diff --git a/CURRENT.md b/CURRENT.md index 0612466..2259fd7 100644 --- a/CURRENT.md +++ b/CURRENT.md @@ -4,6 +4,16 @@ ## 最近已封存结果 +Phase 4AO：**FAIL-POSTPROCESS-MODULE-IDENTITY**，学习效果尚未判断。 + +登记 12 条两轮 delta 衔接验证，仅第一条 reference 神经轨迹完成并封存（120 ms、36,539 spikes、无 NaN/Inf）；随后因两个同源 loader 的缓存归属冲突停止，完整流程完成数为 0/12。未重跑轨迹、未覆盖源码、未启动完整 conditioning。 + +独立 CPU 进程后处理诊断已通过：同一轨迹产生 90 条目标 compartment 非零 delta，跨 compartment 改动为 0；未安装新权重，不能据此认定学习成功。AN/AM 封存与 28 套旧完整性再次通过。下一步单独登记进程隔离协调器，不能恢复或改写本次已封存 AO。 + +[AO 结果、原因与后续边界](docs/progress/phase4ao-interface-status.md) · [正式失败状态](registry/phase4ao-conditioning-interface/evidence/audit/final-status.json) + +## 前一项工程验证 + Phase 4AN：**PASS-EXACT-LIGHT-CONTINUATION-ENGINEERING-NOT-LEARNING**。 固定 zero/static25 输入下，32/32 次运行及 74 项比较通过；分段续跑保留精确事件、末态和延迟队列，独立 CSR 审计覆盖真实非零跨段到达。AM 双封存及 28 套旧完整性记录再次通过。没有训练，未生成 learned candidate；结论只覆盖注册的 100 ms 输入及固定切点，不是普遍动力学证明。 @@ -42,13 +52,13 @@ Phase 4AK D18 表征验证 PASS 原样保留。1.8 ms 为工程 APL 时序假设 ## 当前准备与学习边界 -Phase 4AN 的有限工程门已封存；21 份实际使用源码已按 SHA 锁定，lab 整个目录未封存。科学学习草案仍未登记，没有候选；工程测试不是条件化实验。 +Phase 4AN 的有限工程门已封存；21 份实际使用源码已按 SHA 锁定。AO 进一步登记了包含科学准备代码的 51 份源快照，实验以接口失败封存；lab 整个目录仍未封存，完整科学条件化协议尚未登记执行，没有学习候选。 -新增科学代码仍为未注册 DRAFT：评估清单、按真实 CS 序列对齐的控制政策、完整 63 位种子的输入接口，以及重用固定 AM/J 实现的单轮 eligibility 更新桥。后者只返回 delta，不安装权重。详见[条件化准备进度与限制](docs/progress/conditioning-preparation-2026-09-30.md)。 +评估清单和完整条件化政策仍是科学草案；完整 63 位输入与单轮更新桥已进入 AO 的有限工程登记，但整体门未通过。冻结 AM/J 更新桥在独立 CPU 后处理中可以生成 delta，其跨轮实际安装仍未验证。历史准备记录见[条件化准备进度与限制](docs/progress/conditioning-preparation-2026-09-30.md)，当前结果以 AO 报告为准。 服务器 CPU 合成测试 119/119 通过，0 跳过；21 个 AN 注册源测试前后 SHA 均未变。当前续跑器禁止父子段更换 overlay，因此“保留末态并使用新 delta”的 retention 更新边界仍需新增接口验证，不是现有工程 PASS 已覆盖的能力。 -完整 conditioning 草案为 68,000 epochs、10,760 生物秒（含 idle），新显式评估政策再列出 9,440 次 probes / 1,132.8 生物秒，总计 11,892.8 生物秒。这是未执行的条件性计划库存，不是工期预算。仍缺实际输入/状态/delta 绑定协调器、真实 CUDA 输入一致性验证、反转/连续保持功能门和完整资源预算；先登记有限实现检查，不直接启动大批量训练。 +完整 conditioning 草案为 68,000 epochs、10,760 生物秒（含 idle），新显式评估政策再列出 9,440 次 probes / 1,132.8 生物秒，总计 11,892.8 生物秒。这是未执行的条件性计划库存，不是工期预算。真实 CUDA 输入检查已在 AO 通过；协调器因后处理模块组合问题中止。仍需进程隔离后的跨轮安装回归、反转/连续保持功能门和完整资源预算，不直接启动大批量训练。 目标仍是“学习可以使用”，当前没有达到。接口验证或静态权重效果不能替代真实条件化。 后续须预注册固定 olfactory A/B、具体 DAN type/compartment、至少五个互不重叠训练种子流及独立 evaluation streams；保留原 eligibility-v2、人工 US 与实际 DAN 放电的明确区分，以及配对/控制、反转、延迟奖励、保持等功能验证。 diff --git a/docs/progress/phase4ao-interface-status.md b/docs/progress/phase4ao-interface-status.md new file mode 100644 index 0000000..229f1e6 --- /dev/null +++ b/docs/progress/phase4ao-interface-status.md @@ -0,0 +1,65 @@ +# Phase 4AO：两轮权重接口中止，后处理隔离诊断通过 + +更新：2026-09-30。独立分支 `codex/lif-author-semantics-lab`。 + +正式结果为 **FAIL-POSTPROCESS-MODULE-IDENTITY**。这是执行接口失败，不是 +嗅觉学习或动力学假设的否定证据。完整 conditioning 没有启动，没有 learned candidate。 + +## 实际完成与未完成 + +预登记 12 条短调用：D1 / D0 / frozen × reference / light × 两个 120 ms fresh-reset epoch。 +master 为 20260930，固定第一 PAM15 block；所选两个连续 epoch 均为 A 气味。 +D0/frozen 仍共享同一 CS 和人工 DAN 请求，只改变边界更新命令。没有选择性重抽种子。 + +- 新增协调器的服务器 CPU 合成测试 61/61 通过，0 跳过。 +- 实际 CUDA 抽样与固定原 L 的抽样表达式一致；完整 63 位种子的高位影响已检查。 +- 两轮固定输入分别有 78/65 个 CS 请求，各有 4 个 US 请求。 +- 第一条 reference 全脑轨迹完成：166,700 neurons、25,582,938 edges、1200 ticks、 + 36,539 spikes，所有有限值检查通过。82 个请求中 55 个接受、27 个因实际状态拒绝。 +- 核心 reference 模拟 6.514 s；该次完整 runtime 66.935 s，其中编译/缓存准备 59.222 s。 + 最大已分配显存 518,003,712 bytes。不可把这条含首次编译结果称为优化基准。 +- 轨迹已立即封存，但其后处理失败。因此是 **1 条神经轨迹完成、0/12 条完整流程完成**， + 不是 1/12 整体门通过；其余神经调用没有运行。 + +## 原因和隔离诊断 + +普通 `runtime_light` 与 continuation 内部加载的另一个同源实例各有私有模块缓存， +却共享同一全局 helper 名。第二实例正确拒绝了未由其缓存认证的现有模块。 +没有绕过该安全检查、修改冻结 loader 或重跑已完成轨迹。 + +原 61 个测试没有覆盖“两个真实 loader 同进程组合”这一边界；修复诊断新增实际 loader +进程隔离测试，能复现冲突，并验证干净进程加载成功、伪模块和错误 SHA 仍被拒绝。 + +在独立 CPU 进程、独立目录 `experiments/phase4ao-postprocess-isolation/` 中，原 E +后处理使用完全相同的 sealed 轨迹成功。实际 KC events 为 137；从完整 14,232-edge +mask 中，有 90 条属于目标 compartment 的边产生非零 delta，跨 compartment 改动为 0。 +这些 delta **未安装到脑、未用于下一轮、不是学习成功证据**。原 AO 状态保持 FAIL。 +后处理隔离与发布工具的服务器合成测试最终共 19/19 通过；发布测试另修正了仅适配本地 +目录的 fixture 路径错误，未修改生产模型或已登记源码。 + +## 冻结完整性 + +AN 与 AM 的完整封存重新校验通过；继承的 28 套旧完整性检查通过。 +原 21 个 AN 源、前一批 20 个科学准备源和本次登记的全部 51 源文件均未修改。 +五组旧参考使用已封存 AM 证据，本次没有重新执行，不混称为重新回归。 + +## 下一步 + +为独立进程后处理建立新的有限预登记；保留这次中止与诊断结果，不续写或重启已封存 AO， +不因结果调整 seed、输入、eta、阈值或脑参数。先验证跨 epoch 的 delta 实际安装和所有 +reference/light/零更新对照，再处理新权重与连续 retention 状态的交接。之后才有条件 +登记至少五个训练种子、独立评估种子及完整功能控制。游戏、RL、decoder、视觉和 Web +仍不在范围内。 + +## 来源与保存范围 + +代码版本：`4ff241bdc96319823f5fc83f80312468f5208a5c`。 +注册 SHA：`9e2ed05879ce279da7dc0b4cac09a44d67665c230b56db94e2ca39c645bdc250`。 +实验 manifest SHA：`dcfb24338a39ce0bb4d0bf5fe3c92a1907fb63536b4c57856c2f40203996f979`。 +正式状态 SHA：`f8fff2cb7d4eb0abfc9653c989781c0310b6ad7ec42cce4949bae1a15fd793d8`。 +隔离诊断 manifest SHA：`202efff603a05016b3f530df9eb1d3c52e1c4119dbc4372ad7b75c6ca69013a2`。 + +AO 小证据 registry 已发布 110 份来源文件，连同控制与索引共 117 个文件，本地逐项 +SHA-256 验收通过；隔离诊断的完整 10 个小文件也已本地验收。Git 同步源码、文档、 +AO 小证据与诊断 JSON，原始大型轨迹和完整约 83.8 MB 紧凑语义计划仍以服务器保存为准， +不得把 Git 小证据同步称为全量异机备份。 diff --git a/docs/progress/phase4ao-postprocess-diagnostic.json 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child_process(reg, reg_sha, 'worker', row['row_id'])\n File \"/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_preflight_workflow.py\", line 291, in child_process\n require(result.returncode == 0, 'Child failed; preserve partial files and inspect logs')\n File \"/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/engineering_prepare.py\", line 53, in require\n raise ValueError(message)\nValueError: Child failed; preserve partial files and inspect logs\n" +} diff --git a/registry/phase4ao-conditioning-interface/evidence/cases/D0--light--epoch-000/derivation.json b/registry/phase4ao-conditioning-interface/evidence/cases/D0--light--epoch-000/derivation.json new file mode 100644 index 0000000..99cebf8 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/evidence/cases/D0--light--epoch-000/derivation.json @@ -0,0 +1,130 @@ +{ + "D_and_update_commands_owned_by_epoch_authority": true, + "brain_runs": 0, + 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"/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "cs_input_seed": 5717173809506784725, + "cs_input_seed_bits": 63, + "dt_ms": 0.1, + "duration_ms": 120.0, + "expected_CS_requests": 75.04345290362835, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/full", + "kind": "fullgraph", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "max_steps": 1200, + "mode": "physiological", + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "refractory_ms": 2.2, + "reset_potential_mv": -52.0, + "resting_potential_mv": -52.0, + "synaptic_delay_ms": 1.8, + "synaptic_tau_ms": 5.0, + "threshold_mv": -45.0, + "w_syn_mv_per_anatomical_synapse": 0.275 + }, + "name": "D0--light--epoch-000", + "odor_id": "HXDOZKJGKXYMEW-UHFFFAOYSA-N", + "odor_name": "4-ethylphenol", + "outer_arm": "D0", + "outer_epoch_id": "physiological__PAM15__reinforce_A__rep01/normal/paired/000", + "outer_protocol": "conditioning_interface_preflight_not_full_conditioning", + "output_budget_bytes": 4294967296, + "precision": "float64", + "rate_sum_hz": 750.4344482421875, + "record_history": false, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "seed": 1073741825, + "seed_scope": "seed is separate reset control; full cs_input_seed used only by saved-input sampler", + "source_panel_row": 9, + "source_panel_split": "heldout", + "steps": 1200, + "target_count": 26, + "within_trial_weight_updates": false +} diff --git a/registry/phase4ao-conditioning-interface/evidence/cases/D0--light--epoch-001/derivation.json b/registry/phase4ao-conditioning-interface/evidence/cases/D0--light--epoch-001/derivation.json new file mode 100644 index 0000000..89df534 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/evidence/cases/D0--light--epoch-001/derivation.json @@ -0,0 +1,130 @@ +{ + 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"physiological__PAM15__reinforce_A__rep01/normal/paired/001", + "outer_protocol": "conditioning_interface_preflight_not_full_conditioning", + "output_budget_bytes": 4294967296, + "precision": "float64", + "rate_sum_hz": 750.4344482421875, + "record_history": false, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "seed": 1073741825, + "seed_scope": "seed is separate reset control; full cs_input_seed used only by saved-input sampler", + "source_panel_row": 9, + "source_panel_split": "heldout", + "steps": 1200, + "target_count": 26, + "within_trial_weight_updates": false +} diff --git a/registry/phase4ao-conditioning-interface/evidence/cases/D0--reference--epoch-000/derivation.json b/registry/phase4ao-conditioning-interface/evidence/cases/D0--reference--epoch-000/derivation.json new file mode 100644 index 0000000..323972a --- /dev/null +++ b/registry/phase4ao-conditioning-interface/evidence/cases/D0--reference--epoch-000/derivation.json @@ -0,0 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"4-ethylphenol", + "outer_arm": "D0", + "outer_epoch_id": "physiological__PAM15__reinforce_A__rep01/normal/paired/000", + "outer_protocol": "conditioning_interface_preflight_not_full_conditioning", + "output_budget_bytes": 4294967296, + "precision": "float64", + "rate_sum_hz": 750.4344482421875, + "record_history": false, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "seed": 1073741825, + "seed_scope": "seed is separate reset control; full cs_input_seed used only by saved-input sampler", + "source_panel_row": 9, + "source_panel_split": "heldout", + "steps": 1200, + "target_count": 26, + "within_trial_weight_updates": false +} diff --git a/registry/phase4ao-conditioning-interface/evidence/cases/D0--reference--epoch-001/derivation.json b/registry/phase4ao-conditioning-interface/evidence/cases/D0--reference--epoch-001/derivation.json new file mode 100644 index 0000000..f324e2a --- /dev/null +++ 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b/registry/phase4ao-conditioning-interface/evidence/inputs/generated/actual-CUDA-sampler-check.json @@ -0,0 +1,34 @@ +{ + "additional_probe_matrices": 3, + "exact_sparse_CS_requests": true, + "full_seed": 5717173809506784725, + "highbit_influence_observed": true, + "low32_probe_seed": 4073821653, + "new_input_seed_selected": false, + "old_full_trial_equivalence_claimed": false, + "original_L_sampler_AST_sha256": "e2ad77322ba4d691306c9806c42d7895cf994dc5d55780838de68089baf33e32", + "probe_shape": [ + 1200, + 26 + ], + "uniforms_identities": [ + { + "dtype": " + if args.action == 'worker': worker(args.registration_sha256, args.row_id) + File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_preflight_workflow.py", line 254, in worker + E.postprocess_epoch(paths['authority'], digest(paths['authority']), paths['update']) + File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_epoch.py", line 312, in postprocess_epoch + return _coordinate(value, paths, pins, checkpoint, authority_path, out_dir, dependencies, DIMENSIONS) + File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_epoch.py", line 209, in _coordinate + helper, before, checked_meta, _ = r.load_overlay(paths['overlay_before'], paths['overlay_manifest_before']) + File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/runtime_light.py", line 160, in load_overlay + helper = _load('phase4an_sealed_am_overlay', meta['am_overlay_path'], AM_OVERLAY_SHA) + File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/runtime_light.py", line 99, in _load + require(_LOADED.get(name) == (path, expected, module), 'Existing unverified module binding: ' + name) + File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/runtime_light.py", line 57, in require + raise ValueError(message) +ValueError: Existing unverified module 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+++ b/registry/phase4ao-conditioning-interface/source-snapshot/CONDITIONING_INTERFACE_PROTOCOL.md @@ -0,0 +1,70 @@ +# AO — bounded two-epoch conditioning-interface validation + +This independent branch does not replace the frozen reference, runtime or any +Phase 1–AN result. It tests the installation interface for the existing frozen +eligibility-v2 rule. It is not a new dynamics hypothesis or a learned model. + +## Fixed, pre-outcome selection + +- Master seed: `20260930`; no seed search or outcome-based retries. +- First canonical block: physiological / PAM15 / reinforce_A / rep01. +- First normal paired epoch requesting a US, and its immediate successor. +- Both selected epochs are odor A (4-ethylphenol), not an A/B discrimination + test. Exact recorded L glomerular/PN mapping and PN column order are retained. +- Each epoch is 120 ms: 100 ms CS plus 20 ms tail, US at tick 900. +- D1, D0 and frozen arms receive identical saved CS and DAN requests. The + scheduled artificial D scalar is not measured dopamine concentration and is + not multiplied by actual DAN request acceptance. Delivery is reported apart. +- Two executors (reference and light), two fresh epochs per arm: 12 calls, + 14,400 timesteps. This does not authorize the full conditioning inventory. + +## Immutable neural semantics and authority + +Each epoch resets V, g, refractory counters, APL state and both delay queues; +the zero-initialized eligibility trace is computed from actual sealed spikes. +Only the float32-mV plastic delta carries between epochs. No update occurs +inside an epoch. Original graph, base weights, transmitter signs, numerical +precision, APL parameters, frozen v2 rule and complete 14,232-edge mask remain +unchanged. Full graph is 166,700 neurons and 25,582,938 edges. + +Registration pins source snapshots, actual graph/APL/coefficient files, frozen +L static resources, H mappings, the complete generated semantic plan and the +12 selected call descriptors before CUDA sampling. Sampling receipts bind the +two generated request arrays before any brain call. Every input file is shared +byte-for-byte between arms and executors. The full 63-bit input seed is separate +from the inherited conservative 31-bit neural reset-control value. + +A fixed first-seed CUDA check executes only the four original L sampling AST +statements, compares sparse requests, and compares full63-bit versus low32-bit +uniform matrices. This is a sampler test, not a neural trial or seed search. +Two production and three diagnostic matrices are drawn; each is 1200×PN count, +at most 2,000,000 uniform values. No continuous-time Poisson claim is made. + +Each neural output is immediately sealed. Outside that seal, the coordinator +binds original inputs, checkpoint events, actual v2 recomputation and the new +delta snapshot. `parent_snapshot_sha256` means the previous **overlay NPZ file**, +not its manifest or the neural checkpoint. Epoch 2 must install that exact +snapshot; D0/frozen deltas must stay positive-zero bitwise. + +## Finite gates and reporting limits + +Required: all 12 processes complete; six reference/light pairs have identical +sparse events, counts, final V/g/ref and terminal queues/APL; deltas are exact; +D0/frozen identity holds; epoch 2 actually installs epoch 1 delta. D1 must have +nonzero actual update coverage or the result is `INCOMPLETE`, never rescued by +another input. Nonzero delta is interface coverage, not evidence of memory. + +The light executor does not record intermediate floating states; no all-tick +bitwise claim is inferred. This validation has only one training replicate, two +A epochs, no independent behavioral evaluation, and no acquisition/reversal/ +retention success claim. The post-update retention boundary is still unsupported. + +## Resource and failure policy + +Use only RTX4090 UUID GPU-79f3a85d-adce-7833-7003-6488140ca596; admit a child only +when all three ComfyUI queues are empty. Never stop or evict services. Cap new +output at 4 GiB plus 10 GiB free reserve and each child at 1800 seconds. Preserve +partial output and stop on any failure; no automatic brain retry. End by +rechecking AN and AM seals and the inherited 28-root integrity inventory. Five +old frozen reference results are retained from sealed AM evidence, not claimed +to have been rerun by this small interface gate. diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/ENGINEERING_GATE_DRAFT.md b/registry/phase4ao-conditioning-interface/source-snapshot/ENGINEERING_GATE_DRAFT.md new file mode 100644 index 0000000..60dcadf --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/ENGINEERING_GATE_DRAFT.md @@ -0,0 +1,116 @@ +# Phase 4AN GPU 工程验证门草案 + +状态：**未注册 DRAFT；不是执行授权，尚未运行本门。** 日期：2026-09-30。 + +本门只验证新的轻记录执行器和连续状态接口是否保存冻结 D18 动力学。它不是 conditioning、retention 学习结论，也不产生 learned candidate。AM 是否已正式完成、其最终状态和 seal SHA，必须在本门预注册时从真实证据核验，本文不代填成功状态或哈希。 + +## 1. 固定范围与最小库存 + +只使用一个 AM 已准备的 100 ms 全脑输入，分别安装其原有 `zero` 与 `static25` 固定 overlay。选择规则先于本门任何新 GPU 输出：从 AM `protocol/preregistered.json` 的原 `planned` 顺序取第一条 `repeat == 0` 的记录，保存其完整行、序号、case 路径及 SHA；不得按活动强弱、旧 static25 效果或运行快慢挑 case。其原始 case 实际位于 AK `cases//`；AM `run_suite.py` 没有另造一套 case。若预注册者希望改变此规则，应先修订草案并锁定，不得在看新输出后换输入。 + +每个 overlay 固定以下库存，所有行均使用同一原始 1000 tick 请求序列或其精确前缀/分段： + +| 行组 | 固定执行内容 | 新执行调用数/overlay | +|---|---|---:| +| Fresh 对照 | 完整 1000 tick reference r0、light r0、light r1 | 3 | +| 连续链 | reference、light 各一条，段长 `[5, 13, 1, 481, 500]` | 10 | +| 切点前缀 | 从初态独立运行 reference 至绝对 tick 数 18、19、500 | 3 | + +合计 32 次短执行，11,074 个全脑 tick，即累计 1.1074 生物秒；不是 68,000 个训练 epoch。两条链的第一段均为 fresh 5 tick，其中 reference 第一段兼作第一个切点的前缀参照，不重复运行。切点为累计 tick 数 5、18、19、500，终点为 1000：覆盖短于 18 tick 的父/子段、在绝对 tick 18 的首次合法延迟到达、19-slot 环的相位，以及较晚的活动边界。不得把部分库存完成称作完整门通过。 + +固定参数：166,700 neurons、25,582,938 edges、`dt_ms=0.1`、18 tick synaptic delay、19 行 spike ring、18 行 graded-APL ring、LIF float64 SI 状态与旧 APL float32 操作。保留 14,232 mask、5845 fast-APL 出边置零规则及原 APL 全形矩阵。`static25` 是 AM 原 float32-mV 候选基值的固定 `-0.25 * base` overlay，经原 SI bridge 安装；不是声称 float64 原权重每项恰减 25%。两条 overlay 分别与自己的 AM 参照比较，不能要求 zero 与 static25 相等。 + +禁止：weight updates、eligibility 推进/应用、eta 改动、DAN/US 新请求、odor/seed 新抽样、动力学调参、训练、额外 tail ticks。这里的连续链只检验工程续接，不检验记忆保留。 + +## 2. 当前真实接口与输入派生 + +以下是底层 Python API；本门的独立 coordinator 使用它们，不改旧模块 globals： + +```python +runtime_light.run_light(case_dir, out_dir, build_root, overlay_file, overlay_manifest) +runtime_light.run_reference(case_dir, out_dir, build_root, overlay_file, overlay_manifest) +runtime_continuation.seal_checkpoint(checkpoint_dir) +runtime_continuation.load_checkpoint(checkpoint_dir, expected_sha) +runtime_continuation.run_continued( + case_dir, out_dir, build_root, overlay_file, overlay_manifest, + checkpoint_dir, mode='light' # 或 'reference' +) +``` + +本门的 comparator、有限库存 coordinator、预注册/执行 receipt 已在独立 engineering 模块实现；必须在服务器通过纯合成测试、完成只读代码审查并锁定全部源码后，才能注册执行。现有 AM comparator 对 AM adapter/provenance 和完整 1000 tick 有硬 schema 约束，不应直接冒充 AN comparator 或修改其旧 globals。 + +在新的独占输入目录派生 spec，不改 AK/AM 原文件。完整 fresh case 保留原 `name`、model、APL、图、系数和请求；增加显式 `max_steps=1000`，仅调整新的输出预算根及已列明的非科学标签。要求 `record_history=False`。新旧 spec 的逐字段差异必须列入 receipt，不能用“大致相同”或整类忽略代替。 + +前缀保留原请求中 `tick < end` 的行。分段 `[start, stop)` 只保留该区间原请求，局部 tick 精确减 `start`；全局 neuron_idx 不变。每段 `steps=stop-start`、`duration_ms=steps*0.1`、`max_steps=1000`。将各段请求恢复绝对 tick 后拼接，必须与原请求两个 int32 数组逐位相同、顺序相同、无丢失/重复；不能重抽随机输入。NPZ 容器 SHA 可以因新文件而不同，但数组语义必须 exact，并同时保留文件 SHA。 + +第一段完成后立即 `seal_checkpoint`；外部 coordinator 把返回的实际 manifest SHA 绑定到下一段新 spec 的 `parent_checkpoint_manifest_sha256`，可同时写准确 `global_tick_offset`。此 SHA 是运行后产出的数据依赖，预注册应锁定派生规则而不是预造值。后续 `run_continued` 自身会产生并封存 checkpoint；父 SHA 仍须外部绑定。fresh seal 是 `state.npz` 的首次新增封存绑定，不能声称它追溯证明 seal 前文件未改：应在同一受控流程中立即比较、封存，保存前后 inventory，期间不允许第三方写入。 + +AN overlay manifest 使用现有 `phase4an-overlay-v1` 精确键：`schema, overlay_name, overlay_sha256, mask_path, mask_sha256, base_candidate_path, base_candidate_sha256, aj_model_dir, am_overlay_path, am_overlay_sha256`。复用 AM 原 overlay 数组字节及实际 SHA，仅新建 manifest；不能把 AM 原 manifest 的额外字段直接塞给 AN，也不使用 `learned` 模式。 + +## 3. 预注册必须完整绑定的证据 + +在任何本门 GPU 执行前，保存不可覆盖的 input/source inventory 与其外部 SHA。至少包括： + +- AM 最终状态、model/experiment seal、`protocol/preregistered.json`、`prepared-hashes.json`、所选原 case spec/requests，以及 `gpu/zero/-r0`、`gpu/static25/-r0` 的完整 `output-hashes.json`、comparison、adapter provenance 与全部待比较 artifact。核验完整 AM seal，而非只相信单个 `pass_all`。 +- 图的 `nodes/edge_id/src/dst/ptr/signed_raw/weights.npy` 全部文件；APL `input_matrix/output_matrix/kc_globals/apl_globals.npy`；AF 原 `coefficients.json`；AM `protocol/base-candidate.float32-mV.npy`、两个 overlay NPZ/manifest、原 H mask。图和 bundle 的所有 SHA 来自外部锚定的 AM prereg inputs，不是仅由 AN 本次读入后自报。 +- AN `runtime_light.py`、`runtime_continuation.py`、对应 tests、即将实现的本门 coordinator/comparator/tests，以及本草案最终注册版。固定其实际 SHA 和源 AST 审计；不得在跑到一半后修改源代码继续使用同一 prereg。 +- `runtime_light.PINS` 的整个依赖链：AJ GPU/timing queue、AG-r2 GPU/legacy APL worker、AE backend/C++/CUDA、AF queue helper，以及 `AM_OVERLAY_SHA` 对应原 `overlay.py`。记录 `runtime_continuation.LIGHT_SHA` 与实际 light 文件一致。运行前后都重验旧来源与静态输入，不依赖时间戳缓存来替代字节哈希。 +- 新输入每份 spec/requests/manifest 的 SHA，原始记录到前缀/分段的确定性映射，以及库版本、科学 Python 原 venv 入口、GPU UUID/driver/CUDA/Torch/NumPy、编译标志和 extension 来源。Python 用 `-B`；不可通过 resolve venv executable 改变包环境。Torch import 前完成 GPU/CUDA 环境设置；仅记录并核对原 TF32/CuBLAS/确定性配置，不为追求一致或速度偷偷改默认值。 + +AM 已完成封存并取得小证据下载包；该下载包不含全部大型轨迹，因此预注册仍必须在服务器完整校验 AM 两个 seal。实际 case 名与 seal SHA 依照上述确定规则写入预注册，不以本文代填。缺失 authority、旧输入变化或 AM 不完整时停止，不进入 GPU 门。 + +## 4. Exact 比较，不把摘要布尔当证明 + +所有浮点比较按原 dtype 的连续 little-endian raw bits，包括正负零；禁止 tolerance、round、只比均值或把压缩 NPZ 文件字节相等误作数组相等。所有状态要求 finite，事件、计数和请求库存必须完整。 + +### Fresh light/reference ↔ 同 overlay AM + +逐数组比 `state.npz` 六键：`step/neuron_idx/counts/ref` 为 int32，`v/g` 为 float64；全部 N 个末态和全部稀疏事件相同，events 自身排序唯一且 bincount 等于 counts。light 与 reference 都保留 `requests.csv`，其五字段 `tick, neuron_idx, attempted, accepted, rejected` 必须与 AM 原输出逐行 exact；不能只比较请求总数。reference 另比每 tick `v_sha256/g_sha256/ref_sha256`、全部 APL drive/state/generated inhibition/applied h float32、pending tail 和每 tick queue。来源标签、新 spec SHA、记录范围和计时等确有差异的字段须显式白名单，不能整份忽略 provenance。 + +AN 新 `terminal-queues.npz` 没有对应 AM 原文件时，只从 AM 已记录事实独立重建：1000 tick 最后 19 tick 的全局 sparse events 决定 raw spike ring；末行 `apl-state.float32.npy` 决定 APL state；原 `apl-pending-tail.float32.npy` 决定 chronological pending。比较全部数组及 `next_tick=1000`，不补跑 AM。 + +light 没有全 APL trace、逐 tick V/g/ref hash 或 queue.jsonl；不能把这些缺文件当空记录、不能称 light 已做这些逐 tick 对照。但 `ReportingOnly.visit_With` 只移除 queue/hash 的两个 context item，留下 `request_output`，也保留 `writer.writerows` 和请求接受判定的 GPU→CPU 拷贝。因此 light 的门包括完整 events/counts/终态、逐行五字段请求接受记录、APL state、完整两种 pending queue、有限值覆盖、总到达/接受计数及固定权重身份与 reference exact；不能把它描述为完全无逐请求记录或完全无逐 tick CPU 同步。reference 承担完整逐 tick 状态/队列观测。light r1 要与 r0 的科学数组、请求 CSV、终态队列及有限值/总量一致，计时不是 exact 字段。 + +### Monolithic ↔ segmented + +每个 overlay、每种模式分别比自己的完整 1000 tick fresh 基线，不跨 overlay 借结果。拼接所有段的 sparse events，并按 receipt offset 恢复绝对 tick；逐段 counts 用 int64 求和、验证范围后与单体 counts exact。末态 V/g/ref、最终 APL state/pending、effective weights raw SHA 必须一致；每个中间切点还与相同绝对长度的 fresh reference 前缀末态对照。切点 5 使用已记录的首段 reference，切点 18/19/500 使用预定前缀，终点使用完整基线。 + +spike ring 不能直接比较物理行号：各自按 `terminal.next_tick % 19` 转成“下一写 slot 起”的 19 行顺序后逐位比较；continued 的 `next_tick` 是本段局部相位，绝对时间来自 receipt。APL pending 已按下一次应用顺序保存，直接逐位比较；不得把它当未来脑活动或继续衰减的完整尾迹。短段未覆盖的 spike slots 和未消费 APL prefix 还须精确等于父 carry。 + +reference 的全部逐 tick 状态 hashes 和 APL 四种 trace 按全局时间拼接后与 monolithic 对照；不重复/跳过边界 tick。`queue.jsonl` 的 `joint_sha256` 编码了局部 tick，不能直接跨段相等比较：按全局 tick 对齐，精确比较 `count, accepted_count, ordered_edge_sha256, accepted_mask_sha256`；用完整事件、原 CSR、原 ref 规则独立重建原 source-order queue，并分别验证每份文件自己的 local-header joint hash。不能对 queue 重新按 target 排序后才声称原队列相同。**light 和 reference 两条链的请求 CSV 均须**按 offset 还原 tick、保持其余四字段和行序，拼接后与各自 monolithic CSV 逐行 exact；同时与预定请求库存对应，不能漏掉拒收请求。 + +上述门可证明 reference 完整记录和 light 已记录状态的一致；**不能据此声称不可观测的 light 每 tick 内部 V/g bits 已被直接逐项比较。** 若正式注册要求这一更强结论，须先单独批准新只读观测器和相应 source pin，而不是运行后扩大结论。 + +## 5. 防真空和故障门 + +每个 overlay 都必须从当前 reference 实际记录证明：事件非空；generated 与 applied APL h 至少有非零样本；至少一个预定内部切点同时出现非空 spike carry 和非零 APL pending。还须保存至少一个真实跨边界 synaptic witness：源 spike 在边界之前、`arrival_tick=source_spike_tick+18` 位于边界之后且小于 1000；其真实 canonical edge 权重非零、到达被接受，并包含在原队列哈希重建中。记录 boundary、source/target、edge_id、spike/arrival tick、接受标志及权重，不把这个工程 witness 解释为单一生物学原因。 + +这些仅是“确实测试过 live carry”的覆盖条件，不是调参阈值。不得强迫所有边界都有活动，也不得用空队列 exact 当作 live-carry PASS。固定 case/切点不满足时，结果为 coverage INCOMPLETE，保存全部输出；不可临时加 pulse、选另一个效果强的 case 或后移边界救场。任何扩展需新草案修订及新 prereg，明确看过既有结果。 + +故障检查以纯临时合成测试完成：错误外部 SHA、祖先 state 篡改、局部/绝对时钟错配、未写 ring slot 或未消费 APL prefix 改值、输入数组 dtype/NaN/Inf、重复/遗漏事件、拒收被错算接受、输出/编译目录越入任一祖先或冻结输入、已有目录重用。不要故意破坏 AM 或 GPU 正式输出来造负例。 + +## 6. 计时、压缩与磁盘预算 + +建议注册一个独立新实验输出根，新增 artifact/build 合计硬上限 **8 GiB**，启动前另保留 **10 GiB** 空闲余量；这是待批准的工程资源上限，不是神经科学门。所有图/APL/旧输出仅只读引用，不复制大型图或全脑 dense history。每次运行前后核实已写字节和剩余空间；不足则停止并保留 partial，不删证据续跑，也不缩短注册 tick 数。 + +32 次调用共享新的 extension build cache，但不共享可变神经/APL状态，只有声明的父 checkpoint 传状态。记录每次 wall time、已有 `diagnostic_simulation_seconds`、build/cache 时间、完整校验/压缩在 wall 中的费用及峰值 VRAM。reference r0 先行、light r0/r1 随后；明确首次编译/文件缓存顺序造成的影响，不能把一次差值称为普适性能 benchmark。可报告实测 100 ms trial 的耗时/压缩字节，不以此承诺 conditioning 大库存的总成本。 + +压缩格式按实际来源逐项核对：当前固定 SHA 的 AJ `gpu_timing.py::run_gpu` 写 `state.npz` 的语句是 `np.savez_compressed(target, **state)`，不是 `np.savez`；AN 的 light/reference AST 派生没有替换该语句。`terminal-queues.npz` 则由 AN `_Runtime.finish` 明确使用 `np.savez_compressed`。记录各数组 uncompressed `nbytes`、这两种 NPZ 的实际压缩 bytes、全部 JSONL/CSV/NPY 及 build bytes，并在执行 receipt 核验各 NPZ entry 的实际压缩方式；不得仅凭 `.npz` 扩展名判断。不能只按 NPZ 小体积漏掉 reference APL 原始 `.npy`。reference 四种 APL trace 的主体约为每 tick 32,528 bytes；完整 1000 tick 约 32.528 MB，尚未含事件/末态/队列/头部。所有估计与最终实际 inventory 同报，不预称已节省多少磁盘；不改原输出格式或移除证据以追求预算。 + +## 7. 执行 receipt 与停止结论 + +新 coordinator 须 exclusive 写每行执行 receipt 和全门 inventory：注册 SHA、行 ID/overlay/mode/repeat/区间、case/requests/代码输入 SHA、进程退出码、完整 stdout/stderr、开始/结束记录、实际 completed ticks、原始 output inventory SHA、独立 comparator 结果/输入 SHA、finite coverage、weight identity、父 checkpoint 路径/外部 SHA、全局时钟、计时及磁盘字节。单独的 `summary.complete/pass_all` 不足：它可能先于 wrapper 后置核验失败写出；必须有完整 runtime provenance、无 error/failure marker，并重新验证 input/old integrity。 + +checkpoint manifest、continuation receipt 和外部执行 inventory 各自按现有排除自身的规则封存，不能自哈希循环。新比较报告和 coordinator receipt 放在被封存 checkpoint 目录之外，避免为加报告改坏父链。恢复只可跳过经完整库存/输入/独立比较重验的已完成行；存在 partial、故障 marker 或不完整祖先则停止，不覆盖、不自动科学重试。 + +全部库存、exact 比较、live-carry 覆盖和前后 integrity 均通过时，最高结论仅为“固定 zero/static25 的 GPU 记录模式与连续状态工程等价门通过”；`learning_pass_claimed=False`、`candidate_generated=False`、`weights_updated_during_run=False`。数值不一致为 FAIL；缺证据、预算中断或 live-carry 未覆盖为 INCOMPLETE；不把未执行项记 PASS。任何结果都不自动授权 conditioning，后续训练协议、独立输入/种子/控制与科学门仍需另行审定。 + +## 8. 未来可另行注册的补充，不属于当前库存 + +以下来自独立审查者的工程提案，**不自动追加到本门、不授权执行，也不是当前 32 次调用的 PASS 条件**。需要分别固定新输入、代码/比较器、父 checkpoint 来源和资源预算后重新审定；不可把本草案悄然扩为 90 次调用。 + +第一项是独立全图 synthetic live-carry 短矩阵。可从冻结 mask 的最低 canonical KC source 及其最低 global edge ID 的真实 MBON target 定义 K、M；从冻结 APL input matrix 的非零列按预定 canonical 规则选 Q，不按新输出选人。拟定 80 tick 的两种请求表：K 在 0/1/22/44/66；必要时 Q 在 0；另一表额外 M 在 18，以覆盖到达接收/不应期拒收、child local tick 0 请求及 tick 84 尚未到期的 spike tail。Q 的实际身份和非零列须静态核验，不能仅凭前缀猜 PN/KC；预期 live/accept/reject 条件也须由实际记录证明。zero/static25 × 两表，各比 reference monolithic 与 reference/light 分段 `[5,12,1,1,18,43]`，提案合计 52 次短调用、960 tick（0.096 生物秒）。它测试人工工程请求，不是新增 odor conditioning；本门固定 AM case 的覆盖不足不能未经新注册就用该矩阵救成 PASS。 + +第二项是在上述另注册短矩阵通过并得到明确绑定的 active checkpoint 后，再注册较长的连续 idle 工程门：zero/static25 各比较 light 一段 100,000 tick 与连续 10,000 + 90,000 tick，均零新输入、零权重更新，提案合计 6 次 continuation 调用。比较全部 sparse events、分段 counts 总和、终态 V/g/ref/APL state 及规范化 queues；不记录 10 秒逐 tick CPU hash 时，不能宣称逐 tick 浮点状态已独立对照。1 秒 checkpoint 沿同一 idle 主链续到 10 秒；任何测试 probe 必须另 fork，不能把 probe 插入主轨迹，其调用数和预算另列。所有祖先 manifest 在 fork 前后不变。即使通过，也仅证明较长时钟/状态接口，不是记忆保持或 learning PASS。 + +源码依据：本目录 `runtime_light.py` 的 `validate_spec/load_overlay/derive_ast/_Runtime.finish/_run`；`runtime_continuation.py` 的 `rotate_spike_ring/validate_state/load_checkpoint/run_continued/audit_carried_sequence`；封存 AM `prepare.py/run_suite.py/adapter.py/comparison.py`；封存 AJ `gpu_timing.py` 与 AF `oracle_full.py::queue_summary`。本文只读这些来源，未运行 GPU、未注册或修改其内容。 diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/EVALUATION_POLICY_DRAFT.md b/registry/phase4ao-conditioning-interface/source-snapshot/EVALUATION_POLICY_DRAFT.md new file mode 100644 index 0000000..364063c --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/EVALUATION_POLICY_DRAFT.md @@ -0,0 +1,68 @@ +# Explicit evaluation policy — draft implementation decision + +Status: **DRAFT / NOT REGISTERED / NO SCIENTIFIC EXECUTION**. + +This is a pre-outcome implementation choice by the coordinator for the existing +conditioning proposal. It does not change a frozen brain, eligibility-v2, +anatomical mask, odor identity, five-replicate matrix or scientific pass gate. +The AN engineering result remains separate; none of its 21 sources is edited. + +## Probe timing and snapshots + +- Every evaluation probe lasts 120 ms, with CS requests only in the half-open + interval [0, 100 ms). The remaining 20 ms is a no-CS tail. This matches the + proposed normal training CS duration; it is not the old L full-120-ms input. +- Normal, delayed and reversal curves use 0, 1, 2, 5, 10, 20 and 50 scheduled + reinforcement opportunities. Intermediate snapshots are taken after the + **complete epoch containing** that opportunity and its boundary update, + not immediately at the US tick. +- Terminal checkpoint 50 includes all planned epochs, including trailing + neutral or blank epochs. It is not an early snapshot chosen for response. +- Each snapshot executes both A and B evaluation inputs. No baseline-output + deduplication is assumed. Repeated probes use the same independent evaluation + input within a training replicate; they are paired observations, not new + training replicates. There are still five independent training replicates + per experimental cell and 80 independent A/B evaluation streams overall. +- Probe US and weight updates are disabled. A probe cannot update the training + or retention trajectory. Acquisition/reversal/delayed start from the common + declared reset state; retention forks the actual continuous saved state. + +## Intermediate control alignment + +For normal and delayed experiments, obtain the paired arm's complete ordered +CS prefix at the chosen epoch boundary. Match actual cue, occurrence, odor, +source random stream and full seed, not just the number of epochs. + +- D=0, frozen, random reward and CS-only use that identical CS prefix. +- DAN-only uses the corresponding removed-CS/yoked epoch positions. It has + **zero CS exposure**, not equal CS exposure; this is an intended control. +- Unpaired uses the earliest epoch boundary that has consumed the same ordered + CS prefix, including US-only epochs already inserted before that boundary. + Its terminal checkpoint includes the entire 150-epoch inventory. + +Unpaired intermediate US counts and elapsed time can differ from paired. +Random-reward intermediate US counts can also differ. These differences must +be reported rather than called fully matched exposure. All complete terminal +inventories remain fixed by the original proposal. The terminal gate, not the +most favorable intermediate point, decides the result. + +Reversal and no-reversal use each arm's own kth reinforcement epoch boundary. +Their intermediate CS prefixes may differ because their reinforced cues differ; +their complete 100-epoch inventories remain fixed. The signed readout always +keeps the original acquisition cue orientation and original pre baseline. + +## Inventory, not approval + +Under these choices the pure planner is expected to enumerate 9,440 probes +(1,132.8 biological seconds), in addition to the existing 10,760 seconds of +training, controls, reversal and continuous idle. This arithmetic is not a +measured wall-time or disk estimate and excludes engineering validation, +encoding, output, regressions and reserves. Test fixtures use synthetic masters; +none is adopted as a scientific master seed. + +Before any scientific run, complete the actual input/state/delta file bindings, +exact event-to-eligibility update coordinator, implemented assay gates and a +finite resource plan. Register the protocol and bounded implementation check +before observing scientific outcomes. Only after those checks may the staged +acquisition suite run. A failed acquisition is sealed as failure; it must not +trigger more pairings, parameter changes, odor reselection or control removal. diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/EVIDENCE_DRAFT.md b/registry/phase4ao-conditioning-interface/source-snapshot/EVIDENCE_DRAFT.md new file mode 100644 index 0000000..5fed11b --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/EVIDENCE_DRAFT.md @@ -0,0 +1,84 @@ +# Phase 4AN：条件化接口的生物学证据与解释边界 + +状态：**未注册 DRAFT；不是实验结果、科学 PASS 或学习候选声明。** +日期：2026-09-30（Asia/Shanghai）。 + +本文件记录为后续协议准备的一手文献核对。未修改冻结的脑、符号策略、4J eligibility-v2、mask、eta 或受体假设；未运行远端实验。文献证据不能代替新实验的完整门禁。 + +## 1. 本地身份、文献类型和具体个体必须分层 + +冻结 H 的静态身份依据是 `annotation_class`、`annotation_type`、`annotation_instance` 中精确的 compartment token，以及真实 KC→MBON 和 DAN→MBON 边；不是根据 PAM/PPL1 前缀猜测效价或 compartment。可读源码依据为 `../models/male-cns-lif/phase4h.yaml` 的 `audit`/`conditioning` 字段、`phase4h.py` 的 mask/confirmed-DAN 加载，以及 `phase4h_reinforcement_provenance.json`。 + +下表本地索引、body ID 和侧别是本轮协调者从冻结 H mask/metadata 的实际静态查询确认的目标集合，**不是文献记录的实验个体**。对应冻结 H 文件为 `/data/digital-drosophila/experiments/phase4h-dopamine-plasticity/plastic-mask/plastic-edge-candidates.parquet`，协调者提供的 SHA256 为 `e5075ee3e5a0edf3a4d197453847163cc295b254f21c67203159bba204b12a76`。本次文献核对没有重新下载该表。正式注册仍须将完整静态映射、来源路径及 SHA 与 mask 一同绑定；不得把本表作为绕过静态核验的硬编码替代。 + +| 本地类型与精确 token | 固定本地 neuron_idx | 已提供的本地 body ID | 文献类型对应 | +| --- | --- | --- | --- | +| MBON01 / `y5B'2a` | `9, 130579` | `9 → 10013`；`130579 → 520151` | MBON-01 = MBON-γ5β′2a | +| MBON12 / `y2a'1` | `130136, 131044, 3477, 3513` | `130136 → 519368 (R)`；`131044 → 521086 (R)`；`3477 → 13728 (L)`；`3513 → 13768 (L)` | MBON-12 = MBON-γ2α′1 | + +文献编号与名称的对应来自 [Aso 等，2014，解剖论文 Table 1](https://elifesciences.org/articles/04577/figures)。ASCII token 与希腊字母类型名的对应是本项目跨数据集映射层的一部分，不证明每个 MaleCNS body 的生理效应、性别适用性、行为效价或受体组成。文献不能提供这些本地 neuron_idx/body ID 的身份核验。 + +冻结 mask 仍为 14,232 条边、四个精确 compartment；另外的 MBON07/`a1` 四个细胞和 MBON02/`B2B'2a` 两个细胞不会因为是 mask 成员而被自动赋予与下面两组相同的行为功能，也不因本次聚焦而从 mask 删除。 + +## 2. MBON01 与 MBON12 的一手功能证据 + +### MBON01：avoidance-related 类型，不等于每个本地细胞已验证 + +Stahl 等在同一条件化实验框架中测得，食欲条件化后 MBON-γ5β′2a 的 CS+ 相对 CS− 响应降低；论文将该 compartment 的输出联系到 avoidance-promoting MBON。该结果支持“减少这一类型的条件气味输出可能产生 appetitive-like 偏移”，不证明所有条件下它都是单一行为开关。[Stahl 等，2022，Figure 5 及 Figure 5—figure supplement 1](https://pmc.ncbi.nlm.nih.gov/articles/PMC8956283/) + +较早的光遗传行为实验包含表达于多个 M4/M6 MBON 类型的驱动系；不能把多类型联合激活结果重述为每个 MBON01 个体的独立因果验证。[Aso 等，2014，行为论文 Table 2 与 Figures 2–5](https://cdn.elifesciences.org/articles/04580/elife-04580-v1.pdf) + +### MBON12：approach-related 输出与 aversive-learning depression + +Aso 等明确报告激活 MBON-γ2α′1 可引起趋近。Berry 等在气味与电击配对后观察到该 MBON 对 CS+ 的响应特异性抑制，未配对／气味和 US 分离的控制不产生相同结果；同一 DAN 的后续刺激还可恢复已抑制响应。因此这里有类型特异的趋近相关输出和厌恶记忆证据，但不支持“DAN 的任何刺激只会继续 LTD”。[Aso 等，2014，行为论文 pp.26–27](https://cdn.elifesciences.org/articles/04580/elife-04580-v1.pdf)；[Berry 等，2018，Figures 2–4](https://pmc.ncbi.nlm.nih.gov/articles/PMC6239218/) + +Stahl 等还观察到食欲条件化后 MBON-γ2α′1 的相对 CS+ 响应提高，但不能明确全部归因于 CS+ potentiation 或 CS− depression；不能用该结果论证所有相关 plasticity 都是单向 LTD。[Stahl 等，2022](https://pmc.ncbi.nlm.nih.gov/articles/PMC8956283/) + +## 3. 两个人工 US 接口只具有预先声明的 like 解释 + +| 固定的源类型与 compartment 接口 | 类型层面的预期 | 不可升级成的结论 | +| --- | --- | --- | +| PAM15 → `y5B'2a` / MBON01 | 若目标气味的相关 KC 输入发生 LTD，可预期 avoidance-related 输出相对减弱，即 appetitive-like 输出偏移 | PAM15 全体天然奖励、所有 PAM 为正效价、这些本地细胞已经证明奖赏学习 | +| PPL103 → `y2a'1` / MBON12 | 若目标气味的相关 KC 输入发生 LTD，可预期 approach-related 输出相对减弱，即 aversive-like 输出偏移 | 所有 PPL1 为负效价、DAN 任意刺激均是惩罚、这些本地细胞已经证明厌恶学习 | + +**PAM15 不是 PAM01 的同义词。** Li 等把 PAM15（γ5β′2a）列为相对既有分类新增的一类；PAM01 对应 γ5，PPL103 对应 γ2α′1。旧文献中 PAM-γ5/PAM01 的全部奖励结果不能不经核对就转记为 PAM15 特异性证据。[Li 等，2020，DAN 分类、Figure 6 与 Figures 35–36](https://pmc.ncbi.nlm.nih.gov/articles/PMC7909955/)；[同文图表页](https://elifesciences.org/articles/62576/figures) + +这里的 D 仍是冻结规则中的人工标量命令，不是测得的 dopamine 浓度，也不是由实际 DAN spike/接受刺激自动推定的生物学门控。命令、刺激是否被接受、实际 DAN events、选定 compartment 与权重更新必须分别记录。两个接口共用既定 polarity = −1；不因为接口名字不同而翻转 D、eta、base sign 或下游权重。 + +## 4. 顺序和时间窗证据不等于冻结参数的数值标定 + +- Hige 等在 γ1pedc 测得正向配对后的抑制；气味改为在最后一束 DAN 激活光后 0.5 秒开始的 backward protocol 未产生同样变化。这是特定 compartment 和刺激协议的结果，不是所有类型的通用截止时间。[Hige 等，2015，Figure 1](https://pmc.ncbi.nlm.nih.gov/articles/PMC4674068/) +- Aso–Rubin 显示，单一 DAN 类型可因相对气味的刺激时序不同而写入、削弱或改变记忆效价；不同 compartment 的学习和更新规则并不相同。[Aso–Rubin，2016](https://pmc.ncbi.nlm.nih.gov/articles/PMC4987137/) +- Handler 等在其协议中测得 forward pairing 对应 depression、backward pairing 对应 potentiation，并观察到 PAM/PPL 群均可随时序改变行为偏移方向。这阻止我们把 PAM/PPL 名称当作固定效价开关；本项目不据此加入新受体、LTP 或重设参数。[Handler 等，2019](https://pmc.ncbi.nlm.nih.gov/articles/PMC9012144/) + +出版年复核：该文正式题名为 *Distinct Dopamine Receptor Pathways Underlie the Temporal Sensitivity of Associative Learning*，刊于 **Cell，2019-06-27，178(1):60–75.e19**，online publication 为 2019-06-20，DOI 为 [10.1016/j.cell.2019.05.040](https://doi.org/10.1016/j.cell.2019.05.040)。[PubMed 原文书目记录 PMID 31230716](https://pubmed.ncbi.nlm.nih.gov/31230716/) 明确同时关联 PMC9012144。因此本草稿保留 2019；旧材料中的 2022 不是本论文正式出版年，本次不修改旧冻结材料，也不猜测该旧标注的来源。 + +冻结 4J eligibility-v2 的 τ = 100 ms、trace cap = 1、eta = 0.00275 mV、polarity = −1、最多 25% depression 仍是**既定 causal-LTD 工程假说**，不是上述论文为当前全脑提供的定量标定。负时序零更新和 +1000 ms/零延迟比例门只验证冻结规则的一致性；不能据此声称复现了生物学 relief learning、受体机制、bidirectional plasticity 或遗忘。4J 中另列的 bidirectional branch 也不是本接口可偷偷启用的替代规则。 + +## 5. 固定 B/S：新的离线分析约定，不是 decoder + +首要输出应保留每个目标细胞的原始 spike/count/rate、训练前后变化，以及两个类型各自的结果。可供正式协议审议的固定类型对比为： + +```text +R12(o) = mean of the fixed four MBON12 cells' rates for odor o +R01(o) = mean of the fixed two MBON01 cells' rates for odor o +B(o) = R12(o) - R01(o) +DeltaB(o) = B_post(o) - B_pre(o) +S = DeltaB(CS+) - DeltaB(CS-) +``` + +所有项使用预先锁定的同一评估窗，分别按类型取均值，避免四个 versus 两个细胞的数量差异直接决定符号。CS+/CS− 的角色须事先指定并 counterbalance，不能根据结果挑选气味方向、细胞或 seed。 + +这是**新的、需要预注册的离线分析约定**；不是文献给出的定量行为公式，不进入动力学、不拟合权重或阈值、不构建选择策略、classifier 或 decoder，也不替换旧封存指标。它不要求未训练 B = 0，不把 spike rate 等同于真实选择概率。 + +两个接口永远使用同一个 B 方向：PAM15 分支的假说是 S > 0，PPL103 分支的假说是 S < 0。不得按接口乘效价符号把所有结果改成“正值即成功”，不得取绝对值掩盖方向错误。若反馈使方向相反，应报告为该假说的反证，不改读出或重命名接口。还须与实际运行的预注册控制比较；单独 S 的方向、MBON separation 增大或权重改变都不构成完整 learning PASS。 + +## 6. 正式协议不可省略的解释门 + +- 文献类型功能、本地精确身份、人工 US 命令、实际 DAN 激活和实际 KC→MBON 更新须为不同证据字段；任何一层未知都不得用另一层填补。 +- 冻结脑与 v2 不变；不得新设 receptor sign、改变 eta、扩 mask、选择最有利 compartment、挑训练 checkpoint 或把 direct/static25 诊断作为条件化本身。 +- paired、unpaired、zero-D、frozen、CS-only、DAN-only 应为真实执行控制，不是 summary 中的标签；独立评估 seeds 和预先锁定的 cue 角色不能在看到结果后改变。 +- 必须区分 weight update、实际 MBON 响应变化、类型对比方向和下游功能传递。纯 separation 可能在错误方向上增大；“appetitive-like/aversive-like”不能在没有对应证据时升级为行为学习结论。 +- 本文件不新增 success threshold；完整通过条件应由尚待注册的协议明示，并保留失败、无效应和反向结果。没有训练结果就不生成 learned candidate。 + +浏览核对仅采用上列原始研究的期刊、官方 PDF 或 PMC 全文。没有把综述、搜索摘要的二次推断、Google FlyBody 或其他身体运动项目作为本阶段学习机制证据。 diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/PROTOCOL_DRAFT.md b/registry/phase4ao-conditioning-interface/source-snapshot/PROTOCOL_DRAFT.md new file mode 100644 index 0000000..9353338 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/PROTOCOL_DRAFT.md @@ -0,0 +1,469 @@ +# DRAFT — fixed-v2 olfactory conditioning in the independent D18 branch + +**Status: DRAFT / NOT REGISTERED / NOT APPROVED FOR EXECUTION.** + +This document is a proposal for review. It does not authorize a run, record a +successful gate, freeze a protocol, or generate a learned candidate. It was +written from frozen H/J/L source and the declared AM interface contract, not +from selecting favorable AM outcomes. The coordinator must approve the final +scope, budget, source/input inventory, and exact gate implementation before +registration. No training or remote action is performed by this document. + +## 1. Purpose and entry conditions + +The next scientific endpoint should be actual acquisition under the unchanged +TemporalEligibility-v2 rule, followed, if its registered acquisition gate passes, +by fixed reversal, delayed-reward, and real-time retention assays. File validity, +static 25% sensitivity, or a change in delta alone is not learning. + +Entry requires the **complete** AM whole-brain interface gate, including its +registered zero-overlay comparisons, nonzero internal/repeat validation, old +reference regression, and frozen integrity checks. A partially completed zero +suite is not this gate. Continue to use the independent D18 branch; do not edit +or overwrite Phase 1–AM artifacts. The original base CSR, anatomical counts, +transmitter signs, W_syn, LIF coefficients, APL calibration, D18 timing, +14,232-edge candidate mask, and eligibility-v2 remain unchanged. + +New long-run/continuation plumbing must preserve the exact existing timestep +operation order. Validate zero-delta and fixed-delta continuation, including a +split across pending synaptic/APL arrivals, against an unsplit run before using +it for retention. This is an implementation prerequisite, not another tunable +scientific endpoint. No game, decoder, RL, visual branch, or brain-gain search +is in scope. + +## 2. Fixed population and replicate matrix + +Use the original externally selected 4L odors without re-selection: + +* ODOR_A: **4-ethylphenol**. +* ODOR_B: **3-hexanone**. +* Both frozen encoder modes: `physiological` and `matched_energy`. +* Both reciprocal assignments: A reinforced/B neutral, and B reinforced/A + neutral. +* Both exact DAN/type-compartment branches below. +* Five independent training replicates in each cell. + +Thus there are **2 modes × 2 DAN branches × 2 assignments × 5 replicates = +40 blocks**, or eight experimental cells with five training replicates each. + +| Branch identity | Exact raw compartment string | Proposed signed prediction | +| --- | --- | --- | +| PAM15 | `y5B'2a` | S > 0 | +| PPL103 | `y2a'1` | S < 0 | + +These identities are drawn from old H/J configuration, not from a rule that all +PAM is appetitive or all PPL1 is aversive. Preparation must materialize and hash +the actual DAN body IDs/neuron indices, exact annotation values, confirmed +DAN→MBON/compartment mapping, and affected existing plastic edges from the +sealed H mapping. Missing, ambiguous, or unmatched mappings remain `unknown`; +they cannot be completed by cell-name inference. + +The type-level signed predictions above are **new, falsifiable engineering +hypotheses**. Evidence specifically establishing PAM15's proposed functional +direction is limited. Correspondence of type labels across fly datasets is not +proof that these individual MaleCNS bodies have been functionally validated. +Neither branch is labeled a biologically demonstrated reward/aversion channel. + +### Seed inventory + +Before outcomes, generate and save the complete RNG lineage and explicit seed +table for replicate, cell, arm, epoch, CS events, US ordering, and evaluation. +Use distinct replicate streams, for example an explicitly registered +`SeedSequence` hierarchy with a newly audited master seed. Test expanded seed +identities for collisions. Merely choosing five different base integers is +insufficient: H/J/L used consecutive bases plus the trial number, producing +overlapping nominal replicate streams. + +Matched counterfactual arms may deliberately reuse the same input event table; +list each such common-random-number alias. They are paired controls, not extra +independent replicates. All evaluation RNG streams must be disjoint from all +training/US/control RNG streams. Within a replicate use the same registered +evaluation events for pre/post/control comparisons. If more than one evaluation +stream is used per replicate, report/aggregate it within that replicate; +**five evaluation streams never substitute for five independent training +replicates**. The minimal budget below uses one independent A/B evaluation +stream pair per training replicate, reused for paired contrasts. + +## 3. Frozen update and explicit US interface + +Use the exact frozen J/L v2 implementation and operation order: + +* KC trace float32, tau = 100 ms, cap = 1. +* Every timestep: separate multiply, add actual KC spikes, then clamp. +* At the registered US tick, sample after that tick's multiply/add/clamp; + include its KC spikes. Do not insert an anatomical-delay shift into eligibility. +* eta = 0.00275 mV, compartment polarity = −1. +* Additive update only on the already-confirmed compartment mask, with the + old float32 floor `−0.25 × base_candidate_mV`. +* Update once at the end of the training epoch; it affects subsequent epochs, + not an earlier part of the same epoch. No recovery, potentiation, or new mask. +* Install via the verified AM bridge: immutable base SI float64 plus the + float32 delta widened to float64 and converted from mV to V. Preserve all + untouched weights and fast-APL handling exactly. + +The first proposal preserves the old **scheduled dimensionless D = 1** artificial +unconditioned-signal interface. It is neither measured dopamine concentration +nor an actual-DAN-spike count. At the same scheduled tick, request spikes in the +fixed matched DAN population. Record requested, accepted, rejected, and actual +DAN spikes, including zero-delivery epochs, separately from D. Do not resample, +boost, or silently discard failed delivery to obtain a preferred result. + +Multiplying D by actual acceptance, changing D to a spike count, or adding a +measured dopamine kinetic model would be a distinct, explicitly registered +interface hypothesis; none is hidden in this draft. If neural DAN delivery is +absent, any update still demonstrates scheduled artificial gating, not that +actual DAN activity caused plasticity. This limitation must remain visible even +if an engineering output gate passes. + +## 4. Epochs, timing, and all actual controls + +Training epochs retain the old trial-boundary reset policy: start V/g/ref, +spike-delay queues, APL state/queue and KC trace from their declared initial +values; carry only plastic_delta between epochs. Log the state policy. This is +not continuous natural behavior. Retention deliberately uses the different, +explicit continuous-state policy in section 7. + +Normal epochs last 120 ms. CS occupies 0–100 ms, and a paired US is at 90 ms. +Delayed epochs last 180 ms; the identical CS occupies 0–100 ms, and US is at +150 ms, i.e. **50 ms after CS offset** and 60 ms later than the normal US. +All delayed controls use that same 180 ms duration and 150 ms US slot, including +the neutral epochs; do not reuse 90 ms controls to attribute delayed effects. + +The frozen L encoder mapping/rates and original A/B identity are retained. +However, old `phase4l.py::run_rates` generated PN events throughout its passed +120 ms duration. The present 100 ms CS plus a no-CS tail is an explicit new +experimental timing choice aligned with validated AK input duration, **not** a +claim of bitwise identity to old L's full training input. `matched_energy` +matches the frozen encoder's expected total rate, not an artificially edited +actual Poisson event count; report actual requests and acceptance by condition. + +Within each arm, present 50 reinforced-cue epochs and 50 neutral-cue epochs in +a pre-generated balanced order. The **50 reinforcement opportunities** come +from L's fixed formal conditioning count, not from a new magnitude search. + +| Arm | Actual execution in each normal or delayed suite | +| --- | --- | +| paired | 50 CS+ with its matched DAN/US and 50 CS− without US | +| D=0 | Identical input events and US schedule to paired; D=0 in updates | +| frozen | Identical input events and US schedule; no delta updates | +| random reward | Same 50 A/50 B and 50 US opportunities; exactly 25 A and 25 B get US, selected by a pre-generated random balanced schedule independent of responses | +| unpaired | Same 100 CS epochs and same 50 US events in 50 additional separate, no-CS epochs; preserve total event inventory, declare 150 rather than 100 epochs | +| CS-only | Same 100 CS epochs, no DAN/US; no scheduled D | +| DAN-only | Same 50 US epochs and 50 matched blank epochs, no CS | + +Every control is genuinely executed; neither identity reasoning nor a text label +replaces its trajectory. The unpaired arm is specifically **separate-epoch, +reset-trace unpairing**, not a claim about a long continuous biological +interstimulus interval. Actual DAN-only network activity may recruit KC and +produce eligibility; it must be measured, not assumed zero. Blank and US-only +epochs retain the corresponding 90/150 ms timing slot and 120/180 ms duration. + +## 5. Offline signed readout — do not discard contrary signs + +Preparation defines complete, exact-annotation MBON12 and MBON01 populations +from MaleCNS metadata, with all body/index membership and side information. No +best responding neuron, hemisphere, compartment subset, or evaluation pair is +selected after outcomes. Missing either population makes this specific readout +`NOT-TESTABLE`; do not substitute another type or silently use an empty mean. + +For an evaluation stimulus X and a fixed 0–120 ms probe window, define: + +`B(X) = mean_spike_count(MBON12, X) − mean_spike_count(MBON01, X)`. + +This is **offline analysis only**, not an action decoder, a feedback signal, +or proof of approach/avoidance. Also save both population means separately, +their sizes and counts, all-MBON output, genuine downstream output, and latency. + +Keep two distinct downstream inventories. The frozen H actual-edge table's +complete target union is `legacyDownstream` (11,342 neurons in the verified +local data), and contains **all 97 MBONs**. Do not relabel or silently edit that +old population. Define `nonMBONDownstream` as the exact set difference of that +union and the complete MBON population (11,245 here). Report both. Requiring +an effect outside the MBON population is an additional pre-outcome hypothesis, +not an already inherited H gate; it prevents a purely MBON-local change from +being counted twice as evidence of broader propagation. + +With C denoting that block's reinforced cue and N its neutral cue: + +`S_arm = [B_arm(C) − B_arm(N)] − [B_pre(C) − B_pre(N)]`. + +Always emit the raw signed B and S, pre/post components and control S. For PAM15 +the proposed relation is **S > 0**; for PPL103 it is **S < 0**. Never replace raw +S in the report with `abs(S)`, `polarity × S`, a magnitude-only score, or a +renamed “positive learning score.” An opposite sign is a counterexample to the +prediction, even if a magnitude or unsigned separation statistic increases. + +The expected-control relation is equally explicit: + +* PAM15: `S_paired > S_control` for each registered control. +* PPL103: `S_paired < S_control` for each registered control. + +Use matched evaluation inputs and each arm's matched pre baseline. All probe +updates and US inputs are off. Evaluation begins from the same frozen initial +state for acquisition/reversal/delayed comparisons; retention uses its saved +continuation state and time-matched frozen control as specified below. + +## 6. Acquisition and strict reversal gates + +### 6.1 Acquisition — newly proposed signed gate + +For each of the eight cells, a successful training replicate must satisfy all: + +1. The raw S has the registered direction (PAM15 >0; PPL103 <0). +2. It lies in that direction beyond **each of the six actually executed + timing-matched controls**, not just the easiest control. +3. The inherited L functional requirement holds: conditioned-cue MBON + `Delta_g_max_abs` exceeds neutral-cue `Delta_g_max_abs`, and the conditioned + cue has a nonzero MBON spike-count or first-latency change versus its own pre + probe. Undefined latency is separately reported, not silently zero-filled + into proof of a latency effect. +4. The inherited H MBON and complete `legacyDownstream` A/B Euclidean + separation gains are positive. Independently require positive separation + gain in `nonMBONDownstream` as a newly declared specificity/transfer gate. + Report all three underlying count vectors and gains, never select whichever + downstream definition is more favorable after outcomes. + +Require **at least 3 of the 5 independent training replicates to satisfy the +entire conjunction in the same replicate**. Also calculate the inherited H +mean-control rule separately. This exact new combined signed gate is a proposal, +not a claim that H/L already implemented it. + +Record acquisition at fixed update counts 0, 1, 2, 5, 10, 20 and 50. Only the +registered terminal count 50 determines its gate. Do not stop at the best +earlier count or add pairings after failure. + +### 6.2 Reversal — new hypothesis, not H's permissive sign test + +After normal acquisition, fork its exact delta into two arms: + +* **Reversal:** 50 pairings of the former neutral cue with the same specific DAN + branch, and 50 formerly reinforced cue epochs without US. +* **No-reversal continuation:** 50 further original-cue pairings and 50 original + neutral epochs, with matched epoch totals and registered independent input + streams. This is an additional real control, included in the budget. + +Never reset the learned delta between acquisition and reversal; do not restore +depressed old-cue edges. Keep the old epoch-reset neural state policy in both +arms and evaluate from common fresh states. The pure-LTD/no-recovery rule may +prevent reversal; that is a falsifiable model limitation, not permission to add +potentiation, recovery, or more trials. + +For strict reversal, retain the original C−N orientation when calculating +`S_acquisition`, `S_reversal`, and `S_continuation` against the **same original +pre-training baseline**. At the fixed terminal reversal count 50 require: + +| DAN branch | Acquisition | Reversal | No-reversal continuation | +| --- | --- | --- | --- | +| PAM15 | `S_acquisition > 0` | `S_reversal < 0` | `S_continuation > 0` | +| PPL103 | `S_acquisition < 0` | `S_reversal > 0` | `S_continuation < 0` | + +Additionally require a nonzero change in the former neutral cue's MBON spike +count or defined first latency between acquisition and reversal, and a nonzero +change in its actual-downstream event/count output. Save both cue vectors; +counts alone can conceal a shift confined to the original cue. The same +replicate must satisfy all conditions, with a 3/5 majority per cell. These are +**new terminal reversal hypotheses**, fixed before the learning results. + +Zero is failure of a strict sign-crossing gate, not reversal. Report H's original +`sign(acquisition) != sign(reversal)` separately as a legacy statistic; its +nonzero→zero success case does not satisfy this new gate. Do not claim that +these engineering sign changes establish reversal of real appetitive/aversive +behavior. + +## 7. Delayed reward and genuine retention gates + +### Delayed-reward test — new hypothesis + +Run delayed acquisition from zero delta, using the same fixed 50 reinforcement +opportunities and all six controls in the **180 ms / US-at-150 ms** suite. Sample +the unchanged trace at 150 ms after that tick's update. Do not extend tau or +redefine the trace to force delayed learning. + +At count 50, require the same signed, functional, downstream and control +conjunction as section 6.1, with at least 3/5 independent training replicates per +cell and raw S direction intact. This explicitly tests whether the unchanged +rule supports this particular delayed schedule. H did not supply an independent +delayed-reward success threshold; this is a new pre-outcome gate. + +Report delayed and immediate signed S side by side and their per-replicate +difference. Do not require a invented retained percentage, impose a monotonic +amplitude ratio, pool their seeds, or call an opposite delayed sign a successful +larger absolute response. Delayed controls cannot be substituted by normal ones. + +### Retention — new continuous-state implementation and gate + +Fork the terminal normal-acquisition checkpoint and the matched terminal normal +frozen-control checkpoint. Each includes V/g/ref, pending spike-delay state, +APL state, the complete D18 APL queue, absolute tick, delta, and relevant RNG +state. Run real no-input/no-update dynamics continuously to 1 s and 10 s; the 1 s +checkpoint is on the same idle trajectory used to reach 10 s. Do not reset, +discard pending events, analytically decay only delta, or fabricate elapsed time +by changing a seed. + +At 0, 1, 10 s, fork independent A/B probes from each checkpoint; probes do not feed +back into the idle continuation. Use the same registered evaluation streams +for trained/frozen comparisons at every elapsed time. The probe lasts 120 ms, +without US or updates. Keep delta constant throughout idle; the model does not +contain molecular consolidation or recovery, and a pass cannot prove either. + +Define signed `S_trained(t)` and `S_frozen(t)` using the original C−N orientation +and their registered pre baselines. Require, in the **same replicate at both 1 s +and 10 s**: + +* PAM15: `S_trained(t) > 0` and `S_trained(t) > S_frozen(t)`. +* PPL103: `S_trained(t) < 0` and `S_trained(t) < S_frozen(t)`. +* Conditioned-cue MBON output has a nonzero spike-count or defined first-latency + difference from the time-matched frozen probe; actual-downstream event/count + output also differs. Save neutral-cue differences and the complete signed + specificity contrasts so a generic global response is not presented as memory. + +Require 3/5 independent training replicates per cell, without a fitted amplitude +tolerance or an arbitrarily chosen “percent retained.” This is a new functional +retention hypothesis, not the old H reset-and-retest result. Report an optional +predeclared fresh-state/persistent-delta probe separately as a weight-storage +diagnostic; it cannot satisfy the real-time gate. + +## 8. Legacy gates, new hypotheses, and candidate limits + +Retain the following old predicates verbatim as separately labeled audit fields +where their data definitions apply: + +* H: minimum seed majority 3; positive MBON and downstream Euclidean separation + gains; the smaller mean gain of its two DAN branches exceeds the maximum + mean gain among D0/random/frozen controls. +* H: legacy reversal sign-inequality and last-retention separation>pre. Disclose + the old retention had no actual idle dynamics and is not direct validation of + the new real-time protocol. +* L: conditioned `Delta_g_max_abs`>neutral plus a functional spike/latency change, + in at least 3/5 seeds. Preserve the exact old implementation's legacy result as + well as the new explicitly defined handling of missing latencies. + +There was **no completed inherited delayed-reward gate**, no proven signed +MBON12−MBON01 valence mapping for these bodies, and no complete L reversal or +retention assay. The strict signed gates in sections 6–7 are new proposals, not +historical passes and not thresholds obtained from AL/AM responses. + +For the strongest proposed engineering candidate claim, all eight cells must +pass their registered acquisition and subsequent assay gates, old required +integrity/control gates, and numerical safety checks. This all-cell conjunction +is itself an explicit proposed scope of claim; it is not a pre-existing H/L +aggregate gate. Emit a complete 8-cell×assay table, including contrary signs, +failures, `NOT-RUN`, `NOT-TESTABLE`, and actual delivery limitations. Do not pool +one mode/compartment/assignment to rescue another, or silently retain only +successful cells as the candidate. + +Suggested staged execution: run normal acquisition and all six normal controls +for all 40 blocks first. If the approved overall acquisition gate fails, seal a +real acquisition failure and stop, recording subsequent assays as `NOT-RUN`. +Otherwise execute the already registered complete delayed, reversal, +continuation and retention suites. No gate changes, tuning or new pair choices +occur between stages. A scientific failure is not a reason to delete evidence; +an integrity/numerical/resource failure stops execution without a scientific +success claim. + +Even a full engineering pass would not establish odor preference behavior, +motor learning, natural dopamine concentration, appetitive/aversive valence, +long-term memory, or readiness for a game. Delta change alone, a file/checksum +pass, static-overlay sensitivity, or signed output readout alone is insufficient. + +## 9. Transparent worst-case runtime and data budget + +The original estimate of 38,000 epochs omitted the new no-reversal continuation +and a full timing-matched delayed-control suite. They cannot be silently added +without revising the budget. + +| Per block | Epochs | Biological seconds | +| --- | ---: | ---: | +| Normal paired + five 100-epoch controls + 150-epoch unpaired | 750 | 90 | +| Delayed paired + five 100-epoch controls + 150-epoch unpaired | 750 | 135 | +| Reversal | 100 | 12 | +| No-reversal continuation | 100 | 12 | +| Total training/control/continuation | 1700 | 249 | + +Across 40 blocks this is **68,000 epochs and 9,960 biological seconds**. Real 10 s +idle progression of trained and frozen retention branches adds 800 s, totaling +**at least 10,760 s biological time** (107.6 million dt=0.1 ms steps), before +evaluation probes, implementation validation, and old regressions. Checkpoints +at 1 s are reused within the 10 s trajectory, not separate additional 10 s runs. + +Relative to 38,000: add 4,000 continuation epochs and 26,000 delayed-control epochs. +All delayed neutral/control epochs are 180 ms, not shortened to 120 ms. The table +includes this duration correction. Any approved reduction of scope must be +decided and documented before registration, not after results or a resource +overrun. + +Let measured AM walltime per 100 ms be `t100`. A preliminary linear conversion is +`107,600 × t100`, plus input generation, updates, evaluation, I/O and validation. +If `t100=1 s`, this base is 29.9 h; if 5 s, 149.4 h. These are arithmetic scenarios, not +claimed measured performance: learning may change spike load. Measure and +reserve a conservative actual budget before registration; enforce a resource +stop rather than changing the protocol during a run. + +Full generated+applied APL traces alone use approximately +`2 × 4064 × 4 × 10000 = 325,120,000 bytes/biological-second`, hence **more than +3.18 TiB** for the above minimum. Do not silently carry the small AM experiment +disk budget into this suite. Per-epoch full neuron counts also total about 45 GB +at 68,000×166,700×4 bytes, before events or full checkpoints. + +The proposed recording policy to approve before registration is: all spike +events, actual input/delivery logs, per-epoch counts, signed metrics, every delta +update and full terminal V/g/ref/APL/queue state. Every timestep retains a GPU +finite-value latch and first-failure tick, **not a CPU copy/hash of all state**. +At epoch boundaries hash the files and bind the complete chain of parent, +input, delta-before, events/state/queues, eligibility/D and delta-after. Full +APL/per-tick reference recordings are restricted to a fixed pre-outcome +validation inventory, not a result-selected subset. That subset's exact +comparison must not be described as per-tick regression of all 68,000 epochs. +Compute capacity from measured event/log +sizes plus a stated safety reserve, and register an exclusive-output storage +cap. Do not publish a guessed final compressed size or omit encoder/update/I/O +latency from walltime accounting. + +The frozen L/J metric called `MBON_g_max_abs` is the maximum **across neurons +of the terminal post-minus-pre g difference**, not a temporal maximum. Save +`MBON_final_delta_g_max_abs` with explicit SI units and the legacy alias when +needed. Complete terminal g and sparse events suffice to reconstruct that +metric; adding a dense time-by-neuron recording is not required by its name. + +Even without full histories, all 68,000 terminal state/ring payloads would use +roughly 507 GB uncompressed, before events and evaluation. Use lossless +compression and measure real complete-episode outputs before setting the hard +storage cap. Do not infer final compressed usage from sparse activity alone. + +## 10. Evidence, provenance, and unresolved biology + +Local source basis (read-only, relative to the project): + +* `23-ai-gpu/source/models/male-cns-lif/phase4h.yaml` and `phase4h.py`: + exact DAN configuration, old control/majority predicates, old nominal retention. +* `23-ai-gpu/source/models/male-cns-lif/phase4j.yaml` and `phase4j.py`: + frozen float32 trace order and `apply_v2`. +* `23-ai-gpu/source/models/male-cns-lif/phase4l.yaml` and `phase4l.py`: + original odor mapping, 50 formal pairings, input timing, and acquisition gate. +* `23-ai-gpu/source/wholebrain-plastic-interface-v1/PROTOCOL.md`: + unchanged-base bridge, scheduled-US semantics and later-learning boundary. + +Relevant primary evidence, with narrow use: + +* [Hige et al., Neuron 2015, author institution](https://www.janelia.org/publication/heterosynaptic-plasticity-underlies-aversive-olfactory-learning-drosophila): + compartmental KC→MBON plasticity and odor-specific effects motivate local LTD; + this does not measure this MaleCNS model's eta, tau or DAN concentration. +* [Aso and Rubin, eLife 2016, author institution](https://www.janelia.org/publication/dopaminergic-neurons-write-and-update-memories-cell-type-specific-rules): + cell-type-specific DAN learning/update rules oppose a universal cluster-valence + assumption. They do not functionally identify every MaleCNS body here. +* [Aso et al., eLife 2014](https://elifesciences.org/articles/04580): + MBON population outputs motivate population-level readout. The exact + MBON12−MBON01 signed hypothesis and the two predictions in this draft remain + explicit cross-dataset/model assumptions, not a demonstrated behavioral scale. + +The exact scalar D interface, fixed trace/eta/floor, absence of recovery, +trial-boundary resetting, simplified signed readout, and 100/120/180 ms timings +are declared model/engineering choices. In particular, the 1.8 ms anatomical +synaptic delay is not an eligibility time constant. Physiological applicability, +PAM15 functional direction in these bodies, continuous intertrial learning, +behavioral valence and long-term retention remain unresolved. + +Before formal registration the coordinator must approve and hash the completed +population/seed/input inventory, new readout/gate code, exact continuation +semantics, storage policy/cap, resource budget and final candidate wording. +Until then this remains **DRAFT — NOT REGISTERED — NO EXECUTION AUTHORITY**. diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/README_DRAFT.md b/registry/phase4ao-conditioning-interface/source-snapshot/README_DRAFT.md new file mode 100644 index 0000000..604316b --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/README_DRAFT.md @@ -0,0 +1,118 @@ +# Olfactory conditioning lab — unregistered preparation + +This directory is an independent, unfinished model-branch implementation. +Its 21 engineering sources are registered and sealed by Phase 4AN; those files +must not be edited. Phase 4AM and the finite Phase 4AN engineering gate passed. +The scientific conditioning proposal and additional planning modules remain +unregistered, have not run training, and have not produced a learned candidate. +Engineering PASS is not learning PASS. + +Engineering evidence: `registry/phase4an-engineering-validation` in the project +repository, commit `aeccb7d0ccdcf6788e0a8de60e7ab96f70dbe734`. Its exact +21-source inventory is in `source-identity.json`; this README is not one of +those registered sources. The experiment manifest SHA-256 is +`e8952a48cd3ea6a93c811a1183a1f7b79e19646299924e7688e5f393737a9aa2`. + +## Existing code + +- `runtime_light.py`: derives a fixed-weight trial runner from the SHA-pinned + AJ source. Its full-recording reference retains the entire original tick + loop. The light variant removes an explicit diagnostics whitelist while + preserving the ordered mathematical AST. It retains sparse events, stimulus + acceptance, terminal neural state and delay queues. It supports an explicit + longer admission bound, not hidden time truncation or approximate delays. +- `seed_registry.py`: explicitly separates training, evaluation and schedule + design random streams. Declared same-split aliases can give controls common + random inputs; aliases do not count as independent replicates. Duplicate + keys, cross-split aliases and seed collisions stop preparation. A full + manifest is independently reconstructible. +- `runtime_continuation.py`: carries full terminal state, spike/APL delay + queues and the absolute clock across fixed-weight segments. It protects + every ancestor checkpoint against output/build writes. The explicit + arrival-admission change and reporting hooks are AST-audited. The finite AN + gate passed 32 actual GPU calls and 74 exact comparisons, including pending + arrivals across a split. This does not verify every future 120/180 ms + training epoch or the unexecuted 1/10 s retention protocol. +- `biological_contract.py`: binds observed canonical identity, exact DAN/MBON + compartment tokens and real graph edges without filling unknown relations. +- `trial_plan.py`: constructs and independently validates the proposed complete + 40-block training/control matrix and explicit random-stream aliases. It has + not selected a formal master seed or generated scientific brain inputs. + Complete-protocol measured storage and runtime budgets remain unfinished. +- `evaluation_plan.py`: requires explicit evaluation timing, reversal points + and per-control snapshot bindings. It reconstructs the parent inventory and + retains 80 independent A/B evaluation streams, paired across snapshots within + each training replicate. It does not bind actual state files or authorize a run. +- `evaluation_policy.py`: supplies the explicit draft policy in + `EVALUATION_POLICY_DRAFT.md`: 100 ms CS, 120 ms probes, seven curve points, + actual ordered-CS-prefix control alignment and separate US/time accounting. + Its conditional inventory contains 9,440 probes, not executed trials. +- `conditioning_inputs.py`: consumes a full 63-bit semantic seed in the original + discrete float32 Bernoulli CUDA sampler, with explicit device/draw budget and + CS window. CPU/mock tests do not establish CUDA random-sequence equivalence or + the influence of high seed bits. It never substitutes a CPU random generator. +- `conditioning_update.py`: uses the SHA-pinned AM/J bridge to compute a single + epoch-end delta from supplied actual sparse events. It never installs weights; + scheduled D, actual DAN activity, attempted increments and actual changed + edges remain distinct. Synthetic validation does not establish a trained brain. +- `metrics.py`: pure, explicitly draft functional gates; keeps the historical + downstream population and the non-MBON difference as distinct readouts. + Caller-bound receipts and anatomical membership are still required. +- `test_*.py`: CPU synthetic/structural checks. These cannot establish real + GPU equivalence, biological validity or successful learning. +- `EVIDENCE_DRAFT.md` and `PROTOCOL_DRAFT.md`: evidence and protocol proposals; + neither is an approved or frozen experiment. + +## Required before registration or training + +1. Preserve completed AM/AN engineering evidence. AM completed its 144 zero + and 144 fixed-overlay runs, five old frozen reference trials, and integrity + checks. AN's 32-call continuation gate passed its finite exact comparisons + and 28-root integrity checks; it did not rerun the five old reference trials. + Neither gate executed conditioning. +2. Review and lock the complete experimental matrix, true executed controls, + separate training/evaluation stream table, data budget and stop rules. + Report any additional gates as new model hypotheses, not inherited facts. + The generic seed registry alone does not prove protocol coverage or semantic + independence: preparation must require every planned stream and permit + aliases only within the same training replicate and input role, for named + matched controls/pre-post comparisons. Never alias two training replicates + or CS and US roles. Save generator/version, target ordering, rates, timing, + consumed stream ID and actual requests hash. Do not truncate a 63-bit seed + to 32 bits or derive a new seed by adding the epoch number downstream. +3. Bind **all** graph arrays, APL bundle, coefficient JSON, base-candidate + vector, full 14,232-edge mask, DAN/MBON identity tables and input files to + the registered manifest. A runtime's self-consistent spec or source hash + is not sufficient authority for these data. +4. Preserve the completed finite real-GPU light/reference comparisons, including exact + ordered events, per-neuron counts, terminal V/g/ref, external-request + acceptance, APL terminal state and both pending delay queues. No tolerance + may excuse a spike divergence. Benchmark encoding, loading, simulation, + validation and output time separately and together. +5. Validate the actual retention protocol before claiming retention. The + separate continuation module passed the finite AN GPU gate. V/g/ref, + spike-ring phase, APL state/pending phase and global tick must survive; + segmented and monolithic executions must agree. A checkpoint seal binds + fresh state bytes when created; it cannot prove pre-seal immutability. +6. Verify every learned snapshot's parent chain, input events and exact + frozen eligibility-v2 update. The single-trial runtime verifies the + bounded overlay and parent-hash declaration, but does not prove the + coordinator actually computed that update. Within each neural trial the + delta is fixed. The original base is never written. +7. Record the realized wall-time/output budget before the full acquisition + suite. A failed acquisition gate must not be rescued by tuning eta, + thresholds, APL gain, trial count, mask membership or selected odors. + +## Interpretation limits + +PAM15 and PPL103 are specific, statically mapped artificial US interfaces; +PAM/PPL cluster names are not general valence labels. The fixed eligibility +rule is a single-direction causal-LTD engineering hypothesis, not a complete +model of dopamine, bidirectional plasticity, forgetting or consolidation. +The proposed fixed MBON type contrast is offline analysis, not a decoder. + +No old sealed artifact, raw connectome, transmitter sign, eligibility-v2 or +base weight is modified here. No game, reinforcement-learning policy, +classifier, action decoder, visual encoder, web service or 3D interface is +introduced. A future learning PASS requires functional stimulus-specific +responses and executed controls, never merely a changed delta array. diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/biological_contract.py b/registry/phase4ao-conditioning-interface/source-snapshot/biological_contract.py new file mode 100644 index 0000000..84508c2 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/biological_contract.py @@ -0,0 +1,205 @@ +"""Read-only exact-table contracts for the proposed conditioning branch. + +No body identity, compartment or behavioral function is inferred from a name. +The specific two interfaces are explicit protocol hypotheses checked against +the frozen H maps and actual graph edges. No training or file write occurs. +""" +from __future__ import annotations + +import hashlib +from pathlib import Path + +import numpy as np +import pandas as pd + + +INTERFACES = {"PAM15": ("y5B'2a", "MBON01"), "PPL103": ("y2a'1", "MBON12")} +N = 166700 +E = 25582938 +INPUT_PINS = { + "datasets/male-cns-v1.0/processed/neuron-only/neuron-index/unified-metadata.parquet": + "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "experiments/phase4h-dopamine-plasticity/plastic-mask/plastic-edge-candidates.parquet": + "e5075ee3e5a0edf3a4d197453847163cc295b254f21c67203159bba204b12a76", + "experiments/phase4h-dopamine-plasticity/mappings/dan-to-compartment.parquet": + "a16f72c3121ed0ec638c1a2b249ace446e6ccee020b49679818eb42589d00662", + "experiments/phase4h-dopamine-plasticity/mappings/mbon-to-compartment.parquet": + "54f371f7e35b9def44becd1ba9bd2872861f4876b255b2c8ec46feee6473b70c", + "experiments/phase4h-dopamine-plasticity/mappings/dan-to-mbon-confirmed.parquet": + "730d000f25667b59181d2ee6703c64591229a8beab0856197ed71e68d799d191", +} + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def digest(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(8 << 20), b""): + h.update(block) + return h.hexdigest() + + +def _columns(frame, names, label): + require(isinstance(frame, pd.DataFrame) and set(names) <= set(frame.columns), + "Missing observed schema columns: " + label) + + +def _identity(frame, meta, label): + _columns(frame, ("neuron_idx", "body_id", "annotation_type", "annotation_instance"), label) + require(frame.neuron_idx.dtype == np.int32 and frame.body_id.dtype == np.uint64, + "Canonical identity dtypes changed: " + label) + ids = frame.neuron_idx.to_numpy() + require(len(np.unique(ids)) == len(ids) and np.all((ids >= 0) & (ids < len(meta))), + "Ambiguous/out-of-range identity: " + label) + for field in ("body_id", "annotation_type", "annotation_instance"): + actual = frame[field].reset_index(drop=True) + expected = meta.iloc[ids][field].reset_index(drop=True) + require(actual.equals(expected), "Identity field mismatch: " + label + "/" + field) + + +def _edges(frame, meta, src, dst, signed_raw, label): + names = ("edge_id", "source_neuron_idx", "source_body_id", "target_neuron_idx", "target_body_id", + "raw_synapse_count", "source_annotation_type", "source_annotation_instance", + "target_annotation_type", "target_annotation_instance") + _columns(frame, names, label) + require(frame.edge_id.dtype == np.int64 and frame.edge_id.is_unique, "Invalid edge IDs: " + label) + ids = frame.edge_id.to_numpy() + require(np.all((ids >= 0) & (ids < len(src))), "Edge outside graph: " + label) + require(frame.raw_synapse_count.dtype == np.int64 and (frame.raw_synapse_count > 0).all(), + "Invalid anatomical contact count: " + label) + require(np.array_equal(np.abs(signed_raw[ids]), frame.raw_synapse_count.to_numpy()), + "Anatomical edge weight mismatch: " + label) + for side, expected_idx in (("source", src[ids]), ("target", dst[ids])): + require(frame[side + "_neuron_idx"].dtype == np.int32 and + frame[side + "_body_id"].dtype == np.uint64, + "Canonical edge identity dtype mismatch: " + label) + require(np.array_equal(frame[side + "_neuron_idx"].to_numpy(), expected_idx), + "Actual graph endpoint mismatch: " + label) + for field in ("body_id", "annotation_type", "annotation_instance"): + actual = frame[side + "_" + field].reset_index(drop=True) + expected = meta.iloc[expected_idx][field].reset_index(drop=True) + require(actual.equals(expected), "Actual edge identity mismatch: " + label + "/" + side + "/" + field) + + +def audit_interface_tables(meta, dan, mbon, confirmed, candidates, src, dst, signed_raw): + """Pure table audit; small synthetic graphs are allowed in unit tests only. + + The production loader below separately requires the complete N/E identity. + Supplied annotation population membership is never filled from a prefix. + """ + _columns(meta, ("neuron_idx", "body_id", "annotation_class", "annotation_type", "annotation_instance"), "metadata") + require(meta.neuron_idx.dtype == np.int32 and meta.body_id.dtype == np.uint64, + "Canonical metadata dtype mismatch") + require(np.array_equal(meta.neuron_idx.to_numpy(), np.arange(len(meta), dtype=np.int32)) and meta.body_id.is_unique, + "Metadata must have canonical complete ordered identities") + require(src.dtype == dst.dtype == np.int32 and signed_raw.dtype == np.int64 and + src.ndim == dst.ndim == signed_raw.ndim == 1 and len(src) == len(dst) == len(signed_raw), + "Graph endpoint/weight schema mismatch") + require(np.all((src >= 0) & (src < len(meta))) and np.all((dst >= 0) & (dst < len(meta))), + "Graph endpoint out of range") + _identity(dan, meta, "DAN map") + _identity(mbon, meta, "MBON map") + for table, klass, label in ((dan, "DAN", "DAN"), (mbon, "MBON", "MBON")): + require(np.array_equal(np.sort(table.neuron_idx.to_numpy()), np.flatnonzero(meta.annotation_class.eq(klass))), + "Mapping must include the complete exact-annotation population: " + label) + _columns(table, ("mb_compartment_verbatim", "mb_compartment_mapping_status"), label) + _edges(confirmed, meta, src, dst, signed_raw, "confirmed DAN-MBON") + _edges(candidates, meta, src, dst, signed_raw, "plastic candidates") + _columns(confirmed, ("source_compartment_verbatim", "target_compartment_verbatim", "exact_compartment_match"), "confirmed DAN-MBON") + _columns(candidates, ("target_compartment_verbatim", "plasticity_polarity"), "plastic candidates") + require(confirmed.exact_compartment_match.dtype == np.bool_ and confirmed.exact_compartment_match.all(), + "Confirmed table contains an unknown compartment relation") + require(confirmed.source_compartment_verbatim.notna().all() and + confirmed.source_compartment_verbatim.equals(confirmed.target_compartment_verbatim), + "Confirmed exact compartment relation differs") + require(meta.iloc[candidates.source_neuron_idx.to_numpy()].annotation_class.eq("Kenyon_Cell").all() and + meta.iloc[candidates.target_neuron_idx.to_numpy()].annotation_class.eq("MBON").all(), + "Candidate is not a real KC-MBON edge") + require((signed_raw[candidates.edge_id.to_numpy()] > 0).all() and candidates.plasticity_polarity.eq(-1).all(), + "Frozen KC base sign/polarity changed") + pop = {klass: np.flatnonzero(meta.annotation_class.eq(klass)).astype(np.int32) + for klass in ("Kenyon_Cell", "MBON", "DAN")} + reports = {} + for dan_type, (compartment, mbon_type) in INTERFACES.items(): + dd = dan[dan.annotation_type.eq(dan_type)].sort_values("neuron_idx") + mm = mbon[mbon.annotation_type.eq(mbon_type)].sort_values("neuron_idx") + require(len(dd) > 0 and len(mm) > 0, "Missing exact type: " + dan_type) + for table in (dd, mm): + require(table.mb_compartment_mapping_status.eq("explicit_single_parenthesized_token").all() and + table.mb_compartment_verbatim.eq(compartment).all(), + "Unknown or mismatched compartment: " + dan_type) + edges = confirmed[confirmed.source_neuron_idx.isin(dd.neuron_idx)] + require(len(edges) > 0 and edges.source_compartment_verbatim.eq(compartment).all() and + edges.target_compartment_verbatim.eq(compartment).all() and + edges.target_neuron_idx.isin(mm.neuron_idx).all(), + "Specific DAN interface has unknown or mismatched actual target") + require(set(edges.source_neuron_idx) == set(dd.neuron_idx) and + set(edges.target_neuron_idx) == set(mm.neuron_idx), + "Incomplete actual DAN/MBON type coverage") + plastic = candidates[candidates.target_compartment_verbatim.eq(compartment)] + require(len(plastic) > 0 and plastic.target_neuron_idx.isin(mm.neuron_idx).all() and + set(plastic.target_neuron_idx) == set(mm.neuron_idx), + "Frozen plastic subset is not the exact mapped target population") + pop[dan_type] = dd.neuron_idx.to_numpy(dtype=np.int32) + pop[mbon_type] = mm.neuron_idx.to_numpy(dtype=np.int32) + def members(frame): + return frame[["neuron_idx", "body_id", "annotation_type", "annotation_instance", "mb_compartment_verbatim"]].to_dict(orient="records") + reports[dan_type] = dict(compartment=compartment, MBON_type=mbon_type, + DAN_members=members(dd), MBON_members=members(mm), + confirmed_DAN_MBON_edge_ids=edges.edge_id.tolist(), + confirmed_DAN_MBON_edges=len(edges), confirmed_DAN_MBON_contacts=int(edges.raw_synapse_count.sum()), + candidate_edges=len(plastic), candidate_contacts=int(plastic.raw_synapse_count.sum()), + unknown_selected_interface_relations=0, + functional_valence="cross-dataset type-level hypothesis, not measured MaleCNS body function", + artificial_US=True, D_equals_measured_dopamine=False) + return pop, dict(interfaces=reports, whole_DAN_mapping_unknown=int(dan.mb_compartment_verbatim.isna().sum()), + whole_MBON_mapping_unknown=int(mbon.mb_compartment_verbatim.isna().sum()), + all_candidate_edges_retained=len(candidates), populations={k: len(v) for k, v in pop.items()}, + global_unknowns_filled=False, training=False, new_identity_inferred=False) + + +def _complete_graph_identity(meta, graph, n=N, e=E): + """Exact full-graph dimensions; small explicit sizes are for unit tests.""" + require(len(meta) == n and graph["nodes"].dtype == np.int32 and + np.array_equal(graph["nodes"], np.arange(n, dtype=np.int32)), "Complete canonical neuron set required") + require(graph["edge_id"].dtype == np.int64 and + np.array_equal(graph["edge_id"], np.arange(e, dtype=np.int64)), "Complete canonical edge set required") + require(all(graph[name].shape == (e,) for name in ("src", "dst", "signed_raw")), + "Complete graph endpoint/weight lengths required") + + +def load_bound_tables(root, graph_manifest): + """Require external, registered graph hashes, not self-authorized paths. + + graph_manifest maps the absolute graph paths to their authorized SHA-256. + The coordinator must bind this dictionary to the preregistration/parent. + """ + root = Path(root).resolve() + observed = {} + tables = [] + for relative, sha in INPUT_PINS.items(): + path = root / relative + require(digest(path) == sha, "Frozen table SHA mismatch: " + relative) + observed[str(path)] = sha + tables.append(pd.read_parquet(path)) + meta, candidates, dan, mbon, confirmed = tables + graph_dir = root / "experiments/phase4ag-author-apl-r2/graphs/full" + graph = {} + for name in ("nodes", "edge_id", "src", "dst", "signed_raw"): + path = graph_dir / (name + ".npy") + require(str(path) in graph_manifest and digest(path) == graph_manifest[str(path)], + "Unbound/changed complete graph input: " + name) + observed[str(path)] = graph_manifest[str(path)] + graph[name] = np.load(path, mmap_mode="r", allow_pickle=False) + _complete_graph_identity(meta, graph) + require(len(candidates) == 14232, "Complete frozen mask required") + populations, audit = audit_interface_tables(meta, dan, mbon, confirmed, candidates, + graph["src"], graph["dst"], graph["signed_raw"]) + require(all(digest(path) == sha for path, sha in observed.items()), "Input bytes changed during audit") + return populations, dict(audit, input_sha256=observed, schema={ + name: table.dtypes.astype(str).to_dict() for name, table in + (("metadata", meta), ("candidates", candidates), ("DAN", dan), ("MBON", mbon), ("confirmed", confirmed))}) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_epoch.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_epoch.py new file mode 100644 index 0000000..82c23ee --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_epoch.py @@ -0,0 +1,312 @@ +"""Exclusive postprocessing of one externally authorized sealed fresh epoch. + +No brain execution, source selection, input generation or weight installation. +The external caller must derive and pin authority from its registered row and +successful process receipt; a SHA supplied to this helper is not self-proving +registration authority. Production uses the actual pinned C/R checkpoint and +overlay readers. Small synthetic tests exercise only the private core. +""" +from __future__ import annotations + +import hashlib +import json +import math +from pathlib import Path +import sys +from types import ModuleType + +import numpy as np + + +SCHEMA = 'phase4an-epoch-postprocess-authority-v1' +SOURCE_PINS = { + 'runtime_light_source': '522b8c31ea938890fe15364fccd25ccc5bded54101754909f255532e7ad01aae', + 'runtime_continuation_source': '0b8e8e0a6a23a079dc62a88355ce355d53b3ae22f6665519a8f6aef2dcfc5aba', + 'conditioning_update_source': '96efff678cff910654eb08e86f1a17789e4e5589e340345f419c7027a7d547e2', + 'eligibility_bridge_source': '174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61', + 'phase4j_source': 'abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e', + 'am_overlay_source': '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4', +} +ROLES = frozenset(('spec', 'requests', 'overlay_before', 'overlay_manifest_before', 'state', + 'kc', 'canonical_kc', 'candidate_map', 'mask', 'base_candidate', *SOURCE_PINS)) +GRAPH_FILES = ('nodes.npy', 'edge_id.npy', 'src.npy', 'dst.npy', 'signed_raw.npy', 'ptr.npy', 'weights.npy') +APL_FILES = ('input_matrix.npy', 'output_matrix.npy', 'kc_globals.npy', 'apl_globals.npy') +DIMENSIONS = (166700, 25582938, 4064, 14232) +_MODULES = {} + + +def _require(ok, message): + if not ok: + raise ValueError(message) + + +def _sha(value): + return type(value) is str and len(value) == 64 and all(c in '0123456789abcdef' for c in value) + + +def _digest(path): + h = hashlib.sha256() + with Path(path).open('rb') as stream: + for chunk in iter(lambda: stream.read(8 << 20), b''): + h.update(chunk) + return h.hexdigest() + + +def _read(path): + def pairs(items): + value = {} + for key, item in items: + _require(key not in value, 'Duplicate JSON key: ' + key) + value[key] = item + return value + return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=pairs, + parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value))) + + +def _save(path, value): + with Path(path).open('x', encoding='utf-8') as stream: + json.dump(value, stream, sort_keys=True, indent=2, allow_nan=False) + stream.write('\n') + + +def _path(value): + _require(type(value) is str and Path(value).is_absolute(), 'Input paths must be explicit and absolute') + path = Path(value) + _require(path.is_file() and not path.is_symlink(), 'Input must be an existing regular, unlinked file') + return path.resolve(strict=True) + + +def _check_files(pins): + for path, expected in pins.items(): + _require(_sha(expected) and _digest(path) == expected, 'Input/source SHA mismatch: ' + str(path)) + + +def _authority(path, expected): + path = _path(str(path)) + _require(_sha(expected) and _digest(path) == expected, 'Authority differs from external SHA') + value = _read(path) + keys = {'schema', 'epoch_id', 'diagnostic_mode', 'command', 'checkpoint_dir', + 'checkpoint_manifest_sha256', 'files', 'frozen_inputs'} + _require(type(value) is dict and set(value) == keys and value['schema'] == SCHEMA, 'Invalid authority fields/schema') + _require(type(value['epoch_id']) is str and value['epoch_id'] and value['epoch_id'].strip() == value['epoch_id'], 'Invalid epoch identity') + _require(value['diagnostic_mode'] in ('light', 'reference'), 'Invalid diagnostic mode') + command = value['command'] + _require(type(command) is dict and set(command) == {'steps', 'compartment', 'us_tick', 'd_command', 'frozen', 'trace_backend'}, 'Invalid command fields') + _require(type(command['steps']) is int and command['steps'] > 0, 'Invalid command steps') + _require(command['us_tick'] is None or (type(command['us_tick']) is int and 0 <= command['us_tick'] < command['steps']), 'Invalid US tick') + _require(type(command['d_command']) in (int, float) and math.isfinite(command['d_command']) and command['d_command'] in (0, 1), 'Invalid scheduled D') + _require(command['us_tick'] is not None or command['d_command'] == 0, 'No US requires D=0') + _require(type(command['frozen']) is bool and command['trace_backend'] in ('numpy', 'torch'), 'Explicit frozen/backend required') + _require(command['compartment'] in ("B2B'2a", 'a1', "y2a'1", "y5B'2a"), 'Explicit frozen compartment required') + _require(type(value['files']) is dict and set(value['files']) == ROLES, 'Missing/unknown authority file roles') + paths, pins = {}, {path: expected} + for role, entry in value['files'].items(): + _require(type(entry) is dict and set(entry) == {'path', 'sha256'} and _sha(entry['sha256']), 'Invalid file authority: ' + role) + location = _path(entry['path']) + _require(location not in pins or pins[location] == entry['sha256'], 'Conflicting hashes for the same file') + paths[role], pins[location] = location, entry['sha256'] + _require(type(value['frozen_inputs']) is dict and value['frozen_inputs'], 'Full frozen input pins are required') + for name, sha in value['frozen_inputs'].items(): + location = _path(name) + _require(_sha(sha) and (location not in pins or pins[location] == sha), 'Invalid/conflicting frozen input pin') + pins[location] = sha + checkpoint = Path(value['checkpoint_dir']) + _require(checkpoint.is_absolute() and checkpoint.is_dir() and not checkpoint.is_symlink(), 'Explicit existing checkpoint directory required') + checkpoint = checkpoint.resolve(strict=True) + manifest = checkpoint / 'checkpoint-manifest.json' + _require(_sha(value['checkpoint_manifest_sha256']), 'External checkpoint manifest SHA required') + _require(manifest not in pins or pins[manifest] == value['checkpoint_manifest_sha256'], 'Conflicting checkpoint manifest pins') + pins[manifest] = value['checkpoint_manifest_sha256'] + _require(paths['state'] == checkpoint / 'state.npz', 'Events must come from this checkpoint state.npz') + _check_files(pins) + return value, paths, pins, checkpoint, path + + +def _load_source(path, expected, role): + raw = path.read_bytes() + _require(hashlib.sha256(raw).hexdigest() == expected, 'Pinned source changed: ' + role) + name = '_conditioning_epoch_' + role + if name in _MODULES: + saved_path, saved_sha, module = _MODULES[name] + _require(saved_path == path and saved_sha == expected and sys.modules.get(name) is module, + 'Source module identity collision: ' + role) + return module + _require(name not in sys.modules, 'Unverified source module collision: ' + role) + module = ModuleType(name) + module.__file__ = str(path) + sys.modules[name] = module + try: + exec(compile(raw, str(path), 'exec'), module.__dict__) + except BaseException: + sys.modules.pop(name, None) + raise + _MODULES[name] = (path, expected, module) + return module + + +def _production_dependencies(paths, pins): + for role, expected in SOURCE_PINS.items(): + _require(pins[paths[role]] == expected, 'Authority cannot replace frozen source: ' + role) + _require(paths['runtime_continuation_source'].parent / 'runtime_light.py' == paths['runtime_light_source'], + 'Continuation must load its declared adjacent frozen runtime') + c = _load_source(paths['runtime_continuation_source'], SOURCE_PINS['runtime_continuation_source'], 'continuation') + _require(Path(c.R.__file__).resolve() == paths['runtime_light_source'] and + _digest(c.R.