet layer contains 2,048 row groups. Replacing its + one-bit Q4/Q8 mask with a two-bit Q4/Q6/Q8 code costs 256 additional bytes, + equal to eight 32-byte marginal steps. Therefore a layer with frozen INT8 + row quota ``q`` receives ``K = 2*q - 8`` steps. + + The helper validates every old/new per-layer byte count, the 3,808-step + global sum, and the 2,564,096-byte global state total before returning. + """ + + budgets = { + layer_index: 2 * quota - 8 + for layer_index, quota in FROZEN_QWEN35_INT8_ROW_QUOTAS.items() + } + if any(not 0 <= steps <= 2 * QWEN35_ROWS_PER_LINEAR_LAYER for steps in budgets.values()): + raise RuntimeError("frozen Qwen3.5 multibit step budget is outside [0, 2N]") + if sum(budgets.values()) != QWEN35_FROZEN_TOTAL_MARGINAL_STEPS: + raise RuntimeError("frozen Qwen3.5 multibit step budgets do not sum to 3,808") + + q4_payload_bytes = math.ceil(4 * QWEN35_VALUE_DIM / 8) + q8_payload_bytes = math.ceil(8 * QWEN35_VALUE_DIM / 8) + old_mask_bytes = math.ceil( + QWEN35_ROWS_PER_LINEAR_LAYER * QWEN35_OLD_PRECISION_BITS_PER_ROW / 8 + ) + multibit_mask_bytes = math.ceil( + QWEN35_ROWS_PER_LINEAR_LAYER * QWEN35_MULTIBIT_PRECISION_BITS_PER_ROW / 8 + ) + base_payload_and_scales = QWEN35_ROWS_PER_LINEAR_LAYER * ( + q4_payload_bytes + QWEN35_SCALE_BYTES_PER_ROW + ) + + old_total = 0 + multibit_total = 0 + for layer_index, quota in FROZEN_QWEN35_INT8_ROW_QUOTAS.items(): + old_bytes = ( + base_payload_and_scales + + old_mask_bytes + + quota * (q8_payload_bytes - q4_payload_bytes) + ) + multibit_bytes = ( + base_payload_and_scales + + multibit_mask_bytes + + budgets[layer_index] * QWEN35_MARGINAL_STEP_BYTES + ) + if old_bytes != multibit_bytes: + raise RuntimeError( + f"frozen Qwen3.5 layer {layer_index} byte parity failed: " + f"{old_bytes} != {multibit_bytes}" + ) + old_total += old_bytes + multibit_total += multibit_bytes + + if old_total != QWEN35_FROZEN_TOTAL_STATE_BYTES: + raise RuntimeError("frozen Qwen3.5 Q4/Q8 state bytes do not equal 2,564,096") + if multibit_total != QWEN35_FROZEN_TOTAL_STATE_BYTES: + raise RuntimeError("frozen Qwen3.5 multibit state bytes do not equal 2,564,096") + return budgets diff --git a/src/recurquant/multibit_quantization.py b/src/recurquant/multibit_quantization.py new file mode 100644 index 0000000..16a5fe9 --- /dev/null +++ b/src/recurquant/multibit_quantization.py @@ -0,0 +1,685 @@ +"""Physical mixed-group INT4/INT6/INT8 packing for recurrent states. + +Each quantization group selects one of three symmetric signed precisions: +``0 = INT4``, ``1 = INT6``, and ``2 = INT8``. Four precision codes share one +byte, every group owns exactly one stored scale, and integer payloads are kept +in separate width-specific pools. ``storage_bytes`` counts the resident tensor +bytes exactly; Python metadata is intentionally excluded. + +INT6 uses a little-endian bit stream within each group: four two's-complement +6-bit codes occupy three bytes. Consequently, a 128-element INT6 group occupies +exactly 96 payload bytes. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +import torch + +from .quantization import ( + QuantizationSpec, + _group_tensor, + _scale_dtype, + _stochastic_round, +) + +INT4_PRECISION_CODE = 0 +INT6_PRECISION_CODE = 1 +INT8_PRECISION_CODE = 2 +_BITS_BY_PRECISION_CODE = (4, 6, 8) +_INTEGER_DTYPES = { + torch.uint8, + torch.int8, + torch.int16, + torch.int32, + torch.int64, +} + + +def _validate_multibit_specs( + int4_spec: QuantizationSpec, + int6_spec: QuantizationSpec, + int8_spec: QuantizationSpec, +) -> None: + specs = (int4_spec, int6_spec, int8_spec) + expected_bits = _BITS_BY_PRECISION_CODE + for label, spec, bits in zip( + ("int4_spec", "int6_spec", "int8_spec"), + specs, + expected_bits, + strict=True, + ): + if not isinstance(spec, QuantizationSpec): + raise TypeError(f"{label} must be a QuantizationSpec") + if spec.bits != bits: + raise ValueError(f"{label} must use {bits} bits") + + shared_fields = ( + "group_size", + "scale_bits", + "flatten_last_dims", + "rounding", + "seed", + "epsilon", + ) + mismatched = [ + field + for field in shared_fields + if any(getattr(spec, field) != getattr(int4_spec, field) for spec in specs[1:]) + ] + if mismatched: + raise ValueError( + "INT4, INT6, and INT8 specs must differ only in bits; mismatched fields: " + + ", ".join(mismatched) + ) + + non_byte_widths = [ + bits for bits in expected_bits if (int4_spec.group_size * bits) % 8 != 0 + ] + if non_byte_widths: + widths = ", ".join(f"INT{bits}" for bits in non_byte_widths) + raise ValueError( + f"group_size={int4_spec.group_size} does not give whole-byte payloads for {widths}" + ) + + +def _validate_precision_code_shape( + precision_codes: torch.Tensor, + *, + rows: int, + groups_per_row: int, +) -> None: + if not isinstance(precision_codes, torch.Tensor): + raise TypeError("precision_codes must be a torch.Tensor") + if precision_codes.dtype != torch.uint8: + raise TypeError("precision_codes must use torch.uint8") + + expected_flat = (rows * groups_per_row,) + expected_grouped = (rows, groups_per_row) + if tuple(precision_codes.shape) not in (expected_flat, expected_grouped): + raise ValueError( + "precision_codes must have shape " + f"{expected_flat} or {expected_grouped}, got {tuple(precision_codes.shape)}" + ) + if (precision_codes > INT8_PRECISION_CODE).any().item(): + raise ValueError("precision_codes may contain only 0=INT4, 1=INT6, or 2=INT8") + + +def _pack_precision_codes(precision_codes: torch.Tensor) -> torch.Tensor: + """Pack four canonical two-bit precision codes into each byte.""" + + if not isinstance(precision_codes, torch.Tensor): + raise TypeError("precision_codes must be a torch.Tensor") + if precision_codes.dtype != torch.uint8: + raise TypeError("precision_codes must use torch.uint8") + if (precision_codes > INT8_PRECISION_CODE).any().item(): + raise ValueError("precision code 3 is reserved and must be rejected") + + flat = precision_codes.reshape(-1) + padding = (-flat.numel()) % 4 + if padding: + flat = torch.nn.functional.pad(flat, (0, padding)) + chunks = flat.reshape(flat.numel() // 4, 4).to(torch.int16) + packed = ( + chunks[:, 0] + | torch.bitwise_left_shift(chunks[:, 1], 2) + | torch.bitwise_left_shift(chunks[:, 2], 4) + | torch.bitwise_left_shift(chunks[:, 3], 6) + ) + return packed.to(torch.uint8).contiguous() + + +def _unpack_precision_codes( + packed: torch.Tensor, + total_groups: int, +) -> torch.Tensor: + """Unpack and validate the canonical two-bit precision stream.""" + + if not isinstance(packed, torch.Tensor): + raise TypeError("packed precision codes must be a torch.Tensor") + if packed.dtype != torch.uint8 or packed.ndim != 1: + raise TypeError("packed precision codes must be a one-dimensional torch.uint8 tensor") + if not isinstance(total_groups, int) or total_groups < 0: + raise ValueError("total_groups must be a non-negative integer") + + expected_bytes = math.ceil(total_groups / 4) + if packed.numel() != expected_bytes: + raise ValueError( + f"packed precision stream must contain {expected_bytes} bytes, got {packed.numel()}" + ) + + shifts = torch.tensor((0, 2, 4, 6), dtype=torch.int16, device=packed.device) + expanded = torch.bitwise_right_shift(packed.to(torch.int16).unsqueeze(1), shifts) + all_codes = torch.bitwise_and(expanded, 0x03).reshape(-1).to(torch.uint8) + codes = all_codes[:total_groups] + if (codes == 3).any().item(): + raise ValueError("packed precision stream contains reserved precision code 3") + if (all_codes[total_groups:] != 0).any().item(): + raise ValueError("unused precision-code padding bits must be zero") + return codes + + +def _validate_integer_groups( + codes: torch.Tensor, + *, + bits: int, +) -> None: + if not isinstance(codes, torch.Tensor): + raise TypeError("integer codes must be a torch.Tensor") + if codes.dtype not in _INTEGER_DTYPES: + raise TypeError("integer codes must use an integer dtype") + if codes.ndim != 2: + raise ValueError("integer codes must have shape [groups, group_size]") + if (codes.shape[1] * bits) % 8: + raise ValueError(f"INT{bits} group payload must occupy a whole number of bytes") + + qmax = (1 << (bits - 1)) - 1 + if codes.numel() and (codes.min().item() < -qmax or codes.max().item() > qmax): + raise ValueError(f"INT{bits} symmetric codes must lie in [{-qmax}, {qmax}]") + + +def _pack_int4_groups(codes: torch.Tensor) -> torch.Tensor: + """Pack signed symmetric INT4 groups, preserving each group boundary.""" + + _validate_integer_groups(codes, bits=4) + nibbles = torch.bitwise_and(codes.to(torch.int16), 0x0F) + return ( + nibbles[:, 0::2] | torch.bitwise_left_shift(nibbles[:, 1::2], 4) + ).to(torch.uint8).contiguous() + + +def _unpack_int4_groups(payload: torch.Tensor, group_size: int) -> torch.Tensor: + if payload.dtype != torch.uint8 or payload.ndim != 2: + raise TypeError("INT4 payload must be a two-dimensional torch.uint8 tensor") + expected_width = group_size * 4 // 8 + if group_size <= 0 or group_size * 4 % 8 or payload.shape[1] != expected_width: + raise ValueError("INT4 payload shape is inconsistent with group_size") + + low = torch.bitwise_and(payload, 0x0F).to(torch.int16) + high = torch.bitwise_right_shift(payload, 4).to(torch.int16) + low = torch.where(low >= 8, low - 16, low) + high = torch.where(high >= 8, high - 16, high) + codes = torch.empty( + (payload.shape[0], group_size), + dtype=torch.int16, + device=payload.device, + ) + codes[:, 0::2] = low + codes[:, 1::2] = high + if codes.numel() and (codes == -8).any().item(): + raise ValueError("INT4 payload contains the reserved symmetric code -8") + return codes + + +def _pack_int6_groups(codes: torch.Tensor) -> torch.Tensor: + """Pack four signed two's-complement INT6 codes into three bytes.""" + + _validate_integer_groups(codes, bits=6) + unsigned = torch.bitwise_and(codes.to(torch.int16), 0x3F).reshape( + codes.shape[0], + codes.shape[1] // 4, + 4, + ) + first = unsigned[:, :, 0] + second = unsigned[:, :, 1] + third = unsigned[:, :, 2] + fourth = unsigned[:, :, 3] + + byte0 = first | torch.bitwise_left_shift(torch.bitwise_and(second, 0x03), 6) + byte1 = torch.bitwise_right_shift(second, 2) | torch.bitwise_left_shift( + torch.bitwise_and(third, 0x0F), + 4, + ) + byte2 = torch.bitwise_right_shift(third, 4) | torch.bitwise_left_shift(fourth, 2) + return ( + torch.stack((byte0, byte1, byte2), dim=-1) + .reshape(codes.shape[0], codes.shape[1] * 6 // 8) + .to(torch.uint8) + .contiguous() + ) + + +def _unpack_int6_groups(payload: torch.Tensor, group_size: int) -> torch.Tensor: + """Unpack group-aligned three-byte blocks into signed INT6 codes.""" + + if payload.dtype != torch.uint8 or payload.ndim != 2: + raise TypeError("INT6 payload must be a two-dimensional torch.uint8 tensor") + expected_width = group_size * 6 // 8 + if group_size <= 0 or group_size * 6 % 8 or payload.shape[1] != expected_width: + raise ValueError("INT6 payload shape is inconsistent with group_size") + + triples = payload.reshape(payload.shape[0], group_size // 4, 3).to(torch.int16) + byte0 = triples[:, :, 0] + byte1 = triples[:, :, 1] + byte2 = triples[:, :, 2] + first = torch.bitwise_and(byte0, 0x3F) + second = torch.bitwise_right_shift(byte0, 6) | torch.bitwise_left_shift( + torch.bitwise_and(byte1, 0x0F), + 2, + ) + third = torch.bitwise_right_shift(byte1, 4) | torch.bitwise_left_shift( + torch.bitwise_and(byte2, 0x03), + 4, + ) + fourth = torch.bitwise_right_shift(byte2, 2) + unsigned = torch.stack((first, second, third, fourth), dim=-1).reshape( + payload.shape[0], + group_size, + ) + codes = torch.where(unsigned >= 32, unsigned - 64, unsigned).to(torch.int16) + if codes.numel() and (codes == -32).any().item(): + raise ValueError("INT6 payload contains the reserved symmetric code -32") + return codes + + +def _quantize_multibit_groups( + grouped: torch.Tensor, + precision_codes: torch.Tensor, + spec: QuantizationSpec, +) -> tuple[torch.Tensor, torch.Tensor]: + qmax_lookup = torch.tensor((7.0, 31.0, 127.0), device=grouped.device) + qmax = qmax_lookup[precision_codes.to(torch.long)].unsqueeze(1) + absmax = grouped.abs().amax(dim=-1, keepdim=True) + ideal_scales = torch.where( + absmax > spec.epsilon, + absmax / qmax, + torch.ones_like(absmax), + ) + scale_dtype = _scale_dtype(spec.scale_bits) + if scale_dtype == torch.float16: + ideal_scales = ideal_scales.clamp( + min=2.0**-24, + max=torch.finfo(torch.float16).max, + ) + stored_scales = ideal_scales.to(scale_dtype) + normalized = grouped / stored_scales.to(torch.float32) + if spec.rounding == "nearest": + quantized = torch.round(normalized) + else: + quantized = _stochastic_round(normalized, seed=spec.seed) + quantized = torch.minimum(torch.maximum(quantized, -qmax), qmax) + return quantized.to(torch.int16), stored_scales.squeeze(1).contiguous() + + +@dataclass(frozen=True, slots=True) +class PackedMultiBitQuantizedTensor: + """A tensor with separate INT4, INT6, and INT8 group payload pools.""" + + int4_payload: torch.Tensor + int6_payload: torch.Tensor + int8_payload: torch.Tensor + scales: torch.Tensor + packed_precision_codes: torch.Tensor + int4_spec: QuantizationSpec + int6_spec: QuantizationSpec + int8_spec: QuantizationSpec + original_shape: tuple[int, ...] + original_dtype: torch.dtype + flattened_size: int + padded_size: int + rows: int + groups_per_row: int + + def __post_init__(self) -> None: + """Reject packed objects whose metadata and resident tensors disagree.""" + + _validate_multibit_specs(self.int4_spec, self.int6_spec, self.int8_spec) + metadata = { + "flattened_size": self.flattened_size, + "padded_size": self.padded_size, + "rows": self.rows, + "groups_per_row": self.groups_per_row, + } + for name, value in metadata.items(): + if not isinstance(value, int) or isinstance(value, bool): + raise TypeError(f"{name} must be an integer") + if value < 0: + raise ValueError(f"{name} must be non-negative") + + if not isinstance(self.original_shape, tuple): + raise TypeError("original_shape must be a tuple of integers") + if not self.original_shape: + raise ValueError("original_shape must not be empty") + if any( + not isinstance(dimension, int) or isinstance(dimension, bool) + for dimension in self.original_shape + ): + raise TypeError("original_shape must be a tuple of integers") + if any(dimension < 0 for dimension in self.original_shape): + raise ValueError("original_shape dimensions must be non-negative") + if len(self.original_shape) < self.int4_spec.flatten_last_dims: + raise ValueError("original_shape has fewer dimensions than flatten_last_dims") + + flattened_dimensions = self.original_shape[-self.int4_spec.flatten_last_dims :] + if any(dimension <= 0 for dimension in flattened_dimensions): + raise ValueError("flattened original_shape dimensions must be positive") + expected_flattened_size = math.prod(flattened_dimensions) + if self.flattened_size != expected_flattened_size: + raise ValueError( + "flattened_size is inconsistent with original_shape and flatten_last_dims" + ) + expected_groups_per_row = math.ceil(self.flattened_size / self.int4_spec.group_size) + if self.groups_per_row != expected_groups_per_row: + raise ValueError("groups_per_row is inconsistent with flattened_size and group_size") + expected_padded_size = self.groups_per_row * self.int4_spec.group_size + if self.padded_size != expected_padded_size: + raise ValueError("padded_size is inconsistent with groups_per_row and group_size") + expected_rows = math.prod( + self.original_shape[: -self.int4_spec.flatten_last_dims] + ) + if self.rows != expected_rows: + raise ValueError("rows is inconsistent with original_shape and flatten_last_dims") + + if not isinstance(self.original_dtype, torch.dtype): + raise TypeError("original_dtype must be a torch.dtype") + if not torch.empty((), dtype=self.original_dtype).is_floating_point(): + raise TypeError("original_dtype must be a floating-point dtype") + + resident_tensors = { + "int4_payload": self.int4_payload, + "int6_payload": self.int6_payload, + "int8_payload": self.int8_payload, + "scales": self.scales, + "packed_precision_codes": self.packed_precision_codes, + } + for name, tensor in resident_tensors.items(): + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"{name} must be a torch.Tensor") + if not tensor.is_contiguous(): + raise ValueError(f"{name} must be contiguous") + + resident_device = self.scales.device + mismatched_devices = [ + name + for name, tensor in resident_tensors.items() + if tensor.device != resident_device + ] + if mismatched_devices: + raise ValueError( + "all resident tensors must share one device; mismatched: " + + ", ".join(mismatched_devices) + ) + + expected_scale_dtype = _scale_dtype(self.int4_spec.scale_bits) + if self.scales.dtype != expected_scale_dtype or self.scales.ndim != 1: + raise TypeError( + f"scales must be a one-dimensional {expected_scale_dtype} tensor" + ) + if self.scales.numel() != self.total_groups: + raise ValueError( + f"scales must contain one value per group ({self.total_groups})" + ) + if self.scales.numel(): + if not torch.isfinite(self.scales).all().item(): + raise ValueError("scales must contain only finite values") + if (self.scales <= 0).any().item(): + raise ValueError("scales must be strictly positive") + + precision_codes = _unpack_precision_codes( + self.packed_precision_codes, + self.total_groups, + ) + expected_counts = tuple( + int((precision_codes == code).sum().item()) + for code in ( + INT4_PRECISION_CODE, + INT6_PRECISION_CODE, + INT8_PRECISION_CODE, + ) + ) + payload_layouts = ( + ("int4_payload", self.int4_payload, torch.uint8, 4, expected_counts[0]), + ("int6_payload", self.int6_payload, torch.uint8, 6, expected_counts[1]), + ("int8_payload", self.int8_payload, torch.int8, 8, expected_counts[2]), + ) + for name, payload, dtype, bits, expected_groups in payload_layouts: + if payload.dtype != dtype or payload.ndim != 2: + raise TypeError(f"{name} must be a two-dimensional {dtype} tensor") + expected_width = self.int4_spec.group_size * bits // 8 + if tuple(payload.shape) != (expected_groups, expected_width): + raise ValueError( + f"{name} must have shape {(expected_groups, expected_width)}, " + f"got {tuple(payload.shape)}" + ) + + if self.int4_payload.numel(): + low = torch.bitwise_and(self.int4_payload, 0x0F) + high = torch.bitwise_right_shift(self.int4_payload, 4) + if ((low == 8) | (high == 8)).any().item(): + raise ValueError("int4_payload contains the reserved symmetric code -8") + if self.int6_payload.numel(): + _unpack_int6_groups(self.int6_payload, self.int4_spec.group_size) + if self.int8_payload.numel() and (self.int8_payload == -128).any().item(): + raise ValueError("int8_payload contains the reserved symmetric code -128") + + @property + def elements(self) -> int: + return math.prod(self.original_shape) + + @property + def total_groups(self) -> int: + return self.rows * self.groups_per_row + + @property + def int4_groups(self) -> int: + return self.int4_payload.shape[0] + + @property + def int6_groups(self) -> int: + return self.int6_payload.shape[0] + + @property + def int8_groups(self) -> int: + return self.int8_payload.shape[0] + + @property + def payload_bytes(self) -> int: + return sum( + payload.numel() * payload.element_size() + for payload in (self.int4_payload, self.int6_payload, self.int8_payload) + ) + + @property + def scale_bytes(self) -> int: + return self.scales.numel() * self.scales.element_size() + + @property + def precision_code_bytes(self) -> int: + return self.packed_precision_codes.numel() * self.packed_precision_codes.element_size() + + @property + def storage_bytes(self) -> int: + return self.payload_bytes + self.scale_bytes + self.precision_code_bytes + + def precision_codes(self) -> torch.Tensor: + """Return precision codes in ``[rows, groups_per_row]`` order.""" + + return _unpack_precision_codes( + self.packed_precision_codes, + self.total_groups, + ).reshape(self.rows, self.groups_per_row) + + def _integer_groups(self) -> torch.Tensor: + precision_codes = self.precision_codes().reshape(-1) + codes = torch.empty( + (self.total_groups, self.int4_spec.group_size), + dtype=torch.int16, + device=self.scales.device, + ) + codes[precision_codes == INT4_PRECISION_CODE] = _unpack_int4_groups( + self.int4_payload, + self.int4_spec.group_size, + ) + codes[precision_codes == INT6_PRECISION_CODE] = _unpack_int6_groups( + self.int6_payload, + self.int4_spec.group_size, + ) + int8_codes = self.int8_payload.to(torch.int16) + if int8_codes.numel() and (int8_codes == -128).any().item(): + raise ValueError("INT8 payload contains the reserved symmetric code -128") + codes[precision_codes == INT8_PRECISION_CODE] = int8_codes + return codes + + def dequantize(self) -> torch.Tensor: + """Materialize the mixed packed tensor in its original dtype and shape.""" + + restored_groups = self._integer_groups().to(torch.float32) * self.scales.to( + torch.float32 + ).unsqueeze(1) + restored = restored_groups.reshape(self.rows, self.padded_size)[:, : self.flattened_size] + return restored.reshape(self.original_shape).to(self.original_dtype) + + def to(self, device: torch.device | str) -> PackedMultiBitQuantizedTensor: + """Move every resident tensor without materializing or requantizing.""" + + return PackedMultiBitQuantizedTensor( + int4_payload=self.int4_payload.to(device), + int6_payload=self.int6_payload.to(device), + int8_payload=self.int8_payload.to(device), + scales=self.scales.to(device), + packed_precision_codes=self.packed_precision_codes.to(device), + int4_spec=self.int4_spec, + int6_spec=self.int6_spec, + int8_spec=self.int8_spec, + original_shape=self.original_shape, + original_dtype=self.original_dtype, + flattened_size=self.flattened_size, + padded_size=self.padded_size, + rows=self.rows, + groups_per_row=self.groups_per_row, + ) + + def reorder_batch(self, beam_idx: torch.LongTensor) -> PackedMultiBitQuantizedTensor: + """Select packed batch entries without another quantization round trip.""" + + if len(self.original_shape) <= self.int4_spec.flatten_last_dims: + raise RuntimeError( + "Cannot reorder multibit groups when quantization includes the batch dimension" + ) + if not isinstance(beam_idx, torch.Tensor): + raise TypeError("beam_idx must be a torch.Tensor") + if beam_idx.ndim != 1 or beam_idx.dtype not in (torch.int32, torch.int64): + raise TypeError("beam_idx must be a one-dimensional int32 or int64 tensor") + + batch_size = self.original_shape[0] + if batch_size <= 0 or self.rows % batch_size: + raise RuntimeError("multibit row metadata is inconsistent with its batch dimension") + if beam_idx.numel() and (beam_idx.min().item() < 0 or beam_idx.max().item() >= batch_size): + raise IndexError("beam_idx contains an out-of-range batch index") + + groups_per_batch = self.total_groups // batch_size + selected = beam_idx.to(self.scales.device) + selected_batch_size = selected.numel() + codes = self._integer_groups().reshape( + batch_size, + groups_per_batch, + self.int4_spec.group_size, + ) + scales = self.scales.reshape(batch_size, groups_per_batch) + precision_codes = self.precision_codes().reshape(batch_size, groups_per_batch) + + reordered_codes = codes.index_select(0, selected).reshape( + selected_batch_size * groups_per_batch, + self.int4_spec.group_size, + ) + reordered_scales = scales.index_select(0, selected).reshape(-1).contiguous() + reordered_precision = precision_codes.index_select(0, selected).reshape(-1) + return _from_integer_groups( + reordered_codes, + reordered_scales, + reordered_precision, + int4_spec=self.int4_spec, + int6_spec=self.int6_spec, + int8_spec=self.int8_spec, + original_shape=(selected_batch_size, *self.original_shape[1:]), + original_dtype=self.original_dtype, + flattened_size=self.flattened_size, + padded_size=self.padded_size, + rows=selected_batch_size * (self.rows // batch_size), + groups_per_row=self.groups_per_row, + ) + + +def _from_integer_groups( + codes: torch.Tensor, + scales: torch.Tensor, + precision_codes: torch.Tensor, + *, + int4_spec: QuantizationSpec, + int6_spec: QuantizationSpec, + int8_spec: QuantizationSpec, + original_shape: tuple[int, ...], + original_dtype: torch.dtype, + flattened_size: int, + padded_size: int, + rows: int, + groups_per_row: int, +) -> PackedMultiBitQuantizedTensor: + flat_precision = precision_codes.reshape(-1) + return PackedMultiBitQuantizedTensor( + int4_payload=_pack_int4_groups(codes[flat_precision == INT4_PRECISION_CODE]), + int6_payload=_pack_int6_groups(codes[flat_precision == INT6_PRECISION_CODE]), + int8_payload=codes[flat_precision == INT8_PRECISION_CODE].to(torch.int8).contiguous(), + scales=scales.contiguous(), + packed_precision_codes=_pack_precision_codes(flat_precision), + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + original_shape=original_shape, + original_dtype=original_dtype, + flattened_size=flattened_size, + padded_size=padded_size, + rows=rows, + groups_per_row=groups_per_row, + ) + + +def quantize_pack_multibit( + tensor: torch.Tensor, + precision_codes: torch.Tensor, + *, + int4_spec: QuantizationSpec, + int6_spec: QuantizationSpec, + int8_spec: QuantizationSpec, +) -> PackedMultiBitQuantizedTensor: + """Quantize groups into separate physical INT4, INT6, and INT8 pools. + + ``precision_codes`` may be flat or shaped ``[rows, groups_per_row]`` in the + exact group order produced by the shared grouped quantizer. It must use + ``torch.uint8`` and the canonical values 0, 1, and 2. + """ + + _validate_multibit_specs(int4_spec, int6_spec, int8_spec) + working, original_dtype, flattened_size, padded_size, groups_per_row, grouped = _group_tensor( + tensor, + int4_spec, + ) + rows = grouped.shape[0] + _validate_precision_code_shape( + precision_codes, + rows=rows, + groups_per_row=groups_per_row, + ) + flat_precision = precision_codes.detach().to(grouped.device).reshape(-1) + flat_groups = grouped.reshape(-1, int4_spec.group_size) + codes, scales = _quantize_multibit_groups( + flat_groups, + flat_precision, + int4_spec, + ) + return _from_integer_groups( + codes, + scales, + flat_precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + original_shape=tuple(working.shape), + original_dtype=original_dtype, + flattened_size=flattened_size, + padded_size=padded_size, + rows=rows, + groups_per_row=groups_per_row, + ) diff --git a/src/recurquant/packed_cache.py b/src/recurquant/packed_cache.py index c42e1e6..528bfb3 100644 --- a/src/recurquant/packed_cache.py +++ b/src/recurquant/packed_cache.py @@ -2,19 +2,27 @@ from __future__ import annotations +import hashlib +import math from collections.abc import Callable, Iterator, Mapping, MutableMapping from dataclasses import asdict, dataclass +from numbers import Real import torch from transformers import DynamicCache from transformers.cache_utils import LinearAttentionLayer +from .mixed_quantization import PackedMixedQuantizedTensor, quantize_pack_mixed from .quantization import ( PackedQuantizedTensor, QuantizationSpec, + RoundingMode, + quantize_dequantize, quantize_pack, scheduled_quantization_spec, ) +from .rht import RHT_SEED, right_rht_decode, right_rht_encode +from .row_policy import ExactBudgetRowPlan class _PackedStateView(MutableMapping[int, torch.Tensor | None]): @@ -417,3 +425,2539 @@ def storage_summary(self) -> dict[str, int | float | bool]: resident > 0 and resident < full_precision_equivalent ), } + + +def _validate_exact_budget_plan( + plan: ExactBudgetRowPlan, + linear_layer_indices: set[int], +) -> dict[int, tuple[int, int]]: + """Validate every byte and geometry invariant needed by the mixed cache.""" + + if plan.low_bits != 4 or plan.high_bits != 8: + raise ValueError("mixed row-plan caches require low_bits=4 and high_bits=8") + if plan.group_size <= 0: + raise ValueError("row plan group_size must be positive") + if plan.scale_bits not in (16, 32): + raise ValueError("row plan scale_bits must be 16 or 32") + + shapes: dict[int, tuple[int, int]] = {} + for layer_index, head_count, row_count in plan.score_shapes: + if layer_index in shapes: + raise ValueError(f"row plan contains duplicate geometry for layer {layer_index}") + if layer_index < 0 or head_count <= 0 or row_count <= 0: + raise ValueError( + "row plan layer indices must be nonnegative and dimensions must be positive" + ) + shapes[layer_index] = (head_count, row_count) + + plan_layers = set(shapes) + missing = sorted(linear_layer_indices - plan_layers) + extra = sorted(plan_layers - linear_layer_indices) + if missing or extra: + raise ValueError( + "row plan layers must exactly match linear-attention layers; " + f"missing={missing}, extra={extra}" + ) + + total_groups = sum(heads * rows for heads, rows in shapes.values()) + if plan.total_groups != total_groups: + raise ValueError( + "row plan total_groups is inconsistent with score_shapes: " + f"{plan.total_groups} != {total_groups}" + ) + expected_mask_bytes = math.ceil(total_groups / 8) + if plan.mask_bytes != expected_mask_bytes: + raise ValueError( + "row plan mask_bytes is inconsistent with total_groups: " + f"{plan.mask_bytes} != {expected_mask_bytes}" + ) + + selected = set(plan.high_precision_rows) + if len(selected) != plan.promoted_group_count: + raise ValueError("row plan contains duplicate high-precision row locations") + for location in selected: + geometry = shapes.get(location.layer_index) + if geometry is None: + raise ValueError(f"row plan promotion references unknown layer {location.layer_index}") + head_count, row_count = geometry + if not (0 <= location.head_index < head_count): + raise ValueError(f"row plan promotion has out-of-range head index at {location}") + if not (0 <= location.row_index < row_count): + raise ValueError(f"row plan promotion has out-of-range row index at {location}") + + low_payload_bytes = math.ceil(plan.low_bits * plan.group_size / 8) + high_payload_bytes = math.ceil(plan.high_bits * plan.group_size / 8) + promotion_increment = high_payload_bytes - low_payload_bytes + if plan.promotion_increment_bytes != promotion_increment: + raise ValueError( + "row plan promotion_increment_bytes is inconsistent with its bit widths and " + f"group size: {plan.promotion_increment_bytes} != {promotion_increment}" + ) + minimum_bytes = total_groups * (low_payload_bytes + plan.scale_bits // 8) + ( + expected_mask_bytes + ) + expected_resident = minimum_bytes + plan.promoted_group_count * promotion_increment + if plan.resident_bytes != expected_resident or plan.target_resident_bytes != expected_resident: + raise ValueError( + "row plan resident-byte fields are inconsistent with its physical layout: " + f"expected {expected_resident}" + ) + + # Each layer owns a separately addressable mask tensor. When a layer is not + # byte-aligned, independently packing those masks would exceed the selector's + # globally packed mask model, so reject instead of silently changing the budget. + per_layer_mask_bytes = sum(math.ceil(heads * rows / 8) for heads, rows in shapes.values()) + if per_layer_mask_bytes != expected_mask_bytes: + raise ValueError( + "row plan mask accounting is incompatible with separate per-layer masks; " + f"plan counts {expected_mask_bytes} bytes but layers require " + f"{per_layer_mask_bytes}" + ) + return shapes + + +class _MixedPackedStateView(MutableMapping[int, torch.Tensor | None]): + """Compatibility view that materializes one mixed packed state when read.""" + + def __init__(self, owner: MixedPackedLinearAttentionLayer) -> None: + self._owner = owner + + def __getitem__(self, key: int) -> torch.Tensor | None: + packed = self._owner.packed_states[key] + return None if packed is None else packed.dequantize() + + def __setitem__(self, key: int, value: torch.Tensor | None) -> None: + if value is None: + self._owner.packed_states[key] = None + self._owner.is_recurrent_states_initialized[key] = False + else: + self._owner._store(value, key) + + def __delitem__(self, key: int) -> None: + self.__setitem__(key, None) + + def __iter__(self) -> Iterator[int]: + return iter(self._owner.packed_states) + + def __len__(self) -> int: + return len(self._owner.packed_states) + + +@dataclass(frozen=True, slots=True) +class MixedPackedCacheUpdateEvidence: + update_index: int + layer_index: int + state_index: int + low_bits: int + high_bits: int + group_size: int + scale_bits: int + rounding: str + source_dtype: str + shape: tuple[int, ...] + total_groups: int + high_precision_groups: int + selection_method: str + high_precision_mask_sha256: str + baseline_bytes: int + payload_bytes: int + scale_bytes: int + mask_bytes: int + resident_bytes: int + relative_l2_error: float + mean_squared_error: float + max_absolute_error: float + + def evidence_dict(self) -> dict[str, object]: + return asdict(self) + + +MixedUpdateCallback = Callable[ + [int, int, torch.Tensor, PackedMixedQuantizedTensor, torch.Tensor], + None, +] + + +class MixedPackedLinearAttentionLayer(LinearAttentionLayer): + """Linear-attention cache layer with row-selected INT4/INT8 residency.""" + + is_compileable = False + + def __init__( + self, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + layer_index: int, + expected_heads: int, + expected_rows: int, + high_precision_group_indices: tuple[int, ...], + number_of_states: int = 1, + on_update: MixedUpdateCallback | None = None, + ) -> None: + super().__init__(number_of_states=number_of_states) + if low_spec.bits != 4 or high_spec.bits != 8: + raise ValueError("mixed packed layers require low INT4 and high INT8 specs") + if any( + getattr(low_spec, field) != getattr(high_spec, field) + for field in ( + "group_size", + "scale_bits", + "flatten_last_dims", + "rounding", + "seed", + "epsilon", + ) + ): + raise ValueError("mixed packed layer specs must differ only in bits") + if low_spec.flatten_last_dims != 2: + raise ValueError("mixed row-plan cache requires flatten_last_dims=2") + expected_groups = expected_heads * expected_rows + if len(set(high_precision_group_indices)) != len(high_precision_group_indices): + raise ValueError("high-precision group indices must be unique") + if any(index < 0 or index >= expected_groups for index in high_precision_group_indices): + raise ValueError("high-precision group index is outside the layer geometry") + + self.low_spec = low_spec + self.high_spec = high_spec + self.layer_index = layer_index + self.expected_heads = expected_heads + self.expected_rows = expected_rows + self.high_precision_group_indices = tuple(sorted(high_precision_group_indices)) + self.on_update = on_update + self.packed_states: dict[int, PackedMixedQuantizedTensor | None] = dict.fromkeys( + range(number_of_states) + ) + self.recurrent_states = _MixedPackedStateView(self) + self._update_count = 0 + + def _selected_specs(self) -> tuple[QuantizationSpec, QuantizationSpec]: + return ( + scheduled_quantization_spec( + self.low_spec, + layer_index=self.layer_index, + layer_update_index=self._update_count, + ), + scheduled_quantization_spec( + self.high_spec, + layer_index=self.layer_index, + layer_update_index=self._update_count, + ), + ) + + def _precision_mask( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + del low_spec, high_spec + if recurrent_states.ndim != 4: + raise ValueError( + f"layer {self.layer_index} recurrent state must have rank 4; " + f"got shape {tuple(recurrent_states.shape)}" + ) + expected_shape = ( + recurrent_states.shape[0], + self.expected_heads, + self.expected_rows, + self.low_spec.group_size, + ) + if tuple(recurrent_states.shape) != expected_shape: + rendered = ( + f"[batch, {self.expected_heads}, {self.expected_rows}, {self.low_spec.group_size}]" + ) + raise ValueError( + f"layer {self.layer_index} recurrent state must have shape {rendered}; " + f"got {tuple(recurrent_states.shape)}" + ) + base = torch.zeros( + self.expected_heads * self.expected_rows, + dtype=torch.bool, + device=recurrent_states.device, + ) + if self.high_precision_group_indices: + selected = torch.tensor( + self.high_precision_group_indices, + dtype=torch.long, + device=recurrent_states.device, + ) + base[selected] = True + return base.repeat(recurrent_states.shape[0]).reshape( + recurrent_states.shape[0] * self.expected_heads, + self.expected_rows, + ) + + def _quantized_endpoint( + self, + recurrent_states: torch.Tensor, + spec: QuantizationSpec, + ) -> torch.Tensor: + """Return one aligned QDQ endpoint used by dynamic selectors.""" + + return quantize_dequantize(recurrent_states, spec).tensor + + def _aligned_mse_benefit( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + """Return per-row low-to-high precision MSE reduction.""" + + source = recurrent_states.detach().to(torch.float32) + low = self._quantized_endpoint(recurrent_states, low_spec).to(torch.float32) + high = self._quantized_endpoint(recurrent_states, high_spec).to(torch.float32) + return (low - source).square().mean(dim=-1) - (high - source).square().mean( + dim=-1 + ) + + def _pack_state( + self, + recurrent_states: torch.Tensor, + mask: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> PackedMixedQuantizedTensor: + """Physically pack one state; codec-aware subclasses override this hook.""" + + return quantize_pack_mixed( + recurrent_states, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + + def _store(self, recurrent_states: torch.Tensor, state_idx: int) -> torch.Tensor: + if torch.is_grad_enabled() and recurrent_states.requires_grad: + raise RuntimeError( + "Mixed packed recurrent states are inference-only and cannot accept an " + "autograd-tracked tensor. Wrap every prefill and decode forward in " + "torch.inference_mode() or torch.no_grad(); model.eval() alone does " + "not disable autograd." + ) + low_spec, high_spec = self._selected_specs() + mask = self._precision_mask( + recurrent_states, + low_spec=low_spec, + high_spec=high_spec, + ) + packed = self._pack_state( + recurrent_states, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + self.packed_states[state_idx] = packed + self.is_recurrent_states_initialized[state_idx] = True + if self.device is None: + self.dtype = recurrent_states.dtype + self.device = recurrent_states.device + materialized = packed.dequantize() + if self.on_update is not None: + self.on_update( + self.layer_index, + state_idx, + recurrent_states, + packed, + materialized, + ) + self._update_count += 1 + return materialized + + def update_recurrent_state( + self, + recurrent_states: torch.Tensor, + state_idx: int = 0, + **kwargs: object, + ) -> torch.Tensor: + del kwargs + return self._store(recurrent_states, state_idx) + + def resident_payload_bytes(self) -> int: + return sum( + packed.payload_bytes for packed in self.packed_states.values() if packed is not None + ) + + def resident_scale_bytes(self) -> int: + return sum( + packed.scale_bytes for packed in self.packed_states.values() if packed is not None + ) + + def resident_mask_bytes(self) -> int: + return sum( + packed.mask_bytes for packed in self.packed_states.values() if packed is not None + ) + + def resident_recurrent_state_bytes(self) -> int: + return ( + self.resident_payload_bytes() + + self.resident_scale_bytes() + + self.resident_mask_bytes() + ) + + def high_precision_group_count(self) -> int: + return sum( + packed.high_precision_groups + for packed in self.packed_states.values() + if packed is not None + ) + + def full_precision_equivalent_recurrent_state_bytes(self) -> int: + return sum( + packed.elements * torch.empty((), dtype=packed.original_dtype).element_size() + for packed in self.packed_states.values() + if packed is not None + ) + + def largest_materialized_recurrent_state_bytes(self) -> int: + return max( + ( + packed.elements * torch.empty((), dtype=packed.original_dtype).element_size() + for packed in self.packed_states.values() + if packed is not None + ), + default=0, + ) + + def reset(self) -> None: + for state_idx in range(self.number_of_states): + if self.is_conv_states_initialized[state_idx]: + self.conv_states[state_idx].zero_() + packed = self.packed_states[state_idx] + if packed is not None: + packed.low_payload.zero_() + packed.high_payload.zero_() + packed.scales.zero_() + self.has_previous_state[state_idx] = False + + def reorder_cache(self, beam_idx: torch.LongTensor) -> None: + for state_idx in range(self.number_of_states): + if self.is_conv_states_initialized[state_idx]: + self.conv_states[state_idx] = self.conv_states[state_idx].index_select( + 0, beam_idx.to(self.device) + ) + packed = self.packed_states[state_idx] + if packed is not None: + self.packed_states[state_idx] = packed.reorder_batch(beam_idx) + + def offload(self) -> None: + for state_idx in range(self.number_of_states): + if self.is_conv_states_initialized[state_idx]: + self.conv_states[state_idx] = self.conv_states[state_idx].to( + "cpu", non_blocking=True + ) + packed = self.packed_states[state_idx] + if packed is not None: + self.packed_states[state_idx] = packed.to("cpu") + + def prefetch(self) -> None: + for state_idx in range(self.number_of_states): + if ( + self.is_conv_states_initialized[state_idx] + and self.conv_states[state_idx].device != self.device + ): + self.conv_states[state_idx] = self.conv_states[state_idx].to( + self.device, non_blocking=True + ) + packed = self.packed_states[state_idx] + if packed is not None and packed.low_payload.device != self.device: + self.packed_states[state_idx] = packed.to(self.device) + + +class AdaptiveMixedPackedLinearAttentionLayer(MixedPackedLinearAttentionLayer): + """Batch-one prototype selecting the fixed INT8 quota on each state update. + + The plan determines only this layer's promotion count. The promoted rows are + recomputed from the incoming state using the per-row reduction in aligned + quantize-dequantize MSE from INT4 to INT8. Equal scores keep flattened row + order, making ties deterministic without changing the byte budget. + """ + + def _precision_mask( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + # Reuse the static path's complete rank/geometry validation before + # applying the prototype's deliberately narrow batch-one contract. + super()._precision_mask( + recurrent_states, + low_spec=low_spec, + high_spec=high_spec, + ) + if recurrent_states.shape[0] != 1: + raise ValueError( + "adaptive mixed cache selection currently requires batch size 1; " + f"got {recurrent_states.shape[0]}" + ) + + total_groups = self.expected_heads * self.expected_rows + quota = len(self.high_precision_group_indices) + mask = torch.zeros( + total_groups, + dtype=torch.bool, + device=recurrent_states.device, + ) + if quota == 0: + return mask.reshape(self.expected_heads, self.expected_rows) + if quota == total_groups: + return torch.ones_like(mask).reshape(self.expected_heads, self.expected_rows) + + # quantize_dequantize detaches its input. Keep the scope explicit so this + # selector cannot retain an autograd graph if it is called independently. + with torch.no_grad(): + source = recurrent_states.detach().to(torch.float32) + low = quantize_dequantize(recurrent_states, low_spec).tensor.to(torch.float32) + high = quantize_dequantize(recurrent_states, high_spec).tensor.to(torch.float32) + low_mse = (low - source).square().mean(dim=-1).reshape(-1) + high_mse = (high - source).square().mean(dim=-1).reshape(-1) + benefit = low_mse - high_mse + + # Stable descending sort resolves exact ties by the pre-existing + # flattened [head, row] order (lower index first). + ranked = torch.argsort(benefit, descending=True, stable=True) + mask[ranked[:quota]] = True + return mask.reshape(self.expected_heads, self.expected_rows) + + +_QUERY_EMA_DECAY = 2.0 ** (-1.0 / 32.0) +_QUERY_L2NORM_EPS = 1e-6 +_QUERY_EMA_CHUNK_ATOL = 2e-8 +_QUERY_EMA_CHUNK_RTOL = 2e-6 + +_CORA_L2NORM_EPS = 1e-6 + + +def _pack_row_mask(mask: torch.Tensor) -> torch.Tensor: + """Pack a canonical flattened boolean row mask, least-significant bit first.""" + + flat = mask.detach().reshape(-1).to(torch.uint8) + padding = (-flat.numel()) % 8 + if padding: + flat = torch.nn.functional.pad(flat, (0, padding)) + chunks = flat.reshape(-1, 8).to(torch.int16) + shifts = torch.arange(8, dtype=torch.int16, device=flat.device) + weights = torch.bitwise_left_shift(torch.ones_like(shifts), shifts) + return (chunks * weights).sum(dim=1).to(torch.uint8).contiguous() + + +def _unpack_row_mask(packed: torch.Tensor, total_rows: int) -> torch.Tensor: + """Inverse of :func:`_pack_row_mask` for a known logical row count.""" + + shifts = torch.arange(8, dtype=torch.int16, device=packed.device) + expanded = torch.bitwise_right_shift(packed.to(torch.int16).unsqueeze(1), shifts) + return torch.bitwise_and(expanded, 1).reshape(-1)[:total_rows].to(torch.bool) + + +def _stable_top_mask( + scores: torch.Tensor, + quota: int, + *, + eligible: torch.Tensor | None = None, +) -> torch.Tensor: + """Return a stable exact-quota mask, optionally restricted to eligible rows.""" + + flat_scores = scores.reshape(-1) + total = flat_scores.numel() + mask = torch.zeros(total, dtype=torch.bool, device=flat_scores.device) + if quota == 0: + return mask + if quota == total and eligible is None: + return torch.ones_like(mask) + ranked_scores = flat_scores + if eligible is not None: + flat_eligible = eligible.reshape(-1) + if flat_eligible.dtype != torch.bool or flat_eligible.numel() != total: + raise RuntimeError("confirmation eligibility mask has invalid shape or dtype") + if int(flat_eligible.sum().item()) < quota: + raise RuntimeError("confirmation eligibility contains fewer rows than the quota") + ranked_scores = torch.where( + flat_eligible, + flat_scores, + torch.full_like(flat_scores, -torch.inf), + ) + ranked = torch.argsort(ranked_scores, descending=True, stable=True) + mask[ranked[:quota]] = True + return mask + + +def _score_boundary( + scores: torch.Tensor, + selected: torch.Tensor, +) -> tuple[float | None, float | None, float | None]: + """Return cutoff, absolute gap, and scale-normalized gap for one mask. + + The normalized gap is ``(cutoff - best_unselected) / max(abs(cutoff), + abs(best_unselected), 1e-12)``. This diagnostic definition is deterministic; + it does not participate in row selection. + """ + + flat_scores = scores.reshape(-1) + flat_selected = selected.reshape(-1) + if not flat_selected.any().item(): + return None, None, None + cutoff = flat_scores[flat_selected].min() + if flat_selected.all().item(): + return float(cutoff.item()), None, None + best_unselected = flat_scores[~flat_selected].max() + gap = cutoff - best_unselected + scale = torch.maximum(cutoff.abs(), best_unselected.abs()).clamp_min(1e-12) + return float(cutoff.item()), float(gap.item()), float((gap / scale).item()) + + +def _confirmation_two_mask( + *, + raw_mask: torch.Tensor, + scores: torch.Tensor, + quota: int, + enabled: bool, + has_history: bool, + previous_committed_mask: torch.Tensor | None, + previous_raw_mask_packed: torch.Tensor | None, +) -> torch.Tensor: + """Apply the frozen two-consecutive-hit admission rule.""" + + if not enabled or not has_history: + return raw_mask.clone() + if previous_committed_mask is None or previous_raw_mask_packed is None: + raise RuntimeError("confirmation-2 history is incomplete") + previous_raw = _unpack_row_mask(previous_raw_mask_packed, raw_mask.numel()) + eligible = previous_committed_mask.reshape(-1) | (previous_raw & raw_mask.reshape(-1)) + return _stable_top_mask(scores, quota, eligible=eligible) + + +@dataclass(slots=True) +class _PendingQueryObservation: + update_index: int + token_count: int + candidate_ema: torch.Tensor + consumed: bool = False + raw_mask: torch.Tensor | None = None + committed_mask: torch.Tensor | None = None + raw_cutoff_score: float | None = None + raw_score_gap: float | None = None + raw_normalized_gap: float | None = None + committed_cutoff_score: float | None = None + committed_score_gap: float | None = None + committed_normalized_gap: float | None = None + + +class QueryEmaMixedPackedLinearAttentionLayer(AdaptiveMixedPackedLinearAttentionLayer): + """Select rows by causal query-read energy times aligned MSE benefit. + + A query observation must be staged immediately before every recurrent-state + write. The persistent FP32 EMA is committed only after the corresponding + mixed-precision state was packed successfully. The layer deliberately fails + closed instead of falling back to the MSE-only adaptive selector. + """ + + query_ema_decay = _QUERY_EMA_DECAY + query_l2norm_eps = _QUERY_L2NORM_EPS + query_ema_chunk_atol = _QUERY_EMA_CHUNK_ATOL + query_ema_chunk_rtol = _QUERY_EMA_CHUNK_RTOL + + def __init__(self, **kwargs: object) -> None: + super().__init__(**kwargs) + self.confirmation_two = False + self.query_energy_ema: torch.Tensor | None = None + self.previous_raw_mask_packed: torch.Tensor | None = None + self._pending_query_observation: _PendingQueryObservation | None = None + self.query_observations_committed = 0 + self.query_observations_staged = 0 + self.query_tokens_observed = 0 + self.last_query_token_count: int | None = None + self.last_cutoff_score_margin: float | None = None + self.last_mask_overlap: int | None = None + self.last_mask_churn: int | None = None + self._candidate_cutoff_score_margin: float | None = None + self._has_query_selection_history = False + self.mask_transition_count = 0 + self.raw_xor_churn_total = 0 + self.committed_xor_churn_total = 0 + self.admissions_total = 0 + self.dwell_total = 0 + self.last_raw_cutoff_score: float | None = None + self.last_raw_score_gap: float | None = None + self.last_raw_normalized_gap: float | None = None + self.last_committed_cutoff_score: float | None = None + self.last_committed_score_gap: float | None = None + self.last_committed_normalized_gap: float | None = None + + def configure_confirmation_two(self, enabled: bool) -> None: + """Enable the frozen two-hit admission ablation before the first write.""" + + if not isinstance(enabled, bool): + raise TypeError("confirmation_two must be a bool") + if self._update_count or self._pending_query_observation is not None: + raise RuntimeError("confirmation_two must be configured before staging or writing") + self.confirmation_two = enabled + + def stage_query_observation( + self, + query: torch.Tensor, + *, + l2norm_eps: float = _QUERY_L2NORM_EPS, + ) -> None: + """Stage one ``[1, tokens, heads, rows]`` causal query observation.""" + + if self._pending_query_observation is not None: + self.discard_pending_query_observation() + raise RuntimeError( + f"layer {self.layer_index} received a duplicate query observation; " + "the pending observation was discarded" + ) + if isinstance(l2norm_eps, bool) or not isinstance(l2norm_eps, Real): + raise TypeError("l2norm_eps must be a real number") + epsilon = float(l2norm_eps) + if not math.isfinite(epsilon) or epsilon != _QUERY_L2NORM_EPS: + raise ValueError("query EMA l2norm_eps is frozen at 1e-6") + if not isinstance(query, torch.Tensor): + raise TypeError("query observation must be a tensor") + expected_shape = f"[1, tokens, {self.expected_heads}, {self.expected_rows}]" + if query.ndim != 4: + raise ValueError( + f"layer {self.layer_index} query observation must have shape " + f"{expected_shape}; got {tuple(query.shape)}" + ) + if ( + query.shape[0] != 1 + or query.shape[1] <= 0 + or query.shape[2] != self.expected_heads + or query.shape[3] != self.expected_rows + ): + raise ValueError( + f"layer {self.layer_index} query observation must have shape " + f"{expected_shape}; got {tuple(query.shape)}" + ) + if not query.is_floating_point(): + raise TypeError(f"layer {self.layer_index} query observation must be floating point") + if query.device.type == "meta": + raise ValueError(f"layer {self.layer_index} query observation must be materialized") + if not torch.isfinite(query).all().item(): + raise ValueError(f"layer {self.layer_index} query observation must be finite") + if self.query_energy_ema is not None and self.query_energy_ema.device != query.device: + raise ValueError( + f"layer {self.layer_index} query observation and query EMA must use " + "the same device" + ) + if self.device is not None and torch.device(self.device) != query.device: + raise ValueError( + f"layer {self.layer_index} query observation and recurrent state must use " + "the same device" + ) + + with torch.no_grad(): + source = query.detach().to(torch.float32) + squared = source.square() + energy = squared / (squared.sum(dim=-1, keepdim=True) + epsilon) + energy = energy.squeeze(0) + token_count = energy.shape[0] + previous = self.query_energy_ema + if previous is None: + previous = torch.full( + (self.expected_heads, self.expected_rows), + 1.0 / self.expected_rows, + dtype=torch.float32, + device=query.device, + ) + + # This closed form is the exact mathematical recurrence for applying + # the frozen EMA sequentially over the staged token dimension. + exponents = torch.arange( + token_count - 1, + -1, + -1, + dtype=torch.float32, + device=query.device, + ) + weights = torch.pow( + torch.tensor(_QUERY_EMA_DECAY, dtype=torch.float32, device=query.device), + exponents, + ) + candidate = ( + (_QUERY_EMA_DECAY**token_count) * previous + + (1.0 - _QUERY_EMA_DECAY) + * (energy * weights[:, None, None]).sum(dim=0) + ) + if not torch.isfinite(candidate).all().item(): + raise RuntimeError( + f"layer {self.layer_index} query EMA update produced non-finite values" + ) + + self._pending_query_observation = _PendingQueryObservation( + update_index=self._update_count, + token_count=token_count, + candidate_ema=candidate, + ) + + def discard_pending_query_observation(self) -> None: + """Discard a staged observation; safe to call during exception cleanup.""" + + self._pending_query_observation = None + self._candidate_cutoff_score_margin = None + + def _precision_mask( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + # Use the static base for complete recurrent-state geometry checks while + # deliberately bypassing the MSE-only adaptive selector. + MixedPackedLinearAttentionLayer._precision_mask( + self, + recurrent_states, + low_spec=low_spec, + high_spec=high_spec, + ) + if recurrent_states.shape[0] != 1: + raise ValueError( + "query-EMA mixed cache selection currently requires batch size 1; " + f"got {recurrent_states.shape[0]}" + ) + + pending = self._pending_query_observation + if pending is None: + raise RuntimeError( + f"layer {self.layer_index} has no staged query observation for " + f"state update {self._update_count}" + ) + try: + if pending.consumed: + raise RuntimeError( + f"layer {self.layer_index} query observation was consumed more than once" + ) + if pending.update_index != self._update_count: + raise RuntimeError( + f"layer {self.layer_index} has a stale query observation for update " + f"{pending.update_index}; expected {self._update_count}" + ) + if pending.candidate_ema.device != recurrent_states.device: + raise ValueError( + f"layer {self.layer_index} query observation and recurrent state must use " + "the same device" + ) + expected_ema_shape = (self.expected_heads, self.expected_rows) + if tuple(pending.candidate_ema.shape) != expected_ema_shape: + raise RuntimeError( + f"layer {self.layer_index} staged query EMA must have shape " + f"{expected_ema_shape}; got {tuple(pending.candidate_ema.shape)}" + ) + if pending.candidate_ema.dtype != torch.float32: + raise RuntimeError( + f"layer {self.layer_index} staged query EMA must use torch.float32" + ) + if not torch.isfinite(pending.candidate_ema).all().item(): + raise RuntimeError( + f"layer {self.layer_index} staged query EMA must be finite" + ) + + total_groups = self.expected_heads * self.expected_rows + quota = len(self.high_precision_group_indices) + pending.consumed = True + self._candidate_cutoff_score_margin = None + with torch.no_grad(): + benefit = self._aligned_mse_benefit( + recurrent_states, + low_spec=low_spec, + high_spec=high_spec, + ).reshape(self.expected_heads, self.expected_rows) + scores = (pending.candidate_ema * benefit).reshape(-1) + if not torch.isfinite(scores).all().item(): + raise RuntimeError( + f"layer {self.layer_index} query-weighted selector scores " + "must be finite" + ) + raw_mask = _stable_top_mask(scores, quota) + previous = self.packed_states[0] + previous_committed = ( + previous.high_precision_mask().reshape(-1) + if ( + self.confirmation_two + and previous is not None + and self._has_query_selection_history + ) + else None + ) + committed_mask = _confirmation_two_mask( + raw_mask=raw_mask, + scores=scores, + quota=quota, + enabled=self.confirmation_two, + has_history=self._has_query_selection_history, + previous_committed_mask=previous_committed, + previous_raw_mask_packed=self.previous_raw_mask_packed, + ) + pending.raw_mask = raw_mask + pending.committed_mask = committed_mask + ( + pending.raw_cutoff_score, + pending.raw_score_gap, + pending.raw_normalized_gap, + ) = _score_boundary(scores, raw_mask) + ( + pending.committed_cutoff_score, + pending.committed_score_gap, + pending.committed_normalized_gap, + ) = _score_boundary(scores, committed_mask) + if 0 < quota < total_groups: + ranked = torch.argsort(scores, descending=True, stable=True) + self._candidate_cutoff_score_margin = float( + (scores[ranked[quota - 1]] - scores[ranked[quota]]).item() + ) + return committed_mask.reshape(self.expected_heads, self.expected_rows) + except Exception: + self.discard_pending_query_observation() + raise + + def _store(self, recurrent_states: torch.Tensor, state_idx: int) -> torch.Tensor: + if state_idx not in self.packed_states: + self.discard_pending_query_observation() + raise IndexError( + f"layer {self.layer_index} state_idx {state_idx} is outside " + f"[0, {self.number_of_states})" + ) + previous = self.packed_states[state_idx] + previous_initialized = self.is_recurrent_states_initialized[state_idx] + previous_dtype = self.dtype + previous_device = self.device + previous_update_count = self._update_count + previous_history = self._has_query_selection_history + previous_raw_mask_packed = self.previous_raw_mask_packed + callback = self.on_update + callback_owner = getattr(callback, "__self__", None) + callback_evidence = getattr(callback_owner, "update_evidence", None) + callback_update_index = getattr(callback_owner, "_update_index", None) + evidence_checkpoint = ( + (len(callback_evidence), callback_update_index) + if isinstance(callback_evidence, list) and isinstance(callback_update_index, int) + else None + ) + previous_raw: torch.Tensor | None = None + candidate_raw_packed: torch.Tensor | None = None + raw_xor = committed_xor = admissions = dwell = None + try: + previous_raw = ( + _unpack_row_mask( + previous_raw_mask_packed, + self.expected_heads * self.expected_rows, + ) + if self.confirmation_two + and previous_history + and previous_raw_mask_packed is not None + else None + ) + previous_mask = ( + previous.high_precision_mask().reshape(-1) + if previous is not None and previous_history + else None + ) + # Defer the cache-level evidence callback until packing and every + # query-specific postcondition have succeeded. This keeps a failed + # transaction from appending evidence for a state that is rolled back. + self.on_update = None + try: + materialized = super()._store(recurrent_states, state_idx) + finally: + self.on_update = callback + pending = self._pending_query_observation + if ( + pending is None + or not pending.consumed + or pending.raw_mask is None + or pending.committed_mask is None + ): + raise RuntimeError( + f"layer {self.layer_index} packed a state without consuming exactly " + "one query observation" + ) + current = self.packed_states[state_idx] + if current is None: + raise RuntimeError(f"layer {self.layer_index} packed state disappeared") + current_mask = current.high_precision_mask().reshape(-1) + if not torch.equal(current_mask, pending.committed_mask): + raise RuntimeError("packed precision mask differs from query committed mask") + overlap = ( + None + if previous_mask is None + else int((previous_mask & current_mask).sum().item()) + ) + churn = ( + None + if previous_mask is None + else int((previous_mask ^ current_mask).sum().item()) + ) + if self.confirmation_two: + if pending.raw_mask is None: + raise RuntimeError("query C2 selector did not produce a raw mask") + candidate_raw_packed = _pack_row_mask(pending.raw_mask) + if previous_history: + if previous_raw is None or previous_mask is None: + raise RuntimeError("query C2 transition history is incomplete") + raw_xor = int((previous_raw ^ pending.raw_mask).sum().item()) + committed_xor = int((previous_mask ^ current_mask).sum().item()) + dwell = int((previous_mask & current_mask).sum().item()) + admissions = int((~previous_mask & current_mask).sum().item()) + if callback is not None: + callback( + self.layer_index, + state_idx, + recurrent_states, + current, + materialized, + ) + except Exception: + # Base packing assigns the new representation before materialization. + # Roll back every base field if any later operation fails so a caller + # can never observe a new packed state paired with an old query EMA or + # generation counter. + self.on_update = callback + self.packed_states[state_idx] = previous + self.is_recurrent_states_initialized[state_idx] = previous_initialized + self.dtype = previous_dtype + self.device = previous_device + self._update_count = previous_update_count + self._has_query_selection_history = previous_history + self.previous_raw_mask_packed = previous_raw_mask_packed + if evidence_checkpoint is not None: + evidence_length, update_index = evidence_checkpoint + del callback_evidence[evidence_length:] + callback_owner._update_index = update_index + self.discard_pending_query_observation() + raise + + self.query_energy_ema = pending.candidate_ema + self.previous_raw_mask_packed = ( + candidate_raw_packed if self.confirmation_two else None + ) + self.query_observations_staged += 1 + self.query_observations_committed += 1 + self.query_tokens_observed += pending.token_count + self.last_query_token_count = pending.token_count + self.last_cutoff_score_margin = self._candidate_cutoff_score_margin + self.last_mask_overlap = overlap + self.last_mask_churn = churn + self.last_raw_cutoff_score = pending.raw_cutoff_score + self.last_raw_score_gap = pending.raw_score_gap + self.last_raw_normalized_gap = pending.raw_normalized_gap + self.last_committed_cutoff_score = pending.committed_cutoff_score + self.last_committed_score_gap = pending.committed_score_gap + self.last_committed_normalized_gap = pending.committed_normalized_gap + if self.confirmation_two and previous_history: + assert raw_xor is not None + assert committed_xor is not None + assert admissions is not None + assert dwell is not None + self.mask_transition_count += 1 + self.raw_xor_churn_total += raw_xor + self.committed_xor_churn_total += committed_xor + self.admissions_total += admissions + self.dwell_total += dwell + self._has_query_selection_history = True + self.discard_pending_query_observation() + return materialized + + def selector_auxiliary_bytes(self) -> int: + ema_bytes = ( + 0 + if self.query_energy_ema is None + else self.query_energy_ema.numel() * self.query_energy_ema.element_size() + ) + raw_mask_bytes = ( + 0 + if not self.confirmation_two or self.previous_raw_mask_packed is None + else self.previous_raw_mask_packed.numel() + * self.previous_raw_mask_packed.element_size() + ) + return ema_bytes + raw_mask_bytes + + def query_ema_diagnostics(self) -> dict[str, int | float | bool | str | None]: + packed = self.packed_states[0] + if packed is None: + current_mask_sha256 = None + current_selected_count = 0 + else: + current_mask_sha256 = hashlib.sha256( + bytes(packed.precision_mask.detach().cpu().contiguous().tolist()) + ).hexdigest() + current_selected_count = packed.high_precision_groups + quota = len(self.high_precision_group_indices) + churn_denominator = 2 * quota * self.mask_transition_count + return { + "layer_index": self.layer_index, + "quota": len(self.high_precision_group_indices), + "confirmation_two": self.confirmation_two, + "selection_method": ( + "query_ema32_confirm2_mse_target_fisher_quota" + if self.confirmation_two + else "query_ema32_weighted_aligned_mse_reduction" + ), + "ema_decay": self.query_ema_decay, + "l2norm_eps": self.query_l2norm_eps, + "initial_ema_value": 1.0 / self.expected_rows, + "chunk_equivalence_atol": self.query_ema_chunk_atol, + "chunk_equivalence_rtol": self.query_ema_chunk_rtol, + "state_updates": self._update_count, + "observations_staged": self.query_observations_staged, + "observations_committed": self.query_observations_committed, + "tokens_observed": self.query_tokens_observed, + "last_query_token_count": self.last_query_token_count, + "last_cutoff_score_margin": self.last_cutoff_score_margin, + "last_mask_overlap": self.last_mask_overlap, + "last_mask_churn": self.last_mask_churn, + "current_selected_count": current_selected_count, + "current_mask_sha256": current_mask_sha256, + "raw_mask_sha256": ( + current_mask_sha256 + if self.previous_raw_mask_packed is None + else hashlib.sha256( + bytes( + self.previous_raw_mask_packed.detach() + .cpu() + .contiguous() + .tolist() + ) + ).hexdigest() + ), + "committed_mask_sha256": current_mask_sha256, + "mask_transition_count": self.mask_transition_count, + "raw_xor_churn_total": self.raw_xor_churn_total, + "committed_xor_churn_total": self.committed_xor_churn_total, + "admissions_total": self.admissions_total, + "dwell_total": self.dwell_total, + "raw_normalized_churn": ( + self.raw_xor_churn_total / churn_denominator + if churn_denominator + else None + ), + "committed_normalized_churn": ( + self.committed_xor_churn_total / churn_denominator + if churn_denominator + else None + ), + "last_raw_cutoff_score": self.last_raw_cutoff_score, + "last_raw_score_gap": self.last_raw_score_gap, + "last_raw_normalized_gap": self.last_raw_normalized_gap, + "last_committed_cutoff_score": self.last_committed_cutoff_score, + "last_committed_score_gap": self.last_committed_score_gap, + "last_committed_normalized_gap": self.last_committed_normalized_gap, + "previous_raw_mask_bytes": ( + 0 + if self.previous_raw_mask_packed is None + else self.previous_raw_mask_packed.numel() + * self.previous_raw_mask_packed.element_size() + ), + "pending_observation": self._pending_query_observation is not None, + "selector_auxiliary_bytes": self.selector_auxiliary_bytes(), + } + + def reset(self) -> None: + super().reset() + self.discard_pending_query_observation() + if self.query_energy_ema is not None: + self.query_energy_ema.fill_(1.0 / self.expected_rows) + self._update_count = 0 + self._has_query_selection_history = False + self.previous_raw_mask_packed = None + self.query_observations_committed = 0 + self.query_observations_staged = 0 + self.query_tokens_observed = 0 + self.last_query_token_count = None + self.last_cutoff_score_margin = None + self.last_mask_overlap = None + self.last_mask_churn = None + self.mask_transition_count = 0 + self.raw_xor_churn_total = 0 + self.committed_xor_churn_total = 0 + self.admissions_total = 0 + self.dwell_total = 0 + self.last_raw_cutoff_score = None + self.last_raw_score_gap = None + self.last_raw_normalized_gap = None + self.last_committed_cutoff_score = None + self.last_committed_score_gap = None + self.last_committed_normalized_gap = None + + def _reject_pending_transfer(self, operation: str) -> None: + if self._pending_query_observation is None: + return + self.discard_pending_query_observation() + raise RuntimeError( + f"layer {self.layer_index} cannot {operation} with a pending query " + "observation; it was discarded" + ) + + def offload(self) -> None: + self._reject_pending_transfer("offload") + super().offload() + if self.query_energy_ema is not None: + self.query_energy_ema = self.query_energy_ema.to("cpu") + if self.previous_raw_mask_packed is not None: + self.previous_raw_mask_packed = self.previous_raw_mask_packed.to("cpu") + + def prefetch(self) -> None: + self._reject_pending_transfer("prefetch") + super().prefetch() + if ( + self.query_energy_ema is not None + and self.device is not None + and self.query_energy_ema.device != torch.device(self.device) + ): + self.query_energy_ema = self.query_energy_ema.to( + self.device, + non_blocking=True, + ) + if ( + self.previous_raw_mask_packed is not None + and self.device is not None + and self.previous_raw_mask_packed.device != torch.device(self.device) + ): + self.previous_raw_mask_packed = self.previous_raw_mask_packed.to( + self.device, + non_blocking=True, + ) + + +class RightRhtQueryEmaMixedPackedLinearAttentionLayer( + QueryEmaMixedPackedLinearAttentionLayer +): + """CQER-32 with a deterministic right-side RHT value-axis codec. + + The transform is orthonormal and stays inside each existing + ``[head, key-row]`` quantization group. It therefore preserves CQER's row + identities and packed-state byte contract. The current implementation + materializes transient sign and transform workspaces and makes no latency or + peak-memory claim. + """ + + transform_name = "right_rht_sha256_signs_v1" + transform_seed = RHT_SEED + + def _quantized_endpoint( + self, + recurrent_states: torch.Tensor, + spec: QuantizationSpec, + ) -> torch.Tensor: + encoded = right_rht_encode( + recurrent_states, + layer_index=self.layer_index, + expected_heads=self.expected_heads, + output_dtype=torch.float32, + ) + quantized = quantize_dequantize(encoded, spec).tensor + return right_rht_decode( + quantized, + layer_index=self.layer_index, + expected_heads=self.expected_heads, + output_dtype=torch.float32, + ) + + def _aligned_mse_benefit( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + # Orthonormal right-RHT preserves each row's squared error, so the + # selector can compare transformed endpoints without decoding either. + encoded = right_rht_encode( + recurrent_states, + layer_index=self.layer_index, + expected_heads=self.expected_heads, + output_dtype=torch.float32, + ) + low = quantize_dequantize(encoded, low_spec).tensor.to(torch.float32) + high = quantize_dequantize(encoded, high_spec).tensor.to(torch.float32) + return (low - encoded).square().mean(dim=-1) - (high - encoded).square().mean( + dim=-1 + ) + + def _pack_state( + self, + recurrent_states: torch.Tensor, + mask: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> PackedMixedQuantizedTensor: + return quantize_pack_mixed( + recurrent_states, + mask, + low_spec=low_spec, + high_spec=high_spec, + right_rht_layer_index=self.layer_index, + right_rht_expected_heads=self.expected_heads, + ) + + def query_ema_diagnostics(self) -> dict[str, int | float | bool | str | None]: + diagnostics = super().query_ema_diagnostics() + diagnostics.update( + { + "selection_method": ( + "right_rht_query_ema32_weighted_mse_target_fisher_quota" + ), + "state_codec": self.transform_name, + "state_codec_seed": self.transform_seed, + "state_codec_axis": "value", + "state_codec_normalization": "orthonormal", + "state_codec_persistent_tensor_bytes": 0, + } + ) + return diagnostics + + +@dataclass(slots=True) +class _PendingTransitionObservation: + update_index: int + token_count: int + candidate_diagonal: torch.Tensor + consumed: bool = False + raw_mask: torch.Tensor | None = None + committed_mask: torch.Tensor | None = None + scores: torch.Tensor | None = None + raw_cutoff_score: float | None = None + raw_score_gap: float | None = None + raw_normalized_gap: float | None = None + committed_cutoff_score: float | None = None + committed_score_gap: float | None = None + committed_normalized_gap: float | None = None + + +class CoraMixedPackedLinearAttentionLayer(MixedPackedLinearAttentionLayer): + """Causal observability row selector with optional two-hit confirmation. + + The persistent selector state is a trace-normalized FP32 diagonal over all + ``[head, key_row]`` entries. Every candidate transition is computed in an + FP64 workspace and forms one transaction with the packed recurrent state, + previous raw mask, diagnostics, and cache-level update evidence. + """ + + l2norm_eps = _CORA_L2NORM_EPS + + def __init__(self, **kwargs: object) -> None: + super().