diff --git a/CURRENT.md b/CURRENT.md index deaa51b..6e1f3f1 100644 --- a/CURRENT.md +++ b/CURRENT.md @@ -1,30 +1,25 @@ # Digital Drosophila — 当前状态 -更新：2026-09-30。分支：`codex/lif-author-semantics-lab`。 +更新：2026-09-30。独立分支：`codex/lif-author-semantics-lab`。 ## 最新结果 -Phase4AF：**PASS-SHORT-FULLGRAPH-NUMERICAL-GATE**，已独立封存。 -完整166,700神经元/25,582,938边/124,177,617contacts，保留217isolates和101self-loops。 -无输入10ms、单seed50ms、固定五seed50ms三条轨迹，Brian作者float64与GPU每tick状态逐bit一致，放电/请求/队列顺序一致。 -三个trial事件数0/24,398/24,983；没有NaN/Inf或数值分歧。 -旧五组deterministic-v2 frozen reference再次精确PASS；原230,239项及4AA–4AE封存前后checksum全部PASS。 +Phase 4AG-r2：**FAIL-FIXED-CAL-REPRESENTATION**，已封存。 -前序4AD作者oracle已验证，4AE固定小图数值压力集72/72GPU测试通过。旧phase结论和失败记录保留不改。 +作者 float64 LIF + 原 float32 graded APL 的数值门槛通过；8 个小型场景和两个完整全图 100 ms probe 与共享 APL 组件的 Brian 闭环对照精确一致。全图仍为 166,700 神经元、25,582,938 边，原始 contact sum 124,177,617。 -## 仍未证明 +固定 6 气味 × 5 seed × 2 模式的 60 次 CAL 均完整且有限，12 次重复精确通过。但 KC 平均活跃比例为 **1.2713% / 1.2336%**，低于原固定 3%–9% 门槛。其余原 CAL 条件通过，成对距离判据通过数为 **12/15、13/15**；这不是学习成功率或 held-out 泛化证明。 -尚无learning candidate；没有证明长期全脑等价、生理正确、heldout odor separation或条件反射记忆。 -4AF明确无APL overlay、无训练、无heldout；短轨迹PASS不能替代嗅觉CAL。 -GPU诊断含CPU逐步同步/校验，不是实时性能候选。 +旧五组 deterministic-v2 参考精确通过；原 230,239 项及后续封存目录 checksum 全部通过。v1 的执行入口失败及 r2 修复记录均保留。原 raw/CSR/reference/runtime/APL 参数/eligibility-v2/14,232-edge mask 不改。 + +前序 Phase 4AF 的短时全图数值 PASS 保留。没有学习候选，没有训练，不进入游戏/RL/decoder/Web。 ## 下一步 -1. 先进行author LIF + frozen legacy graded APL显式语义兼容gate，保留原参数/mask；重点验证reset/refractory无条件写入、float32及mV→SI转换。 -2. 通过后再跑固定DoOR CAL，不学习、不用heldout调参。 -3. 信息保持→独立seed/新odor→credit→isolated/wholebrain functional gate均通过后，才开始真正learning验证。 +先只读分析已有 CAL 记录：输入实际接受 → PN→KC 真实延迟投递 → 不应期/reset → APL 写入，解释低 KC 覆盖率。APL 是否为原因尚未证明；不调参、改门槛或增加刺激来追求通过。 -不进入游戏、RL、decoder或Web。旧base CSR/W_syn/sign/APL calibration/eligibility-v2/14,232-edge plastic mask及封存文件只读。 -服务器上保留完整新状态约5.11GB；Git仅同步代码、小报告、provenance和checksum，不等于全量异机备份。 +后续仍需独立 seed/新气味表征、信用分配、隔离功能及全脑功能检查，不能由数值一致性推导学习可用。 -[最新解释](docs/progress/phase4af-interpretation.md) · [实验报告](docs/progress/phase4af-author-fullgraph.md) · [APL兼容审计准备](docs/progress/phase4af-apl-next-gate.md) · [封存校验](registry/phase4af-author-fullgraph/seal-verification.json) +完整新实验数组保存在服务器；Git 只同步代码、小型报告及校验记录，不是全量异机备份。诊断耗时包含逐步校验，不是实时性能基准。 + +[最终解释](docs/progress/phase4ag-interpretation.md) · [正式实验报告](docs/progress/phase4ag-author-apl-r2.md) · [封存校验](registry/phase4ag-author-apl-r2/seal-verification.json) diff --git a/docs/progress/phase4ag-author-apl-r2.md b/docs/progress/phase4ag-author-apl-r2.md new file mode 100644 index 0000000..cb0f1ed --- /dev/null +++ b/docs/progress/phase4ag-author-apl-r2.md @@ -0,0 +1,14 @@ +# Phase 4AG: author LIF + unchanged legacy graded APL + +Status: `FAIL-FIXED-CAL-REPRESENTATION`. + +Numerical closed-loop gate: True; original complete fixed six-CAL gate: False. +The independent Brian LIF and candidate use independent state but share the APL arithmetic component. Original frozen APL AST replay and a float64 error audit are separately recorded. +Author LIF uses float64; APL retains float32, tau20ms/gain0.01/normalization1, full original matrices and order. No learning/parameter search. +The explicitly hypothesized unconditional end-APL write includes refractory KC. Original fast APL mask leaves1,212 non-KC edges unreplaced; this scope limitation was not silently fixed. +Old checksums and five frozen reference trials: True. +Successfully compared cases: [{'case': 'apl_only', 'pass_all': True}, {'case': 'arrival_collision', 'pass_all': True}, {'case': 'cal-matched_energy-00-s51101', 'pass_all': True}, {'case': 'cal-physiological-00-s51101', 'pass_all': True}, {'case': 'high_fanin', 'pass_all': True}, {'case': 'kc_ref_boundary', 'pass_all': True}, {'case': 'natural_request', 'pass_all': True}, {'case': 'pn_pulse', 'pass_all': True}, {'case': 'two_sources', 'pass_all': True}, {'case': 'zero', 'pass_all': True}]. +Detailed CAL, exact comparisons, component errors, event/request traces and execution exceptions are linked from audit/final-status.json. +Wall/VRAM here include per-tick CPU synchronization, IPC and hashes. They are diagnostic costs, not runtime-performance benchmarks. +No heldout dynamic outputs, formal A/B, learning, game, reward, decoder or new candidate. +Next gate: Audit first failure in this explicitly declared hypothesis; no gain search, no heldout tuning, no conditioning diff --git a/docs/progress/phase4ag-author-apl.md b/docs/progress/phase4ag-author-apl.md new file mode 100644 index 0000000..f0d8d02 --- /dev/null +++ b/docs/progress/phase4ag-author-apl.md @@ -0,0 +1,14 @@ +# Phase 4AG: author LIF + unchanged legacy graded APL + +Status: `INCOMPLETE-NUMERICAL-EXECUTION`. + +Numerical closed-loop gate: False; original complete fixed six-CAL gate: False. +The independent Brian LIF and candidate use independent state but share the APL arithmetic component. Original frozen APL AST replay and a float64 error audit are separately recorded. +Author LIF uses float64; APL retains float32, tau20ms/gain0.01/normalization1, full original matrices and order. No learning/parameter search. +The explicitly hypothesized unconditional end-APL write includes refractory KC. Original fast APL mask leaves1,212 non-KC edges unreplaced; this scope limitation was not silently fixed. +Old checksums and five frozen reference trials: True. +Successfully compared cases: []. +Detailed CAL, exact comparisons, component errors, event/request traces and execution exceptions are linked from audit/final-status.json. +Wall/VRAM here include per-tick CPU synchronization, IPC and hashes. They are diagnostic costs, not runtime-performance benchmarks. +No heldout dynamic outputs, formal A/B, learning, game, reward, decoder or new candidate. +Next gate: Audit first failure in this explicitly declared hypothesis; no gain search, no heldout tuning, no conditioning diff --git a/docs/progress/phase4ag-design-and-limits.md b/docs/progress/phase4ag-design-and-limits.md new file mode 100644 index 0000000..602e0ae --- /dev/null +++ b/docs/progress/phase4ag-design-and-limits.md @@ -0,0 +1,91 @@ +# Phase 4AG：设计、证据范围与限制 + +本文是独立于已登记模型和实验文件的解释性文档，不是预注册协议的修订，也不是最终实验报告。状态快照日期：2026-09-30。 + +在本说明对应的检查节点，r2 的 8 个固定小型测试场景已完成每场景 3 次 GPU 运行及 1 次 Brian 对照，数值门禁通过；两个预先指定的全图 100 ms Brian 对照也已通过。后续 60 次 CAL 基础运行和 12 次重复检查仍在进行中。本文不宣布 CAL 通过或失败，更不宣布学习成功。正式状态应以各阶段原始审计记录及最终封存报告为准。 + +## 本阶段到底测试什么 + +Phase 4AG 测试一个明确的新组合：在 MaleCNS 原有全图上，将已经核对过作者语义的 float64 LIF 动力学，与项目旧版 float32 graded APL 模块连接起来，先检查实现是否符合声明，再检查固定旧 CAL 输入下的嗅觉表征是否满足完整原门槛。 + +全图仍为 166,700 个神经元、25,582,938 条规范化有向边。原始连接计数、递质符号、W_syn、旧 APL 参数以及 eligibility-v2 的 14,232 边掩码不修改。本阶段不训练，不搜索增益、阈值、刺激强度或时间窗，不新增连接，不做行为、奖励或游戏演示。 + +作者语义来源是已归档的 Shiu 等人代码，而不是重新选取的最新版本：GitHub commit `91bdd1e7dcf193f3e7ca5a8933497fcef63b7960`，归档 `model.py` 的 SHA-256 为 `fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e`。[固定提交的作者代码](https://github.com/philshiu/Drosophila_brain_model/blob/91bdd1e7dcf193f3e7ca5a8933497fcef63b7960/model.py) 对应 Shiu 等人的论文 [A Drosophila computational brain model reveals sensorimotor processing](https://www.nature.com/articles/s41586-024-07763-9)（Nature，2024）。本项目使用不同的连接组及额外的旧 APL 工程模块，因此不能称为该论文或作者完整模型的严格复现。 + +## 混合精度与每一步的明确顺序 + +LIF 的 `v`、`g` 和解剖边权使用 float64、SI 伏特；这里的 `g` 是作者方程中以伏特表示的状态量，不应仅凭变量名解释成电导。LIF 核心复用已封存的 CUDA 运算，不修改数学语义。固定步长为 0.1 ms，解剖传递延迟为 18 步，不应期为 22 步。 + +APL 则保留旧环境中的 float32 矩阵、状态和运算序列。输入矩阵始终是 `2 × 166700`，输出矩阵始终是 `2 × 4064`；即使测试小型诱导子图，也不裁剪矩阵或重算归一化。每次独立运行从零 APL 状态开始。APL 输入是本步实际产生的二值放电，而不是外部请求：被不应期拒绝的请求不能被当作放电反馈。 + +每步先完成 LIF 核心、请求处理、真实延迟到达和复位的声明调度，再在 `end` 执行旧 APL。APL 衰减与增加保持分开的 eager 操作： + +```text +s32.mul_(float(float32(exp(-0.1 / 20.0)))) +drive32 = torch.mv(I32, actual_spikes32) +s32.add_(drive32) +h32 = torch.matmul(s32, O32) * float32(0.01) + +delta64 = h32.to(float64) * float64(0.001) +g64[KC] = g64[KC] - delta64 +``` + +其中 `h32` 沿用旧模块的 mV 语义。必须先拓宽为 float64，再乘 0.001 转成伏特，最后单独减去；不能先在 float32 中乘 0.001，也不能将 `mul_`、`add_` 或最后的转换与减法融合成另一条运算路径。tau=20 ms、gain=0.01、normalization=1 均不调整，也不额外加入 APL 延迟、裁剪或 `dt/tau` 缩放。 + +APL 不改变原环境的默认 TF32／矩阵乘精度／CuBLAS 设置，而是记录实际设置、版本和设备，并核对组件输出。保留“数学上等价”的表达式并不足以保证逐位相同；PyTorch 官方说明明确指出，浮点运算顺序、后端和版本都可能影响结果。[PyTorch 数值精度说明](https://docs.pytorch.org/docs/2.14/notes/numerical_accuracy.html)、[可复现性说明](https://docs.pytorch.org/docs/2.14/notes/randomness.html) 在此用于解释工程风险，不作为改变冻结环境的理由；文档不能替代实验记录中的实际安装版本和设备信息。 + +## 不应期中的 APL 写入是被检验的假设 + +本组合在复位后的 `end` 阶段，无条件向所有对应 KC 的 `g` 写入 APL 抑制，包括刚放电或仍处于不应期的 KC。这里没有补加 `ref == 0` 条件。 + +这必须与作者核心的普通解剖输入区分：作者方程中带有 `unless refractory` 的状态在不应期冻结，普通突触更新受相应保护；本分支的 APL 是显式的末尾直接状态写入，不是把它伪装成普通受保护突触。[Brian 2.5.1 不应期文档](https://brian2.readthedocs.io/en/2.5.1/user/refractoriness.html) 解释了不应期和状态冻结的区别；具体执行依据仍是已归档作者代码及本项目保存的实际生成代码。 + +由这套代码顺序可以推得：APL 写入的负 `g` 可能在不应期内继续累积，而该期间作者核心不会按通常的 `g` 方程衰减它。这个组合效应是此次被检验的显式工程假设，不是已确认的生理机制，也不是作者原模型的主张。若改成不应期屏蔽、写入后再次清零或更换写入阶段，就已经是另一项假设，不能作为此次结果不好时的即时“修复”。 + +## 5,845 条 fast APL 掩码及 1,212 条未替换边 + +旧 APL 模块的兼容规则是：将全部 5,845 条原始 APL fast 出边的有效权重置零，然后仅向 KC 提供 graded 替代。置零只发生在新运行时的内存副本中；磁盘上的原始图、计数、符号和基础权重不改写，边身份也不删除。 + +其中 1,212 条出边的目标不是 KC，因此其 fast 作用被关闭后，没有获得本模块的 graded 替代。这是已知且保留的覆盖缺口。不能把该组合描述成“所有 APL 输出均被等价替换”，也不能为了得到更好的结果悄悄恢复这些边或新增非 KC 的 graded 通路。这两个数字描述的是本项目图中的有向边计数，不是新增的生物学结论。 + +## 三类数值证据分别能说明什么 + +| 检查 | 能支持的结论 | 不能支持的结论 | +| --- | --- | --- | +| Brian 闭环对照 | Brian 自己推进 LIF、产生实际放电，独立进程中的 APL 状态据此更新；与 GPU 比较逐步状态、事件、请求、队列顺序与接受掩码、APL drive/state/h | APL 算法本身完全独立实现，或所有输入下的普遍等价 | +| 原代码 AST 影子重放 | 从冻结旧初始化代码重建矩阵，并提取旧更新语句；用已记录的真实放电重放，检查组件字节是否保持一致 | 新的独立全脑闭环证明，或摆脱共同 Torch/CUDA 底层原语的验证 | +| float64 算术测量与误差界 | 在明确的非负矩阵、正常有限运算等前提下，量化 float32 组件相对独立 float64 测量的偏差，检查预先定义的误差界 | 用 float64 替换 APL、事后放宽容差，或用误差界替代逐位旧组件一致性 | + +Brian 的 APL worker 不接收候选 GPU 的抑制轨迹，而只接收 Brian 自己产生的实际放电；它有独立的零初始状态，并通过握手核对矩阵哈希与运行环境。但候选与 worker 共享 APL 组件实现，所以准确名称是“共享 APL 组件的闭环 Brian 对照”，不能写成“全部组件独立的 CPU oracle”。 + +AST 影子又提供一层不同的代码来源检查，但仍使用相同的 Torch/CUDA 原语。float64 测量不反馈到任一脑模型，其误差界不根据观测误差拟合。各证据应并列阅读，不能互相替代。 + +8 个固定小型场景检验零输入、真实 PN/KC 输入、不应期边界、自然放电与请求重合、延迟到达、APL mask 和高 fan-in 等情形；每场景 3 次 GPU 运行检查精确重复。两个全图 probe 是预先指定的第一个旧 CAL 气味、首个原始 seed、两种编码模式，各 100 ms。通过这些检查只说明已测试场景符合本组合的声明，不证明所有气味、时间尺度或后续学习任务都有效。同步、IPC、逐步哈希和诊断保存的耗时也不是性能基准。 + +## CAL 必须通过完整原 4L 门槛 + +CAL 设计固定为原 6 个校准气味 × 5 个原始 seed（51101–51105）× 2 种模式（`physiological`、`matched_energy`），共 60 次基础运行，每次 100 ms；每个气味／模式的首个 seed 再运行一次，共 12 次重复。它们不是额外的训练 seed。 + +输入在任何轨迹运行前保存，保留旧 PN 编码、目标／随机数列顺序。matched-energy 的参考值仍取原冻结 12 气味 panel 的中位数，不能换成 6 CAL 的中位数，也不能换成后续其他 panel。读取这份静态参考数据不等于读取 heldout 动态结果。 + +每种模式都必须同时满足以下原始条件，两种模式都通过才满足表征门槛： + +- KC 活跃比例的平均值在闭区间 3%–9%。 +- 单次运行的最小活跃比例严格大于 0，最大值严格小于 50%。 +- 同气味跨 seed 的平均活跃集 Jaccard，严格大于不同气味同 seed 的平均 Jaccard。 +- 不同气味同 seed 的平均欧氏距离，严格大于同气味跨 seed 的平均欧氏距离。 +- 至少 3 对气味通过成对分离检查：该对气味在相同 seed 下的 5 个距离均值，严格大于这两个气味各自全部 10 个跨 seed 距离合并后的 20 项均值。 + +距离来自原始 KC spike-count 向量，欧氏距离先转 float64，不另做向量归一化；Jaccard 使用是否放电的布尔活跃集。6 CAL 每种模式有 15 对气味、60 项 within 距离和 75 项 between 距离。成对比较的基线不是全部气味混在一起的平均 within 距离；只检查平均稀疏率与“至少 3 对”的旧简化 `r4` 不能代替完整原 4L 门槛。 + +本阶段另加输出完整性、有限值和 12 次精确重复要求；缺失文件、失败运行、NaN 或全静默都不能当成通过。原 4L 虽记录 finite，但没有将它写进原最终 gate，因此这里应明确称为新增的完整性前置检查，而非改写历史。 + +即使最终 CAL 通过，含义也仅限于这组固定校准输入下的表征检查。它不等于 heldout 泛化、刺激特异性信用分配、记忆、条件化或可用学习；本阶段没有训练或生成新的候选模型结论。后续学习验证仍需在授权范围内另行登记并通过专门门槛，不能由本阶段的数值一致性推导出来。 + +## 为什么保留 v1，并单独运行 r2 + +第一次登记的 v1 在启动 APL worker 时，对科学虚拟环境的 Python 路径调用了 `resolve()`，跟随符号链接到了系统解释器，丢失预期包环境。异常发生在首个 Brian tick 之前，错误记录的 `last_complete_tick` 为 -1；v1 已保留源代码、部分输出和故障记录，并以 `INCOMPLETE-NUMERICAL-EXECUTION` 封存。 + +这不是已观测到的动力学或科学假设失败，也不能反过来称作数值通过。r2 使用新模型目录 `author-apl-lab-v2` 和新实验目录 `phase4ag-author-apl-r2`，保留虚拟环境的调用入口路径，只修执行入口及相应记录／检查，不更改方程、运算精度、掩码、刺激、门槛、参数或测试选择。全部声明流程从新状态重跑，不覆盖或挑选性省略 v1 的记录。 + +本文所述已通过的 r2 数值门禁与仍在进行的 CAL 是两件事。是否完成、是否通过，以及允许得出的结论，最终都必须回到对应原始证据和封存状态，而不是依据本解释文档升级。 diff --git a/docs/progress/phase4ag-interpretation.md b/docs/progress/phase4ag-interpretation.md new file mode 100644 index 0000000..d4b59c5 --- /dev/null +++ b/docs/progress/phase4ag-interpretation.md @@ -0,0 +1,85 @@ +# Phase 4AG：独立动力学分支的正式结果 + +更新：2026-09-30。实验：`phase4ag-author-apl-r2`；模型：`author-apl-lab-v2`；Git 分支：`codex/lif-author-semantics-lab`。 + +## 结论 + +正式状态：**FAIL-FIXED-CAL-REPRESENTATION**。 + +本轮完成作者 float64 LIF 与冻结旧 float32 graded APL 的显式组合测试。数值一致性、数据完整性和精确重复检查通过；固定 CAL 的完整原门槛未通过。没有训练，没有新学习候选，不升级原 reference/runtime，也不修改旧模型。 + +两种输入模式唯一未达标条件都是 KC 平均活跃比例低于预先固定的 3%–9%。这不证明约 1.2% 的活跃比例在生物学上错误，也不能据此认定 APL 是原因。 + +## 固定 CAL：60 次基础运行及 12 次重复 + +使用原 6 个 CAL 气味、原 5 个 seed、两种输入模式，每次 100 ms；每个气味/模式首个 seed 再运行一次。未读取 held-out 动态结果，未做正式 A/B conditioning。 + +| 指标 | physiological | matched_energy | +| --- | ---: | ---: | +| KC 平均活跃比例 | 1.271325% | 1.233596% | +| 单次最少/最多活跃 KC（共 4,064） | 38 / 79 | 36 / 72 | +| 同气味跨 seed 平均 Jaccard | 0.509072 | 0.516431 | +| 不同气味同 seed 平均 Jaccard | 0.460955 | 0.481702 | +| 同气味平均欧氏距离 | 5.811443 | 5.671828 | +| 不同气味平均欧氏距离 | 6.205091 | 5.932021 | +| 成对距离判据通过数 | 12 / 15 | 13 / 15 | +| 完整原 CAL 门槛 | FAIL | FAIL | + +其余原条件全部通过：单次最小活跃比例大于零、最大值小于 50%、同气味 Jaccard 大于不同气味、不同气味欧氏距离大于同气味、至少 3 对气味满足原成对距离判据。该判据使用每对气味自己的 20 项 within 距离与 5 项 same-seed between 距离，不是全气味混合基线。 + +60/60 基础运行完整、有限；12/12 重复的事件、状态、逐 tick 哈希、APL 轨迹、请求及队列均精确一致。未见 NaN/Inf。12/15 和 13/15 不是学习成功率、统计显著性或 held-out 泛化结论。 + +matched-energy 保持原冻结 12 气味静态 panel 中位数及原随机输入算法；它匹配期望输入量，不保证每次随机运行实际接受事件相等。其期望事件数 CV 约 6.48e-8，实际接受事件 CV 为 0.134355；不得称作实际输入事件数严格匹配实验。 + +## 数值证据与完整性 + +- 8 个固定真实诱导子图场景，各 3 次 GPU 和 1 次 Brian 闭环对照；16 个 GPU 重复比较通过。 +- 两个预指定完整全图 100 ms probe，逐 tick 的 v/g/ref、放电事件、请求、延迟队列及 APL drive/state/h 精确一致。 +- 10 组原 APL AST 组件重放/误差审计通过。两个 Python 环境分别通过 29 项协议/汇总/解释器入口单元测试。 +- 全图保留 166,700 神经元、25,582,938 条边，原始 contact weight sum 为 124,177,617；原始磁盘 CSR 不改。 +- 原 230,239 项冻结清单及后续已封存目录全部校验通过；旧五组 deterministic-v2 参考的事件、计数、最终 v/g 再次精确通过。 +- 新 r2 模型 17 项、新实验 1,718 项已封存并再次验证。实验封存前 payload 为 2,956,599,228 字节（约 2.75 GiB）。 + +Brian 独立推进 LIF，但 APL worker 与候选共享 APL 算术组件；AST 重放也使用相同底层 Torch/CUDA 原语。因此是“共享 APL 组件的闭环对照”，不是全部组件独立的 CPU oracle，也不是任意输入/长期全脑普遍等价证明。float64 误差测量不反馈到模型。 + +诊断包含逐步 CPU 同步、哈希、IPC 和保存，耗时不代表正式 runtime 性能；没有由本轮产生新的实时运行候选。 + +## 已声明的假设与保留的问题 + +APL 保留旧 float32 矩阵、tau=20 ms、gain=0.01、normalization=1；LIF 为作者 float64 语义。APL 在 reset 后的 end 阶段无条件写入 KC g，包括不应期，先拓宽 float64 再由 mV 转 SI。作者核心在不应期冻结 g，与末尾 APL 写入结合可能产生累积，但本轮尚未定位低 KC 覆盖率的原因。 + +旧 APL 规则在独立运行的内存权重副本中关闭 5,845 条 fast APL 出边，只向 KC 提供 graded 替代。其中 1,212 条非 KC 边（13,712 contacts）未获替代。该覆盖缺口原样披露，不静默恢复边或修改归一化。新分支不是作者原始完整模型的严格复现。 + +首次 v1 因虚拟环境 Python 符号链接被错误解析到系统解释器而在首个 Brian tick 前中止，正式保存为 `INCOMPLETE-NUMERICAL-EXECUTION`。r2 只修执行入口及检查，另建目录从头运行；全部 68 组输入请求与 v1 一致。失败代码、日志和结果保留，没有覆盖或删除。 + +## 下一步：先解释失败，不追逐通过率 + +只用现有 60 次 CAL 的记录，先登记一份只读影子记账诊断： + +1. 分解请求数、实际接受量和 PN 实际放电。 +2. 按真实边及 18-step delay 重建 PN→KC 兴奋性投递、拒绝、不应期和 reset。 +3. 区分活跃/静默 KC，审计 APL 写入时相、累积量与 g 的关系。 +4. 先验证重建与已保存事件/最终状态一致，再讨论机制；不足以重建的证据明确标记缺失。 + +这不是一次新脑模拟或参数搜索。不改 gain、刺激、阈值、APL 参数、mask 或通过条件；不以本次 CAL 选择后续最佳参数。任何新的动力学假设须另立协议和目录。表征、独立 seed/新气味、信用分配、隔离功能及全脑功能检查通过之后，才有条件进入真正学习验证。 + +## 封存与证据入口 + +- [正式 r2 报告](phase4ag-author-apl-r2.md) +- [原始最终状态](../../registry/phase4ag-author-apl-r2/evidence/audit/final-status.json) +- [CAL 原始汇总](../../registry/phase4ag-author-apl-r2/evidence/calibration/summary.json) +- [旧数据完整性](../../registry/phase4ag-author-apl-r2/evidence/regression/old-integrity-after.json) +- [旧五组参考回归](../../registry/phase4ag-author-apl-r2/evidence/regression/frozen-five.json) +- [新封存校验](../../registry/phase4ag-author-apl-r2/seal-verification.json) +- [含来源链接的设计/限制历史快照](phase4ag-design-and-limits.md) +- [保留的 v1 执行失败报告](phase4ag-author-apl.md) + +r2 模型 manifest SHA-256：`208c00961b9d895d14ac050bc23fbc18db49d0df1fcf5c20d4c3eaa8e15ff7ff`。 + +r2 实验 manifest SHA-256：`21593297c40bee58516d4d7fd16e9d8d20ffdea5f8e0652fa5dd4e903b4d96ee`。 + +Git 发布代码、文档及小型证据；完整数组和事件文件仍在服务器实验目录。Git 同步不等于全量异机数据备份。 + +## 仓库发布检查 + +原仓库 681 项文件校验零差异，仓库审计单元测试通过，暂存内容的数据/密钥/大文件检查通过。标准空白检查发现两个已封存 `oracle_apl.py` 各有一个文件末尾空行；为保留登记字节未做格式改写。仅关闭末尾空行规则并允许标准 CSV CRLF 后，其余空白检查通过。这是格式例外，不是数值容差或科学门槛例外。 diff --git a/models/male-cns-lif/author-apl-lab-v1/PROTOCOL.md b/models/male-cns-lif/author-apl-lab-v1/PROTOCOL.md new file mode 100644 index 0000000..4676b0c --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/PROTOCOL.md @@ -0,0 +1,76 @@ +# Phase4AG: author LIF + frozen legacy graded APL + +Authorized independent hypothesis branch. All old source, raw connectome, signs, +W_syn, APL calibration, eligibility-v2 and 14,232-edge mask remain read-only. +No learning, gain/threshold changes, new edges, top-K, heldout activity, game or RL. + +Author code provenance is the already archived MIT-licensed +https://github.com/philshiu/Drosophila_brain_model at +91bdd1e7dcf193f3e7ca5a8933497fcef63b7960; original model.py SHA-256 +fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e. +Associated paper: https://www.nature.com/articles/s41586-024-07763-9. +This new MaleCNS/APL combination is not a strict reproduction of that model. + +The declared combination is author float64 LIF plus the exact old float32 graded +APL operator after synapses/resets at end. All 5,845 original APL fast out-edges +are zeroed only in the effective in-memory model, as in the legacy overlay. +Only KC receives graded replacement; known 1,212 non-KC edges remain unreplaced. +This existing scope limitation is not silently repaired in a compatibility test. + +Use the original full normalized I[2,166700]/O[2,4064] float32 bytes and original +normalizers, even for induced real subgraphs. APL reads actual binary spikes, +never requests, and keeps independent float32 state. Preserve unfused operations: +state.mul_(float(f32(exp(-.1/20)))).add_(torch.mv(I, spikes_f32)); +h32=torch.matmul(state,O)*f32(.01). Promote h32 to float64, multiply by float64 +.001, then separately subtract from SI g. Do not alter TF32/default CuBLAS flags; +record them. No added APL delay, drive dt/tau scaling, truncation or clipping. + +APL writes KC g unconditionally after author reset, including refractory KC. +Author g integration is frozen during refractory; therefore negative overlay g +may accumulate and differs from old dynamics. This is an explicit hypothesis, +not an assertion of original author or measured physiology. Guarded writes or +reset-before/after changes would be different hypotheses, not fixes to this one. + +Eight fixed metadata/connectivity-selected fixtures cover zero/reset, real PN/KC +drive, current actual event feedback, duplicate natural/request event, 18-step +arrival vs22-step refractory boundary, full fast APL mask, non-KC scope and mixed +precision/high fan-in. No choice is made from runtime responses. Each GPU case +has3 independent fresh runs. A Brian2 author oracle starts from its own state, +computes its own actual spikes, and uses an isolated standard-I/O legacy-APL +worker with independent state. No candidate inhibition trace is fed into Brian. +All tick states, events, requests, queue order/guard, APL drive/state/h must agree +exactly. The APL component is shared, so this is NOT a fully independent CPU +oracle. Independently reconstruct old matrices using frozen initializer AST, +replay actual events through original update AST, require exact component bytes; +also quantify an independent float64 positive-sum arithmetic error bound. It is +not a replacement for exact legacy equivalence. No arithmetic bound is adjusted. + +Only after all fixture gates pass, run two predeclared full-graph probes: first +old calibration odor, first original seed, both encoder modes. Compare each with +the independently running Brian + isolated APL worker. Save state hashes and sparse +events, not a time-by-all-neurons dense spike matrix. If a numerical gate fails, +stop CAL; keep diagnostic evidence and do not widen tolerance. + +Then run the complete original6 CAL odors ×5 original seeds ×2 original modes, +100ms per run. Retain the ORIGINAL12-odor median for matched-energy encoding, +original pn_drive target/RNG column order, original evaluation seeds51101..51105. +Save Poisson requests before any trajectory. Only calibration dynamic cases are +used; formal A/B and heldout remain unrun. Repeat first seed for every odor/mode. +No per-odor pulse, amplitude, timing or threshold search. No learning. + +Recompute the full original4L representation criteria on CAL only: mean KC active +fraction .03..09, min>0, max<.5, same-odor Jaccard>different, between Euclidean> +within, >=3 pairs whose mean between exceeds THAT PAIR's pooled within distances, +both modes. Add finiteness and exact repeat requirements. CAL PASS does not imply +heldout generalization, timing/credit specificity, memory or learning usability. +If CAL fails stop promotion and report it, not a least-bad candidate. + +Before/after verify SHA-256 of all original230239 files and AA--AF seals; rerun original five +deterministic reference trials to new output. Archive code/env/source SHA, +matrices/norms/masks/identities, requests and all outputs. No published candidate. + +Technical references (queried2026-09-30): Brian2 2.5.1 refractory documentation +https://brian2.readthedocs.io/en/2.5.1/user/refractoriness.html (actual generated code +remains authoritative); PyTorch numerical accuracy notes +https://docs.pytorch.org/docs/2.14/notes/numerical_accuracy.html (cross-backend +floating operations are not assumed bitwise equivalent). diff --git a/models/male-cns-lif/author-apl-lab-v1/calibration.py b/models/male-cns-lif/author-apl-lab-v1/calibration.py new file mode 100644 index 0000000..6d9ac2d --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/calibration.py @@ -0,0 +1,396 @@ +"""Complete original Phase 4L representation metrics on fixed CAL inputs only. + +No simulation, fitting, heldout trajectory access, or candidate generation is +performed here. The original 4L pair-specific within-odor baseline is retained; +the weaker Phase 4P r4 aggregate gate is deliberately not used. +""" +import csv +import hashlib +import itertools +import json +from pathlib import Path +import re + +import numpy as np + + +STATE_DTYPES = {"step": np.int32, "neuron_idx": np.int32, "counts": np.int32, + "v": np.float64, "g": np.float64, "ref": np.int32} +REQUEST_FIELDS = ("tick", "neuron_idx", "attempted", "accepted", "rejected") +APL_FILES = {"apl-drive.float32.npy": 2, "apl-state.float32.npy": 2, + "apl-inhibition.float32.npy": 4064} +ORIGINAL_CAL_IDS = { + "OMSUIQOIVADKIM-RXMQYKEDSA-N", "LABTWGUMFABVFG-ARJAWSKDSA-N", + "QNVRIHYSUZMSGM-ZCFIWIBFSA-N", "VLSVVMPLPMNWBH-UHFFFAOYSA-N", + "XHIUFYZDQBSEMF-UHFFFAOYSA-N", "OMSUIQOIVADKIM-YFKPBYRVSA-N"} +ORIGINAL_MODES = ("physiological", "matched_energy") +ORIGINAL_SEEDS = (51101, 51102, 51103, 51104, 51105) +HEX_SHA = re.compile(r"[0-9a-f]{64}\Z") +_DEFAULT_OUTPUT = object() + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def read_json(path): + with Path(path).open(encoding="utf-8") as stream: + return json.load(stream) + + +def file_hash(path): + digest = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(8 << 20), b""): + digest.update(block) + return digest.hexdigest() + + +def raw_hash(value): + return hashlib.sha256(np.ascontiguousarray(value).tobytes()).hexdigest() + + +def arrays_bitwise_equal(first, second): + """Unlike array_equal, also reject different dtypes and signed-zero bits.""" + return bool(first.dtype == second.dtype and first.shape == second.shape and + np.array_equal(np.ascontiguousarray(first).view(np.uint8), + np.ascontiguousarray(second).view(np.uint8))) + + +def pair_metrics(a, b): + """Exact definitions copied from phase4i.pair_metrics/safe_corr/cosine.""" + x, y = a.astype(np.float64), b.astype(np.float64) + aa, bb = a > 0, b > 0 + union = int(np.count_nonzero(aa | bb)) + intersection = int(np.count_nonzero(aa & bb)) + denominator = float(np.linalg.norm(x) * np.linalg.norm(y)) + cosine = 1.0 if denominator == 0 and np.array_equal(x, y) else ( + 0.0 if denominator == 0 else float(np.dot(x, y) / denominator)) + correlation = (1.0 if np.array_equal(x, y) else 0.0) if np.std(x) == 0 or np.std(y) == 0 else float(np.corrcoef(x, y)[0, 1]) + return {"active_intersection": intersection, "active_union": union, + "active_jaccard": 1.0 if union == 0 else float(intersection / union), + "cosine_similarity": cosine, "correlation": correlation, + "euclidean_distance": float(np.linalg.norm(x - y))} + + +def evaluate_gates(summary, gates): + """Each conjunct in original phase4l.py:239, kept visible for audit.""" + return { + "mean_sparsity_in_closed_interval": bool(float(gates["sparse_min_fraction"]) <= + summary["KC_active_fraction_mean"] <= float(gates["sparse_max_fraction"])), + "maximum_strictly_below_half": bool(summary["KC_active_fraction_max"] < 0.5), + "minimum_strictly_positive": bool(summary["KC_active_fraction_min"] > 0), + "same_jaccard_gt_different": bool(summary["same_odor_jaccard_mean"] > summary["different_odor_jaccard_mean"]), + "different_euclidean_gt_same": bool(summary["different_odor_euclidean_mean"] > summary["same_odor_euclidean_mean"]), + "at_least_original_pair_count": bool(summary["pairwise_separation_pass_count"] >= int(gates["minimum_separable_odor_pairs"]))} + + +def sample_cv(values): + """Original pandas Series.std uses ddof=1. Undefined diagnostic -> null.""" + values = np.asarray(values, dtype=np.float64) + mean = float(values.mean()) + return None if mean == 0 else float(values.std(ddof=1) / mean) + + +def summarize_mode(mode, odor_ids, seeds, runs, gates): + within, within_j, between, between_j, between_cos, between_corr = [], [], [], [], [], [] + pairs = [] + for odor in odor_ids: + arrays = [runs[(mode, odor, seed)]["kc_counts"] for seed in seeds] + for first, second in itertools.combinations(arrays, 2): + metric = pair_metrics(first, second) + within.append(metric["euclidean_distance"]) + within_j.append(metric["active_jaccard"]) + for first_odor, second_odor in itertools.combinations(odor_ids, 2): + distances = [] + for seed in seeds: + metric = pair_metrics(runs[(mode, first_odor, seed)]["kc_counts"], + runs[(mode, second_odor, seed)]["kc_counts"]) + distances.append(metric["euclidean_distance"]) + between.append(metric["euclidean_distance"]) + between_j.append(metric["active_jaccard"]) + between_cos.append(metric["cosine_similarity"]) + between_corr.append(metric["correlation"]) + pair_within = [] + for odor in (first_odor, second_odor): + arrays = [runs[(mode, odor, seed)]["kc_counts"] for seed in seeds] + pair_within.extend(pair_metrics(first, second)["euclidean_distance"] + for first, second in itertools.combinations(arrays, 2)) + cross_mean, within_mean = float(np.mean(distances)), float(np.mean(pair_within)) + pairs.append({"odor_id_a": first_odor, "odor_id_b": second_odor, + "between_same_seed_mean": cross_mean, "within_this_pair_mean": within_mean, + "between_distance_count": len(distances), "within_distance_count": len(pair_within), + "gate": bool(cross_mean > within_mean)}) + selected = [runs[(mode, odor, seed)] for odor in odor_ids for seed in seeds] + fractions = [int(np.count_nonzero(run["kc_counts"])) / len(run["kc_counts"]) for run in selected] + summary = {"KC_active_fraction_mean": float(np.mean(fractions)), + "KC_active_fraction_min": float(np.min(fractions)), "KC_active_fraction_max": float(np.max(fractions)), + "same_odor_jaccard_mean": float(np.mean(within_j)), + "different_odor_jaccard_mean": float(np.mean(between_j)), + "same_odor_euclidean_mean": float(np.mean(within)), + "different_odor_euclidean_mean": float(np.mean(between)), + "different_odor_cosine_mean": float(np.mean(between_cos)), + "different_odor_correlation_mean": float(np.mean(between_corr)), + "within_variance": float(np.var(within)), "between_distance_mean": float(np.mean(between)), + "pairwise_separation_pass_count": sum(row["gate"] for row in pairs), "pair_count": len(pairs), + "expected_event_count_cv": sample_cv([run["expected_external_events"] for run in selected]), + "actual_event_count_cv": sample_cv([run["external_events"] for run in selected]), + "within_distance_count": len(within), "between_distance_count": len(between), + "pairwise_baseline": "Each pair's two odors: 10+10 within-seed distances; never pooled across all odors", + "pairs": pairs} + summary["conditions"] = evaluate_gates(summary, gates) + summary["gate"] = bool(all(summary["conditions"].values())) + return summary + + +def validate_state(state, neurons, steps): + require(set(state) == set(STATE_DTYPES), "state.npz must have exactly the six declared arrays") + for name, dtype in STATE_DTYPES.items(): + value = state[name] + require(value.dtype == dtype and value.ndim == 1, "Invalid state dtype/rank: " + name) + require(bool(np.isfinite(value).all()), "Nonfinite state: " + name) + if name not in ("step", "neuron_idx"): + require(value.shape == (neurons,), "Invalid state length: " + name) + ticks, nodes = state["step"], state["neuron_idx"] + require(ticks.shape == nodes.shape and bool(np.all((ticks >= 0) & (ticks < steps))) and + bool(np.all((nodes >= 0) & (nodes < neurons))), "Invalid sparse event indices") + keys = ticks.astype(np.int64) * neurons + nodes + require(bool(np.all(np.diff(keys) > 0)), "Sparse events not sorted unique by tick/global neuron") + require(np.array_equal(np.bincount(nodes, minlength=neurons), state["counts"]), "Counts disagree with sparse events") + require(bool(np.all((state["ref"] >= 0) & (state["ref"] <= 22))), "Final refractory outside frozen range") + return keys + + +def read_jsonl(path, steps, kind): + with Path(path).open(encoding="utf-8") as source: + rows = [json.loads(line) for line in source if line.strip()] + require(len(rows) == steps and [row.get("tick") for row in rows] == list(range(steps)), + "Missing/duplicate/reordered ticks in " + kind) + for row in rows: + names = ("v_sha256", "g_sha256", "ref_sha256") if kind == "state hashes" else ( + "ordered_edge_sha256", "accepted_mask_sha256", "joint_sha256") + for name in names: + require(isinstance(row.get(name), str) and HEX_SHA.fullmatch(row[name]), "Invalid SHA256 in " + kind) + if kind == "queue": + require(type(row.get("count")) is int and type(row.get("accepted_count")) is int and + 0 <= row["accepted_count"] <= row["count"], "Invalid queue counts") + return rows + + +def read_trial(output, case, spec, kc_globals, neurons, edges): + summary = read_json(output / "summary.json") + steps = spec["steps"] + require(summary.get("complete") is True and summary.get("pass_all") is True and + summary.get("all_finite") is True and summary.get("steps") == steps and + summary.get("completed_ticks") == steps and summary.get("nodes") == neurons and + summary.get("edges") == edges and summary.get("precision") == "float64" and + summary.get("apl_precision") == "float32" and summary.get("case") == spec["name"] and + summary.get("record_history") is False and summary.get("learning") is False, + "Incomplete/failed/wrong-scope GPU summary") + with np.load(output / "state.npz", allow_pickle=False) as source: + state = {name: source[name].copy() for name in source.files} + event_keys = validate_state(state, neurons, steps) + hashes = read_jsonl(output / "tick-state-hashes.jsonl", steps, "state hashes") + for name in ("v", "g", "ref"): + require(hashes[-1][name + "_sha256"] == raw_hash(state[name]), "Final state does not match last tick hash: " + name) + queues = read_jsonl(output / "queue.jsonl", steps, "queue") + apl = {} + for name, width in APL_FILES.items(): + value = np.load(output / name, mmap_mode="r", allow_pickle=False) + require(value.dtype == np.float32 and value.shape == (steps, width) and + bool(np.isfinite(value).all()) and bool(np.all(value >= 0)), "Invalid full APL trace: " + name) + apl[name] = value + with (output / "requests.csv").open(encoding="utf-8", newline="") as source: + reader = csv.DictReader(source) + require(tuple(reader.fieldnames or ()) == REQUEST_FIELDS, "Request CSV schema mismatch") + request_rows = [{name: int(row[name]) for name in REQUEST_FIELDS} for row in reader] + with np.load(case / "requests.npz", allow_pickle=False) as source: + require(set(source.files) == {"tick", "neuron_idx"}, "Input request schema mismatch") + expected_ticks, expected_nodes = source["tick"], source["neuron_idx"] + require(expected_ticks.dtype == np.int32 and expected_nodes.dtype == np.int32 and + expected_ticks.ndim == 1 and expected_ticks.shape == expected_nodes.shape, + "Input request dtype/shape mismatch") + require(len(request_rows) == len(expected_ticks), "Requested event coverage mismatch") + require([row["tick"] for row in request_rows] == expected_ticks.tolist() and + [row["neuron_idx"] for row in request_rows] == expected_nodes.tolist(), "Requests differ from frozen input") + require(all(row["attempted"] == 1 and row["accepted"] in (0, 1) and + row["rejected"] == 1 - row["accepted"] for row in request_rows), "Invalid request acceptance bookkeeping") + requested_keys = expected_ticks.astype(np.int64) * neurons + expected_nodes + require(bool(np.all(np.diff(requested_keys) > 0)) and bool(np.all((expected_ticks >= 0) & (expected_ticks < steps))) and + bool(np.all((expected_nodes >= 0) & (expected_nodes < neurons))), "Input requests not sorted unique/in range") + actual_accepted = np.array([row["accepted"] for row in request_rows], dtype=np.uint8) + require(np.array_equal(np.isin(requested_keys, event_keys, assume_unique=True), actual_accepted.astype(bool)), + "Request accepted/rejected values disagree with actual spikes") + accepted = int(actual_accepted.sum()) + require(summary.get("requested") == len(request_rows) and summary.get("accepted_requests") == accepted and + summary.get("rejected_requests") == len(request_rows) - accepted and + summary.get("total_spikes") == len(state["step"]), "Summary counts disagree with raw output") + component = read_json(output / "apl-component.json") + require(component.get("ok") is True and component.get("n_neurons") == neurons and + component.get("n_kc") == len(kc_globals) and component.get("n_apl") == 2 and + component.get("dt_ms") == .1 and summary.get("apl_component") == component, + "APL component provenance mismatch") + bundle = Path(spec["apl_bundle_dir"]) + require(component.get("files_sha256") == {name: file_hash(bundle / name) for name in + ("input_matrix.npy", "output_matrix.npy", "kc_globals.npy", "apl_globals.npy")}, "APL bundle hashes changed") + inputs = summary.get("input_sha256", {}) + require(inputs.get("spec.json") == file_hash(case / "spec.json") and + inputs.get("requests.npz") == file_hash(case / "requests.npz"), "Case input provenance mismatch") + kc_counts = state["counts"][kc_globals].copy() + kc_event_mask = np.isin(state["neuron_idx"], kc_globals) + record = {"case": spec["name"], "mode": spec["mode"], "odor_id": spec["odor_id"], + "odor_name": spec["odor_name"], "seed": spec["seed"], "split": "calibration", + "KC_active": int(np.count_nonzero(kc_counts)), "KC_active_fraction": float(np.count_nonzero(kc_counts) / len(kc_globals)), + "KC_spikes": int(kc_counts.sum()), "KC_first_latency_ms": + float(state["step"][kc_event_mask][0] * .1) if bool(np.any(kc_event_mask)) else None, + "external_events": accepted, "expected_external_events": float(spec["expected_external_events"]), + "total_PN_rate_hz": float(spec["rate_sum_hz"]), "finite": True, + "total_spikes": len(state["step"]), "requested": len(request_rows)} + require(np.isfinite(record["expected_external_events"]) and record["expected_external_events"] > 0, + "CAL expected external events must be finite and positive") + return {"state": state, "hashes": hashes, "queues": queues, "apl": apl, + "requests": request_rows, "component": component, "kc_counts": kc_counts, + "expected_external_events": record["expected_external_events"], "external_events": accepted, + "record": record, "output": output} + + +def exact_repeat(first, repeat): + checks = {"state/" + name: arrays_bitwise_equal(first["state"][name], repeat["state"][name]) for name in STATE_DTYPES} + checks.update({name: arrays_bitwise_equal(first["apl"][name], repeat["apl"][name]) for name in APL_FILES}) + checks.update(tick_state_hashes=first["hashes"] == repeat["hashes"], + queues=first["queues"] == repeat["queues"], requests=first["requests"] == repeat["requests"], + apl_component=first["component"] == repeat["component"]) + return {"pass_all": bool(all(checks.values())), "checks": checks, + "scope": "All six state arrays, every tick all-neuron hashes, APL raw float32 traces, queues, requests and APL environment"} + + +def validate_inventory(records, cfg): + require(tuple(cfg["calibration"]["modes"]) == ORIGINAL_MODES and + tuple(cfg["calibration"]["seeds"]) == ORIGINAL_SEEDS and + cfg["calibration"]["duration_ms"] == 100.0 and cfg["calibration"]["odor_count"] == 6 and + cfg["calibration"]["split"] == "calibration" and + cfg["calibration"]["repeat_first_seed_each_odor"] is True, "Frozen CAL design changed") + gates = cfg["gates"] + require(gates["sparse_min_fraction"] == .03 and gates["sparse_max_fraction"] == .09 and + gates["minimum_separable_odor_pairs"] == 3 and gates["max_fraction_strict"] == .5 and + gates["min_fraction_strict"] == 0 and all(gates[key] is True for key in + ("same_jaccard_gt_between", "between_euclidean_gt_within", "both_modes_required", "finite_required", "repeat_exact_required")), + "Original representation gates or additional integrity gates changed") + expected = {(mode, index, seed) for mode in ORIGINAL_MODES for index in range(6) for seed in ORIGINAL_SEEDS} + require(len(records) == 60 and {(row["mode"], row["odor_index"], row["seed"]) for row in records} == expected, + "CAL inventory must cover exactly 2 modes x 6 odors x 5 seeds") + odors = [] + for index in range(6): + ids = {row["odor_id"] for row in records if row["odor_index"] == index} + require(len(ids) == 1, "Odor identity changes across seeds/modes") + odors.append(next(iter(ids))) + require(set(odors) == ORIGINAL_CAL_IDS, "CAL identities differ from the original frozen six") + for row in records: + require(row["name"] == f"cal-{row['mode']}-{row['odor_index']:02d}-s{row['seed']}", "Unexpected CAL case name") + return odors + + +def summarize(out_json=_DEFAULT_OUTPUT): + """Recompute; default writes exclusively, out_json=None is strictly read-only.""" + from context import EXP, ROOT, CFG, check + target = EXP / "calibration/summary.json" if out_json is _DEFAULT_OUTPUT else ( + None if out_json is None else Path(out_json)) + require(target is None or not target.exists(), "Refusing existing calibration summary") + failures, runs, run_records, repeats, provenance = [], {}, [], [], [] + modes, odors = {}, [] + prerequisites_ok = False + encoder = None + records = [] + try: + check() + records = read_json(EXP / "protocol/calibration-cases.json") + odors = validate_inventory(records, CFG) + encoder = read_json(EXP / "protocol/encoder-audit.json") + require(encoder["panel_count"] == 12 and encoder["cal_count"] == 6 and + encoder["seeds"] == list(ORIGINAL_SEEDS) and encoder["requested_before_any_dynamics"] is True and + encoder["formal_AB_run"] is False and encoder["heldout_run"] is False, "Invalid frozen encoder scope") + # Only static original-panel metadata is read, never heldout trajectories. + import pandas as pd + original = pd.read_parquet(ROOT / "experiments/phase4l-olfactory-grounding/odor-encoder/frozen-odor-panel.parquet") + require(len(original) == 12 and set(original[original.split == "calibration"].odor_id) == ORIGINAL_CAL_IDS, + "Original static panel identity mismatch") + require(float(np.median(original.mapped_response_sum)) == encoder["median_mapped_response_sum"], + "Matched-energy reference is not the original frozen 12-panel median") + kc_globals = np.load(EXP / "apl-bundle/kc_globals.npy", allow_pickle=False) + require(kc_globals.dtype == np.int32 and kc_globals.shape == (4064,) and + bool(np.all(np.diff(kc_globals) > 0)) and bool(np.all((kc_globals >= 0) & (kc_globals < CFG["neurons"]))), + "Invalid frozen KC identities") + prerequisites_ok = True + except Exception as error: + failures.append({"stage": "prerequisites", "type": type(error).