__file__) == SOURCE_PINS['runtime_light_source'], 'Loaded actual runtime differs from authority') + u = _load_source(paths['conditioning_update_source'], SOURCE_PINS['conditioning_update_source'], 'update') + return c, c.R, u + + +def _npz(path, keys): + with np.load(path, allow_pickle=False) as archive: + _require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected NPZ schema: ' + str(path)) + return {key: archive[key].copy() for key in keys} + + +def _same(a, b): + return a.dtype == b.dtype and a.shape == b.shape and a.tobytes() == b.tobytes() + + +def _vector(a, dtype, count, label): + _require(a.dtype == np.dtype(dtype) and a.shape == (count,), 'Invalid dtype/shape: ' + label) + + +def _coordinate(value, paths, pins, checkpoint, authority_path, out_dir, dependencies, dimensions): + """Private core: explicit tiny-fixture dependencies only in synthetic tests.""" + c, r, u = dependencies + n, edges, kc_count, candidates = dimensions + out = Path(out_dir).resolve(strict=False) + protected = {checkpoint, *(path.parent for path in pins)} + _require(not out.exists() and not any(out.is_relative_to(root) for root in protected), + 'Output must be new and outside checkpoint/source/data/input parent directories') + _require(out.parent.is_dir(), 'Output parent must already exist') + source_sha = _digest(__file__) + spec = r.validate_spec(_read(paths['spec']), paths['spec'].parent) + command = value['command'] + _require(type(spec['steps']) is int and spec['steps'] == command['steps'], 'Command steps differ from authorized spec') + _require(paths['spec'].name == 'spec.json' and paths['requests'] == paths['spec'].parent / 'requests.npz', 'Actual case input filenames differ') + graph = Path(spec['graph_dir']).resolve(strict=True) + apl = Path(spec['apl_bundle_dir']).resolve(strict=True) + required_inputs = [graph / name for name in GRAPH_FILES] + [apl / name for name in APL_FILES] + [Path(spec['coefficients_path']).resolve(strict=True)] + _require(all(path in pins for path in required_inputs), 'Full graph/APL/coefficient authority is incomplete') + _require(paths['canonical_kc'] == apl / 'kc_globals.npy', 'Canonical KC must be the declared frozen APL/KC membership') + old_meta = _read(paths['overlay_manifest_before']) + for role, prefix in (('mask', 'mask'), ('base_candidate', 'base_candidate'), ('am_overlay_source', 'am_overlay')): + _require(Path(old_meta[prefix + '_path']).resolve(strict=True) == paths[role] and + old_meta[prefix + '_sha256'] == pins[paths[role]], 'Overlay authority differs: ' + role) + aj = Path(old_meta['aj_model_dir']).resolve(strict=True) + for relative, expected in r.PINS.items(): + path = (aj.parent / relative).resolve(strict=True) + _require(pins.get(path) == expected, 'Missing exact frozen dependency pin: ' + relative) + record = c.load_checkpoint(checkpoint, value['checkpoint_manifest_sha256']) + _require(record['fresh'] is True and record['global_tick_offset'] == 0 and record['steps'] == command['steps'], 'Only complete fresh epochs may produce an update') + summary, provenance = record['summary'], record['provenance'] + _require(summary['nodes'] == n and summary['edges'] == edges and summary['diagnostic_mode'] == value['diagnostic_mode'], 'Runtime population/mode differs from authority') + _require(record['manifest']['files_sha256']['state.npz'] == pins[paths['state']], 'State/events are not bound by the expected checkpoint seal') + for role in ('spec', 'requests', 'overlay_before', 'overlay_manifest_before'): + _require(provenance['input_sha256'].get(str(paths[role])) == pins[paths[role]], 'Runtime did not bind authorized ' + role) + _require(_digest(checkpoint / 'overlay-input.npz') == pins[paths['overlay_before']] and + _digest(checkpoint / 'overlay-manifest.json') == pins[paths['overlay_manifest_before']], 'Sealed runtime used a different overlay or manifest') + helper, before, checked_meta, _ = r.load_overlay(paths['overlay_before'], paths['overlay_manifest_before']) + _require(checked_meta == old_meta, 'Overlay manifest changed during validation') + mapping = _npz(paths['candidate_map'], ('edge_id', 'source_neuron_idx', 'target_neuron_idx', 'target_compartment_verbatim')) + mask = np.load(paths['mask'], allow_pickle=False) + kc = np.load(paths['kc'], allow_pickle=False) + canonical_kc = np.load(paths['canonical_kc'], allow_pickle=False) + base = np.load(paths['base_candidate'], allow_pickle=False) + _vector(mask, 'int64', candidates, 'complete mask') + _vector(kc, 'int32', kc_count, 'KC') + _vector(canonical_kc, 'int32', kc_count, 'canonical KC') + _vector(base, 'float32', candidates, 'original base mV') + _require(_same(kc, canonical_kc), 'Supplied KC differs from canonical membership bytes') + _require(_same(mapping['edge_id'], mask) and _same(before['edge_id'], mask), 'Candidate mapping/overlay differ from exact mask') + _require(_same(before['base_candidate_mv'], base), 'Overlay used different original base bytes') + for key in ('source_neuron_idx', 'target_neuron_idx'): + _vector(mapping[key], 'int32', candidates, key) + comps = mapping['target_compartment_verbatim'] + _require(comps.shape == (candidates,) and comps.dtype.kind == 'U', 'Compartment mapping must be an exact Unicode vector') + maps = {} + try: + for filename, dtype, length in (('nodes.npy', 'int32', n), ('edge_id.npy', 'int64', edges), + ('src.npy', 'int32', edges), ('dst.npy', 'int32', edges)): + array = np.load(graph / filename, allow_pickle=False, mmap_mode='r') + maps[filename] = array + _vector(array, dtype, length, 'full graph ' + filename) + _require(np.array_equal(maps['nodes.npy'], np.arange(n, dtype=np.int32)), 'Global nodes are not canonical') + _require(np.all((mask >= 0) & (mask < edges)) and np.all(mask[1:] > mask[:-1]), 'Invalid canonical edge mask') + _require(_same(maps['edge_id.npy'][mask], mask), 'Mask is not indexed by original global edge ID') + _require(_same(mapping['source_neuron_idx'], maps['src.npy'][mask]) and + _same(mapping['target_neuron_idx'], maps['dst.npy'][mask]), 'Candidate endpoints differ from actual graph') + finally: + for array in maps.values(): + array._mmap.close() + state = _npz(paths['state'], ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')) + _require(all(_same(state[key], record['state'][key]) for key in state), 'Event/state arrays differ from actual sealed reader') + _require(record['overlay']['delta_raw_sha256'] == r._raw(before['plastic_delta_mv']) and + record['overlay']['effective_weights_raw_sha256'] == summary['effective_weights_raw_sha256'], 'Runtime weight installation is not bound to delta_before') + args = dict(step=state['step'], neuron_idx=state['neuron_idx'], kc=kc, + candidate_edge_id=mask, candidate_sources=mapping['source_neuron_idx'], + candidate_targets=mapping['target_neuron_idx'], candidate_compartments=comps, + delta_before=before['plastic_delta_mv'], base_candidate=base, steps=command['steps'], n_neurons=n, + compartment=command['compartment'], us_tick=command['us_tick'], d_command=command['d_command'], + frozen=command['frozen'], bridge_source=paths['eligibility_bridge_source'], + phase4j_source=paths['phase4j_source'], trace_backend=command['trace_backend']) + result = u.update_epoch(**args) + _check_files(pins) + # The directory creation is the exclusive claim. A later failure remains a + # partial directory and can never silently be retried or overwritten. + out.mkdir() + try: + with (out / 'delta-after.npz').open('xb') as stream: + np.savez_compressed(stream, edge_id=mask, plastic_delta_mv=result.delta_after) + metadata = {key: old_meta[key] for key in ('schema', 'mask_path', 'mask_sha256', + 'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir', 'am_overlay_path', 'am_overlay_sha256')} + metadata.update(overlay_name='learned', overlay_sha256=_digest(out / 'delta-after.npz'), + parent_snapshot_sha256=pins[paths['overlay_before']], eligibility_v2_source_sha256=SOURCE_PINS['phase4j_source']) + _save(out / 'overlay-manifest.json', metadata) + _save(out / 'update-receipt.json', result.receipt) + _, after, _, _ = r.load_overlay(out / 'delta-after.npz', out / 'overlay-manifest.json') + _require(_same(after['plastic_delta_mv'], result.delta_after) and _same(after['edge_id'], mask), 'Written learned snapshot failed actual runtime loader') + # Re-read the sealed neural state after writing, without altering its + # seal or appending coordinator output inside the runtime directory. + c.load_checkpoint(checkpoint, value['checkpoint_manifest_sha256']) + _check_files(pins) + _require(_digest(__file__) == source_sha, 'Coordinator source changed') + output_hashes = {name: _digest(out / name) for name in ('delta-after.npz', 'overlay-manifest.json', 'update-receipt.json')} + chain = dict(schema='phase4an-epoch-postprocess-chain-v1', complete=True, + epoch_id=value['epoch_id'], authority_file=str(authority_path), authority_sha256=pins[authority_path], + coordinator_source_sha256=source_sha, command=dict(command), diagnostic_mode=value['diagnostic_mode'], + before_overlay_file_sha256=pins[paths['overlay_before']], + before_overlay_manifest_sha256=pins[paths['overlay_manifest_before']], + after_overlay_file_sha256=output_hashes['delta-after.npz'], + after_overlay_manifest_sha256=output_hashes['overlay-manifest.json'], + parent_snapshot_sha256_semantics='delta-before overlay NPZ file SHA256, not neural checkpoint or manifest SHA', + source_neural_checkpoint_dir=str(checkpoint), + source_neural_checkpoint_manifest_sha256=value['checkpoint_manifest_sha256'], + source_state_file_sha256=pins[paths['state']], source_spec_sha256=pins[paths['spec']], + source_requests_sha256=pins[paths['requests']], + runtime_delta_before_raw_sha256=record['overlay']['delta_raw_sha256'], + runtime_effective_weights_raw_sha256=summary['effective_weights_raw_sha256'], + input_files_sha256={str(path): sha for path, sha in sorted(pins.items(), key=lambda item: str(item[0]))}, + output_files_sha256=output_hashes, + update_receipt_payload_sha256=result.receipt['payload_sha256'], + runtime_learned_manifest_loader_verified=True, input_and_checkpoint_bytes_reverified=True, + candidate_endpoints_equal_graph=True, KC_equals_supplied_canonical_membership=True, + authority_scope='caller must bind this externally pinned authority to its preregistered row, successful process, anatomical metadata and complete mask audit', + registration_authority_independently_proven=False, anatomical_identity_inferred_from_lengths=False, + neural_checkpoint_and_plastic_parent_are_distinct=True, + weights_installed=False, brain_executed=False, learning_pass_claimed=False, candidate_generated=False) + _save(out / 'chain-receipt.json', chain) + return chain + except BaseException as error: + if not (out / 'postprocess-error.json').exists(): + _save(out / 'postprocess-error.json', dict(complete=False, error_type=type(error).__name__, + message=str(error), partial_preserved=True, retry_forbidden=True, learning_pass_claimed=False)) + raise + + +def postprocess_epoch(authority_json, expected_authority_sha256, out_dir): + """Production entry: full 166700/25582938/4064/14232, no small-mode flag.""" + _require(sys.dont_write_bytecode, 'Use python -B; frozen sources must not receive bytecode writes') + value, paths, pins, checkpoint, authority_path = _authority(authority_json, expected_authority_sha256) + dependencies = _production_dependencies(paths, pins) + return _coordinate(value, paths, pins, checkpoint, authority_path, out_dir, dependencies, DIMENSIONS) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_inputs.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_inputs.py new file mode 100644 index 0000000..53a2140 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_inputs.py @@ -0,0 +1,221 @@ +"""UNREGISTERED input consumer; no I/O, brain execution, or training. + +The frozen L/AK interface is discrete float32 Bernoulli (one request per PN +and tick), not a continuous-time Poisson sampler. PN/rate mapping is supplied +and externally authenticated by the caller; this module never selects odors, +rebuilds mappings, rescales rates, clips probabilities, or resets brain RNGs. + +generate_cs_requests lazily uses the original CUDA Torch random operation. +CPU mocks test its contract only, not CUDA sequence equivalence. A full +(steps, PN-columns) matrix is drawn once, then the explicit half-open CS +window masks requests. A 100 ms CS in a 120 ms epoch is NOT the full old L +120 ms input. Different draw shapes need not share identical random prefixes. +The caller must register source/rate authority, device/environment, budgets, +stream aliases, and a separate bounded CUDA equivalence test before real use. +Returned request/source arrays are owned read-only copies, not immutable +capabilities; the consumer must recheck their recorded hashes before use. +""" +from __future__ import annotations + +import hashlib +import re + +import numpy as np + + +N_NEURONS = 166700 +L_SOURCE_SHA = 'e97d58d8c1f44920d0e953f8f5831ddc86fa0198784e86460e315de4116ac553' +AK_ENCODER_SHA = '0061689abb1832e963718bd836bc8a50009ee38b258143a753b4d59e63414925' + + +def require(value, message): + if not value: + raise ValueError(message) + + +def array_identity(value): + return dict(dtype=value.dtype.str, shape=list(value.shape), order='C-logical-bytes', + sha256=hashlib.sha256(value.tobytes(order='C')).hexdigest()) + + +def _vector(value, dtype, name): + require(isinstance(value, np.ndarray) and value.dtype == np.dtype(dtype) and value.ndim == 1, + name + ' must be an exact one-dimensional ' + np.dtype(dtype).name + ' array') + + +def _targets(value): + _vector(value, np.int32, 'targets') + require(np.all((value >= 0) & (value < N_NEURONS)), 'Target outside canonical global neuron range') + require(len(np.unique(value)) == len(value), 'Duplicate PN columns rejected; never merge rates or deduplicate RNG columns') + + +def _clock(steps, start, stop): + require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max, 'steps must be a positive int32-range integer') + require(type(start) is type(stop) is int and 0 <= start <= stop <= steps, 'Invalid explicit half-open CS tick window') + + +def probabilities_f32(targets, rates_hz, *, dt_ms=0.1): + """Preserve the L expression's separate float32 multiply then divide.""" + _targets(targets); _vector(rates_hz, np.float32, 'rates_hz') + require(rates_hz.shape == targets.shape and np.all(np.isfinite(rates_hz)) and np.all(rates_hz >= 0), 'Invalid PN rate vector') + require(type(dt_ms) in (int, float) and dt_ms == 0.1, 'Only the frozen dt_ms=0.1 contract is supported') + probability = np.divide(np.multiply(rates_hz, np.float32(dt_ms), dtype=np.float32), np.float32(1000.0), dtype=np.float32) + original = rates_hz * dt_ms / 1000.0 + require(original.dtype == np.float32 and original.tobytes() == probability.tobytes(), 'Installed NumPy changed the frozen rate expression') + require(np.all(np.isfinite(probability)) and np.all((probability >= 0) & (probability <= 1)), 'Probability outside [0,1]; no clipping or rate adjustment') + return probability + + +def forced_from_uniforms(uniforms, probability): + """Pure arithmetic fixture helper, NOT a replacement CUDA generator.""" + _vector(probability, np.float32, 'probability') + require(isinstance(uniforms, np.ndarray) and uniforms.dtype == np.float32 and uniforms.ndim == 2 and + uniforms.shape[1] == len(probability), 'Uniform fixture requires exact float32 [ticks,columns]') + require(np.all(np.isfinite(uniforms)) and np.all((uniforms >= 0) & (uniforms < 1)) and + np.all(np.isfinite(probability)) and np.all((probability >= 0) & (probability <= 1)), 'Invalid uniform/probability fixture') + return uniforms < probability[None, :] + + +def requests_from_forced(forced, targets, *, cs_start_tick, cs_stop_tick): + """Sort resulting requests only; never reorder the pre-sampling columns.""" + _targets(targets) + require(isinstance(forced, np.ndarray) and forced.dtype == np.bool_ and forced.ndim == 2 and + forced.shape[1] == len(targets), 'Forced samples must be Boolean [steps,original PN columns]') + steps = forced.shape[0]; _clock(steps, cs_start_tick, cs_stop_tick) + ticks, columns = np.nonzero(forced[cs_start_tick:cs_stop_tick]) + ticks = ticks.astype(np.int32) + np.int32(cs_start_tick) + neurons = targets[columns].copy() + order = np.lexsort((neurons, ticks)) + requests = {'tick': ticks[order], 'neuron_idx': neurons[order]} + validate_requests(requests, steps) + for value in requests.values(): value.setflags(write=False) + generated = int(np.count_nonzero(forced)); retained = len(ticks) + return dict(requests=requests, audit=dict(steps=steps, CS_window=[cs_start_tick, cs_stop_tick], + full_matrix_boolean_hits=generated, retained_CS_requests=retained, + outside_CS_boolean_hits_masked=generated - retained, + original_PN_column_order_preserved=True, sorted_only_after_sampling=True, + duplicate_requests_removed=0, probabilities_clipped=False, + returned_requests_read_only=True, consumer_must_recheck_array_hashes=True, + forced_identity=array_identity(forced), targets_identity=array_identity(targets), + requests_identity={k: array_identity(v) for k, v in requests.items()})) + + +def _load_torch(): + import torch + return torch + + +def generate_cs_requests(targets, rates_hz, *, seed, steps, cs_start_tick, cs_stop_tick, + device, max_uniform_draws, dt_ms=0.1): + """Future explicit CUDA input generation; no implicit CPU fallback. + + ``seed`` is the caller's full semantic seed (Python int in [0,2**63)). + The generator is private to this call. No offset/modulo/truncation or + global/manual brain seed is used. Aliases must reuse the same complete + sampling descriptor, including steps/column order/window, not seed alone. + The caller owns authorization, resource admission, and external rate pins. + ``device`` and ``max_uniform_draws`` must be explicitly supplied. The + latter bounds steps * PN columns, not total process/GPU memory. Retaining + initial_seed proves only integer delivery, not CUDA high-bit influence. + """ + require(type(seed) is int and 0 <= seed < 2**63, 'Exact full 63-bit Python integer seed required') + _clock(steps, cs_start_tick, cs_stop_tick) + probability = probabilities_f32(targets, rates_hz, dt_ms=dt_ms) + require(type(device) is str and re.fullmatch('cuda:[0-9]+', device), 'Explicit indexed CUDA device required; no CPU fallback') + require(type(max_uniform_draws) is int and max_uniform_draws > 0, 'Explicit positive integer uniform-draw budget required') + uniform_draws = steps * len(targets) + require(uniform_draws <= max_uniform_draws, 'Full matrix exceeds caller uniform-draw budget; no chunking or clipping') + torch = _load_torch() + require(torch.cuda.is_available() and torch.get_default_dtype() == torch.float32, 'Original CUDA/default-float32 sampler environment required') + actual_device = torch.device(device) + generator = torch.Generator(device=actual_device) + generator.manual_seed(seed) + require(generator.initial_seed() == seed, 'Generator did not retain the complete semantic seed') + p_gpu = torch.as_tensor(probability, dtype=torch.float32, device=actual_device) + # Exactly one complete draw. No chunking, per-PN loop, or tail-dependent redraw. + uniforms = torch.rand((steps, len(targets)), generator=generator, device=actual_device) + require(uniforms.dtype == torch.float32 and str(uniforms.device) == str(actual_device), 'Sampler dtype/device changed') + forced = (uniforms < p_gpu[None, :]).cpu().numpy() + result = requests_from_forced(forced, targets, cs_start_tick=cs_start_tick, cs_stop_tick=cs_stop_tick) + # Optional identifiers are recorded only when the installed Torch exposes + # them. An indexed device name is not presented as a UUID or external pin. + identity = {'uuid': None, 'pci_bus_id': None, 'query_error': None} + try: + properties = torch.cuda.get_device_properties(actual_device) + for key in ('uuid', 'pci_bus_id'): + value = getattr(properties, key, None) + if value is not None: identity[key] = str(value) + except (AttributeError, RuntimeError) as exc: + identity['query_error'] = type(exc).__name__ + ': ' + str(exc) + result['audit'].update(schema='phase4an-conditioning-input-v1-unregistered', + seed=seed, seed_bits=63, seed_hex=f'{seed:016x}', generator_initial_seed=generator.initial_seed(), + seed_conversion='exact integer; no truncation/modulo/offset', private_generator=True, + sampler='Torch CUDA rand default float32; strict U < float32 probability', + generator_type=type(generator).__module__ + '.' + type(generator).__qualname__, + numpy_version=str(np.__version__), torch_version=str(torch.__version__), torch_cuda_version=torch.version.cuda, + execution_device=str(uniforms.device), device_name=str(torch.cuda.get_device_name(actual_device)), + device_capability=list(torch.cuda.get_device_capability(actual_device)), + device_identity=identity, device_identity_verified_against_external_pin=False, + matrix_draw_calls=1, uniform_matrix_shape=[steps, len(targets)], uniform_scalar_draws=uniform_draws, + max_uniform_draws=max_uniform_draws, uniform_matrix_bytes=uniform_draws * np.dtype(np.float32).itemsize, + budget_scope='Uniform scalar count only, not total process or GPU memory', + sampled_before_window_mask=True, dt_ms=dt_ms, dt_float32_identity=array_identity(np.asarray([dt_ms], np.float32)), + rate_units='Hz', probability_identity=array_identity(probability), rates_identity=array_identity(rates_hz), + input_mapping_authority_verified_by_this_module=False, + reference_L_source_sha256=L_SOURCE_SHA, reference_AK_encoder_source_sha256=AK_ENCODER_SHA, + full_old_L_input_identity_claimed=False, CUDA_sequence_equivalence_tested_here=False, + CUDA_high_seed_bit_sequence_influence_tested_here=False, + timing_scope='Full epoch matrix drawn once, then explicit CS window; old L supplied PN requests over its full duration', + RNG_prefix_invariance_across_shapes_claimed=False, continuous_time_Poisson_claimed=False, + brain_reset_called=False, brain_executed=False, training_executed=False, protocol_registered=False) + return result + + +def validate_requests(requests, steps): + _clock(steps, 0, steps) + require(type(requests) is dict and set(requests) == {'tick', 'neuron_idx'}, 'Exact sparse request schema required') + for key, value in requests.items(): _vector(value, np.int32, key) + tick, neuron = requests['tick'], requests['neuron_idx'] + require(tick.shape == neuron.shape and np.all((tick >= 0) & (tick < steps)) and + np.all((neuron >= 0) & (neuron < N_NEURONS)), 'Requests outside local clock/global neuron range') + keys = tick.astype(np.int64) * N_NEURONS + neuron + require(len(keys) < 2 or np.all(keys[1:] > keys[:-1]), 'Requests must already be sorted unique; no silent sorting/deduplication') + return keys + + +class RequestConflictError(ValueError): + def __init__(self, audit): + super().__init__('CS/US request overlap rejected; explicit boolean_union is required to combine it') + self.audit = audit + + +def merge_requests(cs, us, *, steps, conflict_policy='error'): + """Compose two valid streams, recording every cross-source collision. + + boolean_union explicitly reproduces a single Boolean request per neuron / + tick, not two deliveries. Separate source attempts are retained in audit + and uint8 source_mask (CS=1, US=2, overlap=3). This is not refractory + acceptance, actual spiking, an artificial D value, or an update rule. + """ + require(conflict_policy in ('error', 'boolean_union'), 'Unknown explicit conflict policy') + cs_keys, us_keys = validate_requests(cs, steps), validate_requests(us, steps) + overlap = np.intersect1d(cs_keys, us_keys, assume_unique=True) + conflicts = [dict(tick=int(k // N_NEURONS), neuron_idx=int(k % N_NEURONS), CS_attempts=1, US_attempts=1, + combined_boolean_requests=1 if conflict_policy == 'boolean_union' else None) for k in overlap] + audit = dict(schema='phase4an-request-composition-v1-unregistered', policy=conflict_policy, + CS_attempts=len(cs_keys), US_attempts=len(us_keys), cross_source_conflict_count=len(overlap), conflicts=conflicts, + duplicate_within_source_allowed=False, refractory_acceptance_evaluated=False, actual_spikes_evaluated=False, + input_identity={name: {k: array_identity(v) for k, v in values.items()} for name, values in (('CS', cs), ('US', us))}) + if len(overlap) and conflict_policy == 'error': raise RequestConflictError(audit) + keys = np.union1d(cs_keys, us_keys) + sources = np.zeros(len(keys), np.uint8) + sources[np.searchsorted(keys, cs_keys)] |= np.uint8(1) + sources[np.searchsorted(keys, us_keys)] |= np.uint8(2) + requests = {'tick': (keys // N_NEURONS).astype(np.int32), 'neuron_idx': (keys % N_NEURONS).astype(np.int32)} + validate_requests(requests, steps) + for value in (*requests.values(), sources): value.setflags(write=False) + audit.update(output_requests=len(keys), explicit_boolean_overlap_collapses=len(overlap), + requests_identity={k: array_identity(v) for k, v in requests.items()}, source_mask_identity=array_identity(sources), + returned_arrays_read_only=True, consumer_must_recheck_array_hashes=True, + learning_or_D_command_defined=False, brain_executed=False, protocol_registered=False) + return dict(requests=requests, source_mask=sources, audit=audit) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_compare.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_compare.py new file mode 100644 index 0000000..7ca5874 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_compare.py @@ -0,0 +1,247 @@ +"""Pure CPU comparisons for a separately authorized, finite input/update test. + +No files, checkpoint seals, source pins, parent trees or GPU are read here. +The coordinator must first verify subprocess receipts, externally pinned +checkpoint manifests, registered source/input files and learned-parent chains. +It must independently recompute update receipts with conditioning_update's +verify_receipt. These functions bind supplied arrays and declared metadata; +they cannot replace those external checks or prove functional learning. + +The frozen engineering comparator is reused unchanged. Reference diagnostics +must be complete. Light has events, final state and queues, not per-tick V/g +or APL histories; missing light observations are never fabricated as exact. +""" +from __future__ import annotations + +import hashlib +import json +import re + +import numpy as np + +import engineering_compare as C + + +N, E, N_KC, CANDIDATE_COUNT = 166700, 25582938, 4064, 14232 +COMPARATOR_SHA256 = "5a2403f11bc08d65a9d242ac2cf9686b850e0c3680d65659f785e9b6b50c0238" +PHASE4J_SHA256 = "abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e" +ELIGIBILITY_BRIDGE_SHA256 = "174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61" +MASK_SHA256 = "8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b" +AM_OVERLAY_SHA256 = "7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4" +MANIFEST_FIELDS = frozenset(("schema", "overlay_name", "overlay_sha256", "mask_path", "mask_sha256", + "base_candidate_path", "base_candidate_sha256", "aj_model_dir", "am_overlay_path", "am_overlay_sha256", + "parent_snapshot_sha256", "eligibility_v2_source_sha256")) + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False, allow_nan=False) + + +def _sha(value): + return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest() + + +def _valid_sha(value): + return type(value) is str and re.fullmatch("[0-9a-f]{64}", value) is not None + + +def _evidence(value): + return dict(dtype=value.dtype.str, shape=list(value.shape), nbytes=int(value.nbytes), + raw_c_order_sha256=hashlib.sha256(value.tobytes(order="C")).hexdigest()) + + +def _integer(mapping, key, expected): + return type(mapping.get(key)) is int and mapping[key] == expected + + +def _request_input(value, steps): + _require(isinstance(value, np.ndarray) and value.dtype == np.int32 and value.ndim == 2 and value.shape[1] == 2, + "Expected input requests must be exact int32 [M,2] (local tick, global neuron)") + _require(np.all((value[:, 0] >= 0) & (value[:, 0] < steps)) and + np.all((value[:, 1] >= 0) & (value[:, 1] < N)), "Expected input request is out of range") + keys = value[:, 0].astype(np.int64) * N + value[:, 1] + _require(len(keys) < 2 or np.all(keys[1:] > keys[:-1]), "Expected requests must already be sorted unique") + + +def compare_epoch_pair(reference_record, light_record, *, expected_requests, expected_steps, + expected_effective_weight_sha256): + """Compare fresh reference/light records, not paths or self-asserted seals. + + Inputs are dictionaries returned by frozen engineering_compare.load_run. + The coordinator supplies the original registered request array and the + independently expected effective-weight raw SHA, not a value copied from + the summary being checked. Corrupt array schemas raise ValueError. Valid + but different records return numerical_exact/pass_all=False. pass_all is + ONLY the supplied-record identity check, not a learning or activity gate. + """ + _require(type(expected_steps) is int and 0 < expected_steps <= np.iinfo(np.int32).max, + "Explicit expected epoch steps required") + _require(_valid_sha(expected_effective_weight_sha256), "Expected effective-weight raw SHA is required") + _request_input(expected_requests, expected_steps) + _require(type(reference_record) is dict and type(light_record) is dict, + "Supply already authenticated loaded records, not paths") + C.validate_run(reference_record) + C.validate_run(light_record) + inherited = C.compare_fresh(light_record, reference_record) + checks = dict(inherited["checks"]) + for label, record, mode in (("reference", reference_record, "reference"), ("light", light_record, "light")): + summary = record.get("summary") + _require(type(summary) is dict, "Missing runtime summary") + checks[label + ".full_population"] = len(record["state"]["v"]) == N and _integer(summary, "nodes", N) and _integer(summary, "edges", E) + checks[label + ".expected_clock"] = (record["steps"] == expected_steps and record["global_tick_offset"] == 0 and + _integer(summary, "steps", expected_steps) and _integer(summary, "completed_ticks", expected_steps) and + _integer(summary, "explicit_steps_executed", expected_steps)) + checks[label + ".fresh_fixed_weight_scope"] = (summary.get("checkpoint_continuation_used") is False and + summary.get("within_trial_weight_updates") is False and summary.get("hidden_early_stop") is False and + summary.get("entire_neural_trial_executed") is True and summary.get("record_history") is False and + summary.get("parent_checkpoint_manifest_sha256") is None) + checks[label + ".mode_precision_delay"] = (summary.get("diagnostic_mode") == mode and + summary.get("precision") == "float64" and summary.get("apl_precision") == "float32" and + _integer(summary, "apl_delay_steps", 18) and summary.get("state_units") == "SI volts") + checks[label + ".complete_reported_finite"] = (summary.get("complete") is True and summary.get("pass_all") is True and + summary.get("all_finite") is True and _integer(summary, "first_failure_tick", -1)) + checks[label + ".effective_weight_authority"] = summary.get("effective_weights_raw_sha256") == expected_effective_weight_sha256 + checks[label + ".input_request_array"] = np.array_equal(record["requests"][:, :2], expected_requests.astype(np.int64)) + checks[label + ".request_and_spike_accounting"] = (summary.get("counts_match_sparse_events") is True and + _integer(summary, "total_spikes", len(record["state"]["step"])) and + _integer(summary, "requested", len(expected_requests)) and + _integer(summary, "accepted_requests", int(record["requests"][:, 3].sum(dtype=np.int64))) and + _integer(summary, "rejected_requests", int(record["requests"][:, 4].sum(dtype=np.int64)))) + checks[label + ".recorded_full_APL_terminal"] = (record.get("terminal_origin") == "recorded_terminal_queues" and + record["terminal"]["apl_pending"].shape == (18, N_KC)) + full = reference_record + full_summary = full["summary"] + checks["reference.full_diagnostics"] = (set(full["traces"]) == set(C.TRACE_FILES) and + full.get("state_hashes") is not None and full.get("queues") is not None and + all(full_summary.get(key) is True for key in + ("full_APL_history_recorded", "full_tick_state_hashes_recorded", "full_queue_hashes_recorded", "full_sequence_bitwise_audit_performed"))) + if full.get("queues") is not None: + checks["reference.queue_summary_accounting"] = ( + _integer(full_summary, "arriving_edges", sum(row["count"] for row in full["queues"])) and + _integer(full_summary, "accepted_arriving_edges", sum(row["accepted_count"] for row in full["queues"]))) + else: + checks["reference.queue_summary_accounting"] = False + light_summary = light_record["summary"] + checks["light.declared_recording_scope"] = (not light_record["traces"] and + light_record.get("state_hashes") is None and light_record.get("queues") is None and + all(light_summary.get(key) is False for key in + ("full_APL_history_recorded", "full_tick_state_hashes_recorded", "full_queue_hashes_recorded", "full_sequence_bitwise_audit_performed"))) + for label, summary in (("reference", full_summary), ("light", light_summary)): + inputs = summary.get("input_sha256") + checks[label + ".input_hash_schema"] = (type(inputs) is dict and + set(inputs) == {"base_weights.npy", "coefficients.json", "requests.npz", "spec.json"} and + all(_valid_sha(value) for value in inputs.values())) + for name in ("base_weights.npy", "coefficients.json"): + left = full_summary.get("input_sha256", {}).get(name) + right = light_summary.get("input_sha256", {}).get(name) + checks["pair.input." + name] = _valid_sha(left) and left == right + for name in ("arriving_edges", "accepted_arriving_edges"): + checks["pair.summary." + name] = (type(full_summary.get(name)) is int and + type(light_summary.get(name)) is int and full_summary[name] == light_summary[name]) + checks = {key: bool(value) for key, value in checks.items()} + exact = all(checks.values()) + return dict(schema="conditioning-preflight-epoch-comparison-v1", numerical_exact=exact, pass_all=exact, + status="EXACT-RECORDED-EPOCH-PAIR-NOT-LEARNING" if exact else "FAIL-EPOCH-COMPARISON", + checks=checks, failed_checks=[key for key, ok in checks.items() if not ok], + expected_steps=expected_steps, expected_requests=_evidence(expected_requests), + expected_effective_weight_raw_sha256=expected_effective_weight_sha256, + recorded_pair_availability=inherited["reference_comparisons_available"], + activity=inherited["activity"], nonvacuous_activity=inherited["nonvacuous_activity"], + activity_is_descriptive_not_a_new_gate=True, + full_reference_diagnostics_required=True, light_intermediate_states_observed=False, + light_per_tick_V_g_ref_identity_claimed=False, independent_finite_latch_replay_performed=False, + independent_CSR_queue_reconstruction_performed=False, + independent_wholebrain_CPU_oracle_performed=False, + expected_comparator_source_sha256=COMPARATOR_SHA256, source_pin_verified_here=False, + external_file_seals_process_and_input_authority_verified_here=False, + input_generator_or_parent_delta_chain_verified_here=False, + learning_pass_claimed=False, candidate_generated=False, + scope="Supplied fresh epoch records and original requests only; prerequisite external checkpoint/source/parent verification belongs to coordinator") + + +def _delta(value, name): + _require(isinstance(value, np.ndarray) and value.dtype == np.float32 and value.shape == (CANDIDATE_COUNT,) and + np.isfinite(value).all(), name + " must be the full finite float32 candidate delta vector") + + +def audit_delta_binding(delta_before, delta_after, installed_delta, *, update_receipt, + parent_snapshot_sha256, child_manifest): + """Bind already computed/installed arrays to receipt and learned metadata. + + parent_snapshot_sha256 means the actual PARENT OVERLAY NPZ file hash, + externally established by the coordinator, NOT a checkpoint manifest hash + and NOT a raw delta-array hash. installed_delta must come from the child + overlay subsequently checked by frozen runtime_light.load_overlay. + + This function does not read those files, authenticate anatomical IDs, + recompute eligibility-v2, establish clipping bounds, or prove a causal + behavioral effect. Call conditioning_update.verify_receipt separately + against actual sealed epoch events and complete pinned mappings. + """ + for name, value in (("delta_before", delta_before), ("delta_after", delta_after), ("installed_delta", installed_delta)): + _delta(value, name) + _require(_valid_sha(parent_snapshot_sha256), "Externally established parent overlay FILE SHA is required") + _require(type(update_receipt) is dict and type(child_manifest) is dict, "Receipt and manifest must be dictionaries") + _require(type(update_receipt.get("input_arrays")) is dict and type(update_receipt.get("outputs")) is dict and + type(update_receipt.get("source_pins")) is dict and type(update_receipt.get("command")) is dict and + type(update_receipt.get("semantics")) is dict and type(update_receipt.get("counts")) is dict, + "Incomplete epoch update receipt schema") + before, after, installed = (_evidence(value) for value in (delta_before, delta_after, installed_delta)) + command = update_receipt["command"] + counts = update_receipt["counts"] + checks = { + "receipt.self_hash": update_receipt.get("payload_sha256") == _sha({k: v for k, v in update_receipt.items() if k != "payload_sha256"}), + "receipt.schema": update_receipt.get("schema") == "phase4an-conditioning-update-draft-v1" and + update_receipt.get("status") == "DRAFT-COMPUTED-EPOCH-UPDATE-NOT-INSTALLED", + "receipt.original_source_pins": update_receipt["source_pins"] == { + "eligibility_bridge_sha256": ELIGIBILITY_BRIDGE_SHA256, "phase4j_sha256": PHASE4J_SHA256}, + "receipt.delta_before": _canonical(update_receipt["input_arrays"].get("delta_before")) == _canonical(before), + "receipt.delta_after": _canonical(update_receipt["outputs"].get("delta_after")) == _canonical(after), + "computed_installed_delta_bitwise": C.bit_equal(delta_after, installed_delta), + "delta.nonpositive": bool(np.all(delta_before <= 0) and np.all(delta_after <= 0)), + "delta.no_potentiation": bool(np.all(delta_after <= delta_before)), + "receipt.candidate_count": _integer(counts, "candidates", CANDIDATE_COUNT), + "receipt.changed_bit_count": _integer(counts, "actual_bitwise_changed_edges", int(np.count_nonzero(delta_before.view(np.uint32) != delta_after.view(np.uint32)))), + "receipt.changed_value_count": _integer(counts, "actual_value_changed_edges", int(np.count_nonzero(delta_before != delta_after))), + "receipt.explicit_scheduled_D": type(command.get("D_scheduled")) is float and command["D_scheduled"] in (0.0, 1.0) and + type(command.get("frozen")) is bool, + "receipt.epoch_boundary_only": update_receipt["semantics"].get("application") == "epoch_boundary_after_all_recorded_ticks; affects subsequent epoch only" and + update_receipt["semantics"].get("delta_dtype") == "float32" and update_receipt["semantics"].get("delta_units") == "mV", + "receipt.not_a_DAN_measurement": update_receipt["semantics"].get("D_definition") == "artificial scheduled scalar" and + update_receipt["semantics"].get("actual_DAN_delivery_observed") is False and + update_receipt["semantics"].get("actual_DAN_delivery_used_to_gate") is False, + "manifest.exact_fields": set(child_manifest) == MANIFEST_FIELDS, + "manifest.learned_schema": child_manifest.get("schema") == "phase4an-overlay-v1" and child_manifest.get("overlay_name") == "learned", + "manifest.parent_overlay_file_sha": child_manifest.get("parent_snapshot_sha256") == parent_snapshot_sha256, + "manifest.frozen_rule_mask_helper": child_manifest.get("eligibility_v2_source_sha256") == PHASE4J_SHA256 and + child_manifest.get("mask_sha256") == MASK_SHA256 and child_manifest.get("am_overlay_sha256") == AM_OVERLAY_SHA256, + "manifest.declared_file_hashes": all(_valid_sha(child_manifest.get(key)) for key in ("overlay_sha256", "base_candidate_sha256")), + "manifest.declared_paths": all(type(child_manifest.get(key)) is str and bool(child_manifest[key]) + for key in ("mask_path", "base_candidate_path", "aj_model_dir", "am_overlay_path")), + } + no_update = command.get("frozen") is True or command.get("D_scheduled") == 0.0 + checks["zero_D_or_frozen_identity"] = not no_update or C.bit_equal(delta_before, delta_after) + for name in ("weights_installed", "base_modified", "brain_dynamics_executed", "scientific_learning_pass_claimed", "candidate_generated"): + checks["receipt.scope." + name] = update_receipt.get(name) is False + checks = {key: bool(value) for key, value in checks.items()} + exact = all(checks.values()) + return dict(schema="conditioning-preflight-delta-binding-v1", pass_all=exact, + status="EXACT-DELTA-ARRAY-BINDING-NOT-LEARNING" if exact else "FAIL-DELTA-BINDING", + checks=checks, failed_checks=[key for key, ok in checks.items() if not ok], + delta_before=before, computed_delta_after=after, installed_delta=installed, + parent_overlay_file_sha256=parent_snapshot_sha256, + declared_child_overlay_file_sha256=child_manifest.get("overlay_sha256"), + update_receipt_payload_sha256=update_receipt.get("payload_sha256"), + actual_changed_edges=int(np.count_nonzero(delta_before != delta_after)), + actual_bitwise_changed_edges=int(np.count_nonzero(delta_before.view(np.uint32) != delta_after.view(np.uint32))), + delta_nonzero_or_change_is_not_learning_evidence=True, + eligibility_v2_independently_recomputed_here=False, full_mask_identity_verified_here=False, + candidate_order_or_compartment_mapping_verified_here=False, floor_bound_independently_verified_here=False, + file_bytes_and_parent_snapshot_authority_verified_here=False, installation_in_runtime_verified_here=False, + actual_DAN_delivery_verified_here=False, learning_pass_claimed=False, candidate_generated=False, + scope="Supplied float32 mV arrays and declared parent/update metadata only; coordinator must verify real files, v2 recomputation and next-epoch installation") diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_inputs.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_inputs.py new file mode 100644 index 0000000..aa5a232 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_inputs.py @@ -0,0 +1,288 @@ +"""UNREGISTERED bounded preflight input planning, not a conditioning runner. + +Only the explicitly called generate_epoch_inputs performs random sampling; it +delegates to conditioning_inputs' single full-shape CUDA draw. The plan and +static rate loader do no sampling, neural execution, plasticity, or I/O writes. +The coordinator owns registration, full seals, graph/overlay authority, device +UUID admission, and every epoch update/checkpoint receipt. +""" +from __future__ import annotations + +import ast +import copy +import hashlib +import io +import json +from pathlib import Path, PurePosixPath +from typing import Any + +import numpy as np + +import conditioning_inputs as inputs +import trial_plan + + +MASTER = 20260930 +BLOCK = 'physiological__PAM15__reinforce_A__rep01' +SCHEMA = 'phase4an-conditioning-preflight-inputs-v1-unregistered' +STATIC = 'experiments/phase4l-olfactory-grounding/' +SOURCE_REL = 'models/male-cns-lif/phase4l.py' +CONFIG_REL = 'models/male-cns-lif/olfactory-grounding-v1/phase4l.yaml' +SOURCE_PINS = { + SOURCE_REL: 'e97d58d8c1f44920d0e953f8f5831ddc86fa0198784e86460e315de4116ac553', + CONFIG_REL: '9b8bcb7e031bf9063033a1854a439a7cd0bf6730301c2d2d02ca99a76f098d6e', + STATIC + 'odor-encoder/frozen-odor-panel.parquet': '907f16a56e024a8e2112b5751e53b0e6037b329fa110098af3d31b119c10a1a0', + STATIC + 'odor-encoder/mapped-glomerular-vectors.npz': 'f057563152e0fa22060080af13019f319e7f36563a775eff829bbc1929c451b0', + STATIC + 'odor-encoder/selection.json': '1b832becf9d08935716fe9ba5e3cd3e619e7d208dfb43c4fa197c969d095f113', + STATIC + 'receptor-mapping/olfactory-mapping.parquet': 'a01d27000533e9844458a4c559cac084f1c34ca20a1104e97e6a0f72b0cc93b1', +} + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(',', ':'), ensure_ascii=False, allow_nan=False) + + +def _sha(value): + return hashlib.sha256(_canonical(value).encode('utf-8')).hexdigest() + + +def build_preflight_plan(): + """Rebuild the whole semantic plan, then select by rule, never outcomes. + + D1/D0/frozen share the two paired input epochs. Every neural epoch starts + fresh; only the externally verified plastic delta can cross its boundary. + This inventory does not enable the complete 68,000-epoch protocol. + """ + full = trial_plan.build_trial_plan(MASTER) + trial_plan.validate_trial_plan(full) + block = full['plan']['blocks'][0] + require(block['block_id'] == BLOCK and block['mode'] == 'physiological' and + block['dan_type'] == 'PAM15' and block['compartment_verbatim'] == "y5B'2a", 'First fixed block changed') + suite = next(s for s in block['suites'] if s['assay'] == 'normal') + paired = next(a for a in suite['arms'] if a['arm'] == 'paired') + first = next(i for i, epoch in enumerate(paired['epochs']) if epoch['us']['requested']) + require(first + 1 < len(paired['epochs']), 'Requested US lacks its following complete epoch') + epochs = copy.deepcopy(paired['epochs'][first:first + 2]) + calls = [] + for arm in ('D1', 'D0', 'frozen'): + for mode in ('reference', 'light'): + previous = None + for position, epoch in enumerate(epochs): + _validate_epoch(epoch) + row_id = f'{arm}--{mode}--epoch-{position:03d}' + calls.append(dict(row_id=row_id, arm=arm, diagnostic_mode=mode, + input_epoch_index=position, epoch_id=epoch['epoch_id'], + full_plan_epoch_index=epoch['epoch_index'], steps=1200, + d_command=0.0 if arm == 'D0' else epoch['D_command'], + frozen=arm == 'frozen', previous_delta_row_id=previous, + neural_state='fresh_rest_zero_ref_zero_spike_queue_zero_APL_queue', + input_sharing='same saved CS and US arrays across arms and diagnostic modes')) + previous = row_id + result = dict(schema=SCHEMA, master=MASTER, block_id=BLOCK, mode='physiological', + dan_type='PAM15', compartment_verbatim="y5B'2a", full_trial_plan=full, + selected_epochs=epochs, calls=calls, total_calls=12, total_ticks=14400, + input_generation_calls=2, input_generation_executed=False, brain_runs_executed=0, + complete_conditioning_authorized=False, training_result_claimed=False, + selection_rule='first normal paired requested US epoch in first fixed block, plus its immediate successor') + result['payload_sha256'] = _sha(result) + return result + + +def validate_preflight_plan(value): + require(type(value) is dict and _canonical(value) == _canonical(build_preflight_plan()), + 'Preflight inventory differs from full deterministic reconstruction') + return True + + +def _validate_epoch(epoch): + require(type(epoch) is dict and epoch.get('steps') == 1200 and epoch.get('duration_ms') == 120 and + epoch.get('initial_neural_state') == 'frozen_reset_including_KC_trace_and_both_delay_queues', + 'Only complete fresh normal 120 ms epochs are in this preflight') + cs, us = epoch.get('cs'), epoch.get('us') + require(type(cs) is dict and cs.get('cue') in ('A', 'B') and cs.get('odor') == trial_plan.ODORS[cs['cue']] and + type(cs.get('seed')) is int and 0 <= cs['seed'] < 2**63 and cs.get('seed_bits') == 63 and + cs.get('start_tick') == 0 and cs.get('stop_tick_exclusive') == 1000, 'CS semantic descriptor changed') + require(type(us) is dict and type(us.get('requested')) is bool and us.get('nominal_slot_tick') == 900 and + us.get('rng_seed') is None and us.get('rng_draws') == 0, 'US must remain a deterministic scheduled pulse') + require((us['requested'] and us.get('tick') == 900 and us.get('request_rule') == 'fixed_single_pulse_all_bound_DAN_targets') or + (not us['requested'] and us.get('tick') is None and us.get('request_rule') == 'none'), 'US timing/request rule changed') + + +def _extract_drive(raw, filename): + """Compile only the SHA-pinned pure pn_drive AST; never import old L.""" + import pandas as pd + require(hashlib.sha256(raw).hexdigest() == SOURCE_PINS[SOURCE_REL], 'Frozen L source SHA mismatch') + nodes = [n for n in ast.parse(raw, filename=filename).body if isinstance(n, ast.FunctionDef) and n.name == 'pn_drive'] + require(len(nodes) == 1 and not nodes[0].decorator_list, 'Unique undecorated pn_drive required') + namespace = {'np': np, 'pd': pd, 'Any': Any} + exec(compile(ast.Module(body=nodes, type_ignores=[]), filename, 'exec'), namespace) + return namespace['pn_drive'], hashlib.sha256(ast.dump(nodes[0], include_attributes=False).encode()).hexdigest() + + +def _rate_tables(selection, panel, vectors, mapping, config, drive): + """Pure static-table derivation, preserving every original PN column.""" + require(selection.get('selected_before_KC_simulation') is True and len(panel) == 12 and + panel.odor_id.is_unique and panel.split.tolist() == ['calibration'] * 6 + ['heldout'] * 6, + 'Frozen static odor panel schema changed') + gloms = selection['glomeruli_order'] + require(type(gloms) is list and len(gloms) == len(set(gloms)) and len(gloms) > 0 and + set(vectors) == set(panel.odor_id), 'Frozen vector/glomerulus inventory mismatch') + encoder = config['odor_encoder'] + require(encoder['max_total_rate_per_glomerulus_hz'] == 400.0 and + encoder['modes'] == ['physiological', 'matched_energy'], 'Frozen rate transform changed') + median = float(np.median(panel.mapped_response_sum)) + require(np.isfinite(median) and median > 0, 'Invalid panel response median') + tables = {} + for cue, name in trial_plan.ODORS.items(): + selected = selection['ODOR_' + cue] + require(selected['odor_name'] == name, 'Selected frozen A/B odor name changed') + odor_id = selected['odor_id'] + positions = np.flatnonzero(panel.odor_id.to_numpy() == odor_id) + require(len(positions) == 1, 'Selected odor ID not unique in frozen panel') + panel_row = panel.iloc[int(positions[0])] + require(panel_row.odor_name == name and panel_row.formal_label == 'ODOR_' + cue, 'Panel/selection label mismatch') + vector = vectors[odor_id] + require(isinstance(vector, np.ndarray) and vector.dtype == np.float32 and vector.shape == (len(gloms),) and + np.isfinite(vector).all() and np.all(vector >= 0), 'Invalid frozen glomerular vector; never clip') + targets, rates, frame = drive(odor_id, vector, mapping, gloms, 'physiological', median, config) + inputs.probabilities_f32(targets, rates) + require(len(targets) > 0 and np.all(rates > 0), 'Empty or nonpositive selected PN drive') + require(frame.neuron_idx.to_numpy(np.int32).tobytes() == targets.tobytes() and + frame.rate_hz.to_numpy(np.float32).tobytes() == rates.tobytes(), 'Original frame/array ordering mismatch') + targets, rates = targets.copy(), rates.copy() + targets.setflags(write=False); rates.setflags(write=False) + tables[cue] = dict(cue=cue, odor_id=str(odor_id), odor_name=name, mode='physiological', + source_panel_row=int(positions[0]), source_panel_split=str(panel_row.split), + targets=targets, rates_hz=rates, identities={ + 'targets': inputs.array_identity(targets), 'rates_hz': inputs.array_identity(rates)}, + glomeruli_order=copy.deepcopy(gloms), mapped_vector_identity=inputs.array_identity(vector)) + return tables, median + + +def load_rate_tables(root): + """Read only the six exact static/source files pinned by frozen AK. + + This does not read any historical neural result or rerun odor selection. + Source files are checked before parsing; the parsed bytes are the same + checked snapshots. Full prior seal/registration authority remains external. + """ + import pandas as pd + import yaml + root = Path(root) + raw = {rel: (root / rel).read_bytes() for rel in SOURCE_PINS} + require(all(hashlib.sha256(value).hexdigest() == SOURCE_PINS[rel] for rel, value in raw.items()), + 'Frozen static encoder/source SHA mismatch') + source, ast_sha = _extract_drive(raw[SOURCE_REL], str(root / SOURCE_REL)) + selection = json.loads(raw[STATIC + 'odor-encoder/selection.json']) + panel = pd.read_parquet(io.BytesIO(raw[STATIC + 'odor-encoder/frozen-odor-panel.parquet'])) + mapping = pd.read_parquet(io.BytesIO(raw[STATIC + 'receptor-mapping/olfactory-mapping.parquet'])) + config = yaml.safe_load(raw[CONFIG_REL]) + with np.load(io.BytesIO(raw[STATIC + 'odor-encoder/mapped-glomerular-vectors.npz']), allow_pickle=False) as values: + require(set(values.files) == set(panel.odor_id) | {'glomeruli'} and + values['glomeruli'].tolist() == selection['glomeruli_order'], 'Frozen NPZ inventory/order mismatch') + vectors = {str(oid): values[oid].copy() for oid in panel.odor_id} + tables, median = _rate_tables(selection, panel, vectors, mapping, config, source) + return dict(tables=tables, audit=dict(schema=SCHEMA, static_sources=copy.deepcopy(SOURCE_PINS), + original_pn_drive_AST_sha256=ast_sha, historical_dynamic_results_read=False, + original_function_called_without_PN_reordering=True, mode='physiological', + median_mapped_response_sum=median, full_seal_verification_owned_by_coordinator=True, + random_draws=0, brain_runs=0, numpy_version=np.__version__, pandas_version=pd.__version__)) + + +def _validate_table(table, cue): + require(type(table) is dict and table.get('cue') == cue and table.get('odor_name') == trial_plan.ODORS[cue] and + table.get('mode') == 'physiological' and type(table.get('odor_id')) is str and table['odor_id'], 'Rate-table cue identity mismatch') + inputs.probabilities_f32(table['targets'], table['rates_hz']) + require(table['identities'] == {key: inputs.array_identity(table[key]) for key in ('targets', 'rates_hz')}, + 'Rate-table arrays changed after authority binding') + + +def generate_epoch_inputs(epoch, tables, dan_targets, *, device, max_uniform_draws, conflict_policy='error'): + """Future explicit sampler, called once per selected input epoch, not arm. + + No brain reset is called. The scheduled US request is identical for D1, + D0, and frozen; D-command and update policy belong to the epoch authority. + The caller must preserve and share these returned bytes across all arms. + """ + _validate_epoch(epoch) + cs = epoch['cs']; table = tables[cs['cue']]; _validate_table(table, cs['cue']) + require(isinstance(dan_targets, np.ndarray) and dan_targets.dtype == np.int32 and dan_targets.ndim == 1 and + len(dan_targets) > 0 and np.all((dan_targets >= 0) & (dan_targets < inputs.N_NEURONS)) and + (len(dan_targets) < 2 or np.all(dan_targets[1:] > dan_targets[:-1])), 'Externally bound DAN globals must be sorted unique int32') + require(conflict_policy in ('error', 'boolean_union'), 'Unknown explicit request composition policy') + cs_result = inputs.generate_cs_requests(table['targets'], table['rates_hz'], seed=cs['seed'], steps=1200, + cs_start_tick=0, cs_stop_tick=1000, device=device, max_uniform_draws=max_uniform_draws) + nodes = dan_targets.copy() if epoch['us']['requested'] else np.empty(0, np.int32) + us = {'tick': np.full(len(nodes), 900, np.int32), 'neuron_idx': nodes} + for value in us.values(): value.setflags(write=False) + composed = inputs.merge_requests(cs_result['requests'], us, steps=1200, conflict_policy=conflict_policy) + return dict(requests=composed['requests'], cs_requests=cs_result['requests'], us_requests=us, + source_mask=composed['source_mask'], audit=dict(schema=SCHEMA, epoch_id=epoch['epoch_id'], + CS_sampler=cs_result['audit'], composition=composed['audit'], + DAN_targets_identity=inputs.array_identity(dan_targets), requested_US=epoch['us']['requested'], + scheduled_US_tick=epoch['us']['tick'], CS_shared_across_arms=True, US_shared_across_arms=True, + US_rng_draws=0, semantic_seed_never_passed_to_brain_reset=True, brain_reset_called=False, + source_authority_and_saved_array_rehash_owned_by_coordinator=True)) + + +def derive_case_spec(original, row, epoch, rate_table, *, brain_reset_seed, budget_root, output_budget_bytes): + """Copy an externally authenticated wholegraph spec with explicit changes. + + ``seed`` is an independent conservative 31-bit reset-control value, not a + conversion of the semantic input seed. The inherited deterministic AJ + runtime consumes saved requests, not this input RNG. No checkpoint carry + is admitted: the next epoch carries only its separately verified delta. + """ + _validate_epoch(epoch); _validate_table(rate_table, epoch['cs']['cue']) + require(type(original) is dict and original.get('kind') == 'fullgraph' and original.get('dt_ms') == .1 and + original.get('apl_delay_steps') == 18 and original.get('precision') == 'float64' and + original.get('apl_precision') == 'float32' and original.get('record_history') is False and + original.get('within_trial_weight_updates', False) is False, 'Anchored fresh D18 fullgraph spec required') + require(not any('checkpoint' in str(k).lower() or k in ('initial_state', 'resume', 'continuation', 'global_tick_offset') for k in original), + 'No neural checkpoint continuation in this two-epoch preflight') + require(type(brain_reset_seed) is int and 0 <= brain_reset_seed < 2**31, 'Independent conservative 31-bit brain reset seed required') + require(type(row) is dict and row.get('arm') in ('D1', 'D0', 'frozen') and row.get('diagnostic_mode') in ('reference', 'light') and + row.get('epoch_id') == epoch['epoch_id'] and row.get('steps') == 1200 and + row.get('frozen') is (row['arm'] == 'frozen') and + row.get('d_command') == (0.0 if row['arm'] == 'D0' else epoch['D_command']), 'Row/epoch command mismatch') + require(type(row.get('input_epoch_index')) is int and row['input_epoch_index'] in (0, 1) and + row.get('row_id') == f"{row['arm']}--{row['diagnostic_mode']}--epoch-{row['input_epoch_index']:03d}", 'Unsafe row identity') + require(type(budget_root) is str and budget_root.startswith('/') and not budget_root.startswith('//') and + '..' not in PurePosixPath(budget_root).parts and + type(output_budget_bytes) is int and output_budget_bytes > 0, 'Explicit absolute output budget required') + old = PurePosixPath(original['budget_root']); new = PurePosixPath(budget_root) + require(old.is_absolute() and not new.is_relative_to(old) and not old.is_relative_to(new), 'Budget cannot overlap old experiment') + result = copy.deepcopy(original) + removed = {key: result.pop(key) for key in ('validation_cohort', 'odor_index', 'expected_external_events', 'training') if key in result} + cs = epoch['cs']; rates = rate_table['rates_hz'] + result.update(name=row['row_id'], steps=1200, duration_ms=120.0, max_steps=1200, + budget_root=budget_root, output_budget_bytes=output_budget_bytes, seed=brain_reset_seed, + cs_input_seed=cs['seed'], cs_input_seed_bits=63, + seed_scope='seed is separate reset control; full cs_input_seed used only by saved-input sampler', + mode='physiological', odor_id=rate_table['odor_id'], odor_name=rate_table['odor_name'], + source_panel_row=rate_table['source_panel_row'], source_panel_split=rate_table['source_panel_split'], + rate_sum_hz=float(rates.sum()), target_count=len(rate_table['targets']), + expected_CS_requests=float(np.sum(inputs.probabilities_f32(rate_table['targets'], rates), dtype=np.float64) * 1000), + CS_window=[0, 1000], within_trial_weight_updates=False, + outer_protocol='conditioning_interface_preflight_not_full_conditioning', + outer_epoch_id=epoch['epoch_id'], outer_arm=row['arm']) + changes = {k: dict(present_before=k in original, before=copy.deepcopy(original.get(k)), after=copy.deepcopy(v)) + for k, v in result.items() if k not in original or _canonical(v) != _canonical(original[k])} + allowed = {'name', 'steps', 'duration_ms', 'max_steps', 'budget_root', 'output_budget_bytes', 'seed', + 'cs_input_seed', 'cs_input_seed_bits', 'seed_scope', 'mode', 'odor_id', 'odor_name', 'source_panel_row', + 'source_panel_split', 'rate_sum_hz', 'target_count', 'expected_CS_requests', 'CS_window', + 'within_trial_weight_updates', 'outer_protocol', 'outer_epoch_id', 'outer_arm'} + require(set(changes) <= allowed and all(_canonical(result[k]) == _canonical(v) for k, v in original.items() + if k not in allowed and k not in removed), 'Unexpected frozen spec change') + return result, dict(schema=SCHEMA, explicit_field_changes=changes, removed_stale_input_metadata=removed, + original_spec_sha256=_sha(original), derived_spec_sha256=_sha(result), + graph_APL_model_coefficients_and_other_fields_unchanged=True, + independent_brain_reset_seed=brain_reset_seed, semantic_input_seed=cs['seed'], + neural_state_between_epochs='fresh reset; only plastic delta may carry via separate verified authority', + D_and_update_commands_owned_by_epoch_authority=True, random_draws=0, brain_runs=0, + outer_learning_pass_claimed=False, full_conditioning_authorized=False) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_prepare.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_prepare.py new file mode 100644 index 0000000..1172dad --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_prepare.py @@ -0,0 +1,197 @@ +"""Exclusive CPU preparation for the finite AO conditioning-interface gate. + +This is a two-epoch installation test, not acquisition, retention or a learning +candidate. Source authority and the complete 12-call plan precede CUDA sampling. +Generated requests and learned snapshots receive separate execution receipts; +their not-yet-existing hashes are never invented in the registration. +""" +from __future__ import annotations + +import json +from pathlib import Path +import shutil +import subprocess +import sys + +import numpy as np + +import engineering_prepare as P +import runtime_light as R +import biological_contract as B +import conditioning_preflight_inputs as I + +ROOT = Path('/data/digital-drosophila') +MODEL = Path(__file__).resolve().parent +EXP = ROOT / 'experiments/phase4ao-conditioning-interface' +AN = ROOT / 'experiments/phase4an-engineering-validation' +AN_REG = '7317441126f191ccc6e6d28438870b3921537ebe282a6acb163716a690057945' +AN_MANIFEST = 'e8952a48cd3ea6a93c811a1183a1f7b79e19646299924e7688e5f393737a9aa2' +AN_FINAL = '16630d0d666a2467c56b647d021cbdb627a553795f6afcd7f7eb496a2737d815' +PREP_COMMIT = 'c187d7d5a6047f183467e3d860e2529575dcda97' +DRAFT_INVENTORY_SHA = '2c72d47d09b4ad1949dc6cfdc170f64847ff75f95ded5d20b421fb5a75e0eb84' +BUDGET = dict(output_budget_bytes=4 * 1024**3, minimum_free_reserve_bytes=10 * 1024**3, + max_uniform_draws=2_000_000, child_timeout_seconds=1800) +require, read, save, digest = P.require, P.read, P.save, P.digest + + +def same(a, b): + return json.dumps(a, sort_keys=True, allow_nan=False) == json.dumps(b, sort_keys=True, allow_nan=False) + + +def verify_pins(pins): + for name, sha in pins.items(): + path = Path(name) + require(path.is_absolute() and path.is_file() and not path.is_symlink() and + digest(path) == sha, 'Pinned file changed: ' + name) + + +def save_npz(path, **arrays): + with Path(path).open('xb') as stream: + np.savez_compressed(stream, **arrays) + + +def source_inventory(): + # Explicitly snapshot the current lab's text sources, including this new + # coordinator and its tests. Existing 21/20 files are never rewritten. + paths = sorted([*MODEL.glob('*.py'), *MODEL.glob('*.md')]) + required = ('conditioning_epoch.py', 'conditioning_preflight_inputs.py', + 'conditioning_preflight_compare.py', 'conditioning_preflight_prepare.py', + 'conditioning_preflight_workflow.py', 'test_conditioning_preflight_prepare.py', + 'CONDITIONING_INTERFACE_PROTOCOL.md') + require(all(MODEL / n in paths for n in required), 'Missing complete coordinator/test/protocol source') + require(not any(p.is_symlink() for p in paths), 'Linked source is forbidden') + return {str(p): digest(p) for p in paths} + + +def plan_descriptor(full): + return {k: v for k, v in full.items() if k not in ('full_trial_plan', 'payload_sha256')} + + +def prepare(): + require(sys.dont_write_bytecode, 'Use scientific Python -B') + require(EXP.is_dir() and not any(EXP.iterdir()) and not EXP.is_symlink(), + 'Preparation requires the explicitly created empty AO directory; never overwrite') + require(digest(AN / 'manifest.json') == AN_MANIFEST and digest(AN / 'audit/final-status.json') == AN_FINAL, + 'Externally anchored AN controls changed') + an = P.read_registration(AN_REG) + helper = P.load_module('phase4ao_original_seal_helper', ROOT / P.AI_HELPER_REL, P.AI_HELPER_SHA) + an_seal = helper.verify_seal(AN) + am_seal = P.verify_authority(an['anchors']) + old = P.verify_old_integrity(an) + full = I.build_preflight_plan() + rates = I.load_rate_tables(ROOT) + populations, biology = B.load_bound_tables(ROOT, an['input_sha256']) + pins = dict(an['input_sha256']) + pins.update({str(ROOT / k): v for k, v in I.SOURCE_PINS.items()}) + pins.update(biology['input_sha256']) + pins.update({str(AN / 'manifest.json'): AN_MANIFEST, str(AN / 'audit/final-status.json'): AN_FINAL, + str(AN / 'protocol/preregistered.json'): AN_REG}) + bridge = ROOT / 'models/male-cns-lif/wholebrain-plastic-interface-v1/eligibility_bridge.py' + phasej = ROOT / 'models/male-cns-lif/mb-physiology-v1/phase4j.py' + pins[str(bridge)] = '174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61' + pins[str(phasej)] = 'abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e' + original_path = Path(an['selected_case']['original_dir']) / 'spec.json' + require(str(original_path) in pins, 'Original graph spec not anchored by AN') + original = read(original_path) + previous = MODEL / 'draft-source-inventory.json' + require(digest(previous) == DRAFT_INVENTORY_SHA, 'Previously delivered 20-file source inventory changed') + inventory = read(previous) + require(len(inventory['files']) == 20, 'Previous scientific snapshot must contain 20 files') + pins[str(previous)] = DRAFT_INVENTORY_SHA + for entry in inventory['files']: + require(Path(entry['path']).name == entry['path'], 'Nonlocal scientific source inventory entry') + pins[str(MODEL / entry['path'])] = entry['sha256'] + zero = an['overlays']['zero'] + _, overlay, metadata, _ = R.load_overlay(zero['overlay_file'], zero['overlay_manifest']) + require(not np.any(overlay['plastic_delta_mv'].view(np.uint32)), 'Initial delta must be exact positive zero') + kc = np.load(Path(original['apl_bundle_dir']) / 'kc_globals.npy', allow_pickle=False) + require(np.array_equal(kc, populations['Kenyon_Cell']), 'Canonical metadata and APL KC memberships disagree') + import pandas as pd + candidate = pd.read_parquet(ROOT / 'experiments/phase4h-dopamine-plasticity/plastic-mask/plastic-edge-candidates.parquet') + candidate = candidate.set_index('edge_id').loc[overlay['edge_id']].reset_index() + require(candidate.edge_id.to_numpy().tobytes() == overlay['edge_id'].tobytes() and + candidate.target_compartment_verbatim.notna().all(), 'Complete mask compartment mapping is not exact') + verify_pins(pins) + sources = source_inventory() + code_commit = subprocess.run(['git', '-C', str(ROOT), 'rev-parse', 'HEAD'], check=True, + capture_output=True, text=True).stdout.strip() + require(P.sha(code_commit + '0' * 24), 'Invalid Git code version') + for path, sha in sources.items(): + relative = Path(path).relative_to(ROOT).as_posix() + blob = subprocess.run(['git', '-C', str(ROOT), 'show', code_commit + ':' + relative], + check=True, capture_output=True).stdout + require(__import__('hashlib').sha256(blob).hexdigest() == sha, 'Registered source is not the committed Git blob: ' + relative) + for name in ('protocol/source-snapshot', 'inputs/static', 'cases', 'runs', 'updates', 'receipts', 'logs', 'audit', 'regression'): + (EXP / name).mkdir(parents=True) + for path in sources: + shutil.copyfile(path, EXP / 'protocol/source-snapshot' / Path(path).name) + save(EXP / 'regression/AN-seal-before.json', an_seal) + save(EXP / 'regression/AM-seals-before.json', am_seal) + save(EXP / 'regression/old-integrity-before.json', old) + save(EXP / 'inputs/static/biology-audit.json', biology) + save(EXP / 'inputs/static/rates-audit.json', rates['audit']) + save_npz(EXP / 'inputs/static/populations.npz', **populations) + save_npz(EXP / 'inputs/static/candidate-map.npz', edge_id=candidate.edge_id.to_numpy(np.int64), + source_neuron_idx=candidate.source_neuron_idx.to_numpy(np.int32), + target_neuron_idx=candidate.target_neuron_idx.to_numpy(np.int32), + target_compartment_verbatim=candidate.target_compartment_verbatim.to_numpy(dtype=str)) + table_metadata = {} + for cue, table in rates['tables'].items(): + save_npz(EXP / f'inputs/static/{cue}-rates.npz', targets=table['targets'], rates_hz=table['rates_hz']) + table_metadata[cue] = {k: v for k, v in table.items() if k not in ('targets', 'rates_hz')} + save(EXP / 'inputs/static/rate-tables.json', table_metadata) + save(EXP / 'protocol/full-trial-plan.json', full['full_trial_plan']) + descriptor = plan_descriptor(full) + cases = {} + reset_seed = original['seed'] + for row in descriptor['calls']: + epoch = descriptor['selected_epochs'][row['input_epoch_index']] + case = EXP / 'cases' / row['row_id'] + case.mkdir() + spec, derivation = I.derive_case_spec(original, row, epoch, rates['tables'][epoch['cs']['cue']], + brain_reset_seed=reset_seed, budget_root=str(EXP), output_budget_bytes=BUDGET['output_budget_bytes']) + R.validate_spec(spec, case) + save(case / 'spec.json', spec) + save(case / 'derivation.json', derivation) + cases[row['row_id']] = str(case) + derived = {str(p): digest(p) for parent in ('inputs/static', 'cases', 'protocol/source-snapshot', 'regression') + for p in (EXP / parent).rglob('*') if p.is_file()} + derived[str(EXP / 'protocol/full-trial-plan.json')] = digest(EXP / 'protocol/full-trial-plan.json') + reg = dict(schema='phase4ao-conditioning-interface-registration-v1', plan=descriptor, + full_plan_payload_sha256=full['payload_sha256'], full_trial_plan_file_sha256=derived[str(EXP / 'protocol/full-trial-plan.json')], + source_sha256=sources, input_sha256=pins, prepared_file_sha256=derived, cases=cases, + initial_overlay=zero, original_spec=str(original_path), brain_reset_seed=reset_seed, + bridge_source=str(bridge), phase4j_source=str(phasej), budget=BUDGET, + git_parent_commit=PREP_COMMIT, git_code_commit=code_commit, committed_source_blobs_verified=len(sources), AN_registration_sha256=AN_REG, + generation=dict(device='cuda:0', GPU_UUID='GPU-79f3a85d-adce-7833-7003-6488140ca596', + conflict_policy='error', highbit_probe_seed=descriptor['selected_epochs'][0]['cs']['seed'], + highbit_comparison='full63bit versus low32bit, one fixed 1200xPN matrix each; never a seed search', + original_L_sampling_AST='only rate tensor, private generator and full-shape forced-spike expression', + trace_backend='torch', actual_input_files='created only after external registration SHA recorded'), + scope=dict(epoch_boundary_updates=True, within_trial_updates=False, full_conditioning=False, + candidate_generated=False, learning_success_claimed=False, neural_state_carried=False, + delta_carried=True, independent_model_branch=True), + success_gate='12 complete calls; exact reference/light events and final state/queues; exact D0/frozen identity; real D1 delta installed in epoch 2; nonzero D1 update coverage required', + missing_nonzero_coverage='INCOMPLETE, do not choose another input', + failure_policy='preserve all partials; no automatic run retry or result-selected seed', + retention_supported=False, future_hashes_fabricated=False, new_brain_runs_during_preparation=0) + verify_pins({**pins, **sources, **derived}) + save(EXP / 'protocol/preregistered.json', reg) + return digest(EXP / 'protocol/preregistered.json') + + +def read_registration(expected): + path = EXP / 'protocol/preregistered.json' + require(P.sha(expected) and digest(path) == expected, 'Registration differs from external anchor') + reg = read(path) + require(reg['schema'] == 'phase4ao-conditioning-interface-registration-v1' and + reg['AN_registration_sha256'] == AN_REG and reg['budget'] == BUDGET and + reg['plan']['total_calls'] == 12 and reg['plan']['total_ticks'] == 14400, + 'Unexpected finite registration') + verify_pins({**reg['source_sha256'], **reg['input_sha256'], **reg['prepared_file_sha256']}) + return reg + + +if __name__ == '__main__': + require(sys.argv[1:] == ['prepare'], 'Only exclusive prepare is supported') + print('REGISTRATION_SHA256', prepare(), flush=True) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_workflow.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_workflow.py new file mode 100644 index 0000000..717e232 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_preflight_workflow.py @@ -0,0 +1,474 @@ +"""Finite AO runner: exact frozen brain, two fresh epochs, delta-only carry. + +Never starts the full conditioning protocol. Partial rows are preserved. Each +runtime is sealed immediately; coordinator evidence lives outside its seal. +""" +from __future__ import annotations + +import argparse +import ast +from datetime import datetime, timezone +import json +import os +from pathlib import Path +import shutil +import subprocess +import sys +import time +import traceback +from types import SimpleNamespace + +import numpy as np + +import conditioning_preflight_prepare as P +import conditioning_preflight_inputs as I +import conditioning_inputs as IN +import conditioning_update as U +import conditioning_epoch as E +import conditioning_preflight_compare as Q +import engineering_compare as COMP +import engineering_workflow as ENV +import runtime_light as R +import runtime_continuation as C + +EXP, MODEL, ROOT = P.EXP, P.MODEL, P.ROOT +require, read, save, digest = P.require, P.read, P.save, P.digest + + +def npz(path): + with np.load(path, allow_pickle=False) as z: + return {k: z[k].copy() for k in z.files} + + +def row_by(reg, name): + matches = [r for r in reg['plan']['calls'] if r['row_id'] == name] + require(len(matches) == 1, 'Only an exact registered row may execute') + return matches[0] + + +def locations(name): + require(type(name) is str and name and all(c.isalnum() or c in '-_' for c in name), 'Unsafe row') + return dict(run=EXP / 'runs' / name, update=EXP / 'updates' / name, + runtime=EXP / 'receipts' / (name + '.runtime.json'), execution=EXP / 'receipts' / (name + '.process.json'), + authority=EXP / 'receipts' / (name + '.authority.json'), + stdout=EXP / 'logs' / (name + '.stdout.log'), stderr=EXP / 'logs' / (name + '.stderr.log')) + + +def budget(reg): + files = list(EXP.rglob('*')) + require(not any(p.is_symlink() for p in files), 'Linked output is forbidden') + used = sum(p.stat().st_size for p in files if p.is_file()) + free = shutil.disk_usage(EXP).free + b = reg['budget'] + require(used <= b['output_budget_bytes'] and free >= b['minimum_free_reserve_bytes'] + max(0, b['output_budget_bytes'] - used), + 'Resource cap/reserve exceeded; preserve partials') + return dict(used_bytes=used, free_bytes=free, **b) + + +def tables(): + metadata = read(EXP / 'inputs/static/rate-tables.json') + return {cue: dict(table, **npz(EXP / f'inputs/static/{cue}-rates.npz')) for cue, table in metadata.items()} + + +def original_sampler_nodes(raw): + """Only the four original L sampling statements, no old brain import.""" + require(__import__('hashlib').sha256(raw).hexdigest() == I.SOURCE_PINS[I.SOURCE_REL], 'L source changed') + methods = [n for n in ast.walk(ast.parse(raw)) if isinstance(n, ast.FunctionDef) and n.name == 'run_rates'] + require(len(methods) == 1, 'Original sampling method is not unique') + chosen = [] + for node in methods[0].body: + if isinstance(node, ast.Assign) and any(isinstance(t, ast.Name) and t.id in ('rg', 'gen', 'forced') for t in node.targets): + chosen.append(node) + elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call) and ast.unparse(node.value.func) == 'gen.manual_seed': + chosen.append(node) + require(len(chosen) == 4 and [ast.unparse(n).split('=')[0].strip() for n in chosen[:2]] == ['rg', 'gen'], + 'Original sampler AST structure changed') + return chosen + + +def sampler_check(reg, table, generated): + import torch + seed = reg['generation']['highbit_probe_seed'] + nodes = original_sampler_nodes((ROOT / I.SOURCE_REL).read_bytes()) + env = dict(torch=torch, targets=table['targets'], rates_hz=table['rates_hz'], seed=seed, steps=1200, + self=SimpleNamespace(dt=.1, device=torch.device('cuda:0'))) + exec(compile(ast.Module(body=nodes, type_ignores=[]), str(ROOT / I.SOURCE_REL), 'exec'), env) + original = IN.requests_from_forced(env['forced'].cpu().numpy(), table['targets'], cs_start_tick=0, cs_stop_tick=1000) + require(all(COMP.bit_equal(original['requests'][k], generated['cs_requests'][k]) for k in ('tick', 'neuron_idx')), + 'Actual CUDA original-L sampling expression disagrees') + identities = [] + for value in (seed, seed & 0xffffffff): + gen = torch.Generator(device='cuda:0').manual_seed(value) + matrix = torch.rand((1200, len(table['targets'])), generator=gen, device='cuda:0') + identities.append(IN.array_identity(matrix.cpu().numpy())) + require(identities[0] != identities[1], 'Actual CUDA full63bit and low32bit sequence identical; stop, no seed substitution') + return dict(original_L_sampler_AST_sha256=__import__('hashlib').sha256(ast.dump(ast.Module(body=nodes, type_ignores=[]), include_attributes=False).encode()).hexdigest(), + exact_sparse_CS_requests=True, full_seed=seed, low32_probe_seed=seed & 0xffffffff, + uniforms_identities=identities, highbit_influence_observed=True, + additional_probe_matrices=3, probe_shape=[1200, len(table['targets'])], + new_input_seed_selected=False, old_full_trial_equivalence_claimed=False) + + +def materialize(reg_sha): + reg = P.read_registration(reg_sha) + admission() + ENV.preflight(); budget(reg) + target = EXP / 'inputs/generated' + require(not target.exists(), 'Never rerun or overwrite actual input generation') + target.mkdir() + rate = tables() + dan = npz(EXP / 'inputs/static/populations.npz')[reg['plan']['dan_type']] + records = [] + for index, epoch in enumerate(reg['plan']['selected_epochs']): + started = time.perf_counter() + value = I.generate_epoch_inputs(epoch, rate, dan, device=reg['generation']['device'], + max_uniform_draws=reg['budget']['max_uniform_draws'], conflict_policy=reg['generation']['conflict_policy']) + directory = target / f'epoch-{index:03d}' + directory.mkdir() + for kind in ('requests', 'cs_requests', 'us_requests'): + P.save_npz(directory / (kind + '.npz'), **value[kind]) + P.save_npz(directory / 'source-mask.npz', source_mask=value['source_mask']) + save(directory / 'sampler-receipt.json', value['audit']) + if index == 0: + save(target / 'actual-CUDA-sampler-check.json', sampler_check(reg, rate[epoch['cs']['cue']], value)) + records.append(dict(index=index, epoch_id=epoch['epoch_id'], wall_seconds=time.perf_counter() - started, + CS_requests=len(value['cs_requests']['tick']), US_requests=len(value['us_requests']['tick']))) + for row in reg['plan']['calls']: + src = target / f"epoch-{row['input_epoch_index']:03d}" / 'requests.npz' + dst = Path(reg['cases'][row['row_id']]) / 'requests.npz' + with src.open('rb') as inp, dst.open('xb') as out: + shutil.copyfileobj(inp, out) + require(digest(src) == digest(dst), 'Shared input copy differs') + pins = {str(p): digest(p) for p in target.rglob('*') if p.is_file()} + pins.update({str(Path(path) / 'requests.npz'): digest(Path(path) / 'requests.npz') for path in reg['cases'].values()}) + P.read_registration(reg_sha) + save(EXP / 'receipts/input-generation.json', dict(complete=True, registration_sha256=reg_sha, + inputs_sha256=pins, rows=records, GPU_UUID=ENV.GPU, full63bit_delivery_and_influence_checked=True, + input_generation_calls=2, brain_runs=0, parameters_changed=False)) + + +def generated_inputs(reg_sha): + receipt = read(EXP / 'receipts/input-generation.json') + require(receipt.get('complete') is True and receipt.get('registration_sha256') == reg_sha and receipt.get('brain_runs') == 0, + 'Generated inputs are not bound to registration') + process = read(EXP / 'receipts/input-generation.process.json') + require(process['exit_code'] == 0 and process['registration_sha256'] == reg_sha and + process['receipt_sha256'] == digest(EXP / 'receipts/input-generation.json'), 'Input generation process missing') + for k in ('stdout', 'stderr'): + require(process[k + '_sha256'] == digest(EXP / 'logs' / ('input-generation.' + k + '.log')), 'Input generation log changed') + P.verify_pins(receipt['inputs_sha256']) + return receipt + + +def verify_row(reg, reg_sha, row): + paths = locations(row['row_id']) + record = read(paths['runtime']); process = read(paths['execution']) + require(record['complete'] is True and record['registration_sha256'] == reg_sha and P.same(record['row'], row), + 'Runtime record differs from registered row') + require(process['exit_code'] == 0 and process['registration_sha256'] == reg_sha and + process['runtime_sha256'] == digest(paths['runtime']), 'Runtime process did not complete exactly') + for key in ('stdout', 'stderr'): + require(process[key + '_sha256'] == digest(paths[key]), 'Process log changed') + P.verify_pins(record['files_sha256']) + checkpoint = C.load_checkpoint(paths['run'], record['checkpoint_manifest_sha256']) + require(checkpoint['fresh'] and checkpoint['steps'] == row['steps'] and checkpoint['global_tick_offset'] == 0, + 'Normal epoch must be fresh, not a continuation') + ENV.verify_environment(record['environment'], checkpoint['summary']['apl_component']['environment']) + chain = read(paths['update'] / 'chain-receipt.json') + require(chain['authority_sha256'] == digest(paths['authority']) and + chain['source_neural_checkpoint_manifest_sha256'] == record['checkpoint_manifest_sha256'] and + chain['epoch_id'] == row['epoch_id'], 'Delta does not derive from this actual sealed neural epoch') + P.verify_pins(chain['input_files_sha256']) + for name, sha in chain['output_files_sha256'].items(): + require(digest(paths['update'] / name) == sha, 'Updated snapshot changed') + return record + + +def before_overlay(reg, reg_sha, row): + previous = row['previous_delta_row_id'] + if previous is None: + return reg['initial_overlay'] + parent = row_by(reg, previous) + require(parent['arm'] == row['arm'] and parent['diagnostic_mode'] == row['diagnostic_mode'] and + parent['input_epoch_index'] + 1 == row['input_epoch_index'], 'Invalid cross-epoch delta link') + verify_row(reg, reg_sha, parent) + directory = locations(previous)['update'] + return dict(overlay_file=str(directory / 'delta-after.npz'), overlay_manifest=str(directory / 'overlay-manifest.json')) + + +def make_authority(reg, row, checkpoint_sha, overlay): + paths = locations(row['row_id']); case = Path(reg['cases'][row['row_id']]); spec = read(case / 'spec.json') + metadata = read(overlay['overlay_manifest']) + role_paths = dict(spec=case / 'spec.json', requests=case / 'requests.npz', + overlay_before=overlay['overlay_file'], overlay_manifest_before=overlay['overlay_manifest'], + state=paths['run'] / 'state.npz', kc=Path(spec['apl_bundle_dir']) / 'kc_globals.npy', + canonical_kc=Path(spec['apl_bundle_dir']) / 'kc_globals.npy', candidate_map=EXP / 'inputs/static/candidate-map.npz', + mask=metadata['mask_path'], base_candidate=metadata['base_candidate_path'], + phase4j_source=reg['phase4j_source'], eligibility_bridge_source=reg['bridge_source'], + conditioning_update_source=MODEL / 'conditioning_update.py', runtime_light_source=MODEL / 'runtime_light.py', + runtime_continuation_source=MODEL / 'runtime_continuation.py', am_overlay_source=metadata['am_overlay_path']) + epoch = reg['plan']['selected_epochs'][row['input_epoch_index']] + return dict(schema=E.SCHEMA, epoch_id=row['epoch_id'], diagnostic_mode=row['diagnostic_mode'], + command=dict(steps=row['steps'], compartment=reg['plan']['compartment_verbatim'], + us_tick=epoch['us']['tick'], d_command=row['d_command'], frozen=row['frozen'], trace_backend=reg['generation']['trace_backend']), + checkpoint_dir=str(paths['run']), checkpoint_manifest_sha256=checkpoint_sha, + files={role: dict(path=str(path), sha256=digest(path)) for role, path in role_paths.items()}, + frozen_inputs={k: v for k, v in reg['input_sha256'].items() + if k not in (str(P.AN / 'protocol/preregistered.json'), str(P.AN / 'manifest.json'), str(P.AN / 'audit/final-status.json'))}) + + +def replay_update(authority, receipt): + files = {k: Path(v['path']) for k, v in authority['files'].items()} + state = npz(files['state']); mapping = npz(files['candidate_map']); old = npz(files['overlay_before']) + command = authority['command'] + return U.verify_receipt(receipt, step=state['step'], neuron_idx=state['neuron_idx'], + kc=np.load(files['kc'], allow_pickle=False), candidate_edge_id=mapping['edge_id'], + candidate_sources=mapping['source_neuron_idx'], candidate_targets=mapping['target_neuron_idx'], + candidate_compartments=mapping['target_compartment_verbatim'], delta_before=old['plastic_delta_mv'], + base_candidate=np.load(files['base_candidate'], allow_pickle=False), steps=command['steps'], n_neurons=166700, + compartment=command['compartment'], us_tick=command['us_tick'], d_command=command['d_command'], + frozen=command['frozen'], trace_backend=command['trace_backend'], + bridge_source=files['eligibility_bridge_source'], phase4j_source=files['phase4j_source']) + + +def worker(reg_sha, row_id): + reg = P.read_registration(reg_sha); generated_inputs(reg_sha) + admission() + row = row_by(reg, row_id); paths = locations(row_id) + require(not any(paths[k].exists() for k in ('run', 'update', 'runtime', 'authority')), 'Partial row cannot be retried') + ENV.preflight(); before = budget(reg) + overlay = before_overlay(reg, reg_sha, row) + case = Path(reg['cases'][row_id]) + protected = {Path(p).parent for p in (*reg['input_sha256'], *reg['source_sha256'], *reg['prepared_file_sha256'])} + protected.update({EXP / 'inputs', EXP / 'updates'}) + # Build/run siblings must never touch any current or earlier learned input. + R.execution_paths(case, paths['run'], EXP / 'extension-build', protected=protected) + started = time.perf_counter(); started_utc = datetime.now(timezone.utc).isoformat() + execute = R.run_reference if row['diagnostic_mode'] == 'reference' else R.run_light + execute(case, paths['run'], EXP / 'extension-build', overlay['overlay_file'], overlay['overlay_manifest']) + checkpoint_sha = C.seal_checkpoint(paths['run'])['sha256'] + runtime_seconds = time.perf_counter() - started + authority = make_authority(reg, row, checkpoint_sha, overlay) + save(paths['authority'], authority) + update_started = time.perf_counter() + E.postprocess_epoch(paths['authority'], digest(paths['authority']), paths['update']) + receipt = read(paths['update'] / 'update-receipt.json') + replay_update(authority, receipt) + delta_before = npz(overlay['overlay_file'])['plastic_delta_mv'] + _, after, _, _ = R.load_overlay(paths['update'] / 'delta-after.npz', paths['update'] / 'overlay-manifest.json') + binding = Q.audit_delta_binding(delta_before, after['plastic_delta_mv'], after['plastic_delta_mv'], + update_receipt=receipt, parent_snapshot_sha256=digest(overlay['overlay_file']), + child_manifest=read(paths['update'] / 'overlay-manifest.json')) + require(binding['pass_all'], 'Actual recomputed delta/child binding failed') + save(EXP / 'receipts' / (row_id + '.binding.json'), binding) + record = dict(complete=True, registration_sha256=reg_sha, row=row, + checkpoint_manifest_sha256=checkpoint_sha, environment=ENV.runtime_environment(), + runtime_and_immediate_seal_wall_seconds=runtime_seconds, + postprocess_and_recompute_wall_seconds=time.perf_counter() - update_started, + started_utc=started_utc, budget_before=before, budget_after=budget(reg), + files_sha256={str(p): digest(p) for p in (paths['authority'], *paths['update'].iterdir(), + EXP / 'receipts' / (row_id + '.binding.json'))}, + before_overlay_file_sha256=digest(overlay['overlay_file']), epoch_update_recomputed=True, + learning_success_claimed=False, full_conditioning_executed=False) + ENV.verify_environment(record['environment'], read(paths['run'] / 'summary.json')['apl_component']['environment']) + P.read_registration(reg_sha) + save(paths['runtime'], record) + print('COMPLETE', row_id, receipt['counts']['actual_bitwise_changed_edges'], flush=True) + + +def child_process(reg, reg_sha, action, row_id=None): + if row_id is None: + out, err = [EXP / 'logs' / ('input-generation.' + k + '.log') for k in ('stdout', 'stderr')] + else: + p = locations(row_id); out, err = p['stdout'], p['stderr'] + require(not out.exists() and not err.exists(), 'Partial child logs exist; never retry') + command = [sys.executable, '-B', str(Path(__file__)), action, '--registration-sha256', reg_sha] + if row_id is not None: command += ['--row-id', row_id] + started = time.perf_counter() + with out.open('xb') as stdout, err.open('xb') as stderr: + result = subprocess.run(command, cwd=MODEL, stdout=stdout, stderr=stderr, + timeout=reg['budget']['child_timeout_seconds']) + require(result.returncode == 0, 'Child failed; preserve partial files and inspect logs') + receipt = dict(exit_code=result.returncode, registration_sha256=reg_sha, + stdout_sha256=digest(out), stderr_sha256=digest(err), wall_seconds=time.perf_counter() - started) + if row_id is None: + receipt['receipt_sha256'] = digest(EXP / 'receipts/input-generation.json') + save(EXP / 'receipts/input-generation.process.json', receipt) + else: + receipt['runtime_sha256'] = digest(locations(row_id)['runtime']) + save(locations(row_id)['execution'], receipt) + + +def run(reg_sha): + reg = P.read_registration(reg_sha) + admission() + completed = [] + try: + child_process(reg, reg_sha, 'materialize') + generated_inputs(reg_sha) + for row in reg['plan']['calls']: + print('START', row['row_id'], flush=True) + budget(reg) + child_process(reg, reg_sha, 'worker', row['row_id']) + verify_row(reg, reg_sha, row) + completed.append(row['row_id']) + print('COMPLETE', row['row_id'], flush=True) + save(EXP / 'audit/execution-complete.json', dict(registration_sha256=reg_sha, complete=True, + completed=completed, calls=12, ticks=14400, learning_success_claimed=False)) + except Exception as error: + save(EXP / 'audit/stopped.json', dict(complete=False, registration_sha256=reg_sha, + error_type=type(error).__name__, error=str(error), traceback=traceback.format_exc(), + completed_rows=completed, partials_preserved=True, no_automatic_retry=True)) + raise + + +def admission(): + require(not (EXP / 'audit/stopped.json').exists() and not (EXP / 'audit/execution-complete.json').exists() and + not (EXP / 'manifest.json').exists(), 'Stopped/completed protocol cannot be automatically restarted') + + +def expected_weight_hash(reg, reg_sha, row): + """Independent CPU expected installation from authorized base and delta.""" + overlay = before_overlay(reg, reg_sha, row) + helper, plan, _, _ = R.load_overlay(overlay['overlay_file'], overlay['overlay_manifest']) + spec = read(Path(reg['cases'][row['row_id']]) / 'spec.json') + graph = Path(spec['graph_dir']); arrays = {} + try: + for name in ('nodes', 'weights', 'edge_id', 'src'): + arrays[name] = np.load(graph / (name + '.npy'), mmap_mode='r', allow_pickle=False) + require(np.array_equal(arrays['nodes'], np.arange(166700, dtype=np.int32)), 'Global graph node identity changed') + apl = np.load(Path(spec['apl_bundle_dir']) / 'apl_globals.npy', allow_pickle=False) + mask = np.isin(arrays['src'], apl) + require(int(mask.sum()) == 5845, 'Original fast APL mask differs') + mapping = helper.map_installation(arrays['weights'], arrays['edge_id'], mask, plan) + require(mapping['missing_candidate_count'] == 0, 'Full candidate mask not installed') + base_sha, effective_sha = helper.expected_hashes(arrays['weights'], mask, mapping) + return dict(base_raw_sha256=base_sha, effective_raw_sha256=effective_sha, + expected_from_base_and_overlay_not_run_summary=True) + finally: + for a in arrays.values(): a._mmap.close() + + +def analyze(reg_sha): + reg = P.read_registration(reg_sha); generated_inputs(reg_sha) + require(not (EXP / 'audit/stopped.json').exists(), 'Stopped protocol has no PASS analysis') + complete = read(EXP / 'audit/execution-complete.json') + require(complete['complete'] and complete['registration_sha256'] == reg_sha and + complete['completed'] == [r['row_id'] for r in reg['plan']['calls']], 'Missing finite calls') + records = {r['row_id']: verify_row(reg, reg_sha, r) for r in reg['plan']['calls']} + pairs, summaries, deltas, cross_arm = [], [], {}, [] + for row in reg['plan']['calls']: + paths = locations(row['row_id']); authority = read(paths['authority']) + require(P.same(authority, make_authority(reg, row, records[row['row_id']]['checkpoint_manifest_sha256'], + before_overlay(reg, reg_sha, row))), 'Authority differs from registered row and actual parent') + update = read(paths['update'] / 'update-receipt.json') + replay_update(authority, update) + delta = npz(paths['update'] / 'delta-after.npz')['plastic_delta_mv'] + deltas[row['row_id']] = delta + if row['arm'] in ('D0', 'frozen'): + require(not np.any(delta.view(np.uint32)), 'D0/frozen must remain exact positive zero') + summary = read(paths['run'] / 'summary.json') + population = npz(EXP / 'inputs/static/populations.npz') + state = npz(paths['run'] / 'state.npz') + run_record = COMP.load_run(paths['run']) + try: + us = npz(EXP / f"inputs/generated/epoch-{row['input_epoch_index']:03d}/us_requests.npz") + us_keys = us['tick'].astype(np.int64) * 166700 + us['neuron_idx'] + request_keys = run_record['requests'][:, 0] * 166700 + run_record['requests'][:, 1] + dan_requests = run_record['requests'][np.isin(request_keys, us_keys)] + require(len(dan_requests) == len(us_keys), 'Scheduled US requests not fully accounted') + delivery = dict(scheduled_D=row['d_command'], requested=int(len(dan_requests)), + accepted=int(dan_requests[:, 3].sum()), rejected=int(dan_requests[:, 4].sum()), + actual_DAN_spikes=int(state['counts'][population[reg['plan']['dan_type']]].sum()), + actual_DAN_spikes_gate_update=False, artificial_US_not_dopamine_concentration=True) + finally: + COMP.close_run(run_record) + summaries.append(dict(row_id=row['row_id'], total_spikes=summary['total_spikes'], + active_neurons=int(np.count_nonzero(state['counts'])), all_finite=summary['all_finite'], + update_counts=update['counts'], DAN_delivery=delivery, + runtime_seconds=records[row['row_id']]['runtime_and_immediate_seal_wall_seconds'], + effective_weight_sha256=summary['effective_weights_raw_sha256'])) + for arm in ('D1', 'D0', 'frozen'): + for epoch in range(2): + names = [f'{arm}--{mode}--epoch-{epoch:03d}' for mode in ('reference', 'light')] + a, b = [COMP.load_run(locations(n)['run']) for n in names] + try: + requests = npz(EXP / f'inputs/generated/epoch-{epoch:03d}/requests.npz') + expected = expected_weight_hash(reg, reg_sha, row_by(reg, names[0])) + pair = Q.compare_epoch_pair(a, b, expected_requests=np.column_stack((requests['tick'], requests['neuron_idx'])), + expected_steps=1200, expected_effective_weight_sha256=expected['effective_raw_sha256']) + require(pair['pass_all'] and COMP.bit_equal(deltas[names[0]], deltas[names[1]]), 'Reference/light exact regression failed') + pairs.append(dict(arm=arm, epoch=epoch, exact_delta_after=True, expected_weights=expected, comparison=pair)) + finally: + COMP.close_run(a); COMP.close_run(b) + for mode in ('reference', 'light'): + for left_arm, right_arm, epoch in (('D0', 'frozen', 0), ('D0', 'frozen', 1), ('D1', 'D0', 0)): + left = COMP.load_run(locations(f'{left_arm}--{mode}--epoch-{epoch:03d}')['run']) + right = COMP.load_run(locations(f'{right_arm}--{mode}--epoch-{epoch:03d}')['run']) + try: + comparison = COMP.compare_fresh(left, right) + require(comparison['numerical_exact'], 'Identical-input/zero-delta cross-arm neural behavior differs') + cross_arm.append(dict(left=left_arm, right=right_arm, mode=mode, epoch=epoch, comparison=comparison)) + finally: + COMP.close_run(left); COMP.close_run(right) + d1_rows = [s for s in summaries if s['row_id'].startswith('D1--')] + nonzero = all(s['update_counts']['actual_bitwise_changed_edges'] > 0 for s in d1_rows) + installed = {} + for mode in ('reference', 'light'): + first, second = (locations(f'D1--{mode}--epoch-{j:03d}') for j in range(2)) + raw = R._raw(deltas[f'D1--{mode}--epoch-000']) + installation = read(second['run'] / 'overlay-audit.json') + require(installation['delta_raw_sha256'] == raw and + digest(first['update'] / 'delta-after.npz') == records[f'D1--{mode}--epoch-001']['before_overlay_file_sha256'], + 'Epoch 2 did not install epoch 1 learned delta') + installed[mode] = dict(exact=True, nonzero_delta=int(np.count_nonzero(deltas[f'D1--{mode}--epoch-000']))) + status = 'PASS-EXACT-TWO-EPOCH-DELTA-INTERFACE-NOT-LEARNING' if nonzero else 'INCOMPLETE-NO-NONZERO-UPDATE-COVERAGE' + return dict(status=status, registration_sha256=reg_sha, calls=12, ticks=14400, pairs=pairs, cross_arm=cross_arm, + rows=summaries, epoch2_installations=installed, nonzero_D1_update_coverage=nonzero, + D0_and_frozen_exact_zero=True, complete_conditioning_executed=False, learned_candidate=False, + learning_success_claimed=False, retention_transition_supported=False, + scope='Two selected A epochs, one training replicate, one PAM15 compartment; no acquisition or A/B behavioral test; no general dynamics or learning claim', + light_intermediate_states_observed=False) + + +def finalize(reg_sha): + require(not (EXP / 'manifest.json').exists() and not (EXP / 'audit/final-status.json').exists(), 'Do not overwrite final/partial seal') + report = analyze(reg_sha) + save(EXP / 'audit/interface-analysis.json', report) + an = P.P.read_registration(P.AN_REG) + helper = P.P.load_module('phase4ao_final_seal_helper', ROOT / P.P.AI_HELPER_REL, P.P.AI_HELPER_SHA) + save(EXP / 'regression/AN-seal-after.json', helper.verify_seal(P.AN)) + save(EXP / 'regression/AM-seals-after.json', P.P.verify_authority(an['anchors'])) + old = P.P.verify_old_integrity(an) + save(EXP / 'regression/old-integrity-after.json', old) + P.read_registration(reg_sha) + final = dict(status=report['status'], analysis_sha256=digest(EXP / 'audit/interface-analysis.json'), + registration_sha256=reg_sha, old_integrity_pass=True, old_roots=len(old), + prior_AN_AM_seals_preserved=True, frozen_21_and_previous_draft_20_unchanged=True, + five_reference_trials='Previously sealed AM evidence retained, not rerun in this finite interface gate', + learned_candidate=False, learning_success_claimed=False, full_conditioning_executed=False, + model_directory_sealed=False, new_experiment_only=True, budget=budget(P.read_registration(reg_sha))) + save(EXP / 'audit/final-status.json', final) + paths = sorted(p for p in EXP.rglob('*') if p.is_file()) + require(not any(p.is_symlink() for p in EXP.rglob('*')) and not any('__pycache__' in p.parts for p in paths), 'Unsafe seal payload') + rows = [dict(path=p.relative_to(EXP).as_posix(), bytes=p.stat().st_size, sha256=digest(p)) for p in paths] + save(EXP / 'manifest.json', dict(root=str(EXP), files=rows)) + with (EXP / 'checksums.sha256').open('x', encoding='utf-8', newline='\n') as out: + out.writelines(r['sha256'] + ' ' + r['path'] + '\n' for r in rows) + out.write(digest(EXP / 'manifest.json') + ' manifest.json\n') + helper.verify_seal(EXP) + print(json.dumps(dict(status=report['status'], manifest_sha256=digest(EXP / 'manifest.json'), + final_sha256=digest(EXP / 'audit/final-status.json')), sort_keys=True), flush=True) + + +if __name__ == '__main__': + require(sys.dont_write_bytecode, 'Use scientific Python -B') + parser = argparse.ArgumentParser() + parser.add_argument('action', choices=('materialize', 'worker', 'run', 'finalize')) + parser.add_argument('--registration-sha256', required=True) + parser.add_argument('--row-id') + args = parser.parse_args() + if args.action == 'worker': worker(args.registration_sha256, args.row_id) + elif args.action == 'materialize': materialize(args.registration_sha256) + elif args.action == 'run': run(args.registration_sha256) + else: finalize(args.registration_sha256) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_update.py b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_update.py new file mode 100644 index 0000000..f5f8414 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/conditioning_update.py @@ -0,0 +1,205 @@ +"""Pure epoch-END adapter to the frozen AM/J eligibility implementation. + +Only the two SHA-pinned source files are read. No event files, brain state, +weights, base arrays or artifacts are written. The caller supplies actual sparse +events and externally authenticated COMPLETE KC/mask mappings. Hashing supplied +arrays identifies their bytes; it does not establish anatomical completeness. +The returned overlay is NOT installed. Scheduled D is not actual DAN delivery. +""" +from __future__ import annotations + +from dataclasses import dataclass +import hashlib +import json +import math +from pathlib import Path +import sys +from types import ModuleType + +import numpy as np + + +SCHEMA = "phase4an-conditioning-update-draft-v1" +BRIDGE_SHA256 = "174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61" +PHASE4J_SHA256 = "abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e" + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False, allow_nan=False) + + +def _sha(value): + return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest() + + +def _array(value, dtype, name): + _require(isinstance(value, np.ndarray) and value.ndim == 1 and value.dtype == np.dtype(dtype), + name + " requires an exact one-dimensional " + str(np.dtype(dtype)) + " array") + return value + + +def _evidence(array): + """Original dtype and logical C-order raw bytes; no cast or rounding.""" + return dict(dtype=array.dtype.str, shape=list(array.shape), nbytes=int(array.nbytes), + raw_c_order_sha256=hashlib.sha256(array.tobytes(order="C")).hexdigest()) + + +def _owned(array): + result = np.array(array, copy=True, order="C") + result.setflags(write=False) + return result + + +def _pinned_bytes(path, expected, label): + raw = Path(path).read_bytes() + _require(hashlib.sha256(raw).hexdigest() == expected, label + " source SHA256 mismatch") + return raw + + +def _load_bridge(path): + raw = _pinned_bytes(path, BRIDGE_SHA256, "AM eligibility bridge") + # Execute precisely the verified bytes, not a second path-based import. + # Temporary isolated module registration supports the frozen dataclass; + # restore any prior module, rather than colliding with old globals. + name = "_conditioning_update_bridge_" + BRIDGE_SHA256 + module = ModuleType(name) + module.