__init__(**kwargs) + if self.number_of_states != 1: + raise ValueError("CORA currently requires exactly one recurrent state per layer") + self.confirmation_two = True + self.observability_diagonal: torch.Tensor | None = None + self.previous_raw_mask_packed: torch.Tensor | None = None + self._pending_transition_observation: _PendingTransitionObservation | None = None + self._has_observability_history = False + self.transition_observations_staged = 0 + self.transition_observations_consumed = 0 + self.transition_tokens_processed = 0 + self.mask_transition_count = 0 + self.cumulative_raw_xor_churn = 0 + self.cumulative_committed_xor_churn = 0 + self.cumulative_dwell_count = 0 + self.cumulative_admission_count = 0 + self.last_raw_mask_overlap: int | None = None + self.last_committed_mask_overlap: int | None = None + self.last_dwell_count: int | None = None + self.last_admission_count: int | None = None + self.last_raw_cutoff_score: float | None = None + self.last_raw_score_gap: float | None = None + self.last_raw_normalized_gap: float | None = None + self.last_committed_cutoff_score: float | None = None + self.last_committed_score_gap: float | None = None + self.last_committed_normalized_gap: float | None = None + self.last_token_count: int | None = None + + def configure_confirmation_two(self, enabled: bool) -> None: + """Configure raw CORA or CORA-C2 before the first observation.""" + + if not isinstance(enabled, bool): + raise TypeError("confirmation_two must be a bool") + if self._update_count or self._pending_transition_observation is not None: + raise RuntimeError("confirmation_two must be configured before staging or writing") + self.confirmation_two = enabled + + def _initial_diagonal(self, device: torch.device) -> torch.Tensor: + return torch.full( + (self.expected_heads, self.expected_rows), + 1.0 / (self.expected_heads * self.expected_rows), + dtype=torch.float32, + device=device, + ) + + @staticmethod + def _validate_transition_tensor( + value: object, + *, + name: str, + shape: tuple[int, ...], + ) -> torch.Tensor: + if not isinstance(value, torch.Tensor): + raise TypeError(f"{name} observation must be a tensor") + if tuple(value.shape) != shape: + raise ValueError( + f"{name} observation must have shape {shape}; got {tuple(value.shape)}" + ) + if not value.is_floating_point(): + raise TypeError(f"{name} observation must be floating point") + if value.device.type == "meta": + raise ValueError(f"{name} observation must be materialized") + if not torch.isfinite(value).all().item(): + raise ValueError(f"{name} observation must be finite") + return value + + def stage_transition_observation( + self, + query: torch.Tensor, + key: torch.Tensor, + log_decay: torch.Tensor, + beta: torch.Tensor, + *, + l2norm_eps: float = _CORA_L2NORM_EPS, + ) -> None: + """Stage one chronological Qwen kernel transition sequence. + + ``query`` and ``key`` are ``[1, tokens, heads, key_rows]``; + ``log_decay`` and ``beta`` are ``[1, tokens, heads]``. The observer calls + this only after the corresponding state kernel succeeds. + """ + + if self._pending_transition_observation is not None: + self.discard_pending_transition_observation() + raise RuntimeError( + f"layer {self.layer_index} received a duplicate transition observation; " + "the pending observation was discarded" + ) + if isinstance(l2norm_eps, bool) or not isinstance(l2norm_eps, Real): + raise TypeError("l2norm_eps must be a real number") + epsilon = float(l2norm_eps) + if not math.isfinite(epsilon) or epsilon != _CORA_L2NORM_EPS: + raise ValueError("CORA l2norm_eps is frozen at 1e-6") + if not isinstance(query, torch.Tensor) or query.ndim != 4: + actual = tuple(query.shape) if isinstance(query, torch.Tensor) else type(query).__name__ + raise ValueError(f"query observation must have rank 4; got {actual}") + token_count = query.shape[1] + vector_shape = (1, token_count, self.expected_heads, self.expected_rows) + gate_shape = (1, token_count, self.expected_heads) + if token_count <= 0: + raise ValueError("transition observation must contain at least one token") + query = self._validate_transition_tensor(query, name="query", shape=vector_shape) + key = self._validate_transition_tensor(key, name="key", shape=vector_shape) + log_decay = self._validate_transition_tensor( + log_decay, + name="log_decay", + shape=gate_shape, + ) + beta = self._validate_transition_tensor(beta, name="beta", shape=gate_shape) + devices = {query.device, key.device, log_decay.device, beta.device} + if len(devices) != 1: + raise ValueError("all transition observations must use the same device") + if ( + self.observability_diagonal is not None + and self.observability_diagonal.device != query.device + ): + raise ValueError( + "transition observation and observability diagonal must share a device" + ) + if self.device is not None and torch.device(self.device) != query.device: + raise ValueError("transition observation and recurrent state must share a device") + + with torch.no_grad(): + previous = self.observability_diagonal + if previous is None: + previous = self._initial_diagonal(query.device) + p = previous.detach().to(torch.float64) + q64 = query.detach().squeeze(0).to(torch.float64) + k64 = key.detach().squeeze(0).to(torch.float64) + g64 = log_decay.detach().squeeze(0).to(torch.float64) + beta64 = beta.detach().squeeze(0).to(torch.float64) + for token_index in range(token_count): + q_token = q64[token_index] + k_token = k64[token_index] + q_hat = q_token / torch.sqrt( + q_token.square().sum(dim=-1, keepdim=True) + epsilon + ) + k_hat = k_token / torch.sqrt( + k_token.square().sum(dim=-1, keepdim=True) + epsilon + ) + q_star_squared = q_hat.square() / self.expected_rows + u = k_hat.square() + c = (p * u).sum(dim=-1, keepdim=True) + beta_token = beta64[token_index].unsqueeze(-1) + decay_squared = torch.exp(2.0 * g64[token_index]).unsqueeze(-1) + cross = torch.clamp(c - p * u, min=0.0) + candidate = q_star_squared + decay_squared * ( + p * (1.0 - beta_token * u).square() + + beta_token.square() * u * cross + ) + trace = candidate.sum() + if not torch.isfinite(candidate).all().item() or not torch.isfinite(trace).item(): + raise RuntimeError( + f"layer {self.layer_index} CORA recurrence produced non-finite values" + ) + if trace.item() <= 0.0: + raise RuntimeError( + f"layer {self.layer_index} CORA recurrence produced non-positive trace" + ) + p = candidate / trace + candidate_diagonal = p.to(torch.float32) + if not torch.isfinite(candidate_diagonal).all().item(): + raise RuntimeError( + f"layer {self.layer_index} CORA FP32 diagonal is non-finite" + ) + if candidate_diagonal.sum().item() <= 0.0: + raise RuntimeError( + f"layer {self.layer_index} CORA FP32 diagonal has non-positive trace" + ) + + self._pending_transition_observation = _PendingTransitionObservation( + update_index=self._update_count, + token_count=token_count, + candidate_diagonal=candidate_diagonal, + ) + + def discard_pending_transition_observation(self) -> None: + """Idempotently discard the current uncommitted transition candidate.""" + + self._pending_transition_observation = None + + def _precision_mask( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + MixedPackedLinearAttentionLayer._precision_mask( + self, + recurrent_states, + low_spec=low_spec, + high_spec=high_spec, + ) + if recurrent_states.shape[0] != 1: + raise ValueError( + "CORA mixed cache selection requires batch size 1; " + f"got {recurrent_states.shape[0]}" + ) + pending = self._pending_transition_observation + if pending is None: + raise RuntimeError( + f"layer {self.layer_index} has no staged transition observation for " + f"state update {self._update_count}" + ) + try: + if pending.consumed: + raise RuntimeError("transition observation was consumed more than once") + if pending.update_index != self._update_count: + raise RuntimeError( + f"layer {self.layer_index} has stale transition observation for update " + f"{pending.update_index}; expected {self._update_count}" + ) + expected_shape = (self.expected_heads, self.expected_rows) + diagonal = pending.candidate_diagonal + if tuple(diagonal.shape) != expected_shape or diagonal.dtype != torch.float32: + raise RuntimeError( + f"staged observability diagonal must be FP32 with shape {expected_shape}" + ) + if diagonal.device != recurrent_states.device: + raise ValueError("observability diagonal and recurrent state must share a device") + if not torch.isfinite(diagonal).all().item() or diagonal.sum().item() <= 0.0: + raise RuntimeError( + "staged observability diagonal must be finite with positive trace" + ) + + with torch.no_grad(): + source = recurrent_states.detach().to(torch.float32) + low = quantize_dequantize(recurrent_states, low_spec).tensor.to(torch.float32) + high = quantize_dequantize(recurrent_states, high_spec).tensor.to(torch.float32) + # The protocol freezes physical row SSE, not an averaged row MSE. + benefit = ( + (low - source).square().sum(dim=-1) + - (high - source).square().sum(dim=-1) + ).reshape(self.expected_heads, self.expected_rows) + scores = (diagonal * benefit).reshape(-1) + if not torch.isfinite(scores).all().item(): + raise RuntimeError("CORA row scores must be finite") + quota = len(self.high_precision_group_indices) + raw_mask = _stable_top_mask(scores, quota) + previous = self.packed_states[0] + previous_committed = ( + previous.high_precision_mask().reshape(-1) + if previous is not None and self._has_observability_history + else None + ) + committed_mask = _confirmation_two_mask( + raw_mask=raw_mask, + scores=scores, + quota=quota, + enabled=self.confirmation_two, + has_history=self._has_observability_history, + previous_committed_mask=previous_committed, + previous_raw_mask_packed=self.previous_raw_mask_packed, + ) + ( + pending.raw_cutoff_score, + pending.raw_score_gap, + pending.raw_normalized_gap, + ) = _score_boundary(scores, raw_mask) + ( + pending.committed_cutoff_score, + pending.committed_score_gap, + pending.committed_normalized_gap, + ) = _score_boundary(scores, committed_mask) + pending.scores = scores + pending.raw_mask = raw_mask + pending.committed_mask = committed_mask + pending.consumed = True + return committed_mask.reshape(self.expected_heads, self.expected_rows) + except Exception: + self.discard_pending_transition_observation() + raise + + @staticmethod + def _evidence_checkpoint( + callback: MixedUpdateCallback | None, + ) -> tuple[object, int, int] | None: + owner = getattr(callback, "__self__", None) + evidence = getattr(owner, "update_evidence", None) + update_index = getattr(owner, "_update_index", None) + if isinstance(evidence, list) and isinstance(update_index, int): + return owner, len(evidence), update_index + return None + + @staticmethod + def _restore_evidence(checkpoint: tuple[object, int, int] | None) -> None: + if checkpoint is None: + return + owner, length, update_index = checkpoint + evidence = owner.update_evidence + del evidence[length:] + owner._update_index = update_index + + def _store(self, recurrent_states: torch.Tensor, state_idx: int) -> torch.Tensor: + if state_idx not in self.packed_states: + self.discard_pending_transition_observation() + raise IndexError( + f"layer {self.layer_index} state_idx {state_idx} is outside " + f"[0, {self.number_of_states})" + ) + previous_packed = self.packed_states[state_idx] + previous_initialized = self.is_recurrent_states_initialized[state_idx] + previous_dtype = self.dtype + previous_device = self.device + previous_update_count = self._update_count + previous_history = self._has_observability_history + previous_diagonal = self.observability_diagonal + previous_raw_packed = self.previous_raw_mask_packed + previous_committed: torch.Tensor | None = None + previous_raw: torch.Tensor | None = None + try: + previous_committed = ( + previous_packed.high_precision_mask().reshape(-1) + if previous_packed is not None and previous_history + else None + ) + previous_raw = ( + _unpack_row_mask( + previous_raw_packed, + self.expected_heads * self.expected_rows, + ) + if previous_raw_packed is not None and previous_history + else None + ) + except Exception: + self.discard_pending_transition_observation() + raise + callback = self.on_update + evidence_checkpoint = self._evidence_checkpoint(callback) + candidate_raw_packed: torch.Tensor | None = None + raw_xor = committed_xor = raw_overlap = committed_overlap = admissions = None + try: + if previous_history and (previous_raw is None or previous_committed is None): + raise RuntimeError("CORA transition history is incomplete") + self.on_update = None + try: + materialized = super()._store(recurrent_states, state_idx) + finally: + self.on_update = callback + pending = self._pending_transition_observation + if ( + pending is None + or not pending.consumed + or pending.raw_mask is None + or pending.committed_mask is None + or pending.scores is None + ): + raise RuntimeError( + f"layer {self.layer_index} packed a state without consuming exactly " + "one complete CORA transition observation" + ) + current = self.packed_states[state_idx] + if current is None: + raise RuntimeError("CORA packed state disappeared") + current_mask = current.high_precision_mask().reshape(-1) + quota = len(self.high_precision_group_indices) + if not torch.equal(current_mask, pending.committed_mask): + raise RuntimeError("packed precision mask differs from CORA committed mask") + if ( + int(pending.raw_mask.sum().item()) != quota + or int(current_mask.sum().item()) != quota + ): + raise RuntimeError("CORA raw or committed mask violates the exact quota") + candidate_raw_packed = _pack_row_mask(pending.raw_mask) + if previous_history: + assert previous_raw is not None + assert previous_committed is not None + raw_xor = int((previous_raw ^ pending.raw_mask).sum().item()) + committed_xor = int( + (previous_committed ^ pending.committed_mask).sum().item() + ) + raw_overlap = int((previous_raw & pending.raw_mask).sum().item()) + committed_overlap = int( + (previous_committed & pending.committed_mask).sum().item() + ) + admissions = int( + (~previous_committed & pending.committed_mask).sum().item() + ) + if callback is not None: + callback( + self.layer_index, + state_idx, + recurrent_states, + current, + materialized, + ) + except Exception: + self.on_update = callback + self.packed_states[state_idx] = previous_packed + self.is_recurrent_states_initialized[state_idx] = previous_initialized + self.dtype = previous_dtype + self.device = previous_device + self._update_count = previous_update_count + self._has_observability_history = previous_history + self.observability_diagonal = previous_diagonal + self.previous_raw_mask_packed = previous_raw_packed + self._restore_evidence(evidence_checkpoint) + self.discard_pending_transition_observation() + raise + + # No mutation of selector state or counters occurs before every packing, + # mask, materialization, and evidence postcondition above has succeeded. + assert candidate_raw_packed is not None + self.observability_diagonal = pending.candidate_diagonal + self.previous_raw_mask_packed = candidate_raw_packed + self.transition_observations_staged += 1 + self.transition_observations_consumed += 1 + self.transition_tokens_processed += pending.token_count + self.last_token_count = pending.token_count + if previous_history: + assert raw_xor is not None + assert committed_xor is not None + assert raw_overlap is not None + assert committed_overlap is not None + assert admissions is not None + self.mask_transition_count += 1 + self.cumulative_raw_xor_churn += raw_xor + self.cumulative_committed_xor_churn += committed_xor + self.cumulative_dwell_count += committed_overlap + self.cumulative_admission_count += admissions + self.last_raw_mask_overlap = raw_overlap + self.last_committed_mask_overlap = committed_overlap + self.last_dwell_count = committed_overlap + self.last_admission_count = admissions + else: + self.last_raw_mask_overlap = None + self.last_committed_mask_overlap = None + self.last_dwell_count = None + self.last_admission_count = None + self.last_raw_cutoff_score = pending.raw_cutoff_score + self.last_raw_score_gap = pending.raw_score_gap + self.last_raw_normalized_gap = pending.raw_normalized_gap + self.last_committed_cutoff_score = pending.committed_cutoff_score + self.last_committed_score_gap = pending.committed_score_gap + self.last_committed_normalized_gap = pending.committed_normalized_gap + self._has_observability_history = True + self.discard_pending_transition_observation() + return materialized + + def observability_diagonal_bytes(self) -> int: + if self.observability_diagonal is None: + return 0 + return self.observability_diagonal.numel() * self.observability_diagonal.element_size() + + def previous_raw_mask_bytes(self) -> int: + if self.previous_raw_mask_packed is None: + return 0 + return ( + self.previous_raw_mask_packed.numel() + * self.previous_raw_mask_packed.element_size() + ) + + def selector_auxiliary_bytes(self) -> int: + return self.observability_diagonal_bytes() + self.previous_raw_mask_bytes() + + @staticmethod + def _mask_sha256(packed_mask: torch.Tensor | None) -> str | None: + if packed_mask is None: + return None + return hashlib.sha256( + bytes(packed_mask.detach().cpu().contiguous().tolist()) + ).hexdigest() + + def observability_diagnostics(self) -> dict[str, int | float | bool | str | None]: + packed = self.packed_states[0] + committed_packed = None if packed is None else packed.precision_mask + quota = len(self.high_precision_group_indices) + transitions = self.mask_transition_count + denominator = 2 * quota * transitions + diagonal = self.observability_diagonal + return { + "layer_index": self.layer_index, + "selection_method": ( + "causal_observability_confirm2_mse_target_fisher_quota" + if self.confirmation_two + else "causal_observability_mse_target_fisher_quota" + ), + "confirmation_two": self.confirmation_two, + "quota": quota, + "current_selected_count": 0 if packed is None else packed.high_precision_groups, + "raw_mask_sha256": self._mask_sha256(self.previous_raw_mask_packed), + "committed_mask_sha256": self._mask_sha256(committed_packed), + "observations_staged": self.transition_observations_staged, + "observations_committed": self.transition_observations_consumed, + "tokens_observed": self.transition_tokens_processed, + "last_token_count": self.last_token_count, + "mask_transition_count": transitions, + "raw_xor_churn_total": self.cumulative_raw_xor_churn, + "committed_xor_churn_total": self.cumulative_committed_xor_churn, + "raw_normalized_churn": ( + self.cumulative_raw_xor_churn / denominator if denominator else None + ), + "committed_normalized_churn": ( + self.cumulative_committed_xor_churn / denominator if denominator else None + ), + "last_raw_mask_overlap": self.last_raw_mask_overlap, + "last_committed_mask_overlap": self.last_committed_mask_overlap, + "last_dwell_count": self.last_dwell_count, + "last_admission_count": self.last_admission_count, + "dwell_total": self.cumulative_dwell_count, + "admissions_total": self.cumulative_admission_count, + "last_raw_cutoff_score": self.last_raw_cutoff_score, + "last_raw_score_gap": self.last_raw_score_gap, + "last_raw_normalized_gap": self.last_raw_normalized_gap, + "last_committed_cutoff_score": self.last_committed_cutoff_score, + "last_committed_score_gap": self.last_committed_score_gap, + "last_committed_normalized_gap": self.last_committed_normalized_gap, + "observability_trace": None if diagonal is None else float(diagonal.sum().item()), + "observability_min": None if diagonal is None else float(diagonal.min().item()), + "observability_max": None if diagonal is None else float(diagonal.max().item()), + "observability_dtype": None if diagonal is None else str(diagonal.dtype), + "l2norm_eps": self.l2norm_eps, + "state_updates": self._update_count, + "pending_observation": self._pending_transition_observation is not None, + "observability_diagonal_bytes": self.observability_diagonal_bytes(), + "previous_raw_mask_bytes": self.previous_raw_mask_bytes(), + "selector_auxiliary_bytes": self.selector_auxiliary_bytes(), + } + + def reset(self) -> None: + super().reset() + self.discard_pending_transition_observation() + if self.observability_diagonal is not None: + self.observability_diagonal.fill_( + 1.0 / (self.expected_heads * self.expected_rows) + ) + self.previous_raw_mask_packed = None + self._update_count = 0 + self._has_observability_history = False + self.transition_observations_staged = 0 + self.transition_observations_consumed = 0 + self.transition_tokens_processed = 0 + self.mask_transition_count = 0 + self.cumulative_raw_xor_churn = 0 + self.cumulative_committed_xor_churn = 0 + self.cumulative_dwell_count = 0 + self.cumulative_admission_count = 0 + self.last_raw_mask_overlap = None + self.last_committed_mask_overlap = None + self.last_dwell_count = None + self.last_admission_count = None + self.last_raw_cutoff_score = None + self.last_raw_score_gap = None + self.last_raw_normalized_gap = None + self.last_committed_cutoff_score = None + self.last_committed_score_gap = None + self.last_committed_normalized_gap = None + self.last_token_count = None + + def _reject_pending_transfer(self, operation: str) -> None: + if self._pending_transition_observation is None: + return + self.discard_pending_transition_observation() + raise RuntimeError( + f"layer {self.layer_index} cannot {operation} with a pending transition " + "observation; it was discarded" + ) + + def offload(self) -> None: + self._reject_pending_transfer("offload") + super().offload() + if self.observability_diagonal is not None: + self.observability_diagonal = self.observability_diagonal.to("cpu") + if self.previous_raw_mask_packed is not None: + self.previous_raw_mask_packed = self.previous_raw_mask_packed.to("cpu") + + def prefetch(self) -> None: + self._reject_pending_transfer("prefetch") + super().prefetch() + if self.device is None: + return + device = torch.device(self.device) + if self.observability_diagonal is not None and self.observability_diagonal.device != device: + self.observability_diagonal = self.observability_diagonal.to( + device, + non_blocking=True, + ) + if ( + self.previous_raw_mask_packed is not None + and self.previous_raw_mask_packed.device != device + ): + self.previous_raw_mask_packed = self.previous_raw_mask_packed.to( + device, + non_blocking=True, + ) + + +def _validate_static_rank_weight(value: object) -> float: + if isinstance(value, bool) or not isinstance(value, Real): + raise TypeError("static_rank_weight must be a real number") + weight = float(value) + if not math.isfinite(weight) or not 0.0 <= weight <= 1.0: + raise ValueError("static_rank_weight must be finite and in [0, 1]") + return weight + + +def _descending_rank_positions(values: torch.Tensor) -> torch.Tensor: + """Return deterministic zero-best ordinal rank positions.""" + + flat = values.reshape(-1) + order = torch.argsort(flat, descending=True, stable=True) + ranks = torch.empty(flat.numel(), dtype=torch.int64, device=flat.device) + ranks[order] = torch.arange(flat.numel(), dtype=torch.int64, device=flat.device) + return ranks + + +class RankFusedMixedPackedLinearAttentionLayer(AdaptiveMixedPackedLinearAttentionLayer): + """Fuse calibrated static ranks with causal per-write MSE-benefit ranks.""" + + def __init__(self, **kwargs: object) -> None: + super().__init__(**kwargs) + self.static_rank_weight: float | None = None + self.static_rank_positions: torch.Tensor | None = None + + def configure_rank_fusion( + self, + static_scores: torch.Tensor, + *, + static_rank_weight: float, + ) -> None: + expected_shape = (self.expected_heads, self.expected_rows) + if tuple(static_scores.shape) != expected_shape: + raise ValueError( + f"layer {self.layer_index} static scores must have shape {expected_shape}; " + f"got {tuple(static_scores.shape)}" + ) + if not static_scores.is_floating_point(): + raise TypeError(f"layer {self.layer_index} static scores must be floating point") + if static_scores.device.type == "meta": + raise ValueError(f"layer {self.layer_index} static scores must be materialized") + if not torch.isfinite(static_scores).all().item(): + raise ValueError(f"layer {self.layer_index} static scores must be finite") + self.static_rank_weight = _validate_static_rank_weight(static_rank_weight) + with torch.no_grad(): + self.static_rank_positions = _descending_rank_positions( + static_scores.detach().clone() + ) + + def _precision_mask( + self, + recurrent_states: torch.Tensor, + *, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + ) -> torch.Tensor: + # Reuse the complete geometry checks without applying the static mask. + MixedPackedLinearAttentionLayer._precision_mask( + self, + recurrent_states, + low_spec=low_spec, + high_spec=high_spec, + ) + if recurrent_states.shape[0] != 1: + raise ValueError( + "rank-fused mixed cache selection currently requires batch size 1; " + f"got {recurrent_states.shape[0]}" + ) + if self.static_rank_weight is None or self.static_rank_positions is None: + raise RuntimeError(f"layer {self.layer_index} rank fusion was not configured") + if self.static_rank_positions.device != recurrent_states.device: + raise ValueError( + f"layer {self.layer_index} static scores and recurrent state must use " + "the same device" + ) + + total_groups = self.expected_heads * self.expected_rows + quota = len(self.high_precision_group_indices) + mask = torch.zeros( + total_groups, + dtype=torch.bool, + device=recurrent_states.device, + ) + if quota == 0: + return mask.reshape(self.expected_heads, self.expected_rows) + if quota == total_groups: + return torch.ones_like(mask).reshape(self.expected_heads, self.expected_rows) + + with torch.no_grad(): + source = recurrent_states.detach().to(torch.float32) + low = quantize_dequantize(recurrent_states, low_spec).tensor.to(torch.float32) + high = quantize_dequantize(recurrent_states, high_spec).tensor.to(torch.float32) + dynamic_benefit = ( + (low - source).square().mean(dim=-1) + - (high - source).square().mean(dim=-1) + ).reshape(-1) + dynamic_rank = _descending_rank_positions(dynamic_benefit) + quarter_units = self.static_rank_weight * 4.0 + if quarter_units.is_integer(): + static_units = int(quarter_units) + fused_cost = ( + static_units * self.static_rank_positions + + (4 - static_units) * dynamic_rank + ) + else: + fused_cost = self.static_rank_weight * self.static_rank_positions + ( + 1.0 - self.static_rank_weight + ) * dynamic_rank + ranked = torch.argsort(fused_cost, descending=False, stable=True) + mask[ranked[:quota]] = True + return mask.reshape(self.expected_heads, self.expected_rows) + + +class MixedPackedRecurrentStateCache(DynamicCache): + """Drop-in cache driven by an exact-byte row-level INT4/INT8 plan.""" + + _mixed_layer_class = MixedPackedLinearAttentionLayer + selection_method = "static_plan" + + def __init__( + self, + config: object, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, + ) -> None: + super().__init__(config=config) + self.plan = plan + self.record_evidence = record_evidence + self.update_evidence: list[MixedPackedCacheUpdateEvidence] = [] + self._update_index = 0 + + linear_layer_indices = { + layer_index + for layer_index, layer in enumerate(self.layers) + if isinstance(layer, LinearAttentionLayer) + } + shapes = _validate_exact_budget_plan(plan, linear_layer_indices) + low_spec = QuantizationSpec( + bits=plan.low_bits, + group_size=plan.group_size, + scale_bits=plan.scale_bits, + rounding=rounding, + seed=seed, + ) + high_spec = QuantizationSpec( + bits=plan.high_bits, + group_size=plan.group_size, + scale_bits=plan.scale_bits, + rounding=rounding, + seed=seed, + ) + + replaced = 0 + for layer_index, layer in enumerate(self.layers): + if isinstance(layer, LinearAttentionLayer): + expected_heads, expected_rows = shapes[layer_index] + self.layers[layer_index] = self._mixed_layer_class( + low_spec=low_spec, + high_spec=high_spec, + layer_index=layer_index, + expected_heads=expected_heads, + expected_rows=expected_rows, + high_precision_group_indices=plan.groups_for_layer(layer_index), + number_of_states=layer.number_of_states, + on_update=self._record_update if record_evidence else None, + ) + replaced += 1 + if replaced == 0: + raise TypeError("config did not create any Transformers linear-attention layers") + + def _record_update( + self, + layer_index: int, + state_index: int, + source: torch.Tensor, + packed: PackedMixedQuantizedTensor, + materialized: torch.Tensor, + ) -> None: + error = materialized.to(torch.float32) - source.detach().to(torch.float32) + source_norm = torch.linalg.vector_norm(source.detach().to(torch.float32)) + relative_l2 = torch.linalg.vector_norm(error) / source_norm.clamp_min(1e-12) + self.update_evidence.append( + MixedPackedCacheUpdateEvidence( + update_index=self._update_index, + layer_index=layer_index, + state_index=state_index, + low_bits=packed.low_spec.bits, + high_bits=packed.high_spec.bits, + group_size=packed.low_spec.group_size, + scale_bits=packed.low_spec.scale_bits, + rounding=packed.low_spec.rounding, + source_dtype=str(source.dtype), + shape=tuple(source.shape), + total_groups=packed.total_groups, + high_precision_groups=packed.high_precision_groups, + selection_method=self.selection_method, + high_precision_mask_sha256=hashlib.sha256( + bytes(packed.precision_mask.detach().cpu().contiguous().tolist()) + ).hexdigest(), + baseline_bytes=source.numel() * source.element_size(), + payload_bytes=packed.payload_bytes, + scale_bytes=packed.scale_bytes, + mask_bytes=packed.mask_bytes, + resident_bytes=packed.storage_bytes, + relative_l2_error=float(relative_l2.item()), + mean_squared_error=float(error.square().mean().item()), + max_absolute_error=float(error.abs().max().item()), + ) + ) + self._update_index += 1 + + def mixed_packed_layers(self) -> Iterator[tuple[int, MixedPackedLinearAttentionLayer]]: + for layer_index, layer in enumerate(self.layers): + if isinstance(layer, MixedPackedLinearAttentionLayer): + yield layer_index, layer + + def resident_payload_bytes(self) -> int: + return sum(layer.resident_payload_bytes() for _, layer in self.mixed_packed_layers()) + + def resident_scale_bytes(self) -> int: + return sum(layer.resident_scale_bytes() for _, layer in self.mixed_packed_layers()) + + def resident_mask_bytes(self) -> int: + return sum(layer.resident_mask_bytes() for _, layer in self.mixed_packed_layers()) + + def resident_recurrent_state_bytes(self) -> int: + return ( + self.resident_payload_bytes() + + self.resident_scale_bytes() + + self.resident_mask_bytes() + ) + + def full_precision_equivalent_recurrent_state_bytes(self) -> int: + return sum( + layer.full_precision_equivalent_recurrent_state_bytes() + for _, layer in self.mixed_packed_layers() + ) + + def largest_materialized_recurrent_state_bytes(self) -> int: + return max( + ( + layer.largest_materialized_recurrent_state_bytes() + for _, layer in self.mixed_packed_layers() + ), + default=0, + ) + + def high_precision_group_count(self) -> int: + return sum(layer.high_precision_group_count() for _, layer in self.mixed_packed_layers()) + + def storage_summary(self) -> dict[str, int | float | bool]: + payload = self.resident_payload_bytes() + scales = self.resident_scale_bytes() + mask = self.resident_mask_bytes() + resident = payload + scales + mask + full_precision_equivalent = self.full_precision_equivalent_recurrent_state_bytes() + return { + "payload_bytes": payload, + "scale_bytes": scales, + "mask_bytes": mask, + "resident_bytes": resident, + "high_precision_groups": self.high_precision_group_count(), + "full_precision_equivalent_bytes": full_precision_equivalent, + "largest_materialized_state_bytes": ( + self.largest_materialized_recurrent_state_bytes() + ), + "resident_compression_ratio": ( + full_precision_equivalent / resident if resident else 0.0 + ), + "physical_reduction_realized": ( + resident > 0 and resident < full_precision_equivalent + ), + } + + +class AdaptiveMixedPackedRecurrentStateCache(MixedPackedRecurrentStateCache): + """Exact-byte adaptive-row prototype for batch-one inference diagnostics. + + Per-layer promotion quotas come from the supplied exact-budget plan, so the + resident representation has the same payload, scale, and mask byte counts as + its static counterpart. Row identities may change at every storage update. + This class makes no quality, speed, or novelty claim. + """ + + _mixed_layer_class = AdaptiveMixedPackedLinearAttentionLayer + selection_method = "instantaneous_aligned_mse_reduction" + + +class QueryEmaMixedPackedRecurrentStateCache(MixedPackedRecurrentStateCache): + """Exact-byte packed-state cache with causal query-energy row selection. + + ``resident_bytes`` continues to describe only the physically packed recurrent + states fixed by the supplied plan. Persistent FP32 EMA metadata is reported + separately as ``selector_auxiliary_bytes`` and included only in + ``resident_bytes_including_selector``. + """ + + _mixed_layer_class = QueryEmaMixedPackedLinearAttentionLayer + selection_method = "query_ema32_weighted_aligned_mse_reduction" + + def __init__( + self, + config: object, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, + confirmation_two: bool = False, + ) -> None: + if not isinstance(confirmation_two, bool): + raise TypeError("confirmation_two must be a bool") + super().__init__( + config, + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + self.confirmation_two = confirmation_two + self.selection_method = ( + "query_ema32_confirm2_mse_target_fisher_quota" + if confirmation_two + else "query_ema32_weighted_aligned_mse_reduction" + ) + for _, layer in self.mixed_packed_layers(): + if not isinstance(layer, QueryEmaMixedPackedLinearAttentionLayer): + raise RuntimeError("query-EMA cache contains an incompatible layer") + layer.configure_confirmation_two(confirmation_two) + + def _query_ema_layer(self, layer_index: int) -> QueryEmaMixedPackedLinearAttentionLayer: + if isinstance(layer_index, bool) or not isinstance(layer_index, int): + raise TypeError("layer_index must be an integer") + if layer_index < 0 or layer_index >= len(self.layers): + raise IndexError(f"layer_index {layer_index} is outside this cache") + layer = self.layers[layer_index] + if not isinstance(layer, QueryEmaMixedPackedLinearAttentionLayer): + raise ValueError( + f"layer {layer_index} is not a query-EMA linear-attention layer" + ) + return layer + + def stage_query_observation( + self, + layer_index: int, + query: torch.Tensor, + *, + l2norm_eps: float = _QUERY_L2NORM_EPS, + ) -> None: + """Stage one causal query tensor for the layer's next state write.""" + + self._query_ema_layer(layer_index).stage_query_observation( + query, + l2norm_eps=l2norm_eps, + ) + + def discard_pending_query_observation(self, layer_index: int) -> None: + """Idempotently discard one layer's pending query observation.""" + + self._query_ema_layer(layer_index).discard_pending_query_observation() + + def selector_auxiliary_bytes(self) -> int: + return sum( + layer.selector_auxiliary_bytes() + for _, layer in self.mixed_packed_layers() + if isinstance(layer, QueryEmaMixedPackedLinearAttentionLayer) + ) + + def query_ema_diagnostics( + self, + ) -> list[dict[str, int | float | bool | str | None]]: + return [ + layer.query_ema_diagnostics() + for _, layer in self.mixed_packed_layers() + if isinstance(layer, QueryEmaMixedPackedLinearAttentionLayer) + ] + + def storage_summary(self) -> dict[str, int | float | bool]: + summary = super().storage_summary() + auxiliary = self.selector_auxiliary_bytes() + resident = int(summary["resident_bytes"]) + total = resident + auxiliary + full_precision_equivalent = int(summary["full_precision_equivalent_bytes"]) + summary.update( + { + "selector_auxiliary_bytes": auxiliary, + "resident_bytes_including_selector": total, + "resident_compression_ratio_including_selector": ( + full_precision_equivalent / total if total else 0.0 + ), + "physical_reduction_realized_including_selector": ( + total > 0 and total < full_precision_equivalent + ), + } + ) + return summary + + +class RightRhtQueryEmaMixedPackedRecurrentStateCache( + QueryEmaMixedPackedRecurrentStateCache +): + """Exact-byte CQER-32 cache with a deterministic right-side RHT codec.""" + + _mixed_layer_class = RightRhtQueryEmaMixedPackedLinearAttentionLayer + selection_method = "right_rht_query_ema32_weighted_mse_target_fisher_quota" + + def __init__(self, *args: object, confirmation_two: bool = False, **kwargs: object) -> None: + if confirmation_two: + raise ValueError("right-RHT CQER screening does not support Confirmation-2") + super().__init__( + *args, + confirmation_two=False, + **kwargs, + ) + self.selection_method = "right_rht_query_ema32_weighted_mse_target_fisher_quota" + + +class CoraMixedPackedRecurrentStateCache(MixedPackedRecurrentStateCache): + """CORA exact-quota cache; Confirmation-2 is enabled by default.""" + + _mixed_layer_class = CoraMixedPackedLinearAttentionLayer + selection_method = "causal_observability_confirm2_mse_target_fisher_quota" + + def __init__( + self, + config: object, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, + confirmation_two: bool = True, + ) -> None: + if not isinstance(confirmation_two, bool): + raise TypeError("confirmation_two must be a bool") + super().__init__( + config, + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + self.confirmation_two = confirmation_two + self.selection_method = ( + "causal_observability_confirm2_mse_target_fisher_quota" + if confirmation_two + else "causal_observability_mse_target_fisher_quota" + ) + for _, layer in self.mixed_packed_layers(): + if not isinstance(layer, CoraMixedPackedLinearAttentionLayer): + raise RuntimeError("CORA cache contains an incompatible layer") + layer.configure_confirmation_two(confirmation_two) + + def _cora_layer(self, layer_index: int) -> CoraMixedPackedLinearAttentionLayer: + if isinstance(layer_index, bool) or not isinstance(layer_index, int): + raise TypeError("layer_index must be an integer") + if layer_index < 0 or layer_index >= len(self.layers): + raise IndexError(f"layer_index {layer_index} is outside this cache") + layer = self.layers[layer_index] + if not isinstance(layer, CoraMixedPackedLinearAttentionLayer): + raise ValueError(f"layer {layer_index} is not a CORA linear-attention layer") + return layer + + def stage_transition_observation( + self, + layer_index: int, + query: torch.Tensor, + key: torch.Tensor, + log_decay: torch.Tensor, + beta: torch.Tensor, + *, + l2norm_eps: float = _CORA_L2NORM_EPS, + ) -> None: + """Stage kernel q/k/log-decay/beta for the matching layer write.""" + + self._cora_layer(layer_index).stage_transition_observation( + query, + key, + log_decay, + beta, + l2norm_eps=l2norm_eps, + ) + + def discard_pending_transition_observations(self, layer_index: int) -> None: + """Idempotently discard a layer's staged transition observation.""" + + self._cora_layer(layer_index).discard_pending_transition_observation() + + def observability_diagonal_bytes(self) -> int: + return sum( + layer.observability_diagonal_bytes() + for _, layer in self.mixed_packed_layers() + if isinstance(layer, CoraMixedPackedLinearAttentionLayer) + ) + + def previous_raw_mask_bytes(self) -> int: + return sum( + layer.previous_raw_mask_bytes() + for _, layer in self.mixed_packed_layers() + if isinstance(layer, CoraMixedPackedLinearAttentionLayer) + ) + + def selector_auxiliary_bytes(self) -> int: + return self.observability_diagonal_bytes() + self.previous_raw_mask_bytes() + + def observability_diagnostics( + self, + ) -> list[dict[str, int | float | bool | str | None]]: + return [ + layer.observability_diagnostics() + for _, layer in self.mixed_packed_layers() + if isinstance(layer, CoraMixedPackedLinearAttentionLayer) + ] + + def selection_diagnostics( + self, + ) -> list[dict[str, int | float | bool | str | None]]: + """Generic evaluator alias for :meth:`observability_diagnostics`.""" + + return self.observability_diagnostics() + + def storage_summary(self) -> dict[str, int | float | bool]: + summary = super().storage_summary() + diagonal = self.observability_diagonal_bytes() + raw_mask = self.previous_raw_mask_bytes() + auxiliary = diagonal + raw_mask + resident = int(summary["resident_bytes"]) + total = resident + auxiliary + full_precision_equivalent = int(summary["full_precision_equivalent_bytes"]) + summary.update( + { + "observability_diagonal_bytes": diagonal, + "previous_raw_mask_bytes": raw_mask, + "selector_auxiliary_bytes": auxiliary, + "resident_bytes_including_selector": total, + "resident_compression_ratio_including_selector": ( + full_precision_equivalent / total if total else 0.0 + ), + "physical_reduction_realized_including_selector": ( + total > 0 and total < full_precision_equivalent + ), + } + ) + return summary + + +class RankFusedMixedPackedRecurrentStateCache(MixedPackedRecurrentStateCache): + """Exact-byte cache fusing static selector and instantaneous MSE ranks.""" + + _mixed_layer_class = RankFusedMixedPackedLinearAttentionLayer + selection_method = "quota_preserving_static_dynamic_rank_fusion" + + def __init__( + self, + config: object, + *, + plan: ExactBudgetRowPlan, + static_scores_by_layer: Mapping[int, torch.Tensor], + static_rank_weight: float, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, + ) -> None: + weight = _validate_static_rank_weight(static_rank_weight) + if not isinstance(static_scores_by_layer, Mapping) or not static_scores_by_layer: + raise ValueError("static_scores_by_layer must be a non-empty mapping") + if any( + isinstance(index, bool) or not isinstance(index, int) or index < 0 + for index in static_scores_by_layer + ): + raise ValueError("static score layer indices must be non-negative integers") + + expected_shapes = { + layer_index: (head_count, row_count) + for layer_index, head_count, row_count in plan.score_shapes + } + actual_layers = set(static_scores_by_layer) + expected_layers = set(expected_shapes) + if actual_layers != expected_layers: + missing = sorted(expected_layers - actual_layers) + extra = sorted(actual_layers - expected_layers) + raise ValueError( + "static score layers must exactly match the row plan; " + f"missing={missing}, extra={extra}" + ) + + validated_scores: dict[int, torch.Tensor] = {} + score_devices: set[torch.device] = set() + for layer_index in sorted(expected_layers): + scores = static_scores_by_layer[layer_index] + if not isinstance(scores, torch.Tensor): + raise TypeError(f"layer {layer_index} static scores must be a tensor") + expected_shape = expected_shapes[layer_index] + if tuple(scores.shape) != expected_shape: + raise ValueError( + f"layer {layer_index} static scores must have shape {expected_shape}; " + f"got {tuple(scores.shape)}" + ) + if not scores.is_floating_point(): + raise TypeError(f"layer {layer_index} static scores must be floating point") + if scores.device.type == "meta": + raise ValueError(f"layer {layer_index} static scores must be materialized") + if not torch.isfinite(scores).all().item(): + raise ValueError(f"layer {layer_index} static scores must be finite") + validated_scores[layer_index] = scores.detach().clone() + score_devices.add(scores.device) + if len(score_devices) != 1: + raise ValueError("all static score tensors must use the same device") + + self.static_rank_weight = weight + self.static_scores_by_layer = validated_scores + super().__init__( + config, + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + for layer_index, layer in self.mixed_packed_layers(): + if not isinstance(layer, RankFusedMixedPackedLinearAttentionLayer): + raise RuntimeError(f"layer {layer_index} is not rank-fusion capable") + layer.configure_rank_fusion( + validated_scores[layer_index], + static_rank_weight=weight, + ) diff --git a/src/recurquant/public_data.py b/src/recurquant/public_data.py index 78b387f..fca0a0d 100644 --- a/src/recurquant/public_data.py +++ b/src/recurquant/public_data.py @@ -127,6 +127,61 @@ def load_mbpp_rows( return rows if limit is None else rows[:limit] +def load_mbpp_rows_by_task_ids( + phase: MBPPPhase | str, + *, + task_ids: Sequence[int], + load_dataset_fn: LoadDataset | None = None, +) -> tuple[MBPPRow, ...]: + """Load only an already-pinned ordered row identity from a public split. + + This path is intended for preregistered calibration/development identities: + it streams the source split, reads only ``task_id`` on non-target records, + retains only requested rows, and returns them in the caller's frozen order. + It deliberately refuses confirmation data and duplicate or missing IDs. + """ + + normalized_phase = _coerce_phase(phase) + if normalized_phase is MBPPPhase.CONFIRMATION: + raise ValueError( + "task-ID loading is forbidden for confirmation; use the explicit " + "whole-split confirmation contract" + ) + if isinstance(task_ids, (str, bytes)) or not isinstance(task_ids, Sequence): + raise TypeError("task_ids must be an ordered sequence of integers") + ordered_ids = tuple(_task_id(task_id) for task_id in task_ids) + if not ordered_ids: + raise ValueError("task_ids must not be empty") + if len(set(ordered_ids)) != len(ordered_ids): + raise ValueError("task_ids must be unique") + + targets = set(ordered_ids) + selected: dict[int, MBPPRow] = {} + loader = load_dataset_fn or _load_dataset_lazily + loaded = loader( + MBPP_DATASET_ID, + MBPP_CONFIG, + revision=MBPP_REVISION, + split=mbpp_source_split(normalized_phase), + streaming=True, + ) + for raw_row in loaded: + task_id = _task_id(raw_row) + if task_id not in targets: + continue + if task_id in selected: + raise ValueError(f"duplicate requested MBPP task_id {task_id} in source split") + selected[task_id] = canonical_mbpp_row(raw_row) + if len(selected) == len(targets): + break + + missing = [task_id for task_id in ordered_ids if task_id not in selected] + if missing: + rendered = ", ".join(str(task_id) for task_id in missing) + raise ValueError(f"requested MBPP task IDs are missing from source split: {rendered}") + return tuple(selected[task_id] for task_id in ordered_ids) + + def mbpp_calibration_key(row_or_task_id: Mapping[str, Any] | int) -> str: """Return the frozen SHA-256 ranking key for calibration selection.""" @@ -225,7 +280,21 @@ def mbpp_manifest_sha256( ) -> str: """Hash a canonical, provenance-bearing MBPP phase manifest.""" - return hashlib.sha256(_canonical_json(build_mbpp_manifest(rows, phase=phase))).hexdigest() + return mbpp_manifest_content_sha256(build_mbpp_manifest(rows, phase=phase)) + + +def mbpp_manifest_content_sha256(manifest: Mapping[str, Any]) -> str: + """Hash the exact content of an embedded MBPP manifest. + + MBPP manifest hashes use compact, sorted JSON rather than the indented + serialization used for whole evidence artifacts. Keeping this operation + public lets artifact consumers authenticate an embedded manifest with the + same byte contract used by :func:`mbpp_manifest_sha256`. + """ + + if not isinstance(manifest, Mapping): + raise TypeError("manifest must be a mapping") + return hashlib.sha256(_canonical_json(manifest)).hexdigest() def format_mbpp_prompt_code(row: Mapping[str, Any]) -> MBPPPromptCode: diff --git a/src/recurquant/query_energy.py b/src/recurquant/query_energy.py new file mode 100644 index 0000000..c3c5c08 --- /dev/null +++ b/src/recurquant/query_energy.py @@ -0,0 +1,290 @@ +"""Causal query observation for Qwen3.5 recurrent-state caches. + +The Qwen3.5 Gated DeltaNet forward computes and repeats its query heads before +calling either the chunk or recurrent kernel, then writes the kernel's final +state to ``cache_params``. This context manager observes that exact kernel +query and stages it on selected caches after the kernel succeeds but before the +state write. Caches not supplied at construction are never inspected or +modified. +""" + +from __future__ import annotations + +import math +from contextvars import ContextVar +from dataclasses import dataclass +from types import TracebackType +from typing import Any + +import torch + +_OBSERVER_ATTRIBUTE = "_recurquant_query_energy_observer" +_PACKED_SEQUENCE_ARGUMENTS = ("cu_seq_lens_q", "cu_seq_lens_k", "cu_seqlens") + + +def _argument( + args: tuple[object, ...], + kwargs: dict[str, object], + name: str, + position: int, +) -> object | None: + if name in kwargs: + return kwargs[name] + return args[position] if len(args) > position else None + + +@dataclass(slots=True) +class _ForwardCall: + module: torch.nn.Module + cache: object + layer_index: int + kernel_calls: int = 0 + stage_attempted: bool = False + staged: bool = False + + +class Qwen35QueryEnergyObserver: + """Stage post-convolution Qwen3.5 queries on selected recurrent caches. + + Parameters + ---------- + model: + A model containing ``Qwen3_5GatedDeltaNet`` modules from the pinned + Transformers implementation. + caches: + Cache objects that implement ``stage_query_observation`` and + ``discard_pending_query_observation``. Dispatch is by object identity, + never equality, so reference and static caches pass through unchanged. + l2norm_eps: + Positive finite lower bound forwarded to the cache's query normalizer. + + The observer supports uncached prefill, cached multi-token chunks, and + single-token decode. Packed ``cu_seqlens`` forwards are rejected until a + segment-aware aggregation rule is defined. + """ + + def __init__( + self, + model: torch.nn.Module, + *, + caches: list[object] | tuple[object, ...], + l2norm_eps: float = 1e-6, + ) -> None: + if not isinstance(model, torch.nn.Module): + raise TypeError("model must be a torch.nn.Module") + if not isinstance(caches, (list, tuple)): + raise TypeError("caches must be a list or tuple of query-aware caches") + if not caches: + raise ValueError("caches must contain at least one query-aware cache") + if isinstance(l2norm_eps, bool) or not isinstance(l2norm_eps, (int, float)): + raise TypeError("l2norm_eps must be a positive finite float") + if not math.isfinite(float(l2norm_eps)) or float(l2norm_eps) <= 0: + raise ValueError("l2norm_eps must be a positive finite float") + + identities = [id(cache) for cache in caches] + if len(set(identities)) != len(identities): + raise ValueError("caches must not contain the same object more than once") + for cache in caches: + if not callable(getattr(cache, "stage_query_observation", None)): + raise TypeError( + "each observed cache must implement stage_query_observation" + ) + if not callable(getattr(cache, "discard_pending_query_observation", None)): + raise TypeError( + "each observed cache must implement " + "discard_pending_query_observation" + ) + + self.model = model + self.caches = tuple(caches) + self.l2norm_eps = float(l2norm_eps) + self._cache_by_identity = {id(cache): cache for cache in self.caches} + self._installed: list[tuple[object, str, object]] = [] + self._active_calls: ContextVar[tuple[_ForwardCall, ...]] = ContextVar( + f"recurquant_query_energy_calls_{id(self)}", + default=(), + ) + + def _gdn_modules(self) -> list[torch.nn.Module]: + modules = [ + module + for module in self.model.modules() + if module.__class__.__name__ == "Qwen3_5GatedDeltaNet" + ] + if not modules: + raise TypeError("model does not contain Qwen3_5GatedDeltaNet modules") + return modules + + def _selected_cache(self, candidate: object | None) -> object | None: + if candidate is None: + return None + selected = self._cache_by_identity.get(id(candidate)) + return selected if selected is candidate else None + + @staticmethod + def _discard(cache: object, layer_index: int) -> None: + cache.discard_pending_query_observation(layer_index) # type: ignore[attr-defined] + + def _discard_after_failure( + self, + call: _ForwardCall, + error: BaseException, + ) -> None: + try: + self._discard(call.cache, call.layer_index) + except BaseException as cleanup_error: + error.add_note( + "RecurQuant could not discard the pending query observation after " + f"the forward failed: {cleanup_error!r}" + ) + + def _make_kernel_wrapper( + self, + module: torch.nn.Module, + original: Any, + ): + def wrapped(*args: object, **kwargs: object): + active_calls = self._active_calls.get() + call = active_calls[-1] if active_calls else None + if call is None or call.module is not module: + return original(*args, **kwargs) + if call.kernel_calls: + raise RuntimeError( + "Qwen3.5 Gated DeltaNet called more than one state kernel in one " + "forward; refusing ambiguous query attribution" + ) + + output = original(*args, **kwargs) + call.kernel_calls += 1 + query = _argument(args, kwargs, "query", 0) + if not isinstance(query, torch.Tensor): + raise TypeError( + "Qwen3.5 Gated DeltaNet kernel did not expose its query tensor" + ) + + call.stage_attempted = True + call.cache.stage_query_observation( # type: ignore[attr-defined] + call.layer_index, + query, + l2norm_eps=self.l2norm_eps, + ) + call.staged = True + return output + + return wrapped + + def _make_forward_wrapper( + self, + module: torch.nn.Module, + original: Any, + ): + layer_index = int(module.layer_idx) # type: ignore[attr-defined] + + def wrapped(*args: object, **kwargs: object): + cache = self._selected_cache(_argument(args, kwargs, "cache_params", 1)) + if cache is None: + return original(*args, **kwargs) + + packed_arguments = [ + name + for name in _PACKED_SEQUENCE_ARGUMENTS + if kwargs.get(name) is not None + ] + if packed_arguments: + rendered = ", ".join(packed_arguments) + raise ValueError( + "Qwen3.5 query-energy observation does not yet support packed " + f"cu_seqlens forwards ({rendered})" + ) + + call = _ForwardCall(module=module, cache=cache, layer_index=layer_index) + token = self._active_calls.set((*self._active_calls.get(), call)) + try: + output = original(*args, **kwargs) + if call.kernel_calls != 1 or not call.stage_attempted or not call.staged: + raise RuntimeError( + "Qwen3.5 Gated DeltaNet did not complete exactly one observed " + "state-kernel call; refusing to continue with an ambiguous " + "query observation" + ) + return output + except BaseException as error: + self._discard_after_failure(call, error) + raise + finally: + self._active_calls.reset(token) + + return wrapped + + def install(self) -> None: + """Install wrappers atomically, rejecting any observer already on the model.""" + + if self._installed: + raise RuntimeError("query-energy observer is already installed") + modules = self._gdn_modules() + observed_objects: list[object] = [self.model, *modules] + if any(hasattr(item, _OBSERVER_ATTRIBUTE) for item in observed_objects): + raise RuntimeError("a query-energy observer is already installed on this model") + + try: + setattr(self.model, _OBSERVER_ATTRIBUTE, self) + self._installed.append((self.model, _OBSERVER_ATTRIBUTE, _Missing)) + for module in modules: + setattr(module, _OBSERVER_ATTRIBUTE, self) + self._installed.append((module, _OBSERVER_ATTRIBUTE, _Missing)) + for attribute in ( + "forward", + "chunk_gated_delta_rule", + "recurrent_gated_delta_rule", + ): + original = getattr(module, attribute) + if not callable(original): + raise TypeError( + f"Qwen3_5GatedDeltaNet.{attribute} must be callable" + ) + wrapper = ( + self._make_forward_wrapper(module, original) + if attribute == "forward" + else self._make_kernel_wrapper(module, original) + ) + restore_value = ( + original if attribute in module.__dict__ else _Missing + ) + setattr(module, attribute, wrapper) + self._installed.append((module, attribute, restore_value)) + except BaseException: + self.remove() + raise + + def remove(self) -> None: + """Restore every wrapped callable and remove installation markers.""" + + while self._installed: + target, attribute, original = self._installed.pop() + if original is _Missing: + if attribute == _OBSERVER_ATTRIBUTE: + if getattr(target, attribute, None) is self: + delattr(target, attribute) + elif attribute in getattr(target, "__dict__", {}): + delattr(target, attribute) + else: + setattr(target, attribute, original) + + def __enter__(self) -> Qwen35QueryEnergyObserver: + self.install() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + self.remove() + + +class _MissingType: + pass + + +_Missing = _MissingType() diff --git a/src/recurquant/qwen35.py b/src/recurquant/qwen35.py index 3ae56b9..d25ac5a 100644 --- a/src/recurquant/qwen35.py +++ b/src/recurquant/qwen35.py @@ -10,8 +10,17 @@ from packaging.specifiers import SpecifierSet from packaging.version import InvalidVersion, Version -from .packed_cache import PackedRecurrentStateCache +from .packed_cache import ( + AdaptiveMixedPackedRecurrentStateCache, + CoraMixedPackedRecurrentStateCache, + MixedPackedRecurrentStateCache, + PackedRecurrentStateCache, + QueryEmaMixedPackedRecurrentStateCache, + RankFusedMixedPackedRecurrentStateCache, + RightRhtQueryEmaMixedPackedRecurrentStateCache, +) from .quantization import QuantizationSpec, RoundingMode +from .row_policy import ExactBudgetRowPlan if TYPE_CHECKING: from transformers import Qwen3_5ForCausalLM, Qwen3_5TextConfig @@ -215,6 +224,210 @@ def create_qwen35_packed_cache( ) +def _validated_exact_budget_config( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, +) -> object: + _validate_transformers_compatibility() + config = _validated_text_config(model_or_config) + geometry_fields = { + "linear_num_value_heads": getattr(config, "linear_num_value_heads", None), + "linear_key_head_dim": getattr(config, "linear_key_head_dim", None), + "linear_value_head_dim": getattr(config, "linear_value_head_dim", None), + } + invalid_geometry = { + name: value + for name, value in geometry_fields.items() + if isinstance(value, bool) or not isinstance(value, int) or value <= 0 + } + if invalid_geometry: + raise ValueError( + f"The Qwen3.5 config has invalid recurrent-state geometry: {invalid_geometry}" + ) + + expected_heads = geometry_fields["linear_num_value_heads"] + expected_rows = geometry_fields["linear_key_head_dim"] + expected_group_size = geometry_fields["linear_value_head_dim"] + if plan.group_size != expected_group_size: + raise ValueError( + "row plan group_size must equal Qwen3.5 linear_value_head_dim: " + f"{plan.group_size} != {expected_group_size}" + ) + incompatible_shapes = [ + (layer_index, head_count, row_count) + for layer_index, head_count, row_count in plan.score_shapes + if head_count != expected_heads or row_count != expected_rows + ] + if incompatible_shapes: + raise ValueError( + "row plan score geometry must match Qwen3.5 recurrent states " + f"[heads={expected_heads}, rows={expected_rows}]; incompatible entries: " + f"{incompatible_shapes}" + ) + return config + + +def create_qwen35_exact_budget_cache( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, +) -> MixedPackedRecurrentStateCache: + """Create a validated row-level mixed INT4/INT8 Qwen3.5 cache. + + The plan must cover every linear-attention model-layer index exactly. Its + ``[heads, rows]`` score geometry must equal Qwen3.5's recurrent state geometry, + and its group size must equal ``linear_value_head_dim`` so every state row owns + one independently packed group. No layer, geometry, or byte-budget adaptation + is performed implicitly. + """ + + return MixedPackedRecurrentStateCache( + _validated_exact_budget_config(model_or_config, plan=plan), + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + + +def create_qwen35_adaptive_exact_budget_cache( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, +) -> AdaptiveMixedPackedRecurrentStateCache: + """Create the batch-one adaptive-row mixed-cache prototype for Qwen3.5. + + The exact plan still fixes each layer's INT8 promotion count and resident + byte count. On every state write, row identities are selected by instantaneous + aligned INT4-versus-INT8 quantization-error reduction. This experimental + factory does not establish a quality, latency, or novelty result. + """ + + return AdaptiveMixedPackedRecurrentStateCache( + _validated_exact_budget_config(model_or_config, plan=plan), + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + + +def create_qwen35_query_ema_exact_budget_cache( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, + confirmation_two: bool = False, +) -> QueryEmaMixedPackedRecurrentStateCache: + """Create the frozen half-life-32 query-EMA mixed cache for Qwen3.5. + + The exact plan continues to fix each layer's promotion quota and packed-state + bytes. Before every recurrent-state update, a matching post-convolution query + must be supplied through ``stage_query_observation`` (normally by the Qwen3.5 + query observer). Missing or invalid observations fail closed. Persistent FP32 + EMA metadata is reported separately from packed recurrent-state storage. + """ + + return QueryEmaMixedPackedRecurrentStateCache( + _validated_exact_budget_config(model_or_config, plan=plan), + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + confirmation_two=confirmation_two, + ) + + +def create_qwen35_right_rht_query_ema_exact_budget_cache( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, +) -> RightRhtQueryEmaMixedPackedRecurrentStateCache: + """Create the experimental right-RHT CQER-32 cache for Qwen3.5. + + The orthonormal transform operates only on each recurrent row's value axis, + so the plan's row identities, INT8 quota, and packed-state byte count remain + unchanged. The current PyTorch codec uses transient FP32 workspaces and has + no latency or peak-memory claim. + """ + + return RightRhtQueryEmaMixedPackedRecurrentStateCache( + _validated_exact_budget_config(model_or_config, plan=plan), + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + + +def create_qwen35_cora_exact_budget_cache( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, + confirmation_two: bool = True, +) -> CoraMixedPackedRecurrentStateCache: + """Create the frozen causal-observability Qwen3.5 mixed cache. + + The exact row plan fixes per-layer promotion quotas and packed-state bytes. + A transition observer must stage the successful Gated DeltaNet kernel's + post-convolution query, key, log-decay, and beta tensors before every state + write. ``confirmation_two=False`` selects the frozen raw-CORA ablation. + """ + + return CoraMixedPackedRecurrentStateCache( + _validated_exact_budget_config(model_or_config, plan=plan), + plan=plan, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + confirmation_two=confirmation_two, + ) + + +def create_qwen35_rank_fused_exact_budget_cache( + model_or_config: Qwen35Source, + *, + plan: ExactBudgetRowPlan, + static_scores_by_layer: Mapping[int, torch.Tensor], + static_rank_weight: float, + rounding: RoundingMode = "nearest", + seed: int = 2339, + record_evidence: bool = False, +) -> RankFusedMixedPackedRecurrentStateCache: + """Create an exact-byte static/dynamic rank-fusion cache for Qwen3.5. + + The row plan fixes each layer's promotion quota. Within that quota, one + global weight fuses calibrated static-score ranks with causal per-write ranks + of aligned INT4-to-INT8 MSE reduction. Static score tensors must cover the + plan's layers exactly and reside on the same device as recurrent states. + """ + + return RankFusedMixedPackedRecurrentStateCache( + _validated_exact_budget_config(model_or_config, plan=plan), + plan=plan, + static_scores_by_layer=static_scores_by_layer, + static_rank_weight=static_rank_weight, + rounding=rounding, + seed=seed, + record_evidence=record_evidence, + ) + + def create_qwen35_v02_mixed_cache( model_or_config: Qwen35Source, *, diff --git a/src/recurquant/rht.py b/src/recurquant/rht.py new file mode 100644 index 0000000..9d512da --- /dev/null +++ b/src/recurquant/rht.py @@ -0,0 +1,222 @@ +"""Deterministic right-side randomized Hadamard transforms for recurrent states. + +The codec is stateless. Its sign vectors are derived from the frozen seed, +model-layer index, head index, and value width, so packed objects do not need +to retain a sign tensor or RNG state. All arithmetic uses an FP32 workspace. +""" + +from __future__ import annotations + +import hashlib +import math + +import torch + +RHT_SEED = 2339 +_SIGN_DOMAIN = b"recurquant.right-rht.signs.v1\0" +_SUPPORTED_DTYPES = frozenset((torch.float16, torch.bfloat16, torch.float32)) + + +def _validate_nonnegative_integer(value: object, *, name: str) -> int: + if isinstance(value, bool) or not isinstance(value, int): + raise TypeError(f"{name} must be an integer") + if value < 0: + raise ValueError(f"{name} must be non-negative") + return value + + +def _validate_positive_integer(value: object, *, name: str) -> int: + validated = _validate_nonnegative_integer(value, name=name) + if validated == 0: + raise ValueError(f"{name} must be positive") + return validated + + +def _validate_output_dtype(dtype: torch.dtype) -> None: + if dtype not in _SUPPORTED_DTYPES: + rendered = ", ".join(str(value) for value in sorted(_SUPPORTED_DTYPES, key=str)) + raise TypeError(f"output_dtype must be one of {rendered}") + + +def _validate_floating_tensor(tensor: torch.Tensor, *, name: str) -> None: + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"{name} must be a torch.Tensor") + if tensor.dtype not in _SUPPORTED_DTYPES: + raise TypeError(f"{name} must use float16, bfloat16, or float32; got {tensor.dtype}") + if tensor.numel() == 0: + raise ValueError(f"{name} must not be empty") + if not torch.isfinite(tensor).all().item(): + raise ValueError(f"{name} must contain only finite values") + + +def _validate_power_of_two_width(width: int) -> None: + if width <= 0 or width & (width - 1): + raise ValueError(f"last dimension must be a positive power of two; got {width}") + + +def _validate_state( + state: torch.Tensor, + *, + expected_heads: object, + name: str, +) -> tuple[int, int]: + _validate_floating_tensor(state, name=name) + heads = _validate_positive_integer(expected_heads, name="expected_heads") + if state.ndim != 4: + raise ValueError( + f"{name} must have shape [batch, heads, rows, value]; got rank {state.ndim}" + ) + if any(dimension <= 0 for dimension in state.shape): + raise ValueError(f"{name} dimensions must all be positive") + if state.shape[1] != heads: + raise ValueError( + f"{name} head dimension does not match expected_heads: {state.shape[1]} != {heads}" + ) + width = state.shape[-1] + _validate_power_of_two_width(width) + return heads, width + + +def _fwht_fp32(tensor: torch.Tensor) -> torch.Tensor: + """Return an unnormalized last-axis FWHT from an already validated tensor.""" + + width = tensor.shape[-1] + result = tensor.to(torch.float32).reshape(-1, width) + stride = 1 + while stride < width: + blocks = result.reshape(-1, width // (2 * stride), 2 * stride) + left = blocks[..., :stride] + right = blocks[..., stride:] + result = torch.cat((left + right, left - right), dim=-1).reshape(-1, width) + stride *= 2 + return result.reshape(tensor.shape) + + +def