__name__, "message": str(error)}) + if prerequisites_ok: + for row in records: + name = row["name"] + first = None + try: + case = EXP / "cases" / name + spec = read_json(case / "spec.json") + require(spec["name"] == name and spec["kind"] == "calibration" and spec["steps"] == 1000 and + spec["duration_ms"] == 100.0 and spec["dt_ms"] == .1 and spec["training"] is False and + all(spec[key] == row[key] for key in ("mode", "odor_id", "odor_index", "seed")), + "Case spec differs from fixed CAL inventory") + first = read_trial(EXP / "gpu" / (name + "-r0"), case, spec, kc_globals, CFG["neurons"], CFG["edges"]) + require(first["record"]["requested"] == row["requests"], "Frozen inventory request count mismatch") + runs[(row["mode"], row["odor_id"], row["seed"])] = {key: first[key] for key in + ("kc_counts", "expected_external_events", "external_events")} + run_records.append(first["record"]) + provenance.append({"case": name, "repeat": 0, "state_npz_sha256": file_hash(first["output"] / "state.npz"), + "summary_sha256": file_hash(first["output"] / "summary.json")}) + except Exception as error: + failures.append({"stage": "raw_trial", "case": name, "repeat": 0, + "type": type(error).__name__, "message": str(error)}) + if row["seed"] == ORIGINAL_SEEDS[0]: + try: + require(first is not None, "Baseline unavailable for exact repeat") + repeat = read_trial(EXP / "gpu" / (name + "-r1"), case, spec, kc_globals, CFG["neurons"], CFG["edges"]) + repeat_report = {"case": name, **exact_repeat(first, repeat)} + repeats.append(repeat_report) + provenance.append({"case": name, "repeat": 1, "state_npz_sha256": file_hash(repeat["output"] / "state.npz"), + "summary_sha256": file_hash(repeat["output"] / "summary.json")}) + require(repeat_report["pass_all"], "Exact repeat differs: " + ", ".join( + key for key, value in repeat_report["checks"].items() if not value)) + except Exception as error: + failures.append({"stage": "exact_repeat", "case": name, "repeat": 1, + "type": type(error).__name__, "message": str(error)}) + if len(runs) == 60: + for mode in ORIGINAL_MODES: + try: + modes[mode] = summarize_mode(mode, odors, ORIGINAL_SEEDS, runs, CFG["gates"]) + except Exception as error: + failures.append({"stage": "representation", "mode": mode, + "type": type(error).__name__, "message": str(error)}) + integrity_pass = bool(prerequisites_ok and len(runs) == 60 and not any(row["stage"] in + ("prerequisites", "raw_trial") for row in failures)) + repeat_pass = bool(len(repeats) == 12 and all(row["pass_all"] for row in repeats) and + not any(row["stage"] == "exact_repeat" for row in failures)) + representation_pass = bool(len(modes) == 2 and all(row["gate"] for row in modes.values())) + result = {"phase": "4AG", "scope": "Original fixed six CAL odors only; not heldout generalization or learning", + "pass_all": bool(integrity_pass and repeat_pass and representation_pass), + "numerical_integrity_pass": integrity_pass, "exact_repeat_pass": repeat_pass, + "original_representation_gate_pass": representation_pass, + "expected_base_trials": 60, "valid_base_trials": len(runs), "expected_repeats": 12, + "checked_repeats": len(repeats), "seeds": list(ORIGINAL_SEEDS), "duration_ms": 100.0, + "odor_ids": odors, "modes": modes, "runs": run_records, "repeats": repeats, + "failures": failures, "input_output_provenance": provenance, "encoder_audit": encoder, + "metric_source": "phase4l.representation (full original gate), phase4i.pair_metrics", + "finite_gate_note": "Added integrity prerequisite; original 4L recorded finite but omitted it from its gate", + "repeat_gate_note": "Additional exact repeat requirement; original metrics and numerical thresholds unchanged", + "shared_apl_component_oracle_limit": True, "independent_oracle_equivalence_claim": False, + "candidate_generated": False, "training": False, "heldout_dynamic_access": False} + if target is not None: + target.parent.mkdir(parents=True, exist_ok=True) + with target.open("x", encoding="utf-8") as output: + json.dump(result, output, ensure_ascii=False, indent=2, allow_nan=False) + output.write("\n") + return result + + +if __name__ == "__main__": + print(json.dumps({key: value for key, value in summarize().items() if key in + ("pass_all", "numerical_integrity_pass", "exact_repeat_pass", "original_representation_gate_pass")}), flush=True) diff --git a/models/male-cns-lif/author-apl-lab-v1/checksums.sha256 b/models/male-cns-lif/author-apl-lab-v1/checksums.sha256 new file mode 100644 index 0000000..597e8cc --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/checksums.sha256 @@ -0,0 +1,17 @@ +c14c2d3c35e2b404c516befa66370723fba075516540214ae46e4104ff87e813 PROTOCOL.md +edb9cb2d890471fd07afd88c959ef3540b0cd40af490fac3f26396b5e7f5bf9c calibration.py +6e1616b086f667fa4e4b5087f500f8080281977ca9922e7b055acfddd2003bd5 compare.py +2aa6b4d777ff698018e1a6eaf571215208b8c874a4c848a6d632e2bd778c90e3 config.json +b8b1231d958a3ec935ec1d5a36e08cbd9bcd1127f4d4bb243a9051884a0e7a16 context.py +b6e3c67e668a971b544df32d76435ab5e537061b9fc2e8d9dedb5d2a726f5782 finalize.py +69f5d893f47dafff4ef7c3186c3df0b9014666b32c8f4e943b57016bec92b18a gpu_apl.py +21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291 legacy_apl_worker.py +9b7f3e0b59f2117b94b41a6f542f37e6f7950138d324c905e8e0ac6cb7d17151 oracle_apl.py +197ccc6aae70566c4db4d4de17fa032e853b9bd9a04262905d6c71e12a930b6f prepare.py +afbe6d5a37c29bda66d38c470093584130d1852e67881c681662aec3c76b33c7 run_case.py +84c681030b0032119b5392d0c3f3c136a9c6b0d133eb548c40791a10bbbb8b63 run_suite.py +bab3136eecbc5bb09a985d715844ee845cb72b07930ce376bfb39be33bd1845a test_calibration.py +1a233f47943e01bbd845ac1dacdb1eec3f88c270c65a5f75bce1cf679b5de097 test_protocol.py +5cfc9a4505da6b8fb4e7d2617cdaedaa8475ed46a11ec9bdf0d4d25b1d22c2d3 validate_components.py +0d0e72b2f1348f6648124c8c34f75094f76fc5e1a281fbcca130a5fad2f35293 verify_frozen.py +6aa01a937f1f3c6504ee92e847cf7b3a884e88aa4ef37a68b0b7d4e7deed4bf2 manifest.json diff --git a/models/male-cns-lif/author-apl-lab-v1/compare.py b/models/male-cns-lif/author-apl-lab-v1/compare.py new file mode 100644 index 0000000..464426b --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/compare.py @@ -0,0 +1,508 @@ +"""Fail-closed byte-exact audit of the shared-APL closed-loop fixtures. + +Comparison is independent of either backend's stored PASS. No tolerance is +used, timing fields are never compared, and reports contain no wall timestamp. +""" +from __future__ import annotations + +import csv +import hashlib +import json +import math +from pathlib import Path +import re +import struct + +import numpy as np + + +N_GLOBAL = 166700 +STATE_KEYS = ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref') +REQUEST_FIELDS = ('tick', 'neuron_idx', 'attempted', 'accepted', 'rejected') +HASH_FIELDS = {'tick', 'v_sha256', 'g_sha256', 'ref_sha256'} +QUEUE_FIELDS = {'tick', 'count', 'accepted_count', 'ordered_edge_sha256', + 'accepted_mask_sha256', 'joint_sha256'} +BUNDLE_FILES = ('input_matrix.npy', 'output_matrix.npy', 'kc_globals.npy', 'apl_globals.npy') +COEFFICIENTS = ('gd', 'c0', 'cg', 'cv', 'threshold', 'reset', 'seed_volt') + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def file_hash(path): + h = hashlib.sha256() + with Path(path).open('rb') as source: + for block in iter(lambda: source.read(8 << 20), b''): + h.update(block) + return h.hexdigest() + + +def raw_hash(array, dtype=None): + return hashlib.sha256(np.ascontiguousarray(array, dtype=dtype).tobytes()).hexdigest() + + +def save(path, report): + if path is None: + return + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open('x', encoding='utf-8') as target: + json.dump(report, target, indent=2, sort_keys=True, allow_nan=False) + target.write('\n') + + +def _read_json(path, used): + used.add(Path(path)) + return json.loads(Path(path).read_text(encoding='utf-8')) + + +def _read_jsonl(path, used): + used.add(Path(path)) + with Path(path).open(encoding='utf-8') as source: + return [json.loads(line) for line in source if line.strip()] + + +def _read_array(path, used): + used.add(Path(path)) + return np.load(path, mmap_mode='r', allow_pickle=False) + + +def _check(checks, name, condition): + checks[name] = bool(condition) + + +def _bit_mask(a, b): + if a.dtype.kind == 'f': + unsigned = np.dtype('uint'+str(a.dtype.itemsize*8)) + return np.ascontiguousarray(a).view(unsigned) != np.ascontiguousarray(b).view(unsigned) + return a != b + + +def _compare_array(name, a, b, checks, differences, dtype=None, shape=None): + a, b = np.asarray(a), np.asarray(b) + schema = a.shape == b.shape and a.dtype == b.dtype and \ + (dtype is None or a.dtype == np.dtype(dtype)) and (shape is None or a.shape == shape) + _check(checks, name+'.schema', schema) + if not schema: + differences[name] = {'left_shape': list(a.shape), 'right_shape': list(b.shape), + 'left_dtype': str(a.dtype), 'right_dtype': str(b.dtype), 'expected_shape': None if shape is None else list(shape), + 'expected_dtype': None if dtype is None else str(np.dtype(dtype))} + _check(checks, name+'.bitwise_exact', False) + return False + finite = bool(np.isfinite(a).all() and np.isfinite(b).all()) + _check(checks, name+'.finite', finite) + mask = _bit_mask(a, b) + exact = not bool(np.any(mask)) + _check(checks, name+'.bitwise_exact', exact) + if not exact or not finite: + flat = np.flatnonzero(mask.ravel()) + detail = {'mismatching_elements': int(len(flat)), + 'first_positions': [list(map(int, np.unravel_index(int(index), a.shape))) for index in flat[:5]], + 'left_nonfinite': int(np.count_nonzero(~np.isfinite(a))), + 'right_nonfinite': int(np.count_nonzero(~np.isfinite(b)))} + if finite and a.size: + error = np.abs(a.astype(np.float64)-b.astype(np.float64)) + detail.update(max_abs_error=float(error.max()), mean_abs_error=float(error.mean())) + if a.dtype.kind == 'f' and len(flat): + unsigned = np.dtype('uint'+str(a.dtype.itemsize*8)) + av = np.ascontiguousarray(a).view(unsigned).ravel() + bv = np.ascontiguousarray(b).view(unsigned).ravel() + detail['first_bit_patterns'] = [{'left': hex(int(av[index])), 'right': hex(int(bv[index]))} for index in flat[:5]] + differences[name] = detail + return exact and finite + + +def _sha(value): + return isinstance(value, str) and re.fullmatch('[0-9a-f]{64}', value) is not None + + +def _read_requests(path, steps, used): + used.add(Path(path)) + with Path(path).open(encoding='utf-8', newline='') as source: + reader = csv.DictReader(source) + require(tuple(reader.fieldnames or ()) == REQUEST_FIELDS, 'Request CSV schema mismatch: '+str(path)) + rows = [{key: int(row[key]) for key in REQUEST_FIELDS} for row in reader] + previous = (-1, -1) + for row in rows: + key = row['tick'], row['neuron_idx'] + require(previous < key and 0 <= key[0] < steps and 0 <= key[1] < N_GLOBAL, + 'Request rows must be sorted, unique, and in range: '+str(path)) + require(row['attempted'] == 1 and row['accepted'] in (0, 1) and row['rejected'] == 1-row['accepted'], + 'Request accounting is incomplete: '+str(path)) + previous = key + return rows + + +def _load_run(directory, role, n, edges, steps, record_history, name, used, checks): + directory = Path(directory) + summary = _read_json(directory/'summary.json', used) + _check(checks, role+'.summary_complete', summary.get('complete') is True and summary.get('pass_all') is True and + summary.get('steps') == steps and summary.get('completed_ticks') == steps and + summary.get('nodes') == n and summary.get('edges') == edges and summary.get('precision') == 'float64') + is_cpu = role == 'cpu' + _check(checks, role+'.case_name', summary.get('name' if is_cpu else 'case') == name) + _check(checks, role+'.history_mode', summary.get('history_recorded' if is_cpu else 'record_history') is record_history) + _check(checks, role+'.apl_precision', summary.get('APL_precision' if is_cpu else 'apl_precision') == 'float32') + _check(checks, role+'.no_error_marker', not (directory/'error.json').exists()) + used.add(directory/'state.npz') + with np.load(directory/'state.npz', allow_pickle=False) as source: + require(set(source.files) == set(STATE_KEYS), 'State NPZ keys changed: '+str(directory)) + state = {key: source[key].copy() for key in STATE_KEYS} + for key, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32'), ('counts', 'int32')): + require(state[key].shape == (n,) and state[key].dtype == np.dtype(dtype), 'Invalid final '+key+': '+str(directory)) + require(state['step'].dtype == state['neuron_idx'].dtype == np.int32 and state['step'].ndim == 1 and + state['step'].shape == state['neuron_idx'].shape, 'Invalid sparse event schema: '+str(directory)) + require(bool(np.all((state['step'] >= 0) & (state['step'] < steps))) and + bool(np.all((state['neuron_idx'] >= 0) & (state['neuron_idx'] < N_GLOBAL))), 'Sparse event range error') + event_keys = state['step'].astype(np.int64)*N_GLOBAL+state['neuron_idx'] + _check(checks, role+'.events_sorted_unique', len(event_keys) < 2 or np.all(np.diff(event_keys) > 0)) + _check(checks, role+'.count_nonnegative_total', np.all(state['counts'] >= 0) and int(state['counts'].sum()) == len(event_keys)) + _check(checks, role+'.ref_range', np.all((state['ref'] >= 0) & (state['ref'] <= 22))) + _check(checks, role+'.reported_events', summary.get('spikes' if is_cpu else 'total_spikes') == len(event_keys)) + hashes = _read_jsonl(directory/'tick-state-hashes.jsonl', used) + require(len(hashes) == steps and [row.get('tick') for row in hashes] == list(range(steps)), 'Incomplete tick hash coverage') + require(all(set(row) == HASH_FIELDS and all(_sha(row[key]) for key in HASH_FIELDS-{'tick'}) for row in hashes), + 'Invalid tick state hash schema') + queues = _read_jsonl(directory/'queue.jsonl', used) + require(len(queues) == steps and [row.get('tick') for row in queues] == list(range(steps)), 'Incomplete queue coverage') + require(all(set(row) == QUEUE_FIELDS and type(row['count']) is int and type(row['accepted_count']) is int and + 0 <= row['accepted_count'] <= row['count'] <= edges and + all(_sha(row[key]) for key in QUEUE_FIELDS-{'tick', 'count', 'accepted_count'}) for row in queues), + 'Invalid queue row schema/count/hash') + for key in ('v', 'g', 'ref'): + _check(checks, role+'.last_hash_'+key, hashes[-1][key+'_sha256'] == raw_hash(state[key], '= 0)) + histories, pre = {}, {} + for field, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32')): + files = (directory/(field+'-history.'+dtype+'.npy'), directory/('preAPL-'+field+'.'+dtype+'.npy')) + if record_history: + histories[field], pre[field] = (_read_array(path, used) for path in files) + require(all(array.dtype == np.dtype(dtype) and array.shape == (steps, n) for array in (histories[field], pre[field])), + 'Small pre/post history schema mismatch') + _check(checks, role+'.history_hashes_'+field, all(row[field+'_sha256'] == raw_hash(histories[field][tick]) + for tick, row in enumerate(hashes))) + _check(checks, role+'.history_final_'+field, not np.any(_bit_mask(histories[field][-1], state[field]))) + else: + _check(checks, role+'.no_dense_'+field, not any(path.exists() for path in files)) + if is_cpu: + component = _read_json(directory/'apl-worker-handshake.json', used)['handshake'] + else: + component = _read_json(directory/'apl-component.json', used) + _check(checks, role+'.component_summary_consistent', summary.get('apl_component') == component) + return dict(directory=directory, summary=summary, state=state, hashes=hashes, queues=queues, + requests=requests, apl=apl, histories=histories, pre=pre, component=component) + + +def _pair_outputs(left, right, checks, differences, n, steps, record_history): + for field in STATE_KEYS: + shape = None if field in ('step', 'neuron_idx') else (n,) + dtype = 'float64' if field in ('v', 'g') else 'int32' + _compare_array('state.'+field, left['state'][field], right['state'][field], checks, differences, dtype, shape) + for field, width in (('drive', 2), ('state', 2), ('inhibition', 4064)): + _compare_array('apl.'+field, left['apl'][field], right['apl'][field], checks, differences, 'float32', (steps, width)) + for field in ('hashes', 'queues', 'requests', 'component'): + same = left[field] == right[field] + _check(checks, field+'.parsed_exact', same) + if not same: + if isinstance(left[field], list): + first = next((i for i, (a, b) in enumerate(zip(left[field], right[field])) if a != b), None) + differences[field] = {'left_count': len(left[field]), 'right_count': len(right[field]), + 'first_differing_row': first, + 'left_row': None if first is None else left[field][first], + 'right_row': None if first is None else right[field][first]} + else: + differences[field] = {'left': left[field], 'right': right[field]} + if record_history: + for stage in ('histories', 'pre'): + for field, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32')): + _compare_array(stage+'.'+field, left[stage][field], right[stage][field], checks, differences, dtype, (steps, n)) + + +def _queue_digest(tick, ids, accepted): + ids = np.ascontiguousarray(ids, dtype='= 22 + for index, ok in zip(range(lo, hi), accepted): + row = run['requests'][index] + if row['accepted'] != int(ok) or row['rejected'] != int(not ok): + request_ok = False + first.setdefault('request_ref_mismatch', {'tick': tick, 'neuron_idx': row['neuron_idx']}) + if not np.isin(targets[accepted], fired).all() or np.isin(targets[~accepted], fired).any(): + delivered_ok = False + first.setdefault('request_delivery_mismatch', tick) + last[fired] = tick + ref = np.maximum(22-(tick-last), 0).astype(np.int32) + if raw_hash(ref, '= 18 else np.empty(0, np.int64) + chunks = [np.arange(ptr[source], ptr[source+1], dtype=np.int64) for source in due] + indices = np.concatenate(chunks) if chunks else np.empty(0, np.int64) + expected_queue = _queue_digest(tick, graph['edge_id'][indices], (ref[graph['dst'][indices]] == 0).astype(np.uint8)) + if expected_queue != run['queues'][tick]: + queue_ok = False + first.setdefault('queue_reconstruction_mismatch', tick) + _check(checks, role+'.request_refractory_gate', request_ok) + _check(checks, role+'.accepted_spikes_rejected_absent', delivered_ok) + _check(checks, role+'.all_ref_hashes_from_actual_events', ref_ok) + if run['pre']: + _check(checks, role+'.small_queue_independent_reconstruction', queue_ok) + _check(checks, role+'.spike_g_reset_before_apl', reset_ok) + if first: + differences[role+'.independent_accounting'] = first + + +def _check_apl_application(run, role, nodes, kc, checks, differences): + if not run['pre']: + return + common, local_kc, bundle_kc = np.intersect1d(nodes, kc, assume_unique=True, return_indices=True) + _compare_array(role+'.apl_keeps_voltage', run['pre']['v'], run['histories']['v'], checks, differences, 'float64') + _compare_array(role+'.apl_keeps_ref', run['pre']['ref'], run['histories']['ref'], checks, differences, 'int32') + expected = np.array(run['pre']['g'], copy=True) + delta = run['apl']['inhibition'][:, bundle_kc].astype(np.float64)*np.float64(.001) + expected[:, local_kc] = expected[:, local_kc]-delta + _compare_array(role+'.apl_unconditional_g_write', expected, run['histories']['g'], checks, differences, 'float64') + + +def _finish(report, checks, differences, used, out_json): + report['checks'] = {name: bool(value) for name, value in checks.items()} + report['differences'] = differences + report['pass_all'] = bool(checks) and all(checks.values()) and 'error' not in report + report['failed_checks'] = sorted(name for name, value in checks.items() if not value) + report['evidence_sha256'] = {str(path): file_hash(path) for path in sorted(used) if path.is_file()} + save(out_json, report) + return report + + +def compare(case_dir, gpu_dir, cpu_dir, out_json=None): + """Strict independent closed-loop comparison; out_json=None is read-only.""" + case_dir, gpu_dir, cpu_dir = Path(case_dir), Path(gpu_dir), Path(cpu_dir) + if out_json is not None: + require(not Path(out_json).exists(), 'Refusing existing comparison report') + checks, differences, used = {}, {}, set() + report = {'kind': 'shared-APL-component closed-loop byte-exact comparison', 'pass_all': False, + 'case_dir': str(case_dir), 'gpu_dir': str(gpu_dir), 'cpu_dir': str(cpu_dir), + 'tolerance': None, 'signed_zero_included': True, 'timing_fields_compared': False, + 'independent_CPU_all_components_claim': False} + try: + spec = _read_json(case_dir/'spec.json', used) + steps, history = spec['steps'], spec['record_history'] + require(type(steps) is int and steps > 0 and type(history) is bool and spec['dt_ms'] == .1, + 'Invalid fixed case clock/history schema') + graph_dir, bundle_dir = Path(spec['graph_dir']), Path(spec['apl_bundle_dir']) + graph = {field: _read_array(graph_dir/(field+'.npy'), used) for field in + ('nodes', 'edge_id', 'src', 'dst', 'ptr', 'signed_raw', 'weights')} + nodes = graph['nodes'] + n, edges = len(nodes), len(graph['edge_id']) + require(n > 0 and edges > 0 and nodes.dtype == np.int32 and nodes.ndim == 1 and np.all((nodes >= 0) & (nodes < N_GLOBAL)) and + np.all(np.diff(nodes) > 0), 'Invalid canonical node identities') + require(graph['ptr'].dtype == np.int64 and graph['ptr'].shape == (n+1,) and graph['ptr'][0] == 0 and + graph['ptr'][-1] == edges and np.all(np.diff(graph['ptr']) >= 0), 'Invalid CSR') + for key, dtype in (('edge_id', 'int64'), ('src', 'int32'), ('dst', 'int32'), ('signed_raw', 'int64'), ('weights', 'float64')): + require(graph[key].dtype == np.dtype(dtype) and graph[key].shape == (edges,), 'Invalid graph '+key) + require(np.array_equal(graph['src'], np.repeat(np.arange(n, dtype=np.int32), np.diff(graph['ptr']))) and + np.all((graph['dst'] >= 0) & (graph['dst'] < n)), 'Invalid source/destination row mapping') + require(history == (n < 512) and (n < 512 or n == N_GLOBAL), 'Invalid small/full history declaration') + require(np.all(graph['edge_id'] >= 0) and np.all(np.diff(graph['edge_id']) > 0), + 'Original canonical edge IDs must be unique and increasing') + if n == N_GLOBAL: + require(edges == 25582938 and np.array_equal(nodes, np.arange(n, dtype=np.int32)) and + np.array_equal(graph['edge_id'], np.arange(edges, dtype=np.int64)), 'Full canonical graph identity changed') + bundle = {name: _read_array(bundle_dir/name, used) for name in BUNDLE_FILES} + kc, apl_ids = bundle['kc_globals.npy'], bundle['apl_globals.npy'] + require(kc.dtype == apl_ids.dtype == np.int32 and kc.shape == (4064,) and apl_ids.shape == (2,), 'Invalid APL/KC identities') + require(np.all((kc >= 0) & (kc < N_GLOBAL)) and np.all(np.diff(kc) > 0) and + np.all((apl_ids >= 0) & (apl_ids < N_GLOBAL)) and np.all(np.diff(apl_ids) > 0) and + not np.intersect1d(kc, apl_ids).size, 'APL/KC identities overlap or are invalid') + for key, shape in (('input_matrix.npy', (2, N_GLOBAL)), ('output_matrix.npy', (2, 4064))): + require(bundle[key].dtype == np.float32 and bundle[key].shape == shape and + np.isfinite(bundle[key]).all() and np.all(bundle[key] >= 0), 'Invalid normalized APL matrix') + gpu = _load_run(gpu_dir, 'gpu', n, edges, steps, history, spec['name'], used, checks) + cpu = _load_run(cpu_dir, 'cpu', n, edges, steps, history, spec['name'], used, checks) + _pair_outputs(gpu, cpu, checks, differences, n, steps, history) + used.add(case_dir/'requests.npz') + with np.load(case_dir/'requests.npz', allow_pickle=False) as source: + require(set(source.files) == {'tick', 'neuron_idx'} and source['tick'].dtype == source['neuron_idx'].dtype == np.int32, + 'Fixed request NPZ schema changed') + require(source['tick'].shape == source['neuron_idx'].shape and source['tick'].ndim == 1, 'Invalid sparse request shape') + requested = list(zip(map(int, source['tick']), map(int, source['neuron_idx']))) + for role, run in (('gpu', gpu), ('cpu', cpu)): + _independent_event_accounting(run, role, graph, requested, steps, checks, differences) + _check_apl_application(run, role, nodes, kc, checks, differences) + + # Independently derive the effective clone. Original graph weights stay read-only. + cpu_weights = _read_array(cpu_dir/'weights.npy', used) + require(cpu_weights.dtype == np.float64 and cpu_weights.shape == (edges,), 'CPU effective weight schema mismatch') + expected_hash, base_hash, mask_hash = hashlib.sha256(), hashlib.sha256(), hashlib.sha256() + weight_ok, mask_count = True, 0 + for start in range(0, edges, 1 << 20): + stop = min(edges, start+(1 << 20)) + original = np.asarray(graph['weights'][start:stop]) + require(np.isfinite(original).all(), 'Nonfinite original weights') + raw_expected = graph['signed_raw'][start:stop]*(.275*.001) + require(not np.any(_bit_mask(original, raw_expected)), 'Original SI weights differ from unaltered signed contacts') + mask = np.isin(nodes[graph['src'][start:stop]], apl_ids) + expected = original.copy() + expected[mask] = np.float64(0) + mask_count += int(mask.sum()) + expected_hash.update(np.ascontiguousarray(expected, dtype=' 0 and edges > 0 and steps > 0 and + type(history) is bool and history == (n < 512), 'Invalid repeat dimensions') + left = _load_run(first, 'first_gpu', n, edges, steps, history, name, used, checks) + right = _load_run(second, 'second_gpu', n, edges, steps, history, name, used, checks) + _pair_outputs(left, right, checks, differences, n, steps, history) + stable = ('case', 'complete', 'pass_all', 'steps', 'completed_ticks', 'nodes', 'edges', 'precision', + 'apl_precision', 'state_units', 'total_spikes', 'requested', 'accepted_requests', 'rejected_requests', + 'arriving_edges', 'accepted_arriving_edges', 'counts_match_sparse_events', 'all_finite', 'record_history', + 'apl_fast_edges_zeroed', 'kc_targets_present', 'effective_weights_raw_sha256', 'apl_schedule', + 'apl_si_conversion', 'build', 'sealed_ae_backend_sha256', 'learning', 'input_sha256') + for key in stable: + _check(checks, 'summary.'+key, key in left['summary'] and key in right['summary'] and + left['summary'][key] == right['summary'][key]) + if not checks['summary.'+key]: + differences['summary.'+key] = {'first': left['summary'].get(key), 'second': right['summary'].get(key)} + report.update(case=name, steps=steps, nodes=n, edges=edges, record_history=history, + total_spikes=len(left['state']['step']), + explicit_exclusions=['wall_seconds', 'diagnostic_simulation_seconds', 'build_or_cache_seconds', + 'peak_allocated_vram_bytes', 'peak_reserved_vram_bytes']) + except Exception as error: + report['error'] = {'type': type(error).