__file__ = str(path) + previous = sys.modules.get(name) + sys.modules[name] = module + try: + exec(compile(raw, str(path), "exec"), module.__dict__) + finally: + if previous is None: + del sys.modules[name] + else: + sys.modules[name] = previous + _require(module.PHASE4J_SHA256 == PHASE4J_SHA256, "Frozen bridge/J pin disagreement") + return module + + +@dataclass(frozen=True) +class EpochUpdate: + delta_after: np.ndarray + sampled_trace: np.ndarray + final_trace: np.ndarray + receipt: dict + + +def update_epoch(*, step, neuron_idx, kc, candidate_edge_id, candidate_sources, + candidate_targets, candidate_compartments, delta_before, base_candidate, + steps, n_neurons, compartment, us_tick, d_command, frozen, + bridge_source, phase4j_source, trace_backend): + """Return the epoch-boundary update; never apply it to the running brain. + + trace_backend is mandatory: ``torch`` executes the frozen Torch CPU trace + expression; ``numpy`` remains its candidate compatibility implementation, + without a claim of per-call Torch equivalence. One declared US or no US is + supported. A US-less epoch requires D=0. Actual DAN delivery is deliberately + absent from this API and must be bound as separate coordinator evidence. + """ + _require(type(steps) is int and steps > 0, "steps must be a positive integer") + _require(type(n_neurons) is int and n_neurons > 0, "n_neurons must be a positive integer") + _require(us_tick is None or (type(us_tick) is int and 0 <= us_tick < steps), "us_tick must be None or an integer inside the epoch") + _require(type(d_command) in (int, float) and math.isfinite(d_command) and d_command in (0, 1), + "D must be the explicit scheduled scalar 0 or 1, not Boolean or measured DAN") + _require(us_tick is not None or d_command == 0, "No declared US tick requires D=0") + _require(type(frozen) is bool, "frozen must be Boolean") + _require(trace_backend in ("numpy", "torch"), "Explicit trace_backend must be numpy or torch") + arrays = dict(step=_array(step, "int32", "step"), neuron_idx=_array(neuron_idx, "int32", "neuron_idx"), + kc=_array(kc, "int32", "kc"), candidate_edge_id=_array(candidate_edge_id, "int64", "candidate_edge_id"), + candidate_sources=_array(candidate_sources, "int32", "candidate_sources"), + candidate_targets=_array(candidate_targets, "int32", "candidate_targets"), + delta_before=_array(delta_before, "float32", "delta_before"), + base_candidate=_array(base_candidate, "float32", "base_candidate")) + _require(isinstance(candidate_compartments, np.ndarray) and candidate_compartments.ndim == 1 and + candidate_compartments.dtype.kind == "U", "candidate_compartments must be a Unicode vector") + arrays["candidate_compartments"] = candidate_compartments + size = len(candidate_edge_id) + _require(size > 0 and np.all(candidate_edge_id >= 0) and np.all(candidate_edge_id[1:] > candidate_edge_id[:-1]), + "Candidate edge IDs must be nonempty, canonical sorted unique int64 IDs") + _require(all(len(arrays[name]) == size for name in ("candidate_sources", "candidate_targets", + "candidate_compartments", "delta_before", "base_candidate")), "Candidate mapping columns differ in length") + _require(np.all((candidate_targets >= 0) & (candidate_targets < n_neurons)), "Candidate target outside supplied graph size") + before_evidence = {name: _evidence(value) for name, value in arrays.items()} + # Every computation receives owned copies. Caller views/strides are accepted + # without changing their values or flags; dtype is never silently repaired. + work = {name: np.array(value, copy=True, order="C") for name, value in arrays.items()} + bridge = _load_bridge(bridge_source) + _pinned_bytes(phase4j_source, PHASE4J_SHA256, "Phase 4J") + trace = bridge.trace_from_events(work["step"], work["neuron_idx"], work["kc"], steps, + [] if us_tick is None else [us_tick], phase4j_source=phase4j_source, + backend=trace_backend, n_neurons=n_neurons) + sampled = trace.last_command_trace + after, original = bridge.apply_eligibility(sampled, work["kc"], work["candidate_sources"], + work["candidate_compartments"], compartment, work["delta_before"], work["base_candidate"], + d=d_command, frozen=frozen, phase4j_source=phase4j_source, n_neurons=n_neurons) + # Diagnostics only; the update is exclusively the frozen apply_v2 above. + selected = work["candidate_compartments"] == compartment + local = np.searchsorted(work["kc"], work["candidate_sources"]) + eligible = (sampled[local] > 0) & selected + changed = after.view(np.uint32) != work["delta_before"].view(np.uint32) + value_changed = after != work["delta_before"] + floor = -bridge.FLOOR_FRACTION * work["base_candidate"] + floor_before = work["delta_before"] == floor + floor_after = after == floor + _require(bridge.bit_equal(after[~selected], work["delta_before"][~selected]), "Off-compartment identity failed") + if frozen or d_command == 0: + _require(bridge.bit_equal(after, work["delta_before"]), "Zero-D/frozen identity failed") + original_report = original["original_report"] + attempted = 0 if original_report is None else original_report["updated_edges"] + if original_report is not None: + _require(original_report["eligible_edges"] == int(eligible.sum()), "Frozen eligible-edge report disagrees with its sampled trace") + delta_after, sampled_trace, final_trace = (_owned(a) for a in (after, sampled, trace.final)) + _require(all(_evidence(value) == before_evidence[name] for name, value in arrays.items()), "Caller input bytes changed") + _pinned_bytes(bridge_source, BRIDGE_SHA256, "AM eligibility bridge") + _pinned_bytes(phase4j_source, PHASE4J_SHA256, "Phase 4J") + receipt = dict(schema=SCHEMA, status="DRAFT-COMPUTED-EPOCH-UPDATE-NOT-INSTALLED", + source_pins=dict(eligibility_bridge_sha256=BRIDGE_SHA256, phase4j_sha256=PHASE4J_SHA256), + input_arrays=before_evidence, + command=dict(steps=steps, n_neurons=n_neurons, compartment=compartment, us_tick=us_tick, + D_scheduled=float(d_command), frozen=frozen, trace_backend=trace_backend), + semantics=dict(application="epoch_boundary_after_all_recorded_ticks; affects subsequent epoch only", + sampling="after_current_tick_spike", trace_initial_state="zero at epoch boundary", + no_US_sample="canonical float32 +0, not final trace", D_definition="artificial scheduled scalar", + actual_DAN_delivery_observed=False, actual_DAN_delivery_used_to_gate=False, + actual_DAN_evidence_authority="separate externally bound epoch record, not an input to this function", + original_base_units="mV", delta_units="mV", delta_dtype="float32", + candidate_target_role="identity evidence only; update uses original source/compartment mapping"), + fixed_rule=dict(dt_ms=bridge.DT_MS, trace_tau_ms=bridge.TAU_MS, trace_cap=bridge.TRACE_CAP, + eta_mv=bridge.ETA_MV, polarity=bridge.POLARITY, floor_fraction=bridge.FLOOR_FRACTION), + trace_audit=trace.audit, apply_audit=original, + counts=dict(events=len(step), actual_KC_events=trace.audit["actual_kc_events"], + candidates=size, selected_compartment_edges=int(selected.sum()), sampled_eligible_selected_edges=int(eligible.sum()), + original_updated_edges_attempted=int(attempted), original_apply_v2_executed=original["original_apply_v2_executed"], + actual_value_changed_edges=int(value_changed.sum()), actual_bitwise_changed_edges=int(changed.sum()), + actual_off_compartment_bitwise_changed_edges=int(changed[~selected].sum()), + selected_at_floor_before=int((selected & floor_before).sum()), + selected_at_floor_after=int((selected & floor_after).sum()), + selected_newly_at_floor=int((selected & floor_after & ~floor_before).sum()), + sampled_eligible_selected_already_at_floor=int((eligible & floor_before).sum())), + count_limitations="original updated_edges counts nonzero attempted increments, including saturation; floor counts do not prove why any edge was unchanged", + outputs={"delta_after": _evidence(delta_after), "sampled_trace": _evidence(sampled_trace), "final_trace": _evidence(final_trace)}, + inputs_exact_unchanged=True, off_compartment_exact_unchanged=True, + numpy_torch_bitwise_comparison_performed=False, + frozen_Torch_expression_executed=trace_backend == "torch", + complete_KC_identity_verified=False, complete_mask_identity_verified=False, full_graph_authority_verified=False, + mapping_hash_scope="supplied logical array bytes only; coordinator must bind complete canonical KC and candidate mask", + brain_dynamics_executed=False, weights_installed=False, base_modified=False, + training_executed_by_this_function=False, scientific_learning_pass_claimed=False, candidate_generated=False) + receipt["payload_sha256"] = _sha(receipt) + return EpochUpdate(delta_after, sampled_trace, final_trace, receipt) + + +def verify_receipt(receipt, **epoch_inputs): + """Recompute from supplied arrays/sources; never trust a self-hash alone.""" + _require(type(receipt) is dict and "payload_sha256" in receipt, "Invalid epoch receipt") + _require(receipt["payload_sha256"] == _sha({k: v for k, v in receipt.items() if k != "payload_sha256"}), + "Epoch receipt payload SHA mismatch") + actual = update_epoch(**epoch_inputs) + _require(_canonical(receipt) == _canonical(actual.receipt), "Receipt differs from exact recomputation in this environment") + return True diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_analysis.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_analysis.py new file mode 100644 index 0000000..9eacff6 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_analysis.py @@ -0,0 +1,373 @@ +"""Deterministic, read-only CPU analysis of the registered 32-call AN gate. + +analyze(reg, reg_sha, verified_records) accepts the actual runtime receipts +already verified by engineering_workflow.verify_execution. It does not run +that workflow, publish, seal, save, retry or import a GPU runtime. All external +AM seals/checkpoint chains/process receipts remain the caller's prior duty. +This module rehashes the files it reads, executes every canonical descriptor, +and independently reconstructs the reference queues and effective SI weights. +""" +from __future__ import annotations + +import hashlib +import json +import math +from pathlib import Path +import types + +import numpy as np + +import engineering_compare as compare +import engineering_plan as plan_module +import engineering_queue_audit as queue_audit +import engineering_scopes as scopes + + +N, E, K = 166700, 25582938, 4064 +CANDIDATES, FAST_APL_EDGES = 14232, 5845 +AM_OVERLAY_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4' +MASK_SHA = '8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b' +SUCCESS = 'PASS-EXACT-LIGHT-CONTINUATION-ENGINEERING-NOT-LEARNING' + + +def require(value, message): + if not value: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(',', ':'), ensure_ascii=False, allow_nan=False) + + +def _sha_file(path): + h = hashlib.sha256() + with Path(path).open('rb') as stream: + for data in iter(lambda: stream.read(8 << 20), b''): + h.update(data) + return h.hexdigest() + + +class _Reader: + """Fresh byte hashes, not stat caching; deterministic read manifest.""" + def __init__(self, registered): + self.registered = registered + self.reads = {} + self.arrays = [] + + def bind(self, path, *, registered=True, expected=None): + path = Path(path) + require(path.is_absolute() and path.is_file() and not path.is_symlink(), 'Missing/linked/relative evidence: ' + str(path)) + path = path.resolve(strict=True) + if registered: + require(str(path) in self.registered, 'Unregistered dependency: ' + str(path)) + require(expected is None or expected == self.registered[str(path)], 'Conflicting external hash authority') + expected = self.registered[str(path)] + actual = _sha_file(path) + require(expected is None or (compare._sha(expected) and actual == expected), 'Evidence bytes changed: ' + str(path)) + value = dict(path=str(path), bytes=path.stat().st_size, sha256=actual) + require(str(path) not in self.reads or self.reads[str(path)] == value, 'File changed during analysis') + self.reads[str(path)] = value + return path + + def json(self, path, **kwargs): + return compare._json(self.bind(path, **kwargs)) + + def npy(self, path): + value = np.load(self.bind(path), mmap_mode='r', allow_pickle=False) + self.arrays.append(value) + return value + + def npz(self, path, keys): + with np.load(self.bind(path), allow_pickle=False) as archive: + require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected input NPZ keys') + return {name: archive[name].copy() for name in keys} + + def run(self, directory, *, legacy): + directory = Path(directory) + names = ['summary.json', 'state.npz', 'apl-timing-audit.json', 'apl-pending-tail.float32.npy', 'requests.csv'] + if legacy: + names.append('adapter-provenance.json') + else: + names.append('terminal-queues.npz') + names += [name for name in [*compare.TRACE_FILES.values(), 'tick-state-hashes.jsonl', 'queue.jsonl'] + if (directory / name).is_file()] + for name in names: + self.bind(directory / name, registered=legacy) + return compare.load_run(directory, allow_legacy_am=legacy) + + def finish(self): + for row in self.reads.values(): + path = Path(row['path']) + require(path.is_file() and path.stat().st_size == row['bytes'] and _sha_file(path) == row['sha256'], + 'Read evidence changed during analysis: ' + str(path)) + return [self.reads[key] for key in sorted(self.reads)] + + def close(self): + for value in self.arrays: + mmap = getattr(value, '_mmap', None) + if mmap is not None: + mmap.close() + + +def _registration(reg, reg_sha, verified): + require(type(reg) is dict and reg.get('schema') == 'phase4an-engineering-registration-v1' and + reg.get('training') is False and compare._sha(reg_sha), 'Invalid engineering registration') + plan = plan_module.validate_plan(reg['plan']) + ids = [row['row_id'] for row in plan['rows']] + require(type(verified) is dict and set(verified) == set(ids) and set(reg['cases']) == set(ids), + 'Exactly all 32 registered runtime receipts/cases are required') + exp = None + for row in plan['rows']: + identity = row['row_id']; path = Path(reg['cases'][identity]['case_dir']) + require(path.is_absolute() and path.name == identity and path.parent.name == 'cases', 'Invalid registered case layout') + if exp is None: + exp = path.parent.parent + require(path == exp / 'cases' / identity, 'Cases span inconsistent experiment roots') + receipt = verified[identity] + require(receipt.get('schema') == 'phase4an-engineering-runtime-record-v1' and receipt.get('complete') is True and + receipt.get('training') is False and receipt.get('registration_sha256') == reg_sha and + _canonical(receipt.get('row')) == _canonical(row) and compare._sha(receipt.get('checkpoint_manifest_sha256')), + 'Runtime receipt is not the exact verified registered row: ' + identity) + require(exp.name == 'phase4an-engineering-validation' and exp.parent.name == 'experiments', 'Unexpected fixed engineering output root') + require(set(reg['overlays']) == set(reg['selected_case']['AM_outputs']) == {'zero', 'static25'}, 'Incomplete overlay/AM authority') + return plan, exp + + +def _materialize(reader, reg, graph, stage): + """Use the exact pinned pure AM helper; no original globals are patched.""" + declaration = reg['overlays'][stage] + meta = reader.json(declaration['overlay_manifest']) + require(meta.get('schema') == 'phase4an-overlay-v1' and meta.get('overlay_name') == stage and + meta.get('am_overlay_sha256') == AM_OVERLAY_SHA and meta.get('mask_sha256') == MASK_SHA, + 'Original fixed overlay authority changed') + helper_path = reader.bind(meta['am_overlay_path'], expected=AM_OVERLAY_SHA) + module = types.ModuleType('phase4an_analysis_pinned_overlay') + module.__file__ = str(helper_path) + exec(compile(helper_path.read_bytes(), str(helper_path), 'exec'), module.__dict__) + mask = reader.npy(meta['mask_path']); base_candidate = reader.npy(meta['base_candidate_path']) + require(reader.reads[str(Path(meta['mask_path']).resolve())]['sha256'] == meta['mask_sha256'] and + reader.reads[str(Path(meta['base_candidate_path']).resolve())]['sha256'] == meta['base_candidate_sha256'], + 'Overlay external mask/base hash mismatch') + overlay_file = reader.bind(declaration['overlay_file'], expected=meta['overlay_sha256']) + arrays = reader.npz(overlay_file, {'edge_id', 'plastic_delta_mv'}) + require(mask.dtype == np.int64 and mask.shape == (CANDIDATES,), 'Complete frozen plastic mask required') + values = module.validate_arrays(arrays['edge_id'], arrays['plastic_delta_mv'], mask, base_candidate) + if stage == 'zero': + require(not np.any(values['plastic_delta_mv'] != 0), 'Zero overlay is nonzero') + else: + require(module.bit_equal(values['plastic_delta_mv'], np.multiply(np.float32(-.25), base_candidate)), + 'static25 differs from the original float32-mV floor') + effective, mapping = module.materialize_numpy(graph['weights'], graph['edge_id'], graph['apl_out_mask'], values) + require(mapping['mapped_candidate_count'] == CANDIDATES and mapping['missing_candidate_count'] == 0 and + mapping['apl_overlap_count'] == 0, 'Incomplete/overlapping candidate mapping') + base_sha, effective_sha = module.expected_hashes(graph['weights'], graph['apl_out_mask'], mapping) + require(queue_audit._raw_sha(effective) == effective_sha, 'CPU materialization/hash disagreement') + return effective, dict(effective_weights_raw_sha256=effective_sha, base_weights_raw_sha256=base_sha, + candidate_count=CANDIDATES, installed_nonzero_count=mapping['installed_nonzero_count'], + apl_fast_edges_zeroed=FAST_APL_EDGES, bridge=module.BRIDGE, + helper_source_sha256=AM_OVERLAY_SHA, weights_updated_during_run=False) + + +def _load_context(reg, reg_sha, plan, exp): + reader = _Reader(reg['input_sha256']) + records, legacy = {}, {} + try: + actual_reg = reader.json(exp / 'protocol/preregistered.json', registered=False, expected=reg_sha) + require(_canonical(actual_reg) == _canonical(reg), 'Supplied registration differs from actual registered bytes') + for path, expected in sorted(reg['source_sha256'].items()): + reader.bind(path, registered=False, expected=expected) + original = Path(reg['selected_case']['original_dir']) + spec = reader.json(original / 'spec.json') + require(spec.get('kind') == 'fullgraph' and spec.get('steps') == 1000 and spec.get('dt_ms') == .1 and + spec.get('apl_delay_steps') == 18, 'Original fixed fullgraph D18 spec changed') + requests = reader.npz(original / 'requests.npz', {'tick', 'neuron_idx'}) + graph_dir, bundle = Path(spec['graph_dir']), Path(spec['apl_bundle_dir']) + graph = {key: reader.npy(graph_dir / (key + '.npy')) for key in ('nodes', 'ptr', 'dst', 'edge_id', 'weights')} + require(graph['nodes'].dtype == np.int32 and np.array_equal(graph['nodes'], np.arange(N, dtype=np.int32)), + 'Graph is not the complete canonical neuron set') + n, e = queue_audit._graph(graph['ptr'], graph['dst'], graph['edge_id'], graph['weights']) + require((n, e) == (N, E), 'Original graph dimensions changed') + apl = reader.npy(bundle / 'apl_globals.npy') + require(apl.dtype == np.int32 and apl.shape == (2,) and np.all((apl >= 0) & (apl < N)) and + apl[0] < apl[1], 'Invalid canonical APL source identity') + mask = np.zeros(E, np.bool_) + for source in apl: + mask[graph['ptr'][source]:graph['ptr'][source + 1]] = True + require(int(mask.sum()) == FAST_APL_EDGES, 'Frozen fast-APL source-edge mask changed') + graph['apl_out_mask'] = mask + storage = {} + for row in plan['rows']: + identity = row['row_id']; folder = exp / 'runs' / identity + require(folder.is_dir() and folder.resolve() == folder and not any(p.is_symlink() for p in folder.rglob('*')), + 'Missing/linked runtime evidence folder') + records[identity] = reader.run(folder, legacy=False) + storage[identity] = sum(p.stat().st_size for p in folder.rglob('*') if p.is_file()) + for stage in ('zero', 'static25'): + legacy[stage] = reader.run(reg['selected_case']['AM_outputs'][stage], legacy=True) + return dict(reader=reader, records=records, legacy=legacy, graph=graph, requests=requests, + storage=storage, input_scope='read-only authenticated selected original inputs and AM outputs') + except BaseException: + for record in [*records.values(), *legacy.values()]: + compare.close_run(record) + reader.close() + raise + + +def _pair(candidate, reference): + checks, observed = compare._pair_checks(candidate, reference) + return dict(numerical_exact=all(checks.values()), checks=checks, + failed_checks=[key for key, value in checks.items() if not value], + reference_comparisons_available=observed, learning_pass_claimed=False) + + +def _dispatch(descriptor, records, legacy, overlay): + candidates = [records[name] for name in descriptor['candidate_row_ids']] + reference = records[descriptor['reference_row_id']] if descriptor['reference_row_id'] is not None else None + kind = descriptor['check'] + if kind == 'am_exact': + return compare.compare_fresh(candidates[0], legacy[overlay]) + if kind == 'fresh_exact': + return compare.compare_fresh(candidates[0], reference) + if kind in ('prefix_of_reference', 'segment_of_reference'): + return scopes.compare_slice(candidates[0], reference) + if kind == 'segment_exact': + return _pair(candidates[0], reference) + if kind == 'checkpoint_exact': + return scopes.compare_checkpoint(candidates[0], reference) + if kind == 'parent_carry': + return scopes.compare_parent_carry(candidates[0], reference) + if kind == 'chain_exact': + return compare.compare_segments(reference, candidates) + raise ValueError('Unimplemented registered comparison: ' + str(kind)) + + +def _number(value, name, integer=False): + if value is None: + return None + require((type(value) is int if integer else type(value) in (int, float)) and math.isfinite(value) and value >= 0, + 'Malformed observed metric: ' + name) + return value + + +def _run_checks(row, record, expected_hash, reference): + compare.validate_run(record) + summary = record['summary']; identity = row['row_id'] + require(len(record['state']['v']) == N and record['terminal']['apl_pending'].shape == (18, K), 'Incomplete full brain state: ' + identity) + checks = {key: summary.get(key) is True for key in ('complete', 'pass_all', 'all_finite', 'counts_match_sparse_events')} + fixed = dict(nodes=N, edges=E, steps=row['steps'], completed_ticks=row['steps'], apl_delay_steps=18, + requested=len(record['requests']), total_spikes=len(record['state']['step']), + accepted_requests=int(record['requests'][:, 3].sum(dtype=np.int64)), + rejected_requests=int(record['requests'][:, 4].sum(dtype=np.int64)), + apl_fast_edges_zeroed=FAST_APL_EDGES, kc_targets_present=K) + for key, value in fixed.items(): + checks[key] = type(summary.get(key)) is int and summary[key] == value + checks['registered_clock'] = record['steps'] == row['steps'] and record['global_tick_offset'] == row['start'] + checks['recording_mode'] = summary.get('diagnostic_mode') == row['mode'] + checks['effective_weight_bytes'] = summary.get('effective_weights_raw_sha256') == expected_hash + checks['within_trial_weights_fixed'] = summary.get('within_trial_weight_updates') is False + checks['learning_not_claimed'] = summary.get('learning_pass_claimed') is False + expected_queue = reference['queues'][row['start']:row['stop']] + for field, queue_key in [('arriving_edges', 'count'), ('accepted_arriving_edges', 'accepted_count')]: + expected = sum(q[queue_key] for q in expected_queue) + checks[field] = type(summary.get(field)) is int and summary[field] == expected + return {name: bool(value) for name, value in checks.items()} + + +def analyze(reg, reg_sha, verified_records): + """No writes/runs. The caller rechecks registration and receipts afterward.""" + plan, exp = _registration(reg, reg_sha, verified_records) + context = _load_context(reg, reg_sha, plan, exp) + records, legacy = context['records'], context['legacy'] + try: + require(set(records) == {r['row_id'] for r in plan['rows']} and set(legacy) == {'zero', 'static25'}, + 'Loaded evidence inventory is incomplete') + comparisons = [] + for row in plan['rows']: + for number, descriptor in enumerate(row['expected_comparisons']): + try: + result = _dispatch(descriptor, records, legacy, row['overlay']) + require(type(result.get('numerical_exact')) is bool, 'Comparator lacks an explicit Boolean numerical gate') + except (ValueError, KeyError, TypeError, IndexError, OverflowError) as error: + result = dict(numerical_exact=False, error_type=type(error).__name__, error=str(error), + incomplete_observation_not_fabricated=True) + comparisons.append(dict(descriptor_id=row['row_id'] + '/' + str(number), + descriptor=descriptor, result=result)) + expected_descriptors = sum(len(r['expected_comparisons']) for r in plan['rows']) + require(len(comparisons) == expected_descriptors, 'Not every registered descriptor was evaluated') + overlay_reports, metric_rows, all_row_checks = {}, [], {} + for stage in ('zero', 'static25'): + effective, installation = _materialize(context['reader'], reg, context['graph'], stage) + stage_rows = [r for r in plan['rows'] if r['overlay'] == stage] + ref_row = next(r for r in stage_rows if r['kind'] == 'fresh' and r['mode'] == 'reference') + reference = records[ref_row['row_id']] + reference_chain = [records[r['row_id']] for r in stage_rows if r['kind'] == 'chain' and r['mode'] == 'reference'] + cuts = [r['start'] for r in stage_rows if r['kind'] == 'chain' and r['mode'] == 'reference' and r['start'] > 0] + independent = queue_audit.audit_queues(reference, reference_chain, ptr=context['graph']['ptr'], dst=context['graph']['dst'], + edge_id=context['graph']['edge_id'], effective_weights=effective, requests=context['requests'], cuts=cuts) + del effective + reference_result = next(item['result'] for item in comparisons if item['descriptor']['check'] == 'chain_exact' and + item['descriptor']['reference_row_id'] == ref_row['row_id']) + light_result = next(item['result'] for item in comparisons if item['descriptor']['check'] == 'chain_exact' and + item['descriptor']['reference_row_id'] == plan_module._fresh_id(stage, 'light')) + coverage = reference_result.get('coverage', {}) + apl_values = coverage.get('carried_APL_nonzero_applied_values') + simultaneous_carry = any(type(row.get('pending_source_spike_events')) is int and row['pending_source_spike_events'] > 0 and + type(row.get('carried_APL_nonzero_pending_values')) is int and row['carried_APL_nonzero_pending_values'] > 0 + for row in coverage.get('boundaries', [])) + coverage_pass = (reference_result.get('nonvacuous_temporal_coverage') is True and + type(apl_values) is int and apl_values > 0 and simultaneous_carry and + independent.get('accepted_nonzero_crossing_witness_verified') is True) + legacy_checks = dict(full_dimensions=type(legacy[stage]['summary'].get('nodes')) is int and legacy[stage]['summary']['nodes'] == N and + type(legacy[stage]['summary'].get('edges')) is int and legacy[stage]['summary']['edges'] == E, + effective_weight_bytes=legacy[stage]['summary'].get('effective_weights_raw_sha256') == installation['effective_weights_raw_sha256']) + overlay_reports[stage] = dict(installation=installation, independent_queue_audit=independent, + reference_numerical_exact=reference_result.get('numerical_exact') is True, + light_numerical_exact=light_result.get('numerical_exact') is True, + live_coverage_pass=coverage_pass, legacy_checks=legacy_checks, + same_internal_boundary_has_spike_and_APL_carry=simultaneous_carry, + carried_APL_nonzero_applied_values=apl_values, + light_standalone_coverage=light_result.get('coverage'), + light_per_tick_float_state_directly_observed=False, + reference_coverage_does_not_fabricate_light_observations=True) + for row in stage_rows: + identity = row['row_id']; record = records[identity]; summary = record['summary']; receipt = verified_records[identity] + checks = _run_checks(row, record, installation['effective_weights_raw_sha256'], reference) + all_row_checks[identity] = checks + metric_rows.append(dict(row_id=identity, overlay=stage, mode=row['mode'], start=row['start'], stop=row['stop'], + biological_time_ms=row['steps'] * .1, runtime_and_immediate_seal_wall_seconds=_number(receipt.get('runtime_and_immediate_seal_wall_seconds'), 'runtime wall'), + runtime_reported_wall_seconds=_number(summary.get('wall_seconds'), 'runtime reported wall'), + diagnostic_simulation_seconds=_number(summary.get('diagnostic_simulation_seconds'), 'simulation'), + build_or_cache_seconds=_number(summary.get('build_or_cache_seconds'), 'build/cache'), + peak_allocated_vram_bytes=_number(summary.get('peak_allocated_vram_bytes'), 'allocated VRAM', True), + peak_reserved_vram_bytes=_number(summary.get('peak_reserved_vram_bytes'), 'reserved VRAM', True), + sealed_output_file_bytes=context['storage'][identity], compressed_storage=receipt.get('storage'), + gpu_utilization=None, cpu_utilization=None, unobserved_metrics=['gpu_utilization', 'cpu_utilization'], + checkpoint_manifest_sha256=receipt['checkpoint_manifest_sha256'], + verified_runtime_receipt_canonical_sha256=hashlib.sha256(_canonical(receipt).encode('utf-8')).hexdigest())) + numerical = (all(item['result']['numerical_exact'] for item in comparisons) and + all(all(checks.values()) for checks in all_row_checks.values()) and + all(all(r['legacy_checks'].values()) and r['independent_queue_audit']['queue_ref_request_exact'] for r in overlay_reports.values())) + covered = all(row['live_coverage_pass'] for row in overlay_reports.values()) + read_manifest = context['reader'].finish() + report = dict(schema='phase4an-engineering-analysis-v1', registration_sha256=reg_sha, + status='FAIL-NUMERICAL' if not numerical else (SUCCESS if covered else 'INCOMPLETE-COVERAGE'), + pass_all=bool(numerical and covered), numerical_exact=bool(numerical), nonvacuous_temporal_coverage=bool(covered), + expected_calls=32, analyzed_calls=len(records), executed_ticks=plan['total_ticks'], + expected_comparisons=expected_descriptors, evaluated_comparisons=len(comparisons), comparisons=comparisons, + run_checks=all_row_checks, failed_run_checks={identity: [k for k, ok in checks.items() if not ok] for identity, checks in all_row_checks.items() if not all(checks.values())}, + overlay_reports=overlay_reports, metrics=metric_rows, read_manifest=read_manifest, + source_sha256=reg['source_sha256'], process_receipts_and_checkpoint_chains_verified_by_caller=True, + external_AM_full_seals_reverified_here=False, no_new_brain_runs_during_analysis=True, + GPU_used=False, training=False, learning_pass_claimed=False, candidate_generated=False, + physiology_or_memory_success_claimed=False, + limitations=['Fixed zero/static25 engineering trajectories only; not conditioning or learned memory.', + 'Light intermediate V/g/ref, APL histories and per-tick queue arrays were not directly recorded.', + 'Independent queue/ref reconstruction is conditional on recorded spikes; V/g dynamics are not independently re-simulated.', + 'Missing performance metrics are null, not zero. No universal performance claim is made.', + 'Caller verifies all process receipts, parent chains, registration authority and full AM/frozen seals.']) + _canonical(report) # Fail rather than return non-JSON/nonfinite output. + return report + finally: + for record in [*records.values(), *legacy.values()]: + compare.close_run(record) + context['reader'].close() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_compare.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_compare.py new file mode 100644 index 0000000..c6d15aa --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_compare.py @@ -0,0 +1,413 @@ +"""Read-only CPU comparisons for the UNREGISTERED AN engineering gate. + +load_run(path, allow_legacy_am=False) reads actual AJ/AM/AN artifact schemas. +Call close_run(record) after using explicitly loaded memory-mapped traces. +compare_fresh(left, right, legacy_am=None) and compare_segments(monolithic, +segments) accept loaded dictionaries or paths and return JSON-safe reports. +No report is written, no GPU is imported, no training/biological PASS is claimed. +The caller owns external source/input/root SHA pins and case selection. These +comparisons do not replace seal verification or prove unseen inputs unchanged. + +AM did not save its raw spike ring: its fresh terminal ring is explicitly +reconstructed from sparse events, never described as independently recorded. +Reference queue joint hashes include LOCAL tick headers; cross-segment checks +compare the separate ordered-edge/accepted-mask hashes and counts instead. +""" +from __future__ import annotations + +import csv +import hashlib +import json +from pathlib import Path +import re + +import numpy as np + + +STATE_KEYS = ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref') +REQUEST_FIELDS = ('tick', 'neuron_idx', 'attempted', 'accepted', 'rejected') +TRACE_FILES = {'drive': 'apl-drive.float32.npy', 'state': 'apl-state.float32.npy', + 'generated': 'apl-inhibition.float32.npy', 'applied': 'apl-applied.float32.npy'} +HASH_FIELDS = ('v_sha256', 'g_sha256', 'ref_sha256') +QUEUE_FIELDS = ('count', 'accepted_count', 'ordered_edge_sha256', 'accepted_mask_sha256') + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def bit_equal(a, b): + return (isinstance(a, np.ndarray) and isinstance(b, np.ndarray) and a.dtype == b.dtype and a.shape == b.shape and + np.array_equal(np.ascontiguousarray(a).view(np.uint8), np.ascontiguousarray(b).view(np.uint8))) + + +def _sha(value): + return isinstance(value, str) and re.fullmatch('[0-9a-f]{64}', value) is not None + + +def _json(path): + def pairs(values): + result = {} + for key, value in values: + require(key not in result, 'Duplicate JSON key: ' + key) + result[key] = value + return result + return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=pairs, + parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value))) + + +def _jsonl(path): + with Path(path).open(encoding='utf-8') as stream: + return [json.loads(line) for line in stream if line.strip()] + + +def _npz(path, keys): + with np.load(path, allow_pickle=False) as archive: + require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected array keys: ' + str(path)) + return {key: archive[key].copy() for key in keys} + + +def reconstructed_fresh_ring(state, steps, neurons): + ring = np.zeros((19, neurons), np.uint8) + for tick in range(max(0, steps - 19), steps): + first, last = np.searchsorted(state['step'], (tick, tick + 1)) + ring[tick % 19, state['neuron_idx'][first:last]] = 1 + return ring + + +def normalized_ring(terminal): + ring, phase = terminal['spike_ring'], int(terminal['next_tick']) + return ring[(np.arange(19) + phase % 19) % 19] + + +def _requests(path): + with Path(path).open(encoding='utf-8', newline='') as stream: + reader = csv.DictReader(stream) + require(tuple(reader.fieldnames or ()) == REQUEST_FIELDS, 'requests.csv must use the exact five AJ fields in order') + rows = [] + for row in reader: + require(set(row) == set(REQUEST_FIELDS), 'Extra/missing request CSV field') + rows.append([int(row[key]) for key in REQUEST_FIELDS]) + return np.asarray(rows, dtype=np.int64).reshape(-1, 5) + + +def load_run(directory, *, allow_legacy_am=False): + directory = Path(directory).resolve(strict=True) + require(directory.is_dir(), 'Output is not a directory') + require(not any('error' in p.name.lower() or 'failure' in p.name.lower() for p in directory.iterdir()), + 'Run contains error/failure evidence') + summary = _json(directory / 'summary.json') + require(summary.get('complete') is True and summary.get('pass_all') is True and summary.get('all_finite') is True and + summary.get('steps') == summary.get('completed_ticks'), 'Run is incomplete or failed') + state = _npz(directory / 'state.npz', STATE_KEYS) + steps, neurons = summary['steps'], len(state['v']) + require(summary.get('nodes') == neurons and summary.get('precision') == 'float64' and + summary.get('apl_precision') == 'float32' and summary.get('apl_delay_steps') == 18, 'Unexpected AN/AM numerical schema') + timing = _json(directory / 'apl-timing-audit.json') + pending = np.load(directory / 'apl-pending-tail.float32.npy', mmap_mode='r', allow_pickle=False) + terminal_path = directory / 'terminal-queues.npz' + if terminal_path.is_file(): + terminal = _npz(terminal_path, ('spike_ring', 'apl_state', 'apl_pending', 'next_tick')) + origin = 'recorded_terminal_queues' + else: + require(allow_legacy_am and (directory / 'adapter-provenance.json').is_file(), 'Recorded terminal queues are missing') + bits = timing.get('final_apl_state_u32_bits') + require(isinstance(bits, list) and len(bits) == 2 and all(type(v) is int and 0 <= v < 2**32 for v in bits), + 'AM has no exact terminal APL bits') + terminal = {'spike_ring': reconstructed_fresh_ring(state, steps, neurons), + 'apl_state': np.asarray(bits, dtype=np.uint32).view(np.float32), 'apl_pending': np.asarray(pending), + 'next_tick': np.asarray(steps, np.int64)} + origin = 'AM_spike_ring_derived_from_fresh_events_APL_bits_from_timing' + require(bit_equal(terminal['apl_pending'], pending), 'Terminal/pending file mismatch') + require(timing.get('final_apl_state_u32_bits') == terminal['apl_state'].view(np.uint32).tolist(), 'Terminal APL bit metadata mismatch') + continued = summary.get('checkpoint_continuation_used', False) + require(type(continued) is bool, 'Invalid continuation scope flag') + offset = summary.get('global_tick_offset', 0) + require(not continued or (type(offset) is int and offset > 0 and summary.get('global_next_tick') == offset + steps), + 'Continued output lacks an explicit global clock') + require(continued or offset == 0, 'Fresh output has a nonzero global offset') + traces = {name: np.load(directory / file, mmap_mode='r', allow_pickle=False) + for name, file in TRACE_FILES.items() if (directory / file).is_file()} + reference = summary.get('diagnostic_mode') == 'reference' or origin.startswith('AM_') + if reference or summary.get('full_APL_history_recorded') is True: + require(len(traces) == 4, 'Declared reference APL history is incomplete') + hashes = _jsonl(directory / 'tick-state-hashes.jsonl') if (directory / 'tick-state-hashes.jsonl').is_file() else None + queues = _jsonl(directory / 'queue.jsonl') if (directory / 'queue.jsonl').is_file() else None + if reference or summary.get('full_tick_state_hashes_recorded') is True: + require(hashes is not None, 'Declared state hashes are missing') + if reference or summary.get('full_queue_hashes_recorded') is True: + require(queues is not None, 'Declared queue hashes are missing') + result = {'source': str(directory), 'state': state, 'terminal': terminal, 'requests': _requests(directory / 'requests.csv'), + 'steps': steps, 'global_tick_offset': offset, 'summary': summary, 'traces': traces, + 'state_hashes': hashes, 'queues': queues, 'terminal_origin': origin} + validate_run(result) + return result + + +def close_run(run): + """Release read-only mappings opened by load_run; no files are modified.""" + arrays = [*run.get('traces', {}).values(), *run.get('terminal', {}).values()] + closed = set() + for array in arrays: + current = array + while current is not None: + mapping = getattr(current, '_mmap', None) + if mapping is not None and id(mapping) not in closed: + mapping.close(); closed.add(id(mapping)) + current = getattr(current, 'base', None) + + +def validate_run(run): + """Array-level contract, also usable by synthetic tests without files.""" + require(isinstance(run, dict), 'Run must be a dictionary') + steps, offset = run['steps'], run['global_tick_offset'] + require(type(steps) is int and steps > 0 and type(offset) is int and offset >= 0, 'Invalid segment clock') + state, terminal = run['state'], run['terminal'] + require(set(state) == set(STATE_KEYS), 'Expected exactly the six state keys') + n = len(state['v']) + require(n > 0, 'Empty neuron population') + for name, dtype in (('v', np.float64), ('g', np.float64), ('ref', np.int32), ('counts', np.int32)): + value = state[name] + require(value.dtype == dtype and value.shape == (n,) and np.isfinite(value).all(), 'Invalid state ' + name) + require(np.all((state['ref'] >= 0) & (state['ref'] <= 22)) and np.all(state['counts'] >= 0), 'Invalid ref/count values') + tick, node = state['step'], state['neuron_idx'] + require(tick.dtype == node.dtype == np.int32 and tick.ndim == node.ndim == 1 and tick.shape == node.shape and + np.all((tick >= 0) & (tick < steps)) and np.all((node >= 0) & (node < n)), 'Invalid events') + keys = tick.astype(np.int64) * n + node + require(len(keys) < 2 or np.all(keys[1:] > keys[:-1]), 'Events must be sorted unique') + require(np.array_equal(np.bincount(node, minlength=n), state['counts']), 'Counts disagree with segment events') + request = run['requests'] + require(isinstance(request, np.ndarray) and request.dtype == np.int64 and request.ndim == 2 and request.shape[1] == 5, + 'Requests must be int64 rows in the five AJ fields') + require(np.all((request[:, 0] >= 0) & (request[:, 0] < steps)) and np.all((request[:, 1] >= 0) & (request[:, 1] < n)) and + np.all(request[:, 2] == 1) and np.all((request[:, 3] == 0) | (request[:, 3] == 1)) and + np.all(request[:, 4] == 1 - request[:, 3]), 'Invalid request values') + request_keys = request[:, 0] * n + request[:, 1] + require(len(request_keys) < 2 or np.all(request_keys[1:] > request_keys[:-1]), 'Requests must be sorted unique') + require(set(terminal) == {'spike_ring', 'apl_state', 'apl_pending', 'next_tick'}, 'Invalid terminal keys') + ring = terminal['spike_ring'] + require(ring.dtype == np.uint8 and ring.shape == (19, n) and np.all(ring <= 1), 'Invalid spike ring') + phase = terminal['next_tick'] + require(isinstance(phase, np.ndarray) and phase.dtype == np.int64 and phase.shape == () and int(phase) == steps, + 'Terminal phase must be local segment length') + pending, apl_state = terminal['apl_pending'], terminal['apl_state'] + require(pending.dtype == np.float32 and pending.ndim == 2 and pending.shape[0] == 18 and pending.shape[1] > 0 and + apl_state.dtype == np.float32 and apl_state.shape == (2,) and np.isfinite(pending).all() and np.isfinite(apl_state).all(), + 'Invalid APL terminal state') + for t in range(max(0, steps - 19), steps): + expected = np.zeros(n, np.uint8) + first, last = np.searchsorted(tick, (t, t + 1)); expected[node[first:last]] = 1 + require(bit_equal(ring[t % 19], expected), 'Written ring slot differs from sparse events') + if offset == 0 and steps < 19: + require(not np.any(ring[steps:]), 'Fresh uninitialized ring slots are not zero') + traces = run.get('traces', {}) + require(not traces or set(traces) == set(TRACE_FILES), 'Partial APL trace set') + for key, trace in traces.items(): + width = 2 if key in ('drive', 'state') else pending.shape[1] + require(trace.dtype == np.float32 and trace.shape == (steps, width) and np.isfinite(trace).all(), 'Invalid APL history: ' + key) + if traces: + require(bit_equal(traces['state'][-1], apl_state), 'Final APL history/state mismatch') + kept = min(steps, 18) + require(bit_equal(pending[-kept:], traces['generated'][-kept:]), 'Terminal APL queue does not retain generated suffix') + if steps > 18: + require(bit_equal(traces['applied'][18:], traces['generated'][:-18]), 'Within-segment APL delay differs from 18') + if offset == 0: + require(np.all(traces['applied'][:min(18, steps)].view(np.uint32) == 0), 'Fresh APL application padding is not positive zero') + hashes, queues = run.get('state_hashes'), run.get('queues') + if hashes is not None: + require(len(hashes) == steps, 'State hash coverage incomplete') + for t, row in enumerate(hashes): + require(set(row) == {'tick', *HASH_FIELDS} and type(row['tick']) is int and row['tick'] == t and + all(_sha(row[key]) for key in HASH_FIELDS), 'Invalid state hash row') + for name in ('v', 'g', 'ref'): + raw = np.ascontiguousarray(state[name], dtype=state[name].dtype.newbyteorder('<')).tobytes() + require(hashes[-1][name + '_sha256'] == hashlib.sha256(raw).hexdigest(), 'Last state hash differs from terminal ' + name) + if queues is not None: + require(len(queues) == steps, 'Queue coverage incomplete') + for t, row in enumerate(queues): + require(set(row) == {'tick', *QUEUE_FIELDS, 'joint_sha256'} and type(row['tick']) is int and row['tick'] == t and + type(row['count']) is int and type(row['accepted_count']) is int and 0 <= row['accepted_count'] <= row['count'] and + all(_sha(row[key]) for key in ('ordered_edge_sha256', 'accepted_mask_sha256', 'joint_sha256')), 'Invalid queue row') + return run + + +def _run(value, legacy=False): + return load_run(value, allow_legacy_am=legacy) if isinstance(value, (str, Path)) else validate_run(value) + + +def _activity(run): + terminal = run['terminal'] + result = {'spikes': len(run['state']['step']), 'requested': len(run['requests']), + 'accepted_requests': int(run['requests'][:, 3].sum(dtype=np.int64)), + 'terminal_apl_state_nonzero_values': int(np.count_nonzero(terminal['apl_state'])), + 'terminal_pending_nonzero_values': int(np.count_nonzero(terminal['apl_pending']))} + traces = run.get('traces', {}) + result['generated_h_nonzero_values'] = int(np.count_nonzero(traces['generated'])) if traces else None + result['applied_h_nonzero_values'] = int(np.count_nonzero(traces['applied'])) if traces else None + result['apl_nonzero_observed'] = bool(result['terminal_apl_state_nonzero_values'] or result['terminal_pending_nonzero_values'] or + (result['generated_h_nonzero_values'] or 0) or (result['applied_h_nonzero_values'] or 0)) + return result + + +def _pair_checks(left, right, prefix='pair'): + checks = {prefix + '.steps': left['steps'] == right['steps'], prefix + '.offset': left['global_tick_offset'] == right['global_tick_offset']} + for name in STATE_KEYS: + checks[prefix + '.state.' + name] = bit_equal(left['state'][name], right['state'][name]) + checks[prefix + '.requests_five_fields'] = bit_equal(left['requests'], right['requests']) + for name in ('spike_ring', 'apl_state', 'apl_pending', 'next_tick'): + checks[prefix + '.terminal.' + name] = bit_equal(left['terminal'][name], right['terminal'][name]) + for field in ('effective_weights_raw_sha256',): + a, b = left.get('summary', {}).get(field), right.get('summary', {}).get(field) + if a is not None or b is not None: + checks[prefix + '.' + field] = _sha(a) and a == b + availability = {'APL_traces': bool(left.get('traces')) and bool(right.get('traces')), + 'state_hashes': left.get('state_hashes') is not None and right.get('state_hashes') is not None, + 'queue_hashes': left.get('queues') is not None and right.get('queues') is not None} + if availability['APL_traces']: + for name in TRACE_FILES: + checks[prefix + '.trace.' + name] = bit_equal(left['traces'][name], right['traces'][name]) + if availability['state_hashes']: + checks[prefix + '.state_hashes'] = left['state_hashes'] == right['state_hashes'] + if availability['queue_hashes']: + checks[prefix + '.queues'] = left['queues'] == right['queues'] + return checks, availability + + +def compare_fresh(left, right, legacy_am=None): + left, right = _run(left), _run(right) + require(left['global_tick_offset'] == right['global_tick_offset'] == 0, 'Fresh comparison requires fresh runs') + checks, available = _pair_checks(left, right) + origins = [left.get('terminal_origin', 'caller_arrays'), right.get('terminal_origin', 'caller_arrays')] + if legacy_am is not None: + legacy = _run(legacy_am, legacy=True) + require(legacy['global_tick_offset'] == 0, 'AM control must be fresh') + extra, legacy_available = _pair_checks(left, legacy, 'AM') + checks.update(extra); available['AM'] = legacy_available + # The left run may be light. Do not thereby skip the independent full + # diagnostic comparison between the right reference and sealed AM. + extra, reference_available = _pair_checks(right, legacy, 'reference_vs_AM') + checks.update(extra); available['reference_vs_AM'] = reference_available + origins.append(legacy.get('terminal_origin', 'caller_arrays')) + activity = _activity(left) + exact = bool(all(checks.values())) + nonvacuous = activity['spikes'] > 0 and activity['apl_nonzero_observed'] + return {'schema': 'phase4an-fresh-engineering-comparison-v1', 'numerical_exact': exact, + 'nonvacuous_activity': nonvacuous, 'pass_all': exact and nonvacuous, + 'status': 'FAIL-NUMERICAL' if not exact else ('PASS-FRESH-SCOPE' if nonvacuous else 'INCOMPLETE-COVERAGE'), + 'checks': checks, 'failed_checks': [key for key, value in checks.items() if not value], 'activity': activity, + 'reference_comparisons_available': available, 'terminal_origins': origins, + 'cross_segment_validation_performed': False, 'temporal_validation_complete': False, + 'scope': 'Fresh event/state/request/terminal identity only; no continuation, learning, physiology or performance PASS', + 'independent_CSR_queue_reconstruction_performed': False, + 'accepted_nonzero_weight_edge_witness_verified': False, + 'external_seals_and_input_authority_verified_here': False} + + +def compare_segments(monolithic, segments): + mono = _run(monolithic) + parts = [_run(value) for value in segments] + require(len(parts) >= 2, 'Need a monolithic run and at least two segments') + origin = mono['global_tick_offset'] + if origin: + parent = mono.get('summary', {}).get('parent_checkpoint_manifest_sha256') + require(_sha(parent) and parts[0].get('summary', {}).get('parent_checkpoint_manifest_sha256') == parent, + 'Nonzero-origin comparisons must bind the same starting parent checkpoint') + cursor = origin + for part in parts: + require(part['global_tick_offset'] == cursor, 'Segment clock gap/overlap/order error') + require(len(part['state']['v']) == len(mono['state']['v']), 'Segment population changed') + cursor += part['steps'] + require(cursor == origin + mono['steps'], 'Segments do not cover the monolithic duration') + checks = {} + step = np.concatenate([part['state']['step'].astype(np.int64) + part['global_tick_offset'] for part in parts]) + neuron = np.concatenate([part['state']['neuron_idx'] for part in parts]) + checks['concatenated_global_events'] = np.array_equal(step, mono['state']['step'].astype(np.int64) + origin) and bit_equal(neuron, mono['state']['neuron_idx']) + counts = np.zeros_like(mono['state']['counts'], dtype=np.int64) + for part in parts: + counts += part['state']['counts'].astype(np.int64) + checks['sum_segment_counts'] = np.array_equal(counts, mono['state']['counts'].astype(np.int64)) + requests = [] + for part in parts: + rows = part['requests'].copy(); rows[:, 0] += part['global_tick_offset']; requests.append(rows) + mono_requests = mono['requests'].copy(); mono_requests[:, 0] += origin + checks['concatenated_requests_five_fields'] = bit_equal(np.concatenate(requests), mono_requests) + last = parts[-1] + for name in ('v', 'g', 'ref'): + checks['terminal.' + name] = bit_equal(last['state'][name], mono['state'][name]) + checks['terminal.normalized_spike_ring'] = bit_equal(normalized_ring(last['terminal']), normalized_ring(mono['terminal'])) + for name in ('apl_state', 'apl_pending'): + checks['terminal.' + name] = bit_equal(last['terminal'][name], mono['terminal'][name]) + expected_weight = mono.get('summary', {}).get('effective_weights_raw_sha256') + if expected_weight is not None: + checks['unchanged_effective_weights'] = _sha(expected_weight) and all(part.get('summary', {}).get('effective_weights_raw_sha256') == expected_weight for part in parts) + trace_available = bool(mono.get('traces')) and all(part.get('traces') for part in parts) + hash_available = mono.get('state_hashes') is not None and all(part.get('state_hashes') is not None for part in parts) + queue_available = mono.get('queues') is not None and all(part.get('queues') is not None for part in parts) + if trace_available: + for name in TRACE_FILES: + checks['concatenated_APL.' + name] = all(bit_equal(part['traces'][name], mono['traces'][name][part['global_tick_offset'] - origin:part['global_tick_offset'] - origin + part['steps']]) for part in parts) + if hash_available: + checks['concatenated_per_tick_state_hashes'] = all(all(row[name] == mono['state_hashes'][part['global_tick_offset'] - origin + tick][name] for name in HASH_FIELDS) + for part in parts for tick, row in enumerate(part['state_hashes'])) + if queue_available: + checks['concatenated_queue_order_and_acceptance'] = all(all(row[name] == mono['queues'][part['global_tick_offset'] - origin + tick][name] for name in QUEUE_FIELDS) + for part in parts for tick, row in enumerate(part['queues'])) + boundary_rows = [] + events = mono['state']['step'].astype(np.int64) + origin + for before, part in zip(parts, parts[1:]): + cut, stop = part['global_tick_offset'], part['global_tick_offset'] + part['steps'] + emitted_before = (events < cut) & (events + 18 >= cut) + arriving_this_segment = emitted_before & (events + 18 < stop) + pending = before['terminal']['apl_pending'] + consume = min(part['steps'], 18) + spike_carry = normalized_ring(before['terminal']) + # Future local arrival tick j consumes normalized slot j+1. This also + # covers spikes inherited from before a nonzero monolithic origin, + # which are legitimately absent from that run's local event log. + pending_spikes = int(spike_carry[1:].sum(dtype=np.int64)) + arriving_spikes = int(spike_carry[1:consume + 1].sum(dtype=np.int64)) + if cut - origin >= 18: + checks['boundary.' + str(cut) + '.ring_pending_event_count'] = pending_spikes == int(emitted_before.sum()) + # Slots not overwritten by this short segment must remain bit-identical. + if part['steps'] < 19: + checks['boundary.' + str(cut) + '.unwritten_spike_slots'] = bit_equal(part['terminal']['spike_ring'][part['steps']:], spike_carry[part['steps']:]) + if part['steps'] < 18: + checks['boundary.' + str(cut) + '.unconsumed_APL'] = bit_equal(part['terminal']['apl_pending'][:18 - part['steps']], pending[part['steps']:]) + if part.get('traces'): + checks['boundary.' + str(cut) + '.applied_parent_APL'] = bit_equal(part['traces']['applied'][:consume], pending[:consume]) + queue_rows = (part['queues'][:consume] if part.get('queues') is not None else + mono['queues'][cut - origin:cut - origin + consume] if mono.get('queues') is not None else None) + boundary_rows.append({'global_tick': cut, 'next_segment_steps': part['steps'], + 'pending_source_spike_events': pending_spikes, 'source_spikes_arriving_in_next_segment': arriving_spikes, + 'visible_local_log_source_spikes_arriving_in_next_segment': int(arriving_this_segment.sum()), + 'pending_source_spike_origin': 'recorded normalized ring slots; includes pre-origin carry', + 'observed_carried_arriving_edges': sum(row['count'] for row in queue_rows) if queue_rows is not None else None, + 'observed_carried_accepted_edges': sum(row['accepted_count'] for row in queue_rows) if queue_rows is not None else None, + 'carried_APL_nonzero_pending_values': int(np.count_nonzero(pending)), + 'carried_APL_nonzero_values_applied_in_next_segment': int(np.count_nonzero(pending[:consume])), + 'carried_APL_nonzero_rows_applied_in_next_segment': int(np.count_nonzero(np.any(pending[:consume] != 0, axis=1)))}) + source_arrivals = sum(row['source_spikes_arriving_in_next_segment'] for row in boundary_rows) + edge_counts = [row['observed_carried_arriving_edges'] for row in boundary_rows] + observed_edges = sum(value for value in edge_counts if value is not None) if any(value is not None for value in edge_counts) else None + applied_values = sum(row['carried_APL_nonzero_values_applied_in_next_segment'] for row in boundary_rows) + activity = _activity(mono) + coverage = {'boundaries': boundary_rows, 'source_spikes_crossing_boundaries_and_due_in_receiver': source_arrivals, + 'observed_carried_arriving_edges': observed_edges, 'carried_APL_nonzero_applied_values': applied_values, + 'boundary_pending_event_counts_may_overlap_across_short_segments': True, + 'actual_edge_arrival_coverage_known': observed_edges is not None} + nonvacuous = bool(activity['spikes'] > 0 and activity['apl_nonzero_observed'] and source_arrivals > 0 and + observed_edges is not None and observed_edges > 0 and applied_values > 0) + exact = bool(all(checks.values())) + return {'schema': 'phase4an-segment-engineering-comparison-v1', 'numerical_exact': exact, + 'nonvacuous_temporal_coverage': nonvacuous, 'temporal_validation_complete': exact and nonvacuous, + 'pass_all': exact and nonvacuous, + 'status': 'FAIL-NUMERICAL' if not exact else ('PASS-SEGMENT-SCOPE' if nonvacuous else 'INCOMPLETE-COVERAGE'), + 'checks': checks, 'failed_checks': [key for key, value in checks.items() if not value], + 'activity': activity, 'coverage': coverage, 'segments': len(parts), 'steps': mono['steps'], 'global_tick_offset': origin, + 'reference_comparisons_available': {'APL_traces': trace_available, 'state_hashes': hash_available, 'queue_hashes': queue_available}, + 'queue_joint_hash_compared': False, 'queue_joint_hash_exclusion_reason': 'local tick header differs; ordered-edge/mask hashes and counts compared when available', + 'scope': 'Explicit finite engineering trajectory/terminal/carry checks only; no learning, physiology, or performance PASS', + 'independent_CSR_queue_reconstruction_performed': False, + 'accepted_nonzero_weight_edge_witness_verified': False, + 'external_seals_and_input_authority_verified_here': False} diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_inputs.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_inputs.py new file mode 100644 index 0000000..8b7de96 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_inputs.py @@ -0,0 +1,152 @@ +"""Pure exact input derivations for a future preregistered engineering gate. + +No I/O, random draws, scientific execution or registration. The coordinator +must bind the original case/overlay and every static dependency to the sealed +AM manifest before using these helpers. A self-consistent dictionary is not +independent input authority. Derivation outputs are copies; validate_requests +only checks and returns its original, unmodified input. +""" +from __future__ import annotations + +import copy +import re +from pathlib import PurePosixPath + +import numpy as np + + +N = 166700 +AM_HELPER_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4' +MASK_SHA = '8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b' + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def _positive_int(value, name): + require(type(value) is int and 0 < value <= np.iinfo(np.int32).max, 'Invalid ' + name) + + +def _sha(value): + return type(value) is str and re.fullmatch('[0-9a-f]{64}', value) is not None + + +def _absolute(value): + return (type(value) is str and value.startswith('/') and not value.startswith('//') and + PurePosixPath(value).is_absolute() and + '..' not in PurePosixPath(value).parts) + + +def validate_requests(requests, steps): + _positive_int(steps, 'steps') + require(type(requests) is dict and set(requests) == {'tick', 'neuron_idx'}, 'Exact sparse request schema required') + for key, value in requests.items(): + require(isinstance(value, np.ndarray) and value.dtype == np.int32 and value.ndim == 1, + 'Request requires int32 vector: ' + key) + tick, neuron = requests['tick'], requests['neuron_idx'] + require(tick.shape == neuron.shape, 'Request shape mismatch') + require(np.all((tick >= 0) & (tick < steps)) and np.all((neuron >= 0) & (neuron < N)), 'Request outside fixed trial/canonical set') + key = tick.astype(np.int64) * N + neuron + require(len(key) < 2 or np.all(key[1:] > key[:-1]), 'Requests must be sorted and unique; no implicit deduplication') + return requests + + +def slice_requests(requests, steps, start, stop): + """Half-open exact slice; translate ticks only, never redraw or reorder.""" + validate_requests(requests, steps) + require(type(start) is int and type(stop) is int and 0 <= start < stop <= steps, 'Invalid half-open input interval') + lo, hi = np.searchsorted(requests['tick'], (start, stop)) + result = {'tick': (requests['tick'][lo:hi] - np.int32(start)).copy(), + 'neuron_idx': requests['neuron_idx'][lo:hi].copy()} + validate_requests(result, stop - start) + return result + + +def verify_partition(original, steps, intervals, parts): + """Verify complete coverage plus exact request reconstruction, even blanks.""" + validate_requests(original, steps) + require(type(intervals) in (list, tuple) and type(parts) in (list, tuple) and + len(intervals) == len(parts) > 0, 'Partition needs all intervals and parts') + end, reconstructed = 0, {'tick': [], 'neuron_idx': []} + for interval, part in zip(intervals, parts): + require(type(interval) in (list, tuple) and len(interval) == 2, 'Invalid interval schema') + start, stop = interval + require(type(start) is int and type(stop) is int and start == end and start < stop <= steps, + 'Partition gap/overlap/nonpositive interval') + validate_requests(part, stop - start) + expected = slice_requests(original, steps, start, stop) + require(all(part[k].tobytes() == expected[k].tobytes() for k in part), 'Partition changes actual requests') + reconstructed['tick'].append(part['tick'].astype(np.int64) + start) + reconstructed['neuron_idx'].append(part['neuron_idx']) + end = stop + require(end == steps, 'Partition omits the terminal interval') + require(np.array_equal(np.concatenate(reconstructed['tick']), original['tick']) and + np.array_equal(np.concatenate(reconstructed['neuron_idx']), original['neuron_idx']), 'Reconstructed input mismatch') + return {'complete': True, 'intervals': [list(x) for x in intervals], 'steps': steps, + 'requests': len(original['tick']), 'random_draws': 0, 'input_reselected': False} + + +def derive_spec(original, *, start, stop, budget_root, output_budget_bytes, parent_sha=None): + """Explicit new case admission/budget labels; frozen model fields unchanged. + + New budget_root must be outside the OLD budget tree. Full source/data/ + ancestor containment checks remain the runtime/coordinator's responsibility. + A nonzero interval requires the actual parent manifest's external SHA; + future parent hashes must not be fabricated during preregistration. + """ + require(type(original) is dict and original.get('kind') == 'fullgraph', 'Original fullgraph spec required') + steps = original.get('steps') + _positive_int(steps, 'original steps') + require(original.get('dt_ms') == .1 and original.get('duration_ms') == steps * .1 and + type(original.get('apl_delay_steps')) is int and original['apl_delay_steps'] == 18 and + original.get('record_history') is False and original.get('training') is False and + original.get('within_trial_weight_updates', False) is False, 'Original fixed D18 trial contract required') + require(not any('checkpoint' in k.lower() or k in ('global_tick_offset', 'resume', 'continuation', 'initial_state') + for k in original), 'Original must be a fresh case') + require(type(start) is int and type(stop) is int and 0 <= start < stop <= steps, 'Invalid case interval') + require(_absolute(budget_root) and _absolute(original.get('budget_root')), 'Explicit absolute budget roots required') + new, old = PurePosixPath(budget_root), PurePosixPath(original['budget_root']) + require(not new.is_relative_to(old) and not old.is_relative_to(new), 'New output budget must not overlap old experiment tree') + require(type(output_budget_bytes) is int and output_budget_bytes > 0, 'Explicit positive byte budget required') + require((start == 0 and parent_sha is None) or (start > 0 and _sha(parent_sha)), 'Only a continued interval needs a real parent SHA') + result = copy.deepcopy(original) + result.update(steps=stop - start, duration_ms=(stop - start) * .1, max_steps=steps, + budget_root=budget_root, output_budget_bytes=output_budget_bytes, + within_trial_weight_updates=False, + outer_protocol='phase4an-engineering-validation-no-training') + if start: + result.update(global_tick_offset=start, parent_checkpoint_manifest_sha256=parent_sha) + changes = {key: {'present_before': key in original, 'before': copy.deepcopy(original.get(key)), + 'after': copy.deepcopy(value)} for key, value in result.items() + if key not in original or value != original[key]} + allowed = {'steps', 'duration_ms', 'max_steps', 'budget_root', 'output_budget_bytes', + 'within_trial_weight_updates', 'outer_protocol', 'global_tick_offset', + 'parent_checkpoint_manifest_sha256'} + require(set(changes) <= allowed and all(result[k] == v for k, v in original.items() if k not in allowed), + 'Unexpected scientific spec edit') + return result, {'input_interval': [start, stop], 'explicit_field_changes': changes, + 'all_other_fields_unchanged': True, 'random_draws': 0, 'training': False} + + +def adapt_overlay_manifest(original, am_helper_path): + """Translate observed AM manifest fields; preserve original overlay bytes. + + This function validates declarations only. Callers independently rehash + every referenced file against external AM/source authority before use. + """ + require(type(original) is dict and original.get('schema') == 'phase4am-overlay-v1' and + original.get('overlay_name') in ('zero', 'static25') and + original.get('training') is False and original.get('fixed_for_whole_trial') is True, + 'Only the fixed AM zero/static25 overlays are in this engineering gate') + require(original.get('mask_sha256') == MASK_SHA and _absolute(am_helper_path), 'Original mask/helper identity required') + names = ('overlay_name', 'overlay_sha256', 'mask_path', 'mask_sha256', + 'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir') + require(all(k in original for k in names), 'Missing observed AM manifest field') + require(all(_sha(original[k]) for k in ('overlay_sha256', 'mask_sha256', 'base_candidate_sha256')), + 'Invalid external overlay/base SHA') + require(all(_absolute(original[k]) for k in ('mask_path', 'base_candidate_path', 'aj_model_dir')), + 'Invalid external overlay/base path') + return dict(schema='phase4an-overlay-v1', **{k: copy.deepcopy(original[k]) for k in names}, + am_overlay_path=am_helper_path, am_overlay_sha256=AM_HELPER_SHA) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_plan.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_plan.py new file mode 100644 index 0000000..cedf887 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_plan.py @@ -0,0 +1,153 @@ +"""Pure fixed 32-call engineering inventory; no I/O, GPU or input preparation. + +Rows describe required executions and comparisons, not evidence they occurred. +The external AM reference is a symbolic role: a coordinator must bind its actual +case, overlay, sealed outputs and hashes before execution. A validated plan does +not prove input authority, comparison success, conditioning or learning. +All public results are independently constructed mutable copies. +""" +from __future__ import annotations + + +SCHEMA = 'phase4an-engineering-plan-draft-v1' +OVERLAYS = ('zero', 'static25') +MODES = ('reference', 'light') +CHAIN_LENGTHS = (5, 13, 1, 481, 500) +PREFIX_ENDS = (18, 19, 500) + + +def _fresh_id(overlay, mode, repeat=0): + return f'{overlay}--fresh--{mode}-r{repeat}' + + +def _prefix_id(overlay, end): + return f'{overlay}--prefix--reference-t{end:04d}' + + +def _chain_id(overlay, mode, index): + return f'{overlay}--chain--{mode}-s{index:02d}' + + +def _comparison(check, row, *, reference=None, external=None, scope, candidates=None): + """Exact descriptors; start/stop are always absolute, half-open ticks.""" + return dict(check=check, reference_row_id=reference, external_reference=external, + candidate_row_ids=[row['row_id']] if candidates is None else list(candidates), + start=row['start'], stop=row['stop'], scope=scope) + + +def _row(identity, overlay, mode, kind, start, stop, parent=None): + return dict(row_id=identity, overlay=overlay, mode=mode, kind=kind, + start=start, stop=stop, steps=stop - start, parent_row_id=parent, + expected_comparisons=[]) + + +def _terminal_reference(overlay, stop): + if stop == 5: + return _chain_id(overlay, 'reference', 0) + if stop == 1000: + return _fresh_id(overlay, 'reference') + return _prefix_id(overlay, stop) + + +def build_plan(): + """Return the sole current proposal: 32 calls / 11,074 full-graph ticks. + + kind='chain' with parent_row_id=None is its fresh first segment, NOT a + continuation from an assumed zero checkpoint. Later segments explicitly + reference their predecessor. A chain's local request/event coordinates + are translated using start; terminal.next_tick remains its local phase. + + All fresh/prefix rows globally precede chains. Reference chains precede + light chains, allowing the reference first segment to supply prefix 5. + No extra prefix-5 execution, synthetic 52-call matrix, long idle run, + input selection, training, parameter update or executor is implemented. + + Comparison scopes: + reference_full/reference_prefix/reference_slice: include reference + tick-state hashes, APL traces, queues, requests, events and states; + light_recorded: requests.csv, events/counts, final state, APL state, + normalized pending queues and finite/weight/arrival accounting; + terminal_state_and_normalized_queues: no comparison of segment counts + against cumulative prefix counts; counts are handled by chain_exact; + parent_state_and_pending_queues: bound parent restoration/carry checks. + A comparator must implement these scopes from artifacts, not this plan's + existence. Shift local queue headers before comparing absolute timelines. + """ + rows = [] + for overlay in OVERLAYS: + for mode, repeat in (('reference', 0), ('light', 0), ('light', 1)): + row = _row(_fresh_id(overlay, mode, repeat), overlay, mode, 'fresh', 0, 1000) + scope = 'reference_full' if mode == 'reference' else 'light_recorded' + row['expected_comparisons'].append(_comparison( + 'am_exact', row, external='sealed_AM_selected_case_same_overlay_r0', scope=scope)) + if mode == 'light': + reference = _fresh_id(overlay, 'reference' if repeat == 0 else 'light') + row['expected_comparisons'].append(_comparison( + 'fresh_exact', row, reference=reference, scope='light_recorded')) + rows.append(row) + for end in PREFIX_ENDS: + row = _row(_prefix_id(overlay, end), overlay, 'reference', 'prefix', 0, end) + row['expected_comparisons'].append(_comparison( + 'prefix_of_reference', row, reference=_fresh_id(overlay, 'reference'), scope='reference_prefix')) + rows.append(row) + + for overlay in OVERLAYS: + for mode in MODES: + start, parent, members = 0, None, [] + for index, length in enumerate(CHAIN_LENGTHS): + row = _row(_chain_id(overlay, mode, index), overlay, mode, 'chain', start, start + length, parent) + members.append(row['row_id']) + if mode == 'reference': + row['expected_comparisons'].append(_comparison( + 'segment_of_reference', row, reference=_fresh_id(overlay, 'reference'), scope='reference_slice')) + else: + row['expected_comparisons'].append(_comparison( + 'segment_exact', row, reference=_chain_id(overlay, 'reference', index), scope='light_recorded')) + terminal = _terminal_reference(overlay, row['stop']) + if terminal != row['row_id']: + row['expected_comparisons'].append(_comparison( + 'checkpoint_exact', row, reference=terminal, scope='terminal_state_and_normalized_queues')) + if parent is not None: + row['expected_comparisons'].append(_comparison( + 'parent_carry', row, reference=parent, scope='parent_state_and_pending_queues')) + if index == len(CHAIN_LENGTHS) - 1: + comparison = _comparison('chain_exact', row, reference=_fresh_id(overlay, mode), + scope='reference_full' if mode == 'reference' else 'light_recorded', candidates=members) + comparison['start'] = 0 + row['expected_comparisons'].append(comparison) + rows.append(row) + start, parent = row['stop'], row['row_id'] + + return dict(schema=SCHEMA, overlays=list(OVERLAYS), chain_lengths=list(CHAIN_LENGTHS), + rows=rows, total_calls=32, total_ticks=11074, + training=False, candidate_generated=False) + + +def _exact(actual, expected, path): + """JSON-shaped values with exact Python types; True is not integer 1.""" + if type(actual) is not type(expected): + raise ValueError(f'Plan type mismatch at {path}') + if type(expected) is dict: + if any(type(key) is not str for key in actual) or set(actual) != set(expected): + raise ValueError(f'Plan fields mismatch at {path}') + for key in expected: + _exact(actual[key], expected[key], path + '.' + key) + elif type(expected) is list: + if len(actual) != len(expected): + raise ValueError(f'Plan inventory length mismatch at {path}') + for index, (left, right) in enumerate(zip(actual, expected)): + _exact(left, right, f'{path}[{index}]') + elif actual != expected: + raise ValueError(f'Plan value mismatch at {path}') + + +def validate_plan(plan): + """Reject any noncanonical plan; return a new copy, never a PASS receipt. + + Exact canonical reconstruction rejects unknown fields, order changes, + omitted/extra overlays or calls, interval gaps, false parent links and + weakening/removal of required comparisons. No input files are read. + """ + canonical = build_plan() + _exact(plan, canonical, 'plan') + return canonical diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_prepare.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_prepare.py new file mode 100644 index 0000000..2a7f77c --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_prepare.py @@ -0,0 +1,532 @@ +"""Read-only AM authority verification and exclusive AN engineering preparation. + +Public prepare(model_manifest_sha, experiment_manifest_sha, final_sha) requires +three externally recorded, actual AM evidence hashes. It verifies both complete +AM seals once, checks their declared gates, then fixes the first planned r0 case. +It creates only templates/requests/overlay descriptors and a preregistration; +never a GPU trajectory or fictitious future checkpoint hash. The caller records +the returned preregistration SHA outside this experiment before execution. + +read_registration(expected_sha) rehashes the exact used source/input inventory +and control anchors without rerunning either scientific gate or full AM seal. +Future spec.json/checkpoint receipts are runtime artifacts, not preregistered +bytes. No frozen model module globals are modified; no Torch is imported. +""" +from __future__ import annotations + +import hashlib +import importlib.util +import json +from pathlib import Path, PurePosixPath +import re +import shutil +import sys + +import numpy as np + + +ROOT = Path('/data/digital-drosophila') +MODEL = Path(__file__).resolve().parent +EXP = ROOT / 'experiments/phase4an-engineering-validation' +AM_MODEL_REL = Path('models/male-cns-lif/wholebrain-plastic-interface-v1') +AM_EXP_REL = Path('experiments/phase4am-wholebrain-plastic-interface') +AK_REL = Path('experiments/phase4ak-apl-timing-validation') +AI_HELPER_REL = Path('models/male-cns-lif/pn-recruitment-audit-v1/publication.py') +AI_HELPER_SHA = '7a79c499098b74163e7bb16adabcbc54e7069dafa204d0b99c7f4c03d79f8016' +AM_PREREG_SHA = '465886b78ada08092c2c83de278844707499718977fb6dc6a7608391da0e5304' +AM_OVERLAY_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4' +FINAL_STATUS = 'PASS-EXACT-WHOLEBRAIN-PLASTIC-INTERFACE-NOT-LEARNING' +REG_SCHEMA = 'phase4an-engineering-registration-v1' +BUDGET = {'output_budget_bytes': 8 * 1024**3, 'minimum_free_reserve_bytes': 10 * 1024**3} +MODULES = ('runtime_light', 'runtime_continuation', 'engineering_inputs', 'engineering_plan', + 'engineering_compare', 'engineering_queue_audit', 'engineering_prepare', 'engineering_workflow', + 'engineering_scopes', 'engineering_analysis') +SOURCE_FILES = tuple(sorted([name + '.py' for name in MODULES] + ['test_' + name + '.py' for name in MODULES] + ['ENGINEERING_GATE_DRAFT.md'])) +REFERENCES = ('auditory-10hz', 'auditory-100hz', 'olfactory-50hz', 'mechanosensory-50hz', 'gustatory-50hz') +GRAPH_FILES = ('nodes.npy', 'edge_id.npy', 'src.npy', 'dst.npy', 'signed_raw.npy', 'ptr.npy', 'weights.npy') +APL_FILES = ('input_matrix.npy', 'output_matrix.npy', 'kc_globals.npy', 'apl_globals.npy') +_MODULE_CACHE = {} + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def digest(path): + value = hashlib.sha256() + with Path(path).open('rb') as stream: + for block in iter(lambda: stream.read(8 << 20), b''): + value.update(block) + return value.hexdigest() + + +def sha(value): + return type(value) is str and re.fullmatch('[0-9a-f]{64}', value) is not None + + +def read(path): + def unique(pairs): + result = {} + for key, value in pairs: + require(key not in result, 'Duplicate JSON key: ' + key) + result[key] = value + return result + return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=unique, + parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value))) + + +def save(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open('x', encoding='utf-8') as stream: + json.dump(value, stream, sort_keys=True, indent=2, ensure_ascii=False, allow_nan=False) + stream.write('\n') + + +def load_module(name, path, expected): + path = Path(path).resolve(strict=True) + raw = path.read_bytes() + require(hashlib.sha256(raw).hexdigest() == expected, 'Source pin mismatch: ' + str(path)) + identity = str(path), expected + if name in sys.modules: + require(_MODULE_CACHE.get(name) == (identity, sys.modules[name]), 'Unverified module namespace collision') + return sys.modules[name] + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + try: + exec(compile(raw, str(path), 'exec'), module.__dict__) + except BaseException: + sys.modules.pop(name, None) + raise + _MODULE_CACHE[name] = identity, module + return module + + +def source_inventory(model): + model = Path(model).resolve(strict=True) + result = {} + for name in SOURCE_FILES: + path = model / name + require(path.is_file() and not path.is_symlink() and path.resolve().parent == model, 'Required engineering source missing/linked: ' + name) + result[str(path)] = digest(path) + return result + + +def _relative(value): + require(type(value) is str and value and '\\' not in value, 'Invalid relative inventory path') + path = PurePosixPath(value) + require(not path.is_absolute() and str(path) == value and all(part not in ('', '.', '..') and ':' not in part for part in path.parts), + 'Noncanonical relative inventory path') + return path + + +def _safe_file(root, relative): + relative = _relative(relative) + root = Path(root).resolve(strict=True) + path = root + for part in relative.parts: + path = path / part + require(not path.is_symlink(), 'Linked authority/input file') + require(path.is_file() and path.resolve().is_relative_to(root), 'Missing or escaping input file') + return path + + +def _index(rows): + require(type(rows) is list and rows, 'Missing sealed inventory rows') + result = {} + for row in rows: + require(type(row) is dict and set(row) == {'path', 'bytes', 'sha256'} and + type(row['bytes']) is int and row['bytes'] >= 0 and sha(row['sha256']), 'Invalid sealed inventory row') + _relative(row['path']) + require(row['path'] not in result, 'Duplicate sealed inventory row') + result[row['path']] = row + return result + + +def _bind(inputs, path, expected=None): + path = Path(path).resolve(strict=True) + actual = digest(path) + require(expected is None or actual == expected, 'Authority/input SHA mismatch: ' + str(path)) + require(str(path) not in inputs or inputs[str(path)] == actual, 'Conflicting authority for input') + inputs[str(path)] = actual + return path + + +def _sealed_json(experiment, index, relative): + require(relative in index, 'Required authority absent from AM seal: ' + relative) + path = _safe_file(experiment, relative) + require(digest(path) == index[relative]['sha256'], 'Sealed JSON changed: ' + relative) + return read(path) + + +def validate_final_status(final, scientific, before, after, refs): + """Check actual AM final/summary/report schemas, never infer scientific PASS.""" + require(final.get('status') == scientific.get('status') == FINAL_STATUS, 'AM final status is not the required completed interface gate') + for key in ('zero_overlay_exact', 'static25_repeat_exact', 'old_integrity_pass', 'frozen_five_reference_pass', + 'frozen_eligibility_and_mask_unchanged', 'base_weights_unchanged'): + require(final.get(key) is True, 'AM final gate failed/missing: ' + key) + require(final.get('primary_trials_per_stage') == 120 and final.get('repeats_per_stage') == 24 and + scientific.get('primary_trials_per_stage') == 120 and scientific.get('technical_repeats_per_stage') == 24, + 'AM complete coverage counts changed') + for value in (final, scientific): + require(value.get('training') is False and value.get('learned_candidate') is False, 'AM interface must not claim training/learning') + require(scientific.get('zero_overlay_exact') is True and scientific.get('static25_repeat_exact') is True, 'Scientific summary gate mismatch') + require(type(before) is dict and type(after) is dict and len(before) == len(after) == 28 and set(before) == set(after), + 'AM must retain the same complete 28-root old-integrity inventory') + for name in before: + for report in (before[name], after[name]): + require(type(report) is dict and report.get('pass') is True, 'Old-integrity gate failed: ' + name) + if 'mismatches' in report: + require(report['mismatches'] == [], 'Old-integrity mismatch list is nonempty') + for key in ('manifest_sha256', 'checksums_sha256'): + if key in before[name] or key in after[name]: + require(sha(before[name].get(key)) and before[name][key] == after[name].get(key), 'Old-root control changed: ' + name) + require(refs.get('pass_all') is True and type(refs.get('trials')) is list and + [row.get('trial') for row in refs['trials']] == list(REFERENCES), 'Five old deterministic references are incomplete or reordered') + for row in refs['trials']: + require(type(row.get('exact')) is dict and set(row['exact']) == {'step', 'neuron_idx', 'counts', 'v', 'g'} and + all(value is True for value in row['exact'].values()), 'Old reference arrays are not exact') + + +def validate_planned(planned): + require(type(planned) is list and len(planned) == 144, 'AM planned coverage must be 144') + keys = [] + for row in planned: + require(type(row) is dict and type(row.get('name')) is str and re.fullmatch('[A-Za-z0-9_-]+', row['name']) and + row.get('cohort') in ('seed_validation', 'novel_to_aj') and type(row.get('repeat')) is int and row['repeat'] in (0, 1), + 'Invalid AM planned row identity') + keys.append((row['name'], row['repeat'])) + require(len(set(keys)) == 144 and sum(row['repeat'] == 0 for row in planned) == 120 and sum(row['repeat'] == 1 for row in planned) == 24, + 'AM primary/repeat set incomplete') + require(all((name, 0) in set(keys) for name, repeat in keys if repeat == 1), 'AM repeat lacks primary') + return next(dict(row) for row in planned if row['repeat'] == 0) + + +def validate_stage(experiment, index, stage, planned, root): + """Validate all 144 declared results against the already verified AM seal.""" + receipt = _sealed_json(experiment, index, 'audit/' + stage + '-complete.json') + require(receipt.get('stage') == stage and receipt.get('pass_all') is True and receipt.get('trials') == 144 and + receipt.get('primary_trials') == 120 and receipt.get('technical_repeats') == 24 and receipt.get('training') is False, + 'AM stage is incomplete: ' + stage) + completed = receipt.get('completed') + require(type(completed) is list and len(completed) == 144, 'AM stage completion inventory truncated') + for row, done in zip(planned, completed): + require(done.get('name') == row['name'] and done.get('repeat') == row['repeat'], 'AM stage completion order/identity mismatch') + prefix = 'gpu/' + stage + '/' + row['name'] + '-r' + str(row['repeat']) + '/' + expected = [dict(path=key[len(prefix):], bytes=value['bytes'], sha256=value['sha256']) for key, value in sorted(index.items()) + if key.startswith(prefix) and key != prefix + 'output-hashes.json'] + inventory = _sealed_json(experiment, index, prefix + 'output-hashes.json') + require(inventory == expected and inventory, 'AM completed output inventory differs from sealed payload') + require(done.get('output_hashes_sha256') == index[prefix + 'output-hashes.json']['sha256'] and + done.get('comparison_sha256') == index[prefix + 'comparison.json']['sha256'], 'AM stage receipt SHA mismatch') + summary = _sealed_json(experiment, index, prefix + 'summary.json') + compare = _sealed_json(experiment, index, prefix + 'comparison.json') + folder, case = experiment / prefix.rstrip('/'), root / AK_REL / 'cases' / row['cohort'] / row['name'] + require(summary.get('case') == row['name'] and summary.get('complete') is True and summary.get('pass_all') is True and + summary.get('all_finite') is True and summary.get('steps') == summary.get('completed_ticks') == 1000, + 'AM selected stage contains a failed/truncated trial') + require(compare.get('pass_all') is True and compare.get('overlay_name') == stage and compare.get('case_dir') == str(case) and + compare.get('right') == str(folder), 'AM comparison identity/gate mismatch') + require(type(compare.get('checks')) is dict and compare['checks'] and all(value is True for value in compare['checks'].values()) and + compare.get('failed_checks') == [] and 'error' not in compare and 'read_manifest_error' not in compare and + compare.get('full_queue_reconstructed') is True, 'AM comparison has a hidden negative/missing numerical gate') + mode = 'zero' if stage == 'zero' else ('repeat' if row['repeat'] else 'self') + left = root / AK_REL / 'gpu' / row['cohort'] / (row['name'] + '-r' + str(row['repeat'])) if stage == 'zero' else ( + experiment / 'gpu' / stage / (row['name'] + '-r0') if row['repeat'] else folder) + require(compare.get('mode') == mode and compare.get('left') == str(left), 'AM comparison reference/mode mismatch') + return receipt + + +def _layout(root, model, experiment): + root, model = Path(root).resolve(strict=True), Path(model).resolve(strict=True) + experiment = Path(experiment).resolve(strict=False) + require(experiment == root / 'experiments/phase4an-engineering-validation' and model.is_relative_to(root / 'models/male-cns-lif'), + 'Unexpected project/output layout') + require(not experiment.exists() or (experiment.is_dir() and not any(experiment.iterdir())), 'Refusing populated experiment; preserve all partials') + return root, model, experiment + + +def _budget(experiment): + experiment = Path(experiment) + used = sum(path.stat().st_size for path in experiment.rglob('*') if path.is_file()) if experiment.exists() else 0 + require(used < BUDGET['output_budget_bytes'], '8 GiB engineering budget exhausted; preserve partials') + free = shutil.disk_usage(experiment.parent).free + require(free >= BUDGET['minimum_free_reserve_bytes'] + BUDGET['output_budget_bytes'] - used, + 'Insufficient capacity for remaining 8 GiB budget plus 10 GiB free reserve') + return dict(BUDGET, used_bytes=used, free_bytes=free) + + +def budget(): + return _budget(EXP) + + +def verify_authority(anchors): + """Full read-only AM seal recheck for the finalizer; no gate rerun.""" + require(sys.dont_write_bytecode, 'Use python -B for immutable authority verification') + root = ROOT.resolve(strict=True) + values = [(root / AM_MODEL_REL, 'am_model_manifest_sha256'), (root / AM_EXP_REL, 'am_experiment_manifest_sha256')] + for directory, key in values: + require(sha(anchors.get(key)) and digest(directory / 'manifest.json') == anchors[key], 'External AM manifest anchor mismatch') + require(sha(anchors.get('am_final_sha256')) and digest(root / AM_EXP_REL / 'audit/final-status.json') == anchors['am_final_sha256'], + 'External AM final anchor mismatch') + require(anchors.get('am_preregistration_sha256') == AM_PREREG_SHA and + digest(root / AM_EXP_REL / 'protocol/preregistered.json') == AM_PREREG_SHA, 'AM preregistration anchor mismatch') + require(anchors.get('AI_publication_helper_sha256') == AI_HELPER_SHA, 'Publication helper authority changed') + publication = load_module('phase4an_pinned_AI_publication', root / AI_HELPER_REL, AI_HELPER_SHA) + result = {'model': publication.verify_seal(values[0][0]), 'experiment': publication.verify_seal(values[1][0])} + require(result['model']['manifest_sha256'] == anchors['am_model_manifest_sha256'] and + result['experiment']['manifest_sha256'] == anchors['am_experiment_manifest_sha256'], 'AM seal changed during verification') + return result + + +def _old_integrity(root, context_sha, expected): + """Reuse the pinned AM read-only integrity chain, not a brain runtime.""" + context_path = root / AM_MODEL_REL / 'context.py' + previous_path = list(sys.path) + torch_before = 'torch' in sys.modules + try: + context = load_module('phase4an_sealed_AM_integrity', context_path, context_sha) + require(context.ROOT.resolve() == root.resolve(), 'AM integrity helper belongs to another root') + result = context.integrity() + finally: + sys.path[:] = previous_path + require(torch_before or 'torch' not in sys.modules, 'Integrity unexpectedly imported Torch') + require(type(result) is dict and len(result) == 28 and set(result) == set(expected) and + all(type(row) is dict and row.get('pass') is True for row in result.values()), 'Current 28-root old-integrity gate failed') + for name, row in result.items(): + if 'mismatches' in row: + require(row['mismatches'] == [], 'Current old-integrity mismatch list is nonempty') + for key in ('manifest_sha256', 'checksums_sha256', 'reference_sha256'): + if key in expected[name] or key in row: + require(sha(row.get(key)) and row[key] == expected[name].get(key), 'Old integrity authority changed: ' + name) + return result + + +def verify_old_integrity(registration): + """Rehash the pinned 28 old roots/snapshot via original CPU-only code.""" + require(sys.dont_write_bytecode, 'Use python -B for old-integrity verification') + path = ROOT / AM_MODEL_REL / 'context.py' + expected_sha = registration['input_sha256'].get(str(path.resolve(strict=True))) + require(sha(expected_sha) and digest(path) == expected_sha, 'AM integrity source is not a registered input') + before_path = EXP / 'regression/old-integrity-before.json' + require(digest(before_path) == registration['input_sha256'].get(str(before_path.resolve(strict=True))), 'Before-integrity evidence changed') + return _old_integrity(ROOT, expected_sha, read(before_path)) + + +def _prepare(root, model, experiment, am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha): + require(sys.dont_write_bytecode, 'Preparation requires python -B; old sources must remain byte-for-byte read-only') + require(all(sha(value) for value in (am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha)), 'Three actual external AM SHA-256 anchors are mandatory') + root, model, experiment = _layout(root, model, experiment) + _budget(experiment) + sources = source_inventory(model) + inputs_module = load_module('phase4an_prepare_inputs', model / 'engineering_inputs.py', sources[str(model / 'engineering_inputs.py')]) + plan_module = load_module('phase4an_prepare_plan', model / 'engineering_plan.py', sources[str(model / 'engineering_plan.py')]) + runtime = load_module('phase4an_prepare_light', model / 'runtime_light.py', sources[str(model / 'runtime_light.py')]) + publication = load_module('phase4an_pinned_AI_publication', root / AI_HELPER_REL, AI_HELPER_SHA) + am_model, am = root / AM_MODEL_REL, root / AM_EXP_REL + require(digest(am_model / 'manifest.json') == am_model_manifest_sha and digest(am / 'manifest.json') == am_experiment_manifest_sha and + digest(am / 'audit/final-status.json') == am_final_sha, 'External AM authority mismatch') + verified = {'model': publication.verify_seal(am_model), 'experiment': publication.verify_seal(am)} + require(verified['model']['manifest_sha256'] == am_model_manifest_sha and verified['experiment']['manifest_sha256'] == am_experiment_manifest_sha, + 'AM controls changed during full seal verification') + idx = _index(verified['experiment']['files']); midx = _index(verified['model']['files']) + reg = _sealed_json(am, idx, 'protocol/preregistered.json') + require(idx['protocol/preregistered.json']['sha256'] == AM_PREREG_SHA, 'AM preregistration authority differs from the fixed anchor') + planned = reg.get('planned'); selected = validate_planned(planned) + final = _sealed_json(am, idx, 'audit/final-status.json') + scientific = _sealed_json(am, idx, 'audit/scientific-summary.json') + require(final.get('scientific_summary_sha256') == idx['audit/scientific-summary.json']['sha256'], 'AM final does not bind scientific summary') + before = _sealed_json(am, idx, 'regression/old-integrity-before.json') + after = _sealed_json(am, idx, 'regression/old-integrity-after.json') + refs = _sealed_json(am, idx, 'regression/frozen-five.json') + validate_final_status(final, scientific, before, after, refs) + for stage in ('zero', 'static25'): + validate_stage(am, idx, stage, planned, root) + current_old = _old_integrity(root, midx['context.py']['sha256'], after) + inputs = {} + anchors = {'am_model_manifest_sha256': am_model_manifest_sha, 'am_experiment_manifest_sha256': am_experiment_manifest_sha, + 'am_final_sha256': am_final_sha, 'am_preregistration_sha256': AM_PREREG_SHA, 'AI_publication_helper_sha256': AI_HELPER_SHA} + for path, expected in ((am_model / 'manifest.json', am_model_manifest_sha), (am / 'manifest.json', am_experiment_manifest_sha), + (am / 'audit/final-status.json', am_final_sha), (root / AI_HELPER_REL, AI_HELPER_SHA), + (am_model / 'overlay.py', AM_OVERLAY_SHA)): + _bind(inputs, path, expected) + require(midx.get('overlay.py', {}).get('sha256') == AM_OVERLAY_SHA, 'AM model seal does not contain the pinned overlay helper') + for name in ('context.py', 'config.json'): + _bind(inputs, am_model / name, midx[name]['sha256']) + for path in (am_model / 'checksums.sha256', am / 'checksums.sha256'): + _bind(inputs, path) + for relative in ('protocol/preregistered.json', 'protocol/prepared-hashes.json', 'audit/scientific-summary.json', 'audit/zero-complete.json', 'audit/static25-complete.json', + 'regression/old-integrity-before.json', 'regression/old-integrity-after.json', 'regression/frozen-five.json'): + _bind(inputs, am / relative, idx[relative]['sha256']) + am_inputs = {} + for row in reg['inputs']: + require(set(row) == {'path', 'sha256'} and sha(row['sha256']), 'Invalid AM registered input row') + _relative(row['path']); require(row['path'] not in am_inputs, 'Duplicate AM registered input') + am_inputs[row['path']] = row['sha256'] + def authority(path): + path = Path(path).resolve(strict=True) + require(path.is_relative_to(root), 'AM input leaves project root') + relative = path.relative_to(root).as_posix() + if path.is_relative_to(am): + expected = idx[path.relative_to(am).as_posix()]['sha256'] + else: + require(relative in am_inputs, 'Dependency absent from AM preregistration: ' + relative) + expected = am_inputs[relative] + return _bind(inputs, path, expected) + original_dir = root / AK_REL / 'cases' / selected['cohort'] / selected['name'] + authority(original_dir / 'spec.json'); authority(original_dir / 'requests.npz') + original = read(original_dir / 'spec.json') + require(original['steps'] == 1000 and original['kind'] == 'fullgraph' and original['name'] == selected['name'], 'Fixed selected AM case changed') + with np.load(original_dir / 'requests.npz', allow_pickle=False) as archive: + require(set(archive.files) == {'tick', 'neuron_idx'} and len(archive.files) == 2, 'Unexpected original request arrays') + requests = {key: archive[key].copy() for key in archive.files} + inputs_module.validate_requests(requests, 1000) + for name in GRAPH_FILES: + authority(Path(original['graph_dir']) / name) + for name in APL_FILES: + authority(Path(original['apl_bundle_dir']) / name) + authority(original['coefficients_path']) + selected_outputs = {} + for stage in ('zero', 'static25'): + prefix = 'gpu/' + stage + '/' + selected['name'] + '-r0/' + selected_outputs[stage] = str(am / prefix.rstrip('/')) + for relative, row in idx.items(): + if relative.startswith(prefix): + _bind(inputs, am / relative, row['sha256']) + adapted = {} + for stage in ('zero', 'static25'): + old_manifest = authority(am / 'overlays' / (stage + '.json')) + overlay_file = authority(am / 'overlays' / (stage + '.npz')) + declaration = read(old_manifest) + require(declaration['overlay_name'] == stage and declaration['overlay_sha256'] == digest(overlay_file), 'AM overlay declaration mismatch') + for label in ('mask', 'base_candidate'): + require(digest(authority(declaration[label + '_path'])) == declaration[label + '_sha256'], 'Overlay external array authority mismatch') + adapted[stage] = inputs_module.adapt_overlay_manifest(declaration, str(am_model / 'overlay.py')) + require(adapted[stage]['am_overlay_sha256'] == AM_OVERLAY_SHA, 'Adapted helper pin differs') + for relative, expected in runtime.PINS.items(): + _bind(inputs, model.parent / relative, expected) + plan = plan_module.validate_plan(plan_module.build_plan()) + require(plan['total_calls'] == 32 and plan['total_ticks'] == 11074, 'Engineering plan matrix changed') + # No writes have occurred before all authority/source/space checks above. + experiment.mkdir(parents=True, exist_ok=True) + try: + for folder in ('protocol', 'cases', 'overlays', 'audit', 'runs', 'receipts', 'logs', 'regression', 'extension-build'): + (experiment / folder).mkdir(exist_ok=False) + save(experiment / 'regression/old-integrity-before.json', current_old) + _bind(inputs, experiment / 'regression/old-integrity-before.json') + for source, expected in sources.items(): + target = experiment / 'protocol/source-snapshot' / Path(source).name + publication._copy_verified(Path(source), target, Path(source).stat().st_size, expected) + _bind(inputs, target, expected) + save(experiment / 'protocol/AM-seal-verification.json', verified) + save(experiment / 'protocol/AM-gate-authority.json', dict(anchors=anchors, all_144_per_stage_declared_gates_pass=True, + old_roots=28, old_references=list(REFERENCES), verification_scope='Full AM seals and finalized declared gate evidence; no new scientific execution')) + for name in ('AM-seal-verification.json', 'AM-gate-authority.json'): + _bind(inputs, experiment / 'protocol' / name) + overlays, cases = {}, {} + for stage, value in adapted.items(): + manifest_path = experiment / 'overlays' / (stage + '.json') + save(manifest_path, value) + _bind(inputs, manifest_path) + overlays[stage] = {'overlay_file': str(am / 'overlays' / (stage + '.npz')), 'overlay_manifest': str(manifest_path)} + for row in plan['rows']: + case = experiment / 'cases' / row['row_id']; case.mkdir(exist_ok=False) + template = {'schema': 'phase4an-engineering-case-template-v1', 'row_id': row['row_id'], 'original_spec': original, + 'start': row['start'], 'stop': row['stop'], 'budget_root': str(experiment), 'output_budget_bytes': BUDGET['output_budget_bytes']} + save(case / 'template-spec.json', template) + part = inputs_module.slice_requests(requests, 1000, row['start'], row['stop']) + with (case / 'requests.npz').open('xb') as stream: + np.savez_compressed(stream, **part) + _bind(inputs, case / 'template-spec.json'); _bind(inputs, case / 'requests.npz') + cases[row['row_id']] = {'case_dir': str(case), 'template_spec_sha256': digest(case / 'template-spec.json'), + 'requests_sha256': digest(case / 'requests.npz')} + require(source_inventory(model) == sources and all(digest(path) == expected for path, expected in inputs.items()), 'Source/input changed during preparation') + # Repeat the full AM verification after producing only small new inputs. + after_verified = {'model': publication.verify_seal(am_model), 'experiment': publication.verify_seal(am)} + require(after_verified == verified, 'AM sealed payload changed during preparation') + _budget(experiment) + registration = {'schema': REG_SCHEMA, 'plan': plan, 'selected_case': {'row': selected, 'original_dir': str(original_dir), 'AM_outputs': selected_outputs}, + 'source_sha256': sources, 'input_sha256': inputs, 'cases': cases, 'overlays': overlays, 'budget': dict(BUDGET), 'anchors': anchors, + 'training': False, 'new_brain_runs_during_preparation': 0, 'future_checkpoint_hashes_fabricated': False, + 'runtime_spec_derivation': 'engineering_inputs.derive_spec(template.original_spec, interval, budget, actual verified parent SHA or None)', + 'scope': 'Fixed 32-call engineering gate only; not conditioning or learning registration'} + save(experiment / 'protocol/preregistered.json', registration) + return {'prepared': True, 'registration_path': str(experiment / 'protocol/preregistered.json'), + 'registration_sha256': digest(experiment / 'protocol/preregistered.json'), 'calls': 32, 'brain_runs': 0} + except Exception as error: + failure = experiment / 'audit/preparation-failure.json' + if not failure.exists(): + save(failure, {'prepared': False, 'type': type(error).