fwht_unnormalized( + tensor: torch.Tensor, + *, + output_dtype: torch.dtype | None = None, +) -> torch.Tensor: + """Apply an unnormalized Walsh-Hadamard transform over the last dimension. + + The last dimension must be a positive power of two. Computation is always + performed in FP32. By default the result is converted back to the input + dtype; callers may request another supported output dtype. + """ + + _validate_floating_tensor(tensor, name="tensor") + if tensor.ndim == 0: + raise ValueError("tensor must have a last dimension") + _validate_power_of_two_width(tensor.shape[-1]) + selected_dtype = tensor.dtype if output_dtype is None else output_dtype + _validate_output_dtype(selected_dtype) + return _fwht_fp32(tensor).to(selected_dtype) + + +def _portable_sign_values(*, layer_index: int, head_index: int, width: int) -> list[float]: + values: list[float] = [] + counter = 0 + while len(values) < width: + message = b"".join( + ( + _SIGN_DOMAIN, + RHT_SEED.to_bytes(8, "little", signed=False), + layer_index.to_bytes(8, "little", signed=False), + head_index.to_bytes(8, "little", signed=False), + width.to_bytes(8, "little", signed=False), + counter.to_bytes(8, "little", signed=False), + ) + ) + digest = hashlib.sha256(message).digest() + for byte in digest: + for bit_index in range(8): + values.append(1.0 if byte & (1 << bit_index) else -1.0) + if len(values) == width: + return values + counter += 1 + return values + + +def right_rht_signs( + *, + layer_index: int, + expected_heads: int, + width: int, + device: torch.device | str, +) -> torch.Tensor: + """Materialize the frozen transient sign schedule as ``[1, heads, 1, width]``.""" + + layer = _validate_nonnegative_integer(layer_index, name="layer_index") + heads = _validate_positive_integer(expected_heads, name="expected_heads") + validated_width = _validate_positive_integer(width, name="width") + _validate_power_of_two_width(validated_width) + values = [ + _portable_sign_values( + layer_index=layer, + head_index=head_index, + width=validated_width, + ) + for head_index in range(heads) + ] + return torch.tensor( + values, + dtype=torch.float32, + device=device, + ).reshape(1, heads, 1, validated_width) + + +def right_rht_encode( + state: torch.Tensor, + *, + layer_index: int, + expected_heads: int, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """Encode ``[batch, heads, rows, value]`` state rows with normalized RHT. + + For an unnormalized Hadamard matrix ``H`` and deterministic sign diagonal + ``D``, this computes ``state @ D @ H / sqrt(value)`` independently for each + layer and head. + """ + + layer = _validate_nonnegative_integer(layer_index, name="layer_index") + heads, width = _validate_state( + state, + expected_heads=expected_heads, + name="state", + ) + _validate_output_dtype(output_dtype) + signs = right_rht_signs( + layer_index=layer, + expected_heads=heads, + width=width, + device=state.device, + ) + encoded = _fwht_fp32(state.to(torch.float32) * signs) / math.sqrt(width) + return encoded.to(output_dtype) + + +def right_rht_decode( + encoded: torch.Tensor, + *, + layer_index: int, + expected_heads: int, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """Decode a normalized right-RHT tensor into the original value basis.""" + + layer = _validate_nonnegative_integer(layer_index, name="layer_index") + heads, width = _validate_state( + encoded, + expected_heads=expected_heads, + name="encoded", + ) + _validate_output_dtype(output_dtype) + signs = right_rht_signs( + layer_index=layer, + expected_heads=heads, + width=width, + device=encoded.device, + ) + decoded = (_fwht_fp32(encoded) / math.sqrt(width)) * signs + return decoded.to(output_dtype) diff --git a/src/recurquant/row_policy.py b/src/recurquant/row_policy.py new file mode 100644 index 0000000..c140a4a --- /dev/null +++ b/src/recurquant/row_policy.py @@ -0,0 +1,161 @@ +"""Exact-byte row-level INT4/INT8 policy selection.""" + +from __future__ import annotations + +import math +from collections.abc import Mapping +from dataclasses import asdict, dataclass + +import torch + + +@dataclass(frozen=True, order=True, slots=True) +class RowLocation: + layer_index: int + head_index: int + row_index: int + + +@dataclass(frozen=True, slots=True) +class ExactBudgetRowPlan: + """A deterministic row promotion plan with complete storage accounting.""" + + low_bits: int + high_bits: int + group_size: int + scale_bits: int + total_groups: int + mask_bytes: int + promotion_increment_bytes: int + target_resident_bytes: int + resident_bytes: int + high_precision_rows: tuple[RowLocation, ...] + score_shapes: tuple[tuple[int, int, int], ...] + + @property + def promoted_group_count(self) -> int: + return len(self.high_precision_rows) + + def groups_for_layer(self, layer_index: int) -> tuple[int, ...]: + """Return batch-independent flat ``head * rows + row`` group indices.""" + + matching_shapes = [shape for shape in self.score_shapes if shape[0] == layer_index] + if not matching_shapes: + raise KeyError(f"layer {layer_index} is absent from this row plan") + _, _, row_count = matching_shapes[0] + return tuple( + location.head_index * row_count + location.row_index + for location in self.high_precision_rows + if location.layer_index == layer_index + ) + + def evidence_dict(self) -> dict[str, object]: + evidence = asdict(self) + evidence["promoted_group_count"] = self.promoted_group_count + return evidence + + +def _payload_bytes(*, bits: int, group_size: int) -> int: + return math.ceil(bits * group_size / 8) + + +def select_rows_exact_budget( + scores_by_layer: Mapping[int, torch.Tensor], + *, + target_resident_bytes: int, + low_bits: int = 4, + high_bits: int = 8, + group_size: int = 128, + scale_bits: int = 16, +) -> ExactBudgetRowPlan: + """Promote the highest-scoring rows under an exact physical-byte budget. + + Every score tensor must have shape ``[heads, rows]``. The storage model uses + one group per row, one FP scale per group, and one packed precision-mask bit + per group. A target that cannot be represented exactly is rejected rather + than silently rounded down. + """ + + if not scores_by_layer: + raise ValueError("scores_by_layer must not be empty") + if any( + isinstance(index, bool) or not isinstance(index, int) or index < 0 + for index in scores_by_layer + ): + raise ValueError("layer indices must be nonnegative integers") + if low_bits < 2 or high_bits <= low_bits: + raise ValueError("high_bits must exceed low_bits, and low_bits must be at least 2") + if group_size <= 0: + raise ValueError("group_size must be positive") + if scale_bits not in (16, 32): + raise ValueError("scale_bits must be 16 or 32") + if target_resident_bytes <= 0: + raise ValueError("target_resident_bytes must be positive") + + candidates: list[tuple[float, RowLocation]] = [] + score_shapes: list[tuple[int, int, int]] = [] + for layer_index in sorted(scores_by_layer): + scores = scores_by_layer[layer_index] + if scores.ndim != 2 or scores.numel() == 0: + raise ValueError(f"layer {layer_index} scores must have shape [heads, rows]") + if not scores.is_floating_point(): + raise TypeError(f"layer {layer_index} scores must use a floating-point dtype") + if not torch.isfinite(scores).all().item(): + raise ValueError(f"layer {layer_index} scores must be finite") + head_count, row_count = scores.shape + score_shapes.append((layer_index, head_count, row_count)) + values = scores.detach().to(device="cpu", dtype=torch.float64) + for head_index in range(head_count): + for row_index in range(row_count): + candidates.append( + ( + float(values[head_index, row_index].item()), + RowLocation(layer_index, head_index, row_index), + ) + ) + + total_groups = len(candidates) + scale_bytes = scale_bits // 8 + low_group_bytes = _payload_bytes(bits=low_bits, group_size=group_size) + scale_bytes + promotion_increment = _payload_bytes(bits=high_bits, group_size=group_size) - ( + _payload_bytes(bits=low_bits, group_size=group_size) + ) + if promotion_increment <= 0: + raise ValueError( + "packed low_bits and high_bits must have a positive promotion byte increment; " + "increase group_size or choose bit widths with distinct payload sizes" + ) + mask_bytes = math.ceil(total_groups / 8) + minimum_bytes = total_groups * low_group_bytes + mask_bytes + maximum_bytes = minimum_bytes + total_groups * promotion_increment + if not minimum_bytes <= target_resident_bytes <= maximum_bytes: + raise ValueError( + "target_resident_bytes is outside the representable range " + f"[{minimum_bytes}, {maximum_bytes}]" + ) + extra_bytes = target_resident_bytes - minimum_bytes + if extra_bytes % promotion_increment: + lower = minimum_bytes + (extra_bytes // promotion_increment) * promotion_increment + upper = lower + promotion_increment + raise ValueError( + "target_resident_bytes is not exactly representable; nearest valid targets are " + f"{lower} and {upper}" + ) + promotion_count = extra_bytes // promotion_increment + + ordered = sorted(candidates, key=lambda item: (-item[0], item[1])) + selected = tuple(sorted(location for _, location in ordered[:promotion_count])) + resident_bytes = minimum_bytes + len(selected) * promotion_increment + return ExactBudgetRowPlan( + low_bits=low_bits, + high_bits=high_bits, + group_size=group_size, + scale_bits=scale_bits, + total_groups=total_groups, + mask_bytes=mask_bytes, + promotion_increment_bytes=promotion_increment, + target_resident_bytes=target_resident_bytes, + resident_bytes=resident_bytes, + high_precision_rows=selected, + score_shapes=tuple(score_shapes), + ) diff --git a/src/recurquant/storage_boundary_validation.py b/src/recurquant/storage_boundary_validation.py new file mode 100644 index 0000000..375ff60 --- /dev/null +++ b/src/recurquant/storage_boundary_validation.py @@ -0,0 +1,605 @@ +"""Independent one-row validation at a recurrent-state storage boundary. + +This module checks one local claim made by the loss-sensitivity selector: for a +single recurrent-state row, ``-g dot (Q8 - Q4)`` should approximate the target +token NLL reduction obtained by replacing that stored row's physical INT4 +endpoint with its physical INT8 endpoint. It deliberately does not select a +policy or measure model quality. + +The validation starts from a raw, warm ``transformers.DynamicCache``. Every +initialized recurrent state is physically packed and dequantized as INT4. The +selected row then follows the controlled path + +``Q4(raw) + alpha * (Q8(raw) - Q4(raw))``. + +Only ``alpha=0`` and ``alpha=1`` are deployable quantized endpoints. Interior +and negative values exist solely to make an independent central-difference +check possible at the exact state consumed by the next model token. +""" + +from __future__ import annotations + +import copy +import math +from collections.abc import Mapping +from dataclasses import dataclass, field +from types import MethodType +from typing import Any + +import torch +import torch.nn.functional as F +from transformers import DynamicCache + +from .quantization import QuantizationSpec, quantize_pack + + +@dataclass(frozen=True, order=True, slots=True) +class StorageRowLocation: + """One batch-one Qwen3.5 recurrent-state row.""" + + layer_index: int + head_index: int + row_index: int + + def __post_init__(self) -> None: + values = (self.layer_index, self.head_index, self.row_index) + if any( + isinstance(value, bool) or not isinstance(value, int) or value < 0 + for value in values + ): + raise ValueError("storage-row indices must be non-negative integers") + + +@dataclass(frozen=True, slots=True) +class DirectionalBenefitComparison: + """Scalar derivative and endpoint comparison along one row direction.""" + + epsilon: float + loss_at_zero: float + repeated_loss_at_zero: float + loss_at_minus_epsilon: float + loss_at_plus_epsilon: float + loss_at_int8_endpoint: float + autograd_directional_derivative: float + central_directional_derivative: float + predicted_benefit_autograd: float + predicted_benefit_central: float + measured_endpoint_benefit: float + derivative_absolute_error: float + endpoint_taylor_residual: float + baseline_repeat_absolute_error: float + derivative_sign_agreement: bool | None + endpoint_sign_agreement: bool | None + + +@dataclass(frozen=True, slots=True) +class StorageBoundaryRowValidation: + """Detached result of one real-model storage-boundary validation.""" + + location: StorageRowLocation + comparison: DirectionalBenefitComparison + recurrent_layer_indices: tuple[int, ...] + raw_row: torch.Tensor = field(repr=False) + int4_row: torch.Tensor = field(repr=False) + int8_row: torch.Tensor = field(repr=False) + direction: torch.Tensor = field(repr=False) + gradient_row: torch.Tensor = field(repr=False) + + +def _finite_scalar(name: str, value: float) -> float: + converted = float(value) + if not math.isfinite(converted): + raise ValueError(f"{name} must be finite") + return converted + + +def _directional_dot_float64( + gradient_row: torch.Tensor, + direction: torch.Tensor, +) -> float: + """Accumulate a saved FP32 row dot product reproducibly on CPU in FP64.""" + + if gradient_row.shape != direction.shape: + raise ValueError("gradient row and direction must have the same shape") + if gradient_row.dtype != torch.float32 or direction.dtype != torch.float32: + raise TypeError("gradient row and direction must use float32 before FP64 accumulation") + gradient64 = gradient_row.detach().to(device="cpu", dtype=torch.float64) + direction64 = direction.detach().to(device="cpu", dtype=torch.float64) + value = float((gradient64 * direction64).sum(dtype=torch.float64).item()) + if not math.isfinite(value): + raise RuntimeError("directional dot product became non-finite") + return value + + +def _sign_agreement(left: float, right: float, *, floor: float) -> bool | None: + if abs(left) <= floor and abs(right) <= floor: + return None + if abs(left) <= floor or abs(right) <= floor: + return False + return (left > 0) == (right > 0) + + +def compare_directional_benefits( + *, + epsilon: float, + loss_at_zero: float, + repeated_loss_at_zero: float, + loss_at_minus_epsilon: float, + loss_at_plus_epsilon: float, + loss_at_int8_endpoint: float, + autograd_directional_derivative: float, + sign_floor: float = 1e-12, +) -> DirectionalBenefitComparison: + """Compare autograd, central difference, and the exact INT8 endpoint. + + Derivatives have the sign of increasing NLL. Benefits use the selector's + opposite sign: positive means that moving toward the INT8 row lowers NLL. + A sign comparison is ``None`` only when both quantities are numerically + indistinguishable from zero under ``sign_floor``. + """ + + epsilon_value = _finite_scalar("epsilon", epsilon) + floor_value = _finite_scalar("sign_floor", sign_floor) + if epsilon_value <= 0 or epsilon_value > 1: + raise ValueError("epsilon must be in (0, 1]") + if floor_value < 0: + raise ValueError("sign_floor must be non-negative") + + zero = _finite_scalar("loss_at_zero", loss_at_zero) + repeated_zero = _finite_scalar("repeated_loss_at_zero", repeated_loss_at_zero) + minus = _finite_scalar("loss_at_minus_epsilon", loss_at_minus_epsilon) + plus = _finite_scalar("loss_at_plus_epsilon", loss_at_plus_epsilon) + endpoint = _finite_scalar("loss_at_int8_endpoint", loss_at_int8_endpoint) + autograd = _finite_scalar( + "autograd_directional_derivative", autograd_directional_derivative + ) + central = (plus - minus) / (2.0 * epsilon_value) + predicted_autograd = -autograd + predicted_central = -central + measured_endpoint = zero - endpoint + + return DirectionalBenefitComparison( + epsilon=epsilon_value, + loss_at_zero=zero, + repeated_loss_at_zero=repeated_zero, + loss_at_minus_epsilon=minus, + loss_at_plus_epsilon=plus, + loss_at_int8_endpoint=endpoint, + autograd_directional_derivative=autograd, + central_directional_derivative=central, + predicted_benefit_autograd=predicted_autograd, + predicted_benefit_central=predicted_central, + measured_endpoint_benefit=measured_endpoint, + derivative_absolute_error=abs(autograd - central), + endpoint_taylor_residual=measured_endpoint - predicted_autograd, + baseline_repeat_absolute_error=abs(zero - repeated_zero), + derivative_sign_agreement=_sign_agreement( + autograd, + central, + floor=floor_value, + ), + endpoint_sign_agreement=_sign_agreement( + predicted_autograd, + measured_endpoint, + floor=floor_value, + ), + ) + + +def interpolate_storage_row( + int4_state: torch.Tensor, + direction: torch.Tensor, + *, + location: StorageRowLocation, + alpha: float, +) -> torch.Tensor: + """Return a detached state with exactly one row moved along ``direction``.""" + + alpha_value = _finite_scalar("alpha", alpha) + if int4_state.ndim != 4 or int4_state.shape[0] != 1: + raise ValueError("int4_state must have shape [1, heads, rows, value_dim]") + if not int4_state.is_floating_point() or int4_state.device.type == "meta": + raise TypeError("int4_state must be a materialized floating-point tensor") + if direction.shape != int4_state.shape[-1:]: + raise ValueError("direction must have shape [value_dim]") + if direction.dtype != int4_state.dtype or direction.device != int4_state.device: + raise ValueError("direction must match int4_state dtype and device") + if location.head_index >= int4_state.shape[1]: + raise ValueError("head_index is outside the recurrent-state geometry") + if location.row_index >= int4_state.shape[2]: + raise ValueError("row_index is outside the recurrent-state geometry") + if not torch.isfinite(int4_state).all().item() or not torch.isfinite(direction).all().item(): + raise ValueError("int4_state and direction must contain only finite values") + + moved = int4_state.detach().clone() + moved[0, location.head_index, location.row_index] = ( + moved[0, location.head_index, location.row_index] + alpha_value * direction + ) + return moved + + +def _validate_quantization_specs( + int4_spec: QuantizationSpec, + int8_spec: QuantizationSpec, +) -> None: + if int4_spec.bits != 4 or int8_spec.bits != 8: + raise ValueError("storage-boundary validation requires INT4 and INT8 specs") + matched = ("group_size", "scale_bits", "flatten_last_dims", "rounding", "seed", "epsilon") + mismatched = [name for name in matched if getattr(int4_spec, name) != getattr(int8_spec, name)] + if mismatched: + raise ValueError(f"INT4 and INT8 specs may differ only in bits: {mismatched}") + if int4_spec.flatten_last_dims != 1: + raise ValueError("flatten_last_dims must be 1 so each state row owns its quantizer group") + if int4_spec.rounding != "nearest": + raise ValueError("the deterministic validation path currently requires nearest rounding") + + +def _recurrent_state(cache: DynamicCache, layer_index: int) -> torch.Tensor: + if layer_index >= len(cache.layers): + raise ValueError(f"cache has no layer {layer_index}") + layer = cache.layers[layer_index] + states = getattr(layer, "recurrent_states", None) + initialized = getattr(layer, "is_recurrent_states_initialized", None) + previous = getattr(layer, "has_previous_state", None) + if not isinstance(states, dict) or not isinstance(initialized, dict): + raise ValueError(f"cache layer {layer_index} is not a recurrent-state layer") + if not initialized.get(0, False) or not isinstance(states.get(0), torch.Tensor): + raise ValueError(f"cache layer {layer_index} has no initialized recurrent state") + if not isinstance(previous, dict) or not previous.get(0, False): + raise ValueError(f"cache layer {layer_index} has no previous recurrent state") + state = states[0] + if state.dtype != torch.float32: + raise TypeError(f"cache layer {layer_index} recurrent state must use float32") + if state.ndim != 4 or state.shape[0] != 1: + raise ValueError( + f"cache layer {layer_index} recurrent state must have batch-one rank-4 geometry" + ) + if not torch.isfinite(state).all().item(): + raise ValueError(f"cache layer {layer_index} recurrent state is non-finite") + return state + + +def recurrent_layer_indices(cache: DynamicCache) -> tuple[int, ...]: + """Return initialized recurrent-state layers from a warm DynamicCache.""" + + if not isinstance(cache, DynamicCache): + raise TypeError("cache must be a transformers.DynamicCache") + if getattr(cache, "offloading", False): + raise ValueError("offloaded DynamicCache validation is not supported") + indices: list[int] = [] + for layer_index, layer in enumerate(cache.layers): + states = getattr(layer, "recurrent_states", None) + initialized = getattr(layer, "is_recurrent_states_initialized", None) + if isinstance(states, dict) and isinstance(initialized, dict) and initialized.get(0, False): + _recurrent_state(cache, layer_index) + indices.append(layer_index) + if not indices: + raise ValueError("cache has no initialized recurrent states; run a no-grad prefill first") + return tuple(indices) + + +def store_uniform_int4_boundary( + cache: DynamicCache, + *, + int4_spec: QuantizationSpec, +) -> tuple[int, ...]: + """Physically pack/dequantize every warm recurrent state as INT4 in place.""" + + if int4_spec.bits != 4 or int4_spec.flatten_last_dims != 1: + raise ValueError("int4_spec must use 4 bits and flatten_last_dims=1") + indices = recurrent_layer_indices(cache) + for layer_index in indices: + state = _recurrent_state(cache, layer_index) + if int4_spec.group_size != state.shape[-1]: + raise ValueError( + "int4 group_size must equal recurrent-state value_dim at layer " + f"{layer_index} ({int4_spec.group_size} != {state.shape[-1]})" + ) + packed = quantize_pack(state, int4_spec) + cache.layers[layer_index].recurrent_states[0] = packed.dequantize() + return indices + + +def advance_uniform_int4_trajectory( + model: torch.nn.Module, + raw_cache: DynamicCache, + previous_input_ids: torch.Tensor, + *, + int4_spec: QuantizationSpec, + forward_kwargs: Mapping[str, Any] | None = None, +) -> None: + """Advance prior tokens and stop at the next raw pre-storage boundary. + + ``raw_cache`` must initially contain the raw prefill update. Before every + prior token, all recurrent states are stored through physical INT4 packing. + The last prior token's update remains raw, ready for the validation function + to construct both Q4 and Q8 endpoints. + """ + + if model.training: + raise ValueError("model must be in evaluation mode") + if previous_input_ids.ndim != 2 or previous_input_ids.shape[0] != 1: + raise ValueError("previous_input_ids must have shape [1, tokens]") + if previous_input_ids.dtype not in (torch.int32, torch.int64): + raise TypeError("previous_input_ids must use an integer dtype") + kwargs = dict(forward_kwargs or {}) + conflicts = {"input_ids", "past_key_values", "use_cache", "labels"} & set(kwargs) + if conflicts: + raise ValueError(f"forward_kwargs contains managed arguments: {sorted(conflicts)}") + + for token_index in range(previous_input_ids.shape[1]): + store_uniform_int4_boundary(raw_cache, int4_spec=int4_spec) + with torch.no_grad(): + model( + input_ids=previous_input_ids[:, token_index : token_index + 1], + past_key_values=raw_cache, + use_cache=True, + **kwargs, + ) + + +def _target_nll( + model: torch.nn.Module, + cache: DynamicCache, + input_ids: torch.Tensor, + targets: torch.Tensor, + forward_kwargs: Mapping[str, Any], +) -> torch.Tensor: + output = model( + input_ids=input_ids, + past_key_values=cache, + use_cache=True, + **forward_kwargs, + ) + logits = getattr(output, "logits", None) + if not isinstance(logits, torch.Tensor) or logits.ndim != 3: + raise RuntimeError("model output must contain rank-3 logits") + return F.cross_entropy(logits[:, -1, :].to(torch.float32), targets) + + +def _loss_at_alpha( + model: torch.nn.Module, + base_cache: DynamicCache, + input_ids: torch.Tensor, + targets: torch.Tensor, + *, + location: StorageRowLocation, + direction: torch.Tensor, + alpha: float, + forward_kwargs: Mapping[str, Any], +) -> float: + cache = copy.deepcopy(base_cache) + state = _recurrent_state(cache, location.layer_index) + cache.layers[location.layer_index].recurrent_states[0] = interpolate_storage_row( + state, + direction, + location=location, + alpha=alpha, + ) + with torch.no_grad(): + loss = _target_nll(model, cache, input_ids, targets, forward_kwargs) + value = float(loss.item()) + if not math.isfinite(value): + raise RuntimeError("target NLL became non-finite") + return value + + +def _autograd_directional_derivative( + model: torch.nn.Module, + base_cache: DynamicCache, + input_ids: torch.Tensor, + targets: torch.Tensor, + *, + location: StorageRowLocation, + direction: torch.Tensor, + forward_kwargs: Mapping[str, Any], +) -> tuple[float, float, torch.Tensor]: + cache = copy.deepcopy(base_cache) + state = _recurrent_state(cache, location.layer_index) + leaf = state.detach().clone().requires_grad_(True) + cache.layers[location.layer_index].recurrent_states[0] = leaf + + recurrent_layers = frozenset(recurrent_layer_indices(cache)) + had_instance_update = "update_recurrent_state" in cache.__dict__ + previous_instance_update = cache.__dict__.get("update_recurrent_state") + + def differentiable_update( + working_cache: DynamicCache, + recurrent_states: torch.Tensor, + layer_idx: int, + state_idx: int = 0, + **update_kwargs: Any, + ) -> torch.Tensor: + del update_kwargs + if layer_idx not in recurrent_layers: + raise ValueError( + f"unexpected recurrent-state update for unprepared layer {layer_idx}" + ) + if state_idx != 0: + raise ValueError("Qwen3.5 storage-boundary validation supports state_idx=0 only") + layer = working_cache.layers[layer_idx] + states = getattr(layer, "recurrent_states", None) + initialized = getattr(layer, "is_recurrent_states_initialized", None) + if not isinstance(states, dict) or not isinstance(initialized, dict): + raise RuntimeError(f"cache layer {layer_idx} lost recurrent-state metadata") + if not initialized.get(state_idx, False): + raise RuntimeError(f"cache layer {layer_idx} was not warm at the measured boundary") + + # Transformers normally copy_()s every updated GDN state into its warm + # cache tensor to preserve a static address. During this differentiable + # diagnostic, later GDN updates can depend on the selected leaf. Their + # CopyBackwards destinations would overwrite tensors saved by the + # recurrent kernels before autograd runs. Assignment preserves the exact + # value stored for the next token without mutating any current-token + # input in place. Only ``leaf`` remains a differentiation target. + layer.recurrent_states[state_idx] = recurrent_states + return recurrent_states + + parameters = tuple(model.parameters()) + if any(parameter.grad is not None for parameter in parameters): + raise ValueError("model parameters must not have pre-existing gradients") + original_requires_grad = tuple(parameter.requires_grad for parameter in parameters) + cache.update_recurrent_state = MethodType(differentiable_update, cache) + try: + for parameter in parameters: + parameter.requires_grad_(False) + with torch.enable_grad(): + loss = _target_nll(model, cache, input_ids, targets, forward_kwargs) + (gradient,) = torch.autograd.grad(loss, leaf, retain_graph=False, create_graph=False) + gradient_row = gradient[0, location.head_index, location.row_index].detach() + directional = _directional_dot_float64(gradient_row, direction) + loss_value = float(loss.detach().item()) + finally: + for parameter, requires_grad in zip(parameters, original_requires_grad, strict=True): + parameter.requires_grad_(requires_grad) + if had_instance_update: + cache.update_recurrent_state = previous_instance_update + else: + del cache.__dict__["update_recurrent_state"] + + if not math.isfinite(loss_value) or not math.isfinite(directional): + raise RuntimeError("autograd storage-boundary result became non-finite") + if not torch.isfinite(gradient_row).all().item(): + raise RuntimeError("storage-boundary row gradient became non-finite") + return loss_value, directional, gradient_row.cpu() + + +def validate_qwen_storage_boundary_row( + model: torch.nn.Module, + raw_cache: DynamicCache, + input_ids: torch.Tensor, + target_token_ids: torch.Tensor, + *, + location: StorageRowLocation, + int4_spec: QuantizationSpec, + int8_spec: QuantizationSpec, + epsilon: float = 0.5, + sign_floor: float = 1e-12, + forward_kwargs: Mapping[str, Any] | None = None, +) -> StorageBoundaryRowValidation: + """Validate one Qwen row with autograd, central difference, and endpoints. + + ``raw_cache`` is not mutated. It must describe the raw recurrent update at + the chosen token boundary, after any earlier tokens have followed the + repeated-INT4 trajectory. The function performs five bounded one-token + forwards: one autograd baseline and no-grad evaluations at alpha 0, + ``-epsilon``, ``+epsilon``, and 1. + """ + + _validate_quantization_specs(int4_spec, int8_spec) + if model.training: + raise ValueError("model must be in evaluation mode") + if input_ids.ndim != 2 or input_ids.shape != (1, 1): + raise ValueError("input_ids must have shape [1, 1]") + if input_ids.dtype not in (torch.int32, torch.int64): + raise TypeError("input_ids must use an integer dtype") + targets = target_token_ids + if targets.ndim == 2 and targets.shape == (1, 1): + targets = targets[:, 0] + if targets.ndim != 1 or targets.shape[0] != 1: + raise ValueError("target_token_ids must have shape [1] or [1, 1]") + if targets.dtype not in (torch.int32, torch.int64): + raise TypeError("target_token_ids must use an integer dtype") + if targets.device != input_ids.device: + raise ValueError("input_ids and target_token_ids must be on the same device") + kwargs = dict(forward_kwargs or {}) + conflicts = {"input_ids", "past_key_values", "use_cache", "labels"} & set(kwargs) + if conflicts: + raise ValueError(f"forward_kwargs contains managed arguments: {sorted(conflicts)}") + + raw_state = _recurrent_state(raw_cache, location.layer_index) + if raw_state.device != input_ids.device: + raise ValueError("input_ids and recurrent-state cache must be on the same device") + if int4_spec.group_size != raw_state.shape[-1]: + raise ValueError( + "quantizer group_size must equal recurrent-state value_dim " + f"({int4_spec.group_size} != {raw_state.shape[-1]})" + ) + if location.head_index >= raw_state.shape[1] or location.row_index >= raw_state.shape[2]: + raise ValueError("selected row is outside the recurrent-state geometry") + + int4_state = quantize_pack(raw_state, int4_spec).dequantize() + int8_state = quantize_pack(raw_state, int8_spec).dequantize() + direction = ( + int8_state[0, location.head_index, location.row_index] + - int4_state[0, location.head_index, location.row_index] + ).detach() + if not torch.isfinite(direction).all().item(): + raise RuntimeError("INT4-to-INT8 row direction became non-finite") + + base_cache = copy.deepcopy(raw_cache) + layers = store_uniform_int4_boundary(base_cache, int4_spec=int4_spec) + base_selected = _recurrent_state(base_cache, location.layer_index) + if not torch.equal(base_selected, int4_state): + raise RuntimeError("selected INT4 storage endpoint was not reproduced exactly") + + loss_zero, autograd_derivative, gradient_row = _autograd_directional_derivative( + model, + base_cache, + input_ids, + targets, + location=location, + direction=direction, + forward_kwargs=kwargs, + ) + repeated_zero = _loss_at_alpha( + model, + base_cache, + input_ids, + targets, + location=location, + direction=direction, + alpha=0.0, + forward_kwargs=kwargs, + ) + minus = _loss_at_alpha( + model, + base_cache, + input_ids, + targets, + location=location, + direction=direction, + alpha=-float(epsilon), + forward_kwargs=kwargs, + ) + plus = _loss_at_alpha( + model, + base_cache, + input_ids, + targets, + location=location, + direction=direction, + alpha=float(epsilon), + forward_kwargs=kwargs, + ) + endpoint = _loss_at_alpha( + model, + base_cache, + input_ids, + targets, + location=location, + direction=direction, + alpha=1.0, + forward_kwargs=kwargs, + ) + comparison = compare_directional_benefits( + epsilon=epsilon, + loss_at_zero=loss_zero, + repeated_loss_at_zero=repeated_zero, + loss_at_minus_epsilon=minus, + loss_at_plus_epsilon=plus, + loss_at_int8_endpoint=endpoint, + autograd_directional_derivative=autograd_derivative, + sign_floor=sign_floor, + ) + + return StorageBoundaryRowValidation( + location=location, + comparison=comparison, + recurrent_layer_indices=layers, + raw_row=raw_state[0, location.head_index, location.row_index].detach().cpu(), + int4_row=int4_state[0, location.head_index, location.row_index].detach().cpu(), + int8_row=int8_state[0, location.head_index, location.row_index].detach().cpu(), + direction=direction.cpu(), + gradient_row=gradient_row, + ) diff --git a/src/recurquant/transition_observer.py b/src/recurquant/transition_observer.py new file mode 100644 index 0000000..7343cbf --- /dev/null +++ b/src/recurquant/transition_observer.py @@ -0,0 +1,302 @@ +"""Causal transition observation for Qwen3.5 recurrent-state caches. + +Qwen3.5 constructs and repeats its Gated DeltaNet query and key heads before +calling either its chunk or recurrent state kernel. This context manager +observes those exact kernel inputs together with the log decay and write gate. +It stages them only after the kernel succeeds, immediately before the model +writes the resulting recurrent state to the selected cache. + +Dispatch is by cache identity. Reference, static, and otherwise unregistered +caches are never inspected or modified. +""" + +from __future__ import annotations + +from contextvars import ContextVar +from dataclasses import dataclass +from types import TracebackType +from typing import Any + +import torch + +_OBSERVER_ATTRIBUTE = "_recurquant_transition_observer" +_PACKED_SEQUENCE_ARGUMENTS = ( + "cu_seq_lens_q", + "cu_seq_lens_k", + "cu_seqlens", +) + + +def _argument( + args: tuple[object, ...], + kwargs: dict[str, object], + name: str, + position: int, +) -> object | None: + if name in kwargs: + return kwargs[name] + return args[position] if len(args) > position else None + + +@dataclass(slots=True) +class _ForwardCall: + module: torch.nn.Module + cache: object + layer_index: int + kernel_calls: int = 0 + stage_attempted: bool = False + staged: bool = False + + +class Qwen35TransitionObserver: + """Stage exact Qwen3.5 Gated DeltaNet transition inputs on CORA caches. + + ``caches`` must implement + ``stage_transition_observation(layer_idx, query, key, log_decay, beta)`` + and ``discard_pending_transition_observations(layer_idx)``. Only those + exact objects are observed. The observer supports uncached prefill, + cached multi-token chunks, and cached one-token decode, and composes with + :class:`~recurquant.query_energy.Qwen35QueryEnergyObserver` in either + context-manager nesting order. + + Packed ``cu_seqlens`` forwards are rejected because the frozen CORA-C2 + recurrence requires an unambiguous chronological batch-one sequence. + """ + + def __init__( + self, + model: torch.nn.Module, + *, + caches: list[object] | tuple[object, ...], + ) -> None: + if not isinstance(model, torch.nn.Module): + raise TypeError("model must be a torch.nn.Module") + if not isinstance(caches, (list, tuple)): + raise TypeError("caches must be a list or tuple of CORA caches") + if not caches: + raise ValueError("caches must contain at least one CORA cache") + + identities = [id(cache) for cache in caches] + if len(set(identities)) != len(identities): + raise ValueError("caches must not contain the same object more than once") + for cache in caches: + if not callable(getattr(cache, "stage_transition_observation", None)): + raise TypeError( + "each observed cache must implement stage_transition_observation" + ) + if not callable( + getattr(cache, "discard_pending_transition_observations", None) + ): + raise TypeError( + "each observed cache must implement " + "discard_pending_transition_observations" + ) + + self.model = model + self.caches = tuple(caches) + self._cache_by_identity = {id(cache): cache for cache in self.caches} + self._installed: list[tuple[object, str, object]] = [] + self._active_calls: ContextVar[tuple[_ForwardCall, ...]] = ContextVar( + f"recurquant_transition_calls_{id(self)}", + default=(), + ) + + def _gdn_modules(self) -> list[torch.nn.Module]: + modules = [ + module + for module in self.model.modules() + if module.__class__.__name__ == "Qwen3_5GatedDeltaNet" + ] + if not modules: + raise TypeError("model does not contain Qwen3_5GatedDeltaNet modules") + return modules + + def _selected_cache(self, candidate: object | None) -> object | None: + if candidate is None: + return None + selected = self._cache_by_identity.get(id(candidate)) + return selected if selected is candidate else None + + @staticmethod + def _discard(cache: object, layer_index: int) -> None: + cache.discard_pending_transition_observations( # type: ignore[attr-defined] + layer_index + ) + + def _discard_after_failure( + self, + call: _ForwardCall, + error: BaseException, + ) -> None: + try: + self._discard(call.cache, call.layer_index) + except BaseException as cleanup_error: + error.add_note( + "RecurQuant could not discard the pending transition observation " + f"after the forward failed: {cleanup_error!r}" + ) + + def _make_kernel_wrapper( + self, + module: torch.nn.Module, + original: Any, + ): + def wrapped(*args: object, **kwargs: object): + active_calls = self._active_calls.get() + call = active_calls[-1] if active_calls else None + if call is None or call.module is not module: + return original(*args, **kwargs) + if call.kernel_calls: + raise RuntimeError( + "Qwen3.5 Gated DeltaNet called more than one state kernel in one " + "forward; refusing ambiguous transition attribution" + ) + + # The selected torch or FLA kernel must complete before its inputs + # are eligible to be staged. The Qwen forward writes the returned + # state on the very next cache operation. + output = original(*args, **kwargs) + call.kernel_calls += 1 + + observed = { + "query": _argument(args, kwargs, "query", 0), + "key": _argument(args, kwargs, "key", 1), + "log_decay": _argument(args, kwargs, "g", 3), + "beta": _argument(args, kwargs, "beta", 4), + } + malformed = [ + name for name, value in observed.items() if not isinstance(value, torch.Tensor) + ] + if malformed: + rendered = ", ".join(malformed) + raise TypeError( + "Qwen3.5 Gated DeltaNet kernel did not expose tensor transition " + f"inputs ({rendered})" + ) + + call.stage_attempted = True + call.cache.stage_transition_observation( # type: ignore[attr-defined] + call.layer_index, + observed["query"], + observed["key"], + observed["log_decay"], + observed["beta"], + ) + call.staged = True + return output + + return wrapped + + def _make_forward_wrapper( + self, + module: torch.nn.Module, + original: Any, + ): + layer_index = int(module.layer_idx) # type: ignore[attr-defined] + + def wrapped(*args: object, **kwargs: object): + cache = self._selected_cache(_argument(args, kwargs, "cache_params", 1)) + if cache is None: + return original(*args, **kwargs) + + packed_arguments = [ + name + for name in _PACKED_SEQUENCE_ARGUMENTS + if kwargs.get(name) is not None + ] + if packed_arguments: + rendered = ", ".join(packed_arguments) + raise ValueError( + "Qwen3.5 transition observation does not support packed " + f"cu_seqlens forwards ({rendered})" + ) + + call = _ForwardCall(module=module, cache=cache, layer_index=layer_index) + token = self._active_calls.set((*self._active_calls.get(), call)) + try: + output = original(*args, **kwargs) + if call.kernel_calls != 1 or not call.stage_attempted or not call.staged: + raise RuntimeError( + "Qwen3.5 Gated DeltaNet did not complete exactly one observed " + "state-kernel call; refusing to continue with ambiguous " + "transition inputs" + ) + return output + except BaseException as error: + self._discard_after_failure(call, error) + raise + finally: + self._active_calls.reset(token) + + return wrapped + + def install(self) -> None: + """Install forward and selected torch/FLA kernel wrappers atomically.""" + + if self._installed: + raise RuntimeError("transition observer is already installed") + modules = self._gdn_modules() + observed_objects: list[object] = [self.model, *modules] + if any(hasattr(item, _OBSERVER_ATTRIBUTE) for item in observed_objects): + raise RuntimeError("a transition observer is already installed on this model") + + try: + setattr(self.model, _OBSERVER_ATTRIBUTE, self) + self._installed.append((self.model, _OBSERVER_ATTRIBUTE, _Missing)) + for module in modules: + setattr(module, _OBSERVER_ATTRIBUTE, self) + self._installed.append((module, _OBSERVER_ATTRIBUTE, _Missing)) + for attribute in ( + "forward", + "chunk_gated_delta_rule", + "recurrent_gated_delta_rule", + ): + original = getattr(module, attribute) + if not callable(original): + raise TypeError( + f"Qwen3_5GatedDeltaNet.{attribute} must be callable" + ) + wrapper = ( + self._make_forward_wrapper(module, original) + if attribute == "forward" + else self._make_kernel_wrapper(module, original) + ) + restore_value = original if attribute in module.__dict__ else _Missing + setattr(module, attribute, wrapper) + self._installed.append((module, attribute, restore_value)) + except BaseException: + self.remove() + raise + + def remove(self) -> None: + """Restore every wrapped callable and remove installation markers.""" + + while self._installed: + target, attribute, original = self._installed.pop() + if original is _Missing: + if attribute == _OBSERVER_ATTRIBUTE: + if getattr(target, attribute, None) is self: + delattr(target, attribute) + elif attribute in getattr(target, "__dict__", {}): + delattr(target, attribute) + else: + setattr(target, attribute, original) + + def __enter__(self) -> Qwen35TransitionObserver: + self.install() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + self.remove() + + +class _MissingType: + pass + + +_Missing = _MissingType() diff --git a/tests/test_adaptive_mixed_packed_cache.py b/tests/test_adaptive_mixed_packed_cache.py new file mode 100644 index 0000000..8963bc7 --- /dev/null +++ b/tests/test_adaptive_mixed_packed_cache.py @@ -0,0 +1,275 @@ +from __future__ import annotations + +import hashlib +import math + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant.packed_cache import ( + AdaptiveMixedPackedLinearAttentionLayer, + AdaptiveMixedPackedRecurrentStateCache, +) +from recurquant.quantization import QuantizationSpec, quantize_dequantize, quantize_pack +from recurquant.qwen35 import create_qwen35_adaptive_exact_budget_cache +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from tests.test_transformers_cache import tiny_config + + +def _target_for_promotions( + scores: dict[int, torch.Tensor], + *, + promotions: int, + group_size: int = 8, + scale_bits: int = 16, +) -> int: + total_groups = sum(score.numel() for score in scores.values()) + low_group_bytes = math.ceil(4 * group_size / 8) + scale_bits // 8 + increment = math.ceil(8 * group_size / 8) - math.ceil(4 * group_size / 8) + return total_groups * low_group_bytes + math.ceil(total_groups / 8) + promotions * increment + + +def _plan(*, promotions: int, scores: dict[int, torch.Tensor] | None = None) -> ExactBudgetRowPlan: + if scores is None: + scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + return select_rows_exact_budget( + scores, + target_resident_bytes=_target_for_promotions(scores, promotions=promotions), + group_size=8, + ) + + +def _packed_mask( + cache: AdaptiveMixedPackedRecurrentStateCache, + layer_index: int = 0, +) -> torch.Tensor: + layer = cache.layers[layer_index] + assert isinstance(layer, AdaptiveMixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + return packed.high_precision_mask().reshape(-1) + + +def _state_with_difficult_group(group_index: int) -> torch.Tensor: + state = torch.zeros((1, 2, 8, 8), dtype=torch.float32) + state.reshape(16, 8)[group_index] = torch.tensor( + [1.0, 0.51, -0.37, 0.23, 0.14, -0.08, 0.03, -0.01] + ) + return state + + +def test_adaptive_mask_matches_aligned_per_row_mse_reduction() -> None: + plan = _plan(promotions=5) + cache = create_qwen35_adaptive_exact_budget_cache(tiny_config(), plan=plan) + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(211)) + low_spec = QuantizationSpec(bits=4, group_size=8) + high_spec = QuantizationSpec(bits=8, group_size=8) + source = state.to(torch.float32) + low = quantize_dequantize(state, low_spec).tensor.to(torch.float32) + high = quantize_dequantize(state, high_spec).tensor.to(torch.float32) + benefit = ( + (low - source).square().mean(dim=-1) - (high - source).square().mean(dim=-1) + ).reshape(-1) + expected_indices = torch.argsort(benefit, descending=True, stable=True)[:5] + + cache.update_recurrent_state(state, layer_idx=0) + + actual = _packed_mask(cache) + assert actual.sum().item() == 5 + assert torch.equal(actual.nonzero().reshape(-1), expected_indices.sort().values) + + +def test_adaptive_stored_state_equals_standalone_physical_endpoints_by_computed_mask() -> None: + plan = _plan(promotions=5) + cache = create_qwen35_adaptive_exact_budget_cache(tiny_config(), plan=plan) + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(219)) + layer = cache.layers[0] + assert isinstance(layer, AdaptiveMixedPackedLinearAttentionLayer) + + restored = cache.update_recurrent_state(state, layer_idx=0) + + packed = layer.packed_states[0] + assert packed is not None + computed_mask = packed.high_precision_mask().reshape(1, 2, 8, 1) + standalone_int4 = quantize_pack(state, layer.low_spec).dequantize() + standalone_int8 = quantize_pack(state, layer.high_spec).dequantize() + physical_benefit = ( + (standalone_int4 - state).square().mean(dim=-1) + - (standalone_int8 - state).square().mean(dim=-1) + ).reshape(-1) + selected = torch.argsort(physical_benefit, descending=True, stable=True)[:5] + independent_mask = torch.zeros(16, dtype=torch.bool) + independent_mask[selected] = True + independent_mask = independent_mask.reshape(1, 2, 8, 1) + expected = torch.where(independent_mask, standalone_int8, standalone_int4) + + assert computed_mask.sum().item() == 5 + assert torch.equal(computed_mask, independent_mask) + assert torch.equal(restored, expected) + assert torch.equal(packed.dequantize(), expected) + assert torch.equal(layer.recurrent_states[0], expected) + + +def test_equal_scores_use_stable_flattened_row_order_and_repeat_exactly() -> None: + plan = _plan(promotions=3) + first = create_qwen35_adaptive_exact_budget_cache( + tiny_config(), + plan=plan, + record_evidence=True, + ) + second = create_qwen35_adaptive_exact_budget_cache( + tiny_config(), + plan=plan, + record_evidence=True, + ) + state = torch.zeros((1, 2, 8, 8)) + + first.update_recurrent_state(state, layer_idx=0) + second.update_recurrent_state(state, layer_idx=0) + + expected = torch.zeros(16, dtype=torch.bool) + expected[:3] = True + assert torch.equal(_packed_mask(first), expected) + assert torch.equal(_packed_mask(second), expected) + assert first.update_evidence[0].high_precision_mask_sha256 == ( + second.update_evidence[0].high_precision_mask_sha256 + ) + + +@pytest.mark.parametrize("promotions", [0, 16]) +def test_zero_and_full_layer_quotas_preserve_exact_counts_and_bytes(promotions: int) -> None: + plan = _plan(promotions=promotions) + cache = create_qwen35_adaptive_exact_budget_cache(tiny_config(), plan=plan) + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(223)) + + cache.update_recurrent_state(state, layer_idx=0) + + assert _packed_mask(cache).sum().item() == promotions + assert cache.storage_summary()["high_precision_groups"] == promotions + assert cache.storage_summary()["resident_bytes"] == plan.resident_bytes + + +def test_repeated_updates_can_change_rows_without_changing_quota_or_bytes() -> None: + plan = _plan(promotions=1) + cache = create_qwen35_adaptive_exact_budget_cache( + tiny_config(), + plan=plan, + record_evidence=True, + ) + + cache.update_recurrent_state(_state_with_difficult_group(2), layer_idx=0) + first_mask = _packed_mask(cache).clone() + first_bytes = cache.storage_summary()["resident_bytes"] + cache.update_recurrent_state(_state_with_difficult_group(11), layer_idx=0) + second_mask = _packed_mask(cache).clone() + + assert first_mask.nonzero().reshape(-1).tolist() == [2] + assert second_mask.nonzero().reshape(-1).tolist() == [11] + assert not torch.equal(first_mask, second_mask) + assert first_bytes == cache.storage_summary()["resident_bytes"] == plan.resident_bytes + assert [item.high_precision_groups for item in cache.update_evidence] == [1, 1] + assert ( + cache.update_evidence[0].high_precision_mask_sha256 + != cache.update_evidence[1].high_precision_mask_sha256 + ) + + +def test_each_layer_keeps_its_plan_quota_and_global_resident_byte_count() -> None: + scores = { + 0: torch.tensor([100.0, 99.0, 98.0] + [-100.0] * 13).reshape(2, 8), + 1: torch.tensor([97.0, 96.0, 95.0, 94.0] + [-200.0] * 12).reshape(2, 8), + } + plan = _plan(promotions=7, scores=scores) + assert [len(plan.groups_for_layer(index)) for index in (0, 1)] == [3, 4] + cache = create_qwen35_adaptive_exact_budget_cache( + tiny_config(["linear_attention", "linear_attention"]), + plan=plan, + ) + generator = torch.Generator().manual_seed(227) + + cache.update_recurrent_state(torch.randn((1, 2, 8, 8), generator=generator), layer_idx=0) + cache.update_recurrent_state(torch.randn((1, 2, 8, 8), generator=generator), layer_idx=1) + + assert [_packed_mask(cache, index).sum().item() for index in (0, 1)] == [3, 4] + summary = cache.storage_summary() + assert summary["high_precision_groups"] == plan.promoted_group_count + assert summary["resident_bytes"] == plan.resident_bytes + + +def test_batch_size_greater_than_one_is_rejected_before_storage() -> None: + cache = create_qwen35_adaptive_exact_budget_cache(tiny_config(), plan=_plan(promotions=3)) + + with pytest.raises(ValueError, match="requires batch size 1; got 2"): + cache.update_recurrent_state(torch.zeros((2, 2, 8, 8)), layer_idx=0) + + layer = cache.layers[0] + assert isinstance(layer, AdaptiveMixedPackedLinearAttentionLayer) + assert layer.packed_states[0] is None + + +def test_adaptive_storage_is_inference_only_and_retains_no_autograd_graph() -> None: + cache = create_qwen35_adaptive_exact_budget_cache(tiny_config(), plan=_plan(promotions=3)) + state = torch.randn((1, 2, 8, 8), requires_grad=True) + + with pytest.raises(RuntimeError, match="inference-only"): + cache.update_recurrent_state(state, layer_idx=0) + + with torch.no_grad(): + restored = cache.update_recurrent_state(state, layer_idx=0) + + layer = cache.layers[0] + assert isinstance(layer, AdaptiveMixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + assert not restored.requires_grad + assert all( + not tensor.requires_grad + for tensor in ( + packed.low_payload, + packed.high_payload, + packed.scales, + packed.precision_mask, + ) + ) + + +def test_evidence_records_selection_method_current_mask_hash_and_count() -> None: + plan = _plan(promotions=4) + cache = create_qwen35_adaptive_exact_budget_cache( + tiny_config(), + plan=plan, + record_evidence=True, + ) + cache.update_recurrent_state( + torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(229)), + layer_idx=0, + ) + layer = cache.layers[0] + assert isinstance(layer, AdaptiveMixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + expected_hash = hashlib.sha256(bytes(packed.precision_mask.detach().cpu().tolist())).hexdigest() + + evidence = cache.update_evidence[0] + assert evidence.selection_method == "instantaneous_aligned_mse_reduction" + assert evidence.high_precision_groups == 4 + assert evidence.high_precision_mask_sha256 == expected_hash + assert evidence.resident_bytes == plan.resident_bytes + assert evidence.evidence_dict()["high_precision_mask_sha256"] == expected_hash + + +def test_adaptive_factory_runs_tiny_qwen_prefill_and_decode() -> None: + torch.manual_seed(233) + model = Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + cache = create_qwen35_adaptive_exact_budget_cache(model, plan=_plan(promotions=5)) + + with torch.inference_mode(): + model(torch.randint(0, model.config.vocab_size, (1, 5)), past_key_values=cache) + output = model(torch.randint(0, model.config.vocab_size, (1, 1)), past_key_values=cache) + + assert output.logits.shape == (1, 1, model.config.vocab_size) + assert _packed_mask(cache).sum().item() == 5 diff --git a/tests/test_cora_mixed_packed_cache.py b/tests/test_cora_mixed_packed_cache.py new file mode 100644 index 0000000..b94105a --- /dev/null +++ b/tests/test_cora_mixed_packed_cache.py @@ -0,0 +1,456 @@ +from __future__ import annotations + +import math +from types import MethodType + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant.mixed_quantization import PackedMixedQuantizedTensor +from recurquant.packed_cache import ( + CoraMixedPackedLinearAttentionLayer, + CoraMixedPackedRecurrentStateCache, +) +from recurquant.qwen35 import create_qwen35_cora_exact_budget_cache +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from recurquant.transition_observer import Qwen35TransitionObserver +from tests.test_transformers_cache import tiny_config + + +def _target_for_promotions(promotions: int) -> int: + total_groups = 16 + group_size = 8 + low_group_bytes = math.ceil(4 * group_size / 8) + 2 + high_increment = math.ceil(8 * group_size / 8) - math.ceil(4 * group_size / 8) + return total_groups * low_group_bytes + math.ceil(total_groups / 8) + ( + promotions * high_increment + ) + + +def _plan(promotions: int) -> ExactBudgetRowPlan: + scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + return select_rows_exact_budget( + scores, + target_resident_bytes=_target_for_promotions(promotions), + group_size=8, + ) + + +def _cache( + promotions: int = 2, + *, + confirmation_two: bool = True, + record_evidence: bool = False, +) -> CoraMixedPackedRecurrentStateCache: + return create_qwen35_cora_exact_budget_cache( + tiny_config(), + plan=_plan(promotions), + confirmation_two=confirmation_two, + record_evidence=record_evidence, + ) + + +def _layer(cache: CoraMixedPackedRecurrentStateCache) -> CoraMixedPackedLinearAttentionLayer: + layer = cache.layers[0] + assert isinstance(layer, CoraMixedPackedLinearAttentionLayer) + return layer + + +def _transition( + active_row: int | None = None, + *, + tokens: int = 1, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + query = torch.zeros((1, tokens, 2, 8), dtype=torch.float32) + if active_row is not None: + query.reshape(1, tokens, 16)[:, :, active_row] = 1.0 + key = torch.zeros_like(query) + log_decay = torch.full((1, tokens, 2), -100.0, dtype=torch.float32) + beta = torch.zeros((1, tokens, 2), dtype=torch.float32) + return query, key, log_decay, beta + + +def _state() -> torch.Tensor: + pattern = torch.tensor([1.0, 0.51, -0.37, 0.23, 0.14, -0.08, 0.03, -0.01]) + return pattern.repeat(16).reshape(1, 2, 8, 8) + + +def _committed_mask(cache: CoraMixedPackedRecurrentStateCache) -> torch.Tensor: + packed = _layer(cache).packed_states[0] + assert packed is not None + return packed.high_precision_mask().reshape(-1) + + +def _raw_mask(cache: CoraMixedPackedRecurrentStateCache) -> torch.Tensor: + layer = _layer(cache) + packed = layer.previous_raw_mask_packed + assert packed is not None + shifts = torch.arange(8, dtype=torch.int16) + expanded = torch.bitwise_right_shift(packed.to(torch.int16).unsqueeze(1), shifts) + return torch.bitwise_and(expanded, 1).reshape(-1)[:16].to(torch.bool) + + +def _stage( + cache: CoraMixedPackedRecurrentStateCache, + transition: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor], +) -> None: + cache.stage_transition_observation(0, *transition) + + +def test_diagonal_recurrence_matches_explicit_transition_matrix() -> None: + cache = _cache(confirmation_two=False) + layer = _layer(cache) + generator = torch.Generator().manual_seed(811) + query = torch.randn((1, 1, 2, 8), generator=generator) + key = torch.randn((1, 1, 2, 8), generator=generator) + log_decay = torch.tensor([[[-0.3, -0.7]]], dtype=torch.float32) + beta = torch.tensor([[[0.2, 0.8]]], dtype=torch.float32) + + _stage(cache, (query, key, log_decay, beta)) + pending = layer._pending_transition_observation + assert pending is not None + + p0 = torch.full((2, 8), 1.0 / 16, dtype=torch.float64) + exact = torch.empty_like(p0) + for head in range(2): + q = query[0, 0, head].to(torch.float64) + k = key[0, 0, head].to(torch.float64) + q_hat = q / torch.sqrt(q.square().sum() + 1e-6) + k_hat = k / torch.sqrt(k.square().sum() + 1e-6) + transition = torch.exp(log_decay[0, 0, head].to(torch.float64)) * ( + torch.eye(8, dtype=torch.float64) + - beta[0, 0, head].to(torch.float64) * torch.outer(k_hat, k_hat) + ) + gram = transition.T @ torch.diag(p0[head]) @ transition + exact[head] = q_hat.square() / 8 + torch.diag(gram) + exact /= exact.sum() + + assert pending.candidate_diagonal.dtype == torch.float32 + assert torch.allclose( + pending.candidate_diagonal.to(torch.float64), + exact, + atol=2e-8, + rtol=2e-7, + ) + + +def test_chronological_chunk_matches_one_token_state_writes() -> None: + generator = torch.Generator().manual_seed(821) + query = torch.randn((1, 7, 2, 8), generator=generator) + key = torch.randn((1, 7, 2, 8), generator=generator) + log_decay = -torch.rand((1, 7, 2), generator=generator) + beta = torch.rand((1, 7, 2), generator=generator) + state = torch.randn((1, 2, 8, 8), generator=generator) + chunked = _cache(4, confirmation_two=False) + stepped = _cache(4, confirmation_two=False) + + _stage(chunked, (query, key, log_decay, beta)) + chunked.update_recurrent_state(state, layer_idx=0) + for token in range(7): + selection = slice(token, token + 1) + _stage( + stepped, + ( + query[:, selection], + key[:, selection], + log_decay[:, selection], + beta[:, selection], + ), + ) + stepped.update_recurrent_state(state, layer_idx=0) + + chunked_p = _layer(chunked).observability_diagonal + stepped_p = _layer(stepped).observability_diagonal + assert chunked_p is not None and stepped_p is not None + assert torch.allclose(chunked_p, stepped_p, atol=4e-8, rtol=4e-7) + + +@pytest.mark.parametrize("promotions", [0, 5, 16]) +def test_exact_quota_and_selector_storage(promotions: int) -> None: + plan = _plan(promotions) + cache = _cache(promotions, record_evidence=True) + _stage(cache, _transition(0, tokens=2)) + cache.update_recurrent_state(_state(), layer_idx=0) + + diagnostics = cache.selection_diagnostics()[0] + summary = cache.storage_summary() + assert _committed_mask(cache).sum().item() == promotions + assert diagnostics["quota"] == promotions + assert diagnostics["current_selected_count"] == promotions + assert diagnostics["observability_diagonal_bytes"] == 64 + assert diagnostics["previous_raw_mask_bytes"] == 2 + assert diagnostics["selector_auxiliary_bytes"] == 66 + assert summary["resident_bytes"] == plan.resident_bytes + assert summary["selector_auxiliary_bytes"] == 66 + assert summary["resident_bytes_including_selector"] == plan.resident_bytes + 66 + assert cache.update_evidence[0].selection_method == ( + "causal_observability_confirm2_mse_target_fisher_quota" + ) + + +def test_confirmation_two_requires_two_consecutive_raw_hits() -> None: + cache = _cache(1, confirmation_two=True) + state = _state() + + _stage(cache, _transition(0)) + cache.update_recurrent_state(state, layer_idx=0) + assert _raw_mask(cache).nonzero().flatten().tolist() == [0] + assert _committed_mask(cache).nonzero().flatten().tolist() == [0] + + _stage(cache, _transition(1)) + cache.update_recurrent_state(state, layer_idx=0) + assert _raw_mask(cache).nonzero().flatten().tolist() == [1] + assert _committed_mask(cache).nonzero().flatten().tolist() == [0] + + _stage(cache, _transition(1)) + cache.update_recurrent_state(state, layer_idx=0) + assert _raw_mask(cache).nonzero().flatten().tolist() == [1] + assert _committed_mask(cache).nonzero().flatten().tolist() == [1] + diagnostics = cache.selection_diagnostics()[0] + assert diagnostics["mask_transition_count"] == 2 + assert diagnostics["raw_xor_churn_total"] == 2 + assert diagnostics["committed_xor_churn_total"] == 2 + assert diagnostics["admissions_total"] == 1 + assert diagnostics["dwell_total"] == 1 + assert diagnostics["raw_normalized_churn"] == pytest.approx(0.5) + assert diagnostics["committed_normalized_churn"] == pytest.approx(0.5) + + +def test_raw_cora_switches_without_confirmation_delay() -> None: + cache = _cache(1, confirmation_two=False) + _stage(cache, _transition(0)) + cache.update_recurrent_state(_state(), layer_idx=0) + _stage(cache, _transition(1)) + cache.update_recurrent_state(_state(), layer_idx=0) + assert _committed_mask(cache).nonzero().flatten().tolist() == [1] + + +@pytest.mark.parametrize( + ("replacement", "error_type", "message"), + [ + ("query_batch", ValueError, "shape"), + ("key_width", ValueError, "shape"), + ("gate_width", ValueError, "shape"), + ("integer", TypeError, "floating point"), + ("nonfinite", ValueError, "finite"), + ("empty", ValueError, "at least one token"), + ], +) +def test_invalid_transition_observations_fail_closed( + replacement: str, + error_type: type[Exception], + message: str, +) -> None: + cache = _cache() + query, key, log_decay, beta = _transition(0) + if replacement == "query_batch": + query = query.repeat(2, 1, 1, 1) + elif replacement == "key_width": + key = key[..., :7] + elif replacement == "gate_width": + beta = beta[..., :1] + elif replacement == "integer": + key = key.to(torch.int64) + elif replacement == "nonfinite": + log_decay[0, 0, 0] = float("nan") + elif replacement == "empty": + query = query[:, :0] + key = key[:, :0] + log_decay = log_decay[:, :0] + beta = beta[:, :0] + + with pytest.raises(error_type, match=message): + _stage(cache, (query, key, log_decay, beta)) + assert _layer(cache)._pending_transition_observation is None + + +def test_missing_duplicate_stale_and_invalid_index_fail_closed() -> None: + cache = _cache() + layer = _layer(cache) + with pytest.raises(RuntimeError, match="no staged transition"): + cache.update_recurrent_state(_state(), layer_idx=0) + + _stage(cache, _transition(0)) + with pytest.raises(RuntimeError, match="duplicate transition"): + _stage(cache, _transition(1)) + assert layer._pending_transition_observation is None + + _stage(cache, _transition(0)) + layer._update_count += 1 + with pytest.raises(RuntimeError, match="stale transition"): + cache.update_recurrent_state(_state(), layer_idx=0) + assert layer._pending_transition_observation is None + + layer._update_count = 0 + _stage(cache, _transition(0)) + with pytest.raises(IndexError, match="state_idx 9"): + layer.update_recurrent_state(_state(), state_idx=9) + assert layer._pending_transition_observation is None + + +def test_failed_pack_rolls_back_every_selector_field_and_evidence( + monkeypatch: pytest.MonkeyPatch, +) -> None: + cache = _cache(2, record_evidence=True) + layer = _layer(cache) + _stage(cache, _transition(0, tokens=2)) + cache.update_recurrent_state(_state(), layer_idx=0) + previous_packed = layer.packed_states[0] + previous_diagonal = layer.observability_diagonal + previous_raw = layer.previous_raw_mask_packed + assert previous_packed is not None + assert previous_diagonal is not None + assert previous_raw is not None + previous_diagnostics = layer.observability_diagnostics() + previous_evidence = list(cache.update_evidence) + previous_evidence_index = cache._update_index + + _stage(cache, _transition(1)) + + def fail_dequantize(self: PackedMixedQuantizedTensor) -> torch.Tensor: + raise RuntimeError("injected CORA materialization failure") + + monkeypatch.setattr(PackedMixedQuantizedTensor, "dequantize", fail_dequantize) + with pytest.raises(RuntimeError, match="injected CORA materialization failure"): + cache.update_recurrent_state(_state() * 2, layer_idx=0) + + assert layer.packed_states[0] is previous_packed + assert layer.observability_diagonal is previous_diagonal + assert layer.previous_raw_mask_packed is previous_raw + assert layer.observability_diagnostics() == previous_diagnostics + assert cache.update_evidence == previous_evidence + assert cache._update_index == previous_evidence_index + assert layer._pending_transition_observation is None + + +def test_failed_previous_mask_read_clears_candidate_without_mutation( + monkeypatch: pytest.MonkeyPatch, +) -> None: + cache = _cache(2, record_evidence=True) + layer = _layer(cache) + _stage(cache, _transition(0)) + cache.update_recurrent_state(_state(), layer_idx=0) + previous_packed = layer.packed_states[0] + previous_diagonal = layer.observability_diagonal + previous_raw = layer.previous_raw_mask_packed + previous_diagnostics = layer.observability_diagnostics() + previous_evidence = list(cache.update_evidence) + _stage(cache, _transition(1)) + + def fail_mask(self: PackedMixedQuantizedTensor) -> torch.Tensor: + raise RuntimeError("injected previous-mask failure") + + monkeypatch.setattr(PackedMixedQuantizedTensor, "high_precision_mask", fail_mask) + with pytest.raises(RuntimeError, match="injected previous-mask failure"): + cache.update_recurrent_state(_state() * 2, layer_idx=0) + + assert layer.packed_states[0] is previous_packed + assert layer.observability_diagonal is previous_diagonal + assert layer.previous_raw_mask_packed is previous_raw + assert layer.observability_diagnostics() == previous_diagnostics + assert cache.update_evidence == previous_evidence + assert layer._pending_transition_observation is None + + +def test_callback_failure_rolls_back_appended_evidence_and_every_state() -> None: + cache = _cache(2, record_evidence=True) + layer = _layer(cache) + _stage(cache, _transition(0)) + cache.update_recurrent_state(_state(), layer_idx=0) + previous_packed = layer.packed_states[0] + previous_diagonal = layer.observability_diagonal + previous_raw = layer.previous_raw_mask_packed + previous_diagnostics = layer.observability_diagnostics() + previous_evidence = list(cache.update_evidence) + previous_evidence_index = cache._update_index + _stage(cache, _transition(1)) + + def append_then_fail( + owner: CoraMixedPackedRecurrentStateCache, + layer_index: int, + state_index: int, + source: torch.Tensor, + packed: PackedMixedQuantizedTensor, + materialized: torch.Tensor, + ) -> None: + del layer_index, state_index, source, packed, materialized + owner.update_evidence.append(previous_evidence[0]) + owner._update_index += 1 + raise RuntimeError("injected evidence callback failure") + + layer.on_update = MethodType(append_then_fail, cache) + with pytest.raises(RuntimeError, match="injected evidence callback failure"): + cache.update_recurrent_state(_state() * 2, layer_idx=0) + + assert layer.packed_states[0] is previous_packed + assert layer.observability_diagonal is previous_diagonal + assert layer.previous_raw_mask_packed is previous_raw + assert layer.observability_diagnostics() == previous_diagnostics + assert cache.update_evidence == previous_evidence + assert cache._update_index == previous_evidence_index + assert layer._pending_transition_observation is None + + +def test_reset_offload_prefetch_and_pending_transfer() -> None: + cache = _cache(2) + layer = _layer(cache) + _stage(cache, _transition(0, tokens=3)) + cache.update_recurrent_state(_state(), layer_idx=0) + layer.offload() + assert layer.observability_diagonal is not None + assert layer.observability_diagonal.device.type == "cpu" + assert layer.previous_raw_mask_packed is not None + layer.prefetch() + + cache.reset() + assert torch.equal( + layer.observability_diagonal, + torch.full((2, 8), 1.0 / 16, dtype=torch.float32), + ) + assert layer.previous_raw_mask_packed is None + assert cache.selection_diagnostics()[0]["state_updates"] == 0 + + _stage(cache, _transition(0)) + with pytest.raises(RuntimeError, match="cannot offload with a pending"): + layer.offload() + assert layer._pending_transition_observation is None + + +def test_real_tiny_qwen_observer_runs_cora_prefill_chunk_and_decode() -> None: + model = Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + cache = create_qwen35_cora_exact_budget_cache( + model, + plan=_plan(5), + record_evidence=True, + ) + + with torch.inference_mode(), Qwen35TransitionObserver(model, caches=[cache]): + prefill = model( + torch.randint(0, model.config.vocab_size, (1, 5)), + past_key_values=cache, + ) + chunk = model( + torch.randint(0, model.config.vocab_size, (1, 2)), + past_key_values=cache, + ) + decode = model( + torch.randint(0, model.config.vocab_size, (1, 1)), + past_key_values=cache, + ) + + assert prefill.logits.shape == (1, 5, model.config.vocab_size) + assert chunk.logits.shape == (1, 2, model.config.vocab_size) + assert decode.logits.shape == (1, 1, model.config.vocab_size) + diagnostics = cache.selection_diagnostics()[0] + assert diagnostics["observations_staged"] == 3 + assert diagnostics["observations_committed"] == 3 + assert diagnostics["state_updates"] == 3 + assert diagnostics["tokens_observed"] == 8 + assert diagnostics["mask_transition_count"] == 2 + assert diagnostics["pending_observation"] is False + assert diagnostics["current_selected_count"] == 5 + assert len(cache.update_evidence) == 3 diff --git a/tests/test_evaluate_rht_cqer_stage_b.py b/tests/test_evaluate_rht_cqer_stage_b.py new file mode 100644 index 0000000..7809aea --- /dev/null +++ b/tests/test_evaluate_rht_cqer_stage_b.py @@ -0,0 +1,1772 @@ +from __future__ import annotations + +import copy +import json +from pathlib import Path + +import pytest + +from recurquant.evaluation import