__name__, 'message': str(error)} + return _finish(report, checks, differences, used, out_json) diff --git a/models/male-cns-lif/author-apl-lab-v1/config.json b/models/male-cns-lif/author-apl-lab-v1/config.json new file mode 100644 index 0000000..e8636ee --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/config.json @@ -0,0 +1,22 @@ +{ + "phase": "4AG", + "name": "author-apl-lab-v1", + "human_authorization": "允许独立新模型分支测试动力学假设", + "gpu_uuid": "GPU-79f3a85d-adce-7833-7003-6488140ca596", + "neurons": 166700, + "edges": 25582938, + "raw_weight_sum": 124177617, + "precision": "float64", + "apl_precision": "float32", + "apl": {"tau_ms": 20.0, "gain": 0.01, "normalization": 1.0}, + "hypothesis": "Author LIF plus unchanged legacy graded APL operator at end, after reset, unconditional KC g write. Mixed precision: h32 promoted to float64, then multiplied by float64 .001 SI, then subtracted. Not original author's APL model.", + "fixtures": ["zero", "pn_pulse", "kc_ref_boundary", "two_sources", "arrival_collision", "natural_request", "apl_only", "high_fanin"], + "fixture_repeats": 3, + "calibration": {"split": "calibration", "odor_count": 6, "modes": ["physiological", "matched_energy"], "seeds": [51101,51102,51103,51104,51105], "duration_ms": 100.0, "median_scope": "original frozen 12-odor panel", "full_oracle_probes": ["cal-physiological-00-s51101", "cal-matched_energy-00-s51101"], "repeat_first_seed_each_odor": true}, + "gates": {"sparse_min_fraction": 0.03, "sparse_max_fraction": 0.09, "max_fraction_strict": 0.5, "min_fraction_strict": 0.0, "minimum_separable_odor_pairs": 3, "same_jaccard_gt_between": true, "between_euclidean_gt_within": true, "both_modes_required": true, "finite_required": true, "repeat_exact_required": true}, + "training": false, + "heldout_dynamic_access": false, + "candidate_generation": false, + "output_budget_bytes": 16106127360, + "scope": "Semantic compatibility and fixed old CAL only. Shared-APL-component closed-loop Brian oracle plus independent arithmetic audit; never claim all-component independent CPU equivalence or learning success. No parameter/input/site/window selection from results." +} diff --git a/models/male-cns-lif/author-apl-lab-v1/context.py b/models/male-cns-lif/author-apl-lab-v1/context.py new file mode 100644 index 0000000..937067e --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/context.py @@ -0,0 +1,49 @@ +import hashlib +import importlib.util +import json +from pathlib import Path + +ROOT=Path('/data/digital-drosophila') +MODEL=Path(__file__).resolve().parent +EXP=ROOT/'experiments/phase4ag-author-apl' +AF_MODEL=ROOT/'models/male-cns-lif/author-fullgraph-lab-v1' +AF_EXP=ROOT/'experiments/phase4af-author-fullgraph' +AD_EXP=ROOT/'experiments/phase4ad-author-semantics' +CFG=json.loads((MODEL/'config.json').read_text(encoding='utf-8')) + +def digest(path): + h=hashlib.sha256() + with Path(path).open('rb') as f: + for b in iter(lambda:f.read(8<<20),b''):h.update(b) + return h.hexdigest() + +def save(path,value): + path=Path(path);path.parent.mkdir(parents=True,exist_ok=True) + with path.open('x',encoding='utf-8') as f:json.dump(value,f,indent=2,ensure_ascii=False,allow_nan=False);f.write('\n') + +def sources(): + return {p.name:digest(p) for p in sorted(MODEL.iterdir()) if p.is_file() and p.name not in ('manifest.json','checksums.sha256')} + +def integrity(): + spec=importlib.util.spec_from_file_location('frozen_af_context',AF_MODEL/'context.py') + mod=importlib.util.module_from_spec(spec);spec.loader.exec_module(mod) + result=mod.integrity() + import sys + sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1')) + from diagnostic import manifest_check + for p in (AF_MODEL,AF_EXP):result[str(p.relative_to(ROOT))]=manifest_check(p,digest) + assert all(r['pass'] for r in result.values()) + return result + +def check(): + p=json.loads((EXP/'protocol/preregistered.json').read_text()) + assert p['sources']==sources(),'Source changed after preregistration' + for row in p['inputs']:assert digest(ROOT/row['path'])==row['sha256'],row['path'] + for row in json.loads((EXP/'protocol/prepared-hashes.json').read_text()): + assert digest(EXP/row['path'])==row['sha256'],row['path'] + return p + +def budget(): + size=sum(p.stat().st_size for p in EXP.rglob('*') if p.is_file()) + assert size= 0) & (nodes < N_NEURONS))) and + bool(np.all(np.diff(nodes) > 0)), "Global node IDs must be sorted unique") + require(n < 512 or (n == N_NEURONS and np.array_equal(nodes, np.arange(N_NEURONS, dtype=np.int32))), + "Only small fixtures or the intact frozen full graph are permitted") + require(n != N_NEURONS or e == 25582938, "Full graph edge count changed") + for name in ("src", "dst", "signed_raw", "weights"): + require(graph[name].shape == (e,), "Graph edge array length mismatch: " + name) + ptr = graph["ptr"] + require(ptr.shape == (n + 1,) and ptr[0] == 0 and ptr[-1] == e and + bool(np.all(np.diff(ptr) >= 0)), "Invalid graph CSR") + require(np.array_equal(graph["src"], np.repeat(np.arange(n, dtype=np.int32), np.diff(ptr))), + "Source order does not match CSR") + # Global edge IDs need not equal local row indices in a fixture. + for start in range(0, e, 1 << 20): + stop = min(start + (1 << 20), e) + require(bool(np.all((graph["dst"][start:stop] >= 0) & (graph["dst"][start:stop] < n))), + "Local destination out of range") + require(bool(np.isfinite(graph["weights"][start:stop]).all()), "Nonfinite base SI weights") + require(np.array_equal(np.sign(graph["weights"][start:stop]), np.sign(graph["signed_raw"][start:stop])), + "Base weight sign/zero pattern differs from frozen raw contacts") + lo = max(0, start - 1) + require(bool(np.all(edges[start:stop] >= 0)) and bool(np.all(np.diff(edges[lo:stop]) > 0)), + "Global edge IDs must retain canonical increasing source/edge order") + return graph + + +def load_inputs(case_dir): + spec = json.loads((case_dir / "spec.json").read_text(encoding="utf-8")) + for name in ("name", "steps", "dt_ms", "duration_ms", "model", "graph_dir", "apl", + "apl_bundle_dir", "coefficients_path", "record_history"): + require(name in spec, "Missing case field: " + name) + steps = spec["steps"] + require(type(steps) is int and 0 < steps <= 1000 and float(spec["dt_ms"]) == .1 and + float(spec["duration_ms"]) == steps * .1, "Unexpected steps/dt/duration") + require(type(spec["record_history"]) is bool, "record_history must be Boolean") + require(spec["apl"] == {"tau_ms": 20, "gain": .01, "normalization": 1}, "Frozen APL parameters changed") + for name in ("graph_dir", "apl_bundle_dir", "coefficients_path"): + require(Path(spec[name]).is_absolute(), "Input path must be absolute: " + name) + model = spec["model"] + for name in ("resting_potential_mv", "threshold_mv", "reset_potential_mv", "synaptic_delay_ms", + "refractory_ms", "w_syn_mv_per_anatomical_synapse"): + require(name in model and math.isfinite(float(model[name])), "Invalid model field: " + name) + require(float(model["synaptic_delay_ms"]) == 1.8 and float(model["refractory_ms"]) == 2.2 and + float(model["w_syn_mv_per_anatomical_synapse"]) == .275, "Frozen delay/ref/W_syn changed") + coeff = json.loads(Path(spec["coefficients_path"]).read_text(encoding="utf-8")) + for name in COEFFICIENT_NAMES: + require(name in coeff and math.isfinite(float(coeff[name])), "Invalid author coefficient: " + name) + require(coeff["threshold"] == float(model["threshold_mv"]) * .001 and + coeff["reset"] == float(model["reset_potential_mv"]) * .001, "Coefficient threshold/reset mismatch") + graph = load_graph(str(Path(spec["graph_dir"]).resolve())) + n = len(graph["nodes"]) + require(spec["record_history"] == (n < 512), "Fixtures need dense history; full graph must not duplicate dense histories") + with np.load(case_dir / "requests.npz", allow_pickle=False) as source: + require(set(source.files) == {"tick", "neuron_idx"}, "Request sparse schema changed") + ticks, requested = source["tick"].copy(), source["neuron_idx"].copy() + require(ticks.dtype == np.int32 and requested.dtype == np.int32 and ticks.ndim == 1 and + ticks.shape == requested.shape, "Invalid sparse request dtype/shape") + require(bool(np.all((ticks >= 0) & (ticks < steps))), "Request tick outside trial") + if len(ticks) > 1: + require(bool(np.all((ticks[1:] > ticks[:-1]) | + ((ticks[1:] == ticks[:-1]) & (requested[1:] > requested[:-1])))), + "Requests must be sorted unique by tick/global neuron") + local = np.searchsorted(graph["nodes"], requested) + require(bool(np.all(local < n)) and np.array_equal(graph["nodes"][local], requested), + "Requested neuron absent from graph") + initial = {"initial_v_mv": np.full(n, float(model["resting_potential_mv"]), dtype=np.float64), + "initial_g_mv": np.zeros(n, dtype=np.float64)} + initial_path = case_dir / "arrays.npz" + if initial_path.exists(): + with np.load(initial_path, allow_pickle=False) as source: + require(set(source.files).issubset(initial), "Unexpected initial-state array") + for name in source.files: + value = source[name] + require(value.dtype == np.float64 and value.shape == (n,) and bool(np.isfinite(value).all()), + "Invalid initial state: " + name) + initial[name] = value.copy() + return spec, graph, coeff, ticks, requested, local, initial + + +def new_array(path, dtype, shape): + require(not Path(path).exists(), "Refusing existing output: " + str(path)) + return np.lib.format.open_memmap(path, mode="w+", dtype=dtype, shape=shape) + + +def run_gpu(case_dir, out_dir, build_root): + """Run fresh candidate state; never consume oracle/candidate APL traces. + + pass_all denotes internal recording/finite integrity only. Independent + comparison and the original complete representation gate are separate. + """ + case_dir, out_dir = Path(case_dir), Path(out_dir) + require(not out_dir.exists(), "Refusing existing output directory: " + str(out_dir)) + out_dir.mkdir(parents=True, exist_ok=False) + start, tick, completed = time.perf_counter(), -1, 0 + maps = {} + try: + spec, graph, coeff, request_ticks, request_nodes, request_local, initial = load_inputs(case_dir) + require(torch.cuda.is_available() and torch.cuda.device_count() == 1 and + "4090" in torch.cuda.get_device_name(), "Only the declared single visible RTX4090 is permitted") + ext, build = sealed_backend().build_extension(build_root) + build_done = time.perf_counter() + apl = LegacyAPL(spec["apl_bundle_dir"], spec["dt_ms"]) + write_json(out_dir / "apl-component.json", apl.metadata) + device = apl.device + torch.cuda.reset_peak_memory_stats(device) + n, e, steps = len(graph["nodes"]), len(graph["edge_id"]), spec["steps"] + + def upload(value, dtype): + return torch.from_numpy(np.array(value, copy=True, order="C")).to(device=device, dtype=dtype).contiguous() + + indptr, dst = upload(graph["ptr"], torch.int64), upload(graph["dst"], torch.int32) + global_nodes_gpu = upload(graph["nodes"], torch.int64) + base_weights = upload(graph["weights"], torch.float64) + weights = base_weights.clone() + del base_weights + local_apl = np.flatnonzero(np.isin(graph["nodes"], apl.apl_globals)).astype(np.int64) + apl_out_mask = np.isin(graph["src"], local_apl) + if bool(np.any(apl_out_mask)): + weights[upload(apl_out_mask, torch.bool)] = 0.0 + effective_hash = hashlib.sha256() + for offset in range(0, e, 1 << 20): + effective_hash.update(weights[offset:offset + (1 << 20)].cpu().numpy().astype(" begin: + requested_local_gpu = upload(request_local[begin:end], torch.int64) + requests[requested_local_gpu] = 1 + accepted = (ref[requested_local_gpu] <= 1).to(torch.uint8).cpu().numpy() + accepted_requests_total += int(accepted.sum()) + writer.writerows({"tick": tick, "neuron_idx": int(node), "attempted": 1, + "accepted": int(ok), "rejected": 1 - int(ok)} + for node, ok in zip(request_nodes[begin:end], accepted)) + spikes = ring[tick % 19] + ext.state_step(v, g, ref, requests, spikes, counts, + *[float(coeff[name]) for name in COEFFICIENT_NAMES], 22) + expanded_global = np.empty(0, dtype=np.int64) + accepted_cpu = np.empty(0, dtype=np.uint8) + if tick >= 18: + due = ring[(tick - 18) % 19] + sources = torch.nonzero(due, as_tuple=False).flatten().contiguous() + if sources.numel(): + degrees = indptr[sources + 1] - indptr[sources] + offsets = (torch.cumsum(degrees, dim=0) - degrees).contiguous() + edge_count = int(degrees.sum().item()) + expanded = torch.empty(edge_count, device=device, dtype=torch.int64) + ext.fill_edges(indptr, sources, offsets, expanded) + if edge_count: + destination = dst[expanded] + # Actual active fanout order, BEFORE stable target grouping. + expanded_global = graph["edge_id"][expanded.cpu().numpy()].copy() + accepted_cpu = (ref[destination.long()] == 0).to(torch.uint8).cpu().numpy().copy() + order = torch.argsort(destination, stable=True) + ordered_edges, ordered_targets = expanded[order].contiguous(), destination[order] + boundary = torch.ones(edge_count, device=device, dtype=torch.bool) + boundary[1:] = ordered_targets[1:] != ordered_targets[:-1] + starts = torch.nonzero(boundary, as_tuple=False).flatten().contiguous() + targets = ordered_targets[starts].contiguous() + ext.apply_arrivals(ordered_edges, starts, targets, weights, g, ref) + queue = queue_summary(tick, expanded_global, accepted_cpu) + queue_output.write(json.dumps(queue, allow_nan=False) + "\n") + due_total += queue["count"] + accepted_due_total += queue["accepted_count"] + if spec["record_history"]: + for name, value in (("v", v), ("g", g), ("ref", ref)): + maps["pre_" + name][tick] = value.cpu().numpy() + if n == N_NEURONS: + full_spikes = spikes + else: + apl.spikes.zero_() + apl.spikes[global_nodes_gpu] = spikes + full_spikes = apl.spikes + drive, apl_state, h = apl.advance(full_spikes) + # Mixed precision contract: widen f32 h EXACTLY, THEN multiply + # in f64, THEN subtract in f64. Do not guard refractory KCs. + delta_si = h[kc_full_gpu].to(torch.float64) * 0.001 + g[kc_local_gpu] = g[kc_local_gpu] - delta_si + apl_cpu = {"drive": drive.cpu().numpy().copy(), "state": apl_state.cpu().numpy().copy(), + "inhibition": h.cpu().numpy().copy()} + for name, value in apl_cpu.items(): + maps["apl_" + name][tick] = value + actual = {"v": v.cpu().numpy().copy(), "g": g.cpu().numpy().copy(), + "ref": ref.cpu().numpy().copy()} + finite = all(bool(np.isfinite(value).all()) for value in list(actual.values()) + list(apl_cpu.values())) + row = {"tick": tick, **{name + "_sha256": raw_hash(value, " float64 -> multiply float64 0.001 -> separate float64 subtraction", + "build": build, "sealed_ae_backend_sha256": file_sha256(AE_MODEL / "gpu_backend.py"), + "shared_apl_component_oracle_limitation": True, "oracle_validation_claim": False, + "learning": False, "performance_runtime_claim": False, "cpu_sync_each_tick": True, + "build_or_cache_seconds": build_done - start, + "diagnostic_simulation_seconds": time.perf_counter() - simulation_start, + "wall_seconds": time.perf_counter() - start, + "peak_allocated_vram_bytes": int(torch.cuda.max_memory_allocated(device)), + "peak_reserved_vram_bytes": int(torch.cuda.max_memory_reserved(device)), + "input_sha256": {"spec.json": file_sha256(case_dir / "spec.json"), + "requests.npz": file_sha256(case_dir / "requests.npz"), + "base_weights.npy": file_sha256(Path(spec["graph_dir"]) / "weights.npy"), + "coefficients.json": file_sha256(spec["coefficients_path"])}} + if (case_dir / "arrays.npz").exists(): + summary["input_sha256"]["arrays.npz"] = file_sha256(case_dir / "arrays.npz") + write_json(out_dir / "summary.json", summary) + return summary + except Exception as error: + for value in maps.values(): + value.flush() + write_json(out_dir / "error.json", {"complete": False, "pass_all": False, + "tick_in_progress": tick, "completed_ticks": completed, + "type": type(error).__name__, "message": str(error), "traceback": traceback.format_exc()}) + raise diff --git a/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py b/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py new file mode 100644 index 0000000..84d40e8 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py @@ -0,0 +1,175 @@ +"""Frozen eager Torch float32 APL component and binary oracle sidecar. + +This is the Phase 4P operator sequence, not an author-neuron implementation. +No precision/determinism flags are changed here. The full matrix dimensions +are retained even when the caller simulates a small neuronal fixture. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import struct +import sys +import traceback + +import numpy as np +import torch + + +PROTOCOL = "phase4ag-apl-v1" +N_NEURONS, N_APL, N_KC = 166700, 2, 4064 +BUNDLE_FILES = ("input_matrix.npy", "output_matrix.npy", "kc_globals.npy", "apl_globals.npy") +FRAME = struct.Struct("= 0) & (ids < N_NEURONS))) and + bool(np.all(np.diff(ids) > 0)), "APL/KC globals must be sorted unique: " + name) + require(not np.intersect1d(arrays["kc_globals.npy"], arrays["apl_globals.npy"]).size, + "APL and KC identities overlap") + require(bool(np.all(arrays["input_matrix.npy"] >= 0)) and + bool(np.all(arrays["output_matrix.npy"] >= 0)), "APL raw-contact matrices must be nonnegative") + self.device = torch.device("cuda", torch.cuda.current_device()) + self.input_gpu = torch.from_numpy(arrays["input_matrix.npy"].copy()).to(self.device) + self.output_gpu = torch.from_numpy(arrays["output_matrix.npy"].copy()).to(self.device) + self.kc_globals = arrays["kc_globals.npy"].copy() + self.apl_globals = arrays["apl_globals.npy"].copy() + self.state = torch.zeros(N_APL, dtype=torch.float32, device=self.device) + self.spikes = torch.zeros(N_NEURONS, dtype=torch.uint8, device=self.device) + self.decay = np.float32(math.exp(-float(dt_ms) / 20.0)) + self.gain = np.float32(0.01) + self.metadata = {"ok": True, "protocol": PROTOCOL, "n_neurons": N_NEURONS, + "n_apl": N_APL, "n_kc": N_KC, "dt_ms": float(dt_ms), + "files_sha256": {name: file_sha256(self.bundle_dir / name) for name in BUNDLE_FILES}, + "array_sha256": {name: array_sha256(value) for name, value in arrays.items()}, + "decay_f32_bits": f"{int(self.decay.view(np.uint32)):08x}", + "gain_f32_bits": f"{int(self.gain.view(np.uint32)):08x}", + "environment": environment_metadata(), + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "h_units": "legacy mV", "initial_state": "float32 zeros"} + + @torch.no_grad() + def advance(self, spikes_u8): + require(spikes_u8.device == self.device and spikes_u8.dtype == torch.uint8 and + tuple(spikes_u8.shape) == (N_NEURONS,) and spikes_u8.is_contiguous(), + "APL expects full-width contiguous CUDA uint8 spikes") + # Preserve separate eager operations, including Python evaluation order + # in the frozen state.mul_(...).add_(torch.mv(...)) expression. + self.state.mul_(float(self.decay)) + drive = torch.mv(self.input_gpu, spikes_u8.to(torch.float32)) + self.state.add_(drive) + h = torch.matmul(self.state, self.output_gpu) * self.gain + return drive, self.state, h + + def advance_global_fired(self, fired): + require(fired.dtype == np.int32 and fired.ndim == 1, "Global spikes must be int32 vector") + require(bool(np.all((fired >= 0) & (fired < N_NEURONS))) and + bool(np.all(np.diff(fired) > 0)), "Global spikes must be sorted unique and in range") + self.spikes.zero_() + if fired.size: + indices = torch.from_numpy(fired.copy()).to(device=self.device, dtype=torch.int64) + self.spikes[indices] = 1 + return self.advance(self.spikes) + + +def read_exact(stream, count, boundary_eof=False): + result = bytearray() + while len(result) < count: + block = stream.read(count - len(result)) + if not block: + if boundary_eof and not result: + return None + raise EOFError("Truncated APL request frame") + result.extend(block) + return bytes(result) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--bundle-dir", required=True) + parser.add_argument("--dt-ms", required=True, type=float) + args = parser.parse_args() + # stdout is exclusively the handshake line and binary response frames. + output, source = sys.stdout.buffer, sys.stdin.buffer + component = LegacyAPL(args.bundle_dir, args.dt_ms) + output.write((json.dumps(component.metadata, allow_nan=False) + "\n").encode("utf-8")) + output.flush() + expected_tick = 0 + while True: + header = read_exact(source, FRAME.size, boundary_eof=True) + if header is None: + return + tick, count = FRAME.unpack(header) + require(tick == expected_tick and count <= N_NEURONS, "Invalid APL request tick/count") + fired = np.frombuffer(read_exact(source, count * 4), dtype="{limit}; inputs and duration were not adjusted") + return total + + +class _PipeReader: + """One bounded reader thread, with timeout/EOF checks for every frame.""" + + def __init__(self, pipe, timeout): + self.pipe, self.timeout = pipe, float(timeout) + self.chunks = queue.Queue(maxsize=8) + self.buffer = bytearray() + self.thread = threading.Thread(target=self._pump, daemon=True) + self.thread.start() + + def _pump(self): + try: + while True: + chunk = os.read(self.pipe.fileno(), 65536) + self.chunks.put(chunk) + if not chunk: + return + except BaseException as error: + self.chunks.put(error) + + def _fill(self, deadline): + remaining = deadline - time.monotonic() + if remaining <= 0: + raise TimeoutError("APL sidecar response timed out") + try: + chunk = self.chunks.get(timeout=remaining) + except queue.Empty as error: + raise TimeoutError("APL sidecar response timed out") from error + if isinstance(chunk, BaseException): + raise RuntimeError("APL sidecar stdout reader failed") from chunk + if not chunk: + raise EOFError("APL sidecar ended before a complete response") + self.buffer.extend(chunk) + + def read(self, size): + deadline = time.monotonic() + self.timeout + while len(self.buffer) < size: + self._fill(deadline) + result = bytes(self.buffer[:size]) + del self.buffer[:size] + return result + + def line(self): + deadline = time.monotonic() + self.timeout + while b"\n" not in self.buffer: + _check(len(self.buffer) < 1 << 20, "APL sidecar handshake exceeds 1 MiB") + self._fill(deadline) + end = self.buffer.index(b"\n") + 1 + result = bytes(self.buffer[:end]) + del self.buffer[:end] + return result + + +class _APLWorker: + def __init__(self, python, script, bundle_dir, stderr, timeout=180): + self.command = [str(python), "-B", str(script), "--bundle-dir", str(bundle_dir), "--dt-ms", "0.1"] + self.process = subprocess.Popen(self.command, stdin=subprocess.PIPE, + stdout=subprocess.PIPE, stderr=stderr, bufsize=0, shell=False) + self.reader = _PipeReader(self.process.stdout, timeout) + self.tick = 0 + try: + self.hello = json.loads(self.reader.line().decode("utf-8")) + _check(self.hello.get("ok") is True, "APL sidecar did not acknowledge initialization") + _check(self.hello.get("protocol") == PROTOCOL, "Unexpected APL sidecar protocol") + for key, value in (("n_neurons", 166700), ("n_apl", 2), ("n_kc", 4064)): + _check(self.hello.get(key) == value, f"APL sidecar changed {key}") + _check(self.hello.get("dt_ms") == .1, "APL sidecar changed dt") + expected_decay = f"{int(np.float32(math.exp(-.1 / 20.)).view(np.uint32)):08x}" + expected_gain = f"{int(np.float32(.01).view(np.uint32)):08x}" + _check(self.hello.get("decay_f32_bits") == expected_decay and self.hello.get("gain_f32_bits") == expected_gain, + "APL sidecar decay/gain differs from the frozen float32 scalars") + _check(self.hello.get("files_sha256") == {name: _digest(bundle_dir / name) for name in BUNDLE_FILES}, + "APL sidecar bundle hashes differ from the independently read bundle") + except BaseException: + self.abort() + raise + + def step(self, tick, global_spikes): + _check(tick == self.tick, "APL sidecar request ticks must be consecutive") + nodes = np.asarray(global_spikes) + _check(nodes.ndim == 1 and nodes.dtype == np.dtype("int32"), "APL input spikes must be int32 globals") + _check(not len(nodes) or (nodes[0] >= 0 and nodes[-1] < 166700 and np.all(np.diff(nodes) > 0)), + "APL input spikes must be sorted, unique, and in the frozen global range") + packet = struct.pack("= 0), "APL sidecar returned invalid nonnegative f32 states") + self.tick += 1 + return values[:2], values[2:4], values[4:] + + def finish(self, steps): + _check(self.tick == steps, "APL sidecar did not process every tick") + self.process.stdin.close() + status = self.process.wait(timeout=30) + _check(status == 0, f"APL sidecar exited unsuccessfully: {status}") + self.reader.thread.join(timeout=5) + _check(not self.reader.thread.is_alive(), "APL stdout did not reach EOF") + extra = bytes(self.reader.buffer) + while not self.reader.chunks.empty(): + chunk = self.reader.chunks.get_nowait() + _check(not isinstance(chunk, BaseException), "APL stdout reader failed after the last frame") + extra += chunk + _check(not extra, "APL sidecar emitted unrequested trailing stdout bytes") + self.process.stdout.close() + return status + + def abort(self): + if self.process.poll() is None: + self.process.terminate() + try: + self.process.wait(timeout=5) + except subprocess.TimeoutExpired: + self.process.kill() + self.process.wait(timeout=5) + for handle in (self.process.stdin, self.process.stdout): + if handle is not None and not handle.closed: + handle.close() + + +def _graph_inputs(directory): + arrays = {} + definitions = {"nodes": "int32", "edge_id": "int64", "src": "int32", + "dst": "int32", "signed_raw": "int64", "ptr": "int64", "weights": "float64"} + for name, dtype in definitions.items(): + value = np.load(directory / (name + ".npy"), mmap_mode="r", allow_pickle=False) + _check(value.ndim == 1 and value.dtype == np.dtype(dtype), f"Invalid graph array {name}") + arrays[name] = value + nodes, edge_ids = arrays["nodes"], arrays["edge_id"] + n, edges = len(nodes), len(edge_ids) + _check(0 < n <= 166700 and edges > 0, "A case needs real global nodes and explicit nonempty connectivity") + _check(nodes[0] >= 0 and nodes[-1] < 166700 and np.all(np.diff(nodes) > 0), "Invalid global node order") + for name in ("src", "dst", "signed_raw", "weights"): + _check(arrays[name].shape == (edges,), f"Graph array {name} has the wrong shape") + pointer = arrays["ptr"] + _check(pointer.shape == (n + 1,) and pointer[0] == 0 and pointer[-1] == edges and + np.all(np.diff(pointer) >= 0), "Invalid graph CSR pointer") + source_counts = np.zeros(n, np.int64) + identity_edges = True + previous_source, previous_edge = -1, -1 + width = BLOCK_BYTES // 8 + for begin in range(0, edges, width): + stop = min(edges, begin + width) + source, target, ids = arrays["src"][begin:stop], arrays["dst"][begin:stop], edge_ids[begin:stop] + _check(np.all((source >= 0) & (source < n)) and np.all((target >= 0) & (target < n)), + "Graph edge endpoint is outside the local node array") + _check(np.all(ids >= 0), "Graph edge IDs must be nonnegative") + _check(source[0] > previous_source or (source[0] == previous_source and ids[0] > previous_edge), + "Graph edge blocks are not in canonical source/edge order") + _check(np.all((source[1:] > source[:-1]) | + ((source[1:] == source[:-1]) & (ids[1:] > ids[:-1]))), + "Graph edges are not in canonical source/edge order") + previous_source, previous_edge = int(source[-1]), int(ids[-1]) + identity_edges &= np.array_equal(ids, np.arange(begin, stop, dtype=np.int64)) + source_counts += np.bincount(source, minlength=n) + _check(np.isfinite(arrays["weights"][begin:stop]).all(), "Nonfinite graph weights") + _check(np.array_equal(source_counts, np.diff(pointer)), "CSR pointer and source rows disagree") + if not identity_edges: + _check(len(np.unique(edge_ids)) == edges, "Original edge IDs must be unique") + for name in ("completeness.csv", "connectivity.parquet"): + _check((directory / name).is_file(), f"Missing unchanged author input: {name}") + if n == 166700: + _check(edges == 25582938 and identity_edges and np.array_equal(nodes, np.arange(n, dtype=np.int32)), + "A full probe must use the unchanged complete canonical graph") + return arrays, identity_edges + + +def _requests(case_dir, nodes, steps): + with np.load(case_dir / "requests.npz", allow_pickle=False) as archive: + _check(set(archive.files) == {"tick", "neuron_idx"}, "Unexpected sparse request schema") + ticks, requested = archive["tick"].copy(), archive["neuron_idx"].copy() + _check(ticks.dtype == requested.dtype == np.dtype("int32") and ticks.ndim == requested.ndim == 1 and + ticks.shape == requested.shape, "Requests must be equal-length int32 vectors") + _check(np.all((ticks >= 0) & (ticks < steps)), "Request time outside the case") + _check(not len(ticks) or np.all((ticks[1:] > ticks[:-1]) | + ((ticks[1:] == ticks[:-1]) & (requested[1:] > requested[:-1]))), + "Requests must be sorted and unique by tick/global node") + local = np.searchsorted(nodes, requested) + _check(np.all(local < len(nodes)) and np.array_equal(nodes[local], requested), + "Request references a global neuron absent from this case") + counts = np.bincount(ticks, minlength=steps).astype(np.int32) + pointer = np.concatenate((np.zeros(1, np.int64), np.cumsum(counts, dtype=np.int64))) + return requested, local.astype(np.int32), pointer, counts + + +class _QueueProxy: + def __init__(self, original, get_tick, available, arrays, identity_edges, + due_counts, spike_ring, delay_steps, handle, records, summarize): + self._original_queue, self._get_tick = original, get_tick + self._available, self._arrays, self._identity_edges = available, arrays, identity_edges + self._due_counts, self._spike_ring, self._delay_steps = due_counts, spike_ring, delay_steps + self._handle, self._records, self._summarize = handle, records, summarize + + def peek(self, *args, **kwargs): + original = self._original_queue.peek(*args, **kwargs) + local_edges = np.asarray(original) + tick = self._get_tick() + _check(tick == len(self._records["rows"]), "Queue peek ticks must be consecutive") + _check(local_edges.ndim == 1 and np.all((local_edges >= 0) & (local_edges < len(self._arrays["edge_id"]))), + "Original queue contains an invalid local edge index") + _check(len(local_edges) == self._due_counts[tick % len(self._due_counts)], + "Queue arrivals differ from the fixed-delay outgoing degree of actual spikes") + fired_then = np.zeros(len(self._arrays["nodes"]), dtype=bool) + if tick >= self._delay_steps: + fired_then[self._spike_ring[(tick - self._delay_steps) % len(self._spike_ring)]] = True + + def accepted(begin, stop): + part = local_edges[begin:stop] + _check(np.all(fired_then[self._arrays["src"][part]]), "Arrival lacks a presynaptic spike at author delay") + return self._available[self._arrays["dst"][part]] + + # Only the observed ID view changes; the queue is returned verbatim. + edge_ids = local_edges if self._identity_edges else self._arrays["edge_id"][local_edges] + summary = self._summarize(tick, edge_ids, accepted) + self._handle.write(json.dumps(summary, sort_keys=True) + "\n") + self._records["rows"].append(summary) + self._records["peek_calls"] += 1 + return original + + def advance(self, *args, **kwargs): + self._records["advance_calls"] += 1 + return self._original_queue.advance(*args, **kwargs) + + def __getattr__(self, name): + return getattr(self._original_queue, name) + + +def run_case(case_dir: Path, out_dir: Path) -> dict: + """Run one continuous case, with exclusive outputs and no candidate trace input. + + Graph inputs are the AF array/author-table schema plus original weights.npy. + requests.npz contains sparse tick/neuron_idx int32 vectors. Optional + arrays.npz contains initial_v_mv/initial_g_mv float64 vectors only. + Full probes write state hashes, sparse events and APL traces, not dense + whole-brain histories. Each case requires a fresh process. + """ + global _PROCESS_USED + _check(not _PROCESS_USED, "Every oracle case requires a fresh process") + _PROCESS_USED = True + case_dir, out_dir = Path(case_dir).resolve(strict=True), Path(out_dir).resolve() + spec = json.loads((case_dir / "spec.json").read_text(encoding="utf-8")) + graph_dir = Path(spec["graph_dir"]).resolve(strict=True) + bundle_dir = Path(spec["apl_bundle_dir"]).resolve(strict=True) + worker_path = Path(spec["legacy_apl_worker"]).resolve(strict=True) + science_python = Path(spec["science_python"]).resolve(strict=True) + author_path = Path(spec.get("author_path", AUTHOR_PATH)).resolve(strict=True) + ae_path = Path(spec.get("ae_oracle_path", AE_PATH)).resolve(strict=True) + af_path = Path(spec.get("af_oracle_path", AF_PATH)).resolve(strict=True) + coefficients_path = Path(spec["coefficients_path"]).resolve(strict=True) + steps = int(spec["steps"]) + _check(steps == spec["steps"] and 0 < steps <= np.iinfo(np.int32).max, "Invalid number of steps") + _check(float(spec["dt_ms"]) == .1 and np.isclose(float(spec["duration_ms"]), steps * .1, rtol=0, atol=1e-12), + "The frozen APL protocol requires dt_ms=0.1 and a matching duration") + _check(spec.get("precision", "float64") == "float64", "Author state must be float64") + _check(spec.get("training", False) is False and spec.get("poi_used", False) is False, + "Training and original poi are outside this gate") + _check(spec["apl"] == {"tau_ms": 20, "gain": .01, "normalization": 1}, "Frozen APL parameters changed") + record_history = spec["record_history"] + _check(isinstance(record_history, bool), "record_history must be a boolean") + budget_root = Path(spec.get("budget_root", out_dir.parent)).resolve() + budget_limit = int(spec.get("output_budget_bytes", 15 * 1024 ** 3)) + _check(budget_limit > 0 and out_dir.is_relative_to(budget_root), "Invalid output budget/root") + out_dir.mkdir(parents=True, exist_ok=False) + mapped_files = [] + saved_preferences, old_dt, worker = None, None, None + last_complete_tick = -1 + try: + arrays, identity_edges = _graph_inputs(graph_dir) + nodes, n, edges = arrays["nodes"], len(arrays["nodes"]), len(arrays["edge_id"]) + _check(n < 166700 or not record_history, "Full probes must not save dense V/g/ref histories") + bundle = {name: np.load(bundle_dir / (name + ".npy"), mmap_mode="r", allow_pickle=False) + for name in ("input_matrix", "output_matrix", "kc_globals", "apl_globals")} + for name, shape in (("input_matrix", (2, 166700)), ("output_matrix", (2, 4064))): + value = bundle[name] + _check(value.dtype == np.dtype("float32") and value.shape == shape and value.flags.c_contiguous, + f"Frozen APL {name} shape/dtype/layout changed") + _check(np.isfinite(value).all() and np.all(value >= 0), f"Invalid positive raw-normalized APL {name}") + for name, count in (("kc_globals", 4064), ("apl_globals", 2)): + value = bundle[name] + _check(value.dtype == np.dtype("int32") and value.shape == (count,) and value[0] >= 0 and + value[-1] < 166700 and np.all(np.diff(value) > 0), f"Frozen APL identity {name} changed") + _check(not np.intersect1d(bundle["kc_globals"], bundle["apl_globals"]).size, "APL and KC identities overlap") + kc_positions = np.searchsorted(nodes, bundle["kc_globals"]) + present = kc_positions < n + present[present] &= nodes[kc_positions[present]] == bundle["kc_globals"][present] + kc_local, kc_in_bundle = kc_positions[present], np.flatnonzero(present) + requested_globals, request_local, request_ptr, request_count = _requests(case_dir, nodes, steps) + initial = None + if (case_dir / "arrays.npz").exists(): + with np.load(case_dir / "arrays.npz", allow_pickle=False) as archive: + _check(set(archive.files) == {"initial_v_mv", "initial_g_mv"}, "Unexpected initial-state schema") + initial = {name: archive[name].copy() for name in archive.files} + for name, value in initial.items(): + _check(value.dtype == np.dtype("float64") and value.shape == (n,) and np.isfinite(value).all(), + f"Invalid diagnostic initial state: {name}") + history_bytes = steps * n * 40 if record_history else 0 + _budget(budget_root, budget_limit, history_bytes + steps * (2 + 2 + 4064) * 4 + edges * 8 + (64 << 20)) + versions = {name: importlib.metadata.version(name) for name in + ("brian2", "numpy", "pandas", "pyarrow", "sympy", "scipy", "cython")} + _save(out_dir / "environment.json", {"python": sys.version, "platform": platform.platform(), + "packages": versions, "codegen": "numpy", "author_precision": "float64", + "APL_precision": "float32 in separate science-Python process", "Torch_imported_into_Brian": False}) + for name, required in (("brian2", "2.5.1"), ("numpy", "1.22.3"), ("sympy", "1.12")): + _check(versions[name] == required, f"{name} must remain pinned to {required}") + import brian2 as b2 + import pandas as pd + from brian2.core.base import BrianObject + + ae = _load_checked(ae_path, AE_SHA256, "phase4ag_sealed_ae_helpers") + af = _load_checked(af_path, AF_SHA256, "phase4ag_sealed_af_helpers") + author = _load_checked(author_path, AUTHOR_SHA256, "phase4ag_unchanged_author") + _check("torch" not in sys.modules, "Torch must not be imported into the pinned Brian process") + comp = pd.read_csv(graph_dir / "completeness.csv", index_col=0) + _check(np.array_equal(comp.index.to_numpy(), nodes) and + np.array_equal(comp["local_index"].to_numpy(), np.arange(n)), "Author CSV neuron identity differs") + del comp + graph_hashes = {name: _digest(graph_dir / name) for name in + tuple(key + ".npy" for key in arrays) + ("completeness.csv", "connectivity.parquet")} + bundle_hashes = {name: _digest(bundle_dir / name) for name in BUNDLE_FILES} + case_hashes = {name: _digest(case_dir / name) for name in ("spec.json", "requests.npz")} + if initial is not None: + case_hashes["arrays.npz"] = _digest(case_dir / "arrays.npz") + worker_hash = _digest(worker_path) + coefficient_hash = _digest(coefficients_path) + baseline_coefficients = json.loads(coefficients_path.read_text(encoding="utf-8")) + if "legacy_apl_worker_sha256" in spec: + _check(worker_hash == spec["legacy_apl_worker_sha256"], "APL worker differs from preregistered source") + _save(out_dir / "input-audit.json", {"spec": spec, "case_hashes": case_hashes, + "graph_hashes": graph_hashes, "bundle_hashes": bundle_hashes, + "author_sha256": AUTHOR_SHA256, "ae_sha256": AE_SHA256, "af_sha256": AF_SHA256, + "legacy_apl_worker_sha256": worker_hash, "coefficients_sha256": coefficient_hash, + "candidate_trace_used_as_input": False, + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence"}) + saved_preferences = {key: b2.prefs[key] for key in + ("codegen.target", "core.default_float_dtype", "legacy.refractory_timing")} + old_dt = b2.defaultclock.dt + b2.start_scope() + b2.prefs["codegen.target"] = "numpy" + b2.prefs["core.default_float_dtype"] = np.float64 + b2.prefs["legacy.refractory_timing"] = False + b2.defaultclock.dt = .1 * b2.ms + parameters = dict(author.default_params) + _save(out_dir / "parameter-checks.json", ae._parameters_match(parameters, spec["model"], b2)) + neu, syn, spikes = author.create_model(graph_dir / "completeness.csv", graph_dir / "connectivity.parquet", parameters) + _check(len(neu) == n and len(syn) == edges, "Author loaded an unexpected graph size") + _check(all(np.dtype(neu.variables[key].dtype) == np.float64 for key in ("v", "g")) and + np.dtype(syn.variables["w"].dtype) == np.float64, "Author states/weights are not float64") + effective = _new_memmap(out_dir / "weights.npy", "= 0) + last_ticks.fill(0) + last_ticks[seen] = np.rint(last_raw[seen] / .0001).astype(np.int64) + result = np.where(seen, np.maximum(ref_steps - (tick - last_ticks), 0), 0).astype(np.int32) + _check(np.array_equal(result == 0, available_raw), "Ref history disagrees with author not_refractory") + return result + + with ExitStack() as stack: + stderr = stack.enter_context((out_dir / "apl-worker.stderr.log").open("xb")) + worker = _APLWorker(science_python, worker_path, bundle_dir, stderr, float(spec.get("worker_timeout_seconds", 180))) + _save(out_dir / "apl-worker-handshake.json", {"command": worker.command, "handshake": worker.hello}) + queue_file = stack.enter_context((out_dir / "queue.jsonl").open("x", encoding="utf-8")) + event_file = stack.enter_context((out_dir / "events.bin").open("xb")) + hash_file = stack.enter_context((out_dir / "tick-state-hashes.jsonl").open("x", encoding="utf-8")) + tick_file = stack.enter_context((out_dir / "ticks.jsonl").open("x", encoding="utf-8")) + requests_file = stack.enter_context((out_dir / "requests.csv").open("x", encoding="utf-8", newline="")) + request_writer = csv.DictWriter(requests_file, fieldnames=["tick", "neuron_idx", "attempted", "accepted", "rejected"]) + request_writer.writeheader() + + def request_operation(): + nonlocal permitted + tick = tick_now() + _check(0 <= tick < steps and not request_seen[tick], "Invalid/duplicate common request tick") + request_seen[tick] = 1 + begin, stop = request_ptr[tick:tick + 2] + local = request_local[begin:stop] + available = available_raw[local].copy() + permitted = local[available] + applicable_count[tick] = len(permitted) + rejected_count[tick] = len(local) - len(permitted) + for node, accepted in zip(requested_globals[begin:stop], available): + request_writer.writerow({"tick": tick, "neuron_idx": int(node), "attempted": 1, + "accepted": int(accepted), "rejected": int(not accepted)}) + if len(permitted): + neu.v[permitted] = parameters["v_th"] + 1 * b2.mV + + def apl_operation(): + tick = tick_now() + _check(0 <= tick < steps and not apl_seen[tick] and request_seen[tick], "Invalid/duplicate APL tick") + active = np.asarray(neu.spikes, dtype=np.int32) + _check(not len(active) or np.all(np.diff(active) > 0), "Brian actual spikes are not canonical/unique") + _check(np.all(g_raw[active] == 0.0), "APL ran before the original author g reset") + if record_history: + pre_apl_histories["v"][tick] = v_raw + pre_apl_histories["g"][tick] = g_raw + pre_apl_histories["ref"][tick] = current_ref(tick) + drive, state, inhibition = worker.step(tick, np.asarray(nodes[active], dtype=np.int32)) + apl_history["drive"][tick] = drive + apl_history["state"][tick] = state + apl_history["inhibition"][tick] = inhibition + # These are separate array operations. Do not move the unit + # conversion into a fused subtract; do not guard by refractory. + delta_si = inhibition[kc_in_bundle].astype(np.float64) * np.float64(.001) + g_raw[kc_local] = g_raw[kc_local] - delta_si + apl_seen[tick] = 1 + + def record_end(): + nonlocal last_complete_tick, total_events, final_ref + tick = tick_now() + _check(tick == last_complete_tick + 1 and tick < steps and apl_seen[tick], "End tick is incomplete/out of order") + _check(len(records["rows"]) == tick + 1, "End state lacks queue observation") + _check(np.isfinite(v_raw).all() and np.isfinite(g_raw).all(), "Nonfinite author/APL state") + final_ref = current_ref(tick) + if record_history: + histories["v"][tick], histories["g"][tick], histories["ref"][tick] = v_raw, g_raw, final_ref + active = np.asarray(neu.spikes, dtype=np.int32) + _check(np.isin(permitted, active).all(), "An applicable request did not produce a real spike") + spike_count[tick] = len(active) + total_counts[active] += 1 + if len(active): + events = np.empty(len(active), EVENT_DTYPE) + events["step"], events["neuron_idx"] = tick, nodes[active] + event_file.write(memoryview(events).cast("B")) + total_events += len(active) + spike_ring[tick % queue_len] = active.copy() + due_counts[(tick + delay_steps) % queue_len] = int(outdegree[active].sum(dtype=np.int64)) + hash_file.write(json.dumps({"tick": tick, "v_sha256": _array_hash(v_raw, "=0 and requests[:,0].max()30*1024**3 + for d in ('protocol','apl-bundle','graphs','cases','gpu','oracle','component-audit','regression','calibration','audit','extension-build','logs'):(EXP/d).mkdir(parents=True,exist_ok=True) + can=ROOT/'datasets/male-cns-v1.0/processed/neuron-only';legacy=ROOT/'models/male-cns-lif';lp=legacy/'olfactory-grounding-v1/phase4l.yaml' + lock=ROOT/'experiments/phase4j-mb-physiology-repair/calibration/locked-parameters.json' + files=[AF_MODEL/'manifest.json',AF_EXP/'manifest.json',AF_EXP/'oracle/no_input/coefficients.json', + legacy/'deterministic-runtime-v2/phase4p_revalidate.py',lp,lock,can/'neuron-index/unified-metadata.parquet',can/'gpu/csr-weights.int64.npy', + legacy/'author-runtime-lab-v1/author_ext.cpp',legacy/'author-runtime-lab-v1/author_ext.cu',legacy/'author-runtime-lab-v1/gpu_backend.py', + legacy/'author-runtime-lab-v1/oracle_stress.py',AD_EXP/'external-resources/model.py',AF_MODEL/'oracle_full.py'] + lroot=ROOT/'experiments/phase4l-olfactory-grounding' + files += [lroot/'odor-encoder'/x for x in ('frozen-odor-panel.parquet','mapped-glomerular-vectors.npz','selection.json')] + files += [lroot/'receptor-mapping/olfactory-mapping.parquet'] + save(EXP/'protocol/preregistered.json',dict(timestamp_utc=datetime.now(timezone.utc).isoformat(),sources=sources(),config=CFG, + inputs=[dict(path=str(p.relative_to(ROOT)),sha256=digest(p)) for p in files],before_any_trajectory=True, + parent_commit=subprocess.check_output(['git','-C',str(ROOT),'rev-parse','HEAD'],text=True).strip())) + save(EXP/'regression/old-integrity-before.json',integrity()) + locked=json.loads(lock.read_text());assert locked['parameters_locked'] and locked['calibration_only'] + assert {k:locked['selected'][k] for k in ('gain','normalization','tau_ms')}==CFG['apl'] + meta=pd.read_parquet(can/'neuron-index/unified-metadata.parquet').sort_values('neuron_idx').reset_index(drop=True) + assert len(meta)==CFG['neurons'] and np.array_equal(meta.neuron_idx,np.arange(CFG['neurons'])) + cls=meta.annotation_class.fillna('').astype(str).to_numpy();typ=meta.annotation_type.fillna('').astype(str).to_numpy() + kc=np.flatnonzero(cls=='Kenyon_Cell').astype(np.int32);pn=np.flatnonzero(cls=='ALPN').astype(np.int32);apl=np.flatnonzero(typ=='APL').astype(np.int32) + assert len(kc)==4064 and len(apl)==2 + fg=AF_EXP/'graph';ptr=np.load(fg/'ptr.npy',mmap_mode='r');dst=np.load(fg/'dst.npy',mmap_mode='r');src=np.load(fg/'src.npy',mmap_mode='r') + signed=np.load(fg/'signed_raw.npy',mmap_mode='r');base=np.load(AF_EXP/'oracle/no_input/weights.npy',mmap_mode='r');raw=np.load(can/'gpu/csr-weights.int64.npy',mmap_mode='r') + assert len(dst)==CFG['edges'] and int(raw.sum())==CFG['raw_weight_sum'] + aplout=np.isin(src,apl);aplids=np.flatnonzero(aplout).astype(np.int64);assert len(aplids)==5845 + I=np.zeros((2,len(meta)),np.float32);O=np.zeros((2,len(kc)),np.float32) + alook={int(v):i for i,v in enumerate(apl)};kl=np.full(len(meta),-1,np.int32);kl[kc]=np.arange(len(kc),dtype=np.int32) + allowed=np.isin(src,np.r_[kc,pn])&np.isin(dst,apl) + for eid in np.flatnonzero(allowed):I[alook[int(dst[eid])],int(src[eid])]+=float(raw[eid]) + for eid in np.flatnonzero(aplout&np.isin(dst,kc)):O[alook[int(src[eid])],kl[dst[eid]]]+=float(raw[eid]) + iscale=float(I[I>0].sum()/np.count_nonzero(np.any(I>0,axis=0)));oscale=float(O[O>0].mean());bundle=EXP/'apl-bundle' + for name,value in dict(input_matrix=I/iscale,output_matrix=O/oscale,input_raw=I,output_raw=O,kc_globals=kc,pn_globals=pn,apl_globals=apl,apl_out_ids=aplids).items():arr(bundle/(name+'.npy'),value) + save(bundle/'scales.json',dict(input_scale=iscale,output_scale=oscale)) + save(bundle/'provenance.json',dict(canonical_root=str(can),source=str(legacy/'deterministic-runtime-v2/phase4p_revalidate.py'),source_sha256=digest(legacy/'deterministic-runtime-v2/phase4p_revalidate.py'), + parameters=CFG['apl'],lock_file=str(lock),lock_sha256=digest(lock),full_dimensions=[list(I.shape),list(O.shape)], + APL=len(apl),KC=len(kc),ALPN=len(pn),APL_fast_edges=len(aplids),APL_nonKC_edges=int(np.count_nonzero(aplout&~np.isin(dst,kc))), + APL_nonKC_contacts=int(raw[aplout&~np.isin(dst,kc)].sum()),normalization_recomputed_in_original_order=True, + raw_files_modified=False)) + full=(ptr,dst,src,signed,base);fullnodes=graph(EXP/'graphs/full',np.arange(len(meta),dtype=np.int32),full) + pscore=I.sum(axis=0);pnrank=pn[np.lexsort((pn,-pscore[pn]))];kscore=O.sum(axis=0);kcrank=kc[np.lexsort((kc,-kscore))] + candidates=np.flatnonzero(np.isin(src,pn)&np.isin(dst,kc));best=int(candidates[np.argmax(raw[candidates])]);pairpn=int(src[best]) + pe=np.arange(ptr[pairpn],ptr[pairpn+1],dtype=np.int64);pe=pe[np.isin(dst[pe],kc)];pe=pe[np.lexsort((dst[pe],-raw[pe]))] + collision_kc=np.unique(dst[pe[:3]]);assert len(collision_kc)==3 + nonkc=aplids[~np.isin(dst[aplids],kc)];nonkc_target=int(dst[nonkc[np.argmax(raw[nonkc])]]) + nodes=np.unique(np.r_[apl,pnrank[:16],kcrank[:16],pairpn,collision_kc,nonkc_target,[1197,1381,1511,82312,133666]]).astype(np.int32) + fixturegraph=EXP/'graphs/fixtures';graph(fixturegraph,nodes,full) + p=int(pnrank[0]);k=int(kcrank[0]);kc1,kc2,kc3=map(int,collision_kc) + designs=[('zero',100,[],None),('pn_pulse',300,[(0,p)],None), + ('kc_ref_boundary',100,[(t,k) for t in (0,1,21,22,23,44)],None), + ('two_sources',300,[(t,n) for t in (0,22,44,66,88) for n in (p,k)],None), + ('arrival_collision',120,[(0,pairpn),(0,kc1),(18,kc2),(21,kc1)],None), + ('natural_request',100,[(0,k)],dict(v={k:-44.0})), + ('apl_only',100,[(0,int(a)) for a in apl],None), + ('high_fanin',400,[(t,int(n)) for t in (0,22,44) for n in nodes if pscore[n]>0],dict(g={k:.123}))] + assert [v[0] for v in designs]==CFG['fixtures'] + for name,steps,requests,initial in designs:case(name,'fixture',steps,nodes,fixturegraph,requests,initial) + meta.iloc[nodes][['neuron_idx','body_id','annotation_class','annotation_type','neurotransmitter']].to_csv(EXP/'protocol/fixture-identities.csv',index=False) + save(EXP/'protocol/fixture-selection.json',dict(pn=p,kc=k,pair_PN=pairpn,arrival_KCs=collision_kc.tolist(),real_PN_KC_edge=best, + nonKC_APL_target=nonkc_target,method='Static raw-count ranks and exact metadata; retain ALL existing induced edges and full APL matrices/norms',runtime_selection=False)) + os.environ['CUDA_VISIBLE_DEVICES']=CFG['gpu_uuid'];sys.path.insert(0,str(legacy));sys.path.insert(0,str(legacy/'deterministic-runtime-v2')) + sys.path.insert(0,str(legacy/'terminal-scope-diagnostic-v1'));from diagnostic import queues + queues() + import torch + from phase4p_revalidate import door_inputs,make_drive + oc,panel,vectors,selection,mapping=door_inputs();cal=panel[panel.split=='calibration'].copy();assert len(panel)==12 and len(cal)==6 + assert oc['odor_encoder']['evaluation_seeds']==CFG['calibration']['seeds'] + assert float(oc['odor_encoder']['duration_ms'])==CFG['calibration']['duration_ms'] + cal.to_parquet(EXP/'protocol/calibration-panel.parquet',index=False);calrecords=[] + for mode in CFG['calibration']['modes']: + for oi,(_,odor) in enumerate(cal.iterrows()): + targets,rates=make_drive(oc,panel,vectors,selection,mapping,odor.odor_id,mode) + assert len(np.unique(targets))==len(targets),'Original encoder emits duplicate PN targets; audit instead of silently merging' + rates=np.asarray(rates,np.float32);steps=round(CFG['calibration']['duration_ms']/.1) + for seed in CFG['calibration']['seeds']: + gen=torch.Generator(device='cuda');gen.manual_seed(seed) + original_probability=rates*.1/1000.0 + explicit_probability=rates*np.float32(.1)/np.float32(1000.) + assert original_probability.dtype==np.float32 and np.array_equal(original_probability.view(np.uint32),explicit_probability.view(np.uint32)) + probs=torch.as_tensor(original_probability,dtype=torch.float32,device='cuda') + forced=(torch.rand((steps,len(targets)),generator=gen,device='cuda') 0, 'Missing/ambiguous self.base cutoff') + prefix = init.body[:cuts[0]] + # Executing no import or file-writing call is allowed in the extracted prefix. + require(not any(isinstance(node, (ast.Import, ast.ImportFrom, ast.With, ast.AsyncWith)) + for stmt in prefix for node in ast.walk(stmt)), 'Unexpected side-effect construct in initializer prefix') + expressions = [node for node in ast.walk(run) if isinstance(node, ast.Expr) and + isinstance(node.value, ast.Call) and isinstance(node.value.func, ast.Attribute) and + node.value.func.attr == 'add_' and isinstance(node.value.func.value, ast.Call) and + isinstance(node.value.func.value.func, ast.Attribute) and + isinstance(node.value.func.value.func.value, ast.Name) and + node.value.func.value.func.value.id == 'apl_state' and node.value.func.value.func.attr == 'mul_'] + inhibition = [node for node in ast.walk(run) if isinstance(node, ast.Assign) and + any(isinstance(target, ast.Name) and target.id == 'inhibition' for target in node.targets)] + decay = [node for node in ast.walk(run) if isinstance(node, ast.Assign) and + any(isinstance(target, ast.Name) and target.id == 'apl_decay' for target in node.targets)] + require(len(expressions) == len(inhibition) == len(decay) == 1, 'Ambiguous frozen APL operator extraction') + require(expressions[0].lineno < inhibition[0].lineno, 'Unexpected frozen APL update order') + rhs = inhibition[0].value + require(isinstance(rhs, ast.BinOp) and isinstance(rhs.op, ast.Mult), 'Unexpected inhibition gain expression') + gain_expression = ast.Expression(copy.deepcopy(rhs.right)) + provenance = {'source': str(SOURCE), 'sha256': SOURCE_SHA256, + 'initializer_line_start': prefix[0].lineno, 'initializer_line_end': prefix[-1].end_lineno, + 'excluded_self_base_line': init.body[cuts[0]].lineno, + 'decay_line': decay[0].lineno, 'state_update_line': expressions[0].lineno, + 'inhibition_line': inhibition[0].lineno, + 'initializer_source': '\n'.join(ast.get_source_segment(text, node) for node in prefix), + 'decay_source': ast.get_source_segment(text, decay[0]), + 'state_update_source': ast.get_source_segment(text, expressions[0]), + 'inhibition_source': ast.get_source_segment(text, inhibition[0]), + 'learning_initializer_suffix_executed': False, 'original_run_loop_executed': False} + return (_code(prefix, SOURCE), _code(decay, SOURCE), + _code([expressions[0], inhibition[0]], SOURCE), + compile(ast.fix_missing_locations(gain_expression), str(SOURCE), 'eval'), provenance) + + +def _canonical_files(): + return {name: CANONICAL/path for name, path in { + 'metadata': 'neuron-index/unified-metadata.parquet', + 'indptr': 'gpu/csr-indptr.int64.npy', 'indices': 'gpu/csr-indices.int32.npy', + 'raw': 'gpu/csr-weights.int64.npy'}.items()} + + +@lru_cache(maxsize=2) +def reconstruct(canonical_hashes, device_name): + """Cache only reconstructions tied to exact canonical bytes and source hash.""" + files = _canonical_files() + require(dict(canonical_hashes) == {name: digest(path) for name, path in files.items()}, + 'Canonical bytes changed before matrix reconstruction') + arrays = {name: np.load(files[name], mmap_mode='r', allow_pickle=False) + for name in ('indptr', 'indices', 'raw')} + require(arrays['indptr'].dtype == np.int64 and arrays['indices'].dtype == np.int32 and + arrays['raw'].dtype == np.int64, 'Canonical CSR dtypes changed') + require(arrays['indptr'].shape == (N + 1,) and arrays['indptr'][-1] == 25582938 and + arrays['indices'].shape == arrays['raw'].shape == (25582938,), 'Canonical graph shape changed') + require(int(arrays['raw'].sum()) == 124177617 and bool(np.all(arrays['raw'] > 0)), 'Canonical raw weights changed') + table = pq.read_table(files['metadata']) + require(table.num_rows == N, 'Canonical metadata count changed') + rt = ReadOnlyNamespace(n=N, dt=.1, device=torch.device(device_name), table=table, + indptr_np=arrays['indptr'], indices_np=arrays['indices'], raw_np=arrays['raw']) + view = types.SimpleNamespace() + init_code, decay_code, update_code, gain_code, provenance = extract_statements() + scope = {'self': view, 'rt': rt, 'np': np, 'torch': torch} + exec(init_code, scope) + require(not hasattr(view, 'base') and not hasattr(view, 'identity'), 'Initializer crossed the learning cutoff') + expected = {'input_matrix': view.input_gpu.detach().cpu().numpy().copy(), + 'output_matrix': view.output_gpu.detach().cpu().numpy().copy(), + 'input_raw': scope['input_matrix'], 'output_raw': scope['output_matrix'], + 'kc_globals': view.kc, 'apl_globals': view.apl, 'pn_globals': view.pn, + 'apl_out_ids': view.apl_out_ids} + for value in expected.values(): + value.flags.writeable = False + return view, expected, provenance, (decay_code, update_code, gain_code) + + +class TorchObservationProxy: + """Observe the result of the actual extracted torch.mv call unchanged.""" + def __init__(self): + self.drive = None + self.calls = 0 + + def __getattr__(self, name): + return getattr(torch, name) + + def mv(self, *args, **kwargs): + value = torch.mv(*args, **kwargs) + self.drive = value + self.calls += 1 + return value + + +def _up(value): + return np.nextafter(np.asarray(value, dtype=np.float64), np.inf) + + +def _add(a, b): + return _up(np.asarray(a, dtype=np.float64) + np.asarray(b, dtype=np.float64)) + + +def _mul(a, b): + return _up(np.asarray(a, dtype=np.float64) * np.asarray(b, dtype=np.float64)) + + +def _gamma(count, unit_roundoff): + require(count * unit_roundoff < 1, 'Gamma bound is undefined') + # Outward rounding on the division; the denominator is rounded downward. + numerator = _mul(count, unit_roundoff) + denominator = np.nextafter(1.0 - numerator, -np.inf) + return _up(numerator / denominator) + + +def _positive_dot_upper(state, matrix): + result = np.zeros(matrix.shape[1], np.float64) + for index in range(len(state)): + result = _add(result, _mul(state[index], matrix[index])) + return result + + +def _normal_or_zero(value, tiny): + value = np.asarray(value) + return bool(np.all((value == 0) | (value >= tiny))) + + +def _new_metric(): + return {'max_abs_error': 0.0, 'sum_abs_error': 0.0, 'elements': 0, + 'max_relative_error_nonzero_reference': 0.0, 'zero_reference_nonzero_actual': 0, + 'max_error_bound': 0.0, 'max_bound_utilization': 0.0, 'bound_violations': 0} + + +def _accumulate(metric, actual, reference, bound): + error = np.abs(np.asarray(actual, np.float64) - reference) + error_upper = np.where(error == 0, 0.0, _up(error)) + nonzero = reference != 0 + ratio = np.divide(error, np.abs(reference), out=np.zeros_like(error), where=nonzero) + usage = np.divide(error_upper, bound, out=np.zeros_like(error), where=bound > 0) + metric['max_abs_error'] = max(metric['max_abs_error'], float(error.max(initial=0))) + metric['sum_abs_error'] += float(error.sum(dtype=np.float64)) + metric['elements'] += error.size + metric['max_relative_error_nonzero_reference'] = max(metric['max_relative_error_nonzero_reference'], float(ratio.max(initial=0))) + metric['zero_reference_nonzero_actual'] += int(np.count_nonzero((~nonzero) & (error != 0))) + metric['max_error_bound'] = max(metric['max_error_bound'], float(np.max(bound, initial=0))) + metric['max_bound_utilization'] = max(metric['max_bound_utilization'], float(usage.max(initial=0))) + metric['bound_violations'] += int(np.count_nonzero(error_upper > bound)) + + +def validate(gpu_case_dir, bundle_dir, out_json): + """Validate one completed fixture; write one exclusive pure-JSON audit.""" + gpu_case_dir, bundle_dir, out_json = Path(gpu_case_dir), Path(bundle_dir), Path(out_json) + require(not out_json.exists(), 'Refusing to overwrite an existing component audit') + report = {'pass_all': False, 'scope': 'Frozen APL component extraction and recorded-spike replay only', + 'independent_cpu_closed_loop_oracle': False, 'brain_or_learning_parameters_changed': False, + 'candidate_function_called': False, 'gpu_case_dir': str(gpu_case_dir), 'bundle_dir': str(bundle_dir)} + try: + require(digest(SOURCE) == SOURCE_SHA256, 'Frozen source changed before component validation') + require(torch.cuda.is_available() and torch.cuda.device_count() == 1 and + '4090' in torch.cuda.get_device_name(), 'Require the one declared RTX4090') + environment = {'torch': str(torch.__version__), 'numpy': np.__version__, + 'gpu': torch.cuda.get_device_name(), 'cuda': torch.version.cuda, + 'cuda_matmul_allow_tf32': torch.backends.cuda.matmul.allow_tf32, + 'float32_matmul_precision': torch.get_float32_matmul_precision(), + 'deterministic_algorithms': torch.are_deterministic_algorithms_enabled(), + 'precision_flags_changed': False} + report['environment'] = environment + canonical_hashes = tuple(sorted((name, digest(path)) for name, path in _canonical_files().items())) + view, reconstructed, provenance, code = reconstruct(canonical_hashes, 'cuda:0') + bundle = {} + matrix_checks = {} + for name, (dtype, shape) in BUNDLE_ARRAYS.items(): + value = np.load(bundle_dir/(name+'.npy'), mmap_mode='r', allow_pickle=False) + require(value.dtype == dtype and value.ndim == len(shape or (0,)) and + (shape is None or value.shape == shape), 'Bundle schema mismatch: '+name) + bundle[name] = value + matrix_checks[name] = bit_equal(value, reconstructed[name]) + scales = json.loads((bundle_dir/'scales.json').read_text()) + for name in ('input_scale', 'output_scale'): + matrix_checks[name] = struct.pack(' 0, 'Empty APL trajectory cannot pass') + for name, width in (('drive', A), ('state', A), ('inhibition', K)): + require(traces[name].dtype == np.float32 and traces[name].shape == (steps, width) and + bool(np.isfinite(traces[name]).all()) and bool(np.all(traces[name] >= 0)), 'Invalid APL trace: '+name) + summary = json.loads((gpu_case_dir/'summary.json').read_text()) + require(summary.get('complete') is True and summary.get('pass_all') is True and + summary.get('steps') == steps and summary.get('completed_ticks') == steps, + 'GPU fixture did not complete its internal execution checks') + with np.load(gpu_case_dir/'state.npz', allow_pickle=False) as archive: + event_step, event_node = archive['step'].copy(), archive['neuron_idx'].copy() + require(event_step.dtype == event_node.dtype == np.int32 and event_step.ndim == 1 and + event_step.shape == event_node.shape, 'Invalid sparse actual spike event schema') + require(bool(np.all((event_step >= 0) & (event_step < steps))) and + bool(np.all((event_node >= 0) & (event_node < N))), 'Sparse event outside its clock/identity range') + require(np.array_equal(np.lexsort((event_node, event_step)), np.arange(len(event_step))), 'Actual events not sorted') + keys = event_step.astype(np.int64)*N + event_node + require(len(keys) < 2 or bool(np.all(np.diff(keys) > 0)), 'Duplicate actual spikes') + boundaries = np.searchsorted(event_step, np.arange(steps + 1)) + + proxy = TorchObservationProxy() + # Reconstructed original matrices are already on the original GPU/dtype. + replay_rt = types.SimpleNamespace(spike_u8=torch.zeros(N, dtype=torch.uint8, device=view.device)) + shadow = types.SimpleNamespace(input_gpu=view.input_gpu, output_gpu=view.output_gpu, rt=replay_rt, dt=.1) + scope = {'self': shadow, 'np': np, 'math': math, 'torch': proxy, + 'apl_state': torch.zeros(A, dtype=torch.float32, device=view.device)} + decay_code, update_code, gain_code = code + exec(decay_code, scope) + decay32 = np.float32(scope['apl_decay']) + gain32 = np.float32(eval(gain_code, {'np': np})) + require(0 < decay32 < 1 and gain32 > 0, 'Invalid extracted positive APL constants') + exact = {name: True for name in traces} + first_difference = None + I64 = np.asarray(bundle['input_matrix'], np.float64) + O64 = np.asarray(bundle['output_matrix'], np.float64) + require(bool(np.all(I64 >= 0)) and bool(np.all(O64 >= 0)), 'Positive-sum theory requires nonnegative matrices') + d, q = float(decay32), float(gain32) + u32, u64 = 2.0**-24, 2.0**-53 + gd32, gd64 = _gamma(N-1, u32), _gamma(N-1, u64) + g232, g264 = _gamma(2, u32), _gamma(2, u64) + gh32, gh64 = _gamma(2*A+1, u32), _gamma(2*A+1, u64) + state64 = np.zeros(A, np.float64) + e32 = np.zeros(A, np.float64) + e64 = np.zeros(A, np.float64) + previous32 = np.zeros(A, np.float64) + statistics = {name: _new_metric() for name in traces} + normal_arithmetic = _normal_or_zero(I64, np.finfo(np.float32).tiny) and _normal_or_zero(O64, np.finfo(np.float32).tiny) + max_active_terms = 0 + with torch.no_grad(): + for tick in range(steps): + fired = event_node[boundaries[tick]:boundaries[tick+1]] + replay_rt.spike_u8.zero_() + if len(fired): + replay_rt.spike_u8[torch.as_tensor(fired.astype(np.int64), device=view.device)] = 1 + before_calls = proxy.calls + exec(update_code, scope) + require(proxy.calls == before_calls + 1, 'Original update did not issue exactly one mv') + actual = {'drive': proxy.drive.detach().cpu().numpy(), + 'state': scope['apl_state'].detach().cpu().numpy(), + 'inhibition': scope['inhibition'].detach().cpu().numpy()} + for name in traces: + same = bit_equal(actual[name], traces[name][tick]) + exact[name] &= same + if not same and first_difference is None: + first_difference = {'tick': tick, 'field': name, + 'max_abs_error': float(np.max(np.abs(actual[name].astype(np.float64)-traces[name][tick])))} + # Float64 reference is measurement only and never feeds either brain. + selected = I64[:, fired] + drive64 = selected.sum(axis=1, dtype=np.float64) + max_active_terms = max(max_active_terms, int(np.count_nonzero(selected, axis=1).max(initial=0))) + Dupper = _up(drive64 / np.nextafter(1.0-gd64, -np.inf)) + drive_bound = _add(_mul(gd32, Dupper), _mul(gd64, Dupper)) + state32 = traces['state'][tick].astype(np.float64) + normal_arithmetic &= _normal_or_zero(d*previous32, np.finfo(np.float32).tiny) + normal_arithmetic &= _normal_or_zero(O64*state32[:, None], np.finfo(np.float32).tiny) + e32 = _add(_mul(d, e32), _add(_mul(g232, _add(_mul(d, previous32), Dupper)), + _mul(1.0+u32, _mul(gd32, Dupper)))) + e64 = _add(_mul(d, e64), _add(_mul(g264, _add(_mul(d, state64), Dupper)), + _mul(1.0+u64, _mul(gd64, Dupper)))) + state64 = d*state64 + drive64 + inhibition64 = np.matmul(state64, O64)*q + h32bound = _add(_mul(q, _positive_dot_upper(e32, O64)), + _mul(gh32, _mul(q, _positive_dot_upper(state32, O64)))) + h64bound = _add(_mul(q, _positive_dot_upper(e64, O64)), + _mul(gh64, _mul(q, _positive_dot_upper(state64, O64)))) + normal_arithmetic &= _normal_or_zero(q*np.matmul(state32, O64), np.finfo(np.float32).tiny) + _accumulate(statistics['drive'], traces['drive'][tick], drive64, drive_bound) + _accumulate(statistics['state'], traces['state'][tick], state64, _add(e32, e64)) + _accumulate(statistics['inhibition'], traces['inhibition'][tick], inhibition64, _add(h32bound, h64bound)) + previous32 = state32 + for metric in statistics.values(): + metric['mean_abs_error'] = metric.pop('sum_abs_error') / metric['elements'] + theory_applicable = bool(normal_arithmetic and not environment['cuda_matmul_allow_tf32'] and + environment['float32_matmul_precision'] == 'highest') + report.update(steps=steps, actual_spike_events=len(event_step), + ast_replay_bitwise_exact=exact, first_ast_difference=first_difference, + value_units={'drive': 'legacy normalized-contact spike drive', + 'state': 'legacy normalized-drive accumulator', 'inhibition': 'legacy mV'}, + decay_f32_bits=f'{int(decay32.view(np.uint32)):08x}', gain_f32_bits=f'{int(gain32.view(np.uint32)):08x}', + float64_measurement=statistics, + theoretical_bound={'applicable': theory_applicable, 'normal_or_zero_arithmetic': bool(normal_arithmetic), + 'gamma_definition': 'gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53', + 'drive_n': N-1, 'max_observed_nonzero_drive_terms': max_active_terms, + 'gamma_drive_f32': float(gd32), 'gamma_drive_f64': float(gd64), + 'state_recurrence': 'E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper', + 'output_bound': 'q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64', + 'assumptions': 'nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul', + 'bound_arithmetic': 'Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity', + 'tolerance_fitted_to_observed_error': False, + 'f64_values_used_to_drive_brain': False, + 'no_cpu_gpu_bitwise_expectation_for_f64_measurement': True}, + input_sha256={name: digest(gpu_case_dir/name) for name in + ('state.npz', 'summary.json', 'apl-drive.float32.npy', 'apl-state.float32.npy', 'apl-inhibition.float32.npy')}) + report['pass_all'] = bool(all(matrix_checks.values()) and all(exact.values()) and theory_applicable and + all(value['bound_violations'] == 0 for value in statistics.values())) + report['limitations'] = [ + 'AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.', + 'Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.', + 'Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.', + 'The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning.'] + save(out_json, report) + return report + except Exception as error: + report['error'] = {'type': type(error).__name__, 'message': str(error)} + if not out_json.exists(): + save(out_json, report) + raise + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('gpu_case_dir', type=Path) + parser.add_argument('bundle_dir', type=Path) + parser.add_argument('out_json', type=Path) + args = parser.parse_args() + result = validate(args.gpu_case_dir, args.bundle_dir, args.out_json) + print(json.dumps({'pass_all': result['pass_all'], 'steps': result['steps']}), flush=True) + if not result['pass_all']: + raise SystemExit(1) diff --git a/models/male-cns-lif/author-apl-lab-v1/verify_frozen.py b/models/male-cns-lif/author-apl-lab-v1/verify_frozen.py new file mode 100644 index 0000000..1a57885 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v1/verify_frozen.py @@ -0,0 +1,35 @@ +"""Run old reference unchanged, writing only new experiment output.""" +import os +import sys +import time +import numpy as np +import pandas as pd +import yaml +from context import ROOT,EXP,CFG,save,check + + +def main(): + check();assert not (EXP/'regression/frozen-five.json').exists() + rtpath=ROOT/'models/male-cns-lif/deterministic-runtime-v2';pc=yaml.safe_load((rtpath/'phase4p.yaml').read_text()) + os.environ['CUDA_VISIBLE_DEVICES']=CFG['gpu_uuid'];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' + sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1')) + from diagnostic import queues + queues();sys.path.insert(0,str(ROOT/'models/male-cns-lif'));sys.path.insert(0,str(rtpath)) + import torch,phase4p + assert torch.cuda.mem_get_info()[0]>2*1024**3 + torch.cuda.reset_peak_memory_stats();t0=time.perf_counter();rt=phase4p.CanonicalRuntime(pc,rtpath,EXP);rows=[] + for spec in pc['reference_trials']: + queues();base=ROOT/'experiments/phase4p-deterministic-runtime/phase4c-v2/reference'/spec['name'] + ev=pd.read_parquet(base/'spike-events.parquet') + ref=dict(step=ev.step.to_numpy(),neuron_idx=ev.neuron_idx.to_numpy(),counts=np.load(base/'per-neuron-spike-counts.int32.npy'),v=np.load(base/'final-voltage.float32.npy'),g=np.load(base/'final-g.float32.npy')) + result=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms']) + row=dict(trial=spec['name'],exact={k:bool(np.array_equal(v,result[k])) for k,v in ref.items()},wall_seconds=result['metrics']['wall_seconds']) + rows.append(row);print(row,flush=True) + save(EXP/'regression/frozen-five.json',dict(pass_all=all(all(r['exact'].values()) for r in rows),trials=rows, + wall_seconds=time.perf_counter()-t0,peak_allocated_vram=int(torch.cuda.max_memory_allocated()),oracle='old frozen deterministic-v2 only')) + assert all(all(r['exact'].values()) for r in rows) + + +if __name__=='__main__':main() diff --git a/models/male-cns-lif/author-apl-lab-v2/PROTOCOL.md b/models/male-cns-lif/author-apl-lab-v2/PROTOCOL.md new file mode 100644 index 0000000..28f4896 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/PROTOCOL.md @@ -0,0 +1,84 @@ +# Phase4AG: author LIF + frozen legacy graded APL + +Execution revision r2: the first registered v1 attempt stopped before the first +Brian tick because resolving the science-venv Python symlink launched system +Python without NumPy. All v1 files and partial outputs are preserved separately. +This revision retains the declared venv invocation path. It does not change any +equation, arithmetic, mask, stimulus, gate, parameter or choice of trial. +The complete original protocol is rerun from fresh state; no failed trial is +overwritten or omitted, and the first version cannot be claimed numerical PASS. + +Authorized independent hypothesis branch. All old source, raw connectome, signs, +W_syn, APL calibration, eligibility-v2 and 14,232-edge mask remain read-only. +No learning, gain/threshold changes, new edges, top-K, heldout activity, game or RL. + +Author code provenance is the already archived MIT-licensed +https://github.com/philshiu/Drosophila_brain_model at +91bdd1e7dcf193f3e7ca5a8933497fcef63b7960; original model.py SHA-256 +fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e. +Associated paper: https://www.nature.com/articles/s41586-024-07763-9. +This new MaleCNS/APL combination is not a strict reproduction of that model. + +The declared combination is author float64 LIF plus the exact old float32 graded +APL operator after synapses/resets at end. All 5,845 original APL fast out-edges +are zeroed only in the effective in-memory model, as in the legacy overlay. +Only KC receives graded replacement; known 1,212 non-KC edges remain unreplaced. +This existing scope limitation is not silently repaired in a compatibility test. + +Use the original full normalized I[2,166700]/O[2,4064] float32 bytes and original +normalizers, even for induced real subgraphs. APL reads actual binary spikes, +never requests, and keeps independent float32 state. Preserve unfused operations: +state.mul_(float(f32(exp(-.1/20)))).add_(torch.mv(I, spikes_f32)); +h32=torch.matmul(state,O)*f32(.01). Promote h32 to float64, multiply by float64 +.001, then separately subtract from SI g. Do not alter TF32/default CuBLAS flags; +record them. No added APL delay, drive dt/tau scaling, truncation or clipping. + +APL writes KC g unconditionally after author reset, including refractory KC. +Author g integration is frozen during refractory; therefore negative overlay g +may accumulate and differs from old dynamics. This is an explicit hypothesis, +not an assertion of original author or measured physiology. Guarded writes or +reset-before/after changes would be different hypotheses, not fixes to this one. + +Eight fixed metadata/connectivity-selected fixtures cover zero/reset, real PN/KC +drive, current actual event feedback, duplicate natural/request event, 18-step +arrival vs22-step refractory boundary, full fast APL mask, non-KC scope and mixed +precision/high fan-in. No choice is made from runtime responses. Each GPU case +has3 independent fresh runs. A Brian2 author oracle starts from its own state, +computes its own actual spikes, and uses an isolated standard-I/O legacy-APL +worker with independent state. No candidate inhibition trace is fed into Brian. +All tick states, events, requests, queue order/guard, APL drive/state/h must agree +exactly. The APL component is shared, so this is NOT a fully independent CPU +oracle. Independently reconstruct old matrices using frozen initializer AST, +replay actual events through original update AST, require exact component bytes; +also quantify an independent float64 positive-sum arithmetic error bound. It is +not a replacement for exact legacy equivalence. No arithmetic bound is adjusted. + +Only after all fixture gates pass, run two predeclared full-graph probes: first +old calibration odor, first original seed, both encoder modes. Compare each with +the independently running Brian + isolated APL worker. Save state hashes and sparse +events, not a time-by-all-neurons dense spike matrix. If a numerical gate fails, +stop CAL; keep