__name__, 'message': str(error), 'partial_outputs_preserved': True}) + raise + + +def prepare(am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha): + return _prepare(ROOT, MODEL, EXP, am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha) + + +def row_path(registration, row_id): + require(type(row_id) is str and row_id in {row['row_id'] for row in registration['plan']['rows']}, 'Unknown engineering row ID') + expected = EXP.resolve(strict=True) / 'cases' / row_id + actual = Path(registration['cases'][row_id]['case_dir']) + require(actual == expected and actual.resolve(strict=True) == expected and actual.is_dir(), 'Row case path is not its fixed safe mapping') + return actual + + +def read_registration(expected_sha): + require(sys.dont_write_bytecode and sha(expected_sha), 'Require python -B and an external preregistration SHA') + path = EXP / 'protocol/preregistered.json' + require(digest(path) == expected_sha, 'Preregistration differs from external authority') + require(not (EXP / 'audit/preparation-failure.json').exists(), 'Preparation failed; preserve partials') + registration = read(path) + require(registration.get('schema') == REG_SCHEMA and registration.get('training') is False and registration.get('budget') == BUDGET, + 'Unexpected registration schema/scope/budget') + require(registration['source_sha256'] == source_inventory(MODEL), 'Registered engineering sources changed or inventory incomplete') + for name, expected in registration['input_sha256'].items(): + require(Path(name).is_absolute() and sha(expected) and digest(name) == expected, 'Registered input changed: ' + name) + plan_module = load_module('phase4an_read_plan', MODEL / 'engineering_plan.py', registration['source_sha256'][str(MODEL / 'engineering_plan.py')]) + inputs_module = load_module('phase4an_read_inputs', MODEL / 'engineering_inputs.py', registration['source_sha256'][str(MODEL / 'engineering_inputs.py')]) + plan_module.validate_plan(registration['plan']) + selected = validate_planned(read(ROOT / AM_EXP_REL / 'protocol/preregistered.json')['planned']) + original_dir = ROOT / AK_REL / 'cases' / selected['cohort'] / selected['name'] + require(registration['selected_case']['row'] == selected and registration['selected_case']['original_dir'] == str(original_dir), + 'Registered case is not the fixed first AM planned primary') + original = read(original_dir / 'spec.json') + with np.load(original_dir / 'requests.npz', allow_pickle=False) as archive: + require(set(archive.files) == {'tick', 'neuron_idx'} and len(archive.files) == 2, 'Unexpected original sparse request schema') + requests = {key: archive[key].copy() for key in archive.files} + inputs_module.validate_requests(requests, 1000) + require(set(registration['cases']) == {row['row_id'] for row in registration['plan']['rows']}, 'Registered case inventory differs from plan') + for row in registration['plan']['rows']: + case = row_path(registration, row['row_id']); entry = registration['cases'][row['row_id']] + require(entry['template_spec_sha256'] == digest(case / 'template-spec.json') and entry['requests_sha256'] == digest(case / 'requests.npz'), + 'Template/request metadata does not bind registered bytes') + template = read(case / 'template-spec.json') + require(set(template) == {'schema', 'row_id', 'original_spec', 'start', 'stop', 'budget_root', 'output_budget_bytes'} and + template['schema'] == 'phase4an-engineering-case-template-v1' and template['original_spec'] == original and + template['row_id'] == row['row_id'] and template['start'] == row['start'] and template['stop'] == row['stop'] and + template['budget_root'] == str(EXP) and template['output_budget_bytes'] == BUDGET['output_budget_bytes'], 'Template rule differs from canonical row') + expected = inputs_module.slice_requests(requests, 1000, row['start'], row['stop']) + with np.load(case / 'requests.npz', allow_pickle=False) as archive: + require(len(archive.files) == 2 and set(archive.files) == set(expected) and + all(archive[key].dtype == expected[key].dtype and archive[key].shape == expected[key].shape and + archive[key].tobytes() == expected[key].tobytes() for key in expected), 'Registered request slice is not exact') + require(set(registration['overlays']) == {'zero', 'static25'}, 'Registered overlay inventory changed') + for stage in ('zero', 'static25'): + entry = registration['overlays'][stage] + expected_file = ROOT / AM_EXP_REL / 'overlays' / (stage + '.npz') + expected_manifest = EXP / 'overlays' / (stage + '.json') + require(entry == {'overlay_file': str(expected_file), 'overlay_manifest': str(expected_manifest)}, 'Overlay path mapping changed') + original_overlay = read(ROOT / AM_EXP_REL / 'overlays' / (stage + '.json')) + require(read(expected_manifest) == inputs_module.adapt_overlay_manifest(original_overlay, str(ROOT / AM_MODEL_REL / 'overlay.py')), + 'Adapted overlay manifest is not the exact declared transformation') + anchors = registration['anchors'] + for name, relative in (('am_model_manifest_sha256', AM_MODEL_REL / 'manifest.json'), + ('am_experiment_manifest_sha256', AM_EXP_REL / 'manifest.json'), + ('am_final_sha256', AM_EXP_REL / 'audit/final-status.json')): + require(sha(anchors[name]) and digest(ROOT / relative) == anchors[name], 'AM external anchor changed') + require(anchors['am_preregistration_sha256'] == AM_PREREG_SHA and digest(ROOT / AM_EXP_REL / 'protocol/preregistered.json') == AM_PREREG_SHA and + anchors['AI_publication_helper_sha256'] == AI_HELPER_SHA and digest(ROOT / AI_HELPER_REL) == AI_HELPER_SHA, 'Fixed AM/helper authority changed') + require(digest(path) == expected_sha, 'Registration changed during verification') + return registration diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_queue_audit.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_queue_audit.py new file mode 100644 index 0000000..d69d4dc --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_queue_audit.py @@ -0,0 +1,243 @@ +"""Independent, read-only CPU queue/ref audit for the draft AN 32-call gate. + +The caller supplies externally authenticated records and CSR arrays. No file +I/O, runtime import, GPU, membrane integration, weight update or learning is +performed. Spikes are observed facts, not independently predicted here. + +queue digest bytes independently implement frozen AF oracle_full.queue_summary +(source SHA below): header, little-endian signed int64 local tick/count, all +global edge IDs in source/CSR order, then uint8 post-threshold acceptance bits. +The function never target-sorts or drops zero-weight edges from the queue. +""" +from __future__ import annotations + +import hashlib +import struct + +import numpy as np + + +AF_QUEUE_SOURCE_SHA256 = 'd72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961' +QUEUE_HEADER = b'phase4af-queue-v1\0' +DELAY, REFRACTORY = 18, 22 +CHUNK = 1 << 18 +QUEUE_KEYS = {'tick', 'count', 'accepted_count', 'ordered_edge_sha256', 'accepted_mask_sha256', 'joint_sha256'} + + +def require(value, message): + if not value: + raise ValueError(message) + + +def _array(value, dtype, shape, name): + require(isinstance(value, np.ndarray) and value.dtype == np.dtype(dtype) and value.shape == shape, + 'Invalid dtype/shape: ' + name) + + +def _raw_sha(value): + result = hashlib.sha256() + for start in range(0, len(value), CHUNK): + result.update(np.ascontiguousarray(value[start:start + CHUNK], dtype=value.dtype.newbyteorder('<')).tobytes()) + return result.hexdigest() + + +def _event_schema(tick, node, steps, n, label): + require(isinstance(tick, np.ndarray) and tick.ndim == 1, 'Invalid event vector: ' + label) + _array(tick, np.int32, tick.shape, label + '.tick') + _array(node, np.int32, tick.shape, label + '.neuron_idx') + require(np.all((tick >= 0) & (tick < steps)) and np.all((node >= 0) & (node < n)), + 'Out-of-range event: ' + label) + key = tick.astype(np.int64) * n + node + require(len(key) < 2 or np.all(key[1:] > key[:-1]), 'Events must be sorted/unique: ' + label) + + +def _record_schema(record, n, label): + require(isinstance(record, dict), 'Record must be a dictionary: ' + label) + steps, offset = record['steps'], record['global_tick_offset'] + require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max and + type(offset) is int and offset >= 0, 'Invalid record clock: ' + label) + state = record['state'] + require(set(state) == {'step', 'neuron_idx', 'counts', 'v', 'g', 'ref'}, 'Invalid state keys: ' + label) + _event_schema(state['step'], state['neuron_idx'], steps, n, label) + for name, dtype in [('counts', np.int32), ('ref', np.int32), ('v', np.float64), ('g', np.float64)]: + _array(state[name], dtype, (n,), label + '.' + name) + require(np.isfinite(state[name]).all(), 'Nonfinite state: ' + label + '.' + name) + require(np.all(state['counts'] >= 0) and np.all((state['ref'] >= 0) & (state['ref'] <= REFRACTORY)), + 'Invalid state count/ref: ' + label) + rows = record['requests'] + require(isinstance(rows, np.ndarray) and rows.ndim == 2 and rows.shape[1] == 5, + 'Invalid request table: ' + label) + _array(rows, np.int64, rows.shape, label + '.requests') + queues, hashes = record.get('queues'), record.get('state_hashes') + require(isinstance(queues, list) and isinstance(hashes, list) and len(queues) == len(hashes) == steps, + 'Complete reference queue and state hashes are required: ' + label) + for t, (queue, state_hash) in enumerate(zip(queues, hashes)): + require(isinstance(queue, dict) and set(queue) == QUEUE_KEYS and type(queue['tick']) is int and queue['tick'] == t, + 'Invalid local queue row: ' + label) + require(isinstance(state_hash, dict) and set(state_hash) == {'tick', 'v_sha256', 'g_sha256', 'ref_sha256'} and + type(state_hash['tick']) is int and state_hash['tick'] == t, + 'Invalid local state-hash row: ' + label) + return record + + +def _graph(ptr, dst, edge_id, effective_weights): + require(isinstance(ptr, np.ndarray) and ptr.ndim == 1 and len(ptr) > 1, 'Invalid CSR ptr') + n, e = len(ptr) - 1, len(dst) + _array(ptr, np.int64, (n + 1,), 'ptr') + _array(dst, np.int32, (e,), 'dst') + _array(edge_id, np.int64, (e,), 'edge_id') + _array(effective_weights, np.float64, (e,), 'effective_weights_SI') + require(ptr[0] == 0 and ptr[-1] == e and np.all(ptr[1:] >= ptr[:-1]), 'Invalid CSR pointers') + for start in range(0, e, CHUNK): + stop = min(start + CHUNK, e) + require(np.all((dst[start:stop] >= 0) & (dst[start:stop] < n)), 'CSR destination out of range') + require(np.all(edge_id[start:stop] >= 0) and + np.all(np.diff(edge_id[max(0, start - 1):stop]) > 0), 'Global edge IDs must preserve canonical increasing CSR order') + require(np.isfinite(effective_weights[start:stop]).all(), 'Nonfinite effective SI weight') + return n, e + + +def _queue(tick, local_tick, sources, ref, ptr, dst, edge_id, weights, boundaries): + """Two headers, shared source-ordered payload; memory bounded by one chunk.""" + count = sum(int(ptr[int(s) + 1] - ptr[int(s)]) for s in sources) + joints = [hashlib.sha256(QUEUE_HEADER + struct.pack('= 2, 'At least two reference segments required') + parts = [_record_schema(p, n, 'segment-' + str(i)) for i, p in enumerate(segments)] + cursor, actual_cuts = 0, [] + for i, part in enumerate(parts): + require(part['global_tick_offset'] == cursor, 'Segment clock gap/overlap') + if i: + actual_cuts.append(cursor) + cursor += part['steps'] + require(cursor == mono['steps'] and type(cuts) in (list, tuple) and all(type(t) is int for t in cuts) and + list(cuts) == actual_cuts, 'Cuts/segments must cover the exact complete monolithic interval') + require(isinstance(requests, dict) and set(requests) == {'tick', 'neuron_idx'}, 'Exact original request arrays required') + _event_schema(requests['tick'], requests['neuron_idx'], mono['steps'], n, 'original_requests') + records = [('monolithic', mono), *[('segment-' + str(i), p) for i, p in enumerate(parts)]] + differences, difference_count = [], 0 + + def check(value, field, **location): + nonlocal difference_count + if not value: + difference_count += 1 + if len(differences) < 100: + differences.append({'field': field, **location}) + + tick, node = mono['state']['step'], mono['state']['neuron_idx'] + for label, record in records: + start, stop = record['global_tick_offset'], record['global_tick_offset'] + record['steps'] + lo, hi = np.searchsorted(tick, (start, stop)) + check(np.array_equal(record['state']['step'].astype(np.int64) + start, tick[lo:hi]), 'events.tick', record=label) + check(np.array_equal(record['state']['neuron_idx'], node[lo:hi]), 'events.neuron_idx', record=label) + check(np.array_equal(record['state']['counts'], np.bincount(record['state']['neuron_idx'], minlength=n)), 'counts', record=label) + expected_requests = np.empty((len(requests['tick']), 5), np.int64) + if len(expected_requests): + expected_requests[:, 0] = requests['tick']; expected_requests[:, 1] = requests['neuron_idx']; expected_requests[:, 2] = 1 + ref = np.zeros(n, np.int32) + boundary_report = {t: {'boundary_tick': t, 'accepted_nonzero_crossing_edge_events': 0, 'first_witness': None} for t in cuts} + due_total, accepted_total, segment_index = 0, 0, 0 + for t in range(mono['steps']): + while t >= parts[segment_index]['global_tick_offset'] + parts[segment_index]['steps']: + segment_index += 1 + part = parts[segment_index]; local = t - part['global_tick_offset'] + rlo, rhi = np.searchsorted(requests['tick'], (t, t + 1)) + requested = requests['neuron_idx'][rlo:rhi] + accepted_requests = ref[requested] <= 1 + expected_requests[rlo:rhi, 3] = accepted_requests + expected_requests[rlo:rhi, 4] = ~accepted_requests + np.maximum(ref - np.int32(1), np.int32(0), out=ref) + slo, shi = np.searchsorted(tick, (t, t + 1)); fired = node[slo:shi] + check(np.all(ref[fired] == 0), 'spike_during_refractory', global_tick=t) + check(np.isin(requested[accepted_requests], fired, assume_unique=True).all(), 'accepted_request_without_spike', global_tick=t) + ref[fired] = REFRACTORY + sources = node[0:0] + if t >= DELAY: + dlo, dhi = np.searchsorted(tick, (t - DELAY, t - DELAY + 1)); sources = node[dlo:dhi] + crossing = [cut for cut in cuts if t - DELAY < cut <= t] + rows, witness, live_count = _queue(t, local, sources, ref, ptr, dst, edge_id, effective_weights, crossing) + due_total += rows[0]['count']; accepted_total += rows[0]['accepted_count'] + ref_sha = _raw_sha(ref) + for label, record, at, expected in [('monolithic', mono, t, rows[0]), ('segment-' + str(segment_index), part, local, rows[1])]: + for key in sorted(QUEUE_KEYS): + check(type(record['queues'][at][key]) is type(expected[key]) and record['queues'][at][key] == expected[key], + 'queue.' + key, record=label, local_tick=at, global_tick=t) + check(record['state_hashes'][at]['ref_sha256'] == ref_sha, 'state_hash.ref_sha256', record=label, local_tick=at, global_tick=t) + if at == record['steps'] - 1: + check(np.array_equal(record['state']['ref'], ref), 'terminal.ref', record=label) + for cut in crossing: + boundary_report[cut]['accepted_nonzero_crossing_edge_events'] += live_count + if witness is not None and boundary_report[cut]['first_witness'] is None: + boundary_report[cut]['first_witness'] = {'boundary_tick': cut, **witness} + for label, record in records: + start, stop = record['global_tick_offset'], record['global_tick_offset'] + record['steps'] + lo, hi = np.searchsorted(requests['tick'], (start, stop)) + expected = expected_requests[lo:hi].copy(); expected[:, 0] -= start + check(np.array_equal(record['requests'], expected), 'requests.five_fields', record=label) + first_witness = min((row['first_witness'] for row in boundary_report.values() if row['first_witness'] is not None), + key=lambda row: (row['arrival_tick'], row['source_neuron_idx'], row['csr_edge_index'], row['boundary_tick']), default=None) + exact, live = difference_count == 0, first_witness is not None + return {'schema': 'phase4an-independent-queue-audit-v1', 'pass_all': exact and live, + 'status': 'FAIL-QUEUE-OR-REF' if not exact else ('PASS-QUEUE-WITNESS-SCOPE' if live else 'INCOMPLETE-NO-ACCEPTED-NONZERO-CROSSING'), + 'queue_ref_request_exact': exact, 'accepted_nonzero_crossing_witness_verified': exact and live, + 'difference_count': difference_count, 'differences': differences, 'differences_truncated': difference_count > len(differences), + 'neurons': n, 'edges': e, 'steps': mono['steps'], 'segments': len(parts), 'cuts': list(cuts), + 'arriving_edges': due_total, 'accepted_arriving_edges': accepted_total, 'rejected_arriving_edges': due_total - accepted_total, + 'requests': len(expected_requests), 'accepted_requests': int(expected_requests[:, 3].sum(dtype=np.int64)), + 'rejected_requests': int(expected_requests[:, 4].sum(dtype=np.int64)), + 'first_witness': first_witness, 'boundaries': list(boundary_report.values()), + 'same_edge_event_may_cross_multiple_boundaries': True, + 'input_array_sha256': {name: _raw_sha(value) for name, value in [('ptr', ptr), ('dst', dst), ('edge_id', edge_id), + ('effective_weights_float64_SI', effective_weights), ('request_tick', requests['tick']), ('request_neuron_idx', requests['neuron_idx'])]}, + 'digest_definition': {'AF_source_sha256': AF_QUEUE_SOURCE_SHA256, 'header_hex': QUEUE_HEADER.hex(), + 'payload_order': 'local tick/count 0), label + ' continuation scope mismatch') + declared_offset = summary.get('global_tick_offset', 0) + _require(type(declared_offset) is int and declared_offset == offset, label + ' summary offset mismatch') + if continued or 'global_next_tick' in summary: + _require(type(summary.get('global_next_tick')) is int and summary['global_next_tick'] == offset + steps, + label + ' summary global end mismatch') + _require(ec._sha(summary.get('effective_weights_raw_sha256')), label + ' effective weight SHA is missing') + mode = summary.get('diagnostic_mode') + legacy = str(value.get('terminal_origin', '')).startswith('AM_') + _require(mode in ('light', 'reference') or (mode is None and legacy), label + ' recording mode is not declared') + observations = {'apl_traces': bool(value.get('traces')), 'state_hashes': value.get('state_hashes') is not None, + 'queues': value.get('queues') is not None} + for name, field in (('apl_traces', 'full_APL_history_recorded'), + ('state_hashes', 'full_tick_state_hashes_recorded'), ('queues', 'full_queue_hashes_recorded')): + if field in summary: + _require(type(summary[field]) is bool, label + ' observation flag must be Boolean') + if mode == 'reference' or legacy or summary.get(field) is True: + _require(observations[name], label + ' declared reference observation missing: ' + name) + return value, observations + except (KeyError, TypeError, AttributeError, IndexError, OverflowError) as error: + raise ValueError(label + ' has an invalid run-record schema') from error + + +def _common(left, right): + return {'same_neuron_count': len(left['state']['v']) == len(right['state']['v']), + 'same_APL_width': left['terminal']['apl_pending'].shape[1] == right['terminal']['apl_pending'].shape[1], + 'same_effective_weights': left['summary']['effective_weights_raw_sha256'] == + right['summary']['effective_weights_raw_sha256']} + + +def _report(comparison, checks, observed, limitations): + # All predicates are JSON booleans, not array scalars or success claims. + checks = {key: bool(value) for key, value in checks.items()} + return dict(schema=SCHEMA, comparison=comparison, numerical_exact=all(checks.values()), + checks=checks, failed_checks=[key for key, value in checks.items() if not value], + observed_scope=observed, limitations=list(limitations), learning_pass_claimed=False, + complete_dynamical_equivalence_proven=False, external_authority_verified_here=False, + independent_CSR_queue_reconstruction_performed=False) + + +def compare_slice(candidate, monolithic): + """Compare candidate's absolute [start,stop) with an origin-zero full reference. + + The final candidate V/g/ref bytes must match the reference's tick stop-1 + hashes even for light candidates. Missing light intermediate observations + are disclosed, never replaced by fabricated zero arrays or inferred hashes. + """ + candidate, observed = _record(candidate, 'candidate') + monolithic, full = _record(monolithic, 'monolithic') + _require(monolithic['global_tick_offset'] == 0, 'Monolithic slice reference must have origin zero') + _require(monolithic['summary'].get('diagnostic_mode') == 'reference' or + str(monolithic.get('terminal_origin', '')).startswith('AM_'), + 'Monolithic slice source must be declared reference or loaded legacy AM') + _require(all(full.values()), 'Monolithic slice reference needs all four APL traces, state hashes and queues') + start, steps = candidate['global_tick_offset'], candidate['steps'] + stop = start + steps + _require(stop <= monolithic['steps'], 'Candidate slice extends beyond monolithic reference') + checks = _common(candidate, monolithic) + state, whole = candidate['state'], monolithic['state'] + lo, hi = np.searchsorted(whole['step'], (start, stop)) + expected_ticks = (whole['step'][lo:hi].astype(np.int64) - start).astype(np.int32) + expected_nodes = whole['neuron_idx'][lo:hi] + checks['slice.event_steps'] = ec.bit_equal(state['step'], expected_ticks) + checks['slice.event_neurons'] = ec.bit_equal(state['neuron_idx'], expected_nodes) + counts = np.bincount(expected_nodes, minlength=len(whole['v'])).astype(np.int32) + checks['slice.segment_counts'] = ec.bit_equal(state['counts'], counts) + requests = monolithic['requests'] + lo, hi = np.searchsorted(requests[:, 0], (start, stop)) + expected_requests = requests[lo:hi].copy() + expected_requests[:, 0] -= start + checks['slice.requests_five_fields'] = ec.bit_equal(candidate['requests'], expected_requests) + for name in ('v', 'g', 'ref'): + checks['slice.final_' + name + '_raw_hash'] = _raw_sha(state[name]) == monolithic['state_hashes'][stop - 1][name + '_sha256'] + raw_ring = ec.reconstructed_fresh_ring(whole, stop, len(whole['v'])) + expected_ring = raw_ring[(np.arange(19) + stop % 19) % 19] + checks['slice.normalized_spike_ring'] = ec.bit_equal(ec.normalized_ring(candidate['terminal']), expected_ring) + traces = monolithic['traces'] + checks['slice.APL_state'] = ec.bit_equal(candidate['terminal']['apl_state'], traces['state'][stop - 1]) + pending = np.zeros((18, traces['generated'].shape[1]), dtype=np.float32) + kept = min(stop, 18) + pending[-kept:] = traces['generated'][stop - kept:stop] + checks['slice.APL_pending'] = ec.bit_equal(candidate['terminal']['apl_pending'], pending) + if observed['apl_traces']: + for name in ec.TRACE_FILES: + checks['slice.APL_trace.' + name] = ec.bit_equal(candidate['traces'][name], traces[name][start:stop]) + if observed['state_hashes']: + checks['slice.per_tick_state_hashes'] = all( + all(left[key] == right[key] for key in ec.HASH_FIELDS) + for left, right in zip(candidate['state_hashes'], monolithic['state_hashes'][start:stop])) + if observed['queues']: + checks['slice.queue_order_and_acceptance'] = all( + all(left[key] == right[key] for key in ec.QUEUE_FIELDS) + for left, right in zip(candidate['queues'], monolithic['queues'][start:stop])) + limitations = ['External input/seal/case/parent authority is not established by these records.', + 'Queue joint hashes contain local tick headers; their independent CSR reconstruction is not performed here.', + 'Numerical equality of this observed slice is not a learning, live-coverage or complete dynamics proof.'] + for key, label in (('apl_traces', 'APL histories'), ('state_hashes', 'intermediate V/g/ref state hashes'), + ('queues', 'per-tick queue order/acceptance')): + if not observed[key]: + limitations.append('Candidate ' + label + ' are unobserved; no direct comparison is claimed.') + return _report('slice', checks, dict(start=start, stop=stop, segment_steps=steps, + events=True, segment_counts=True, requests_five_fields=True, final_state_against_reference_tick_hash=True, + normalized_spike_ring_from_reference_prefix_events=True, terminal_APL_state_and_pending=True, + candidate_APL_histories=observed['apl_traces'], candidate_per_tick_state_hashes=observed['state_hashes'], + candidate_per_tick_queue_fields=observed['queues'], queue_joint_hashes_compared=False), limitations) + + +def compare_checkpoint(candidate, prefix): + """Compare equal absolute endpoints, not local phases or segment counts.""" + candidate, _ = _record(candidate, 'candidate') + prefix, _ = _record(prefix, 'prefix') + checks = _common(candidate, prefix) + end = candidate['global_tick_offset'] + candidate['steps'] + reference_end = prefix['global_tick_offset'] + prefix['steps'] + checks['checkpoint.same_absolute_end'] = end == reference_end + for name in ('v', 'g', 'ref'): + checks['checkpoint.state.' + name] = ec.bit_equal(candidate['state'][name], prefix['state'][name]) + checks['checkpoint.normalized_spike_ring'] = ec.bit_equal( + ec.normalized_ring(candidate['terminal']), ec.normalized_ring(prefix['terminal'])) + for name in ('apl_state', 'apl_pending'): + checks['checkpoint.' + name] = ec.bit_equal(candidate['terminal'][name], prefix['terminal'][name]) + return _report('checkpoint', checks, dict(candidate_absolute_end=end, reference_absolute_end=reference_end, + candidate_local_steps=candidate['steps'], reference_local_steps=prefix['steps'], terminal_v_g_ref=True, + normalized_spike_ring=True, terminal_APL_state_and_pending=True, counts_compared=False, + event_histories_compared=False, per_tick_state_histories_compared=False), [ + 'Segment counts and cumulative prefix counts are deliberately not compared in this endpoint scope.', + 'Matching terminal records do not prove matching intermediate states or complete event histories.', + 'External input/seal/parent authority and live-carry coverage remain separate gates.']) + + +def compare_parent_carry(candidate, parent): + """Check recorded short-segment carry invariants, not unobserved restoration. + + The parent ends where the candidate starts. Slots already overwritten by + this segment cannot be checked as initial carry from terminal data alone. + When present, the child's applied trace directly tests consumed APL rows. + """ + candidate, observed = _record(candidate, 'candidate') + parent, _ = _record(parent, 'parent') + _require(candidate['global_tick_offset'] > 0, 'Parent-carry candidate must be a continued segment') + checks = _common(candidate, parent) + start, steps = candidate['global_tick_offset'], candidate['steps'] + parent_end = parent['global_tick_offset'] + parent['steps'] + checks['parent.contiguous_absolute_clock'] = parent_end == start + restored_ring = ec.normalized_ring(parent['terminal']) + unconsumed_spikes, unconsumed_apl = steps < 19, steps < 18 + if unconsumed_spikes: + checks['parent.unwritten_spike_slots'] = ec.bit_equal( + candidate['terminal']['spike_ring'][steps:], restored_ring[steps:]) + pending = parent['terminal']['apl_pending'] + if unconsumed_apl: + checks['parent.unconsumed_APL_prefix'] = ec.bit_equal( + candidate['terminal']['apl_pending'][:18 - steps], pending[steps:]) + consume = min(18, steps) + if observed['apl_traces']: + checks['parent.applied_APL_prefix'] = ec.bit_equal(candidate['traces']['applied'][:consume], pending[:consume]) + limitations = ['Initial V/g/ref/APL-state restoration is not directly observed by this terminal-only carry scope.', + 'Overwritten spike slots and consumed pending slots require the separate full-reference slice/endpoint gates.', + 'Parent manifest hashes and ancestor provenance must be independently bound by the coordinator.', + 'No queue arrival/acceptance reconstruction, live-carry or learning success is claimed here.'] + if not observed['apl_traces']: + limitations.append('Candidate applied APL history is unobserved; consumed parent APL values are not directly compared.') + carry_observed = unconsumed_spikes or unconsumed_apl or observed['apl_traces'] + if not carry_observed: + limitations.append('No carry array remains directly observable in this long light segment; this scope checks clock/shape/weight identity only.') + return _report('parent_carry', checks, dict(parent_absolute_start=parent['global_tick_offset'], + parent_absolute_end=parent_end, candidate_absolute_start=start, candidate_absolute_end=start + steps, + candidate_local_steps=steps, unwritten_spike_slots_checked=unconsumed_spikes, + unwritten_spike_slot_count=max(19 - steps, 0), unconsumed_APL_prefix_checked=unconsumed_apl, + unconsumed_APL_row_count=max(18 - steps, 0), applied_parent_APL_prefix_checked=observed['apl_traces'], + applied_parent_APL_rows_compared=consume if observed['apl_traces'] else None, + any_carry_values_directly_compared=carry_observed, + initial_neural_state_restoration_directly_observed=False), limitations) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/engineering_workflow.py b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_workflow.py new file mode 100644 index 0000000..ab58144 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/engineering_workflow.py @@ -0,0 +1,421 @@ +"""Finite, no-training execution of the separately registered 32-call gate. + +All files are exclusive creations. A partial/failed row is preserved and is +not retried. Runtime outputs are sealed before external reports are written. +The preparation module owns source/input authority; no summary Boolean alone +is accepted as proof that a subprocess or a complete engineering gate passed. +""" +from __future__ import annotations + +import argparse +from datetime import datetime, timezone +import importlib +import json +import os +from pathlib import Path +import shutil +import subprocess +import sys +import time +import traceback +import urllib.request +import zipfile + +import numpy as np + +import engineering_inputs as I +import engineering_plan as PLAN +import runtime_light as R +import runtime_continuation as C + + +GPU = 'GPU-79f3a85d-adce-7833-7003-6488140ca596' +ROOT = Path('/data/digital-drosophila') +MODEL = Path(__file__).resolve().parent +EXP = ROOT / 'experiments/phase4an-engineering-validation' +require = R.require + + +def canonical(value): + return json.dumps(value, sort_keys=True, separators=(',', ':'), allow_nan=False) + + +def same_json(left, right): + return canonical(left) == canonical(right) + + +def preparation(): + return importlib.import_module('engineering_prepare') + + +def row_from(registration, row_id): + plan = PLAN.validate_plan(registration['plan']) + rows = [row for row in plan['rows'] if row['row_id'] == row_id] + require(len(rows) == 1, 'Only an exact registered row may execute') + return rows[0] + + +def locations(row_id): + require(type(row_id) is str and row_id and all(ch.isalnum() or ch in '-_' for ch in row_id), + 'Unsafe row identity') + return dict(output=EXP / 'runs' / row_id, + runtime=EXP / 'receipts' / (row_id + '.runtime.json'), + execution=EXP / 'receipts' / (row_id + '.json'), + derivation=EXP / 'receipts' / (row_id + '.derivation.json'), + stdout=EXP / 'logs' / (row_id + '.stdout.log'), + stderr=EXP / 'logs' / (row_id + '.stderr.log')) + + +def disk_budget(registration): + limit = registration['budget']['output_budget_bytes'] + reserve = registration['budget']['minimum_free_reserve_bytes'] + require(type(limit) is type(reserve) is int and limit > 0 and reserve > 0, 'Invalid registered budget') + files = [p for p in EXP.rglob('*') if p.is_file()] + require(not any(p.is_symlink() for p in EXP.rglob('*')), 'Experiment contains a linked output') + used = sum(p.stat().st_size for p in files) + free = shutil.disk_usage(EXP).free + require(used <= limit and free >= reserve + max(0, limit - used), 'Engineering budget/reserve exceeded; preserve partials') + return dict(used_bytes=used, free_bytes=free, hard_cap_bytes=limit, reserve_bytes=reserve) + + +def preflight(): + """Never evict an existing service; configure only this child process.""" + os.environ['CUDA_VISIBLE_DEVICES'] = GPU + os.environ['CUDA_HOME'] = '/usr/local/cuda' + os.environ['PATH'] = str(ROOT / 'models/male-cns-lif/.venv/bin') + ':/usr/local/cuda/bin:' + os.environ['PATH'] + os.environ['MAX_JOBS'] = '2' + os.environ['TORCH_CUDA_ARCH_LIST'] = '8.9' + for port in (8188, 8189, 8190): + with urllib.request.urlopen(f'http://192.168.10.51:{port}/queue', timeout=5) as response: + queue = json.load(response) + require(queue['queue_running'] == [] and queue['queue_pending'] == [], + 'ComfyUI busy: defer rather than evict') + + +def derive_case(template, row, parent_sha): + require(type(template) is dict and set(template) == {'schema', 'row_id', 'original_spec', 'start', 'stop', + 'budget_root', 'output_budget_bytes'}, 'Unexpected registered case template fields') + require(template['schema'] == 'phase4an-engineering-case-template-v1' and template['row_id'] == row['row_id'] and + type(template['start']) is int and type(template['stop']) is int and + (template['start'], template['stop']) == (row['start'], row['stop']), 'Template differs from exact plan') + require((row['parent_row_id'] is None) == (parent_sha is None), 'Parent linkage differs from registered plan') + return I.derive_spec(template['original_spec'], start=row['start'], stop=row['stop'], + budget_root=template['budget_root'], output_budget_bytes=template['output_budget_bytes'], parent_sha=parent_sha) + + +def npz_storage(path): + with zipfile.ZipFile(path) as archive: + rows = archive.infolist() + require(rows and all(r.compress_type == zipfile.ZIP_DEFLATED and r.filename.endswith('.npy') for r in rows), + 'Declared lossless compressed state format differs from actual archive') + return dict(file_bytes=Path(path).stat().st_size, + uncompressed_entry_bytes=sum(r.file_size for r in rows), + compressed_entry_bytes=sum(r.compress_size for r in rows), + entries=[dict(name=r.filename, bytes=r.file_size, compressed_bytes=r.compress_size, + compression=r.compress_type) for r in rows]) + + +def runtime_environment(): + """Read settings after the runtime imported Torch; change none of them.""" + torch = sys.modules.get('torch') + require(torch is not None and os.environ.get('CUDA_VISIBLE_DEVICES') == GPU and + torch.cuda.device_count() == 1 and '4090' in torch.cuda.get_device_name(0), + 'Actual runtime is not the registered single RTX4090') + properties = torch.cuda.get_device_properties(0) + inventory = subprocess.run(['nvidia-smi', '-i', GPU, + '--query-gpu=uuid,name,memory.total,driver_version', '--format=csv,noheader'], + check=True, capture_output=True, text=True, timeout=20).stdout.strip() + require(inventory.startswith(GPU + ','), 'GPU hardware inventory does not match the registered UUID') + return dict(python_executable=sys.executable, python_version=sys.version, numpy_version=np.__version__, + torch_version=str(torch.__version__), torch_cuda_version=torch.version.cuda, + device_name=properties.name, total_memory_bytes=int(properties.total_memory), + gpu_uuid=GPU, nvidia_smi_inventory=inventory, + matmul_allow_tf32=bool(torch.backends.cuda.matmul.allow_tf32), + cudnn_allow_tf32=bool(torch.backends.cudnn.allow_tf32), + deterministic_algorithms=bool(torch.are_deterministic_algorithms_enabled()), + cudnn_deterministic=bool(torch.backends.cudnn.deterministic), + cudnn_benchmark=bool(torch.backends.cudnn.benchmark), + float32_matmul_precision=torch.get_float32_matmul_precision(), + CUBLAS_WORKSPACE_CONFIG=os.environ.get('CUBLAS_WORKSPACE_CONFIG'), + TORCH_CUDA_ARCH_LIST=os.environ.get('TORCH_CUDA_ARCH_LIST'), + settings_changed_by_environment_recorder=False, + environment_recorder_adds_no_timing=True, + build_timing_source='Use inherited summary.build_or_cache_seconds; build/cache is also included in runtime wall time') + + +def verify_environment(recorded, component): + require(type(recorded) is type(component) is dict and + recorded.get('gpu_uuid') == GPU and recorded.get('settings_changed_by_environment_recorder') is False, + 'Missing or invalid actual environment receipt') + require(component.get('cuda_matmul_allow_tf32') is False and + component.get('float32_matmul_precision') == 'highest' and + component.get('precision_configuration_changed_by_component') is False, + 'Frozen AM precision configuration changed; never correct it automatically') + pairs = {'matmul_allow_tf32': 'cuda_matmul_allow_tf32', 'cudnn_allow_tf32': 'cudnn_allow_tf32', + 'deterministic_algorithms': 'deterministic_algorithms', 'cudnn_deterministic': 'cudnn_deterministic', + 'cudnn_benchmark': 'cudnn_benchmark', 'float32_matmul_precision': 'float32_matmul_precision', + 'CUBLAS_WORKSPACE_CONFIG': 'CUBLAS_WORKSPACE_CONFIG', 'device_name': 'device_name', + 'torch_version': 'torch', 'torch_cuda_version': 'cuda', 'numpy_version': 'numpy'} + for left, right in pairs.items(): + require(left in recorded and right in component and same_json(recorded[left], component[right]), + 'Actual runtime/APL environment disagreement: ' + left) + require(type(recorded['device_name']) is str and '4090' in recorded['device_name'], 'Wrong physical device class') + + +def check_runtime_record(reg, reg_sha, row, record): + """Recheck actual bytes, runtime completion and parent chain, not intent.""" + paths = locations(row['row_id']) + require(record.get('complete') is True and record.get('registration_sha256') == reg_sha and + same_json(record.get('row'), row) and record.get('training') is False, + 'Runtime record is not bound to this registered row') + case = Path(reg['cases'][row['row_id']]['case_dir']) + checkpoint = C.load_checkpoint(paths['output'], record['checkpoint_manifest_sha256']) + summary = checkpoint['summary'] + verify_environment(record['environment'], summary['apl_component']['environment']) + require(checkpoint['steps'] == row['steps'] and checkpoint['global_tick_offset'] == row['start'] and + checkpoint['global_next_tick'] == row['stop'] and summary['nodes'] == 166700 and summary['edges'] == 25582938 and + summary['diagnostic_mode'] == row['mode'], 'Runtime clock/population/mode differs from plan') + require(record['case_sha256'] == R.digest(case / 'spec.json') and + record['requests_sha256'] == R.digest(case / 'requests.npz') and + record['derivation_sha256'] == R.digest(paths['derivation']), 'Derived input/receipt bytes changed') + template = R._read_json(case / 'template-spec.json') + parent_pin = None + if row['parent_row_id'] is not None: + parent = verify_execution(reg, reg_sha, row_from(reg, row['parent_row_id'])) + parent_pin = parent['checkpoint_manifest_sha256'] + expected, derivation = derive_case(template, row, parent_pin) + require(same_json(R._read_json(case / 'spec.json'), expected) and + same_json(R._read_json(paths['derivation']), derivation), 'Case was not generated by the registered exact rule') + require(record['parent_checkpoint_manifest_sha256'] == parent_pin and + summary.get('parent_checkpoint_manifest_sha256') == parent_pin, 'Execution parent SHA mismatch') + require(record['checkpoint_inventory_sha256'] == R.digest(paths['output'] / C.MANIFEST), 'Checkpoint manifest changed') + for name in ('state.npz', 'terminal-queues.npz'): + require(same_json(record['storage'][name], npz_storage(paths['output'] / name)), 'Storage measurement changed') + return record + + +def verify_execution(reg, reg_sha, row): + paths = locations(row['row_id']) + receipt = R._read_json(paths['execution']) + require(receipt.get('complete') is True and type(receipt.get('process_exit_code')) is int and receipt['process_exit_code'] == 0 and + receipt.get('registration_sha256') == reg_sha and receipt.get('row_id') == row['row_id'], + 'Missing completed process receipt') + for field, key in (('runtime_record_sha256', 'runtime'), ('stdout_sha256', 'stdout'), ('stderr_sha256', 'stderr')): + require(receipt[field] == R.digest(paths[key]), 'Execution log/runtime receipt changed') + require(not (EXP / 'audit/stopped.json').exists(), 'A failed/deferred run requires explicit review, not automatic retry') + record = check_runtime_record(reg, reg_sha, row, R._read_json(paths['runtime'])) + require(receipt['checkpoint_manifest_sha256'] == record['checkpoint_manifest_sha256'], 'Execution/checkpoint linkage mismatch') + return record + + +def worker(reg_sha, row_id): + require(sys.dont_write_bytecode, 'Use the scientific venv with -B') + reg = preparation().read_registration(reg_sha) + row = row_from(reg, row_id) + paths = locations(row_id) + require(not paths['output'].exists() and not paths['runtime'].exists() and not paths['derivation'].exists(), + 'Never overwrite/retry a partial row') + budget_before = disk_budget(reg) + preflight() + parent_sha = None + parent_dir = None + if row['parent_row_id'] is not None: + parent_record = verify_execution(reg, reg_sha, row_from(reg, row['parent_row_id'])) + parent_sha = parent_record['checkpoint_manifest_sha256'] + parent_dir = locations(row['parent_row_id'])['output'] + case = Path(reg['cases'][row_id]['case_dir']) + template = R._read_json(case / 'template-spec.json') + spec, derivation = derive_case(template, row, parent_sha) + require(not (case / 'spec.json').exists(), 'Derived spec already exists; preserve partial instead of rerun') + R._save(case / 'spec.json', spec) + R._save(paths['derivation'], derivation) + overlay = reg['overlays'][row['overlay']] + start_utc = datetime.now(timezone.utc).isoformat() + start = time.perf_counter() + if parent_dir is None: + function = R.run_reference if row['mode'] == 'reference' else R.run_light + function(case, paths['output'], EXP / 'extension-build', overlay['overlay_file'], overlay['overlay_manifest']) + pin = C.seal_checkpoint(paths['output'])['sha256'] + else: + result = C.run_continued(case, paths['output'], EXP / 'extension-build', + overlay['overlay_file'], overlay['overlay_manifest'], parent_dir, mode=row['mode']) + pin = result['checkpoint_manifest_sha256'] + elapsed = time.perf_counter() - start + budget_after = disk_budget(reg) + record = dict(schema='phase4an-engineering-runtime-record-v1', complete=True, training=False, + registration_sha256=reg_sha, row=row, case_sha256=R.digest(case / 'spec.json'), + requests_sha256=R.digest(case / 'requests.npz'), derivation_sha256=R.digest(paths['derivation']), + checkpoint_manifest_sha256=pin, checkpoint_inventory_sha256=R.digest(paths['output'] / C.MANIFEST), + parent_checkpoint_manifest_sha256=parent_sha, runtime_and_immediate_seal_wall_seconds=elapsed, + started_utc=start_utc, completed_utc=datetime.now(timezone.utc).isoformat(), + environment=runtime_environment(), + budget_before=budget_before, budget_after=budget_after, + storage={name: npz_storage(paths['output'] / name) for name in ('state.npz', 'terminal-queues.npz')}, + no_result_selected_retry=True, learning_pass_claimed=False) + # Do not write this or any later report INSIDE the sealed checkpoint. + check_runtime_record(reg, reg_sha, row, record) + preparation().read_registration(reg_sha) + R._save(paths['runtime'], record) + print('COMPLETED ENGINEERING ROW', row_id, flush=True) + + +def run_registered(reg_sha): + require(sys.dont_write_bytecode, 'Use python -B') + reg = preparation().read_registration(reg_sha) + require(not (EXP / 'audit/stopped.json').exists() and not (EXP / 'audit/execution-complete.json').exists(), + 'Stopped/completed gate must not be restarted') + started = time.perf_counter() + completed = [] + try: + for row in reg['plan']['rows']: + paths = locations(row['row_id']) + if paths['execution'].exists(): + verify_execution(reg, reg_sha, row) + completed.append(dict(row_id=row['row_id'], execution_sha256=R.digest(paths['execution']), + reverified_existing=True)) + continue + require(not any(paths[k].exists() for k in ('output', 'runtime', 'stdout', 'stderr', 'derivation')), + 'Partial row present; no automatic retry') + disk_budget(reg) + preflight() + began = time.perf_counter() + print('START', row['row_id'], flush=True) + with paths['stdout'].open('xb') as out, paths['stderr'].open('xb') as err: + result = subprocess.run([sys.executable, '-B', str(Path(__file__)), 'worker', + '--registration-sha256', reg_sha, '--row-id', row['row_id']], cwd=MODEL, stdout=out, stderr=err) + require(result.returncode == 0, 'Worker failed; preserve its stdout/stderr and partial output') + record = check_runtime_record(reg, reg_sha, row, R._read_json(paths['runtime'])) + receipt = dict(schema='phase4an-engineering-execution-receipt-v1', complete=True, + process_exit_code=result.returncode, row_id=row['row_id'], registration_sha256=reg_sha, + runtime_record_sha256=R.digest(paths['runtime']), stdout_sha256=R.digest(paths['stdout']), + stderr_sha256=R.digest(paths['stderr']), process_wall_seconds=time.perf_counter() - began, + checkpoint_manifest_sha256=record['checkpoint_manifest_sha256'], scientific_pass_claimed=False) + R._save(paths['execution'], receipt) + verify_execution(reg, reg_sha, row) + completed.append(dict(row_id=row['row_id'], execution_sha256=R.digest(paths['execution']), + reverified_existing=False)) + print('COMPLETE', row['row_id'], flush=True) + require(len(completed) == 32, 'Incomplete registered inventory') + preparation().read_registration(reg_sha) + R._save(EXP / 'audit/execution-complete.json', dict(complete=True, registration_sha256=reg_sha, + completed=completed, expected_calls=32, executed_ticks=11074, wall_seconds=time.perf_counter() - started, + numerical_gate_evaluated=False, training=False, learning_pass_claimed=False)) + except Exception as error: + R._save(EXP / 'audit/stopped.json', dict(complete=False, type=type(error).__name__, message=str(error), + traceback=traceback.format_exc(), completed_before_error=completed, partial_outputs_preserved=True, + no_automatic_retry=True, scientific_failure_not_inferred_from_engineering_failure=True)) + raise + + +def verified_inventory(reg, reg_sha): + """A completed child and every planned descriptor must remain auditable.""" + plan = PLAN.validate_plan(reg['plan']) + receipt = R._read_json(EXP / 'audit/execution-complete.json') + require(receipt.get('complete') is True and receipt.get('registration_sha256') == reg_sha and + type(receipt.get('expected_calls')) is int and receipt['expected_calls'] == plan['total_calls'] and + type(receipt.get('executed_ticks')) is int and receipt['executed_ticks'] == plan['total_ticks'] and + receipt.get('training') is False and receipt.get('learning_pass_claimed') is False, + 'Execution inventory is not a complete no-training registered gate') + require(type(receipt.get('completed')) is list and len(receipt['completed']) == plan['total_calls'], + 'Execution inventory truncated') + records = {} + for row, entry in zip(plan['rows'], receipt['completed']): + require(entry.get('row_id') == row['row_id'] and + entry.get('execution_sha256') == R.digest(locations(row['row_id'])['execution']), + 'Execution completion inventory reordered or receipt changed') + records[row['row_id']] = verify_execution(reg, reg_sha, row) + return records + + +def evaluate(reg_sha): + reg = preparation().read_registration(reg_sha) + records = verified_inventory(reg, reg_sha) + report = importlib.import_module('engineering_analysis').analyze(reg, reg_sha, records) + require(type(report) is dict and report.get('training') is False and report.get('learning_pass_claimed') is False, + 'Engineering analysis must not claim training or learning') + preparation().read_registration(reg_sha) + return reg, report + + +def analyze(reg_sha): + require(sys.dont_write_bytecode, 'Use python -B') + target = EXP / 'audit/engineering-analysis.json' + require(not target.exists() and not (EXP / 'manifest.json').exists(), 'Analysis/seal already exists; do not overwrite') + began = time.perf_counter() + _, report = evaluate(reg_sha) + R._save(target, report) + R._save(EXP / 'audit/analysis-execution.json', dict(registration_sha256=reg_sha, + analysis_sha256=R.digest(target), wall_seconds=time.perf_counter() - began, GPU_used=False, + training=False, learning_pass_claimed=False)) + print(report['status'], flush=True) + + +def seal_experiment(): + """Seal only the new engineering experiment, not the unfinished lab model.""" + controls = ('manifest.json', 'checksums.sha256') + require(not any((EXP / name).exists() for name in controls), 'Do not replace an existing or partial seal') + paths = sorted(EXP.rglob('*')) + require(not any(path.is_symlink() for path in paths), 'Cannot seal linked artifacts') + require(not any('__pycache__' in path.relative_to(EXP).parts for path in paths), 'Unexpected bytecode cache in new experiment') + rows = [dict(path=path.relative_to(EXP).as_posix(), bytes=path.stat().st_size, sha256=R.digest(path)) + for path in paths if path.is_file()] + require(rows, 'Cannot seal an empty experiment') + # Only ROOT controls are excluded from their own hash. Nested checkpoint + # manifests and source snapshots are ordinary payload and remain included. + R._save(EXP / controls[0], dict(root=str(EXP), files=rows)) + with (EXP / controls[1]).open('x', encoding='utf-8', newline='\n') as stream: + stream.writelines(row['sha256'] + ' ' + row['path'] + '\n' for row in rows) + stream.write(R.digest(EXP / controls[0]) + ' manifest.json\n') + return dict(manifest_sha256=R.digest(EXP / controls[0]), checksums_sha256=R.digest(EXP / controls[1])) + + +def finalize(reg_sha): + require(sys.dont_write_bytecode, 'Use python -B') + require(not (EXP / 'manifest.json').exists() and not (EXP / 'audit/final-status.json').exists(), + 'Final/partial seal already exists; preserve it') + reg, actual = evaluate(reg_sha) + target = EXP / 'audit/engineering-analysis.json' + require(same_json(actual, R._read_json(target)), 'Recomputed analysis differs from saved evidence') + execution = R._read_json(EXP / 'audit/analysis-execution.json') + require(execution.get('registration_sha256') == reg_sha and execution.get('analysis_sha256') == R.digest(target), + 'Saved analysis is not bound to its execution receipt') + authority = preparation().verify_authority(reg['anchors']) + old = preparation().verify_old_integrity(reg) + R._save(EXP / 'regression/AM-seals-after.json', authority) + R._save(EXP / 'regression/old-integrity-after.json', old) + preparation().read_registration(reg_sha) + final = dict(schema='phase4an-engineering-final-v1', status=actual['status'], + registration_sha256=reg_sha, analysis_sha256=R.digest(target), + analysis_recomputed_exactly=True, completed_calls=32, executed_ticks=11074, + old_integrity_pass=True, old_integrity_roots=len(old), AM_seals_preserved=True, + old_five_reference_evidence='Sealed AM five-trial evidence retained; not rerun by this engineering gate', + training=False, learning_pass_claimed=False, learned_candidate=False, + model_directory_sealed=False, used_engineering_source_snapshots_sealed=True, + budget=disk_budget(reg), + scope='Finite registered 100 ms input and fixed continuation cuts only; no universal determinism, learning or physiology claim') + R._save(EXP / 'audit/final-status.json', final) + pins = seal_experiment() + helper = preparation().load_module('phase4an_pinned_AI_publication', + ROOT / preparation().AI_HELPER_REL, preparation().AI_HELPER_SHA) + verified = helper.verify_seal(EXP) + require(verified['manifest_sha256'] == pins['manifest_sha256'] and + verified['checksums_sha256'] == pins['checksums_sha256'], 'Final experiment seal verification failed') + print(canonical(dict(status=final['status'], **pins)), flush=True) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('action', choices=('run', 'worker', 'analyze', 'finalize')) + parser.add_argument('--registration-sha256', required=True) + parser.add_argument('--row-id') + args = parser.parse_args() + if args.action == 'worker': + require(args.row_id is not None, 'Worker requires a row ID') + worker(args.registration_sha256, args.row_id) + else: + require(args.row_id is None, 'Full inventory run has no selected row') + {'run': run_registered, 'analyze': analyze, 'finalize': finalize}[args.action](args.registration_sha256) + + +if __name__ == '__main__': + main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/evaluation_plan.py b/registry/phase4ao-conditioning-interface/source-snapshot/evaluation_plan.py new file mode 100644 index 0000000..e44991a --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/evaluation_plan.py @@ -0,0 +1,260 @@ +"""Pure, UNREGISTERED evaluation inventory; no files, RNG draws or execution. + +build_evaluation_plan(trial_payload, config) requires the complete existing +trial_plan payload, including its caller-chosen master and seed manifest. It +reuses that payload's 80 evaluation inputs; repeated probes are paired +observations, never extra independent training replicates. No defaults select +CS offset, reversal curve checkpoints, control snapshot boundaries, or baseline +execution policy. See required_choices(). A complete inventory is not approval, +an executed snapshot, an externally verified source pin, or a scientific gate. +""" +from __future__ import annotations + +from collections import Counter +import hashlib +import json + +import seed_registry +import trial_plan + + +SCHEMA = "phase4an-evaluation-plan-draft-v1" +CONFIG_SCHEMA = "phase4an-evaluation-config-draft-v1" +CONFIG_FIELDS = frozenset(("schema", "probe_steps", "cs_start_tick", + "cs_stop_tick_exclusive", "reversal_checkpoints", "baseline_execution", + "snapshot_binding_policy", "snapshot_bindings")) +BINDING_FIELDS = frozenset(("block_id", "assay", "arm", "checkpoint", "completed_epochs")) + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(",", ":"), + ensure_ascii=False, allow_nan=False) + + +def _sha(value): + return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest() + + +def required_choices(): + """Describe mandatory choices, without filling or approving any of them.""" + return { + "probe_steps": "Explicit integer 1200: the draft's fixed 120 ms metric window.", + "cs_start_tick": "Explicit integer 0: stimulus onset defines probe latency.", + "cs_stop_tick_exclusive": "Explicit integer in [1,1200]; draft does not settle evaluation CS offset.", + "reversal_checkpoints": "Explicit sorted subset of [0,1,2,5,10,20,50], including 0 and 50.", + "baseline_execution": "Only execute_each_snapshot is implemented; declare it explicitly. No output deduplication.", + "snapshot_binding_policy": "Explicit completed_epochs; nominal control labels are not actual update counts.", + "snapshot_bindings": "Every block/assay/arm/checkpoint, in trial-plan order, with explicit completed_epochs. No control alignment is guessed.", + "authority": "Provide the complete existing trial payload; no master seed is chosen here. Source/input pins remain coordinator responsibilities.", + } + + +def _configuration(config): + _require(type(config) is dict, "Explicit evaluation config is required; see required_choices()") + _require(set(config) == CONFIG_FIELDS, + "Evaluation config fields: missing=" + str(sorted(CONFIG_FIELDS - set(config))) + + "; unknown=" + str(sorted(set(config) - CONFIG_FIELDS))) + _require(config["schema"] == CONFIG_SCHEMA, "Wrong evaluation config schema") + _require(type(config["probe_steps"]) is int and config["probe_steps"] == 1200, + "Explicit probe_steps must be integer 1200") + _require(type(config["cs_start_tick"]) is int and config["cs_start_tick"] == 0, + "Explicit CS onset must be integer tick 0") + stop = config["cs_stop_tick_exclusive"] + _require(type(stop) is int and 0 < stop <= 1200, + "Evaluation CS stop tick is required; no training-CS default is inferred") + checks = config["reversal_checkpoints"] + _require(type(checks) is list and len(checks) >= 2 and + all(type(v) is int and v in trial_plan.PROBE_CHECKPOINTS for v in checks) and + checks == sorted(set(checks)) and checks[0] == 0 and checks[-1] == 50, + "Explicit reversal checkpoints must include 0/50, sorted, unique, and within the draft curve") + _require(config["baseline_execution"] == "execute_each_snapshot", + "Declare execute_each_snapshot explicitly; baseline reuse is not implemented or inferred") + _require(config["snapshot_binding_policy"] == "explicit_completed_epochs", + "Explicit control snapshot epoch bindings are mandatory") + _require(type(config["snapshot_bindings"]) is list, "snapshot_bindings must be an explicit ordered list") + return json.loads(_canonical(config)) + + +def _binding_inventory(plan, config): + expected, specs = [], [] + for block in plan["blocks"]: + for suite in block["suites"]: + assay = suite["assay"] + points = config["reversal_checkpoints"] if assay == "reversal" else block["evaluation_checkpoints"] + for arm in suite["arms"]: + for point in points: + expected.append((block["block_id"], assay, arm["arm"], point)) + specs.append((block, suite, arm)) + bindings = config["snapshot_bindings"] + _require(len(bindings) == len(expected), "Missing/extra snapshot bindings: require every block, arm and checkpoint") + previous = {} + result = [] + for binding, identity, spec in zip(bindings, expected, specs): + _require(type(binding) is dict and set(binding) == BINDING_FIELDS, "Invalid snapshot binding fields") + actual = tuple(binding[key] for key in ("block_id", "assay", "arm", "checkpoint")) + _require(all(type(v) is str for v in actual[:3]) and type(actual[3]) is int and actual == identity, + "Snapshot bindings must match exact canonical coverage/order/types") + block, suite, arm = spec + count = binding["completed_epochs"] + _require(type(count) is int and 0 <= count <= len(arm["epochs"]), "Invalid completed_epochs") + point = identity[-1] + _require((count == 0) == (point == 0), "Checkpoint zero is before the arm's first epoch") + _require(point != 50 or count == len(arm["epochs"]), "Terminal checkpoint must include the entire arm, including neutral/blank epochs") + key = identity[:3] + _require(key not in previous or count > previous[key], "Snapshot epochs must advance strictly") + previous[key] = count + us = sum(e["us"]["requested"] for e in arm["epochs"][:count]) + updates = sum(e["update_enabled"] and e["D_command"] != 0 for e in arm["epochs"][:count]) + if arm["arm"] in ("paired", "reversal", "no_reversal"): + _require(us == point and updates == point, + "Paired/reversal checkpoint must contain exactly its declared reinforcement opportunities") + result.append((binding, block, suite, arm, us, updates)) + return result + + +def _streams(trial): + """Reconstruct evaluation semantic keys, not just plausible seed strings.""" + manifest = trial["seed_manifest"] + master = trial["plan"]["master"] + independent = {(r["split"], tuple(r["key"])): r for r in manifest["independent_streams"]} + non_eval = {r["seed"] for r in manifest["independent_streams"] if r["split"] != "evaluation"} + used, result = set(), {} + for block in trial["plan"]["blocks"]: + _require([v["cue"] for v in block["evaluation_streams"]] == ["A", "B"], "Both original evaluation cues are required") + for row in block["evaluation_streams"]: + cue = row["cue"] + key = (block["block_id"], "evaluation", "CS", cue, "00", "base-probe") + seed, derivation = seed_registry.seed_for(master, "evaluation", key) + registered = independent.get(("evaluation", key)) + _require(registered == {"split": "evaluation", "key": list(key), "seed": seed, + "derivation_sha256": derivation}, "Missing exact evaluation registry key") + stream_id = "evaluation:" + _sha([trial_plan.SCHEMA, "stream-id", master, "evaluation", list(key)]) + _require(type(row["seed"]) is int and row["seed"] == seed and row["seed_bits"] == 63 and + row["stream_id"] == stream_id and row["odor"] == trial_plan.ODORS[cue] and row["role"] == "CS", + "Evaluation seed/key/odor lineage mismatch") + _require(seed not in used and seed not in non_eval, "Evaluation seed overlaps another independent stream") + used.add(seed) + result[(block["block_id"], cue)] = dict(stream_id=stream_id, split="evaluation", + semantic_key=list(key), seed=seed, seed_bits=63, derivation_sha256=derivation, + input_id=block["block_id"] + "/evaluation/input-" + cue, + reuse="paired repeated probes within this training replicate; not additional independent seeds") + _require(len(result) == manifest["split_counts"]["evaluation"] == 80, "Exactly the existing 80 evaluation streams are required") + return result + + +def _snapshot_id(block_id, assay, arm, checkpoint): + return f"{block_id}/{assay}/{arm}/checkpoint-{checkpoint:02d}" + + +def _assemble(trial, config): + """Internal assembly; public entry points first validate full trial authority.""" + config = _configuration(config) + records = _binding_inventory(trial["plan"], config) + streams = _streams(trial) + snapshots, probes = [], [] + + def emit(block, snapshot): + common = dict(block_id=block["block_id"], mode=block["mode"], dan_type=block["dan_type"], + compartment_verbatim=block["compartment_verbatim"], training_replicate=block["replicate"], + original_reinforced_cue=block["initial_reinforced_cue"], original_neutral_cue=block["initial_neutral_cue"], + original_S_orientation="original reinforced minus original neutral, never flipped for reversal") + snapshot = dict(common, **snapshot) + snapshots.append(snapshot) + for cue in trial_plan.CUES: + probes.append(dict(common, probe_id=snapshot["snapshot_id"] + "/probe-" + cue, + snapshot_id=snapshot["snapshot_id"], baseline_snapshot_id=snapshot["baseline_snapshot_id"], + assay=snapshot["assay"], arm=snapshot["arm"], checkpoint=snapshot["checkpoint"], + elapsed_idle_ticks=snapshot["elapsed_idle_ticks"], cue=cue, odor=trial_plan.ODORS[cue], + steps=config["probe_steps"], dt_ms=0.1, cs_start_tick=config["cs_start_tick"], + cs_stop_tick_exclusive=config["cs_stop_tick_exclusive"], + input=streams[(block["block_id"], cue)], execution="independent fork; discard probe terminal state", + US_requested=False, D_command=0.0, weight_updates_enabled=False, + feedback_to_training_or_idle=False, adds_training_replicate=False)) + + for binding, block, suite, arm, us, updates in records: + block_id, assay, name = block["block_id"], suite["assay"], arm["arm"] + point, count = binding["checkpoint"], binding["completed_epochs"] + last = None if count == 0 else arm["epochs"][count - 1]["epoch_id"] + delta_source = (dict(arm["delta_start"]) if count == 0 else + {"kind": "epoch_delta_after", "source": last + "/delta-after"}) + baseline = _snapshot_id(block_id, "normal" if assay == "reversal" else assay, + "paired" if assay == "reversal" else name, 0) + emit(block, dict(snapshot_id=_snapshot_id(block_id, assay, name, point), assay=assay, arm=name, + checkpoint=point, checkpoint_semantics="nominal reinforcement progress; control epoch boundary explicitly supplied", + completed_epochs=count, last_completed_epoch_id=last, + actual_US_requests_completed=us, enabled_nonzero_D_update_opportunities_completed=updates, + arm_reinforced_cue=arm["reinforced_cue"], delta_source=delta_source, + neural_state_source={"kind": "common_frozen_reset", "from_training_terminal_state": False}, + baseline_snapshot_id=baseline, elapsed_idle_ticks=None, + probe_count=2, snapshot_mutated_by_planning=False)) + + for block in trial["plan"]["blocks"]: + block_id = block["block_id"] + normal = {a["arm"]: a for a in block["suites"][0]["arms"]} + for branch in block["retention"]["branches"]: + name = branch.split("/")[1] + terminal_epoch = normal[name]["epochs"][-1]["epoch_id"] + lineage = block_id + "/retention/" + name + "/idle-10s" + for seconds in block["retention"]["checkpoint_seconds"]: + ticks = seconds * 10000 + emit(block, dict(snapshot_id=f"{block_id}/retention/{name}/idle-{seconds:02d}s", + assay="retention", arm=name, checkpoint=None, completed_epochs=len(normal[name]["epochs"]), + last_completed_epoch_id=terminal_epoch, delta_source={"kind": "epoch_delta_after", "source": terminal_epoch + "/delta-after"}, + neural_state_source={"kind": "actual_terminal_checkpoint" if seconds == 0 else "continuous_idle_checkpoint", + "terminal_epoch_id": terminal_epoch, "idle_lineage_id": lineage, + "elapsed_ticks": ticks, "total_idle_ticks": 100000, + "one_second_is_prefix_of_same_ten_second_idle": True, + "carry": ["v", "g", "ref", "spike_ring", "apl_state", "apl_pending", "absolute_tick", "delta", "relevant_rng_state"], + "reset_or_analytic_time_advance": False}, + baseline_snapshot_id=_snapshot_id(block_id, "normal", name, 0), + elapsed_idle_ticks=ticks, probe_count=2, snapshot_mutated_by_planning=False)) + + ids = {row["snapshot_id"] for row in snapshots} + _require(len(ids) == len(snapshots) and all(p["baseline_snapshot_id"] in ids for p in probes), + "Duplicate snapshot or missing original pre baseline") + by_assay = Counter(p["assay"] for p in probes) + steps = len(probes) * config["probe_steps"] + result = dict(schema=SCHEMA, status="DRAFT-NOT-REGISTERED", config=config, + trial_payload_sha256=trial["payload_sha256"], seed_manifest_sha256=trial["seed_manifest"]["payload_sha256"], + config_sha256=_sha(config), snapshots=snapshots, probes=probes, + budget=dict(snapshot_count=len(snapshots), evaluation_probes=len(probes), + probes_by_assay=dict(sorted(by_assay.items())), evaluation_steps=steps, + evaluation_biological_seconds=steps / 10000, + preexisting_training_control_reversal_epochs=trial["budget"]["training_control_reversal_epochs"], + preexisting_minimum_steps_excluding_evaluation=trial["budget"]["minimum_steps_excluding_evaluation"], + planned_scientific_steps_including_evaluation=trial["budget"]["minimum_steps_excluding_evaluation"] + steps, + planned_scientific_biological_seconds=(trial["budget"]["minimum_steps_excluding_evaluation"] + steps) / 10000, + independent_evaluation_inputs=80, independent_training_blocks=40, + evaluation_probes_are_training_replicates=False, baseline_output_deduplication=False, + scope="conditional full draft inventory, not an executed count or wall-time/storage estimate", + excludes=["implementation validation", "frozen regressions", "encoding", "I/O", "resource reserve"]), + evaluation_inventory_structurally_complete=True, scientific_gate_added=False, + protocol_approval_claimed=False, execution_authorized=False, training_executed=False, + inputs_generated=False, input_generator_implementation_verified=False, candidate_generated=False, + learning_pass_claimed=False, external_source_and_snapshot_authority_verified=False, + subsequent_assays_condition="not authorized here; full planned inventory remains subject to the draft's acquisition gate") + result["payload_sha256"] = _sha(result) + # No returned nested object aliases caller-owned trial/config structures. + return json.loads(_canonical(result)) + + +def build_evaluation_plan(trial_payload, config): + """Build a complete draft inventory, with mandatory explicit choices.""" + _configuration(config) # Report missing choices before expensive reconstruction. + trial_plan.validate_trial_plan(trial_payload) + return _assemble(trial_payload, config) + + +def validate_evaluation_plan(payload, trial_payload, config): + """Reconstruct every probe/binding; an updated self-hash cannot excuse drift.""" + _require(type(payload) is dict and "payload_sha256" in payload, "Invalid evaluation payload") + _require(payload["payload_sha256"] == _sha({k: v for k, v in payload.items() if k != "payload_sha256"}), + "Evaluation payload SHA mismatch") + expected = build_evaluation_plan(trial_payload, config) + _require(_canonical(payload) == _canonical(expected), "Evaluation coverage, binding, type, seed, or order differs from exact reconstruction") + return True diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/evaluation_policy.py b/registry/phase4ao-conditioning-interface/source-snapshot/evaluation_policy.py new file mode 100644 index 0000000..8c08061 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/evaluation_policy.py @@ -0,0 +1,294 @@ +"""Pure pre-outcome DRAFT snapshot policy; no I/O, inputs, execution or RNG. + +The caller must supply the complete existing trial_plan payload. This module +does not select a master seed. Its fixed policy choices are newly proposed +planning choices, not inherited biological measurements or scientific gates. + +build_evaluation_policy(trial_payload) returns a config directly consumable by +evaluation_plan.build_evaluation_plan, plus a separate ordered prefix audit. +Every snapshot boundary is AFTER the complete selected epoch and its scheduled +delta update, not immediately after the US tick. All returned timing and counts +describe planned exposures, never observed or accepted DAN spikes. +""" +from __future__ import annotations + +from collections import Counter +import hashlib +import json + +import evaluation_plan +import trial_plan + + +SCHEMA = "phase4an-evaluation-policy-draft-v1" +ALGORITHM = "fixed-ordered-CS-prefix-completed-epoch-v1" +POINTS = (0, 1, 2, 5, 10, 20, 50) +CS_FIELDS = frozenset(("cue", "odor", "occurrence", "stream_id", "source_stream_id", + "seed", "seed_bits", "seed_conversion", "role", "start_tick", "stop_tick_exclusive")) +IDENTITY_FIELDS = tuple(sorted(CS_FIELDS - {"stream_id"})) +SAME_POSITION_ARMS = frozenset(("d_zero", "frozen", "random_reward", "cs_only")) + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(",", ":"), + ensure_ascii=False, allow_nan=False) + + +def _sha(value): + return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest() + + +def _registry_index(trial): + manifest = trial["seed_manifest"] + independent = {(r["split"], tuple(r["key"])): r for r in manifest["independent_streams"]} + aliases = {(r["split"], tuple(r["key"])): r for r in manifest["aliases"]} + _require(len(independent) == len(manifest["independent_streams"]) and + len(aliases) == len(manifest["aliases"]) and not (independent.keys() & aliases.keys()), + "Duplicate or overlapping seed registry keys") + return independent, aliases + + +def _stream_id(master, key): + return "training:" + _sha([trial_plan.SCHEMA, "stream-id", master, "training", list(key)]) + + +def _cs_identity(cs, *, block_id, assay, arm, base_arm, master, registry): + """Verify actual alias source/seed; omit only the control's own alias ID.""" + _require(type(cs) is dict and set(cs) == CS_FIELDS, "CS binding schema differs") + _require(cs["cue"] in trial_plan.CUES and cs["odor"] == trial_plan.ODORS[cs["cue"]] and + type(cs["occurrence"]) is int and 0 <= cs["occurrence"] < 50 and + type(cs["seed"]) is int and 0 <= cs["seed"] < 2**63 and + type(cs["seed_bits"]) is int and cs["seed_bits"] == 63 and cs["role"] == "CS" and + type(cs["start_tick"]) is int and cs["start_tick"] == 0 and + type(cs["stop_tick_exclusive"]) is int and cs["stop_tick_exclusive"] == 1000 and + cs["seed_conversion"] == "exact integer; no truncation or addition", + "CS cue/occurrence/full seed/role/timing differs") + key = (block_id, assay, "CS", cs["cue"], f'{cs["occurrence"]:02d}', arm) + source_key = key[:-1] + (base_arm,) + independent, aliases = registry + source = independent.get(("training", source_key)) + _require(source is not None and source["seed"] == cs["seed"] and + cs["source_stream_id"] == _stream_id(master, source_key) and + cs["stream_id"] == _stream_id(master, key), "CS seed/source stream authority differs") + if arm == base_arm: + _require(("training", key) not in aliases and cs["stream_id"] == cs["source_stream_id"], + "Original CS must be its own independent source") + else: + alias = aliases.get(("training", key)) + _require(alias is not None and alias["source_split"] == "training" and + alias["source_key"] == list(source_key) and alias["seed"] == cs["seed"] and + alias["independent_replicate"] is False, + "CS alias is not registered to this block/assay/cue/occurrence source") + return {key: cs[key] for key in IDENTITY_FIELDS} + + +def _prefixes(arm, *, block_id, assay, base_arm, master, registry): + """Return verified ordered CS records indexed by actual completed epochs.""" + identities = [] + prefixes = [[]] + for epoch in arm["epochs"]: + if epoch["cs"] is not None: + identities.append(_cs_identity(epoch["cs"], block_id=block_id, assay=assay, + arm=arm["arm"], base_arm=base_arm, master=master, registry=registry)) + prefixes.append(list(identities)) + if arm["arm"] == "dan_only": + _require(not identities, "DAN-only must contain no delivered CS") + else: + _require(len(identities) == 100, "Every CS-present arm must retain all 100 CS epochs") + counters = Counter() + for row in identities: + _require(row["occurrence"] == counters[row["cue"]], "CS occurrence order is not canonical") + counters[row["cue"]] += 1 + _require(dict(counters) == {"A": 50, "B": 50}, "Both original odors require 50 CS occurrences") + return prefixes + + +def _reinforcement_boundary(arm, point): + """The whole epoch containing the kth requested US; terminal includes all.""" + if point == 0: + return 0 + epochs = arm["epochs"] + us_indices = [i for i, e in enumerate(epochs) if e["us"]["requested"]] + _require(len(us_indices) == 50, "Reference arm must have exactly 50 requested US epochs") + return len(epochs) if point == 50 else us_indices[point - 1] + 1 + + +def _exposure(arm, count, cs_prefix): + epochs = arm["epochs"][:count] + return dict(completed_epochs=count, CS_epochs=len(cs_prefix), + CS_counts_by_cue={cue: sum(c["cue"] == cue for c in cs_prefix) for cue in trial_plan.CUES}, + ordered_CS_source_identity_sha256=_sha(cs_prefix), + requested_US_epochs=sum(e["us"]["requested"] for e in epochs), + enabled_nonzero_D_update_opportunities=sum(e["update_enabled"] and e["D_command"] != 0 for e in epochs), + elapsed_training_ticks=sum(e["steps"] for e in epochs), + actual_accepted_US_or_DAN_spikes_known=False) + + +def _suite_policy(block, suite, master, registry): + """Independent prefix/accounting construction, separate from evaluation_plan.""" + assay, block_id = suite["assay"], block["block_id"] + _require(assay in ("normal", "delayed", "reversal"), "Unexpected assay") + expected_arms = trial_plan.REVERSAL_ARMS if assay == "reversal" else trial_plan.ARMS + _require([a["arm"] for a in suite["arms"]] == list(expected_arms), "Missing, reordered or duplicate control arms") + arms = {a["arm"]: a for a in suite["arms"]} + base_name = "reversal" if assay == "reversal" else "paired" + base = arms[base_name] + _require(len(base["epochs"]) == 100, "Base arm must contain all 100 epochs") + prefixes = {name: _prefixes(arm, block_id=block_id, assay=assay, base_arm=base_name, + master=master, registry=registry) for name, arm in arms.items()} + base_full = prefixes[base_name][-1] + for name, arm in arms.items(): + _require(len(arm["epochs"]) == (150 if name == "unpaired" else 100), "Unexpected epoch coverage") + if name == "dan_only": + for i, epoch in enumerate(arm["epochs"]): + original = base["epochs"][i] + _require(epoch["cs"] is None and + epoch["yoked_cue_removed"] == original["cs"]["cue"] and + type(epoch["yoked_occurrence_removed"]) is int and + epoch["yoked_occurrence_removed"] == original["cs"]["occurrence"] and + _canonical(epoch["us"]) == _canonical(original["us"]) and + epoch["steps"] == original["steps"], + "DAN-only omitted CS/yoked US/epoch position differs from paired") + else: + _require(_canonical(prefixes[name][-1]) == _canonical(base_full), + "Actual ordered CS cue/occurrence/seed/source sequence differs") + if name != "unpaired": + _require(all(_canonical(a) == _canonical(b) + for a, b in zip(prefixes[name], prefixes[base_name])), + "CS sequence differs at a paired epoch position") + + bindings, audits = [], [] + for name in expected_arms: + arm = arms[name] + previous = -1 + for point in POINTS: + base_count = _reinforcement_boundary(base, point) + expected_prefix = prefixes[base_name][base_count] + if assay == "reversal": + count = _reinforcement_boundary(arm, point) + alignment = "own reinforced-US ordinal; intermediate CS prefixes need not match across arms" + elif name == "unpaired": + if point == 0: + count = 0 + elif point == 50: + count = len(arm["epochs"]) + else: + matches = [i for i, prefix in enumerate(prefixes[name]) if len(prefix) == len(expected_prefix)] + _require(bool(matches), "Unpaired arm never reaches the required CS prefix") + count = matches[0] + alignment = "earliest ordered-CS-prefix boundary; terminal includes all 150 epochs" + else: + count = base_count + alignment = ("paired yoked epoch positions; zero delivered CS, not equal CS exposure" + if name == "dan_only" else "exact paired completed-epoch and ordered-CS prefix") + _require(count > previous, "Policy snapshot epochs must advance strictly") + previous = count + own_prefix = prefixes[name][count] + same_cs = _canonical(own_prefix) == _canonical(expected_prefix) + if assay != "reversal" and name != "dan_only": + _require(same_cs, "Ordered CS prefix mismatch at a control checkpoint") + if name == "unpaired" and point not in (0, 50): + _require(count > 0 and len(prefixes[name][count - 1]) < len(expected_prefix), + "Unpaired snapshot is not the earliest matching completed-epoch boundary") + actual = _exposure(arm, count, own_prefix) + reference = _exposure(base, base_count, expected_prefix) + binding = dict(block_id=block_id, assay=assay, arm=name, + checkpoint=point, completed_epochs=count) + bindings.append(binding) + audits.append(dict(binding=binding, alignment=alignment, + reference_arm=base_name, reference_completed_epochs=base_count, + boundary="after complete epoch and scheduled delta update; not at the US tick", + terminal_includes_all_epochs=point == 50, + actual=actual, reference=reference, + ordered_CS_prefix_exact=same_cs if name != "dan_only" else None, + CS_exposure_equal=same_cs if name != "dan_only" else False, + DAN_only_yoke_verified=name == "dan_only", + requested_US_count_equal_to_reference=actual["requested_US_epochs"] == reference["requested_US_epochs"], + requested_US_count_difference=actual["requested_US_epochs"] - reference["requested_US_epochs"], + elapsed_ticks_equal_to_reference=actual["elapsed_training_ticks"] == reference["elapsed_training_ticks"], + elapsed_ticks_difference=actual["elapsed_training_ticks"] - reference["elapsed_training_ticks"], + nonzero_D_update_opportunity_difference=actual["enabled_nonzero_D_update_opportunities"] - reference["enabled_nonzero_D_update_opportunities"], + all_US_timing_or_total_exposure_matched_claimed=False)) + return bindings, audits + + +def _assemble(trial): + registry = _registry_index(trial) + bindings, audits = [], [] + blocks = trial["plan"]["blocks"] + _require(len(blocks) == 40, "Exactly 40 original training blocks are required") + for block in blocks: + _require(block["evaluation_checkpoints"] == list(POINTS) and + [s["assay"] for s in block["suites"]] == ["normal", "delayed", "reversal"], + "Original assay/checkpoint coverage differs") + for suite in block["suites"]: + suite_bindings, suite_audits = _suite_policy(block, suite, trial["plan"]["master"], registry) + bindings.extend(suite_bindings) + audits.extend(suite_audits) + config = dict(schema=evaluation_plan.CONFIG_SCHEMA, probe_steps=1200, + cs_start_tick=0, cs_stop_tick_exclusive=1000, reversal_checkpoints=list(POINTS), + baseline_execution="execute_each_snapshot", snapshot_binding_policy="explicit_completed_epochs", + snapshot_bindings=bindings) + # Check the unchanged consumer's declared schema/coverage as well as our + # independent ordered-prefix construction. Do not generate its probes here. + evaluation_plan._configuration(config) + evaluation_plan._binding_inventory(trial["plan"], config) + _require(len(bindings) == 4480 and len({_canonical(v) for v in bindings}) == 4480, + "Incomplete or duplicate snapshot coverage") + counts = Counter(row["assay"] for row in bindings) + result = dict(schema=SCHEMA, status="DRAFT-NOT-REGISTERED", algorithm=ALGORITHM, + trial_payload_sha256=trial["payload_sha256"], + seed_manifest_sha256=trial["seed_manifest"]["payload_sha256"], + config_sha256=_sha(config), config=config, + audit=dict(bindings=audits, + coverage=dict(training_blocks=40, training_replicates_per_cell=5, + nonretention_snapshots=4480, snapshots_by_assay=dict(sorted(counts.items())), + original_full_CS_alias_sequences_verified=True, + new_random_streams=0, new_master_seed_selected=False), + exposure_limits=[ + "CS-present normal/delayed controls match actual ordered CS prefixes, not just counts.", + "US counts, nonzero-D opportunities and elapsed time are separately reported, not assumed matched.", + "Unpaired boundaries include earlier inserted US; terminal includes all 150 epochs and trailing US.", + "DAN-only has zero delivered CS; yoked positions are not equal CS exposure.", + "Reversal and continuation use their own kth US; intermediate CS prefixes can differ.", + "All snapshots are after a full epoch plus its delta update, not the US tick.", + "Requested US is not accepted DAN activity, dopamine concentration or learning evidence."], + unchanged_retention_scope=dict(arms=["normal/paired", "normal/frozen"], + idle_checkpoints_seconds=[0, 1, 10], + same_continuous_ten_second_lineage=True, probes_fork_and_do_not_advance_idle=True, + retained_snapshot_execution_or_lineage_verified=False), + conditional_evaluation_inventory=dict(nonretention_probes=8960, + retention_snapshots=240, retention_probes=480, all_snapshots=4720, + all_probes=9440, probe_steps=1200, evaluation_steps=11328000, + includes_full_seven_point_reversal=True, + baseline_output_deduplication=False, + actual_execution_count=0)), + policy_structural_checks_complete=True, choices_fixed_before_outcomes=True, + choices_are_new_draft_proposals_not_inherited_gates=True, + protocol_approval_claimed=False, execution_authorized=False, training_executed=False, + inputs_generated=False, input_generator_implementation_verified=False, + source_and_snapshot_authority_verified=False, candidate_generated=False, + learning_pass_claimed=False, scientific_gate_added=False) + result["payload_sha256"] = _sha(result) + return json.loads(_canonical(result)) + + +def build_evaluation_policy(trial_payload): + """Return fixed DRAFT config/audit; caller provides, we never choose, master.""" + trial_plan.validate_trial_plan(trial_payload) + return _assemble(trial_payload) + + +def validate_evaluation_policy(payload, trial_payload): + """Exact reconstruction, including JSON types; a replaced self-hash is not authority.""" + _require(type(payload) is dict and "payload_sha256" in payload, "Invalid policy payload") + _require(payload["payload_sha256"] == _sha({k: v for k, v in payload.items() if k != "payload_sha256"}), + "Actual evaluation policy SHA mismatch") + expected = build_evaluation_policy(trial_payload) + _require(_canonical(payload) == _canonical(expected), "Policy config/audit/coverage differs from exact reconstruction") + return True diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/metrics.py b/registry/phase4ao-conditioning-interface/source-snapshot/metrics.py new file mode 100644 index 0000000..e09274f --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/metrics.py @@ -0,0 +1,494 @@ +"""Pure Phase 4AN proposal metrics; no file I/O, simulation or fitted decoder. + +The caller must bind complete anatomical population membership (not a plastic +mask subset), finalized execution receipts, input identities and seed lineages +to its preregistration. These functions validate arrays/declared coverage; they +cannot establish provenance by trusting a caller's Boolean or SHA string. +All gates below are NEW DRAFT hypotheses, not a historical learning result. +""" +from __future__ import annotations + +import hashlib +import json +import math +from collections.abc import Mapping + +import numpy as np + + +SCHEMA = 'phase4an-metrics-draft-v1' +CUES = ('4-ethylphenol', '3-hexanone') +MODES = ('physiological', 'matched_energy') +DAN_TYPES = ('PAM15', 'PPL103') +CONTROLS = ('D=0', 'frozen', 'random reward', 'unpaired', 'CS-only', 'DAN-only') +ARMS = ('paired',) + CONTROLS +POPULATIONS = ('MBON', 'legacyDownstream', 'nonMBONDownstream', 'MBON01', 'MBON12') +SEPARATION_POPULATIONS = ('MBON', 'legacyDownstream', 'nonMBONDownstream') +POPULATION_SEMANTICS = { + 'MBON': 'complete caller-bound anatomical MBON population', + 'legacyDownstream': 'complete frozen H downstream membership, including MBONs; never mask-filtered', + 'nonMBONDownstream': 'exact legacyDownstream minus MBON; NEW hypothesis readout, not the old H population', + 'MBON01': 'complete caller-bound MBON01 type, not plastic-mask targets only', + 'MBON12': 'complete caller-bound MBON12 type, not plastic-mask targets only', +} +STATE_KEYS = frozenset(('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')) + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def _hash(value): + return hashlib.sha256(np.ascontiguousarray(value, dtype=value.dtype.newbyteorder('<')).tobytes()).hexdigest() + + +def _json_hash(value): + raw = json.dumps(value, sort_keys=True, separators=(',', ':'), allow_nan=False).encode('utf-8') + return hashlib.sha256(raw).hexdigest() + + +def _sha(value): + return type(value) is str and len(value) == 64 and all(c in '0123456789abcdef' for c in value) + + +def _token(value, name): + require(type(value) is str and bool(value) and value.strip() == value, 'Invalid ' + name) + return value + + +def _vector(value, dtype, name, size=None): + require(isinstance(value, np.ndarray) and value.dtype == np.dtype(dtype) and value.ndim == 1, + name + ' requires an exact ' + dtype + ' vector') + require(size is None or value.shape == (size,), name + ' shape mismatch') + return value + + +def _ids(value, name): + _vector(value, 'int32', name) + require(len(value) > 0 and np.all((value >= 0) & (value < 166700)) and np.all(value[1:] > value[:-1]), + name + ' must be nonempty sorted unique global neuron indices') + return value + + +def _positions(nodes, values, name): + position = np.searchsorted(nodes, values) + require(np.all(position < len(nodes)), name + ' contains absent neurons') + require(np.array_equal(nodes[position], values), name + ' contains absent neurons') + return position + + +def validate_populations(nodes, populations): + """Validate full caller-bound type lists; never derive them from an edge mask.""" + _ids(nodes, 'nodes') + require(isinstance(populations, Mapping) and set(populations) == set(POPULATIONS), + 'Require exactly MBON/legacyDownstream/nonMBONDownstream/MBON01/MBON12 memberships') + for name in POPULATIONS: + _positions(nodes, _ids(populations[name], name), name) + for name in ('MBON01', 'MBON12'): + _positions(populations['MBON'], populations[name], name + ' outside MBON') + require(not len(np.intersect1d(populations['MBON01'], populations['MBON12'])), 'Type populations overlap') + _positions(populations['legacyDownstream'], populations['MBON'], 'MBON outside legacyDownstream') + require(np.array_equal(populations['nonMBONDownstream'], + np.setdiff1d(populations['legacyDownstream'], populations['MBON'])), + 'nonMBONDownstream must equal the complete legacyDownstream minus MBON') + payload = {name: populations[name].tolist() for name in POPULATIONS} + return dict(indices=payload, membership_sha256=_json_hash(payload), + population_semantics=dict(POPULATION_SEMANTICS), + complete_anatomical_membership_authority='caller preregistration; never inferred from mask') + + +def validate_state(state, *, nodes, steps, dt_ms=.1): + """Check every original state element and all sparse events without mutation.""" + _ids(nodes, 