paired_bootstrap_mean_improvement +from scripts import evaluate_rht_cqer_stage_b as stage_b + + +def _metric( + *, + task_id: int, + token_count: int, + delta_nll: float, + mean_kl: float, + cvar95_kl: float, + top1_agreement: float, +) -> dict[str, float | int | bool]: + return { + "task_id": task_id, + "token_count": token_count, + "mean_kl": mean_kl, + "cvar95_kl": cvar95_kl, + "max_kl": cvar95_kl + 0.25, + "top1_agreement": top1_agreement, + "reference_nll": 1.0, + "candidate_nll": 1.0 + delta_nll, + "delta_nll": delta_nll, + "all_logits_finite": True, + } + + +def _task_records() -> list[dict[str, int | str]]: + digest = "a" * 64 + return [ + { + "rank": stage_b.STAGE_B_OFFSET + index, + "task_id": 10_000 + ((index * 17) % stage_b.STAGE_B_LIMIT), + "row_sha256": f"{index + 1:064x}", + "prompt_tokens": 3, + "code_tokens": 4, + "aligned_scored_tokens": 3, + "full_code_scored_tokens": 4, + "prompt_text_sha256": digest, + "code_text_sha256": digest, + "prompt_token_ids_sha256": digest, + "code_token_ids_sha256": digest, + } + for index in range(stage_b.STAGE_B_LIMIT) + ] + + +def _frozen_row_plan_inputs() -> tuple[dict[str, object], dict[str, object]]: + resolver = stage_b.identity_resolver + rows = [ + { + "layer_index": layer, + "head_index": flat_index // 128, + "row_index": flat_index % 128, + } + for layer in stage_b.FROZEN_LINEAR_LAYERS + for flat_index in range(stage_b.FROZEN_LAYER_QUOTAS[layer]) + ] + selector_binding = { + "selector_file_sha256": resolver.SELECTOR_FILE_SHA256, + "selector_canonical_evidence_sha256": ( + resolver.SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": resolver.LOSS_SELECTOR_FILE_SHA256, + "loss_selector_canonical_evidence_sha256": ( + resolver.LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + } + row_plan: dict[str, object] = { + "schema": resolver.ROW_PLAN_SCHEMA, + "method": stage_b.TARGET_FISHER_SCORE, + "selector_binding": selector_binding, + "model": { + "id": resolver.MODEL_ID, + "revision": resolver.MODEL_REVISION, + }, + "quantization": { + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + }, + "accounting": { + "total_groups": 36_864, + "mask_bytes": stage_b.TARGET_MASK_BYTES, + "promotion_increment_bytes": 64, + "target_resident_bytes": stage_b.TARGET_PACKED_STATE_BYTES, + "resident_bytes": stage_b.TARGET_PACKED_STATE_BYTES, + "promoted_group_count": stage_b.TARGET_PROMOTED_ROWS, + }, + "score_shapes": [ + {"layer_index": layer, "heads": 16, "rows": 128} + for layer in stage_b.FROZEN_LINEAR_LAYERS + ], + "layer_quotas": { + str(layer): quota + for layer, quota in stage_b.FROZEN_LAYER_QUOTAS.items() + }, + "high_precision_rows": rows, + } + row_plan["canonical_plan_sha256"] = stage_b._sha256_bytes( # noqa: SLF001 + stage_b.canonical_json_bytes(row_plan) + ) + identity = { + "row_plan": row_plan, + "model_contract": { + "id": resolver.MODEL_ID, + "revision": resolver.MODEL_REVISION, + "weights_loaded": False, + }, + } + stage_a = {"selector_artifacts": dict(selector_binding)} + return identity, stage_a + + +def _rehash_row_plan(identity: dict[str, object]) -> None: + row_plan = dict(identity["row_plan"]) # type: ignore[arg-type] + row_plan.pop("canonical_plan_sha256") + row_plan["canonical_plan_sha256"] = stage_b._sha256_bytes( # noqa: SLF001 + stage_b.canonical_json_bytes(row_plan) + ) + identity["row_plan"] = row_plan + + +def _per_task_metrics( + task_records: list[dict[str, int | str]], + *, + token_field: str = "aligned_scored_tokens", +) -> dict[str, list[dict[str, float | int | bool]]]: + values = { + stage_b.STATIC_METHOD: (0.95, 0.9, 1.8, 0.88), + stage_b.ADAPTIVE_METHOD: (0.90, 0.8, 1.7, 0.89), + stage_b.CQER_METHOD: (1.00, 1.0, 2.0, 0.900), + stage_b.RHT_METHOD: (0.70, 0.5, 1.5, 0.897), + } + return { + method: [ + _metric( + task_id=int(task["task_id"]), + token_count=int(task[token_field]), + delta_nll=method_values[0], + mean_kl=method_values[1], + cvar95_kl=method_values[2], + top1_agreement=method_values[3], + ) + for task in task_records + ] + for method, method_values in values.items() + } + + +def _aggregates( + per_task: dict[str, list[dict[str, float | int | bool]]], +) -> dict[str, dict[str, float | int]]: + return stage_b._expected_aggregates(per_task) # noqa: SLF001 + + +def _bootstrap( + per_task: dict[str, list[dict[str, float | int | bool]]], +) -> dict[str, object]: + return paired_bootstrap_mean_improvement( + [float(row["delta_nll"]) for row in per_task[stage_b.CQER_METHOD]], + [float(row["delta_nll"]) for row in per_task[stage_b.RHT_METHOD]], + samples=stage_b.BOOTSTRAP_SAMPLES, + seed=stage_b.SEED, + ) + + +def _passing_gate_inputs() -> dict[str, object]: + tasks = _task_records() + per_task = _per_task_metrics(tasks) + selector_diagnostics = _selector_diagnostics(tasks) + per_task_state_errors = _per_task_state_errors(tasks) + return { + "aggregates": _aggregates(per_task), + "per_task": per_task, + "paired_bootstrap": _bootstrap(per_task), + "aggregate_state_error": stage_b.aggregate_state_errors( + per_task_state_errors + ), + "per_task_state_errors": per_task_state_errors, + "selector_diagnostics": selector_diagnostics, + "task_records": tasks, + "integrity_gate": {"passed": True}, + } + + +def _storage() -> dict[str, dict[str, int]]: + common = { + "payload_bytes": stage_b.TARGET_PAYLOAD_BYTES, + "scale_bytes": stage_b.TARGET_SCALE_BYTES, + "mask_bytes": stage_b.TARGET_MASK_BYTES, + "resident_bytes": stage_b.TARGET_PACKED_STATE_BYTES, + "high_precision_groups": stage_b.TARGET_PROMOTED_ROWS, + } + storage = {method: dict(common) for method in stage_b.METHODS} + for method in stage_b.QUERY_METHODS: + storage[method].update( + { + "selector_auxiliary_bytes": stage_b.TARGET_SELECTOR_BYTES, + "resident_bytes_including_selector": stage_b.TARGET_TOTAL_BYTES, + } + ) + return storage + + +def _selector_diagnostics( + tasks: list[dict[str, int | str]], +) -> dict[str, list[dict[str, object]]]: + result: dict[str, list[dict[str, object]]] = {} + for method in stage_b.QUERY_METHODS: + task_rows: list[dict[str, object]] = [] + for task in tasks: + layers: list[dict[str, object]] = [] + for layer in stage_b.FROZEN_LINEAR_LAYERS: + record: dict[str, object] = { + "layer_index": layer, + "quota": stage_b.FROZEN_LAYER_QUOTAS[layer], + "current_selected_count": stage_b.FROZEN_LAYER_QUOTAS[layer], + "state_updates": 4, + "observations_staged": 4, + "observations_committed": 4, + "tokens_observed": 6, + "pending_observation": False, + "confirmation_two": False, + "selection_method": ( + "query_ema32_weighted_aligned_mse_reduction" + if method == stage_b.CQER_METHOD + else stage_b.RHT_METHOD + ), + "current_mask_sha256": "b" * 64, + } + if method == stage_b.RHT_METHOD: + record.update( + { + "state_codec": "right_rht_sha256_signs_v1", + "state_codec_seed": stage_b.SEED, + "state_codec_axis": "value", + "state_codec_normalization": "orthonormal", + "state_codec_persistent_tensor_bytes": 0, + } + ) + layers.append(record) + task_rows.append({"task_id": int(task["task_id"]), "layers": layers}) + result[method] = task_rows + return result + + +def _state_error_summary(*, method: str, sse_per_record: float) -> dict[str, object]: + writes = 4 + layers = stage_b.FROZEN_LINEAR_LAYERS + elements_per_record = 16 * 128 * 128 + record_count = writes * len(layers) + element_count = record_count * elements_per_record + selection_method = ( + "query_ema32_weighted_aligned_mse_reduction" + if method == stage_b.CQER_METHOD + else stage_b.RHT_METHOD + ) + records: list[dict[str, object]] = [] + for write in range(writes): + for layer in layers: + quota = stage_b.FROZEN_LAYER_QUOTAS[layer] + payload = 2_048 * 64 + quota * 64 + scale = 2_048 * 2 + mask = 2_048 // 8 + records.append( + { + "update_index": len(records), + "layer_index": layer, + "state_index": 0, + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + "rounding": "nearest", + "source_dtype": "torch.float32", + "shape": [1, 16, 128, 128], + "total_groups": 2_048, + "high_precision_groups": quota, + "selection_method": selection_method, + "high_precision_mask_sha256": "b" * 64, + "baseline_bytes": elements_per_record * 4, + "payload_bytes": payload, + "scale_bytes": scale, + "mask_bytes": mask, + "resident_bytes": payload + scale + mask, + "relative_l2_error": 0.1, + "mean_squared_error": sse_per_record / elements_per_record, + "max_absolute_error": 0.2, + "element_count": elements_per_record, + "state_sse": sse_per_record, + "write_ordinal": write, + } + ) + coverage = [ + { + "write_ordinal": int(record["write_ordinal"]), + "layer_index": int(record["layer_index"]), + "state_index": 0, + "shape": [1, 16, 128, 128], + } + for record in records + ] + total_sse = sse_per_record * record_count + return { + "record_count": record_count, + "element_count": element_count, + "aggregate_state_sse": total_sse, + "aggregate_state_mse": total_sse / element_count, + "coverage": coverage, + "per_layer": { + str(layer): { + "record_count": writes, + "element_count": writes * elements_per_record, + "state_sse": sum(sse_per_record for _ in range(writes)), + } + for layer in layers + }, + "per_write": { + str(write): { + "record_count": len(layers), + "element_count": len(layers) * elements_per_record, + "state_sse": sum(sse_per_record for _ in layers), + } + for write in range(writes) + }, + "records": records, + } + + +def _per_task_state_errors( + tasks: list[dict[str, int | str]], +) -> dict[str, list[dict[str, object]]]: + return { + method: [ + { + "task_id": int(task["task_id"]), + "state_error": _state_error_summary( + method=method, + sse_per_record=( + 1.0 if method == stage_b.CQER_METHOD else 0.4 + ), + ), + } + for task in tasks + ] + for method in stage_b.QUERY_METHODS + } + + +def _reference_aligned_summary(*, sse_per_layer: float) -> dict[str, object]: + expected_shape = [1, 16, 128, 128] + elements = 16 * 128 * 128 + writes: list[dict[str, object]] = [] + for write in range(4): + layers = [ + { + "layer_index": layer, + "state_index": 0, + "shape": expected_shape, + "element_count": elements, + "state_sse": sse_per_layer, + "state_mse": sse_per_layer / elements, + "relative_l2_error": 0.1, + "max_absolute_error": 0.2, + } + for layer in stage_b.FROZEN_LINEAR_LAYERS + ] + writes.append( + { + "write_ordinal": write, + "record_count": len(layers), + "element_count": len(layers) * elements, + "state_sse": sum(sse_per_layer for _ in layers), + "layers": layers, + } + ) + total_elements = sum(int(write["element_count"]) for write in writes) + total_sse = sum(float(write["state_sse"]) for write in writes) + return { + "metric": "candidate_materialized_state_minus_matched_fp32_state", + "write_count": len(writes), + "record_count": len(writes) * len(stage_b.FROZEN_LINEAR_LAYERS), + "element_count": total_elements, + "aggregate_state_sse": total_sse, + "aggregate_state_mse": total_sse / total_elements, + "writes": writes, + } + + +def _per_task_reference_aligned_state_errors( + tasks: list[dict[str, int | str]], +) -> dict[str, list[dict[str, object]]]: + return { + method: [ + { + "task_id": int(task["task_id"]), + "state_error": _reference_aligned_summary( + sse_per_layer=( + 2.0 if method == stage_b.CQER_METHOD else 1.0 + ) + ), + } + for task in tasks + ] + for method in stage_b.QUERY_METHODS + } + + +def _unit_evidence() -> dict[str, object]: + return { + "production_self_check": { + "inverse_relative_l2": 1e-7, + "inverse_relative_l2_threshold": stage_b.MAX_RHT_INVERSE_RELATIVE_L2, + "physical_pack_matches_transformed_qdq": True, + "physical_pack_max_abs_difference": 0.0, + "physical_pack_storage_bytes": ( + stage_b.PRODUCTION_SELF_CHECK_STORAGE_BYTES + ), + "sign_schedule_sha256": stage_b.EXPECTED_SIGN_SCHEDULE_SHA256, + "expected_sign_schedule_sha256": ( + stage_b.EXPECTED_SIGN_SCHEDULE_SHA256 + ), + "seed": stage_b.SEED, + "device": "cpu", + "dtype": "torch.float32", + }, + "independent_dense_reference": { + "reference": stage_b.INDEPENDENT_REFERENCE_DESCRIPTION, + "passed": True, + "signs_exact": True, + "encode_max_abs_difference": 1e-7, + "encode_max_abs_threshold": ( + stage_b.INDEPENDENT_ENCODE_MAX_ABS_THRESHOLD + ), + "physical_pack_max_abs_difference": 1e-7, + "physical_pack_max_abs_threshold": ( + stage_b.INDEPENDENT_PACK_MAX_ABS_THRESHOLD + ), + }, + } + + +def _integrity_flags() -> dict[str, bool]: + return { + "repository_clean_at_start": True, + "repository_clean_at_end": True, + "repository_commit_stable": True, + "source_hashes_stable": True, + "stage_a_artifact_committed_authenticated_and_passed": True, + "identity_artifact_committed_and_authenticated": True, + "identity_authenticated_before_model_weights": True, + "identity_row_plan_authenticated": True, + "identity_source_freeze_matches_current_bytes": True, + "imported_modules_resolved_to_authenticated_repository": True, + "runtime_environment_authenticated_before_dataset_access": True, + ( + "protected_window_8_16_content_selected_retained_canonicalized_" + "formatted_tokenized_passed_to_model_or_evaluated" + ): False, + } + + +def _token_traces( + tasks: list[dict[str, int | str]], + *, + token_field: str, +) -> dict[str, list[dict[str, object]]]: + method_values = { + stage_b.STATIC_METHOD: (0.95, 0.9), + stage_b.ADAPTIVE_METHOD: (0.90, 0.8), + stage_b.CQER_METHOD: (1.00, 1.0), + stage_b.RHT_METHOD: (0.70, 0.5), + } + result: dict[str, list[dict[str, object]]] = {} + for method, (delta, kl) in method_values.items(): + rows: list[dict[str, object]] = [] + for task in tasks: + count = int(task[token_field]) + payload = { + "token_count": count, + "kl": [kl] * count, + "reference_nll": [1.0] * count, + "candidate_nll": [1.0 + delta] * count, + "top1_agreement": [True] * count, + "outputs_finite": [True] * count, + } + rows.append( + { + "task_id": int(task["task_id"]), + **payload, + "canonical_primitives_sha256": stage_b._sha256_bytes( # noqa: SLF001 + stage_b.canonical_json_bytes(payload) + ), + } + ) + result[method] = rows + return result + + +def _summaries_from_traces( + traces: dict[str, list[dict[str, object]]], +) -> dict[str, list[dict[str, float | int | bool]]]: + return { + method: [ + { + "task_id": int(trace["task_id"]), + **stage_b._summary_from_trace(trace), # noqa: SLF001 + } + for trace in method_traces + ] + for method, method_traces in traces.items() + } + + +def _passing_integrity_inputs() -> dict[str, object]: + tasks = _task_records() + traces = _token_traces(tasks, token_field="aligned_scored_tokens") + full_traces = _token_traces(tasks, token_field="full_code_scored_tokens") + per_task = _summaries_from_traces(traces) + full = _summaries_from_traces(full_traces) + return { + "per_task": per_task, + "per_task_full_code": full, + "per_task_token_traces": traces, + "per_task_full_code_token_traces": full_traces, + "aggregates": _aggregates(per_task), + "aggregates_full_code": _aggregates(full), + "storage": _storage(), + "selector_diagnostics": _selector_diagnostics(tasks), + "per_task_state_errors": _per_task_state_errors(tasks), + "per_task_reference_aligned_state_errors": ( + _per_task_reference_aligned_state_errors(tasks) + ), + "task_records": tasks, + "unit_evidence": _unit_evidence(), + "integrity": _integrity_flags(), + } + + +def _result_identity_dataset( + tasks: list[dict[str, int | str]], +) -> dict[str, object]: + resolver = stage_b.identity_resolver + ordered_ids = [int(task["task_id"]) for task in tasks] + manifest = { + "schema": resolver.MBPP_MANIFEST_SCHEMA, + "dataset_id": resolver.MBPP_DATASET_ID, + "config": resolver.MBPP_CONFIG, + "revision": resolver.MBPP_REVISION, + "phase": "calibration", + "source_split": resolver.mbpp_source_split("calibration"), + "selection_namespace": resolver.MBPP_SELECTION_NAMESPACE, + "formatter_version": resolver.MBPP_FORMATTER_VERSION, + "row_count": stage_b.STAGE_B_LIMIT, + "rows": sorted( + ( + {"task_id": int(task["task_id"]), "sha256": task["row_sha256"]} + for task in tasks + ), + key=lambda row: row["task_id"], + ), + } + content_hash = stage_b.mbpp_manifest_content_sha256(manifest) + token_hash = resolver.token_manifest_sha256(tasks) + ordered_hash = resolver.ordered_identity_sha256( + content_manifest_sha256=content_hash, + task_records=tasks, + ) + source_rows = resolver.MBPP_CALIBRATION_SIZE + target_rows_seen = stage_b.STAGE_B_STOP + application_keys = ( + "selected", + "retained", + "canonicalized", + "formatted", + "tokenized", + "passed_to_model", + "evaluated", + ) + data_access = { + "transport_limitation": stage_b.DATA_ACCESS_TRANSPORT_LIMITATION, + "ranking_pass": { + "transport_records_yielded": source_rows, + "task_id_fields_inspected": source_rows, + "non_task_id_fields_read_by_recurquant": 0, + "row_mappings_retained": 0, + }, + "target_load_pass": { + "transport_records_yielded": target_rows_seen, + "task_id_fields_inspected": target_rows_seen, + "non_target_content_fields_read_by_recurquant": 0, + "target_rows_retained_and_canonicalized": stage_b.STAGE_B_LIMIT, + }, + "application_task_id_sets": { + "selected": ordered_ids, + "retained": ordered_ids, + "canonicalized": ordered_ids, + "formatted": ordered_ids, + "tokenized": ordered_ids, + "passed_to_model": [], + "evaluated": [], + }, + "protected_window_intersection": { + key: False for key in application_keys + }, + } + return { + "id": resolver.MBPP_DATASET_ID, + "config": resolver.MBPP_CONFIG, + "revision": resolver.MBPP_REVISION, + "phase": "calibration", + "source_split": resolver.mbpp_source_split("calibration"), + "selection_namespace": resolver.MBPP_SELECTION_NAMESPACE, + "formatter_version": resolver.MBPP_FORMATTER_VERSION, + "selection_mode": "task_id_ranking_then_exact_task_id_stream", + "selection_window": { + "offset": stage_b.STAGE_B_OFFSET, + "limit": stage_b.STAGE_B_LIMIT, + "stop_exclusive": stage_b.STAGE_B_STOP, + }, + "protected_window": { + "offset": stage_b.PROTECTED_WINDOW[0], + "stop_exclusive": stage_b.PROTECTED_WINDOW[1], + "content_retained_canonicalized_or_tokenized": False, + }, + "ordered_task_ids": ordered_ids, + "manifest": manifest, + "content_manifest_sha256": content_hash, + "token_manifest_sha256": token_hash, + "ordered_identity_sha256": ordered_hash, + "tasks": tasks, + "totals": { + "source_train_rows_seen_by_task_id_only": source_rows, + "retained_rows": stage_b.STAGE_B_LIMIT, + "prompt_tokens": sum(int(task["prompt_tokens"]) for task in tasks), + "code_tokens": sum(int(task["code_tokens"]) for task in tasks), + "aligned_scored_tokens": sum( + int(task["aligned_scored_tokens"]) for task in tasks + ), + "full_code_scored_tokens": sum( + int(task["full_code_scored_tokens"]) for task in tasks + ), + }, + "data_access": data_access, + } + + +def _result_runtime_environment() -> dict[str, object]: + resolver = stage_b.identity_resolver + version = ".".join(str(item) for item in resolver.STAGE_A_PYTHON_VERSION) + return { + "schema": resolver.RUNTIME_ENVIRONMENT_SCHEMA, + "stage_a_binding": { + "artifact_kind": stage_b.STAGE_A_ARTIFACT_KIND, + "file_sha256": resolver.STAGE_A_FILE_SHA256, + "canonical_evidence_sha256": resolver.STAGE_A_CANONICAL_EVIDENCE_SHA256, + }, + "python": { + "major": resolver.STAGE_A_PYTHON_VERSION[0], + "minor": resolver.STAGE_A_PYTHON_VERSION[1], + "micro": resolver.STAGE_A_PYTHON_VERSION[2], + "version": version, + }, + "packages": dict(resolver.STAGE_A_PACKAGE_VERSIONS), + "cuda": dict(resolver.STAGE_A_CUDA_CONTRACT), + "runtime_matches_stage_a": True, + "local_files_only": False, + } + + +def _stage_b_result_document() -> dict[str, object]: + inputs = _passing_integrity_inputs() + per_task = inputs["per_task"] + state_aggregates = stage_b.aggregate_state_errors( + inputs["per_task_state_errors"] + ) + bootstrap = _bootstrap(per_task) + integrity = stage_b.evaluate_stage_b_integrity(**inputs) + gate = stage_b.evaluate_stage_b_gate( + aggregates=inputs["aggregates"], + per_task=per_task, + paired_bootstrap=bootstrap, + aggregate_state_error=state_aggregates, + per_task_state_errors=inputs["per_task_state_errors"], + selector_diagnostics=inputs["selector_diagnostics"], + task_records=inputs["task_records"], + integrity_gate=integrity, + ) + tasks = inputs["task_records"] + ordered_ids = [int(task["task_id"]) for task in tasks] + identity_dataset = _result_identity_dataset(tasks) + identity_access = identity_dataset["data_access"] + application_keys = ( + "selected", + "retained", + "canonicalized", + "formatted", + "tokenized", + "passed_to_model", + "evaluated", + ) + source_hashes = {path: "c" * 64 for path in stage_b.SOURCE_FILES} + runtime_environment = _result_runtime_environment() + python_version = runtime_environment["python"]["version"] + evidence: dict[str, object] = { + "schema_version": 1, + "artifact_kind": stage_b.ARTIFACT_KIND, + "diagnostic_only": True, + "claim_boundary": stage_b.RESULT_CLAIM_BOUNDARY, + "created_at_utc": "2026-07-26T00:00:00+00:00", + "protocol": { + "name": "Experiment 009 Stage B", + "ranked_window": [stage_b.STAGE_B_OFFSET, stage_b.STAGE_B_STOP], + "protected_ranked_window": list(stage_b.PROTECTED_WINDOW), + "protected_window_application_content_intersection": False, + "methods_locked": True, + "thresholds_locked": True, + "bootstrap_samples": stage_b.BOOTSTRAP_SAMPLES, + "bootstrap_seed": stage_b.SEED, + }, + "prerequisite_artifacts": { + "stage_a": { + "path": stage_b.identity_resolver.STAGE_A_ARTIFACT_RELATIVE_PATH, + "sha256": stage_b.identity_resolver.STAGE_A_FILE_SHA256, + "git_blob_sha256": stage_b.identity_resolver.STAGE_A_FILE_SHA256, + "artifact_sha256": stage_b.identity_resolver.STAGE_A_FILE_SHA256, + "canonical_evidence_sha256": ( + stage_b.identity_resolver.STAGE_A_CANONICAL_EVIDENCE_SHA256 + ), + "implementation_commit": ( + stage_b.identity_resolver.STAGE_A_IMPLEMENTATION_COMMIT + ), + "gate_recomputed_and_passed": "true", + "artifact_kind": stage_b.STAGE_A_ARTIFACT_KIND, + "historical_path_privacy_limitation": ( + stage_b.STAGE_A_PATH_PRIVACY_LIMITATION + ), + }, + "stage_b_identity": { + "path": "evidence/experiment009-stage-b-identity.json", + "sha256": "d" * 64, + "git_blob_sha256": "d" * 64, + "artifact_sha256": "d" * 64, + "canonical_evidence_sha256": "e" * 64, + "resolver_commit": "f" * 40, + "artifact_kind": stage_b.IDENTITY_ARTIFACT_KIND, + }, + "committed_row_plan": { + "passed": True, + "canonical_plan_sha256": "1" * 64, + "method": stage_b.TARGET_FISHER_SCORE, + "promoted_group_count": stage_b.TARGET_PROMOTED_ROWS, + "resident_bytes": stage_b.TARGET_PACKED_STATE_BYTES, + "selector_binding": { + "selector_file_sha256": ( + stage_b.identity_resolver.SELECTOR_FILE_SHA256 + ), + "selector_canonical_evidence_sha256": ( + stage_b.identity_resolver.SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": ( + stage_b.identity_resolver.LOSS_SELECTOR_FILE_SHA256 + ), + "loss_selector_canonical_evidence_sha256": ( + stage_b.identity_resolver.LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + }, + }, + "identity_source_freeze": { + "passed": True, + "path_count": len(stage_b.SOURCE_FILES), + "paths": list(stage_b.SOURCE_FILES), + "sha256": source_hashes, + }, + }, + "model": { + "id": stage_b.identity_resolver.MODEL_ID, + "revision": stage_b.identity_resolver.MODEL_REVISION, + "dtype": "torch.bfloat16", + "device": "cuda", + }, + "methods": list(stage_b.METHODS), + "dataset": { + "phase": "calibration", + "selection_mode": ( + "committed_exact_task_ids_from_ranked_window_32_64" + ), + "identity": identity_dataset, + "identity_validation": { + "passed": True, + "authenticated_before_model_weights": True, + "ordered_task_ids": ordered_ids, + "content_manifest_sha256": ( + identity_dataset["content_manifest_sha256"] + ), + "token_manifest_sha256": identity_dataset["token_manifest_sha256"], + "ordered_identity_sha256": identity_dataset["ordered_identity_sha256"], + }, + "data_access": { + "transport_limitation": stage_b.DATA_ACCESS_TRANSPORT_LIMITATION, + "identity_resolution": copy.deepcopy(identity_access), + "evaluator_target_load": copy.deepcopy( + identity_access["target_load_pass"] + ), + "evaluator_application_task_id_sets": { + key: ordered_ids for key in application_keys + }, + "protected_window_intersection": { + key: False for key in application_keys + }, + "non_target_source_records": { + "recurquant_fields_inspected": ["task_id"], + ( + "content_retained_canonicalized_formatted_tokenized_" + "or_evaluated" + ): False, + }, + }, + "identity_authenticated_before_model_weights": True, + stage_b.PROTECTED_EVALUATION_FIELD: False, + }, + "metric_contract": { + "primary": ( + "task-macro aligned excess next-token NLL versus FP32 state" + ), + "aligned_excludes": "prompt-to-first-code-token prediction", + "secondary": "task-macro full-code metrics", + "paired_bootstrap_samples": stage_b.BOOTSTRAP_SAMPLES, + "paired_bootstrap_seed": stage_b.SEED, + }, + "storage": { + "fp32_reference_recurrent_state_bytes": 10_000_000, + "candidates": inputs["storage"], + }, + "aggregates": inputs["aggregates"], + "aggregates_full_code_secondary": inputs["aggregates_full_code"], + "per_task": per_task, + "per_task_full_code_secondary": inputs["per_task_full_code"], + "per_task_token_primitives": inputs["per_task_token_traces"], + "per_task_full_code_token_primitives": ( + inputs["per_task_full_code_token_traces"] + ), + "paired_bootstrap_cqer_minus_rht_aligned_delta_nll": bootstrap, + "selector_diagnostics": inputs["selector_diagnostics"], + "state_error": { + "primary_gate_metric": stage_b.PRIMARY_STATE_ERROR_METRIC, + "aggregates": state_aggregates, + "per_task": inputs["per_task_state_errors"], + "reference_aligned_secondary": { + "metric": stage_b.REFERENCE_ALIGNED_STATE_ERROR_METRIC, + "per_task": inputs["per_task_reference_aligned_state_errors"] + }, + }, + "unit_evidence": inputs["unit_evidence"], + "integrity_inputs": inputs["integrity"], + "stage_b_integrity": integrity, + "stage_b_gate": gate, + "runtime_environment": runtime_environment, + "environment": { + "python": f"{python_version} test-build", + "platform": "Windows-test", + "packages": dict(stage_b.identity_resolver.STAGE_A_PACKAGE_VERSIONS), + "cuda_available": True, + "cuda_runtime": ( + stage_b.identity_resolver.STAGE_A_CUDA_CONTRACT[ + "runtime_version" + ] + ), + "gpu": "Test CUDA GPU", + }, + "repository": { + "commit": "2" * 40, + "start": { + "commit": "2" * 40, + "worktree_clean": True, + "status": [], + }, + "end": { + "commit": "2" * 40, + "worktree_clean": True, + "status": [], + }, + "stable_commit": True, + }, + "source_files": { + "paths": list(stage_b.SOURCE_FILES), + "sha256_start": source_hashes, + "sha256_end": dict(source_hashes), + "stable": True, + "imported_module_paths": dict(stage_b.RESULT_IMPORTED_MODULE_PATHS), + }, + "command_template": [ + "python", + "scripts/evaluate_rht_cqer_stage_b.py", + "--stage-a-artifact", + "", + "--identity-artifact", + "", + "--output", + "", + "--device", + "auto", + ], + } + return { + "schema_version": 1, + "artifact_kind": stage_b.ARTIFACT_KIND, + "canonical_evidence_sha256": stage_b._sha256_bytes( # noqa: SLF001 + stage_b.canonical_json_bytes(evidence) + ), + "evidence": evidence, + } + + +def _write_result_document(path: Path, document: dict[str, object]) -> None: + path.write_bytes(stage_b.canonical_json_bytes(document)) + + +def _rehash_result_document(document: dict[str, object]) -> None: + document["canonical_evidence_sha256"] = stage_b._sha256_bytes( # noqa: SLF001 + stage_b.canonical_json_bytes(document["evidence"]) + ) + + +def _recompute_result_outcomes(document: dict[str, object]) -> None: + evidence = document["evidence"] + assert isinstance(evidence, dict) + dataset = evidence["dataset"] + assert isinstance(dataset, dict) + identity_dataset = dataset["identity"] + assert isinstance(identity_dataset, dict) + state_error = evidence["state_error"] + assert isinstance(state_error, dict) + reference_aligned = state_error["reference_aligned_secondary"] + assert isinstance(reference_aligned, dict) + storage = evidence["storage"] + assert isinstance(storage, dict) + integrity = stage_b.evaluate_stage_b_integrity( + per_task=evidence["per_task"], + per_task_full_code=evidence["per_task_full_code_secondary"], + per_task_token_traces=evidence["per_task_token_primitives"], + per_task_full_code_token_traces=( + evidence["per_task_full_code_token_primitives"] + ), + aggregates=evidence["aggregates"], + aggregates_full_code=evidence["aggregates_full_code_secondary"], + storage=storage["candidates"], + selector_diagnostics=evidence["selector_diagnostics"], + per_task_state_errors=state_error["per_task"], + per_task_reference_aligned_state_errors=reference_aligned["per_task"], + task_records=identity_dataset["tasks"], + unit_evidence=evidence["unit_evidence"], + integrity=evidence["integrity_inputs"], + ) + evidence["stage_b_integrity"] = integrity + evidence["stage_b_gate"] = stage_b.evaluate_stage_b_gate( + aggregates=evidence["aggregates"], + per_task=evidence["per_task"], + paired_bootstrap=( + evidence["paired_bootstrap_cqer_minus_rht_aligned_delta_nll"] + ), + aggregate_state_error=state_error["aggregates"], + per_task_state_errors=state_error["per_task"], + selector_diagnostics=evidence["selector_diagnostics"], + task_records=identity_dataset["tasks"], + integrity_gate=integrity, + ) + _rehash_result_document(document) + + +def test_stage_b_gate_passes_exactly_eight_frozen_checks() -> None: + gate = stage_b.evaluate_stage_b_gate(**_passing_gate_inputs()) + + assert gate["passed"] is True + assert gate["integrity_passed"] is True + assert len(gate["advancement_checks"]) == 8 + assert all(check["passed"] is True for check in gate["advancement_checks"].values()) + + +@pytest.mark.parametrize( + "check_name", + [ + "macro_excess_nll_relative_reduction", + "paired_95pct_lower_bound_above_zero", + "at_least_20_task_level_excess_nll_wins", + "lower_macro_mean_kl", + "macro_cvar95_kl_not_higher", + "macro_top1_disadvantage_at_most_0_005", + "maximum_task_excess_nll_disadvantage_at_most_0_25", + "aggregate_state_sse_relative_reduction", + ], +) +def test_each_stage_b_advancement_condition_fails_closed(check_name: str) -> None: + inputs = copy.deepcopy(_passing_gate_inputs()) + per_task = inputs["per_task"] + + if check_name == "macro_excess_nll_relative_reduction": + for row in per_task[stage_b.RHT_METHOD]: + row.update({"candidate_nll": 1.85, "delta_nll": 0.85}) + inputs["aggregates"] = _aggregates(per_task) + inputs["paired_bootstrap"] = _bootstrap(per_task) + elif check_name == "paired_95pct_lower_bound_above_zero": + inputs["paired_bootstrap"]["confidence_interval"] = [-0.01, 0.2] + elif check_name == "at_least_20_task_level_excess_nll_wins": + for index, row in enumerate(per_task[stage_b.RHT_METHOD]): + delta = 0.5 if index < 19 else 1.1 + row.update({"candidate_nll": 1.0 + delta, "delta_nll": delta}) + inputs["aggregates"] = _aggregates(per_task) + inputs["paired_bootstrap"] = _bootstrap(per_task) + elif check_name == "lower_macro_mean_kl": + for row in per_task[stage_b.RHT_METHOD]: + row["mean_kl"] = 1.0 + inputs["aggregates"] = _aggregates(per_task) + elif check_name == "macro_cvar95_kl_not_higher": + for row in per_task[stage_b.RHT_METHOD]: + row.update({"cvar95_kl": 2.1, "max_kl": 2.35}) + inputs["aggregates"] = _aggregates(per_task) + elif check_name == "macro_top1_disadvantage_at_most_0_005": + for row in per_task[stage_b.RHT_METHOD]: + row["top1_agreement"] = 0.894 + inputs["aggregates"] = _aggregates(per_task) + elif check_name == "maximum_task_excess_nll_disadvantage_at_most_0_25": + per_task[stage_b.RHT_METHOD][0].update( + {"candidate_nll": 2.3, "delta_nll": 1.3} + ) + inputs["aggregates"] = _aggregates(per_task) + inputs["paired_bootstrap"] = _bootstrap(per_task) + elif check_name == "aggregate_state_sse_relative_reduction": + for task in inputs["per_task_state_errors"][stage_b.RHT_METHOD]: + task["state_error"] = _state_error_summary( + method=stage_b.RHT_METHOD, + sse_per_record=0.51, + ) + inputs["aggregate_state_error"] = stage_b.aggregate_state_errors( + inputs["per_task_state_errors"] + ) + + gate = stage_b.evaluate_stage_b_gate(**inputs) + + assert gate["passed"] is False + assert gate["advancement_checks"][check_name]["passed"] is False + + +def test_stage_b_gate_cannot_pass_when_integrity_fails() -> None: + inputs = _passing_gate_inputs() + inputs["integrity_gate"] = {"passed": False} + + gate = stage_b.evaluate_stage_b_gate(**inputs) + + assert gate["passed"] is False + assert gate["integrity_passed"] is False + assert all(check["passed"] is True for check in gate["advancement_checks"].values()) + + +def test_stage_b_gate_rejects_detached_macro_aggregate() -> None: + inputs = _passing_gate_inputs() + for row in inputs["per_task"][stage_b.RHT_METHOD]: + row.update({"candidate_nll": 1.99, "delta_nll": 0.99}) + inputs["paired_bootstrap"] = _bootstrap(inputs["per_task"]) + + with pytest.raises(ValueError, match="aggregate differs"): + stage_b.evaluate_stage_b_gate(**inputs) + + +def test_stage_b_gate_rejects_detached_state_aggregate() -> None: + inputs = _passing_gate_inputs() + inputs["aggregate_state_error"][stage_b.RHT_METHOD][ + "aggregate_state_sse" + ] = 0.0 + + with pytest.raises(ValueError, match="differs from raw state-error"): + stage_b.evaluate_stage_b_gate(**inputs) + + +def test_stage_b_result_loader_recomputes_the_exact_canonical_result( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + path = tmp_path / "stage-b.json" + _write_result_document(path, document) + + evidence, verification = stage_b.load_and_validate_stage_b_result_artifact( + path + ) + + assert evidence == document["evidence"] + assert verification["passed"] is True + assert verification["integrity_passed"] is True + assert verification["advancement_passed"] is True + assert verification["canonical_round_trip"] is True + assert verification["advancement_check_count"] == 8 + identity = evidence["dataset"]["identity"] + task_ids = [record["task_id"] for record in identity["tasks"]] + manifest_ids = [row["task_id"] for row in identity["manifest"]["rows"]] + assert task_ids != manifest_ids + assert manifest_ids == sorted(task_ids) + + +def _rehash_result_identity_dataset(identity: dict[str, object]) -> None: + manifest = identity["manifest"] + tasks = identity["tasks"] + assert isinstance(manifest, dict) + assert isinstance(tasks, list) + content_hash = stage_b.mbpp_manifest_content_sha256(manifest) + identity["content_manifest_sha256"] = content_hash + identity["token_manifest_sha256"] = ( + stage_b.identity_resolver.token_manifest_sha256(tasks) + ) + identity["ordered_identity_sha256"] = ( + stage_b.identity_resolver.ordered_identity_sha256( + content_manifest_sha256=content_hash, + task_records=tasks, + ) + ) + + +def test_stage_b_result_identity_rejects_swapped_manifest_hashes() -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + rows[0]["sha256"], rows[1]["sha256"] = rows[1]["sha256"], rows[0]["sha256"] + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest rows do not match tasks"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +@pytest.mark.parametrize("mutation", ["missing", "duplicate", "extra"]) +def test_stage_b_result_identity_rejects_manifest_row_set_drift( + mutation: str, +) -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + if mutation == "missing": + rows.pop() + elif mutation == "duplicate": + rows[1] = copy.deepcopy(rows[0]) + else: + rows.append({"task_id": 99_999, "sha256": "f" * 64}) + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest rows|manifest task IDs"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_identity_rejects_unique_unknown_manifest_task_id() -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + rows[-1]["task_id"] = 99_999 + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest rows do not match tasks"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +@pytest.mark.parametrize("field_drift", ["missing", "extra"]) +def test_stage_b_result_identity_rejects_manifest_row_field_drift( + field_drift: str, +) -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + if field_drift == "missing": + del rows[0]["sha256"] + else: + rows[0]["unexpected"] = "field" + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest row 0 fields drifted"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_identity_rejects_invalid_manifest_sha256() -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + rows[0]["sha256"] = "not-a-sha256" + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest row 0.*SHA-256"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_identity_rejects_boolean_manifest_task_id() -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + rows[0]["task_id"] = True + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest row 0 task_id"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_identity_rejects_noncanonical_manifest_order() -> None: + identity = _result_identity_dataset(_task_records()) + manifest = identity["manifest"] + assert isinstance(manifest, dict) + rows = manifest["rows"] + assert isinstance(rows, list) + rows[0], rows[1] = rows[1], rows[0] + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="canonical task-ID order"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_identity_rejects_rehashed_task_reorder() -> None: + identity = _result_identity_dataset(_task_records()) + tasks = identity["tasks"] + assert isinstance(tasks, list) + tasks[0], tasks[1] = tasks[1], tasks[0] + identity["ordered_task_ids"] = [task["task_id"] for task in tasks] + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="ordered window"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_identity_rejects_task_row_hash_tamper() -> None: + identity = _result_identity_dataset(_task_records()) + tasks = identity["tasks"] + assert isinstance(tasks, list) + tasks[0]["row_sha256"] = "f" * 64 + _rehash_result_identity_dataset(identity) + + with pytest.raises(ValueError, match="manifest rows do not match tasks"): + stage_b._validate_result_identity_dataset(identity) # noqa: SLF001 + + +def test_stage_b_result_loader_authenticates_an_exact_negative_result( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + evidence = document["evidence"] + traces = evidence["per_task_token_primitives"] + for trace in traces[stage_b.RHT_METHOD]: + token_count = int(trace["token_count"]) + trace["top1_agreement"] = [False] * token_count + payload = { + key: trace[key] + for key in ( + "token_count", + "kl", + "reference_nll", + "candidate_nll", + "top1_agreement", + "outputs_finite", + ) + } + trace["canonical_primitives_sha256"] = stage_b._sha256_bytes( # noqa: SLF001 + stage_b.canonical_json_bytes(payload) + ) + evidence["per_task"] = _summaries_from_traces(traces) + evidence["aggregates"] = _aggregates(evidence["per_task"]) + evidence["paired_bootstrap_cqer_minus_rht_aligned_delta_nll"] = _bootstrap( + evidence["per_task"] + ) + _recompute_result_outcomes(document) + path = tmp_path / "stage-b-negative.json" + _write_result_document(path, document) + + loaded, verification = stage_b.load_and_validate_stage_b_result_artifact(path) + + assert verification["passed"] is True + assert verification["integrity_passed"] is True + assert verification["advancement_passed"] is False + assert ( + loaded["stage_b_gate"]["advancement_checks"][ + "macro_top1_disadvantage_at_most_0_005" + ]["passed"] + is False + ) + + +def test_stage_b_result_loader_rejects_contradictory_unit_passed_flag( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + document["evidence"]["unit_evidence"]["independent_dense_reference"][ + "passed" + ] = False + _recompute_result_outcomes(document) + path = tmp_path / "contradictory-unit-pass.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="passed flag contradicts"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_result_loader_rejects_incomplete_unit_evidence_schema( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + del document["evidence"]["unit_evidence"]["independent_dense_reference"][ + "reference" + ] + _recompute_result_outcomes(document) + path = tmp_path / "incomplete-unit-schema.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="reference fields drifted"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_result_loader_rejects_missing_production_provenance( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + del document["evidence"]["protocol"] + _rehash_result_document(document) + path = tmp_path / "missing-provenance.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="evidence fields drifted"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_result_loader_rejects_local_prerequisite_path( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + document["evidence"]["prerequisite_artifacts"]["stage_b_identity"][ + "path" + ] = "/private/identity.json" + _rehash_result_document(document) + path = tmp_path / "local-path.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="repository-relative path"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_result_loader_rejects_state_metric_label_drift( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + document["evidence"]["state_error"]["primary_gate_metric"] = "generic MSE" + _rehash_result_document(document) + path = tmp_path / "metric-label.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="state-error metric label drifted"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_result_loader_rejects_rehashed_aggregate_tamper( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + document["evidence"]["aggregates"][stage_b.RHT_METHOD][ + "macro_delta_nll" + ] = 0.01 + _rehash_result_document(document) + path = tmp_path / "aggregate-tamper.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="integrity"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_result_loader_rejects_rehashed_state_aggregate_tamper( + tmp_path: Path, +) -> None: + document = _stage_b_result_document() + document["evidence"]["state_error"]["aggregates"][stage_b.RHT_METHOD][ + "aggregate_state_sse" + ] = 0.0 + _rehash_result_document(document) + path = tmp_path / "state-tamper.json" + _write_result_document(path, document) + + with pytest.raises(ValueError, match="differs from raw state-error"): + stage_b.load_and_validate_stage_b_result_artifact(path) + + +def test_stage_b_integrity_accepts_complete_exact_contract() -> None: + integrity = stage_b.evaluate_stage_b_integrity(**_passing_integrity_inputs()) + + assert integrity["passed"] is True + assert all(check["passed"] is True for check in integrity["checks"].values()) + + +@pytest.mark.parametrize( + ("section", "mutation", "failed_check"), + [ + ( + "integrity", + lambda values: values.update( + { + ( + "protected_window_8_16_content_selected_retained_" + "canonicalized_formatted_tokenized_passed_to_model_or_evaluated" + ): True + } + ), + "committed_clean_stable_provenance", + ), + ( + "storage", + lambda values: values[stage_b.RHT_METHOD].update({"resident_bytes": 1}), + "exact_physical_storage", + ), + ( + "selector_diagnostics", + lambda values: values[stage_b.RHT_METHOD][0]["layers"][0].update( + {"state_codec_seed": 1} + ), + "exact_quotas_and_query_handshakes", + ), + ( + "per_task_state_errors", + lambda values: values[stage_b.RHT_METHOD][0]["state_error"].update( + {"coverage": []} + ), + "complete_matched_state_error_coverage", + ), + ( + "per_task", + lambda values: values[stage_b.RHT_METHOD][0].update( + {"candidate_nll": 99.0} + ), + "finite_exact_task_macro_metrics", + ), + ( + "per_task", + lambda values: values[stage_b.RHT_METHOD][0].update({"mean_kl": -0.1}), + "finite_exact_task_macro_metrics", + ), + ( + "per_task", + lambda values: values[stage_b.RHT_METHOD][0].update( + {"mean_kl": 2.0, "cvar95_kl": 1.0} + ), + "finite_exact_task_macro_metrics", + ), + ( + "per_task", + lambda values: values[stage_b.RHT_METHOD][0].update( + {"top1_agreement": 1.1} + ), + "finite_exact_task_macro_metrics", + ), + ( + "per_task", + lambda values: values[stage_b.RHT_METHOD][0].update( + {"reference_nll": 1.1, "candidate_nll": 1.8} + ), + "finite_exact_task_macro_metrics", + ), + ( + "per_task_token_traces", + lambda values: values[stage_b.RHT_METHOD][0].update( + {"canonical_primitives_sha256": "0" * 64} + ), + "finite_exact_task_macro_metrics", + ), + ( + "per_task_state_errors", + lambda values: values[stage_b.RHT_METHOD][0]["state_error"]["records"][ + 0 + ].update({"mean_squared_error": 0.5}), + "complete_matched_state_error_coverage", + ), + ( + "per_task_state_errors", + lambda values: values[stage_b.RHT_METHOD][0]["state_error"]["records"][ + 0 + ].update({"resident_bytes": 1}), + "complete_matched_state_error_coverage", + ), + ( + "per_task_state_errors", + lambda values: values[stage_b.RHT_METHOD][0]["state_error"]["records"][ + -1 + ].update({"high_precision_mask_sha256": "0" * 64}), + "complete_matched_state_error_coverage", + ), + ( + "per_task_reference_aligned_state_errors", + lambda values: values[stage_b.RHT_METHOD][0]["state_error"].update( + {"aggregate_state_sse": 999.0} + ), + "authenticated_reference_aligned_state_secondary", + ), + ( + "unit_evidence", + lambda values: values["production_self_check"].update( + {"inverse_relative_l2": 1e-3} + ), + "deterministic_rht_codec_self_check", + ), + ], +) +def test_stage_b_integrity_fails_closed_on_contract_drift( + section: str, + mutation: object, + failed_check: str, +) -> None: + inputs = copy.deepcopy(_passing_integrity_inputs()) + mutation(inputs[section]) + + integrity = stage_b.evaluate_stage_b_integrity(**inputs) + + assert integrity["passed"] is False + assert integrity["checks"][failed_check]["passed"] is False + + +def test_stage_b_identity_tasks_accept_only_exact_ordered_window() -> None: + tasks = _task_records() + identity = { + "dataset": { + "tasks": tasks, + "ordered_task_ids": [record["task_id"] for record in tasks], + "selection_window": { + "offset": stage_b.STAGE_B_OFFSET, + "limit": stage_b.STAGE_B_LIMIT, + "stop_exclusive": stage_b.STAGE_B_STOP, + }, + "protected_window": { + "offset": stage_b.PROTECTED_WINDOW[0], + "stop_exclusive": stage_b.PROTECTED_WINDOW[1], + "content_retained_canonicalized_or_tokenized": False, + }, + } + } + + assert stage_b._identity_task_records(identity) == tuple(tasks) # noqa: SLF001 + + drifted = copy.deepcopy(identity) + drifted["dataset"]["tasks"][0]["rank"] = 31 + with pytest.raises(ValueError, match=r"\[32, 64\)"): + stage_b._identity_task_records(drifted) # noqa: SLF001 + + +def test_stage_b_cli_exposes_no_quality_or_window_tuning_flags() -> None: + required = [ + "--stage-a-artifact", + "stage-a.json", + "--identity-artifact", + "identity.json", + "--output", + "result.json", + ] + parsed = stage_b.parse_args(required) + assert parsed.device == "auto" + + with pytest.raises(SystemExit): + stage_b.parse_args([*required, "--bootstrap-samples", "10"]) + + +def test_identity_source_freeze_survives_canonical_json_round_trip() -> None: + source_hashes = { + path: stage_b._sha256_bytes(path.encode("utf-8")) # noqa: SLF001 + for path in stage_b.SOURCE_FILES + } + identity = { + "source_files": { + "paths": list(stage_b.SOURCE_FILES), + "sha256_start": source_hashes, + "sha256_end": source_hashes, + "stable": True, + } + } + round_tripped = json.loads(stage_b.canonical_json_bytes(identity)) + + authentication = stage_b.authenticate_identity_source_freeze( + round_tripped, + current_source_hashes=source_hashes, + ) + + assert stage_b.SOURCE_FILES is stage_b.identity_resolver.STAGE_B_SOURCE_FILES + assert authentication["passed"] is True + assert authentication["path_count"] == len(stage_b.SOURCE_FILES) + + +def test_identity_source_freeze_rejects_current_byte_drift() -> None: + source_hashes = { + path: stage_b._sha256_bytes(path.encode("utf-8")) # noqa: SLF001 + for path in stage_b.SOURCE_FILES + } + identity = { + "source_files": { + "paths": list(stage_b.SOURCE_FILES), + "sha256_start": source_hashes, + "sha256_end": source_hashes, + "stable": True, + } + } + current = dict(source_hashes) + current[stage_b.SOURCE_FILES[0]] = "0" * 64 + + with pytest.raises(ValueError, match="differ from the identity freeze"): + stage_b.authenticate_identity_source_freeze( + identity, + current_source_hashes=current, + ) + + +def test_runtime_environment_must_equal_the_committed_identity( + monkeypatch: pytest.MonkeyPatch, +) -> None: + authenticated = { + "schema": "recurquant.experiment009-stage-b-runtime-environment.v1", + "runtime_matches_stage_a": True, + "local_files_only": True, + } + calls: list[tuple[object, bool]] = [] + + def fake_authenticate( + stage_a: object, + *, + local_files_only: bool, + ) -> dict[str, object]: + calls.append((stage_a, local_files_only)) + return dict(authenticated) + + monkeypatch.setattr( + stage_b.identity_resolver, + "authenticate_runtime_environment", + fake_authenticate, + ) + stage_a = {"artifact_kind": stage_b.STAGE_A_ARTIFACT_KIND} + identity = {"environment": dict(authenticated)} + + assert stage_b.authenticate_stage_b_runtime_environment( + stage_a, + identity, + local_files_only=True, + ) == authenticated + assert calls == [(stage_a, True)] + + identity["environment"]["local_files_only"] = False + with pytest.raises(ValueError, match="differs from the Stage-B identity"): + stage_b.authenticate_stage_b_runtime_environment( + stage_a, + identity, + local_files_only=True, + ) + + +def test_identity_row_plan_reconstructs_the_only_frozen_plan() -> None: + identity, stage_a = _frozen_row_plan_inputs() + + plan, authentication = stage_b.plan_from_identity(identity, stage_a=stage_a) + + assert plan.promoted_group_count == stage_b.TARGET_PROMOTED_ROWS + assert plan.resident_bytes == stage_b.TARGET_PACKED_STATE_BYTES + assert { + layer: len(plan.groups_for_layer(layer)) + for layer in stage_b.FROZEN_LINEAR_LAYERS + } == stage_b.FROZEN_LAYER_QUOTAS + assert authentication["passed"] is True + + +def test_identity_row_plan_rejects_canonical_hash_drift() -> None: + identity, stage_a = _frozen_row_plan_inputs() + identity["row_plan"]["canonical_plan_sha256"] = "0" * 64 # type: ignore[index] + + with pytest.raises(ValueError, match="canonical hash"): + stage_b.plan_from_identity(identity, stage_a=stage_a) + + +def test_identity_row_plan_rejects_duplicate_promoted_row() -> None: + identity, stage_a = _frozen_row_plan_inputs() + row_plan = identity["row_plan"] # type: ignore[assignment] + rows = row_plan["high_precision_rows"] # type: ignore[index] + rows[1] = copy.deepcopy(rows[0]) # type: ignore[index] + _rehash_row_plan(identity) + + with pytest.raises(ValueError, match="duplicated"): + stage_b.plan_from_identity(identity, stage_a=stage_a) + + +def test_identity_row_plan_rejects_selector_binding_drift() -> None: + identity, stage_a = _frozen_row_plan_inputs() + row_plan = identity["row_plan"] # type: ignore[assignment] + selector_binding = row_plan["selector_binding"] # type: ignore[index] + selector_binding["selector_file_sha256"] = "0" * 64 # type: ignore[index] + _rehash_row_plan(identity) + + with pytest.raises(ValueError, match="exact Stage-A selectors"): + stage_b.plan_from_identity(identity, stage_a=stage_a) + + +def test_committed_stage_a_artifact_recomputes_as_a_passing_prerequisite() -> None: + repository_root = Path(__file__).resolve().parents[1] + path = ( + repository_root + / "evidence" + / "experiment009-rht-cqer-stage-a-666-5be8d48.json" + ) + + evidence, authentication = stage_b.authenticate_stage_a_prerequisite( + path, + repository_root=repository_root, + ) + + assert evidence["stage_a_gate"]["passed"] is True + assert authentication["gate_recomputed_and_passed"] == "true" + + +def test_independent_dense_rht_reference_passes() -> None: + evidence = stage_b.compute_independent_dense_rht_evidence() + + assert evidence["passed"] is True + assert evidence["signs_exact"] is True + assert evidence["encode_max_abs_difference"] <= 2e-6 + assert evidence["physical_pack_max_abs_difference"] <= 6e-6 + + +def test_imported_evaluator_modules_resolve_to_repository_sources() -> None: + repository_root = Path(__file__).resolve().parents[1] + controlled_modules = { + "stage_b": stage_b, + "identity_resolver": stage_b.identity_resolver, + "pilot": stage_b.pilot, + "screen": stage_b.screen, + "recurquant.cache": stage_b.importlib.import_module("recurquant.cache"), + "recurquant.evaluation": stage_b.importlib.import_module( + "recurquant.evaluation" + ), + "recurquant.packed_cache": stage_b.importlib.import_module( + "recurquant.packed_cache" + ), + "recurquant.qwen35": stage_b.importlib.import_module("recurquant.qwen35"), + "recurquant.rht": stage_b.importlib.import_module("recurquant.rht"), + } + + paths = stage_b.authenticate_imported_module_paths( + repository_root, + modules=controlled_modules, + ) + + assert paths["stage_b_identity_resolver"] == ( + "scripts/resolve_rht_cqer_stage_b_identity.py" + ) + assert paths["recurquant_qwen35"] == "src/recurquant/qwen35.py" + + +def test_stage_b_artifact_source_avoids_local_path_and_raw_command_fields() -> None: + source = Path(stage_b.__file__).read_text(encoding="utf-8") + + assert '"path_supplied"' not in source + assert '"selector_path"' not in source + assert '"loss_selector_path"' not in source + assert '"command": [sys.executable, *sys.argv]' not in source + assert '"command_template"' in source diff --git a/tests/test_finite_difference.py b/tests/test_finite_difference.py new file mode 100644 index 0000000..4aa5664 --- /dev/null +++ b/tests/test_finite_difference.py @@ -0,0 +1,109 @@ +from __future__ import annotations + +import pytest +import torch + +from recurquant.finite_difference import check_directional_derivative + + +def test_quadratic_directional_derivative_matches_central_difference() -> None: + state = torch.tensor([0.5, -1.25, 2.0], dtype=torch.float64) + direction = torch.tensor([0.2, 0.4, -0.3], dtype=torch.float64) + + result = check_directional_derivative( + lambda value: value.square().sum(), + state, + direction, + ) + + expected = float((2 * state * direction).sum().item()) + assert result.autograd_derivative == pytest.approx(expected) + assert result.gradient_l2_norm == pytest.approx(float(torch.linalg.vector_norm(2 * state))) + assert result.direction_l2_norm == pytest.approx(float(torch.linalg.vector_norm(direction))) + assert all(point.central_derivative == pytest.approx(expected) for point in result.points) + assert result.median_relative_error < 1e-12 + assert result.nonzero_sign_agreement_rate == 1.0 + + +def test_nonlinear_central_difference_error_shrinks_with_epsilon() -> None: + state = torch.tensor([0.7, -0.4], dtype=torch.float64) + direction = torch.tensor([0.3, 0.2], dtype=torch.float64) + + result = check_directional_derivative( + lambda value: value.pow(3).sum(), + state, + direction, + epsilons=(0.5, 0.25, 0.125, 0.0625), + ) + + errors = [point.absolute_error for point in result.points] + assert errors == sorted(errors, reverse=True) + assert result.points[-1].relative_error < result.points[0].relative_error + assert result.nonzero_sign_agreement_rate == 1.0 + + +def test_near_zero_derivative_preserves_error_without_forcing_a_sign() -> None: + state = torch.zeros(3, dtype=torch.float32) + direction = torch.tensor([1.0, -1.0, 0.5], dtype=torch.float32) + + result = check_directional_derivative( + lambda value: value.square().sum(), + state, + direction, + epsilons=(0.25, 0.125), + near_zero_floor=1e-6, + ) + + assert result.autograd_derivative == 0.0 + assert all(point.near_zero for point in result.points) + assert all(point.sign_agreement is None for point in result.points) + assert result.nonzero_sign_agreement_rate is None + + +@pytest.mark.parametrize( + ("loss_fn", "expected_autograd", "expected_central"), + ( + (lambda value: value[0].pow(3) - value[0], -1.0, 0.0), + (lambda value: value[0].pow(3), 0.0, 1.0), + ), +) +def test_exactly_zero_derivative_does_not_manufacture_sign_agreement( + loss_fn: object, + expected_autograd: float, + expected_central: float, +) -> None: + result = check_directional_derivative( + loss_fn, # type: ignore[arg-type] + torch.zeros(1, dtype=torch.float64), + torch.ones(1, dtype=torch.float64), + epsilons=(1.0,), + near_zero_floor=1e-8, + ) + + assert result.autograd_derivative == expected_autograd + assert result.points[0].central_derivative == expected_central + assert not result.points[0].near_zero + assert result.points[0].sign_agreement is False + assert result.nonzero_sign_agreement_rate == 0.0 + + +def test_directional_derivative_validation_rejects_bad_inputs_and_losses() -> None: + state = torch.ones(2, dtype=torch.float32) + direction = torch.ones(2, dtype=torch.float32) + with pytest.raises(ValueError, match="same shape"): + check_directional_derivative(lambda value: value.sum(), state, direction[:1]) + with pytest.raises(ValueError, match="positive finite"): + check_directional_derivative( + lambda value: value.sum(), + state, + direction, + epsilons=(0.1, 0.0), + ) + with pytest.raises(ValueError, match="one floating-point scalar"): + check_directional_derivative(lambda value: value, state, direction) + with pytest.raises(ValueError, match="finite"): + check_directional_derivative( + lambda value: value.sum() * torch.tensor(float("nan")), + state, + direction, + ) diff --git a/tests/test_fisher_sensitivity.py b/tests/test_fisher_sensitivity.py new file mode 100644 index 0000000..028fbdf --- /dev/null +++ b/tests/test_fisher_sensitivity.py @@ -0,0 +1,621 @@ +from __future__ import annotations + +from types import MethodType +from typing import Literal + +import pytest +import torch +import torch.nn.functional as F +from transformers import DynamicCache, Qwen3_5ForCausalLM + +from recurquant.fisher_sensitivity import ( + FisherStepResult, + GDNInt4TrajectoryFisherCalibrator, + GDNOneStepFisherCalibrator, + RowPromotionSensitivityScores, + TaskMacroSensitivityAccumulator, + row_promotion_scores_from_errors, + row_promotion_sensitivity_scores, +) +from recurquant.quantization import QuantizationSpec, RoundingMode, quantize_dequantize +from recurquant.transformers_cache import RecurrentStateQDQCache +from tests.test_transformers_cache import tiny_config + + +def _spec(bits: int) -> QuantizationSpec: + return QuantizationSpec( + bits=bits, + group_size=8, + flatten_last_dims=1, + ) + + +def _tiny_model() -> Qwen3_5ForCausalLM: + torch.manual_seed(2339) + return Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + + +def _warm_cache( + model: Qwen3_5ForCausalLM, + prompt: torch.Tensor, +) -> DynamicCache: + cache = DynamicCache(config=model.config) + with torch.no_grad(): + model(prompt, past_key_values=cache, use_cache=True) + return cache + + +def _all_cached_tensors(cache: DynamicCache) -> list[torch.Tensor]: + tensors: list[torch.Tensor] = [] + for layer in cache.layers: + for name in ("keys", "values"): + value = getattr(layer, name, None) + if isinstance(value, torch.Tensor): + tensors.append(value) + for name in ("conv_states", "recurrent_states"): + values = getattr(layer, name, None) + if isinstance(values, dict): + tensors.extend( + value for value in values.values() if isinstance(value, torch.Tensor) + ) + return tensors + + +def test_row_promotion_scores_match_signed_taylor_and_fisher_formulas() -> None: + state = torch.tensor( + [ + [ + [ + [0.04, -0.13, 0.21, -0.37, 0.55, -0.72, 0.81, -0.94], + [-0.02, 0.11, -0.29, 0.41, -0.58, 0.69, -0.83, 0.97], + ] + ], + [ + [ + [-0.09, 0.17, -0.24, 0.38, -0.49, 0.63, -0.79, 0.91], + [0.01, -0.15, 0.27, -0.44, 0.57, -0.68, 0.86, -0.99], + ] + ], + ], + dtype=torch.float32, + ) + gradient = torch.linspace(-0.4, 0.7, state.numel(), dtype=torch.float32).reshape_as(state) + int4_spec = _spec(4) + int8_spec = _spec(8) + error4 = quantize_dequantize(state, int4_spec).tensor - state + error8 = quantize_dequantize(state, int8_spec).tensor - state + delta = error8 - error4 + expected_taylor = -(gradient * delta).sum(dim=-1).mean(dim=0) + projected4 = (gradient * error4).sum(dim=-1) + projected8 = (gradient * error8).sum(dim=-1) + expected_directional_difference = (projected4.square() - projected8.square()).mean(dim=0) + expected_diagonal_difference = ( + (gradient.square() * (error4.square() - error8.square())).sum(dim=-1).mean(dim=0) + ) + expected_delta_magnitude = (gradient * (error4 - error8)).sum(dim=-1).square().mean(dim=0) + + taylor, directional_difference, diagonal_difference, delta_magnitude = ( + row_promotion_sensitivity_scores( + state, + gradient, + int4_spec=int4_spec, + int8_spec=int8_spec, + ) + ) + + assert taylor.shape == (1, 2) + assert directional_difference.shape == (1, 2) + assert diagonal_difference.shape == (1, 2) + assert delta_magnitude.shape == (1, 2) + assert torch.equal(taylor, expected_taylor) + assert torch.equal(directional_difference, expected_directional_difference) + assert torch.equal(diagonal_difference, expected_diagonal_difference) + assert torch.equal(delta_magnitude, expected_delta_magnitude) + assert (delta_magnitude >= 0).all() + + from_errors = row_promotion_scores_from_errors( + gradient, + error4, + error8, + ) + assert all( + torch.equal(left, right) + for left, right in zip( + from_errors, + ( + taylor, + directional_difference, + diagonal_difference, + delta_magnitude, + ), + strict=True, + ) + ) + + +def test_signed_taylor_benefit_uses_promotion_loss_reduction_sign() -> None: + gradient = torch.tensor([[[[2.0, -1.0]]]], dtype=torch.float32) + error4 = torch.tensor([[[[0.5, 0.25]]]], dtype=torch.float32) + error8 = torch.tensor([[[[0.1, 0.05]]]], dtype=torch.float32) + + taylor, _, diagonal, _ = row_promotion_scores_from_errors( + gradient, + error4, + error8, + ) + + delta = error8 - error4 + assert taylor.item() == pytest.approx(-(gradient * delta).sum().item()) + assert taylor.item() > 0 + # Signed diagnostics are intentionally not clamped. + assert diagonal.item() == pytest.approx( + (gradient.square() * (error4.square() - error8.square())).sum().item() + ) + + +def _expected_scores( + state: torch.Tensor, + gradient: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + return row_promotion_sensitivity_scores( + state, + gradient, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + + +def test_full_model_step_uses_pre_token_state_and_preserves_continuity() -> None: + model = _tiny_model() + prompt = torch.tensor([[1, 2, 3, 4]]) + measured_cache = _warm_cache(model, prompt) + reference_cache = _warm_cache(model, prompt) + before = measured_cache.layers[0].recurrent_states[0].clone() + attention = model.model.layers[0].linear_attn + original_kernel = attention.recurrent_gated_delta_rule + captured_states: list[torch.Tensor] = [] + captured_gradients: list[torch.Tensor] = [] + + def capture_kernel(*args: object, **kwargs: object): + state = kwargs["initial_state"] + assert isinstance(state, torch.Tensor) + assert state.is_leaf + assert state.requires_grad + assert state.dtype == torch.float32 + captured_states.append(state.detach().clone()) + state.register_hook(lambda gradient: captured_gradients.append(gradient.detach().clone())) + return original_kernel(*args, **kwargs) + + attention.recurrent_gated_delta_rule = capture_kernel + flags_before = tuple(parameter.requires_grad for parameter in model.parameters()) + next_input = torch.tensor([[5]]) + target = torch.tensor([6]) + calibrator = GDNOneStepFisherCalibrator( + model, + measured_cache, + layer_indices=[0], + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + try: + result = calibrator.step(next_input, target) + finally: + attention.recurrent_gated_delta_rule = original_kernel + + with torch.no_grad(): + reference_output = model( + next_input, + past_key_values=reference_cache, + use_cache=True, + ) + expected_nll = F.cross_entropy(reference_output.logits[:, -1, :].float(), target) + expected = _expected_scores(captured_states[0], captured_gradients[0]) + + assert len(captured_states) == 1 + assert len(captured_gradients) == 1 + assert torch.equal(captured_states[0], before) + assert result.mean_nll == pytest.approx(expected_nll.item(), abs=1e-6) + assert result.trajectory == "fp32" + assert torch.equal(result.layer(0).taylor_benefit, expected[0]) + assert torch.equal(result.layer(0).directional_fisher_difference, expected[1]) + assert torch.equal(result.layer(0).diagonal_fisher_difference, expected[2]) + assert torch.equal(result.layer(0).delta_direction_fisher_magnitude, expected[3]) + assert torch.allclose( + measured_cache.layers[0].recurrent_states[0], + reference_cache.layers[0].recurrent_states[0], + ) + assert not torch.equal(measured_cache.layers[0].recurrent_states[0], before) + assert tuple(parameter.requires_grad for parameter in model.parameters()) == flags_before + assert all(parameter.grad is None for parameter in model.parameters()) + assert "update_recurrent_state" not in measured_cache.__dict__ + assert all( + not tensor.requires_grad and tensor.grad_fn is None + for tensor in _all_cached_tensors(measured_cache) + ) + + +def test_one_reverse_pass_scores_every_selected_gdn_state() -> None: + config = tiny_config(["linear_attention", "linear_attention", "full_attention"]) + torch.manual_seed(71) + model = Qwen3_5ForCausalLM._from_config(config, attn_implementation="eager").eval() + cache = _warm_cache(model, torch.tensor([[1, 3, 5]])) + before = { + layer_index: cache.layers[layer_index].recurrent_states[0].clone() for layer_index in (0, 1) + } + calibrator = GDNOneStepFisherCalibrator( + model, + cache, + layer_indices=[1, 0], + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + + result = calibrator.step(torch.tensor([[7]]), torch.tensor([9])) + + assert tuple(scores.layer_index for scores in result.layers) == (0, 1) + assert all(scores.shape == (2, 8) for scores in result.layers) + assert all( + not torch.equal(cache.layers[layer_index].recurrent_states[0], before[layer_index]) + for layer_index in (0, 1) + ) + assert all(parameter.grad is None for parameter in model.parameters()) + + +@pytest.mark.parametrize("rounding", ["nearest", "stochastic"]) +def test_repeated_int4_trajectory_matches_qdq_cache_and_uses_aligned_delta( + rounding: RoundingMode, +) -> None: + model = _tiny_model() + prompt = torch.tensor([[9, 10, 11, 12]]) + measured_cache = _warm_cache(model, prompt) + raw_boundary = measured_cache.layers[0].recurrent_states[0].clone() + int4_spec = QuantizationSpec( + bits=4, + group_size=8, + flatten_last_dims=1, + rounding=rounding, + ) + int8_spec = QuantizationSpec( + bits=8, + group_size=8, + flatten_last_dims=1, + rounding=rounding, + ) + reference_cache = RecurrentStateQDQCache( + model.config, + spec=int4_spec, + record_evidence=False, + ) + with torch.no_grad(): + model(prompt, past_key_values=reference_cache, use_cache=True) + + calibrator = GDNInt4TrajectoryFisherCalibrator( + model, + measured_cache, + int4_spec=int4_spec, + int8_spec=int8_spec, + ) + assert torch.equal( + measured_cache.layers[0].recurrent_states[0], + reference_cache.layers[0].recurrent_states[0], + ) + + attention = model.model.layers[0].linear_attn + original_kernel = attention.recurrent_gated_delta_rule + captured_states: list[torch.Tensor] = [] + captured_gradients: list[torch.Tensor] = [] + + def capture_kernel(*args: object, **kwargs: object): + state = kwargs["initial_state"] + assert isinstance(state, torch.Tensor) + assert state.is_leaf and state.requires_grad + captured_states.append(state.detach().clone()) + state.register_hook(lambda gradient: captured_gradients.append(gradient.detach().clone())) + return original_kernel(*args, **kwargs) + + attention.recurrent_gated_delta_rule = capture_kernel + token = torch.tensor([[13]]) + target = torch.tensor([14]) + try: + result = calibrator.step(token, target) + finally: + attention.recurrent_gated_delta_rule = original_kernel + with torch.no_grad(): + reference_output = model(token, past_key_values=reference_cache, use_cache=True) + + q4 = quantize_dequantize(raw_boundary, int4_spec).tensor.to(torch.float32) + q8 = quantize_dequantize(raw_boundary, int8_spec).tensor.to(torch.float32) + expected = row_promotion_scores_from_errors( + captured_gradients[0], + q4 - raw_boundary, + q8 - raw_boundary, + ) + + assert result.trajectory == "int4" + assert torch.equal(captured_states[0], q4) + assert result.mean_nll == pytest.approx( + F.cross_entropy(reference_output.logits[:, -1, :].float(), target).item(), + abs=1e-6, + ) + assert torch.equal(result.layer(0).taylor_benefit, expected[0]) + assert torch.equal(result.layer(0).directional_fisher_difference, expected[1]) + assert torch.equal(result.layer(0).diagonal_fisher_difference, expected[2]) + assert torch.equal(result.layer(0).delta_direction_fisher_magnitude, expected[3]) + assert torch.equal( + measured_cache.layers[0].recurrent_states[0], + reference_cache.layers[0].recurrent_states[0], + ) + + second_token = torch.tensor([[15]]) + second_target = torch.tensor([16]) + second = calibrator.step(second_token, second_target) + with torch.no_grad(): + second_reference = model( + second_token, + past_key_values=reference_cache, + use_cache=True, + ) + assert second.mean_nll == pytest.approx( + F.cross_entropy(second_reference.logits[:, -1, :].float(), second_target).item(), + abs=1e-6, + ) + assert torch.equal( + measured_cache.layers[0].recurrent_states[0], + reference_cache.layers[0].recurrent_states[0], + ) + assert all( + not tensor.requires_grad and tensor.grad_fn is None + for tensor in _all_cached_tensors(measured_cache) + ) + + +def test_partial_multilayer_failure_rolls_back_int4_trajectory_atomically() -> None: + config = tiny_config(["linear_attention", "linear_attention", "full_attention"]) + torch.manual_seed(91) + model = Qwen3_5ForCausalLM._from_config(config, attn_implementation="eager").eval() + prompt = torch.tensor([[3, 6, 9]]) + measured_cache = _warm_cache(model, prompt) + reference_cache = RecurrentStateQDQCache( + config, + spec=_spec(4), + record_evidence=False, + ) + with torch.no_grad(): + model(prompt, past_key_values=reference_cache, use_cache=True) + calibrator = GDNInt4TrajectoryFisherCalibrator( + model, + measured_cache, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + before = [tensor.clone() for tensor in _all_cached_tensors(measured_cache)] + second_attention = model.model.layers[1].linear_attn + original_kernel = second_attention.recurrent_gated_delta_rule + + def fail_after_first_layer(*args: object, **kwargs: object): + del args, kwargs + raise RuntimeError("failure after first GDN update") + + second_attention.recurrent_gated_delta_rule = fail_after_first_layer + try: + with pytest.raises(RuntimeError, match="after first GDN update"): + calibrator.step(torch.tensor([[12]]), torch.tensor([15])) + finally: + second_attention.recurrent_gated_delta_rule = original_kernel + + after = _all_cached_tensors(measured_cache) + assert len(after) == len(before) + assert all( + torch.equal(current, original) for current, original in zip(after, before, strict=True) + ) + assert all(parameter.grad is None for parameter in model.parameters()) + assert "update_recurrent_state" not in measured_cache.