diagnostic evidence and do not widen tolerance. + +Then run the complete original6 CAL odors ×5 original seeds ×2 original modes, +100ms per run. Retain the ORIGINAL12-odor median for matched-energy encoding, +original pn_drive target/RNG column order, original evaluation seeds51101..51105. +Save Poisson requests before any trajectory. Only calibration dynamic cases are +used; formal A/B and heldout remain unrun. Repeat first seed for every odor/mode. +No per-odor pulse, amplitude, timing or threshold search. No learning. + +Recompute the full original4L representation criteria on CAL only: mean KC active +fraction .03..09, min>0, max<.5, same-odor Jaccard>different, between Euclidean> +within, >=3 pairs whose mean between exceeds THAT PAIR's pooled within distances, +both modes. Add finiteness and exact repeat requirements. CAL PASS does not imply +heldout generalization, timing/credit specificity, memory or learning usability. +If CAL fails stop promotion and report it, not a least-bad candidate. + +Before/after verify SHA-256 of all original230239 files and AA--AF seals; rerun original five +deterministic reference trials to new output. Archive code/env/source SHA, +matrices/norms/masks/identities, requests and all outputs. No published candidate. + +Technical references (queried2026-09-30): Brian2 2.5.1 refractory documentation +https://brian2.readthedocs.io/en/2.5.1/user/refractoriness.html (actual generated code +remains authoritative); PyTorch numerical accuracy notes +https://docs.pytorch.org/docs/2.14/notes/numerical_accuracy.html (cross-backend +floating operations are not assumed bitwise equivalent). diff --git a/models/male-cns-lif/author-apl-lab-v2/calibration.py b/models/male-cns-lif/author-apl-lab-v2/calibration.py new file mode 100644 index 0000000..6d9ac2d --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/calibration.py @@ -0,0 +1,396 @@ +"""Complete original Phase 4L representation metrics on fixed CAL inputs only. + +No simulation, fitting, heldout trajectory access, or candidate generation is +performed here. The original 4L pair-specific within-odor baseline is retained; +the weaker Phase 4P r4 aggregate gate is deliberately not used. +""" +import csv +import hashlib +import itertools +import json +from pathlib import Path +import re + +import numpy as np + + +STATE_DTYPES = {"step": np.int32, "neuron_idx": np.int32, "counts": np.int32, + "v": np.float64, "g": np.float64, "ref": np.int32} +REQUEST_FIELDS = ("tick", "neuron_idx", "attempted", "accepted", "rejected") +APL_FILES = {"apl-drive.float32.npy": 2, "apl-state.float32.npy": 2, + "apl-inhibition.float32.npy": 4064} +ORIGINAL_CAL_IDS = { + "OMSUIQOIVADKIM-RXMQYKEDSA-N", "LABTWGUMFABVFG-ARJAWSKDSA-N", + "QNVRIHYSUZMSGM-ZCFIWIBFSA-N", "VLSVVMPLPMNWBH-UHFFFAOYSA-N", + "XHIUFYZDQBSEMF-UHFFFAOYSA-N", "OMSUIQOIVADKIM-YFKPBYRVSA-N"} +ORIGINAL_MODES = ("physiological", "matched_energy") +ORIGINAL_SEEDS = (51101, 51102, 51103, 51104, 51105) +HEX_SHA = re.compile(r"[0-9a-f]{64}\Z") +_DEFAULT_OUTPUT = object() + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def read_json(path): + with Path(path).open(encoding="utf-8") as stream: + return json.load(stream) + + +def file_hash(path): + digest = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(8 << 20), b""): + digest.update(block) + return digest.hexdigest() + + +def raw_hash(value): + return hashlib.sha256(np.ascontiguousarray(value).tobytes()).hexdigest() + + +def arrays_bitwise_equal(first, second): + """Unlike array_equal, also reject different dtypes and signed-zero bits.""" + return bool(first.dtype == second.dtype and first.shape == second.shape and + np.array_equal(np.ascontiguousarray(first).view(np.uint8), + np.ascontiguousarray(second).view(np.uint8))) + + +def pair_metrics(a, b): + """Exact definitions copied from phase4i.pair_metrics/safe_corr/cosine.""" + x, y = a.astype(np.float64), b.astype(np.float64) + aa, bb = a > 0, b > 0 + union = int(np.count_nonzero(aa | bb)) + intersection = int(np.count_nonzero(aa & bb)) + denominator = float(np.linalg.norm(x) * np.linalg.norm(y)) + cosine = 1.0 if denominator == 0 and np.array_equal(x, y) else ( + 0.0 if denominator == 0 else float(np.dot(x, y) / denominator)) + correlation = (1.0 if np.array_equal(x, y) else 0.0) if np.std(x) == 0 or np.std(y) == 0 else float(np.corrcoef(x, y)[0, 1]) + return {"active_intersection": intersection, "active_union": union, + "active_jaccard": 1.0 if union == 0 else float(intersection / union), + "cosine_similarity": cosine, "correlation": correlation, + "euclidean_distance": float(np.linalg.norm(x - y))} + + +def evaluate_gates(summary, gates): + """Each conjunct in original phase4l.py:239, kept visible for audit.""" + return { + "mean_sparsity_in_closed_interval": bool(float(gates["sparse_min_fraction"]) <= + summary["KC_active_fraction_mean"] <= float(gates["sparse_max_fraction"])), + "maximum_strictly_below_half": bool(summary["KC_active_fraction_max"] < 0.5), + "minimum_strictly_positive": bool(summary["KC_active_fraction_min"] > 0), + "same_jaccard_gt_different": bool(summary["same_odor_jaccard_mean"] > summary["different_odor_jaccard_mean"]), + "different_euclidean_gt_same": bool(summary["different_odor_euclidean_mean"] > summary["same_odor_euclidean_mean"]), + "at_least_original_pair_count": bool(summary["pairwise_separation_pass_count"] >= int(gates["minimum_separable_odor_pairs"]))} + + +def sample_cv(values): + """Original pandas Series.std uses ddof=1. Undefined diagnostic -> null.""" + values = np.asarray(values, dtype=np.float64) + mean = float(values.mean()) + return None if mean == 0 else float(values.std(ddof=1) / mean) + + +def summarize_mode(mode, odor_ids, seeds, runs, gates): + within, within_j, between, between_j, between_cos, between_corr = [], [], [], [], [], [] + pairs = [] + for odor in odor_ids: + arrays = [runs[(mode, odor, seed)]["kc_counts"] for seed in seeds] + for first, second in itertools.combinations(arrays, 2): + metric = pair_metrics(first, second) + within.append(metric["euclidean_distance"]) + within_j.append(metric["active_jaccard"]) + for first_odor, second_odor in itertools.combinations(odor_ids, 2): + distances = [] + for seed in seeds: + metric = pair_metrics(runs[(mode, first_odor, seed)]["kc_counts"], + runs[(mode, second_odor, seed)]["kc_counts"]) + distances.append(metric["euclidean_distance"]) + between.append(metric["euclidean_distance"]) + between_j.append(metric["active_jaccard"]) + between_cos.append(metric["cosine_similarity"]) + between_corr.append(metric["correlation"]) + pair_within = [] + for odor in (first_odor, second_odor): + arrays = [runs[(mode, odor, seed)]["kc_counts"] for seed in seeds] + pair_within.extend(pair_metrics(first, second)["euclidean_distance"] + for first, second in itertools.combinations(arrays, 2)) + cross_mean, within_mean = float(np.mean(distances)), float(np.mean(pair_within)) + pairs.append({"odor_id_a": first_odor, "odor_id_b": second_odor, + "between_same_seed_mean": cross_mean, "within_this_pair_mean": within_mean, + "between_distance_count": len(distances), "within_distance_count": len(pair_within), + "gate": bool(cross_mean > within_mean)}) + selected = [runs[(mode, odor, seed)] for odor in odor_ids for seed in seeds] + fractions = [int(np.count_nonzero(run["kc_counts"])) / len(run["kc_counts"]) for run in selected] + summary = {"KC_active_fraction_mean": float(np.mean(fractions)), + "KC_active_fraction_min": float(np.min(fractions)), "KC_active_fraction_max": float(np.max(fractions)), + "same_odor_jaccard_mean": float(np.mean(within_j)), + "different_odor_jaccard_mean": float(np.mean(between_j)), + "same_odor_euclidean_mean": float(np.mean(within)), + "different_odor_euclidean_mean": float(np.mean(between)), + "different_odor_cosine_mean": float(np.mean(between_cos)), + "different_odor_correlation_mean": float(np.mean(between_corr)), + "within_variance": float(np.var(within)), "between_distance_mean": float(np.mean(between)), + "pairwise_separation_pass_count": sum(row["gate"] for row in pairs), "pair_count": len(pairs), + "expected_event_count_cv": sample_cv([run["expected_external_events"] for run in selected]), + "actual_event_count_cv": sample_cv([run["external_events"] for run in selected]), + "within_distance_count": len(within), "between_distance_count": len(between), + "pairwise_baseline": "Each pair's two odors: 10+10 within-seed distances; never pooled across all odors", + "pairs": pairs} + summary["conditions"] = evaluate_gates(summary, gates) + summary["gate"] = bool(all(summary["conditions"].values())) + return summary + + +def validate_state(state, neurons, steps): + require(set(state) == set(STATE_DTYPES), "state.npz must have exactly the six declared arrays") + for name, dtype in STATE_DTYPES.items(): + value = state[name] + require(value.dtype == dtype and value.ndim == 1, "Invalid state dtype/rank: " + name) + require(bool(np.isfinite(value).all()), "Nonfinite state: " + name) + if name not in ("step", "neuron_idx"): + require(value.shape == (neurons,), "Invalid state length: " + name) + ticks, nodes = state["step"], state["neuron_idx"] + require(ticks.shape == nodes.shape and bool(np.all((ticks >= 0) & (ticks < steps))) and + bool(np.all((nodes >= 0) & (nodes < neurons))), "Invalid sparse event indices") + keys = ticks.astype(np.int64) * neurons + nodes + require(bool(np.all(np.diff(keys) > 0)), "Sparse events not sorted unique by tick/global neuron") + require(np.array_equal(np.bincount(nodes, minlength=neurons), state["counts"]), "Counts disagree with sparse events") + require(bool(np.all((state["ref"] >= 0) & (state["ref"] <= 22))), "Final refractory outside frozen range") + return keys + + +def read_jsonl(path, steps, kind): + with Path(path).open(encoding="utf-8") as source: + rows = [json.loads(line) for line in source if line.strip()] + require(len(rows) == steps and [row.get("tick") for row in rows] == list(range(steps)), + "Missing/duplicate/reordered ticks in " + kind) + for row in rows: + names = ("v_sha256", "g_sha256", "ref_sha256") if kind == "state hashes" else ( + "ordered_edge_sha256", "accepted_mask_sha256", "joint_sha256") + for name in names: + require(isinstance(row.get(name), str) and HEX_SHA.fullmatch(row[name]), "Invalid SHA256 in " + kind) + if kind == "queue": + require(type(row.get("count")) is int and type(row.get("accepted_count")) is int and + 0 <= row["accepted_count"] <= row["count"], "Invalid queue counts") + return rows + + +def read_trial(output, case, spec, kc_globals, neurons, edges): + summary = read_json(output / "summary.json") + steps = spec["steps"] + require(summary.get("complete") is True and summary.get("pass_all") is True and + summary.get("all_finite") is True and summary.get("steps") == steps and + summary.get("completed_ticks") == steps and summary.get("nodes") == neurons and + summary.get("edges") == edges and summary.get("precision") == "float64" and + summary.get("apl_precision") == "float32" and summary.get("case") == spec["name"] and + summary.get("record_history") is False and summary.get("learning") is False, + "Incomplete/failed/wrong-scope GPU summary") + with np.load(output / "state.npz", allow_pickle=False) as source: + state = {name: source[name].copy() for name in source.files} + event_keys = validate_state(state, neurons, steps) + hashes = read_jsonl(output / "tick-state-hashes.jsonl", steps, "state hashes") + for name in ("v", "g", "ref"): + require(hashes[-1][name + "_sha256"] == raw_hash(state[name]), "Final state does not match last tick hash: " + name) + queues = read_jsonl(output / "queue.jsonl", steps, "queue") + apl = {} + for name, width in APL_FILES.items(): + value = np.load(output / name, mmap_mode="r", allow_pickle=False) + require(value.dtype == np.float32 and value.shape == (steps, width) and + bool(np.isfinite(value).all()) and bool(np.all(value >= 0)), "Invalid full APL trace: " + name) + apl[name] = value + with (output / "requests.csv").open(encoding="utf-8", newline="") as source: + reader = csv.DictReader(source) + require(tuple(reader.fieldnames or ()) == REQUEST_FIELDS, "Request CSV schema mismatch") + request_rows = [{name: int(row[name]) for name in REQUEST_FIELDS} for row in reader] + with np.load(case / "requests.npz", allow_pickle=False) as source: + require(set(source.files) == {"tick", "neuron_idx"}, "Input request schema mismatch") + expected_ticks, expected_nodes = source["tick"], source["neuron_idx"] + require(expected_ticks.dtype == np.int32 and expected_nodes.dtype == np.int32 and + expected_ticks.ndim == 1 and expected_ticks.shape == expected_nodes.shape, + "Input request dtype/shape mismatch") + require(len(request_rows) == len(expected_ticks), "Requested event coverage mismatch") + require([row["tick"] for row in request_rows] == expected_ticks.tolist() and + [row["neuron_idx"] for row in request_rows] == expected_nodes.tolist(), "Requests differ from frozen input") + require(all(row["attempted"] == 1 and row["accepted"] in (0, 1) and + row["rejected"] == 1 - row["accepted"] for row in request_rows), "Invalid request acceptance bookkeeping") + requested_keys = expected_ticks.astype(np.int64) * neurons + expected_nodes + require(bool(np.all(np.diff(requested_keys) > 0)) and bool(np.all((expected_ticks >= 0) & (expected_ticks < steps))) and + bool(np.all((expected_nodes >= 0) & (expected_nodes < neurons))), "Input requests not sorted unique/in range") + actual_accepted = np.array([row["accepted"] for row in request_rows], dtype=np.uint8) + require(np.array_equal(np.isin(requested_keys, event_keys, assume_unique=True), actual_accepted.astype(bool)), + "Request accepted/rejected values disagree with actual spikes") + accepted = int(actual_accepted.sum()) + require(summary.get("requested") == len(request_rows) and summary.get("accepted_requests") == accepted and + summary.get("rejected_requests") == len(request_rows) - accepted and + summary.get("total_spikes") == len(state["step"]), "Summary counts disagree with raw output") + component = read_json(output / "apl-component.json") + require(component.get("ok") is True and component.get("n_neurons") == neurons and + component.get("n_kc") == len(kc_globals) and component.get("n_apl") == 2 and + component.get("dt_ms") == .1 and summary.get("apl_component") == component, + "APL component provenance mismatch") + bundle = Path(spec["apl_bundle_dir"]) + require(component.get("files_sha256") == {name: file_hash(bundle / name) for name in + ("input_matrix.npy", "output_matrix.npy", "kc_globals.npy", "apl_globals.npy")}, "APL bundle hashes changed") + inputs = summary.get("input_sha256", {}) + require(inputs.get("spec.json") == file_hash(case / "spec.json") and + inputs.get("requests.npz") == file_hash(case / "requests.npz"), "Case input provenance mismatch") + kc_counts = state["counts"][kc_globals].copy() + kc_event_mask = np.isin(state["neuron_idx"], kc_globals) + record = {"case": spec["name"], "mode": spec["mode"], "odor_id": spec["odor_id"], + "odor_name": spec["odor_name"], "seed": spec["seed"], "split": "calibration", + "KC_active": int(np.count_nonzero(kc_counts)), "KC_active_fraction": float(np.count_nonzero(kc_counts) / len(kc_globals)), + "KC_spikes": int(kc_counts.sum()), "KC_first_latency_ms": + float(state["step"][kc_event_mask][0] * .1) if bool(np.any(kc_event_mask)) else None, + "external_events": accepted, "expected_external_events": float(spec["expected_external_events"]), + "total_PN_rate_hz": float(spec["rate_sum_hz"]), "finite": True, + "total_spikes": len(state["step"]), "requested": len(request_rows)} + require(np.isfinite(record["expected_external_events"]) and record["expected_external_events"] > 0, + "CAL expected external events must be finite and positive") + return {"state": state, "hashes": hashes, "queues": queues, "apl": apl, + "requests": request_rows, "component": component, "kc_counts": kc_counts, + "expected_external_events": record["expected_external_events"], "external_events": accepted, + "record": record, "output": output} + + +def exact_repeat(first, repeat): + checks = {"state/" + name: arrays_bitwise_equal(first["state"][name], repeat["state"][name]) for name in STATE_DTYPES} + checks.update({name: arrays_bitwise_equal(first["apl"][name], repeat["apl"][name]) for name in APL_FILES}) + checks.update(tick_state_hashes=first["hashes"] == repeat["hashes"], + queues=first["queues"] == repeat["queues"], requests=first["requests"] == repeat["requests"], + apl_component=first["component"] == repeat["component"]) + return {"pass_all": bool(all(checks.values())), "checks": checks, + "scope": "All six state arrays, every tick all-neuron hashes, APL raw float32 traces, queues, requests and APL environment"} + + +def validate_inventory(records, cfg): + require(tuple(cfg["calibration"]["modes"]) == ORIGINAL_MODES and + tuple(cfg["calibration"]["seeds"]) == ORIGINAL_SEEDS and + cfg["calibration"]["duration_ms"] == 100.0 and cfg["calibration"]["odor_count"] == 6 and + cfg["calibration"]["split"] == "calibration" and + cfg["calibration"]["repeat_first_seed_each_odor"] is True, "Frozen CAL design changed") + gates = cfg["gates"] + require(gates["sparse_min_fraction"] == .03 and gates["sparse_max_fraction"] == .09 and + gates["minimum_separable_odor_pairs"] == 3 and gates["max_fraction_strict"] == .5 and + gates["min_fraction_strict"] == 0 and all(gates[key] is True for key in + ("same_jaccard_gt_between", "between_euclidean_gt_within", "both_modes_required", "finite_required", "repeat_exact_required")), + "Original representation gates or additional integrity gates changed") + expected = {(mode, index, seed) for mode in ORIGINAL_MODES for index in range(6) for seed in ORIGINAL_SEEDS} + require(len(records) == 60 and {(row["mode"], row["odor_index"], row["seed"]) for row in records} == expected, + "CAL inventory must cover exactly 2 modes x 6 odors x 5 seeds") + odors = [] + for index in range(6): + ids = {row["odor_id"] for row in records if row["odor_index"] == index} + require(len(ids) == 1, "Odor identity changes across seeds/modes") + odors.append(next(iter(ids))) + require(set(odors) == ORIGINAL_CAL_IDS, "CAL identities differ from the original frozen six") + for row in records: + require(row["name"] == f"cal-{row['mode']}-{row['odor_index']:02d}-s{row['seed']}", "Unexpected CAL case name") + return odors + + +def summarize(out_json=_DEFAULT_OUTPUT): + """Recompute; default writes exclusively, out_json=None is strictly read-only.""" + from context import EXP, ROOT, CFG, check + target = EXP / "calibration/summary.json" if out_json is _DEFAULT_OUTPUT else ( + None if out_json is None else Path(out_json)) + require(target is None or not target.exists(), "Refusing existing calibration summary") + failures, runs, run_records, repeats, provenance = [], {}, [], [], [] + modes, odors = {}, [] + prerequisites_ok = False + encoder = None + records = [] + try: + check() + records = read_json(EXP / "protocol/calibration-cases.json") + odors = validate_inventory(records, CFG) + encoder = read_json(EXP / "protocol/encoder-audit.json") + require(encoder["panel_count"] == 12 and encoder["cal_count"] == 6 and + encoder["seeds"] == list(ORIGINAL_SEEDS) and encoder["requested_before_any_dynamics"] is True and + encoder["formal_AB_run"] is False and encoder["heldout_run"] is False, "Invalid frozen encoder scope") + # Only static original-panel metadata is read, never heldout trajectories. + import pandas as pd + original = pd.read_parquet(ROOT / "experiments/phase4l-olfactory-grounding/odor-encoder/frozen-odor-panel.parquet") + require(len(original) == 12 and set(original[original.split == "calibration"].odor_id) == ORIGINAL_CAL_IDS, + "Original static panel identity mismatch") + require(float(np.median(original.mapped_response_sum)) == encoder["median_mapped_response_sum"], + "Matched-energy reference is not the original frozen 12-panel median") + kc_globals = np.load(EXP / "apl-bundle/kc_globals.npy", allow_pickle=False) + require(kc_globals.dtype == np.int32 and kc_globals.shape == (4064,) and + bool(np.all(np.diff(kc_globals) > 0)) and bool(np.all((kc_globals >= 0) & (kc_globals < CFG["neurons"]))), + "Invalid frozen KC identities") + prerequisites_ok = True + except Exception as error: + failures.append({"stage": "prerequisites", "type": type(error).__name__, "message": str(error)}) + if prerequisites_ok: + for row in records: + name = row["name"] + first = None + try: + case = EXP / "cases" / name + spec = read_json(case / "spec.json") + require(spec["name"] == name and spec["kind"] == "calibration" and spec["steps"] == 1000 and + spec["duration_ms"] == 100.0 and spec["dt_ms"] == .1 and spec["training"] is False and + all(spec[key] == row[key] for key in ("mode", "odor_id", "odor_index", "seed")), + "Case spec differs from fixed CAL inventory") + first = read_trial(EXP / "gpu" / (name + "-r0"), case, spec, kc_globals, CFG["neurons"], CFG["edges"]) + require(first["record"]["requested"] == row["requests"], "Frozen inventory request count mismatch") + runs[(row["mode"], row["odor_id"], row["seed"])] = {key: first[key] for key in + ("kc_counts", "expected_external_events", "external_events")} + run_records.append(first["record"]) + provenance.append({"case": name, "repeat": 0, "state_npz_sha256": file_hash(first["output"] / "state.npz"), + "summary_sha256": file_hash(first["output"] / "summary.json")}) + except Exception as error: + failures.append({"stage": "raw_trial", "case": name, "repeat": 0, + "type": type(error).__name__, "message": str(error)}) + if row["seed"] == ORIGINAL_SEEDS[0]: + try: + require(first is not None, "Baseline unavailable for exact repeat") + repeat = read_trial(EXP / "gpu" / (name + "-r1"), case, spec, kc_globals, CFG["neurons"], CFG["edges"]) + repeat_report = {"case": name, **exact_repeat(first, repeat)} + repeats.append(repeat_report) + provenance.append({"case": name, "repeat": 1, "state_npz_sha256": file_hash(repeat["output"] / "state.npz"), + "summary_sha256": file_hash(repeat["output"] / "summary.json")}) + require(repeat_report["pass_all"], "Exact repeat differs: " + ", ".join( + key for key, value in repeat_report["checks"].items() if not value)) + except Exception as error: + failures.append({"stage": "exact_repeat", "case": name, "repeat": 1, + "type": type(error).__name__, "message": str(error)}) + if len(runs) == 60: + for mode in ORIGINAL_MODES: + try: + modes[mode] = summarize_mode(mode, odors, ORIGINAL_SEEDS, runs, CFG["gates"]) + except Exception as error: + failures.append({"stage": "representation", "mode": mode, + "type": type(error).__name__, "message": str(error)}) + integrity_pass = bool(prerequisites_ok and len(runs) == 60 and not any(row["stage"] in + ("prerequisites", "raw_trial") for row in failures)) + repeat_pass = bool(len(repeats) == 12 and all(row["pass_all"] for row in repeats) and + not any(row["stage"] == "exact_repeat" for row in failures)) + representation_pass = bool(len(modes) == 2 and all(row["gate"] for row in modes.values())) + result = {"phase": "4AG", "scope": "Original fixed six CAL odors only; not heldout generalization or learning", + "pass_all": bool(integrity_pass and repeat_pass and representation_pass), + "numerical_integrity_pass": integrity_pass, "exact_repeat_pass": repeat_pass, + "original_representation_gate_pass": representation_pass, + "expected_base_trials": 60, "valid_base_trials": len(runs), "expected_repeats": 12, + "checked_repeats": len(repeats), "seeds": list(ORIGINAL_SEEDS), "duration_ms": 100.0, + "odor_ids": odors, "modes": modes, "runs": run_records, "repeats": repeats, + "failures": failures, "input_output_provenance": provenance, "encoder_audit": encoder, + "metric_source": "phase4l.representation (full original gate), phase4i.pair_metrics", + "finite_gate_note": "Added integrity prerequisite; original 4L recorded finite but omitted it from its gate", + "repeat_gate_note": "Additional exact repeat requirement; original metrics and numerical thresholds unchanged", + "shared_apl_component_oracle_limit": True, "independent_oracle_equivalence_claim": False, + "candidate_generated": False, "training": False, "heldout_dynamic_access": False} + if target is not None: + target.parent.mkdir(parents=True, exist_ok=True) + with target.open("x", encoding="utf-8") as output: + json.dump(result, output, ensure_ascii=False, indent=2, allow_nan=False) + output.write("\n") + return result + + +if __name__ == "__main__": + print(json.dumps({key: value for key, value in summarize().items() if key in + ("pass_all", "numerical_integrity_pass", "exact_repeat_pass", "original_representation_gate_pass")}), flush=True) diff --git a/models/male-cns-lif/author-apl-lab-v2/checksums.sha256 b/models/male-cns-lif/author-apl-lab-v2/checksums.sha256 new file mode 100644 index 0000000..b4beb82 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/checksums.sha256 @@ -0,0 +1,18 @@ +49f53924f758317bfcbd2b53cab9b8b785121eb99f3234a8343c9b483c5701d8 PROTOCOL.md +edb9cb2d890471fd07afd88c959ef3540b0cd40af490fac3f26396b5e7f5bf9c calibration.py +6e1616b086f667fa4e4b5087f500f8080281977ca9922e7b055acfddd2003bd5 compare.py +a8bafb9deec49d918976f2836d977ef9f13c26a6fb7f70e015879e29cf986c9a config.json +274eb13ccf38dbbc5c7f14d9d642130eb0f69072d91a090f208c30c27f87dd2b context.py +b6e3c67e668a971b544df32d76435ab5e537061b9fc2e8d9dedb5d2a726f5782 finalize.py +69f5d893f47dafff4ef7c3186c3df0b9014666b32c8f4e943b57016bec92b18a gpu_apl.py +21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291 legacy_apl_worker.py +357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea oracle_apl.py +e9328567753e144fc315491970f7e02af13725b93f35e4d4b97f7c4c48ce3338 prepare.py +afbe6d5a37c29bda66d38c470093584130d1852e67881c681662aec3c76b33c7 run_case.py +84c681030b0032119b5392d0c3f3c136a9c6b0d133eb548c40791a10bbbb8b63 run_suite.py +bab3136eecbc5bb09a985d715844ee845cb72b07930ce376bfb39be33bd1845a test_calibration.py +1a233f47943e01bbd845ac1dacdb1eec3f88c270c65a5f75bce1cf679b5de097 test_protocol.py +0ed665f686e3499f8543e48f1712ccb4523946971cb7a0e096cf4f6d10d3bcba test_python_entry.py +5cfc9a4505da6b8fb4e7d2617cdaedaa8475ed46a11ec9bdf0d4d25b1d22c2d3 validate_components.py +0d0e72b2f1348f6648124c8c34f75094f76fc5e1a281fbcca130a5fad2f35293 verify_frozen.py +208c00961b9d895d14ac050bc23fbc18db49d0df1fcf5c20d4c3eaa8e15ff7ff manifest.json diff --git a/models/male-cns-lif/author-apl-lab-v2/compare.py b/models/male-cns-lif/author-apl-lab-v2/compare.py new file mode 100644 index 0000000..464426b --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/compare.py @@ -0,0 +1,508 @@ +"""Fail-closed byte-exact audit of the shared-APL closed-loop fixtures. + +Comparison is independent of either backend's stored PASS. No tolerance is +used, timing fields are never compared, and reports contain no wall timestamp. +""" +from __future__ import annotations + +import csv +import hashlib +import json +import math +from pathlib import Path +import re +import struct + +import numpy as np + + +N_GLOBAL = 166700 +STATE_KEYS = ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref') +REQUEST_FIELDS = ('tick', 'neuron_idx', 'attempted', 'accepted', 'rejected') +HASH_FIELDS = {'tick', 'v_sha256', 'g_sha256', 'ref_sha256'} +QUEUE_FIELDS = {'tick', 'count', 'accepted_count', 'ordered_edge_sha256', + 'accepted_mask_sha256', 'joint_sha256'} +BUNDLE_FILES = ('input_matrix.npy', 'output_matrix.npy', 'kc_globals.npy', 'apl_globals.npy') +COEFFICIENTS = ('gd', 'c0', 'cg', 'cv', 'threshold', 'reset', 'seed_volt') + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def file_hash(path): + h = hashlib.sha256() + with Path(path).open('rb') as source: + for block in iter(lambda: source.read(8 << 20), b''): + h.update(block) + return h.hexdigest() + + +def raw_hash(array, dtype=None): + return hashlib.sha256(np.ascontiguousarray(array, dtype=dtype).tobytes()).hexdigest() + + +def save(path, report): + if path is None: + return + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open('x', encoding='utf-8') as target: + json.dump(report, target, indent=2, sort_keys=True, allow_nan=False) + target.write('\n') + + +def _read_json(path, used): + used.add(Path(path)) + return json.loads(Path(path).read_text(encoding='utf-8')) + + +def _read_jsonl(path, used): + used.add(Path(path)) + with Path(path).open(encoding='utf-8') as source: + return [json.loads(line) for line in source if line.strip()] + + +def _read_array(path, used): + used.add(Path(path)) + return np.load(path, mmap_mode='r', allow_pickle=False) + + +def _check(checks, name, condition): + checks[name] = bool(condition) + + +def _bit_mask(a, b): + if a.dtype.kind == 'f': + unsigned = np.dtype('uint'+str(a.dtype.itemsize*8)) + return np.ascontiguousarray(a).view(unsigned) != np.ascontiguousarray(b).view(unsigned) + return a != b + + +def _compare_array(name, a, b, checks, differences, dtype=None, shape=None): + a, b = np.asarray(a), np.asarray(b) + schema = a.shape == b.shape and a.dtype == b.dtype and \ + (dtype is None or a.dtype == np.dtype(dtype)) and (shape is None or a.shape == shape) + _check(checks, name+'.schema', schema) + if not schema: + differences[name] = {'left_shape': list(a.shape), 'right_shape': list(b.shape), + 'left_dtype': str(a.dtype), 'right_dtype': str(b.dtype), 'expected_shape': None if shape is None else list(shape), + 'expected_dtype': None if dtype is None else str(np.dtype(dtype))} + _check(checks, name+'.bitwise_exact', False) + return False + finite = bool(np.isfinite(a).all() and np.isfinite(b).all()) + _check(checks, name+'.finite', finite) + mask = _bit_mask(a, b) + exact = not bool(np.any(mask)) + _check(checks, name+'.bitwise_exact', exact) + if not exact or not finite: + flat = np.flatnonzero(mask.ravel()) + detail = {'mismatching_elements': int(len(flat)), + 'first_positions': [list(map(int, np.unravel_index(int(index), a.shape))) for index in flat[:5]], + 'left_nonfinite': int(np.count_nonzero(~np.isfinite(a))), + 'right_nonfinite': int(np.count_nonzero(~np.isfinite(b)))} + if finite and a.size: + error = np.abs(a.astype(np.float64)-b.astype(np.float64)) + detail.update(max_abs_error=float(error.max()), mean_abs_error=float(error.mean())) + if a.dtype.kind == 'f' and len(flat): + unsigned = np.dtype('uint'+str(a.dtype.itemsize*8)) + av = np.ascontiguousarray(a).view(unsigned).ravel() + bv = np.ascontiguousarray(b).view(unsigned).ravel() + detail['first_bit_patterns'] = [{'left': hex(int(av[index])), 'right': hex(int(bv[index]))} for index in flat[:5]] + differences[name] = detail + return exact and finite + + +def _sha(value): + return isinstance(value, str) and re.fullmatch('[0-9a-f]{64}', value) is not None + + +def _read_requests(path, steps, used): + used.add(Path(path)) + with Path(path).open(encoding='utf-8', newline='') as source: + reader = csv.DictReader(source) + require(tuple(reader.fieldnames or ()) == REQUEST_FIELDS, 'Request CSV schema mismatch: '+str(path)) + rows = [{key: int(row[key]) for key in REQUEST_FIELDS} for row in reader] + previous = (-1, -1) + for row in rows: + key = row['tick'], row['neuron_idx'] + require(previous < key and 0 <= key[0] < steps and 0 <= key[1] < N_GLOBAL, + 'Request rows must be sorted, unique, and in range: '+str(path)) + require(row['attempted'] == 1 and row['accepted'] in (0, 1) and row['rejected'] == 1-row['accepted'], + 'Request accounting is incomplete: '+str(path)) + previous = key + return rows + + +def _load_run(directory, role, n, edges, steps, record_history, name, used, checks): + directory = Path(directory) + summary = _read_json(directory/'summary.json', used) + _check(checks, role+'.summary_complete', summary.get('complete') is True and summary.get('pass_all') is True and + summary.get('steps') == steps and summary.get('completed_ticks') == steps and + summary.get('nodes') == n and summary.get('edges') == edges and summary.get('precision') == 'float64') + is_cpu = role == 'cpu' + _check(checks, role+'.case_name', summary.get('name' if is_cpu else 'case') == name) + _check(checks, role+'.history_mode', summary.get('history_recorded' if is_cpu else 'record_history') is record_history) + _check(checks, role+'.apl_precision', summary.get('APL_precision' if is_cpu else 'apl_precision') == 'float32') + _check(checks, role+'.no_error_marker', not (directory/'error.json').exists()) + used.add(directory/'state.npz') + with np.load(directory/'state.npz', allow_pickle=False) as source: + require(set(source.files) == set(STATE_KEYS), 'State NPZ keys changed: '+str(directory)) + state = {key: source[key].copy() for key in STATE_KEYS} + for key, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32'), ('counts', 'int32')): + require(state[key].shape == (n,) and state[key].dtype == np.dtype(dtype), 'Invalid final '+key+': '+str(directory)) + require(state['step'].dtype == state['neuron_idx'].dtype == np.int32 and state['step'].ndim == 1 and + state['step'].shape == state['neuron_idx'].shape, 'Invalid sparse event schema: '+str(directory)) + require(bool(np.all((state['step'] >= 0) & (state['step'] < steps))) and + bool(np.all((state['neuron_idx'] >= 0) & (state['neuron_idx'] < N_GLOBAL))), 'Sparse event range error') + event_keys = state['step'].astype(np.int64)*N_GLOBAL+state['neuron_idx'] + _check(checks, role+'.events_sorted_unique', len(event_keys) < 2 or np.all(np.diff(event_keys) > 0)) + _check(checks, role+'.count_nonnegative_total', np.all(state['counts'] >= 0) and int(state['counts'].sum()) == len(event_keys)) + _check(checks, role+'.ref_range', np.all((state['ref'] >= 0) & (state['ref'] <= 22))) + _check(checks, role+'.reported_events', summary.get('spikes' if is_cpu else 'total_spikes') == len(event_keys)) + hashes = _read_jsonl(directory/'tick-state-hashes.jsonl', used) + require(len(hashes) == steps and [row.get('tick') for row in hashes] == list(range(steps)), 'Incomplete tick hash coverage') + require(all(set(row) == HASH_FIELDS and all(_sha(row[key]) for key in HASH_FIELDS-{'tick'}) for row in hashes), + 'Invalid tick state hash schema') + queues = _read_jsonl(directory/'queue.jsonl', used) + require(len(queues) == steps and [row.get('tick') for row in queues] == list(range(steps)), 'Incomplete queue coverage') + require(all(set(row) == QUEUE_FIELDS and type(row['count']) is int and type(row['accepted_count']) is int and + 0 <= row['accepted_count'] <= row['count'] <= edges and + all(_sha(row[key]) for key in QUEUE_FIELDS-{'tick', 'count', 'accepted_count'}) for row in queues), + 'Invalid queue row schema/count/hash') + for key in ('v', 'g', 'ref'): + _check(checks, role+'.last_hash_'+key, hashes[-1][key+'_sha256'] == raw_hash(state[key], '= 0)) + histories, pre = {}, {} + for field, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32')): + files = (directory/(field+'-history.'+dtype+'.npy'), directory/('preAPL-'+field+'.'