'nodes') + require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max, 'Invalid steps') + require(type(dt_ms) in (int, float) and math.isfinite(dt_ms) and dt_ms > 0, 'Invalid dt_ms') + require(isinstance(state, Mapping) and set(state) == STATE_KEYS, 'State must contain exactly six runtime keys') + n = len(nodes) + for key, dtype in (('counts', 'int32'), ('ref', 'int32'), ('v', 'float64'), ('g', 'float64')): + _vector(state[key], dtype, key, n) + require(np.isfinite(state[key]).all(), 'Nonfinite state: ' + key) + require(np.all((state['counts'] >= 0) & (state['counts'] <= steps)), 'Invalid counts') + require(np.all((state['ref'] >= 0) & (state['ref'] <= 22)), 'Invalid refractory state') + ticks = _vector(state['step'], 'int32', 'step') + events = _vector(state['neuron_idx'], 'int32', 'neuron_idx', len(ticks)) + require(np.all((ticks >= 0) & (ticks < steps)), 'Event tick outside trial') + require(len(ticks) < 2 or np.all((ticks[1:] > ticks[:-1]) | + ((ticks[1:] == ticks[:-1]) & (events[1:] > events[:-1]))), + 'Events must be sorted unique by tick/global neuron') + local = _positions(nodes, events, 'events') + require(np.array_equal(np.bincount(local, minlength=n), state['counts']), 'Sparse events/counts mismatch') + first = np.full(n, -1, dtype=np.int32) + if len(ticks): + first.fill(steps) + np.minimum.at(first, local, ticks) + first[first == steps] = -1 + return dict(first_tick=first, local_events=local, + hashes={key: _hash(state[key]) for key in sorted(STATE_KEYS)}) + + +def _first_ms(first, dt_ms): + present = first[first >= 0] + return float(int(present.min()) * dt_ms) if len(present) else None + + +def _latencies(first, dt_ms): + return [None if int(tick) < 0 else float(int(tick) * dt_ms) for tick in first] + + +def _population_change(pre, post, checked_pre, checked_post, nodes, local, dt_ms, vectors): + cp, cq = pre['counts'][local].astype(np.int64), post['counts'][local].astype(np.int64) + fp, fq = checked_pre['first_tick'][local], checked_post['first_tick'][local] + lp, lq = _first_ms(fp, dt_ms), _first_ms(fq, dt_ms) + both = (fp >= 0) & (fq >= 0) + ep = np.isin(checked_pre['local_events'], local) + eq = np.isin(checked_post['local_events'], local) + events_same = (np.array_equal(pre['step'][ep], post['step'][eq]) and + np.array_equal(pre['neuron_idx'][ep], post['neuron_idx'][eq])) + try: + with np.errstate(over='raise', invalid='raise'): + dv, dg = post['v'][local] - pre['v'][local], post['g'][local] - pre['g'][local] + vmax, gmax = float(np.max(np.abs(dv))), float(np.max(np.abs(dg))) + except FloatingPointError as error: + raise ValueError('Nonfinite derived final-state difference') from error + result = dict(neurons=len(local), pre_total_spikes=int(cp.sum()), post_total_spikes=int(cq.sum()), + spike_count_l1=int(np.abs(cq - cp).sum()), spike_count_changed_neurons=int(np.count_nonzero(cp != cq)), + events_changed=not events_same, pre_first_latency_ms=lp, post_first_latency_ms=lq, + first_latency_delta_ms=None if lp is None or lq is None else float(lq - lp), + defined_first_latency_changed=bool(lp is not None and lq is not None and lp != lq), + first_latency_both_defined_neurons=int(both.sum()), + first_latency_changed_among_both_defined_neurons=int(np.count_nonzero(both & (fp != fq))), + first_latency_became_defined_neurons=int(np.count_nonzero((fp < 0) & (fq >= 0))), + first_latency_became_undefined_neurons=int(np.count_nonzero((fp >= 0) & (fq < 0))), + final_delta_v_max_abs=vmax, final_delta_g_max_abs=gmax, + final_ref_changed_neurons=int(np.count_nonzero(pre['ref'][local] != post['ref'][local])), + final_state_units='SI volts; refractory integer ticks', + max_abs_scope='maximum across population neurons at terminal tick; NOT a temporal peak') + if vectors: + result.update(neuron_idx=nodes[local].tolist(), pre_counts=cp.tolist(), post_counts=cq.tolist(), + pre_first_latency_ms_per_neuron=_latencies(fp, dt_ms), + post_first_latency_ms_per_neuron=_latencies(fq, dt_ms)) + for key in ('v', 'g', 'ref'): + result['pre_final_' + key] = pre[key][local].tolist() + result['post_final_' + key] = post[key][local].tolist() + return result + + +def functional_summary(pre, post, *, nodes, populations, steps, dt_ms=.1): + """Full-state validation plus wholebrain and anatomical-group differences. + + The population's earliest event defines the legacy-style first latency. + Per-neuron first events and missingness transitions are additional diagnostics. + A missing latency is None, never zero or evidence of a defined latency shift. + """ + membership = validate_populations(nodes, populations) + a = validate_state(pre, nodes=nodes, steps=steps, dt_ms=dt_ms) + b = validate_state(post, nodes=nodes, steps=steps, dt_ms=dt_ms) + groups = {name: _population_change(pre, post, a, b, nodes, + _positions(nodes, populations[name], name), dt_ms, True) for name in POPULATIONS} + return dict(schema=SCHEMA, full_six_key_state_validated=True, steps=steps, dt_ms=float(dt_ms), + nodes=len(nodes), nodes_raw_sha256=_hash(nodes), membership=membership, + pre_state_raw_sha256=a['hashes'], post_state_raw_sha256=b['hashes'], + wholebrain=_population_change(pre, post, a, b, nodes, np.arange(len(nodes)), dt_ms, False), + populations=groups, MBON_final_delta_g_max_abs=groups['MBON']['final_delta_g_max_abs'], + MBON_final_delta_g_units='SI volts', MBON_final_delta_g_scope='terminal across-neuron maximum, not time peak') + + +def arm_metrics(pre_by_cue, post_by_cue, *, conditioned_cue, nodes, populations, steps=1200, dt_ms=.1): + """New fixed, unscaled type-count contrast; neither decoder nor feedback.""" + require(conditioned_cue in CUES, 'Unknown conditioned odor label') + require(isinstance(pre_by_cue, Mapping) and isinstance(post_by_cue, Mapping) and + set(pre_by_cue) == set(post_by_cue) == set(CUES), 'Both original odor labels are required') + neutral = next(cue for cue in CUES if cue != conditioned_cue) + summaries = {cue: functional_summary(pre_by_cue[cue], post_by_cue[cue], nodes=nodes, + populations=populations, steps=steps, dt_ms=dt_ms) for cue in CUES} + b, means = {}, {} + for phase in ('pre', 'post'): + b[phase], means[phase] = {}, {} + for cue in CUES: + groups = summaries[cue]['populations'] + means[phase][cue] = {name: float(np.mean(groups[name][phase + '_counts'])) for name in ('MBON01', 'MBON12')} + b[phase][cue] = means[phase][cue]['MBON12'] - means[phase][cue]['MBON01'] + raw_s = (b['post'][conditioned_cue] - b['post'][neutral]) - (b['pre'][conditioned_cue] - b['pre'][neutral]) + separation = {} + for name in SEPARATION_POPULATIONS: + values = {} + for phase in ('pre', 'post'): + first = np.asarray(summaries[CUES[0]]['populations'][name][phase + '_counts'], dtype=np.float64) + second = np.asarray(summaries[CUES[1]]['populations'][name][phase + '_counts'], dtype=np.float64) + values[phase] = float(np.linalg.norm(first - second)) + separation[name] = dict(values, gain=values['post'] - values['pre'], + vector='all bound member spike counts', population_semantics=POPULATION_SEMANTICS[name]) + return dict(schema=SCHEMA, analysis_status='DRAFT_NEW_HYPOTHESIS', conditioned_cue=conditioned_cue, + neutral_cue=neutral, steps=steps, dt_ms=float(dt_ms), rawB=b, rawS=float(raw_s), + type_mean_counts=means, B_definition='mean full MBON12 counts - mean full MBON01 counts', + B_is_decoder=False, S_orientation='conditioned minus neutral; post minus own pre', + separation=separation, cues=summaries, + historical_implementation_equivalence_claimed=False, learning_pass_claimed=False) + + +def _finite_tree(value): + if isinstance(value, Mapping): + for item in value.values(): + _finite_tree(item) + elif isinstance(value, (list, tuple)): + for item in value: + _finite_tree(item) + elif isinstance(value, (float, np.floating)): + require(math.isfinite(float(value)), 'Nonfinite metric value') + + +def _bound_metric(metric, cue): + require(isinstance(metric, Mapping) and metric.get('schema') == SCHEMA and + metric.get('analysis_status') == 'DRAFT_NEW_HYPOTHESIS', 'Unexpected metric schema') + _finite_tree(metric) + require(metric['conditioned_cue'] == cue and metric['neutral_cue'] == next(c for c in CUES if c != cue), + 'Cue role mismatch') + require(metric['steps'] == 1200 and metric['dt_ms'] == .1, 'Evaluation must use the fixed 120 ms probe') + # Derive gate quantities from recorded raw vectors, not a supplied gate Boolean. + for odor in CUES: + summary = metric['cues'][odor] + require(summary.get('full_six_key_state_validated') is True and summary['steps'] == 1200 and + summary['dt_ms'] == .1 and _sha(summary['nodes_raw_sha256']), 'Unvalidated cue state') + membership = summary['membership']['indices'] + require(set(membership) == set(POPULATIONS) and summary['membership']['membership_sha256'] == _json_hash(membership), + 'Population membership digest mismatch') + require(summary['membership'].get('population_semantics') == POPULATION_SEMANTICS, + 'Population semantics must distinguish old H membership from the new non-MBON readout') + for key in ('pre_state_raw_sha256', 'post_state_raw_sha256'): + require(set(summary[key]) == STATE_KEYS and all(_sha(v) for v in summary[key].values()), 'Missing state digest') + for name in POPULATIONS: + group = summary['populations'][name] + ids, count = group['neuron_idx'], group['neurons'] + require(type(count) is int and count > 0 and len(ids) == count and ids == membership[name] and + all(type(v) is int and 0 <= v < 166700 for v in ids) and ids == sorted(set(ids)), + 'Empty or mismatched anatomical population') + for phase in ('pre', 'post'): + counts = group[phase + '_counts'] + latency = group[phase + '_first_latency_ms_per_neuron'] + require(len(counts) == len(latency) == count and + all(type(v) is int and 0 <= v <= 1200 for v in counts), 'Invalid count/latency vector') + for c, value in zip(counts, latency): + require((c == 0 and value is None) or (c > 0 and type(value) in (int, float) and + 0 <= value < 120 and value == round(value / .1) * .1), 'Missing/nonphysical first latency') + present = [v for v in latency if v is not None] + require(group[phase + '_first_latency_ms'] == (min(present) if present else None), + 'Population first latency differs from raw cells') + for key in ('v', 'g', 'ref'): + values = group[phase + '_final_' + key] + require(len(values) == count and all(type(v) in (int, float) and math.isfinite(v) for v in values), + 'Invalid final-state vector') + if key == 'ref': + require(all(type(v) is int and 0 <= v <= 22 for v in values), 'Invalid final ref') + l1 = sum(abs(a - b) for a, b in zip(group['pre_counts'], group['post_counts'])) + lp, lq = group['pre_first_latency_ms'], group['post_first_latency_ms'] + changed = lp is not None and lq is not None and lp != lq + require(type(group['spike_count_l1']) is int and group['spike_count_l1'] == l1 and + group['defined_first_latency_changed'] is changed and + group['first_latency_delta_ms'] == (None if lp is None or lq is None else lq - lp), + 'Functional predicate differs from raw counts/defined latency') + try: + with np.errstate(over='raise', invalid='raise'): + for key in ('v', 'g'): + difference = np.asarray(group['post_final_' + key]) - np.asarray(group['pre_final_' + key]) + require(group['final_delta_' + key + '_max_abs'] == float(np.max(np.abs(difference))), + 'Final-state maximum differs from raw terminal vectors') + except FloatingPointError as error: + raise ValueError('Nonfinite derived terminal metric') from error + require(not set(membership['MBON01']).intersection(membership['MBON12']), 'Type population overlap') + require(set(membership['MBON']) <= set(membership['legacyDownstream']), + 'Legacy downstream must retain the complete MBON population') + require(membership['nonMBONDownstream'] == sorted(set(membership['legacyDownstream']) - set(membership['MBON'])), + 'nonMBONDownstream differs from exact legacyDownstream minus MBON') + for name, parent in (('MBON01', 'MBON'), ('MBON12', 'MBON'), + ('MBON', 'legacyDownstream'), ('nonMBONDownstream', 'legacyDownstream')): + require(set(membership[name]) <= set(membership[parent]), 'Population is not a subset of its bound parent') + lookup = {v: i for i, v in enumerate(membership[parent])} + for phase in ('pre', 'post'): + for field in ('counts', 'first_latency_ms_per_neuron', 'final_v', 'final_g', 'final_ref'): + key = phase + '_' + field + require(summary['populations'][name][key] == [ + summary['populations'][parent][key][lookup[v]] for v in membership[name]], + 'Overlapping population vectors disagree with the bound parent') + for phase in ('pre', 'post'): + for odor in CUES: + groups = metric['cues'][odor]['populations'] + means = [] + for name in ('MBON12', 'MBON01'): + counts = groups[name][phase + '_counts'] + require(len(counts) > 0 and all(type(v) is int and 0 <= v <= 1200 for v in counts), + 'Empty or invalid type count vector') + means.append(sum(counts) / len(counts)) + require(metric['type_mean_counts'][phase][odor][name] == means[-1], 'Type mean differs from raw counts') + require(metric['rawB'][phase][odor] == means[0] - means[1], 'Raw B disagrees with full type vectors') + neutral = metric['neutral_cue'] + calculated = (metric['rawB']['post'][cue] - metric['rawB']['post'][neutral]) - ( + metric['rawB']['pre'][cue] - metric['rawB']['pre'][neutral]) + require(type(metric['rawS']) in (int, float) and metric['rawS'] == calculated, 'Raw S disagrees with signed components') + for name in SEPARATION_POPULATIONS: + calculated = {} + for phase in ('pre', 'post'): + a = metric['cues'][CUES[0]]['populations'][name][phase + '_counts'] + b = metric['cues'][CUES[1]]['populations'][name][phase + '_counts'] + require(metric['cues'][CUES[0]]['membership'] == metric['cues'][CUES[1]]['membership'], + 'A/B population mismatch') + calculated[phase] = float(np.linalg.norm(np.asarray(a, dtype=np.float64) - np.asarray(b, dtype=np.float64))) + require(metric['separation'][name]['pre'] == calculated['pre'] and + metric['separation'][name]['post'] == calculated['post'] and + metric['separation'][name]['gain'] == calculated['post'] - calculated['pre'], + 'Separation gain differs from raw count vectors') + require(metric['separation'][name].get('population_semantics') == POPULATION_SEMANTICS[name], + 'Separation population semantics mismatch') + return metric + + +def _execution(arm, name, assay, used_receipts): + require(isinstance(arm, Mapping) and set(arm) == {'metrics', 'execution'}, 'Arm needs metrics and actual execution evidence') + e = arm['execution'] + required = {'complete', 'recording_verified', 'receipt_sha256', 'assay', 'epoch_duration_ms', + 'cs_duration_ms', 'us_tick', 'completed_epochs', 'reinforcement_opportunities', 'terminal_pairings'} + require(isinstance(e, Mapping) and set(e) == required, 'Incomplete execution evidence') + require(e['complete'] is True and e['recording_verified'] is True and _sha(e['receipt_sha256']), + 'All arms require an actual verified completed execution receipt') + require(e['receipt_sha256'] not in used_receipts, 'Execution receipt reused across arms/replicates') + used_receipts.add(e['receipt_sha256']) + require(e['assay'] == assay and e['epoch_duration_ms'] == (120. if assay == 'normal' else 180.) and + e['cs_duration_ms'] == 100. and type(e['us_tick']) is int and + e['us_tick'] == (900 if assay == 'normal' else 1500), 'Normal/delayed control timing mismatch') + require(type(e['completed_epochs']) is int and e['completed_epochs'] == (150 if name == 'unpaired' else 100), + 'Incomplete arm epoch inventory') + require(type(e['reinforcement_opportunities']) is int and e['reinforcement_opportunities'] == (0 if name == 'CS-only' else 50), + 'Wrong arm US opportunity inventory') + require(type(e['terminal_pairings']) is int and e['terminal_pairings'] == 50, 'Only fixed terminal count 50 may gate') + + +def evaluate_acquisition(replicates, *, assay, mode, dan_type, conditioned_cue, registered_training_streams): + """Strict new normal/delayed 3-of-5 SAME-training-replicate conjunction. + + Each row has replicate_id, replicate_unit='independent_training', + training_stream_ids (all independent source IDs, not aliases), + evaluation_stream_ids, assay/mode/dan_type/conditioned_cue, and seven arms. + Arm = {metrics: arm_metrics(...), execution: complete receipt declaration}. + registered_training_streams maps exactly five predeclared replicate IDs to + their source-ID lists. Aliased controls stay within a row; evaluation cannot + become new training replication. This pure function does NOT verify a file + receipt/seed derivation; the coordinator must independently do so. + """ + require(assay in ('normal', 'delayed') and mode in MODES and dan_type in DAN_TYPES and conditioned_cue in CUES, + 'Unknown raw assay/mode/DAN/odor label') + require(isinstance(registered_training_streams, Mapping) and len(registered_training_streams) == 5, + 'Require five predeclared independent training replicates') + registered, training_all = {}, set() + for identity, sources in registered_training_streams.items(): + _token(identity, 'training replicate identity') + require(isinstance(sources, (tuple, list)) and len(sources) > 0, 'Missing actual training source identities') + values = tuple(_token(v, 'training source identity') for v in sources) + require(len(set(values)) == len(values) and not training_all.intersection(values), + 'Training source reused across independent replicates') + training_all.update(values) + registered[identity] = values + require(isinstance(replicates, (tuple, list)) and len(replicates) == 5, 'Exactly five training rows required; do not count evaluation draws') + used_ids, used_eval, receipts, reports = set(), set(), set(), [] + for row in replicates: + keys = {'replicate_id', 'replicate_unit', 'training_stream_ids', 'evaluation_stream_ids', + 'assay', 'mode', 'dan_type', 'conditioned_cue', 'arms'} + require(isinstance(row, Mapping) and set(row) == keys, 'Invalid replicate schema') + identity = row['replicate_id'] + require(identity in registered and identity not in used_ids and row['replicate_unit'] == 'independent_training', + 'Unknown/duplicate training identity or evaluation pseudo-replication') + used_ids.add(identity) + require(isinstance(row['training_stream_ids'], (list, tuple)) and + tuple(row['training_stream_ids']) == registered[identity], 'Training lineage does not match preregistration') + require((row['assay'], row['mode'], row['dan_type'], row['conditioned_cue']) == (assay, mode, dan_type, conditioned_cue), + 'Cannot pool modes, DAN branches, cue assignments or timing suites') + ev = row['evaluation_stream_ids'] + require(isinstance(ev, (tuple, list)) and len(ev) > 0, 'Missing independent evaluation identity') + ev = tuple(_token(v, 'evaluation identity') for v in ev) + require(len(set(ev)) == len(ev) and not training_all.intersection(ev) and not used_eval.intersection(ev), + 'Evaluation sources overlap training or another training replicate') + used_eval.update(ev) + require(isinstance(row['arms'], Mapping) and set(row['arms']) == set(ARMS), 'All six actual controls and paired arm are mandatory') + metrics = {} + for name in ARMS: + _execution(row['arms'][name], name, assay, receipts) + metrics[name] = _bound_metric(row['arms'][name]['metrics'], conditioned_cue) + baseline = metrics['paired'] + # State baseline and populations must really be matched for each control. + for metric in metrics.values(): + for cue in CUES: + a, b = baseline['cues'][cue], metric['cues'][cue] + require(a['membership'] == b['membership'] and a['nodes_raw_sha256'] == b['nodes_raw_sha256'] and + a['pre_state_raw_sha256'] == b['pre_state_raw_sha256'], 'Control population or pre-baseline mismatch') + s = baseline['rawS'] + control_s = {name: metrics[name]['rawS'] for name in CONTROLS} + direction = s > 0 if dan_type == 'PAM15' else s < 0 + beyond = {name: (s > value if dan_type == 'PAM15' else s < value) for name, value in control_s.items()} + neutral = baseline['neutral_cue'] + c = baseline['cues'][conditioned_cue]['populations']['MBON'] + n = baseline['cues'][neutral]['populations']['MBON'] + functional = c['spike_count_l1'] > 0 or c['defined_first_latency_changed'] is True + predicates = dict(raw_signed_direction=bool(direction), beyond_each_actual_control=all(beyond.values()), + conditioned_final_g_exceeds_neutral=c['final_delta_g_max_abs'] > n['final_delta_g_max_abs'], + conditioned_count_or_defined_population_first_latency_change=bool(functional), + all_MBON_AB_euclidean_gain_positive=baseline['separation']['MBON']['gain'] > 0, + legacyDownstream_AB_euclidean_gain_positive=baseline['separation']['legacyDownstream']['gain'] > 0, + nonMBONDownstream_AB_euclidean_gain_positive=baseline['separation']['nonMBONDownstream']['gain'] > 0) + reports.append(dict(replicate_id=identity, rawS=s, raw_control_S=control_s, + population_separation_gains={name: baseline['separation'][name]['gain'] for name in SEPARATION_POPULATIONS}, + beyond_controls= beyond, predicates=predicates, conjunction_pass=all(predicates.values()), + legacy_audit=dict(L_like_functional_with_explicit_missing_latency=bool( + predicates['conditioned_final_g_exceeds_neutral'] and functional), + H_like_MBON_positive_gain=predicates['all_MBON_AB_euclidean_gain_positive'], + H_like_downstream_population='legacyDownstream', + H_like_downstream_positive_gain=predicates['legacyDownstream_AB_euclidean_gain_positive'], + original_implementation_equivalence_claimed=False), + new_hypothesis_audit=dict(nonMBONDownstream_positive_gain= + predicates['nonMBONDownstream_AB_euclidean_gain_positive'], + inherited_from_old_H=False, either_downstream_may_rescue_other=False))) + reports.sort(key=lambda r: r['replicate_id']) + successful = sum(r['conjunction_pass'] for r in reports) + return dict(schema=SCHEMA, status='DRAFT-PROPOSAL-GATE-PASS' if successful >= 3 else 'DRAFT-PROPOSAL-GATE-FAIL', + proposal_gate_pass=successful >= 3, scientific_learning_pass_claimed=False, candidate_generated=False, + assay=assay, mode=mode, dan_type=dan_type, conditioned_cue=conditioned_cue, + required_training_replicates=5, successful_same_replicate_conjunctions=successful, required_majority=3, + actual_control_receipts=len(receipts) - 5, evaluation_draws_count_as_replicates=False, + execution_receipt_authority='caller must verify finalized receipts and seed registry; pure metrics do not read files', + historical_full_H_mean_control_rule_computed=False, replicates=reports) + + +def legacy_h_mean_control_audit(protocol_mbon_gains): + """Old H scalar predicate only, kept separate from the new signed gate. + + Input uses the ORIGINAL five protocol labels, each with five already-bound + training-replicate MBON Euclidean gains. The caller must explicitly define + any AN-to-old-protocol mapping; this function never picks a DAN/control or + pools AN cells. It is not the entire old H gate or a reproduced old result. + """ + names = ('appetitive-conditioning', 'aversive-conditioning', 'd-zero-control', + 'random-reward-control', 'frozen-brain-control') + require(isinstance(protocol_mbon_gains, Mapping) and set(protocol_mbon_gains) == set(names), + 'Legacy audit requires exactly the five original H protocol labels') + means = {} + for name in names: + values = protocol_mbon_gains[name] + require(isinstance(values, (list, tuple)) and len(values) == 5 and + all(type(v) in (int, float) and math.isfinite(v) for v in values), + 'Legacy audit needs five finite, separately bound training-replicate gains') + means[name] = float(np.mean(values)) + require(math.isfinite(means[name]), 'Nonfinite legacy mean') + floor, ceiling = min(means[names[0]], means[names[1]]), max(means[name] for name in names[2:]) + return dict(schema=SCHEMA, audit='legacy_H_mean_control_predicate_only', protocol_means=means, + condition_floor=floor, control_ceiling=ceiling, predicate_pass=floor > ceiling, + source_reference='frozen phase4h.py:412-414', full_old_H_gate_computed=False, + scientific_learning_pass_claimed=False, AN_to_legacy_protocol_mapping_authority='caller; no automatic pooling') + + +def unavailable_assay(assay): + """Do not turn a terminal delta or fresh reset into a retention/reversal PASS.""" + require(assay in ('reversal', 'retention'), 'Unknown unavailable assay') + missing = {'reversal': ['bound acquisition/reversal/no-reversal lineage', + 'original pre baseline and original cue orientation', 'terminal reversal count and both cue event outputs'], + 'retention': ['validated continuous-state checkpoint/queue/RNG schema', + 'bound same idle trajectory at 1 s and 10 s', 'matched frozen continuation and non-feedback probes']} + return dict(schema=SCHEMA, assay=assay, status='NOT-IMPLEMENTED', proposal_gate_pass=None, + scientific_learning_pass_claimed=False, candidate_generated=False, missing=missing[assay]) diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/runtime_continuation.py b/registry/phase4ao-conditioning-interface/source-snapshot/runtime_continuation.py new file mode 100644 index 0000000..5ebf75e --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/runtime_continuation.py @@ -0,0 +1,478 @@ +"""Unregistered fixed-weight continuation of the reporting-only AN executor. + +run_continued(case, out, build, overlay, manifest, checkpoint_dir, mode='light') +also accepts mode='reference'. Inputs/events/counts use segment-local ticks; +summary and receipts expose global_tick_offset/global_next_tick. No training, +weight update, early stopping, or implicit extra tail is performed. + +The caller must externally pin spec.parent_checkpoint_manifest_sha256. A fresh +parent's original runtime receipt did NOT bind state.npz: seal_checkpoint adds +that binding, after checking the available original receipts and current bytes. +It cannot retroactively prove that an unsealed state file was never modified. +Full graph/APL/coefficient/preregistered-input authority remains the coordinator's +responsibility, as in runtime_light. No executable/venv path is resolved here. +""" +from __future__ import annotations + +import ast +import copy +import hashlib +import importlib.util +from pathlib import Path +import sys + +import numpy as np + + +LIGHT_SHA = '522b8c31ea938890fe15364fccd25ccc5bded54101754909f255532e7ad01aae' +N = 166700 +E = 25582938 +MANIFEST = 'checkpoint-manifest.json' +RECEIPT = 'continuation-receipt.json' +RESTORE = '_continuation.restore(v, g, ref, counts, ring, apl_ring, apl, upload)' +CLOCK_OLD = 'tick >= 18' +CLOCK_NEW = 'tick + _continuation.global_offset >= 18' + + +def _light(): + path = Path(__file__).resolve().parent / 'runtime_light.py' + raw = path.read_bytes() + if hashlib.sha256(raw).hexdigest() != LIGHT_SHA: + raise ValueError('Fresh runtime source pin changed') + name = 'phase4an_continuation_pinned_light' + existing = sys.modules.get(name) + if existing is not None: + if getattr(existing, '__continuation_pin__', None) != LIGHT_SHA or Path(existing.__file__).resolve() != path: + raise ValueError('Unverified runtime module binding') + return existing + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + try: + exec(compile(raw, str(path), 'exec'), module.__dict__) + except BaseException: + sys.modules.pop(name, None) + raise + module.__continuation_pin__ = LIGHT_SHA + return module + + +R = _light() # Pure NumPy/stdlib; this does not import Torch or run a brain. + + +def rotate_spike_ring(raw_ring, phase_tick): + """Parent's local next-write slot becomes the child's local slot zero.""" + R.require(isinstance(raw_ring, np.ndarray) and raw_ring.dtype == np.uint8 and + raw_ring.ndim == 2 and raw_ring.shape[0] == 19, 'Invalid spike ring dtype/shape') + R.require(type(phase_tick) is int and phase_tick >= 0 and np.all(raw_ring <= 1), 'Invalid ring phase/content') + return raw_ring[(np.arange(19) + phase_tick % 19) % 19].copy() + + +def _tree_hashes(directory): + """Exact regular-file inventory, no symlink/reparse escapes, no self-hash.""" + directory = Path(directory).resolve(strict=True) + R.require(directory.is_dir(), 'Checkpoint must be a directory') + result = {} + for path in sorted(directory.rglob('*')): + R.require(not path.is_symlink() and path.resolve().is_relative_to(directory), 'Checkpoint contains a linked/escaping path') + if path.is_dir(): + continue + R.require(path.is_file(), 'Checkpoint contains a nonregular entry') + relative = path.relative_to(directory).as_posix() + if relative == MANIFEST: + continue + R.require('error' not in path.name.lower() and 'failure' not in path.name.lower(), 'Parent has failure/error evidence') + result[relative] = R.digest(path) + return result + + +def _npz(path, keys): + with np.load(path, allow_pickle=False) as archive: + R.require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected checkpoint array keys: ' + str(path)) + return {name: archive[name].copy() for name in keys} + + +def validate_state(state, terminal, steps, *, fresh): + """Validate local events/counts and the slots written by this segment.""" + R.require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max, 'Invalid segment length') + for name in ('v', 'g'): + value = state[name] + R.require(value.dtype == np.float64 and value.shape == (N,) and np.isfinite(value).all(), 'Invalid parent ' + name) + for name in ('ref', 'counts'): + value = state[name] + R.require(value.dtype == np.int32 and value.shape == (N,) and np.all(value >= 0), 'Invalid parent ' + name) + R.require(np.all(state['ref'] <= 22), 'Parent refractory counter exceeds frozen 22-step contract') + tick, neuron = state['step'], state['neuron_idx'] + R.require(tick.dtype == neuron.dtype == np.int32 and tick.ndim == neuron.ndim == 1 and tick.shape == neuron.shape, + 'Invalid sparse event dtype/shape') + R.require(np.all((tick >= 0) & (tick < steps)) and np.all((neuron >= 0) & (neuron < N)), 'Parent event outside segment') + key = tick.astype(np.int64) * N + neuron + R.require(len(key) < 2 or np.all(key[1:] > key[:-1]), 'Parent events must be sorted and unique') + R.require(np.array_equal(np.bincount(neuron, minlength=N), state['counts']), 'Parent counts are not segment-local event counts') + ring = terminal['spike_ring'] + R.require(ring.dtype == np.uint8 and ring.shape == (19, N) and np.all(ring <= 1), 'Invalid parent spike ring') + for name, shape in (('apl_state', (2,)), ('apl_pending', (18, 4064))): + value = terminal[name] + R.require(value.dtype == np.float32 and value.shape == shape and np.isfinite(value).all(), 'Invalid terminal ' + name) + phase = terminal['next_tick'] + R.require(phase.dtype == np.int64 and phase.shape == () and phase.item() == steps, 'Terminal local phase does not match segment length') + for local_tick in range(max(0, steps - 19), steps): + start, stop = np.searchsorted(tick, (local_tick, local_tick + 1)) + expected = np.zeros(N, dtype=np.uint8) + expected[neuron[start:stop]] = 1 + R.require(np.array_equal(ring[local_tick % 19], expected), 'Spike ring does not match emitted segment events') + if fresh and steps < 19: + R.require(not np.any(ring[steps:]), 'Fresh parent has nonzero uninitialized spike slots') + return state, terminal + + +def _verify_receipt_hashes(directory, receipt): + for key, name in (('summary_sha256', 'summary.json'), ('finite_audit_sha256', 'finite-audit.json'), + ('terminal_queues_sha256', 'terminal-queues.npz'), ('overlay_audit_sha256', 'overlay-audit.json')): + R.require(receipt.get(key) == R.digest(directory / name), 'Parent receipt hash mismatch: ' + name) + inputs = receipt.get('input_sha256') + R.require(isinstance(inputs, dict) and inputs, 'Parent receipt lacks immutable input bindings') + for name, sha in inputs.items(): + R.require(Path(name).is_absolute() and R._sha(sha) and R.digest(name) == sha, 'Parent input/source bytes changed: ' + name) + + +def _validate_record(directory, seen): + summary = R._read_json(directory / 'summary.json') + finite = R._read_json(directory / 'finite-audit.json') + provenance = R._read_json(directory / 'runtime-provenance.json') + steps = summary.get('steps') + R.require(summary.get('complete') is True and summary.get('pass_all') is True and summary.get('all_finite') is True and + summary.get('completed_ticks') == steps and summary.get('nodes') == N and summary.get('edges') == E and + summary.get('precision') == 'float64' and summary.get('apl_precision') == 'float32' and + summary.get('within_trial_weight_updates') is False, 'Parent is not a complete fixed-weight fullgraph trial') + R.require(finite.get('all_finite') is True and finite.get('first_failure_tick') == -1 and + finite.get('finite_observed_ticks') == steps, 'Parent finite coverage failed') + R.require(provenance.get('complete') is True and provenance.get('weights_updated_during_run') is False and + provenance.get('old_globals_patched') is False, 'Invalid parent runtime receipt') + _verify_receipt_hashes(directory, provenance) + fresh = provenance.get('schema') == 'phase4an-reporting-adapter-v1' + if fresh: + R.require(provenance.get('source_sha256') == LIGHT_SHA and R.digest(provenance['source_file']) == LIGHT_SHA and + summary.get('checkpoint_continuation_used') is False, 'Fresh parent source/continuation scope mismatch') + offset = 0 + else: + R.require(provenance.get('schema') == 'phase4an-continuation-runtime-v1' and + provenance.get('source_sha256') == R.digest(__file__) and + R.digest(provenance['source_file']) == R.digest(__file__) and + summary.get('checkpoint_continuation_used') is True, 'Unrecognized continued parent source') + receipt = R._read_json(directory / RECEIPT) + R.require(receipt.get('schema') == 'phase4an-continuation-receipt-v1' and receipt.get('complete') is True, + 'Invalid continuation receipt') + R.require(receipt.get('weights_updated_during_run') is False and receipt.get('segment_steps') == steps and + receipt.get('counts_scope') == 'segment_only_not_lifetime' and + provenance.get('state_sha256') == R.digest(directory / 'state.npz'), 'Continued state/scope receipt mismatch') + expected_payload = _tree_hashes(directory) + expected_payload.pop(RECEIPT, None) + R.require(receipt.get('payload_sha256') == expected_payload, 'Continuation receipt does not bind the exact payload') + parent_pin = receipt.get('parent_checkpoint_manifest_sha256') + case_spec = _parent_spec({'summary': summary, 'provenance': provenance}) + R.require(R._sha(parent_pin) and provenance.get('parent_checkpoint_manifest_sha256') == parent_pin and + summary.get('parent_checkpoint_manifest_sha256') == parent_pin and + case_spec.get('parent_checkpoint_manifest_sha256') == parent_pin, + 'Parent checkpoint pin disagrees across case/summary/provenance/continuation receipts') + R.require(Path(receipt['parent_checkpoint_dir']).is_absolute(), 'Parent checkpoint directory is not absolute') + parent = load_checkpoint(receipt['parent_checkpoint_dir'], parent_pin, _seen=seen) + offset = parent['global_next_tick'] + R.require(receipt.get('global_tick_offset') == offset and receipt.get('global_next_tick') == offset + steps and + summary.get('global_tick_offset') == offset and summary.get('global_next_tick') == offset + steps and + provenance.get('global_tick_offset') == offset and provenance.get('global_next_tick') == offset + steps, + 'Continuation global clock chain mismatch') + R.require(summary.get('effective_weights_raw_sha256') == parent['summary'].get('effective_weights_raw_sha256'), + 'Retention effective weights changed between parent and child') + state = _npz(directory / 'state.npz', ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')) + terminal = _npz(directory / 'terminal-queues.npz', ('spike_ring', 'apl_state', 'apl_pending', 'next_tick')) + validate_state(state, terminal, steps, fresh=fresh) + initial_padding = max(18 - (offset + steps), 0) + R.require(np.all(terminal['apl_pending'][:initial_padding].view(np.uint32) == 0), + 'Unconsumed original APL padding is not positive zero') + if not fresh: + if steps < 19: + restored = rotate_spike_ring(parent['terminal']['spike_ring'], int(parent['terminal']['next_tick'])) + R.require(np.array_equal(terminal['spike_ring'][steps:], restored[steps:]), 'Unwritten carried spike slots changed') + if steps < 18: + carried = parent['terminal']['apl_pending'][steps:] + R.require(np.array_equal(terminal['apl_pending'][:18 - steps].view(np.uint32), carried.view(np.uint32)), + 'Unconsumed carried APL slots changed') + overlay = R._read_json(directory / 'overlay-audit.json') + R.require(overlay.get('complete') is True and overlay.get('weights_updated_during_run') is False and + overlay.get('effective_weights_raw_sha256') == summary.get('effective_weights_raw_sha256') and + overlay.get('mask_sha256') == R.MASK_SHA and overlay.get('am_helper_sha256') == R.AM_OVERLAY_SHA, + 'Parent effective overlay receipt mismatch') + overlay_manifest = R._read_json(directory / 'overlay-manifest.json') + R.require(R.digest(directory / 'overlay-input.npz') == overlay_manifest.get('overlay_sha256') and + R.digest(directory / 'overlay-manifest.json') in provenance['input_sha256'].values() and + overlay_manifest.get('mask_sha256') == R.MASK_SHA and + overlay_manifest.get('base_candidate_sha256') == overlay.get('base_candidate_sha256'), + 'Parent copied overlay inputs do not match their original receipt') + timing = R._read_json(directory / 'apl-timing-audit.json') + with (directory / 'apl-pending-tail.float32.npy').open('rb') as stream: + pending = np.load(stream, allow_pickle=False) + R.require(pending.dtype == np.float32 and pending.shape == (18, 4064) and + pending.tobytes() == terminal['apl_pending'].tobytes(), 'Parent terminal/pending output mismatch') + R.require(timing.get('pending_raw_sha256') == R._raw(pending) and timing.get('apl_delay_steps') == 18 and + timing.get('brain_tail_advanced') is False and + timing.get('final_apl_state_u32_bits') == terminal['apl_state'].view(np.uint32).tolist(), 'Parent APL terminal audit mismatch') + return {'directory': directory, 'summary': summary, 'provenance': provenance, 'overlay': overlay, + 'state': state, 'terminal': terminal, 'steps': steps, 'global_tick_offset': offset, + 'global_next_tick': offset + steps, 'fresh': fresh, + 'ancestry_directories': [directory] if fresh else [directory, *parent['ancestry_directories']]} + + +def seal_checkpoint(checkpoint_dir): + """Exclusive file-hash seal, not a brain run or proof of pre-seal integrity. + + The returned manifest SHA must be externally pinned into the next case spec. + No existing receipt is overwritten. Fresh state.npz is newly bound here. + """ + directory = Path(checkpoint_dir).resolve(strict=True) + R.require(not (directory / MANIFEST).exists(), 'Checkpoint is already sealed') + before = _tree_hashes(directory) + record = _validate_record(directory, {directory}) + R.require(before == _tree_hashes(directory), 'Checkpoint changed while sealing') + value = {'schema': 'phase4an-checkpoint-v1', 'complete': True, 'files_sha256': before, + 'segment_steps': record['steps'], 'global_tick_offset': record['global_tick_offset'], + 'global_next_tick': record['global_next_tick'], 'counts_scope': 'segment_only', + 'state_binding_origin': 'checkpoint seal; fresh runtime receipt did not hash state.npz', + 'pre_seal_state_integrity_independently_proven': False} + R._save(directory / MANIFEST, value) + return {'path': str(directory / MANIFEST), 'sha256': R.digest(directory / MANIFEST), **value} + + +def load_checkpoint(checkpoint_dir, expected_sha, *, _seen=None): + directory = Path(checkpoint_dir).resolve(strict=True) + R.require(R._sha(expected_sha) and R.digest(directory / MANIFEST) == expected_sha, 'Parent checkpoint manifest differs from external pin') + seen = set() if _seen is None else set(_seen) + R.require(directory not in seen, 'Checkpoint ancestry cycle') + seen.add(directory) + manifest = R._read_json(directory / MANIFEST) + R.require(manifest.get('schema') == 'phase4an-checkpoint-v1' and manifest.get('complete') is True and + manifest.get('counts_scope') == 'segment_only' and manifest.get('files_sha256') == _tree_hashes(directory), + 'Checkpoint file inventory/hash mismatch') + record = _validate_record(directory, seen) + R.require(manifest.get('segment_steps') == record['steps'] and manifest.get('global_tick_offset') == record['global_tick_offset'] and + manifest.get('global_next_tick') == record['global_next_tick'], 'Checkpoint clock metadata mismatch') + R.require(manifest['files_sha256'] == _tree_hashes(directory) and R.digest(directory / MANIFEST) == expected_sha, + 'Checkpoint changed while loading') + record.update(manifest=manifest, manifest_sha256=expected_sha) + return record + + +def derive_continued(source_bytes, mode): + """One explicit arrival-admission edit plus one pre-loop restore hook.""" + tree, inherited = R.derive_ast(source_bytes, mode) + original = copy.deepcopy(tree) + function = tree.body[-1] + body = next(node for node in function.body if isinstance(node, ast.Try)).body + candidates = [i for i, node in enumerate(body) if isinstance(node, ast.Assign) and + len(node.targets) == 1 and isinstance(node.targets[0], ast.Name) and node.targets[0].id == 'apl_ring'] + R.require(len(candidates) == 1, 'Expected one APL ring initialization') + body.insert(candidates[0] + 1, ast.parse(RESTORE).body[0]) + clocks = [node for node in ast.walk(function) if isinstance(node, ast.If) and ast.unparse(node.test) == CLOCK_OLD] + R.require(len(clocks) == 1, 'Expected exact original local arrival-admission condition') + clocks[0].test = ast.parse(CLOCK_NEW, mode='eval').body + tree = ast.fix_missing_locations(tree) + # Independently reverse exactly the two edits, then require the ENTIRE AST. + rollback = copy.deepcopy(tree) + reverse_body = next(node for node in rollback.body[-1].body if isinstance(node, ast.Try)).body + restores = [i for i, node in enumerate(reverse_body) if ast.unparse(node) == RESTORE] + reverse_clocks = [node for node in ast.walk(rollback) if isinstance(node, ast.If) and ast.unparse(node.test) == CLOCK_NEW] + R.require(len(restores) == len(reverse_clocks) == 1, 'Ambiguous continuation edit rollback') + del reverse_body[restores[0]] + reverse_clocks[0].test = ast.parse(CLOCK_OLD, mode='eval').body + R.require(R._dump(rollback) == R._dump(original), 'Additional unlisted continuation AST edit') + audit = {'base': inherited, 'restore_hook': RESTORE, 'clock_admission_old': CLOCK_OLD, + 'clock_admission_new': CLOCK_NEW, 'full_AST_rollback_identical': True, + 'kernels_sorting_and_local_ring_math_unchanged': True, + 'derived_module_ast_sha256': hashlib.sha256(R._dump(tree).encode()).hexdigest()} + return tree, audit + + +def audit_carried_sequence(generated, applied, pending, initial_pending): + """Bitwise FIFO accounting with a carried queue, never a new brain step.""" + for name, value in (('generated', generated), ('applied', applied), ('pending', pending), ('initial', initial_pending)): + R.require(isinstance(value, np.ndarray) and value.dtype == np.float32 and value.ndim == 2 and + np.isfinite(value).all() and np.all(value >= 0), 'Invalid continued APL trace: ' + name) + R.require(generated.shape == applied.shape and pending.shape == initial_pending.shape == (18, generated.shape[1]), + 'Continued APL sequence shapes differ') + steps = len(generated) + for tick in range(steps + 18): + actual = applied[tick] if tick < steps else pending[tick - steps] + expected = initial_pending[tick] if tick < 18 else generated[tick - 18] + R.require(np.array_equal(actual.view(np.uint32), expected.view(np.uint32)), + 'Carried FIFO bitwise accounting failed at local application tick ' + str(tick)) + return {'pass_all': True, 'apl_delay_steps': 18, 'steps': steps, 'sample_bits_preserved': True, + 'initial_queue_from_parent': True, 'initial_queue_raw_sha256': R._raw(initial_pending), + 'full_sequence_bitwise_audit_performed': True, 'brain_tail_advanced': False} + + +class ContinuedRuntime(R._Runtime): + def __init__(self, *args, parent, **kwargs): + super().__init__(*args, **kwargs) + self.parent = parent + self.global_offset = parent['global_next_tick'] + self.restorations = 0 + + def install(self, *args, **kwargs): + super().install(*args, **kwargs) + R.require(self.effective_sha == self.parent['summary']['effective_weights_raw_sha256'], 'Retention changed effective weights') + + def restore(self, v, g, ref, counts, ring, apl_ring, apl, upload): + R.require(self.installations == 1 and self.restorations == 0, 'Restore must occur once after fixed-weight installation') + R.require(not bool(counts.any().item()), 'Segment counts must start at zero') + state, terminal = self.parent['state'], self.parent['terminal'] + for target, value, dtype in ((v, state['v'], self.torch.float64), (g, state['g'], self.torch.float64), + (ref, state['ref'], self.torch.int32), + (ring, rotate_spike_ring(terminal['spike_ring'], int(terminal['next_tick'])), self.torch.uint8), + (apl_ring, terminal['apl_pending'], self.torch.float32), + (apl.state, terminal['apl_state'], self.torch.float32)): + target.copy_(upload(value, dtype)) + self.restorations += 1 + + def finish(self, *args, **kwargs): + R.require(self.restorations == 1, 'Continuation was not restored') + return super().finish(*args, **kwargs) + + def audit_sequence(self, generated, applied, pending, delay): + R.require(delay == 18, 'Continuation changed D18') + return audit_carried_sequence(generated, applied, pending, self.parent['terminal']['apl_pending']) + + def _timing_metadata(self, value): + result = dict(value) + next_tick, steps = self.global_offset + self.spec['steps'], self.spec['steps'] + result.pop('initial_padding_positive_zero', None) + result.update(initial_queue_from_parent=True, initial_queue_raw_sha256=R._raw(self.parent['terminal']['apl_pending']), + global_tick_offset=self.global_offset, global_next_tick=next_tick, tick_coordinate='global', + segment_steps=steps, segment_generated_rows_applied=max(steps - 18, 0), + applied_parent_pending_rows=min(steps, 18), + applied_initial_zero_rows=min(steps, max(18 - self.global_offset, 0)), + applied_parent_generated_rows=min(steps, 18) - min(steps, max(18 - self.global_offset, 0)), + pending_segment_generated_rows=min(steps, 18), + pending_parent_generated_rows=max(18 - steps, 0) - max(18 - next_tick, 0), + pending_generated_count=min(next_tick, 18), pending_initial_zero_prefix=max(18 - next_tick, 0), + pending_application_tick_start=next_tick, pending_application_tick_stop_exclusive=next_tick + 18, + pending_generated_tick_start=max(0, next_tick - 18), pending_generated_tick_stop_exclusive=next_tick, + brain_tail_advanced_scope='no extra brain ticks beyond this explicit segment') + if self.mode == 'light': + result.update(generated_trace=None, applied_trace=None, full_sequence_bitwise_audit_performed=False, + scope='Original D18 ring math retained with restored queue; histories not recorded') + return result + + def timing(self, steps, pending): + return self._timing_metadata(super().timing(steps, pending)) + + def summary(self, value): + result = super().summary(value) + result.update(checkpoint_continuation_used=True, global_tick_offset=self.global_offset, + global_next_tick=self.global_offset + self.spec['steps'], event_tick_scope='segment_local', + counts_scope='segment_only_not_lifetime', parent_checkpoint_manifest_sha256=self.parent['manifest_sha256']) + result.update(pending_generated_count=min(result['global_next_tick'], 18), + pending_initial_zero_prefix=max(18 - result['global_next_tick'], 0)) + if 'apl_timing' in result: + result['apl_timing'] = self._timing_metadata(result['apl_timing']) + return result + + def write_json(self, path, value): + if Path(path).name == 'apl-timing-audit.json': + value = self._timing_metadata(value) + return super().write_json(path, value) + + +def compile_continued(aj, runtime, mode): + tree, audit = derive_continued(Path(aj.__file__).read_bytes(), mode) + namespace = dict(aj.__dict__) + namespace.update(__name__=__name__ + '.derived_' + mode, __file__=str(Path(__file__).resolve()), + _runtime=runtime, _continuation=runtime, write_json=runtime.write_json, + audit_sequence=runtime.audit_sequence) + audit['diagnostic_namespace_binding'] = 'audit_sequence checks parent_pending + generated, not an assumed zero-initial queue' + exec(compile(tree, str(Path(__file__).resolve()) + '::continued-AJ-AST-' + mode, 'exec'), namespace) + return namespace['run_gpu'], audit + + +def _parent_spec(parent): + expected = parent['summary']['input_sha256']['spec.json'] + paths = [Path(path) for path, sha in parent['provenance']['input_sha256'].items() + if Path(path).name == 'spec.json' and sha == expected] + R.require(len(paths) == 1, 'Ambiguous original parent case spec') + return R._read_json(paths[0]) + + +def run_continued(case_dir, out_dir, build_root, overlay_file, overlay_manifest, checkpoint_dir, *, mode='light'): + R.require(sys.dont_write_bytecode, 'Use python -B; frozen sources must not receive bytecode writes') + R.require(mode in ('light', 'reference'), 'Invalid diagnostic mode') + case_dir, out_dir, build_root = R.execution_paths(case_dir, out_dir, build_root, (checkpoint_dir,)) + spec = R._read_json(case_dir / 'spec.json') + parent_sha = spec.get('parent_checkpoint_manifest_sha256') + fresh_admission = dict(spec) + fresh_admission.pop('parent_checkpoint_manifest_sha256', None) + R.validate_spec(fresh_admission, case_dir) + parent = load_checkpoint(checkpoint_dir, parent_sha) + # A continuation may never create output or a build cache inside ANY + # immutable ancestor, not only its immediate checkpoint parent. This check + # precedes overlay imports, Torch imports, compilation and output creation. + case_dir, out_dir, build_root = R.execution_paths(case_dir, out_dir, build_root, parent['ancestry_directories']) + if 'global_tick_offset' in spec: + R.require(type(spec['global_tick_offset']) is int and spec['global_tick_offset'] == parent['global_next_tick'], + 'Declared global offset disagrees with checkpoint chain') + R.require(parent['global_next_tick'] + spec['steps'] <= np.iinfo(np.int64).max, 'Absolute tick overflow') + helper, plan, meta, inputs = R.load_overlay(overlay_file, overlay_manifest) + R.require(R.digest(overlay_file) == R.digest(parent['directory'] / 'overlay-input.npz') and + R.digest(overlay_manifest) == R.digest(parent['directory'] / 'overlay-manifest.json'), 'Retention overlay snapshot/manifest changed') + previous = _parent_spec(parent) + for key in ('dt_ms', 'model', 'apl', 'apl_delay_steps'): + R.require(spec[key] == previous[key], 'Continuation changes frozen dynamics: ' + key) + for key in ('graph_dir', 'apl_bundle_dir'): + R.require(Path(spec[key]).resolve(strict=True) == Path(previous[key]).resolve(strict=True), 'Continuation changes input directory: ' + key) + R.require(R.digest(spec['coefficients_path']) == parent['summary']['input_sha256']['coefficients.json'], 'Continuation coefficients changed') + R.require(R.digest(Path(spec['graph_dir']) / 'weights.npy') == parent['summary']['input_sha256']['base_weights.npy'], 'Continuation base weights changed') + aj_dir = Path(meta['aj_model_dir']).resolve(strict=True) + protected = (aj_dir.parent, Path(meta['am_overlay_path']).resolve().parent, spec['graph_dir'], spec['apl_bundle_dir'], + *parent['ancestry_directories'], + *(Path(path).resolve(strict=True).parent for path in + (overlay_file, overlay_manifest, meta['mask_path'], meta['base_candidate_path'], spec['coefficients_path']))) + case_dir, out_dir, build_root = R.execution_paths(case_dir, out_dir, build_root, protected) + for path in (case_dir / 'spec.json', case_dir / 'requests.npz', Path(spec['graph_dir']) / 'weights.npy', + Path(spec['coefficients_path']), Path(checkpoint_dir).resolve() / MANIFEST, Path(R.__file__).resolve()): + inputs[str(path)] = R.digest(path) + source_sha = R.digest(__file__) + R._load('timing_queue', aj_dir / 'timing_queue.py', R.PINS['apl-timing-lab-v1/timing_queue.py']) + aj = R._load('phase4an_continued_sealed_aj_gpu', aj_dir / 'gpu_timing.py', R.AJ_SHA) + runtime = ContinuedRuntime(mode, spec, helper, plan, meta, inputs, aj.torch, overlay_file, overlay_manifest, parent=parent) + function, ast_audit = compile_continued(aj, runtime, mode) + try: + inherited = function(case_dir, out_dir, build_root) + R.require(runtime.finished and runtime.restorations == 1 and runtime.installations == 1 and inherited['complete'], 'Incomplete continuation') + R.require(inherited['effective_weights_raw_sha256'] == runtime.effective_sha, 'Effective weight identity changed') + R.require(source_sha == R.digest(__file__) and all(R.digest(path) == sha for path, sha in inputs.items()), 'Source/input changed during continuation') + load_checkpoint(checkpoint_dir, parent_sha) + summary = R._read_json(out_dir / 'summary.json') + R.require(summary == runtime.summary(inherited), 'Stored continuation summary mismatch') + provenance = {'schema': 'phase4an-continuation-runtime-v1', 'complete': True, 'mode': mode, + 'source_file': str(Path(__file__).resolve()), 'source_sha256': source_sha, 'fresh_runtime_sha256': LIGHT_SHA, + 'ast': ast_audit, 'input_sha256': inputs, 'old_globals_patched': False, 'weights_updated_during_run': False, + 'learning_pass_claimed': False, 'global_tick_offset': runtime.global_offset, + 'global_next_tick': runtime.global_offset + spec['steps'], + 'parent_checkpoint_manifest_sha256': parent_sha} + for key, name in (('summary_sha256', 'summary.json'), ('finite_audit_sha256', 'finite-audit.json'), + ('terminal_queues_sha256', 'terminal-queues.npz'), ('overlay_audit_sha256', 'overlay-audit.json'), + ('state_sha256', 'state.npz')): + provenance[key] = R.digest(out_dir / name) + R._save(out_dir / 'runtime-provenance.json', provenance) + receipt = {'schema': 'phase4an-continuation-receipt-v1', 'complete': True, + 'parent_checkpoint_dir': str(parent['directory']), 'parent_checkpoint_manifest_sha256': parent_sha, + 'global_tick_offset': runtime.global_offset, 'global_next_tick': runtime.global_offset + spec['steps'], + 'segment_steps': spec['steps'], 'counts_scope': 'segment_only_not_lifetime', + 'payload_sha256': _tree_hashes(out_dir), 'weights_updated_during_run': False, + 'clock_admission_edit': CLOCK_NEW, 'checkpoint_continuation_verified_by_GPU_comparison': False} + R._save(out_dir / RECEIPT, receipt) + sealed = seal_checkpoint(out_dir) + return dict(summary, checkpoint_manifest_sha256=sealed['sha256']) + except Exception as error: + if out_dir.is_dir() and not (out_dir / 'continuation-error.json').exists(): + R._save(out_dir / 'continuation-error.json', {'complete': False, 'type': type(error).__name__, + 'message': str(error), 'learning_update_forbidden': True}) + raise diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/runtime_light.py b/registry/phase4ao-conditioning-interface/source-snapshot/runtime_light.py new file mode 100644 index 0000000..4503765 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/runtime_light.py @@ -0,0 +1,559 @@ +"""Engineering-only, fixed-weight trial execution derived from frozen AJ AST. + +Public APIs run_light(case_dir,out_dir,build_root,overlay_file,overlay_manifest) +and run_reference(...) have identical arguments. Neither trains nor carries a +previous trial's neural state. The reference retains AJ's eager diagnostics; +the light function removes only an explicit diagnostic whitelist. Both accept +an explicit spec.max_steps instead of AJ's 1000-step admission bound. + +The parent protocol owns conditioning, cue/tail schedules, frozen input/mask +authority and success criteria. A completed trial is not a learning PASS. +Before any scientific execution, the coordinator must verify externally fixed +hashes for the entire graph, APL bundle, coefficients/model, and preregistered +case spec (including max_steps). Inherited AJ consistency checks and this +adapter's input hashes are not a replacement for those external authorities. +""" +from __future__ import annotations + +import ast +import copy +import hashlib +import importlib.util +import json +from pathlib import Path +import re +import sys + +import numpy as np + + +AJ_SHA = 'af1975ba5e5e700208c0a598d3cbe648a3cf10885c6231239b6e4b28f76d173b' +AM_OVERLAY_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4' +MASK_SHA = '8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b' +V2_SHA = 'abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e' +PINS = { + 'apl-timing-lab-v1/gpu_timing.py': AJ_SHA, + 'apl-timing-lab-v1/timing_queue.py': '54df6d4818e2a0c26e96fb0aaf5d9b71cd964981706da937562d48af79aca14e', + 'author-apl-lab-v2/gpu_apl.py': '69f5d893f47dafff4ef7c3186c3df0b9014666b32c8f4e943b57016bec92b18a', + 'author-apl-lab-v2/legacy_apl_worker.py': '21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291', + 'author-runtime-lab-v1/gpu_backend.py': '03758d07572f4b44d272059d9308fac534268ad201b57bdd77d69fee95e67278', + 'author-runtime-lab-v1/author_ext.cpp': '208a937d17bbb041b9bf6b25b89264ee566830b60609e58491b44583d8541f15', + 'author-runtime-lab-v1/author_ext.cu': '669f166e18ce9a3f63fc723db8b0a8b74f7fa4757194b1bd65b4cf6008a7787e', + 'author-fullgraph-lab-v1/oracle_full.py': 'd72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961', +} +_LOADED = {} +_INSTALL = '_runtime.install(weights, graph, apl_out_mask, upload, out_dir, source_provenance, device)' +_FINISH = '_runtime.finish(ring, apl_ring, apl, steps, out_dir)' +_MATH_TARGETS = frozenset(('indptr', 'dst', 'global_nodes_gpu', 'base_weights', 'weights', + 'local_apl', 'apl_out_mask', 'common_kc', 'local_kc', 'full_kc', 'kc_local_gpu', 'kc_full_gpu', + 'v', 'g', 'ref', 'counts', 'requests', 'ring', 'apl_delay', 'apl_ring', 'requested_local_gpu', + 'spikes', 'due', 'sources', 'degrees', 'offsets', 'edge_count', 'expanded', 'destination', + 'order', 'ordered_edges', 'ordered_targets', 'boundary', 'starts', 'targets', 'full_spikes', + 'drive', 'apl_state', 'h', 'slot', 'applied_h', 'delta_si')) + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def digest(path): + h = hashlib.sha256() + with Path(path).open('rb') as stream: + for block in iter(lambda: stream.read(8 << 20), b''): + h.update(block) + return h.hexdigest() + + +def _read_json(path): + def pairs(items): + out = {} + for key, value in items: + require(key not in out, 'Duplicate JSON key: ' + key) + out[key] = value + return out + return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=pairs, + parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value))) + + +def _save(path, value): + with Path(path).open('x', encoding='utf-8') as stream: + json.dump(value, stream, sort_keys=True, indent=2, allow_nan=False) + stream.write('\n') + + +def _raw(value): + return hashlib.sha256(np.ascontiguousarray(value, dtype=value.dtype.newbyteorder('<')).tobytes()).hexdigest() + + +def _sha(value): + return isinstance(value, str) and re.fullmatch('[0-9a-f]{64}', value) is not None + + +def _load(name, path, expected): + path = Path(path).absolute() + raw = path.read_bytes() + require(hashlib.sha256(raw).hexdigest() == expected, 'Unpinned source: ' + str(path)) + if name in sys.modules: + module = sys.modules[name] + require(_LOADED.get(name) == (path, expected, module), 'Existing unverified module binding: ' + name) + return module + spec = importlib.util.spec_from_file_location(name, path) + require(spec is not None and spec.loader is not None, 'Missing module loader') + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + try: + exec(compile(raw, str(path), 'exec'), module.__dict__) + except BaseException: + sys.modules.pop(name, None) + raise + _LOADED[name] = (path, expected, module) + return module + + +def validate_spec(spec, case_dir): + """Admission policy only. No implicit early stopping or checkpoint support.""" + require(type(spec.get('max_steps')) is int and 0 < spec['max_steps'] <= np.iinfo(np.int32).max, + 'An explicit positive int32-range max_steps is mandatory') + require(type(spec.get('steps')) is int and 0 < spec['steps'] <= spec['max_steps'], 'steps exceeds its declared bound') + require(spec.get('dt_ms') == .1 and spec.get('duration_ms') == spec['steps'] * .1 and + type(spec.get('apl_delay_steps')) is int and spec['apl_delay_steps'] == 18, + 'Frozen clock/duration/D18 contract changed') + require(spec.get('record_history') is False, 'Only wholebrain fresh-state trials are supported') + require(not (Path(case_dir) / 'arrays.npz').exists(), 'Initial arrays/checkpoint continuation are not supported in this version') + require(not any('checkpoint' in str(key).lower() or str(key) in ('initial_state', 'resume', 'continuation') for key in spec), + 'Checkpoint/continuation configuration is unsupported') + require(spec.get('within_trial_weight_updates', False) is False, 'No within-trial plasticity is supported') + return spec + + +def load_overlay(overlay_file, manifest_file): + """New manifest policy, frozen AM array validators; no old-global mutation. + + phase4an-overlay-v1 requires overlay_name zero/static25/learned, + overlay_sha256, original mask_path/mask_sha256, external positive f32-mV + base_candidate_path/base_candidate_sha256, aj_model_dir, + am_overlay_path/am_overlay_sha256. Learned snapshots additionally require + parent_snapshot_sha256 and eligibility_v2_source_sha256=the frozen 4J pin. + These hashes bind provenance; the coordinator must verify the parent chain. + NPZ contains ONLY edge_id:int64[14232], plastic_delta_mv:float32[14232]. + """ + overlay_file, manifest_file = Path(overlay_file).absolute(), Path(manifest_file).absolute() + manifest_sha = digest(manifest_file) + meta = _read_json(manifest_file) + keys = {'schema', 'overlay_name', 'overlay_sha256', 'mask_path', 'mask_sha256', + 'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir', 'am_overlay_path', 'am_overlay_sha256'} + require(isinstance(meta, dict) and keys <= meta.keys() and meta['schema'] == 'phase4an-overlay-v1', 'Invalid overlay manifest schema') + require(meta['overlay_name'] in ('zero', 'static25', 'learned'), 'Unknown overlay snapshot type') + learned_keys = {'parent_snapshot_sha256', 'eligibility_v2_source_sha256'} + allowed = keys | (learned_keys if meta['overlay_name'] == 'learned' else set()) + require(not (meta.keys() - allowed), 'Unknown or inapplicable overlay manifest fields') + require(meta['mask_sha256'] == MASK_SHA and meta['am_overlay_sha256'] == AM_OVERLAY_SHA, + 'Original mask or frozen AM helper authority changed') + require(_sha(meta['overlay_sha256']) and digest(overlay_file) == meta['overlay_sha256'], 'Overlay SHA mismatch') + inputs = {str(overlay_file): meta['overlay_sha256'], str(manifest_file): manifest_sha} + for label in ('mask', 'base_candidate', 'am_overlay'): + path = Path(meta[label + '_path']) + require(path.is_absolute() and _sha(meta[label + '_sha256']) and digest(path) == meta[label + '_sha256'], + 'Missing/mismatched anchored input: ' + label) + inputs[str(path)] = meta[label + '_sha256'] + helper = _load('phase4an_sealed_am_overlay', meta['am_overlay_path'], AM_OVERLAY_SHA) + mask = np.load(meta['mask_path'], allow_pickle=False) + base = np.load(meta['base_candidate_path'], allow_pickle=False) + require(mask.dtype == np.int64 and mask.shape == (helper.CANDIDATE_COUNT,), 'Original complete 14,232 mask is required') + with np.load(overlay_file, allow_pickle=False) as archive: + require(len(archive.files) == 2 and set(archive.files) == {'edge_id', 'plastic_delta_mv'}, 'No embedded base or additional overlay arrays allowed') + plan = helper.validate_arrays(archive['edge_id'], archive['plastic_delta_mv'], mask, base) + delta = plan['plastic_delta_mv'] + if meta['overlay_name'] == 'zero': + require(not np.any(delta != 0), 'Zero snapshot contains a nonzero delta') + elif meta['overlay_name'] == 'static25': + require(helper.bit_equal(delta, np.multiply(np.float32(-.25), base)), 'static25 must equal the original f32 floor') + else: + require(_sha(meta.get('parent_snapshot_sha256')) and meta.get('eligibility_v2_source_sha256') == V2_SHA, + 'Learned snapshot needs parent hash and explicit frozen eligibility-v2 authority') + aj_dir = Path(meta['aj_model_dir']) + require(aj_dir.is_absolute() and aj_dir.name == 'apl-timing-lab-v1', 'AJ must use its real original directory') + for relative, expected in PINS.items(): + path = aj_dir.parent / relative + require(digest(path) == expected, 'Sealed dependency changed: ' + relative) + inputs[str(path)] = expected + require(all(digest(path) == expected for path, expected in inputs.items()), 'Input changed while loading') + return helper, plan, meta, inputs + + +def _dump(node): + return ast.dump(node, annotate_fields=True, include_attributes=False) + + +def _target_roots(node): + if isinstance(node, ast.Name): + return {node.id} + if isinstance(node, (ast.Tuple, ast.List)): + return set().union(*(_target_roots(value) for value in node.elts)) + if isinstance(node, (ast.Subscript, ast.Attribute)): + return _target_roots(node.value) + return set() + + +def math_projection(function): + """Ordered/control-structured frozen math statements, excluding diagnostics. + + Whitelist explicitly covers state/weight initialization, request mutation, + expansion/stable grouping, original CUDA calls, LegacyAPL and ring/g write. + Tests also require the entire reference loop AST to equal the original. + """ + def block(statements): + result = [] + for node in statements: + if isinstance(node, ast.FunctionDef) and node.name == 'upload': + result.append(copy.deepcopy(node)) + elif isinstance(node, ast.Assign) and set().union(*(_target_roots(t) for t in node.targets)) & _MATH_TARGETS: + result.append(copy.deepcopy(node)) + elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): + text = ast.unparse(node.value.func) + if text.startswith('ext.') or text in ('requests.zero_', 'apl.spikes.zero_', 'apl_ring[slot].copy_'): + result.append(copy.deepcopy(node)) + elif isinstance(node, (ast.With, ast.Try)): + result.extend(block(node.body)) + elif isinstance(node, (ast.If, ast.For)): + body, other = block(node.body), block(node.orelse) + if body or other: + keep = copy.deepcopy(node) + keep.body, keep.orelse = body, other + result.append(keep) + return result + return _dump(ast.Module(body=block(function.body), type_ignores=[])) + + +def derive_ast(source_bytes, mode): + """Derive only from exact AJ bytes; return ASTs plus an explicit edit receipt.""" + require(mode in ('light', 'reference'), 'Unknown diagnostic mode') + require(hashlib.sha256(source_bytes).hexdigest() == AJ_SHA, 'AJ source pin mismatch') + source = ast.parse(source_bytes) + original = next(node for node in source.body if isinstance(node, ast.FunctionDef) and node.name == 'run_gpu') + loader = copy.deepcopy(next(node for node in source.body if isinstance(node, ast.FunctionDef) and node.name == 'load_inputs')) + bounds = [node for node in ast.walk(loader) if isinstance(node, ast.Compare) and + ast.unparse(node) == '0 < steps <= 1000'] + require(len(bounds) == 1, 'Expected one explicit step-admission bound') + bounds[0].comparators[-1] = ast.parse("spec['max_steps']", mode='eval').body + function = copy.deepcopy(original) + body = next(node for node in function.body if isinstance(node, ast.Try)).body + anchor = next(i for i, node in enumerate(body) if ast.unparse(node) == 'effective_hash = hashlib.sha256()') + require(ast.unparse(body[anchor - 1]) == 'if bool(np.any(apl_out_mask)):\n weights[upload(apl_out_mask, torch.bool)] = 0.0', + 'Installation no longer follows APL fast-edge zeroing') + body.insert(anchor, ast.parse(_INSTALL).body[0]) + sync = [i for i, node in enumerate(body) if ast.unparse(node) == 'torch.cuda.synchronize(device)'] + require(len(sync) == 1, 'Expected one post-loop synchronization') + body.insert(sync[0] + 1, ast.parse(_FINISH).body[0]) + removed, replacements = [], [] + + class ReportingOnly(ast.NodeTransformer): + def note(self, node): + removed.append(_dump(node)) + return None + + def visit_Assign(self, node): + names = set().union(*(_target_roots(t) for t in node.targets)) + if names & {'expanded_global', 'accepted_cpu', 'queue', 'apl_cpu', 'actual', 'finite', 'row'}: + return self.note(node) + if names == {'maps'} and isinstance(node.targets[0], ast.Subscript): + return self.note(node) + if names == {'timing'}: + require(ast.unparse(node.value.func) == 'audit_sequence', 'Unexpected timing assignment') + replacements.append(_dump(node)) + return ast.parse('timing = _runtime.timing(steps, pending)').body[0] + if names == {'destination'}: + return [node, ast.parse('_runtime.arrivals(expanded, destination, ref)').body[0]] + if ast.unparse(node) == 'g[kc_local_gpu] = g[kc_local_gpu] - delta_si': + return [node, ast.parse('_runtime.after_tick(tick, v, g, ref, drive, apl_state, h, applied_h)').body[0]] + return node + + def visit_AugAssign(self, node): + if _target_roots(node.target) & {'due_total', 'accepted_due_total'}: + return self.note(node) + return node + + def visit_For(self, node): + text = ast.unparse(node.iter) + if text in ("(('drive', 2), ('state', 2), ('inhibition', 4064))", 'apl_cpu.items()', 'actual.items()'): + return self.note(node) + return self.generic_visit(node) + + def visit_If(self, node): + if ast.unparse(node.test) in ("spec['record_history']", 'not finite'): + return self.note(node) + return self.generic_visit(node) + + def visit_Expr(self, node): + if isinstance(node.value, ast.Call) and ast.unparse(node.value.func) in ('queue_output.write', 'hash_output.write'): + return self.note(node) + return node + + def visit_With(self, node): + targets = [getattr(item.optional_vars, 'id', None) for item in node.items] + if targets == ['queue_output', 'hash_output', 'request_output']: + replacements.append(_dump(node.items[0])); replacements.append(_dump(node.items[1])) + node.items = [node.items[-1]] + return self.generic_visit(node) + + if mode == 'light': + function = ReportingOnly().visit(function) + require(len(removed) == 20 and len(replacements) == 3, 'Reporting whitelist changed; stop instead of broad rewriting') + loops = lambda f: [node for node in ast.walk(f) if isinstance(node, ast.For) and + isinstance(node.target, ast.Name) and node.target.id == 'tick'] + require(len(loops(original)) == len(loops(function)) == 1, 'Missing/duplicated trial loop') + if mode == 'reference': + require(_dump(loops(original)[0]) == _dump(loops(function)[0]), 'Reference loop must stay entirely unchanged') + original_math, derived_math = math_projection(original), math_projection(function) + require(original_math == derived_math, 'Ordered mathematical AST changed') + extra_calls = [ast.unparse(node) for node in ast.walk(function) if isinstance(node, ast.Call) and + ast.unparse(node.func).startswith('_runtime.')] + expected_calls = [_INSTALL, _FINISH] + if mode == 'light': + expected_calls += ['_runtime.arrivals(expanded, destination, ref)', + '_runtime.after_tick(tick, v, g, ref, drive, apl_state, h, applied_h)', + '_runtime.timing(steps, pending)'] + require(sorted(extra_calls) == sorted(expected_calls), 'Unlisted runtime callback added') + tree = ast.fix_missing_locations(ast.Module(body=[loader, function], type_ignores=[])) + sha = lambda value: hashlib.sha256(value.encode('utf-8')).hexdigest() + receipt = {'mode': mode, 'aj_source_sha256': AJ_SHA, 'math_ast_sha256': sha(original_math), + 'derived_math_ast_sha256': sha(derived_math), 'mathematical_ast_identical': True, + 'reference_entire_tick_loop_identical': mode == 'reference', + 'admission_edit': '0 < steps <= 1000 -> 0 < steps <= spec.max_steps', + 'pre_loop_installation': _INSTALL, 'post_loop_terminal_recording': _FINISH, + 'added_runtime_callbacks': extra_calls, + 'removed_diagnostic_statements': removed, 'replaced_diagnostic_nodes': replacements, + 'derived_module_ast_sha256': sha(_dump(tree)), 'old_globals_patched': False} + return tree, receipt + + +class FiniteObserver: + """GPU scalar diagnostics only; never writes a neuronal/APL input tensor.""" + def __init__(self, torch, device): + self.torch = torch + self.ok = torch.ones((), device=device, dtype=torch.bool) + self.first = torch.full((), -1, device=device, dtype=torch.int64) + self.accepted = torch.zeros((), device=device, dtype=torch.int64) + self.due = 0 + self.ticks = 0 + + def arrivals(self, expanded, destination, ref): + self.due += expanded.numel() + self.accepted.add_((ref[destination.long()] == 0).to(self.torch.int64).sum()) + + def observe(self, tick, *values): + require(tick == self.ticks, 'Finite observer tick coverage changed') + finite = self.torch.ones_like(self.ok) + for value in values: + finite.logical_and_(self.torch.isfinite(value).all()) + self.first = self.torch.where((self.first < 0) & (~finite), self.torch.full_like(self.first, tick), self.first) + self.ok.logical_and_(finite) + self.ticks += 1 + + def result(self): + return {'all_finite': bool(self.ok.item()), 'first_failure_tick': int(self.first.item()), + 'finite_observed_ticks': self.ticks, 'arriving_edges': self.due, + 'accepted_arriving_edges': int(self.accepted.item())} + + +def _copy_input(source, target, expected): + with Path(source).open('rb') as src, Path(target).open('xb') as dst: + for block in iter(lambda: src.read(8 << 20), b''): + dst.write(block) + require(digest(target) == expected, 'Copied input differs') + + +class _Runtime: + def __init__(self, mode, spec, helper, plan, meta, inputs, torch, overlay_file, manifest_file): + self.mode, self.spec, self.helper, self.plan, self.meta, self.inputs = mode, spec, helper, plan, meta, inputs + self.torch, self.overlay_file, self.manifest_file = torch, Path(overlay_file).absolute(), Path(manifest_file).absolute() + self.observer = None + self.installations = 0 + self.finished = False + self.result = None + + def install(self, weights, graph, apl_mask, upload, out_dir, inherited, device): + require(self.installations == 0, 'Repeated installation is forbidden') + require(len(graph['nodes']) == 166700 and len(graph['edge_id']) == self.helper.EDGE_COUNT, + 'Only the intact full graph is permitted') + require(int(apl_mask.sum()) == 5845, 'Original fast APL mask changed') + mapping = self.helper.map_installation(graph['weights'], graph['edge_id'], apl_mask, self.plan) + require(mapping['missing_candidate_count'] == 0, 'All original candidates must be present') + rows = mapping['rows'] + if len(rows): + index = upload(rows, self.torch.int64) + nonzero = self.plan['plastic_delta_mv'] != np.float32(0) + change = upload(self.plan['plastic_delta_mv'][nonzero], self.torch.float64) * 0.001 + weights[index] = weights[index] + change + require(self.helper.bit_equal(weights[index].cpu().numpy(), mapping['expected_si']), 'Independent SI installation mismatch') + raw_base, expected = self.helper.expected_hashes(graph['weights'], apl_mask, mapping) + actual = hashlib.sha256() + for start in range(0, weights.numel(), 1 << 20): + actual.update(weights[start:start + (1 << 20)].cpu().numpy().astype(' 0, + "A nonempty list/tuple semantic key is required") + _require(all(type(v) is str and v and v.strip() == v for v in key), + "Semantic key tokens must be nonempty, unpadded strings") + return tuple(key) + + +def seed_for(master, split, key): + _require(type(master) is int and 0 <= master < 2**63, "master must be a 63-bit integer") + _require(type(split) is str and split in SPLITS, "Unknown stream split") + key = _key(key) + message = _canonical([SCHEMA, master, split, list(key)]).encode("utf-8") + full_sha = hashlib.sha256(message).hexdigest() + return int(full_sha[:16], 16) & ((1 << 63) - 1), full_sha + + +class SeedRegistry: + """Fail closed on duplicate keys, collisions or cross-split aliasing. + + No collision rehashing or seed search is performed: such a failure must be + resolved before registration, never after observing a neural response. + """ + + def __init__(self, master): + seed_for(master, "design", ("validate-master",)) + self.master = master + self._streams = {} + self._aliases = {} + self._seed_to_key = {} + + def add(self, split, key): + key = _key(key) + identity = (split, key) + _require(identity not in self._streams and identity not in self._aliases, + "Duplicate semantic stream key") + seed, full_sha = seed_for(self.master, split, key) + _require(seed not in self._seed_to_key, "Seed collision: stop before registration") + row = dict(split=split, key=list(key), seed=seed, derivation_sha256=full_sha) + self._streams[identity] = row + self._seed_to_key[seed] = identity + return seed + + def alias(self, split, key, source_split, source_key, *, reason): + """Declare paired controls, never silently create/reuse a stream.""" + key, source_key = _key(key), _key(source_key) + identity, source = (split, key), (source_split, source_key) + _require(split == source_split and split in SPLITS, + "Cross-split random-stream sharing is forbidden") + _require(identity not in self._streams and identity not in self._aliases, + "Duplicate semantic stream key") + _require(source in self._streams, "Alias must target an independent stream, not another alias") + _require(type(reason) is str and bool(reason.strip()), "Intentional sharing requires a reason") + seed = self._streams[source]["seed"] + self._aliases[identity] = dict(split=split, key=list(key), seed=seed, + source_split=source_split, source_key=list(source_key), reason=reason, + independent_replicate=False) + return seed + + def manifest(self): + streams = [dict(self._streams[k]) for k in sorted(self._streams)] + aliases = [dict(self._aliases[k]) for k in sorted(self._aliases)] + split_counts = {split: sum(r["split"] == split for r in streams) for split in sorted(SPLITS)} + result = dict(schema=SCHEMA, master=self.master, + algorithm="SHA-256 canonical JSON [schema,master,split,key]; first64 bits masked to63", + independent_streams=streams, aliases=aliases, + independent_count=len(streams), alias_count=len(aliases), split_counts=split_counts, + all_independent_seeds_unique=True, training_evaluation_seed_overlap=0, + aliases_count_as_independent_replicates=False, + training_executed=False, protocol_approval_claimed=False) + result["payload_sha256"] = hashlib.sha256(_canonical(result).encode("utf-8")).hexdigest() + # JSON round-trip makes nested data independent of the mutable registry. + return json.loads(_canonical(result)) + + +def validate_manifest(manifest): + """Reconstruct every seed/alias and require the complete canonical payload.""" + _require(isinstance(manifest, dict) and manifest.get("schema") == SCHEMA, "Invalid seed manifest") + payload = {key: value for key, value in manifest.items() if key != "payload_sha256"} + actual_sha = hashlib.sha256(_canonical(payload).encode("utf-8")).hexdigest() + _require(manifest.get("payload_sha256") == actual_sha, "Actual manifest payload SHA mismatch") + rebuilt = SeedRegistry(manifest["master"]) + for row in manifest["independent_streams"]: + rebuilt.add(row["split"], row["key"]) + for row in manifest["aliases"]: + rebuilt.alias(row["split"], row["key"], row["source_split"], row["source_key"], reason=row["reason"]) + # Python dict equality conflates False/0 and 1/1.0; the serialized contract + # must retain JSON Boolean versus integer versus floating-point identity. + _require(_canonical(manifest) == _canonical(rebuilt.manifest()), + "Seed manifest differs from full independent reconstruction") + return True diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_biological_contract.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_biological_contract.py new file mode 100644 index 0000000..d3a83ec --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_biological_contract.py @@ -0,0 +1,153 @@ +import unittest + +import numpy as np +import pandas as pd + +import biological_contract as b + + +def fixture(): + types = ["KC-a", "KC-b", "PAM15", "PPL103", "MBON01", "MBON12", "other-DAN"] + classes = ["Kenyon_Cell", "Kenyon_Cell", "DAN", "DAN", "MBON", "MBON", "DAN"] + instances = ["KC-a_L", "KC-b_R", "PAM15(y5B'2a)_L", "PPL103(y2a'1)_R", + "MBON01(y5B'2a)_L", "MBON12(y2a'1)_R", "other-DAN"] + meta = pd.DataFrame(dict(neuron_idx=np.arange(7, dtype=np.int32), + body_id=np.arange(7, dtype=np.uint64) + np.uint64(2**63), + annotation_type=pd.Series(types, dtype=object), annotation_class=pd.Series(classes, dtype=object), + annotation_instance=pd.Series(instances, dtype=object))) + dan = meta.iloc[[2, 3, 6]].drop(columns="annotation_class").copy() + mbon = meta.iloc[[4, 5]].drop(columns="annotation_class").copy() + dan["mb_compartment_verbatim"] = ["y5B'2a", "y2a'1", None] + dan["mb_compartment_mapping_status"] = ["explicit_single_parenthesized_token"] * 2 + ["no_parenthesized_token"] + mbon["mb_compartment_verbatim"] = ["y5B'2a", "y2a'1"] + mbon["mb_compartment_mapping_status"] = "explicit_single_parenthesized_token" + src = np.array([0, 0, 1, 1, 2, 3], np.int32) + dst = np.array([4, 5, 4, 5, 4, 5], np.int32) + raw = np.array([4, 8, 12, 16, 20, 24], np.int64) + def table(ids): + rows = [] + for i in ids: + row = dict(edge_id=i, raw_synapse_count=int(raw[i])) + for label, node in (("source", src[i]), ("target", dst[i])): + for key in ("neuron_idx", "body_id", "annotation_type", "annotation_instance"): + row[label + "_" + key] = meta.iloc[node][key] + row["target_compartment_verbatim"] = "y5B'2a" if dst[i] == 4 else "y2a'1" + rows.append(row) + out = pd.DataFrame(rows) + for key in ("source_neuron_idx", "target_neuron_idx"): + out[key] = out[key].astype(np.int32) + for key in ("source_body_id", "target_body_id"): + out[key] = out[key].astype(np.uint64) + # Match the observed frozen parquet object dtype; local pandas 3 may + # infer its new string extension dtype from constructor literals. + for prefix in ("source", "target"): + for suffix in ("annotation_type", "annotation_instance"): + out[prefix + "_" + suffix] = out[prefix + "_" + suffix].astype(object) + out["edge_id"] = out.edge_id.astype(np.int64) + out["raw_synapse_count"] = out.raw_synapse_count.astype(np.int64) + return out + candidates = table(range(4)) + candidates["plasticity_polarity"] = np.int64(-1) + confirmed = table([4, 5]) + confirmed["source_compartment_verbatim"] = confirmed.target_compartment_verbatim + confirmed["exact_compartment_match"] = True + return meta, dan, mbon, confirmed, candidates, src, dst, raw + + +class Biology(unittest.TestCase): + def test_complete_graph_binds_every_edge_array_length(self): + meta, *_, src, dst, raw = fixture() + graph = dict(nodes=np.arange(7, dtype=np.int32), edge_id=np.arange(6, dtype=np.int64), + src=src, dst=dst, signed_raw=raw) + b._complete_graph_identity(meta, graph, n=7, e=6) + for names in (("src",), ("dst",), ("signed_raw",), ("src", "dst", "signed_raw")): + changed = dict(graph) + for name in names: + changed[name] = changed[name][:-1] + with self.subTest(names=names), self.assertRaisesRegex(ValueError, "endpoint/weight lengths"): + b._complete_graph_identity(meta, changed, n=7, e=6) + for name in ("nodes", "edge_id"): + changed = dict(graph) + changed[name] = changed[name].reshape(1, -1) + with self.subTest(name=name), self.assertRaises(ValueError): + b._complete_graph_identity(meta, changed, n=7, e=6) + + def test_exact_id_compartment_and_real_edge_contract(self): + values = fixture() + populations, audit = b.audit_interface_tables(*values) + self.assertEqual(populations["PAM15"].tolist(), [2]) + self.assertEqual(populations["MBON12"].tolist(), [5]) + self.assertEqual(audit["whole_DAN_mapping_unknown"], 1) + self.assertFalse(audit["global_unknowns_filled"]) + self.assertEqual(audit["all_candidate_edges_retained"], 4) + self.assertEqual(audit["interfaces"]["PAM15"]["candidate_contacts"], 16) + self.assertEqual(audit["interfaces"]["PPL103"]["confirmed_DAN_MBON_contacts"], 24) + self.assertGreater(audit["interfaces"]["PAM15"]["DAN_members"][0]["body_id"], 2**63) + self.assertTrue(pd.isna(values[1].iloc[-1].mb_compartment_verbatim)) + + def test_unknown_selected_mapping_is_not_filled_from_type(self): + for field, new_value in (("mb_compartment_verbatim", None), + ("mb_compartment_mapping_status", "unknown")): + values = fixture() + values[1].loc[2, field] = new_value + with self.assertRaisesRegex(ValueError, "Unknown or mismatched"): + b.audit_interface_tables(*values) + + def test_changed_body_id_or_type_or_instance_rejected(self): + for field, new_value in (("body_id", np.uint64(5)), ("annotation_type", "PAM_OTHER"), + ("annotation_instance", "inferred")): + values = fixture() + values[1].loc[2, field] = new_value + with self.assertRaisesRegex(ValueError, "Identity field mismatch"): + b.audit_interface_tables(*values) + + def test_unmapped_dan_cannot_be_silently_removed(self): + values = list(fixture()) + values[1] = values[1].iloc[:2].copy() + with self.assertRaisesRegex(ValueError, "complete exact-annotation"): + b.audit_interface_tables(*values) + + def test_missing_actual_edge_fails_not_guessed(self): + values = list(fixture()) + values[3] = values[3].iloc[:1].copy() + with self.assertRaisesRegex(ValueError, "unknown or mismatched actual target"): + b.audit_interface_tables(*values) + + def test_edge_identity_and_contacts_and_polarity(self): + changes = ((3, "source_body_id", np.uint64(8)), + (3, "target_annotation_type", "MBON_OTHER"), + (4, "raw_synapse_count", np.int64(1)), + (4, "plasticity_polarity", np.int64(1))) + for which, field, value in changes: + values = fixture() + values[which].loc[values[which].index[0], field] = value + with self.subTest(field=field), self.assertRaises(ValueError): + b.audit_interface_tables(*values) + + def test_false_or_nonboolean_confirmed_flag_rejected(self): + for value in (False, "True"): + values = fixture() + values[3]["exact_compartment_match"] = value + with self.assertRaisesRegex(ValueError, "unknown compartment"): + b.audit_interface_tables(*values) + + def test_graph_endpoint_and_sign_change_rejected(self): + for where in (5, 6, 7): + values = fixture() + values[where][0] = -1 if where == 7 else 6 + with self.assertRaises(ValueError): + b.audit_interface_tables(*values) + + def test_schema_and_duplicate_identity_rejected(self): + values = list(fixture()) + values[0] = values[0].drop(columns="annotation_class") + with self.assertRaises(ValueError): + b.audit_interface_tables(*values) + values = fixture() + values[0].loc[1, "body_id"] = values[0].loc[0, "body_id"] + with self.assertRaises(ValueError): + b.audit_interface_tables(*values) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_epoch.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_epoch.py new file mode 100644 index 0000000..516f45f --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_epoch.py @@ -0,0 +1,298 @@ +"""Tiny temporary CPU fixtures, never scientific events or a real brain run.""" +import copy +import json +from pathlib import Path +import tempfile +from types import SimpleNamespace +import unittest +from unittest.mock import patch + +import numpy as np + +import conditioning_epoch as m +import conditioning_update as update +from test_conditioning_update import BRIDGE, SOURCE + + +def save(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, sort_keys=True, allow_nan=False), encoding='utf-8') + + +class TinyFixture: + def __init__(self, root): + self.root = root + self.inputs = root / 'inputs'; self.inputs.mkdir() + self.case = self.inputs / 'case'; self.case.mkdir() + self.graph = self.inputs / 'graph'; self.graph.mkdir() + self.apl = self.inputs / 'apl'; self.apl.mkdir() + self.checkpoint = root / 'runtime'; self.checkpoint.mkdir() + self.authority_dir = root / 'authority'; self.authority_dir.mkdir() + self.output_parent = root / 'outputs'; self.output_parent.mkdir() + self.authority_path = self.authority_dir / 'epoch.json' + self.mask = np.array([1, 3], np.int64) + self.kc = np.array([1, 3], np.int32) + self.base = np.array([.275, .55], np.float32) + self.before = np.zeros(2, np.float32) + graph = dict(nodes=np.arange(5, dtype=np.int32), edge_id=np.arange(6, dtype=np.int64), + src=np.array([0, 1, 1, 3, 3, 4], np.int32), dst=np.array([1, 2, 4, 2, 4, 0], np.int32), + signed_raw=np.ones(6, np.int64), ptr=np.array([0, 1, 3, 3, 5, 6], np.int64), weights=np.full(6, .000275)) + for name, array in graph.items(): + np.save(self.graph / (name + '.npy'), array) + for name, array in dict(input_matrix=np.zeros((2, 5), np.float32), + output_matrix=np.zeros((2, 2), np.float32), kc_globals=self.kc, apl_globals=np.array([0, 4], np.int32)).items(): + np.save(self.apl / (name + '.npy'), array) + np.save(self.inputs / 'kc.npy', self.kc) + np.save(self.inputs / 'mask.npy', self.mask) + np.save(self.inputs / 'base.npy', self.base) + np.savez_compressed(self.inputs / 'mapping.npz', edge_id=self.mask, + source_neuron_idx=graph['src'][self.mask], target_neuron_idx=graph['dst'][self.mask], + target_compartment_verbatim=np.array(["y5B'2a", 'a1'])) + np.savez_compressed(self.inputs / 'before.npz', edge_id=self.mask, plastic_delta_mv=self.before) + save(self.inputs / 'coeff.json', {'synthetic': True}) + source_root = Path(m.__file__).resolve().parent + source_files = dict(runtime_light_source=source_root / 'runtime_light.py', + runtime_continuation_source=source_root / 'runtime_continuation.py', + conditioning_update_source=source_root / 'conditioning_update.py', + eligibility_bridge_source=BRIDGE.resolve(), phase4j_source=SOURCE.resolve(), + am_overlay_source=source_root.parent / 'wholebrain-plastic-interface-v1/overlay.py') + metadata = dict(schema='phase4an-overlay-v1', overlay_name='zero', + overlay_sha256=m._digest(self.inputs / 'before.npz'), mask_path=str(self.inputs / 'mask.npy'), + mask_sha256=m._digest(self.inputs / 'mask.npy'), base_candidate_path=str(self.inputs / 'base.npy'), + base_candidate_sha256=m._digest(self.inputs / 'base.npy'), aj_model_dir=str(self.graph), + am_overlay_path=str(source_files['am_overlay_source']), am_overlay_sha256=m._digest(source_files['am_overlay_source'])) + save(self.inputs / 'before.json', metadata) + spec = dict(name='synthetic', steps=4, max_steps=4, dt_ms=.1, duration_ms=.4, + apl_delay_steps=18, record_history=False, within_trial_weight_updates=False, + graph_dir=str(self.graph), apl_bundle_dir=str(self.apl), coefficients_path=str(self.inputs / 'coeff.json')) + save(self.case / 'spec.json', spec) + np.savez_compressed(self.case / 'requests.npz', tick=np.array([0], np.int32), neuron_idx=np.array([1], np.int32)) + state = dict(step=np.array([0], np.int32), neuron_idx=np.array([1], np.int32), + counts=np.array([0, 1, 0, 0, 0], np.int32), v=np.full(5, -.052), g=np.zeros(5), ref=np.zeros(5, np.int32)) + np.savez_compressed(self.checkpoint / 'state.npz', **state) + (self.checkpoint / 'overlay-input.npz').write_bytes((self.inputs / 'before.npz').read_bytes()) + (self.checkpoint / 'overlay-manifest.json').write_bytes((self.inputs / 'before.json').read_bytes()) + summary = dict(nodes=5, edges=6, diagnostic_mode='light', effective_weights_raw_sha256='e' * 64) + save(self.checkpoint / 'summary.json', summary) + provenance = dict(input_sha256={str(path): m._digest(path) for path in + (self.case / 'spec.json', self.case / 'requests.npz', self.inputs / 'before.npz', self.inputs / 'before.json')}) + save(self.checkpoint / 'runtime-provenance.json', provenance) + save(self.checkpoint / 'overlay-audit.json', dict(delta_raw_sha256=self.raw(self.before), effective_weights_raw_sha256='e' * 64)) + paths = dict(spec=self.case / 'spec.json', requests=self.case / 'requests.npz', + overlay_before=self.inputs / 'before.npz', overlay_manifest_before=self.inputs / 'before.json', + state=self.checkpoint / 'state.npz', kc=self.inputs / 'kc.npy', canonical_kc=self.apl / 'kc_globals.npy', + candidate_map=self.inputs / 'mapping.npz', mask=self.inputs / 'mask.npy', base_candidate=self.inputs / 'base.npy', **source_files) + self.value = dict(schema=m.SCHEMA, epoch_id='synthetic/epoch-0', diagnostic_mode='light', + command=dict(steps=4, compartment="y5B'2a", us_tick=1, d_command=1., frozen=False, trace_backend='numpy'), + checkpoint_dir=str(self.checkpoint), checkpoint_manifest_sha256=None, + files={role: dict(path=str(path), sha256=m._digest(path)) for role, path in paths.items()}, + frozen_inputs={str(path): m._digest(path) for path in [ + *(self.graph / name for name in m.GRAPH_FILES), *(self.apl / name for name in m.APL_FILES), self.inputs / 'coeff.json']}) + self.reseal() + + @staticmethod + def raw(array): + return __import__('hashlib').sha256(array.tobytes()).hexdigest() + + def reseal(self): + files = {path.name: m._digest(path) for path in self.checkpoint.iterdir() if path.name != 'checkpoint-manifest.json'} + save(self.checkpoint / 'checkpoint-manifest.json', dict(files_sha256=files)) + self.value['checkpoint_manifest_sha256'] = m._digest(self.checkpoint / 'checkpoint-manifest.json') + for entry in self.value['files'].values(): + entry['sha256'] = m._digest(entry['path']) + for path in self.value['frozen_inputs']: + self.value['frozen_inputs'][path] = m._digest(path) + save(self.authority_path, self.value) + + def load_checkpoint(self, directory, pin): + self.asserted_loads += 1 + directory = Path(directory) + m._require(m._digest(directory / 'checkpoint-manifest.json') == pin, 'synthetic seal pin mismatch') + manifest = m._read(directory / 'checkpoint-manifest.json') + actual = {path.name: m._digest(path) for path in directory.iterdir() if path.name != 'checkpoint-manifest.json'} + m._require(manifest['files_sha256'] == actual, 'synthetic sealed inventory mismatch') + return dict(fresh=True, global_tick_offset=0, steps=4, summary=m._read(directory / 'summary.json'), + provenance=m._read(directory / 'runtime-provenance.json'), manifest=manifest, + state=m._npz(directory / 'state.npz', ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')), + overlay=m._read(directory / 'overlay-audit.json')) + + def load_overlay(self, file, manifest): + meta = m._read(manifest) + m._require(meta['overlay_sha256'] == m._digest(file), 'synthetic overlay SHA mismatch') + value = m._npz(file, ('edge_id', 'plastic_delta_mv')) + value['base_candidate_mv'] = np.load(meta['base_candidate_path'], allow_pickle=False) + if meta['overlay_name'] == 'learned': + expected = {'schema', 'overlay_name', 'overlay_sha256', 'mask_path', 'mask_sha256', + 'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir', 'am_overlay_path', 'am_overlay_sha256', + 'parent_snapshot_sha256', 'eligibility_v2_source_sha256'} + m._require(set(meta) == expected, 'learned manifest fields differ from real runtime contract') + return None, value, meta, {} + + def run(self, out=None): + self.asserted_loads = 0 + parsed = m._authority(self.authority_path, m._digest(self.authority_path)) + c = SimpleNamespace(load_checkpoint=self.load_checkpoint) + r = SimpleNamespace(PINS={}, validate_spec=lambda value, _: value, + load_overlay=self.load_overlay, _raw=self.raw) + return m._coordinate(*parsed, self.output_parent / 'result' if out is None else out, + (c, r, update), (5, 6, 2, 2)) + + +class EpochPostprocessing(unittest.TestCase): + def setUp(self): + self.tmp = tempfile.TemporaryDirectory() + self.f = TinyFixture(Path(self.tmp.name).resolve()) + + def tearDown(self): + self.tmp.cleanup() + + def test_exclusive_complete_output_and_distinct_parent_hashes(self): + chain = self.f.run() + output = self.f.output_parent / 'result' + self.assertEqual({p.name for p in output.iterdir()}, {'delta-after.npz', 'overlay-manifest.json', 'update-receipt.json', 'chain-receipt.json'}) + meta = m._read(output / 'overlay-manifest.json') + self.assertEqual(meta['parent_snapshot_sha256'], self.f.value['files']['overlay_before']['sha256']) + self.assertNotEqual(meta['parent_snapshot_sha256'], self.f.value['checkpoint_manifest_sha256']) + self.assertEqual(chain['before_overlay_manifest_sha256'], self.f.value['files']['overlay_manifest_before']['sha256']) + self.assertEqual(chain['source_neural_checkpoint_manifest_sha256'], self.f.value['checkpoint_manifest_sha256']) + self.assertEqual(self.f.asserted_loads, 2) + self.assertFalse(chain['weights_installed']) + self.assertFalse(chain['registration_authority_independently_proven']) + after = m._npz(output / 'delta-after.npz', ('edge_id', 'plastic_delta_mv')) + self.assertLess(after['plastic_delta_mv'][0], 0) + self.assertEqual(after['plastic_delta_mv'][1].view(np.uint32), 0) + with self.assertRaisesRegex(ValueError, 'Output must be new'): + self.f.run() + + def test_every_external_sha_before_processing(self): + with self.assertRaisesRegex(ValueError, 'Authority differs'): + m._authority(self.f.authority_path, '0' * 64) + with (self.f.inputs / 'mapping.npz').open('ab') as stream: + stream.write(b'changed') + with self.assertRaisesRegex(ValueError, 'SHA mismatch'): + self.f.run() + self.assertFalse((self.f.output_parent / 'result').exists()) + + def test_sealed_event_substitution_and_unbound_case_are_rejected(self): + path = self.f.checkpoint / 'runtime-provenance.json' + provenance = m._read(path) + provenance['input_sha256'][str(self.f.case / 'requests.npz')] = 'f' * 64 + save(path, provenance) + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'did not bind authorized requests'): + self.f.run() + + def test_changed_runtime_overlay_cannot_match_only_its_own_seal(self): + (self.f.checkpoint / 'overlay-input.npz').write_bytes(b'different') + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'used a different overlay'): + self.f.run() + + def test_candidate_endpoint_and_canonical_kc_must_really_match(self): + path = self.f.inputs / 'mapping.npz' + mapping = m._npz(path, ('edge_id', 'source_neuron_idx', 'target_neuron_idx', 'target_compartment_verbatim')) + mapping['target_neuron_idx'][0] = 4 + np.savez_compressed(path, **mapping) + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'endpoints differ'): + self.f.run() + mapping['target_neuron_idx'][0] = 2 + np.savez_compressed(path, **mapping) + np.save(self.f.inputs / 'kc.npy', np.array([1, 4], np.int32)) + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'KC differs'): + self.f.run() + + def test_command_steps_and_full_frozen_inventory_required(self): + self.f.value['command']['steps'] = 5 + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'Command steps differ'): + self.f.run() + self.f.value['command']['steps'] = 4 + del self.f.value['frozen_inputs'][str(self.f.graph / 'signed_raw.npy')] + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'authority is incomplete'): + self.f.run() + + def test_protected_directories_and_existing_output_fail_before_write(self): + for path in (self.f.checkpoint / 'new', self.f.inputs / 'new', self.f.graph / 'new', + self.f.authority_dir / 'new', self.f.checkpoint): + with self.subTest(path=path), self.assertRaisesRegex(ValueError, 'Output must be new'): + self.f.run(path) + + def test_partial_is_preserved_and_not_retryable(self): + with patch.object(self.f, 'load_overlay', side_effect=[ + self.f.load_overlay(self.f.inputs / 'before.npz', self.f.inputs / 'before.json'), + ValueError('synthetic output validation failed')]): + with self.assertRaisesRegex(ValueError, 'output validation'): + self.f.run() + output = self.f.output_parent / 'result' + self.assertTrue((output / 'postprocess-error.json').is_file()) + self.assertTrue((output / 'delta-after.npz').is_file()) + self.assertFalse((output / 'chain-receipt.json').exists()) + with self.assertRaisesRegex(ValueError, 'Output must be new'): + self.f.run() + + def test_actual_pinned_dependencies_load_without_gpu(self): + _, paths, pins, _, _ = m._authority(self.f.authority_path, m._digest(self.f.authority_path)) + c, r, u = m._production_dependencies(paths, pins) + self.assertEqual(Path(r.