__dict__ + + token = torch.tensor([[12]]) + target = torch.tensor([15]) + measured = calibrator.step(token, target) + with torch.no_grad(): + reference = model(token, past_key_values=reference_cache, use_cache=True) + assert measured.mean_nll == pytest.approx( + F.cross_entropy(reference.logits[:, -1, :].float(), target).item(), + abs=1e-6, + ) + for layer_index in (0, 1): + assert torch.equal( + measured_cache.layers[layer_index].recurrent_states[0], + reference_cache.layers[layer_index].recurrent_states[0], + ) + + second_input = torch.tensor([[7]]) + calibrator.step(second_input, torch.tensor([8])) + with torch.no_grad(): + model(second_input, past_key_values=reference_cache, use_cache=True) + assert torch.allclose( + measured_cache.layers[0].recurrent_states[0], + reference_cache.layers[0].recurrent_states[0], + ) + assert all( + not tensor.requires_grad and tensor.grad_fn is None + for tensor in _all_cached_tensors(measured_cache) + ) + + +def test_step_restores_cache_method_parameters_and_graph_state_after_failure() -> None: + model = _tiny_model() + cache = _warm_cache(model, torch.tensor([[1, 2, 3]])) + state_before = cache.layers[0].recurrent_states[0].clone() + parameters = tuple(model.parameters()) + parameters[0].requires_grad_(False) + flags_before = tuple(parameter.requires_grad for parameter in parameters) + original_forward = model.forward + + def broken_forward(_model: torch.nn.Module, **kwargs: object): + del kwargs + raise RuntimeError("synthetic forward failure") + + model.forward = MethodType(broken_forward, model) + calibrator = GDNOneStepFisherCalibrator( + model, + cache, + layer_indices=[0], + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + try: + with pytest.raises(RuntimeError, match="synthetic forward failure"): + calibrator.step(torch.tensor([[4]]), torch.tensor([5])) + finally: + model.forward = original_forward + + assert tuple(parameter.requires_grad for parameter in parameters) == flags_before + assert all(parameter.grad is None for parameter in parameters) + assert "update_recurrent_state" not in cache.__dict__ + assert torch.equal(cache.layers[0].recurrent_states[0], state_before) + assert all( + not tensor.requires_grad and tensor.grad_fn is None for tensor in _all_cached_tensors(cache) + ) + + +def test_step_rejects_preexisting_parameter_gradients_without_mutating_cache() -> None: + model = _tiny_model() + cache = _warm_cache(model, torch.tensor([[2, 4, 6]])) + state_before = cache.layers[0].recurrent_states[0].clone() + parameter = next(model.parameters()) + parameter.grad = torch.ones_like(parameter) + calibrator = GDNOneStepFisherCalibrator( + model, + cache, + layer_indices=[0], + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + + with pytest.raises(ValueError, match="pre-existing gradients"): + calibrator.step(torch.tensor([[8]]), torch.tensor([10])) + + assert torch.equal(cache.layers[0].recurrent_states[0], state_before) + assert "update_recurrent_state" not in cache.__dict__ + + +def _step( + value: float, + *, + nll: float = 1.0, + trajectory: Literal["fp32", "int4"] = "fp32", +) -> FisherStepResult: + scores = torch.full((2, 3), value, dtype=torch.float32) + return FisherStepResult( + mean_nll=nll, + batch_size=1, + trajectory=trajectory, + layers=( + RowPromotionSensitivityScores( + layer_index=0, + taylor_benefit=scores, + directional_fisher_difference=scores * 2, + diagonal_fisher_difference=scores * 3, + delta_direction_fisher_magnitude=scores * 4, + ), + ), + ) + + +def test_task_macro_accumulator_weights_tasks_not_token_count() -> None: + accumulator = TaskMacroSensitivityAccumulator() + accumulator.add_task([_step(0, nll=2)]) + accumulator.add_task([_step(6, nll=4), _step(9, nll=8), _step(12, nll=12)]) + + summary = accumulator.summary(0) + + # Task means are 0 and 9, so equal-task macro is 4.5. A token mean would be 6.75. + assert summary.tasks == 2 + assert summary.steps == 4 + assert summary.mean_nll == pytest.approx(5.0) + assert summary.trajectory == "fp32" + assert torch.equal(summary.taylor_benefit, torch.full((2, 3), 4.5, dtype=torch.float64)) + assert torch.equal( + summary.directional_fisher_difference, + torch.full((2, 3), 9.0, dtype=torch.float64), + ) + assert torch.equal( + summary.diagonal_fisher_difference, + torch.full((2, 3), 13.5, dtype=torch.float64), + ) + assert torch.equal( + summary.delta_direction_fisher_magnitude, + torch.full((2, 3), 18.0, dtype=torch.float64), + ) + + +def test_validation_rejects_non_row_specs_shapes_and_unready_cache() -> None: + state = torch.zeros((1, 2, 8, 8), dtype=torch.float32) + gradient = torch.zeros_like(state) + with pytest.raises(ValueError, match="group_size"): + row_promotion_sensitivity_scores( + state, + gradient, + int4_spec=QuantizationSpec(bits=4, group_size=4, flatten_last_dims=1), + int8_spec=QuantizationSpec(bits=8, group_size=4, flatten_last_dims=1), + ) + with pytest.raises(ValueError, match="differ only in bit width"): + row_promotion_sensitivity_scores( + state, + gradient, + int4_spec=_spec(4), + int8_spec=QuantizationSpec(bits=8, group_size=8, scale_bits=32, flatten_last_dims=1), + ) + with pytest.raises(ValueError, match="same shape"): + row_promotion_sensitivity_scores( + state, + gradient[..., :7], + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + nonfinite = gradient.clone() + nonfinite[0, 0, 0, 0] = torch.nan + with pytest.raises(ValueError, match="finite"): + row_promotion_sensitivity_scores( + state, + nonfinite, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + + model = _tiny_model() + empty_cache = DynamicCache(config=model.config) + calibrator = GDNOneStepFisherCalibrator( + model, + empty_cache, + layer_indices=[0], + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + with pytest.raises(ValueError, match="run a no-grad prefill"): + calibrator.step(torch.tensor([[1]]), torch.tensor([2])) + + +def test_task_macro_rejects_shape_changes_and_nonfinite_values() -> None: + accumulator = TaskMacroSensitivityAccumulator() + accumulator.add_task([_step(1)]) + wrong_shape = FisherStepResult( + mean_nll=1.0, + batch_size=1, + trajectory="fp32", + layers=( + RowPromotionSensitivityScores( + layer_index=0, + taylor_benefit=torch.ones(2, 2), + directional_fisher_difference=torch.ones(2, 2), + diagonal_fisher_difference=torch.ones(2, 2), + delta_direction_fisher_magnitude=torch.ones(2, 2), + ), + ), + ) + with pytest.raises(ValueError, match="layer set or score shapes changed"): + accumulator.add_task([wrong_shape]) + nonfinite = _step(1, nll=float("nan")) + with pytest.raises(ValueError, match="mean_nll"): + accumulator.add_task([nonfinite]) diff --git a/tests/test_generate_readme_assets.py b/tests/test_generate_readme_assets.py new file mode 100644 index 0000000..ff129ea --- /dev/null +++ b/tests/test_generate_readme_assets.py @@ -0,0 +1,387 @@ +from __future__ import annotations + +import hashlib +import json +import math +import sys +import xml.etree.ElementTree as ET +from pathlib import Path + +import pytest + +from scripts import generate_readme_assets as assets + + +def _synthetic_stage_b() -> tuple[dict[str, object], dict[str, object]]: + task_ids = list(range(700, 732)) + improvements = [0.1] * 27 + [-0.05] * 5 + per_task: dict[str, list[dict[str, object]]] = {} + macro = { + assets.STAGE_B_METHODS[0]: 0.42, + assets.STAGE_B_METHODS[1]: 0.45, + assets.STAGE_B_CQER_METHOD: 0.3, + assets.STAGE_B_RHT_METHOD: 0.2234375, + } + for method in assets.STAGE_B_METHODS: + if method == assets.STAGE_B_CQER_METHOD: + values = [0.3] * 32 + elif method == assets.STAGE_B_RHT_METHOD: + values = [0.3 - improvement for improvement in improvements] + else: + values = [macro[method]] * 32 + per_task[method] = [ + {"task_id": task_id, "delta_nll": value} + for task_id, value in zip(task_ids, values, strict=True) + ] + + mean = math.fsum(improvements) / len(improvements) + evidence: dict[str, object] = { + "artifact_kind": assets.STAGE_B_ARTIFACT_KIND, + "methods": list(assets.STAGE_B_METHODS), + "repository": { + "commit": assets.STAGE_B_RESULT_COMMIT, + "stable_commit": True, + }, + "stage_b_integrity": {"passed": True}, + "stage_b_gate": { + "passed": True, + "advancement_checks": { + "at_least_20_task_level_excess_nll_wins": { + "rht_wins": 27, + "ties": 0, + } + }, + }, + "dataset": {"identity": {"ordered_task_ids": task_ids}}, + "aggregates": {method: {"macro_delta_nll": value} for method, value in macro.items()}, + "per_task": per_task, + "state_error": { + "aggregates": { + assets.STAGE_B_CQER_METHOD: { + "aggregate_state_sse": 36_000.0, + }, + assets.STAGE_B_RHT_METHOD: { + "aggregate_state_sse": 15_000.0, + }, + } + }, + "paired_bootstrap_cqer_minus_rht_aligned_delta_nll": { + "bootstrap_samples": 10_000, + "confidence": 0.95, + "confidence_interval": [0.04, 0.11], + "mean_improvement": mean, + "paired_examples": 32, + "seed": 2339, + }, + "claim_boundary": "Frozen development evidence only.", + } + verification: dict[str, object] = { + "passed": True, + "integrity_passed": True, + "advancement_passed": True, + "task_count": 32, + "bootstrap_samples": 10_000, + "bootstrap_seed": 2339, + "advancement_check_count": 8, + "artifact_sha256": assets.STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": assets.STAGE_B_CANONICAL_SHA256, + "canonical_round_trip": True, + } + return evidence, verification + + +def _metadata(svg: str) -> dict[str, object]: + root = ET.fromstring(svg) + metadata = root.find("{http://www.w3.org/2000/svg}metadata") + assert metadata is not None + assert metadata.text is not None + return json.loads(metadata.text) + + +def test_stage_b_assets_use_strict_loader_and_embed_authenticated_provenance( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + evidence, verification = _synthetic_stage_b() + calls: list[Path] = [] + source = tmp_path / "stage-b-result.json" + source.write_bytes(b"strict loader owns these bytes") + + def load(path: Path) -> tuple[dict[str, object], dict[str, object]]: + calls.append(path) + return evidence, verification + + monkeypatch.setattr(assets, "STAGE_B_SOURCE", source) + monkeypatch.setattr(assets, "_load_validated_stage_b_result", load) + data = assets._stage_b_data() + + assert calls == [source] + assert data["wins"] == 27 + assert data["ties"] == 0 + assert data["losses"] == 5 + assert [ + point["task_id"] + for point in data["paired"] # type: ignore[index] + ] == list(range(700, 732)) + + overview = assets._stage_b_overview_svg(data) + paired = assets._stage_b_paired_svg(data) + assets._validate_svg( + overview, + assets.ASSETS / "experiment009-stage-b-overview.svg", + ) + assets._validate_svg( + paired, + assets.ASSETS / "experiment009-stage-b-paired.svg", + ) + assert overview == assets._stage_b_overview_svg(data) + assert paired == assets._stage_b_paired_svg(data) + + for record in (_metadata(overview), _metadata(paired)): + assert record["artifact_kind"] == assets.STAGE_B_ARTIFACT_KIND + assert record["source_artifact_sha256"] == assets.STAGE_B_ARTIFACT_SHA256 + assert record["canonical_evidence_sha256"] == assets.STAGE_B_CANONICAL_SHA256 + assert record["original_result_commit"] == assets.STAGE_B_RESULT_COMMIT + assert record["verifier_fix_commit"] == assets.STAGE_B_VERIFIER_FIX_COMMIT + assert record["integrity_passed"] is True + assert record["advancement_gate_passed"] is True + + +def test_stage_b_data_rejects_verification_drift( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + evidence, verification = _synthetic_stage_b() + verification["artifact_sha256"] = "0" * 64 + source = tmp_path / "stage-b-result.json" + source.write_bytes(b"strict loader owns these bytes") + monkeypatch.setattr(assets, "STAGE_B_SOURCE", source) + monkeypatch.setattr( + assets, + "_load_validated_stage_b_result", + lambda _path: (evidence, verification), + ) + + with pytest.raises( + ValueError, + match="verification record drifted", + ): + assets._stage_b_data() + + +def test_check_detects_tampered_stage_b_asset( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + output = tmp_path / "assets" / "experiment009-stage-b-overview.svg" + output.parent.mkdir() + expected = 'authenticated\n' + output.write_text(expected, encoding="utf-8", newline="\n") + source = tmp_path / "stage-b-result.json" + source.write_bytes(b"present") + monkeypatch.setattr(assets, "ROOT", tmp_path) + monkeypatch.setattr(assets, "STAGE_B_SOURCE", source) + monkeypatch.setattr(assets, "_outputs", lambda: {output: expected}) + monkeypatch.setattr(sys, "argv", ["generate_readme_assets.py", "--check"]) + + assert assets.main() == 0 + + output.write_text(expected.replace("authenticated", "tampered"), encoding="utf-8") + assert assets.main() == 1 + + +def _write_clean_clone_fixture( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> tuple[Path, Path, dict[str, object]]: + evidence, verification = _synthetic_stage_b() + generation_source = tmp_path / "generation-source.json" + generation_source.write_bytes(b"strict loader owns these bytes") + monkeypatch.setattr(assets, "STAGE_B_SOURCE", generation_source) + monkeypatch.setattr( + assets, + "_load_validated_stage_b_result", + lambda _path: (evidence, verification), + ) + data = assets._stage_b_data() + rendered = { + "assets/experiment009-stage-b-overview.svg": ( + assets._stage_b_overview_svg(data), + "experiment009-stage-b-overview", + ), + "assets/experiment009-stage-b-paired.svg": ( + assets._stage_b_paired_svg(data), + "experiment009-stage-b-paired-cqer-minus-rht", + ), + } + + graph_receipts: dict[str, dict[str, object]] = {} + for relative_path, (content, chart) in rendered.items(): + raw = content.encode("utf-8") + path = tmp_path / relative_path + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(raw) + graph_receipts[relative_path] = { + "bytes": len(raw), + "sha256": hashlib.sha256(raw).hexdigest(), + "chart": chart, + } + + manifest: dict[str, object] = { + "artifact_kind": "recurquant_experiment009_stage_b_release_manifest", + "schema_version": 1, + "result": { + "artifact_kind": assets.STAGE_B_ARTIFACT_KIND, + "raw_filename": "experiment009-rht-cqer-stage-b-result-cdc603b.json", + "raw_sha256": assets.STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": assets.STAGE_B_CANONICAL_SHA256, + "evaluation_commit": assets.STAGE_B_RESULT_COMMIT, + "verifier_fix_commit": assets.STAGE_B_VERIFIER_FIX_COMMIT, + }, + "verification": { + "strict_loader_passed": True, + "canonical_round_trip": True, + "integrity_passed": True, + "advancement_passed": True, + "advancement_check_count": 8, + "task_count": 32, + "bootstrap_samples": 10_000, + "bootstrap_seed": 2339, + }, + "graph_assets": { + "source_artifact_sha256": assets.STAGE_B_ARTIFACT_SHA256, + "canonical_evidence_sha256": assets.STAGE_B_CANONICAL_SHA256, + "original_result_commit": assets.STAGE_B_RESULT_COMMIT, + "verifier_fix_commit": assets.STAGE_B_VERIFIER_FIX_COMMIT, + "artifact_kind": assets.STAGE_B_ARTIFACT_KIND, + "integrity_passed": True, + "advancement_gate_passed": True, + "assets": graph_receipts, + }, + } + manifest_path = tmp_path / "evidence" / "experiment009-rht-cqer-stage-b-result-manifest.json" + manifest_path.parent.mkdir(parents=True, exist_ok=True) + manifest_path.write_text( + json.dumps(manifest, indent=2) + "\n", + encoding="utf-8", + newline="\n", + ) + + legacy = tmp_path / "assets" / "legacy.svg" + legacy_content = 'legacy\n' + legacy.write_text(legacy_content, encoding="utf-8", newline="\n") + monkeypatch.setattr(assets, "ROOT", tmp_path) + monkeypatch.setattr(assets, "STAGE_B_MANIFEST", manifest_path) + monkeypatch.setattr( + assets, + "STAGE_B_SOURCE", + tmp_path / "artifacts" / "experiment009-rht-cqer-stage-b-result-cdc603b.json", + ) + monkeypatch.setattr(assets, "_legacy_outputs", lambda: {legacy: legacy_content}) + monkeypatch.setattr(sys, "argv", ["generate_readme_assets.py", "--check"]) + return ( + tmp_path / "assets" / "experiment009-stage-b-overview.svg", + manifest_path, + manifest, + ) + + +def test_clean_clone_check_uses_manifest_and_rejects_tampered_bytes( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + overview, _manifest_path, _manifest = _write_clean_clone_fixture( + monkeypatch, + tmp_path, + ) + + assert assets.main() == 0 + + overview.write_text( + overview.read_text(encoding="utf-8").replace( + "Stage B development", + "Stage B tampered", + 1, + ), + encoding="utf-8", + newline="\n", + ) + assert assets.main() == 1 + + +def test_clean_clone_check_rejects_tampered_metadata_even_with_new_byte_receipt( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + overview, manifest_path, manifest = _write_clean_clone_fixture( + monkeypatch, + tmp_path, + ) + raw = overview.read_bytes().replace( + assets.STAGE_B_RESULT_COMMIT.encode(), + ("0" * 40).encode(), + 1, + ) + overview.write_bytes(raw) + graph_assets = manifest["graph_assets"] + assert isinstance(graph_assets, dict) + receipts = graph_assets["assets"] + assert isinstance(receipts, dict) + overview_receipt = receipts["assets/experiment009-stage-b-overview.svg"] + assert isinstance(overview_receipt, dict) + overview_receipt["bytes"] = len(raw) + overview_receipt["sha256"] = hashlib.sha256(raw).hexdigest() + manifest_path.write_text( + json.dumps(manifest, indent=2) + "\n", + encoding="utf-8", + newline="\n", + ) + + assert assets.main() == 1 + + +def test_clean_clone_check_rejects_missing_manifest( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + _write_clean_clone_fixture(monkeypatch, tmp_path) + monkeypatch.setattr( + assets, + "STAGE_B_MANIFEST", + tmp_path / "evidence" / "missing-manifest.json", + ) + + assert assets.main() == 1 + + +def test_clean_clone_check_rejects_missing_graph_asset( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + overview, _manifest_path, _manifest = _write_clean_clone_fixture( + monkeypatch, + tmp_path, + ) + overview.unlink() + + assert assets.main() == 1 + + +def test_generation_without_raw_stage_b_source_fails_with_download_instruction( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + capsys: pytest.CaptureFixture[str], +) -> None: + monkeypatch.setattr(assets, "ROOT", tmp_path) + monkeypatch.setattr( + assets, + "STAGE_B_SOURCE", + tmp_path / "artifacts" / "experiment009-rht-cqer-stage-b-result-cdc603b.json", + ) + monkeypatch.setattr(sys, "argv", ["generate_readme_assets.py"]) + + assert assets.main() == 2 + error = capsys.readouterr().err + assert assets.STAGE_B_RELEASE_DOWNLOAD in error + assert assets.STAGE_B_ARTIFACT_SHA256 in error + assert "extract the JSON to" in error diff --git a/tests/test_horizon.py b/tests/test_horizon.py new file mode 100644 index 0000000..0d8052c --- /dev/null +++ b/tests/test_horizon.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import pytest +import torch + +from recurquant.horizon import ( + finite_horizon_row_read_risk, + finite_horizon_row_read_risk_from_energies, +) + + +def _brute_force_scores( + errors: torch.Tensor, + queries: torch.Tensor, + keys: torch.Tensor, + log_decays: torch.Tensor, + betas: torch.Tensor, + *, + horizon: int, +) -> torch.Tensor: + time_steps, batch_size, heads, key_dim, value_dim = errors.shape + result = torch.zeros((time_steps, batch_size, heads, key_dim), dtype=torch.float32) + scale = key_dim**-0.5 + for start in range(time_steps): + stop = min(time_steps, start + horizon) + for batch in range(batch_size): + for head in range(heads): + for source_row in range(key_dim): + propagated = torch.zeros((key_dim, value_dim), dtype=torch.float32) + propagated[source_row] = errors[start, batch, head, source_row] + energy = 0.0 + for step in range(start, stop): + key = keys[step, batch, head] + projected = (key.unsqueeze(-1) * propagated).sum(dim=0) + propagated = log_decays[step, batch, head].exp() * ( + propagated - betas[step, batch, head] * key.unsqueeze(-1) * projected + ) + read = (queries[step, batch, head] * scale) @ propagated + energy += float(read.square().sum().item()) + result[start, batch, head, source_row] = energy / (stop - start) + return result + + +def test_horizon_scores_match_brute_force_isolated_row_propagation() -> None: + generator = torch.Generator().manual_seed(2339) + errors = torch.randn((4, 1, 2, 3, 2), generator=generator) * 0.05 + queries = torch.nn.functional.normalize(torch.randn((4, 1, 2, 3), generator=generator), dim=-1) + keys = torch.nn.functional.normalize(torch.randn((4, 1, 2, 3), generator=generator), dim=-1) + log_decays = -torch.rand((4, 1, 2), generator=generator) * 0.3 + betas = torch.rand((4, 1, 2), generator=generator) + + result = finite_horizon_row_read_risk( + errors, + queries, + keys, + log_decays, + betas, + horizon=3, + normalize_qk=False, + ) + expected = _brute_force_scores( + errors, + queries, + keys, + log_decays, + betas, + horizon=3, + ) + + assert result.horizon == 3 + assert torch.allclose(result.per_state_scores, expected, atol=1e-7, rtol=1e-5) + assert torch.allclose(result.scores, expected.mean(dim=(0, 1)), atol=1e-7, rtol=1e-5) + assert result.future_read_counts.tolist() == [3, 3, 2, 1] + + +def test_zero_state_error_has_zero_horizon_risk() -> None: + errors = torch.zeros((2, 1, 1, 3, 4)) + queries = torch.ones((2, 1, 1, 3)) + keys = torch.ones((2, 1, 1, 3)) + log_decays = torch.zeros((2, 1, 1)) + betas = torch.full((2, 1, 1), 0.5) + + result = finite_horizon_row_read_risk(errors, queries, keys, log_decays, betas) + + assert torch.count_nonzero(result.scores).item() == 0 + + +def test_row_energy_api_matches_full_error_api_exactly() -> None: + generator = torch.Generator().manual_seed(72) + errors = torch.randn((5, 2, 3, 4, 7), generator=generator) * 0.03 + queries = torch.randn((5, 2, 3, 4), generator=generator) + keys = torch.randn((5, 2, 3, 4), generator=generator) + log_decays = -torch.rand((5, 2, 3), generator=generator) + betas = torch.rand((5, 2, 3), generator=generator) + + full = finite_horizon_row_read_risk( + errors, + queries, + keys, + log_decays, + betas, + horizon=4, + ) + energies = errors.to(torch.float32).square().sum(dim=-1) + reduced = finite_horizon_row_read_risk_from_energies( + energies, + queries, + keys, + log_decays, + betas, + horizon=4, + ) + + assert full.horizon == reduced.horizon + assert torch.equal(full.future_read_counts, reduced.future_read_counts) + assert torch.equal(full.per_state_scores, reduced.per_state_scores) + assert torch.equal(full.scores, reduced.scores) + + +def test_row_energy_api_rejects_negative_energy() -> None: + energies = torch.zeros((1, 1, 1, 2)) + energies[..., 0] = -1 + vectors = torch.ones((1, 1, 1, 2)) + scalars = torch.zeros((1, 1, 1)) + + with pytest.raises(ValueError, match="non-negative"): + finite_horizon_row_read_risk_from_energies( + energies, + vectors, + vectors, + scalars, + scalars, + ) + + +def test_row_energy_api_rejects_shape_and_dtype_mismatch() -> None: + energies = torch.zeros((2, 1, 1, 3)) + vectors = torch.ones((2, 1, 1, 3)) + scalars = torch.zeros((2, 1, 1)) + + with pytest.raises(ValueError, match="query/key dimensions"): + finite_horizon_row_read_risk_from_energies( + energies, + vectors[..., :2], + vectors, + scalars, + scalars, + ) + with pytest.raises(TypeError, match="floating-point"): + finite_horizon_row_read_risk_from_energies( + energies.to(torch.int64), + vectors, + vectors, + scalars, + scalars, + ) + + +@pytest.mark.parametrize("horizon", [0, -1, True]) +def test_invalid_horizon_is_rejected(horizon: int) -> None: + errors = torch.zeros((1, 1, 1, 2, 2)) + vectors = torch.ones((1, 1, 1, 2)) + scalars = torch.zeros((1, 1, 1)) + with pytest.raises(ValueError, match="positive integer"): + finite_horizon_row_read_risk( + errors, + vectors, + vectors, + scalars, + scalars, + horizon=horizon, + ) diff --git a/tests/test_horizon_calibration.py b/tests/test_horizon_calibration.py new file mode 100644 index 0000000..1010523 --- /dev/null +++ b/tests/test_horizon_calibration.py @@ -0,0 +1,309 @@ +from __future__ import annotations + +from collections.abc import Callable + +import pytest +import torch + +from recurquant.horizon_calibration import ( + GDNHorizonCalibrationRecorder, + TaskMacroHorizonAccumulator, + row_quantization_error_energies, + score_gdn_calibration_trace, +) +from recurquant.quantization import QuantizationSpec, quantize_dequantize + + +class Qwen3_5GatedDeltaNet(torch.nn.Module): + def __init__(self, layer_idx: int) -> None: + super().__init__() + self.layer_idx = layer_idx + self.calls = 0 + + def recurrent_kernel( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + initial_state: torch.Tensor, + output_final_state: bool, + use_qk_l2norm_in_kernel: bool, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + del key, g, beta, use_qk_l2norm_in_kernel + self.calls += 1 + output = value.clone() + final = initial_state + 1 if output_final_state else None + return output, final + + self.recurrent_gated_delta_rule: Callable[..., object] = recurrent_kernel + + +class FakeModel(torch.nn.Module): + def __init__(self, layers: int = 2) -> None: + super().__init__() + self.layers = torch.nn.ModuleList( + [Qwen3_5GatedDeltaNet(layer_idx=index) for index in range(layers)] + ) + + +def _inputs( + *, + batch: int = 1, + sequence: int = 1, + heads: int = 2, + key_dim: int = 4, + value_dim: int = 4, +) -> tuple[torch.Tensor, ...]: + generator = torch.Generator().manual_seed(2339) + query = torch.randn((batch, sequence, heads, key_dim), generator=generator) + key = torch.randn((batch, sequence, heads, key_dim), generator=generator) + value = torch.randn((batch, sequence, heads, value_dim), generator=generator) + log_decay = -torch.rand((batch, sequence, heads), generator=generator) * 0.2 + beta = torch.rand((batch, sequence, heads), generator=generator) + state = torch.randn((batch, heads, key_dim, value_dim), generator=generator) * 0.3 + return query, key, value, log_decay, beta, state + + +def _call( + module: Qwen3_5GatedDeltaNet, + inputs: tuple[torch.Tensor, ...], + *, + normalized: bool = True, +) -> object: + query, key, value, log_decay, beta, state = inputs + return module.recurrent_gated_delta_rule( + query, + key, + value, + g=log_decay, + beta=beta, + initial_state=state, + output_final_state=True, + use_qk_l2norm_in_kernel=normalized, + ) + + +def _spec(bits: int) -> QuantizationSpec: + return QuantizationSpec( + bits=bits, + group_size=4, + flatten_last_dims=1, + ) + + +def test_recorder_is_non_mutating_and_restores_selected_hook() -> None: + model = FakeModel() + selected = model.layers[1] + untouched = model.layers[0] + original_selected = selected.recurrent_gated_delta_rule + original_untouched = untouched.recurrent_gated_delta_rule + inputs = _inputs() + snapshots = tuple(tensor.clone() for tensor in inputs) + + recorder = GDNHorizonCalibrationRecorder( + model, + layer_indices=[1], + max_tokens_per_layer=3, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + with recorder: + assert selected.recurrent_gated_delta_rule is not original_selected + assert untouched.recurrent_gated_delta_rule is original_untouched + output, final_state = _call(selected, inputs) + + assert selected.recurrent_gated_delta_rule is original_selected + assert untouched.recurrent_gated_delta_rule is original_untouched + assert selected.calls == 1 + assert output.shape == inputs[0].shape + assert torch.equal(final_state, inputs[-1] + 1) + for value, snapshot in zip(inputs, snapshots, strict=True): + assert torch.equal(value, snapshot) + + +def test_context_restores_hook_after_exception() -> None: + model = FakeModel(layers=1) + module = model.layers[0] + original = module.recurrent_gated_delta_rule + recorder = GDNHorizonCalibrationRecorder( + model, + layer_indices=[0], + max_tokens_per_layer=1, + ) + + with pytest.raises(RuntimeError, match="stop"), recorder: + raise RuntimeError("stop") + + assert module.recurrent_gated_delta_rule is original + + +def test_recorder_captures_normalized_compact_trace_and_exact_row_energies() -> None: + model = FakeModel(layers=1) + module = model.layers[0] + inputs = _inputs(value_dim=5) + recorder = GDNHorizonCalibrationRecorder( + model, + layer_indices=[0], + max_tokens_per_layer=2, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + + with recorder: + _call(module, inputs) + _call(module, inputs) + _call(module, inputs) + + assert recorder.captured_tokens == {0: 2} + assert recorder.dropped_calls == {0: 1} + with pytest.raises(RuntimeError, match="trace is incomplete"): + recorder.drain_traces() + traces = recorder.drain_traces(require_complete=False) + trace = traces[0] + query, key, _, log_decay, beta, state = inputs + expected_query = query[:, 0].to(torch.float32) + expected_query *= torch.rsqrt(expected_query.square().sum(dim=-1, keepdim=True) + 1e-6) + expected_key = key[:, 0].to(torch.float32) + expected_key *= torch.rsqrt(expected_key.square().sum(dim=-1, keepdim=True) + 1e-6) + expected_int4 = row_quantization_error_energies(state, _spec(4)) + expected_int8 = row_quantization_error_energies(state, _spec(8)) + + assert trace.tokens == 2 + assert trace.dropped_calls == 1 + assert not trace.complete + assert trace.queries.shape == (2, 1, 2, 4) + assert trace.int4_row_error_energies.shape == (2, 1, 2, 4) + assert trace.int8_row_error_energies.shape == (2, 1, 2, 4) + assert trace.log_decays.shape == (2, 1, 2) + assert trace.betas.shape == (2, 1, 2) + assert all( + tensor.dtype == torch.float32 and tensor.device.type == "cpu" + for tensor in ( + trace.queries, + trace.keys, + trace.log_decays, + trace.betas, + trace.int4_row_error_energies, + trace.int8_row_error_energies, + ) + ) + assert torch.allclose(trace.queries[0], expected_query) + assert torch.allclose(trace.keys[0], expected_key) + assert torch.equal(trace.log_decays[0], log_decay[:, 0]) + assert torch.equal(trace.betas[0], beta[:, 0]) + assert torch.equal(trace.int4_row_error_energies[0], expected_int4) + assert torch.equal(trace.int8_row_error_energies[0], expected_int8) + assert recorder.captured_tokens == {0: 0} + assert recorder.dropped_calls == {0: 0} + + with pytest.raises(ValueError, match="incomplete calibration trace"): + score_gdn_calibration_trace(trace) + + +@pytest.mark.parametrize("bits", [4, 8]) +def test_row_energy_uses_fp32_source_and_matches_direct_qdq(bits: int) -> None: + state = torch.tensor( + [[[[0.03125, -0.1171875, 0.203125, 0.4453125, -0.6875]]]], + dtype=torch.bfloat16, + ) + spec = _spec(bits) + source = state.to(torch.float32) + qdq = quantize_dequantize(source, spec).tensor.to(torch.float32) + expected = (qdq - source).square().sum(dim=-1) + + actual = row_quantization_error_energies(state, spec) + + assert actual.dtype == torch.float32 + assert torch.equal(actual, expected) + + +def test_capture_validation_rejects_shape_dtype_device_and_kernel_mode() -> None: + model = FakeModel(layers=1) + module = model.layers[0] + recorder = GDNHorizonCalibrationRecorder( + model, + layer_indices=[0], + max_tokens_per_layer=2, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + good = _inputs() + + with recorder: + with pytest.raises(ValueError, match="single-token"): + _call(module, _inputs(sequence=2)) + + invalid_dtype = list(good) + invalid_dtype[0] = invalid_dtype[0].to(torch.int64) + with pytest.raises(TypeError, match="floating-point"): + _call(module, tuple(invalid_dtype)) + + lower_precision_state = list(good) + lower_precision_state[-1] = lower_precision_state[-1].to(torch.bfloat16) + with pytest.raises(TypeError, match="initial_state must use torch.float32"): + _call(module, tuple(lower_precision_state)) + + mixed_device = list(good) + mixed_device[-1] = torch.empty(good[-1].shape, device="meta") + with pytest.raises(ValueError, match="same device"): + _call(module, tuple(mixed_device)) + + with pytest.raises(ValueError, match="l2norm"): + _call(module, good, normalized=False) + + +def test_recorder_requires_existing_bounded_layers_and_row_specs() -> None: + model = FakeModel(layers=1) + with pytest.raises(ValueError, match="at least one layer"): + GDNHorizonCalibrationRecorder( + model, + layer_indices=[], + max_tokens_per_layer=1, + ) + with ( + pytest.raises(ValueError, match="selected GDN layers"), + GDNHorizonCalibrationRecorder( + model, + layer_indices=[7], + max_tokens_per_layer=1, + ), + ): + pass + with pytest.raises(ValueError, match="flatten_last_dims=1"): + GDNHorizonCalibrationRecorder( + model, + layer_indices=[0], + max_tokens_per_layer=1, + int4_spec=QuantizationSpec(bits=4, flatten_last_dims=2), + ) + + +def test_task_macro_accumulator_scores_then_discards_token_traces() -> None: + model = FakeModel(layers=1) + module = model.layers[0] + recorder = GDNHorizonCalibrationRecorder( + model, + layer_indices=[0], + max_tokens_per_layer=3, + int4_spec=_spec(4), + int8_spec=_spec(8), + ) + with recorder: + for _ in range(3): + _call(module, _inputs()) + traces = recorder.drain_traces() + expected = score_gdn_calibration_trace(traces[0], horizon=2) + accumulator = TaskMacroHorizonAccumulator(horizon=2) + + accumulator.add_task(traces) + accumulator.add_task(traces) + with pytest.raises(ValueError, match="layer set changed"): + accumulator.add_task({1: traces[0]}) + summary = accumulator.summary(0) + + assert summary.tasks == 2 + assert summary.horizon == 2 + assert torch.allclose(summary.int4_scores, expected.int4.scores.to(torch.float64)) + assert torch.allclose(summary.int8_scores, expected.int8.scores.to(torch.float64)) + assert recorder.captured_tokens == {0: 0} diff --git a/tests/test_intervention.py b/tests/test_intervention.py new file mode 100644 index 0000000..b6be59f --- /dev/null +++ b/tests/test_intervention.py @@ -0,0 +1,203 @@ +from __future__ import annotations + +import math + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +import recurquant.intervention as intervention_module +from recurquant.intervention import ( + evaluate_physical_row_promotions, + target_nll_values, +) +from recurquant.row_policy import RowLocation +from tests.test_transformers_cache import tiny_config + + +def _tiny_model(layer_types: list[str] | None = None) -> Qwen3_5ForCausalLM: + torch.manual_seed(211) + return Qwen3_5ForCausalLM._from_config( + tiny_config(layer_types), + attn_implementation="eager", + ).eval() + + +def test_physical_row_promotions_use_fixed_budget_and_repeated_cache_updates() -> None: + model = _tiny_model() + prompt = torch.tensor([[1, 2, 3]]) + continuation = torch.tensor([[4, 5]]) + candidates = (RowLocation(0, 1, 7), RowLocation(0, 0, 0)) + + result = evaluate_physical_row_promotions( + model, + prompt_ids=prompt, + continuation_ids=continuation, + candidate_rows=candidates, + ) + + # 16 rows, each with a four-byte INT4 payload and two-byte scale, plus + # a two-byte packed mask. One promoted eight-element row adds four bytes. + assert result.baseline_plan_bytes == 98 + assert result.intervention_plan_bytes == 102 + assert result.promotion_increment_bytes == 4 + assert result.baseline.resident_bytes == 98 + assert result.baseline.high_precision_groups == 0 + assert result.baseline.token_count == 2 + assert result.baseline.cache_update_count == 2 + assert tuple(measurement.location for measurement in result.measurements) == ( + RowLocation(0, 0, 0), + RowLocation(0, 1, 7), + ) + for measurement in result.measurements: + assert measurement.run.resident_bytes == 102 + assert measurement.run.high_precision_groups == 1 + assert measurement.run.cache_update_count == 2 + assert math.isfinite(measurement.run.mean_metric) + assert measurement.metric_delta == pytest.approx( + measurement.run.mean_metric - result.baseline.mean_metric + ) + assert measurement.improvement == pytest.approx(-measurement.metric_delta) + assert sorted(result.ranked_measurements(), key=lambda item: -item.improvement) == list( + result.ranked_measurements() + ) + + +def test_background_intervention_and_custom_metric_are_deterministic() -> None: + model = _tiny_model() + prompt = torch.tensor([[9, 10]]) + continuation = torch.tensor([[11]]) + + def target_log_probability(logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor: + return -target_nll_values(logits, targets) + + arguments = { + "prompt_ids": prompt, + "continuation_ids": continuation, + "candidate_rows": (RowLocation(0, 0, 1),), + "background_rows": (RowLocation(0, 0, 0),), + "metric": target_log_probability, + "metric_name": "target_log_probability", + "lower_is_better": False, + } + first = evaluate_physical_row_promotions(model, **arguments) + second = evaluate_physical_row_promotions(model, **arguments) + + assert first == second + assert first.baseline.high_precision_groups == 1 + assert first.measurements[0].run.high_precision_groups == 2 + assert first.baseline_plan_bytes == 102 + assert first.intervention_plan_bytes == 106 + assert first.measurements[0].improvement == pytest.approx(first.measurements[0].metric_delta) + + +def test_cache_update_count_covers_every_recurrent_layer_and_forward() -> None: + model = _tiny_model( + ["linear_attention", "linear_attention", "full_attention"] + ) + + result = evaluate_physical_row_promotions( + model, + prompt_ids=torch.tensor([[1, 2]]), + continuation_ids=torch.tensor([[3, 4, 5]]), + candidate_rows=(RowLocation(0, 0, 0),), + ) + + # One prompt forward plus two continuation transitions, each updating one + # recurrent state in both linear-attention layers. + assert result.baseline.cache_update_count == 6 + assert result.measurements[0].run.cache_update_count == 6 + + +def test_intervention_rejects_unbounded_invalid_or_inexact_candidates() -> None: + model = _tiny_model() + prompt = torch.tensor([[1, 2]]) + continuation = torch.tensor([[3]]) + location = RowLocation(0, 0, 0) + + common = { + "model": model, + "prompt_ids": prompt, + "continuation_ids": continuation, + } + with pytest.raises(ValueError, match="must not contain duplicates"): + evaluate_physical_row_promotions( + **common, + candidate_rows=(location, location), + ) + with pytest.raises(ValueError, match="must not overlap"): + evaluate_physical_row_promotions( + **common, + candidate_rows=(location,), + background_rows=(location,), + ) + with pytest.raises(ValueError, match="exceeding max_candidates"): + evaluate_physical_row_promotions( + **common, + candidate_rows=(location, RowLocation(0, 0, 1)), + max_candidates=1, + ) + with pytest.raises(ValueError, match="out-of-range"): + evaluate_physical_row_promotions( + **common, + candidate_rows=(RowLocation(0, 2, 0),), + ) + with pytest.raises(ValueError, match="must exactly encode"): + evaluate_physical_row_promotions( + **common, + candidate_rows=(location,), + intervention_resident_bytes=101, + ) + + +def test_intervention_validates_metric_output_per_target() -> None: + model = _tiny_model() + + def scalar_metric(logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor: + del targets + return logits.mean() + + with pytest.raises(ValueError, match="one value per target"): + evaluate_physical_row_promotions( + model, + prompt_ids=torch.tensor([[1, 2]]), + continuation_ids=torch.tensor([[3]]), + candidate_rows=(RowLocation(0, 0, 0),), + metric=scalar_metric, + ) + + +def test_intervention_rejects_an_incomplete_cache_update_trace( + monkeypatch: pytest.MonkeyPatch, +) -> None: + original_factory = intervention_module.create_qwen35_exact_budget_cache + + class DropFirstEvidence(list[object]): + def __init__(self) -> None: + super().__init__() + self._dropped = False + + def append(self, value: object) -> None: + if not self._dropped: + self._dropped = True + return + super().append(value) + + def create_cache_with_incomplete_evidence(*args: object, **kwargs: object) -> object: + cache = original_factory(*args, **kwargs) # type: ignore[arg-type] + cache.update_evidence = DropFirstEvidence() # type: ignore[assignment] + return cache + + monkeypatch.setattr( + intervention_module, + "create_qwen35_exact_budget_cache", + create_cache_with_incomplete_evidence, + ) + + with pytest.raises(RuntimeError, match="update trace did not cover"): + evaluate_physical_row_promotions( + _tiny_model(), + prompt_ids=torch.tensor([[1, 2]]), + continuation_ids=torch.tensor([[3, 4]]), + candidate_rows=(RowLocation(0, 0, 0),), + ) diff --git a/tests/test_mixed_packed_cache.py b/tests/test_mixed_packed_cache.py new file mode 100644 index 0000000..0508aac --- /dev/null +++ b/tests/test_mixed_packed_cache.py @@ -0,0 +1,274 @@ +from __future__ import annotations + +import math + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant.packed_cache import ( + MixedPackedLinearAttentionLayer, + MixedPackedRecurrentStateCache, +) +from recurquant.quantization import QuantizationSpec +from recurquant.qwen35 import create_qwen35_exact_budget_cache +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from recurquant.transformers_cache import RecurrentStateQDQCache +from tests.test_transformers_cache import tiny_config + + +def _target_for_promotions( + scores: dict[int, torch.Tensor], + *, + promotions: int, + group_size: int = 8, + scale_bits: int = 16, +) -> int: + total_groups = sum(score.numel() for score in scores.values()) + low_group_bytes = math.ceil(4 * group_size / 8) + scale_bits // 8 + increment = math.ceil(8 * group_size / 8) - math.ceil(4 * group_size / 8) + return total_groups * low_group_bytes + math.ceil(total_groups / 8) + (promotions * increment) + + +def _tiny_plan( + *, + layer_indices: tuple[int, ...] = (0,), + promotions: int = 5, + group_size: int = 8, + heads: int = 2, + rows: int = 8, +) -> ExactBudgetRowPlan: + scores = { + layer_index: torch.arange(heads * rows, dtype=torch.float32).reshape(heads, rows) + + offset * 100 + for offset, layer_index in enumerate(layer_indices) + } + return select_rows_exact_budget( + scores, + target_resident_bytes=_target_for_promotions( + scores, + promotions=promotions, + group_size=group_size, + ), + group_size=group_size, + ) + + +def _tiny_model(layer_types: list[str] | None = None) -> Qwen3_5ForCausalLM: + return Qwen3_5ForCausalLM._from_config( + tiny_config(layer_types), + attn_implementation="eager", + ).eval() + + +def test_exact_budget_factory_runs_prefill_decode_and_reports_physical_bytes() -> None: + torch.manual_seed(131) + model = _tiny_model() + plan = _tiny_plan(promotions=5) + cache = create_qwen35_exact_budget_cache( + model, + plan=plan, + record_evidence=True, + ) + prompt = torch.randint(0, model.config.vocab_size, (1, 6)) + next_token = torch.randint(0, model.config.vocab_size, (1, 1)) + + with torch.inference_mode(): + model(prompt, past_key_values=cache, use_cache=True) + output = model(next_token, past_key_values=cache, use_cache=True) + + assert output.logits.shape == (1, 1, model.config.vocab_size) + assert isinstance(cache, MixedPackedRecurrentStateCache) + layer = cache.layers[0] + assert isinstance(layer, MixedPackedLinearAttentionLayer) + assert layer.recurrent_states[0].shape == (1, 2, 8, 8) + assert cache.storage_summary() == { + "payload_bytes": 84, + "scale_bytes": 32, + "mask_bytes": 2, + "resident_bytes": 118, + "high_precision_groups": 5, + "full_precision_equivalent_bytes": 512, + "largest_materialized_state_bytes": 512, + "resident_compression_ratio": 512 / 118, + "physical_reduction_realized": True, + } + assert len(cache.update_evidence) == 2 + assert cache.update_evidence[-1].high_precision_groups == 5 + assert cache.update_evidence[-1].resident_bytes == 118 + + +def test_all_int4_row_plan_matches_qdq_cache_state_and_logits_exactly() -> None: + torch.manual_seed(137) + model = _tiny_model() + config = model.config + plan = _tiny_plan(promotions=0) + qdq_cache = RecurrentStateQDQCache( + config, + spec=QuantizationSpec(bits=4, group_size=8), + ) + mixed_cache = create_qwen35_exact_budget_cache(model, plan=plan) + prompt = torch.randint(0, config.vocab_size, (1, 6)) + continuation = torch.randint(0, config.vocab_size, (1, 2)) + + with torch.inference_mode(): + model(prompt, past_key_values=qdq_cache, use_cache=True) + qdq_output = model(continuation, past_key_values=qdq_cache, use_cache=True) + model(prompt, past_key_values=mixed_cache, use_cache=True) + mixed_output = model(continuation, past_key_values=mixed_cache, use_cache=True) + + assert torch.equal(mixed_output.logits, qdq_output.logits) + assert torch.equal( + mixed_cache.layers[0].recurrent_states[0], + qdq_cache.layers[0].recurrent_states[0], + ) + + +def test_batch_independent_policy_repeats_exactly_for_each_batch_item() -> None: + plan = _tiny_plan(promotions=7) + cache = create_qwen35_exact_budget_cache(tiny_config(), plan=plan) + state = torch.randn((2, 2, 8, 8), generator=torch.Generator().manual_seed(139)) + + cache.update_recurrent_state(state, layer_idx=0) + + layer = cache.layers[0] + assert isinstance(layer, MixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + unpacked = packed.high_precision_mask().reshape(2, 16) + assert torch.equal(unpacked[0], unpacked[1]) + assert unpacked[0].sum().item() == 7 + assert cache.storage_summary()["resident_bytes"] == 2 * plan.resident_bytes + assert cache.storage_summary()["high_precision_groups"] == 14 + + +def test_exact_budget_cache_supports_beam_generation_and_lossless_reorder( + monkeypatch: pytest.MonkeyPatch, +) -> None: + torch.manual_seed(149) + model = _tiny_model() + plan = _tiny_plan(promotions=5) + cache = create_qwen35_exact_budget_cache(model, plan=plan) + prompt = torch.tensor([[1, 2, 3, 4]]) + reorder_indices: list[torch.Tensor] = [] + original_reorder = MixedPackedLinearAttentionLayer.reorder_cache + + def record_reorder( + layer: MixedPackedLinearAttentionLayer, + beam_idx: torch.LongTensor, + ) -> None: + reorder_indices.append(beam_idx.detach().cpu().clone()) + original_reorder(layer, beam_idx) + + monkeypatch.setattr(MixedPackedLinearAttentionLayer, "reorder_cache", record_reorder) + with torch.inference_mode(): + generated = model.generate( + prompt, + past_key_values=cache, + use_cache=True, + max_new_tokens=3, + do_sample=False, + num_beams=3, + pad_token_id=0, + ) + + assert generated.shape == (1, 7) + assert reorder_indices + assert all(indices.shape == (3,) for indices in reorder_indices) + assert cache.storage_summary()["resident_bytes"] == 3 * plan.resident_bytes + assert cache.storage_summary()["high_precision_groups"] == 15 + + +def test_factory_rejects_missing_and_extra_plan_layers() -> None: + missing_plan = _tiny_plan(layer_indices=(0,), promotions=0) + with pytest.raises(ValueError, match=r"missing=\[1\].*extra=\[\]"): + create_qwen35_exact_budget_cache( + tiny_config(["linear_attention", "linear_attention", "full_attention"]), + plan=missing_plan, + ) + + extra_plan = _tiny_plan(layer_indices=(0, 1), promotions=0) + with pytest.raises(ValueError, match=r"missing=\[\].*extra=\[1\]"): + create_qwen35_exact_budget_cache(tiny_config(), plan=extra_plan) + + +def test_factory_rejects_incompatible_plan_and_runtime_geometry() -> None: + incompatible_scores = {0: torch.ones((1, 16))} + incompatible_shape = select_rows_exact_budget( + incompatible_scores, + target_resident_bytes=_target_for_promotions( + incompatible_scores, + promotions=0, + ), + group_size=8, + ) + with pytest.raises(ValueError, match="score geometry"): + create_qwen35_exact_budget_cache(tiny_config(), plan=incompatible_shape) + + wrong_group_size = _tiny_plan(promotions=0, group_size=4) + with pytest.raises(ValueError, match="linear_value_head_dim"): + create_qwen35_exact_budget_cache(tiny_config(), plan=wrong_group_size) + + cache = create_qwen35_exact_budget_cache(tiny_config(), plan=_tiny_plan()) + with pytest.raises(ValueError, match=r"\[batch, 2, 8, 8\]"): + cache.update_recurrent_state(torch.ones((1, 2, 4, 16)), layer_idx=0) + + +def test_factory_rejects_global_mask_budget_that_grows_when_split_by_layer() -> None: + config = tiny_config(["linear_attention", "linear_attention"]) + config.linear_num_value_heads = 1 + config.linear_key_head_dim = 4 + config.linear_value_head_dim = 8 + scores = {0: torch.ones((1, 4)), 1: torch.ones((1, 4))} + plan = select_rows_exact_budget( + scores, + target_resident_bytes=_target_for_promotions(scores, promotions=0), + group_size=8, + ) + + with pytest.raises(ValueError, match="separate per-layer masks"): + create_qwen35_exact_budget_cache(config, plan=plan) + + +def test_qwen_eighteen_recurrent_layer_layout_realizes_exact_target() -> None: + layer_types = [ + layer_type + for _ in range(6) + for layer_type in ( + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + ) + ] + linear_indices = tuple( + index for index, layer_type in enumerate(layer_types) if layer_type == "linear_attention" + ) + config = tiny_config(layer_types) + config.linear_num_value_heads = 16 + config.linear_key_head_dim = 128 + config.linear_value_head_dim = 128 + scores = { + layer_index: torch.arange(16 * 128, dtype=torch.float32).reshape(16, 128) + order * 10_000 + for order, layer_index in enumerate(linear_indices) + } + plan = select_rows_exact_budget(scores, target_resident_bytes=2_564_096) + cache = create_qwen35_exact_budget_cache(config, plan=plan) + state = torch.zeros((1, 16, 128, 128)) + + for layer_index in linear_indices: + cache.update_recurrent_state(state, layer_idx=layer_index) + + assert len(linear_indices) == 18 + assert plan.promoted_group_count == 1_976 + assert cache.storage_summary() == { + "payload_bytes": 2_485_760, + "scale_bytes": 73_728, + "mask_bytes": 4_608, + "resident_bytes": 2_564_096, + "high_precision_groups": 1_976, + "full_precision_equivalent_bytes": 18_874_368, + "largest_materialized_state_bytes": 1_048_576, + "resident_compression_ratio": 18_874_368 / 2_564_096, + "physical_reduction_realized": True, + } diff --git a/tests/test_mixed_quantization.py b/tests/test_mixed_quantization.py new file mode 100644 index 0000000..508a0e3 --- /dev/null +++ b/tests/test_mixed_quantization.py @@ -0,0 +1,292 @@ +from __future__ import annotations + +import math +from dataclasses import replace + +import pytest +import torch + +from recurquant.mixed_quantization import quantize_pack_mixed +from recurquant.quantization import QuantizationSpec, quantize_dequantize + + +def _per_group_qdq_reference( + tensor: torch.Tensor, + mask: torch.Tensor, + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, +) -> torch.Tensor: + flattened_size = math.prod(tensor.shape[-low_spec.flatten_last_dims :]) + rows = tensor.detach().to(torch.float32).reshape(-1, flattened_size) + groups_per_row = math.ceil(flattened_size / low_spec.group_size) + padded_size = groups_per_row * low_spec.group_size + if padded_size != flattened_size: + rows = torch.nn.functional.pad(rows, (0, padded_size - flattened_size)) + groups = rows.reshape(-1, low_spec.group_size) + flat_mask = mask.reshape(-1) + + restored_groups = [] + for index, group in enumerate(groups): + selected = high_spec if flat_mask[index] else low_spec + restored_groups.append( + quantize_dequantize( + group, + replace(selected, flatten_last_dims=1), + ).tensor + ) + restored = torch.stack(restored_groups).reshape(-1, padded_size)[:, :flattened_size] + return restored.reshape(tensor.shape).to(tensor.dtype) + + +def _specs(**kwargs: object) -> tuple[QuantizationSpec, QuantizationSpec]: + return QuantizationSpec(bits=4, **kwargs), QuantizationSpec(bits=8, **kwargs) + + +def test_mixed_pack_matches_independent_per_group_qdq() -> None: + state = torch.linspace(-2.7, 3.1, 60, dtype=torch.float32).reshape(2, 2, 3, 5) + low_spec, high_spec = _specs(group_size=4) + mask = torch.tensor( + [ + [False, True, False, True], + [True, False, False, True], + [False, False, True, True], + [True, True, False, False], + ] + ) + + packed = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + reference = _per_group_qdq_reference(state, mask, low_spec, high_spec) + + assert torch.equal(packed.dequantize(), reference) + assert torch.equal(packed.high_precision_mask(), mask) + assert packed.low_payload.dtype == torch.uint8 + assert packed.high_payload.dtype == torch.int8 + assert packed.scales.dtype == torch.float16 + assert packed.low_precision_groups == 8 + assert packed.high_precision_groups == 8 + + +def test_qwen35_row_layout_hits_v02_exact_byte_target() -> None: + total_groups = 18 * 16 * 128 + promoted_groups = 1_976 + state = torch.zeros((1, 18 * 16, 128, 128), dtype=torch.float32) + mask = torch.zeros(total_groups, dtype=torch.bool) + mask[:promoted_groups] = True + low_spec, high_spec = _specs(group_size=128) + + packed = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + + assert packed.total_groups == 36_864 + assert packed.high_precision_groups == 1_976 + assert packed.low_precision_groups == 34_888 + assert packed.payload_bytes == 2_485_760 + assert packed.scale_bytes == 73_728 + assert packed.mask_bytes == 4_608 + assert packed.storage_bytes == 2_564_096 + + +def test_odd_group_size_and_ninth_mask_bit_have_exact_storage() -> None: + state = torch.linspace(-1.3, 2.1, 25) + mask = torch.tensor([True, False, True, False, False, False, False, False, True]) + low_spec, high_spec = _specs(group_size=3, flatten_last_dims=1) + + packed = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + reference = _per_group_qdq_reference(state, mask, low_spec, high_spec) + + assert packed.precision_mask.tolist() == [0b00000101, 0b00000001] + assert packed.low_payload.shape == (6, 2) + assert packed.high_payload.shape == (3, 3) + assert packed.payload_bytes == 21 + assert packed.scale_bytes == 18 + assert packed.mask_bytes == 2 + assert packed.storage_bytes == 41 + assert torch.equal(packed.dequantize(), reference) + + +def test_stochastic_mixed_pack_is_seeded_and_deterministic() -> None: + state = torch.linspace(-1.0, 1.0, 77) + mask = torch.tensor([False, True, False, True, True, False, True]) + low_spec, high_spec = _specs( + group_size=12, + flatten_last_dims=1, + rounding="stochastic", + seed=71, + ) + + first = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + second = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + + assert torch.equal(first.low_payload, second.low_payload) + assert torch.equal(first.high_payload, second.high_payload) + assert torch.equal(first.scales, second.scales) + assert torch.equal(first.dequantize(), second.dequantize()) + + +def test_batch_reorder_reuses_integer_codes_and_supports_duplicates() -> None: + generator = torch.Generator().manual_seed(103) + state = torch.randn((3, 2, 3, 5), generator=generator).to(torch.bfloat16) + low_spec, high_spec = _specs(group_size=4) + mask = (torch.arange(24) % 3 == 1).reshape(6, 4) + packed = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + before = packed.dequantize() + before_codes = packed._integer_groups().reshape(3, 8, 4) + beam_idx = torch.tensor([2, 2, 0], dtype=torch.long) + + reordered = packed.reorder_batch(beam_idx) + + assert torch.equal(reordered.dequantize(), before.index_select(0, beam_idx)) + assert torch.equal( + reordered._integer_groups().reshape(3, 8, 4), + before_codes.index_select(0, beam_idx), + ) + assert reordered.original_shape == state.shape + + +@pytest.mark.parametrize("high_value", [False, True]) +def test_all_groups_can_use_one_payload_width(high_value: bool) -> None: + state = torch.linspace(-2.0, 2.0, 16) + mask = torch.full((2,), high_value, dtype=torch.bool) + low_spec, high_spec = _specs(group_size=8, flatten_last_dims=1) + + packed = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + + assert packed.high_precision_groups == (2 if high_value else 0) + assert packed.low_precision_groups == (0 if high_value else 2) + assert torch.equal( + packed.dequantize(), + _per_group_qdq_reference(state, mask, low_spec, high_spec), + ) + + +@pytest.mark.parametrize( + ("low_spec", "high_spec", "message"), + [ + (QuantizationSpec(bits=6), QuantizationSpec(bits=8), "low INT4"), + (QuantizationSpec(bits=4), QuantizationSpec(bits=6), "high INT8"), + ( + QuantizationSpec(bits=4, group_size=8), + QuantizationSpec(bits=8, group_size=16), + "group_size", + ), + ( + QuantizationSpec(bits=4, scale_bits=16), + QuantizationSpec(bits=8, scale_bits=32), + "scale_bits", + ), + ( + QuantizationSpec(bits=4, seed=1), + QuantizationSpec(bits=8, seed=2), + "seed", + ), + ], +) +def test_invalid_mixed_specs_are_rejected( + low_spec: QuantizationSpec, + high_spec: QuantizationSpec, + message: str, +) -> None: + with pytest.raises(ValueError, match=message): + quantize_pack_mixed( + torch.ones(128), + torch.zeros(1, dtype=torch.bool), + low_spec=low_spec, + high_spec=high_spec, + ) + + +def test_invalid_masks_are_rejected() -> None: + state = torch.ones((2, 8)) + low_spec, high_spec = _specs(group_size=4, flatten_last_dims=1) + + with pytest.raises(TypeError, match="torch.bool"): + quantize_pack_mixed( + state, + torch.zeros((2, 2), dtype=torch.int8), + low_spec=low_spec, + high_spec=high_spec, + ) + with pytest.raises(ValueError, match="must have shape"): + quantize_pack_mixed( + state, + torch.zeros((1, 4), dtype=torch.bool), + low_spec=low_spec, + high_spec=high_spec, + ) + + +def test_invalid_reorder_indices_are_rejected() -> None: + low_spec, high_spec = _specs(group_size=4, flatten_last_dims=1) + packed = quantize_pack_mixed( + torch.ones((2, 8)), + torch.zeros((2, 2), dtype=torch.bool), + low_spec=low_spec, + high_spec=high_spec, + ) + + with pytest.raises(TypeError, match="int32 or int64"): + packed.reorder_batch(torch.tensor([0.0, 1.0])) + with pytest.raises(IndexError, match="out-of-range"): + packed.reorder_batch(torch.tensor([2], dtype=torch.long)) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is unavailable") +def test_to_cuda_preserves_storage_and_reorder_exactly() -> None: + state = torch.linspace(-2.0, 2.0, 48).reshape(3, 2, 8) + mask = (torch.arange(12) % 2 == 0).reshape(6, 2) + low_spec, high_spec = _specs(group_size=4, flatten_last_dims=1) + packed = quantize_pack_mixed( + state, + mask, + low_spec=low_spec, + high_spec=high_spec, + ) + + on_cuda = packed.to("cuda") + beam_idx = torch.tensor([2, 0, 2], dtype=torch.long, device="cuda") + reordered = on_cuda.reorder_batch(beam_idx) + + assert on_cuda.low_payload.device.type == "cuda" + assert on_cuda.high_payload.device.type == "cuda" + assert on_cuda.scales.device.type == "cuda" + assert on_cuda.precision_mask.device.type == "cuda" + assert on_cuda.storage_bytes == packed.storage_bytes + assert torch.equal(on_cuda.dequantize().cpu(), packed.dequantize()) + assert torch.equal( + reordered.dequantize().cpu(), + packed.dequantize().index_select(0, beam_idx.cpu()), + ) diff --git a/tests/test_model_fisher.py b/tests/test_model_fisher.py new file mode 100644 index 0000000..eba05d3 --- /dev/null +++ b/tests/test_model_fisher.py @@ -0,0 +1,217 @@ +from __future__ import annotations + +import pytest +import torch + +from recurquant.model_fisher import ( + row_block_model_fisher_risk, + sample_model_pseudo_labels, +) + + +def _manual_endpoint_risk( + gradients: torch.Tensor, + errors: torch.Tensor, +) -> torch.Tensor: + probes, batch_size, heads, key_dim, _ = gradients.shape + result = torch.zeros((heads, key_dim), dtype=gradients.dtype) + for probe in range(probes): + for batch in range(batch_size): + for head in range(heads): + for row in range(key_dim): + projection = torch.dot( + gradients[probe, batch, head, row], + errors[batch, head, row], + ) + result[head, row] += 0.5 * projection.square() + return result / (probes * batch_size) + + +def test_row_risk_matches_manual_probe_and_batch_average() -> None: + gradients = torch.tensor( + [ + [ + [[[1.0, 2.0], [-1.0, 0.5]]], + [[[0.0, 3.0], [2.0, -2.0]]], + ], + [ + [[[2.0, -1.0], [0.25, 4.0]]], + [[[-3.0, 1.0], [1.5, 0.5]]], + ], + ], + dtype=torch.float64, + ) + int4_errors = torch.tensor( + [ + [[[0.4, -0.2], [0.5, 0.25]]], + [[[0.1, 0.3], [-0.2, 0.6]]], + ], + dtype=torch.float64, + ) + int8_errors = torch.tensor( + [ + [[[0.05, -0.1], [0.2, 0.1]]], + [[[0.15, 0.05], [-0.1, 0.2]]], + ], + dtype=torch.float64, + ) + + result = row_block_model_fisher_risk(gradients, int4_errors, int8_errors) + expected4 = _manual_endpoint_risk(gradients, int4_errors) + expected8 = _manual_endpoint_risk(gradients, int8_errors) + + assert result.probes == 2 + assert result.batch_size == 2 + assert result.shape == torch.Size((1, 2)) + assert torch.allclose(result.int4_risk, expected4) + assert torch.allclose(result.int8_risk, expected8) + assert torch.allclose(result.risk_difference, expected4 - expected8) + + +def test_row_dot_is_squared_after_sum_and_negative_difference_is_preserved() -> None: + gradients = torch.tensor([[[[[1.0, 1.0]]]]]) + # Within-row cancellation makes the INT4 projection exactly zero. A + # coordinate-diagonal approximation would incorrectly assign positive risk. + int4_errors = torch.tensor([[[[1.0, -1.0]]]]) + int8_errors = torch.tensor([[[[1.0, 0.0]]]]) + + result = row_block_model_fisher_risk(gradients, int4_errors, int8_errors) + + assert result.int4_risk.item() == 0.0 + assert result.int8_risk.item() == pytest.approx(0.5) + assert result.risk_difference.item() == pytest.approx(-0.5) + + +def test_positive_difference_means_int8_has_lower_endpoint_risk() -> None: + gradients = torch.tensor([[[[[1.0, 0.0]]]]]) + int4_errors = torch.tensor([[[[2.0, 0.0]]]]) + int8_errors = torch.tensor([[[[1.0, 0.0]]]]) + + result = row_block_model_fisher_risk(gradients, int4_errors, int8_errors) + + assert result.int4_risk.item() == pytest.approx(2.0) + assert result.int8_risk.item() == pytest.approx(0.5) + assert result.risk_difference.item() == pytest.approx(1.5) + + +def test_scoring_is_deterministic_detached_and_does_not_mutate_inputs() -> None: + generator = torch.Generator().manual_seed(2339) + gradients = torch.randn((3, 2, 2, 4, 5), generator=generator, requires_grad=True) + int4_errors = torch.randn((2, 2, 4, 5), generator=generator, requires_grad=True) + int8_errors = torch.randn((2, 2, 4, 5), generator=generator, requires_grad=True) + before = tuple(tensor.detach().clone() for tensor in (gradients, int4_errors, int8_errors)) + + first = row_block_model_fisher_risk(gradients, int4_errors, int8_errors) + second = row_block_model_fisher_risk(gradients, int4_errors, int8_errors) + + assert torch.equal(first.int4_risk, second.int4_risk) + assert torch.equal(first.int8_risk, second.int8_risk) + assert torch.equal(first.risk_difference, second.risk_difference) + assert not first.int4_risk.requires_grad + assert not first.int8_risk.requires_grad + assert not first.risk_difference.requires_grad + assert all(tensor.grad is None for tensor in (gradients, int4_errors, int8_errors)) + assert all( + torch.equal(current.detach(), original) + for current, original in zip( + (gradients, int4_errors, int8_errors), + before, + strict=True, + ) + ) + + +def test_model_pseudo_label_sampling_is_explicit_and_reproducible() -> None: + logits = torch.tensor([[0.2, -0.4, 1.1], [1.3, 0.7, -0.2]]) + first_generator = torch.Generator().manual_seed(72) + second_generator = torch.Generator().manual_seed(72) + + first = sample_model_pseudo_labels( + logits, + probes=64, + generator=first_generator, + ) + second = sample_model_pseudo_labels( + logits, + probes=64, + generator=second_generator, + ) + + assert first.shape == (64, 2) + assert first.dtype == torch.int64 + assert torch.equal(first, second) + assert first.min().item() >= 0 + assert first.max().item() < logits.shape[1] + assert not first.requires_grad + + +def test_model_pseudo_label_sampling_uses_unmodified_softmax_distribution() -> None: + logits = torch.tensor([[-1000.0, 1000.0], [1000.0, -1000.0]]) + labels = sample_model_pseudo_labels( + logits, + probes=16, + generator=torch.Generator().manual_seed(9), + ) + + assert torch.equal(labels[:, 0], torch.ones(16, dtype=torch.int64)) + assert torch.equal(labels[:, 1], torch.zeros(16, dtype=torch.int64)) + + +def test_row_risk_rejects_invalid_shapes_dtypes_and_empty_dimensions() -> None: + gradients = torch.zeros((2, 1, 1, 2, 3)) + errors = torch.zeros((1, 1, 2, 3)) + + with pytest.raises(ValueError, match="pseudo_label_score_gradients"): + row_block_model_fisher_risk(gradients[0], errors, errors) + with pytest.raises(ValueError, match="int4_errors and int8_errors"): + row_block_model_fisher_risk(gradients, errors[0], errors) + with pytest.raises(ValueError, match="gradient dimensions"): + row_block_model_fisher_risk(gradients[:, :, :, :, :2], errors, errors) + with pytest.raises(ValueError, match="same shape"): + row_block_model_fisher_risk(gradients, errors, errors[..., :2]) + with pytest.raises(ValueError, match="non-empty"): + row_block_model_fisher_risk(gradients[:0], errors, errors) + with pytest.raises(TypeError, match="floating-point"): + row_block_model_fisher_risk(gradients.to(torch.int64), errors, errors) + with pytest.raises(TypeError, match="same dtype"): + row_block_model_fisher_risk(gradients, errors, errors.to(torch.float64)) + + +@pytest.mark.parametrize("bad_value", [float("nan"), float("inf")]) +def test_row_risk_rejects_nonfinite_inputs(bad_value: float) -> None: + gradients = torch.ones((1, 1, 1, 1, 2)) + errors = torch.ones((1, 1, 1, 2)) + gradients[0, 0, 0, 0, 0] = bad_value + + with pytest.raises(ValueError, match="finite"): + row_block_model_fisher_risk(gradients, errors, errors) + + +def test_row_risk_rejects_nonfinite_computation() -> None: + gradients = torch.full((1, 1, 1, 1, 2), 1e30) + errors = torch.full((1, 1, 1, 2), 1e30) + + with pytest.raises(RuntimeError, match="became non-finite"): + row_block_model_fisher_risk(gradients, errors, errors) + + +def test_pseudo_label_sampling_validation() -> None: + generator = torch.Generator().manual_seed(1) + logits = torch.ones((2, 3)) + + with pytest.raises(ValueError, match=r"\[batch, vocabulary\]"): + sample_model_pseudo_labels(logits[0], probes=2, generator=generator) + with pytest.raises(ValueError, match="at least one"): + sample_model_pseudo_labels(logits[:, :0], probes=2, generator=generator) + with pytest.raises(ValueError, match="positive integer"): + sample_model_pseudo_labels(logits, probes=0, generator=generator) + with pytest.raises(ValueError, match="positive integer"): + sample_model_pseudo_labels(logits, probes=True, generator=generator) + with pytest.raises(TypeError, match="torch.Generator"): + sample_model_pseudo_labels(logits, probes=2, generator=object()) # type: ignore[arg-type] + with pytest.raises(TypeError, match="floating-point"): + sample_model_pseudo_labels(logits.to(torch.int64), probes=2, generator=generator) + nonfinite = logits.clone() + nonfinite[0, 0] = torch.nan + with pytest.raises(ValueError, match="finite"): + sample_model_pseudo_labels(nonfinite, probes=2, generator=generator) diff --git a/tests/test_multibit_policy.py b/tests/test_multibit_policy.py new file mode 100644 index 0000000..af6a52a --- /dev/null +++ b/tests/test_multibit_policy.