+dtype+'.npy')) + if record_history: + histories[field], pre[field] = (_read_array(path, used) for path in files) + require(all(array.dtype == np.dtype(dtype) and array.shape == (steps, n) for array in (histories[field], pre[field])), + 'Small pre/post history schema mismatch') + _check(checks, role+'.history_hashes_'+field, all(row[field+'_sha256'] == raw_hash(histories[field][tick]) + for tick, row in enumerate(hashes))) + _check(checks, role+'.history_final_'+field, not np.any(_bit_mask(histories[field][-1], state[field]))) + else: + _check(checks, role+'.no_dense_'+field, not any(path.exists() for path in files)) + if is_cpu: + component = _read_json(directory/'apl-worker-handshake.json', used)['handshake'] + else: + component = _read_json(directory/'apl-component.json', used) + _check(checks, role+'.component_summary_consistent', summary.get('apl_component') == component) + return dict(directory=directory, summary=summary, state=state, hashes=hashes, queues=queues, + requests=requests, apl=apl, histories=histories, pre=pre, component=component) + + +def _pair_outputs(left, right, checks, differences, n, steps, record_history): + for field in STATE_KEYS: + shape = None if field in ('step', 'neuron_idx') else (n,) + dtype = 'float64' if field in ('v', 'g') else 'int32' + _compare_array('state.'+field, left['state'][field], right['state'][field], checks, differences, dtype, shape) + for field, width in (('drive', 2), ('state', 2), ('inhibition', 4064)): + _compare_array('apl.'+field, left['apl'][field], right['apl'][field], checks, differences, 'float32', (steps, width)) + for field in ('hashes', 'queues', 'requests', 'component'): + same = left[field] == right[field] + _check(checks, field+'.parsed_exact', same) + if not same: + if isinstance(left[field], list): + first = next((i for i, (a, b) in enumerate(zip(left[field], right[field])) if a != b), None) + differences[field] = {'left_count': len(left[field]), 'right_count': len(right[field]), + 'first_differing_row': first, + 'left_row': None if first is None else left[field][first], + 'right_row': None if first is None else right[field][first]} + else: + differences[field] = {'left': left[field], 'right': right[field]} + if record_history: + for stage in ('histories', 'pre'): + for field, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32')): + _compare_array(stage+'.'+field, left[stage][field], right[stage][field], checks, differences, dtype, (steps, n)) + + +def _queue_digest(tick, ids, accepted): + ids = np.ascontiguousarray(ids, dtype='= 22 + for index, ok in zip(range(lo, hi), accepted): + row = run['requests'][index] + if row['accepted'] != int(ok) or row['rejected'] != int(not ok): + request_ok = False + first.setdefault('request_ref_mismatch', {'tick': tick, 'neuron_idx': row['neuron_idx']}) + if not np.isin(targets[accepted], fired).all() or np.isin(targets[~accepted], fired).any(): + delivered_ok = False + first.setdefault('request_delivery_mismatch', tick) + last[fired] = tick + ref = np.maximum(22-(tick-last), 0).astype(np.int32) + if raw_hash(ref, '= 18 else np.empty(0, np.int64) + chunks = [np.arange(ptr[source], ptr[source+1], dtype=np.int64) for source in due] + indices = np.concatenate(chunks) if chunks else np.empty(0, np.int64) + expected_queue = _queue_digest(tick, graph['edge_id'][indices], (ref[graph['dst'][indices]] == 0).astype(np.uint8)) + if expected_queue != run['queues'][tick]: + queue_ok = False + first.setdefault('queue_reconstruction_mismatch', tick) + _check(checks, role+'.request_refractory_gate', request_ok) + _check(checks, role+'.accepted_spikes_rejected_absent', delivered_ok) + _check(checks, role+'.all_ref_hashes_from_actual_events', ref_ok) + if run['pre']: + _check(checks, role+'.small_queue_independent_reconstruction', queue_ok) + _check(checks, role+'.spike_g_reset_before_apl', reset_ok) + if first: + differences[role+'.independent_accounting'] = first + + +def _check_apl_application(run, role, nodes, kc, checks, differences): + if not run['pre']: + return + common, local_kc, bundle_kc = np.intersect1d(nodes, kc, assume_unique=True, return_indices=True) + _compare_array(role+'.apl_keeps_voltage', run['pre']['v'], run['histories']['v'], checks, differences, 'float64') + _compare_array(role+'.apl_keeps_ref', run['pre']['ref'], run['histories']['ref'], checks, differences, 'int32') + expected = np.array(run['pre']['g'], copy=True) + delta = run['apl']['inhibition'][:, bundle_kc].astype(np.float64)*np.float64(.001) + expected[:, local_kc] = expected[:, local_kc]-delta + _compare_array(role+'.apl_unconditional_g_write', expected, run['histories']['g'], checks, differences, 'float64') + + +def _finish(report, checks, differences, used, out_json): + report['checks'] = {name: bool(value) for name, value in checks.items()} + report['differences'] = differences + report['pass_all'] = bool(checks) and all(checks.values()) and 'error' not in report + report['failed_checks'] = sorted(name for name, value in checks.items() if not value) + report['evidence_sha256'] = {str(path): file_hash(path) for path in sorted(used) if path.is_file()} + save(out_json, report) + return report + + +def compare(case_dir, gpu_dir, cpu_dir, out_json=None): + """Strict independent closed-loop comparison; out_json=None is read-only.""" + case_dir, gpu_dir, cpu_dir = Path(case_dir), Path(gpu_dir), Path(cpu_dir) + if out_json is not None: + require(not Path(out_json).exists(), 'Refusing existing comparison report') + checks, differences, used = {}, {}, set() + report = {'kind': 'shared-APL-component closed-loop byte-exact comparison', 'pass_all': False, + 'case_dir': str(case_dir), 'gpu_dir': str(gpu_dir), 'cpu_dir': str(cpu_dir), + 'tolerance': None, 'signed_zero_included': True, 'timing_fields_compared': False, + 'independent_CPU_all_components_claim': False} + try: + spec = _read_json(case_dir/'spec.json', used) + steps, history = spec['steps'], spec['record_history'] + require(type(steps) is int and steps > 0 and type(history) is bool and spec['dt_ms'] == .1, + 'Invalid fixed case clock/history schema') + graph_dir, bundle_dir = Path(spec['graph_dir']), Path(spec['apl_bundle_dir']) + graph = {field: _read_array(graph_dir/(field+'.npy'), used) for field in + ('nodes', 'edge_id', 'src', 'dst', 'ptr', 'signed_raw', 'weights')} + nodes = graph['nodes'] + n, edges = len(nodes), len(graph['edge_id']) + require(n > 0 and edges > 0 and nodes.dtype == np.int32 and nodes.ndim == 1 and np.all((nodes >= 0) & (nodes < N_GLOBAL)) and + np.all(np.diff(nodes) > 0), 'Invalid canonical node identities') + require(graph['ptr'].dtype == np.int64 and graph['ptr'].shape == (n+1,) and graph['ptr'][0] == 0 and + graph['ptr'][-1] == edges and np.all(np.diff(graph['ptr']) >= 0), 'Invalid CSR') + for key, dtype in (('edge_id', 'int64'), ('src', 'int32'), ('dst', 'int32'), ('signed_raw', 'int64'), ('weights', 'float64')): + require(graph[key].dtype == np.dtype(dtype) and graph[key].shape == (edges,), 'Invalid graph '+key) + require(np.array_equal(graph['src'], np.repeat(np.arange(n, dtype=np.int32), np.diff(graph['ptr']))) and + np.all((graph['dst'] >= 0) & (graph['dst'] < n)), 'Invalid source/destination row mapping') + require(history == (n < 512) and (n < 512 or n == N_GLOBAL), 'Invalid small/full history declaration') + require(np.all(graph['edge_id'] >= 0) and np.all(np.diff(graph['edge_id']) > 0), + 'Original canonical edge IDs must be unique and increasing') + if n == N_GLOBAL: + require(edges == 25582938 and np.array_equal(nodes, np.arange(n, dtype=np.int32)) and + np.array_equal(graph['edge_id'], np.arange(edges, dtype=np.int64)), 'Full canonical graph identity changed') + bundle = {name: _read_array(bundle_dir/name, used) for name in BUNDLE_FILES} + kc, apl_ids = bundle['kc_globals.npy'], bundle['apl_globals.npy'] + require(kc.dtype == apl_ids.dtype == np.int32 and kc.shape == (4064,) and apl_ids.shape == (2,), 'Invalid APL/KC identities') + require(np.all((kc >= 0) & (kc < N_GLOBAL)) and np.all(np.diff(kc) > 0) and + np.all((apl_ids >= 0) & (apl_ids < N_GLOBAL)) and np.all(np.diff(apl_ids) > 0) and + not np.intersect1d(kc, apl_ids).size, 'APL/KC identities overlap or are invalid') + for key, shape in (('input_matrix.npy', (2, N_GLOBAL)), ('output_matrix.npy', (2, 4064))): + require(bundle[key].dtype == np.float32 and bundle[key].shape == shape and + np.isfinite(bundle[key]).all() and np.all(bundle[key] >= 0), 'Invalid normalized APL matrix') + gpu = _load_run(gpu_dir, 'gpu', n, edges, steps, history, spec['name'], used, checks) + cpu = _load_run(cpu_dir, 'cpu', n, edges, steps, history, spec['name'], used, checks) + _pair_outputs(gpu, cpu, checks, differences, n, steps, history) + used.add(case_dir/'requests.npz') + with np.load(case_dir/'requests.npz', allow_pickle=False) as source: + require(set(source.files) == {'tick', 'neuron_idx'} and source['tick'].dtype == source['neuron_idx'].dtype == np.int32, + 'Fixed request NPZ schema changed') + require(source['tick'].shape == source['neuron_idx'].shape and source['tick'].ndim == 1, 'Invalid sparse request shape') + requested = list(zip(map(int, source['tick']), map(int, source['neuron_idx']))) + for role, run in (('gpu', gpu), ('cpu', cpu)): + _independent_event_accounting(run, role, graph, requested, steps, checks, differences) + _check_apl_application(run, role, nodes, kc, checks, differences) + + # Independently derive the effective clone. Original graph weights stay read-only. + cpu_weights = _read_array(cpu_dir/'weights.npy', used) + require(cpu_weights.dtype == np.float64 and cpu_weights.shape == (edges,), 'CPU effective weight schema mismatch') + expected_hash, base_hash, mask_hash = hashlib.sha256(), hashlib.sha256(), hashlib.sha256() + weight_ok, mask_count = True, 0 + for start in range(0, edges, 1 << 20): + stop = min(edges, start+(1 << 20)) + original = np.asarray(graph['weights'][start:stop]) + require(np.isfinite(original).all(), 'Nonfinite original weights') + raw_expected = graph['signed_raw'][start:stop]*(.275*.001) + require(not np.any(_bit_mask(original, raw_expected)), 'Original SI weights differ from unaltered signed contacts') + mask = np.isin(nodes[graph['src'][start:stop]], apl_ids) + expected = original.copy() + expected[mask] = np.float64(0) + mask_count += int(mask.sum()) + expected_hash.update(np.ascontiguousarray(expected, dtype=' 0 and edges > 0 and steps > 0 and + type(history) is bool and history == (n < 512), 'Invalid repeat dimensions') + left = _load_run(first, 'first_gpu', n, edges, steps, history, name, used, checks) + right = _load_run(second, 'second_gpu', n, edges, steps, history, name, used, checks) + _pair_outputs(left, right, checks, differences, n, steps, history) + stable = ('case', 'complete', 'pass_all', 'steps', 'completed_ticks', 'nodes', 'edges', 'precision', + 'apl_precision', 'state_units', 'total_spikes', 'requested', 'accepted_requests', 'rejected_requests', + 'arriving_edges', 'accepted_arriving_edges', 'counts_match_sparse_events', 'all_finite', 'record_history', + 'apl_fast_edges_zeroed', 'kc_targets_present', 'effective_weights_raw_sha256', 'apl_schedule', + 'apl_si_conversion', 'build', 'sealed_ae_backend_sha256', 'learning', 'input_sha256') + for key in stable: + _check(checks, 'summary.'+key, key in left['summary'] and key in right['summary'] and + left['summary'][key] == right['summary'][key]) + if not checks['summary.'+key]: + differences['summary.'+key] = {'first': left['summary'].get(key), 'second': right['summary'].get(key)} + report.update(case=name, steps=steps, nodes=n, edges=edges, record_history=history, + total_spikes=len(left['state']['step']), + explicit_exclusions=['wall_seconds', 'diagnostic_simulation_seconds', 'build_or_cache_seconds', + 'peak_allocated_vram_bytes', 'peak_reserved_vram_bytes']) + except Exception as error: + report['error'] = {'type': type(error).__name__, 'message': str(error)} + return _finish(report, checks, differences, used, out_json) diff --git a/models/male-cns-lif/author-apl-lab-v2/config.json b/models/male-cns-lif/author-apl-lab-v2/config.json new file mode 100644 index 0000000..50ecd5f --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/config.json @@ -0,0 +1,23 @@ +{ + "phase": "4AG-r2", + "name": "author-apl-lab-v2", + "execution_revision": "Preserve declared venv Python entry rather than resolving its symlink to system Python. No dynamics or inputs changed.", + "human_authorization": "允许独立新模型分支测试动力学假设", + "gpu_uuid": "GPU-79f3a85d-adce-7833-7003-6488140ca596", + "neurons": 166700, + "edges": 25582938, + "raw_weight_sum": 124177617, + "precision": "float64", + "apl_precision": "float32", + "apl": {"tau_ms": 20.0, "gain": 0.01, "normalization": 1.0}, + "hypothesis": "Author LIF plus unchanged legacy graded APL operator at end, after reset, unconditional KC g write. Mixed precision: h32 promoted to float64, then multiplied by float64 .001 SI, then subtracted. Not original author's APL model.", + "fixtures": ["zero", "pn_pulse", "kc_ref_boundary", "two_sources", "arrival_collision", "natural_request", "apl_only", "high_fanin"], + "fixture_repeats": 3, + "calibration": {"split": "calibration", "odor_count": 6, "modes": ["physiological", "matched_energy"], "seeds": [51101,51102,51103,51104,51105], "duration_ms": 100.0, "median_scope": "original frozen 12-odor panel", "full_oracle_probes": ["cal-physiological-00-s51101", "cal-matched_energy-00-s51101"], "repeat_first_seed_each_odor": true}, + "gates": {"sparse_min_fraction": 0.03, "sparse_max_fraction": 0.09, "max_fraction_strict": 0.5, "min_fraction_strict": 0.0, "minimum_separable_odor_pairs": 3, "same_jaccard_gt_between": true, "between_euclidean_gt_within": true, "both_modes_required": true, "finite_required": true, "repeat_exact_required": true}, + "training": false, + "heldout_dynamic_access": false, + "candidate_generation": false, + "output_budget_bytes": 16106127360, + "scope": "Semantic compatibility and fixed old CAL only. Shared-APL-component closed-loop Brian oracle plus independent arithmetic audit; never claim all-component independent CPU equivalence or learning success. No parameter/input/site/window selection from results." +} diff --git a/models/male-cns-lif/author-apl-lab-v2/context.py b/models/male-cns-lif/author-apl-lab-v2/context.py new file mode 100644 index 0000000..4315785 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/context.py @@ -0,0 +1,50 @@ +import hashlib +import importlib.util +import json +from pathlib import Path + +ROOT=Path('/data/digital-drosophila') +MODEL=Path(__file__).resolve().parent +EXP=ROOT/'experiments/phase4ag-author-apl-r2' +AF_MODEL=ROOT/'models/male-cns-lif/author-fullgraph-lab-v1' +AF_EXP=ROOT/'experiments/phase4af-author-fullgraph' +AD_EXP=ROOT/'experiments/phase4ad-author-semantics' +CFG=json.loads((MODEL/'config.json').read_text(encoding='utf-8')) + +def digest(path): + h=hashlib.sha256() + with Path(path).open('rb') as f: + for b in iter(lambda:f.read(8<<20),b''):h.update(b) + return h.hexdigest() + +def save(path,value): + path=Path(path);path.parent.mkdir(parents=True,exist_ok=True) + with path.open('x',encoding='utf-8') as f:json.dump(value,f,indent=2,ensure_ascii=False,allow_nan=False);f.write('\n') + +def sources(): + return {p.name:digest(p) for p in sorted(MODEL.iterdir()) if p.is_file() and p.name not in ('manifest.json','checksums.sha256')} + +def integrity(): + spec=importlib.util.spec_from_file_location('frozen_af_context',AF_MODEL/'context.py') + mod=importlib.util.module_from_spec(spec);spec.loader.exec_module(mod) + result=mod.integrity() + import sys + sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1')) + from diagnostic import manifest_check + for p in (AF_MODEL,AF_EXP,ROOT/'models/male-cns-lif/author-apl-lab-v1',ROOT/'experiments/phase4ag-author-apl'): + result[str(p.relative_to(ROOT))]=manifest_check(p,digest) + assert all(r['pass'] for r in result.values()) + return result + +def check(): + p=json.loads((EXP/'protocol/preregistered.json').read_text()) + assert p['sources']==sources(),'Source changed after preregistration' + for row in p['inputs']:assert digest(ROOT/row['path'])==row['sha256'],row['path'] + for row in json.loads((EXP/'protocol/prepared-hashes.json').read_text()): + assert digest(EXP/row['path'])==row['sha256'],row['path'] + return p + +def budget(): + size=sum(p.stat().st_size for p in EXP.rglob('*') if p.is_file()) + assert size= 0) & (nodes < N_NEURONS))) and + bool(np.all(np.diff(nodes) > 0)), "Global node IDs must be sorted unique") + require(n < 512 or (n == N_NEURONS and np.array_equal(nodes, np.arange(N_NEURONS, dtype=np.int32))), + "Only small fixtures or the intact frozen full graph are permitted") + require(n != N_NEURONS or e == 25582938, "Full graph edge count changed") + for name in ("src", "dst", "signed_raw", "weights"): + require(graph[name].shape == (e,), "Graph edge array length mismatch: " + name) + ptr = graph["ptr"] + require(ptr.shape == (n + 1,) and ptr[0] == 0 and ptr[-1] == e and + bool(np.all(np.diff(ptr) >= 0)), "Invalid graph CSR") + require(np.array_equal(graph["src"], np.repeat(np.arange(n, dtype=np.int32), np.diff(ptr))), + "Source order does not match CSR") + # Global edge IDs need not equal local row indices in a fixture. + for start in range(0, e, 1 << 20): + stop = min(start + (1 << 20), e) + require(bool(np.all((graph["dst"][start:stop] >= 0) & (graph["dst"][start:stop] < n))), + "Local destination out of range") + require(bool(np.isfinite(graph["weights"][start:stop]).all()), "Nonfinite base SI weights") + require(np.array_equal(np.sign(graph["weights"][start:stop]), np.sign(graph["signed_raw"][start:stop])), + "Base weight sign/zero pattern differs from frozen raw contacts") + lo = max(0, start - 1) + require(bool(np.all(edges[start:stop] >= 0)) and bool(np.all(np.diff(edges[lo:stop]) > 0)), + "Global edge IDs must retain canonical increasing source/edge order") + return graph + + +def load_inputs(case_dir): + spec = json.loads((case_dir / "spec.json").read_text(encoding="utf-8")) + for name in ("name", "steps", "dt_ms", "duration_ms", "model", "graph_dir", "apl", + "apl_bundle_dir", "coefficients_path", "record_history"): + require(name in spec, "Missing case field: " + name) + steps = spec["steps"] + require(type(steps) is int and 0 < steps <= 1000 and float(spec["dt_ms"]) == .1 and + float(spec["duration_ms"]) == steps * .1, "Unexpected steps/dt/duration") + require(type(spec["record_history"]) is bool, "record_history must be Boolean") + require(spec["apl"] == {"tau_ms": 20, "gain": .01, "normalization": 1}, "Frozen APL parameters changed") + for name in ("graph_dir", "apl_bundle_dir", "coefficients_path"): + require(Path(spec[name]).is_absolute(), "Input path must be absolute: " + name) + model = spec["model"] + for name in ("resting_potential_mv", "threshold_mv", "reset_potential_mv", "synaptic_delay_ms", + "refractory_ms", "w_syn_mv_per_anatomical_synapse"): + require(name in model and math.isfinite(float(model[name])), "Invalid model field: " + name) + require(float(model["synaptic_delay_ms"]) == 1.8 and float(model["refractory_ms"]) == 2.2 and + float(model["w_syn_mv_per_anatomical_synapse"]) == .275, "Frozen delay/ref/W_syn changed") + coeff = json.loads(Path(spec["coefficients_path"]).read_text(encoding="utf-8")) + for name in COEFFICIENT_NAMES: + require(name in coeff and math.isfinite(float(coeff[name])), "Invalid author coefficient: " + name) + require(coeff["threshold"] == float(model["threshold_mv"]) * .001 and + coeff["reset"] == float(model["reset_potential_mv"]) * .001, "Coefficient threshold/reset mismatch") + graph = load_graph(str(Path(spec["graph_dir"]).resolve())) + n = len(graph["nodes"]) + require(spec["record_history"] == (n < 512), "Fixtures need dense history; full graph must not duplicate dense histories") + with np.load(case_dir / "requests.npz", allow_pickle=False) as source: + require(set(source.files) == {"tick", "neuron_idx"}, "Request sparse schema changed") + ticks, requested = source["tick"].copy(), source["neuron_idx"].copy() + require(ticks.dtype == np.int32 and requested.dtype == np.int32 and ticks.ndim == 1 and + ticks.shape == requested.shape, "Invalid sparse request dtype/shape") + require(bool(np.all((ticks >= 0) & (ticks < steps))), "Request tick outside trial") + if len(ticks) > 1: + require(bool(np.all((ticks[1:] > ticks[:-1]) | + ((ticks[1:] == ticks[:-1]) & (requested[1:] > requested[:-1])))), + "Requests must be sorted unique by tick/global neuron") + local = np.searchsorted(graph["nodes"], requested) + require(bool(np.all(local < n)) and np.array_equal(graph["nodes"][local], requested), + "Requested neuron absent from graph") + initial = {"initial_v_mv": np.full(n, float(model["resting_potential_mv"]), dtype=np.float64), + "initial_g_mv": np.zeros(n, dtype=np.float64)} + initial_path = case_dir / "arrays.npz" + if initial_path.exists(): + with np.load(initial_path, allow_pickle=False) as source: + require(set(source.files).issubset(initial), "Unexpected initial-state array") + for name in source.files: + value = source[name] + require(value.dtype == np.float64 and value.shape == (n,) and bool(np.isfinite(value).all()), + "Invalid initial state: " + name) + initial[name] = value.copy() + return spec, graph, coeff, ticks, requested, local, initial + + +def new_array(path, dtype, shape): + require(not Path(path).exists(), "Refusing existing output: " + str(path)) + return np.lib.format.open_memmap(path, mode="w+", dtype=dtype, shape=shape) + + +def run_gpu(case_dir, out_dir, build_root): + """Run fresh candidate state; never consume oracle/candidate APL traces. + + pass_all denotes internal recording/finite integrity only. Independent + comparison and the original complete representation gate are separate. + """ + case_dir, out_dir = Path(case_dir), Path(out_dir) + require(not out_dir.exists(), "Refusing existing output directory: " + str(out_dir)) + out_dir.mkdir(parents=True, exist_ok=False) + start, tick, completed = time.perf_counter(), -1, 0 + maps = {} + try: + spec, graph, coeff, request_ticks, request_nodes, request_local, initial = load_inputs(case_dir) + require(torch.cuda.is_available() and torch.cuda.device_count() == 1 and + "4090" in torch.cuda.get_device_name(), "Only the declared single visible RTX4090 is permitted") + ext, build = sealed_backend().build_extension(build_root) + build_done = time.perf_counter() + apl = LegacyAPL(spec["apl_bundle_dir"], spec["dt_ms"]) + write_json(out_dir / "apl-component.json", apl.metadata) + device = apl.device + torch.cuda.reset_peak_memory_stats(device) + n, e, steps = len(graph["nodes"]), len(graph["edge_id"]), spec["steps"] + + def upload(value, dtype): + return torch.from_numpy(np.array(value, copy=True, order="C")).to(device=device, dtype=dtype).contiguous() + + indptr, dst = upload(graph["ptr"], torch.int64), upload(graph["dst"], torch.int32) + global_nodes_gpu = upload(graph["nodes"], torch.int64) + base_weights = upload(graph["weights"], torch.float64) + weights = base_weights.clone() + del base_weights + local_apl = np.flatnonzero(np.isin(graph["nodes"], apl.apl_globals)).astype(np.int64) + apl_out_mask = np.isin(graph["src"], local_apl) + if bool(np.any(apl_out_mask)): + weights[upload(apl_out_mask, torch.bool)] = 0.0 + effective_hash = hashlib.sha256() + for offset in range(0, e, 1 << 20): + effective_hash.update(weights[offset:offset + (1 << 20)].cpu().numpy().astype(" begin: + requested_local_gpu = upload(request_local[begin:end], torch.int64) + requests[requested_local_gpu] = 1 + accepted = (ref[requested_local_gpu] <= 1).to(torch.uint8).cpu().numpy() + accepted_requests_total += int(accepted.sum()) + writer.writerows({"tick": tick, "neuron_idx": int(node), "attempted": 1, + "accepted": int(ok), "rejected": 1 - int(ok)} + for node, ok in zip(request_nodes[begin:end], accepted)) + spikes = ring[tick % 19] + ext.state_step(v, g, ref, requests, spikes, counts, + *[float(coeff[name]) for name in COEFFICIENT_NAMES], 22) + expanded_global = np.empty(0, dtype=np.int64) + accepted_cpu = np.empty(0, dtype=np.uint8) + if tick >= 18: + due = ring[(tick - 18) % 19] + sources = torch.nonzero(due, as_tuple=False).flatten().contiguous() + if sources.numel(): + degrees = indptr[sources + 1] - indptr[sources] + offsets = (torch.cumsum(degrees, dim=0) - degrees).contiguous() + edge_count = int(degrees.sum().item()) + expanded = torch.empty(edge_count, device=device, dtype=torch.int64) + ext.fill_edges(indptr, sources, offsets, expanded) + if edge_count: + destination = dst[expanded] + # Actual active fanout order, BEFORE stable target grouping. + expanded_global = graph["edge_id"][expanded.cpu().numpy()].copy() + accepted_cpu = (ref[destination.long()] == 0).to(torch.uint8).cpu().numpy().copy() + order = torch.argsort(destination, stable=True) + ordered_edges, ordered_targets = expanded[order].contiguous(), destination[order] + boundary = torch.ones(edge_count, device=device, dtype=torch.bool) + boundary[1:] = ordered_targets[1:] != ordered_targets[:-1] + starts = torch.nonzero(boundary, as_tuple=False).flatten().contiguous() + targets = ordered_targets[starts].contiguous() + ext.apply_arrivals(ordered_edges, starts, targets, weights, g, ref) + queue = queue_summary(tick, expanded_global, accepted_cpu) + queue_output.write(json.dumps(queue, allow_nan=False) + "\n") + due_total += queue["count"] + accepted_due_total += queue["accepted_count"] + if spec["record_history"]: + for name, value in (("v", v), ("g", g), ("ref", ref)): + maps["pre_" + name][tick] = value.cpu().numpy() + if n == N_NEURONS: + full_spikes = spikes + else: + apl.spikes.zero_() + apl.spikes[global_nodes_gpu] = spikes + full_spikes = apl.spikes + drive, apl_state, h = apl.advance(full_spikes) + # Mixed precision contract: widen f32 h EXACTLY, THEN multiply + # in f64, THEN subtract in f64. Do not guard refractory KCs. + delta_si = h[kc_full_gpu].to(torch.float64) * 0.001 + g[kc_local_gpu] = g[kc_local_gpu] - delta_si + apl_cpu = {"drive": drive.cpu().numpy().copy(), "state": apl_state.cpu().numpy().copy(), + "inhibition": h.cpu().numpy().copy()} + for name, value in apl_cpu.items(): + maps["apl_" + name][tick] = value + actual = {"v": v.cpu().numpy().copy(), "g": g.cpu().numpy().copy(), + "ref": ref.cpu().numpy().copy()} + finite = all(bool(np.isfinite(value).all()) for value in list(actual.values()) + list(apl_cpu.values())) + row = {"tick": tick, **{name + "_sha256": raw_hash(value, " float64 -> multiply float64 0.001 -> separate float64 subtraction", + "build": build, "sealed_ae_backend_sha256": file_sha256(AE_MODEL / "gpu_backend.py"), + "shared_apl_component_oracle_limitation": True, "oracle_validation_claim": False, + "learning": False, "performance_runtime_claim": False, "cpu_sync_each_tick": True, + "build_or_cache_seconds": build_done - start, + "diagnostic_simulation_seconds": time.perf_counter() - simulation_start, + "wall_seconds": time.perf_counter() - start, + "peak_allocated_vram_bytes": int(torch.cuda.max_memory_allocated(device)), + "peak_reserved_vram_bytes": int(torch.cuda.max_memory_reserved(device)), + "input_sha256": {"spec.json": file_sha256(case_dir / "spec.json"), + "requests.npz": file_sha256(case_dir / "requests.npz"), + "base_weights.npy": file_sha256(Path(spec["graph_dir"]) / "weights.npy"), + "coefficients.json": file_sha256(spec["coefficients_path"])}} + if (case_dir / "arrays.npz").exists(): + summary["input_sha256"]["arrays.npz"] = file_sha256(case_dir / "arrays.npz") + write_json(out_dir / "summary.json", summary) + return summary + except Exception as error: + for value in maps.values(): + value.flush() + write_json(out_dir / "error.json", {"complete": False, "pass_all": False, + "tick_in_progress": tick, "completed_ticks": completed, + "type": type(error).__name__, "message": str(error), "traceback": traceback.format_exc()}) + raise diff --git a/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py b/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py new file mode 100644 index 0000000..84d40e8 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py @@ -0,0 +1,175 @@ +"""Frozen eager Torch float32 APL component and binary oracle sidecar. + +This is the Phase 4P operator sequence, not an author-neuron implementation. +No precision/determinism flags are changed here. The full matrix dimensions +are retained even when the caller simulates a small neuronal fixture. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import struct +import sys +import traceback + +import numpy as np +import torch + + +PROTOCOL = "phase4ag-apl-v1" +N_NEURONS, N_APL, N_KC = 166700, 2, 4064 +BUNDLE_FILES = ("input_matrix.npy", "output_matrix.npy", "kc_globals.npy", "apl_globals.npy") +FRAME = struct.Struct("= 0) & (ids < N_NEURONS))) and + bool(np.all(np.diff(ids) > 0)), "APL/KC globals must be sorted unique: " + name) + require(not np.intersect1d(arrays["kc_globals.npy"], arrays["apl_globals.npy"]).size, + "APL and KC identities overlap") + require(bool(np.all(arrays["input_matrix.npy"] >= 0)) and + bool(np.all(arrays["output_matrix.npy"] >= 0)), "APL raw-contact matrices must be nonnegative") + self.device = torch.device("cuda", torch.cuda.current_device()) + self.input_gpu = torch.from_numpy(arrays["input_matrix.npy"].copy()).to(self.device) + self.output_gpu = torch.from_numpy(arrays["output_matrix.npy"].copy()).to(self.device) + self.kc_globals = arrays["kc_globals.npy"].copy() + self.apl_globals = arrays["apl_globals.npy"].copy() + self.state = torch.zeros(N_APL, dtype=torch.float32, device=self.device) + self.spikes = torch.zeros(N_NEURONS, dtype=torch.uint8, device=self.device) + self.decay = np.float32(math.exp(-float(dt_ms) / 20.0)) + self.gain = np.float32(0.01) + self.metadata = {"ok": True, "protocol": PROTOCOL, "n_neurons": N_NEURONS, + "n_apl": N_APL, "n_kc": N_KC, "dt_ms": float(dt_ms), + "files_sha256": {name: file_sha256(self.bundle_dir / name) for name in BUNDLE_FILES}, + "array_sha256": {name: array_sha256(value) for name, value in arrays.items()}, + "decay_f32_bits": f"{int(self.decay.view(np.uint32)):08x}", + "gain_f32_bits": f"{int(self.gain.view(np.uint32)):08x}", + "environment": environment_metadata(), + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "h_units": "legacy mV", "initial_state": "float32 zeros"} + + @torch.no_grad() + def advance(self, spikes_u8): + require(spikes_u8.device == self.device and spikes_u8.dtype == torch.uint8 and + tuple(spikes_u8.shape) == (N_NEURONS,) and spikes_u8.is_contiguous(), + "APL expects full-width contiguous CUDA uint8 spikes") + # Preserve separate eager operations, including Python evaluation order + # in the frozen state.mul_(...).add_(torch.mv(...)) expression. + self.state.mul_(float(self.decay)) + drive = torch.mv(self.input_gpu, spikes_u8.to(torch.float32)) + self.state.add_(drive) + h = torch.matmul(self.state, self.output_gpu) * self.gain + return drive, self.state, h + + def advance_global_fired(self, fired): + require(fired.dtype == np.int32 and fired.ndim == 1, "Global spikes must be int32 vector") + require(bool(np.all((fired >= 0) & (fired < N_NEURONS))) and + bool(np.all(np.diff(fired) > 0)), "Global spikes must be sorted unique and in range") + self.spikes.zero_() + if fired.size: + indices = torch.from_numpy(fired.copy()).to(device=self.device, dtype=torch.int64) + self.spikes[indices] = 1 + return self.advance(self.spikes) + + +def read_exact(stream, count, boundary_eof=False): + result = bytearray() + while len(result) < count: + block = stream.read(count - len(result)) + if not block: + if boundary_eof and not result: + return None + raise EOFError("Truncated APL request frame") + result.extend(block) + return bytes(result) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--bundle-dir", required=True) + parser.add_argument("--dt-ms", required=True, type=float) + args = parser.parse_args() + # stdout is exclusively the handshake line and binary response frames. + output, source = sys.stdout.buffer, sys.stdin.buffer + component = LegacyAPL(args.bundle_dir, args.dt_ms) + output.write((json.dumps(component.metadata, allow_nan=False) + "\n").encode("utf-8")) + output.flush() + expected_tick = 0 + while True: + header = read_exact(source, FRAME.size, boundary_eof=True) + if header is None: + return + tick, count = FRAME.unpack(header) + require(tick == expected_tick and count <= N_NEURONS, "Invalid APL request tick/count") + fired = np.frombuffer(read_exact(source, count * 4), dtype="{limit}; inputs and duration were not adjusted") + return total + + +class _PipeReader: + """One bounded reader thread, with timeout/EOF checks for every frame.""" + + def __init__(self, pipe, timeout): + self.pipe, self.timeout = pipe, float(timeout) + self.chunks = queue.Queue(maxsize=8) + self.buffer = bytearray() + self.thread = threading.Thread(target=self._pump, daemon=True) + self.thread.start() + + def _pump(self): + try: + while True: + chunk = os.read(self.pipe.fileno(), 65536) + self.chunks.put(chunk) + if not chunk: + return + except BaseException as error: + self.chunks.put(error) + + def _fill(self, deadline): + remaining = deadline - time.monotonic() + if remaining <= 0: + raise TimeoutError("APL sidecar response timed out") + try: + chunk = self.chunks.get(timeout=remaining) + except queue.Empty as error: + raise TimeoutError("APL sidecar response timed out") from error + if isinstance(chunk, BaseException): + raise RuntimeError("APL sidecar stdout reader failed") from chunk + if not chunk: + raise EOFError("APL sidecar ended before a complete response") + self.buffer.extend(chunk) + + def read(self, size): + deadline = time.monotonic() + self.timeout + while len(self.buffer) < size: + self._fill(deadline) + result = bytes(self.buffer[:size]) + del self.buffer[:size] + return result + + def line(self): + deadline = time.monotonic() + self.timeout + while b"\n" not in self.buffer: + _check(len(self.buffer) < 1 << 20, "APL sidecar handshake exceeds 1 MiB") + self._fill(deadline) + end = self.buffer.index(b"\n") + 1 + result = bytes(self.buffer[:end]) + del self.buffer[:end] + return result + + +class _APLWorker: + def __init__(self, python, script, bundle_dir, stderr, timeout=180): + self.command = [str(python), "-B", str(script), "--bundle-dir", str(bundle_dir), "--dt-ms", "0.1"] + self.process = subprocess.Popen(self.command, stdin=subprocess.PIPE, + stdout=subprocess.PIPE, stderr=stderr, bufsize=0, shell=False) + self.reader = _PipeReader(self.process.stdout, timeout) + self.tick = 0 + try: + self.hello = json.loads(self.reader.line().decode("utf-8")) + _check(self.hello.get("ok") is True, "APL sidecar did not acknowledge initialization") + _check(self.hello.get("protocol") == PROTOCOL, "Unexpected APL sidecar protocol") + for key, value in (("n_neurons", 166700), ("n_apl", 2), ("n_kc", 4064)): + _check(self.hello.get(key) == value, f"APL sidecar changed {key}") + _check(self.hello.get("dt_ms") == .1, "APL sidecar changed dt") + expected_decay = f"{int(np.float32(math.exp(-.1 / 20.)).view(np.uint32)):08x}" + expected_gain = f"{int(np.float32(.01).view(np.uint32)):08x}" + _check(self.hello.get("decay_f32_bits") == expected_decay and self.hello.get("gain_f32_bits") == expected_gain, + "APL sidecar decay/gain differs from the frozen float32 scalars") + _check(self.hello.get("files_sha256") == {name: _digest(bundle_dir / name) for name in BUNDLE_FILES}, + "APL sidecar bundle hashes differ from the independently read bundle") + except BaseException: + self.abort() + raise + + def step(self, tick, global_spikes): + _check(tick == self.tick, "APL