__file__).resolve(), paths['runtime_light_source']) + self.assertEqual(c.N, 166700) + self.assertEqual(u.BRIDGE_SHA256, update.BRIDGE_SHA256) + for role in m.SOURCE_PINS: + self.assertEqual(m._digest(paths[role]), m.SOURCE_PINS[role]) + + def test_production_dimensions_cannot_be_set_by_authority(self): + self.assertEqual(m.DIMENSIONS, (166700, 25582938, 4064, 14232)) + self.f.value['small_test_mode'] = True + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'fields/schema'): + m.postprocess_epoch(self.f.authority_path, m._digest(self.f.authority_path), self.f.output_parent / 'result') + + def test_public_entry_rejects_tiny_population_even_with_valid_fixture_seal(self): + self.f.asserted_loads = 0 + c = SimpleNamespace(load_checkpoint=self.f.load_checkpoint) + r = SimpleNamespace(PINS={}, validate_spec=lambda value, _: value, + load_overlay=self.f.load_overlay, _raw=self.f.raw) + with patch.object(m, '_production_dependencies', return_value=(c, r, update)): + with self.assertRaisesRegex(ValueError, 'Runtime population/mode'): + m.postprocess_epoch(self.f.authority_path, m._digest(self.f.authority_path), self.f.output_parent / 'result') + self.assertFalse((self.f.output_parent / 'result').exists()) + + def test_authorized_event_file_cannot_be_an_unsealed_copy(self): + other = self.f.inputs / 'state-copy.npz' + other.write_bytes((self.f.checkpoint / 'state.npz').read_bytes()) + self.f.value['files']['state'] = dict(path=str(other), sha256=m._digest(other)) + self.f.reseal() + with self.assertRaisesRegex(ValueError, 'Events must come from this checkpoint'): + self.f.run() + + def test_late_input_change_preserves_partial_but_never_complete_chain(self): + original = self.f.load_checkpoint + calls = 0 + def late_change(directory, pin): + nonlocal calls + calls += 1 + result = original(directory, pin) + if calls == 2: + with (self.f.graph / 'weights.npy').open('ab') as stream: + stream.write(b'changed after outputs were written') + return result + with patch.object(self.f, 'load_checkpoint', side_effect=late_change): + with self.assertRaisesRegex(ValueError, 'SHA mismatch'): + self.f.run() + output = self.f.output_parent / 'result' + self.assertTrue((output / 'postprocess-error.json').is_file()) + self.assertFalse((output / 'chain-receipt.json').exists()) + + def test_zero_d_and_frozen_return_identity_without_installation(self): + for index, (d, frozen) in enumerate(((0., False), (1., True))): + self.f.value['command'].update(d_command=d, frozen=frozen) + self.f.reseal() + self.f.run(self.f.output_parent / str(index)) + value = m._npz(self.f.output_parent / str(index) / 'delta-after.npz', ('edge_id', 'plastic_delta_mv')) + self.assertEqual(value['plastic_delta_mv'].tobytes(), self.f.before.tobytes()) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_inputs.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_inputs.py new file mode 100644 index 0000000..b8d1123 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_inputs.py @@ -0,0 +1,258 @@ +"""Synthetic CPU/mocked-CUDA contract tests; never run a real input generator.""" +import ast +import hashlib +import json +from pathlib import Path +import types +import unittest +from unittest.mock import patch + +import numpy as np + +import conditioning_inputs as c + + +def req(rows): + values = np.asarray(rows, np.int32).reshape(-1, 2) + return {'tick': values[:, 0].copy(), 'neuron_idx': values[:, 1].copy()} + + +class FakeTensor: + def __init__(self, values, device): + self.values = np.array(values, copy=True) + self.device, self.dtype = device, self.values.dtype + + def __getitem__(self, key): return FakeTensor(self.values[key], self.device) + def __lt__(self, other): return FakeTensor(self.values < other.values, self.device) + def cpu(self): return self + def numpy(self): return self.values.copy() + + +class FakeTorch: + """Observable adapter, not a CUDA or random-number emulation.""" + float32 = np.dtype(np.float32) + __version__ = 'SYNTHETIC-CONTRACT-MOCK-NOT-TORCH' + version = types.SimpleNamespace(cuda=None) + + def __init__(self, uniforms, truncate=False): + self.uniforms = uniforms + self.calls, self.seeds, self.truncate = [], [], truncate + self.cuda = types.SimpleNamespace(is_available=lambda: True, + get_device_name=lambda device: 'SYNTHETIC-NOT-A-REAL-GPU', get_device_capability=lambda device: (0, 0), + get_device_properties=lambda device: types.SimpleNamespace()) + + def get_default_dtype(self): return self.float32 + def device(self, name): return name + + def Generator(self, device): + owner = self + class Generator: + def manual_seed(self, value): + owner.seeds.append(value); self.seed = value & 0xffffffff if owner.truncate else value + def initial_seed(self): return self.seed + self.calls.append(('generator', device)); return Generator() + + def as_tensor(self, values, dtype, device): + self.calls.append(('probability', np.array(values, copy=True), dtype, device)) + return FakeTensor(values, device) + + def rand(self, shape, generator, device): + self.calls.append(('rand', shape, generator.initial_seed(), device)) + if shape != self.uniforms.shape: raise AssertionError('Unexpected draw shape') + return FakeTensor(self.uniforms, device) + + +class InputsTests(unittest.TestCase): + def setUp(self): + self.targets = np.array([19, 3], np.int32) + self.rates = np.array([5000, 10000], np.float32) + + def test_frozen_probability_dtype_order_signed_zero_and_no_mutation(self): + targets = np.arange(6, dtype=np.int32) + rates = np.array([0., -0., np.nextafter(np.float32(0), np.float32(1)), .1, 123.456, 10000.], np.float32) + before = rates.tobytes(); rates.setflags(write=False) + actual = c.probabilities_f32(targets, rates) + self.assertEqual(actual.dtype, np.float32) + self.assertEqual(actual.tobytes(), (rates * .1 / 1000.).tobytes()) + self.assertEqual(rates.tobytes(), before) + + def test_uniform_threshold_is_strict_and_bernoulli_not_poisson_counts(self): + p = np.array([0, .5, 1], np.float32) + u = np.array([[0, .5, np.nextafter(np.float32(1), np.float32(0))], [0, .499, 0]], np.float32) + forced = c.forced_from_uniforms(u, p) + np.testing.assert_array_equal(forced, [[False, False, True], [False, True, True]]) + self.assertEqual(forced.dtype, np.bool_) + + def test_original_PN_column_order_then_sparse_tick_global_sort_only(self): + forced = np.array([[True, True], [True, False], [False, True]], np.bool_) + before = forced.copy(); self.targets.setflags(write=False) + result = c.requests_from_forced(forced, self.targets, cs_start_tick=0, cs_stop_tick=3) + np.testing.assert_array_equal(result['requests']['tick'], [0, 0, 1, 2]) + np.testing.assert_array_equal(result['requests']['neuron_idx'], [3, 19, 19, 3]) + np.testing.assert_array_equal(forced, before) + self.assertEqual(result['audit']['duplicate_requests_removed'], 0) + + def test_half_open_window_masks_full_matrix_including_empty_window(self): + forced = np.ones((5, 2), np.bool_) + result = c.requests_from_forced(forced, self.targets, cs_start_tick=1, cs_stop_tick=4) + self.assertEqual(result['audit']['full_matrix_boolean_hits'], 10) + self.assertEqual(result['audit']['outside_CS_boolean_hits_masked'], 4) + self.assertEqual(set(result['requests']['tick']), {1, 2, 3}) + empty = c.requests_from_forced(forced, self.targets, cs_start_tick=2, cs_stop_tick=2) + self.assertEqual(len(empty['requests']['tick']), 0) + self.assertEqual(empty['audit']['outside_CS_boolean_hits_masked'], 10) + + def test_full_63bit_seed_single_full_draw_and_mock_scope_receipt(self): + seed = (1 << 62) + (1 << 32) + 19 + fake = FakeTorch(np.array([[.2, .6], [.7, .7], [.1, .2], [.8, .1], [.1, .1]], np.float32)) + with patch.object(c, '_load_torch', return_value=fake): + result = c.generate_cs_requests(self.targets, self.rates, seed=seed, steps=5, cs_start_tick=1, cs_stop_tick=4, + device='cuda:0', max_uniform_draws=10) + self.assertEqual(fake.seeds, [seed]); draws = [x for x in fake.calls if x[0] == 'rand'] + self.assertEqual(draws, [('rand', (5, 2), seed, 'cuda:0')]) + a = result['audit']; self.assertEqual(a['seed_hex'], f'{seed:016x}') + self.assertEqual(a['numpy_version'], np.__version__) + self.assertEqual(a['torch_version'], fake.__version__) + self.assertEqual(a['device_name'], 'SYNTHETIC-NOT-A-REAL-GPU') + self.assertFalse(a['CUDA_sequence_equivalence_tested_here']) + self.assertFalse(a['CUDA_high_seed_bit_sequence_influence_tested_here']) + self.assertFalse(a['full_old_L_input_identity_claimed']) + self.assertEqual(a['uniform_scalar_draws'], 10) + self.assertEqual((a['max_uniform_draws'], a['uniform_matrix_bytes']), (10, 40)) + self.assertEqual(a['device_identity'], {'uuid': None, 'pci_bus_id': None, 'query_error': None}) + np.testing.assert_array_equal(result['requests']['tick'], [1, 2, 2, 3]) + np.testing.assert_array_equal(result['requests']['neuron_idx'], [3, 3, 19, 3]) + json.dumps(a, allow_nan=False) + + def test_high_seed_bits_preserved_and_bad_consumer_detected_before_draw(self): + for seed in (0, 17, (1 << 32) + 17, (1 << 63) - 1): + fake = FakeTorch(np.zeros((1, 2), np.float32)) + with patch.object(c, '_load_torch', return_value=fake): + result = c.generate_cs_requests(self.targets, self.rates, seed=seed, steps=1, cs_start_tick=0, cs_stop_tick=1, + device='cuda:0', max_uniform_draws=2) + self.assertEqual(result['audit']['generator_initial_seed'], seed) + self.assertEqual(fake.seeds, [seed]) + fake = FakeTorch(np.zeros((1, 2), np.float32), truncate=True) + with patch.object(c, '_load_torch', return_value=fake), self.assertRaisesRegex(ValueError, 'complete semantic seed'): + c.generate_cs_requests(self.targets, self.rates, seed=(1 << 32) + 17, steps=1, cs_start_tick=0, cs_stop_tick=1, + device='cuda:0', max_uniform_draws=2) + self.assertFalse(any(x[0] == 'rand' for x in fake.calls)) + + def test_invalid_seed_clock_or_rate_never_imports_torch(self): + for seed in (-1, 2**63, True, np.int64(3), 3.): + with patch.object(c, '_load_torch') as loader, self.assertRaises(ValueError): + c.generate_cs_requests(self.targets, self.rates, seed=seed, steps=5, cs_start_tick=0, cs_stop_tick=5, + device='cuda:0', max_uniform_draws=10) + loader.assert_not_called() + for start, stop in ((-1, 1), (2, 1), (0, 6), (False, 1)): + with patch.object(c, '_load_torch') as loader, self.assertRaises(ValueError): + c.generate_cs_requests(self.targets, self.rates, seed=3, steps=5, cs_start_tick=start, cs_stop_tick=stop, + device='cuda:0', max_uniform_draws=10) + loader.assert_not_called() + + def test_invalid_arrays_and_no_clipping_or_duplicate_merging(self): + for rates in (np.array([-1, 2], np.float32), np.array([np.nan, 2], np.float32), np.array([np.inf, 2], np.float32), + np.array([10001, 2], np.float32), self.rates.astype(np.float64), self.rates[:1]): + with self.assertRaises(ValueError): c.probabilities_f32(self.targets, rates) + for targets in (np.array([3, 3], np.int32), np.array([-1, 3], np.int32), np.array([166700, 3], np.int32), self.targets.astype(np.int64)): + with self.assertRaises(ValueError): c.probabilities_f32(targets, self.rates) + with self.assertRaises(ValueError): c.probabilities_f32(self.targets, self.rates, dt_ms=.2) + with self.assertRaises(ValueError): c.requests_from_forced(np.ones((2, 2), np.uint8), self.targets, cs_start_tick=0, cs_stop_tick=2) + + def test_no_cpu_fallback_or_changed_default_float_dtype(self): + for variant in ('cpu', 'unavailable', 'float64'): + fake = FakeTorch(np.zeros((1, 2), np.float32)); device = 'cuda:0' + if variant == 'cpu': device = 'cpu' + if variant == 'unavailable': fake.cuda.is_available = lambda: False + if variant == 'float64': fake.get_default_dtype = lambda: np.dtype(np.float64) + with patch.object(c, '_load_torch', return_value=fake), self.assertRaises(ValueError): + c.generate_cs_requests(self.targets, self.rates, seed=3, steps=1, cs_start_tick=0, cs_stop_tick=1, + device=device, max_uniform_draws=2) + self.assertFalse(any(x[0] == 'rand' for x in fake.calls)) + + def test_explicit_device_and_budget_required_and_budget_checked_before_torch(self): + common = dict(seed=3, steps=5, cs_start_tick=0, cs_stop_tick=1) + for missing in ({}, {'device': 'cuda:0'}, {'max_uniform_draws': 10}): + with patch.object(c, '_load_torch') as loader, self.assertRaises(TypeError): + c.generate_cs_requests(self.targets, self.rates, **common, **missing) + loader.assert_not_called() + for budget in (0, -1, 9, True, 10., np.int64(10)): + with patch.object(c, '_load_torch') as loader, self.assertRaises(ValueError): + c.generate_cs_requests(self.targets, self.rates, **common, device='cuda:0', max_uniform_draws=budget) + loader.assert_not_called() + # Full 5x2 matrix admission, not merely the 1-tick active CS window. + fake = FakeTorch(np.zeros((5, 2), np.float32)) + with patch.object(c, '_load_torch', return_value=fake): + result = c.generate_cs_requests(self.targets, self.rates, **common, device='cuda:1', max_uniform_draws=10) + self.assertEqual(result['audit']['execution_device'], 'cuda:1') + self.assertEqual(result['audit']['uniform_scalar_draws'], 10) + + def test_optional_device_uuid_recorded_as_reported_or_explicitly_unavailable(self): + fake = FakeTorch(np.zeros((1, 2), np.float32)) + fake.cuda.get_device_properties = lambda device: types.SimpleNamespace(uuid='SYNTHETIC-UUID', pci_bus_id='SYNTHETIC-PCI') + args = dict(seed=3, steps=1, cs_start_tick=0, cs_stop_tick=1, device='cuda:7', max_uniform_draws=2) + with patch.object(c, '_load_torch', return_value=fake): + audit = c.generate_cs_requests(self.targets, self.rates, **args)['audit'] + self.assertEqual(audit['device_identity'], {'uuid': 'SYNTHETIC-UUID', 'pci_bus_id': 'SYNTHETIC-PCI', 'query_error': None}) + self.assertFalse(audit['device_identity_verified_against_external_pin']) + def unavailable(device): raise RuntimeError('Synthetic property unavailable') + fake.cuda.get_device_properties = unavailable + with patch.object(c, '_load_torch', return_value=fake): + audit = c.generate_cs_requests(self.targets, self.rates, **args)['audit'] + self.assertIsNone(audit['device_identity']['uuid']) + self.assertEqual(audit['device_identity']['query_error'], 'RuntimeError: Synthetic property unavailable') + + def test_returned_audited_arrays_are_owned_readonly_and_inputs_not_frozen(self): + forced = np.ones((2, 2), np.bool_) + generated = c.requests_from_forced(forced, self.targets, cs_start_tick=0, cs_stop_tick=2) + merged = c.merge_requests(req([[0, 9]]), req([[1, 8]]), steps=2) + for value in (*generated['requests'].values(), *merged['requests'].values(), merged['source_mask']): + self.assertTrue(value.flags.owndata) + self.assertFalse(value.flags.writeable) + with self.assertRaises(ValueError): value[0] = 0 + self.assertTrue(forced.flags.writeable) + self.assertTrue(self.targets.flags.writeable) + + def test_empty_targets_and_p0_p1_are_explicit_valid_limits(self): + result = c.requests_from_forced(np.zeros((3, 0), np.bool_), np.empty(0, np.int32), cs_start_tick=0, cs_stop_tick=3) + self.assertEqual(result['requests']['tick'].dtype, np.int32); self.assertEqual(len(result['requests']['tick']), 0) + np.testing.assert_array_equal(c.probabilities_f32(self.targets, np.array([0, 10000], np.float32)), [0, 1]) + + def test_merging_disjoint_sources_is_sorted_without_mutating_inputs(self): + cs, us = req([[0, 9], [3, 4]]), req([[0, 2], [2, 5]]) + for value in [*cs.values(), *us.values()]: value.setflags(write=False) + result = c.merge_requests(cs, us, steps=4) + np.testing.assert_array_equal(result['requests']['tick'], [0, 0, 2, 3]) + np.testing.assert_array_equal(result['requests']['neuron_idx'], [2, 9, 5, 4]) + np.testing.assert_array_equal(result['source_mask'], [2, 1, 2, 1]) + self.assertEqual(result['audit']['explicit_boolean_overlap_collapses'], 0) + + def test_collision_rejected_by_default_or_explicitly_audited_boolean_union(self): + cs, us = req([[0, 9], [3, 4]]), req([[0, 9], [2, 5], [3, 4]]) + with self.assertRaises(c.RequestConflictError) as error: c.merge_requests(cs, us, steps=4) + self.assertEqual(error.exception.audit['cross_source_conflict_count'], 2) + result = c.merge_requests(cs, us, steps=4, conflict_policy='boolean_union') + np.testing.assert_array_equal(result['source_mask'], [3, 2, 3]) + a = result['audit']; self.assertEqual((a['CS_attempts'], a['US_attempts'], a['output_requests']), (2, 3, 3)) + self.assertEqual(a['explicit_boolean_overlap_collapses'], 2) + self.assertEqual([x['combined_boolean_requests'] for x in a['conflicts']], [1, 1]) + json.dumps(a, allow_nan=False) + + def test_within_source_duplicate_unsorted_and_bad_range_never_deduped(self): + for rows in ([[0, 3], [0, 3]], [[1, 3], [0, 3]], [[4, 3]], [[0, -1]]): + with self.assertRaises(ValueError): c.merge_requests(req(rows), req([]), steps=4, conflict_policy='boolean_union') + with self.assertRaises(ValueError): c.merge_requests(req([]), req([]), steps=4, conflict_policy='clip') + + def test_frozen_AK_pure_conversion_AST_matches_for_full_window(self): + local = Path(__file__).resolve().parent.parent / 'apl-timing-validation-v1/encoder_inputs.py' + raw = local.read_bytes(); self.assertEqual(hashlib.sha256(raw).hexdigest(), c.AK_ENCODER_SHA) + tree = ast.parse(raw); node = next(n for n in tree.body if isinstance(n, ast.FunctionDef) and n.name == 'requests_from_forced') + namespace = {'np': np}; exec(compile(ast.Module(body=[node], type_ignores=[]), str(local), 'exec'), namespace) + forced = np.array([[True, True], [False, True], [True, False]], np.bool_) + expected = namespace['requests_from_forced'](forced, self.targets) + actual = c.requests_from_forced(forced, self.targets, cs_start_tick=0, cs_stop_tick=3)['requests'] + np.testing.assert_array_equal(expected, np.column_stack([actual['tick'], actual['neuron_idx']])) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_compare.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_compare.py new file mode 100644 index 0000000..6d60ee8 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_compare.py @@ -0,0 +1,308 @@ +"""Synthetic CPU array/receipt mutation tests; no files or neural execution.""" +import copy +import hashlib +import json +import unittest +from unittest import mock + +import numpy as np + +import conditioning_preflight_compare as P +from test_engineering_compare import synthetic_parts + + +def rehash(receipt): + receipt["payload_sha256"] = P._sha({k: v for k, v in receipt.items() if k != "payload_sha256"}) + + +def pair(*, active=True): + reference = synthetic_parts([64], active=active)[0] + light = synthetic_parts([64], active=active, traces=False, queues=False)[0] + for record, mode in ((reference, "reference"), (light, "light")): + summary = record["summary"] + summary.update(edges=6, explicit_steps_executed=64, within_trial_weight_updates=False, + hidden_early_stop=False, entire_neural_trial_executed=True, record_history=False, + state_units="SI volts", first_failure_tick=-1, counts_match_sparse_events=True, + total_spikes=len(record["state"]["step"]), requested=len(record["requests"]), + accepted_requests=int(record["requests"][:, 3].sum()), rejected_requests=int(record["requests"][:, 4].sum()), + full_APL_history_recorded=mode == "reference", full_tick_state_hashes_recorded=mode == "reference", + full_queue_hashes_recorded=mode == "reference", full_sequence_bitwise_audit_performed=mode == "reference", + arriving_edges=sum(row["count"] for row in reference["queues"]), + accepted_arriving_edges=sum(row["accepted_count"] for row in reference["queues"]), + input_sha256={"base_weights.npy": "a" * 64, "coefficients.json": "b" * 64, + "requests.npz": "c" * 64, "spec.json": "d" * 64}) + record["terminal_origin"] = "recorded_terminal_queues" + return reference, light, reference["requests"][:, :2].astype(np.int32) + + +def delta_fixture(): + before = np.zeros(P.CANDIDATE_COUNT, np.float32) + after = before.copy() + after[1] = np.float32(-.001) + receipt = dict(schema="phase4an-conditioning-update-draft-v1", + status="DRAFT-COMPUTED-EPOCH-UPDATE-NOT-INSTALLED", + source_pins={"eligibility_bridge_sha256": P.ELIGIBILITY_BRIDGE_SHA256, "phase4j_sha256": P.PHASE4J_SHA256}, + input_arrays={"delta_before": P._evidence(before)}, outputs={"delta_after": P._evidence(after)}, + command={"D_scheduled": 1.0, "frozen": False}, + semantics={"application": "epoch_boundary_after_all_recorded_ticks; affects subsequent epoch only", + "delta_dtype": "float32", "delta_units": "mV", "D_definition": "artificial scheduled scalar", + "actual_DAN_delivery_observed": False, "actual_DAN_delivery_used_to_gate": False}, + counts={"candidates": P.CANDIDATE_COUNT, "actual_bitwise_changed_edges": 1, "actual_value_changed_edges": 1}, + weights_installed=False, base_modified=False, brain_dynamics_executed=False, + scientific_learning_pass_claimed=False, candidate_generated=False) + rehash(receipt) + manifest = dict(schema="phase4an-overlay-v1", overlay_name="learned", overlay_sha256="1" * 64, + mask_path="/synthetic/mask.npy", mask_sha256=P.MASK_SHA256, + base_candidate_path="/synthetic/base.npy", base_candidate_sha256="2" * 64, + aj_model_dir="/synthetic/apl-timing-lab-v1", am_overlay_path="/synthetic/overlay.py", + am_overlay_sha256=P.AM_OVERLAY_SHA256, parent_snapshot_sha256="3" * 64, + eligibility_v2_source_sha256=P.PHASE4J_SHA256) + return before, after, after.copy(), receipt, manifest + + +class EpochPairTests(unittest.TestCase): + def setUp(self): + # Pure three-neuron fixtures; production constants remain full MaleCNS. + self.patches = [mock.patch.object(P, "N", 3), mock.patch.object(P, "E", 6), mock.patch.object(P, "N_KC", 2)] + for patch in self.patches: + patch.start() + self.addCleanup(patch.stop) + + def compare(self, reference, light, requests): + return P.compare_epoch_pair(reference, light, expected_requests=requests, + expected_steps=64, expected_effective_weight_sha256="e" * 64) + + def test_exact_actual_arrays_with_explicit_light_limits(self): + reference, light, requests = pair() + report = self.compare(reference, light, requests) + self.assertTrue(report["pass_all"], report["failed_checks"]) + self.assertTrue(report["numerical_exact"]) + self.assertTrue(report["nonvacuous_activity"]) + self.assertFalse(report["light_intermediate_states_observed"]) + self.assertFalse(report["light_per_tick_V_g_ref_identity_claimed"]) + self.assertEqual(report["recorded_pair_availability"], + {"APL_traces": False, "state_hashes": False, "queue_hashes": False}) + self.assertFalse(report["external_file_seals_process_and_input_authority_verified_here"]) + self.assertFalse(report["learning_pass_claimed"]) + self.assertFalse(report["candidate_generated"]) + json.dumps(report, allow_nan=False) + + def test_equal_quiet_records_are_not_new_activity_or_learning_pass(self): + reference, light, requests = pair(active=False) + report = self.compare(reference, light, requests) + self.assertTrue(report["numerical_exact"]) + self.assertFalse(report["nonvacuous_activity"]) + self.assertTrue(report["activity_is_descriptive_not_a_new_gate"]) + self.assertFalse(report["learning_pass_claimed"]) + + def test_signed_zero_final_voltage_difference_fails(self): + reference, light, requests = pair() + light["state"]["g"][0] = np.float64(0.) + report = self.compare(reference, light, requests) + self.assertFalse(report["pass_all"]) + self.assertIn("pair.state.g", report["failed_checks"]) + + def test_equal_but_wrong_original_requests_fail(self): + reference, light, requests = pair() + wrong = requests.copy() + wrong[0, 1] = 1 + report = self.compare(reference, light, wrong) + self.assertFalse(report["pass_all"]) + self.assertIn("reference.input_request_array", report["failed_checks"]) + self.assertIn("light.input_request_array", report["failed_checks"]) + + def test_reference_missing_diagnostics_cannot_be_excused_by_light(self): + for key in ("traces", "state_hashes", "queues"): + with self.subTest(key=key): + reference, light, requests = pair() + reference[key] = {} if key == "traces" else None + report = self.compare(reference, light, requests) + self.assertFalse(report["pass_all"]) + self.assertIn("reference.full_diagnostics", report["failed_checks"]) + + def test_partial_or_corrupt_array_schema_fails_closed(self): + reference, light, requests = pair() + del reference["traces"]["drive"] + with self.assertRaises(ValueError): + self.compare(reference, light, requests) + reference, light, requests = pair() + light["state"]["v"][0] = np.nan + with self.assertRaises(ValueError): + self.compare(reference, light, requests) + + def test_reported_modes_population_delay_and_scope_fail_closed(self): + mutations = (("nodes", 2), ("edges", 7), ("apl_delay_steps", 17), + ("diagnostic_mode", "reference"), ("first_failure_tick", 0), + ("within_trial_weight_updates", True), ("explicit_steps_executed", 63), + ("all_finite", 1), ("parent_checkpoint_manifest_sha256", "f" * 64)) + for key, value in mutations: + with self.subTest(key=key): + reference, light, requests = pair() + light["summary"][key] = value + self.assertFalse(self.compare(reference, light, requests)["pass_all"]) + + def test_effective_weight_and_base_coefficient_hash_binding(self): + for mutation in ("effective", "base_weights.npy", "coefficients.json"): + with self.subTest(mutation=mutation): + reference, light, requests = pair() + if mutation == "effective": + for record in (reference, light): + record["summary"]["effective_weights_raw_sha256"] = "f" * 64 + else: + light["summary"]["input_sha256"][mutation] = "f" * 64 + self.assertFalse(self.compare(reference, light, requests)["pass_all"]) + + def test_performance_is_not_compared_and_path_only_spec_identity_is_external(self): + reference, light, requests = pair() + reference["summary"].update(wall_seconds=1., peak_allocated_vram_bytes=1) + light["summary"].update(wall_seconds=99., peak_allocated_vram_bytes=999) + light["summary"]["input_sha256"]["spec.json"] = "f" * 64 + report = self.compare(reference, light, requests) + self.assertTrue(report["pass_all"]) + self.assertFalse(report["external_file_seals_process_and_input_authority_verified_here"]) + + def test_declared_light_full_diagnostics_or_derived_terminal_is_rejected(self): + for mutation in ("recorded", "origin"): + reference, light, requests = pair() + if mutation == "recorded": + light["summary"]["full_tick_state_hashes_recorded"] = True + else: + light["terminal_origin"] = "AM_spike_ring_derived_from_fresh_events_APL_bits_from_timing" + self.assertFalse(self.compare(reference, light, requests)["pass_all"]) + + def test_actual_summary_accounting_cannot_be_self_reported_wrong(self): + for key in ("requested", "accepted_requests", "rejected_requests", "total_spikes", "arriving_edges", "accepted_arriving_edges"): + with self.subTest(key=key): + reference, light, requests = pair() + light["summary"][key] += 1 + self.assertFalse(self.compare(reference, light, requests)["pass_all"]) + + def test_invalid_planned_request_seed_arrays_are_not_silently_coerced(self): + reference, light, requests = pair() + for bad in (requests.astype(np.int64), requests[::-1], np.concatenate((requests[:1], requests))): + with self.assertRaises(ValueError): + self.compare(reference, light, bad) + with self.assertRaises(ValueError): + P.compare_epoch_pair(reference, light, expected_requests=requests, expected_steps=True, + expected_effective_weight_sha256="e" * 64) + + +class DeltaBindingTests(unittest.TestCase): + def audit(self, values): + before, after, installed, receipt, manifest = values + return P.audit_delta_binding(before, after, installed, update_receipt=receipt, + parent_snapshot_sha256="3" * 64, child_manifest=manifest) + + def test_exact_array_binding_preserves_explicit_external_authority_limits(self): + values = delta_fixture() + for value in values[:3]: + value.setflags(write=False) + original = [value.tobytes() for value in values[:3]] + result = self.audit(values) + self.assertTrue(result["pass_all"], result["failed_checks"]) + self.assertEqual(result["actual_changed_edges"], 1) + for name in ("eligibility_v2_independently_recomputed_here", "full_mask_identity_verified_here", + "file_bytes_and_parent_snapshot_authority_verified_here", "installation_in_runtime_verified_here", + "actual_DAN_delivery_verified_here", "learning_pass_claimed"): + self.assertFalse(result[name]) + self.assertEqual([value.tobytes() for value in values[:3]], original) + json.dumps(result, allow_nan=False) + + def test_parent_overlay_file_sha_is_not_raw_delta_or_checkpoint_sha(self): + values = delta_fixture() + self.assertNotEqual(values[4]["overlay_sha256"], P._evidence(values[1])["raw_c_order_sha256"]) + self.assertTrue(self.audit(values)["pass_all"]) + for false_parent in (P._evidence(values[0])["raw_c_order_sha256"], "4" * 64): + values[4]["parent_snapshot_sha256"] = false_parent + result = self.audit(values) + self.assertFalse(result["pass_all"]) + self.assertIn("manifest.parent_overlay_file_sha", result["failed_checks"]) + + def test_installed_delta_signed_zero_and_row_mutation_rejected(self): + for mutation in ("zero", "row"): + values = delta_fixture() + if mutation == "zero": + values[2][0] = np.float32(-0.) + else: + values[2][1], values[2][2] = values[2][2], values[2][1] + result = self.audit(values) + self.assertFalse(result["pass_all"]) + self.assertIn("computed_installed_delta_bitwise", result["failed_checks"]) + + def test_rehashed_receipt_must_still_bind_supplied_arrays(self): + for location, key in (("input_arrays", "delta_before"), ("outputs", "delta_after")): + values = delta_fixture() + values[3][location][key]["raw_c_order_sha256"] = "f" * 64 + rehash(values[3]) + result = self.audit(values) + self.assertFalse(result["pass_all"]) + self.assertIn("receipt." + key, result["failed_checks"]) + + def test_rehashed_false_count_and_boolean_integer_ambiguity_rejected(self): + for field, value in (("actual_bitwise_changed_edges", 2), ("actual_value_changed_edges", True), ("candidates", float(P.CANDIDATE_COUNT))): + values = delta_fixture() + values[3]["counts"][field] = value + rehash(values[3]) + self.assertFalse(self.audit(values)["pass_all"]) + + def test_stale_receipt_or_changed_frozen_rule_manifest_rejected(self): + for mutation in ("hash", "v2", "mask", "AMhelper", "extra", "wrong_name"): + values = delta_fixture() + if mutation == "hash": + values[3]["payload_sha256"] = "0" * 64 + elif mutation == "extra": + values[4]["unregistered_override"] = True + elif mutation == "wrong_name": + values[4]["overlay_name"] = "static25" + else: + key = {"v2": "eligibility_v2_source_sha256", "mask": "mask_sha256", "AMhelper": "am_overlay_sha256"}[mutation] + values[4][key] = "0" * 64 + self.assertFalse(self.audit(values)["pass_all"]) + + def test_zero_D_and_frozen_require_exact_no_update_including_signed_zero(self): + for mode in ("D0", "frozen"): + values = delta_fixture() + values[3]["command"]["D_scheduled" if mode == "D0" else "frozen"] = 0.0 if mode == "D0" else True + rehash(values[3]) + result = self.audit(values) + self.assertFalse(result["pass_all"]) + self.assertIn("zero_D_or_frozen_identity", result["failed_checks"]) + values[1][:] = values[0] + values[2][:] = values[0] + values[3]["outputs"]["delta_after"] = P._evidence(values[1]) + values[3]["counts"].update(actual_value_changed_edges=0, actual_bitwise_changed_edges=0) + rehash(values[3]) + self.assertTrue(self.audit(values)["pass_all"]) + + def test_actual_DAN_or_immediate_US_update_claim_does_not_match_interface(self): + for field, value in (("actual_DAN_delivery_used_to_gate", True), ("actual_DAN_delivery_observed", True), + ("application", "at US tick"), ("delta_units", "V")): + values = delta_fixture() + values[3]["semantics"][field] = value + rehash(values[3]) + self.assertFalse(self.audit(values)["pass_all"]) + + def test_wrong_dtype_shape_nonfinite_or_positive_delta_rejected(self): + values = delta_fixture() + with self.assertRaises(ValueError): + P.audit_delta_binding(values[0].astype(np.float64), values[1], values[2], update_receipt=values[3], + parent_snapshot_sha256="3" * 64, child_manifest=values[4]) + values = list(delta_fixture()) + values[0] = values[0][:-1] + with self.assertRaises(ValueError): + self.audit(values) + values = delta_fixture() + values[0][0] = np.nan + with self.assertRaises(ValueError): + self.audit(values) + values = delta_fixture() + values[1][1] = values[2][1] = np.float32(.001) + values[3]["outputs"]["delta_after"] = P._evidence(values[1]) + rehash(values[3]) + result = self.audit(values) + self.assertFalse(result["pass_all"]) + self.assertIn("delta.nonpositive", result["failed_checks"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_inputs.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_inputs.py new file mode 100644 index 0000000..81072b1 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_inputs.py @@ -0,0 +1,229 @@ +"""Pure/synthetic preflight tests: no actual rates, CUDA draws, or brain run.""" +import copy +import hashlib +import io +import json +from pathlib import Path +import tempfile +import types +import unittest +from unittest.mock import patch + +import numpy as np +import pandas as pd + +import conditioning_preflight_inputs as p + + +def synthetic_tables(): + names = ['synthetic-' + str(i) for i in range(12)] + names[6], names[7] = '4-ethylphenol', '3-hexanone' + ids = ['synthetic-id-' + str(i) for i in range(12)] + panel = pd.DataFrame(dict(odor_id=ids, odor_name=names, + split=['calibration'] * 6 + ['heldout'] * 6, mapped_response_sum=np.arange(1, 13, dtype=float), + formal_label=[''] * 6 + ['ODOR_A', 'ODOR_B'] + [''] * 4)) + selection = dict(selected_before_KC_simulation=True, glomeruli_order=['G2', 'G1'], + ODOR_A=dict(odor_id=ids[6], odor_name=names[6]), ODOR_B=dict(odor_id=ids[7], odor_name=names[7])) + vectors = {oid: np.array([.5, .25], np.float32) for oid in ids} + mapping = pd.DataFrame(dict(glomerulus=['G2', 'G2', 'G2', 'G1', 'G1'], + mapping_confidence=['exact', 'alias-normalized', 'exact', 'exact', 'unmatched'], + neuron_idx=[19., 3., 19., 8., 100.])) + config = dict(odor_encoder=dict(max_total_rate_per_glomerulus_hz=400., modes=['physiological', 'matched_energy'])) + return selection, panel, vectors, mapping, config + + +def sparse(rows): + values = np.asarray(rows, np.int32).reshape(-1, 2) + return {'tick': values[:, 0].copy(), 'neuron_idx': values[:, 1].copy()} + + +class PreflightTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.plan = p.build_preflight_plan() + cls.epoch = cls.plan['selected_epochs'][0] + source_parent = Path(__file__).resolve().parent.parent + candidates = (source_parent / 'phase4l.py', source_parent / 'models/male-cns-lif/phase4l.py') + path = next((candidate for candidate in candidates if candidate.is_file()), None) + if path is None: + raise FileNotFoundError('Pinned L source missing at both known server/local paths: ' + repr(candidates)) + cls.source_raw = path.read_bytes() + if hashlib.sha256(cls.source_raw).hexdigest() != p.SOURCE_PINS[p.SOURCE_REL]: + raise ValueError('Selected server/local L source differs from the frozen SHA') + cls.drive, cls.ast_sha = p._extract_drive(cls.source_raw, str(path)) + cls.tables, _ = p._rate_tables(*synthetic_tables(), cls.drive) + + def test_fixed_full_plan_master_first_two_real_epochs_and_12_calls(self): + full = self.plan['full_trial_plan'] + self.assertEqual(full['plan']['master'], 20260930) + self.assertEqual(full['payload_sha256'], 'a8e8620875e8213f3cc3b4f153fc5c06e1351bf1d91c7edaf4f18c1e2cdef62e') + self.assertEqual([e['epoch_index'] for e in self.plan['selected_epochs']], [0, 1]) + self.assertEqual([e['cs']['cue'] for e in self.plan['selected_epochs']], ['A', 'A']) + self.assertEqual([e['cs']['seed'] for e in self.plan['selected_epochs']], [5717173809506784725, 4342084263790487177]) + self.assertTrue(all(e['us']['requested'] for e in self.plan['selected_epochs'])) + self.assertEqual(self.plan['total_calls'], 12) + self.assertEqual(sum(r['steps'] for r in self.plan['calls']), 14400) + self.assertEqual(len({r['row_id'] for r in self.plan['calls']}), 12) + self.assertFalse(self.plan['complete_conditioning_authorized']) + + def test_plan_rows_share_inputs_but_keep_D_and_freeze_distinct(self): + for row in self.plan['calls']: + self.assertEqual(row['epoch_id'], self.plan['selected_epochs'][row['input_epoch_index']]['epoch_id']) + self.assertEqual(row['d_command'], 0. if row['arm'] == 'D0' else 1.) + self.assertEqual(row['frozen'], row['arm'] == 'frozen') + self.assertEqual(row['previous_delta_row_id'], None if row['input_epoch_index'] == 0 else + f"{row['arm']}--{row['diagnostic_mode']}--epoch-000") + self.assertEqual(self.plan['input_generation_calls'], 2) + + def test_whole_plan_validator_rejects_wrong_master_and_row_mutation(self): + # Reuse an already independently rebuilt expected fixture here; the + # production validator calls its full builder, not a count-only gate. + with patch.object(p, 'build_preflight_plan', return_value=self.plan) as rebuild: + self.assertTrue(p.validate_preflight_plan(copy.deepcopy(self.plan))) + bad = copy.deepcopy(self.plan); bad['master'] = 461 + with self.assertRaises(ValueError): p.validate_preflight_plan(bad) + bad = copy.deepcopy(self.plan); bad['calls'][0]['d_command'] = 0. + with self.assertRaises(ValueError): p.validate_preflight_plan(bad) + self.assertEqual(rebuild.call_count, 3) + + def test_actual_frozen_function_AST_preserves_mapping_order_rates_and_dtype(self): + table = self.tables['A'] + np.testing.assert_array_equal(table['targets'], [19, 3, 8]) + self.assertEqual(table['targets'].dtype, np.int32) + np.testing.assert_array_equal(table['rates_hz'], np.array([100, 100, 100], np.float32)) + self.assertEqual(table['source_panel_row'], 6) + self.assertEqual(table['source_panel_split'], 'heldout') # original static label only + self.assertFalse(table['targets'].flags.writeable) + self.assertFalse(table['rates_hz'].flags.writeable) + self.assertEqual(hashlib.sha256(self.source_raw).hexdigest(), p.SOURCE_PINS[p.SOURCE_REL]) + self.assertEqual(len(self.ast_sha), 64) + + def test_static_mapping_fails_closed_on_selection_order_schema_or_duplicate_PN(self): + for kind in ('name', 'split', 'vector', 'duplicate'): + args = list(synthetic_tables()) + if kind == 'name': args[0]['ODOR_A']['odor_name'] = 'different' + if kind == 'split': args[1].loc[0, 'split'] = 'heldout' + if kind == 'vector': args[2][args[0]['ODOR_A']['odor_id']] = np.array([np.nan, .25], np.float32) + if kind == 'duplicate': args[3].loc[3, 'neuron_idx'] = 19. + with self.assertRaises(ValueError): p._rate_tables(*args, type(self).drive) + + def test_loader_checks_all_static_bytes_then_uses_original_AST_no_old_import(self): + selection, panel, vectors, mapping, config = synthetic_tables() + buffer = io.BytesIO(); np.savez(buffer, glomeruli=np.array(selection['glomeruli_order']), **vectors) + payload = { + p.SOURCE_REL: self.source_raw, p.CONFIG_REL: json.dumps(config).encode(), + p.STATIC + 'odor-encoder/frozen-odor-panel.parquet': b'SYNTHETIC-PANEL', + p.STATIC + 'odor-encoder/mapped-glomerular-vectors.npz': buffer.getvalue(), + p.STATIC + 'odor-encoder/selection.json': json.dumps(selection).encode(), + p.STATIC + 'receptor-mapping/olfactory-mapping.parquet': b'SYNTHETIC-MAPPING', + } + pins = {k: hashlib.sha256(v).hexdigest() for k, v in payload.items()} + with tempfile.TemporaryDirectory() as td: + for name, raw in payload.items(): + target = Path(td) / name; target.parent.mkdir(parents=True, exist_ok=True); target.write_bytes(raw) + # Synthetic config is JSON (a YAML subset); local runtime lacks + # PyYAML. This mocks parsing only, not any scientific input/RNG. + yaml_mock = types.SimpleNamespace(safe_load=json.loads) + with patch.object(p, 'SOURCE_PINS', pins), patch.object(pd, 'read_parquet', side_effect=[panel, mapping]), \ + patch.dict('sys.modules', {'yaml': yaml_mock}): + result = p.load_rate_tables(td) + self.assertEqual(result['audit']['static_sources'], pins) + self.assertFalse(result['audit']['historical_dynamic_results_read']) + self.assertEqual(result['audit']['random_draws'], 0) + self.assertEqual(result['tables']['B']['odor_name'], '3-hexanone') + (Path(td) / p.CONFIG_REL).write_bytes(b'CHANGED') + with patch.object(p, 'SOURCE_PINS', pins), patch.object(pd, 'read_parquet') as parse, \ + patch.dict('sys.modules', {'yaml': yaml_mock}), self.assertRaises(ValueError): + p.load_rate_tables(td) + parse.assert_not_called() + + def test_generate_delegates_full_shape_full_seed_and_shared_US_without_real_draw(self): + fake = dict(requests=sparse([[2, 19], [999, 8]]), audit={'synthetic_sampler': True}) + dan = np.array([80, 90], np.int32) + with patch.object(p.inputs, 'generate_cs_requests', return_value=fake) as sampler: + result = p.generate_epoch_inputs(self.epoch, self.tables, dan, device='cuda:1', max_uniform_draws=3600) + args, kwargs = sampler.call_args + self.assertIs(args[0], self.tables['A']['targets']) + self.assertEqual(kwargs, dict(seed=5717173809506784725, steps=1200, cs_start_tick=0, cs_stop_tick=1000, + device='cuda:1', max_uniform_draws=3600)) + np.testing.assert_array_equal(result['requests']['tick'], [2, 900, 900, 999]) + np.testing.assert_array_equal(result['requests']['neuron_idx'], [19, 80, 90, 8]) + self.assertTrue(result['audit']['US_shared_across_arms']) + self.assertFalse(result['audit']['brain_reset_called']) + + def test_explicit_conflict_union_and_default_error_preserve_US_attempts(self): + fake = dict(requests=sparse([[900, 19]]), audit={'synthetic_sampler': True}) + with patch.object(p.inputs, 'generate_cs_requests', return_value=fake): + with self.assertRaises(p.inputs.RequestConflictError): + p.generate_epoch_inputs(self.epoch, self.tables, np.array([19], np.int32), device='cuda:0', max_uniform_draws=3600) + value = p.generate_epoch_inputs(self.epoch, self.tables, np.array([19], np.int32), + device='cuda:0', max_uniform_draws=3600, conflict_policy='boolean_union') + self.assertEqual(value['audit']['composition']['cross_source_conflict_count'], 1) + np.testing.assert_array_equal(value['source_mask'], [3]) + + def test_no_US_epoch_still_draws_CS_but_no_DAN_pulse(self): + epoch = copy.deepcopy(self.epoch) + epoch['us'].update(requested=False, tick=None, request_rule='none'); epoch['D_command'] = 0. + with patch.object(p.inputs, 'generate_cs_requests', return_value=dict(requests=sparse([]), audit={})): + value = p.generate_epoch_inputs(epoch, self.tables, np.array([80], np.int32), device='cuda:0', max_uniform_draws=3600) + self.assertEqual(len(value['us_requests']['tick']), 0) + + def test_invalid_targets_and_mutated_rates_rejected_before_sampling(self): + with patch.object(p.inputs, 'generate_cs_requests') as sampler: + for dan in (np.array([2, 2], np.int32), np.array([9, 2], np.int32), np.array([], np.int32), np.array([2], np.int64)): + with self.assertRaises(ValueError): + p.generate_epoch_inputs(self.epoch, self.tables, dan, device='cuda:0', max_uniform_draws=3600) + tables = copy.deepcopy(self.tables); tables['A']['rates_hz'][0] = 3. + with self.assertRaises(ValueError): + p.generate_epoch_inputs(self.epoch, tables, np.array([80], np.int32), device='cuda:0', max_uniform_draws=3600) + sampler.assert_not_called() + + def original_spec(self): + return dict(name='old-case', kind='fullgraph', steps=1000, dt_ms=.1, duration_ms=100., + model={'immutable': 'synthetic model'}, graph_dir='/old/graph', apl_bundle_dir='/old/apl', + coefficients_path='/old/coeff.json', science_python='/old/venv/bin/python', + apl_delay_steps=18, precision='float64', apl_precision='float32', record_history=False, + training=False, validation_cohort='old_cohort', odor_index=0, expected_external_events=123., + odor_id='old-id', odor_name='old-name', source_panel_row=0, source_panel_split='calibration', + rate_sum_hz=999., target_count=100, seed=22, budget_root='/old/experiment', output_budget_bytes=100) + + def test_derive_spec_changes_only_explicit_input_scope_and_clock_budget_fields(self): + old = self.original_spec(); before = copy.deepcopy(old) + value, audit = p.derive_case_spec(old, self.plan['calls'][0], self.epoch, self.tables['A'], + brain_reset_seed=20260930, budget_root='/new/preflight', output_budget_bytes=10000) + self.assertEqual(old, before) + self.assertEqual((value['steps'], value['duration_ms'], value['max_steps']), (1200, 120., 1200)) + self.assertEqual(value['seed'], 20260930) + self.assertEqual(value['cs_input_seed'], 5717173809506784725) + for key in ('model', 'graph_dir', 'apl_bundle_dir', 'coefficients_path', 'science_python'): + self.assertEqual(value[key], old[key]) + self.assertFalse(value['within_trial_weight_updates']) + self.assertEqual(value['odor_name'], '4-ethylphenol') + self.assertNotIn('validation_cohort', value); self.assertNotIn('training', value) + self.assertEqual(set(audit['removed_stale_input_metadata']), {'validation_cohort', 'training', 'odor_index', 'expected_external_events'}) + self.assertFalse(audit['full_conditioning_authorized']) + json.dumps(audit, allow_nan=False) + + def test_seed_or_neural_continuation_or_budget_alias_invalid(self): + args = dict(brain_reset_seed=3, budget_root='/new/preflight', output_budget_bytes=10000) + for seed in (-1, 2**31, self.epoch['cs']['seed'], True, np.int32(3)): + with self.assertRaises(ValueError): + p.derive_case_spec(self.original_spec(), self.plan['calls'][0], self.epoch, self.tables['A'], **{**args, 'brain_reset_seed': seed}) + for root in ('/old/experiment/new', '/old', '/new/../old', 'relative'): + with self.assertRaises(ValueError): + p.derive_case_spec(self.original_spec(), self.plan['calls'][0], self.epoch, self.tables['A'], **{**args, 'budget_root': root}) + old = self.original_spec(); old['parent_checkpoint_manifest_sha256'] = 'a' * 64 + with self.assertRaises(ValueError): p.derive_case_spec(old, self.plan['calls'][0], self.epoch, self.tables['A'], **args) + + def test_row_command_mismatch_and_rate_label_mismatch_fail_closed(self): + row = copy.deepcopy(self.plan['calls'][0]); row['d_command'] = 0. + with self.assertRaises(ValueError): + p.derive_case_spec(self.original_spec(), row, self.epoch, self.tables['A'], + brain_reset_seed=3, budget_root='/new/preflight', output_budget_bytes=10000) + with self.assertRaises(ValueError): + p.derive_case_spec(self.original_spec(), self.plan['calls'][0], self.epoch, self.tables['B'], + brain_reset_seed=3, budget_root='/new/preflight', output_budget_bytes=10000) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_prepare.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_prepare.py new file mode 100644 index 0000000..19c03a1 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_preflight_prepare.py @@ -0,0 +1,109 @@ +"""CPU-only coordinator contract tests; no real brain/input sampling.""" +import ast +from pathlib import Path +import tempfile +import unittest +from unittest.mock import patch + +import numpy as np + +import conditioning_preflight_prepare as P +import conditioning_preflight_workflow as W + + +class CoordinatorTests(unittest.TestCase): + def test_npz_exclusive_roundtrip(self): + with tempfile.TemporaryDirectory() as d: + path = Path(d) / 'x.npz' + a = np.asarray([0, 3], np.int32) + P.save_npz(path, tick=a) + self.assertEqual(W.npz(path)['tick'].tobytes(), a.tobytes()) + with self.assertRaises(FileExistsError): P.save_npz(path, tick=a) + + def test_exact_pins_and_mutation(self): + with tempfile.TemporaryDirectory() as d: + p = Path(d) / 'input' + p.write_bytes(b'original') + pins = {str(p): P.digest(p)} + P.verify_pins(pins) + p.write_bytes(b'changed') + with self.assertRaisesRegex(ValueError, 'Pinned file'): P.verify_pins(pins) + + def test_rows_exact_unique(self): + row = dict(row_id='D1--reference--epoch-000') + self.assertEqual(W.row_by({'plan': {'calls': [row]}}, row['row_id']), row) + for rows in ([], [row, row]): + with self.assertRaises(ValueError): W.row_by({'plan': {'calls': rows}}, row['row_id']) + + def test_locations_reject_path_traversal(self): + for value in ('../x', '/x', 'a/b', 'a\\b', '', None): + with self.assertRaises(ValueError): W.locations(value) + + def test_plan_descriptor_avoids_huge_payload_duplication(self): + value = dict(full_trial_plan={'large': [1]}, payload_sha256='x', calls=[{'row_id': 'a'}]) + self.assertEqual(P.plan_descriptor(value), {'calls': [{'row_id': 'a'}]}) + self.assertIn('full_trial_plan', value) + + def test_original_AST_rejects_unanchored_bytes(self): + with self.assertRaisesRegex(ValueError, 'L source changed'): + W.original_sampler_nodes(b'def run_rates(): pass') + + def test_original_AST_if_local_frozen_source_available(self): + # Both repository checkout and local source mirror are supported; this + # test never reads historical dynamics or executes the extracted code. + candidates = [Path(__file__).resolve().parents[1] / 'phase4l.py', + Path(__file__).resolve().parents[1] / 'models/male-cns-lif/phase4l.py'] + source = next((p for p in candidates if p.is_file()), None) + self.assertIsNotNone(source, 'Original L source required for actual AST extraction test') + nodes = W.original_sampler_nodes(source.read_bytes()) + self.assertEqual(len(nodes), 4) + self.assertNotIn('reset', ast.unparse(ast.Module(body=nodes, type_ignores=[]))) + + def test_registration_external_hash_rejection_before_parse(self): + with tempfile.TemporaryDirectory() as d: + exp = Path(d); (exp / 'protocol').mkdir() + P.save(exp / 'protocol/preregistered.json', {'fake': True}) + with patch.object(P, 'EXP', exp): + with self.assertRaisesRegex(ValueError, 'external anchor'): + P.read_registration('0' * 64) + + def test_initial_overlay_has_no_parent_process_lookup(self): + expected = dict(overlay_file='/immutable/zero.npz', overlay_manifest='/immutable/zero.json') + self.assertIs(W.before_overlay({'initial_overlay': expected}, 'unused', {'previous_delta_row_id': None}), expected) + + def test_wrong_parent_arm_rejected_before_runtime_read(self): + parent = dict(row_id='parent', arm='D0', diagnostic_mode='light', input_epoch_index=0) + child = dict(previous_delta_row_id='parent', arm='D1', diagnostic_mode='light', input_epoch_index=1) + with self.assertRaisesRegex(ValueError, 'cross-epoch'): + W.before_overlay({'plan': {'calls': [parent]}}, 'unused', child) + + def test_budget_refuses_overrun_without_delete(self): + with tempfile.TemporaryDirectory() as d: + exp = Path(d); p = exp / 'keep' + p.write_bytes(b'1234') + with patch.object(W, 'EXP', exp): + with self.assertRaisesRegex(ValueError, 'Resource cap'): + W.budget({'budget': {'output_budget_bytes': 1, 'minimum_free_reserve_bytes': 1}}) + self.assertEqual(p.read_bytes(), b'1234') + + def test_partial_log_is_not_retried(self): + with tempfile.TemporaryDirectory() as d: + exp = Path(d); (exp / 'logs').mkdir() + (exp / 'logs/input-generation.stdout.log').write_bytes(b'partial') + with patch.object(W, 'EXP', exp), patch.object(W.subprocess, 'run') as run: + with self.assertRaisesRegex(ValueError, 'never retry'): W.child_process({}, 'sha', 'materialize') + run.assert_not_called() + + def test_admission_blocks_stopped_complete_or_sealed(self): + with tempfile.TemporaryDirectory() as d: + exp = Path(d); (exp / 'audit').mkdir() + with patch.object(W, 'EXP', exp): + W.admission() + for name in ('audit/stopped.json', 'audit/execution-complete.json', 'manifest.json'): + p = exp / name; p.write_bytes(b'{}') + with self.assertRaisesRegex(ValueError, 'Stopped/completed'): W.admission() + p.unlink() + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_update.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_update.py new file mode 100644 index 0000000..742e4aa --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_conditioning_update.py @@ -0,0 +1,197 @@ +"""Synthetic sparse-event updates only; no recorded trajectories or training.""" +import copy +import hashlib +import importlib.util +import json +import os +from pathlib import Path +import tempfile +import unittest + +import numpy as np + +import conditioning_update as m + + +def source_path(): + # Same PHASE4J_SOURCE and ordered local candidates as frozen AM tests. + explicit = os.environ.get("PHASE4J_SOURCE") + if explicit: + return Path(explicit) + parent = Path(__file__).resolve().parents[1] + paths = (parent / "models/male-cns-lif/phase4j.py", parent / "mb-physiology-v1/phase4j.py") + for path in paths: + if path.is_file(): + return path + raise FileNotFoundError("Set PHASE4J_SOURCE to the SHA-pinned original phase4j.py") + + +SOURCE = source_path() +BRIDGE = Path(os.environ.get("AM_ELIGIBILITY_BRIDGE", + str(Path(__file__).resolve().parents[1] / "wholebrain-plastic-interface-v1/eligibility_bridge.py"))) + + +def args(rows=((0, 1), (3, 3)), us_tick=3, steps=8): + events = np.asarray(rows, np.int32).reshape(-1, 2) + return dict(step=events[:, 0], neuron_idx=events[:, 1], kc=np.array([1, 3, 7], np.int32), + candidate_edge_id=np.array([1, 4, 5, 10, 12], np.int64), + candidate_sources=np.array([1, 1, 3, 7, 3], np.int32), + candidate_targets=np.array([2, 2, 5, 5, 9], np.int32), + candidate_compartments=np.array(["y5B'2a", "a1", "y5B'2a", "y5B'2a", "y2a'1"]), + delta_before=np.zeros(5, np.float32), base_candidate=np.array([.275, .55, .0001, .275, 2.75], np.float32), + steps=steps, n_neurons=10, compartment="y5B'2a", us_tick=us_tick, + d_command=1.0 if us_tick is not None else 0.0, frozen=False, + bridge_source=BRIDGE, phase4j_source=SOURCE, trace_backend="numpy") + + +class ConditioningUpdate(unittest.TestCase): + def test_pinned_original_calls_and_recomputable_receipt(self): + inputs = args() + result = m.update_epoch(**inputs) + bridge = m._load_bridge(BRIDGE) + traced = bridge.trace_from_events(inputs["step"], inputs["neuron_idx"], inputs["kc"], 8, [3], + phase4j_source=SOURCE, backend="numpy", n_neurons=10) + expected, _ = bridge.apply_eligibility(traced.last_command_trace, inputs["kc"], + inputs["candidate_sources"], inputs["candidate_compartments"], inputs["compartment"], + inputs["delta_before"], inputs["base_candidate"], d=1., phase4j_source=SOURCE, n_neurons=10) + self.assertTrue(bridge.bit_equal(result.delta_after, expected)) + self.assertTrue(m.verify_receipt(result.receipt, **inputs)) + self.assertEqual(result.receipt["source_pins"]["eligibility_bridge_sha256"], hashlib.sha256(BRIDGE.read_bytes()).hexdigest()) + self.assertEqual(result.receipt["trace_audit"]["sampling"], "after_current_tick_spike") + json.dumps(result.receipt, allow_nan=False) + + def test_zero_d_and_frozen_preserve_existing_delta(self): + for d, frozen in ((0., False), (1., True), (0., True)): + inputs = args() + inputs["delta_before"][:] = -.25 * inputs["base_candidate"] + inputs.update(d_command=d, frozen=frozen) + result = m.update_epoch(**inputs) + self.assertEqual(result.delta_after.tobytes(), inputs["delta_before"].tobytes()) + self.assertEqual(result.receipt["counts"]["original_updated_edges_attempted"], 0) + self.assertEqual(result.receipt["counts"]["actual_bitwise_changed_edges"], 0) + + def test_us_before_cs_does_not_use_future_trace(self): + result = m.update_epoch(**args(rows=((2, 1),), us_tick=1)) + self.assertFalse(np.any(result.sampled_trace)) + self.assertTrue(np.any(result.final_trace)) + self.assertFalse(np.any(result.delta_after)) + + def test_current_tick_and_reentrant_kc_saturate_before_sample(self): + result = m.update_epoch(**args(rows=((0, 1), (1, 1), (3, 1), (3, 3)), us_tick=3)) + self.assertEqual(result.sampled_trace.tolist(), [1., 1., 0.]) + self.assertEqual(result.delta_after[0].view(np.uint32), np.float32(-.00275).view(np.uint32)) + self.assertEqual(result.receipt["counts"]["actual_KC_events"], 4) + + def test_non_kc_only_changes_event_evidence_not_update(self): + a = m.update_epoch(**args(rows=((0, 1), (3, 3)))) + b = m.update_epoch(**args(rows=((0, 0), (0, 1), (2, 9), (3, 3)))) + self.assertEqual(a.delta_after.tobytes(), b.delta_after.tobytes()) + self.assertEqual(a.sampled_trace.tobytes(), b.sampled_trace.tobytes()) + self.assertNotEqual(a.receipt["input_arrays"], b.receipt["input_arrays"]) + + def test_no_us_uses_zero_not_final_trace(self): + result = m.update_epoch(**args(us_tick=None)) + self.assertFalse(np.any(result.sampled_trace)) + self.assertFalse(np.any(np.signbit(result.sampled_trace))) + self.assertTrue(np.any(result.final_trace)) + self.assertEqual(result.receipt["counts"]["actual_value_changed_edges"], 0) + + def test_attempts_and_fully_saturated_changes_are_distinct(self): + inputs = args(rows=((3, 1), (3, 3), (3, 7))) + inputs["delta_before"][:] = -.25 * inputs["base_candidate"] + result = m.update_epoch(**inputs) + counts = result.receipt["counts"] + self.assertEqual(counts["original_updated_edges_attempted"], 3) + self.assertEqual(counts["actual_value_changed_edges"], 0) + self.assertEqual(counts["selected_at_floor_before"], 3) + self.assertEqual(counts["selected_at_floor_after"], 3) + self.assertEqual(counts["selected_newly_at_floor"], 0) + + def test_new_floor_and_off_compartment_identity(self): + inputs = args() + inputs["delta_before"][1] = -.01 + result = m.update_epoch(**inputs) + self.assertEqual(result.receipt["counts"]["selected_newly_at_floor"], 1) + self.assertEqual(result.receipt["counts"]["actual_bitwise_changed_edges"], 2) + self.assertEqual(result.delta_after[[1, 3, 4]].tobytes(), inputs["delta_before"][[1, 3, 4]].tobytes()) + + def test_owned_readonly_results_and_all_input_bytes_unchanged(self): + inputs = args() + before = {k: v.tobytes() for k, v in inputs.items() if isinstance(v, np.ndarray)} + result = m.update_epoch(**inputs) + for key, raw in before.items(): + self.assertEqual(inputs[key].tobytes(), raw) + for output in (result.delta_after, result.sampled_trace, result.final_trace): + self.assertTrue(output.flags.owndata) + self.assertFalse(output.flags.writeable) + self.assertTrue(all(not np.shares_memory(output, inputs[key]) for key in before)) + inputs["delta_before"][:] = -.000001 + self.assertNotEqual(result.delta_after.tobytes(), inputs["delta_before"].tobytes()) + + def test_scope_claims_do_not_prove_graph_or_dan_delivery(self): + receipt = m.update_epoch(**args()).receipt + for field in ("complete_KC_identity_verified", "complete_mask_identity_verified", "full_graph_authority_verified", + "brain_dynamics_executed", "weights_installed", "base_modified", "training_executed_by_this_function", + "scientific_learning_pass_claimed", "candidate_generated", "numpy_torch_bitwise_comparison_performed", + "frozen_Torch_expression_executed"): + self.assertIs(receipt[field], False) + self.assertFalse(receipt["semantics"]["actual_DAN_delivery_observed"]) + self.assertFalse(receipt["semantics"]["actual_DAN_delivery_used_to_gate"]) + + def test_invalid_events_mapping_commands_and_values_fail_closed(self): + mutations = ( + ("step", np.array([0, 3], np.int64)), ("steps", True), ("us_tick", False), + ("d_command", True), ("d_command", float("nan")), ("frozen", 1), ("trace_backend", "auto"), + ("candidate_edge_id", np.array([1, 1, 5, 10, 12], np.int64)), + ("candidate_targets", np.array([2, 2, 5, 5, 10], np.int32)), + ("candidate_sources", np.array([0, 1, 3, 7, 3], np.int32)), + ("base_candidate", np.array([.275, .55, float("inf"), .275, 2.75], np.float32)), + ("delta_before", np.full(5, -0., np.float32)), + ("kc", np.array([1, 1, 7], np.int32)), + ) + for key, value in mutations: + inputs = args() + inputs[key] = value + with self.subTest(key=key), self.assertRaises(ValueError): + m.update_epoch(**inputs) + for rows in (((0, 1), (0, 1)), ((3, 3), (0, 1)), ((8, 1),)): + with self.assertRaises(ValueError): + m.update_epoch(**args(rows=rows)) + inputs = args(us_tick=None) + inputs["d_command"] = 1 + with self.assertRaises(ValueError): + m.update_epoch(**inputs) + + def test_receipt_forgery_fails_after_new_self_hash(self): + inputs = args() + bad = copy.deepcopy(m.update_epoch(**inputs).receipt) + bad["counts"]["actual_value_changed_edges"] = 99 + bad["payload_sha256"] = m._sha({k: v for k, v in bad.items() if k != "payload_sha256"}) + with self.assertRaisesRegex(ValueError, "exact recomputation"): + m.verify_receipt(bad, **inputs) + + def test_source_drift_rejected_before_loading(self): + with tempfile.TemporaryDirectory() as directory: + for field, source in (("bridge_source", BRIDGE), ("phase4j_source", SOURCE)): + path = Path(directory) / (field + ".py") + path.write_bytes(source.read_bytes() + b"\nraise RuntimeError('never execute')\n") + inputs = args() + inputs[field] = path + with self.subTest(field=field), self.assertRaisesRegex(ValueError, "SHA256 mismatch"): + m.update_epoch(**inputs) + + @unittest.skipUnless(importlib.util.find_spec("torch") is not None, + "Torch absent: per-call NumPy/Torch exact compatibility NOT established locally") + def test_frozen_torch_cpu_matches_numpy_on_synthetic_events(self): + inputs = args(rows=((0, 1), (1, 1), (3, 3), (7, 7)), us_tick=7) + numpy_result = m.update_epoch(**inputs) + inputs["trace_backend"] = "torch" + torch_result = m.update_epoch(**inputs) + for field in ("delta_after", "sampled_trace", "final_trace"): + self.assertEqual(getattr(numpy_result, field).tobytes(), getattr(torch_result, field).tobytes()) + self.assertTrue(torch_result.receipt["frozen_Torch_expression_executed"]) + self.assertFalse(torch_result.receipt["numpy_torch_bitwise_comparison_performed"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_analysis.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_analysis.py new file mode 100644 index 0000000..7312d1d --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_analysis.py @@ -0,0 +1,227 @@ +"""Synthetic/mocked dispatch + temporary-array tests; no GPU or brain runs.""" +import copy +from contextlib import ExitStack +import hashlib +import json +from pathlib import Path +import tempfile +import unittest +from unittest.mock import Mock, patch + +import numpy as np + +import engineering_analysis as a +import engineering_plan as p + + +PIN = 'a' * 64 +WEIGHT = 'b' * 64 + + +def raw(value): + return hashlib.sha256(value.tobytes()).hexdigest() + + +def record(row): + n, k, steps, start = 3, 2, row['steps'], row['start'] + state = dict(step=np.empty(0, np.int32), neuron_idx=np.empty(0, np.int32), counts=np.zeros(n, np.int32), + v=np.zeros(n, np.float64), g=np.zeros(n, np.float64), ref=np.zeros(n, np.int32)) + reference = row['mode'] == 'reference' + summary = dict(complete=True, pass_all=True, all_finite=True, counts_match_sparse_events=True, + steps=steps, completed_ticks=steps, nodes=n, edges=3, requested=0, total_spikes=0, accepted_requests=0, + rejected_requests=0, arriving_edges=0, accepted_arriving_edges=0, apl_fast_edges_zeroed=1, kc_targets_present=k, + precision='float64', apl_precision='float32', apl_delay_steps=18, diagnostic_mode=row['mode'], + effective_weights_raw_sha256=WEIGHT, within_trial_weight_updates=False, learning_pass_claimed=False, + checkpoint_continuation_used=start > 0, global_tick_offset=start, global_next_tick=start + steps, + wall_seconds=1., diagnostic_simulation_seconds=.5, build_or_cache_seconds=.2, + peak_allocated_vram_bytes=1000, peak_reserved_vram_bytes=2000) + return dict(source=row['row_id'], state=state, steps=steps, global_tick_offset=start, summary=summary, + terminal=dict(spike_ring=np.zeros((19, n), np.uint8), apl_state=np.zeros(2, np.float32), + apl_pending=np.zeros((18, k), np.float32), next_tick=np.asarray(steps, np.int64)), + requests=np.empty((0, 5), np.int64), + traces={name: np.zeros((steps, 2), np.float32) for name in a.compare.TRACE_FILES} if reference else {}, + state_hashes=[dict(tick=t, v_sha256=raw(state['v']), g_sha256=raw(state['g']), ref_sha256=raw(state['ref'])) for t in range(steps)] if reference else None, + queues=[dict(tick=t, count=0, accepted_count=0, ordered_edge_sha256='c' * 64, + accepted_mask_sha256='c' * 64, joint_sha256='d' * 64) for t in range(steps)] if reference else None) + + +def fixture(): + plan = p.build_plan(); exp = Path(tempfile.gettempdir()).resolve() / 'synthetic-project' / 'experiments/phase4an-engineering-validation' + reg = dict(schema='phase4an-engineering-registration-v1', plan=plan, training=False, + cases={r['row_id']: dict(case_dir=str(exp / 'cases' / r['row_id'])) for r in plan['rows']}, + source_sha256={}, input_sha256={}, selected_case=dict(AM_outputs={'zero': 'mock', 'static25': 'mock'}), + overlays={'zero': {}, 'static25': {}}) + verified = {r['row_id']: dict(schema='phase4an-engineering-runtime-record-v1', complete=True, training=False, + row=copy.deepcopy(r), registration_sha256=PIN, checkpoint_manifest_sha256=PIN, + runtime_and_immediate_seal_wall_seconds=2., storage={'state.npz': {'file_bytes': 10}, 'terminal-queues.npz': {'file_bytes': 20}}) + for r in plan['rows']} + records = {r['row_id']: record(r) for r in plan['rows']} + reader = Mock(); reader.finish.return_value = []; reader.close.return_value = None + context = dict(reader=reader, records=records, + legacy={stage: record(next(r for r in plan['rows'] if r['overlay'] == stage and r['kind'] == 'fresh' and r['mode'] == 'reference')) for stage in p.OVERLAYS}, + graph=dict(ptr=np.array([0, 1, 2, 3], np.int64), dst=np.array([1, 2, 0], np.int32), edge_id=np.arange(3, dtype=np.int64)), + requests=dict(tick=np.empty(0, np.int32), neuron_idx=np.empty(0, np.int32)), storage={r['row_id']: 600 for r in plan['rows']}) + return reg, verified, context + + +class Orchestration(unittest.TestCase): + def mocked(self, reg, verified, context, *, fail=None, live=True, queue_exact=True, joint=True): + calls = [] + def ordinary(name): + def f(*args): + calls.append(name) + return dict(numerical_exact=name != fail, checks={}, observed_scope={'mock_only': True}) + return f + def chain(mono, parts): + calls.append('chain_exact') + reference = mono['summary']['diagnostic_mode'] == 'reference' + return dict(numerical_exact=fail != ('reference_chain' if reference else 'light_chain'), + pass_all=reference and live, nonvacuous_temporal_coverage=reference and live, + status='PASS' if reference else 'INCOMPLETE-COVERAGE', + coverage={'carried_APL_nonzero_applied_values': 1 if live and reference else 0, + 'boundaries': [{'pending_source_spike_events': 1, 'carried_APL_nonzero_pending_values': 1 if joint else 0}]} if reference else None) + with ExitStack() as stack: + for name, value in dict(N=3, E=3, K=2, FAST_APL_EDGES=1).items(): + stack.enter_context(patch.object(a, name, value)) + stack.enter_context(patch.object(a, '_load_context', return_value=context)) + stack.enter_context(patch.object(a, '_materialize', return_value=(np.ones(3, np.float64), {'effective_weights_raw_sha256': WEIGHT}))) + stack.enter_context(patch.object(a.compare, 'compare_fresh', side_effect=ordinary('fresh'))) + stack.enter_context(patch.object(a.scopes, 'compare_slice', side_effect=ordinary('slice'))) + stack.enter_context(patch.object(a.scopes, 'compare_checkpoint', side_effect=ordinary('checkpoint'))) + stack.enter_context(patch.object(a.scopes, 'compare_parent_carry', side_effect=ordinary('carry'))) + stack.enter_context(patch.object(a, '_pair', side_effect=ordinary('pair'))) + stack.enter_context(patch.object(a.compare, 'compare_segments', side_effect=chain)) + queue_mock = stack.enter_context(patch.object(a.queue_audit, 'audit_queues', return_value=dict( + queue_ref_request_exact=queue_exact, accepted_nonzero_crossing_witness_verified=live and queue_exact))) + result = a.analyze(reg, PIN, verified) + return result, calls, queue_mock + + def test_executes_all_descriptors_and_both_independent_queues(self): + reg, verified, ctx = fixture() + report, calls, queued = self.mocked(reg, verified, ctx) + expected = [r['row_id'] + '/' + str(i) for r in reg['plan']['rows'] for i, _ in enumerate(r['expected_comparisons'])] + self.assertEqual([r['descriptor_id'] for r in report['comparisons']], expected) + self.assertEqual(len(calls), len(expected)) + self.assertEqual(report['evaluated_comparisons'], 74) + self.assertEqual(queued.call_count, 2) + self.assertEqual(queued.call_args.kwargs['cuts'], [5, 18, 19, 500]) + self.assertEqual(report['status'], a.SUCCESS) + self.assertFalse(report['learning_pass_claimed']) + self.assertEqual(len(report['metrics']), 32) + self.assertTrue(all(row['gpu_utilization'] is None for row in report['metrics'])) + self.assertTrue(all(r['light_standalone_coverage'] is None for r in report['overlay_reports'].values())) + json.dumps(report, allow_nan=False) + ctx['reader'].finish.assert_called_once() + ctx['reader'].close.assert_called_once() + + def test_numerical_failure_still_evaluates_every_descriptor(self): + for failure in ('fresh', 'slice', 'checkpoint', 'carry', 'pair', 'reference_chain', 'light_chain'): + reg, verified, ctx = fixture() + with self.subTest(failure=failure): + report, calls, _ = self.mocked(reg, verified, ctx, fail=failure) + self.assertEqual(len(calls), 74) + self.assertEqual(report['status'], 'FAIL-NUMERICAL') + self.assertFalse(report['pass_all']) + + def test_queue_failure_and_absent_live_coverage_are_distinct(self): + for arguments, expected in [({'queue_exact': False}, 'FAIL-NUMERICAL'), + ({'live': False}, 'INCOMPLETE-COVERAGE'), ({'joint': False}, 'INCOMPLETE-COVERAGE')]: + reg, verified, ctx = fixture() + report, _, _ = self.mocked(reg, verified, ctx, **arguments) + self.assertEqual(report['status'], expected) + self.assertFalse(report['pass_all']) + + def test_receipt_inventory_alias_and_descriptor_removal_fail_closed(self): + for mutation in ('receipt', 'descriptor', 'pin', 'case', 'row_alias'): + reg, verified, ctx = fixture(); identity = reg['plan']['rows'][0]['row_id'] + if mutation == 'receipt': + verified.pop(identity) + elif mutation == 'descriptor': + reg['plan']['rows'][0]['expected_comparisons'] = [] + elif mutation == 'pin': + verified[identity]['registration_sha256'] = 'f' * 64 + elif mutation == 'row_alias': + verified[identity]['row']['steps'] = True + else: + reg['cases'][identity]['case_dir'] += '-another' + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + self.mocked(reg, verified, ctx) + + def test_summary_uses_actual_AJ_requested_names_and_checks_total_accounting(self): + for key in ('requested', 'accepted_requests', 'arriving_edges', 'accepted_arriving_edges', 'edges', 'effective_weights_raw_sha256'): + reg, verified, ctx = fixture(); identity = reg['plan']['rows'][0]['row_id'] + ctx['records'][identity]['summary'][key] = 'e' * 64 if key.endswith('sha256') else 1 + with self.subTest(key=key): + report, _, _ = self.mocked(reg, verified, ctx) + self.assertEqual(report['status'], 'FAIL-NUMERICAL') + self.assertIn(identity, report['failed_run_checks']) + + def test_identical_bytes_give_deterministic_report_and_unknown_is_null(self): + reg, verified, ctx = fixture(); identity = reg['plan']['rows'][0]['row_id'] + ctx['records'][identity]['summary'].pop('peak_reserved_vram_bytes') + one, _, _ = self.mocked(reg, verified, ctx) + two, _, _ = self.mocked(reg, verified, ctx) + self.assertEqual(a._canonical(one), a._canonical(two)) + row = next(r for r in one['metrics'] if r['row_id'] == identity) + self.assertIsNone(row['peak_reserved_vram_bytes']) + + def test_AM_reference_full_comparison_is_not_hidden_by_light(self): + reg, _, ctx = fixture() + row = reg['plan']['rows'][0]; descriptor = row['expected_comparisons'][0] + ctx['records'][row['row_id']]['traces']['drive'][10, 0] = np.float32(1) + report = a._dispatch(descriptor, ctx['records'], ctx['legacy'], row['overlay']) + self.assertFalse(report['numerical_exact']) + self.assertIn('pair.trace.drive', report['failed_checks']) + + def test_missing_loaded_population_or_reference_inventory_rejected(self): + reg, verified, ctx = fixture(); ctx['records'].pop(next(iter(ctx['records']))) + with self.assertRaises(ValueError): + self.mocked(reg, verified, ctx) + + +class FileAndWeightInputs(unittest.TestCase): + def test_reader_rehashes_actual_content_and_never_uses_stat_cache(self): + with tempfile.TemporaryDirectory() as folder: + path = Path(folder).resolve() / 'data.bin'; path.write_bytes(b'AAAA') + reader = a._Reader({str(path): a._sha_file(path)}) + reader.bind(path); path.write_bytes(b'BBBB') + with self.assertRaises(ValueError): + reader.bind(path) + with self.assertRaises(ValueError): + reader.finish() + + def test_original_pinned_overlay_math_signed_zero_and_nonzero_only_bridge(self): + helper = Path(__file__).resolve().parents[1] / 'wholebrain-plastic-interface-v1/overlay.py' + self.assertEqual(a._sha_file(helper), a.AM_OVERLAY_SHA) + with tempfile.TemporaryDirectory() as folder: + folder = Path(folder).resolve() + mask = folder / 'mask.npy'; base = folder / 'base.npy' + np.save(mask, np.array([0], np.int64)); np.save(base, np.array([1.5], np.float32)) + original = np.array([.002, -0., -0., .009], np.float64) + graph = dict(weights=original, edge_id=np.arange(4, dtype=np.int64), apl_out_mask=np.array([False, True, False, False])) + before = original.tobytes() + for stage in ('zero', 'static25'): + delta = np.array([-0. if stage == 'zero' else -.375], np.float32) + overlay = folder / (stage + '.npz'); manifest = folder / (stage + '.json') + np.savez_compressed(overlay, edge_id=np.array([0], np.int64), plastic_delta_mv=delta) + meta = dict(schema='phase4an-overlay-v1', overlay_name=stage, am_overlay_path=str(helper), am_overlay_sha256=a.AM_OVERLAY_SHA, + mask_path=str(mask), mask_sha256=a._sha_file(mask), base_candidate_path=str(base), base_candidate_sha256=a._sha_file(base), + overlay_sha256=a._sha_file(overlay)) + manifest.write_text(json.dumps(meta), encoding='utf-8') + paths = [mask, base, overlay, manifest, helper] + reader = a._Reader({str(path): a._sha_file(path) for path in paths}) + reg = {'overlays': {stage: dict(overlay_file=str(overlay), overlay_manifest=str(manifest))}} + with patch.object(a, 'MASK_SHA', meta['mask_sha256']), patch.object(a, 'CANDIDATES', 1), patch.object(a, 'FAST_APL_EDGES', 1): + actual, info = a._materialize(reader, reg, graph, stage) + expected = original.copy(); expected[1] = 0. + if stage == 'static25': + expected[0] = np.add(original[0], np.multiply(delta[0].astype(np.float64), np.float64(.001))) + self.assertEqual(actual.tobytes(), expected.tobytes()) + self.assertEqual(info['effective_weights_raw_sha256'], raw(expected)) + self.assertEqual(info['installed_nonzero_count'], 0 if stage == 'zero' else 1) + self.assertEqual(original.tobytes(), before) + reader.finish(); reader.close() + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_compare.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_compare.py new file mode 100644 index 0000000..7b54b1a --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_compare.py @@ -0,0 +1,246 @@ +"""Synthetic artifact comparisons only; no neural equation or GPU execution.""" +import copy +import hashlib +import json +from pathlib import Path +import tempfile +import unittest + +import numpy as np + +import engineering_compare as c + + +def sha(value): + return hashlib.sha256(np.ascontiguousarray(value).tobytes()).hexdigest() + + +def synthetic_parts(lengths, *, active=True, traces=True, queues=True): + n, k = 3, 2 + total = sum(lengths) + all_ticks = np.array([value for value in (0, 7, 19, 21, 41, 61) if value < total], np.int32) if active else np.empty(0, np.int32) + all_nodes = np.array([0, 1, 2, 0, 2, 1][:len(all_ticks)], np.int32) + ring, apl_ring = np.zeros((19, n), np.uint8), np.zeros((18, k), np.float32) + phase, offset, result = 0, 0, [] + for length in lengths: + ring = ring[(np.arange(19) + phase % 19) % 19].copy() + head = phase % 18 + apl_ring = np.concatenate((apl_ring[head:], apl_ring[:head])) + event_tick, event_node, requested, hashes, queue_rows = [], [], [], [], [] + generated, applied, drives, apl_states = [], [], [], [] + for tick in range(length): + absolute = offset + tick + fired = all_nodes[all_ticks == absolute] + ring[tick % 19] = 0; ring[tick % 19, fired] = 1 + for neuron in fired: + event_tick.append(tick); event_node.append(neuron) + requested.append([tick, int(neuron), 1, 1, 0]) + due = ring[(tick - 18) % 19] if absolute >= 18 else np.zeros(n, np.uint8) + edges = np.array([int(node) * 2 + i for node in np.flatnonzero(due) for i in (0, 1)], np.int64) + accepted = np.ones(len(edges), np.uint8) + queue_rows.append({'tick': tick, 'count': len(edges), 'accepted_count': len(edges), + 'ordered_edge_sha256': sha(edges), 'accepted_mask_sha256': sha(accepted), + 'joint_sha256': hashlib.sha256(str(tick).encode() + edges.tobytes() + accepted.tobytes()).hexdigest()}) + v = np.array([-.05, -.04, -.03], np.float64) + g = np.array([-0., float(absolute), -.001], np.float64) + ref = np.zeros(n, np.int32) + hashes.append({'tick': tick, 'v_sha256': sha(v), 'g_sha256': sha(g), 'ref_sha256': sha(ref)}) + h = np.full(k, np.float32(absolute + 1 if active else 0), np.float32) + application = apl_ring[tick % 18].copy(); apl_ring[tick % 18] = h + generated.append(h); applied.append(application) + drives.append(np.full(2, np.float32(absolute if active else 0), np.float32)) + apl_states.append(np.full(2, np.float32(absolute + 2 if active else 0), np.float32)) + local_ticks, nodes = np.array(event_tick, np.int32), np.array(event_node, np.int32) + state = {'step': local_ticks, 'neuron_idx': nodes, 'counts': np.bincount(nodes, minlength=n).astype(np.int32), 'v': v, 'g': g, 'ref': ref} + head = length % 18 + pending = np.concatenate((apl_ring[head:], apl_ring[:head])) + terminal = {'spike_ring': ring.copy(), 'apl_state': apl_states[-1].copy(), 'apl_pending': pending, + 'next_tick': np.asarray(length, np.int64)} + summary = {'complete': True, 'pass_all': True, 'all_finite': True, 'steps': length, 'completed_ticks': length, + 'nodes': n, 'precision': 'float64', 'apl_precision': 'float32', 'apl_delay_steps': 18, + 'checkpoint_continuation_used': offset > 0, 'global_tick_offset': offset, 'global_next_tick': offset + length, + 'effective_weights_raw_sha256': 'e' * 64, 'diagnostic_mode': 'reference' if traces else 'light'} + result.append({'source': 'synthetic', 'steps': length, 'global_tick_offset': offset, 'state': state, + 'requests': np.asarray(requested, np.int64).reshape(-1, 5), 'terminal': terminal, + 'summary': summary, 'terminal_origin': 'synthetic_recorded', + 'traces': {'generated': np.asarray(generated), 'applied': np.asarray(applied), + 'drive': np.asarray(drives), 'state': np.asarray(apl_states)} if traces else {}, + 'state_hashes': hashes if traces else None, 'queues': queue_rows if queues else None}) + offset += length; phase = length + return result + + +def write_output(path, run, *, legacy=False): + path.mkdir() + for name, value in (('summary.json', run['summary']), ('apl-timing-audit.json', + {'final_apl_state_u32_bits': run['terminal']['apl_state'].view(np.uint32).tolist()})): + (path / name).write_text(json.dumps(value), encoding='utf-8') + np.savez_compressed(path / 'state.npz', **run['state']) + np.save(path / 'apl-pending-tail.float32.npy', run['terminal']['apl_pending']) + if legacy: + (path / 'adapter-provenance.json').write_text('{}', encoding='utf-8') + else: + np.savez_compressed(path / 'terminal-queues.npz', **run['terminal']) + with (path / 'requests.csv').open('w', encoding='utf-8', newline='') as stream: + import csv + writer = csv.writer(stream); writer.writerow(c.REQUEST_FIELDS); writer.writerows(run['requests'].tolist()) + for name, file in c.TRACE_FILES.items(): + if name in run['traces']: + np.save(path / file, run['traces'][name]) + for name, file in (('state_hashes', 'tick-state-hashes.jsonl'), ('queues', 'queue.jsonl')): + if run[name] is not None: + (path / file).write_text(''.join(json.dumps(row) + '\n' for row in run[name]), encoding='utf-8') + + +class Comparisons(unittest.TestCase): + def test_fresh_light_reference_exact_without_fabricating_light_traces(self): + reference = synthetic_parts([64])[0] + light = synthetic_parts([64], traces=False, queues=False)[0] + report = c.compare_fresh(light, reference) + self.assertTrue(report['pass_all']) + self.assertFalse(report['reference_comparisons_available']['APL_traces']) + self.assertFalse(report['temporal_validation_complete']) + + def test_light_does_not_hide_reference_vs_AM_interior_trace_mismatch(self): + light = synthetic_parts([64], traces=False, queues=False)[0] + reference = synthetic_parts([64])[0] + legacy = copy.deepcopy(reference) + reference['traces']['drive'][10, 0] += np.float32(1.) + report = c.compare_fresh(light, reference, legacy) + self.assertFalse(report['pass_all']) + self.assertIn('reference_vs_AM.trace.drive', report['failed_checks']) + + def test_nonzero_origin_monolithic_and_split_require_same_parent(self): + mono = synthetic_parts([20, 44])[1] + parts = synthetic_parts([20, 5, 39])[1:] + with self.assertRaisesRegex(ValueError, 'same starting parent'): + c.compare_segments(mono, parts) + mono['summary']['parent_checkpoint_manifest_sha256'] = 'a' * 64 + parts[0]['summary']['parent_checkpoint_manifest_sha256'] = 'a' * 64 + report = c.compare_segments(mono, parts) + self.assertTrue(report['pass_all'], report['failed_checks']) + self.assertEqual(report['global_tick_offset'], 20) + parts[0]['summary']['parent_checkpoint_manifest_sha256'] = 'b' * 64 + with self.assertRaisesRegex(ValueError, 'same starting parent'): + c.compare_segments(mono, parts) + + def test_monolithic_segments_exact_and_cross_boundary_coverage(self): + mono = synthetic_parts([64])[0] + parts = synthetic_parts([5, 12, 1, 1, 18, 19, 8]) + report = c.compare_segments(mono, parts) + self.assertTrue(report['pass_all'], report['failed_checks']) + self.assertTrue(report['reference_comparisons_available']['APL_traces']) + self.assertTrue(report['reference_comparisons_available']['state_hashes']) + self.assertGreater(report['coverage']['observed_carried_arriving_edges'], 0) + self.assertGreater(report['coverage']['carried_APL_nonzero_applied_values'], 0) + self.assertFalse(report['queue_joint_hash_compared']) + self.assertNotEqual(mono['queues'][18]['joint_sha256'], parts[3]['queues'][0]['joint_sha256']) + + def test_raw_ring_phase_must_be_normalized(self): + mono = synthetic_parts([64])[0] + parts = synthetic_parts([20, 44]) + self.assertFalse(c.bit_equal(mono['terminal']['spike_ring'], parts[-1]['terminal']['spike_ring'])) + self.assertTrue(c.bit_equal(c.normalized_ring(mono['terminal']), c.normalized_ring(parts[-1]['terminal']))) + self.assertTrue(c.compare_segments(mono, parts)['pass_all']) + + def test_signed_zero_final_state_difference_is_not_equal(self): + left = synthetic_parts([64], traces=False, queues=False)[0] + right = copy.deepcopy(left); right['state']['g'][0] = 0. + report = c.compare_fresh(left, right) + self.assertFalse(report['numerical_exact']) + self.assertIn('pair.state.g', report['failed_checks']) + + def test_zero_activity_is_not_a_complete_temporal_pass(self): + mono = synthetic_parts([64], active=False)[0] + parts = synthetic_parts([20, 44], active=False) + report = c.compare_segments(mono, parts) + self.assertTrue(report['numerical_exact']) + self.assertFalse(report['pass_all']) + self.assertFalse(report['temporal_validation_complete']) + self.assertEqual(report['status'], 'INCOMPLETE-COVERAGE') + self.assertFalse(c.compare_fresh(mono, copy.deepcopy(mono))['pass_all']) + + def test_missing_observed_queue_coverage_is_reported_unknown_not_zero_or_pass(self): + mono = synthetic_parts([64], traces=False, queues=False)[0] + parts = synthetic_parts([20, 44], traces=False, queues=False) + report = c.compare_segments(mono, parts) + self.assertTrue(report['numerical_exact']) + self.assertIsNone(report['coverage']['observed_carried_arriving_edges']) + self.assertFalse(report['temporal_validation_complete']) + + def test_request_acceptance_difference_fails_even_if_events_are_equal(self): + left = synthetic_parts([64])[0]; right = copy.deepcopy(left) + right['requests'][0, 3:] = [0, 1] + report = c.compare_fresh(left, right) + self.assertIn('pair.requests_five_fields', report['failed_checks']) + + def test_clock_gaps_and_empty_segment_list_rejected(self): + mono = synthetic_parts([64])[0] + with self.assertRaises(ValueError): + c.compare_segments(mono, []) + parts = synthetic_parts([20, 44]); parts[1]['global_tick_offset'] += 1 + with self.assertRaisesRegex(ValueError, 'clock gap'): + c.compare_segments(mono, parts) + + def test_partial_traces_hash_coverage_and_bad_dtype_rejected(self): + for mutation in ('trace', 'hash', 'dtype', 'duplicate'): + run = synthetic_parts([64])[0] + if mutation == 'trace': + run['traces'].pop('applied') + elif mutation == 'hash': + run['state_hashes'].pop() + elif mutation == 'dtype': + run['state']['v'] = run['state']['v'].astype(np.float32) + else: + run['state']['step'][1] = run['state']['step'][0] + run['state']['neuron_idx'][1] = run['state']['neuron_idx'][0] + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + c.validate_run(run) + + def test_per_step_state_hash_and_queue_mask_mismatch_fail(self): + mono = synthetic_parts([64])[0]; parts = synthetic_parts([20, 44]) + parts[1]['state_hashes'][1]['g_sha256'] = '0' * 64 + parts[1]['queues'][1]['accepted_mask_sha256'] = '1' * 64 + report = c.compare_segments(mono, parts) + self.assertFalse(report['pass_all']) + self.assertIn('concatenated_per_tick_state_hashes', report['failed_checks']) + self.assertIn('concatenated_queue_order_and_acceptance', report['failed_checks']) + + def test_effective_weight_change_fails(self): + mono = synthetic_parts([64])[0]; parts = synthetic_parts([20, 44]) + parts[1]['summary']['effective_weights_raw_sha256'] = 'f' * 64 + self.assertFalse(c.compare_segments(mono, parts)['checks']['unchanged_effective_weights']) + + +class FileSchemas(unittest.TestCase): + def test_actual_five_request_fields_and_AM_derived_ring_are_labeled(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + run = synthetic_parts([64])[0] + write_output(root / 'AN', run) + write_output(root / 'AM', run, legacy=True) + actual, legacy = c.load_run(root / 'AN'), c.load_run(root / 'AM', allow_legacy_am=True) + try: + self.assertIn('derived_from_fresh_events', legacy['terminal_origin']) + self.assertTrue(c.compare_fresh(actual, copy.deepcopy(actual), legacy)['pass_all']) + with self.assertRaisesRegex(ValueError, 'missing'): + c.load_run(root / 'AM') + finally: + c.close_run(actual); c.close_run(legacy) + + def test_missing_reference_file_or_wrong_request_header_rejected(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + run = synthetic_parts([64])[0] + write_output(root / 'partial', run) + (root / 'partial' / 'apl-applied.float32.npy').unlink() + with self.assertRaisesRegex(ValueError, 'incomplete'): + c.load_run(root / 'partial') + write_output(root / 'header', run) + (root / 'header' / 'requests.csv').write_text('tick,neuron_idx,accepted\n', encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'five AJ'): + c.load_run(root / 'header') + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_inputs.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_inputs.py new file mode 100644 index 0000000..7837395 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_inputs.py @@ -0,0 +1,115 @@ +import copy +import unittest +import numpy as np +import engineering_inputs as e + + +def inputs(): + return dict(tick=np.array([0, 4, 5, 17, 18, 18, 19, 499, 500, 999], np.int32), + neuron_idx=np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 166699], np.int32)) + + +def spec(): + return dict(name='unchanged', kind='fullgraph', steps=1000, dt_ms=.1, duration_ms=100., + apl_delay_steps=18, record_history=False, training=False, + model={'threshold_mv': -45, 'w_syn_mv_per_anatomical_synapse': .275}, + apl={'gain': .01}, budget_root='/project/old', output_budget_bytes=2048, + graph_dir='/project/frozen-graph', seed=1073741825) + + +class ExactInputs(unittest.TestCase): + def test_exact_boundaries_reconstruct_all_requests(self): + original = inputs() + intervals = [(0, 5), (5, 18), (18, 19), (19, 500), (500, 1000)] + parts = [e.slice_requests(original, 1000, *span) for span in intervals] + report = e.verify_partition(original, 1000, intervals, parts) + self.assertTrue(report['complete']) + self.assertEqual(parts[2]['tick'].tolist(), [0, 0]) + self.assertEqual(parts[-1]['tick'].tolist(), [0, 499]) + self.assertEqual(report['random_draws'], 0) + for value in parts: + for key in original: + self.assertFalse(np.shares_memory(original[key], value[key])) + + def test_empty_intervals_are_explicit_not_lost(self): + original = inputs() + intervals = [(0, 20), (20, 499), (499, 1000)] + parts = [e.slice_requests(original, 1000, *span) for span in intervals] + self.assertEqual(len(parts[1]['tick']), 0) + self.assertTrue(e.verify_partition(original, 1000, intervals, parts)['complete']) + + def test_bad_requests_never_silently_fixed(self): + for mut in ('dtype', 'duplicate', 'order', 'bounds', 'shape', 'extra'): + req = inputs() + if mut == 'dtype': req['tick'] = req['tick'].astype(np.int64) + elif mut == 'duplicate': req['tick'][1], req['neuron_idx'][1] = 0, 1 + elif mut == 'order': req['tick'][1] = 10 + elif mut == 'bounds': req['neuron_idx'][-1] = 166700 + elif mut == 'shape': req['tick'] = req['tick'][:-1] + else: req['seed'] = 1 + with self.subTest(mut=mut), self.assertRaises(ValueError): + e.slice_requests(req, 1000, 0, 1000) + + def test_partition_gap_overlap_changed_input_and_missing_tail_rejected(self): + req = inputs() + spans = [(0, 500), (500, 1000)] + parts = [e.slice_requests(req, 1000, *x) for x in spans] + for bad in ([(0, 500), (501, 1000)], [(0, 500), (499, 1000)], [(0, 500)]): + with self.assertRaises(ValueError): + e.verify_partition(req, 1000, bad, parts[:len(bad)]) + parts[0]['neuron_idx'][0] = 123 + with self.assertRaisesRegex(ValueError, 'changes actual requests'): + e.verify_partition(req, 1000, spans, parts) + + def test_fresh_prefix_and_continued_specs_preserve_original(self): + original = spec() + before = copy.deepcopy(original) + for start, stop, parent in ((0, 1000, None), (0, 18, None), (18, 19, 'a' * 64)): + derived, audit = e.derive_spec(original, start=start, stop=stop, budget_root='/project/new', + output_budget_bytes=8 * 1024**3, parent_sha=parent) + self.assertEqual(original, before) + self.assertEqual(derived['model'], original['model']) + self.assertEqual(derived['apl'], original['apl']) + self.assertIsNot(derived['model'], original['model']) + self.assertEqual(derived['max_steps'], 1000) + self.assertEqual(derived['steps'], stop - start) + self.assertTrue(audit['all_other_fields_unchanged']) + if start: self.assertEqual(derived['global_tick_offset'], start) + + def test_invalid_continuation_root_and_duration_rejected(self): + for root in ('/project/old/sub', '/project', 'relative', '/project/../old', '//project/old', '//project/new'): + with self.assertRaises(ValueError): + e.derive_spec(spec(), start=0, stop=1000, budget_root=root, output_budget_bytes=10) + for start, stop, parent in ((1, 20, None), (0, 10, 'a'*64), (True, 10, None), (0, 1001, None)): + with self.assertRaises(ValueError): + e.derive_spec(spec(), start=start, stop=stop, budget_root='/project/new', output_budget_bytes=10, parent_sha=parent) + original = spec(); original['duration_ms'] = 1. + with self.assertRaises(ValueError): + e.derive_spec(original, start=0, stop=10, budget_root='/project/new', output_budget_bytes=10) + + def test_original_clock_type_and_no_within_trial_update_match_runtime(self): + for key, value in (('apl_delay_steps', 18.), ('apl_delay_steps', True), + ('within_trial_weight_updates', True), ('budget_root', '//project/old')): + original = spec(); original[key] = value + with self.subTest(key=key, value=value), self.assertRaises(ValueError): + e.derive_spec(original, start=0, stop=10, budget_root='/project/new', output_budget_bytes=10) + + def test_overlay_is_declaration_translation_not_self_authorized_learned(self): + original = dict(schema='phase4am-overlay-v1', overlay_name='zero', training=False, fixed_for_whole_trial=True, + overlay_sha256='b'*64, mask_path='/project/mask.npy', mask_sha256=e.MASK_SHA, + base_candidate_path='/project/base.npy', base_candidate_sha256='c'*64, + aj_model_dir='/project/apl-timing-lab-v1', old_extra_field='preserved in original only') + before = copy.deepcopy(original) + result = e.adapt_overlay_manifest(original, '/project/overlay.py') + self.assertEqual(original, before) + self.assertEqual(result['schema'], 'phase4an-overlay-v1') + self.assertNotIn('old_extra_field', result) + self.assertEqual(result['overlay_sha256'], original['overlay_sha256']) + for key, val in (('overlay_name', 'learned'), ('mask_sha256', 'd'*64), ('training', True), ('base_candidate_path', 'local')): + bad = dict(original); bad[key] = val + with self.subTest(key=key), self.assertRaises(ValueError): + e.adapt_overlay_manifest(bad, '/project/overlay.py') + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_plan.