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +import itertools + +import pytest +import torch + +from recurquant.multibit_policy import ( + FROZEN_QWEN35_INT8_ROW_QUOTAS, + QWEN35_FROZEN_TOTAL_MARGINAL_STEPS, + allocate_exact_multibit_codes, + frozen_qwen35_multibit_step_budgets, +) + + +def _objective( + codes: torch.Tensor, + d4: torch.Tensor, + d6: torch.Tensor, + d8: torch.Tensor, +) -> float: + stacked = torch.stack((d4, d6, d8), dim=-1).reshape(-1, 3).to(torch.float64) + selected = stacked.gather(1, codes.reshape(-1, 1).to(torch.long)) + return float(selected.sum().item()) + + +def _brute_force_codes( + d4: torch.Tensor, + d6: torch.Tensor, + d8: torch.Tensor, + *, + marginal_steps: int, +) -> torch.Tensor: + shape = d4.shape + candidates: list[tuple[float, tuple[int, ...]]] = [] + for codes in itertools.product((0, 1, 2), repeat=d4.numel()): + if sum(codes) != marginal_steps: + continue + tensor = torch.tensor(codes, dtype=torch.uint8).reshape(shape) + candidates.append((_objective(tensor, d4, d6, d8), codes)) + _, best = min(candidates, key=lambda item: (item[0], tuple(-code for code in item[1]))) + return torch.tensor(best, dtype=torch.uint8).reshape(shape) + + +def test_exact_allocator_handles_increasing_second_marginal_globally() -> None: + # Row 0 gains only 1 unit at 4->6 but 100 at 6->8. An available-marginal + # greedy allocator takes row 1's 60-unit first step and misses the optimum. + d4 = torch.tensor([[101.0, 60.0]]) + d6 = torch.tensor([[100.0, 0.0]]) + d8 = torch.tensor([[0.0, 0.0]]) + + codes = allocate_exact_multibit_codes(d4, d6, d8, marginal_steps=2) + + assert torch.equal(codes, torch.tensor([[2, 0]], dtype=torch.uint8)) + assert codes.sum().item() == 2 + assert _objective(codes, d4, d6, d8) == 60.0 + + +@pytest.mark.parametrize("marginal_steps", range(9)) +def test_exact_allocator_matches_exhaustive_search(marginal_steps: int) -> None: + d4 = torch.tensor([[8.0, 2.0], [5.0, 9.0]], dtype=torch.float64) + d6 = torch.tensor([[7.0, 3.0], [1.0, 4.0]], dtype=torch.float64) + d8 = torch.tensor([[0.0, 1.0], [2.0, 6.0]], dtype=torch.float64) + + actual = allocate_exact_multibit_codes( + d4, + d6, + d8, + marginal_steps=marginal_steps, + ) + expected = _brute_force_codes( + d4, + d6, + d8, + marginal_steps=marginal_steps, + ) + + assert torch.equal(actual, expected) + assert actual.dtype == torch.uint8 + assert actual.device.type == "cpu" + assert tuple(actual.shape) == (2, 2) + assert actual.sum().item() == marginal_steps + + +def test_exact_ties_give_higher_codes_to_earlier_flattened_rows() -> None: + equal = torch.ones((2, 2)) + + codes = allocate_exact_multibit_codes( + equal, + equal, + equal, + marginal_steps=3, + ) + + assert torch.equal(codes, torch.tensor([[2, 1], [0, 0]], dtype=torch.uint8)) + + +def test_endpoint_budgets_return_all_int4_or_all_int8() -> None: + d4 = torch.ones((2, 3)) + d6 = torch.ones((2, 3)) + d8 = torch.ones((2, 3)) + + low = allocate_exact_multibit_codes(d4, d6, d8, marginal_steps=0) + high = allocate_exact_multibit_codes(d4, d6, d8, marginal_steps=12) + + assert torch.equal(low, torch.zeros((2, 3), dtype=torch.uint8)) + assert torch.equal(high, torch.full((2, 3), 2, dtype=torch.uint8)) + + +@pytest.mark.parametrize( + ("values", "match"), + [ + ((torch.tensor([[float("nan")]]), torch.ones((1, 1)), torch.ones((1, 1))), "finite"), + ((-torch.ones((1, 1)), torch.ones((1, 1)), torch.ones((1, 1))), "nonnegative"), + ((torch.ones(1), torch.ones(1), torch.ones(1)), r"shape \[heads, rows\]"), + ( + (torch.ones((1, 2)), torch.ones((2, 1)), torch.ones((1, 2))), + "identical shapes", + ), + ], +) +def test_invalid_distortion_values_and_shapes_are_rejected( + values: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + match: str, +) -> None: + with pytest.raises(ValueError, match=match): + allocate_exact_multibit_codes(*values, marginal_steps=0) + + +def test_integer_distortions_and_invalid_step_budgets_are_rejected() -> None: + integer = torch.ones((1, 2), dtype=torch.int64) + floating = torch.ones((1, 2)) + + with pytest.raises(TypeError, match="floating-point"): + allocate_exact_multibit_codes(integer, floating, floating, marginal_steps=1) + with pytest.raises(TypeError, match="integer"): + allocate_exact_multibit_codes(floating, floating, floating, marginal_steps=True) + with pytest.raises(ValueError, match="representable range"): + allocate_exact_multibit_codes(floating, floating, floating, marginal_steps=5) + + +def test_frozen_qwen35_step_budgets_match_old_bytes_exactly() -> None: + budgets = frozen_qwen35_multibit_step_budgets() + + assert budgets == { + 0: 702, + 1: 752, + 2: 530, + 4: 350, + 5: 362, + 6: 202, + 8: 152, + 9: 78, + 10: 160, + 12: 52, + 13: 116, + 14: 100, + 16: 82, + 17: 46, + 18: 6, + 20: 10, + 21: 6, + 22: 102, + } + assert sum(FROZEN_QWEN35_INT8_ROW_QUOTAS.values()) == 1_976 + assert sum(budgets.values()) == QWEN35_FROZEN_TOTAL_MARGINAL_STEPS == 3_808 + + rows = 16 * 128 + old_base_per_layer = rows * (64 + 2) + 256 + multibit_base_per_layer = rows * (64 + 2) + 512 + for layer_index, old_quota in FROZEN_QWEN35_INT8_ROW_QUOTAS.items(): + assert old_base_per_layer + 64 * old_quota == ( + multibit_base_per_layer + 32 * budgets[layer_index] + ) diff --git a/tests/test_multibit_quantization.py b/tests/test_multibit_quantization.py new file mode 100644 index 0000000..9d579a7 --- /dev/null +++ b/tests/test_multibit_quantization.py @@ -0,0 +1,707 @@ +from __future__ import annotations + +import math +from dataclasses import replace + +import pytest +import torch + +from recurquant.multibit_quantization import ( + INT6_PRECISION_CODE, + _pack_int6_groups, + _pack_precision_codes, + _unpack_int6_groups, + _unpack_precision_codes, + quantize_pack_multibit, +) +from recurquant.quantization import QuantizationSpec + + +def _specs( + *, + group_size: int = 128, + scale_bits: int = 16, + flatten_last_dims: int = 2, + rounding: str = "nearest", + seed: int = 2339, +) -> tuple[QuantizationSpec, QuantizationSpec, QuantizationSpec]: + common = { + "group_size": group_size, + "scale_bits": scale_bits, + "flatten_last_dims": flatten_last_dims, + "rounding": rounding, + "seed": seed, + } + return ( + QuantizationSpec(bits=4, **common), + QuantizationSpec(bits=6, **common), + QuantizationSpec(bits=8, **common), + ) + + +def _independent_nearest_reference( + tensor: torch.Tensor, + precision_codes: torch.Tensor, + spec: QuantizationSpec, +) -> torch.Tensor: + working = tensor.detach().to(torch.float32) + flattened_size = math.prod(working.shape[-spec.flatten_last_dims :]) + rows = working.reshape(-1, flattened_size) + groups_per_row = math.ceil(flattened_size / spec.group_size) + padded_size = groups_per_row * spec.group_size + if padded_size != flattened_size: + rows = torch.nn.functional.pad(rows, (0, padded_size - flattened_size)) + groups = rows.reshape(-1, spec.group_size) + + qmax = torch.tensor((7.0, 31.0, 127.0))[precision_codes.reshape(-1).long()] + absmax = groups.abs().amax(dim=1) + ideal_scales = torch.where( + absmax > spec.epsilon, + absmax / qmax, + torch.ones_like(absmax), + ) + scale_dtype = torch.float16 if spec.scale_bits == 16 else torch.float32 + if scale_dtype == torch.float16: + ideal_scales = ideal_scales.clamp( + min=2.0**-24, + max=torch.finfo(torch.float16).max, + ) + scales = ideal_scales.to(scale_dtype).to(torch.float32) + integer_codes = torch.round(groups / scales.unsqueeze(1)) + integer_codes = torch.minimum( + torch.maximum(integer_codes, -qmax.unsqueeze(1)), + qmax.unsqueeze(1), + ) + restored = (integer_codes * scales.unsqueeze(1)).reshape(-1, padded_size) + restored = restored[:, :flattened_size] + return restored.reshape(tensor.shape).to(tensor.dtype) + + +def _packed_validation_fixture(): + state = torch.linspace(-1.0, 1.0, 12).reshape(1, 12) + precision = torch.tensor([[0, 1, 2]], dtype=torch.uint8) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + return quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + +def test_int6_exhaustive_symmetric_code_roundtrip() -> None: + values = torch.arange(-31, 32, dtype=torch.int16) + codes = values.unsqueeze(1).repeat(1, 4) + + payload = _pack_int6_groups(codes) + restored = _unpack_int6_groups(payload, group_size=4) + + assert payload.shape == (63, 3) + assert payload.dtype == torch.uint8 + assert torch.equal(restored, codes) + + +def test_int6_uses_little_endian_four_code_three_byte_layout() -> None: + codes = torch.tensor( + [ + [0, 1, 2, 3], + [-1, -2, -3, -31], + ], + dtype=torch.int16, + ) + unsigned = torch.bitwise_and(codes.to(torch.int64), 0x3F) + words = ( + unsigned[:, 0] + | (unsigned[:, 1] << 6) + | (unsigned[:, 2] << 12) + | (unsigned[:, 3] << 18) + ) + independent_payload = torch.stack( + ( + torch.bitwise_and(words, 0xFF), + torch.bitwise_and(words >> 8, 0xFF), + torch.bitwise_and(words >> 16, 0xFF), + ), + dim=1, + ).to(torch.uint8) + + assert torch.equal(_pack_int6_groups(codes), independent_payload) + + +def test_group_size_128_int6_payload_is_exactly_96_bytes() -> None: + codes = torch.arange(128, dtype=torch.int16).remainder(63) - 31 + + payload = _pack_int6_groups(codes.reshape(1, 128)) + + assert payload.shape == (1, 96) + assert payload.numel() * payload.element_size() == 96 + assert torch.equal(_unpack_int6_groups(payload, 128), codes.reshape(1, 128)) + + +def test_precision_codes_have_canonical_two_bit_encoding() -> None: + precision = torch.tensor([0, 1, 2, 0, 2, 1, 0, 2], dtype=torch.uint8) + + first = _pack_precision_codes(precision) + second = _pack_precision_codes(precision.clone()) + + assert first.tolist() == [0x24, 0x86] + assert torch.equal(first, second) + assert torch.equal(_unpack_precision_codes(first, 8), precision) + + +def test_mixed_qdq_matches_independent_reference_exactly() -> None: + state = torch.tensor( + [ + [ + [0.0, -0.23, 0.91, -1.7, 3.2], + [0.07, -0.51, 0.34, 1.2, -2.8], + [0.03, -0.11, 0.57, -0.8, 2.1], + ], + [ + [-0.4, 0.0, 0.2, 0.8, -1.4], + [2.7, -1.3, 0.33, -0.09, 0.72], + [-3.1, 1.8, -0.63, 0.14, 0.01], + ], + ], + dtype=torch.float32, + ) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + precision = torch.tensor( + [ + [0, 1], + [2, 0], + [1, 2], + [0, 2], + [1, 0], + [2, 1], + ], + dtype=torch.uint8, + ) + + packed = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + reference = _independent_nearest_reference(state, precision, int4_spec) + + assert torch.equal(packed.dequantize(), reference) + assert torch.equal(packed.precision_codes(), precision) + assert packed.int4_payload.dtype == torch.uint8 + assert packed.int6_payload.dtype == torch.uint8 + assert packed.int8_payload.dtype == torch.int8 + assert packed.scales.dtype == torch.float16 + assert (packed.int4_groups, packed.int6_groups, packed.int8_groups) == (4, 4, 4) + + +def test_qwen_layout_hits_old_target_with_corrected_two_bit_metadata() -> None: + total_groups = 18 * 16 * 128 + int6_groups = 3_808 + state = torch.zeros((1, 18 * 16, 128, 128), dtype=torch.float32) + precision = torch.zeros(total_groups, dtype=torch.uint8) + precision[:int6_groups] = INT6_PRECISION_CODE + int4_spec, int6_spec, int8_spec = _specs(group_size=128) + + packed = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + assert packed.total_groups == 36_864 + assert packed.int4_groups == 33_056 + assert packed.int6_groups == 3_808 + assert packed.int8_groups == 0 + assert packed.payload_bytes == 2_481_152 + assert packed.scale_bytes == 73_728 + assert packed.precision_code_bytes == 9_216 + assert packed.storage_bytes == 2_564_096 + + +def test_exact_storage_with_all_three_widths_and_fp32_scales() -> None: + state = torch.linspace(-2.0, 2.0, 48) + precision = torch.tensor( + [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], + dtype=torch.uint8, + ) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + scale_bits=32, + flatten_last_dims=1, + ) + + packed = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + assert packed.int4_payload.shape == (4, 2) + assert packed.int6_payload.shape == (4, 3) + assert packed.int8_payload.shape == (4, 4) + assert packed.payload_bytes == 36 + assert packed.scale_bytes == 48 + assert packed.precision_code_bytes == 3 + assert packed.storage_bytes == 87 + assert packed.scales.dtype == torch.float32 + + +def test_stochastic_quantization_is_seeded_and_deterministic() -> None: + state = torch.linspace(-1.13, 0.91, 400) + precision = torch.arange(100, dtype=torch.uint8).remainder(3) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + rounding="stochastic", + seed=71, + ) + + first = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + second = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + different_seed = quantize_pack_multibit( + state, + precision, + int4_spec=replace(int4_spec, seed=72), + int6_spec=replace(int6_spec, seed=72), + int8_spec=replace(int8_spec, seed=72), + ) + + assert torch.equal(first.int4_payload, second.int4_payload) + assert torch.equal(first.int6_payload, second.int6_payload) + assert torch.equal(first.int8_payload, second.int8_payload) + assert torch.equal(first.scales, second.scales) + assert torch.equal(first.packed_precision_codes, second.packed_precision_codes) + assert not torch.equal(first.dequantize(), different_seed.dequantize()) + + +def test_batch_reorder_preserves_integer_codes_scales_and_duplicates() -> None: + generator = torch.Generator().manual_seed(103) + state = torch.randn((3, 2, 3, 5), generator=generator).to(torch.bfloat16) + precision = torch.arange(24, dtype=torch.uint8).remainder(3).reshape(6, 4) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=2, + ) + packed = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + before = packed.dequantize() + before_codes = packed._integer_groups().reshape(3, 8, 4) + before_precision = packed.precision_codes().reshape(3, 8) + beam_idx = torch.tensor([2, 2, 0], dtype=torch.long) + + reordered = packed.reorder_batch(beam_idx) + + assert torch.equal(reordered.dequantize(), before.index_select(0, beam_idx)) + assert torch.equal( + reordered._integer_groups().reshape(3, 8, 4), + before_codes.index_select(0, beam_idx), + ) + assert torch.equal( + reordered.precision_codes().reshape(3, 8), + before_precision.index_select(0, beam_idx), + ) + assert reordered.original_shape == state.shape + + +def test_to_cpu_preserves_every_resident_tensor_and_storage() -> None: + state = torch.linspace(-2.0, 2.0, 48).reshape(3, 2, 8) + precision = torch.arange(12, dtype=torch.uint8).remainder(3).reshape(6, 2) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + packed = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + transferred = packed.to(torch.device("cpu")) + + assert transferred.int4_payload.device.type == "cpu" + assert transferred.int6_payload.device.type == "cpu" + assert transferred.int8_payload.device.type == "cpu" + assert transferred.scales.device.type == "cpu" + assert transferred.packed_precision_codes.device.type == "cpu" + assert transferred.storage_bytes == packed.storage_bytes + assert torch.equal(transferred.dequantize(), packed.dequantize()) + + +def test_empty_batch_reorder_remains_a_valid_packed_object() -> None: + packed = _packed_validation_fixture() + + reordered = packed.reorder_batch(torch.empty(0, dtype=torch.long)) + + assert reordered.original_shape == (0, 12) + assert reordered.rows == 0 + assert reordered.total_groups == 0 + assert reordered.storage_bytes == 0 + assert reordered.dequantize().shape == (0, 12) + + +@pytest.mark.parametrize( + ("changes", "message"), + [ + ({"flattened_size": 13}, "flattened_size"), + ({"padded_size": 16}, "padded_size"), + ({"rows": 2}, "rows"), + ({"groups_per_row": 4}, "groups_per_row"), + ({"original_shape": (1, 16)}, "flattened_size"), + ({"original_shape": [1, 12]}, "original_shape must be a tuple"), + ({"original_shape": (1, 0)}, "flattened.*must be positive"), + ({"original_dtype": torch.int32}, "floating-point dtype"), + ], +) +def test_direct_construction_rejects_inconsistent_metadata( + changes: dict[str, object], + message: str, +) -> None: + packed = _packed_validation_fixture() + + with pytest.raises((TypeError, ValueError), match=message): + replace(packed, **changes) + + +def test_direct_construction_rejects_invalid_specs() -> None: + packed = _packed_validation_fixture() + + with pytest.raises(TypeError, match="int4_spec.*QuantizationSpec"): + replace(packed, int4_spec=object()) + with pytest.raises(ValueError, match="mismatched fields: group_size"): + replace(packed, int6_spec=replace(packed.int6_spec, group_size=8)) + + +def test_direct_construction_rejects_invalid_scales() -> None: + packed = _packed_validation_fixture() + + with pytest.raises(TypeError, match="one-dimensional torch.float16"): + replace(packed, scales=packed.scales.to(torch.float32)) + with pytest.raises(ValueError, match="one value per group"): + replace(packed, scales=packed.scales[:-1].clone()) + + nonfinite = packed.scales.clone() + nonfinite[0] = float("nan") + with pytest.raises(ValueError, match="only finite"): + replace(packed, scales=nonfinite) + + nonpositive = packed.scales.clone() + nonpositive[0] = 0 + with pytest.raises(ValueError, match="strictly positive"): + replace(packed, scales=nonpositive) + + backing = torch.empty( + packed.scales.numel() * 2, + dtype=packed.scales.dtype, + ) + noncontiguous = backing[::2] + noncontiguous.copy_(packed.scales) + with pytest.raises(ValueError, match="scales must be contiguous"): + replace(packed, scales=noncontiguous) + + +def test_direct_construction_rejects_invalid_precision_stream() -> None: + packed = _packed_validation_fixture() + + with pytest.raises(TypeError, match="packed precision codes.*torch.uint8"): + replace( + packed, + packed_precision_codes=packed.packed_precision_codes.to(torch.int16), + ) + with pytest.raises(ValueError, match="must contain 1 bytes"): + replace( + packed, + packed_precision_codes=packed.packed_precision_codes[:0].clone(), + ) + + reserved = packed.packed_precision_codes.clone() + reserved[0] = torch.bitwise_or( + torch.bitwise_and(reserved[0], 0xFC), + torch.tensor(3, dtype=torch.uint8), + ) + with pytest.raises(ValueError, match="reserved precision code 3"): + replace(packed, packed_precision_codes=reserved) + + noncanonical_padding = packed.packed_precision_codes.clone() + noncanonical_padding[0] = torch.bitwise_or( + noncanonical_padding[0], + torch.tensor(0x40, dtype=torch.uint8), + ) + with pytest.raises(ValueError, match="padding bits must be zero"): + replace(packed, packed_precision_codes=noncanonical_padding) + + +@pytest.mark.parametrize( + ("field", "dtype", "message"), + [ + ("int4_payload", torch.int8, "int4_payload.*torch.uint8"), + ("int6_payload", torch.int8, "int6_payload.*torch.uint8"), + ("int8_payload", torch.uint8, "int8_payload.*torch.int8"), + ], +) +def test_direct_construction_rejects_payload_dtypes( + field: str, + dtype: torch.dtype, + message: str, +) -> None: + packed = _packed_validation_fixture() + + with pytest.raises(TypeError, match=message): + replace(packed, **{field: getattr(packed, field).to(dtype)}) + + +@pytest.mark.parametrize( + ("field", "message"), + [ + ("int4_payload", "int4_payload must have shape"), + ("int6_payload", "int6_payload must have shape"), + ("int8_payload", "int8_payload must have shape"), + ], +) +def test_direct_construction_rejects_payload_counts( + field: str, + message: str, +) -> None: + packed = _packed_validation_fixture() + payload = getattr(packed, field) + + with pytest.raises(ValueError, match=message): + replace(packed, **{field: payload[:0].clone()}) + + +def test_direct_construction_rejects_payload_row_widths_and_devices() -> None: + packed = _packed_validation_fixture() + + with pytest.raises(ValueError, match="int6_payload must have shape"): + replace(packed, int6_payload=packed.int6_payload[:, :-1].clone()) + with pytest.raises(ValueError, match="all resident tensors must share one device"): + replace(packed, int8_payload=packed.int8_payload.to("meta")) + + +def test_direct_construction_rejects_reserved_symmetric_payload_codes() -> None: + packed = _packed_validation_fixture() + + invalid_int4 = packed.int4_payload.clone() + invalid_int4[0, 0] = int(invalid_int4[0, 0].item()) & 0xF0 | 0x08 + with pytest.raises(ValueError, match="int4_payload.*-8"): + replace(packed, int4_payload=invalid_int4) + + invalid_int6 = packed.int6_payload.clone() + invalid_int6[0, 0] = int(invalid_int6[0, 0].item()) & 0xC0 | 0x20 + with pytest.raises(ValueError, match="INT6 payload.*-32"): + replace(packed, int6_payload=invalid_int6) + + invalid_int8 = packed.int8_payload.clone() + invalid_int8[0, 0] = -128 + with pytest.raises(ValueError, match="int8_payload.*-128"): + replace(packed, int8_payload=invalid_int8) + + +@pytest.mark.parametrize( + ("spec_index", "replacement", "message"), + [ + (0, QuantizationSpec(bits=5), "int4_spec"), + (1, QuantizationSpec(bits=5), "int6_spec"), + (2, QuantizationSpec(bits=7), "int8_spec"), + (1, QuantizationSpec(bits=6, group_size=64), "group_size"), + (2, QuantizationSpec(bits=8, scale_bits=32), "scale_bits"), + ], +) +def test_invalid_specs_are_rejected( + spec_index: int, + replacement: QuantizationSpec, + message: str, +) -> None: + specs = list(_specs()) + specs[spec_index] = replacement + + with pytest.raises(ValueError, match=message): + quantize_pack_multibit( + torch.ones(128), + torch.zeros(1, dtype=torch.uint8), + int4_spec=specs[0], + int6_spec=specs[1], + int8_spec=specs[2], + ) + + +def test_group_size_must_make_every_width_byte_representable() -> None: + int4_spec, int6_spec, int8_spec = _specs( + group_size=2, + flatten_last_dims=1, + ) + + with pytest.raises(ValueError, match="whole-byte payloads.*INT6"): + quantize_pack_multibit( + torch.ones(4), + torch.zeros(2, dtype=torch.uint8), + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + +def test_invalid_precision_codes_are_rejected() -> None: + state = torch.ones((2, 8)) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + + with pytest.raises(TypeError, match="torch.uint8"): + quantize_pack_multibit( + state, + torch.zeros((2, 2), dtype=torch.int64), + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + with pytest.raises(ValueError, match="0=INT4"): + quantize_pack_multibit( + state, + torch.tensor([[0, 3], [1, 2]], dtype=torch.uint8), + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + with pytest.raises(ValueError, match="must have shape"): + quantize_pack_multibit( + state, + torch.zeros((1, 4), dtype=torch.uint8), + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + with pytest.raises(ValueError, match="reserved precision code 3"): + _unpack_precision_codes(torch.tensor([0b00000011], dtype=torch.uint8), 1) + + +def test_nonfloating_nonfinite_and_empty_sources_are_rejected() -> None: + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + kwargs = { + "int4_spec": int4_spec, + "int6_spec": int6_spec, + "int8_spec": int8_spec, + } + + with pytest.raises(TypeError, match="floating-point"): + quantize_pack_multibit( + torch.ones(4, dtype=torch.int32), + torch.zeros(1, dtype=torch.uint8), + **kwargs, + ) + with pytest.raises(ValueError, match="finite"): + quantize_pack_multibit( + torch.tensor([1.0, float("nan"), 2.0, 3.0]), + torch.zeros(1, dtype=torch.uint8), + **kwargs, + ) + with pytest.raises(ValueError, match="must not be empty"): + quantize_pack_multibit( + torch.empty(0), + torch.empty(0, dtype=torch.uint8), + **kwargs, + ) + + +def test_invalid_int6_codes_and_payload_shapes_are_rejected() -> None: + with pytest.raises(ValueError, match=r"\[-31, 31\]"): + _pack_int6_groups(torch.tensor([[-32, 0, 0, 0]], dtype=torch.int16)) + with pytest.raises(ValueError, match="whole number of bytes"): + _pack_int6_groups(torch.zeros((1, 2), dtype=torch.int16)) + with pytest.raises(TypeError, match="two-dimensional"): + _unpack_int6_groups(torch.zeros(3, dtype=torch.uint8), 4) + with pytest.raises(ValueError, match="inconsistent"): + _unpack_int6_groups(torch.zeros((1, 4), dtype=torch.uint8), 4) + + +def test_invalid_reorder_indices_are_rejected() -> None: + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + packed = quantize_pack_multibit( + torch.ones((2, 8)), + torch.zeros((2, 2), dtype=torch.uint8), + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + with pytest.raises(TypeError, match="int32 or int64"): + packed.reorder_batch(torch.tensor([0.0, 1.0])) + with pytest.raises(IndexError, match="out-of-range"): + packed.reorder_batch(torch.tensor([2], dtype=torch.long)) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is unavailable") +def test_to_cuda_preserves_storage_dequantization_and_reorder() -> None: + state = torch.linspace(-2.0, 2.0, 48).reshape(3, 2, 8) + precision = torch.arange(12, dtype=torch.uint8).remainder(3).reshape(6, 2) + int4_spec, int6_spec, int8_spec = _specs( + group_size=4, + flatten_last_dims=1, + ) + packed = quantize_pack_multibit( + state, + precision, + int4_spec=int4_spec, + int6_spec=int6_spec, + int8_spec=int8_spec, + ) + + on_cuda = packed.to("cuda") + beam_idx = torch.tensor([2, 0, 2], dtype=torch.long, device="cuda") + reordered = on_cuda.reorder_batch(beam_idx) + + assert on_cuda.storage_bytes == packed.storage_bytes + assert torch.equal(on_cuda.dequantize().cpu(), packed.dequantize()) + assert torch.equal( + reordered.dequantize().cpu(), + packed.dequantize().index_select(0, beam_idx.cpu()), + ) + + +def test_public_package_exports_multibit_primitives() -> None: + import recurquant + + assert recurquant.quantize_pack_multibit is quantize_pack_multibit + assert recurquant.INT4_PRECISION_CODE == 0 + assert recurquant.INT6_PRECISION_CODE == 1 + assert recurquant.INT8_PRECISION_CODE == 2 + assert callable(recurquant.allocate_exact_multibit_codes) + assert callable(recurquant.frozen_qwen35_multibit_step_budgets) diff --git a/tests/test_pilot_evaluate_hrr.py b/tests/test_pilot_evaluate_hrr.py new file mode 100644 index 0000000..7c1e7e5 --- /dev/null +++ b/tests/test_pilot_evaluate_hrr.py @@ -0,0 +1,1742 @@ +from __future__ import annotations + +from copy import deepcopy + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from scripts import pilot_evaluate_hrr as evaluator +from scripts import pilot_hrr_rows, pilot_loss_sensitivity_rows +from scripts import pilot_validate_storage_boundary as storage_validator +from tests.test_transformers_cache import tiny_config + + +def _quantizers() -> dict[str, object]: + common = { + "group_size": 128, + "scale_bits": 16, + "flatten_last_dims": 1, + "rounding": "nearest", + "seed": 2339, + "epsilon": 1e-12, + } + return { + "axis_contract": "one independent group per recurrent [head, key-row]", + "int4": {"bits": 4, **common}, + "int8": {"bits": 8, **common}, + } + + +def _selector(kind: str) -> dict[str, object]: + manifest = { + "schema": "recurquant.mbpp-manifest.v1", + "dataset_id": "google-research-datasets/mbpp", + "config": "full", + "revision": "dataset-revision", + "phase": "calibration", + "source_split": "train", + "selection_namespace": "rq-v0.2", + "formatter_version": "recurquant.mbpp-prompt-code.v1", + "row_count": 2, + "rows": [ + {"task_id": 10, "sha256": "row-10"}, + {"task_id": 20, "sha256": "row-20"}, + ], + } + affected_field = ( + "captured_decode_tokens" if kind == evaluator.HRR_ARTIFACT_KIND else "scored_transitions" + ) + method: dict[str, object] = {} + if kind == evaluator.HRR_ARTIFACT_KIND: + method["normalization_epsilon"] = 1e-6 + return { + "artifact_kind": kind, + "seed": 2339, + "model": { + "id": "model", + "revision": "model-revision", + "dtype": "torch.bfloat16", + "linear_attention_layers": [0, 2], + }, + "dataset": { + "manifest": manifest, + "manifest_sha256": evaluator.mbpp_manifest_content_sha256(manifest), + "tasks": [ + { + "task_id": 20, + "prompt_tokens": 7, + "code_tokens": 4, + affected_field: 3, + }, + { + "task_id": 10, + "prompt_tokens": 9, + "code_tokens": 3, + affected_field: 2, + }, + ], + }, + "method": method, + "quantizers": _quantizers(), + "byte_budget": { + "target_resident_bytes": 1000, + "group_size": 128, + "scale_bits": 16, + "precision_mask_bits_per_group": 1, + }, + } + + +@pytest.mark.parametrize( + "git_state", + ( + pilot_hrr_rows.git_state, + pilot_loss_sensitivity_rows.git_state, + storage_validator._git_state, + ), +) +def test_artifact_generators_emit_evaluator_repository_contract(git_state) -> None: + repository = git_state() + + assert {"commit", "worktree_clean", "status"} <= repository.keys() + assert repository["worktree_clean"] is (repository["status"] == []) + + +@pytest.mark.parametrize( + ("generator", "kind", "affected_field"), + ( + (pilot_hrr_rows, evaluator.HRR_ARTIFACT_KIND, "captured_decode_tokens"), + ( + pilot_loss_sensitivity_rows, + evaluator.LOSS_ARTIFACT_KIND, + "scored_transitions", + ), + ), +) +def test_generator_manifest_hash_contract_is_accepted_and_content_authenticated( + generator, + kind: str, + affected_field: str, +) -> None: + rows = ( + { + "task_id": 10, + "text": "Return ten.", + "code": "def answer():\n return 10", + "test_list": ["assert answer() == 10"], + "test_setup_code": "", + "challenge_test_list": [], + }, + { + "task_id": 20, + "text": "Return twenty.", + "code": "def answer():\n return 20", + "test_list": ["assert answer() == 20"], + "test_setup_code": "", + "challenge_test_list": [], + }, + ) + manifest = generator.mbpp_manifest(rows, phase="calibration") + recorded_hash = generator.mbpp_manifest_sha256(rows, phase="calibration") + selector = _selector(kind) + selector["dataset"] = { + "manifest": manifest, + "manifest_sha256": recorded_hash, + "tasks": [ + { + "task_id": row["task_id"], + "prompt_tokens": 8, + "code_tokens": 4, + affected_field: 3, + } + for row in rows + ], + } + + evaluator.validate_selector_contract(selector) + assert recorded_hash == evaluator.mbpp_manifest_content_sha256(manifest) + + selector["dataset"]["manifest"]["rows"][0]["sha256"] = "tampered" + with pytest.raises(ValueError, match="manifest hash"): + evaluator.validate_selector_contract(selector) + + +def _frozen_selector(kind: str, *, commit: str = "frozen-commit") -> dict[str, object]: + selector = _selector(kind) + affected_field = ( + "captured_decode_tokens" if kind == evaluator.HRR_ARTIFACT_KIND else "scored_transitions" + ) + ordered_ids = list(range(100, 108)) + tasks = [ + { + "task_id": task_id, + "prompt_tokens": 10 + index, + "code_tokens": 4 + index, + affected_field: 3 + index, + } + for index, task_id in enumerate(ordered_ids) + ] + manifest = { + **selector["dataset"]["manifest"], + "row_count": 8, + "rows": [ + {"task_id": task_id, "sha256": f"row-{task_id}"} for task_id in sorted(ordered_ids) + ], + } + selector["dataset"] = { + "manifest": manifest, + "manifest_sha256": evaluator.mbpp_manifest_content_sha256(manifest), + "tasks": tasks, + } + selector["repository"] = { + "commit": commit, + "worktree_clean": True, + "status": [], + } + if kind == evaluator.HRR_ARTIFACT_KIND: + selector["method"]["horizon"] = 32 + return selector + + +def _passing_gate_inputs() -> dict[str, object]: + primary = evaluator.ADAPTIVE_TARGET_FISHER + adaptive_h1 = evaluator.ADAPTIVE_H1 + aggregates = { + primary: { + "macro_delta_nll": 0.60, + "macro_top1_agreement": 0.900, + "macro_cvar95_kl": 0.20, + }, + adaptive_h1: { + "macro_delta_nll": 0.90, + "macro_top1_agreement": 0.895, + "macro_cvar95_kl": 0.22, + }, + "hrr_h1": { + "macro_delta_nll": 1.00, + "macro_top1_agreement": 0.905, + "macro_cvar95_kl": 0.15, + }, + "row_mse": { + "macro_delta_nll": 1.20, + "macro_top1_agreement": 0.89, + "macro_cvar95_kl": 0.30, + }, + "uniform_int4": { + "macro_delta_nll": 2.00, + "macro_top1_agreement": 0.80, + "macro_cvar95_kl": 0.50, + }, + } + per_task = { + primary: [ + {"task_id": 1, "delta_nll": 0.4}, + {"task_id": 2, "delta_nll": 0.8}, + ], + adaptive_h1: [ + {"task_id": 1, "delta_nll": 0.8}, + {"task_id": 2, "delta_nll": 1.0}, + ], + "hrr_h1": [ + {"task_id": 1, "delta_nll": 0.5}, + {"task_id": 2, "delta_nll": 1.5}, + ], + } + contrasts = { + adaptive_h1: {"confidence_interval": [0.05, 0.50]}, + "hrr_h1": {"confidence_interval": [0.10, 0.70]}, + } + storage = { + primary: {"resident_bytes": evaluator.TARGET_RESIDENT_BYTES}, + adaptive_h1: {"resident_bytes": evaluator.TARGET_RESIDENT_BYTES}, + "hrr_h1": {"resident_bytes": evaluator.TARGET_RESIDENT_BYTES}, + "row_mse": {"resident_bytes": evaluator.TARGET_RESIDENT_BYTES}, + "uniform_int4": {"resident_bytes": evaluator.TARGET_RESIDENT_BYTES - 1}, + } + for index, name in enumerate(evaluator.FROZEN_STATIC_COMPARATORS, start=1): + aggregates.setdefault( + name, + { + "macro_delta_nll": 1.20 + index / 100, + "macro_top1_agreement": 0.89, + "macro_cvar95_kl": 0.30, + }, + ) + storage.setdefault(name, {"resident_bytes": evaluator.TARGET_RESIDENT_BYTES}) + return { + "aggregates": aggregates, + "per_task": per_task, + "contrasts": contrasts, + "storage": storage, + "primary_name": primary, + } + + +def _storage_boundary_evidence() -> dict[str, object]: + repository = {"commit": "frozen-commit", "worktree_clean": True, "status": []} + source_hashes = {"scripts/pilot_validate_storage_boundary.py": "abc123"} + return { + "artifact_kind": evaluator.STORAGE_BOUNDARY_ARTIFACT_KIND, + "model": { + "id": "model", + "revision": "model-revision", + "dtype": "torch.float32", + }, + "derivative_gate": { + "passed": True, + "failures": [], + "thresholds": { + "model_dtype": "torch.float32", + "baseline_repeat_absolute_tolerance": 1e-7, + "derivative_informative_floor": 1e-8, + "near_zero_absolute_tolerance": 2e-7, + "minimum_informative_rows": 3, + "minimum_sign_agreement": 0.95, + "maximum_median_relative_error": 0.10, + "minimum_converged_row_fraction": 0.75, + }, + "observed": { + "rows": 4, + "informative_rows": 4, + "maximum_baseline_repeat_absolute_error": 1e-8, + "sign_agreement": 1.0, + "median_relative_error": 0.05, + "converged_row_fraction": 1.0, + "near_zero_checks": 0, + "near_zero_checks_passed": 0, + }, + }, + "implementation": { + "source_hashes_start": source_hashes, + "source_hashes_end": source_hashes, + "unchanged_during_run": True, + }, + "repository": {"start": repository, "end": repository}, + } + + +def test_scores_from_artifact_verifies_hash_and_preserves_float64() -> None: + arrays = {"0": [[1.0000000000000002, -2.5]]} + evidence = { + "scores": { + "candidate": { + "arrays": arrays, + "canonical_arrays_sha256": evaluator.sha256_bytes( + evaluator.canonical_json_bytes(arrays) + ), + } + } + } + + scores = evaluator.scores_from_artifact(evidence, "candidate") + + assert scores[0].dtype == torch.float64 + assert scores[0][0, 0].item() == 1.0000000000000002 + + evidence["scores"]["candidate"]["arrays"]["0"][0][0] = 9.0 + with pytest.raises(ValueError, match="array hash"): + evaluator.scores_from_artifact(evidence, "candidate") + + +def test_compatible_selectors_compare_full_evidence_contract() -> None: + hrr = _selector(evaluator.HRR_ARTIFACT_KIND) + loss = _selector(evaluator.LOSS_ARTIFACT_KIND) + + evaluator.validate_compatible_selector(hrr, loss) + + incompatible = deepcopy(loss) + incompatible["quantizers"]["int4"]["epsilon"] = 1e-9 + incompatible["quantizers"]["int8"]["epsilon"] = 1e-9 + with pytest.raises(ValueError, match="quantizer contracts"): + evaluator.validate_compatible_selector(hrr, incompatible) + + +@pytest.mark.parametrize( + ("mutation", "message"), + [ + (lambda value: value["model"].update(dtype="torch.float32"), "dtype"), + (lambda value: value["model"].update(linear_attention_layers=[0, 3]), "layers"), + ( + lambda value: value["dataset"]["tasks"][0].update( + prompt_tokens=8, + ), + "token manifests", + ), + ], +) +def test_compatible_selectors_reject_provenance_mismatch(mutation, message: str) -> None: + hrr = _selector(evaluator.HRR_ARTIFACT_KIND) + loss = _selector(evaluator.LOSS_ARTIFACT_KIND) + mutation(loss) + + with pytest.raises(ValueError, match=message): + evaluator.validate_compatible_selector(hrr, loss) + + +def test_selector_requires_explicit_quantizer_metadata() -> None: + selector = _selector(evaluator.HRR_ARTIFACT_KIND) + del selector["quantizers"] + + with pytest.raises(ValueError, match="selector quantizers"): + evaluator.validate_selector_contract(selector) + + +def test_calibration_prefix_verifies_authenticated_row_content() -> None: + selector = _selector(evaluator.HRR_ARTIFACT_KIND) + full_manifest = selector["dataset"]["manifest"] + actual_manifest = { + **{key: value for key, value in full_manifest.items() if key not in ("row_count", "rows")}, + "row_count": 1, + "rows": [{"task_id": 20, "sha256": "row-20"}], + } + + evaluator.validate_calibration_prefix( + selector, + actual_manifest=actual_manifest, + expected_task_ids=[20], + ) + + actual_manifest["rows"][0]["sha256"] = "tampered" + with pytest.raises(ValueError, match="content manifest"): + evaluator.validate_calibration_prefix( + selector, + actual_manifest=actual_manifest, + expected_task_ids=[20], + ) + + +def test_calibration_window_slices_disjoint_ranked_holdout() -> None: + selector = _selector(evaluator.HRR_ARTIFACT_KIND) + ranked_rows = tuple({"task_id": task_id} for task_id in (10, 20, 30, 40)) + + selected = evaluator.select_calibration_window( + ranked_rows, + offset=2, + limit=2, + selectors=[selector], + ) + + assert [row["task_id"] for row in selected] == [30, 40] + + +def test_calibration_window_offset_zero_retains_selector_prefix() -> None: + selector = _selector(evaluator.HRR_ARTIFACT_KIND) + ranked_rows = tuple({"task_id": task_id} for task_id in (20, 10, 30)) + + selected = evaluator.select_calibration_window( + ranked_rows, + offset=0, + limit=2, + selectors=[selector], + ) + + assert [row["task_id"] for row in selected] == [20, 10] + + +def test_calibration_holdout_refuses_any_selector_overlap() -> None: + hrr = _selector(evaluator.HRR_ARTIFACT_KIND) + loss = _selector(evaluator.LOSS_ARTIFACT_KIND) + ranked_rows = tuple({"task_id": task_id} for task_id in (99, 20, 30)) + + with pytest.raises(ValueError, match="overlap selector artifact tasks: 20"): + evaluator.select_calibration_window( + ranked_rows, + offset=1, + limit=2, + selectors=[hrr, loss], + ) + + +def test_calibration_holdout_refuses_out_of_range_window() -> None: + selector = _selector(evaluator.HRR_ARTIFACT_KIND) + ranked_rows = tuple({"task_id": task_id} for task_id in (10, 20, 30)) + + with pytest.raises(ValueError, match=r"window \[2:4\] exceeds 3"): + evaluator.select_calibration_window( + ranked_rows, + offset=2, + limit=2, + selectors=[selector], + ) + + +def test_legacy_frozen_holdout_is_locked_after_experiment008_freeze() -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + + with pytest.raises(ValueError, match="protected by Experiment 008"): + evaluator.validate_frozen_holdout_request( + offset=8, + limit=8, + bootstrap_samples=10_000, + selectors=selectors, + loss_selector_present=True, + storage_boundary_present=True, + ) + + +def test_experiment006_rank_fusion_holdout_remains_fail_closed() -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + + with pytest.raises(ValueError, match="rank-fusion holdout remains closed"): + evaluator.validate_frozen_holdout_request( + offset=8, + limit=8, + bootstrap_samples=10_000, + selectors=selectors, + loss_selector_present=True, + storage_boundary_present=True, + rank_fusion_enabled=True, + ) + + +def test_experiment007_query_ema_holdout_remains_fail_closed() -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + + with pytest.raises(ValueError, match="CQER-32 holdout remains closed"): + evaluator.validate_frozen_holdout_request( + offset=8, + limit=8, + bootstrap_samples=10_000, + selectors=selectors, + loss_selector_present=True, + storage_boundary_present=True, + query_ema_enabled=True, + ) + + +@pytest.mark.parametrize( + "changes", + [ + {"offset": 1}, + {"limit": 7}, + {"bootstrap_samples": 9999}, + ], +) +def test_experiment007_development_request_refuses_partial_or_drifted_runs( + changes: dict[str, int], +) -> None: + arguments = { + "enabled": True, + "offset": 0, + "limit": 8, + "bootstrap_samples": 10_000, + } + arguments.update(changes) + + with pytest.raises(ValueError, match="offset 0, exactly 8 tasks"): + evaluator.validate_cqer_development_request(**arguments) + + evaluator.validate_cqer_development_request( + enabled=False, + offset=99, + limit=1, + bootstrap_samples=1, + ) + + +def test_experiment007_requires_exact_eight_task_selector_pair( + monkeypatch: pytest.MonkeyPatch, +) -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + monkeypatch.setattr( + evaluator, + "CQER_DEVELOPMENT_TASK_IDS", + tuple(range(100, 108)), + ) + monkeypatch.setattr( + evaluator, + "CQER_FROZEN_SELECTOR_CANONICAL_SHA256S", + tuple( + evaluator.sha256_bytes(evaluator.canonical_json_bytes(selector)) + for selector in selectors + ), + ) + evaluator.validate_cqer_selector_artifacts(enabled=True, selectors=selectors) + + selectors[1]["dataset"]["tasks"].append( + { + "task_id": 999, + "prompt_tokens": 1, + "code_tokens": 2, + "scored_transitions": 1, + } + ) + with pytest.raises(ValueError, match="eight-task selector artifacts"): + evaluator.validate_cqer_selector_artifacts(enabled=True, selectors=selectors) + + +def test_experiment007_rejects_selector_content_with_drifted_canonical_hash( + monkeypatch: pytest.MonkeyPatch, +) -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + monkeypatch.setattr( + evaluator, + "CQER_DEVELOPMENT_TASK_IDS", + tuple(range(100, 108)), + ) + monkeypatch.setattr( + evaluator, + "CQER_FROZEN_SELECTOR_CANONICAL_SHA256S", + tuple( + evaluator.sha256_bytes(evaluator.canonical_json_bytes(selector)) + for selector in selectors + ), + ) + selectors[1]["byte_budget"]["target_resident_bytes"] = 999 + + with pytest.raises(ValueError, match="canonical hashes"): + evaluator.validate_cqer_selector_artifacts(enabled=True, selectors=selectors) + + +def test_experiment007_pins_ordered_task_ids_and_layer_quotas() -> None: + evaluator.validate_cqer_development_task_ids( + enabled=True, + task_ids=evaluator.CQER_DEVELOPMENT_TASK_IDS, + ) + evaluator.validate_cqer_layer_quotas( + enabled=True, + quotas=dict(evaluator.CQER_FROZEN_LAYER_QUOTAS), + ) + + with pytest.raises(ValueError, match="frozen ordered prefix"): + evaluator.validate_cqer_development_task_ids( + enabled=True, + task_ids=tuple(reversed(evaluator.CQER_DEVELOPMENT_TASK_IDS)), + ) + drifted = dict(evaluator.CQER_FROZEN_LAYER_QUOTAS) + drifted[0] -= 1 + drifted[1] += 1 + with pytest.raises(ValueError, match="frozen target-Fisher allocation"): + evaluator.validate_cqer_layer_quotas(enabled=True, quotas=drifted) + + +@pytest.mark.parametrize( + ("changes", "message"), + [ + ({"offset": 9}, "exact ranked window"), + ({"limit": 7}, "exact ranked window"), + ({"bootstrap_samples": 9999}, "exactly 10000"), + ({"loss_selector_present": False}, "requires both HRR and loss"), + ({"storage_boundary_present": False}, "storage-boundary artifact"), + ], +) +def test_frozen_holdout_request_refuses_protocol_drift(changes, message: str) -> None: + arguments = { + "offset": 8, + "limit": 8, + "bootstrap_samples": 10_000, + "selectors": [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ], + "loss_selector_present": True, + "storage_boundary_present": True, + } + arguments.update(changes) + + with pytest.raises(ValueError, match=message): + evaluator.validate_frozen_holdout_request(**arguments) + + +def test_frozen_holdout_request_refuses_non_eight_task_selector() -> None: + hrr = _frozen_selector(evaluator.HRR_ARTIFACT_KIND) + loss = _frozen_selector(evaluator.LOSS_ARTIFACT_KIND) + loss["dataset"]["tasks"].pop() + + with pytest.raises(ValueError, match="exactly 8 tasks"): + evaluator.validate_frozen_holdout_request( + offset=8, + limit=8, + bootstrap_samples=10_000, + selectors=[hrr, loss], + loss_selector_present=True, + storage_boundary_present=True, + ) + + +def test_frozen_holdout_request_refuses_non_h32_selector() -> None: + hrr = _frozen_selector(evaluator.HRR_ARTIFACT_KIND) + loss = _frozen_selector(evaluator.LOSS_ARTIFACT_KIND) + hrr["method"]["horizon"] = 64 + + with pytest.raises(ValueError, match="requires HRR horizon 32"): + evaluator.validate_frozen_holdout_request( + offset=8, + limit=8, + bootstrap_samples=10_000, + selectors=[hrr, loss], + loss_selector_present=True, + storage_boundary_present=True, + ) + + +def test_authenticate_selector_prefix_checks_order_and_row_content() -> None: + selector = _frozen_selector(evaluator.HRR_ARTIFACT_KIND) + manifest = selector["dataset"]["manifest"] + ordered_ids = [record["task_id"] for record in selector["dataset"]["tasks"]] + + evaluator.authenticate_selector_prefix( + selector, + ranked_prefix_manifest=manifest, + ranked_prefix_task_ids=ordered_ids, + ) + + with pytest.raises(ValueError, match="ordered task IDs"): + evaluator.authenticate_selector_prefix( + selector, + ranked_prefix_manifest=manifest, + ranked_prefix_task_ids=list(reversed(ordered_ids)), + ) + + tampered = deepcopy(manifest) + tampered["rows"][0]["sha256"] = "tampered" + with pytest.raises(ValueError, match="content manifest"): + evaluator.authenticate_selector_prefix( + selector, + ranked_prefix_manifest=tampered, + ranked_prefix_task_ids=ordered_ids, + ) + + +def test_frozen_selector_token_manifest_matches_every_prefix_task() -> None: + selector = _frozen_selector(evaluator.HRR_ARTIFACT_KIND) + records = [ + { + "task_id": record["task_id"], + "prompt_tokens": record["prompt_tokens"], + "code_tokens": record["code_tokens"], + "aligned_scored_tokens": record["captured_decode_tokens"], + "full_code_scored_tokens": record["code_tokens"], + } + for record in selector["dataset"]["tasks"] + ] + + evaluator.validate_actual_token_manifest(selector, records) + + records[-1]["code_tokens"] += 1 + with pytest.raises(ValueError, match="formatter/token manifest"): + evaluator.validate_actual_token_manifest(selector, records) + + +def test_heldout_repository_requires_clean_matching_selector_commit() -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + repository = {"commit": "frozen-commit", "worktree_clean": True, "status": []} + + evaluator.validate_heldout_repository_start(repository, selectors) + + dirty = {**repository, "worktree_clean": False, "status": [" M source.py"]} + with pytest.raises(ValueError, match="clean worktree"): + evaluator.validate_heldout_repository_start(dirty, selectors) + + selectors[1]["repository"]["commit"] = "different" + with pytest.raises(ValueError, match="commit does not match"): + evaluator.validate_heldout_repository_start(repository, selectors) + + +def test_heldout_repository_end_refuses_commit_or_source_drift() -> None: + clean = {"commit": "abc", "worktree_clean": True, "status": []} + hashes = {"source.py": "hash"} + + evaluator.validate_heldout_repository_end( + start_repository=clean, + end_repository=clean, + start_source_hashes=hashes, + end_source_hashes=hashes, + ) + + with pytest.raises(RuntimeError, match="source files changed"): + evaluator.validate_heldout_repository_end( + start_repository=clean, + end_repository=clean, + start_source_hashes=hashes, + end_source_hashes={"source.py": "changed"}, + ) + + +def test_storage_boundary_prerequisite_recomputes_numeric_gate_and_provenance() -> None: + selector = _frozen_selector(evaluator.HRR_ARTIFACT_KIND) + evidence = _storage_boundary_evidence() + + evaluator.validate_storage_boundary_prerequisite( + evidence, + expected_commit="frozen-commit", + expected_model=selector["model"], + ) + + failed = deepcopy(evidence) + failed["derivative_gate"]["observed"]["sign_agreement"] = 0.94 + with pytest.raises(ValueError, match="numeric observations"): + evaluator.validate_storage_boundary_prerequisite( + failed, + expected_commit="frozen-commit", + expected_model=selector["model"], + ) + + wrong_commit = deepcopy(evidence) + wrong_commit["repository"]["end"]["commit"] = "different" + with pytest.raises(ValueError, match="commit does not match"): + evaluator.validate_storage_boundary_prerequisite( + wrong_commit, + expected_commit="frozen-commit", + expected_model=selector["model"], + ) + + +def test_evaluator_source_freeze_covers_metric_implementation() -> None: + assert "src/recurquant/metrics.py" in evaluator.EVALUATOR_SOURCE_FILES + + +def test_source_file_hashes_are_path_stable_and_detect_changes(tmp_path) -> None: + source = tmp_path / "source.py" + source.write_bytes(b"first") + first = evaluator.source_file_hashes(tmp_path, ("source.py",)) + source.write_bytes(b"second") + second = evaluator.source_file_hashes(tmp_path, ("source.py",)) + + assert set(first) == {"source.py"} + assert first != second + + +def test_actual_token_manifest_must_match_selected_prefix() -> None: + selector = _selector(evaluator.HRR_ARTIFACT_KIND) + records = [ + { + "task_id": 20, + "prompt_tokens": 7, + "code_tokens": 4, + "aligned_scored_tokens": 3, + "full_code_scored_tokens": 4, + } + ] + + evaluator.validate_actual_token_manifest(selector, records) + + records[0]["prompt_tokens"] = 8 + with pytest.raises(ValueError, match="formatter/token manifest"): + evaluator.validate_actual_token_manifest(selector, records) + + +def test_one_task_contrast_is_labeled_descriptive_without_interval() -> None: + result = evaluator.paired_contrast([2.0], [1.25], samples=100, seed=17) + + assert result["mean_improvement"] == pytest.approx(0.75) + assert result["confidence_interval"] is None + assert result["bootstrap_samples"] == 0 + assert "descriptive only" in result["note"] + + +def test_multi_task_contrast_keeps_bootstrap_interval() -> None: + result = evaluator.paired_contrast([2.0, 3.0], [1.0, 2.0], samples=100, seed=17) + + assert result["paired_examples"] == 2 + assert result["confidence_interval"] == pytest.approx([1.0, 1.0]) + + +def test_frozen_holdout_gate_passes_all_prespecified_thresholds() -> None: + gate = evaluator.evaluate_frozen_holdout_gate(**_passing_gate_inputs()) + + assert gate["passed"] is True + assert gate["strongest_equal_byte_static"] == "hrr_h1" + assert all(check["passed"] is True for check in gate["checks"].values()) + + +@pytest.mark.parametrize( + ("mutation", "failed_check"), + [ + ( + lambda inputs: inputs["aggregates"][evaluator.ADAPTIVE_TARGET_FISHER].update( + macro_delta_nll=0.81 + ), + "relative_nll_reduction_vs_strongest_static", + ), + ( + lambda inputs: inputs["aggregates"][evaluator.ADAPTIVE_TARGET_FISHER].update( + macro_top1_agreement=0.894 + ), + "top1_disadvantage_margin_vs_strongest_static", + ), + ( + lambda inputs: inputs["aggregates"][evaluator.ADAPTIVE_TARGET_FISHER].update( + macro_cvar95_kl=0.251 + ), + "cvar95_disadvantage_margin_vs_strongest_static", + ), + ( + lambda inputs: inputs["per_task"][evaluator.ADAPTIVE_TARGET_FISHER][0].update( + delta_nll=1.6 + ), + "maximum_per_task_nll_disadvantage_vs_strongest_static", + ), + ( + lambda inputs: inputs["contrasts"]["hrr_h1"].update(confidence_interval=[0.0, 0.5]), + "paired_lower_ci_vs_strongest_static", + ), + ( + lambda inputs: inputs["contrasts"][evaluator.ADAPTIVE_H1].update( + confidence_interval=[0.0, 0.5] + ), + "paired_lower_ci_vs_adaptive_h1", + ), + ( + lambda inputs: inputs["storage"][evaluator.ADAPTIVE_TARGET_FISHER].update( + resident_bytes=evaluator.TARGET_RESIDENT_BYTES - 1 + ), + "exact_resident_bytes", + ), + ], +) +def test_frozen_holdout_gate_reports_each_threshold_failure(mutation, failed_check: str) -> None: + inputs = _passing_gate_inputs() + mutation(inputs) + + gate = evaluator.evaluate_frozen_holdout_gate(**inputs) + + assert gate["passed"] is False + assert gate["checks"][failed_check]["passed"] is False + + +def _passing_cqer_development_inputs() -> dict[str, object]: + methods = { + evaluator.QUERY_EMA_PRIMARY: { + "macro_delta_nll": 0.40, + "macro_mean_kl": 0.40, + "macro_cvar95_kl": 1.00, + "macro_top1_agreement": 0.80, + "task_count": 1, + "token_count": 10, + }, + evaluator.ADAPTIVE_TARGET_FISHER: { + "macro_delta_nll": 0.50, + "macro_mean_kl": 0.50, + "macro_cvar95_kl": 0.95, + "macro_top1_agreement": 0.795, + "task_count": 1, + "token_count": 10, + }, + "target_directional_fisher_difference_int4": { + "macro_delta_nll": 0.60, + "macro_mean_kl": 0.60, + "macro_cvar95_kl": 1.10, + "macro_top1_agreement": 0.805, + "task_count": 1, + "token_count": 10, + }, + } + per_task = { + name: [ + { + "task_id": task_id, + "delta_nll": values["macro_delta_nll"], + "mean_kl": values["macro_mean_kl"], + "cvar95_kl": values["macro_cvar95_kl"], + "top1_agreement": values["macro_top1_agreement"], + "token_count": 10, + "candidate_nll": 1.0, + "reference_nll": 0.5, + "max_kl": 1.5, + "all_logits_finite": True, + } + for task_id in range(8) + ] + for name, values in methods.items() + } + storage = { + name: { + "resident_bytes": 100, + "high_precision_groups": 2, + } + for name in methods + } + storage[evaluator.QUERY_EMA_PRIMARY].update( + selector_auxiliary_bytes=8, + resident_bytes_including_selector=108, + ) + diagnostics = { + evaluator.QUERY_EMA_PRIMARY: [ + { + "task_id": task_id, + "layers": [ + { + "layer_index": 0, + "quota": 2, + "current_selected_count": 2, + "observations_committed": 3, + "state_updates": 3, + "selector_auxiliary_bytes": 8, + "current_mask_sha256": "a" * 64, + "pending_observation": False, + "last_cutoff_score_margin": 0.01, + } + ], + } + for task_id in range(8) + ] + } + return { + "aggregates": methods, + "per_task": per_task, + "per_task_full_code": deepcopy(per_task), + "storage": storage, + "query_ema_diagnostics": diagnostics, + "expected_quotas": {0: 2}, + "expected_packed_bytes": 100, + "expected_selector_auxiliary_bytes": 8, + } + + +def test_cqer_development_gate_passes_only_the_frozen_conjunction() -> None: + inputs = _passing_cqer_development_inputs() + + gate = evaluator.evaluate_cqer_development_gate(**inputs) + + assert gate["passed"] is True + assert all(check["passed"] is True for check in gate["checks"].values()) + + inputs = _passing_cqer_development_inputs() + inputs["aggregates"][evaluator.QUERY_EMA_PRIMARY]["macro_delta_nll"] = 0.49 + gate = evaluator.evaluate_cqer_development_gate(**inputs) + assert gate["passed"] is False + assert gate["checks"]["relative_nll_reduction_vs_plain_adaptive"]["passed"] is False + assert gate["checks"]["relative_nll_reduction_vs_strongest_static"]["passed"] is False + + +def test_cqer_development_gate_rejects_stale_stage_consume_state() -> None: + inputs = _passing_cqer_development_inputs() + layer = inputs["query_ema_diagnostics"][evaluator.QUERY_EMA_PRIMARY][0]["layers"][0] + layer["pending_observation"] = True + + gate = evaluator.evaluate_cqer_development_gate(**inputs) + + assert gate["passed"] is False + assert gate["checks"]["exact_stage_consume_handshake"]["passed"] is False + + inputs = _passing_cqer_development_inputs() + inputs["per_task_full_code"][evaluator.QUERY_EMA_PRIMARY][0][ + "all_logits_finite" + ] = False + gate = evaluator.evaluate_cqer_development_gate(**inputs) + assert gate["passed"] is False + assert gate["checks"]["all_values_finite"]["passed"] is False + + +def test_evaluator_runs_cqer_observer_through_prefill_and_decode() -> None: + torch.manual_seed(443) + config = tiny_config() + model = Qwen3_5ForCausalLM._from_config( + config, + attn_implementation="eager", + ).eval() + scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + plan = evaluator.select_rows_exact_budget( + scores, + target_resident_bytes=118, + group_size=8, + ) + prompt_ids = torch.randint(0, config.vocab_size, (1, 4)) + code_ids = torch.randint(0, config.vocab_size, (1, 3)) + + with torch.inference_mode(): + summaries, _, storage, diagnostics, reference_bytes = evaluator.evaluate_task( + model, + prompt_ids=prompt_ids, + code_ids=code_ids, + plans={}, + adaptive_plans={}, + query_ema_plans={evaluator.QUERY_EMA_PRIMARY: plan}, + ) + + query_summary = summaries[evaluator.QUERY_EMA_PRIMARY] + query_storage = storage[evaluator.QUERY_EMA_PRIMARY] + layer = diagnostics[evaluator.QUERY_EMA_PRIMARY][0] + assert query_summary["token_count"] == 2 + assert query_storage["resident_bytes"] == plan.resident_bytes + assert query_storage["selector_auxiliary_bytes"] == 64 + assert layer["observations_committed"] == 3 + assert layer["state_updates"] == 3 + assert layer["tokens_observed"] == 6 + assert layer["pending_observation"] is False + assert reference_bytes > 0 + + +def test_claim_and_exit_code_follow_actual_primary_and_gate() -> None: + static_claim = evaluator.primary_claim_text("hrr_h32") + adaptive_claim = evaluator.primary_claim_text(evaluator.ADAPTIVE_TARGET_FISHER) + fusion_claim = evaluator.primary_claim_text(evaluator.RANK_FUSION_PRIMARY) + query_ema_claim = evaluator.primary_claim_text(evaluator.QUERY_EMA_PRIMARY) + + assert "static HRR" in static_claim + assert "no loss-selector" in static_claim + assert "target-directional-Fisher" in adaptive_claim + assert "equal-weight ordinal rank fusion" in fusion_claim + assert "normalized-query-energy EMA" in query_ema_claim + assert "32-token half-life" in query_ema_claim + assert evaluator.QUERY_EMA_HALF_LIFE == 32 + assert evaluator.RANK_FUSION_METHODS == ( + ("rank_fusion_l025_target_fisher_adaptive_mse", 0.25), + ("rank_fusion_l050_target_fisher_adaptive_mse", 0.50), + ("rank_fusion_l075_target_fisher_adaptive_mse", 0.75), + ) + assert evaluator.diagnostic_exit_code(heldout_calibration=True, gate_passed=False) == 2 + assert evaluator.diagnostic_exit_code(heldout_calibration=True, gate_passed=True) == 0 + assert evaluator.diagnostic_exit_code(heldout_calibration=False, gate_passed=False) == 0 + + +def _cora_metric( + *, + delta_nll: float, + top1: float, + cvar95: float, +) -> dict[str, float | int]: + return { + "macro_delta_nll": delta_nll, + "macro_mean_kl": delta_nll, + "macro_cvar95_kl": cvar95, + "macro_top1_agreement": top1, + "task_count": evaluator.E008_DEVELOPMENT_LIMIT, + "token_count": 2 * evaluator.E008_DEVELOPMENT_LIMIT, + } + + +def _cora_task_rows( + summary: dict[str, float | int], +) -> list[dict[str, float | int | bool]]: + return [ + { + "task_id": task_id, + "delta_nll": summary["macro_delta_nll"], + "mean_kl": summary["macro_mean_kl"], + "cvar95_kl": summary["macro_cvar95_kl"], + "top1_agreement": summary["macro_top1_agreement"], + "token_count": 2, + "candidate_nll": 1.0, + "reference_nll": 0.5, + "max_kl": 1.5, + "all_logits_finite": True, + } + for task_id in evaluator.E008_DEVELOPMENT_TASK_IDS + ] + + +def _query_selector_layer(*, confirmation_two: bool) -> dict[str, object]: + return { + "layer_index": 0, + "quota": 2, + "confirmation_two": confirmation_two, + "ema_decay": 2.0 ** (-1.0 / evaluator.QUERY_EMA_HALF_LIFE), + "l2norm_eps": 1e-6, + "state_updates": 3, + "observations_staged": 3, + "observations_committed": 3, + "tokens_observed": 6, + "last_query_token_count": 1, + "last_cutoff_score_margin": 0.01, + "last_mask_overlap": 1, + "last_mask_churn": 2, + "current_selected_count": 2, + "current_mask_sha256": "a" * 64, + "raw_mask_sha256": "b" * 64, + "committed_mask_sha256": "a" * 64, + "pending_observation": False, + "selector_auxiliary_bytes": 9 if confirmation_two else 8, + } + + +def _cora_selector_layer( + *, + confirmation_two: bool, + committed_xor: int, +) -> dict[str, object]: + transitions = 2 + quota = 2 + denominator = 2 * quota * transitions + raw_xor = 4 + admissions = committed_xor // 2 + dwell = quota * transitions - admissions + return { + "layer_index": 0, + "selection_method": ( + evaluator.CORA_C2_PRIMARY if confirmation_two else evaluator.CORA_RAW + ), + "confirmation_two": confirmation_two, + "quota": quota, + "current_selected_count": quota, + "raw_mask_sha256": "b" * 64, + "committed_mask_sha256": "c" * 64, + "observations_staged": 3, + "observations_committed": 3, + "tokens_observed": 6, + "last_token_count": 1, + "mask_transition_count": transitions, + "raw_xor_churn_total": raw_xor, + "committed_xor_churn_total": committed_xor, + "raw_normalized_churn": raw_xor / denominator, + "committed_normalized_churn": committed_xor / denominator, + "last_raw_mask_overlap": 1, + "last_committed_mask_overlap": 1, + "last_dwell_count": 1, + "last_admission_count": 1, + "dwell_total": dwell, + "admissions_total": admissions, + "last_raw_cutoff_score": 0.1, + "last_raw_score_gap": 0.01, + "last_raw_normalized_gap": 0.1, + "last_committed_cutoff_score": 0.1, + "last_committed_score_gap": 0.01, + "last_committed_normalized_gap": 0.1, + "observability_trace": 1.0, + "observability_min": 0.1, + "observability_max": 0.9, + "observability_dtype": "torch.float32", + "l2norm_eps": 1e-6, + "state_updates": 3, + "pending_observation": False, + "observability_diagonal_bytes": 8, + # Raw CORA also keeps its previous raw mask to report cumulative churn. + "previous_raw_mask_bytes": 1, + "selector_auxiliary_bytes": 9, + } + + +def _selector_task_diagnostics(layer: dict[str, object]) -> list[dict[str, object]]: + return [ + {"task_id": task_id, "layers": [deepcopy(layer)]} + for task_id in evaluator.E008_DEVELOPMENT_TASK_IDS + ] + + +def _passing_cora_c2_development_inputs() -> dict[str, object]: + static = "target_directional_fisher_difference_int4" + aggregates = { + static: _cora_metric(delta_nll=1.00, top1=0.800, cvar95=1.00), + evaluator.ADAPTIVE_TARGET_FISHER: _cora_metric( + delta_nll=0.90, + top1=0.810, + cvar95=0.95, + ), + evaluator.QUERY_EMA_PRIMARY: _cora_metric( + delta_nll=0.80, + top1=0.800, + cvar95=1.00, + ), + evaluator.CORA_RAW: _cora_metric( + delta_nll=0.72, + top1=0.804, + cvar95=1.02, + ), + evaluator.QUERY_EMA_C2: _cora_metric( + delta_nll=0.78, + top1=0.801, + cvar95=1.00, + ), + evaluator.CORA_C2_PRIMARY: _cora_metric( + delta_nll=0.70, + top1=0.805, + cvar95=1.04, + ), + } + per_task = {name: _cora_task_rows(values) for name, values in aggregates.items()} + per_task_full_code = deepcopy(per_task) + for task_rows in per_task_full_code.values(): + for row in task_rows: + row["token_count"] = 3 + aggregates_full_code = deepcopy(aggregates) + for summary in aggregates_full_code.values(): + summary["token_count"] = 3 * evaluator.E008_DEVELOPMENT_LIMIT + packed_bytes = 100 + storage = { + name: { + "resident_bytes": packed_bytes, + "high_precision_groups": 2, + } + for name in aggregates + } + storage[evaluator.QUERY_EMA_PRIMARY].update( + selector_auxiliary_bytes=8, + resident_bytes_including_selector=108, + ) + storage[evaluator.CORA_RAW].update( + selector_auxiliary_bytes=9, + resident_bytes_including_selector=109, + ) + for name in (evaluator.QUERY_EMA_C2, evaluator.CORA_C2_PRIMARY): + storage[name].update( + selector_auxiliary_bytes=9, + resident_bytes_including_selector=109, + ) + selector_diagnostics = { + evaluator.QUERY_EMA_PRIMARY: _selector_task_diagnostics( + _query_selector_layer(confirmation_two=False) + ), + evaluator.QUERY_EMA_C2: _selector_task_diagnostics( + _query_selector_layer(confirmation_two=True) + ), + evaluator.CORA_RAW: _selector_task_diagnostics( + _cora_selector_layer(confirmation_two=False, committed_xor=4) + ), + evaluator.CORA_C2_PRIMARY: _selector_task_diagnostics( + _cora_selector_layer(confirmation_two=True, committed_xor=2) + ), + } + return { + "aggregates": aggregates, + "aggregates_full_code": aggregates_full_code, + "per_task": per_task, + "per_task_full_code": per_task_full_code, + "storage": storage, + "selector_diagnostics": selector_diagnostics, + "contrasts": { + static: {"confidence_interval": [0.10, 0.50]}, + evaluator.QUERY_EMA_PRIMARY: {"confidence_interval": [0.02, 0.20]}, + }, + "token_manifest": [ + { + "task_id": task_id, + "prompt_tokens": 4, + "code_tokens": 3, + "aligned_scored_tokens": 2, + "full_code_scored_tokens": 3, + } + for task_id in evaluator.E008_DEVELOPMENT_TASK_IDS + ], + "expected_quotas": {0: 2}, + "expected_packed_bytes": packed_bytes, + "expected_query_auxiliary_bytes": 8, + "expected_cora_auxiliary_bytes": 9, + } + + +def _set_all_cora_c2_committed_churn(values: dict[str, object], churn: int) -> None: + for task in values["selector_diagnostics"][evaluator.CORA_C2_PRIMARY]: + layer = task["layers"][0] + layer["committed_xor_churn_total"] = churn + layer["committed_normalized_churn"] = churn / 8 + layer["admissions_total"] = churn // 2 + layer["dwell_total"] = 4 - churn // 2 + + +@pytest.mark.parametrize( + "changes", + ( + {"offset": 8}, + {"offset": 15}, + {"offset": 17}, + {"limit": 15}, + {"bootstrap_samples": 9_999}, + ), +) +def test_experiment008_request_pins_development_and_protects_eight_to_sixteen( + changes: dict[str, int], +) -> None: + arguments = { + "enabled": True, + "offset": evaluator.E008_DEVELOPMENT_OFFSET, + "limit": evaluator.E008_DEVELOPMENT_LIMIT, + "bootstrap_samples": evaluator.FROZEN_BOOTSTRAP_SAMPLES, + } + evaluator.validate_cora_development_request(**arguments) + arguments.update(changes) + + with pytest.raises(ValueError, match=r"\[16, 32\).