sidecar request ticks must be consecutive") + nodes = np.asarray(global_spikes) + _check(nodes.ndim == 1 and nodes.dtype == np.dtype("int32"), "APL input spikes must be int32 globals") + _check(not len(nodes) or (nodes[0] >= 0 and nodes[-1] < 166700 and np.all(np.diff(nodes) > 0)), + "APL input spikes must be sorted, unique, and in the frozen global range") + packet = struct.pack("= 0), "APL sidecar returned invalid nonnegative f32 states") + self.tick += 1 + return values[:2], values[2:4], values[4:] + + def finish(self, steps): + _check(self.tick == steps, "APL sidecar did not process every tick") + self.process.stdin.close() + status = self.process.wait(timeout=30) + _check(status == 0, f"APL sidecar exited unsuccessfully: {status}") + self.reader.thread.join(timeout=5) + _check(not self.reader.thread.is_alive(), "APL stdout did not reach EOF") + extra = bytes(self.reader.buffer) + while not self.reader.chunks.empty(): + chunk = self.reader.chunks.get_nowait() + _check(not isinstance(chunk, BaseException), "APL stdout reader failed after the last frame") + extra += chunk + _check(not extra, "APL sidecar emitted unrequested trailing stdout bytes") + self.process.stdout.close() + return status + + def abort(self): + if self.process.poll() is None: + self.process.terminate() + try: + self.process.wait(timeout=5) + except subprocess.TimeoutExpired: + self.process.kill() + self.process.wait(timeout=5) + for handle in (self.process.stdin, self.process.stdout): + if handle is not None and not handle.closed: + handle.close() + + +def _graph_inputs(directory): + arrays = {} + definitions = {"nodes": "int32", "edge_id": "int64", "src": "int32", + "dst": "int32", "signed_raw": "int64", "ptr": "int64", "weights": "float64"} + for name, dtype in definitions.items(): + value = np.load(directory / (name + ".npy"), mmap_mode="r", allow_pickle=False) + _check(value.ndim == 1 and value.dtype == np.dtype(dtype), f"Invalid graph array {name}") + arrays[name] = value + nodes, edge_ids = arrays["nodes"], arrays["edge_id"] + n, edges = len(nodes), len(edge_ids) + _check(0 < n <= 166700 and edges > 0, "A case needs real global nodes and explicit nonempty connectivity") + _check(nodes[0] >= 0 and nodes[-1] < 166700 and np.all(np.diff(nodes) > 0), "Invalid global node order") + for name in ("src", "dst", "signed_raw", "weights"): + _check(arrays[name].shape == (edges,), f"Graph array {name} has the wrong shape") + pointer = arrays["ptr"] + _check(pointer.shape == (n + 1,) and pointer[0] == 0 and pointer[-1] == edges and + np.all(np.diff(pointer) >= 0), "Invalid graph CSR pointer") + source_counts = np.zeros(n, np.int64) + identity_edges = True + previous_source, previous_edge = -1, -1 + width = BLOCK_BYTES // 8 + for begin in range(0, edges, width): + stop = min(edges, begin + width) + source, target, ids = arrays["src"][begin:stop], arrays["dst"][begin:stop], edge_ids[begin:stop] + _check(np.all((source >= 0) & (source < n)) and np.all((target >= 0) & (target < n)), + "Graph edge endpoint is outside the local node array") + _check(np.all(ids >= 0), "Graph edge IDs must be nonnegative") + _check(source[0] > previous_source or (source[0] == previous_source and ids[0] > previous_edge), + "Graph edge blocks are not in canonical source/edge order") + _check(np.all((source[1:] > source[:-1]) | + ((source[1:] == source[:-1]) & (ids[1:] > ids[:-1]))), + "Graph edges are not in canonical source/edge order") + previous_source, previous_edge = int(source[-1]), int(ids[-1]) + identity_edges &= np.array_equal(ids, np.arange(begin, stop, dtype=np.int64)) + source_counts += np.bincount(source, minlength=n) + _check(np.isfinite(arrays["weights"][begin:stop]).all(), "Nonfinite graph weights") + _check(np.array_equal(source_counts, np.diff(pointer)), "CSR pointer and source rows disagree") + if not identity_edges: + _check(len(np.unique(edge_ids)) == edges, "Original edge IDs must be unique") + for name in ("completeness.csv", "connectivity.parquet"): + _check((directory / name).is_file(), f"Missing unchanged author input: {name}") + if n == 166700: + _check(edges == 25582938 and identity_edges and np.array_equal(nodes, np.arange(n, dtype=np.int32)), + "A full probe must use the unchanged complete canonical graph") + return arrays, identity_edges + + +def _requests(case_dir, nodes, steps): + with np.load(case_dir / "requests.npz", allow_pickle=False) as archive: + _check(set(archive.files) == {"tick", "neuron_idx"}, "Unexpected sparse request schema") + ticks, requested = archive["tick"].copy(), archive["neuron_idx"].copy() + _check(ticks.dtype == requested.dtype == np.dtype("int32") and ticks.ndim == requested.ndim == 1 and + ticks.shape == requested.shape, "Requests must be equal-length int32 vectors") + _check(np.all((ticks >= 0) & (ticks < steps)), "Request time outside the case") + _check(not len(ticks) or np.all((ticks[1:] > ticks[:-1]) | + ((ticks[1:] == ticks[:-1]) & (requested[1:] > requested[:-1]))), + "Requests must be sorted and unique by tick/global node") + local = np.searchsorted(nodes, requested) + _check(np.all(local < len(nodes)) and np.array_equal(nodes[local], requested), + "Request references a global neuron absent from this case") + counts = np.bincount(ticks, minlength=steps).astype(np.int32) + pointer = np.concatenate((np.zeros(1, np.int64), np.cumsum(counts, dtype=np.int64))) + return requested, local.astype(np.int32), pointer, counts + + +class _QueueProxy: + def __init__(self, original, get_tick, available, arrays, identity_edges, + due_counts, spike_ring, delay_steps, handle, records, summarize): + self._original_queue, self._get_tick = original, get_tick + self._available, self._arrays, self._identity_edges = available, arrays, identity_edges + self._due_counts, self._spike_ring, self._delay_steps = due_counts, spike_ring, delay_steps + self._handle, self._records, self._summarize = handle, records, summarize + + def peek(self, *args, **kwargs): + original = self._original_queue.peek(*args, **kwargs) + local_edges = np.asarray(original) + tick = self._get_tick() + _check(tick == len(self._records["rows"]), "Queue peek ticks must be consecutive") + _check(local_edges.ndim == 1 and np.all((local_edges >= 0) & (local_edges < len(self._arrays["edge_id"]))), + "Original queue contains an invalid local edge index") + _check(len(local_edges) == self._due_counts[tick % len(self._due_counts)], + "Queue arrivals differ from the fixed-delay outgoing degree of actual spikes") + fired_then = np.zeros(len(self._arrays["nodes"]), dtype=bool) + if tick >= self._delay_steps: + fired_then[self._spike_ring[(tick - self._delay_steps) % len(self._spike_ring)]] = True + + def accepted(begin, stop): + part = local_edges[begin:stop] + _check(np.all(fired_then[self._arrays["src"][part]]), "Arrival lacks a presynaptic spike at author delay") + return self._available[self._arrays["dst"][part]] + + # Only the observed ID view changes; the queue is returned verbatim. + edge_ids = local_edges if self._identity_edges else self._arrays["edge_id"][local_edges] + summary = self._summarize(tick, edge_ids, accepted) + self._handle.write(json.dumps(summary, sort_keys=True) + "\n") + self._records["rows"].append(summary) + self._records["peek_calls"] += 1 + return original + + def advance(self, *args, **kwargs): + self._records["advance_calls"] += 1 + return self._original_queue.advance(*args, **kwargs) + + def __getattr__(self, name): + return getattr(self._original_queue, name) + + +def run_case(case_dir: Path, out_dir: Path) -> dict: + """Run one continuous case, with exclusive outputs and no candidate trace input. + + Graph inputs are the AF array/author-table schema plus original weights.npy. + requests.npz contains sparse tick/neuron_idx int32 vectors. Optional + arrays.npz contains initial_v_mv/initial_g_mv float64 vectors only. + Full probes write state hashes, sparse events and APL traces, not dense + whole-brain histories. Each case requires a fresh process. + """ + global _PROCESS_USED + _check(not _PROCESS_USED, "Every oracle case requires a fresh process") + _PROCESS_USED = True + case_dir, out_dir = Path(case_dir).resolve(strict=True), Path(out_dir).resolve() + spec = json.loads((case_dir / "spec.json").read_text(encoding="utf-8")) + graph_dir = Path(spec["graph_dir"]).resolve(strict=True) + bundle_dir = Path(spec["apl_bundle_dir"]).resolve(strict=True) + worker_path = Path(spec["legacy_apl_worker"]).resolve(strict=True) + science_python = _python_entry(spec["science_python"]) + author_path = Path(spec.get("author_path", AUTHOR_PATH)).resolve(strict=True) + ae_path = Path(spec.get("ae_oracle_path", AE_PATH)).resolve(strict=True) + af_path = Path(spec.get("af_oracle_path", AF_PATH)).resolve(strict=True) + coefficients_path = Path(spec["coefficients_path"]).resolve(strict=True) + steps = int(spec["steps"]) + _check(steps == spec["steps"] and 0 < steps <= np.iinfo(np.int32).max, "Invalid number of steps") + _check(float(spec["dt_ms"]) == .1 and np.isclose(float(spec["duration_ms"]), steps * .1, rtol=0, atol=1e-12), + "The frozen APL protocol requires dt_ms=0.1 and a matching duration") + _check(spec.get("precision", "float64") == "float64", "Author state must be float64") + _check(spec.get("training", False) is False and spec.get("poi_used", False) is False, + "Training and original poi are outside this gate") + _check(spec["apl"] == {"tau_ms": 20, "gain": .01, "normalization": 1}, "Frozen APL parameters changed") + record_history = spec["record_history"] + _check(isinstance(record_history, bool), "record_history must be a boolean") + budget_root = Path(spec.get("budget_root", out_dir.parent)).resolve() + budget_limit = int(spec.get("output_budget_bytes", 15 * 1024 ** 3)) + _check(budget_limit > 0 and out_dir.is_relative_to(budget_root), "Invalid output budget/root") + out_dir.mkdir(parents=True, exist_ok=False) + mapped_files = [] + saved_preferences, old_dt, worker = None, None, None + last_complete_tick = -1 + try: + arrays, identity_edges = _graph_inputs(graph_dir) + nodes, n, edges = arrays["nodes"], len(arrays["nodes"]), len(arrays["edge_id"]) + _check(n < 166700 or not record_history, "Full probes must not save dense V/g/ref histories") + bundle = {name: np.load(bundle_dir / (name + ".npy"), mmap_mode="r", allow_pickle=False) + for name in ("input_matrix", "output_matrix", "kc_globals", "apl_globals")} + for name, shape in (("input_matrix", (2, 166700)), ("output_matrix", (2, 4064))): + value = bundle[name] + _check(value.dtype == np.dtype("float32") and value.shape == shape and value.flags.c_contiguous, + f"Frozen APL {name} shape/dtype/layout changed") + _check(np.isfinite(value).all() and np.all(value >= 0), f"Invalid positive raw-normalized APL {name}") + for name, count in (("kc_globals", 4064), ("apl_globals", 2)): + value = bundle[name] + _check(value.dtype == np.dtype("int32") and value.shape == (count,) and value[0] >= 0 and + value[-1] < 166700 and np.all(np.diff(value) > 0), f"Frozen APL identity {name} changed") + _check(not np.intersect1d(bundle["kc_globals"], bundle["apl_globals"]).size, "APL and KC identities overlap") + kc_positions = np.searchsorted(nodes, bundle["kc_globals"]) + present = kc_positions < n + present[present] &= nodes[kc_positions[present]] == bundle["kc_globals"][present] + kc_local, kc_in_bundle = kc_positions[present], np.flatnonzero(present) + requested_globals, request_local, request_ptr, request_count = _requests(case_dir, nodes, steps) + initial = None + if (case_dir / "arrays.npz").exists(): + with np.load(case_dir / "arrays.npz", allow_pickle=False) as archive: + _check(set(archive.files) == {"initial_v_mv", "initial_g_mv"}, "Unexpected initial-state schema") + initial = {name: archive[name].copy() for name in archive.files} + for name, value in initial.items(): + _check(value.dtype == np.dtype("float64") and value.shape == (n,) and np.isfinite(value).all(), + f"Invalid diagnostic initial state: {name}") + history_bytes = steps * n * 40 if record_history else 0 + _budget(budget_root, budget_limit, history_bytes + steps * (2 + 2 + 4064) * 4 + edges * 8 + (64 << 20)) + versions = {name: importlib.metadata.version(name) for name in + ("brian2", "numpy", "pandas", "pyarrow", "sympy", "scipy", "cython")} + _save(out_dir / "environment.json", {"python": sys.version, "platform": platform.platform(), + "packages": versions, "codegen": "numpy", "author_precision": "float64", + "APL_precision": "float32 in separate science-Python process", "Torch_imported_into_Brian": False}) + for name, required in (("brian2", "2.5.1"), ("numpy", "1.22.3"), ("sympy", "1.12")): + _check(versions[name] == required, f"{name} must remain pinned to {required}") + import brian2 as b2 + import pandas as pd + from brian2.core.base import BrianObject + + ae = _load_checked(ae_path, AE_SHA256, "phase4ag_sealed_ae_helpers") + af = _load_checked(af_path, AF_SHA256, "phase4ag_sealed_af_helpers") + author = _load_checked(author_path, AUTHOR_SHA256, "phase4ag_unchanged_author") + _check("torch" not in sys.modules, "Torch must not be imported into the pinned Brian process") + comp = pd.read_csv(graph_dir / "completeness.csv", index_col=0) + _check(np.array_equal(comp.index.to_numpy(), nodes) and + np.array_equal(comp["local_index"].to_numpy(), np.arange(n)), "Author CSV neuron identity differs") + del comp + graph_hashes = {name: _digest(graph_dir / name) for name in + tuple(key + ".npy" for key in arrays) + ("completeness.csv", "connectivity.parquet")} + bundle_hashes = {name: _digest(bundle_dir / name) for name in BUNDLE_FILES} + case_hashes = {name: _digest(case_dir / name) for name in ("spec.json", "requests.npz")} + if initial is not None: + case_hashes["arrays.npz"] = _digest(case_dir / "arrays.npz") + worker_hash = _digest(worker_path) + coefficient_hash = _digest(coefficients_path) + baseline_coefficients = json.loads(coefficients_path.read_text(encoding="utf-8")) + if "legacy_apl_worker_sha256" in spec: + _check(worker_hash == spec["legacy_apl_worker_sha256"], "APL worker differs from preregistered source") + _save(out_dir / "input-audit.json", {"spec": spec, "case_hashes": case_hashes, + "graph_hashes": graph_hashes, "bundle_hashes": bundle_hashes, + "author_sha256": AUTHOR_SHA256, "ae_sha256": AE_SHA256, "af_sha256": AF_SHA256, + "legacy_apl_worker_sha256": worker_hash, "coefficients_sha256": coefficient_hash, + "candidate_trace_used_as_input": False, + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence"}) + saved_preferences = {key: b2.prefs[key] for key in + ("codegen.target", "core.default_float_dtype", "legacy.refractory_timing")} + old_dt = b2.defaultclock.dt + b2.start_scope() + b2.prefs["codegen.target"] = "numpy" + b2.prefs["core.default_float_dtype"] = np.float64 + b2.prefs["legacy.refractory_timing"] = False + b2.defaultclock.dt = .1 * b2.ms + parameters = dict(author.default_params) + _save(out_dir / "parameter-checks.json", ae._parameters_match(parameters, spec["model"], b2)) + neu, syn, spikes = author.create_model(graph_dir / "completeness.csv", graph_dir / "connectivity.parquet", parameters) + _check(len(neu) == n and len(syn) == edges, "Author loaded an unexpected graph size") + _check(all(np.dtype(neu.variables[key].dtype) == np.float64 for key in ("v", "g")) and + np.dtype(syn.variables["w"].dtype) == np.float64, "Author states/weights are not float64") + effective = _new_memmap(out_dir / "weights.npy", "= 0) + last_ticks.fill(0) + last_ticks[seen] = np.rint(last_raw[seen] / .0001).astype(np.int64) + result = np.where(seen, np.maximum(ref_steps - (tick - last_ticks), 0), 0).astype(np.int32) + _check(np.array_equal(result == 0, available_raw), "Ref history disagrees with author not_refractory") + return result + + with ExitStack() as stack: + stderr = stack.enter_context((out_dir / "apl-worker.stderr.log").open("xb")) + worker = _APLWorker(science_python, worker_path, bundle_dir, stderr, float(spec.get("worker_timeout_seconds", 180))) + _save(out_dir / "apl-worker-handshake.json", {"command": worker.command, "handshake": worker.hello}) + queue_file = stack.enter_context((out_dir / "queue.jsonl").open("x", encoding="utf-8")) + event_file = stack.enter_context((out_dir / "events.bin").open("xb")) + hash_file = stack.enter_context((out_dir / "tick-state-hashes.jsonl").open("x", encoding="utf-8")) + tick_file = stack.enter_context((out_dir / "ticks.jsonl").open("x", encoding="utf-8")) + requests_file = stack.enter_context((out_dir / "requests.csv").open("x", encoding="utf-8", newline="")) + request_writer = csv.DictWriter(requests_file, fieldnames=["tick", "neuron_idx", "attempted", "accepted", "rejected"]) + request_writer.writeheader() + + def request_operation(): + nonlocal permitted + tick = tick_now() + _check(0 <= tick < steps and not request_seen[tick], "Invalid/duplicate common request tick") + request_seen[tick] = 1 + begin, stop = request_ptr[tick:tick + 2] + local = request_local[begin:stop] + available = available_raw[local].copy() + permitted = local[available] + applicable_count[tick] = len(permitted) + rejected_count[tick] = len(local) - len(permitted) + for node, accepted in zip(requested_globals[begin:stop], available): + request_writer.writerow({"tick": tick, "neuron_idx": int(node), "attempted": 1, + "accepted": int(accepted), "rejected": int(not accepted)}) + if len(permitted): + neu.v[permitted] = parameters["v_th"] + 1 * b2.mV + + def apl_operation(): + tick = tick_now() + _check(0 <= tick < steps and not apl_seen[tick] and request_seen[tick], "Invalid/duplicate APL tick") + active = np.asarray(neu.spikes, dtype=np.int32) + _check(not len(active) or np.all(np.diff(active) > 0), "Brian actual spikes are not canonical/unique") + _check(np.all(g_raw[active] == 0.0), "APL ran before the original author g reset") + if record_history: + pre_apl_histories["v"][tick] = v_raw + pre_apl_histories["g"][tick] = g_raw + pre_apl_histories["ref"][tick] = current_ref(tick) + drive, state, inhibition = worker.step(tick, np.asarray(nodes[active], dtype=np.int32)) + apl_history["drive"][tick] = drive + apl_history["state"][tick] = state + apl_history["inhibition"][tick] = inhibition + # These are separate array operations. Do not move the unit + # conversion into a fused subtract; do not guard by refractory. + delta_si = inhibition[kc_in_bundle].astype(np.float64) * np.float64(.001) + g_raw[kc_local] = g_raw[kc_local] - delta_si + apl_seen[tick] = 1 + + def record_end(): + nonlocal last_complete_tick, total_events, final_ref + tick = tick_now() + _check(tick == last_complete_tick + 1 and tick < steps and apl_seen[tick], "End tick is incomplete/out of order") + _check(len(records["rows"]) == tick + 1, "End state lacks queue observation") + _check(np.isfinite(v_raw).all() and np.isfinite(g_raw).all(), "Nonfinite author/APL state") + final_ref = current_ref(tick) + if record_history: + histories["v"][tick], histories["g"][tick], histories["ref"][tick] = v_raw, g_raw, final_ref + active = np.asarray(neu.spikes, dtype=np.int32) + _check(np.isin(permitted, active).all(), "An applicable request did not produce a real spike") + spike_count[tick] = len(active) + total_counts[active] += 1 + if len(active): + events = np.empty(len(active), EVENT_DTYPE) + events["step"], events["neuron_idx"] = tick, nodes[active] + event_file.write(memoryview(events).cast("B")) + total_events += len(active) + spike_ring[tick % queue_len] = active.copy() + due_counts[(tick + delay_steps) % queue_len] = int(outdegree[active].sum(dtype=np.int64)) + hash_file.write(json.dumps({"tick": tick, "v_sha256": _array_hash(v_raw, "=0 and requests[:,0].max()30*1024**3 + for d in ('protocol','apl-bundle','graphs','cases','gpu','oracle','component-audit','regression','calibration','audit','extension-build','logs'):(EXP/d).mkdir(parents=True,exist_ok=True) + can=ROOT/'datasets/male-cns-v1.0/processed/neuron-only';legacy=ROOT/'models/male-cns-lif';lp=legacy/'olfactory-grounding-v1/phase4l.yaml' + lock=ROOT/'experiments/phase4j-mb-physiology-repair/calibration/locked-parameters.json' + files=[AF_MODEL/'manifest.json',AF_EXP/'manifest.json',AF_EXP/'oracle/no_input/coefficients.json', + legacy/'deterministic-runtime-v2/phase4p_revalidate.py',lp,lock,can/'neuron-index/unified-metadata.parquet',can/'gpu/csr-weights.int64.npy', + legacy/'author-runtime-lab-v1/author_ext.cpp',legacy/'author-runtime-lab-v1/author_ext.cu',legacy/'author-runtime-lab-v1/gpu_backend.py', + legacy/'author-runtime-lab-v1/oracle_stress.py',AD_EXP/'external-resources/model.py',AF_MODEL/'oracle_full.py'] + lroot=ROOT/'experiments/phase4l-olfactory-grounding' + files += [lroot/'odor-encoder'/x for x in ('frozen-odor-panel.parquet','mapped-glomerular-vectors.npz','selection.json')] + files += [lroot/'receptor-mapping/olfactory-mapping.parquet'] + prior=ROOT/'experiments/phase4ag-author-apl' + files += [prior/'manifest.json',ROOT/'models/male-cns-lif/author-apl-lab-v1/manifest.json'] + save(EXP/'protocol/preregistered.json',dict(timestamp_utc=datetime.now(timezone.utc).isoformat(),sources=sources(),config=CFG, + inputs=[dict(path=str(p.relative_to(ROOT)),sha256=digest(p)) for p in files],before_any_trajectory=True, + parent_commit=subprocess.check_output(['git','-C',str(ROOT),'rev-parse','HEAD'],text=True).strip())) + save(EXP/'regression/old-integrity-before.json',integrity()) + science_entry=ROOT/'models/male-cns-lif/.venv/bin/python' + command=[str(science_entry),'-B','-c', + 'import sys,json,importlib.util; print(json.dumps(dict(executable=sys.executable,prefix=sys.prefix,base_prefix=sys.base_prefix,numpy_visible=importlib.util.find_spec("numpy") is not None,torch_visible=importlib.util.find_spec("torch") is not None)))'] + environment=json.loads(subprocess.check_output(command,text=True)) + assert environment['executable']==str(science_entry) and environment['prefix']==str(science_entry.parent.parent) + assert environment['prefix']!=environment['base_prefix'] and environment['numpy_visible'] and environment['torch_visible'] + save(EXP/'protocol/execution-preflight.json',dict(command=command,observed=environment, + before_any_trajectory=True,torch_imported=False,CUDA_initialized=False)) + locked=json.loads(lock.read_text());assert locked['parameters_locked'] and locked['calibration_only'] + assert {k:locked['selected'][k] for k in ('gain','normalization','tau_ms')}==CFG['apl'] + meta=pd.read_parquet(can/'neuron-index/unified-metadata.parquet').sort_values('neuron_idx').reset_index(drop=True) + assert len(meta)==CFG['neurons'] and np.array_equal(meta.neuron_idx,np.arange(CFG['neurons'])) + cls=meta.annotation_class.fillna('').astype(str).to_numpy();typ=meta.annotation_type.fillna('').astype(str).to_numpy() + kc=np.flatnonzero(cls=='Kenyon_Cell').astype(np.int32);pn=np.flatnonzero(cls=='ALPN').astype(np.int32);apl=np.flatnonzero(typ=='APL').astype(np.int32) + assert len(kc)==4064 and len(apl)==2 + fg=AF_EXP/'graph';ptr=np.load(fg/'ptr.npy',mmap_mode='r');dst=np.load(fg/'dst.npy',mmap_mode='r');src=np.load(fg/'src.npy',mmap_mode='r') + signed=np.load(fg/'signed_raw.npy',mmap_mode='r');base=np.load(AF_EXP/'oracle/no_input/weights.npy',mmap_mode='r');raw=np.load(can/'gpu/csr-weights.int64.npy',mmap_mode='r') + assert len(dst)==CFG['edges'] and int(raw.sum())==CFG['raw_weight_sum'] + aplout=np.isin(src,apl);aplids=np.flatnonzero(aplout).astype(np.int64);assert len(aplids)==5845 + I=np.zeros((2,len(meta)),np.float32);O=np.zeros((2,len(kc)),np.float32) + alook={int(v):i for i,v in enumerate(apl)};kl=np.full(len(meta),-1,np.int32);kl[kc]=np.arange(len(kc),dtype=np.int32) + allowed=np.isin(src,np.r_[kc,pn])&np.isin(dst,apl) + for eid in np.flatnonzero(allowed):I[alook[int(dst[eid])],int(src[eid])]+=float(raw[eid]) + for eid in np.flatnonzero(aplout&np.isin(dst,kc)):O[alook[int(src[eid])],kl[dst[eid]]]+=float(raw[eid]) + iscale=float(I[I>0].sum()/np.count_nonzero(np.any(I>0,axis=0)));oscale=float(O[O>0].mean());bundle=EXP/'apl-bundle' + for name,value in dict(input_matrix=I/iscale,output_matrix=O/oscale,input_raw=I,output_raw=O,kc_globals=kc,pn_globals=pn,apl_globals=apl,apl_out_ids=aplids).items():arr(bundle/(name+'.npy'),value) + save(bundle/'scales.json',dict(input_scale=iscale,output_scale=oscale)) + save(bundle/'provenance.json',dict(canonical_root=str(can),source=str(legacy/'deterministic-runtime-v2/phase4p_revalidate.py'),source_sha256=digest(legacy/'deterministic-runtime-v2/phase4p_revalidate.py'), + parameters=CFG['apl'],lock_file=str(lock),lock_sha256=digest(lock),full_dimensions=[list(I.shape),list(O.shape)], + APL=len(apl),KC=len(kc),ALPN=len(pn),APL_fast_edges=len(aplids),APL_nonKC_edges=int(np.count_nonzero(aplout&~np.isin(dst,kc))), + APL_nonKC_contacts=int(raw[aplout&~np.isin(dst,kc)].sum()),normalization_recomputed_in_original_order=True, + raw_files_modified=False)) + full=(ptr,dst,src,signed,base);fullnodes=graph(EXP/'graphs/full',np.arange(len(meta),dtype=np.int32),full) + pscore=I.sum(axis=0);pnrank=pn[np.lexsort((pn,-pscore[pn]))];kscore=O.sum(axis=0);kcrank=kc[np.lexsort((kc,-kscore))] + candidates=np.flatnonzero(np.isin(src,pn)&np.isin(dst,kc));best=int(candidates[np.argmax(raw[candidates])]);pairpn=int(src[best]) + pe=np.arange(ptr[pairpn],ptr[pairpn+1],dtype=np.int64);pe=pe[np.isin(dst[pe],kc)];pe=pe[np.lexsort((dst[pe],-raw[pe]))] + collision_kc=np.unique(dst[pe[:3]]);assert len(collision_kc)==3 + nonkc=aplids[~np.isin(dst[aplids],kc)];nonkc_target=int(dst[nonkc[np.argmax(raw[nonkc])]]) + nodes=np.unique(np.r_[apl,pnrank[:16],kcrank[:16],pairpn,collision_kc,nonkc_target,[1197,1381,1511,82312,133666]]).astype(np.int32) + fixturegraph=EXP/'graphs/fixtures';graph(fixturegraph,nodes,full) + p=int(pnrank[0]);k=int(kcrank[0]);kc1,kc2,kc3=map(int,collision_kc) + designs=[('zero',100,[],None),('pn_pulse',300,[(0,p)],None), + ('kc_ref_boundary',100,[(t,k) for t in (0,1,21,22,23,44)],None), + ('two_sources',300,[(t,n) for t in (0,22,44,66,88) for n in (p,k)],None), + ('arrival_collision',120,[(0,pairpn),(0,kc1),(18,kc2),(21,kc1)],None), + ('natural_request',100,[(0,k)],dict(v={k:-44.0})), + ('apl_only',100,[(0,int(a)) for a in apl],None), + ('high_fanin',400,[(t,int(n)) for t in (0,22,44) for n in nodes if pscore[n]>0],dict(g={k:.123}))] + assert [v[0] for v in designs]==CFG['fixtures'] + for name,steps,requests,initial in designs:case(name,'fixture',steps,nodes,fixturegraph,requests,initial) + meta.iloc[nodes][['neuron_idx','body_id','annotation_class','annotation_type','neurotransmitter']].to_csv(EXP/'protocol/fixture-identities.csv',index=False) + save(EXP/'protocol/fixture-selection.json',dict(pn=p,kc=k,pair_PN=pairpn,arrival_KCs=collision_kc.tolist(),real_PN_KC_edge=best, + nonKC_APL_target=nonkc_target,method='Static raw-count ranks and exact metadata; retain ALL existing induced edges and full APL matrices/norms',runtime_selection=False)) + os.environ['CUDA_VISIBLE_DEVICES']=CFG['gpu_uuid'];sys.path.insert(0,str(legacy));sys.path.insert(0,str(legacy/'deterministic-runtime-v2')) + sys.path.insert(0,str(legacy/'terminal-scope-diagnostic-v1'));from diagnostic import queues + queues() + import torch + from phase4p_revalidate import door_inputs,make_drive + oc,panel,vectors,selection,mapping=door_inputs();cal=panel[panel.split=='calibration'].copy();assert len(panel)==12 and len(cal)==6 + assert oc['odor_encoder']['evaluation_seeds']==CFG['calibration']['seeds'] + assert float(oc['odor_encoder']['duration_ms'])==CFG['calibration']['duration_ms'] + cal.to_parquet(EXP/'protocol/calibration-panel.parquet',index=False);calrecords=[] + for mode in CFG['calibration']['modes']: + for oi,(_,odor) in enumerate(cal.iterrows()): + targets,rates=make_drive(oc,panel,vectors,selection,mapping,odor.odor_id,mode) + assert len(np.unique(targets))==len(targets),'Original encoder emits duplicate PN targets; audit instead of silently merging' + rates=np.asarray(rates,np.float32);steps=round(CFG['calibration']['duration_ms']/.1) + for seed in CFG['calibration']['seeds']: + gen=torch.Generator(device='cuda');gen.manual_seed(seed) + original_probability=rates*.1/1000.0 + explicit_probability=rates*np.float32(.1)/np.float32(1000.) + assert original_probability.dtype==np.float32 and np.array_equal(original_probability.view(np.uint32),explicit_probability.view(np.uint32)) + probs=torch.as_tensor(original_probability,dtype=torch.float32,device='cuda') + forced=(torch.rand((steps,len(targets)),generator=gen,device='cuda') 0, 'Missing/ambiguous self.base cutoff') + prefix = init.body[:cuts[0]] + # Executing no import or file-writing call is allowed in the extracted prefix. + require(not any(isinstance(node, (ast.Import, ast.ImportFrom, ast.With, ast.AsyncWith)) + for stmt in prefix for node in ast.walk(stmt)), 'Unexpected side-effect construct in initializer prefix') + expressions = [node for node in ast.walk(run) if isinstance(node, ast.Expr) and + isinstance(node.value, ast.Call) and isinstance(node.value.func, ast.Attribute) and + node.value.func.attr == 'add_' and isinstance(node.value.func.value, ast.Call) and + isinstance(node.value.func.value.func, ast.Attribute) and + isinstance(node.value.func.value.func.value, ast.Name) and + node.value.func.value.func.value.id == 'apl_state' and node.value.func.value.func.attr == 'mul_'] + inhibition = [node for node in ast.walk(run) if isinstance(node, ast.Assign) and + any(isinstance(target, ast.Name) and target.id == 'inhibition' for target in node.targets)] + decay = [node for node in ast.walk(run) if isinstance(node, ast.Assign) and + any(isinstance(target, ast.Name) and target.id == 'apl_decay' for target in node.targets)] + require(len(expressions) == len(inhibition) == len(decay) == 1, 'Ambiguous frozen APL operator extraction') + require(expressions[0].lineno < inhibition[0].lineno, 'Unexpected frozen APL update order') + rhs = inhibition[0].value + require(isinstance(rhs, ast.BinOp) and isinstance(rhs.op, ast.Mult), 'Unexpected inhibition gain expression') + gain_expression = ast.Expression(copy.deepcopy(rhs.right)) + provenance = {'source': str(SOURCE), 'sha256': SOURCE_SHA256, + 'initializer_line_start': prefix[0].lineno, 'initializer_line_end': prefix[-1].end_lineno, + 'excluded_self_base_line': init.body[cuts[0]].lineno, + 'decay_line': decay[0].lineno, 'state_update_line': expressions[0].lineno, + 'inhibition_line': inhibition[0].lineno, + 'initializer_source': '\n'.join(ast.get_source_segment(text, node) for node in prefix), + 'decay_source': ast.get_source_segment(text, decay[0]), + 'state_update_source': ast.get_source_segment(text, expressions[0]), + 'inhibition_source': ast.get_source_segment(text, inhibition[0]), + 'learning_initializer_suffix_executed': False, 'original_run_loop_executed': False} + return (_code(prefix, SOURCE), _code(decay, SOURCE), + _code([expressions[0], inhibition[0]], SOURCE), + compile(ast.fix_missing_locations(gain_expression), str(SOURCE), 'eval'), provenance) + + +def _canonical_files(): + return {name: CANONICAL/path for name, path in { + 'metadata': 'neuron-index/unified-metadata.parquet', + 'indptr': 'gpu/csr-indptr.int64.npy', 'indices': 'gpu/csr-indices.int32.npy', + 'raw': 'gpu/csr-weights.int64.npy'}.items()} + + +@lru_cache(maxsize=2) +def reconstruct(canonical_hashes, device_name): + """Cache only reconstructions tied to exact canonical bytes and source hash.""" + files = _canonical_files() + require(dict(canonical_hashes) == {name: digest(path) for name, path in files.items()}, + 'Canonical bytes changed before matrix reconstruction') + arrays = {name: np.load(files[name], mmap_mode='r', allow_pickle=False) + for name in ('indptr', 'indices', 'raw')} + require(arrays['indptr'].dtype == np.int64 and arrays['indices'].dtype == np.int32 and + arrays['raw'].dtype == np.int64, 'Canonical CSR dtypes changed') + require(arrays['indptr'].shape == (N + 1,) and arrays['indptr'][-1] == 25582938 and + arrays['indices'].shape == arrays['raw'].shape == (25582938,), 'Canonical graph shape changed') + require(int(arrays['raw'].sum()) == 124177617 and bool(np.all(arrays['raw'] > 0)), 'Canonical raw weights changed') + table = pq.read_table(files['metadata']) + require(table.num_rows == N, 'Canonical metadata count changed') + rt = ReadOnlyNamespace(n=N, dt=.1, device=torch.device(device_name), table=table, + indptr_np=arrays['indptr'], indices_np=arrays['indices'], raw_np=arrays['raw']) + view = types.SimpleNamespace() + init_code, decay_code, update_code, gain_code, provenance = extract_statements() + scope = {'self': view, 'rt': rt, 'np': np, 'torch': torch} + exec(init_code, scope) + require(not hasattr(view, 'base') and not hasattr(view, 'identity'), 'Initializer crossed the learning cutoff') + expected = {'input_matrix': view.input_gpu.detach().cpu().numpy().copy(), + 'output_matrix': view.output_gpu.detach().cpu().numpy().copy(), + 'input_raw': scope['input_matrix'], 'output_raw': scope['output_matrix'], + 'kc_globals': view.kc, 'apl_globals': view.apl, 'pn_globals': view.pn, + 'apl_out_ids': view.apl_out_ids} + for value in expected.values(): + value.flags.writeable = False + return view, expected, provenance, (decay_code, update_code, gain_code) + + +class TorchObservationProxy: + """Observe the result of the actual extracted torch.mv call unchanged.""" + def __init__(self): + self.drive = None + self.calls = 0 + + def __getattr__(self, name): + return getattr(torch, name) + + def mv(self, *args, **kwargs): + value = torch.mv(*args, **kwargs) + self.drive = value + self.calls += 1 + return value + + +def _up(value): + return np.nextafter(np.asarray(value, dtype=np.float64), np.inf) + + +def _add(a, b): + return _up(np.asarray(a, dtype=np.float64) + np.asarray(b, dtype=np.float64)) + + +def _mul(a, b): + return _up(np.asarray(a, dtype=np.float64) * np.asarray(b, dtype=np.float64)) + + +def _gamma(count, unit_roundoff): + require(count * unit_roundoff < 1, 'Gamma bound is undefined') + # Outward rounding on the division; the denominator is rounded downward. + numerator = _mul(count, unit_roundoff) + denominator = np.nextafter(1.0 - numerator, -np.inf) + return _up(numerator / denominator) + + +def _positive_dot_upper(state, matrix): + result = np.zeros(matrix.shape[1], np.float64) + for index in range(len(state)): + result = _add(result, _mul(state[index], matrix[index])) + return result + + +def _normal_or_zero(value, tiny): + value = np.asarray(value) + return bool(np.all((value == 0) | (value >= tiny))) + + +def _new_metric(): + return {'max_abs_error': 0.0, 'sum_abs_error': 0.0, 'elements': 0, + 'max_relative_error_nonzero_reference': 0.0, 'zero_reference_nonzero_actual': 0, + 'max_error_bound': 0.0, 'max_bound_utilization': 0.0, 'bound_violations': 0} + + +def _accumulate(metric, actual, reference, bound): + error = np.abs(np.asarray(actual, np.float64) - reference) + error_upper = np.where(error == 0, 0.0, _up(error)) + nonzero = reference != 0 + ratio = np.divide(error, np.abs(reference), out=np.zeros_like(error), where=nonzero) + usage = np.divide(error_upper, bound, out=np.zeros_like(error), where=bound > 0) + metric['max_abs_error'] = max(metric['max_abs_error'], float(error.max(initial=0))) + metric['sum_abs_error'] += float(error.sum(dtype=np.float64)) + metric['elements'] += error.size + metric['max_relative_error_nonzero_reference'] = max(metric['max_relative_error_nonzero_reference'], float(ratio.max(initial=0))) + metric['zero_reference_nonzero_actual'] += int(np.count_nonzero((~nonzero) & (error != 0))) + metric['max_error_bound'] = max(metric['max_error_bound'], float(np.max(bound, initial=0))) + metric['max_bound_utilization'] = max(metric['max_bound_utilization'], float(usage.max(initial=0))) + metric['bound_violations'] += int(np.count_nonzero(error_upper > bound)) + + +def validate(gpu_case_dir, bundle_dir, out_json): + """Validate one completed fixture; write one exclusive pure-JSON audit.""" + gpu_case_dir, bundle_dir, out_json = Path(gpu_case_dir), Path(bundle_dir), Path(out_json) + require(not out_json.exists(), 'Refusing to overwrite an existing component audit') + report = {'pass_all': False, 'scope': 'Frozen APL component extraction and recorded-spike replay only', + 'independent_cpu_closed_loop_oracle': False, 'brain_or_learning_parameters_changed': False, + 'candidate_function_called': False, 'gpu_case_dir': str(gpu_case_dir), 'bundle_dir': str(bundle_dir)} + try: + require(digest(SOURCE) == SOURCE_SHA256, 'Frozen source changed before component validation') + require(torch.cuda.is_available() and torch.cuda.device_count() == 1 and + '4090' in torch.cuda.get_device_name(), 'Require the one declared RTX4090') + environment = {'torch': str(torch.