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_plan.py new file mode 100644 index 0000000..709109e --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_plan.py @@ -0,0 +1,152 @@ +"""Pure inventory/dependency tests; no I/O, GPU, scientific input or runtime.""" +import copy +import json +import unittest + +import engineering_plan as p + + +class FixedPlan(unittest.TestCase): + def test_complete_current_inventory_not_future_supplements(self): + plan = p.build_plan() + self.assertEqual(plan['total_calls'], len(plan['rows'])) + self.assertEqual(plan['total_calls'], 32) + self.assertEqual(plan['total_ticks'], sum(row['steps'] for row in plan['rows'])) + self.assertEqual(plan['total_ticks'], 11074) + self.assertEqual(plan['overlays'], ['zero', 'static25']) + self.assertEqual(plan['chain_lengths'], [5, 13, 1, 481, 500]) + self.assertFalse(plan['training']) + self.assertFalse(plan['candidate_generated']) + self.assertEqual(p.validate_plan(json.loads(json.dumps(plan))), plan) + for overlay in plan['overlays']: + rows = [r for r in plan['rows'] if r['overlay'] == overlay] + self.assertEqual(len(rows), 16) + self.assertEqual(sum(r['steps'] for r in rows), 5537) + self.assertEqual([(r['mode'], r['steps']) for r in rows if r['kind'] == 'fresh'], + [('reference', 1000), ('light', 1000), ('light', 1000)]) + self.assertEqual([r['stop'] for r in rows if r['kind'] == 'prefix'], [18, 19, 500]) + self.assertFalse(any(r['stop'] > 1000 for r in rows)) + + def test_all_prerequisites_precede_rows_and_fresh_prefixes_precede_chains(self): + rows = p.build_plan()['rows'] + self.assertTrue(all(r['kind'] in ('fresh', 'prefix') for r in rows[:12])) + self.assertTrue(all(r['kind'] == 'chain' for r in rows[12:])) + positions = {r['row_id']: i for i, r in enumerate(rows)} + self.assertEqual(len(positions), 32) + for index, row in enumerate(rows): + if row['parent_row_id'] is not None: + self.assertLess(positions[row['parent_row_id']], index) + for check in row['expected_comparisons']: + reference = check['reference_row_id'] + if reference is not None: + self.assertLess(positions[reference], index) + for candidate in check['candidate_row_ids']: + self.assertLessEqual(positions[candidate], index) + self.assertLess(check['start'], check['stop']) + + def test_each_chain_is_exact_partition_with_local_phase_distinct_from_global(self): + for overlay in p.OVERLAYS: + for mode in p.MODES: + chain = [r for r in p.build_plan()['rows'] if + (r['overlay'], r['mode'], r['kind']) == (overlay, mode, 'chain')] + self.assertEqual([(r['start'], r['stop']) for r in chain], + [(0, 5), (5, 18), (18, 19), (19, 500), (500, 1000)]) + self.assertEqual([r['steps'] % 19 for r in chain], [5, 13, 1, 6, 6]) + self.assertEqual([r['stop'] % 19 for r in chain], [5, 18, 0, 6, 12]) + self.assertIsNone(chain[0]['parent_row_id']) + for earlier, later in zip(chain, chain[1:]): + self.assertEqual(later['parent_row_id'], earlier['row_id']) + self.assertEqual(later['start'], earlier['stop']) + self.assertEqual(chain[2]['start'], 18) # source tick 0 arrives at this child's local 0. + self.assertEqual(chain[2]['steps'], 1) + last = next(c for c in chain[-1]['expected_comparisons'] if c['check'] == 'chain_exact') + self.assertEqual(last['candidate_row_ids'], [r['row_id'] for r in chain]) + self.assertEqual((last['start'], last['stop']), (0, 1000)) + + def test_prefix5_is_reference_first_segment_without_extra_run(self): + rows = p.build_plan()['rows'] + self.assertFalse(any(r['kind'] == 'prefix' and r['stop'] == 5 for r in rows)) + for overlay in p.OVERLAYS: + first = {r['mode']: r for r in rows if r['kind'] == 'chain' and + r['overlay'] == overlay and r['start'] == 0} + refs = [c for c in first['light']['expected_comparisons'] if c['check'] == 'checkpoint_exact'] + self.assertEqual(len(refs), 1) + self.assertEqual(refs[0]['reference_row_id'], first['reference']['row_id']) + self.assertFalse(any(c['reference_row_id'] == first['reference']['row_id'] + for c in first['reference']['expected_comparisons'])) + + def test_fresh_am_and_light_repeat_comparisons_are_explicit(self): + rows = p.build_plan()['rows'] + for overlay in p.OVERLAYS: + fresh = [r for r in rows if r['overlay'] == overlay and r['kind'] == 'fresh'] + self.assertEqual(len(fresh), 3) + for row in fresh: + am = [c for c in row['expected_comparisons'] if c['check'] == 'am_exact'] + self.assertEqual(len(am), 1) + self.assertEqual(am[0]['external_reference'], 'sealed_AM_selected_case_same_overlay_r0') + self.assertIsNone(am[0]['reference_row_id']) + self.assertEqual(fresh[1]['expected_comparisons'][1]['reference_row_id'], fresh[0]['row_id']) + self.assertEqual(fresh[2]['expected_comparisons'][1]['reference_row_id'], fresh[1]['row_id']) + + def test_every_result_is_independent_not_shared_mutable_inventory(self): + original = p.build_plan() + checked = p.validate_plan(original) + checked['rows'][0]['expected_comparisons'][0]['candidate_row_ids'].append('mutation') + checked['chain_lengths'][0] = 99 + self.assertEqual(original, p.build_plan()) + original['rows'][0]['expected_comparisons'][0]['scope'] = 'mutation' + self.assertNotEqual(original, p.build_plan()) + + def test_exact_types_reject_bool_int_float_and_sequence_conflation(self): + mutations = ( + lambda x: x.update(total_calls=32.0), + lambda x: x.update(training=0), + lambda x: x['rows'][0].update(start=False), + lambda x: x['rows'][14].update(steps=True), + lambda x: x['rows'][14].update(steps=1.0), + lambda x: x['rows'][0]['expected_comparisons'][0].update(start=False), + lambda x: x.update(rows=tuple(x['rows'])), + ) + for mutate in mutations: + plan = p.build_plan(); mutate(plan) + with self.subTest(mutation=repr(mutate)), self.assertRaisesRegex(ValueError, 'type'): + p.validate_plan(plan) + + def test_unknown_fields_missing_overlays_and_reordering_are_rejected(self): + mutations = ( + lambda x: x.update(extra=True), + lambda x: x['rows'][0].update(extra='not allowed'), + lambda x: x['rows'][0]['expected_comparisons'][0].update(tolerance=0), + lambda x: x.update(overlays=['zero']), + lambda x: x['rows'].pop(0), + lambda x: x['rows'].append(copy.deepcopy(x['rows'][-1])), + lambda x: x['rows'].__setitem__(slice(0, 2), x['rows'][1::-1]), + lambda x: x['rows'][0].update(overlay='static25'), + lambda x: x['rows'][0].update(mode='light'), + lambda x: x['rows'][0].update(row_id='arbitrary'), + ) + for mutate in mutations: + plan = p.build_plan(); mutate(plan) + with self.subTest(mutation=repr(mutate)), self.assertRaises(ValueError): + p.validate_plan(plan) + + def test_gaps_wrong_parents_and_dropped_comparisons_never_pass(self): + mutations = ( + lambda x: x['rows'][13].update(start=6, steps=12), + lambda x: x['rows'][13].update(stop=17, steps=12), + lambda x: x['rows'][13].update(parent_row_id=None), + lambda x: x['rows'][12].update(parent_row_id=x['rows'][13]['row_id']), + lambda x: x['rows'][16].update(stop=999, steps=499), + lambda x: x['rows'][0].update(expected_comparisons=[]), + lambda x: x['rows'][2]['expected_comparisons'].pop(), + lambda x: x['rows'][16]['expected_comparisons'][-1]['candidate_row_ids'].pop(), + lambda x: x['rows'][16]['expected_comparisons'][-1].update(scope='final_only'), + ) + for mutate in mutations: + plan = p.build_plan(); mutate(plan) + with self.subTest(mutation=repr(mutate)), self.assertRaises(ValueError): + p.validate_plan(plan) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_prepare.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_prepare.py new file mode 100644 index 0000000..adda80c --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_prepare.py @@ -0,0 +1,216 @@ +"""Temporary-directory and small authority tests only; never touch real ROOT.""" +import copy +import hashlib +import json +from pathlib import Path +import tempfile +import types +import unittest +from unittest.mock import patch + +import engineering_prepare as p + + +LOCAL = Path(__file__).resolve().parent +AI = LOCAL.parent / 'pn-recruitment-audit-v1/publication.py' + + +def valid_status(): + final = dict(status=p.FINAL_STATUS, primary_trials_per_stage=120, repeats_per_stage=24, + zero_overlay_exact=True, static25_repeat_exact=True, old_integrity_pass=True, + frozen_five_reference_pass=True, frozen_eligibility_and_mask_unchanged=True, + base_weights_unchanged=True, training=False, learned_candidate=False) + scientific = dict(status=p.FINAL_STATUS, primary_trials_per_stage=120, technical_repeats_per_stage=24, + zero_overlay_exact=True, static25_repeat_exact=True, training=False, learned_candidate=False) + old = {'root-' + str(i): {'pass': True, 'checked': 1, 'mismatches': [], 'manifest_sha256': 'a'*64, 'checksums_sha256': 'b'*64} + for i in range(28)} + refs = {'pass_all': True, 'trials': [{'trial': name, 'exact': {key: True for key in ('step', 'neuron_idx', 'counts', 'v', 'g')}} for name in p.REFERENCES]} + return final, scientific, old, copy.deepcopy(old), refs + + +def planned(): + return [dict(name='fixed-' + str(i), cohort='seed_validation' if i < 60 else 'novel_to_aj', repeat=r) + for i in range(120) for r in ((0, 1) if i < 24 else (0,))] + + +class AuthorityTests(unittest.TestCase): + def test_required_final_status_and_all_28_roots_five_references(self): + p.validate_final_status(*valid_status()) + for mutation in ('status', 'hidden-learning', 'root', 'reference', 'root-hash'): + args = copy.deepcopy(valid_status()) + if mutation == 'status': + args[0]['status'] = 'INCOMPLETE' + elif mutation == 'hidden-learning': + args[1]['learned_candidate'] = True + elif mutation == 'root': + args[3].pop('root-27') + elif mutation == 'reference': + args[4]['trials'][2]['exact']['g'] = False + else: + args[3]['root-1']['manifest_sha256'] = 'c' * 64 + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + p.validate_final_status(*args) + + def test_first_actual_planned_primary_is_selected_without_dynamic_ranking(self): + rows = planned() + self.assertEqual(p.validate_planned(rows), rows[0]) + for altered in (rows[:-1], rows + [rows[0]], [dict(rows[0], name='../escape'), *rows[1:]], + [rows[1], rows[1], *rows[2:]]): + with self.assertRaises(ValueError): + p.validate_planned(altered) + + def test_three_external_sha_anchors_have_no_defaults_or_placeholders(self): + for anchors in ((None, 'a'*64, 'b'*64), ('', '', ''), ('x'*64, 'a'*64, 'b'*64)): + with self.assertRaisesRegex(ValueError, 'actual external'): + p._prepare(Path('not-read'), Path('not-read'), Path('not-read'), *anchors) + + def test_path_inventory_rejects_duplicates_and_traversal(self): + for name in ('../bad', '/absolute', 'a\\b', 'a/../b', 'C:/bad'): + with self.assertRaises(ValueError): + p._relative(name) + row = {'path': 'good.json', 'bytes': 3, 'sha256': 'a'*64} + with self.assertRaisesRegex(ValueError, 'Duplicate'): + p._index([row, row]) + + def test_pinned_AI_verifier_rechecks_complete_synthetic_seal(self): + helper = p.load_module('phase4an_test_AI_publication', AI, p.AI_HELPER_SHA) + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve() + payload = root / 'small.json'; payload.write_text('{}', encoding='utf-8') + row = {'path': payload.name, 'bytes': payload.stat().st_size, 'sha256': p.digest(payload)} + p.save(root / 'manifest.json', {'root': str(root), 'files': [row]}) + (root / 'checksums.sha256').write_text(row['sha256'] + ' small.json\n' + p.digest(root / 'manifest.json') + ' manifest.json\n', encoding='utf-8') + self.assertTrue(helper.verify_seal(root)['pass_all']) + payload.write_text('{"changed":true}', encoding='utf-8') + with self.assertRaises(ValueError): + helper.verify_seal(root) + + def test_finalizer_authority_helper_checks_external_anchors_and_both_full_seals(self): + helper = p.load_module('phase4an_test_AI_publication', AI, p.AI_HELPER_SHA) + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve() + model, experiment = root / p.AM_MODEL_REL, root / p.AM_EXP_REL + model.mkdir(parents=True); experiment.mkdir(parents=True) + p.save(model / 'payload.json', {'synthetic': True}) + p.save(experiment / 'protocol/preregistered.json', {'planned': []}) + p.save(experiment / 'audit/final-status.json', {'status': p.FINAL_STATUS}) + for tree in (model, experiment): + rows = [dict(path=path.relative_to(tree).as_posix(), bytes=path.stat().st_size, sha256=p.digest(path)) + for path in sorted(tree.rglob('*')) if path.is_file()] + p.save(tree / 'manifest.json', {'root': str(tree), 'files': rows}) + (tree / 'checksums.sha256').write_text(''.join(row['sha256'] + ' ' + row['path'] + '\n' for row in rows) + + p.digest(tree / 'manifest.json') + ' manifest.json\n', encoding='utf-8') + prereg = p.digest(experiment / 'protocol/preregistered.json') + anchors = dict(am_model_manifest_sha256=p.digest(model / 'manifest.json'), + am_experiment_manifest_sha256=p.digest(experiment / 'manifest.json'), am_final_sha256=p.digest(experiment / 'audit/final-status.json'), + am_preregistration_sha256=prereg, AI_publication_helper_sha256=p.AI_HELPER_SHA) + with patch.object(p, 'ROOT', root), patch.object(p, 'AM_PREREG_SHA', prereg), patch.object(p, 'load_module', return_value=helper): + self.assertEqual(set(p.verify_authority(anchors)), {'model', 'experiment'}) + (model / 'payload.json').write_text('changed', encoding='utf-8') + with self.assertRaises(ValueError): + p.verify_authority(anchors) + + def test_old_integrity_bridge_restores_import_path_and_rejects_failed_root(self): + import sys + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve(); expected = valid_status()[3] + before_path = list(sys.path) + def good(): + sys.path.insert(0, 'synthetic-old-context-search-path') + return copy.deepcopy(expected) + module = types.SimpleNamespace(ROOT=root, integrity=good) + with patch.object(p, 'load_module', return_value=module): + self.assertEqual(p._old_integrity(root, 'a'*64, expected), expected) + self.assertEqual(sys.path, before_path) + bad = copy.deepcopy(expected); bad['root-1']['pass'] = False + module.integrity = lambda: bad + with patch.object(p, 'load_module', return_value=module), self.assertRaisesRegex(ValueError, '28-root'): + p._old_integrity(root, 'a'*64, expected) + self.assertEqual(sys.path, before_path) + + def test_all_144_stage_completions_must_match_sealed_inventory(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve(); experiment = root / p.AM_EXP_REL; experiment.mkdir(parents=True) + rows, index, completed = planned(), {}, [] + def add(relative, value): + path = experiment / relative; p.save(path, value) + index[relative] = {'path': relative, 'bytes': path.stat().st_size, 'sha256': p.digest(path)} + for row in rows: + prefix = 'gpu/zero/' + row['name'] + '-r' + str(row['repeat']) + '/' + folder = experiment / prefix.rstrip('/') + case = root / p.AK_REL / 'cases' / row['cohort'] / row['name'] + add(prefix + 'summary.json', {'case': row['name'], 'complete': True, 'pass_all': True, 'all_finite': True, + 'steps': 1000, 'completed_ticks': 1000}) + add(prefix + 'comparison.json', {'pass_all': True, 'overlay_name': 'zero', 'case_dir': str(case), 'right': str(folder), + 'mode': 'zero', 'left': str(root / p.AK_REL / 'gpu' / row['cohort'] / (row['name'] + '-r' + str(row['repeat']))), + 'checks': {'synthetic_gate': True}, 'failed_checks': [], 'full_queue_reconstructed': True}) + inventory = [dict(path=k[len(prefix):], bytes=v['bytes'], sha256=v['sha256']) for k, v in sorted(index.items()) if k.startswith(prefix)] + add(prefix + 'output-hashes.json', inventory) + completed.append(dict(name=row['name'], repeat=row['repeat'], output_hashes_sha256=index[prefix+'output-hashes.json']['sha256'], + comparison_sha256=index[prefix+'comparison.json']['sha256'])) + receipt = {'stage': 'zero', 'pass_all': True, 'trials': 144, 'primary_trials': 120, 'technical_repeats': 24, + 'training': False, 'completed': completed} + add('audit/zero-complete.json', receipt) + self.assertTrue(p.validate_stage(experiment, index, 'zero', rows, root)['pass_all']) + wrong = copy.deepcopy(rows); wrong[0], wrong[1] = wrong[1], wrong[0] + with self.assertRaisesRegex(ValueError, 'order/identity'): + p.validate_stage(experiment, index, 'zero', wrong, root) + # A missing sealed payload entry cannot be hidden by a PASS flag. + altered = dict(index); altered.pop('gpu/zero/fixed-0-r0/summary.json') + with self.assertRaises(ValueError): + p.validate_stage(experiment, altered, 'zero', rows, root) + + +class PreparationSafetyTests(unittest.TestCase): + def test_exclusive_layout_allows_only_nonexistent_or_empty_experiment(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve() + model = root / 'models/male-cns-lif/olfactory-conditioning-lab-v1'; model.mkdir(parents=True) + experiment = root / 'experiments/phase4an-engineering-validation'; experiment.parent.mkdir() + self.assertEqual(p._layout(root, model, experiment)[2], experiment) + experiment.mkdir(); self.assertEqual(p._layout(root, model, experiment)[2], experiment) + (experiment / 'preserve.txt').write_text('partial', encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'populated'): + p._layout(root, model, experiment) + self.assertEqual((experiment / 'preserve.txt').read_text(), 'partial') + + def test_required_runtime_workflow_scope_sources_must_all_exist(self): + with tempfile.TemporaryDirectory() as directory: + model = Path(directory).resolve() + for name in p.SOURCE_FILES: + (model / name).write_text('# pure test source\n', encoding='utf-8') + result = p.source_inventory(model) + self.assertEqual(len(result), 21) + self.assertIn(str(model / 'engineering_workflow.py'), result) + self.assertIn(str(model / 'engineering_scopes.py'), result) + self.assertIn(str(model / 'engineering_analysis.py'), result) + (model / 'engineering_workflow.py').unlink() + with self.assertRaisesRegex(ValueError, 'Required engineering source'): + p.source_inventory(model) + + def test_byte_budget_reserve_is_not_a_license_to_delete_partials(self): + with tempfile.TemporaryDirectory() as directory: + experiment = Path(directory).resolve() / 'new' + experiment.mkdir(); (experiment / 'partial').write_bytes(b'x') + with patch.object(p.shutil, 'disk_usage', return_value=types.SimpleNamespace(free=30 * 1024**3)): + self.assertEqual(p._budget(experiment)['used_bytes'], 1) + with patch.object(p.shutil, 'disk_usage', return_value=types.SimpleNamespace(free=5 * 1024**3)): + with self.assertRaisesRegex(ValueError, 'Insufficient'): + p._budget(experiment) + self.assertTrue((experiment / 'partial').is_file()) + + def test_row_mapping_rejects_unplanned_or_escaping_paths(self): + with tempfile.TemporaryDirectory() as directory: + exp = Path(directory).resolve(); case = exp / 'cases/safe-row'; case.mkdir(parents=True) + reg = {'plan': {'rows': [{'row_id': 'safe-row'}]}, 'cases': {'safe-row': {'case_dir': str(case)}}} + with patch.object(p, 'EXP', exp): + self.assertEqual(p.row_path(reg, 'safe-row'), case) + with self.assertRaises(ValueError): + p.row_path(reg, '../outside') + reg['cases']['safe-row']['case_dir'] = str(exp) + with self.assertRaises(ValueError): + p.row_path(reg, 'safe-row') + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_queue_audit.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_queue_audit.py new file mode 100644 index 0000000..0415acd --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_queue_audit.py @@ -0,0 +1,229 @@ +"""Small synthetic CSR/record tests only; no brain, GPU or artifact writes.""" +import ast +import copy +import hashlib +import json +from pathlib import Path +import struct +import unittest + +import numpy as np + +import engineering_queue_audit as audit + + +def digest(array): + return hashlib.sha256(np.ascontiguousarray(array).tobytes()).hexdigest() + + +def queue_row(tick, edges, accepted): + # Independent simple whole-payload construction, unlike the chunked audit. + edge_bytes = np.asarray(edges, dtype='= 22) + request_rows.append([tick, neuron, 1, accepted, 1 - accepted]) + for when, neuron in events: + if when == tick: + last[neuron] = tick + ref = np.maximum(22 - (tick - last), 0).astype(np.int32) + refs.append(ref) + edges = [j for when, neuron in events if when == tick - 18 for j in range(int(ptr[neuron]), int(ptr[neuron + 1]))] + expanded.append(np.asarray(edges, np.int64)) + masks.append((ref[dst[edges]] == 0).astype(np.uint8)) + + def record(start, stop): + selected = (ticks >= start) & (ticks < stop) + ev_t, ev_n = (ticks[selected] - start).copy(), nodes[selected].copy() + rows = np.array([row for row in request_rows if start <= row[0] < stop], np.int64).reshape(-1, 5) + rows[:, 0] -= start + return {'steps': stop - start, 'global_tick_offset': start, + 'summary': {'pass_all': False}, # Never treated as authority. + 'state': {'step': ev_t, 'neuron_idx': ev_n, 'counts': np.bincount(ev_n, minlength=n).astype(np.int32), + 'v': np.zeros(n, np.float64), 'g': np.zeros(n, np.float64), 'ref': refs[stop - 1].copy()}, + 'requests': rows, + 'queues': [queue_row(t - start, edge_id[expanded[t]], masks[t]) for t in range(start, stop)], + 'state_hashes': [{'tick': t - start, 'v_sha256': digest(np.zeros(n, np.float64)), + 'g_sha256': digest(np.zeros(n, np.float64)), 'ref_sha256': digest(refs[t])} for t in range(start, stop)]} + intervals = list(zip([0, *cuts], [*cuts, total])) + return {'mono': record(0, total), 'parts': [record(a, b) for a, b in intervals], 'ptr': ptr, 'dst': dst, + 'edge_id': edge_id, 'effective_weights': weights, 'requests': requests, 'cuts': list(cuts)} + + +def run(data): + return audit.audit_queues(data['mono'], data['parts'], **{k: data[k] for k in + ('ptr', 'dst', 'edge_id', 'effective_weights', 'requests', 'cuts')}) + + +class QueueAudit(unittest.TestCase): + def test_real_edge_witness_and_local_headers_cross_18_19(self): + data = fixture() + result = run(data) + self.assertTrue(result['pass_all'], result['differences']) + witness = result['first_witness'] + self.assertEqual((witness['boundary_tick'], witness['source_spike_tick'], witness['arrival_tick']), (37, 22, 40)) + self.assertEqual((witness['source_neuron_idx'], witness['target_neuron_idx'], witness['global_edge_id']), (0, 2, 10)) + self.assertEqual(witness['effective_weight_float64_le_hex'], np.asarray(.001, dtype='= start) & (ticks < stop) + event_ticks, event_nodes = ticks[selected] - np.int32(start), nodes[selected].copy() + v = np.array([-.05, -.04, -.03], np.float64) + g = np.array([-0., float(stop - 1), -.001], np.float64) + ref = np.zeros(n, np.int32) + state = dict(step=event_ticks, neuron_idx=event_nodes, counts=np.bincount(event_nodes, minlength=n).astype(np.int32), + v=v, g=g, ref=ref) + raw_global = np.zeros((19, n), np.uint8) + for tick, node in zip(ticks, nodes): + if max(0, stop - 19) <= tick < stop: + raw_global[tick % 19, node] = 1 + normalized = raw_global[(np.arange(19) + stop % 19) % 19] + raw_local = normalized[(np.arange(19) - length % 19) % 19].copy() + generated_all = np.repeat(np.arange(1, stop + 1, dtype=np.float32)[:, None], k, axis=1) + applied_all = np.concatenate((np.zeros((18, k), np.float32), generated_all))[:stop] + pending = np.zeros((18, k), np.float32) + kept = min(stop, 18); pending[-kept:] = generated_all[-kept:] + apl_state = np.full(2, np.float32(stop + 1), np.float32) + terminal = dict(spike_ring=raw_local, apl_state=apl_state, apl_pending=pending, next_tick=np.asarray(length, np.int64)) + trace = dict(generated=generated_all[start:stop].copy(), applied=applied_all[start:stop].copy(), + drive=np.repeat(np.arange(start, stop, dtype=np.float32)[:, None], 2, axis=1), + state=np.repeat(np.arange(start + 2, stop + 2, dtype=np.float32)[:, None], 2, axis=1)) + hashes, queues = [], [] + for local, global_tick in enumerate(range(start, stop)): + hashes.append(dict(tick=local, v_sha256=sha(v), g_sha256=sha(np.array([-0., float(global_tick), -.001], np.float64)), + ref_sha256=sha(ref))) + edges, accepted = np.array([global_tick], np.int64), np.array([global_tick % 2], np.uint8) + queues.append(dict(tick=local, count=1, accepted_count=int(accepted.sum()), ordered_edge_sha256=sha(edges), + accepted_mask_sha256=sha(accepted), joint_sha256=hashlib.sha256( + str(local).encode() + edges.tobytes() + accepted.tobytes()).hexdigest())) + requests = np.array([[int(t), int(i), 1, 1, 0] for t, i in zip(event_ticks, event_nodes)], np.int64).reshape(-1, 5) + if blank_requests: + requests = np.empty((0, 5), np.int64) + summary = dict(complete=True, pass_all=True, all_finite=True, steps=length, completed_ticks=length, nodes=n, + precision='float64', apl_precision='float32', apl_delay_steps=18, + checkpoint_continuation_used=start > 0, global_tick_offset=start, global_next_tick=stop, + effective_weights_raw_sha256='e' * 64, diagnostic_mode='reference' if reference else 'light', + full_APL_history_recorded=reference, full_tick_state_hashes_recorded=reference, + full_queue_hashes_recorded=reference) + return dict(source='synthetic', state=state, terminal=terminal, requests=requests, steps=length, + global_tick_offset=start, summary=summary, traces=trace if reference else {}, + state_hashes=hashes if reference else None, queues=queues if reference else None, + terminal_origin='synthetic_recorded') + + +class SliceTests(unittest.TestCase): + def test_reference_slice_all_observations_exact_and_inputs_unchanged(self): + candidate, whole = record(20, 44), record(0, 64) + before = candidate['terminal']['spike_ring'].tobytes(), whole['requests'].tobytes() + report = s.compare_slice(candidate, whole) + self.assertTrue(report['numerical_exact'], report['failed_checks']) + self.assertTrue(report['observed_scope']['candidate_per_tick_state_hashes']) + self.assertTrue(report['observed_scope']['candidate_per_tick_queue_fields']) + self.assertFalse(report['observed_scope']['queue_joint_hashes_compared']) + self.assertNotEqual(candidate['queues'][0]['joint_sha256'], whole['queues'][20]['joint_sha256']) + self.assertEqual(before, (candidate['terminal']['spike_ring'].tobytes(), whole['requests'].tobytes())) + json.dumps(report, allow_nan=False) + + def test_light_uses_final_raw_hash_but_does_not_fabricate_optional_history(self): + report = s.compare_slice(record(20, 44, reference=False), record(0, 64)) + self.assertTrue(report['numerical_exact']) + self.assertTrue(report['observed_scope']['final_state_against_reference_tick_hash']) + for name in ('candidate_APL_histories', 'candidate_per_tick_state_hashes', 'candidate_per_tick_queue_fields'): + self.assertFalse(report['observed_scope'][name]) + self.assertNotIn('slice.per_tick_state_hashes', report['checks']) + self.assertTrue(any('unobserved' in item for item in report['limitations'])) + + def test_short_fresh_positive_zero_padding_and_prefix_events(self): + self.assertTrue(s.compare_slice(record(0, 5), record(0, 64))['numerical_exact']) + candidate = record(0, 5, reference=False) + candidate['terminal']['apl_pending'][0, 0] = np.float32(-0.) + report = s.compare_slice(candidate, record(0, 64)) + self.assertFalse(report['checks']['slice.APL_pending']) + + def test_blank_requests_are_valid_but_wrong_acceptance_is_not_equal(self): + self.assertTrue(s.compare_slice(record(20, 44, blank_requests=True), record(0, 64, blank_requests=True))['numerical_exact']) + candidate = record(20, 44) + candidate['requests'][0, 3:] = [0, 1] + report = s.compare_slice(candidate, record(0, 64)) + self.assertFalse(report['checks']['slice.requests_five_fields']) + + def test_wrong_count_or_final_hash_binding_is_invalid_not_a_pass(self): + for mutation in ('count', 'terminal', 'last_hash'): + candidate = record(20, 44) + if mutation == 'count': candidate['state']['counts'][0] += 1 + elif mutation == 'terminal': candidate['state']['g'][1] += 1. + else: candidate['state_hashes'][-1]['g_sha256'] = '0' * 64 + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + s.compare_slice(candidate, record(0, 64)) + + def test_signed_zero_light_terminal_vs_reference_hash_is_detected(self): + candidate = record(20, 44, reference=False) + candidate['state']['g'][0] = 0. + report = s.compare_slice(candidate, record(0, 64)) + self.assertFalse(report['checks']['slice.final_g_raw_hash']) + + def test_interior_trace_state_hash_and_queue_mismatch_are_not_hidden(self): + candidate = record(20, 44) + candidate['traces']['drive'][1, 0] += np.float32(1.) + candidate['state_hashes'][1]['v_sha256'] = '0' * 64 + candidate['queues'][1]['accepted_mask_sha256'] = '1' * 64 + report = s.compare_slice(candidate, record(0, 64)) + for key in ('slice.APL_trace.drive', 'slice.per_tick_state_hashes', 'slice.queue_order_and_acceptance'): + self.assertFalse(report['checks'][key]) + + def test_missing_reference_observations_and_invalid_bounds_fail_closed(self): + for mutation in ('light_mono', 'light_labeled_full_mono', 'mono_hashes', 'candidate_traces', 'partial_trace', 'out_of_bounds', 'nonzero_mono'): + candidate, mono = record(20, 44), record(0, 64) + if mutation == 'light_mono': mono = record(0, 64, reference=False) + elif mutation == 'light_labeled_full_mono': mono['summary']['diagnostic_mode'] = 'light' + elif mutation == 'mono_hashes': mono['state_hashes'] = None + elif mutation == 'candidate_traces': candidate['traces'] = {} + elif mutation == 'partial_trace': candidate['traces'].pop('drive') + elif mutation == 'out_of_bounds': candidate = record(60, 65) + else: mono = record(1, 65) + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + s.compare_slice(candidate, mono) + + def test_summary_type_scope_nonfinite_and_paths_rejected(self): + for mutation in ('bool', 'failed', 'offset', 'mode', 'weight', 'nan'): + candidate = record(20, 44) + if mutation == 'bool': candidate['summary']['steps'] = True + elif mutation == 'failed': candidate['summary']['pass_all'] = False + elif mutation == 'offset': candidate['summary']['global_tick_offset'] = 0 + elif mutation == 'mode': candidate['summary']['diagnostic_mode'] = 'unknown' + elif mutation == 'weight': candidate['summary'].pop('effective_weights_raw_sha256') + else: candidate['state']['g'][0] = np.nan + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + s.compare_slice(candidate, record(0, 64)) + with self.assertRaises(ValueError): s.compare_slice('/never/open/this', record(0, 64)) + + +class CheckpointTests(unittest.TestCase): + def test_local_phase_normalization_without_segment_cumulative_counts_confusion(self): + candidate, prefix = record(20, 64), record(0, 64) + self.assertFalse(s.ec.bit_equal(candidate['terminal']['spike_ring'], prefix['terminal']['spike_ring'])) + self.assertFalse(s.ec.bit_equal(candidate['state']['counts'], prefix['state']['counts'])) + report = s.compare_checkpoint(candidate, prefix) + self.assertTrue(report['numerical_exact'], report['failed_checks']) + self.assertFalse(report['observed_scope']['counts_compared']) + self.assertFalse(report['observed_scope']['per_tick_state_histories_compared']) + + def test_signed_zero_different_stop_or_effective_weights_cannot_pass(self): + for mutation in ('zero', 'stop', 'weight'): + candidate, prefix = record(20, 64, reference=False), record(0, 64, reference=False) + if mutation == 'zero': candidate['state']['g'][0] = 0. + elif mutation == 'stop': prefix = record(0, 63, reference=False) + else: candidate['summary']['effective_weights_raw_sha256'] = 'f' * 64 + with self.subTest(mutation=mutation): + self.assertFalse(s.compare_checkpoint(candidate, prefix)['numerical_exact']) + + +class ParentCarryTests(unittest.TestCase): + def test_short_parent_carry_observed_and_json_safe(self): + report = s.compare_parent_carry(record(20, 25), record(0, 20)) + self.assertTrue(report['numerical_exact'], report['failed_checks']) + self.assertEqual(report['observed_scope']['unwritten_spike_slot_count'], 14) + self.assertEqual(report['observed_scope']['unconsumed_APL_row_count'], 13) + self.assertEqual(report['observed_scope']['applied_parent_APL_rows_compared'], 5) + json.dumps(report, allow_nan=False) + + def test_unwritten_or_unconsumed_corruption_detected_even_if_terminal_is_self_consistent(self): + for mutation in ('ring', 'pending', 'applied'): + candidate = record(20, 25) + if mutation == 'ring': candidate['terminal']['spike_ring'][10, 0] ^= np.uint8(1) + elif mutation == 'pending': candidate['terminal']['apl_pending'][0, 0] += np.float32(1.) + else: candidate['traces']['applied'][0, 0] += np.float32(1.) + with self.subTest(mutation=mutation): + self.assertFalse(s.compare_parent_carry(candidate, record(0, 20))['numerical_exact']) + + def test_light_missing_optional_applied_trace_is_not_fabricated(self): + report = s.compare_parent_carry(record(20, 25, reference=False), record(0, 20)) + self.assertTrue(report['numerical_exact']) + self.assertFalse(report['observed_scope']['applied_parent_APL_prefix_checked']) + self.assertIsNone(report['observed_scope']['applied_parent_APL_rows_compared']) + self.assertNotIn('parent.applied_APL_prefix', report['checks']) + + def test_long_segment_discloses_no_unwritten_slots_and_wrong_parent_clock_fails(self): + report = s.compare_parent_carry(record(20, 44), record(0, 20)) + self.assertTrue(report['numerical_exact']) + self.assertFalse(report['observed_scope']['unwritten_spike_slots_checked']) + self.assertFalse(report['observed_scope']['unconsumed_APL_prefix_checked']) + self.assertEqual(report['observed_scope']['applied_parent_APL_rows_compared'], 18) + self.assertFalse(s.compare_parent_carry(record(20, 44), record(0, 19))['numerical_exact']) + with self.assertRaises(ValueError): s.compare_parent_carry(record(0, 5), record(0, 20)) + + def test_long_light_carry_scope_explicitly_has_no_observed_carry_array(self): + report = s.compare_parent_carry(record(20, 44, reference=False), record(0, 20)) + self.assertTrue(report['numerical_exact']) + self.assertFalse(report['observed_scope']['any_carry_values_directly_compared']) + self.assertTrue(any('clock/shape/weight identity only' in line for line in report['limitations'])) + self.assertFalse(report['complete_dynamical_equivalence_proven']) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_workflow.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_workflow.py new file mode 100644 index 0000000..9f04dbd --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_engineering_workflow.py @@ -0,0 +1,207 @@ +import copy +import hashlib +import json +from pathlib import Path +import tempfile +import types +import unittest +from unittest.mock import patch + +import numpy as np + +import engineering_workflow as w +from test_engineering_inputs import spec + + +def template(row): + return dict(schema='phase4an-engineering-case-template-v1', row_id=row['row_id'], original_spec=spec(), + start=row['start'], stop=row['stop'], budget_root='/project/new', output_budget_bytes=8 * 1024**3) + + +class Workflow(unittest.TestCase): + def test_only_exact_plan_row_and_safe_output_identity(self): + reg = {'plan': w.PLAN.build_plan()} + row = reg['plan']['rows'][0] + self.assertEqual(w.row_from(reg, row['row_id']), row) + for bad in ('unknown', '../escape', '/absolute'): + with self.assertRaises(ValueError): w.row_from(reg, bad) + for bad in ('../escape', '/absolute', 'a/b', 'a\\b', '', 'a:b'): + with self.assertRaises(ValueError): w.locations(bad) + reg['plan']['rows'][0]['steps'] = True + with self.assertRaises(ValueError): w.row_from(reg, row['row_id']) + + def test_real_parent_is_required_only_for_later_segments(self): + for row in w.PLAN.build_plan()['rows']: + before = template(row) + pin = None if row['parent_row_id'] is None else 'a' * 64 + derived, audit = w.derive_case(before, row, pin) + self.assertEqual(derived['steps'], row['steps']) + self.assertEqual(before, template(row)) + self.assertEqual(audit['input_interval'], [row['start'], row['stop']]) + with self.assertRaises(ValueError): + w.derive_case(before, row, 'a' * 64 if pin is None else None) + + def test_forged_template_and_boolean_interval_rejected(self): + row = w.PLAN.build_plan()['rows'][0] + for key, value in (('schema', 'wrong'), ('row_id', 'wrong'), ('start', False), ('stop', 18)): + changed = template(row); changed[key] = value + with self.subTest(key=key), self.assertRaises(ValueError): w.derive_case(changed, row, None) + changed = template(row); changed['extra'] = True + with self.assertRaises(ValueError): w.derive_case(changed, row, None) + + def test_json_comparison_does_not_conflate_booleans_and_numbers(self): + self.assertFalse(w.same_json({'n': True}, {'n': 1})) + self.assertFalse(w.same_json({'n': 1.}, {'n': 1})) + with self.assertRaises(ValueError): w.canonical({'n': float('nan')}) + + def test_actual_precision_flags_are_checked_not_merely_recorded(self): + component = dict(cuda_matmul_allow_tf32=False, cudnn_allow_tf32=True, deterministic_algorithms=False, + cudnn_deterministic=False, cudnn_benchmark=False, float32_matmul_precision='highest', + CUBLAS_WORKSPACE_CONFIG=None, device_name='NVIDIA GeForce RTX 4090', torch='2.test', cuda='13.0', + numpy='2.test', precision_configuration_changed_by_component=False) + recorded = dict(gpu_uuid=w.GPU, settings_changed_by_environment_recorder=False, + matmul_allow_tf32=False, cudnn_allow_tf32=True, deterministic_algorithms=False, + cudnn_deterministic=False, cudnn_benchmark=False, float32_matmul_precision='highest', + CUBLAS_WORKSPACE_CONFIG=None, device_name='NVIDIA GeForce RTX 4090', torch_version='2.test', + torch_cuda_version='13.0', numpy_version='2.test') + w.verify_environment(recorded, component) + for key, value in [('cuda_matmul_allow_tf32', True), ('float32_matmul_precision', 'high'), + ('precision_configuration_changed_by_component', True), ('cudnn_allow_tf32', False)]: + altered = dict(component, **{key: value}) + with self.subTest(key=key), self.assertRaises(ValueError): w.verify_environment(recorded, altered) + with self.assertRaises(ValueError): w.verify_environment(dict(recorded, matmul_allow_tf32=0), component) + + def test_npz_real_compression_measured_not_assumed(self): + with tempfile.TemporaryDirectory() as name: + root = Path(name) + with (root / 'compressed.npz').open('xb') as stream: + np.savez_compressed(stream, v=np.zeros(50, np.float64)) + measured = w.npz_storage(root / 'compressed.npz') + self.assertGreater(measured['uncompressed_entry_bytes'], measured['compressed_entry_bytes']) + self.assertEqual(measured['entries'][0]['compression'], 8) + with (root / 'stored.npz').open('xb') as stream: + np.savez(stream, v=np.zeros(50, np.float64)) + with self.assertRaises(ValueError): w.npz_storage(root / 'stored.npz') + + def test_resource_limit_preserves_existing_payload(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + path = Path(name) / 'payload.bin' + path.write_bytes(b'not deleted') + reg = {'budget': {'output_budget_bytes': 1, 'minimum_free_reserve_bytes': 1}} + with self.assertRaises(ValueError): w.disk_budget(reg) + self.assertEqual(path.read_bytes(), b'not deleted') + + def _temporary_gate(self, root): + for name in ('audit', 'logs', 'receipts', 'runs', 'cases'): + (root / name).mkdir() + reg = {'plan': w.PLAN.build_plan(), 'budget': {'output_budget_bytes': 1 << 20, + 'minimum_free_reserve_bytes': 1}} + return reg, types.SimpleNamespace(read_registration=lambda sha: copy.deepcopy(reg)) + + def test_controller_refuses_partial_without_rerun_or_deletion(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); reg, prep = self._temporary_gate(root) + paths = w.locations(reg['plan']['rows'][0]['row_id']) + paths['stdout'].write_bytes(b'original evidence') + with (patch.object(w, 'preparation', return_value=prep), patch.object(w, 'preflight') as pre, + patch.object(w.subprocess, 'run') as runner): + with self.assertRaisesRegex(ValueError, 'Partial row'): + w.run_registered('a' * 64) + runner.assert_not_called(); pre.assert_not_called() + self.assertEqual(paths['stdout'].read_bytes(), b'original evidence') + self.assertFalse(paths['execution'].exists()) + self.assertTrue((root / 'audit/stopped.json').exists()) + + def test_nonzero_worker_exit_cannot_become_completed_receipt(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); reg, prep = self._temporary_gate(root) + paths = w.locations(reg['plan']['rows'][0]['row_id']) + failure = types.SimpleNamespace(returncode=1) + with (patch.object(w, 'preparation', return_value=prep), patch.object(w, 'preflight'), + patch.object(w.subprocess, 'run', return_value=failure) as runner): + with self.assertRaisesRegex(ValueError, 'Worker failed'): + w.run_registered('a' * 64) + self.assertEqual(runner.call_count, 1) + self.assertTrue(paths['stdout'].exists() and paths['stderr'].exists()) + self.assertFalse(paths['execution'].exists()) + self.assertTrue((root / 'audit/stopped.json').exists()) + + def test_stopped_inventory_cannot_auto_resume(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); _, prep = self._temporary_gate(root) + (root / 'audit/stopped.json').write_text('{}') + with patch.object(w, 'preparation', return_value=prep), patch.object(w.subprocess, 'run') as runner: + with self.assertRaisesRegex(ValueError, 'Stopped/completed'): + w.run_registered('a' * 64) + runner.assert_not_called() + + def _completion(self, reg, root): + entries = [] + for row in reg['plan']['rows']: + path = w.locations(row['row_id'])['execution'] + path.write_bytes(row['row_id'].encode()) + entries.append(dict(row_id=row['row_id'], execution_sha256=w.R.digest(path), reverified_existing=False)) + return dict(complete=True, registration_sha256='a' * 64, expected_calls=32, executed_ticks=11074, + training=False, learning_pass_claimed=False, completed=entries) + + def test_inventory_rechecks_every_actual_receipt(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); reg, _ = self._temporary_gate(root) + complete = self._completion(reg, root) + w.R._save(root / 'audit/execution-complete.json', complete) + with patch.object(w, 'verify_execution', side_effect=lambda r, s, row: dict(row=row)) as verify: + records = w.verified_inventory(reg, 'a' * 64) + self.assertEqual(verify.call_count, 32) + self.assertEqual(list(records), [row['row_id'] for row in reg['plan']['rows']]) + + def test_inventory_rejects_reorder_missing_and_changed_receipt(self): + for corruption in ('reorder', 'missing', 'changed', 'claimed_training', 'wrong_ticks'): + with self.subTest(corruption=corruption), tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); reg, _ = self._temporary_gate(root) + complete = self._completion(reg, root) + if corruption == 'reorder': complete['completed'].reverse() + if corruption == 'missing': complete['completed'].pop() + if corruption == 'changed': w.locations(reg['plan']['rows'][0]['row_id'])['execution'].write_bytes(b'changed') + if corruption == 'claimed_training': complete['training'] = True + if corruption == 'wrong_ticks': complete['executed_ticks'] += 1 + w.R._save(root / 'audit/execution-complete.json', complete) + with patch.object(w, 'verify_execution'), self.assertRaises(ValueError): + w.verified_inventory(reg, 'a' * 64) + + def test_seal_contains_nested_manifests_and_self_checksum(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name) + (root / 'runs' / 'one').mkdir(parents=True) + (root / 'runs/one/manifest.json').write_bytes(b'nested evidence') + (root / 'payload').write_bytes(b'payload') + result = w.seal_experiment() + manifest = json.loads((root / 'manifest.json').read_text()) + self.assertEqual([row['path'] for row in manifest['files']], ['payload', 'runs/one/manifest.json']) + lines = (root / 'checksums.sha256').read_text().splitlines() + self.assertIn(result['manifest_sha256'] + ' manifest.json', lines) + self.assertEqual(len(lines), 3) + self.assertEqual(result['checksums_sha256'], hashlib.sha256((root / 'checksums.sha256').read_bytes()).hexdigest()) + before = (root / 'manifest.json').read_bytes() + with self.assertRaises(ValueError): w.seal_experiment() + self.assertEqual((root / 'manifest.json').read_bytes(), before) + + def test_seal_rejects_bytecode_instead_of_silently_losing_it(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); (root / '__pycache__').mkdir() + (root / '__pycache__/a.pyc').write_bytes(b'cache') + with self.assertRaises(ValueError): w.seal_experiment() + self.assertFalse((root / 'manifest.json').exists()) + + def test_finalize_rejects_recomputed_difference_before_sealing(self): + with tempfile.TemporaryDirectory() as name, patch.object(w, 'EXP', Path(name)): + root = Path(name); _, prep = self._temporary_gate(root) + w.R._save(root / 'audit/engineering-analysis.json', dict(status='saved')) + with patch.object(w, 'evaluate', return_value=({}, {'status': 'different'})), patch.object(w, 'seal_experiment') as seal: + with self.assertRaisesRegex(ValueError, 'Recomputed analysis'): + w.finalize('a' * 64) + seal.assert_not_called() + self.assertFalse((root / 'audit/final-status.json').exists()) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_evaluation_plan.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_evaluation_plan.py new file mode 100644 index 0000000..c4c879d --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_evaluation_plan.py @@ -0,0 +1,195 @@ +"""Synthetic planning tests only; no input generation, files or brain runs.""" +import copy +from collections import Counter +import unittest +from unittest.mock import patch + +import evaluation_plan as m +import trial_plan + + +def synthetic_config(trial, reversal_points=(0, 50)): + """An explicit TEST choice, not a proposed production default. + + Controls here use a simple fixed epoch fraction; the planner never supplies + this policy. Only paired/reversal checkpoints track actual scheduled USs. + """ + config = dict(schema=m.CONFIG_SCHEMA, probe_steps=1200, cs_start_tick=0, + cs_stop_tick_exclusive=1000, reversal_checkpoints=list(reversal_points), + baseline_execution="execute_each_snapshot", snapshot_binding_policy="explicit_completed_epochs", + snapshot_bindings=[]) + for block in trial["plan"]["blocks"]: + for suite in block["suites"]: + points = list(reversal_points) if suite["assay"] == "reversal" else block["evaluation_checkpoints"] + for arm in suite["arms"]: + for point in points: + if point in (0, 50): + completed = 0 if point == 0 else len(arm["epochs"]) + elif arm["arm"] in ("paired", "reversal", "no_reversal"): + rewarded = [i + 1 for i, e in enumerate(arm["epochs"]) if e["us"]["requested"]] + completed = rewarded[point - 1] + else: + completed = point * len(arm["epochs"]) // 50 + config["snapshot_bindings"].append(dict(block_id=block["block_id"], assay=suite["assay"], + arm=arm["arm"], checkpoint=point, completed_epochs=completed)) + return config + + +class EvaluationPlan(unittest.TestCase): + @classmethod + def setUpClass(cls): + # Existing synthetic master from the trial-plan tests, not a real choice. + cls.trial = trial_plan.build_trial_plan(461) + cls.config = synthetic_config(cls.trial) + cls.plan = m.build_evaluation_plan(cls.trial, cls.config) + + def test_public_full_authority_reconstruction(self): + self.assertTrue(m.validate_evaluation_plan(self.plan, self.trial, self.config)) + + def test_exact_inventory_budget_and_no_execution_claim(self): + b = self.plan["budget"] + self.assertEqual(b["probes_by_assay"], dict(normal=3920, delayed=3920, reversal=320, retention=480)) + self.assertEqual(b["evaluation_probes"], 8640) + self.assertEqual(b["snapshot_count"], 4320) + self.assertEqual(b["evaluation_steps"], 10368000) + self.assertEqual(b["planned_scientific_steps_including_evaluation"], 117968000) + self.assertEqual(b["independent_evaluation_inputs"], 80) + for name in ("execution_authorized", "training_executed", "inputs_generated", "candidate_generated", + "learning_pass_claimed", "scientific_gate_added", "external_source_and_snapshot_authority_verified"): + self.assertIs(self.plan[name], False) + + def test_full_cells_and_all_control_coverage(self): + cells = {} + for p in self.plan["probes"]: + cells.setdefault((p["mode"], p["dan_type"], p["original_reinforced_cue"]), set()).add(p["training_replicate"]) + self.assertEqual(p["odor"], trial_plan.ODORS[p["cue"]]) + self.assertFalse(p["weight_updates_enabled"]) + self.assertFalse(p["US_requested"]) + self.assertFalse(p["feedback_to_training_or_idle"]) + self.assertFalse(p["adds_training_replicate"]) + self.assertEqual(len(cells), 8) + self.assertTrue(all(v == {1, 2, 3, 4, 5} for v in cells.values())) + first = self.trial["plan"]["blocks"][0]["block_id"] + normal = [p for p in self.plan["probes"] if p["block_id"] == first and p["assay"] == "normal"] + self.assertEqual(Counter(p["arm"] for p in normal), Counter({arm: 14 for arm in trial_plan.ARMS})) + + def test_stream_keys_disjoint_and_reused_as_paired_probes(self): + non_eval = {r["seed"] for r in self.trial["seed_manifest"]["independent_streams"] if r["split"] != "evaluation"} + grouped = {} + for p in self.plan["probes"]: + grouped.setdefault((p["block_id"], p["cue"]), set()).add((p["input"]["seed"], p["input"]["stream_id"])) + self.assertEqual(p["input"]["semantic_key"], [p["block_id"], "evaluation", "CS", p["cue"], "00", "base-probe"]) + self.assertEqual(p["input"]["seed_bits"], 63) + seeds = {next(iter(values))[0] for values in grouped.values()} + self.assertEqual(len(grouped), 80) + self.assertTrue(all(len(v) == 1 for v in grouped.values())) + self.assertEqual(len(seeds), 80) + self.assertFalse(seeds & non_eval) + self.assertTrue(any(seed > 2**32 for seed in seeds)) + + def test_reversal_original_baseline_and_orientation(self): + for s in self.plan["snapshots"]: + if s["assay"] == "reversal": + self.assertEqual(s["baseline_snapshot_id"], s["block_id"] + "/normal/paired/checkpoint-00") + if s["checkpoint"] == 0: + self.assertEqual(s["delta_source"], {"kind": "fork", "source": s["block_id"] + "/normal/paired/terminal-delta"}) + self.assertEqual(s["neural_state_source"]["kind"], "common_frozen_reset") + + def test_retention_same_idle_lineage_independent_forks(self): + grouped = {} + for s in self.plan["snapshots"]: + if s["assay"] != "retention": + continue + src = s["neural_state_source"] + grouped.setdefault((s["block_id"], s["arm"]), []).append(src) + self.assertFalse(src["reset_or_analytic_time_advance"]) + self.assertFalse(s["snapshot_mutated_by_planning"]) + self.assertEqual(s["baseline_snapshot_id"], s["block_id"] + "/normal/" + s["arm"] + "/checkpoint-00") + self.assertEqual(len(grouped), 80) + for rows in grouped.values(): + self.assertEqual([r["elapsed_ticks"] for r in rows], [0, 10000, 100000]) + self.assertEqual(len({r["idle_lineage_id"] for r in rows}), 1) + self.assertEqual(rows[0]["kind"], "actual_terminal_checkpoint") + self.assertEqual(rows[1]["kind"], "continuous_idle_checkpoint") + + def test_missing_choices_no_hidden_default(self): + for key in m.CONFIG_FIELDS: + bad = copy.deepcopy(self.config) + del bad[key] + with self.subTest(key=key), self.assertRaisesRegex(ValueError, "missing="): + m.build_evaluation_plan(self.trial, bad) + self.assertIn("snapshot_bindings", m.required_choices()) + + def test_bad_configuration_types_and_unimplemented_reuse(self): + for key, value in (("probe_steps", 1200.0), ("cs_start_tick", False), + ("cs_stop_tick_exclusive", True), ("cs_stop_tick_exclusive", 1201), + ("reversal_checkpoints", [False, 50]), ("reversal_checkpoints", [50]), + ("baseline_execution", "reuse_identical"), ("snapshot_binding_policy", "guess_from_paired")): + bad = copy.deepcopy(self.config) + bad[key] = value + with self.subTest(key=key, value=value), self.assertRaises(ValueError): + m._assemble(self.trial, bad) + + def test_binding_coverage_order_types_and_pairing_boundary(self): + changes = ( + lambda rows: rows.pop(), lambda rows: rows.reverse(), + lambda rows: rows[0].update(checkpoint=False), + lambda rows: rows[0].update(completed_epochs=False), + lambda rows: rows[1].update(completed_epochs=0), + lambda rows: rows[6].update(completed_epochs=99), + lambda rows: rows[1].update(completed_epochs=100), + lambda rows: rows[0].update(extra="unknown"), + ) + for index, change in enumerate(changes): + bad = copy.deepcopy(self.config) + change(bad["snapshot_bindings"]) + with self.subTest(index=index), self.assertRaises(ValueError): + m._assemble(self.trial, bad) + + def test_explicit_alternative_curve_changes_budget_not_seed_inventory(self): + config = synthetic_config(self.trial, trial_plan.PROBE_CHECKPOINTS) + result = m._assemble(self.trial, config) + self.assertEqual(result["budget"]["evaluation_probes"], 9440) + self.assertEqual(result["budget"]["probes_by_assay"]["reversal"], 1120) + self.assertEqual(result["budget"]["independent_evaluation_inputs"], 80) + config["cs_stop_tick_exclusive"] = 1200 + alternate = m._assemble(self.trial, config) + self.assertNotEqual(result["config_sha256"], alternate["config_sha256"]) + self.assertTrue(all(p["cs_stop_tick_exclusive"] == 1200 for p in alternate["probes"])) + + def test_payload_mutations_rejected_even_when_self_hash_updated(self): + changes = ( + lambda p: p["probes"].pop(), lambda p: p["probes"].reverse(), + lambda p: p["probes"][0]["input"].update(seed=1), + lambda p: p["probes"][0].update(training_replicate=True), + lambda p: p["probes"][0].update(adds_training_replicate=True), + lambda p: p.update(execution_authorized=True), + ) + # Full trial authority was validated in setUpClass and again above. + # Isolate evaluation reconstruction here, avoiding six repeated rebuilds + # of the large, unchanged 68k-epoch synthetic training schedule. + with patch.object(m.trial_plan, "validate_trial_plan", return_value=True): + for index, change in enumerate(changes): + bad = copy.deepcopy(self.plan) + change(bad) + bad["payload_sha256"] = m._sha({k: v for k, v in bad.items() if k != "payload_sha256"}) + with self.subTest(index=index), self.assertRaises(ValueError): + m.validate_evaluation_plan(bad, self.trial, self.config) + + def test_parent_authority_failure_propagates(self): + with patch.object(m.trial_plan, "validate_trial_plan", side_effect=ValueError("invalid full parent")): + with self.assertRaisesRegex(ValueError, "invalid full parent"): + m.build_evaluation_plan(self.trial, self.config) + + def test_no_mutable_alias_to_caller(self): + result = m._assemble(self.trial, self.config) + result["config"]["snapshot_bindings"][0]["completed_epochs"] = 900 + result["snapshots"][0]["delta_source"]["kind"] = "corruption" + result["probes"][0]["input"]["semantic_key"][0] = "corruption" + self.assertEqual(self.config["snapshot_bindings"][0]["completed_epochs"], 0) + self.assertEqual(self.trial["plan"]["blocks"][0]["suites"][0]["arms"][0]["delta_start"]["kind"], "zero") + self.assertEqual(m._assemble(self.trial, self.config), self.plan) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_evaluation_policy.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_evaluation_policy.py new file mode 100644 index 0000000..fac653b --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_evaluation_policy.py @@ -0,0 +1,262 @@ +"""Synthetic plans only; no neural input generation, files, remote or execution.""" +import copy +import unittest +from unittest import mock + +import evaluation_plan +import evaluation_policy as policy +import trial_plan + + +class EvaluationPolicyTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + # Explicit synthetic fixture seed, never a proposed production master. + cls.trial = trial_plan.build_trial_plan(461) + cls.result = policy.build_evaluation_policy(cls.trial) + cls.registry = policy._registry_index(cls.trial) + cls.rows = cls.result["audit"]["bindings"] + + def suite(self, assay="normal"): + block = self.trial["plan"]["blocks"][0] + suite = next(s for s in block["suites"] if s["assay"] == assay) + return block, copy.deepcopy(suite) + + def check_suite(self, block, suite, registry=None): + return policy._suite_policy(block, suite, self.trial["plan"]["master"], + self.registry if registry is None else registry) + + def test_fixed_config_and_complete_consumer_inventory(self): + config = self.result["config"] + self.assertEqual(set(config), evaluation_plan.CONFIG_FIELDS) + self.assertEqual(config["probe_steps"], 1200) + self.assertEqual(config["cs_start_tick"], 0) + self.assertEqual(config["cs_stop_tick_exclusive"], 1000) + self.assertEqual(config["reversal_checkpoints"], [0, 1, 2, 5, 10, 20, 50]) + self.assertEqual(config["baseline_execution"], "execute_each_snapshot") + self.assertEqual(len(config["snapshot_bindings"]), 4480) + # The original public consumer performs its own full authority check. + inventory = evaluation_plan.build_evaluation_plan(self.trial, config) + self.assertEqual(inventory["budget"]["evaluation_probes"], 9440) + self.assertEqual(inventory["budget"]["snapshot_count"], 4720) + self.assertEqual(inventory["budget"]["evaluation_steps"], 11328000) + self.assertEqual(inventory["budget"]["planned_scientific_steps_including_evaluation"], 118928000) + self.assertEqual(inventory["budget"]["probes_by_assay"], + {"normal": 3920, "delayed": 3920, "reversal": 1120, "retention": 480}) + self.assertFalse(inventory["execution_authorized"]) + self.assertFalse(inventory["budget"]["baseline_output_deduplication"]) + + def test_each_binding_audited_in_exact_order(self): + bindings = self.result["config"]["snapshot_bindings"] + self.assertEqual([r["binding"] for r in self.rows], bindings) + identities = [(r["block_id"], r["assay"], r["arm"], r["checkpoint"]) for r in bindings] + self.assertEqual(len(identities), len(set(identities))) + self.assertEqual({r["checkpoint"] for r in bindings if r["assay"] == "reversal"}, set(policy.POINTS)) + self.assertEqual(self.result["audit"]["coverage"]["snapshots_by_assay"], + {"normal": 1960, "delayed": 1960, "reversal": 560}) + + def test_five_independent_replicates_not_relabelled_from_probes(self): + coverage = self.result["audit"]["coverage"] + self.assertEqual(coverage["training_blocks"], 40) + self.assertEqual(coverage["training_replicates_per_cell"], 5) + self.assertEqual(coverage["new_random_streams"], 0) + self.assertFalse(coverage["new_master_seed_selected"]) + self.assertEqual(self.result["seed_manifest_sha256"], self.trial["seed_manifest"]["payload_sha256"]) + + def test_normal_and_delayed_controls_match_actual_ordered_source_prefixes(self): + selected = [r for r in self.rows if r["binding"]["assay"] in ("normal", "delayed") and + r["binding"]["arm"] != "dan_only"] + self.assertEqual(len(selected), 3360) + for row in selected: + self.assertIs(row["ordered_CS_prefix_exact"], True) + self.assertEqual(row["actual"]["ordered_CS_source_identity_sha256"], + row["reference"]["ordered_CS_source_identity_sha256"]) + self.assertEqual(row["actual"]["CS_counts_by_cue"], row["reference"]["CS_counts_by_cue"]) + if row["binding"]["arm"] != "unpaired": + self.assertEqual(row["binding"]["completed_epochs"], row["reference_completed_epochs"]) + self.assertTrue(row["elapsed_ticks_equal_to_reference"]) + + def test_unpaired_earliest_matching_prefix_and_full_terminal_including_tail(self): + any_earlier_US = False + any_trailing_US = False + for block in self.trial["plan"]["blocks"]: + for suite in block["suites"][:2]: + arms = {a["arm"]: a for a in suite["arms"]} + paired, unpaired = arms["paired"], arms["unpaired"] + rows = [r for r in self.rows if r["binding"]["block_id"] == block["block_id"] and + r["binding"]["assay"] == suite["assay"] and r["binding"]["arm"] == "unpaired"] + for row in rows: + point = row["binding"]["checkpoint"] + count = row["binding"]["completed_epochs"] + needed = row["reference_completed_epochs"] + if point == 0: + self.assertEqual(count, 0) + elif point == 50: + self.assertEqual(count, 150) + self.assertEqual(row["actual"]["CS_epochs"], 100) + self.assertEqual(row["actual"]["requested_US_epochs"], 50) + self.assertEqual(row["elapsed_ticks_difference"], 50 * paired["epochs"][0]["steps"]) + any_trailing_US |= unpaired["epochs"][-1]["cs"] is None + else: + actual_positions = [i + 1 for i, e in enumerate(unpaired["epochs"]) if e["cs"] is not None] + self.assertEqual(count, actual_positions[needed - 1]) + self.assertIsNotNone(unpaired["epochs"][count - 1]["cs"]) + self.assertEqual(row["actual"]["requested_US_epochs"], count - needed) + any_earlier_US |= count > needed + self.assertTrue(any_earlier_US) + self.assertTrue(any_trailing_US) + + def test_DAN_only_is_yoked_zero_CS_not_equal_exposure(self): + rows = [r for r in self.rows if r["binding"]["arm"] == "dan_only"] + self.assertEqual(len(rows), 560) + for row in rows: + self.assertEqual(row["actual"]["CS_epochs"], 0) + self.assertIsNone(row["ordered_CS_prefix_exact"]) + self.assertFalse(row["CS_exposure_equal"]) + self.assertTrue(row["DAN_only_yoke_verified"]) + self.assertTrue(row["requested_US_count_equal_to_reference"]) + self.assertTrue(row["elapsed_ticks_equal_to_reference"]) + self.assertEqual(row["binding"]["completed_epochs"], row["reference_completed_epochs"]) + + def test_reversal_each_own_US_boundary_and_original_terminal(self): + saw_different_middle_CS_prefix = False + for block in self.trial["plan"]["blocks"]: + suite = block["suites"][2] + arms = {a["arm"]: a for a in suite["arms"]} + for row in [r for r in self.rows if r["binding"]["block_id"] == block["block_id"] and + r["binding"]["assay"] == "reversal"]: + binding = row["binding"] + arm = arms[binding["arm"]] + point, count = binding["checkpoint"], binding["completed_epochs"] + if point in (0, 50): + self.assertEqual(count, 0 if point == 0 else 100) + self.assertTrue(row["ordered_CS_prefix_exact"]) + else: + ordinal_positions = [i + 1 for i, e in enumerate(arm["epochs"]) if e["us"]["requested"]] + self.assertEqual(count, ordinal_positions[point - 1]) + self.assertTrue(arm["epochs"][count - 1]["us"]["requested"]) + saw_different_middle_CS_prefix |= not row["ordered_CS_prefix_exact"] + self.assertEqual(row["actual"]["requested_US_epochs"], point) + self.assertEqual(row["actual"]["enabled_nonzero_D_update_opportunities"], point) + self.assertTrue(saw_different_middle_CS_prefix) + + def test_intermediate_US_time_and_update_mismatches_are_explicit_not_failed(self): + middle = [r for r in self.rows if r["binding"]["checkpoint"] not in (0, 50)] + self.assertTrue(any(r["binding"]["arm"] == "random_reward" and r["requested_US_count_difference"] != 0 for r in middle)) + self.assertTrue(any(r["binding"]["arm"] == "unpaired" and r["elapsed_ticks_difference"] > 0 for r in middle)) + self.assertTrue(any(r["binding"]["arm"] == "frozen" and r["nonzero_D_update_opportunity_difference"] < 0 for r in middle)) + self.assertTrue(all(r["all_US_timing_or_total_exposure_matched_claimed"] is False for r in self.rows)) + self.assertTrue(all(r["actual"]["actual_accepted_US_or_DAN_spikes_known"] is False for r in self.rows)) + + def test_all_boundaries_follow_complete_epoch_update_not_US_tick(self): + for row in self.rows: + self.assertEqual(row["boundary"], "after complete epoch and scheduled delta update; not at the US tick") + steps = 1800 if row["binding"]["assay"] == "delayed" else 1200 + self.assertEqual(row["actual"]["elapsed_training_ticks"], row["binding"]["completed_epochs"] * steps) + self.assertEqual(row["terminal_includes_all_epochs"], row["binding"]["checkpoint"] == 50) + + def test_control_seed_or_source_alias_mutations_fail_without_relying_on_counts(self): + for field, change in (("seed", lambda v: v + 1), + ("source_stream_id", lambda v: v + "x"), + ("stream_id", lambda v: v + "x"), + ("start_tick", lambda v: False)): + with self.subTest(field=field): + block, suite = self.suite() + cs = suite["arms"][1]["epochs"][0]["cs"] + cs[field] = change(cs[field]) + with self.assertRaises(ValueError): + self.check_suite(block, suite) + + def test_reordered_cues_with_same_counts_and_valid_individual_aliases_fail(self): + block, suite = self.suite() + epochs = suite["arms"][1]["epochs"] + index = next(i for i in range(1, len(epochs)) if epochs[i]["cs"]["cue"] != epochs[0]["cs"]["cue"]) + epochs[0]["cs"], epochs[index]["cs"] = epochs[index]["cs"], epochs[0]["cs"] + with self.assertRaisesRegex(ValueError, "CS occurrence order|ordered CS|CS sequence"): + self.check_suite(block, suite) + + def test_corrupt_alias_registry_is_not_excused_by_equal_CS_seed(self): + block, suite = self.suite() + independent, aliases = self.registry + changed = copy.deepcopy(aliases) + cs = suite["arms"][1]["epochs"][0]["cs"] + key = (block["block_id"], "normal", "CS", cs["cue"], f'{cs["occurrence"]:02d}', "d_zero") + changed[("training", key)]["source_key"][-1] = "frozen" + with self.assertRaisesRegex(ValueError, "alias is not registered"): + self.check_suite(block, suite, (independent, changed)) + + def test_DAN_yoked_cue_US_or_added_CS_mutations_fail(self): + for mutation in ("cue", "US", "CS"): + with self.subTest(mutation=mutation): + block, suite = self.suite() + paired = suite["arms"][0] + dan = suite["arms"][-1] + epoch = dan["epochs"][0] + if mutation == "cue": + epoch["yoked_cue_removed"] = "B" if epoch["yoked_cue_removed"] == "A" else "A" + elif mutation == "US": + epoch["us"]["nominal_slot_tick"] += 1 + else: + epoch["cs"] = copy.deepcopy(paired["epochs"][0]["cs"]) + with self.assertRaises(ValueError): + self.check_suite(block, suite) + + def test_missing_reordered_or_duplicate_control_arms_fail(self): + for mutation in ("missing", "reorder", "duplicate"): + with self.subTest(mutation=mutation): + block, suite = self.suite() + if mutation == "missing": + suite["arms"].pop() + elif mutation == "reorder": + suite["arms"][0], suite["arms"][1] = suite["arms"][1], suite["arms"][0] + else: + suite["arms"].append(suite["arms"][0]) + with self.assertRaisesRegex(ValueError, "control arms"): + self.check_suite(block, suite) + + def test_result_not_aliased_no_execution_or_new_scientific_gate(self): + fields = ("protocol_approval_claimed", "execution_authorized", "training_executed", "inputs_generated", + "input_generator_implementation_verified", "source_and_snapshot_authority_verified", + "candidate_generated", "learning_pass_claimed", "scientific_gate_added") + self.assertTrue(all(self.result[field] is False for field in fields)) + self.assertEqual(self.result["status"], "DRAFT-NOT-REGISTERED") + self.assertTrue(self.result["choices_are_new_draft_proposals_not_inherited_gates"]) + self.assertTrue(self.result["audit"]["unchanged_retention_scope"]["same_continuous_ten_second_lineage"]) + self.assertFalse(self.result["audit"]["unchanged_retention_scope"]["retained_snapshot_execution_or_lineage_verified"]) + # Config and audit may describe the same row, but neither is a caller alias. + block = self.trial["plan"]["blocks"][0] + self.assertIsNot(self.result["config"]["reversal_checkpoints"], block["evaluation_checkpoints"]) + self.assertEqual(self.result["trial_payload_sha256"], self.trial["payload_sha256"]) + + def test_validation_rejects_rehashed_config_type_and_audit_mutations(self): + # Separate reconstruction is supplied by the already independently built + # fixture to keep mutation tests small; public full reconstruction is + # exercised once below, not hidden behind this mocked trust boundary. + with mock.patch.object(policy, "build_evaluation_policy", return_value=self.result): + for mutation in ("point", "type", "audit", "authorization"): + with self.subTest(mutation=mutation): + changed = copy.deepcopy(self.result) + if mutation == "point": + changed["config"]["snapshot_bindings"][1]["completed_epochs"] += 1 + elif mutation == "type": + changed["config"]["cs_start_tick"] = False + elif mutation == "audit": + changed["audit"]["bindings"][0]["actual"]["CS_epochs"] += 1 + else: + changed["execution_authorized"] = True + changed["payload_sha256"] = policy._sha({k: v for k, v in changed.items() if k != "payload_sha256"}) + with self.assertRaisesRegex(ValueError, "exact reconstruction"): + policy.validate_evaluation_policy(changed, self.trial) + + def test_public_validation_full_reconstruction(self): + self.assertTrue(policy.validate_evaluation_policy(self.result, self.trial)) + + def test_trial_authority_hash_rejected_before_planning(self): + changed = dict(self.trial) + changed["payload_sha256"] = "0" * 64 + with self.assertRaisesRegex(ValueError, "payload SHA"): + policy.build_evaluation_policy(changed) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_metrics.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_metrics.py new file mode 100644 index 0000000..53286ad --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_metrics.py @@ -0,0 +1,318 @@ +"""Synthetic arrays/declared receipts only; no real recordings or science run.""" +import copy +import hashlib +import json +import unittest + +import numpy as np + +import metrics as m + + +NODES = np.arange(6, dtype=np.int32) +POPS = {'MBON': np.array([0, 1, 2], np.int32), 'legacyDownstream': np.arange(6, dtype=np.int32), + 'nonMBONDownstream': np.array([3, 4, 5], np.int32), + 'MBON01': np.array([0], np.int32), 'MBON12': np.array([1], np.int32)} + + +def state(counts=(1, 1, 0, 1, 0, 0), *, times=None, g=None): + events = [(10 + 20 * k if times is None else times[i][k], i) for i, count in enumerate(counts) for k in range(count)] + events.sort() + return dict(step=np.array([t for t, _ in events], np.int32), + neuron_idx=np.array([i for _, i in events], np.int32), counts=np.array(counts, np.int32), + v=np.full(6, -.052, np.float64), g=np.zeros(6, np.float64) if g is None else np.array(g, np.float64), + ref=np.zeros(6, np.int32)) + + +def effect(*, direction=1, gain=True, g_effect=True, conditioned=m.CUES[0], neutral_g=0.): + pre = {cue: state() for cue in m.CUES} + post = {cue: state() for cue in m.CUES} + counts = [1, 1, 0, 1, 0, 0] + counts[1 if direction > 0 else 0] += abs(direction) + if gain: + counts[3] += abs(direction) + g = np.zeros(6, np.float64) + if g_effect: + g[0] = .002 + post[conditioned] = state(counts, g=g) + neutral = next(c for c in m.CUES if c != conditioned) + post[neutral]['g'][0] = neutral_g + return m.arm_metrics(pre, post, conditioned_cue=conditioned, nodes=NODES, populations=POPS) + + +def inventory(*, assay='normal', dan='PAM15', paired=None): + registration = {f'rep{i}': [f'training-source-{i}'] for i in range(5)} + rows = [] + for i in range(5): + arms = {} + for name in m.ARMS: + metric = effect(direction=0, g_effect=False) if name != 'paired' else ( + effect(direction=1 if dan == 'PAM15' else -1) if paired is None else copy.deepcopy(paired[i])) + evidence = dict(complete=True, recording_verified=True, + receipt_sha256=hashlib.sha256(f'{i}:{name}:{assay}'.encode()).hexdigest(), assay=assay, + epoch_duration_ms=120. if assay == 'normal' else 180., cs_duration_ms=100., + us_tick=900 if assay == 'normal' else 1500, completed_epochs=150 if name == 'unpaired' else 100, + reinforcement_opportunities=0 if name == 'CS-only' else 50, terminal_pairings=50) + arms[name] = dict(metrics=metric, execution=evidence) + rows.append(dict(replicate_id=f'rep{i}', replicate_unit='independent_training', + training_stream_ids=registration[f'rep{i}'], evaluation_stream_ids=[f'evaluation-source-{i}'], + assay=assay, mode='physiological', dan_type=dan, conditioned_cue=m.CUES[0], arms=arms)) + return rows, registration + + +def evaluate(rows, registration, *, assay='normal', dan='PAM15'): + return m.evaluate_acquisition(rows, assay=assay, mode='physiological', dan_type=dan, + conditioned_cue=m.CUES[0], registered_training_streams=registration) + + +class StateTests(unittest.TestCase): + def test_every_key_validated_and_no_input_mutation(self): + a, b = state(), state((1, 2, 0, 2, 0, 0), g=[.001, -.004, 0, 0, 0, 0]) + before = {key: value.tobytes() for key, value in a.items()} + r = m.functional_summary(a, b, nodes=NODES, populations=POPS, steps=1200) + self.assertEqual(r['MBON_final_delta_g_max_abs'], .004) + self.assertEqual(r['MBON_final_delta_g_units'], 'SI volts') + self.assertIn('not time peak', r['MBON_final_delta_g_scope']) + self.assertEqual(r['wholebrain']['spike_count_l1'], 2) + self.assertEqual(before, {key: value.tobytes() for key, value in a.items()}) + json.dumps(r, allow_nan=False) + + def test_first_event_minimum_not_last_and_zero_tick_is_defined(self): + a = state((3, 0, 0, 0, 0, 0), times=[[0, 20, 100], [], [], [], [], []]) + r = m.validate_state(a, nodes=NODES, steps=1200) + self.assertEqual(r['first_tick'].tolist(), [0, -1, -1, -1, -1, -1]) + + def test_missing_latencies_never_zero_filled(self): + a, b = state((0, 0, 0, 0, 0, 0)), state((1, 0, 0, 0, 0, 0)) + r = m.functional_summary(a, b, nodes=NODES, populations=POPS, steps=1200)['populations']['MBON'] + self.assertIsNone(r['pre_first_latency_ms']) + self.assertIsNone(r['first_latency_delta_ms']) + self.assertFalse(r['defined_first_latency_changed']) + self.assertEqual(r['first_latency_became_defined_neurons'], 1) + self.assertGreater(r['spike_count_l1'], 0) + + def test_nonfinite_input_all_neurons_not_only_scored_targets(self): + for key in ('v', 'g'): + for value in (np.nan, np.inf, -np.inf): + with self.subTest(key=key, value=value): + a = state(); a[key][5] = value + with self.assertRaisesRegex(ValueError, 'Nonfinite'): + m.functional_summary(a, state(), nodes=NODES, populations=POPS, steps=1200) + + def test_nonfinite_derived_difference_rejected(self): + a, b = state(), state() + a['g'][0], b['g'][0] = -1e308, 1e308 + with self.assertRaisesRegex(ValueError, 'Nonfinite derived'): + m.functional_summary(a, b, nodes=NODES, populations=POPS, steps=1200) + + def test_bad_state_dtype_shape_counts_order_bounds_rejected(self): + variants = [] + a = state(); a['counts'] = a['counts'].astype(np.int64); variants.append(a) + a = state(); a['counts'][0] += 1; variants.append(a) + a = state(); a['step'][0] = -1; variants.append(a) + a = state(); a['neuron_idx'][0] = 99; variants.append(a) + a = state(); a['ref'][0] = 23; variants.append(a) + a = state(); a['v'] = a['v'][:-1]; variants.append(a) + a = state(); a['neuron_idx'][1] = 0; variants.append(a) + a = state(); a['extra'] = np.zeros(1); variants.append(a) + for a in variants: + with self.subTest(keys=list(a)), self.assertRaises(ValueError): + m.validate_state(a, nodes=NODES, steps=1200) + + def test_empty_wrong_duplicate_and_overlapping_memberships_rejected(self): + for key, value in [('MBON12', []), ('MBON01', [0, 0]), ('MBON12', [3]), + ('MBON12', [0]), ('nonMBONDownstream', [2, 3]), ('MBON', [0, 1, 99]), + ('nonMBONDownstream', [3, 4]), ('legacyDownstream', [0, 1, 3, 4, 5])]: + populations = dict(POPS, **{key: np.array(value, np.int32)}) + with self.subTest(key=key, value=value), self.assertRaises(ValueError): + m.validate_populations(NODES, populations) + + def test_legacy_downstream_retains_mbon_and_new_setdiff_is_explicit(self): + result = m.validate_populations(NODES, POPS) + self.assertEqual(result['indices']['legacyDownstream'], [0, 1, 2, 3, 4, 5]) + self.assertEqual(result['indices']['nonMBONDownstream'], [3, 4, 5]) + self.assertIn('NEW hypothesis', result['population_semantics']['nonMBONDownstream']) + old_alias = dict(POPS) + old_alias['trueDownstream'] = old_alias.pop('nonMBONDownstream') + with self.assertRaisesRegex(ValueError, 'exactly'): + m.validate_populations(NODES, old_alias) + + +class ContrastTests(unittest.TestCase): + def test_fixed_types_not_mask_and_type_mean_not_population_sum(self): + p = dict(POPS, MBON=np.array([0, 1, 2], np.int32), MBON01=np.array([0], np.int32), MBON12=np.array([1, 2], np.int32)) + pre = {c: state() for c in m.CUES} + post = {c: state() for c in m.CUES}; post[m.CUES[0]] = state((1, 3, 1, 2, 0, 0)) + r = m.arm_metrics(pre, post, conditioned_cue=m.CUES[0], nodes=NODES, populations=p) + self.assertEqual(r['rawB']['post'][m.CUES[0]], 1.) + self.assertEqual(r['rawS'], 1.5) + self.assertFalse(r['B_is_decoder']) + + def test_reciprocal_role_keeps_signed_orientation(self): + first, second = effect(), effect(conditioned=m.CUES[1]) + self.assertEqual(first['rawS'], second['rawS']) + self.assertEqual(second['conditioned_cue'], m.CUES[1]) + + def test_raw_labels_not_aliases(self): + with self.assertRaises(ValueError): + m.arm_metrics({'A': state()}, {'B': state()}, conditioned_cue='A', nodes=NODES, populations=POPS) + + def test_legacy_and_non_mbon_gains_are_separate_not_an_alias(self): + result = effect(gain=False) + self.assertEqual(result['separation']['MBON']['gain'], 1.) + self.assertEqual(result['separation']['legacyDownstream']['gain'], 1.) + self.assertEqual(result['separation']['nonMBONDownstream']['gain'], 0.) + pre = {cue: state((4, 0, 0, 0, 0, 0) if cue == m.CUES[0] else (0, 0, 0, 0, 0, 0)) for cue in m.CUES} + post = {cue: state((3, 0, 0, 1, 0, 0) if cue == m.CUES[0] else (0, 0, 0, 0, 0, 0)) for cue in m.CUES} + result = m.arm_metrics(pre, post, conditioned_cue=m.CUES[0], nodes=NODES, populations=POPS) + self.assertLess(result['separation']['legacyDownstream']['gain'], 0.) + self.assertGreater(result['separation']['nonMBONDownstream']['gain'], 0.) + + +class GateTests(unittest.TestCase): + def test_both_directions_and_suites_pass_only_as_draft_proposal(self): + for assay in ('normal', 'delayed'): + for dan in m.DAN_TYPES: + with self.subTest(assay=assay, dan=dan): + rows, registration = inventory(assay=assay, dan=dan) + r = evaluate(rows, registration, assay=assay, dan=dan) + self.assertTrue(r['proposal_gate_pass']) + self.assertEqual(r['actual_control_receipts'], 30) + self.assertEqual(r['successful_same_replicate_conjunctions'], 5) + self.assertFalse(r['scientific_learning_pass_claimed']) + self.assertFalse(r['candidate_generated']) + self.assertTrue(all(v['rawS'] < 0 for v in r['replicates']) if dan == 'PPL103' else + all(v['rawS'] > 0 for v in r['replicates'])) + json.dumps(r, allow_nan=False) + + def test_opposite_or_zero_signed_effect_is_failure_not_abs_success(self): + for direction in (-1, 0): + rows, registry = inventory(paired=[effect(direction=direction)] * 5) + r = evaluate(rows, registry) + self.assertFalse(r['proposal_gate_pass']) + self.assertEqual(r['successful_same_replicate_conjunctions'], 0) + + def test_equal_control_is_failure(self): + rows, registry = inventory() + for row in rows: + row['arms']['CS-only']['metrics'] = copy.deepcopy(row['arms']['paired']['metrics']) + self.assertFalse(evaluate(rows, registry)['proposal_gate_pass']) + + def test_same_replicate_conjunction_not_majority_of_each_predicate(self): + effects = [effect(), effect(), effect(gain=False), effect(g_effect=False), effect(direction=-1)] + rows, registry = inventory(paired=effects) + r = evaluate(rows, registry) + self.assertEqual(r['successful_same_replicate_conjunctions'], 2) + self.assertFalse(r['proposal_gate_pass']) + + def test_exact_three_of_five_pass(self): + rows, registry = inventory(paired=[effect()] * 3 + [effect(direction=0)] * 2) + self.assertTrue(evaluate(rows, registry)['proposal_gate_pass']) + + def test_positive_legacy_gain_cannot_rescue_new_non_mbon_gate(self): + rows, registry = inventory(paired=[effect(gain=False)] * 5) + result = evaluate(rows, registry) + self.assertFalse(result['proposal_gate_pass']) + for row in result['replicates']: + self.assertTrue(row['predicates']['legacyDownstream_AB_euclidean_gain_positive']) + self.assertFalse(row['predicates']['nonMBONDownstream_AB_euclidean_gain_positive']) + self.assertEqual(row['population_separation_gains']['legacyDownstream'], 1.) + self.assertEqual(row['population_separation_gains']['nonMBONDownstream'], 0.) + self.assertEqual(row['legacy_audit']['H_like_downstream_population'], 'legacyDownstream') + self.assertFalse(row['new_hypothesis_audit']['inherited_from_old_H']) + + def test_population_metadata_or_overlapping_raw_vectors_cannot_be_forged(self): + for mutation in ('semantics', 'setdiff', 'subset_final'): + rows, registry = inventory() + summary = rows[0]['arms']['paired']['metrics']['cues'][m.CUES[0]] + if mutation == 'semantics': + summary['membership']['population_semantics']['nonMBONDownstream'] = 'old H population' + elif mutation == 'setdiff': + ids = summary['membership']['indices'] + ids['nonMBONDownstream'] = [2, 3, 4] + summary['membership']['membership_sha256'] = m._json_hash(ids) + summary['populations']['nonMBONDownstream']['neuron_idx'] = [2, 3, 4] + else: + # Self-consistent terminal deltas can still contradict the full parent population. + child = summary['populations']['nonMBONDownstream'] + child['pre_final_v'][0] += .001 + child['post_final_v'][0] += .001 + with self.subTest(mutation=mutation), self.assertRaises(ValueError): + evaluate(rows, registry) + + def test_missing_control_incomplete_receipt_and_wrong_delayed_schedule_rejected(self): + for mutation in ('missing', 'incomplete', 'timing', 'epochs', 'duplicate_receipt', 'best_checkpoint'): + rows, registry = inventory() + if mutation == 'missing': + rows[0]['arms'].pop('unpaired') + else: + e = rows[0]['arms']['unpaired']['execution'] + if mutation == 'incomplete': e['complete'] = False + if mutation == 'timing': e['us_tick'] = 1500 + if mutation == 'epochs': e['completed_epochs'] = 100 + if mutation == 'duplicate_receipt': e['receipt_sha256'] = rows[0]['arms']['paired']['execution']['receipt_sha256'] + if mutation == 'best_checkpoint': e['terminal_pairings'] = 20 + with self.subTest(mutation=mutation), self.assertRaises(ValueError): evaluate(rows, registry) + + def test_evaluation_draws_cannot_be_training_replicates(self): + for mutation in ('unit', 'duplicate_id', 'training_overlap', 'eval_training_overlap', 'eval_overlap', 'extra_draw'): + rows, registry = inventory() + if mutation == 'unit': rows[0]['replicate_unit'] = 'evaluation' + if mutation == 'duplicate_id': rows[1]['replicate_id'] = rows[0]['replicate_id'] + if mutation == 'training_overlap': registry['rep1'] = registry['rep0'] + if mutation == 'eval_training_overlap': rows[0]['evaluation_stream_ids'] = registry['rep0'] + if mutation == 'eval_overlap': rows[1]['evaluation_stream_ids'] = rows[0]['evaluation_stream_ids'] + if mutation == 'extra_draw': rows.append(copy.deepcopy(rows[0])) + with self.subTest(mutation=mutation), self.assertRaises(ValueError): evaluate(rows, registry) + + def test_raw_nan_forged_B_g_function_and_separation_rejected(self): + for mutation in ('nan', 'rawB', 'g', 'count', 'separation', 'latency'): + rows, registry = inventory() + metric = rows[0]['arms']['paired']['metrics'] + group = metric['cues'][m.CUES[0]]['populations']['MBON'] + if mutation == 'nan': metric['rawS'] = np.inf + if mutation == 'rawB': metric['rawB']['post'][m.CUES[0]] = 100. + if mutation == 'g': group['final_delta_g_max_abs'] = 100. + if mutation == 'count': group['spike_count_l1'] = 100 + if mutation == 'separation': metric['separation']['MBON']['gain'] = 100. + if mutation == 'latency': group['defined_first_latency_changed'] = True + with self.subTest(mutation=mutation), self.assertRaises(ValueError): evaluate(rows, registry) + + def test_control_baseline_and_cell_pooling_rejected(self): + for mutation in ('baseline', 'mode', 'DAN', 'assay', 'cue'): + rows, registry = inventory() + if mutation == 'baseline': rows[0]['arms']['frozen']['metrics']['cues'][m.CUES[0]]['pre_state_raw_sha256']['g'] = 'a' * 64 + if mutation == 'mode': rows[0]['mode'] = 'matched_energy' + if mutation == 'DAN': rows[0]['dan_type'] = 'PAM-positive' + if mutation == 'assay': rows[0]['assay'] = 'delayed' + if mutation == 'cue': rows[0]['conditioned_cue'] = m.CUES[1] + with self.subTest(mutation=mutation), self.assertRaises(ValueError): evaluate(rows, registry) + + def test_reversal_retention_remain_missing_not_fabricated_pass(self): + for assay in ('reversal', 'retention'): + r = m.unavailable_assay(assay) + self.assertEqual(r['status'], 'NOT-IMPLEMENTED') + self.assertIsNone(r['proposal_gate_pass']) + self.assertTrue(r['missing']) + + def test_legacy_mean_control_audit_separate_and_strict_not_new_signed_gate(self): + raw = {'appetitive-conditioning': [2.] * 5, 'aversive-conditioning': [1.] * 5, + 'd-zero-control': [0.] * 5, 'random-reward-control': [.5] * 5, 'frozen-brain-control': [0.] * 5} + r = m.legacy_h_mean_control_audit(raw) + self.assertTrue(r['predicate_pass']) + self.assertFalse(r['full_old_H_gate_computed']) + raw['random-reward-control'] = [1.] * 5 + self.assertFalse(m.legacy_h_mean_control_audit(raw)['predicate_pass']) + raw.pop('d-zero-control') + with self.assertRaises(ValueError): m.legacy_h_mean_control_audit(raw) + + def test_legacy_nan_or_evaluation_count_not_admitted(self): + raw = {'appetitive-conditioning': [2.] * 5, 'aversive-conditioning': [1.] * 5, + 'd-zero-control': [0.] * 5, 'random-reward-control': [.5] * 5, 'frozen-brain-control': [0.] * 5} + for values in ([0.] * 25, [0., 0., np.nan, 0., 0.]): + raw['frozen-brain-control'] = values + with self.assertRaises(ValueError): m.legacy_h_mean_control_audit(raw) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_runtime_continuation.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_runtime_continuation.py new file mode 100644 index 0000000..f8f7714 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_runtime_continuation.py @@ -0,0 +1,399 @@ +"""CPU synthetic checkpoint/clock tests; not a brain or GPU equivalence test.""" +import ast +import copy +import json +from pathlib import Path +import tempfile +import types +import unittest +from unittest.mock import patch + +import numpy as np + +import runtime_continuation as c + + +R = c.R +AJ = Path(__file__).resolve().parent.parent / 'apl-timing-lab-v1/gpu_timing.py' + + +class RingTests(unittest.TestCase): + def execute(self, start, steps, raw, phase, apl_pending, events, *, admit_global=True): + ring = c.rotate_spike_ring(raw, phase) + apl = apl_pending.copy() + due, applied = [], [] + for tick in range(steps): + absolute = start + tick + ring[tick % 19] = events[absolute] + admitted = absolute >= 18 if admit_global else tick >= 18 + due.append(ring[(tick - 18) % 19].copy() if admitted else np.zeros(ring.shape[1], np.uint8)) + slot = tick % 18 + applied.append(apl[slot].copy()) + apl[slot] = np.float32(absolute + 1) + head = steps % 18 + return ring, steps, np.concatenate((apl[head:], apl[:head])), np.asarray(due), np.asarray(applied) + + def test_multi_segments_preserve_all_future_arrivals_and_apl_slots(self): + rng = np.random.default_rng(193) + events = rng.integers(0, 2, (80, 4), dtype=np.uint8) + initial, pending = np.zeros((19, 4), np.uint8), np.zeros((18, 2), np.float32) + full = self.execute(0, 80, initial, 0, pending, events) + for lengths in ((5, 13, 1, 22, 39), (17, 1, 1, 18, 43), (19, 19, 19, 23), (1,) * 80): + ring, phase, tail, offset, due, applied = initial, 0, pending, 0, [], [] + for steps in lengths: + ring, phase, tail, d, a = self.execute(offset, steps, ring, phase, tail, events) + due.append(d); applied.append(a); offset += steps + np.testing.assert_array_equal(np.concatenate(due), full[3]) + np.testing.assert_array_equal(np.concatenate(applied), full[4]) + np.testing.assert_array_equal(c.rotate_spike_ring(ring, phase), c.rotate_spike_ring(full[0], 80)) + np.testing.assert_array_equal(tail, full[2]) + + def test_parent_shorter_than_delay_needs_global_admission(self): + events = np.zeros((24, 2), np.uint8); events[0, 1] = 1 + parent = self.execute(0, 5, np.zeros((19, 2), np.uint8), 0, np.zeros((18, 2), np.float32), events) + child = self.execute(5, 19, *parent[:3], events) + wrong = self.execute(5, 19, *parent[:3], events, admit_global=False) + self.assertEqual(child[3][13, 1], 1) # absolute tick 18, local 13 + self.assertEqual(wrong[3][13, 1], 0) + + def test_local_tick_zero_request_uses_global_clock_and_ring_phase(self): + events = np.zeros((50, 3), np.uint8) + events[19, 2] = 1 # Synthetic US request at child's local tick zero. + first = self.execute(0, 19, np.zeros((19, 3), np.uint8), 0, np.zeros((18, 2), np.float32), events) + child = self.execute(19, 31, *first[:3], events) + self.assertEqual(child[3][18, 2], 1) + self.assertEqual(np.count_nonzero(child[3][:18, 2]), 0) + + def test_rotation_is_copy_and_rejects_invalid_ring(self): + raw = np.zeros((19, 2), np.uint8); raw[18, 1] = 1 + before = raw.tobytes() + result = c.rotate_spike_ring(raw, 19) + result[:] = 1 + self.assertEqual(raw.tobytes(), before) + for value, phase in ((raw.astype(np.int32), 0), (raw[:18], 0), (raw, -1), (raw, True)): + with self.assertRaises(ValueError): + c.rotate_spike_ring(value, phase) + + +class ASTTests(unittest.TestCase): + def test_only_restore_and_global_admission_edits_in_both_modes(self): + for mode in ('light', 'reference'): + tree, audit = c.derive_continued(AJ.read_bytes(), mode) + self.assertTrue(audit['full_AST_rollback_identical']) + self.assertTrue(audit['kernels_sorting_and_local_ring_math_unchanged']) + self.assertEqual(sum(isinstance(node, ast.If) and ast.unparse(node.test) == c.CLOCK_NEW for node in ast.walk(tree)), 1) + self.assertEqual(sum(isinstance(node, ast.Expr) and ast.unparse(node) == c.RESTORE for node in ast.walk(tree)), 1) + compile(tree, '', 'exec') + + def test_reference_loop_diff_is_exactly_admission_condition(self): + base, _ = R.derive_ast(AJ.read_bytes(), 'reference') + actual, _ = c.derive_continued(AJ.read_bytes(), 'reference') + loops = lambda tree: next(node for node in ast.walk(tree) if isinstance(node, ast.For) and + isinstance(node.target, ast.Name) and node.target.id == 'tick') + modified = copy.deepcopy(loops(actual)) + changed = [node for node in ast.walk(modified) if isinstance(node, ast.If) and ast.unparse(node.test) == c.CLOCK_NEW] + changed[0].test = ast.parse(c.CLOCK_OLD, mode='eval').body + self.assertEqual(R._dump(modified), R._dump(loops(base))) + + def test_compile_never_mutates_sealed_module_namespace(self): + module = types.ModuleType('fake_aj'); module.__file__ = str(AJ) + original = dict(module.__dict__) + runtime = types.SimpleNamespace(write_json=lambda *_: None, audit_sequence=lambda *_: None) + function, _ = c.compile_continued(module, runtime, 'light') + self.assertEqual(module.__dict__, original) + self.assertEqual(function.__globals__['__file__'], str(Path(c.__file__).resolve())) + self.assertIs(function.__globals__['_continuation'], runtime) + + +class ArrayTarget: + def __init__(self, value): + self.value = np.array(value, copy=True) + + def copy_(self, value): + self.value[...] = value + + def any(self): + return np.asarray(self.value.any()) + + +class RestoreTests(unittest.TestCase): + def test_reference_fifo_audit_preserves_carried_bits_for_short_segment(self): + initial = np.arange(36, dtype=np.float32).reshape(18, 2) + initial[0, 0] = np.float32(-0.) + generated = np.arange(10, dtype=np.float32).reshape(5, 2) + joined = np.concatenate((initial, generated)) + applied, pending = joined[:5], joined[5:] + result = c.audit_carried_sequence(generated, applied, pending, initial) + self.assertTrue(result['sample_bits_preserved']) + bad = applied.copy(); bad[0, 0] = np.float32(0.) + with self.assertRaisesRegex(ValueError, 'bitwise'): + c.audit_carried_sequence(generated, bad, pending, initial) + + def test_timing_counts_and_ranges_use_absolute_clock_not_segment_length(self): + obj = object.__new__(c.ContinuedRuntime) + obj.global_offset, obj.spec, obj.mode = 41, {'steps': 5}, 'reference' + obj.parent = {'terminal': {'apl_pending': np.zeros((18, 4064), np.float32)}} + result = obj._timing_metadata({'pending_generated_count': 5, 'pending_initial_zero_prefix': 13, + 'initial_padding_positive_zero': True}) + self.assertEqual(result['pending_generated_count'], 18) + self.assertEqual(result['pending_initial_zero_prefix'], 0) + self.assertEqual(result['pending_application_tick_start'], 46) + self.assertEqual(result['pending_generated_tick_start'], 28) + self.assertEqual(result['pending_segment_generated_rows'], 5) + self.assertEqual(result['pending_parent_generated_rows'], 13) + self.assertEqual(result['applied_parent_generated_rows'], 5) + self.assertNotIn('initial_padding_positive_zero', result) + + def test_restore_carries_exact_state_and_queues_but_not_segment_counts(self): + parent = {'global_next_tick': 41, 'state': {'v': np.array([-.05, -.04], np.float64), + 'g': np.array([-0., -.002], np.float64), 'ref': np.array([22, 3], np.int32)}, + 'terminal': {'spike_ring': np.zeros((19, 2), np.uint8), 'next_tick': np.asarray(5, np.int64), + 'apl_pending': np.arange(36, dtype=np.float32).reshape(18, 2), 'apl_state': np.array([2., 3.], np.float32)}} + parent['terminal']['spike_ring'][4, 1] = 1 + obj = object.__new__(c.ContinuedRuntime) + obj.parent, obj.installations, obj.restorations = parent, 1, 0 + obj.torch = types.SimpleNamespace(float64=np.float64, float32=np.float32, int32=np.int32, uint8=np.uint8) + v, g, ref, counts = (ArrayTarget(np.zeros(2, dtype)) for dtype in (np.float64, np.float64, np.int32, np.int32)) + ring, apl_ring = ArrayTarget(np.zeros((19, 2), np.uint8)), ArrayTarget(np.zeros((18, 2), np.float32)) + apl = types.SimpleNamespace(state=ArrayTarget(np.zeros(2, np.float32))) + upload = lambda value, dtype: np.array(value, dtype=dtype, copy=True) + obj.restore(v, g, ref, counts, ring, apl_ring, apl, upload) + self.assertEqual(g.value.tobytes(), parent['state']['g'].tobytes()) + np.testing.assert_array_equal(ref.value, parent['state']['ref']) + np.testing.assert_array_equal(counts.value, [0, 0]) + np.testing.assert_array_equal(ring.value, c.rotate_spike_ring(parent['terminal']['spike_ring'], 5)) + np.testing.assert_array_equal(apl_ring.value, parent['terminal']['apl_pending']) + np.testing.assert_array_equal(apl.state.value, parent['terminal']['apl_state']) + with self.assertRaises(ValueError): + obj.restore(v, g, ref, counts, ring, apl_ring, apl, upload) + + +class CheckpointTests(unittest.TestCase): + def setUp(self): + self.tmp = tempfile.TemporaryDirectory() + self.root = Path(self.tmp.name).resolve() + self.n = patch.object(c, 'N', 4); self.e = patch.object(c, 'E', 6) + self.n.start(); self.e.start() + + def tearDown(self): + self.e.stop(); self.n.stop(); self.tmp.cleanup() + + def save(self, path, value): + R._save(path, value) + + def make(self, name, steps=5, parent=None, corrupt_carry=None): + directory = self.root / name; directory.mkdir() + inputs = self.root / (name + '-inputs'); inputs.mkdir() + spec = {'steps': steps} + if parent is not None: + spec['parent_checkpoint_manifest_sha256'] = parent['manifest_sha256'] + self.save(inputs / 'spec.json', spec) + overlay_bytes = b'synthetic-bound-overlay-not-a-brain' + (inputs / 'overlay.npz').write_bytes(overlay_bytes) + meta = {'overlay_sha256': R.digest(inputs / 'overlay.npz'), 'mask_sha256': R.MASK_SHA, + 'base_candidate_sha256': 'b' * 64} + self.save(inputs / 'overlay-manifest.json', meta) + (directory / 'overlay-input.npz').write_bytes(overlay_bytes) + self.save(directory / 'overlay-manifest.json', meta) + event_tick, event_neuron = np.array([0], np.int32), np.array([1], np.int32) + counts = np.array([0, 1, 0, 0], np.int32) + state = {'step': event_tick, 'neuron_idx': event_neuron, 'counts': counts, + 'v': np.full(4, -.052, np.float64), 'g': np.array([-0., -.003, 0., 1.], np.float64), + 'ref': np.array([0, 22, 3, 0], np.int32)} + ring = np.zeros((19, 4), np.uint8) if parent is None else c.rotate_spike_ring(parent['terminal']['spike_ring'], parent['steps']) + for tick in range(steps): + ring[tick % 19] = 0 + if tick == 0: + ring[0, 1] = 1 + pending = np.zeros((18, 4064), np.float32) + if corrupt_carry == 'spike': + ring[18, 0] ^= np.uint8(1) + elif corrupt_carry == 'apl': + pending[0, 0] = np.float32(1.) + terminal = {'spike_ring': ring, 'apl_pending': pending, 'apl_state': np.array([1., 2.], np.float32), + 'next_tick': np.asarray(steps, np.int64)} + np.savez_compressed(directory / 'state.npz', **state) + np.savez_compressed(directory / 'terminal-queues.npz', **terminal) + np.save(directory / 'apl-pending-tail.float32.npy', pending) + self.save(directory / 'apl-timing-audit.json', {'apl_delay_steps': 18, 'brain_tail_advanced': False, + 'pending_raw_sha256': R._raw(pending), 'final_apl_state_u32_bits': terminal['apl_state'].view(np.uint32).tolist()}) + offset = 0 if parent is None else parent['global_next_tick'] + summary = {'complete': True, 'pass_all': True, 'all_finite': True, 'steps': steps, 'completed_ticks': steps, + 'nodes': 4, 'edges': 6, 'precision': 'float64', 'apl_precision': 'float32', + 'within_trial_weight_updates': False, 'checkpoint_continuation_used': parent is not None, + 'effective_weights_raw_sha256': 'e'*64, 'global_tick_offset': offset, 'global_next_tick': offset + steps, + 'input_sha256': {'spec.json': R.digest(inputs / 'spec.json')}} + if parent is not None: + summary['parent_checkpoint_manifest_sha256'] = parent['manifest_sha256'] + self.save(directory / 'summary.json', summary) + self.save(directory / 'finite-audit.json', {'all_finite': True, 'first_failure_tick': -1, 'finite_observed_ticks': steps}) + self.save(directory / 'overlay-audit.json', {'complete': True, 'weights_updated_during_run': False, + 'effective_weights_raw_sha256': 'e'*64, 'mask_sha256': R.MASK_SHA, 'am_helper_sha256': R.AM_OVERLAY_SHA, + 'base_candidate_sha256': 'b'*64}) + provenance = {'schema': 'phase4an-reporting-adapter-v1' if parent is None else 'phase4an-continuation-runtime-v1', + 'complete': True, 'weights_updated_during_run': False, 'old_globals_patched': False, + 'source_file': R.