*\[8, 16\) remains closed"): + evaluator.validate_cora_development_request(**arguments) + + +def test_experiment008_disabled_request_is_a_noop() -> None: + evaluator.validate_cora_development_request( + enabled=False, + offset=8, + limit=1, + bootstrap_samples=1, + ) + + +def test_experiment008_requires_a_clean_committed_repository_before_data_access() -> None: + clean = {"commit": "a" * 40, "worktree_clean": True, "status": []} + evaluator.validate_cora_development_repository_start(clean) + + with pytest.raises(ValueError, match="clean committed worktree"): + evaluator.validate_cora_development_repository_start( + {"commit": "a" * 40, "worktree_clean": False, "status": [" M source.py"]} + ) + with pytest.raises(ValueError, match="40-character Git commit"): + evaluator.validate_cora_development_repository_start( + {"commit": "short", "worktree_clean": True, "status": []} + ) + + +@pytest.mark.parametrize( + ("field", "value", "message"), + ( + ("task_ids", tuple(reversed(evaluator.E008_DEVELOPMENT_TASK_IDS)), "task IDs"), + ("content_manifest_sha256", "0" * 64, "content manifest"), + ("token_manifest_sha256", "0" * 64, "token manifest"), + ), +) +def test_experiment008_identity_rejects_every_frozen_identity_drift( + field: str, + value: object, + message: str, +) -> None: + arguments = { + "enabled": True, + "task_ids": evaluator.E008_DEVELOPMENT_TASK_IDS, + "content_manifest_sha256": evaluator.E008_DEVELOPMENT_CONTENT_MANIFEST_SHA256, + "token_manifest_sha256": evaluator.E008_DEVELOPMENT_TOKEN_MANIFEST_SHA256, + } + evaluator.validate_cora_development_identity(**arguments) + arguments[field] = value + + with pytest.raises(ValueError, match=message): + evaluator.validate_cora_development_identity(**arguments) + + +def test_experiment008_authenticates_exact_selector_pair_and_quotas( + monkeypatch: pytest.MonkeyPatch, +) -> None: + selectors = [ + _frozen_selector(evaluator.HRR_ARTIFACT_KIND), + _frozen_selector(evaluator.LOSS_ARTIFACT_KIND), + ] + monkeypatch.setattr(evaluator, "CQER_DEVELOPMENT_TASK_IDS", tuple(range(100, 108))) + monkeypatch.setattr( + evaluator, + "CQER_FROZEN_SELECTOR_CANONICAL_SHA256S", + tuple( + evaluator.sha256_bytes(evaluator.canonical_json_bytes(selector)) + for selector in selectors + ), + ) + + evaluator.validate_cora_selector_artifacts(enabled=True, selectors=selectors) + evaluator.validate_cora_layer_quotas( + enabled=True, + quotas=dict(evaluator.CQER_FROZEN_LAYER_QUOTAS), + ) + + drifted_selector = deepcopy(selectors) + drifted_selector[1]["byte_budget"]["target_resident_bytes"] = 999 + with pytest.raises(ValueError, match="canonical hashes"): + evaluator.validate_cora_selector_artifacts( + enabled=True, + selectors=drifted_selector, + ) + + drifted_quotas = dict(evaluator.CQER_FROZEN_LAYER_QUOTAS) + drifted_quotas[0] -= 1 + drifted_quotas[1] += 1 + with pytest.raises(ValueError, match="target-Fisher allocation"): + evaluator.validate_cora_layer_quotas(enabled=True, quotas=drifted_quotas) + + +def test_experiment008_cora_path_does_not_authorize_the_protected_window() -> None: + with pytest.raises(ValueError, match=r"\[8, 16\) remains closed"): + evaluator.validate_cora_development_request( + enabled=True, + offset=8, + limit=8, + bootstrap_samples=evaluator.FROZEN_BOOTSTRAP_SAMPLES, + ) + + +@pytest.mark.parametrize( + ("offset", "limit", "bootstrap_samples"), + ( + (8, 8, 10_000), + (16, 15, 10_000), + (16, 16, 9_999), + ), +) +def test_generic_holdout_validator_only_bypasses_for_exact_e008_development_request( + offset: int, + limit: int, + bootstrap_samples: int, +) -> None: + evaluator.validate_frozen_holdout_request( + offset=16, + limit=16, + bootstrap_samples=10_000, + selectors=[], + loss_selector_present=False, + storage_boundary_present=False, + cora_c2_enabled=True, + ) + + with pytest.raises(ValueError, match="Experiment 008 CORA-C2"): + evaluator.validate_frozen_holdout_request( + offset=offset, + limit=limit, + bootstrap_samples=bootstrap_samples, + selectors=[], + loss_selector_present=False, + storage_boundary_present=False, + cora_c2_enabled=True, + ) + + +def test_cora_c2_development_gate_passes_the_complete_frozen_conjunction() -> None: + gate = evaluator.evaluate_cora_c2_development_gate( + **_passing_cora_c2_development_inputs() + ) + + assert gate["schema"] == "recurquant.experiment008-cora-c2-development-gate.v1" + assert gate["primary"] == evaluator.CORA_C2_PRIMARY + assert gate["passed"] is True + assert set(gate["checks"]) == { + "exact_per_layer_quotas", + "exact_packed_and_selector_bytes", + "exact_stage_consume_handshake", + "all_values_finite", + "lower_nll_than_primary_comparators", + "relative_nll_reduction_vs_static", + "relative_nll_reduction_vs_adaptive", + "relative_nll_reduction_vs_cqer", + "paired_lower_ci_vs_static", + "paired_lower_ci_vs_cqer", + "top1_margin_vs_static_adaptive", + "top1_not_lower_than_cqer", + "cvar95_margin_vs_static_adaptive", + "raw_cora_relative_nll_reduction_vs_cqer", + "c2_churn_reduction_vs_raw", + "c2_top1_not_lower_than_raw", + "c2_nll_worsening_vs_raw", + } + assert all(check["passed"] is True for check in gate["checks"].values()) + + +def test_cora_c2_gate_fails_closed_when_staging_evidence_is_missing() -> None: + inputs = _passing_cora_c2_development_inputs() + layer = inputs["selector_diagnostics"][evaluator.CORA_C2_PRIMARY][0]["layers"][0] + del layer["observations_staged"] + + gate = evaluator.evaluate_cora_c2_development_gate(**inputs) + + assert gate["passed"] is False + assert gate["checks"]["exact_stage_consume_handshake"]["passed"] is False + + +def test_cora_c2_gate_rejects_method_or_metric_token_drift() -> None: + inputs = _passing_cora_c2_development_inputs() + inputs["aggregates"]["posthoc_method"] = deepcopy( + inputs["aggregates"][evaluator.CORA_C2_PRIMARY] + ) + with pytest.raises(ValueError, match="frozen six-method set"): + evaluator.evaluate_cora_c2_development_gate(**inputs) + + inputs = _passing_cora_c2_development_inputs() + inputs["per_task"][evaluator.CORA_C2_PRIMARY][0]["token_count"] = 1 + with pytest.raises(ValueError, match="aligned token counts"): + evaluator.evaluate_cora_c2_development_gate(**inputs) + + +def test_cora_c2_gate_rejects_dropped_transition_tokens() -> None: + inputs = _passing_cora_c2_development_inputs() + layer = inputs["selector_diagnostics"][evaluator.CORA_C2_PRIMARY][0]["layers"][0] + layer["tokens_observed"] = 5 + + gate = evaluator.evaluate_cora_c2_development_gate(**inputs) + + assert gate["passed"] is False + assert gate["checks"]["exact_stage_consume_handshake"]["passed"] is False + + +def test_evaluator_runs_all_e008_dynamic_methods_with_disjoint_observers() -> None: + torch.manual_seed(449) + config = tiny_config() + model = Qwen3_5ForCausalLM._from_config( + config, + attn_implementation="eager", + ).eval() + scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + plan = evaluator.select_rows_exact_budget( + scores, + target_resident_bytes=118, + group_size=8, + ) + prompt_ids = torch.randint(0, config.vocab_size, (1, 4)) + code_ids = torch.randint(0, config.vocab_size, (1, 3)) + dynamic_methods = { + evaluator.QUERY_EMA_PRIMARY, + evaluator.QUERY_EMA_C2, + evaluator.CORA_RAW, + evaluator.CORA_C2_PRIMARY, + } + + with torch.inference_mode(): + summaries, _, storage, diagnostics, reference_bytes = evaluator.evaluate_task( + model, + prompt_ids=prompt_ids, + code_ids=code_ids, + plans={}, + adaptive_plans={}, + query_ema_plans={evaluator.QUERY_EMA_PRIMARY: plan}, + query_ema_c2_plans={evaluator.QUERY_EMA_C2: plan}, + cora_specs={ + evaluator.CORA_RAW: (plan, False), + evaluator.CORA_C2_PRIMARY: (plan, True), + }, + include_default_baselines=False, + ) + + assert set(summaries) == dynamic_methods + assert set(diagnostics) == dynamic_methods + assert all(summaries[name]["token_count"] == 2 for name in dynamic_methods) + assert all(storage[name]["resident_bytes"] == plan.resident_bytes for name in dynamic_methods) + assert storage[evaluator.QUERY_EMA_PRIMARY]["selector_auxiliary_bytes"] == 64 + assert storage[evaluator.QUERY_EMA_C2]["selector_auxiliary_bytes"] == 66 + assert storage[evaluator.CORA_RAW]["selector_auxiliary_bytes"] == 66 + assert storage[evaluator.CORA_C2_PRIMARY]["selector_auxiliary_bytes"] == 66 + + for name in dynamic_methods: + assert len(diagnostics[name]) == 1 + layer = diagnostics[name][0] + assert layer["state_updates"] == 3 + assert layer["observations_staged"] == 3 + assert layer["observations_committed"] == 3 + assert layer["tokens_observed"] == 6 + assert layer["current_selected_count"] == len(plan.groups_for_layer(0)) + assert layer["pending_observation"] is False + + assert diagnostics[evaluator.QUERY_EMA_PRIMARY][0]["confirmation_two"] is False + assert diagnostics[evaluator.QUERY_EMA_C2][0]["confirmation_two"] is True + assert diagnostics[evaluator.CORA_RAW][0]["selection_method"] == evaluator.CORA_RAW + assert ( + diagnostics[evaluator.CORA_C2_PRIMARY][0]["selection_method"] + == evaluator.CORA_C2_PRIMARY + ) + assert reference_bytes > 0 + + +@pytest.mark.parametrize( + ("mutation", "failed_check"), + ( + ( + lambda values: values["selector_diagnostics"][evaluator.CORA_C2_PRIMARY][0][ + "layers" + ][0].update(current_selected_count=1), + "exact_per_layer_quotas", + ), + ( + lambda values: values["storage"][evaluator.CORA_C2_PRIMARY].update( + resident_bytes_including_selector=110 + ), + "exact_packed_and_selector_bytes", + ), + ( + lambda values: values["selector_diagnostics"][evaluator.CORA_C2_PRIMARY][0][ + "layers" + ][0].update(observations_committed=2), + "exact_stage_consume_handshake", + ), + ( + lambda values: values["per_task_full_code"][evaluator.CORA_C2_PRIMARY][0].update( + all_logits_finite=False + ), + "all_values_finite", + ), + ( + lambda values: values["aggregates"][evaluator.CORA_C2_PRIMARY].update( + macro_delta_nll=0.80 + ), + "lower_nll_than_primary_comparators", + ), + ( + lambda values: values["aggregates"][ + "target_directional_fisher_difference_int4" + ].update(macro_delta_nll=0.874), + "relative_nll_reduction_vs_static", + ), + ( + lambda values: values["aggregates"][evaluator.ADAPTIVE_TARGET_FISHER].update( + macro_delta_nll=0.735 + ), + "relative_nll_reduction_vs_adaptive", + ), + ( + lambda values: values["aggregates"][evaluator.QUERY_EMA_PRIMARY].update( + macro_delta_nll=0.735 + ), + "relative_nll_reduction_vs_cqer", + ), + ( + lambda values: values["contrasts"][ + "target_directional_fisher_difference_int4" + ].update(confidence_interval=[0.0, 0.5]), + "paired_lower_ci_vs_static", + ), + ( + lambda values: values["contrasts"][evaluator.QUERY_EMA_PRIMARY].update( + confidence_interval=[0.0, 0.2] + ), + "paired_lower_ci_vs_cqer", + ), + ( + lambda values: values["aggregates"][evaluator.CORA_C2_PRIMARY].update( + macro_top1_agreement=0.799 + ), + "top1_margin_vs_static_adaptive", + ), + ( + lambda values: values["aggregates"][evaluator.QUERY_EMA_PRIMARY].update( + macro_top1_agreement=0.806 + ), + "top1_not_lower_than_cqer", + ), + ( + lambda values: values["aggregates"][evaluator.CORA_C2_PRIMARY].update( + macro_cvar95_kl=1.051 + ), + "cvar95_margin_vs_static_adaptive", + ), + ( + lambda values: values["aggregates"][evaluator.CORA_RAW].update( + macro_delta_nll=0.777 + ), + "raw_cora_relative_nll_reduction_vs_cqer", + ), + ( + lambda values: _set_all_cora_c2_committed_churn(values, 4), + "c2_churn_reduction_vs_raw", + ), + ( + lambda values: values["aggregates"][evaluator.CORA_RAW].update( + macro_top1_agreement=0.806 + ), + "c2_top1_not_lower_than_raw", + ), + ( + lambda values: values["aggregates"][evaluator.CORA_RAW].update( + macro_delta_nll=0.69 + ), + "c2_nll_worsening_vs_raw", + ), + ), +) +def test_cora_c2_development_gate_fails_each_frozen_condition( + mutation, + failed_check: str, +) -> None: + inputs = _passing_cora_c2_development_inputs() + mutation(inputs) + + gate = evaluator.evaluate_cora_c2_development_gate(**inputs) + + assert gate["passed"] is False + assert gate["checks"][failed_check]["passed"] is False diff --git a/tests/test_pilot_validate_storage_boundary.py b/tests/test_pilot_validate_storage_boundary.py new file mode 100644 index 0000000..1de1697 --- /dev/null +++ b/tests/test_pilot_validate_storage_boundary.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import importlib.util +import json +from pathlib import Path + +import pytest +import torch +from transformers import DynamicCache + +from recurquant.storage_boundary_validation import ( + StorageRowLocation, +) +from recurquant.storage_boundary_validation import ( + _directional_dot_float64 as _module_directional_dot_float64, +) +from tests.test_transformers_cache import tiny_config + + +def _load_script(): + path = Path(__file__).parents[1] / "scripts" / "pilot_validate_storage_boundary.py" + spec = importlib.util.spec_from_file_location("pilot_validate_storage_boundary", path) + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_fixed_grid_and_strata_resolve_without_scores() -> None: + script = _load_script() + recurrent_layers = tuple(range(0, 36, 2)) + + resolved = script._resolve_strata(recurrent_layers, heads=16, rows=128) + + assert script.EPSILONS == (0.25, 0.125, 0.0625, 0.03125) + assert resolved == ( + ("early_low", StorageRowLocation(0, 0, 0)), + ("early_mid", StorageRowLocation(10, 5, 42)), + ("late_mid", StorageRowLocation(22, 10, 85)), + ("late_high", StorageRowLocation(34, 15, 127)), + ) + + +def test_fixed_strata_refuse_incompatible_geometry() -> None: + script = _load_script() + + with pytest.raises(ValueError, match="recurrent-layer geometry"): + script._resolve_strata(tuple(range(17)), heads=16, rows=128) + with pytest.raises(ValueError, match="recurrent-state geometry"): + script._resolve_strata(tuple(range(18)), heads=8, rows=128) + + +def test_cache_fingerprint_covers_recurrent_history_flags() -> None: + script = _load_script() + cache = DynamicCache(config=tiny_config()) + before = script._cache_fingerprint(cache) + + cache.layers[0].has_previous_state[0] = True + + assert script._cache_fingerprint(cache) != before + + +def test_saved_directional_dot_uses_matching_float64_accumulation() -> None: + script = _load_script() + gradient = torch.tensor([100_000_000.0, 1.0, -100_000_000.0], dtype=torch.float32) + direction = torch.ones_like(gradient) + + assert float((gradient * direction).sum().item()) == 0.0 + assert _module_directional_dot_float64(gradient, direction) == 1.0 + assert script._directional_dot_float64(gradient, direction) == 1.0 + + +def _gate_rows(script, *, central_scale: float = 1.0, repeat_error: float = 0.0): + rows = [] + for row_index, (label, *_coordinates) in enumerate(script.ROW_STRATA, start=1): + autograd = row_index * 1e-4 + rows.append( + { + "stratum": label, + "epsilon_results": [ + { + "epsilon": epsilon, + "autograd_directional_derivative": autograd, + "central_directional_derivative": autograd * central_scale, + "baseline_repeat_absolute_error": repeat_error, + } + for epsilon in script.EPSILONS + ], + } + ) + return rows + + +def test_frozen_derivative_gate_passes_consistent_fp32_grid() -> None: + script = _load_script() + + gate = script._evaluate_derivative_gate(_gate_rows(script, central_scale=1.02)) + + assert gate["passed"] is True + assert gate["failures"] == [] + assert gate["observed"]["informative_rows"] == 4 + assert gate["observed"]["sign_agreement"] == 1.0 + assert gate["observed"]["median_relative_error"] == pytest.approx(0.02) + + +def test_frozen_derivative_gate_rejects_sign_and_repeat_failures() -> None: + script = _load_script() + + gate = script._evaluate_derivative_gate( + _gate_rows(script, central_scale=-1.0, repeat_error=1e-4) + ) + + assert gate["passed"] is False + assert any("sign agreement" in failure for failure in gate["failures"]) + assert any("alpha=0" in failure for failure in gate["failures"]) + + +def test_frozen_derivative_gate_rejects_too_many_near_zero_rows() -> None: + script = _load_script() + rows = _gate_rows(script) + for row in rows[:2]: + for result in row["epsilon_results"]: + result["autograd_directional_derivative"] = 0.0 + result["central_directional_derivative"] = 0.0 + + gate = script._evaluate_derivative_gate(rows) + + assert gate["passed"] is False + assert gate["observed"]["informative_rows"] == 2 + assert any("informative rows" in failure for failure in gate["failures"]) + + +def test_frozen_derivative_gate_all_near_zero_is_json_safe_failure() -> None: + script = _load_script() + rows = _gate_rows(script) + for row in rows: + for result in row["epsilon_results"]: + result["autograd_directional_derivative"] = script.DERIVATIVE_INFORMATIVE_FLOOR + result["central_directional_derivative"] = 0.0 + + gate = script._evaluate_derivative_gate(rows) + + assert gate["passed"] is False + assert gate["observed"]["informative_rows"] == 0 + assert gate["observed"]["near_zero_rows"] == 4 + assert gate["observed"]["sign_agreement"] is None + assert gate["observed"]["median_relative_error"] is None + assert gate["observed"]["converged_row_fraction"] is None + json.dumps(gate, allow_nan=False) + + +def test_frozen_derivative_gate_rejects_nonfinite_inputs_and_wrong_strata() -> None: + script = _load_script() + rows = _gate_rows(script) + rows[0]["epsilon_results"][0]["central_directional_derivative"] = float("nan") + + with pytest.raises(ValueError, match="must be finite"): + script._evaluate_derivative_gate(rows) + + rows = _gate_rows(script) + rows[0]["stratum"] = "substituted_after_results" + with pytest.raises(ValueError, match="source order"): + script._evaluate_derivative_gate(rows) diff --git a/tests/test_public_data.py b/tests/test_public_data.py index c326394..cdff26f 100644 --- a/tests/test_public_data.py +++ b/tests/test_public_data.py @@ -19,7 +19,9 @@ format_mbpp_prompt_code, load_mbpp_phase, load_mbpp_rows, + load_mbpp_rows_by_task_ids, mbpp_manifest, + mbpp_manifest_content_sha256, mbpp_manifest_sha256, mbpp_row_sha256, mbpp_source_split, @@ -97,11 +99,24 @@ def test_manifest_hash_is_order_independent_but_phase_bound() -> None: assert first != confirmation manifest = build_mbpp_manifest(rows, phase="development") assert mbpp_manifest(rows, phase="development") == manifest + assert mbpp_manifest_content_sha256(manifest) == first assert manifest["source_split"] == "validation" assert manifest["selection_namespace"] is None assert [item["task_id"] for item in manifest["rows"]] == [1, 2, 3] +def test_embedded_manifest_hash_authenticates_exact_content() -> None: + manifest = build_mbpp_manifest([row(1)], phase="calibration") + expected = mbpp_manifest_sha256([row(1)], phase="calibration") + + assert mbpp_manifest_content_sha256(dict(reversed(list(manifest.items())))) == expected + + changed = {**manifest, "unexpected_field": "authenticated too"} + assert mbpp_manifest_content_sha256(changed) != expected + with pytest.raises(TypeError, match="mapping"): + mbpp_manifest_content_sha256([manifest]) # type: ignore[arg-type] + + def test_prompt_code_formatter_keeps_target_separate_and_normalizes_newlines() -> None: example = row(9, newline="\r\n", code="def answer():\r\n return 9\r\n\r\n") example["text"] = "Write task 9." @@ -168,6 +183,53 @@ def fake_loader(*args: Any, **kwargs: Any) -> Iterable[Mapping[str, Any]]: load_mbpp_rows("development", limit=0) +def test_task_id_loader_retains_only_targets_in_frozen_order() -> None: + calls: list[tuple[tuple[Any, ...], dict[str, Any]]] = [] + malformed_non_target = {"task_id": 99, "text": object()} + + def fake_loader(*args: Any, **kwargs: Any) -> Iterable[Mapping[str, Any]]: + calls.append((args, kwargs)) + return [malformed_non_target, row(2), row(1), row(3)] + + loaded = load_mbpp_rows_by_task_ids( + "calibration", + task_ids=(3, 1), + load_dataset_fn=fake_loader, + ) + + assert [item["task_id"] for item in loaded] == [3, 1] + assert calls == [ + ( + (MBPP_DATASET_ID, MBPP_CONFIG), + {"revision": MBPP_REVISION, "split": "train", "streaming": True}, + ) + ] + + +def test_task_id_loader_rejects_duplicate_missing_and_confirmation_ids() -> None: + def fake_loader(*args: Any, **kwargs: Any) -> Iterable[Mapping[str, Any]]: + return [row(1), row(2)] + + with pytest.raises(ValueError, match="unique"): + load_mbpp_rows_by_task_ids( + "calibration", + task_ids=(1, 1), + load_dataset_fn=fake_loader, + ) + with pytest.raises(ValueError, match="missing.*3"): + load_mbpp_rows_by_task_ids( + "calibration", + task_ids=(1, 3), + load_dataset_fn=fake_loader, + ) + with pytest.raises(ValueError, match="forbidden for confirmation"): + load_mbpp_rows_by_task_ids( + "confirmation", + task_ids=(1,), + load_dataset_fn=fake_loader, + ) + + def test_confirmation_guard_blocks_before_loader_and_accepts_explicit_token() -> None: calls = 0 diff --git a/tests/test_query_ema_mixed_packed_cache.py b/tests/test_query_ema_mixed_packed_cache.py new file mode 100644 index 0000000..cdda58d --- /dev/null +++ b/tests/test_query_ema_mixed_packed_cache.py @@ -0,0 +1,443 @@ +from __future__ import annotations + +import math + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant import ( + QueryEmaMixedPackedLinearAttentionLayer, + QueryEmaMixedPackedRecurrentStateCache, + create_qwen35_query_ema_exact_budget_cache, +) +from recurquant.mixed_quantization import PackedMixedQuantizedTensor +from recurquant.query_energy import Qwen35QueryEnergyObserver +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from tests.test_transformers_cache import tiny_config + + +def _target_for_promotions(promotions: int) -> int: + total_groups = 16 + group_size = 8 + low_group_bytes = math.ceil(4 * group_size / 8) + 2 + high_increment = math.ceil(8 * group_size / 8) - math.ceil(4 * group_size / 8) + return total_groups * low_group_bytes + math.ceil(total_groups / 8) + ( + promotions * high_increment + ) + + +def _plan(promotions: int) -> ExactBudgetRowPlan: + scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + return select_rows_exact_budget( + scores, + target_resident_bytes=_target_for_promotions(promotions), + group_size=8, + ) + + +def _cache( + promotions: int = 1, + *, + record_evidence: bool = False, + confirmation_two: bool = False, +) -> QueryEmaMixedPackedRecurrentStateCache: + return create_qwen35_query_ema_exact_budget_cache( + tiny_config(), + plan=_plan(promotions), + record_evidence=record_evidence, + confirmation_two=confirmation_two, + ) + + +def _layer( + cache: QueryEmaMixedPackedRecurrentStateCache, +) -> QueryEmaMixedPackedLinearAttentionLayer: + layer = cache.layers[0] + assert isinstance(layer, QueryEmaMixedPackedLinearAttentionLayer) + return layer + + +def _query(*active_rows: int, tokens: int = 1, scale: float = 1.0) -> torch.Tensor: + query = torch.zeros((1, tokens, 2, 8), dtype=torch.float32) + for token in range(tokens): + for row in active_rows: + query.reshape(1, tokens, 16)[0, token, row] = scale + return query + + +def _difficult_state() -> torch.Tensor: + pattern = torch.tensor([1.0, 0.51, -0.37, 0.23, 0.14, -0.08, 0.03, -0.01]) + state = torch.zeros((1, 2, 8, 8), dtype=torch.float32) + state.reshape(16, 8)[0] = pattern * 10.0 + state.reshape(16, 8)[1] = pattern + return state + + +def _packed_mask(cache: QueryEmaMixedPackedRecurrentStateCache) -> torch.Tensor: + packed = _layer(cache).packed_states[0] + assert packed is not None + return packed.high_precision_mask().reshape(-1) + + +def test_query_energy_can_override_an_irrelevant_high_mse_row() -> None: + cache = _cache(promotions=1) + cache.stage_query_observation(0, _query(1, tokens=256)) + + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + + assert _packed_mask(cache).nonzero().reshape(-1).tolist() == [1] + + +def test_first_update_starts_from_frozen_uniform_ema() -> None: + cache = _cache(promotions=1) + cache.stage_query_observation(0, _query(0)) + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + layer = _layer(cache) + assert layer.query_energy_ema is not None + + expected = torch.full((2, 8), layer.query_ema_decay / 8, dtype=torch.float32) + expected[0, 0] += (1.0 - layer.query_ema_decay) / (1.0 + layer.query_l2norm_eps) + assert torch.allclose(layer.query_energy_ema, expected, atol=1e-8, rtol=1e-6) + + +@pytest.mark.parametrize("promotions", [0, 5, 16]) +def test_fixed_quota_packed_bytes_and_selector_auxiliary_bytes(promotions: int) -> None: + plan = _plan(promotions) + cache = create_qwen35_query_ema_exact_budget_cache( + tiny_config(), + plan=plan, + record_evidence=True, + ) + cache.stage_query_observation(0, _query(0, 3, tokens=2)) + + cache.update_recurrent_state(torch.zeros((1, 2, 8, 8)), layer_idx=0) + + summary = cache.storage_summary() + assert _packed_mask(cache).sum().item() == promotions + assert summary["resident_bytes"] == plan.resident_bytes + assert summary["selector_auxiliary_bytes"] == 2 * 8 * 4 + assert summary["resident_bytes_including_selector"] == plan.resident_bytes + 64 + assert cache.update_evidence[0].selection_method == ( + "query_ema32_weighted_aligned_mse_reduction" + ) + + +def test_chunk_ema_matches_sequential_one_token_updates() -> None: + generator = torch.Generator().manual_seed(401) + queries = torch.randn((1, 7, 2, 8), generator=generator) + state = torch.randn((1, 2, 8, 8), generator=generator) + chunked = _cache(promotions=3) + sequential = _cache(promotions=3) + + chunked.stage_query_observation(0, queries) + chunked.update_recurrent_state(state, layer_idx=0) + for token in range(queries.shape[1]): + sequential.stage_query_observation(0, queries[:, token : token + 1]) + sequential.update_recurrent_state(state, layer_idx=0) + + chunked_ema = _layer(chunked).query_energy_ema + sequential_ema = _layer(sequential).query_energy_ema + assert chunked_ema is not None + assert sequential_ema is not None + layer = _layer(chunked) + assert torch.allclose( + chunked_ema, + sequential_ema, + atol=layer.query_ema_chunk_atol, + rtol=layer.query_ema_chunk_rtol, + ) + + +def test_query_scaling_preserves_row_selection() -> None: + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(409)) + first = _cache(promotions=4) + second = _cache(promotions=4) + query = torch.randn((1, 3, 2, 8), generator=torch.Generator().manual_seed(419)) + + first.stage_query_observation(0, query) + second.stage_query_observation(0, query * 32.0) + first.update_recurrent_state(state, layer_idx=0) + second.update_recurrent_state(state, layer_idx=0) + + assert torch.equal(_packed_mask(first), _packed_mask(second)) + + +def test_query_ema_confirmation_two_requires_two_consecutive_raw_hits() -> None: + cache = _cache(promotions=1, confirmation_two=True) + for row, expected in zip((0, 1, 1), (0, 0, 1), strict=True): + cache.stage_query_observation(0, _query(row, tokens=256)) + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + assert _packed_mask(cache).nonzero().flatten().tolist() == [expected] + + diagnostics = cache.query_ema_diagnostics()[0] + assert _packed_mask(cache).nonzero().flatten().tolist() == [1] + assert diagnostics["confirmation_two"] is True + assert diagnostics["observations_staged"] == 3 + assert diagnostics["observations_committed"] == 3 + assert diagnostics["mask_transition_count"] == 2 + assert diagnostics["raw_xor_churn_total"] == 2 + assert diagnostics["committed_xor_churn_total"] == 2 + assert diagnostics["admissions_total"] == 1 + assert diagnostics["dwell_total"] == 1 + assert diagnostics["previous_raw_mask_bytes"] == 2 + assert diagnostics["selector_auxiliary_bytes"] == 66 + assert isinstance(diagnostics["raw_mask_sha256"], str) + assert isinstance(diagnostics["committed_mask_sha256"], str) + + cache.reset() + reset = cache.query_ema_diagnostics()[0] + assert reset["observations_staged"] == 0 + assert reset["observations_committed"] == 0 + assert reset["mask_transition_count"] == 0 + assert reset["raw_xor_churn_total"] == 0 + assert reset["committed_xor_churn_total"] == 0 + assert reset["previous_raw_mask_bytes"] == 0 + assert reset["last_raw_cutoff_score"] is None + assert reset["last_committed_normalized_gap"] is None + + +def test_missing_and_duplicate_query_observations_fail_closed() -> None: + cache = _cache(promotions=2) + state = torch.zeros((1, 2, 8, 8)) + + with pytest.raises(RuntimeError, match="no staged query observation"): + cache.update_recurrent_state(state, layer_idx=0) + assert _layer(cache).packed_states[0] is None + + cache.stage_query_observation(0, _query(0)) + with pytest.raises(RuntimeError, match="duplicate query observation"): + cache.stage_query_observation(0, _query(1)) + with pytest.raises(RuntimeError, match="no staged query observation"): + cache.update_recurrent_state(state, layer_idx=0) + assert _layer(cache).query_energy_ema is None + + +@pytest.mark.parametrize( + ("query", "error_type", "message"), + [ + (torch.zeros((2, 1, 2, 8)), ValueError, "must have shape"), + (torch.zeros((1, 0, 2, 8)), ValueError, "must have shape"), + (torch.zeros((1, 1, 1, 8)), ValueError, "must have shape"), + (torch.zeros((1, 1, 2, 7)), ValueError, "must have shape"), + (torch.zeros((1, 1, 2, 8), dtype=torch.int64), TypeError, "floating point"), + (torch.full((1, 1, 2, 8), float("nan")), ValueError, "must be finite"), + (torch.zeros((1, 1, 2, 8), device="meta"), ValueError, "materialized"), + ], +) +def test_invalid_query_observations_are_rejected( + query: torch.Tensor, + error_type: type[Exception], + message: str, +) -> None: + cache = _cache() + + with pytest.raises(error_type, match=message): + cache.stage_query_observation(0, query) + + assert _layer(cache)._pending_query_observation is None + + +def test_query_observation_must_share_the_persistent_ema_device() -> None: + layer = _layer(_cache()) + layer.query_energy_ema = torch.empty((2, 8), dtype=torch.float32, device="meta") + + with pytest.raises(ValueError, match="query observation and query EMA"): + layer.stage_query_observation(_query(0)) + + assert layer._pending_query_observation is None + + +@pytest.mark.parametrize("epsilon", [0.0, 1e-5, float("nan"), float("inf")]) +def test_frozen_normalization_epsilon_cannot_be_tuned(epsilon: float) -> None: + with pytest.raises(ValueError, match="frozen at 1e-6"): + _cache().stage_query_observation(0, _query(0), l2norm_eps=epsilon) + + +def test_stale_observation_and_failed_pack_do_not_commit_ema() -> None: + stale = _cache() + stale.stage_query_observation(0, _query(0)) + stale_layer = _layer(stale) + stale_layer._update_count += 1 + with pytest.raises(RuntimeError, match="stale query observation"): + stale.update_recurrent_state(torch.zeros((1, 2, 8, 8)), layer_idx=0) + assert stale_layer.query_energy_ema is None + assert stale_layer._pending_query_observation is None + + tracked = _cache() + tracked.stage_query_observation(0, _query(0)) + with pytest.raises(RuntimeError, match="inference-only"): + tracked.update_recurrent_state( + torch.zeros((1, 2, 8, 8), requires_grad=True), + layer_idx=0, + ) + assert _layer(tracked).query_energy_ema is None + assert _layer(tracked)._pending_query_observation is None + + +def test_reset_offload_prefetch_and_diagnostics_handle_fp32_ema() -> None: + cache = _cache(promotions=2) + cache.stage_query_observation(0, _query(0, 1, tokens=3)) + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + layer = _layer(cache) + assert layer.query_energy_ema is not None + assert layer.query_energy_ema.dtype == torch.float32 + diagnostics = cache.query_ema_diagnostics()[0] + assert diagnostics["observations_committed"] == 1 + assert diagnostics["tokens_observed"] == 3 + assert diagnostics["selector_auxiliary_bytes"] == 64 + + layer.offload() + assert layer.query_energy_ema.device.type == "cpu" + layer.prefetch() + assert layer.query_energy_ema.device == torch.device("cpu") + + cache.reset() + assert torch.equal( + layer.query_energy_ema, + torch.full((2, 8), 1.0 / 8, dtype=torch.float32), + ) + assert cache.query_ema_diagnostics()[0]["observations_committed"] == 0 + assert cache.query_ema_diagnostics()[0]["state_updates"] == 0 + + cache.stage_query_observation(0, _query(2)) + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + after_reset = cache.query_ema_diagnostics()[0] + assert after_reset["observations_committed"] == 1 + assert after_reset["state_updates"] == 1 + assert after_reset["last_mask_overlap"] is None + assert after_reset["last_mask_churn"] is None + + +def test_pending_observation_is_discarded_if_transfer_is_attempted() -> None: + layer = _layer(_cache()) + layer.stage_query_observation(_query(0)) + + with pytest.raises(RuntimeError, match="cannot offload with a pending"): + layer.offload() + + assert layer._pending_query_observation is None + layer.discard_pending_query_observation() + + +@pytest.mark.parametrize("confirmation_two", [False, True]) +def test_failed_materialization_rolls_back_packed_state_ema_and_generation( + monkeypatch: pytest.MonkeyPatch, + confirmation_two: bool, +) -> None: + cache = _cache(promotions=2, confirmation_two=confirmation_two) + layer = _layer(cache) + cache.stage_query_observation(0, _query(0, 1, tokens=2)) + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + previous_packed = layer.packed_states[0] + assert previous_packed is not None + assert layer.query_energy_ema is not None + previous_ema = layer.query_energy_ema.clone() + previous_diagnostics = layer.query_ema_diagnostics() + + cache.stage_query_observation(0, _query(3)) + + def fail_dequantize(self: PackedMixedQuantizedTensor) -> torch.Tensor: + raise RuntimeError("injected dequantize failure") + + monkeypatch.setattr(PackedMixedQuantizedTensor, "dequantize", fail_dequantize) + with pytest.raises(RuntimeError, match="injected dequantize failure"): + cache.update_recurrent_state(_difficult_state() * 2, layer_idx=0) + + assert layer.packed_states[0] is previous_packed + assert torch.equal(layer.query_energy_ema, previous_ema) + assert layer.query_ema_diagnostics() == previous_diagnostics + assert layer._pending_query_observation is None + + +def test_failed_postvalidation_rolls_back_without_appending_evidence( + monkeypatch: pytest.MonkeyPatch, +) -> None: + cache = _cache(promotions=2, record_evidence=True) + layer = _layer(cache) + cache.stage_query_observation(0, _query(0, 1, tokens=2)) + cache.update_recurrent_state(_difficult_state(), layer_idx=0) + previous_packed = layer.packed_states[0] + assert previous_packed is not None + assert layer.query_energy_ema is not None + previous_ema = layer.query_energy_ema.clone() + previous_diagnostics = layer.query_ema_diagnostics() + previous_evidence = list(cache.update_evidence) + previous_update_index = cache._update_index + + cache.stage_query_observation(0, _query(3)) + original_high_precision_mask = PackedMixedQuantizedTensor.high_precision_mask + mask_calls = 0 + + def fail_postvalidation_mask( + self: PackedMixedQuantizedTensor, + ) -> torch.Tensor: + nonlocal mask_calls + mask_calls += 1 + if mask_calls == 3: + raise RuntimeError("injected postvalidation failure") + return original_high_precision_mask(self) + + monkeypatch.setattr( + PackedMixedQuantizedTensor, + "high_precision_mask", + fail_postvalidation_mask, + ) + with pytest.raises(RuntimeError, match="injected postvalidation failure"): + cache.update_recurrent_state(_difficult_state() * 2, layer_idx=0) + + assert mask_calls == 3 + assert layer.packed_states[0] is previous_packed + assert torch.equal(layer.query_energy_ema, previous_ema) + assert layer.query_ema_diagnostics() == previous_diagnostics + assert cache.update_evidence == previous_evidence + assert cache._update_index == previous_update_index + assert layer._pending_query_observation is None + + +def test_invalid_state_index_discards_pending_observation() -> None: + layer = _layer(_cache()) + layer.stage_query_observation(_query(0)) + + with pytest.raises(IndexError, match="state_idx 99"): + layer.update_recurrent_state(_difficult_state(), state_idx=99) + + assert layer._pending_query_observation is None + + +def test_real_tiny_qwen_observer_runs_prefill_cached_chunk_and_decode() -> None: + model = Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + cache = create_qwen35_query_ema_exact_budget_cache( + model, + plan=_plan(5), + record_evidence=True, + ) + + with torch.inference_mode(), Qwen35QueryEnergyObserver(model, caches=[cache]): + prefill = model( + torch.randint(0, model.config.vocab_size, (1, 5)), + past_key_values=cache, + ) + chunk = model( + torch.randint(0, model.config.vocab_size, (1, 2)), + past_key_values=cache, + ) + decode = model( + torch.randint(0, model.config.vocab_size, (1, 1)), + past_key_values=cache, + ) + + assert prefill.logits.shape == (1, 5, model.config.vocab_size) + assert chunk.logits.shape == (1, 2, model.config.vocab_size) + assert decode.logits.shape == (1, 1, model.config.vocab_size) + diagnostics = cache.query_ema_diagnostics()[0] + assert diagnostics["observations_committed"] == 3 + assert diagnostics["state_updates"] == diagnostics["observations_committed"] + assert diagnostics["tokens_observed"] == 8 + assert diagnostics["pending_observation"] is False + assert diagnostics["current_selected_count"] == 5 + assert isinstance(diagnostics["current_mask_sha256"], str) + assert len(cache.update_evidence) == 3 diff --git a/tests/test_query_energy.py b/tests/test_query_energy.py new file mode 100644 index 0000000..482658b --- /dev/null +++ b/tests/test_query_energy.py @@ -0,0 +1,365 @@ +from __future__ import annotations + +from typing import Any + +import pytest +import torch + +from recurquant.query_energy import Qwen35QueryEnergyObserver + + +class _QueryAwareCache: + def __init__(self, *, has_previous_state: bool = False) -> None: + self.previous = has_previous_state + self.pending: dict[int, torch.Tensor] = {} + self.staged: list[tuple[int, torch.Tensor, float]] = [] + self.updates: list[tuple[int, torch.Tensor]] = [] + self.discards: list[int] = [] + self.fail_stage = False + self.fail_update = False + + def has_previous_state(self, layer_index: int) -> bool: + return self.previous + + def stage_query_observation( + self, + layer_index: int, + query: torch.Tensor, + *, + l2norm_eps: float, + ) -> None: + if self.fail_stage: + raise RuntimeError("stage failed") + snapshot = query.detach().clone() + self.pending[layer_index] = snapshot + self.staged.append((layer_index, snapshot, l2norm_eps)) + + def discard_pending_query_observation(self, layer_index: int) -> None: + self.pending.pop(layer_index, None) + self.discards.append(layer_index) + + def update_recurrent_state( + self, + state: torch.Tensor, + layer_idx: int, + ) -> torch.Tensor: + if self.fail_update: + raise RuntimeError("update failed") + if layer_idx not in self.pending: + raise RuntimeError("query was not staged before recurrent update") + self.pending.pop(layer_idx) + self.updates.append((layer_idx, state.detach().clone())) + self.previous = True + return state + + +class _ReferenceCache: + def __init__(self, *, has_previous_state: bool = False) -> None: + self.previous = has_previous_state + self.updates = 0 + + def has_previous_state(self, layer_index: int) -> bool: + return self.previous + + def update_recurrent_state( + self, + state: torch.Tensor, + layer_idx: int, + ) -> torch.Tensor: + self.previous = True + self.updates += 1 + return state + + +class Qwen3_5GatedDeltaNet(torch.nn.Module): + def __init__(self, layer_idx: int) -> None: + super().__init__() + self.layer_idx = layer_idx + self.kernel_calls: list[str] = [] + self.queries: list[torch.Tensor] = [] + self.chunk_gated_delta_rule = self._chunk + self.recurrent_gated_delta_rule = self._recurrent + + def _chunk(self, query: torch.Tensor, *args: object, **kwargs: object): + self.kernel_calls.append("chunk") + self.queries.append(query.detach().clone()) + return query, query.sum(dim=1) + + def _recurrent(self, query: torch.Tensor, *args: object, **kwargs: object): + self.kernel_calls.append("recurrent") + self.queries.append(query.detach().clone()) + return query, query.sum(dim=1) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: object | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: object, + ) -> torch.Tensor: + del attention_mask + # Stand in for Qwen3.5's causal convolution, reshape, and key-head repeat. + query = (hidden_states + 7.0).repeat_interleave(2, dim=2) + previous = cache_params is not None and cache_params.has_previous_state( # type: ignore[attr-defined] + self.layer_idx + ) + if previous and query.shape[1] == 1: + output, final_state = self.recurrent_gated_delta_rule(query) + else: + output, final_state = self.chunk_gated_delta_rule( + query, + cu_seqlens=kwargs.get("cu_seq_lens_q"), + ) + if cache_params is not None: + cache_params.update_recurrent_state( # type: ignore[attr-defined] + final_state, + self.layer_idx, + ) + return output + + +class _TinyModel(torch.nn.Module): + def __init__(self, layers: int = 1) -> None: + super().__init__() + self.layers = torch.nn.ModuleList( + [Qwen3_5GatedDeltaNet(index) for index in range(layers)] + ) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: object | None = None, + **kwargs: object, + ) -> torch.Tensor: + output = hidden_states + for layer in self.layers: + output = layer(output, cache_params=cache_params, **kwargs) + return output + + +def _input(sequence_length: int) -> torch.Tensor: + return torch.arange(sequence_length * 2, dtype=torch.float32).reshape( + 1, + sequence_length, + 1, + 2, + ) + + +def test_observes_post_convolution_repeated_query_for_all_forward_modes() -> None: + model = _TinyModel() + cache = _QueryAwareCache() + + with Qwen35QueryEnergyObserver(model, caches=[cache], l2norm_eps=2e-5): + model(_input(3), cache_params=cache) # uncached prefill + model(_input(2), cache_params=cache) # cached multi-token chunk + model(_input(1), cache_params=cache) # cached one-token decode + + assert model.layers[0].kernel_calls == ["chunk", "chunk", "recurrent"] + assert len(cache.staged) == 3 + assert len(cache.updates) == 3 + assert not cache.pending + for (layer_index, staged, eps), kernel_query in zip( + cache.staged, + model.layers[0].queries, + strict=True, + ): + assert layer_index == 0 + assert torch.equal(staged, kernel_query) + assert staged.shape[2] == 2 + assert eps == 2e-5 + + +def test_dispatches_strictly_by_identity_across_two_caches() -> None: + model = _TinyModel() + first = _QueryAwareCache() + second = _QueryAwareCache() + + with Qwen35QueryEnergyObserver(model, caches=[first, second]): + model(_input(2), cache_params=first) + model(_input(2), cache_params=second) + + assert len(first.staged) == 1 + assert len(second.staged) == 1 + assert first.staged[0][1].data_ptr() != second.staged[0][1].data_ptr() + + +def test_reference_cache_and_direct_kernel_calls_pass_through_unchanged() -> None: + model = _TinyModel() + observed = _QueryAwareCache() + reference = _ReferenceCache() + kernel = model.layers[0].chunk_gated_delta_rule + + with Qwen35QueryEnergyObserver(model, caches=[observed]): + output = model(_input(2), cache_params=reference) + direct_output = model.layers[0].chunk_gated_delta_rule(_input(1)) + + assert output.shape == (1, 2, 2, 2) + assert isinstance(direct_output, tuple) + assert reference.updates == 1 + assert not observed.staged + assert not observed.discards + assert model.layers[0].chunk_gated_delta_rule is kernel + + +def test_restores_forward_and_both_kernels_after_normal_and_exceptional_exit() -> None: + model = _TinyModel() + cache = _QueryAwareCache() + module = model.layers[0] + originals = ( + module.forward, + module.chunk_gated_delta_rule, + module.recurrent_gated_delta_rule, + ) + + with Qwen35QueryEnergyObserver(model, caches=[cache]): + assert module.forward is not originals[0] + assert module.chunk_gated_delta_rule is not originals[1] + assert module.recurrent_gated_delta_rule is not originals[2] + + assert module.forward.__func__ is originals[0].__func__ + assert "forward" not in module.__dict__ + assert module.chunk_gated_delta_rule is originals[1] + assert module.recurrent_gated_delta_rule is originals[2] + + with ( + pytest.raises(RuntimeError, match="body failed"), + Qwen35QueryEnergyObserver(model, caches=[cache]), + ): + raise RuntimeError("body failed") + + assert module.forward.__func__ is originals[0].__func__ + assert "forward" not in module.__dict__ + assert module.chunk_gated_delta_rule is originals[1] + assert module.recurrent_gated_delta_rule is originals[2] + + +@pytest.mark.parametrize("failure", ["stage", "update"]) +def test_forward_failure_discards_unconsumed_observation(failure: str) -> None: + model = _TinyModel() + cache = _QueryAwareCache() + if failure == "stage": + cache.fail_stage = True + else: + cache.fail_update = True + + with ( + Qwen35QueryEnergyObserver(model, caches=[cache]), + pytest.raises(RuntimeError, match=f"{failure} failed"), + ): + model(_input(2), cache_params=cache) + + assert not cache.pending + assert cache.discards == [0] + + +def test_packed_cu_seqlens_is_rejected_before_kernel_or_cache_mutation() -> None: + model = _TinyModel() + cache = _QueryAwareCache() + + with ( + Qwen35QueryEnergyObserver(model, caches=[cache]), + pytest.raises(ValueError, match="does not yet support packed cu_seqlens"), + ): + model( + _input(2), + cache_params=cache, + cu_seq_lens_q=torch.tensor([0, 2]), + ) + + assert not model.layers[0].kernel_calls + assert not cache.staged + assert not cache.updates + + +def test_duplicate_observers_and_duplicate_cache_entries_are_rejected() -> None: + model = _TinyModel() + cache = _QueryAwareCache() + + with pytest.raises(ValueError, match="same object"): + Qwen35QueryEnergyObserver(model, caches=[cache, cache]) + + first = Qwen35QueryEnergyObserver(model, caches=[cache]) + second = Qwen35QueryEnergyObserver(model, caches=[cache]) + with first, pytest.raises(RuntimeError, match="already installed"): + second.install() + + +@pytest.mark.parametrize("cache_argument", [None, _ReferenceCache()]) +def test_unselected_or_missing_cache_bypasses_observer(cache_argument: object | None) -> None: + model = _TinyModel() + observed = _QueryAwareCache() + + with Qwen35QueryEnergyObserver(model, caches=[observed]): + model(_input(2), cache_params=cache_argument) + + assert not observed.staged + assert not observed.discards + + +@pytest.mark.parametrize( + ("caches", "error", "message"), + [ + ([], ValueError, "at least one"), + ([object()], TypeError, "stage_query_observation"), + ], +) +def test_constructor_fails_closed_on_invalid_cache_contract( + caches: list[object], + error: type[Exception], + message: str, +) -> None: + with pytest.raises(error, match=message): + Qwen35QueryEnergyObserver(_TinyModel(), caches=caches) + + +def test_install_fails_atomically_when_a_kernel_attribute_is_invalid() -> None: + model = _TinyModel(layers=2) + cache = _QueryAwareCache() + first = model.layers[0] + first_originals = ( + first.forward, + first.chunk_gated_delta_rule, + first.recurrent_gated_delta_rule, + ) + model.layers[1].recurrent_gated_delta_rule = None # type: ignore[assignment] + + observer = Qwen35QueryEnergyObserver(model, caches=[cache]) + with pytest.raises(TypeError, match="must be callable"): + observer.install() + + assert first.forward.__func__ is first_originals[0].__func__ + assert "forward" not in first.__dict__ + assert first.chunk_gated_delta_rule is first_originals[1] + assert first.recurrent_gated_delta_rule is first_originals[2] + assert not hasattr(model, "_recurquant_query_energy_observer") + assert all( + not hasattr(module, "_recurquant_query_energy_observer") + for module in model.layers + ) + + +def test_selected_cache_with_non_tensor_kernel_query_fails_closed() -> None: + model = _TinyModel() + cache = _QueryAwareCache() + module = model.layers[0] + + def malformed_forward( + hidden_states: torch.Tensor, + cache_params: object | None = None, + **kwargs: Any, + ) -> torch.Tensor: + del kwargs + module.chunk_gated_delta_rule("not-a-tensor") + raise AssertionError("unreachable") + + module.forward = malformed_forward # type: ignore[method-assign] + module.chunk_gated_delta_rule = lambda query: (query, query) + with ( + Qwen35QueryEnergyObserver(model, caches=[cache]), + pytest.raises(TypeError, match="did not expose its query tensor"), + ): + module(_input(2), cache_params=cache) + + assert not cache.pending + assert cache.discards == [0] diff --git a/tests/test_rank_fused_mixed_packed_cache.py b/tests/test_rank_fused_mixed_packed_cache.py new file mode 100644 index 0000000..89bf886 --- /dev/null +++ b/tests/test_rank_fused_mixed_packed_cache.py @@ -0,0 +1,315 @@ +from __future__ import annotations + +import hashlib +import math + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant import ( + RankFusedMixedPackedRecurrentStateCache, + create_qwen35_rank_fused_exact_budget_cache, +) +from recurquant.packed_cache import ( + AdaptiveMixedPackedLinearAttentionLayer, + RankFusedMixedPackedLinearAttentionLayer, +) +from recurquant.qwen35 import create_qwen35_adaptive_exact_budget_cache +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from tests.test_transformers_cache import tiny_config + + +def _target_for_promotions( + scores: dict[int, torch.Tensor], + *, + promotions: int, + group_size: int = 8, + scale_bits: int = 16, +) -> int: + total_groups = sum(score.numel() for score in scores.values()) + low_group_bytes = math.ceil(4 * group_size / 8) + scale_bits // 8 + increment = math.ceil(8 * group_size / 8) - math.ceil(4 * group_size / 8) + return total_groups * low_group_bytes + math.ceil(total_groups / 8) + promotions * increment + + +def _plan( + *, + promotions: int, + scores: dict[int, torch.Tensor] | None = None, +) -> ExactBudgetRowPlan: + if scores is None: + scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + return select_rows_exact_budget( + scores, + target_resident_bytes=_target_for_promotions(scores, promotions=promotions), + group_size=8, + ) + + +def _rank_fused_mask( + cache: RankFusedMixedPackedRecurrentStateCache, + layer_index: int = 0, +) -> torch.Tensor: + layer = cache.layers[layer_index] + assert isinstance(layer, RankFusedMixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + return packed.high_precision_mask().reshape(-1) + + +def _adaptive_mask(cache: object, layer_index: int = 0) -> torch.Tensor: + layer = cache.layers[layer_index] # type: ignore[attr-defined] + assert isinstance(layer, AdaptiveMixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + return packed.high_precision_mask().reshape(-1) + + +def test_zero_weight_exactly_matches_existing_adaptive_selector_and_storage() -> None: + static_scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + plan = _plan(promotions=5, scores=static_scores) + adaptive = create_qwen35_adaptive_exact_budget_cache(tiny_config(), plan=plan) + rank_fused = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.0, + ) + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(307)) + + adaptive_result = adaptive.update_recurrent_state(state, layer_idx=0) + rank_fused_result = rank_fused.update_recurrent_state(state, layer_idx=0) + + assert torch.equal(_rank_fused_mask(rank_fused), _adaptive_mask(adaptive)) + assert torch.equal(rank_fused_result, adaptive_result) + adaptive_packed = adaptive.layers[0].packed_states[0] # type: ignore[attr-defined] + rank_fused_packed = rank_fused.layers[0].packed_states[0] # type: ignore[attr-defined] + assert adaptive_packed is not None + assert rank_fused_packed is not None + assert torch.equal(rank_fused_packed.low_payload, adaptive_packed.low_payload) + assert torch.equal(rank_fused_packed.high_payload, adaptive_packed.high_payload) + assert torch.equal(rank_fused_packed.scales, adaptive_packed.scales) + assert torch.equal(rank_fused_packed.precision_mask, adaptive_packed.precision_mask) + assert rank_fused.storage_summary() == adaptive.storage_summary() + + +def test_one_weight_exactly_selects_static_plan_rows() -> None: + static_scores = { + 0: torch.tensor( + [1.0, 8.0, 3.0, 14.0, 0.0, 5.0, 12.0, 4.0, 11.0, 2.0, 15.0, 7.0, + 9.0, 13.0, 6.0, 10.0] + ).reshape(2, 8) + } + plan = _plan(promotions=5, scores=static_scores) + cache = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=1.0, + ) + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(311)) + + cache.update_recurrent_state(state, layer_idx=0) + + expected = torch.zeros(16, dtype=torch.bool) + expected[list(plan.groups_for_layer(0))] = True + assert torch.equal(_rank_fused_mask(cache), expected) + + +def test_equal_static_and_dynamic_scores_use_stable_flattened_order() -> None: + static_scores = {0: torch.zeros((2, 8), dtype=torch.float32)} + plan = _plan(promotions=3, scores=static_scores) + first = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + second = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + state = torch.zeros((1, 2, 8, 8)) + + first.update_recurrent_state(state, layer_idx=0) + second.update_recurrent_state(state, layer_idx=0) + + expected = torch.zeros(16, dtype=torch.bool) + expected[:3] = True + assert torch.equal(_rank_fused_mask(first), expected) + assert torch.equal(_rank_fused_mask(second), expected) + + +@pytest.mark.parametrize("promotions", [0, 16]) +def test_zero_and_full_quotas_keep_exact_counts_and_bytes(promotions: int) -> None: + static_scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + plan = _plan(promotions=promotions, scores=static_scores) + cache = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + + cache.update_recurrent_state( + torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(309)), + layer_idx=0, + ) + + assert _rank_fused_mask(cache).sum().item() == promotions + assert cache.storage_summary()["high_precision_groups"] == promotions + assert cache.storage_summary()["resident_bytes"] == plan.resident_bytes + + +@pytest.mark.parametrize("weight", [-0.01, 1.01, float("nan"), float("inf")]) +def test_invalid_static_rank_weight_values_are_rejected(weight: float) -> None: + scores = {0: torch.zeros((2, 8))} + + with pytest.raises(ValueError, match=r"finite and in \[0, 1\]"): + create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=_plan(promotions=3, scores=scores), + static_scores_by_layer=scores, + static_rank_weight=weight, + ) + + +@pytest.mark.parametrize("weight", [True, "0.5"]) +def test_non_real_static_rank_weights_are_rejected(weight: object) -> None: + scores = {0: torch.zeros((2, 8))} + + with pytest.raises(TypeError, match="must be a real number"): + create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=_plan(promotions=3, scores=scores), + static_scores_by_layer=scores, + static_rank_weight=weight, # type: ignore[arg-type] + ) + + +@pytest.mark.parametrize( + ("static_scores", "error_type", "message"), + [ + ({1: torch.zeros((2, 8))}, ValueError, "exactly match the row plan"), + ({0: torch.zeros(16)}, ValueError, "must have shape"), + ({0: torch.zeros((2, 8), dtype=torch.int64)}, TypeError, "floating point"), + ({0: torch.full((2, 8), float("nan"))}, ValueError, "must be finite"), + ({0: torch.zeros((2, 8), device="meta")}, ValueError, "must be materialized"), + ({0: object()}, TypeError, "must be a tensor"), + ], +) +def test_invalid_static_score_maps_are_rejected( + static_scores: dict[int, object], + error_type: type[Exception], + message: str, +) -> None: + plan_scores = {0: torch.zeros((2, 8))} + + with pytest.raises(error_type, match=message): + create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=_plan(promotions=3, scores=plan_scores), + static_scores_by_layer=static_scores, # type: ignore[arg-type] + static_rank_weight=0.5, + ) + + +def test_static_scores_must_share_the_recurrent_state_device() -> None: + static_scores = {0: torch.zeros((2, 8))} + cache = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=_plan(promotions=3, scores=static_scores), + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + + with pytest.raises(ValueError, match="same device"): + cache.update_recurrent_state(torch.zeros((1, 2, 8, 8), device="meta"), layer_idx=0) + + +def test_each_layer_preserves_quota_exact_bytes_and_distinct_evidence_method() -> None: + static_scores = { + 0: torch.tensor([100.0, 99.0, 98.0] + [-100.0] * 13).reshape(2, 8), + 1: torch.tensor([97.0, 96.0, 95.0, 94.0] + [-200.0] * 12).reshape(2, 8), + } + plan = _plan(promotions=7, scores=static_scores) + cache = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(["linear_attention", "linear_attention"]), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.25, + record_evidence=True, + ) + generator = torch.Generator().manual_seed(313) + + cache.update_recurrent_state(torch.randn((1, 2, 8, 8), generator=generator), layer_idx=0) + cache.update_recurrent_state(torch.randn((1, 2, 8, 8), generator=generator), layer_idx=1) + + assert [_rank_fused_mask(cache, index).sum().item() for index in (0, 1)] == [3, 4] + summary = cache.storage_summary() + assert summary["high_precision_groups"] == plan.promoted_group_count == 7 + assert summary["resident_bytes"] == plan.resident_bytes + assert len(cache.update_evidence) == 2 + assert all( + evidence.selection_method == "quota_preserving_static_dynamic_rank_fusion" + for evidence in cache.update_evidence + ) + for evidence, layer_index in zip(cache.update_evidence, (0, 1), strict=True): + packed = cache.layers[layer_index].packed_states[0] # type: ignore[attr-defined] + assert packed is not None + expected_hash = hashlib.sha256( + bytes(packed.precision_mask.detach().cpu().tolist()) + ).hexdigest() + assert evidence.high_precision_mask_sha256 == expected_hash + + +def test_batch_one_and_inference_only_contracts_are_preserved() -> None: + static_scores = {0: torch.zeros((2, 8))} + plan = _plan(promotions=3, scores=static_scores) + batch_cache = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + + with pytest.raises(ValueError, match="requires batch size 1; got 2"): + batch_cache.update_recurrent_state(torch.zeros((2, 2, 8, 8)), layer_idx=0) + + grad_cache = create_qwen35_rank_fused_exact_budget_cache( + tiny_config(), + plan=plan, + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + state = torch.randn((1, 2, 8, 8), requires_grad=True) + with pytest.raises(RuntimeError, match="inference-only"): + grad_cache.update_recurrent_state(state, layer_idx=0) + with torch.no_grad(): + restored = grad_cache.update_recurrent_state(state, layer_idx=0) + assert not restored.requires_grad + + +def test_rank_fused_factory_runs_tiny_qwen_prefill_and_decode() -> None: + torch.manual_seed(317) + model = Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + static_scores = {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)} + cache = create_qwen35_rank_fused_exact_budget_cache( + model, + plan=_plan(promotions=5, scores=static_scores), + static_scores_by_layer=static_scores, + static_rank_weight=0.5, + ) + + with torch.inference_mode(): + model(torch.randint(0, model.config.vocab_size, (1, 5)), past_key_values=cache) + output = model(torch.randint(0, model.config.vocab_size, (1, 1)), past_key_values=cache) + + assert output.logits.shape == (1, 1, model.config.vocab_size) + assert _rank_fused_mask(cache).sum().item() == 5 diff --git a/tests/test_resolve_rht_cqer_stage_b_identity.py b/tests/test_resolve_rht_cqer_stage_b_identity.py new file mode 100644 index 0000000..8056c03 --- /dev/null +++ b/tests/test_resolve_rht_cqer_stage_b_identity.py @@ -0,0 +1,807 @@ +from __future__ import annotations + +import copy +import hashlib +import importlib +import json +import random +import sys +from collections.abc import Iterator, Mapping +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +import pytest + +from recurquant.evidence import canonical_json_bytes +from recurquant.public_data import ( + MBPP_CALIBRATION_SIZE, + mbpp_calibration_key, + mbpp_manifest, + mbpp_manifest_content_sha256, + select_mbpp_calibration, +) + +resolver = importlib.import_module("scripts.resolve_rht_cqer_stage_b_identity") +REPOSITORY_ROOT = Path(__file__).resolve().parents[1] + + +def _resolver_runtime_modules() -> dict[str, Any]: + modules: dict[str, Any] = {} + frozen_paths = set(resolver.STAGE_B_SOURCE_FILES) + for name, module in sys.modules.items(): + if not ( + name == "recurquant" + or name.startswith("recurquant.") + or name == resolver.__name__ + ): + continue + raw_path = getattr(module, "__file__", None) + if not isinstance(raw_path, str): + continue + path = Path(raw_path).resolve() + try: + relative = path.relative_to(REPOSITORY_ROOT).as_posix() + except ValueError: + continue + if relative in frozen_paths: + modules[name] = module + return modules + + +def test_all_imported_repository_modules_are_source_frozen() -> None: + imported = resolver.validate_all_imported_repository_modules_frozen( + REPOSITORY_ROOT, + modules=_resolver_runtime_modules(), + ) + + assert imported["recurquant"] == "src/recurquant/__init__.py" + assert set(imported.values()) <= set(resolver.STAGE_B_SOURCE_FILES) + + +def test_unfrozen_imported_repository_module_is_rejected() -> None: + unfrozen = SimpleNamespace( + __file__=str(REPOSITORY_ROOT / "src" / "recurquant" / "cli.py") + ) + + with pytest.raises(ValueError, match="outside the Stage-B source freeze"): + resolver.validate_all_imported_repository_modules_frozen( + REPOSITORY_ROOT, + modules={"recurquant.unfrozen_test": unfrozen}, + ) + + +def _row(task_id: int) -> dict[str, Any]: + return { + "task_id": task_id, + "text": f"Return {task_id}.", + "code": f"def answer_{task_id}():\n return {task_id}\n", + "test_list": [f"assert answer_{task_id}() == {task_id}"], + "test_setup_code": "", + "challenge_test_list": [], + } + + +class _TrackedRow(Mapping[str, Any]): + def __init__(self, row: Mapping[str, Any], accesses: list[tuple[int, str]]) -> None: + self._row = row + self._accesses = accesses + + def __getitem__(self, key: str) -> Any: + self._accesses.append((int(self._row["task_id"]), key)) + return self._row[key] + + def __iter__(self) -> Iterator[str]: + return iter(self._row) + + def __len__(self) -> int: + return len(self._row) + + def get(self, key: str, default: Any = None) -> Any: + self._accesses.append((int(self._row["task_id"]), key)) + return self._row.get(key, default) + + +class Qwen2Tokenizer: + def __call__( + self, + text: str, + *, + add_special_tokens: bool, + return_attention_mask: bool, + return_token_type_ids: bool, + ) -> dict[str, list[int]]: + assert return_attention_mask is False + assert return_token_type_ids is False + payload = list(text.encode("utf-8")) + return {"input_ids": ([1] if add_special_tokens else []) + payload} + + +def _compact_row_plan() -> dict[str, Any]: + locations = [ + { + "layer_index": layer, + "head_index": flat_index // 128, + "row_index": flat_index % 128, + } + for layer in resolver.FROZEN_LINEAR_LAYERS + for flat_index in range(resolver.FROZEN_LAYER_QUOTAS[layer]) + ] + plan: dict[str, Any] = { + "schema": resolver.ROW_PLAN_SCHEMA, + "method": resolver.ROW_PLAN_METHOD, + "selector_binding": { + "selector_file_sha256": resolver.SELECTOR_FILE_SHA256, + "selector_canonical_evidence_sha256": ( + resolver.SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + "loss_selector_file_sha256": resolver.LOSS_SELECTOR_FILE_SHA256, + "loss_selector_canonical_evidence_sha256": ( + resolver.LOSS_SELECTOR_CANONICAL_EVIDENCE_SHA256 + ), + }, + "model": { + "id": resolver.MODEL_ID, + "revision": resolver.MODEL_REVISION, + }, + "quantization": { + "low_bits": 4, + "high_bits": 8, + "group_size": 128, + "scale_bits": 16, + }, + "accounting": { + "total_groups": 36_864, + "mask_bytes": 4_608, + "promotion_increment_bytes": 64, + "target_resident_bytes": 2_564_096, + "resident_bytes": 2_564_096, + "promoted_group_count": 1_976, + }, + "score_shapes": [ + {"layer_index": layer, "heads": 16, "rows": 128} + for layer in resolver.FROZEN_LINEAR_LAYERS + ], + "layer_quotas": { + str(layer): quota + for layer, quota in resolver.FROZEN_LAYER_QUOTAS.items() + }, + "high_precision_rows": locations, + } + plan["canonical_plan_sha256"] = hashlib.sha256( + canonical_json_bytes(plan) + ).hexdigest() + return plan + + +def _loader_with_tracking( + rows: list[dict[str, Any]], + accesses: list[tuple[int, str]], +) -> Any: + def loader(*args: Any, **kwargs: Any) -> tuple[_TrackedRow, ...]: + assert args[:2] == ( + resolver.MBPP_DATASET_ID, + resolver.MBPP_CONFIG, + ) + assert kwargs == { + "revision": resolver.MBPP_REVISION, + "split": "train", + "streaming": True, + } + return tuple(_TrackedRow(row, accesses) for row in rows) + + return loader + + +def _identity_evidence( + rows: tuple[Mapping[str, Any], ...], + records: tuple[dict[str, Any], ...], +) -> dict[str, Any]: + content_manifest = mbpp_manifest(rows, phase="calibration") + content_hash = mbpp_manifest_content_sha256(content_manifest) + source_hashes = { + path: hashlib.sha256(path.encode()).hexdigest() + for path in resolver.SOURCE_FILES + } + commit = "a" * 40 + return { + "schema_version": 1, + "artifact_kind": resolver.ARTIFACT_KIND, + "identity_schema": resolver.IDENTITY_SCHEMA, + "identity_only": True, + "claim_boundary": resolver.CLAIM_BOUNDARY, + "created_at_utc": "2026-07-26T00:00:00+00:00", + "authorization": { + "stage_a_artifact_kind": resolver.STAGE_A_ARTIFACT_KIND, + "stage_a_file_sha256": resolver.STAGE_A_FILE_SHA256, + "stage_a_canonical_evidence_sha256": ( + resolver.STAGE_A_CANONICAL_EVIDENCE_SHA256 + ), + "stage_a_implementation_commit": resolver.STAGE_A_IMPLEMENTATION_COMMIT, + "stage_a_result_commit": resolver.STAGE_A_RESULT_COMMIT, + "stage_a_gate_passed": True, + "verified_before_dataset_access": True, + }, + "row_plan": _compact_row_plan(), + "model_contract": { + "id": resolver.MODEL_ID, + "revision": resolver.MODEL_REVISION, + "weights_loaded": False, + }, + "tokenizer_contract": { + "source_id": resolver.MODEL_ID, + "revision": resolver.MODEL_REVISION, + "class": resolver.TOKENIZER_CLASS, + "transformers_version": resolver.TRANSFORMERS_VERSION, + "trust_remote_code": False, + "prompt_add_special_tokens": True, + "code_add_special_tokens": False, + "formatter_version": resolver.MBPP_FORMATTER_VERSION, + "token_id_hash_serialization": resolver.TOKEN_ID_HASH_SERIALIZATION, + "text_hash_encoding": resolver.TEXT_HASH_ENCODING, + }, + "dataset": { + "id": resolver.MBPP_DATASET_ID, + "config": resolver.MBPP_CONFIG, + "revision": resolver.MBPP_REVISION, + "phase": "calibration", + "source_split": "train", + "selection_namespace": resolver.MBPP_SELECTION_NAMESPACE, + "formatter_version": resolver.MBPP_FORMATTER_VERSION, + "selection_mode": "task_id_ranking_then_exact_task_id_stream", + "selection_window": { + "offset": resolver.STAGE_B_OFFSET, + "limit": resolver.STAGE_B_LIMIT, + "stop_exclusive": resolver.STAGE_B_STOP, + }, + "protected_window": { + "offset": resolver.PROTECTED_WINDOW[0], + "stop_exclusive": resolver.PROTECTED_WINDOW[1], + "content_retained_canonicalized_or_tokenized": False, + }, + "ordered_task_ids": [int(row["task_id"]) for row in rows], + "manifest": content_manifest, + "content_manifest_sha256": content_hash, + "token_manifest_sha256": resolver.token_manifest_sha256(records), + "ordered_identity_sha256": resolver.ordered_identity_sha256( + content_manifest_sha256=content_hash, + task_records=records, + ), + "tasks": list(records), + "totals": { + "source_train_rows_seen_by_task_id_only": MBPP_CALIBRATION_SIZE, + "retained_rows": resolver.STAGE_B_LIMIT, + "prompt_tokens": sum(record["prompt_tokens"] for record in records), + "code_tokens": sum(record["code_tokens"] for record in records), + "aligned_scored_tokens": sum( + record["aligned_scored_tokens"] for record in records + ), + "full_code_scored_tokens": sum( + record["full_code_scored_tokens"] for record in records + ), + }, + "data_access": resolver.StageBDataAccessAudit( + ranking_transport_records_yielded=MBPP_CALIBRATION_SIZE, + ranking_task_id_fields_inspected=MBPP_CALIBRATION_SIZE, + target_transport_records_yielded=MBPP_CALIBRATION_SIZE, + target_task_id_fields_inspected=MBPP_CALIBRATION_SIZE, + target_rows_retained_and_canonicalized=resolver.STAGE_B_LIMIT, + ).as_dict( + selected_task_ids=[int(row["task_id"]) for row in rows], + ), + }, + "integrity": { + "stage_a_authenticated_before_dataset_access": True, + "runtime_environment_authenticated_before_dataset_access": True, + "selector_artifacts_authenticated_before_dataset_access": True, + "repository_clean_at_start": True, + "repository_clean_at_end": True, + "repository_commit_stable": True, + "source_hashes_stable": True, + "task_id_only_ranking_pass": True, + "only_stage_b_content_retained_canonicalized_and_tokenized": True, + "imported_modules_resolved_to_authenticated_repository": True, + "protected_window_8_16_content_retained_canonicalized_or_tokenized": False, + "model_weights_loaded": False, + "model_forward_pass_run": False, + "logits_or_quality_metrics_observed": False, + "output_path_external_or_git_ignored": True, + }, + "repository": { + "commit": commit, + "start": {"commit": commit, "worktree_clean": True, "status": []}, + "end": {"commit": commit, "worktree_clean": True, "status": []}, + "stable_commit": True, + }, + "source_files": { + "paths": list(resolver.SOURCE_FILES), + "sha256_start": source_hashes, + "sha256_end": source_hashes, + "stable": True, + "imported_modules": dict(resolver.IMPORTED_MODULE_PATHS), + }, + "environment": { + "schema": resolver.RUNTIME_ENVIRONMENT_SCHEMA, + "stage_a_binding": { + "artifact_kind": resolver.STAGE_A_ARTIFACT_KIND, + "file_sha256": resolver.STAGE_A_FILE_SHA256, + "canonical_evidence_sha256": ( + resolver.STAGE_A_CANONICAL_EVIDENCE_SHA256 + ), + }, + "python": { + "major": resolver.STAGE_A_PYTHON_VERSION[0], + "minor": resolver.STAGE_A_PYTHON_VERSION[1], + "micro": resolver.STAGE_A_PYTHON_VERSION[2], + "version": ".".join( + str(component) + for component in resolver.STAGE_A_PYTHON_VERSION + ), + }, + "packages": dict(resolver.STAGE_A_PACKAGE_VERSIONS), + "cuda": dict(resolver.STAGE_A_CUDA_CONTRACT), + "runtime_matches_stage_a": True, + "local_files_only": True, + }, + } + + +@pytest.fixture +def resolved_identity() -> tuple[ + list[dict[str, Any]], + tuple[Mapping[str, Any], ...], + tuple[dict[str, Any], ...], + dict[str, Any], +]: + source_rows = [_row(task_id) for task_id in range(1000, 1000 + MBPP_CALIBRATION_SIZE)] + ranked = sorted( + source_rows, + key=lambda row: (mbpp_calibration_key(row["task_id"]), row["task_id"]), + ) + rows = tuple(ranked[resolver.STAGE_B_OFFSET : resolver.STAGE_B_STOP]) + _, records = resolver.tokenize_stage_b_rows(Qwen2Tokenizer(), rows) + evidence = _identity_evidence(rows, records) + return source_rows, rows, records, evidence + + +def test_resolver_reads_only_task_id_outside_stage_b_window() -> None: + source_rows = [_row(task_id) for task_id in range(1000, 1000 + MBPP_CALIBRATION_SIZE)] + ranked = sorted( + source_rows, + key=lambda row: (mbpp_calibration_key(row["task_id"]), row["task_id"]), + ) + protected_ids = { + row["task_id"] + for row in ranked[resolver.PROTECTED_WINDOW[0] : resolver.PROTECTED_WINDOW[1]] + } + expected_ids = tuple( + row["task_id"] for row in ranked[resolver.STAGE_B_OFFSET : resolver.STAGE_B_STOP] + ) + accesses: list[tuple[int, str]] = [] + + resolved = resolver.resolve_stage_b_rows( + load_dataset_fn=_loader_with_tracking(source_rows, accesses) + ) + + assert resolved.ordered_task_ids == expected_ids + assert tuple(int(row["task_id"]) for row in resolved.rows) == expected_ids + assert ( + resolved.access_audit.ranking_transport_records_yielded + == MBPP_CALIBRATION_SIZE + ) + assert all(key == "task_id" for task_id, key in accesses if task_id in protected_ids) + content_access_ids = {task_id for task_id, key in accesses if key != "task_id"} + assert content_access_ids == set(expected_ids) + _, token_records = resolver.tokenize_stage_b_rows( + Qwen2Tokenizer(), + resolved.rows, + ) + assert {record["task_id"] for record in token_records}.isdisjoint(protected_ids) + audit = resolved.access_audit.as_dict(selected_task_ids=resolved.ordered_task_ids) + for name in ("selected", "retained", "canonicalized", "formatted", "tokenized"): + assert set(audit["application_task_id_sets"][name]).isdisjoint(protected_ids) + assert audit["protected_window_intersection"][name] is False + assert audit["application_task_id_sets"]["passed_to_model"] == [] + assert audit["application_task_id_sets"]["evaluated"] == [] + + +def test_resolver_rejects_duplicate_or_undersized_source() -> None: + rows = [_row(task_id) for task_id in range(MBPP_CALIBRATION_SIZE)] + duplicate_rows = [*rows, _row(0)] + with pytest.raises(ValueError, match="duplicate MBPP task_id"): + resolver.resolve_stage_b_task_ids( + load_dataset_fn=_loader_with_tracking(duplicate_rows, []) + ) + with pytest.raises(ValueError, match="too small"): + resolver.resolve_stage_b_task_ids( + load_dataset_fn=_loader_with_tracking(rows[:-1], []) + ) + + +def test_id_only_ranking_matches_full_selector_on_shuffled_large_source() -> None: + source_rows = [_row(task_id) for task_id in range(2000, 2500)] + random.Random(2339).shuffle(source_rows) + expected = select_mbpp_calibration( + source_rows, + size=MBPP_CALIBRATION_SIZE, + )[resolver.STAGE_B_OFFSET : resolver.STAGE_B_STOP] + + actual, source_count = resolver.resolve_stage_b_task_ids( + load_dataset_fn=_loader_with_tracking(source_rows, []) + ) + + assert source_count == 500 + assert actual == tuple(int(row["task_id"]) for row in expected) + + +def test_token_records_bind_row_text_and_exact_token_ids( + resolved_identity: tuple[ + list[dict[str, Any]], + tuple[Mapping[str, Any], ...], + tuple[dict[str, Any], ...], + dict[str, Any], + ], +) -> None: + _, rows, records, _ = resolved_identity + encoded, repeated = resolver.tokenize_stage_b_rows(Qwen2Tokenizer(), rows) + + assert repeated == records + assert len(encoded) == resolver.STAGE_B_LIMIT + assert [record["rank"] for record in records] == list( + range(resolver.STAGE_B_OFFSET, resolver.STAGE_B_STOP) + ) + assert all( + record["aligned_scored_tokens"] == record["code_tokens"] - 1 + for record in records + ) + assert all(record["full_code_scored_tokens"] == record["code_tokens"] for record in records) + assert resolver.token_manifest_sha256(records) == resolver.token_manifest_sha256(repeated) + + +def test_identity_validator_and_runtime_authentication_round_trip( + monkeypatch: pytest.MonkeyPatch, + resolved_identity: tuple[ + list[dict[str, Any]], + tuple[Mapping[str, Any], ...], + tuple[dict[str, Any], ...], + dict[str, Any], + ], +) -> None: + source_rows, rows, _, evidence = resolved_identity + resolver.validate_stage_b_identity_evidence(evidence) + monkeypatch.setattr( + resolver.importlib.metadata, + "version", + lambda name: resolver.TRANSFORMERS_VERSION, + ) + accesses: list[tuple[int, str]] = [] + loaded = resolver.load_authenticated_stage_b_rows( + evidence, + load_dataset_fn=_loader_with_tracking(source_rows, accesses), + ) + authentication = resolver.authenticate_stage_b_runtime_identity( + evidence, + loaded, + Qwen2Tokenizer(), + ) + assert len(authentication.tasks) == resolver.STAGE_B_LIMIT + assert tuple(task.record for task in authentication.tasks) == tuple( + evidence["dataset"]["tasks"] + ) + assert loaded.ordered_task_ids == tuple(evidence["dataset"]["ordered_task_ids"]) + assert authentication.access_audit == loaded.access_audit + assert authentication.ordered_identity_sha256 == evidence["dataset"][ + "ordered_identity_sha256" + ] + + +def