__file__ if parent is None else c.__file__, + 'source_sha256': c.LIGHT_SHA if parent is None else R.digest(c.__file__), + 'input_sha256': {str(path): R.digest(path) for path in inputs.iterdir()}} + if parent is not None: + provenance.update(parent_checkpoint_manifest_sha256=parent['manifest_sha256'], + global_tick_offset=offset, global_next_tick=offset + steps) + for key, file in (('summary_sha256', 'summary.json'), ('finite_audit_sha256', 'finite-audit.json'), + ('terminal_queues_sha256', 'terminal-queues.npz'), ('overlay_audit_sha256', 'overlay-audit.json'), + ('state_sha256', 'state.npz')): + provenance[key] = R.digest(directory / file) + self.save(directory / 'runtime-provenance.json', provenance) + if parent is not None: + self.save(directory / c.RECEIPT, {'schema': 'phase4an-continuation-receipt-v1', 'complete': True, + 'parent_checkpoint_dir': str(parent['directory']), 'parent_checkpoint_manifest_sha256': parent['manifest_sha256'], + 'segment_steps': steps, 'counts_scope': 'segment_only_not_lifetime', 'weights_updated_during_run': False, + 'global_tick_offset': offset, 'global_next_tick': offset + steps, 'payload_sha256': c._tree_hashes(directory)}) + return directory + + def test_fresh_seal_is_exclusive_and_binds_state_bytes(self): + directory = self.make('fresh') + sealed = c.seal_checkpoint(directory) + loaded = c.load_checkpoint(directory, sealed['sha256']) + self.assertEqual(loaded['global_next_tick'], 5) + self.assertEqual(loaded['state']['g'].tobytes(), np.array([-0., -.003, 0., 1.], np.float64).tobytes()) + self.assertIn('state.npz', sealed['files_sha256']) + self.assertFalse(sealed['pre_seal_state_integrity_independently_proven']) + with self.assertRaisesRegex(ValueError, 'already sealed'): + c.seal_checkpoint(directory) + with (directory / 'state.npz').open('ab') as stream: + stream.write(b'tampered') + with self.assertRaisesRegex(ValueError, 'inventory/hash'): + c.load_checkpoint(directory, sealed['sha256']) + + def test_multisegment_receipts_bind_absolute_offset_and_local_phase(self): + parent = None + total = 0 + for i, steps in enumerate((5, 13, 1, 19)): + directory = self.make('segment-' + str(i), steps, parent) + sealed = c.seal_checkpoint(directory) + parent = c.load_checkpoint(directory, sealed['sha256']) + total += steps + self.assertEqual(parent['global_next_tick'], total) + self.assertEqual(parent['terminal']['next_tick'].item(), steps) + self.assertEqual(parent['ancestry_directories'], [self.root / ('segment-' + str(j)) for j in range(i, -1, -1)]) + self.assertEqual(total, 38) + + def test_run_rejects_output_and_build_in_every_ancestor_before_overlay_or_gpu(self): + parent = None + for i in range(3): + directory = self.make('ancestor-' + str(i), 20, parent) + seal = c.seal_checkpoint(directory) + parent = c.load_checkpoint(directory, seal['sha256']) + case = self.root / 'next-case'; case.mkdir() + self.save(case / 'spec.json', {'max_steps': 5, 'steps': 5, 'dt_ms': .1, 'duration_ms': .5, + 'apl_delay_steps': 18, 'record_history': False, + 'parent_checkpoint_manifest_sha256': parent['manifest_sha256']}) + before = {path: c._tree_hashes(path) for path in parent['ancestry_directories']} + with patch.object(R, 'load_overlay', side_effect=AssertionError('Overlay/GPU preparation must not begin')) as overlay: + for ancestor in parent['ancestry_directories']: + for forbidden in (ancestor, ancestor / 'new-child'): + for output, build in ((forbidden, self.root / 'safe-build'), (self.root / 'safe-output', forbidden)): + with self.subTest(ancestor=ancestor.name, output=output, build=build), self.assertRaises(ValueError): + c.run_continued(case, output, build, self.root / 'unused-overlay.npz', + self.root / 'unused-manifest.json', parent['directory']) + overlay.assert_not_called() + self.assertEqual(before, {path: c._tree_hashes(path) for path in parent['ancestry_directories']}) + self.assertFalse((self.root / 'safe-output').exists()) + self.assertFalse((self.root / 'safe-build').exists()) + + def test_reject_bad_external_pin_extra_file_and_error_marker(self): + directory = self.make('fresh') + sealed = c.seal_checkpoint(directory) + with self.assertRaisesRegex(ValueError, 'external pin'): + c.load_checkpoint(directory, '0' * 64) + (directory / 'unexpected.bin').write_bytes(b'extra') + with self.assertRaisesRegex(ValueError, 'inventory/hash'): + c.load_checkpoint(directory, sealed['sha256']) + failure = self.make('failure') + self.save(failure / 'runtime-error.json', {'complete': False}) + with self.assertRaisesRegex(ValueError, 'failure/error'): + c.seal_checkpoint(failure) + + def test_self_consistent_receipts_do_not_hide_unwritten_carry_slot_corruption(self): + directory = self.make('parent', 20) + sealed = c.seal_checkpoint(directory) + parent = c.load_checkpoint(directory, sealed['sha256']) + for kind, message in (('spike', 'carried spike'), ('apl', 'carried APL')): + child = self.make('bad-' + kind, 5, parent, corrupt_carry=kind) + # The synthetic writer hashes its incorrect output consistently; + # the independent parent-to-child carry invariant must still reject. + with self.subTest(kind=kind), self.assertRaisesRegex(ValueError, message): + c.seal_checkpoint(child) + + def test_reject_partial_receipt_and_modified_original_input(self): + directory = self.make('bad-receipt') + (directory / 'summary.json').write_text('{}', encoding='utf-8') + with self.assertRaises(ValueError): + c.seal_checkpoint(directory) + directory = self.make('modified-input') + (self.root / 'modified-input-inputs' / 'spec.json').write_text('{}', encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'input/source'): + c.seal_checkpoint(directory) + + def test_descendant_rejects_mutated_ancestor_even_if_own_files_are_unchanged(self): + directory = self.make('ancestor', 20) + sealed = c.seal_checkpoint(directory) + parent = c.load_checkpoint(directory, sealed['sha256']) + child = self.make('descendant', 5, parent) + child_seal = c.seal_checkpoint(child) + with (directory / 'state.npz').open('ab') as stream: + stream.write(b'ancestor changed after child seal') + with self.assertRaisesRegex(ValueError, 'inventory/hash'): + c.load_checkpoint(child, child_seal['sha256']) + + def test_self_consistent_hashes_cannot_hide_parent_pin_or_weight_disagreement(self): + directory = self.make('bound-parent', 20) + sealed = c.seal_checkpoint(directory) + parent = c.load_checkpoint(directory, sealed['sha256']) + def rewrite(path, value): + path.write_text(json.dumps(value, sort_keys=True), encoding='utf-8') + for kind in ('case', 'summary', 'provenance', 'weights'): + child = self.make('bound-child-' + kind, 5, parent) + spec_path = self.root / ('bound-child-' + kind + '-inputs') / 'spec.json' + summary = R._read_json(child / 'summary.json') + provenance = R._read_json(child / 'runtime-provenance.json') + if kind == 'case': + spec = R._read_json(spec_path); spec['parent_checkpoint_manifest_sha256'] = '0' * 64 + rewrite(spec_path, spec) + summary['input_sha256']['spec.json'] = R.digest(spec_path) + provenance['input_sha256'][str(spec_path)] = R.digest(spec_path) + elif kind in ('summary', 'provenance'): + target = summary if kind == 'summary' else provenance + target['parent_checkpoint_manifest_sha256'] = '0' * 64 + else: + summary['effective_weights_raw_sha256'] = 'f' * 64 + audit = R._read_json(child / 'overlay-audit.json'); audit['effective_weights_raw_sha256'] = 'f' * 64 + rewrite(child / 'overlay-audit.json', audit) + provenance['overlay_audit_sha256'] = R.digest(child / 'overlay-audit.json') + rewrite(child / 'summary.json', summary) + provenance['summary_sha256'] = R.digest(child / 'summary.json') + rewrite(child / 'runtime-provenance.json', provenance) + receipt = R._read_json(child / c.RECEIPT) + receipt['payload_sha256'] = c._tree_hashes(child); receipt['payload_sha256'].pop(c.RECEIPT) + rewrite(child / c.RECEIPT, receipt) + # All individual byte hashes are now consistent. Only semantic + # cross-receipt binding or parent/child weight equality rejects it. + with self.subTest(kind=kind), self.assertRaisesRegex(ValueError, 'weights changed|pin disagrees'): + c.seal_checkpoint(child) + + def test_reject_invalid_state_dtype_events_ref_and_ring(self): + directory = self.make('arrays') + state = c._npz(directory / 'state.npz', ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')) + terminal = c._npz(directory / 'terminal-queues.npz', ('spike_ring', 'apl_state', 'apl_pending', 'next_tick')) + for key, value in (('v', state['v'].astype(np.float32)), ('g', np.full(4, np.nan)), + ('ref', np.full(4, 23, np.int32)), ('step', np.array([5], np.int32)), + ('counts', np.zeros(4, np.int32))): + with self.subTest(key=key), self.assertRaises(ValueError): + c.validate_state(dict(state, **{key: value}), terminal, 5, fresh=True) + bad = dict(terminal, spike_ring=terminal['spike_ring'].copy()); bad['spike_ring'][0, 1] = 0 + with self.assertRaisesRegex(ValueError, 'emitted'): + c.validate_state(state, bad, 5, fresh=True) + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_runtime_light.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_runtime_light.py new file mode 100644 index 0000000..248c575 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_runtime_light.py @@ -0,0 +1,298 @@ +"""Synthetic AST/array diagnostics only. No real brain/GPU or old-file writes.""" +import ast +import copy +import json +from pathlib import Path +import tempfile +import types +import unittest +from unittest.mock import patch + +import numpy as np + +import runtime_light as r + + +AJ = Path(__file__).absolute().parent.parent / 'apl-timing-lab-v1/gpu_timing.py' +AM = Path(__file__).absolute().parent.parent / 'wholebrain-plastic-interface-v1/overlay.py' + + +class ScalarTensor: + """Tiny NumPy scalar stub for diagnostic algebra, not a neuron simulator.""" + def __init__(self, value): + self.value = np.asarray(value) + + def logical_and_(self, other): + self.value = self.value & other.value + return self + + def __lt__(self, other): + return ScalarTensor(self.value < other) + + def __and__(self, other): + return ScalarTensor(self.value & other.value) + + def __invert__(self): + return ScalarTensor(~self.value) + + def all(self): + return ScalarTensor(self.value.all()) + + def item(self): + return self.value.item() + + +class TorchScalars: + bool, int64 = np.bool_, np.int64 + ones = staticmethod(lambda shape, device, dtype: ScalarTensor(np.ones(shape, dtype))) + zeros = staticmethod(lambda shape, device, dtype: ScalarTensor(np.zeros(shape, dtype))) + full = staticmethod(lambda shape, value, device, dtype: ScalarTensor(np.full(shape, value, dtype))) + ones_like = staticmethod(lambda value: ScalarTensor(np.ones_like(value.value))) + full_like = staticmethod(lambda value, fill: ScalarTensor(np.full_like(value.value, fill))) + isfinite = staticmethod(lambda value: ScalarTensor(np.isfinite(value))) + where = staticmethod(lambda condition, first, other: ScalarTensor(np.where(condition.value, first.value, other.value))) + + +class AstTests(unittest.TestCase): + def source(self): + if not AJ.is_file(): + self.skipTest('Original pinned AJ source is not installed') + return AJ.read_bytes() + + def test_math_projection_equal_light_and_reference_and_guard_explicit(self): + for mode in ('light', 'reference'): + tree, audit = r.derive_ast(self.source(), mode) + self.assertTrue(audit['mathematical_ast_identical']) + self.assertEqual(audit['math_ast_sha256'], audit['derived_math_ast_sha256']) + self.assertIn("spec['max_steps']", ast.unparse(tree.body[0])) + compile(tree, '', 'exec') + + def test_reference_entire_tick_loop_remains_identical(self): + source = ast.parse(self.source()) + original = next(node for node in source.body if isinstance(node, ast.FunctionDef) and node.name == 'run_gpu') + tree, _ = r.derive_ast(self.source(), 'reference') + tick = lambda f: next(n for n in ast.walk(f) if isinstance(n, ast.For) and isinstance(n.target, ast.Name) and n.target.id == 'tick') + self.assertEqual(r._dump(tick(original)), r._dump(tick(tree.body[-1]))) + + def test_light_retains_events_requests_kernels_and_no_full_state_cpu_copy(self): + tree, _ = r.derive_ast(self.source(), 'light') + function = tree.body[-1] + tick = next(n for n in ast.walk(function) if isinstance(n, ast.For) and isinstance(n.target, ast.Name) and n.target.id == 'tick') + text = ast.unparse(tick) + for expected in ('ext.state_step', 'ext.fill_edges', 'ext.apply_arrivals', 'apl.advance', + 'event_steps.append', 'event_nodes.append', 'writer.writerows', '_runtime.after_tick', + 'apl_ring[slot].clone()', 'apl_ring[slot].copy_(h)'): + self.assertIn(expected, text) + for forbidden in ('v.cpu()', 'g.cpu()', 'ref.cpu()', 'drive.cpu()', 'h.cpu()', 'queue_summary(', + 'queue_output', 'hash_output', 'np.isfinite'): + self.assertNotIn(forbidden, text) + + def test_altered_math_is_detected_and_source_pin_rejects_comments(self): + tree, audit = r.derive_ast(self.source(), 'light') + function = copy.deepcopy(tree.body[-1]) + node = next(n for n in ast.walk(function) if isinstance(n, ast.Assign) and + ast.unparse(n) == 'g[kc_local_gpu] = g[kc_local_gpu] - delta_si') + node.value.op = ast.Add() + self.assertNotEqual(r.math_projection(function), r.math_projection(tree.body[-1])) + with self.assertRaises(ValueError): + r.derive_ast(self.source() + b'\n# altered\n', 'light') + + def test_fresh_namespace_no_fake_file_no_old_globals_changed(self): + self.source() + module = types.ModuleType('synthetic_AJ') + module.__file__ = str(AJ) + module.marker = object() + before = dict(module.__dict__) + runtime = types.SimpleNamespace(write_json=lambda *_: None) + function, audit = r.compile_runtime(module, runtime, 'light') + self.assertEqual(before, module.__dict__) + self.assertEqual(function.__globals__['__file__'], str(Path(r.__file__).absolute())) + self.assertNotEqual(function.__globals__['__file__'], str(AJ)) + self.assertFalse(audit['old_globals_patched']) + + +class ContractTests(unittest.TestCase): + def spec(self, steps=1200): + return dict(max_steps=steps, steps=steps, dt_ms=.1, duration_ms=steps*.1, + apl_delay_steps=18, record_history=False) + + def test_directory_parent_traversal_cannot_bypass_input_or_source_boundaries(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve() + case, models, graph, bundle = (root / name for name in ('case', 'models', 'graph', 'bundle')) + for path in (case, models, graph, bundle, root / 'new'): + path.mkdir() + safe = root / 'new' + for protected in (case, models, graph, bundle): + alias = safe / '..' / protected.name / 'cache' + for output, build in ((alias, safe / 'build'), (safe / 'out', alias)): + with self.subTest(protected=protected.name, output=output), self.assertRaisesRegex(ValueError, 'cannot write'): + r.execution_paths(case, output, build, (models, graph, bundle)) + actual = r.execution_paths(case, safe / '..' / 'safe-output', safe / '..' / 'safe-build', (models, graph, bundle)) + self.assertEqual(actual, (case, root / 'safe-output', root / 'safe-build')) + self.assertFalse((root / 'safe-output').exists()) + self.assertFalse((root / 'safe-build').exists()) + + def test_directory_link_alias_cannot_bypass_protected_boundary(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory).resolve() + case, models = root / 'case', root / 'models' + case.mkdir(); models.mkdir() + alias = root / 'alias-to-models' + try: + alias.symlink_to(models, target_is_directory=True) + except (OSError, NotImplementedError) as error: + self.skipTest('Directory symlink unavailable on this host: ' + str(error)) + for output, build in ((alias / 'out', root / 'build'), (root / 'out', alias / 'build')): + with self.subTest(output=output), self.assertRaisesRegex(ValueError, 'cannot write'): + r.execution_paths(case, output, build, (models,)) + # A protected root supplied through an alias is also canonicalized. + with self.assertRaisesRegex(ValueError, 'cannot write'): + r.execution_paths(case, root / 'out', models / 'build', (alias,)) + + def test_explicit_bounds_1000_1200_retention_no_hidden_duration(self): + with tempfile.TemporaryDirectory() as directory: + for steps in (1000, 1200, 100000): + self.assertEqual(r.validate_spec(self.spec(steps), directory)['steps'], steps) + for update in ({'max_steps': 999}, {'max_steps': True}, {'steps': 1200.}, + {'duration_ms': 100.}, {'dt_ms': .2}, {'apl_delay_steps': 0}, + {'within_trial_weight_updates': True}, {'checkpoint': None}): + with self.subTest(update=update), self.assertRaises(ValueError): + r.validate_spec(dict(self.spec(), **update), directory) + Path(directory, 'arrays.npz').write_bytes(b'unsupported checkpoint') + with self.assertRaises(ValueError): + r.validate_spec(self.spec(), directory) + + def test_finite_observer_remembers_nan_that_later_disappears(self): + observer = r.FiniteObserver(TorchScalars, 'CPU-test-stub') + initial = np.array([1., -2.], np.float64) + saved = initial.tobytes() + observer.observe(0, initial) + observer.observe(1, np.array([np.nan], np.float32)) + observer.observe(2, np.array([0.], np.float64)) + result = observer.result() + self.assertFalse(result['all_finite']) + self.assertEqual(result['first_failure_tick'], 1) + self.assertEqual(result['finite_observed_ticks'], 3) + self.assertEqual(saved, initial.tobytes()) + with self.assertRaises(ValueError): + observer.observe(4, initial) + + def test_finite_observer_all_valid_reports_minus_one(self): + observer = r.FiniteObserver(TorchScalars, 'CPU-test-stub') + for tick in range(4): + observer.observe(tick, np.array([1, 0], np.int32), np.array([-0., 1.], np.float64)) + self.assertTrue(observer.result()['all_finite']) + self.assertEqual(observer.result()['first_failure_tick'], -1) + + def test_summary_has_no_fake_outer_learning_claim_and_light_audit_is_not_full(self): + obj = object.__new__(r._Runtime) + obj.mode, obj.spec = 'light', dict(max_steps=1200, steps=1200, outer_protocol='conditioning-owned-by-parent') + obj.result = dict(first_failure_tick=-1, arriving_edges=11, accepted_arriving_edges=7) + inherited = dict(learning=False, arriving_edges=0, accepted_arriving_edges=0, complete=True, + apl_timing={'generated_trace': 'absent.npy', 'applied_trace': 'absent.npy'}) + final = obj.summary(inherited) + self.assertNotIn('learning', final) + self.assertFalse(final['within_trial_weight_updates']) + self.assertFalse(final['learning_pass_claimed']) + self.assertFalse(final['full_sequence_bitwise_audit_performed']) + self.assertEqual(final['arriving_edges'], 11) + self.assertIsNone(final['apl_timing']['generated_trace']) + self.assertIsNone(final['apl_timing']['applied_trace']) + self.assertFalse(obj.timing(1200, np.zeros((18, 4064), np.float32))['full_sequence_bitwise_audit_performed']) + self.assertEqual(inherited['arriving_edges'], 0) + + def test_terminal_queue_schema_chronological_pending_and_fresh_tick(self): + class CPUArray: + def __init__(self, array): + self.value = array + + def cpu(self): + return self + + def numpy(self): + return self.value + + ring = np.zeros((19, 166700), np.uint8) + ring[2, 3] = 1 + apl_ring = np.repeat(np.arange(18, dtype=np.float32)[:, None], 4064, axis=1) + apl = types.SimpleNamespace(state=CPUArray(np.array([1., 2.], np.float32))) + obj = object.__new__(r._Runtime) + obj.mode, obj.installations, obj.finished = 'light', 1, False + obj.observer = types.SimpleNamespace(result=lambda: dict(all_finite=True, first_failure_tick=-1, + finite_observed_ticks=1200, arriving_edges=11, accepted_arriving_edges=7)) + with tempfile.TemporaryDirectory() as directory: + obj.finish(CPUArray(ring), CPUArray(apl_ring), apl, 1200, Path(directory)) + with np.load(Path(directory)/'terminal-queues.npz', allow_pickle=False) as output: + self.assertEqual(set(output.files), {'spike_ring', 'apl_state', 'apl_pending', 'next_tick'}) + self.assertEqual(output['spike_ring'].dtype, np.uint8) + self.assertEqual(output['spike_ring'].tobytes(), ring.tobytes()) + self.assertEqual(output['next_tick'].dtype, np.int64) + self.assertEqual(output['next_tick'].item(), 1200) + expected = np.concatenate((apl_ring[1200 % 18:], apl_ring[:1200 % 18])) + np.testing.assert_array_equal(output['apl_pending'], expected) + self.assertTrue(obj.finished) + with self.assertRaises(ValueError): + obj.finish(CPUArray(ring), CPUArray(apl_ring), apl, 1200, Path(directory)) + + def test_pinned_overlay_helpers_toy_units_and_missing_nonzero_fail_closed(self): + if not AM.is_file(): + self.skipTest('Original frozen AM overlay helper not installed') + helper = r._load('phase4an_test_overlay_helper', AM, r.AM_OVERLAY_SHA) + ids, base32 = np.array([3, 9], np.int64), np.array([.275, 1.1], np.float32) + delta = np.float32(-.25) * base32 + plan = helper.validate_arrays(ids, delta, ids, base32) + base = np.array([.000275, .0011], np.float64) + result, _ = helper.materialize_numpy(base, ids, np.zeros(2, bool), plan) + self.assertTrue(helper.bit_equal(result, base + delta.astype(np.float64) * .001)) + with self.assertRaises(ValueError): + helper.map_installation(base[:1], ids[:1], np.zeros(1, bool), plan) + with self.assertRaises(ValueError): + helper.validate_arrays(ids, np.array([.1, 0], np.float32), ids, base32) + + def test_manifest_cannot_self_authorize_replacement_mask(self): + with tempfile.TemporaryDirectory() as directory: + path = Path(directory) + manifest = dict(schema='phase4an-overlay-v1', overlay_name='learned', overlay_sha256='0'*64, + mask_path=str(path/'mask.npy'), mask_sha256='0'*64, base_candidate_path=str(path/'base.npy'), + base_candidate_sha256='0'*64, aj_model_dir=str(AJ.parent), am_overlay_path=str(AM), + am_overlay_sha256=r.AM_OVERLAY_SHA) + (path/'manifest.json').write_text(json.dumps(manifest), encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'authority'): + r.load_overlay(path/'delta.npz', path/'manifest.json') + + def test_learned_manifest_uses_external_base_and_requires_parent_v2(self): + if not AM.is_file() or not AJ.is_file(): + self.skipTest('Pinned static source dependencies are not installed') + with tempfile.TemporaryDirectory() as directory: + path = Path(directory) + ids = np.arange(14232, dtype=np.int64) + base = np.full(14232, .275, np.float32) + delta = np.zeros(14232, np.float32); delta[0] = np.float32(-.00275) + np.save(path/'mask.npy', ids); np.save(path/'base.npy', base) + np.savez(path/'delta.npz', edge_id=ids, plastic_delta_mv=delta) + mask_sha = r.digest(path/'mask.npy') + manifest = dict(schema='phase4an-overlay-v1', overlay_name='learned', + overlay_sha256=r.digest(path/'delta.npz'), mask_path=str(path/'mask.npy'), mask_sha256=mask_sha, + base_candidate_path=str(path/'base.npy'), base_candidate_sha256=r.digest(path/'base.npy'), + aj_model_dir=str(AJ.parent), am_overlay_path=str(AM), am_overlay_sha256=r.AM_OVERLAY_SHA, + parent_snapshot_sha256='a'*64, eligibility_v2_source_sha256=r.V2_SHA) + (path/'manifest.json').write_text(json.dumps(manifest), encoding='utf-8') + # Only the authority fixture is patched; production pin remains fixed. + with patch.object(r, 'MASK_SHA', mask_sha): + helper, plan, meta, inputs = r.load_overlay(path/'delta.npz', path/'manifest.json') + self.assertEqual(plan['plastic_delta_mv'].tobytes(), delta.tobytes()) + self.assertFalse(plan['plastic_delta_mv'].flags.writeable) + self.assertEqual(meta['parent_snapshot_sha256'], 'a'*64) + unexpected = dict(manifest, within_trial_weight_updates=True) + (path/'manifest.json').write_text(json.dumps(unexpected), encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'Unknown'): + r.load_overlay(path/'delta.npz', path/'manifest.json') + manifest.pop('parent_snapshot_sha256') + (path/'manifest.json').write_text(json.dumps(manifest), encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'parent'): + r.load_overlay(path/'delta.npz', path/'manifest.json') + + +if __name__ == '__main__': + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_seed_registry.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_seed_registry.py new file mode 100644 index 0000000..933b8ef --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_seed_registry.py @@ -0,0 +1,123 @@ +import copy +import hashlib +import json +import unittest +from unittest.mock import patch + +import seed_registry as m + + +class Seeds(unittest.TestCase): + def test_order_independent(self): + a, b = m.SeedRegistry(461), m.SeedRegistry(461) + keys = [("mode", "PAM15", str(replicate), str(epoch)) for replicate in range(5) for epoch in range(50)] + for key in keys: + a.add("training", key) + for key in reversed(keys): + b.add("training", key) + self.assertEqual(a.manifest(), b.manifest()) + self.assertEqual(a.manifest()["independent_count"], 250) + self.assertTrue(m.validate_manifest(a.manifest())) + + def test_splits_disjoint_and_no_sequential_overlap(self): + r = m.SeedRegistry(461) + for split in ("training", "evaluation", "design"): + for replicate in range(5): + for epoch in range(100): + r.add(split, ("shared-name", str(replicate), str(epoch))) + out = r.manifest() + seeds = [x["seed"] for x in out["independent_streams"]] + self.assertEqual(len(set(seeds)), 1500) + self.assertEqual(out["training_evaluation_seed_overlap"], 0) + self.assertTrue(all(0 <= s < 2**63 for s in seeds)) + + def test_alias_explicit_not_independent(self): + r = m.SeedRegistry(461) + seed = r.add("training", ("paired", "0")) + self.assertEqual(seed, r.alias("training", ("frozen", "0"), "training", ("paired", "0"), reason="Same prescheduled CS inputs, different learning policy")) + out = r.manifest() + self.assertEqual(out["independent_count"], 1) + self.assertEqual(out["alias_count"], 1) + self.assertFalse(out["aliases_count_as_independent_replicates"]) + self.assertTrue(m.validate_manifest(out)) + + def test_no_cross_split_alias(self): + r = m.SeedRegistry(1) + r.add("training", ("a",)) + with self.assertRaisesRegex(ValueError, "Cross-split"): + r.alias("evaluation", ("a",), "training", ("a",), reason="Not permitted") + + def test_no_alias_chain(self): + r = m.SeedRegistry(1) + r.add("training", ("a",)) + r.alias("training", ("b",), "training", ("a",), reason="Control") + with self.assertRaisesRegex(ValueError, "not another alias"): + r.alias("training", ("c",), "training", ("b",), reason="Invalid chain") + + def test_no_silent_duplicate(self): + r = m.SeedRegistry(1) + r.add("training", ("a",)) + with self.assertRaisesRegex(ValueError, "Duplicate"): + r.add("training", ("a",)) + with self.assertRaisesRegex(ValueError, "Duplicate"): + r.alias("training", ("a",), "training", ("a",), reason="Invalid") + r.alias("training", ("b",), "training", ("a",), reason="Control") + with self.assertRaisesRegex(ValueError, "Duplicate"): + r.add("training", ("b",)) + + def test_collision_fails_without_resampling(self): + r = m.SeedRegistry(1) + with patch.object(m, "seed_for", return_value=(123, "a" * 64)): + r.add("training", ("a",)) + with self.assertRaisesRegex(ValueError, "Seed collision"): + r.add("evaluation", ("b",)) + self.assertEqual(len(r._streams), 1) + + def test_mutations_detected(self): + r = m.SeedRegistry(1) + r.add("training", ("a",)) + original = r.manifest() + for mutate in (lambda x: x["independent_streams"][0].update(seed=7), + lambda x: x.update(training_evaluation_seed_overlap=1), + lambda x: x.update(independent_count=2), + lambda x: x.update(payload_sha256="a" * 64), + lambda x: x.update(training_executed=True)): + corrupt = copy.deepcopy(original) + mutate(corrupt) + with self.assertRaises(ValueError): + m.validate_manifest(corrupt) + + def test_manifest_copy_not_mutable_alias(self): + r = m.SeedRegistry(1) + r.add("training", ("a",)) + out = r.manifest() + out["independent_streams"][0]["key"][0] = "changed" + self.assertEqual(r.manifest()["independent_streams"][0]["key"], ["a"]) + self.assertTrue(m.validate_manifest(json.loads(json.dumps(r.manifest())))) + + def test_json_numeric_boolean_type_changes_rejected_even_when_rehashed(self): + r = m.SeedRegistry(1) + r.add("training", ("a",)) + for field, replacement in (("training_executed", 0), ("independent_count", 1.0)): + for rehash in (False, True): + corrupt = r.manifest() + corrupt[field] = replacement + if rehash: + payload = {key: value for key, value in corrupt.items() if key != "payload_sha256"} + corrupt["payload_sha256"] = hashlib.sha256(m._canonical(payload).encode("utf-8")).hexdigest() + with self.subTest(field=field, rehash=rehash), self.assertRaises(ValueError): + m.validate_manifest(corrupt) + + def test_reject_invalid_values(self): + for master in (True, -1, 2**63, 1.0, "1"): + with self.assertRaises(ValueError): + m.SeedRegistry(master) + for key in ([], "a", [""], [" a"], ["a "]): + with self.assertRaises(ValueError): + m.seed_for(1, "training", key) + with self.assertRaises(ValueError): + m.seed_for(1, "unknown", ["a"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/test_trial_plan.py b/registry/phase4ao-conditioning-interface/source-snapshot/test_trial_plan.py new file mode 100644 index 0000000..03f61d2 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/test_trial_plan.py @@ -0,0 +1,171 @@ +import copy +from collections import Counter +import unittest +from unittest.mock import patch + +import seed_registry +import trial_plan as m + + +class TrialPlan(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.original = m.build_trial_plan(461) + + def test_complete_reconstruction_and_budget(self): + self.assertTrue(m.validate_trial_plan(self.original)) + budget = self.original["budget"] + self.assertEqual(budget["training_control_reversal_epochs"], 68000) + self.assertEqual(budget["training_control_reversal_steps"], 99600000) + self.assertEqual(budget["minimum_biological_seconds_excluding_evaluation"], 10760) + self.assertEqual(budget["minimum_steps_excluding_evaluation"], 107600000) + self.assertFalse(budget["evaluation_probe_inventory_complete"]) + self.assertIsNone(budget["evaluation_epochs"]) + self.assertFalse(self.original["plan"]["training_executed"]) + + def test_exact_cells_five_training_replicates_not_evaluation_streams(self): + cells = {} + for block in self.original["plan"]["blocks"]: + key = block["mode"], block["dan_type"], block["initial_reinforced_cue"] + cells.setdefault(key, []).append(block["replicate"]) + self.assertEqual(len(block["evaluation_streams"]), 2) + self.assertEqual(len(cells), 8) + self.assertTrue(all(replicates == [1, 2, 3, 4, 5] for replicates in cells.values())) + + def test_all_epoch_control_roles_and_matched_delayed_slots(self): + for block in self.original["plan"]["blocks"]: + for suite in block["suites"]: + assay = suite["assay"] + arms = {a["arm"]: a for a in suite["arms"]} + self.assertEqual(tuple(arms), m.REVERSAL_ARMS if assay == "reversal" else m.ARMS) + expected_steps, expected_us = (1800, 1500) if assay == "delayed" else (1200, 900) + for arm, spec in arms.items(): + epochs = spec["epochs"] + self.assertEqual(len(epochs), 150 if arm == "unpaired" else 100) + cues = Counter(e["cs"]["cue"] for e in epochs if e["cs"] is not None) + self.assertEqual(cues, Counter() if arm == "dan_only" else Counter(A=50, B=50)) + us = [e for e in epochs if e["us"]["requested"]] + self.assertEqual(len(us), 0 if arm == "cs_only" else 50) + self.assertEqual(len({e["us"]["inventory_id"] for e in us}), len(us)) + for i, e in enumerate(epochs): + self.assertEqual(e["epoch_index"], i) + self.assertEqual(e["steps"], expected_steps) + self.assertEqual(e["us"]["nominal_slot_tick"], expected_us) + self.assertEqual(e["us"]["tick"], expected_us if e["us"]["requested"] else None) + self.assertIsNone(e["us"]["rng_seed"]) + self.assertEqual(e["us"]["rng_draws"], 0) + self.assertEqual(e["update_enabled"], arm != "frozen") + self.assertEqual(e["D_command"], 1.0 if e["us"]["requested"] and arm != "d_zero" else 0.0) + if arm == "random_reward": + self.assertEqual(Counter(e["cs"]["cue"] for e in us), Counter(A=25, B=25)) + if arm == "unpaired": + self.assertTrue(all(e["cs"] is None for e in us)) + if arm == "dan_only": + self.assertEqual(sum(not e["us"]["requested"] for e in epochs), 50) + + def test_alias_binding_same_occurrence_even_unpaired_insertion(self): + for block in self.original["plan"]["blocks"]: + for suite in block["suites"]: + base = suite["arms"][0] + lookup = {(e["cs"]["cue"], e["cs"]["occurrence"]): e["cs"] + for e in base["epochs"]} + base_order = [(e["cs"]["cue"], e["cs"]["occurrence"]) for e in base["epochs"]] + for arm in suite["arms"][1:]: + actual_order = [] + for e in arm["epochs"]: + if e["cs"] is None: + continue + cs = e["cs"] + source = lookup[(cs["cue"], cs["occurrence"])] + self.assertEqual(cs["seed"], source["seed"]) + self.assertEqual(cs["source_stream_id"], source["stream_id"]) + self.assertNotEqual(cs["stream_id"], source["stream_id"]) + actual_order.append((cs["cue"], cs["occurrence"])) + if arm["arm"] != "dan_only": + self.assertEqual(actual_order, base_order) + + def test_reversal_not_extra_acquisition_or_new_compartment(self): + for b in self.original["plan"]["blocks"]: + reversal, continuation = b["suites"][2]["arms"] + original = b["initial_reinforced_cue"] + other = b["initial_neutral_cue"] + for spec, cue in ((reversal, other), (continuation, original)): + self.assertEqual(spec["delta_start"], {"kind": "fork", "source": b["block_id"] + "/normal/paired/terminal-delta"}) + self.assertEqual({e["cs"]["cue"] for e in spec["epochs"] if e["us"]["requested"]}, {cue}) + + def test_split_and_replicate_seeds_are_independent_fullwidth(self): + manifest = self.original["seed_manifest"] + all_seeds = [r["seed"] for r in manifest["independent_streams"]] + self.assertEqual(len(all_seeds), len(set(all_seeds))) + by_block = {} + by_split = {} + for row in manifest["independent_streams"]: + by_block.setdefault(row["key"][0], set()).add(row["seed"]) + by_split.setdefault(row["split"], set()).add(row["seed"]) + self.assertEqual(len(by_block), 40) + blocks = list(by_block) + for i, block in enumerate(blocks): + for other in blocks[i + 1:]: + self.assertFalse(by_block[block] & by_block[other]) + self.assertFalse(by_split["training"] & by_split["evaluation"]) + self.assertTrue(any(seed > 2**32 for seed in all_seeds)) + for block in self.original["plan"]["blocks"]: + for probe in block["evaluation_streams"]: + self.assertIn(probe["seed"], by_split["evaluation"]) + self.assertEqual(probe["seed_bits"], 63) + + def test_generic_valid_alias_can_still_be_protocol_illegal(self): + for source, destination in ( + (("b-rep1", "normal", "CS", "A", "00", "paired"), ("b-rep2", "normal", "CS", "A", "00", "frozen")), + (("b", "normal", "US", "A", "00", "paired"), ("b", "normal", "CS", "A", "00", "frozen")), + (("b", "normal", "CS", "A", "00", "paired"), ("b", "delayed", "CS", "A", "00", "frozen")), + (("b", "normal", "CS", "A", "00", "paired"), ("b", "normal", "CS", "B", "00", "frozen")), + (("b", "normal", "CS", "A", "00", "paired"), ("b", "normal", "CS", "A", "01", "frozen")), + (("b", "normal", "CS", "A", "00", "paired"), ("b", "normal", "CS", "A", "00", "undeclared-control")), + ): + r = seed_registry.SeedRegistry(1) + r.add("training", source) + r.alias("training", destination, "training", source, reason="Synthetic prohibited crossing") + self.assertTrue(seed_registry.validate_manifest(r.manifest())) + with self.assertRaises(ValueError): + m._validate_alias_permissions(r.manifest()) + + def test_mutations_fail_even_after_forging_payload_sha(self): + def first(p): + return p["plan"]["blocks"][0]["suites"][0]["arms"][0]["epochs"][0] + mutations = ( + lambda p: p["plan"]["blocks"].pop(), + lambda p: p["plan"]["blocks"].__setitem__(1, copy.deepcopy(p["plan"]["blocks"][0])), + lambda p: p["plan"]["blocks"][0]["suites"][0]["arms"][0]["epochs"].pop(), + lambda p: first(p)["cs"].update(seed=first(p)["cs"]["seed"] & 0xffffffff), + lambda p: first(p)["cs"].update(seed_bits=32), + lambda p: first(p)["cs"].update(odor="post-outcome-selected odor"), + lambda p: p["plan"].update(execution_authorized=0), + lambda p: p["budget"].update(evaluation_epochs=0), + ) + for index, mutate in enumerate(mutations): + payload = copy.deepcopy(self.original) + mutate(payload) + payload["payload_sha256"] = m._sha({k: v for k, v in payload.items() if k != "payload_sha256"}) + with self.subTest(mutation=index), self.assertRaises(ValueError): + m.validate_trial_plan(payload) + + def test_deterministic_small_shuffle_and_fullwidth_seed(self): + a = m._Shuffle(23).shuffled(range(30)) + self.assertEqual(a, m._Shuffle(23).shuffled(range(30))) + self.assertNotEqual(a, m._Shuffle(23 + 2**32).shuffled(range(30))) + self.assertEqual(sorted(a), list(range(30))) + with self.assertRaises(ValueError): + m._Shuffle(True) + + def test_integer_rejection_advances_counter_not_seed(self): + shuffle = m._Shuffle(23 + 2**32) + with patch.object(m, "_sha", side_effect=["f" * 64, "0" * 64]) as hashed: + self.assertEqual(shuffle.below(3), 0) + self.assertEqual(shuffle.counter, 2) + self.assertEqual(hashed.call_args_list[0].args[0], [m.SHUFFLE_VERSION, 23 + 2**32, 0]) + self.assertEqual(hashed.call_args_list[1].args[0], [m.SHUFFLE_VERSION, 23 + 2**32, 1]) + + +if __name__ == "__main__": + unittest.main() diff --git a/registry/phase4ao-conditioning-interface/source-snapshot/trial_plan.py b/registry/phase4ao-conditioning-interface/source-snapshot/trial_plan.py new file mode 100644 index 0000000..4020541 --- /dev/null +++ b/registry/phase4ao-conditioning-interface/source-snapshot/trial_plan.py @@ -0,0 +1,325 @@ +"""Pure, unregistered conditioning inventory; no files, brain inputs or runs. + +build_trial_plan(master) returns plan/seed_manifest/budget. validate_trial_plan +checks seed authority, alias permissions, and exact deterministic reconstruction +of the complete inventory, not just totals. A consumer must still bind the exact +63-bit seed and generator to its generated input receipt; this module cannot +certify a future consumer that silently truncates an integer. +""" +from __future__ import annotations + +from collections import Counter +import hashlib +import json + +import seed_registry + + +SCHEMA = "phase4an-trial-plan-v1-draft" +SHUFFLE_VERSION = "sha256-counter-rejection-fisher-yates-v1" +MODES = ("physiological", "matched_energy") +DAN_BRANCHES = (("PAM15", "y5B'2a"), ("PPL103", "y2a'1")) +CUES = ("A", "B") +ODORS = {"A": "4-ethylphenol", "B": "3-hexanone"} +ARMS = ("paired", "d_zero", "frozen", "random_reward", "unpaired", "cs_only", "dan_only") +REVERSAL_ARMS = ("reversal", "no_reversal") +CONTROL_ALIASES = frozenset(("d_zero", "frozen", "random_reward", "unpaired", "cs_only")) +PROBE_CHECKPOINTS = (0, 1, 2, 5, 10, 20, 50) + + +def _require(value, message): + if not value: + raise ValueError(message) + + +def _canonical(value): + return json.dumps(value, sort_keys=True, separators=(",", ":"), + ensure_ascii=False, allow_nan=False) + + +def _sha(value): + return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest() + + +class _Shuffle: + """Explicit portable integer algorithm, unrelated to neural input RNG. + + Each draw hashes canonical UTF-8 JSON [version, full63bitseed, counter]. + Interpret all 256 digest bits as a big-endian unsigned integer; increment + counter even for a rejection. Reject x >= 2**256 - (2**256 % bound), then + return x % bound. Fisher-Yates descends i=n-1..1 and swaps with draw(i+1). + No Python random/NumPy algorithm, modulo bias, or seed search is used. + """ + def __init__(self, seed): + _require(type(seed) is int and 0 <= seed < 2**63, "Full 63-bit shuffle seed required") + self.seed = seed + self.counter = 0 + + def below(self, bound): + _require(type(bound) is int and 0 < bound <= 2**256, "Invalid shuffle bound") + limit = 2**256 - (2**256 % bound) + while True: + raw = bytes.fromhex(_sha([SHUFFLE_VERSION, self.seed, self.counter])) + self.counter += 1 + value = int.from_bytes(raw, "big") + if value < limit: + return value % bound + + def shuffled(self, values): + result = list(values) + for i in range(len(result) - 1, 0, -1): + j = self.below(i + 1) + result[i], result[j] = result[j], result[i] + return result + + +def _stream_id(master, split, key): + return split + ":" + _sha([SCHEMA, "stream-id", master, split, list(key)]) + + +def _block_id(mode, dan, reinforced, replicate): + return f"{mode}__{dan}__reinforce_{reinforced}__rep{replicate:02d}" + + +def _input_key(block, assay, cue, occurrence, arm): + return (block, assay, "CS", cue, f"{occurrence:02d}", arm) + + +def _cs_binding(registry, block, assay, cue, occurrence, arm, base_arm): + key = _input_key(block, assay, cue, occurrence, arm) + source_key = _input_key(block, assay, cue, occurrence, base_arm) + if arm == base_arm: + seed = registry.add("training", key) + else: + seed = registry.alias("training", key, "training", source_key, + reason="Same block/assay/CS cue/occurrence; named counterfactual control") + return {"cue": cue, "odor": ODORS[cue], "occurrence": occurrence, + "stream_id": _stream_id(registry.master, "training", key), + "source_stream_id": _stream_id(registry.master, "training", source_key), + "seed": seed, "seed_bits": 63, "seed_conversion": "exact integer; no truncation or addition", + "role": "CS", "start_tick": 0, "stop_tick_exclusive": 1000} + + +def _ordered_cues(seed): + counts = {"A": 0, "B": 0} + result = [] + for cue in _Shuffle(seed).shuffled(["A"] * 50 + ["B"] * 50): + result.append((cue, counts[cue])) + counts[cue] += 1 + return result + + +def _epoch(registry, block, assay, arm, ordinal, cue, occurrence, base_arm, + us_ordinal, yoked_cue=None, yoked_occurrence=None): + delayed = assay == "delayed" + steps, us_tick = (1800, 1500) if delayed else (1200, 900) + requested = us_ordinal is not None + cs = None if cue is None else _cs_binding(registry, block, assay, cue, occurrence, arm, base_arm) + return { + "epoch_id": f"{block}/{assay}/{arm}/{ordinal:03d}", + "epoch_index": ordinal, "steps": steps, "duration_ms": steps // 10, + "initial_neural_state": "frozen_reset_including_KC_trace_and_both_delay_queues", + "cs": cs, + "us": {"requested": requested, + "inventory_id": None if not requested else f"{block}/{assay}/US/{us_ordinal:02d}", + "target_binding": None if not requested else block + "/confirmed-DAN-targets", + "tick": us_tick if requested else None, + "nominal_slot_tick": us_tick, + "request_rule": "fixed_single_pulse_all_bound_DAN_targets" if requested else "none", + "rng_seed": None, "rng_draws": 0}, + "D_command": 1.0 if requested and arm != "d_zero" else 0.0, + "D_definition": "scheduled artificial scalar, not multiplied by acceptance", + "update_enabled": arm != "frozen", + "update_schedule": "epoch_boundary_only", + "yoked_cue_removed": yoked_cue, + "yoked_occurrence_removed": yoked_occurrence, + } + + +def _suite(registry, block, assay, reinforced): + base_arm = "reversal" if assay == "reversal" else "paired" + order_key = (block, assay, "cue-order") + order_seed = registry.add("design", order_key) + ordered = _ordered_cues(order_seed) + designs = [{"purpose": "cue-order", "stream_id": _stream_id(registry.master, "design", order_key), + "seed": order_seed, "algorithm": SHUFFLE_VERSION}] + random_sets = None + placements = None + if assay != "reversal": + key = (block, assay, "random-reward-placement") + seed = registry.add("design", key) + shuffle = _Shuffle(seed) + random_sets = {cue: sorted(shuffle.shuffled(range(50))[:25]) for cue in CUES} + designs.append({"purpose": "random-reward-placement", "stream_id": _stream_id(registry.master, "design", key), + "seed": seed, "algorithm": SHUFFLE_VERSION}) + key = (block, assay, "unpaired-US-placement") + seed = registry.add("design", key) + placements = _Shuffle(seed).shuffled(["CS"] * 100 + ["US"] * 50) + designs.append({"purpose": "unpaired-US-placement", "stream_id": _stream_id(registry.master, "design", key), + "seed": seed, "algorithm": SHUFFLE_VERSION}) + + arms = [] + for arm in REVERSAL_ARMS if assay == "reversal" else ARMS: + events = [] + arm_reinforced = ("B" if reinforced == "A" else "A") if arm == "reversal" else reinforced + if arm == "unpaired": + cursor, us_cursor = 0, 0 + for token in placements: + if token == "CS": + cue, occurrence = ordered[cursor] + cursor += 1 + events.append(_epoch(registry, block, assay, arm, len(events), cue, occurrence, base_arm, None)) + else: + events.append(_epoch(registry, block, assay, arm, len(events), None, None, base_arm, us_cursor)) + us_cursor += 1 + else: + reward_us_ids = None + if arm == "random_reward": + reward_us_ids = {(cue, occurrence): offset + i + for cue, offset in (("A", 0), ("B", 25)) + for i, occurrence in enumerate(random_sets[cue])} + for cue, occurrence in ordered: + if arm == "random_reward": + us_ordinal = reward_us_ids.get((cue, occurrence)) + elif arm == "cs_only": + us_ordinal = None + else: + us_ordinal = occurrence if cue == arm_reinforced else None + if arm == "dan_only": + events.append(_epoch(registry, block, assay, arm, len(events), None, None, base_arm, + us_ordinal, yoked_cue=cue, yoked_occurrence=occurrence)) + else: + events.append(_epoch(registry, block, assay, arm, len(events), cue, occurrence, base_arm, us_ordinal)) + arms.append({ + "arm": arm, + "delta_start": {"kind": "fork", "source": f"{block}/normal/paired/terminal-delta"} + if assay == "reversal" else {"kind": "zero", "source": None}, + "carry_between_epochs": ["plastic_delta_only"], + "reinforced_cue": arm_reinforced if arm not in ("random_reward", "unpaired", "cs_only", "dan_only") else None, + "random_reward_occurrences": random_sets if arm == "random_reward" else None, + "epochs": events, + }) + return {"assay": assay, "design_streams": designs, "arms": arms} + + +def _assemble(master): + registry = seed_registry.SeedRegistry(master) + blocks = [] + for mode in MODES: + for dan, compartment in DAN_BRANCHES: + for reinforced in CUES: + for replicate in range(1, 6): + block = _block_id(mode, dan, reinforced, replicate) + evaluations = [] + for cue in CUES: + key = _input_key(block, "evaluation", cue, 0, "base-probe") + seed = registry.add("evaluation", key) + evaluations.append({"cue": cue, "odor": ODORS[cue], "role": "CS", "seed": seed, + "seed_bits": 63, "stream_id": _stream_id(master, "evaluation", key), + "use": "same saved input across pre/post/controls; never extra training replicate"}) + blocks.append({ + "block_id": block, "mode": mode, "dan_type": dan, + "compartment_verbatim": compartment, "replicate": replicate, + "initial_reinforced_cue": reinforced, + "initial_neutral_cue": "B" if reinforced == "A" else "A", + "DAN_target_binding": {"binding_id": block + "/confirmed-DAN-targets", + "source": "sealed H exact confirmed mapping; not materialized by this planner", + "required_before_execution": True, "body_ids": None}, + "suites": [_suite(registry, block, assay, reinforced) + for assay in ("normal", "delayed", "reversal")], + "evaluation_streams": evaluations, + "evaluation_checkpoints": list(PROBE_CHECKPOINTS), + "evaluation_policy": "separate future probe inventory required; evaluation exposure not included in epoch budget", + "retention": {"branches": ["normal/paired", "normal/frozen"], + "checkpoint_seconds": [0, 1, 10], "idle_seconds_each": 10, + "state_continuous": True, "probes_fork_and_do_not_advance_idle": True}, + }) + + plan = { + "schema": SCHEMA, "status": "DRAFT-NOT-REGISTERED", "master": master, + "execution_authorized": False, "brain_inputs_generated": False, + "training_executed": False, "shuffle_algorithm": SHUFFLE_VERSION, + "shuffle_specification": _Shuffle.__doc__.strip(), + "input_seed_contract": "full63bit integer; no uint32 truncation, offset, modulo or hidden global RNG", + "consumer_generator_implementation_verified": False, + "fixed_odors": dict(ODORS), "dt_ms": 0.1, + "training_replicates_per_cell": 5, "blocks": blocks, + "alias_policy": "training CS only; same block/assay/cue/occurrence; named controls only", + "scheduled_US_rng": "none; deterministic pulse; design shuffle randomness is separately registered", + } + counts, steps = Counter(), 0 + cs_epochs, us_epochs, effective_updates = 0, 0, 0 + for block in blocks: + for suite in block["suites"]: + for arm in suite["arms"]: + counts[(suite["assay"], arm["arm"])] += len(arm["epochs"]) + for epoch in arm["epochs"]: + steps += epoch["steps"] + cs_epochs += epoch["cs"] is not None + us_epochs += epoch["us"]["requested"] + effective_updates += epoch["update_enabled"] and epoch["D_command"] != 0 + budget = { + "blocks": 40, "training_replicates_per_cell": 5, + "epoch_counts_by_assay_arm": [{"assay": a, "arm": b, "epochs": count} + for (a, b), count in sorted(counts.items())], + "training_control_reversal_epochs": sum(counts.values()), + "CS_epochs": cs_epochs, "US_request_epochs": us_epochs, + "enabled_nonzero_D_update_opportunities": effective_updates, + "training_control_reversal_steps": steps, + "training_control_reversal_biological_seconds": steps // 10000, + "retention_idle_seconds": 40 * 2 * 10, + "retention_idle_steps": 40 * 2 * 10 * 10000, + "minimum_biological_seconds_excluding_evaluation": steps // 10000 + 800, + "minimum_steps_excluding_evaluation": steps + 8000000, + "evaluation_epochs": None, "evaluation_biological_seconds": None, + "evaluation_probe_inventory_complete": False, + "excludes": ["evaluation probes", "implementation validation", "frozen regressions", "I/O and encoding wall time"], + "reason_not_total_runtime": "Only training/control/reversal and continuous retention idle are budgeted; never label this complete total exposure", + } + _require(budget["training_control_reversal_epochs"] == 68000 and steps == 99600000, + "Internal protocol inventory arithmetic changed") + result = {"plan": plan, "seed_manifest": registry.manifest(), "budget": budget} + result["payload_sha256"] = _sha(result) + return result + + +def _validate_alias_permissions(manifest): + for row in manifest["aliases"]: + key, source = row["key"], row["source_key"] + _require(row["split"] == row["source_split"] == "training", + "Only named training CS controls may alias streams") + _require(len(key) == len(source) == 6 and key[2] == source[2] == "CS", + "CS/US/design/evaluation role aliasing is forbidden") + _require(key[:5] == source[:5], "Alias crosses block/replicate/assay/cue/occurrence") + if key[1] in ("normal", "delayed"): + _require(source[5] == "paired" and key[5] in CONTROL_ALIASES, + "Unknown normal/delayed control alias") + else: + _require(key[1] == "reversal" and source[5] == "reversal" and key[5] == "no_reversal", + "Unknown reversal control alias") + + +def validate_trial_plan(payload): + """Fail closed on incomplete coverage, illegal aliasing, mutation or types. + + Rebuild the whole fixed 40-block schedule and every RNG derivation. This + does not trust a count, a caller's declared sha, or a row labeled replicate. + Canonical comparison distinguishes Boolean/integer/floating-point JSON. + """ + _require(type(payload) is dict and set(payload) == {"plan", "seed_manifest", "budget", "payload_sha256"}, + "Invalid complete trial-plan envelope") + actual = {key: value for key, value in payload.items() if key != "payload_sha256"} + _require(type(payload["payload_sha256"]) is str and payload["payload_sha256"] == _sha(actual), + "Actual trial-plan payload SHA mismatch") + seed_registry.validate_manifest(payload["seed_manifest"]) + _validate_alias_permissions(payload["seed_manifest"]) + _require(payload["plan"]["master"] == payload["seed_manifest"]["master"], "Master identities differ") + expected = _assemble(payload["plan"]["master"]) + _require(_canonical(payload) == _canonical(expected), + "Full protocol coverage/input binding/order/type reconstruction mismatch") + return True + + +def build_trial_plan(master): + """Return a deterministic JSON-serializable DRAFT inventory without I/O.""" + return _assemble(master) diff --git a/tools/phase4ao-publication/diagnose_postprocess_isolation.py b/tools/phase4ao-publication/diagnose_postprocess_isolation.py new file mode 100644 index 0000000..c698524 --- /dev/null +++ b/tools/phase4ao-publication/diagnose_postprocess_isolation.py @@ -0,0 +1,77 @@ +"""One fresh-process, CPU-only diagnosis of the stopped AO postprocessor. + +Consumes the existing sealed first trajectory; never executes a neural trial, +changes the stopped protocol, or installs a delta. Diagnostic files are new. +""" +import argparse +import hashlib +import json +import os +from pathlib import Path +import sys +import time + +ROOT = Path('/data/digital-drosophila') +EXP = ROOT / 'experiments/phase4ao-conditioning-interface' +MODEL = ROOT / 'models/male-cns-lif/olfactory-conditioning-lab-v1' +DIAG = ROOT / 'experiments/phase4ao-postprocess-isolation' +E_SHA = '757935a4815d9f625640df685184ed3fe44a8845323562796da7aa43cbb194e1' + + +def digest(p): + h = hashlib.sha256() + with Path(p).open('rb') as f: + for data in iter(lambda: f.read(8 << 20), b''): h.update(data) + return h.hexdigest() + + +def main(reg_sha, checkpoint_sha, authority_sha, stopped_sha): + assert sys.dont_write_bytecode + assert not any(k in sys.modules for k in ('runtime_light', 'runtime_continuation', 'phase4an_sealed_am_overlay')) + regpath = EXP / 'protocol/preregistered.json' + authority = EXP / 'receipts/D1--reference--epoch-000.authority.json' + stopped = EXP / 'audit/stopped.json' + checkpoint = EXP / 'runs/D1--reference--epoch-000/checkpoint-manifest.json' + pins = {regpath: reg_sha, authority: authority_sha, stopped: stopped_sha, checkpoint: checkpoint_sha, + MODEL / 'conditioning_epoch.py': E_SHA} + assert all(digest(p) == h for p, h in pins.items()) + reg = json.loads(regpath.read_text()) + assert all(digest(p) == h for field in ('source_sha256', 'input_sha256', 'prepared_file_sha256') for p, h in reg[field].items()) + value = json.loads(authority.read_text()) + row = reg['plan']['calls'][0] + assert row['row_id'] == 'D1--reference--epoch-000' and value['epoch_id'] == row['epoch_id'] + assert value['checkpoint_manifest_sha256'] == checkpoint_sha + assert value['command'] == dict(steps=1200, compartment=reg['plan']['compartment_verbatim'], us_tick=900, + d_command=1.0, frozen=False, trace_backend='torch') + out = DIAG / 'postprocess' + receipt = DIAG / 'diagnostic-receipt.json' + assert DIAG.is_dir() and not out.exists() and not receipt.exists() + os.environ['CUDA_VISIBLE_DEVICES'] = '' + sys.path.insert(0, str(MODEL)) + import conditioning_epoch as E + assert digest(E.__file__) == E_SHA + started = time.perf_counter() + chain = E.postprocess_epoch(authority, authority_sha, out) + update = json.loads((out / 'update-receipt.json').read_text()) + assert all(digest(p) == h for p, h in pins.items()) + result = dict(status='PASS-ISOLATED-POSTPROCESS-DIAGNOSTIC-ONLY', registration_sha256=reg_sha, + authority_sha256=authority_sha, checkpoint_manifest_sha256=checkpoint_sha, stopped_sha256=stopped_sha, + diagnostic_script_sha256=digest(__file__), postprocess_source_sha256=E_SHA, + result_files_sha256={str(p): digest(p) for p in out.iterdir()}, counts=update['counts'], + before_overlay_sha256=chain['before_overlay_file_sha256'], after_overlay_sha256=chain['after_overlay_file_sha256'], + wall_seconds=time.perf_counter() - started, CUDA_VISIBLE_DEVICES='', + new_brain_runs=0, old_trajectory_rerun=False, weights_installed=False, + original_protocol_remains_stopped=True, original_12_call_gate_passed=False, + learning_success_claimed=False, candidate_generated=False, + interpretation='The same pinned E succeeds in a fresh CPU process without the competing ordinary runtime loader cache. This is post-hoc diagnosis, not completion of AO.') + with receipt.open('x', encoding='utf-8') as f: + json.dump(result, f, sort_keys=True, indent=2, allow_nan=False); f.write('\n') + print(json.dumps(dict(status=result['status'], receipt_sha256=digest(receipt), counts=result['counts'])), flush=True) + + +if __name__ == '__main__': + p = argparse.ArgumentParser() + for name in ('registration-sha256', 'checkpoint-sha256', 'authority-sha256', 'stopped-sha256'): + p.add_argument('--' + name, required=True) + a = p.parse_args() + main(a.registration_sha256, a.checkpoint_sha256, a.authority_sha256, a.stopped_sha256) diff --git a/tools/phase4ao-publication/finalize_failed_phase4ao.py b/tools/phase4ao-publication/finalize_failed_phase4ao.py new file mode 100644 index 0000000..8a4c9f6 --- /dev/null +++ b/tools/phase4ao-publication/finalize_failed_phase4ao.py @@ -0,0 +1,128 @@ +"""Additive failure sealing, never an AO execution retry or scientific PASS.""" +import argparse +import hashlib +import json +from pathlib import Path +import shutil +import sys + +ROOT = Path('/data/digital-drosophila') +EXP = ROOT / 'experiments/phase4ao-conditioning-interface' +DIAG = ROOT / 'experiments/phase4ao-postprocess-isolation' +MODEL = ROOT / 'models/male-cns-lif/olfactory-conditioning-lab-v1' + + +def digest(p): + h = hashlib.sha256() + with Path(p).open('rb') as f: + for b in iter(lambda: f.read(8 << 20), b''): h.update(b) + return h.hexdigest() + + +def read(p): return json.loads(Path(p).read_text()) + + +def save(p, value): + with Path(p).open('x', encoding='utf-8') as f: + json.dump(value, f, sort_keys=True, indent=2, allow_nan=False); f.write('\n') + + +def seal(directory, helper): + assert not (directory / 'manifest.json').exists() and not (directory / 'checksums.sha256').exists() + paths = sorted(directory.rglob('*')) + assert not any(p.is_symlink() or '__pycache__' in p.parts for p in paths) + rows = [dict(path=p.relative_to(directory).as_posix(), bytes=p.stat().st_size, sha256=digest(p)) + for p in paths if p.is_file()] + save(directory / 'manifest.json', dict(root=str(directory), files=rows)) + with (directory / 'checksums.sha256').open('x', encoding='utf-8', newline='\n') as f: + f.writelines(r['sha256'] + ' ' + r['path'] + '\n' for r in rows) + f.write(digest(directory / 'manifest.json') + ' manifest.json\n') + return helper.verify_seal(directory) + + +def main(reg_sha, checkpoint_sha, stopped_sha, diagnostic_sha): + assert sys.dont_write_bytecode + assert not (EXP / 'manifest.json').exists() and not (EXP / 'audit/final-status.json').exists() + assert digest(EXP / 'audit/stopped.json') == stopped_sha + assert digest(DIAG / 'diagnostic-receipt.json') == diagnostic_sha + sys.path.insert(0, str(MODEL)) + import conditioning_preflight_prepare as P + import runtime_continuation as C + reg = P.read_registration(reg_sha) + assert reg['plan']['total_calls'] == 12 + row_id = reg['plan']['calls'][0]['row_id'] + assert row_id == 'D1--reference--epoch-000' + assert sorted(p.name for p in (EXP / 'runs').iterdir()) == [row_id] + assert not any((EXP / 'updates').iterdir()) + assert not (EXP / 'audit/execution-complete.json').exists() + assert sorted(p.name for p in (EXP / 'receipts').glob('*.process.json')) == ['input-generation.process.json'] + stopped = read(EXP / 'audit/stopped.json') + assert stopped['completed_rows'] == [] and stopped['registration_sha256'] == reg_sha + stderr = (EXP / 'logs' / (row_id + '.stderr.log')).read_text() + assert 'E.postprocess_epoch' in stderr and 'Existing unverified module binding: phase4an_sealed_am_overlay' in stderr + checkpoint = C.load_checkpoint(EXP / 'runs' / row_id, checkpoint_sha) + assert checkpoint['fresh'] and checkpoint['steps'] == 1200 and checkpoint['summary']['all_finite'] + diagnostic = read(DIAG / 'diagnostic-receipt.json') + assert diagnostic['status'] == 'PASS-ISOLATED-POSTPROCESS-DIAGNOSTIC-ONLY' + assert diagnostic['registration_sha256'] == reg_sha and diagnostic['checkpoint_manifest_sha256'] == checkpoint_sha + assert diagnostic['stopped_sha256'] == stopped_sha and diagnostic['new_brain_runs'] == 0 + assert diagnostic['weights_installed'] is False and diagnostic['original_protocol_remains_stopped'] is True + P.verify_pins(diagnostic['result_files_sha256']) + original = P.P.read_registration(P.AN_REG) + helper = P.P.load_module('phase4ao_failure_original_seal', ROOT / P.P.AI_HELPER_REL, P.P.AI_HELPER_SHA) + # Recheck previous complete seals without rerunning their neural trials. + save(EXP / 'regression/AN-seal-after.json', helper.verify_seal(P.AN)) + save(EXP / 'regression/AM-seals-after.json', P.P.verify_authority(original['anchors'])) + old = P.P.verify_old_integrity(original) + save(EXP / 'regression/old-integrity-after.json', old) + P.read_registration(reg_sha) + C.load_checkpoint(EXP / 'runs' / row_id, checkpoint_sha) + source = Path(__file__).resolve().parent + for name in ('diagnose_postprocess_isolation.py', 'test_postprocess_isolation.py', 'finalize_failed_phase4ao.py'): + target = DIAG / name + assert not target.exists() + shutil.copyfile(source / name, target) + diag_seal = seal(DIAG, helper) + summary = checkpoint['summary'] + analysis = dict(status='FAIL-POSTPROCESS-MODULE-IDENTITY', registration_sha256=reg_sha, + planned_brain_calls=12, actual_complete_brain_trajectories=1, complete_workflow_rows=0, + executed_biological_ms=120, executed_ticks=1200, stopped_sha256=stopped_sha, + first_checkpoint_manifest_sha256=checkpoint_sha, + failure_stage='after neural runtime and immediate seal, before epoch delta output creation', + cause='Two separately loaded frozen runtime_light module objects use private _LOADED caches but share a sys.modules helper name; the second correctly rejects unowned existing binding.', + previous_synthetic_test_gap='Tests covered isolated loader and synthetic overlay fixture, not the mixed actual loader integration.', + unchanged_protection='No loader cache or frozen source was patched or bypassed.', + runtime=dict(neurons=summary['nodes'], edges=summary['edges'], spikes=summary['total_spikes'], + all_finite=summary['all_finite'], simulation_seconds=summary['diagnostic_simulation_seconds'], + wall_seconds=summary['wall_seconds'], build_or_cache_seconds=summary['build_or_cache_seconds'], + peak_allocated_vram_bytes=summary['peak_allocated_vram_bytes']), + diagnostic=dict(receipt_sha256=diagnostic_sha, experiment=str(DIAG), manifest_sha256=diag_seal['manifest_sha256'], + status=diagnostic['status'], actual_changed_edges=diagnostic['counts']['actual_bitwise_changed_edges'], + off_compartment_changed_edges=diagnostic['counts']['actual_off_compartment_bitwise_changed_edges'], + weights_installed=False, old_trajectory_rerun=False, new_brain_runs=0), + old_integrity_roots=len(old), old_integrity_pass=True, prior_AN_AM_seals_preserved=True, + original_sources_unchanged=True, scientific_learning_assessed=False, learning_success_claimed=False, + full_conditioning_executed=False, learned_candidate=False, + next_step='Separately register a new process-isolated coordinator. Do not overwrite or resume this stopped AO inventory; no result-selected seed or brain parameter change.') + save(EXP / 'audit/failure-analysis.json', analysis) + final = dict(status=analysis['status'], analysis_path='audit/failure-analysis.json', + analysis_sha256=digest(EXP / 'audit/failure-analysis.json'), registration_sha256=reg_sha, + planned_calls=12, actual_complete_brain_trajectories=1, completed_calls=0, + old_integrity_roots=len(old), old_integrity_pass=True, prior_AN_AM_seals_preserved=True, + frozen_21_and_previous_draft_20_unchanged=True, registered_51_sources_unchanged=True, + learned_candidate=False, learning_success_claimed=False, full_conditioning_executed=False, + old_trajectory_rerun=False, original_failure_preserved=True, model_directory_sealed=False, + five_reference_trials='Sealed AM evidence retained; not rerun by AO or the CPU diagnosis.', + finalizer_source_sha256=digest(__file__), diagnostic_manifest_sha256=diag_seal['manifest_sha256']) + save(EXP / 'audit/final-status.json', final) + verified = seal(EXP, helper) + print(json.dumps(dict(status=final['status'], manifest_sha256=verified['manifest_sha256'], + final_sha256=digest(EXP / 'audit/final-status.json'), diagnostic_manifest_sha256=diag_seal['manifest_sha256'])), flush=True) + + +if __name__ == '__main__': + p = argparse.ArgumentParser() + for name in ('registration-sha256', 'checkpoint-sha256', 'stopped-sha256', 'diagnostic-sha256'): + p.add_argument('--' + name, required=True) + a = p.parse_args() + main(a.registration_sha256, a.checkpoint_sha256, a.stopped_sha256, a.diagnostic_sha256) diff --git a/tools/phase4ao-publication/publish_phase4ao.py b/tools/phase4ao-publication/publish_phase4ao.py new file mode 100644 index 0000000..04cce9a --- /dev/null +++ b/tools/phase4ao-publication/publish_phase4ao.py @@ -0,0 +1,291 @@ +"""Publish the sealed AO interface evidence, not a backup or learning verdict. + +All three external anchors are mandatory. The original complete experiment is +verified before and after copying; only this new exclusive registry is written. +The registry then receives its own independently verified small-file seal. +""" +from __future__ import annotations + +import argparse +import hashlib +import importlib.util +import json +from pathlib import Path, PurePosixPath +import re +import sys + + +ROOT = Path('/data/digital-drosophila') +MODEL_REL = Path('models/male-cns-lif/olfactory-conditioning-lab-v1') +EXP_REL = Path('experiments/phase4ao-conditioning-interface') +REGISTRY_REL = Path('registry/phase4ao-conditioning-interface') +HELPER_REL = Path('models/male-cns-lif/pn-recruitment-audit-v1/publication.py') +HELPER_SHA = '7a79c499098b74163e7bb16adabcbc54e7069dafa204d0b99c7f4c03d79f8016' +REGISTRATION = 'protocol/preregistered.json' +FINAL = 'audit/final-status.json' +ANALYSIS = 'audit/interface-analysis.json' +FAILURE_ANALYSIS = 'audit/failure-analysis.json' +FULL_PLAN = 'protocol/full-trial-plan.json' +SNAPSHOT = 'protocol/source-snapshot/' +MAX_BYTES = 4 * 1024 ** 2 +SUFFIXES = frozenset(('.json', '.md', '.log', '.sha256')) +SHA = re.compile(r'^[0-9a-f]{64}$') + + +def require(ok, message): + if not ok: + raise ValueError(message) + + +def digest(path): + value = hashlib.sha256() + with Path(path).open('rb') as handle: + for block in iter(lambda: handle.read(8 << 20), b''): + value.update(block) + return value.hexdigest() + + +def load_helper(path): + raw = Path(path).read_bytes() + require(hashlib.sha256(raw).hexdigest() == HELPER_SHA, 'Frozen seal helper SHA mismatch') + spec = importlib.util.spec_from_file_location('phase4ao_pinned_seal_helper', path) + module = importlib.util.module_from_spec(spec) + # Execute only the exact pinned helper; never import old experiment runners + # or change any helper globals. No import-generated __pycache__ is written. + exec(compile(raw, str(path), 'exec'), module.__dict__) + return module + + +def write_json(path, value): + raw = (json.dumps(value, sort_keys=True, indent=2, ensure_ascii=False, + allow_nan=False) + '\n').encode('utf-8') + require(len(raw) <= MAX_BYTES, 'Generated evidence exceeds the 4 MiB limit') + with Path(path).open('xb') as handle: + handle.write(raw) + + +def small_json(helper, experiment, rows, name): + require(name in rows and rows[name]['bytes'] <= MAX_BYTES, 'Missing/oversize required JSON: ' + name) + path = helper._regular_file(experiment, name) + require(digest(path) == rows[name]['sha256'], 'Evidence changed: ' + name) + value = helper._json_bytes(path.read_bytes()) + require(isinstance(value, dict), 'Expected JSON object: ' + name) + return value + + +def source_identity(helper, root, experiment, rows, registration, registration_sha): + model = root / MODEL_REL + source_pins = registration.get('source_sha256') + prepared = registration.get('prepared_file_sha256') + require(isinstance(source_pins, dict) and source_pins and isinstance(prepared, dict), + 'Missing registered source/prepared inventory') + files, sealed_names = [], set() + for text, checksum in sorted(source_pins.items()): + require(isinstance(text, str) and isinstance(checksum, str) and SHA.fullmatch(checksum), + 'Malformed registered source identity') + path = Path(text) + require(path.is_absolute() and path.parent == model and path.suffix in ('.py', '.md') and + path.name not in ('', '.', '..') and '..' not in path.parts, + 'Registered source escapes the fixed model directory') + original = helper._regular_file(root, path.relative_to(root).as_posix()) + name = SNAPSHOT + path.name + require(name not in sealed_names and name in rows, 'Missing/duplicate registered snapshot') + sealed_names.add(name) + snapshot = helper._regular_file(experiment, name) + row = rows[name] + require(prepared.get(str(snapshot)) == checksum and row['sha256'] == checksum and + row['bytes'] <= MAX_BYTES and original.stat().st_size == row['bytes'] and + digest(original) == checksum and digest(snapshot) == checksum, + 'Model, registration, prepared inventory and sealed snapshot differ: ' + path.name) + files.append(dict(model_source=path.relative_to(root).as_posix(), sealed_source=name, + copy='source-snapshot/' + path.name, bytes=row['bytes'], sha256=checksum)) + actual = {name for name in rows if name.startswith(SNAPSHOT)} + require(actual == sealed_names, 'Source snapshot inventory differs from registration') + # Prepared AO files must also be the same sealed payload, not merely listed + # in a registration whose sources happen to match. + for text, checksum in prepared.items(): + require(isinstance(text, str) and isinstance(checksum, str) and SHA.fullmatch(checksum), + 'Malformed prepared-file identity') + path = Path(text) + require(path.is_absolute() and path.is_relative_to(experiment) and '..' not in path.parts, + 'Prepared file escapes this experiment') + name = path.relative_to(experiment).as_posix() + helper._relative_name(name) + require(name in rows and rows[name]['sha256'] == checksum, 'Prepared file differs from seal: ' + name) + require(FULL_PLAN in rows and registration.get('full_trial_plan_file_sha256') == rows[FULL_PLAN]['sha256'] and + prepared.get(str(experiment / FULL_PLAN)) == rows[FULL_PLAN]['sha256'], + 'Full plan identity is not bound to the registration') + require(type(registration.get('committed_source_blobs_verified')) is int and + registration['committed_source_blobs_verified'] == len(files) and + isinstance(registration.get('git_code_commit'), str) and + re.fullmatch(r'[0-9a-f]{40}', registration['git_code_commit']), 'Missing registered source version') + return dict(registration_sha256=registration_sha, source_count=len(files), files=files, + git_code_commit=registration['git_code_commit'], + original_snapshot_registration_exact=True, + git_blobs_reverified_by_publisher=False, model_directory_sealed=False) + + +def selection(row): + name = row['path'] + parsed = PurePosixPath(name) + if name == FULL_PLAN: + return None, 'full_trial_plan_intentionally_not_exported' + if 'extension-build' in parsed.parts or 'build' in parsed.parts or '__pycache__' in parsed.parts: + return None, 'build_or_cache_artifact' + if row['bytes'] > MAX_BYTES: + return None, 'exceeds_4_MiB' + if name.startswith(SNAPSHOT): + return 'source-snapshot/' + parsed.name, None + if parsed.suffix.lower() in SUFFIXES: + return 'evidence/' + name, None + return None, 'binary_or_non_selected_extension' + + +def _check_roots(root, paths): + for path in paths: + current = root + for part in path.relative_to(root).parts: + current /= part + require(not current.is_symlink(), 'Symlink in publication path: ' + str(current)) + + +def _registry_seal(helper, registry): + files = [dict(path=p.relative_to(registry).as_posix(), bytes=p.stat().st_size, sha256=digest(p)) + for p in sorted(registry.rglob('*')) if p.is_file()] + write_json(registry / 'manifest.json', dict(root=str(registry), files=files)) + raw = ''.join(row['sha256'] + ' ' + row['path'] + '\n' for row in files) + raw += digest(registry / 'manifest.json') + ' manifest.json\n' + require(len(raw.encode('utf-8')) <= MAX_BYTES, 'Registry checksums exceed small-file limit') + with (registry / 'checksums.sha256').open('xb') as handle: + handle.write(raw.encode('utf-8')) + return helper.verify_seal(registry) + + +def publish(registration_sha256, experiment_manifest_sha256, final_sha256, *, root=ROOT): + """Create only the fixed AO registry; verify integrity, not science. + + The root override exists for isolated synthetic tests. Existing and partial + registries are never overwritten, removed, or resumed. + """ + require(sys.dont_write_bytecode, 'Use Python -B') + for value in (registration_sha256, experiment_manifest_sha256, final_sha256): + require(isinstance(value, str) and SHA.fullmatch(value), 'Three external SHA-256 anchors are mandatory') + root = Path(root) + require(root.is_absolute() and root.is_dir() and not root.is_symlink(), 'Invalid project root') + root = root.resolve(strict=True) + experiment, registry, helper_path = (root / part for part in (EXP_REL, REGISTRY_REL, HELPER_REL)) + _check_roots(root, (experiment, root / MODEL_REL, registry, helper_path)) + require(not registry.exists(), 'Registry already exists; no overwrite or resume') + helper = load_helper(helper_path) + publisher_sha = digest(__file__) + for relative, expected in ((REGISTRATION, registration_sha256), ('manifest.json', experiment_manifest_sha256), + (FINAL, final_sha256)): + require(digest(helper._regular_file(experiment, relative)) == expected, 'External anchor mismatch: ' + relative) + verified = helper.verify_seal(experiment) + rows = {row['path']: row for row in verified['files']} + require(verified['manifest_sha256'] == experiment_manifest_sha256 and + rows.get(REGISTRATION, {}).get('sha256') == registration_sha256 and + rows.get(FINAL, {}).get('sha256') == final_sha256, + 'Verified seal differs from external registration/manifest/final anchors') + registration = small_json(helper, experiment, rows, REGISTRATION) + require(registration.get('schema') == 'phase4ao-conditioning-interface-registration-v1', 'Wrong phase registration') + final = small_json(helper, experiment, rows, FINAL) + status = final.get('status') + require(isinstance(status, str) and re.fullmatch(r'(PASS|FAIL|INCOMPLETE)(-[A-Z0-9]+)*', status), + 'Unknown finalized status family') + require(final.get('registration_sha256') == registration_sha256, 'Final status names another registration') + for flag in ('learned_candidate', 'learning_success_claimed', 'full_conditioning_executed'): + require(final.get(flag) is False, 'Final scope flag is not explicitly false: ' + flag) + analysis_sha = final.get('analysis_sha256') + require(isinstance(analysis_sha, str) and SHA.fullmatch(analysis_sha), 'Missing finalized analysis SHA') + linked = [name for name in (ANALYSIS, FAILURE_ANALYSIS) + if name in rows and rows[name]['sha256'] == analysis_sha] + require(len(linked) == 1, 'Final analysis link must identify one sealed interface/failure analysis') + analysis_path = linked[0] + if 'analysis_path' in final: + require(final['analysis_path'] == analysis_path, 'Explicit analysis path disagrees with SHA') + analysis = small_json(helper, experiment, rows, analysis_path) + require(analysis.get('status') == status and analysis.get('registration_sha256') == registration_sha256, + 'Final and analysis identities differ') + sources = source_identity(helper, root, experiment, rows, registration, registration_sha256) + inventory, plans = [], [] + for row in verified['files'] + helper._control_rows(experiment, verified): + target, reason = selection(row) + inventory.append(dict(**row, disposition='copied' if target else 'omitted', copy=target, reason=reason)) + if target: + plans.append((row, target)) + targets = [target for _, target in plans] + require(len(set(targets)) == len(targets), 'Publication destination collision') + required = {REGISTRATION, FINAL, 'manifest.json', 'checksums.sha256'} + required.add(analysis_path) + require(all('evidence/' + name in targets for name in required), 'Mandatory evidence is missing/oversize') + require(all(row['copy'] in targets for row in sources['files']), 'A registered source is not exportable') + registry.parent.mkdir(parents=True, exist_ok=True) + registry.mkdir(exist_ok=False) + try: + records = [] + for row, target in plans: + source = helper._regular_file(experiment, row['path']) + helper._copy_verified(source, registry / target, row['bytes'], row['sha256']) + records.append(dict(source=source.relative_to(root).as_posix(), copy=target, + bytes=row['bytes'], sha256=row['sha256'])) + after = helper.verify_seal(experiment) + require(after == verified, 'Experiment changed during publication') + require(source_identity(helper, root, experiment, rows, registration, registration_sha256) == sources, + 'Registered source changed during publication') + require(digest(helper_path) == HELPER_SHA and digest(__file__) == publisher_sha, 'Publication/helper source changed') + write_json(registry / 'seal-verification.json', {k: v for k, v in verified.items() if k != 'files'}) + write_json(registry / 'evidence-index.json', records) + write_json(registry / 'source-identity.json', sources) + write_json(registry / 'artifact-inventory.json', dict( + experiment_manifest_sha256=experiment_manifest_sha256, files=inventory, + omitted_files=sum(row['disposition'] == 'omitted' for row in inventory), + omitted_bytes=sum(row['bytes'] for row in inventory if row['disposition'] == 'omitted'), + inventory_covers_complete_experiment_and_controls=True, complete_bulk_backup=False)) + receipt_files = [dict(path=p.relative_to(registry).as_posix(), bytes=p.stat().st_size, sha256=digest(p)) + for p in sorted(registry.rglob('*')) if p.is_file()] + receipt = dict(schema='phase4ao-publication-v1', publication_complete=True, + registry=REGISTRY_REL.as_posix(), project_root=str(root), declared_audit_status=status, + registration_sha256=registration_sha256, experiment_manifest_sha256=experiment_manifest_sha256, + experiment_checksums_sha256=verified['checksums_sha256'], final_sha256=final_sha256, + analysis_sha256=analysis_sha, analysis_path=analysis_path, + helper_sha256=HELPER_SHA, publisher_sha256=publisher_sha, + helper_globals_modified=False, source_count=sources['source_count'], + copied_evidence_files=len(records), copied_evidence_bytes=sum(row['bytes'] for row in records), + files=receipt_files, bulk_remains_at=EXP_REL.as_posix(), complete_bulk_backup=False, + model_directory_sealed=False, scientific_gate_executed_by_publisher=False, + scientific_gate_certified_by_publication=False, candidate_generated=False, learning_achieved=False, + existing_files_modified=False, new_brain_run=False, + scope='Complete source experiment byte seal verified; small evidence copied. Original status preserved, not reassessed.', + receipt_self_hash='Returned externally; registry manifest separately seals this receipt.', + registry_controls='manifest.json and checksums.sha256 are created after this receipt and independently verified.', + limits=dict(max_selected_file_bytes=MAX_BYTES, selected_suffixes=sorted(SUFFIXES), + full_trial_plan_always_omitted=True, registered_source_snapshot_exception=True)) + write_json(registry / 'publication-receipt.json', receipt) + small_seal = _registry_seal(helper, registry) + return dict(publication_complete=True, registry=str(registry), declared_audit_status=status, + registration_sha256=registration_sha256, receipt_sha256=digest(registry / 'publication-receipt.json'), + registry_manifest_sha256=small_seal['manifest_sha256'], registry_checksums_sha256=small_seal['checksums_sha256'], + registry_payload_files=small_seal['payload_file_count'], source_count=sources['source_count'], + copied_evidence_files=len(records), complete_bulk_backup=False, + scientific_gate_executed_by_publisher=False, scientific_gate_certified_by_publication=False) + except Exception as error: + if not (registry / 'publication-failure.json').exists(): + write_json(registry / 'publication-failure.json', dict(publication_complete=False, + error_type=type(error).__name__, error=str(error), partial_outputs_preserved=True, + existing_files_modified=False)) + raise + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--registration-sha256', required=True) + parser.add_argument('--experiment-manifest-sha256', required=True) + parser.add_argument('--final-sha256', required=True) + args = parser.parse_args() + result = publish(args.registration_sha256, args.experiment_manifest_sha256, args.final_sha256) + print(json.dumps(result, ensure_ascii=False, sort_keys=True, allow_nan=False), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tools/phase4ao-publication/test_postprocess_isolation.py b/tools/phase4ao-publication/test_postprocess_isolation.py new file mode 100644 index 0000000..013e227 --- /dev/null +++ b/tools/phase4ao-publication/test_postprocess_isolation.py @@ -0,0 +1,53 @@ +"""Actual pinned loader integration in fresh CPU subprocesses, no brain.""" +import os +from pathlib import Path +import subprocess +import sys +import unittest + + +class IsolationTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + server = Path('/data/digital-drosophila/models/male-cns-lif') + cls.root = server if server.is_dir() else Path(__file__).resolve().parents[1] + cls.model = cls.root / 'olfactory-conditioning-lab-v1' + cls.helper = cls.root / 'wholebrain-plastic-interface-v1/overlay.py' + assert cls.helper.is_file() + + def run_child(self, body): + setup = ('import sys, pathlib, types\n' + f'sys.path.insert(0,{str(self.model)!r})\n' + f'helper=pathlib.Path({str(self.helper)!r})\n' + 'import conditioning_epoch as E\n' + 'c=E._load_source(pathlib.Path(E.__file__).parent / "runtime_continuation.py", E.SOURCE_PINS["runtime_continuation_source"], "continuation")\n' + 'r=c.R\n') + return subprocess.run([sys.executable, '-B', '-c', setup + body], + env=dict(os.environ, CUDA_VISIBLE_DEVICES=''), capture_output=True, text=True, timeout=60) + + def test_fresh_E_uses_one_verified_helper_cache(self): + result = self.run_child('a=r._load("phase4an_sealed_am_overlay",helper,r.AM_OVERLAY_SHA)\n' + 'b=r._load("phase4an_sealed_am_overlay",helper,r.AM_OVERLAY_SHA)\n' + 'assert a is b\nassert "runtime_light" not in sys.modules\nassert "torch" not in sys.modules\n') + self.assertEqual(result.returncode, 0, result.stderr) + + def test_mixed_actual_loaders_reproduce_exact_failure(self): + result = self.run_child('import runtime_light as ordinary\n' + 'ordinary._load("phase4an_sealed_am_overlay",helper,ordinary.AM_OVERLAY_SHA)\n' + 'r._load("phase4an_sealed_am_overlay",helper,r.AM_OVERLAY_SHA)\n') + self.assertNotEqual(result.returncode, 0) + self.assertIn('Existing unverified module binding: phase4an_sealed_am_overlay', result.stderr) + + def test_fake_preexisting_helper_is_still_rejected(self): + result = self.run_child('sys.modules["phase4an_sealed_am_overlay"]=types.ModuleType("fake")\n' + 'r._load("phase4an_sealed_am_overlay",helper,r.AM_OVERLAY_SHA)\n') + self.assertNotEqual(result.returncode, 0) + self.assertIn('Existing unverified module binding', result.stderr) + + def test_wrong_source_pin_is_still_rejected(self): + result = self.run_child('r._load("phase4an_sealed_am_overlay",helper,"0"*64)\n') + self.assertNotEqual(result.returncode, 0) + self.assertIn('pin', result.stderr.lower()) + + +if __name__ == '__main__': unittest.main() diff --git a/tools/phase4ao-publication/test_publish_phase4ao.py b/tools/phase4ao-publication/test_publish_phase4ao.py new file mode 100644 index 0000000..692256b --- /dev/null +++ b/tools/phase4ao-publication/test_publish_phase4ao.py @@ -0,0 +1,274 @@ +"""Temporary-directory, CPU-only publication mutations; no real experiment.""" +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +import tempfile +import unittest +from unittest import mock + +import publish_phase4ao as P + + +_KNOWN_HELPERS = (P.ROOT / P.HELPER_REL, + Path(__file__).resolve().parents[1] / 'pn-recruitment-audit-v1/publication.py') +HELPER = next((p for p in _KNOWN_HELPERS if p.is_file()), None) +if HELPER is None or P.digest(HELPER) != P.HELPER_SHA: + raise ValueError('Pinned publication helper absent or changed in server/local source layout') + + +def put(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + data = value if isinstance(value, bytes) else (json.dumps(value, sort_keys=True) + '\n').encode() + path.write_bytes(data) + return hashlib.sha256(data).hexdigest() + + +def read(path): + return json.loads(path.read_text(encoding='utf-8')) + + +def seal(exp): + rows = [dict(path=p.relative_to(exp).as_posix(), bytes=p.stat().st_size, sha256=P.digest(p)) + for p in sorted(exp.rglob('*')) if p.is_file() and p.relative_to(exp).as_posix() + not in ('manifest.json', 'checksums.sha256')] + put(exp / 'manifest.json', dict(root=str(exp), files=rows)) + data = ''.join(r['sha256'] + ' ' + r['path'] + '\n' for r in rows) + data += P.digest(exp / 'manifest.json') + ' manifest.json\n' + put(exp / 'checksums.sha256', data.encode()) + + +class PublisherTest(unittest.TestCase): + def setUp(self): + self.temp = tempfile.TemporaryDirectory() + self.addCleanup(self.temp.cleanup) + self.root = Path(self.temp.name).resolve() + self.exp = self.root / P.EXP_REL + self.registry = self.root / P.REGISTRY_REL + self.model = self.root / P.MODEL_REL + put(self.root / P.HELPER_REL, HELPER.read_bytes()) + + def fixture(self, status='PASS-EXACT-TWO-EPOCH-DELTA-INTERFACE-NOT-LEARNING', count=3): + sources, prepared = {}, {} + names = ['worker.py', 'test_worker.py', 'PROTOCOL.md'] + [f'source_{i:03d}.py' for i in range(max(0, count - 3))] + for name in names[:count]: + raw = ('# synthetic registered source ' + name + '\n').encode() + original = self.model / name + snapshot = self.exp / P.SNAPSHOT / name + sha = put(original, raw) + put(snapshot, raw) + sources[str(original)] = sha + prepared[str(snapshot)] = sha + full_plan = put(self.exp / P.FULL_PLAN, dict(draft_only=True, no_brain_inputs=True)) + prepared[str(self.exp / P.FULL_PLAN)] = full_plan + self.registration = dict(schema='phase4ao-conditioning-interface-registration-v1', + source_sha256=sources, prepared_file_sha256=prepared, + full_trial_plan_file_sha256=full_plan, git_code_commit='4' * 40, + committed_source_blobs_verified=len(sources)) + self.reg_sha = put(self.exp / P.REGISTRATION, self.registration) + analysis_path = P.FAILURE_ANALYSIS if status.startswith('FAIL') else P.ANALYSIS + analysis_sha = put(self.exp / analysis_path, dict(status=status, registration_sha256=self.reg_sha, + complete_conditioning_executed=False, completed_rows=[] if status.startswith('FAIL') else ['synthetic'])) + self.final = dict(status=status, registration_sha256=self.reg_sha, analysis_sha256=analysis_sha, + learned_candidate=False, learning_success_claimed=False, full_conditioning_executed=False) + put(self.exp / P.FINAL, self.final) + put(self.exp / 'logs/worker.log', b'partial failures are retained\n') + put(self.exp / 'runs/first/summary.json', dict(complete=True)) + put(self.exp / 'runs/first/state.npz', b'fake binary') + put(self.exp / 'runs/first/hashes.jsonl', b'{}\n') + put(self.exp / 'extension-build/compiler.log', b'build output not exported') + put(self.exp / 'reference.npy', b'not a text file') + put(self.exp / 'large.json', b' ' * (P.MAX_BYTES + 1)) + self.reseal() + + def reseal(self): + seal(self.exp) + self.anchors = (P.digest(self.exp / P.REGISTRATION), P.digest(self.exp / 'manifest.json'), + P.digest(self.exp / P.FINAL)) + + def publish(self, *anchors): + return P.publish(*(anchors or self.anchors), root=self.root) + + def test_success_complete_small_seal_inventory_and_51_dynamic_sources(self): + self.fixture(count=51) + original = {p: P.digest(p) for p in self.exp.rglob('*') if p.is_file()} + result = self.publish() + self.assertTrue(result['publication_complete']) + self.assertEqual(result['source_count'], 51) + self.assertFalse(result['complete_bulk_backup']) + helper = P.load_helper(self.root / P.HELPER_REL) + verified = helper.verify_seal(self.registry) + self.assertEqual(verified['manifest_sha256'], result['registry_manifest_sha256']) + receipt = read(self.registry / 'publication-receipt.json') + self.assertEqual(result['receipt_sha256'], P.digest(self.registry / 'publication-receipt.json')) + self.assertEqual(receipt['registration_sha256'], self.anchors[0]) + self.assertFalse(receipt['scientific_gate_certified_by_publication']) + for entry in receipt['files']: + self.assertEqual(P.digest(self.registry / entry['path']), entry['sha256']) + inventory = read(self.registry / 'artifact-inventory.json')['files'] + rows = {r['path']: r for r in inventory} + self.assertEqual(set(rows), {p.relative_to(self.exp).as_posix() for p in original}) + self.assertEqual(rows[P.FULL_PLAN]['reason'], 'full_trial_plan_intentionally_not_exported') + self.assertEqual(rows['large.json']['reason'], 'exceeds_4_MiB') + for name in ('runs/first/state.npz', 'reference.npy', 'runs/first/hashes.jsonl', 'extension-build/compiler.log'): + self.assertEqual(rows[name]['disposition'], 'omitted') + self.assertEqual(rows[name]['sha256'], P.digest(self.exp / name)) + self.assertEqual((self.registry / 'evidence/manifest.json').read_bytes(), (self.exp / 'manifest.json').read_bytes()) + self.assertEqual((self.registry / 'evidence/checksums.sha256').read_bytes(), (self.exp / 'checksums.sha256').read_bytes()) + self.assertTrue(all(p.stat().st_size <= P.MAX_BYTES for p in self.registry.rglob('*') if p.is_file())) + self.assertEqual(original, {p: P.digest(p) for p in self.exp.rglob('*') if p.is_file()}) + + def test_fail_with_partial_run_no_execution_complete_is_preserved(self): + self.fixture('FAIL-POSTPROCESS-MODULE-CACHE') + result = self.publish() + self.assertEqual(result['declared_audit_status'], 'FAIL-POSTPROCESS-MODULE-CACHE') + receipt = read(self.registry / 'publication-receipt.json') + self.assertEqual(receipt['analysis_path'], P.FAILURE_ANALYSIS) + self.assertFalse((self.registry / 'evidence/audit/execution-complete.json').exists()) + self.assertFalse(receipt['learning_achieved']) + + def test_incomplete_status_is_not_upgraded(self): + self.fixture('INCOMPLETE-NO-NONZERO-UPDATE-COVERAGE') + self.assertEqual(self.publish()['declared_audit_status'], 'INCOMPLETE-NO-NONZERO-UPDATE-COVERAGE') + + def test_three_external_anchors_required_and_checked(self): + self.fixture() + for index in range(3): + values = list(self.anchors) + values[index] = '0' * 64 + with self.subTest(index=index), self.assertRaisesRegex(ValueError, 'External anchor mismatch'): + self.publish(*values) + with self.assertRaises(ValueError): + self.publish('', self.anchors[1], self.anchors[2]) + self.assertFalse(self.registry.exists()) + + def test_existing_registry_is_never_touched(self): + self.fixture() + marker = self.registry / 'do-not-touch.txt' + put(marker, b'keep') + with self.assertRaisesRegex(ValueError, 'already exists'): + self.publish() + self.assertEqual(marker.read_bytes(), b'keep') + self.assertEqual(len(list(self.registry.iterdir())), 1) + + def test_original_source_mutation_rejected_even_with_valid_experiment_seal(self): + self.fixture() + put(self.model / 'worker.py', b'# other source') + with self.assertRaisesRegex(ValueError, 'differ'): + self.publish() + self.assertFalse(self.registry.exists()) + + def test_snapshot_mutation_resealed_still_rejected_by_registration(self): + self.fixture() + put(self.exp / P.SNAPSHOT / 'worker.py', b'# changed') + self.reseal() + with self.assertRaisesRegex(ValueError, 'differ'): + self.publish() + + def test_extra_snapshot_is_not_silently_exported(self): + self.fixture() + put(self.exp / P.SNAPSHOT / 'unregistered.py', b'# not registered') + self.reseal() + with self.assertRaisesRegex(ValueError, 'snapshot inventory'): + self.publish() + + def test_source_escape_rejected_after_self_consistent_resealing(self): + self.fixture() + old = str(self.model / 'worker.py') + checksum = self.registration['source_sha256'].pop(old) + external = self.root / 'unrelated/worker.py' + put(external, (self.model / 'worker.py').read_bytes()) + self.registration['source_sha256'][str(external)] = checksum + self.reg_sha = put(self.exp / P.REGISTRATION, self.registration) + analysis = read(self.exp / P.ANALYSIS) + analysis['registration_sha256'] = self.reg_sha + self.final['analysis_sha256'] = put(self.exp / P.ANALYSIS, analysis) + self.final['registration_sha256'] = self.reg_sha + put(self.exp / P.FINAL, self.final) + self.reseal() + with self.assertRaisesRegex(ValueError, 'escapes the fixed model'): + self.publish() + + def test_unlisted_file_or_bad_checksum_control_rejected(self): + self.fixture() + put(self.exp / 'unlisted.txt', b'not sealed') + with self.assertRaisesRegex(ValueError, 'unlisted'): + self.publish() + (self.exp / 'unlisted.txt').unlink() + with (self.exp / 'checksums.sha256').open('ab') as handle: + handle.write(b'bad line\n') + with self.assertRaisesRegex(ValueError, 'Malformed checksums'): + self.publish() + + def test_final_analysis_link_and_status_must_agree(self): + self.fixture() + self.final['analysis_sha256'] = '0' * 64 + put(self.exp / P.FINAL, self.final) + self.reseal() + with self.assertRaisesRegex(ValueError, 'analysis link'): + self.publish() + self.final['analysis_sha256'] = P.digest(self.exp / P.ANALYSIS) + self.final['status'] = 'FAIL-OTHER' + put(self.exp / P.FINAL, self.final) + self.reseal() + with self.assertRaisesRegex(ValueError, 'identities differ'): + self.publish() + + def test_no_learning_claim_and_helper_pin_cannot_be_bypassed(self): + self.fixture() + self.final['learning_success_claimed'] = True + put(self.exp / P.FINAL, self.final) + self.reseal() + with self.assertRaisesRegex(ValueError, 'scope flag'): + self.publish() + put(self.root / P.HELPER_REL, b'# alternate helper') + with self.assertRaisesRegex(ValueError, 'helper SHA mismatch'): + self.publish() + + def test_postcopy_full_seal_detects_change_in_omitted_bulk_and_preserves_partial(self): + self.fixture() + helper = P.load_helper(self.root / P.HELPER_REL) + original_copy = helper._copy_verified + changed = False + def interrupted_copy(*args): + nonlocal changed + original_copy(*args) + if not changed: + changed = True + put(self.exp / 'reference.npy', b'changed omitted bytes') + # Test-local injected failure only; production never changes helper globals. + with mock.patch.object(helper, '_copy_verified', side_effect=interrupted_copy), mock.patch.object(P, 'load_helper', return_value=helper): + with self.assertRaisesRegex(ValueError, 'Payload byte count|Payload SHA'): + self.publish() + self.assertTrue(self.registry.exists()) + self.assertTrue(read(self.registry / 'publication-failure.json')['partial_outputs_preserved']) + self.assertFalse((self.registry / 'publication-receipt.json').exists()) + with self.assertRaisesRegex(ValueError, 'already exists'): + self.publish() + + def test_small_registry_seal_catches_copied_payload_mutation(self): + self.fixture() + self.publish() + path = self.registry / 'evidence/logs/worker.log' + put(path, b'tampered after publication') + helper = P.load_helper(self.root / P.HELPER_REL) + with self.assertRaisesRegex(ValueError, 'Payload byte count|Payload SHA'): + helper.verify_seal(self.registry) + + def test_valid_reseal_between_anchor_read_and_verification_is_rejected(self): + self.fixture() + helper = P.load_helper(self.root / P.HELPER_REL) + original_verify = helper.verify_seal + def resealed_verify(directory): + put(self.exp / 'logs/worker.log', b'changed after external anchor read') + seal(self.exp) + return original_verify(directory) + with mock.patch.object(helper, 'verify_seal', side_effect=resealed_verify), mock.patch.object(P, 'load_helper', return_value=helper): + with self.assertRaisesRegex(ValueError, 'differs from external'): + self.publish() + self.assertFalse(self.registry.exists()) + + +if __name__ == '__main__': + unittest.main()