test_runtime_rejects_coherent_wrong_rank_identity_before_content_access() -> None: + source_rows = [ + _row(task_id) for task_id in range(1000, 1000 + MBPP_CALIBRATION_SIZE) + ] + ranked = sorted( + source_rows, + key=lambda row: (mbpp_calibration_key(row["task_id"]), row["task_id"]), + ) + wrong_rows = tuple(ranked[: resolver.STAGE_B_LIMIT]) + _, wrong_records = resolver.tokenize_stage_b_rows( + Qwen2Tokenizer(), + wrong_rows, + ) + coherent_wrong_evidence = _identity_evidence(wrong_rows, wrong_records) + accesses: list[tuple[int, str]] = [] + + with pytest.raises(ValueError, match=r"ranked window \[32, 64\)"): + resolver.load_authenticated_stage_b_rows( + coherent_wrong_evidence, + load_dataset_fn=_loader_with_tracking(source_rows, accesses), + ) + + assert accesses + assert all(key == "task_id" for _, key in accesses) + + +def test_imported_module_paths_must_match_authenticated_repository( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + modules = _resolver_runtime_modules() + assert resolver.validate_imported_module_paths( + REPOSITORY_ROOT, + modules=modules, + ) == dict(resolver.IMPORTED_MODULE_PATHS) + evidence_module = resolver.sys.modules["recurquant.evidence"] + monkeypatch.setattr( + evidence_module, + "__file__", + str(tmp_path / "untrusted" / "evidence.py"), + ) + + with pytest.raises(ValueError, match="did not resolve"): + resolver.validate_imported_module_paths( + REPOSITORY_ROOT, + modules=modules, + ) + + +@pytest.mark.skipif( + not ( + REPOSITORY_ROOT / resolver.SELECTOR_ARTIFACT_RELATIVE_PATH + ).is_file() + or not ( + REPOSITORY_ROOT / resolver.LOSS_SELECTOR_ARTIFACT_RELATIVE_PATH + ).is_file(), + reason="original frozen selector artifacts are not present in this clone", +) +def test_exact_selector_pair_builds_a_complete_public_compact_plan() -> None: + plan = resolver.build_compact_row_plan( + REPOSITORY_ROOT / resolver.SELECTOR_ARTIFACT_RELATIVE_PATH, + REPOSITORY_ROOT / resolver.LOSS_SELECTOR_ARTIFACT_RELATIVE_PATH, + ) + + resolver.validate_compact_row_plan(plan) + assert len(plan["high_precision_rows"]) == 1_976 + assert plan["accounting"]["resident_bytes"] == 2_564_096 + assert plan["selector_binding"]["selector_file_sha256"] == ( + resolver.SELECTOR_FILE_SHA256 + ) + + +@pytest.mark.parametrize( + ("mutation", "message"), + [ + ( + lambda evidence: evidence["dataset"]["selection_window"].update({"offset": 8}), + "ranked window", + ), + ( + lambda evidence: evidence["integrity"].update( + {"protected_window_8_16_content_retained_canonicalized_or_tokenized": True} + ), + "forbidden", + ), + ( + lambda evidence: evidence["model_contract"].update({"weights_loaded": True}), + "model contract", + ), + ( + lambda evidence: evidence.update( + { + "claim_boundary": ( + "This is not performance evidence. It proves a breakthrough." + ) + } + ), + "claim boundary", + ), + ( + lambda evidence: evidence["row_plan"]["high_precision_rows"][0].update( + {"row_index": 127} + ), + "row.plan", + ), + ( + lambda evidence: evidence["dataset"]["tasks"][0].update({"prompt_tokens": 1}), + "token manifest hash", + ), + ( + lambda evidence: evidence["dataset"]["manifest"]["rows"][0].update( + {"sha256": "0" * 64} + ), + "content manifest hash", + ), + ( + lambda evidence: evidence["environment"]["python"].update({"micro": 14}), + "runtime environment contract", + ), + ( + lambda evidence: evidence["environment"]["packages"].update( + {"numpy": "2.4.5"} + ), + "runtime environment contract", + ), + ( + lambda evidence: evidence["environment"]["packages"].pop("safetensors"), + "runtime environment contract", + ), + ( + lambda evidence: evidence["environment"]["cuda"].update( + {"runtime_version": "12.7"} + ), + "runtime environment contract", + ), + ( + lambda evidence: evidence["environment"]["cuda"].update( + {"available": False, "device_type": "cpu"} + ), + "runtime environment contract", + ), + ( + lambda evidence: evidence["environment"]["stage_a_binding"].update( + {"file_sha256": "0" * 64} + ), + "runtime environment contract", + ), + ( + lambda evidence: evidence["environment"].update( + {"runtime_matches_stage_a": False} + ), + "runtime environment contract", + ), + ], +) +def test_identity_validator_fails_closed_on_contract_drift( + resolved_identity: tuple[ + list[dict[str, Any]], + tuple[Mapping[str, Any], ...], + tuple[dict[str, Any], ...], + dict[str, Any], + ], + mutation: Any, + message: str, +) -> None: + evidence = copy.deepcopy(resolved_identity[3]) + mutation(evidence) + with pytest.raises(ValueError, match=message): + resolver.validate_stage_b_identity_evidence(evidence) + + +def test_artifact_loader_rejects_outer_or_internal_tampering( + tmp_path: Path, + resolved_identity: tuple[ + list[dict[str, Any]], + tuple[Mapping[str, Any], ...], + tuple[dict[str, Any], ...], + dict[str, Any], + ], +) -> None: + evidence = resolved_identity[3] + artifact = { + "canonical_evidence_sha256": hashlib.sha256( + canonical_json_bytes(evidence) + ).hexdigest(), + "evidence": evidence, + } + path = tmp_path / "identity.json" + path.write_bytes(canonical_json_bytes(artifact)) + loaded, file_hash = resolver.load_stage_b_identity_artifact(path) + assert loaded == evidence + assert file_hash == hashlib.sha256(path.read_bytes()).hexdigest() + + tampered = json.loads(path.read_text(encoding="utf-8")) + tampered["evidence"]["dataset"]["tasks"][0]["task_id"] += 1 + path.write_bytes(canonical_json_bytes(tampered)) + with pytest.raises(ValueError, match="canonical verification"): + resolver.load_stage_b_identity_artifact(path) + + +def test_stage_a_artifact_is_the_exact_authenticated_pass() -> None: + repository_root = Path(__file__).resolve().parents[1] + stage_a_path = repository_root / resolver.STAGE_A_ARTIFACT_RELATIVE_PATH + evidence, file_hash = resolver.authenticate_stage_a_artifact(stage_a_path) + + assert file_hash == resolver.STAGE_A_FILE_SHA256 + assert evidence["stage_a_gate"]["passed"] is True + assert ( + evidence["dataset"]["protected_window_8_16_loaded_tokenized_or_evaluated"] + is False + ) + + +def test_runtime_environment_is_derived_from_authenticated_stage_a() -> None: + stage_a_path = REPOSITORY_ROOT / resolver.STAGE_A_ARTIFACT_RELATIVE_PATH + stage_a_evidence, _ = resolver.authenticate_stage_a_artifact(stage_a_path) + derived = resolver.derive_stage_a_runtime_environment(stage_a_evidence) + + assert derived == { + "python": { + "major": 3, + "minor": 11, + "micro": 15, + "version": "3.11.15", + }, + "packages": { + "datasets": "4.8.5", + "numpy": "2.4.6", + "safetensors": "0.8.0", + "torch": "2.11.0+cu128", + "transformers": "5.14.1", + }, + "cuda": { + "available": True, + "runtime_version": "12.8", + "device_type": "cuda", + }, + } + contract = resolver.authenticate_runtime_environment( + stage_a_evidence, + local_files_only=True, + runtime_environment=copy.deepcopy(derived), + ) + assert contract["runtime_matches_stage_a"] is True + assert contract["stage_a_binding"]["file_sha256"] == resolver.STAGE_A_FILE_SHA256 + assert "gpu" not in contract + assert "platform" not in contract + assert "executable" not in contract + + +@pytest.mark.parametrize( + "mutation", + [ + lambda runtime: runtime["python"].update({"micro": 14}), + lambda runtime: runtime["packages"].update({"datasets": "4.8.4"}), + lambda runtime: runtime["packages"].update({"numpy": "2.4.5"}), + lambda runtime: runtime["packages"].pop("safetensors"), + lambda runtime: runtime["packages"].update({"torch": "2.11.0"}), + lambda runtime: runtime["packages"].update({"transformers": "5.14.0"}), + lambda runtime: runtime["cuda"].update({"runtime_version": "12.7"}), + lambda runtime: runtime["cuda"].update( + {"available": False, "device_type": "cpu"} + ), + lambda runtime: runtime["cuda"].update({"device_type": "cpu"}), + lambda runtime: runtime.update({"unexpected_path": "C:/private/python.exe"}), + ], +) +def test_runtime_environment_authentication_rejects_any_drift( + mutation: Any, +) -> None: + stage_a_path = REPOSITORY_ROOT / resolver.STAGE_A_ARTIFACT_RELATIVE_PATH + stage_a_evidence, _ = resolver.authenticate_stage_a_artifact(stage_a_path) + runtime = resolver.derive_stage_a_runtime_environment(stage_a_evidence) + mutation(runtime) + + with pytest.raises(ValueError, match="does not exactly match"): + resolver.authenticate_runtime_environment( + stage_a_evidence, + local_files_only=True, + runtime_environment=runtime, + ) + + +def test_runtime_environment_rejects_stage_a_contract_drift() -> None: + stage_a_path = REPOSITORY_ROOT / resolver.STAGE_A_ARTIFACT_RELATIVE_PATH + stage_a_evidence, _ = resolver.authenticate_stage_a_artifact(stage_a_path) + runtime = resolver.derive_stage_a_runtime_environment(stage_a_evidence) + drifted_stage_a = copy.deepcopy(stage_a_evidence) + drifted_stage_a["environment"]["packages"]["numpy"] = "2.4.5" + + with pytest.raises(ValueError, match="authenticated Stage-A runtime"): + resolver.authenticate_runtime_environment( + drifted_stage_a, + local_files_only=True, + runtime_environment=runtime, + ) + + +def test_output_path_must_be_external_or_git_ignored(tmp_path: Path) -> None: + repository_root = Path(__file__).resolve().parents[1] + resolver.validate_output_path(tmp_path / "identity.json", repository_root) + resolver.validate_output_path( + repository_root / "artifacts" / "identity.json", + repository_root, + ) + with pytest.raises(ValueError, match="must be Git-ignored"): + resolver.validate_output_path( + repository_root / "research" / "identity.json", + repository_root, + ) diff --git a/tests/test_rht.py b/tests/test_rht.py new file mode 100644 index 0000000..57cab1c --- /dev/null +++ b/tests/test_rht.py @@ -0,0 +1,249 @@ +from __future__ import annotations + +import hashlib + +import pytest +import torch + +from recurquant.rht import ( + fwht_unnormalized, + right_rht_decode, + right_rht_encode, + right_rht_signs, +) + + +def _q4_absmax(tensor: torch.Tensor) -> torch.Tensor: + scale = tensor.abs().amax(dim=-1, keepdim=True) / 7.0 + scale = torch.where(scale > 0, scale, torch.ones_like(scale)) + return torch.round(tensor / scale).clamp(-7, 7) * scale + + +def test_unnormalized_fwht_has_expected_small_example_and_dtype_restore() -> None: + vector = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float16) + + transformed = fwht_unnormalized(vector, output_dtype=torch.float32) + + assert transformed.dtype == torch.float32 + assert torch.equal(transformed, torch.tensor([10.0, -2.0, -4.0, 0.0])) + assert fwht_unnormalized(vector).dtype == torch.float16 + + +def test_unnormalized_fwht_rejects_scalar_and_unsupported_output_dtype() -> None: + with pytest.raises(ValueError, match="last dimension"): + fwht_unnormalized(torch.tensor(1.0)) + with pytest.raises(TypeError, match="output_dtype"): + fwht_unnormalized(torch.ones(8), output_dtype=torch.float64) + + +def test_encode_decode_inverse_has_tight_fp32_error_bound() -> None: + generator = torch.Generator().manual_seed(11) + state = torch.randn((2, 3, 5, 128), generator=generator) + + encoded = right_rht_encode(state, layer_index=7, expected_heads=3) + restored = right_rht_decode(encoded, layer_index=7, expected_heads=3) + relative_l2 = torch.linalg.vector_norm(restored - state) / torch.linalg.vector_norm(state) + + assert float(relative_l2.item()) < 3e-7 + torch.testing.assert_close(restored, state, rtol=2e-6, atol=1e-6) + + +def test_sign_schedule_is_deterministic_portable_and_rng_independent() -> None: + torch.manual_seed(1) + first = right_rht_signs( + layer_index=4, + expected_heads=3, + width=128, + device="cpu", + ) + torch.manual_seed(999_999) + second = right_rht_signs( + layer_index=4, + expected_heads=3, + width=128, + device="cpu", + ) + digest = hashlib.sha256(first.to(torch.int8).numpy().tobytes()).hexdigest() + + assert torch.equal(first, second) + assert digest == "3cc14ffaf1ad8de3d77a1d277cb027c3dee9360429c0746232780acc55d42f55" + + +def test_layers_and_heads_receive_different_sign_vectors() -> None: + first_layer = right_rht_signs( + layer_index=2, + expected_heads=4, + width=128, + device="cpu", + ) + second_layer = right_rht_signs( + layer_index=3, + expected_heads=4, + width=128, + device="cpu", + ) + + assert all( + not torch.equal(first_layer[0, left, 0], first_layer[0, right, 0]) + for left in range(4) + for right in range(left + 1, 4) + ) + assert not torch.equal(first_layer, second_layer) + + +def test_normalized_encode_preserves_every_row_norm() -> None: + generator = torch.Generator().manual_seed(23) + state = torch.randn((2, 4, 7, 128), generator=generator) + + encoded = right_rht_encode(state, layer_index=5, expected_heads=4) + + torch.testing.assert_close( + encoded.square().sum(dim=-1), + state.square().sum(dim=-1), + rtol=2e-6, + atol=2e-5, + ) + + +def test_decode_preserves_quantization_error_energy_per_row() -> None: + generator = torch.Generator().manual_seed(41) + state = torch.randn((2, 3, 6, 128), generator=generator) + encoded = right_rht_encode(state, layer_index=9, expected_heads=3) + quantized = _q4_absmax(encoded) + restored = right_rht_decode(quantized, layer_index=9, expected_heads=3) + + source_error = (restored - state).square().sum(dim=-1) + transformed_error = (quantized - encoded).square().sum(dim=-1) + + torch.testing.assert_close(source_error, transformed_error, rtol=8e-6, atol=2e-5) + + +def test_zero_state_is_exact_and_batch_rows_broadcast_independently() -> None: + state = torch.zeros((3, 2, 5, 64), dtype=torch.float32) + + encoded = right_rht_encode(state, layer_index=1, expected_heads=2) + restored = right_rht_decode(encoded, layer_index=1, expected_heads=2) + + assert torch.equal(encoded, state) + assert torch.equal(restored, state) + + +def test_hadamard_aligned_vector_maps_to_one_coordinate() -> None: + spike = torch.zeros((1, 1, 1, 128), dtype=torch.float32) + spike[..., 37] = 3.25 + aligned = right_rht_decode(spike, layer_index=6, expected_heads=1) + + encoded = right_rht_encode(aligned, layer_index=6, expected_heads=1) + + torch.testing.assert_close(encoded, spike, rtol=2e-6, atol=1e-6) + + +def test_exact_grid_adversary_documents_that_rotation_is_not_always_better() -> None: + values = torch.arange(128, dtype=torch.float32).remainder(15).sub(7) + state = values.reshape(1, 1, 1, 128) + baseline = _q4_absmax(state) + encoded = right_rht_encode(state, layer_index=0, expected_heads=1) + restored = right_rht_decode( + _q4_absmax(encoded), + layer_index=0, + expected_heads=1, + ) + + assert torch.equal(baseline, state) + assert float((restored - state).square().sum().item()) > 0.0 + + +@pytest.mark.parametrize( + ("state", "expected_heads", "message"), + [ + (torch.ones((1, 2, 128)), 2, "shape"), + (torch.ones((1, 2, 3, 96)), 2, "power of two"), + (torch.ones((1, 2, 3, 128)), 3, "head dimension"), + (torch.ones((1, 2, 3, 128), dtype=torch.int64), 2, "float16"), + (torch.full((1, 2, 3, 128), float("nan")), 2, "finite"), + (torch.full((1, 2, 3, 128), float("inf")), 2, "finite"), + (torch.empty((0, 2, 3, 128)), 2, "empty"), + ], +) +def test_encode_fails_closed_on_invalid_state( + state: torch.Tensor, + expected_heads: int, + message: str, +) -> None: + with pytest.raises((TypeError, ValueError), match=message): + right_rht_encode( + state, + layer_index=0, + expected_heads=expected_heads, + ) + + +@pytest.mark.parametrize( + ("layer_index", "expected_heads", "error", "message"), + [ + (-1, 2, ValueError, "non-negative"), + (True, 2, TypeError, "integer"), + (0, 0, ValueError, "positive"), + (0, True, TypeError, "integer"), + ], +) +def test_encode_rejects_invalid_identity( + layer_index: int, + expected_heads: int, + error: type[Exception], + message: str, +) -> None: + with pytest.raises(error, match=message): + right_rht_encode( + torch.ones((1, 2, 3, 128)), + layer_index=layer_index, + expected_heads=expected_heads, + ) + + +def test_decode_can_restore_requested_recurrent_state_dtype() -> None: + state = torch.randn((1, 2, 3, 128), dtype=torch.float32).to(torch.bfloat16) + + encoded = right_rht_encode( + state, + layer_index=8, + expected_heads=2, + output_dtype=torch.float32, + ) + restored = right_rht_decode( + encoded, + layer_index=8, + expected_heads=2, + output_dtype=torch.bfloat16, + ) + + assert encoded.dtype == torch.float32 + assert restored.dtype == torch.bfloat16 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is unavailable") +def test_cuda_matches_cpu_sign_schedule_and_inverse_bound() -> None: + generator = torch.Generator().manual_seed(71) + state_cpu = torch.randn((2, 3, 4, 128), generator=generator) + state_cuda = state_cpu.cuda() + + cpu_signs = right_rht_signs( + layer_index=11, + expected_heads=3, + width=128, + device="cpu", + ) + cuda_signs = right_rht_signs( + layer_index=11, + expected_heads=3, + width=128, + device="cuda", + ) + encoded = right_rht_encode(state_cuda, layer_index=11, expected_heads=3) + restored = right_rht_decode(encoded, layer_index=11, expected_heads=3) + relative_l2 = torch.linalg.vector_norm(restored - state_cuda) / torch.linalg.vector_norm( + state_cuda + ) + + assert torch.equal(cpu_signs, cuda_signs.cpu()) + assert float(relative_l2.item()) < 3e-7 diff --git a/tests/test_rht_independent_reference.py b/tests/test_rht_independent_reference.py new file mode 100644 index 0000000..2541177 --- /dev/null +++ b/tests/test_rht_independent_reference.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +import hashlib +import math + +import numpy as np +import torch + +from recurquant.mixed_quantization import quantize_pack_mixed +from recurquant.quantization import QuantizationSpec +from recurquant.rht import right_rht_encode, right_rht_signs + +LOW = QuantizationSpec(bits=4, group_size=128, flatten_last_dims=2) +HIGH = QuantizationSpec(bits=8, group_size=128, flatten_last_dims=2) +DOMAIN = b"recurquant.right-rht.signs.v1\0" +SEED = 2339 + + +def _reference_signs(layer: int, heads: int, width: int) -> np.ndarray: + result = np.empty((1, heads, 1, width), dtype=np.float32) + for head in range(heads): + values: list[float] = [] + counter = 0 + while len(values) < width: + message = b"".join( + ( + DOMAIN, + SEED.to_bytes(8, "little"), + layer.to_bytes(8, "little"), + head.to_bytes(8, "little"), + width.to_bytes(8, "little"), + counter.to_bytes(8, "little"), + ) + ) + for byte in hashlib.sha256(message).digest(): + for bit in range(8): + values.append(1.0 if byte & (1 << bit) else -1.0) + if len(values) == width: + break + if len(values) == width: + break + counter += 1 + result[0, head, 0] = np.asarray(values, dtype=np.float32) + return result + + +def _dense_hadamard(width: int) -> np.ndarray: + matrix = np.ones((1, 1), dtype=np.float32) + while matrix.shape[0] < width: + matrix = np.block([[matrix, matrix], [matrix, -matrix]]) + return matrix + + +def _reference_encode(state: np.ndarray, *, layer: int) -> np.ndarray: + width = state.shape[-1] + signed = state.astype(np.float32) * _reference_signs( + layer, + state.shape[1], + width, + ) + return np.matmul(signed, _dense_hadamard(width)) / math.sqrt(width) + + +def _reference_decode(encoded: np.ndarray, *, layer: int) -> np.ndarray: + width = encoded.shape[-1] + decoded = np.matmul(encoded.astype(np.float32), _dense_hadamard(width)) + decoded /= math.sqrt(width) + return decoded * _reference_signs(layer, encoded.shape[1], width) + + +def _reference_mixed_qdq( + state: np.ndarray, + mask: np.ndarray, + *, + layer: int, +) -> np.ndarray: + encoded = _reference_encode(state, layer=layer) + rows = encoded.reshape(state.shape[0] * state.shape[1], -1) + groups = rows.reshape(-1, 128) + flat_mask = mask.reshape(-1) + qmax = np.where(flat_mask, 127.0, 7.0).reshape(-1, 1) + ideal_scales = np.max(np.abs(groups), axis=1, keepdims=True) / qmax + ideal_scales = np.where(ideal_scales > 1e-8, ideal_scales, 1.0) + ideal_scales = np.clip( + ideal_scales, + 2.0**-24, + np.finfo(np.float16).max, + ) + scales = ideal_scales.astype(np.float16).astype(np.float32) + codes = np.rint(groups / scales) + codes = np.maximum(np.minimum(codes, qmax), -qmax) + quantized = (codes * scales).reshape(encoded.shape) + return _reference_decode(quantized, layer=layer) + + +def test_production_rht_matches_independent_dense_reference() -> None: + state = torch.randn( + (1, 2, 3, 128), + generator=torch.Generator().manual_seed(811), + dtype=torch.float32, + ) + reference_signs = _reference_signs(layer=7, heads=2, width=128) + reference_encoded = _reference_encode(state.numpy(), layer=7) + + production_signs = right_rht_signs( + layer_index=7, + expected_heads=2, + width=128, + device="cpu", + ) + production_encoded = right_rht_encode( + state, + layer_index=7, + expected_heads=2, + ) + + np.testing.assert_array_equal(production_signs.numpy(), reference_signs) + np.testing.assert_allclose( + production_encoded.numpy(), + reference_encoded, + rtol=2e-6, + atol=2e-6, + ) + + +def test_physical_rht_pack_matches_independent_dense_quantizer() -> None: + state = torch.randn( + (1, 2, 4, 128), + generator=torch.Generator().manual_seed(823), + dtype=torch.float32, + ) + mask = torch.tensor( + [[False, True, False, True], [True, False, False, True]], + dtype=torch.bool, + ) + packed = quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=9, + right_rht_expected_heads=2, + ) + + reference = _reference_mixed_qdq( + state.numpy(), + mask.numpy(), + layer=9, + ) + + np.testing.assert_allclose( + packed.dequantize().numpy(), + reference, + rtol=5e-6, + atol=6e-6, + ) diff --git a/tests/test_rht_mixed_quantization.py b/tests/test_rht_mixed_quantization.py new file mode 100644 index 0000000..65d9bb7 --- /dev/null +++ b/tests/test_rht_mixed_quantization.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +import pytest +import torch + +from recurquant.mixed_quantization import quantize_pack_mixed +from recurquant.quantization import QuantizationSpec, quantize_dequantize +from recurquant.rht import right_rht_decode, right_rht_encode + +LOW = QuantizationSpec(bits=4, group_size=128, flatten_last_dims=2) +HIGH = QuantizationSpec(bits=8, group_size=128, flatten_last_dims=2) + + +def _independent_rht_qdq( + state: torch.Tensor, + mask: torch.Tensor, + *, + layer_index: int, +) -> torch.Tensor: + encoded = right_rht_encode( + state, + layer_index=layer_index, + expected_heads=state.shape[1], + ) + low = quantize_dequantize(encoded, LOW).tensor + high = quantize_dequantize(encoded, HIGH).tensor + selected = torch.where(mask.reshape(1, state.shape[1], state.shape[2], 1), high, low) + return right_rht_decode( + selected, + layer_index=layer_index, + expected_heads=state.shape[1], + output_dtype=state.dtype, + ) + + +def test_rht_mixed_pack_matches_independent_transformed_qdq() -> None: + generator = torch.Generator().manual_seed(91) + state = torch.randn((1, 3, 7, 128), generator=generator, dtype=torch.float32) + mask = torch.zeros((3, 7), dtype=torch.bool) + mask[0, 2] = True + mask[2, 4:] = True + + packed = quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=5, + right_rht_expected_heads=3, + ) + + expected = _independent_rht_qdq(state, mask, layer_index=5) + torch.testing.assert_close(packed.dequantize(), expected, rtol=0, atol=0) + assert packed.high_precision_groups == int(mask.sum().item()) + assert packed.storage_bytes == ( + packed.payload_bytes + packed.scale_bytes + packed.mask_bytes + ) + + +def test_rht_changes_error_without_changing_physical_byte_contract() -> None: + state = torch.randn((1, 2, 8, 128), generator=torch.Generator().manual_seed(101)) + mask = torch.rand((2, 8), generator=torch.Generator().manual_seed(102)) > 0.6 + + baseline = quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + ) + transformed = quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=4, + right_rht_expected_heads=2, + ) + + assert transformed.storage_bytes == baseline.storage_bytes + assert transformed.payload_bytes == baseline.payload_bytes + assert transformed.scale_bytes == baseline.scale_bytes + assert transformed.mask_bytes == baseline.mask_bytes + assert not torch.equal(transformed.dequantize(), baseline.dequantize()) + + +def test_rht_metadata_survives_transfer_and_batch_reorder() -> None: + state = torch.randn((3, 2, 4, 128), generator=torch.Generator().manual_seed(111)) + mask = torch.zeros((state.shape[0] * state.shape[1], state.shape[2]), dtype=torch.bool) + mask[::2, 1] = True + packed = quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=8, + right_rht_expected_heads=2, + ) + expected = packed.dequantize() + + moved = packed.to("cpu") + assert moved.right_rht_layer_index == 8 + assert moved.right_rht_expected_heads == 2 + torch.testing.assert_close(moved.dequantize(), expected, rtol=0, atol=0) + + indices = torch.tensor([2, 0, 2], dtype=torch.long) + reordered = packed.reorder_batch(indices) + assert reordered.right_rht_layer_index == 8 + assert reordered.right_rht_expected_heads == 2 + torch.testing.assert_close( + reordered.dequantize(), + expected.index_select(0, indices), + rtol=0, + atol=0, + ) + + +@pytest.mark.parametrize( + ("layer_index", "expected_heads"), + [ + (0, None), + (None, 2), + ], +) +def test_rht_configuration_must_be_complete( + layer_index: int | None, + expected_heads: int | None, +) -> None: + state = torch.ones((1, 2, 3, 128)) + mask = torch.zeros((2, 3), dtype=torch.bool) + + with pytest.raises(ValueError, match="configured together"): + quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=layer_index, + right_rht_expected_heads=expected_heads, + ) + + +def test_rht_pack_rejects_wrong_head_geometry() -> None: + state = torch.ones((1, 2, 3, 128)) + mask = torch.zeros((2, 3), dtype=torch.bool) + + with pytest.raises(ValueError, match="head dimension"): + quantize_pack_mixed( + state, + mask, + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=0, + right_rht_expected_heads=3, + ) + + +def test_rht_reorder_rejects_empty_batch() -> None: + state = torch.ones((1, 2, 3, 128)) + packed = quantize_pack_mixed( + state, + torch.zeros((2, 3), dtype=torch.bool), + low_spec=LOW, + high_spec=HIGH, + right_rht_layer_index=0, + right_rht_expected_heads=2, + ) + + with pytest.raises(ValueError, match="empty batch"): + packed.reorder_batch(torch.empty(0, dtype=torch.long)) diff --git a/tests/test_right_rht_query_ema_cache.py b/tests/test_right_rht_query_ema_cache.py new file mode 100644 index 0000000..ce07b42 --- /dev/null +++ b/tests/test_right_rht_query_ema_cache.py @@ -0,0 +1,137 @@ +from __future__ import annotations + +import math + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant import ( + Qwen35QueryEnergyObserver, + RightRhtQueryEmaMixedPackedLinearAttentionLayer, + RightRhtQueryEmaMixedPackedRecurrentStateCache, + create_qwen35_right_rht_query_ema_exact_budget_cache, +) +from recurquant.quantization import QuantizationSpec +from recurquant.row_policy import ExactBudgetRowPlan, select_rows_exact_budget +from tests.test_transformers_cache import tiny_config + + +def _target_for_promotions(promotions: int) -> int: + total_groups = 16 + group_size = 8 + low_group_bytes = math.ceil(4 * group_size / 8) + 2 + high_increment = math.ceil(8 * group_size / 8) - math.ceil(4 * group_size / 8) + return ( + total_groups * low_group_bytes + + math.ceil(total_groups / 8) + + promotions * high_increment + ) + + +def _plan(promotions: int = 3) -> ExactBudgetRowPlan: + return select_rows_exact_budget( + {0: torch.arange(16, dtype=torch.float32).reshape(2, 8)}, + target_resident_bytes=_target_for_promotions(promotions), + group_size=8, + ) + + +def _query() -> torch.Tensor: + query = torch.zeros((1, 2, 2, 8), dtype=torch.float32) + query[:, :, :, 3] = 1 + return query + + +def test_right_rht_query_cache_realizes_same_exact_byte_plan() -> None: + plan = _plan() + cache = create_qwen35_right_rht_query_ema_exact_budget_cache( + tiny_config(), + plan=plan, + record_evidence=True, + ) + cache.stage_query_observation(0, _query()) + + cache.update_recurrent_state( + torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(121)), + layer_idx=0, + ) + + assert isinstance(cache, RightRhtQueryEmaMixedPackedRecurrentStateCache) + layer = cache.layers[0] + assert isinstance(layer, RightRhtQueryEmaMixedPackedLinearAttentionLayer) + packed = layer.packed_states[0] + assert packed is not None + assert packed.right_rht_layer_index == 0 + assert packed.right_rht_expected_heads == 2 + summary = cache.storage_summary() + assert summary["resident_bytes"] == plan.resident_bytes + assert summary["selector_auxiliary_bytes"] == 2 * 8 * 4 + assert summary["resident_bytes_including_selector"] == plan.resident_bytes + 64 + assert cache.high_precision_group_count() == plan.promoted_group_count + + diagnostics = cache.query_ema_diagnostics() + assert diagnostics[0]["selection_method"] == ( + "right_rht_query_ema32_weighted_mse_target_fisher_quota" + ) + assert diagnostics[0]["state_codec"] == "right_rht_sha256_signs_v1" + assert diagnostics[0]["state_codec_seed"] == 2339 + assert diagnostics[0]["state_codec_persistent_tensor_bytes"] == 0 + + +def test_right_rht_query_cache_rejects_confirmation_two() -> None: + with pytest.raises(ValueError, match="does not support Confirmation-2"): + RightRhtQueryEmaMixedPackedRecurrentStateCache( + tiny_config(), + plan=_plan(), + confirmation_two=True, + ) + + +def test_right_rht_selector_benefit_matches_decoded_endpoint_error() -> None: + cache = create_qwen35_right_rht_query_ema_exact_budget_cache( + tiny_config(), + plan=_plan(), + ) + layer = cache.layers[0] + assert isinstance(layer, RightRhtQueryEmaMixedPackedLinearAttentionLayer) + state = torch.randn((1, 2, 8, 8), generator=torch.Generator().manual_seed(127)) + low_spec = QuantizationSpec(bits=4, group_size=8, flatten_last_dims=2) + high_spec = QuantizationSpec(bits=8, group_size=8, flatten_last_dims=2) + + transformed_benefit = layer._aligned_mse_benefit( + state, + low_spec=low_spec, + high_spec=high_spec, + ) + low = layer._quantized_endpoint(state, low_spec) + high = layer._quantized_endpoint(state, high_spec) + decoded_benefit = (low - state).square().mean(dim=-1) - ( + high - state + ).square().mean(dim=-1) + + torch.testing.assert_close(transformed_benefit, decoded_benefit, rtol=2e-5, atol=2e-7) + + +def test_right_rht_query_cache_runs_tiny_qwen_prefill_and_decode() -> None: + torch.manual_seed(131) + model = Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + cache = create_qwen35_right_rht_query_ema_exact_budget_cache( + model, + plan=_plan(), + ) + prompt = torch.tensor([[1, 2, 3, 4]]) + + with torch.inference_mode(), Qwen35QueryEnergyObserver(model, caches=[cache]): + prefill = model(prompt, past_key_values=cache, use_cache=True) + next_token = prefill.logits[:, -1, :].argmax(dim=-1, keepdim=True) + decode = model(next_token, past_key_values=cache, use_cache=True) + + assert decode.logits.shape == (1, 1, model.config.vocab_size) + diagnostics = cache.query_ema_diagnostics() + assert diagnostics[0]["state_updates"] == 2 + assert diagnostics[0]["observations_committed"] == 2 + assert diagnostics[0]["tokens_observed"] == 5 diff --git a/tests/test_row_policy.py b/tests/test_row_policy.py new file mode 100644 index 0000000..d7e1036 --- /dev/null +++ b/tests/test_row_policy.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import pytest +import torch + +from recurquant.row_policy import RowLocation, select_rows_exact_budget + + +def test_qwen35_equal_byte_row_plan_accounts_for_mask_and_promotions() -> None: + scores = { + layer_index: torch.arange(16 * 128, dtype=torch.float32).reshape(16, 128) + + layer_index * 10_000 + for layer_index in range(18) + } + + plan = select_rows_exact_budget(scores, target_resident_bytes=2_564_096) + + assert plan.total_groups == 36_864 + assert plan.mask_bytes == 4_608 + assert plan.promotion_increment_bytes == 64 + assert plan.promoted_group_count == 1_976 + assert plan.resident_bytes == 2_564_096 + assert plan.target_resident_bytes == 2_564_096 + assert plan.high_precision_rows[-1] == RowLocation(17, 15, 127) + + +def test_row_selection_breaks_score_ties_by_location() -> None: + scores = {4: torch.ones((1, 4)), 1: torch.ones((1, 4))} + minimum_with_mask = 8 * 66 + 1 + + plan = select_rows_exact_budget( + scores, + target_resident_bytes=minimum_with_mask + 2 * 64, + ) + + assert plan.high_precision_rows == ( + RowLocation(1, 0, 0), + RowLocation(1, 0, 1), + ) + assert plan.groups_for_layer(1) == (0, 1) + assert plan.groups_for_layer(4) == () + + +def test_nonrepresentable_target_is_rejected() -> None: + scores = {0: torch.ones((1, 2))} + minimum_with_mask = 2 * 66 + 1 + + with pytest.raises(ValueError, match="nearest valid targets"): + select_rows_exact_budget(scores, target_resident_bytes=minimum_with_mask + 1) + + +def test_payload_rounding_must_leave_a_positive_promotion_increment() -> None: + scores = {0: torch.ones((1, 1))} + + with pytest.raises(ValueError, match="positive promotion byte increment"): + select_rows_exact_budget( + scores, + target_resident_bytes=4, + low_bits=4, + high_bits=8, + group_size=1, + ) diff --git a/tests/test_screen_rht_cqer.py b/tests/test_screen_rht_cqer.py new file mode 100644 index 0000000..bd3bce6 --- /dev/null +++ b/tests/test_screen_rht_cqer.py @@ -0,0 +1,432 @@ +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path + +import pytest + +from scripts import screen_rht_cqer as screen + +REPOSITORY_ROOT = Path(__file__).resolve().parents[1] +STAGE_A_EVIDENCE = ( + REPOSITORY_ROOT + / "evidence" + / "experiment009-rht-cqer-stage-a-666-5be8d48.json" +) + + +def _metric( + *, + tokens: int, + delta_nll: float, + mean_kl: float, + top1: float, +) -> dict[str, float | int | bool]: + return { + "token_count": tokens, + "mean_kl": mean_kl, + "cvar95_kl": mean_kl * 2, + "max_kl": mean_kl * 3, + "top1_agreement": top1, + "reference_nll": 2.0, + "candidate_nll": 2.0 + delta_nll, + "delta_nll": delta_nll, + "all_logits_finite": True, + } + + +def _diagnostics(method: str) -> list[dict[str, object]]: + records: list[dict[str, object]] = [] + for layer, quota in screen.FROZEN_LAYER_QUOTAS.items(): + records.append( + { + "layer_index": layer, + "quota": quota, + "current_selected_count": quota, + "state_updates": screen.EXPECTED_STATE_WRITES, + "observations_staged": screen.EXPECTED_STATE_WRITES, + "observations_committed": screen.EXPECTED_STATE_WRITES, + "tokens_observed": screen.EXPECTED_QUERY_TOKENS, + "pending_observation": False, + "confirmation_two": False, + "selection_method": ( + "query_ema32_weighted_aligned_mse_reduction" + if method == screen.CQER_METHOD + else screen.RHT_METHOD + ), + **( + { + "state_codec": "right_rht_sha256_signs_v1", + "state_codec_seed": screen.SEED, + "state_codec_axis": "value", + "state_codec_normalization": "orthonormal", + "state_codec_persistent_tensor_bytes": 0, + } + if method == screen.RHT_METHOD + else {} + ), + "current_mask_sha256": ( + "a" * 64 if method == screen.CQER_METHOD else "b" * 64 + ), + } + ) + return records + + +def _state_error(*, sse: float, method: str) -> dict[str, object]: + record_count = ( + screen.EXPECTED_STATE_WRITES * len(screen.FROZEN_LINEAR_LAYERS) + ) + element_count = 16 * 128 * 128 + mse = sse / (record_count * element_count) + mask_hash = "a" * 64 if method == screen.CQER_METHOD else "b" * 64 + selection_method = ( + "query_ema32_weighted_aligned_mse_reduction" + if method == screen.CQER_METHOD + else screen.RHT_METHOD + ) + records = [ + { + "update_index": write * len(screen.FROZEN_LINEAR_LAYERS) + offset, + "layer_index": layer, + "state_index": 0, + "shape": [1, 16, 128, 128], + "mean_squared_error": mse, + "max_absolute_error": 0.1, + "relative_l2_error": 0.1, + "source_dtype": "torch.float32", + "selection_method": selection_method, + "total_groups": 2048, + "high_precision_groups": screen.FROZEN_LAYER_QUOTAS[layer], + "high_precision_mask_sha256": mask_hash, + } + for write in range(screen.EXPECTED_STATE_WRITES) + for offset, layer in enumerate(screen.FROZEN_LINEAR_LAYERS) + ] + return screen.aggregate_state_error_evidence(records) + + +def _passing_gate_inputs() -> dict[str, object]: + aligned = { + screen.CQER_METHOD: _metric( + tokens=screen.ALIGNED_TOKENS, + delta_nll=1.0, + mean_kl=0.5, + top1=0.75, + ), + screen.RHT_METHOD: _metric( + tokens=screen.ALIGNED_TOKENS, + delta_nll=0.8, + mean_kl=0.4, + top1=0.76, + ), + } + full = { + screen.CQER_METHOD: _metric( + tokens=screen.CODE_TOKENS, + delta_nll=0.9, + mean_kl=0.45, + top1=0.76, + ), + screen.RHT_METHOD: _metric( + tokens=screen.CODE_TOKENS, + delta_nll=0.7, + mean_kl=0.35, + top1=0.77, + ), + } + storage_record = { + "payload_bytes": screen.TARGET_PAYLOAD_BYTES, + "scale_bytes": screen.TARGET_SCALE_BYTES, + "mask_bytes": screen.TARGET_MASK_BYTES, + "resident_bytes": screen.TARGET_PACKED_STATE_BYTES, + "selector_auxiliary_bytes": screen.TARGET_SELECTOR_BYTES, + "resident_bytes_including_selector": screen.TARGET_TOTAL_BYTES, + "high_precision_groups": screen.TARGET_PROMOTED_ROWS, + } + return { + "aligned_metrics": aligned, + "full_code_metrics": full, + "storage": { + screen.CQER_METHOD: dict(storage_record), + screen.RHT_METHOD: dict(storage_record), + }, + "selector_diagnostics": { + method: _diagnostics(method) for method in screen.METHODS + }, + "state_errors": { + screen.CQER_METHOD: _state_error( + sse=100.0, + method=screen.CQER_METHOD, + ), + screen.RHT_METHOD: _state_error( + sse=40.0, + method=screen.RHT_METHOD, + ), + }, + "unit_evidence": { + "inverse_relative_l2": 2e-7, + "physical_pack_matches_transformed_qdq": True, + "physical_pack_max_abs_difference": 0.0, + "sign_schedule_sha256": screen.EXPECTED_SIGN_SCHEDULE_SHA256, + }, + "integrity": { + "repository_clean_at_start": True, + "repository_clean_at_end": True, + "repository_commit_stable": True, + "source_hashes_stable": True, + "identity_authenticated_before_model_weights": True, + "protected_window_8_16_accessed": False, + }, + } + + +def test_stage_a_gate_passes_only_the_complete_frozen_conjunction() -> None: + gate = screen.evaluate_stage_a_gate(**_passing_gate_inputs()) + + assert gate["passed"] is True + assert all(check["passed"] is True for check in gate["checks"].values()) + assert ( + gate["checks"]["state_sse_relative_reduction"]["relative_reduction"] + == pytest.approx(0.6) + ) + assert ( + gate["checks"]["aligned_excess_nll_relative_reduction"][ + "relative_reduction" + ] + == pytest.approx(0.2) + ) + + +@pytest.mark.parametrize( + ("mutation", "failed_check"), + [ + ( + lambda values: values["integrity"].__setitem__( + "repository_clean_at_end", False + ), + "clean_stable_repository", + ), + ( + lambda values: values["storage"][screen.RHT_METHOD].__setitem__( + "resident_bytes", screen.TARGET_PACKED_STATE_BYTES - 1 + ), + "exact_equal_storage", + ), + ( + lambda values: values["state_errors"][screen.RHT_METHOD].__setitem__( + "aggregate_state_sse", 60.0 + ), + "state_sse_relative_reduction", + ), + ( + lambda values: values["aligned_metrics"][screen.RHT_METHOD].__setitem__( + "delta_nll", 0.95 + ), + "aligned_excess_nll_relative_reduction", + ), + ( + lambda values: values["unit_evidence"].__setitem__( + "inverse_relative_l2", screen.MAX_RHT_INVERSE_RELATIVE_L2 + ), + "independent_rht_numeric_evidence", + ), + ], +) +def test_stage_a_gate_fails_closed_on_frozen_condition_drift( + mutation, + failed_check: str, +) -> None: + values = deepcopy(_passing_gate_inputs()) + mutation(values) + + gate = screen.evaluate_stage_a_gate(**values) + + assert gate["passed"] is False + assert gate["checks"][failed_check]["passed"] is False + + +@pytest.mark.parametrize( + ("field", "value"), + [ + ("selection_method", "unexpected_selector"), + ("state_codec_seed", screen.SEED + 1), + ("state_codec_axis", "key"), + ("state_codec_persistent_tensor_bytes", 1), + ], +) +def test_stage_a_gate_rejects_rht_method_or_codec_drift( + field: str, + value: object, +) -> None: + inputs = deepcopy(_passing_gate_inputs()) + inputs["selector_diagnostics"][screen.RHT_METHOD][0][field] = value + + gate = screen.evaluate_stage_a_gate(**inputs) + + assert gate["passed"] is False + assert ( + gate["checks"]["finite_metrics_exact_quotas_and_handshakes"]["passed"] + is False + ) + + +@pytest.mark.parametrize( + "mutation", + [ + lambda values: values["aligned_metrics"][screen.RHT_METHOD].__setitem__( + "candidate_nll", 999.0 + ), + lambda values: values["aligned_metrics"][screen.RHT_METHOD].__setitem__( + "reference_nll", 1.5 + ), + lambda values: values["aligned_metrics"][screen.RHT_METHOD].__setitem__( + "mean_kl", -0.1 + ), + lambda values: values["aligned_metrics"][screen.RHT_METHOD].__setitem__( + "top1_agreement", 1.2 + ), + lambda values: values["state_errors"][screen.RHT_METHOD]["records"][ + 0 + ].__setitem__("mean_squared_error", 1.0), + lambda values: values["state_errors"][screen.RHT_METHOD]["records"][ + -1 + ].__setitem__("high_precision_mask_sha256", "c" * 64), + ], +) +def test_stage_a_gate_recomputes_metrics_state_totals_and_mask_links( + mutation, +) -> None: + inputs = deepcopy(_passing_gate_inputs()) + mutation(inputs) + + gate = screen.evaluate_stage_a_gate(**inputs) + + assert gate["passed"] is False + assert ( + gate["checks"]["finite_metrics_exact_quotas_and_handshakes"]["passed"] + is False + ) + + +def test_committed_stage_a_artifact_passes_the_hardened_gate() -> None: + document = json.loads(STAGE_A_EVIDENCE.read_text(encoding="utf-8")) + evidence = document["evidence"] + repository = evidence["repository"] + integrity = { + "repository_clean_at_start": repository["start"]["worktree_clean"], + "repository_clean_at_end": repository["end"]["worktree_clean"], + "repository_commit_stable": repository["stable_commit"], + "source_hashes_stable": evidence["source_files"]["stable"], + "identity_authenticated_before_model_weights": evidence["dataset"][ + "identity_authenticated_before_model_weights" + ], + "protected_window_8_16_accessed": False, + } + + gate = screen.evaluate_stage_a_gate( + aligned_metrics=evidence["metrics_aligned"], + full_code_metrics=evidence["metrics_full_code_secondary"], + storage=evidence["storage"]["candidates"], + selector_diagnostics=evidence["selector_diagnostics"], + state_errors=evidence["state_error"], + unit_evidence=evidence["unit_evidence"], + integrity=integrity, + ) + + assert gate["passed"] is True + assert all(check["passed"] is True for check in gate["checks"].values()) + + +def test_stage_a_identity_accepts_only_frozen_task_hash_and_token_counts() -> None: + identity = screen.validate_stage_a_identity( + task_id=screen.TASK_ID, + row_sha256=screen.TASK_ROW_SHA256, + prompt_tokens=screen.PROMPT_TOKENS, + code_tokens=screen.CODE_TOKENS, + aligned_scored_tokens=screen.ALIGNED_TOKENS, + ) + + assert identity["authenticated_before_model_weights"] is True + assert identity["aligned_scored_tokens"] == identity["code_tokens"] - 1 + + +@pytest.mark.parametrize( + ("field", "value", "message"), + [ + ("task_id", 667, "locked"), + ("row_sha256", "0" * 64, "row SHA"), + ("prompt_tokens", 68, "token identity"), + ("code_tokens", 40, "token identity"), + ("aligned_scored_tokens", 37, "token identity"), + ], +) +def test_stage_a_identity_rejects_every_frozen_identity_drift( + field: str, + value: object, + message: str, +) -> None: + values: dict[str, object] = { + "task_id": screen.TASK_ID, + "row_sha256": screen.TASK_ROW_SHA256, + "prompt_tokens": screen.PROMPT_TOKENS, + "code_tokens": screen.CODE_TOKENS, + "aligned_scored_tokens": screen.ALIGNED_TOKENS, + } + values[field] = value + + with pytest.raises(ValueError, match=message): + screen.validate_stage_a_identity(**values) + + +def test_state_error_evidence_aggregates_mse_to_sse_by_layer_and_write() -> None: + records = [ + { + "update_index": 0, + "layer_index": 0, + "state_index": 0, + "shape": [1, 1, 2, 2], + "mean_squared_error": 0.25, + "max_absolute_error": 1.0, + "relative_l2_error": 0.1, + }, + { + "update_index": 1, + "layer_index": 1, + "state_index": 0, + "shape": [1, 1, 2, 3], + "mean_squared_error": 0.5, + "max_absolute_error": 1.5, + "relative_l2_error": 0.2, + }, + ] + + aggregate = screen.aggregate_state_error_evidence(records) + + assert aggregate["record_count"] == 2 + assert aggregate["element_count"] == 10 + assert aggregate["aggregate_state_sse"] == pytest.approx(4.0) + assert aggregate["aggregate_state_mse"] == pytest.approx(0.4) + assert aggregate["per_layer"]["0"]["state_sse"] == pytest.approx(1.0) + assert aggregate["per_layer"]["1"]["state_sse"] == pytest.approx(3.0) + assert aggregate["per_write"]["0"]["record_count"] == 2 + assert [record["write_ordinal"] for record in aggregate["records"]] == [0, 0] + + +def test_state_error_evidence_rejects_duplicate_or_nonfinite_records() -> None: + record = { + "update_index": 0, + "layer_index": 0, + "state_index": 0, + "shape": [1, 2], + "mean_squared_error": 0.1, + "max_absolute_error": 0.2, + "relative_l2_error": 0.3, + } + with pytest.raises(ValueError, match="duplicate"): + screen.aggregate_state_error_evidence([record, dict(record)]) + with pytest.raises(ValueError, match="finite"): + screen.aggregate_state_error_evidence( + [{**record, "mean_squared_error": float("nan")}] + ) diff --git a/tests/test_storage_boundary_validation.py b/tests/test_storage_boundary_validation.py new file mode 100644 index 0000000..4f2e52e --- /dev/null +++ b/tests/test_storage_boundary_validation.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +import pytest +import torch +from transformers import DynamicCache, Qwen3_5ForCausalLM + +from recurquant.quantization import QuantizationSpec +from recurquant.storage_boundary_validation import ( + StorageRowLocation, + _directional_dot_float64, + advance_uniform_int4_trajectory, + compare_directional_benefits, + interpolate_storage_row, + validate_qwen_storage_boundary_row, +) +from tests.test_transformers_cache import tiny_config + + +def _spec(bits: int) -> QuantizationSpec: + return QuantizationSpec(bits=bits, group_size=8, flatten_last_dims=1) + + +def _tiny_model() -> Qwen3_5ForCausalLM: + torch.manual_seed(2339) + return Qwen3_5ForCausalLM._from_config( + tiny_config(), + attn_implementation="eager", + ).eval() + + +def _warm_cache(model: Qwen3_5ForCausalLM, prompt: torch.Tensor) -> DynamicCache: + cache = DynamicCache(config=model.config) + with torch.no_grad(): + model(prompt, past_key_values=cache, use_cache=True) + return cache + + +def test_directional_comparison_preserves_loss_and_benefit_signs() -> None: + # L(alpha) = 3 + 2 alpha + 0.5 alpha^2. + epsilon = 0.25 + def loss(alpha: float) -> float: + return 3.0 + 2.0 * alpha + 0.5 * alpha**2 + + result = compare_directional_benefits( + epsilon=epsilon, + loss_at_zero=loss(0), + repeated_loss_at_zero=loss(0), + loss_at_minus_epsilon=loss(-epsilon), + loss_at_plus_epsilon=loss(epsilon), + loss_at_int8_endpoint=loss(1), + autograd_directional_derivative=2.0, + ) + + assert result.central_directional_derivative == pytest.approx(2.0) + assert result.predicted_benefit_autograd == pytest.approx(-2.0) + assert result.predicted_benefit_central == pytest.approx(-2.0) + assert result.measured_endpoint_benefit == pytest.approx(-2.5) + assert result.derivative_sign_agreement is True + assert result.endpoint_sign_agreement is True + assert result.endpoint_taylor_residual == pytest.approx(-0.5) + + +def test_directional_comparison_handles_exact_zero_without_false_sign() -> None: + result = compare_directional_benefits( + epsilon=0.5, + loss_at_zero=1.0, + repeated_loss_at_zero=1.0, + loss_at_minus_epsilon=1.0, + loss_at_plus_epsilon=1.0, + loss_at_int8_endpoint=1.0, + autograd_directional_derivative=0.0, + ) + + assert result.derivative_sign_agreement is None + assert result.endpoint_sign_agreement is None + + +def test_interpolation_changes_only_the_selected_row() -> None: + state = torch.arange(1 * 2 * 3 * 4, dtype=torch.float32).reshape(1, 2, 3, 4) + direction = torch.tensor([0.25, -0.5, 0.75, -1.0]) + location = StorageRowLocation(layer_index=7, head_index=1, row_index=2) + + moved = interpolate_storage_row(state, direction, location=location, alpha=0.5) + + difference = moved - state + assert torch.equal(difference[0, 1, 2], direction * 0.5) + difference[0, 1, 2] = 0 + assert torch.count_nonzero(difference).item() == 0 + assert torch.equal(state, torch.arange(24, dtype=torch.float32).reshape_as(state)) + + +def test_real_tiny_qwen_storage_boundary_matches_central_difference() -> None: + model = _tiny_model() + cache = _warm_cache(model, torch.tensor([[1, 2, 3, 4]])) + raw_before = cache.layers[0].recurrent_states[0].clone() + location = StorageRowLocation(layer_index=0, head_index=0, row_index=0) + + result = validate_qwen_storage_boundary_row( + model, + cache, + torch.tensor([[5]]), + torch.tensor([[6]]), + location=location, + int4_spec=_spec(4), + int8_spec=_spec(8), + epsilon=0.25, + sign_floor=1e-9, + forward_kwargs={"logits_to_keep": 1}, + ) + + assert torch.equal(cache.layers[0].recurrent_states[0], raw_before) + assert result.recurrent_layer_indices == (0,) + assert torch.equal(result.direction, result.int8_row - result.int4_row) + assert result.comparison.baseline_repeat_absolute_error < 1e-7 + assert result.comparison.autograd_directional_derivative == pytest.approx( + result.comparison.central_directional_derivative, + rel=3e-2, + abs=2e-6, + ) + assert result.comparison.predicted_benefit_autograd == pytest.approx( + result.comparison.measured_endpoint_benefit, + rel=0.15, + abs=5e-6, + ) + assert all(parameter.grad is None for parameter in model.parameters()) + + +def test_multilayer_qwen_gradient_avoids_downstream_cache_copy_inplace() -> None: + torch.manual_seed(2341) + model = Qwen3_5ForCausalLM._from_config( + tiny_config(["linear_attention", "linear_attention", "full_attention"]), + attn_implementation="eager", + ).eval() + cache = _warm_cache(model, torch.tensor([[2, 3, 5, 7]])) + raw_before = { + layer_index: cache.layers[layer_index].recurrent_states[0].clone() + for layer_index in (0, 1) + } + + result = validate_qwen_storage_boundary_row( + model, + cache, + torch.tensor([[11]]), + torch.tensor([[13]]), + location=StorageRowLocation(layer_index=0, head_index=1, row_index=3), + int4_spec=_spec(4), + int8_spec=_spec(8), + epsilon=0.25, + sign_floor=1e-9, + forward_kwargs={"logits_to_keep": 1}, + ) + + assert result.recurrent_layer_indices == (0, 1) + assert result.comparison.autograd_directional_derivative == _directional_dot_float64( + result.gradient_row, + result.direction, + ) + assert result.comparison.baseline_repeat_absolute_error < 1e-7 + assert result.comparison.autograd_directional_derivative == pytest.approx( + result.comparison.central_directional_derivative, + rel=5e-2, + abs=2e-6, + ) + assert all( + torch.equal(cache.layers[layer_index].recurrent_states[0], raw_before[layer_index]) + for layer_index in (0, 1) + ) + assert all(parameter.grad is None for parameter in model.parameters()) + + +def test_advancing_prior_token_stops_on_raw_update() -> None: + model = _tiny_model() + prompt = torch.tensor([[3, 4, 5]]) + advanced = _warm_cache(model, prompt) + + advance_uniform_int4_trajectory( + model, + advanced, + torch.tensor([[6]]), + int4_spec=_spec(4), + forward_kwargs={"logits_to_keep": 1}, + ) + + reference = _warm_cache(model, prompt) + # Quantizing before the prior token changes the update, while leaving the + # post-token state raw instead of immediately QDQing it again. + assert not torch.equal( + advanced.layers[0].recurrent_states[0], + reference.layers[0].recurrent_states[0], + ) + assert advanced.layers[0].has_previous_state[0] + + +@pytest.mark.parametrize("bad_epsilon", [0.0, -0.1, 1.1, float("nan")]) +def test_directional_comparison_rejects_invalid_epsilon(bad_epsilon: float) -> None: + with pytest.raises(ValueError, match="epsilon"): + compare_directional_benefits( + epsilon=bad_epsilon, + loss_at_zero=1, + repeated_loss_at_zero=1, + loss_at_minus_epsilon=1, + loss_at_plus_epsilon=1, + loss_at_int8_endpoint=1, + autograd_directional_derivative=0, + ) diff --git a/tests/test_transition_observer.py b/tests/test_transition_observer.py new file mode 100644 index 0000000..1d79993 --- /dev/null +++ b/tests/test_transition_observer.py @@ -0,0 +1,585 @@ +from __future__ import annotations + +from contextlib import ExitStack +from typing import Any + +import pytest +import torch +from transformers import Qwen3_5ForCausalLM + +from recurquant.quantization import QuantizationSpec +from recurquant.query_energy import Qwen35QueryEnergyObserver +from recurquant.transformers_cache import RecurrentStateQDQCache +from recurquant.transition_observer import Qwen35TransitionObserver +from tests.test_transformers_cache import tiny_config + + +class _TransitionCache: + def __init__( + self, + *, + has_previous_state: bool = False, + require_query: bool = False, + ) -> None: + self.previous = has_previous_state + self.require_query = require_query + self.pending: dict[int, tuple[torch.Tensor, ...]] = {} + self.query_pending: dict[int, torch.Tensor] = {} + self.staged: list[tuple[int, tuple[torch.Tensor, ...]]] = [] + self.query_staged: list[tuple[int, torch.Tensor]] = [] + self.updates: list[tuple[int, torch.Tensor]] = [] + self.discards: list[int] = [] + self.query_discards: list[int] = [] + self.events: list[str] = [] + self.fail_stage = False + self.fail_update = False + + def has_previous_state(self, layer_index: int) -> bool: + del layer_index + return self.previous + + def stage_transition_observation( + self, + layer_index: int, + query: torch.Tensor, + key: torch.Tensor, + log_decay: torch.Tensor, + beta: torch.Tensor, + ) -> None: + if self.fail_stage: + raise RuntimeError("stage failed") + snapshot = tuple( + tensor.detach().clone() for tensor in (query, key, log_decay, beta) + ) + self.pending[layer_index] = snapshot + self.staged.append((layer_index, snapshot)) + self.events.append("transition-stage") + + def discard_pending_transition_observations(self, layer_index: int) -> None: + self.pending.pop(layer_index, None) + self.discards.append(layer_index) + + def stage_query_observation( + self, + layer_index: int, + query: torch.Tensor, + *, + l2norm_eps: float, + ) -> None: + assert l2norm_eps > 0 + snapshot = query.detach().clone() + self.query_pending[layer_index] = snapshot + self.query_staged.append((layer_index, snapshot)) + self.events.append("query-stage") + + def discard_pending_query_observation(self, layer_index: int) -> None: + self.query_pending.pop(layer_index, None) + self.query_discards.append(layer_index) + + def update_recurrent_state( + self, + state: torch.Tensor, + layer_idx: int, + ) -> torch.Tensor: + self.events.append("cache-write") + if self.fail_update: + raise RuntimeError("update failed") + if layer_idx not in self.pending: + raise RuntimeError("transition was not staged before recurrent update") + if self.require_query and layer_idx not in self.query_pending: + raise RuntimeError("query was not staged before recurrent update") + self.pending.pop(layer_idx) + self.query_pending.pop(layer_idx, None) + self.updates.append((layer_idx, state.detach().clone())) + self.previous = True + return state + + +class _ReferenceCache: + def __init__(self) -> None: + self.previous = False + self.updates = 0 + + def has_previous_state(self, layer_index: int) -> bool: + del layer_index + return self.previous + + def update_recurrent_state( + self, + state: torch.Tensor, + layer_idx: int, + ) -> torch.Tensor: + del layer_idx + self.previous = True + self.updates += 1 + return state + + +class Qwen3_5GatedDeltaNet(torch.nn.Module): + def __init__(self, layer_idx: int) -> None: + super().__init__() + self.layer_idx = layer_idx + self.kernel_calls: list[str] = [] + self.kernel_inputs: list[tuple[torch.Tensor, ...]] = [] + self.events: list[str] = [] + self.fail_kernel = False + self.bypass_kernel_wrapper = False + self.skip_cache_write = False + self.call_kernel_twice = False + self.chunk_gated_delta_rule = self._chunk + self.recurrent_gated_delta_rule = self._recurrent + + def _kernel( + self, + kind: str, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + *, + g: torch.Tensor, + beta: torch.Tensor, + **kwargs: object, + ) -> tuple[torch.Tensor, torch.Tensor]: + del kwargs + self.events.append("kernel-start") + if self.fail_kernel: + raise RuntimeError("kernel failed") + self.kernel_calls.append(kind) + snapshot = tuple( + tensor.detach().clone() for tensor in (query, key, g, beta) + ) + self.kernel_inputs.append(snapshot) + self.events.append("kernel-done") + return value, query.sum(dim=1) + + def _chunk( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + *, + g: torch.Tensor, + beta: torch.Tensor, + **kwargs: object, + ) -> tuple[torch.Tensor, torch.Tensor]: + return self._kernel("chunk", query, key, value, g=g, beta=beta, **kwargs) + + def _recurrent( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + *, + g: torch.Tensor, + beta: torch.Tensor, + **kwargs: object, + ) -> tuple[torch.Tensor, torch.Tensor]: + return self._kernel( + "recurrent", + query, + key, + value, + g=g, + beta=beta, + **kwargs, + ) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: object | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: object, + ) -> torch.Tensor: + del attention_mask + # Stand in for Qwen3.5's causal convolution, reshape, and key-head repeat. + query = (hidden_states + 7.0).repeat_interleave(2, dim=2) + key = (hidden_states - 3.0).repeat_interleave(2, dim=2) + value = hidden_states.repeat_interleave(2, dim=2) + log_decay = -torch.arange( + 1, + query.shape[1] + 1, + dtype=query.dtype, + device=query.device, + ).reshape(1, -1, 1).expand(query.shape[0], -1, query.shape[2]) + beta = torch.linspace( + 0.1, + 0.9, + query.shape[1], + dtype=query.dtype, + device=query.device, + ).reshape(1, -1, 1).expand(query.shape[0], -1, query.shape[2]) + previous = cache_params is not None and cache_params.has_previous_state( # type: ignore[attr-defined] + self.layer_idx + ) + kernel = ( + self.recurrent_gated_delta_rule + if previous and query.shape[1] == 1 + else self.chunk_gated_delta_rule + ) + if self.bypass_kernel_wrapper: + kernel = self._recurrent if previous and query.shape[1] == 1 else self._chunk + output, final_state = kernel( + query, + key, + value, + g=log_decay, + beta=beta, + cu_seqlens=kwargs.get("cu_seq_lens_q"), + ) + if self.call_kernel_twice: + kernel(query, key, value, g=log_decay, beta=beta) + if cache_params is not None and not self.skip_cache_write: + cache_params.update_recurrent_state( # type: ignore[attr-defined] + final_state, + self.layer_idx, + ) + return output + + +class _TinyModel(torch.nn.Module): + def __init__(self, layers: int = 1) -> None: + super().__init__() + self.layers = torch.nn.ModuleList( + [Qwen3_5GatedDeltaNet(index) for index in range(layers)] + ) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: object | None = None, + **kwargs: object, + ) -> torch.Tensor: + output = hidden_states + for layer in self.layers: + output = layer(output, cache_params=cache_params, **kwargs) + return output + + +def _input(sequence_length: int) -> torch.Tensor: + return torch.arange(sequence_length * 2, dtype=torch.float32).reshape( + 1, + sequence_length, + 1, + 2, + ) + + +def test_observes_exact_transition_inputs_for_prefill_chunk_and_decode() -> None: + model = _TinyModel() + cache = _TransitionCache() + cache.events = model.layers[0].events + + with Qwen35TransitionObserver(model, caches=[cache]): + model(_input(3), cache_params=cache) + model(_input(2), cache_params=cache) + model(_input(1), cache_params=cache) + + assert model.layers[0].kernel_calls == ["chunk", "chunk", "recurrent"] + assert len(cache.staged) == 3 + assert len(cache.updates) == 3 + assert not cache.pending + for (layer_index, staged), kernel_inputs in zip( + cache.staged, + model.layers[0].kernel_inputs, + strict=True, + ): + assert layer_index == 0 + assert len(staged) == 4 + for observed, expected in zip(staged, kernel_inputs, strict=True): + assert torch.equal(observed, expected) + query, key, log_decay, beta = staged + assert query.shape == key.shape + assert query.shape[2] == 2 + assert log_decay.shape == beta.shape == query.shape[:3] + + assert cache.events == [ + "kernel-start", + "kernel-done", + "transition-stage", + "cache-write", + "kernel-start", + "kernel-done", + "transition-stage", + "cache-write", + "kernel-start", + "kernel-done", + "transition-stage", + "cache-write", + ] + + +def test_dispatches_strictly_by_identity_and_leaves_reference_cache_alone() -> None: + model = _TinyModel() + first = _TransitionCache() + second = _TransitionCache() + reference = _ReferenceCache() + + with Qwen35TransitionObserver(model, caches=[first, second]): + model(_input(2), cache_params=first) + model(_input(2), cache_params=second) + model(_input(2), cache_params=reference) + + assert len(first.staged) == len(second.staged) == 1 + assert first.staged[0][1][0].data_ptr() != second.staged[0][1][0].data_ptr() + assert reference.updates == 1 + + +@pytest.mark.parametrize("failure", ["kernel", "stage", "update"]) +def test_every_forward_failure_clears_pending_observation(failure: str) -> None: + model = _TinyModel() + cache = _TransitionCache() + module = model.layers[0] + original_forward = module.forward + original_chunk = module.chunk_gated_delta_rule + original_recurrent = module.recurrent_gated_delta_rule + if failure == "kernel": + model.layers[0].fail_kernel = True + elif failure == "stage": + cache.fail_stage = True + else: + cache.fail_update = True + + with ( + Qwen35TransitionObserver(model, caches=[cache]), + pytest.raises(RuntimeError, match=f"{failure} failed"), + ): + model(_input(2), cache_params=cache) + + assert not cache.pending + assert cache.discards == [0] + assert module.forward.__func__ is original_forward.__func__ + assert "forward" not in module.__dict__ + assert module.chunk_gated_delta_rule is original_chunk + assert module.recurrent_gated_delta_rule is original_recurrent + + +@pytest.mark.parametrize("packed_name", ["cu_seq_lens_q", "cu_seq_lens_k", "cu_seqlens"]) +def test_rejects_packed_cu_seqlens_before_kernel_or_cache_mutation( + packed_name: str, +) -> None: + model = _TinyModel() + cache = _TransitionCache() + + with ( + Qwen35TransitionObserver(model, caches=[cache]), + pytest.raises(ValueError, match="does not support packed cu_seqlens"), + ): + model( + _input(2), + cache_params=cache, + **{packed_name: torch.tensor([0, 2])}, + ) + + assert not model.layers[0].kernel_calls + assert not cache.staged + assert not cache.updates + + +def test_fails_closed_if_forward_bypasses_wrapped_kernel() -> None: + model = _TinyModel() + cache = _TransitionCache() + model.layers[0].bypass_kernel_wrapper = True + model.layers[0].skip_cache_write = True + + with ( + Qwen35TransitionObserver(model, caches=[cache]), + pytest.raises(RuntimeError, match="exactly one observed state-kernel call"), + ): + model(_input(2), cache_params=cache) + + assert not cache.pending + assert not cache.staged + assert cache.discards == [0] + + +def test_rejects_a_second_kernel_call_without_staging_twice() -> None: + model = _TinyModel() + cache = _TransitionCache() + model.layers[0].call_kernel_twice = True + + with ( + Qwen35TransitionObserver(model, caches=[cache]), + pytest.raises(RuntimeError, match="more than one state kernel"), + ): + model(_input(2), cache_params=cache) + + assert len(cache.staged) == 1 + assert not cache.pending + assert cache.discards == [0] + + +@pytest.mark.parametrize("transition_outermost", [False, True]) +def test_nests_with_query_energy_observer_in_either_order( + transition_outermost: bool, +) -> None: + model = _TinyModel() + cache = _TransitionCache(require_query=True) + module = model.layers[0] + original_forward = module.forward + original_chunk = module.chunk_gated_delta_rule + original_recurrent = module.recurrent_gated_delta_rule + transition = Qwen35TransitionObserver(model, caches=[cache]) + query = Qwen35QueryEnergyObserver(model, caches=[cache]) + observers = (query, transition) if transition_outermost else (transition, query) + + with ExitStack() as stack: + for observer in observers: + stack.enter_context(observer) + model(_input(2), cache_params=cache) + + assert len(cache.staged) == len(cache.query_staged) == len(cache.updates) == 1 + assert not cache.pending + assert not cache.query_pending + assert module.forward.__func__ is original_forward.__func__ + assert "forward" not in module.__dict__ + assert module.chunk_gated_delta_rule is original_chunk + assert module.recurrent_gated_delta_rule is original_recurrent + + +def test_nested_observers_both_discard_when_cache_write_fails() -> None: + model = _TinyModel() + cache = _TransitionCache(require_query=True) + cache.fail_update = True + + with ( + Qwen35TransitionObserver(model, caches=[cache]), + Qwen35QueryEnergyObserver(model, caches=[cache]), + pytest.raises(RuntimeError, match="update failed"), + ): + model(_input(2), cache_params=cache) + + assert not cache.pending + assert not cache.query_pending + assert cache.discards == [0] + assert cache.query_discards == [0] + + +def test_installation_is_atomic_and_duplicate_installation_is_rejected() -> None: + model = _TinyModel(layers=2) + cache = _TransitionCache() + first = model.layers[0] + originals = ( + first.forward, + first.chunk_gated_delta_rule, + first.recurrent_gated_delta_rule, + ) + model.layers[1].recurrent_gated_delta_rule = None # type: ignore[assignment] + + observer = Qwen35TransitionObserver(model, caches=[cache]) + with pytest.raises(TypeError, match="must be callable"): + observer.install() + + assert first.forward.__func__ is originals[0].__func__ + assert "forward" not in first.__dict__ + assert first.chunk_gated_delta_rule is originals[1] + assert first.recurrent_gated_delta_rule is originals[2] + assert not hasattr(model, "_recurquant_transition_observer") + + valid_model = _TinyModel() + first_observer = Qwen35TransitionObserver(valid_model, caches=[cache]) + second_observer = Qwen35TransitionObserver(valid_model, caches=[cache]) + with first_observer, pytest.raises(RuntimeError, match="already installed"): + second_observer.install() + + +@pytest.mark.parametrize( + ("caches", "error", "message"), + [ + ([], ValueError, "at least one"), + ([object()], TypeError, "stage_transition_observation"), + ], +) +def test_constructor_rejects_invalid_cache_contract( + caches: list[object], + error: type[Exception], + message: str, +) -> None: + with pytest.raises(error, match=message): + Qwen35TransitionObserver(_TinyModel(), caches=caches) + + +def test_constructor_rejects_the_same_cache_identity_twice() -> None: + cache = _TransitionCache() + + with pytest.raises(ValueError, match="same object"): + Qwen35TransitionObserver(_TinyModel(), caches=[cache, cache]) + + +class _ObservedQDQCache(RecurrentStateQDQCache): + def __init__(self, config: Any) -> None: + super().__init__(config, spec=QuantizationSpec(bits=4, group_size=8)) + self.transition_observations: list[tuple[int, tuple[torch.Tensor, ...]]] = [] + self.pending_transitions: dict[int, tuple[torch.Tensor, ...]] = {} + + def stage_transition_observation( + self, + layer_index: int, + query: torch.Tensor, + key: torch.Tensor, + log_decay: torch.Tensor, + beta: torch.Tensor, + ) -> None: + snapshot = tuple( + tensor.detach().clone() for tensor in (query, key, log_decay, beta) + ) + self.pending_transitions[layer_index] = snapshot + self.transition_observations.append((layer_index, snapshot)) + + def discard_pending_transition_observations(self, layer_index: int) -> None: + self.pending_transitions.pop(layer_index, None) + + def update_recurrent_state( + self, + recurrent_state: torch.Tensor, + layer_idx: int, + ) -> torch.Tensor: + if layer_idx not in self.pending_transitions: + raise RuntimeError("transition was not staged before recurrent update") + self.pending_transitions.pop(layer_idx) + return super().update_recurrent_state(recurrent_state, layer_idx) + + +def test_real_tiny_qwen_runs_prefill_cached_chunk_and_decode() -> None: + device = ( + torch.device("cuda", torch.cuda.current_device()) + if torch.cuda.is_available() + else torch.device("cpu") + ) + config = tiny_config() + model = Qwen3_5ForCausalLM._from_config( + config, + attn_implementation="eager", + ).to(device).eval() + cache = _ObservedQDQCache(config) + + with torch.inference_mode(), Qwen35TransitionObserver(model, caches=[cache]): + prefill = model( + torch.randint(0, config.vocab_size, (1, 5), device=device), + past_key_values=cache, + ) + chunk = model( + torch.randint(0, config.vocab_size, (1, 2), device=device), + past_key_values=cache, + ) + decode = model( + torch.randint(0, config.vocab_size, (1, 1), device=device), + past_key_values=cache, + ) + + assert prefill.logits.shape == (1, 5, config.vocab_size) + assert chunk.logits.shape == (1, 2, config.vocab_size) + assert decode.logits.shape == (1, 1, config.vocab_size) + assert len(cache.transition_observations) == 3 + assert not cache.pending_transitions + assert [item[1][0].shape[1] for item in cache.transition_observations] == [5, 2, 1] + for layer_index, (query, key, log_decay, beta) in cache.transition_observations: + assert layer_index == 0 + assert query.shape == key.shape == ( + 1, + query.shape[1], + config.linear_num_value_heads, + config.linear_key_head_dim, + ) + assert log_decay.shape == beta.shape == query.shape[:3] + assert query.device == key.device == log_decay.device == beta.device == device