__version__), 'numpy': np.__version__, + 'gpu': torch.cuda.get_device_name(), 'cuda': torch.version.cuda, + 'cuda_matmul_allow_tf32': torch.backends.cuda.matmul.allow_tf32, + 'float32_matmul_precision': torch.get_float32_matmul_precision(), + 'deterministic_algorithms': torch.are_deterministic_algorithms_enabled(), + 'precision_flags_changed': False} + report['environment'] = environment + canonical_hashes = tuple(sorted((name, digest(path)) for name, path in _canonical_files().items())) + view, reconstructed, provenance, code = reconstruct(canonical_hashes, 'cuda:0') + bundle = {} + matrix_checks = {} + for name, (dtype, shape) in BUNDLE_ARRAYS.items(): + value = np.load(bundle_dir/(name+'.npy'), mmap_mode='r', allow_pickle=False) + require(value.dtype == dtype and value.ndim == len(shape or (0,)) and + (shape is None or value.shape == shape), 'Bundle schema mismatch: '+name) + bundle[name] = value + matrix_checks[name] = bit_equal(value, reconstructed[name]) + scales = json.loads((bundle_dir/'scales.json').read_text()) + for name in ('input_scale', 'output_scale'): + matrix_checks[name] = struct.pack(' 0, 'Empty APL trajectory cannot pass') + for name, width in (('drive', A), ('state', A), ('inhibition', K)): + require(traces[name].dtype == np.float32 and traces[name].shape == (steps, width) and + bool(np.isfinite(traces[name]).all()) and bool(np.all(traces[name] >= 0)), 'Invalid APL trace: '+name) + summary = json.loads((gpu_case_dir/'summary.json').read_text()) + require(summary.get('complete') is True and summary.get('pass_all') is True and + summary.get('steps') == steps and summary.get('completed_ticks') == steps, + 'GPU fixture did not complete its internal execution checks') + with np.load(gpu_case_dir/'state.npz', allow_pickle=False) as archive: + event_step, event_node = archive['step'].copy(), archive['neuron_idx'].copy() + require(event_step.dtype == event_node.dtype == np.int32 and event_step.ndim == 1 and + event_step.shape == event_node.shape, 'Invalid sparse actual spike event schema') + require(bool(np.all((event_step >= 0) & (event_step < steps))) and + bool(np.all((event_node >= 0) & (event_node < N))), 'Sparse event outside its clock/identity range') + require(np.array_equal(np.lexsort((event_node, event_step)), np.arange(len(event_step))), 'Actual events not sorted') + keys = event_step.astype(np.int64)*N + event_node + require(len(keys) < 2 or bool(np.all(np.diff(keys) > 0)), 'Duplicate actual spikes') + boundaries = np.searchsorted(event_step, np.arange(steps + 1)) + + proxy = TorchObservationProxy() + # Reconstructed original matrices are already on the original GPU/dtype. + replay_rt = types.SimpleNamespace(spike_u8=torch.zeros(N, dtype=torch.uint8, device=view.device)) + shadow = types.SimpleNamespace(input_gpu=view.input_gpu, output_gpu=view.output_gpu, rt=replay_rt, dt=.1) + scope = {'self': shadow, 'np': np, 'math': math, 'torch': proxy, + 'apl_state': torch.zeros(A, dtype=torch.float32, device=view.device)} + decay_code, update_code, gain_code = code + exec(decay_code, scope) + decay32 = np.float32(scope['apl_decay']) + gain32 = np.float32(eval(gain_code, {'np': np})) + require(0 < decay32 < 1 and gain32 > 0, 'Invalid extracted positive APL constants') + exact = {name: True for name in traces} + first_difference = None + I64 = np.asarray(bundle['input_matrix'], np.float64) + O64 = np.asarray(bundle['output_matrix'], np.float64) + require(bool(np.all(I64 >= 0)) and bool(np.all(O64 >= 0)), 'Positive-sum theory requires nonnegative matrices') + d, q = float(decay32), float(gain32) + u32, u64 = 2.0**-24, 2.0**-53 + gd32, gd64 = _gamma(N-1, u32), _gamma(N-1, u64) + g232, g264 = _gamma(2, u32), _gamma(2, u64) + gh32, gh64 = _gamma(2*A+1, u32), _gamma(2*A+1, u64) + state64 = np.zeros(A, np.float64) + e32 = np.zeros(A, np.float64) + e64 = np.zeros(A, np.float64) + previous32 = np.zeros(A, np.float64) + statistics = {name: _new_metric() for name in traces} + normal_arithmetic = _normal_or_zero(I64, np.finfo(np.float32).tiny) and _normal_or_zero(O64, np.finfo(np.float32).tiny) + max_active_terms = 0 + with torch.no_grad(): + for tick in range(steps): + fired = event_node[boundaries[tick]:boundaries[tick+1]] + replay_rt.spike_u8.zero_() + if len(fired): + replay_rt.spike_u8[torch.as_tensor(fired.astype(np.int64), device=view.device)] = 1 + before_calls = proxy.calls + exec(update_code, scope) + require(proxy.calls == before_calls + 1, 'Original update did not issue exactly one mv') + actual = {'drive': proxy.drive.detach().cpu().numpy(), + 'state': scope['apl_state'].detach().cpu().numpy(), + 'inhibition': scope['inhibition'].detach().cpu().numpy()} + for name in traces: + same = bit_equal(actual[name], traces[name][tick]) + exact[name] &= same + if not same and first_difference is None: + first_difference = {'tick': tick, 'field': name, + 'max_abs_error': float(np.max(np.abs(actual[name].astype(np.float64)-traces[name][tick])))} + # Float64 reference is measurement only and never feeds either brain. + selected = I64[:, fired] + drive64 = selected.sum(axis=1, dtype=np.float64) + max_active_terms = max(max_active_terms, int(np.count_nonzero(selected, axis=1).max(initial=0))) + Dupper = _up(drive64 / np.nextafter(1.0-gd64, -np.inf)) + drive_bound = _add(_mul(gd32, Dupper), _mul(gd64, Dupper)) + state32 = traces['state'][tick].astype(np.float64) + normal_arithmetic &= _normal_or_zero(d*previous32, np.finfo(np.float32).tiny) + normal_arithmetic &= _normal_or_zero(O64*state32[:, None], np.finfo(np.float32).tiny) + e32 = _add(_mul(d, e32), _add(_mul(g232, _add(_mul(d, previous32), Dupper)), + _mul(1.0+u32, _mul(gd32, Dupper)))) + e64 = _add(_mul(d, e64), _add(_mul(g264, _add(_mul(d, state64), Dupper)), + _mul(1.0+u64, _mul(gd64, Dupper)))) + state64 = d*state64 + drive64 + inhibition64 = np.matmul(state64, O64)*q + h32bound = _add(_mul(q, _positive_dot_upper(e32, O64)), + _mul(gh32, _mul(q, _positive_dot_upper(state32, O64)))) + h64bound = _add(_mul(q, _positive_dot_upper(e64, O64)), + _mul(gh64, _mul(q, _positive_dot_upper(state64, O64)))) + normal_arithmetic &= _normal_or_zero(q*np.matmul(state32, O64), np.finfo(np.float32).tiny) + _accumulate(statistics['drive'], traces['drive'][tick], drive64, drive_bound) + _accumulate(statistics['state'], traces['state'][tick], state64, _add(e32, e64)) + _accumulate(statistics['inhibition'], traces['inhibition'][tick], inhibition64, _add(h32bound, h64bound)) + previous32 = state32 + for metric in statistics.values(): + metric['mean_abs_error'] = metric.pop('sum_abs_error') / metric['elements'] + theory_applicable = bool(normal_arithmetic and not environment['cuda_matmul_allow_tf32'] and + environment['float32_matmul_precision'] == 'highest') + report.update(steps=steps, actual_spike_events=len(event_step), + ast_replay_bitwise_exact=exact, first_ast_difference=first_difference, + value_units={'drive': 'legacy normalized-contact spike drive', + 'state': 'legacy normalized-drive accumulator', 'inhibition': 'legacy mV'}, + decay_f32_bits=f'{int(decay32.view(np.uint32)):08x}', gain_f32_bits=f'{int(gain32.view(np.uint32)):08x}', + float64_measurement=statistics, + theoretical_bound={'applicable': theory_applicable, 'normal_or_zero_arithmetic': bool(normal_arithmetic), + 'gamma_definition': 'gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53', + 'drive_n': N-1, 'max_observed_nonzero_drive_terms': max_active_terms, + 'gamma_drive_f32': float(gd32), 'gamma_drive_f64': float(gd64), + 'state_recurrence': 'E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper', + 'output_bound': 'q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64', + 'assumptions': 'nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul', + 'bound_arithmetic': 'Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity', + 'tolerance_fitted_to_observed_error': False, + 'f64_values_used_to_drive_brain': False, + 'no_cpu_gpu_bitwise_expectation_for_f64_measurement': True}, + input_sha256={name: digest(gpu_case_dir/name) for name in + ('state.npz', 'summary.json', 'apl-drive.float32.npy', 'apl-state.float32.npy', 'apl-inhibition.float32.npy')}) + report['pass_all'] = bool(all(matrix_checks.values()) and all(exact.values()) and theory_applicable and + all(value['bound_violations'] == 0 for value in statistics.values())) + report['limitations'] = [ + 'AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.', + 'Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.', + 'Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.', + 'The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning.'] + save(out_json, report) + return report + except Exception as error: + report['error'] = {'type': type(error).__name__, 'message': str(error)} + if not out_json.exists(): + save(out_json, report) + raise + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('gpu_case_dir', type=Path) + parser.add_argument('bundle_dir', type=Path) + parser.add_argument('out_json', type=Path) + args = parser.parse_args() + result = validate(args.gpu_case_dir, args.bundle_dir, args.out_json) + print(json.dumps({'pass_all': result['pass_all'], 'steps': result['steps']}), flush=True) + if not result['pass_all']: + raise SystemExit(1) diff --git a/models/male-cns-lif/author-apl-lab-v2/verify_frozen.py b/models/male-cns-lif/author-apl-lab-v2/verify_frozen.py new file mode 100644 index 0000000..1a57885 --- /dev/null +++ b/models/male-cns-lif/author-apl-lab-v2/verify_frozen.py @@ -0,0 +1,35 @@ +"""Run old reference unchanged, writing only new experiment output.""" +import os +import sys +import time +import numpy as np +import pandas as pd +import yaml +from context import ROOT,EXP,CFG,save,check + + +def main(): + check();assert not (EXP/'regression/frozen-five.json').exists() + rtpath=ROOT/'models/male-cns-lif/deterministic-runtime-v2';pc=yaml.safe_load((rtpath/'phase4p.yaml').read_text()) + os.environ['CUDA_VISIBLE_DEVICES']=CFG['gpu_uuid'];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' + sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1')) + from diagnostic import queues + queues();sys.path.insert(0,str(ROOT/'models/male-cns-lif'));sys.path.insert(0,str(rtpath)) + import torch,phase4p + assert torch.cuda.mem_get_info()[0]>2*1024**3 + torch.cuda.reset_peak_memory_stats();t0=time.perf_counter();rt=phase4p.CanonicalRuntime(pc,rtpath,EXP);rows=[] + for spec in pc['reference_trials']: + queues();base=ROOT/'experiments/phase4p-deterministic-runtime/phase4c-v2/reference'/spec['name'] + ev=pd.read_parquet(base/'spike-events.parquet') + ref=dict(step=ev.step.to_numpy(),neuron_idx=ev.neuron_idx.to_numpy(),counts=np.load(base/'per-neuron-spike-counts.int32.npy'),v=np.load(base/'final-voltage.float32.npy'),g=np.load(base/'final-g.float32.npy')) + result=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms']) + row=dict(trial=spec['name'],exact={k:bool(np.array_equal(v,result[k])) for k,v in ref.items()},wall_seconds=result['metrics']['wall_seconds']) + rows.append(row);print(row,flush=True) + save(EXP/'regression/frozen-five.json',dict(pass_all=all(all(r['exact'].values()) for r in rows),trials=rows, + wall_seconds=time.perf_counter()-t0,peak_allocated_vram=int(torch.cuda.max_memory_allocated()),oracle='old frozen deterministic-v2 only')) + assert all(all(r['exact'].values()) for r in rows) + + +if 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closed-loop gate: True; original complete fixed six-CAL gate: False. +The independent Brian LIF and candidate use independent state but share the APL arithmetic component. Original frozen APL AST replay and a float64 error audit are separately recorded. +Author LIF uses float64; APL retains float32, tau20ms/gain0.01/normalization1, full original matrices and order. No learning/parameter search. +The explicitly hypothesized unconditional end-APL write includes refractory KC. Original fast APL mask leaves1,212 non-KC edges unreplaced; this scope limitation was not silently fixed. +Old checksums and five frozen reference trials: True. +Successfully compared cases: [{'case': 'apl_only', 'pass_all': True}, {'case': 'arrival_collision', 'pass_all': True}, {'case': 'cal-matched_energy-00-s51101', 'pass_all': True}, {'case': 'cal-physiological-00-s51101', 'pass_all': True}, {'case': 'high_fanin', 'pass_all': True}, {'case': 'kc_ref_boundary', 'pass_all': True}, {'case': 'natural_request', 'pass_all': True}, {'case': 'pn_pulse', 'pass_all': True}, {'case': 'two_sources', 'pass_all': True}, {'case': 'zero', 'pass_all': True}]. +Detailed CAL, exact comparisons, component errors, event/request traces and execution exceptions are linked from audit/final-status.json. +Wall/VRAM here include per-tick CPU synchronization, IPC and hashes. They are diagnostic costs, not runtime-performance benchmarks. +No heldout dynamic outputs, formal A/B, learning, game, reward, decoder or new candidate. +Next gate: Audit first failure in this explicitly declared hypothesis; no gain search, no heldout tuning, no conditioning diff --git a/registry/phase4ag-author-apl-r2/evidence/apl-bundle/provenance.json b/registry/phase4ag-author-apl-r2/evidence/apl-bundle/provenance.json new file mode 100644 index 0000000..8bb4ef7 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/apl-bundle/provenance.json @@ -0,0 +1,30 @@ +{ + "canonical_root": "/data/digital-drosophila/datasets/male-cns-v1.0/processed/neuron-only", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "source_sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "parameters": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "lock_file": "/data/digital-drosophila/experiments/phase4j-mb-physiology-repair/calibration/locked-parameters.json", + "lock_sha256": "1894cc4b4082ba9b2d069edf87c5eccb75292aa11947ad9c48acf661037ff3e7", + "full_dimensions": [ + [ + 2, + 166700 + ], + [ + 2, + 4064 + ] + ], + "APL": 2, + "KC": 4064, + "ALPN": 686, + "APL_fast_edges": 5845, + "APL_nonKC_edges": 1212, + "APL_nonKC_contacts": 13712, + "normalization_recomputed_in_original_order": true, + "raw_files_modified": false +} diff --git a/registry/phase4ag-author-apl-r2/evidence/apl-bundle/scales.json b/registry/phase4ag-author-apl-r2/evidence/apl-bundle/scales.json new file mode 100644 index 0000000..ce97422 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/apl-bundle/scales.json @@ -0,0 +1,4 @@ +{ + "input_scale": 49.65891647338867, + "output_scale": 42.34836959838867 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/audit/calibration-gate.json b/registry/phase4ag-author-apl-r2/evidence/audit/calibration-gate.json new file mode 100644 index 0000000..f4bdc67 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/audit/calibration-gate.json @@ -0,0 +1,5 @@ +{ + "pass_all": false, + "scope": "Complete original six-CAL representation gate, not heldout or learning", + "summary": "calibration/summary.json" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/audit/compare-apl_only.json b/registry/phase4ag-author-apl-r2/evidence/audit/compare-apl_only.json new file mode 100644 index 0000000..947eef6 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/audit/compare-apl_only.json @@ -0,0 +1,263 @@ +{ + "accepted_arriving_edges": 47, + "accepted_requests": 2, + "active_neurons": 2, + "apl_fast_edges_zeroed": 50, + "arriving_edges": 50, + "case": "apl_only", + "case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/cases/apl_only", + "checks": { + "apl.drive.bitwise_exact": true, + "apl.drive.finite": true, + "apl.drive.schema": true, + "apl.inhibition.bitwise_exact": true, + 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protocol/revision-input-equivalence.json +9057437695a0c2bd565de3600d1185e6351cbeb8128198fe2d8686437d8117ae regression/frozen-five.json +d6f97efe8f126034e417bbd612c1bb55f201e9c815ab7c1ddd8470979a4806b9 regression/old-integrity-after.json +d6f97efe8f126034e417bbd612c1bb55f201e9c815ab7c1ddd8470979a4806b9 regression/old-integrity-before.json +70f41bb2a262a7775c502627ef9bb6bb78e020167f518ba95e9b245c35642b9f regression/unit-tests-pinned-brian.log +99505b2b006e668633a612c4f0ea3affbeec185b8db7d0f44bad9b31177c4ea0 regression/unit-tests-science.log +7059ec8f8949cd2f5cbb723c77f754803ec8585492a0581647dbd832290d3c8d regression/unit-tests.json +21593297c40bee58516d4d7fd16e9d8d20ffdea5f8e0652fa5dd4e903b4d96ee manifest.json diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/apl_only.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/apl_only.json new file mode 100644 index 0000000..2f47373 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/apl_only.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 2, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 1.5e-323, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 406400, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 2.6e-322, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 4.69e-321, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/apl_only-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "15fe3726f298f2ebcf01f147d8dbd995d41c1e9413138d858f341b8aa0df48bd", + "apl-inhibition.float32.npy": "429a69417d057704b5a9364e5dc9968d7a690c83f43b096c9284be47835d4c9f", + "apl-state.float32.npy": "15fe3726f298f2ebcf01f147d8dbd995d41c1e9413138d858f341b8aa0df48bd", + "state.npz": "0a2750ea21a095925edb6f6642777f204b176eaa717f3b0aa429f0568fb9e809", + "summary.json": "fde9a6206c089b29761374a548bea0f249f32a47fe7724f954b0c4a02d9a2d85" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 100, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 0, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/arrival_collision.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/arrival_collision.json new file mode 100644 index 0000000..4493515 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/arrival_collision.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 3, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 240, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 0.0169758646495657, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 487680, + "max_abs_error": 4.8516818562660546e-08, + "max_bound_utilization": 6.446459311349652e-05, + "max_error_bound": 0.0014135775322797544, + "max_relative_error_nonzero_reference": 6.477506295857017e-07, + "mean_abs_error": 3.025335278028543e-09, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 240, + "max_abs_error": 9.64070396403116e-07, + "max_bound_utilization": 5.2935382778105945e-05, + "max_error_bound": 0.029058704062549626, + "max_relative_error_nonzero_reference": 5.319253846070637e-07, + "mean_abs_error": 2.718757893987664e-07, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/arrival_collision-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "5c00bd6558f55c0ac7b2b7dd1a3de32c1b66357de1cec433dc7e6bfb8e6650ec", + "apl-inhibition.float32.npy": "04a2f0809f96a13a8e07649f11f546c78e4173583f599f6c8a60109df8d8005a", + "apl-state.float32.npy": "fd345540fb68691ab401b60b39daf097ed1e5667c39396575bcd8f33e23e4edf", + "state.npz": "1b5d50cfd92a11ccc8217bae24b6899d59d0092a75ddf90e5b74bc21ec31bc98", + "summary.json": "641b1286caf31b42be66ea3197d6d7ffc34f774fa36b64e282127a0230786bbe" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 120, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 2, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/cal-matched_energy-00-s51101.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/cal-matched_energy-00-s51101.json new file mode 100644 index 0000000..85b989f --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/cal-matched_energy-00-s51101.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 29518, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 2000, + "max_abs_error": 1.1883676052093506e-06, + "max_bound_utilization": 1.3425302775238804e-05, + "max_error_bound": 0.08851700591819335, + "max_relative_error_nonzero_reference": 1.347329858244693e-07, + "mean_abs_error": 1.6856938600540162e-08, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 4064000, + "max_abs_error": 5.454211202682302e-06, + "max_bound_utilization": 9.84223576203938e-05, + "max_error_bound": 0.07556047939475269, + "max_relative_error_nonzero_reference": 9.900927935120239e-07, + "mean_abs_error": 4.106206890725126e-07, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 2000, + "max_abs_error": 0.0001467736110498663, + "max_bound_utilization": 9.011960851284882e-05, + "max_error_bound": 1.7234670898130762, + "max_relative_error_nonzero_reference": 9.065433270955913e-07, + "mean_abs_error": 3.576079289893559e-05, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/cal-matched_energy-00-s51101-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "664e679a6ff1d0a98a8a9c833b1bc6e5a48556487e2a181dba6a2b7db3c22f47", + "apl-inhibition.float32.npy": "5c61b09f281cb180e88f7753df1537790606b8a4d3bd38af6ce47ee73e6723d4", + "apl-state.float32.npy": "1912491567db6f8c9ef5d574a80a3a79e9ae22e215d9f027bd22b3afa73be185", + "state.npz": "3413e5df842dcae18784eac3fbc3ab2e5e01da74e8285e2fdee10a49f8b9eab9", + "summary.json": "236bd133f1256a41f14615129c4b1809ec185d3f0d24feb7c0c154214a6d1850" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 1000, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 11, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/cal-physiological-00-s51101.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/cal-physiological-00-s51101.json new file mode 100644 index 0000000..6ddf357 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/cal-physiological-00-s51101.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 29938, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 2000, + "max_abs_error": 5.327165126800537e-07, + "max_bound_utilization": 1.3081829823259631e-05, + "max_error_bound": 0.07437045311528964, + "max_relative_error_nonzero_reference": 1.312859770571549e-07, + "mean_abs_error": 1.6927719116210936e-08, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 4064000, + "max_abs_error": 7.491359403921649e-06, + "max_bound_utilization": 0.00011989178922831249, + "max_error_bound": 0.07492270812507368, + "max_relative_error_nonzero_reference": 1.205880367596719e-06, + "mean_abs_error": 4.967732996601039e-07, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 2000, + "max_abs_error": 0.0001710029721095907, + "max_bound_utilization": 0.00010836545907437195, + "max_error_bound": 1.7577510177979658, + "max_relative_error_nonzero_reference": 1.089951834730382e-06, + "mean_abs_error": 4.368356717728657e-05, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/cal-physiological-00-s51101-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "2a492856ea680dd6509ae2e5924228744beb6f37f34c0ac0ec1ef6bdbcb7ec2a", + "apl-inhibition.float32.npy": "0227a22a7c8f54321e7f02ef320a3bc22142b7efd63d9fc4a475653c86d4f025", + "apl-state.float32.npy": "fcf12e155c5922d0a93d3618205cc4828455e67f805271d8018743fe44c46721", + "state.npz": "ea7080a05e476eb987f536ec7fb327dc78f6e2f18a7d6e4cd91601d8446ffeae", + "summary.json": "71e5049fe55738ebd42830920c605865f096c6de32bcc7d44c643fcee27e1434" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 1000, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 10, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/high_fanin.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/high_fanin.json new file mode 100644 index 0000000..edf7dd0 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/high_fanin.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 157, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 800, + "max_abs_error": 4.887580871582031e-06, + "max_bound_utilization": 1.6511086743180562e-05, + "max_error_bound": 0.3599287436704336, + "max_relative_error_nonzero_reference": 1.6570114308471973e-07, + "mean_abs_error": 3.116670995950699e-08, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 1625600, + "max_abs_error": 1.2484126887812863e-06, + "max_bound_utilization": 8.058559947951667e-05, + "max_error_bound": 0.03670469855006011, + "max_relative_error_nonzero_reference": 8.123768266382489e-07, + "mean_abs_error": 1.3598974519396686e-07, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 800, + "max_abs_error": 3.000281999732124e-05, + "max_bound_utilization": 7.192595541700556e-05, + "max_error_bound": 0.9714612652236844, + "max_relative_error_nonzero_reference": 7.250582286303385e-07, + "mean_abs_error": 1.1814070948781286e-05, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/high_fanin-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "a4b78272e91043441d6c17b2aa9844122546a0a20332cf37d08f28c324a545fb", + "apl-inhibition.float32.npy": "c1950e4c348b57a70516a73fbd7b22cef62de32b2d39ee6bce72605d6768dd50", + "apl-state.float32.npy": "951c2cc005cace059b0648d23809566f03a45d3c5e001de9c795fc84c42936b8", + "state.npz": "d4408b09e7099f9293340ece2bfd381d5827829e7efb2e27f1ac8de59e70ac40", + "summary.json": "605ad105f67caba0649c96c51810cd1409e84cad3833143c80af125dded958d2" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 400, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 25, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/kc_ref_boundary.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/kc_ref_boundary.json new file mode 100644 index 0000000..5051bce --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/kc_ref_boundary.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 4, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 0.0440564111223974, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 406400, + "max_abs_error": 1.9029258452318842e-07, + "max_bound_utilization": 4.4126254222663114e-05, + "max_error_bound": 0.005784441916246177, + "max_relative_error_nonzero_reference": 4.4317091098579625e-07, + "mean_abs_error": 6.804216737954364e-09, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 3.576507783265015e-06, + "max_bound_utilization": 3.6536565512615554e-05, + "max_error_bound": 0.11890991658573181, + "max_relative_error_nonzero_reference": 3.669348968748365e-07, + "mean_abs_error": 5.966829373615923e-07, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/kc_ref_boundary-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "87b9b4d999a0ae584aafffbbce8603333b55467122495501a81fd3f396a0de15", + "apl-inhibition.float32.npy": "f915b04b78d1660e20f0f97874564454c97dc306a84ae0383cdb09b0ae5bf67f", + "apl-state.float32.npy": "db75fd304090f4d00ed3c7e5f80c14be74c0bfc99ac95984a710a3a143ef3625", + "state.npz": "cfabddcd199a03da9acb981c3791031d8d5926f244e3d3b5d9556133e4b23868", + "summary.json": "c9a200cdabe861ba45985d027b7766e5ef5e7f30878080ab9dd1fdc4d67ce2f3" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 100, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 1, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/natural_request.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/natural_request.json new file mode 100644 index 0000000..c9a0e91 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/natural_request.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 2, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 0.0440564111223974, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 406400, + "max_abs_error": 2.983958111157925e-08, + "max_bound_utilization": 2.592836429347364e-05, + "max_error_bound": 0.002143175293067466, + "max_relative_error_nonzero_reference": 2.6046869456955393e-07, + "mean_abs_error": 1.4655995503399407e-09, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 5.368426725382847e-07, + "max_bound_utilization": 1.816095230598335e-05, + "max_error_bound": 0.044056937070883116, + "max_relative_error_nonzero_reference": 1.824341540183155e-07, + "mean_abs_error": 1.2347406080026603e-07, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/natural_request-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "694944eb4e7ede2146035bd4a3725a7ca84b59a2eb499ebab036998fc05c88b4", + "apl-inhibition.float32.npy": "6652e316e26fdb7a1089fd8d586eecbb53d9643fa02c2f79722956fb70ce28d2", + "apl-state.float32.npy": "1b5d2c9959509781490fd5518682f448eaaafa0737d4de841ea0b816aeac0a11", + "state.npz": "770b7e29adbab3d29de20a77c9e3977f0da7d9cf2aaf68f346e1953683fb632c", + "summary.json": "9dd57f49a08b8dc16aa76a73fad8d5beaca3a3a13c7b0b6bd3377df3b8ed22a5" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 100, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 1, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/pn_pulse.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/pn_pulse.json new file mode 100644 index 0000000..424e9e8 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/pn_pulse.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 1, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 600, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 0.021624017139767585, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 1219200, + "max_abs_error": 2.5551622206965163e-08, + "max_bound_utilization": 8.782876291952778e-05, + "max_error_bound": 0.0008170294997358125, + "max_relative_error_nonzero_reference": 8.841340802705443e-07, + "mean_abs_error": 1.8512844276510413e-09, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 600, + "max_abs_error": 6.090067741215677e-07, + "max_bound_utilization": 8.10124097075475e-05, + "max_error_bound": 0.021624275288781088, + "max_relative_error_nonzero_reference": 8.154926639445106e-07, + "mean_abs_error": 1.5725084035383974e-07, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/pn_pulse-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "d36ee1bc57cdb36c38fefe93da0493e17dabf899b02b2222468bd67bcad5edf5", + "apl-inhibition.float32.npy": "5d3c55e13884d2a86296e3826d4ebab1ca491458fee90cbb11972b0d895c76aa", + "apl-state.float32.npy": "b680c32977a56e9551cbebef9c2d14acca31e32f44c6109a7f0b2fa90a9bb63a", + "state.npz": "cd3596d06b03fc4702dea4b528c7c704659ff9316e46bdf0b804b6a3ec3b9804", + "summary.json": "c4894ccbd533402b4347c778e60b7e100f9dca625e063075a38ae95f7a56fc4d" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 300, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 1, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/two_sources.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/two_sources.json new file mode 100644 index 0000000..a00e4c2 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/two_sources.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 13, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 600, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 0.0440564111223974, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 1219200, + "max_abs_error": 2.626378379222416e-07, + "max_bound_utilization": 5.477468793595109e-05, + "max_error_bound": 0.008708151261175854, + "max_relative_error_nonzero_reference": 5.510089963658295e-07, + "mean_abs_error": 1.4450305411278388e-08, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 600, + "max_abs_error": 4.6134957809584876e-06, + "max_bound_utilization": 4.419862852467115e-05, + "max_error_bound": 0.17901217817239432, + "max_relative_error_nonzero_reference": 4.44605370798596e-07, + "mean_abs_error": 1.2647788049158225e-06, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/two_sources-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "656c1fb8a53ca1ed7c23b31f0b87a394ebca2af6e8be118d2dda6c972a5da180", + "apl-inhibition.float32.npy": "c31530f2d09305d73bd37438ca1ed7e1d2b3cedfcfe6e4cced94279377b32081", + "apl-state.float32.npy": "919e10a7ccb6c6ff4bd240dfe7a70f654df72068c254c63e67d8ffd6eea1ca03", + "state.npz": "ad50b02424e0dda0894e245442ae07ec02f4508ad4110795ebdd146067571975", + "summary.json": "773bcb4f7acdf4ac8863d9bc0c4fe5fc3604231e01ebb6e225c6ffbf0adcdee5" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 300, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 1, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/component-audit/zero.json b/registry/phase4ag-author-apl-r2/evidence/component-audit/zero.json new file mode 100644 index 0000000..43a9bb2 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/component-audit/zero.json @@ -0,0 +1,151 @@ +{ + "actual_spike_events": 0, + "ast_replay_bitwise_exact": { + "drive": true, + "inhibition": true, + "state": true + }, + "brain_or_learning_parameters_changed": false, + "bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "bundle_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "apl_out_ids.npy": "51e2cb25752d8eaa3ab1950fdd4ae9580f71644b8d435f875abb63efa6ab344c", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "input_raw.npy": "6da61bb9db93e11c687632f8ae7a5ffec3734d506b790f40c38da16c7e6d1028", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "output_raw.npy": "a142a2b49b070d1a03c8ba4b7fa53fa97fa3005ad70326e0a93204570e5beea1", + "pn_globals.npy": "ed574f42559455069679f8ce3b339395efa9398ec13e199eab47959533370805", + "provenance.json": "c3441f8424032f6f9c28aa19370928407c46eaea942eb7581a77214ed38bf57e", + "scales.json": "fe2decd3949f73006966227cd6a1c4a7d43885f977311ffabb9ababdedd44daa" + }, + "candidate_function_called": false, + "canonical_sha256": { + "indices": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "indptr": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "metadata": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63", + "raw": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b" + }, + "decay_f32_bits": "3f7eb923", + "environment": { + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "deterministic_algorithms": false, + "float32_matmul_precision": "highest", + "gpu": "NVIDIA GeForce RTX 4090", + "numpy": "2.2.6", + "precision_flags_changed": false, + "torch": "2.14.0+cu130" + }, + "extracted_source": { + "decay_line": 126, + "decay_source": "apl_decay = np.float32(math.exp(-self.dt / 20.0))", + "excluded_self_base_line": 83, + "inhibition_line": 154, + "inhibition_source": "inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)", + "initializer_line_end": 82, + "initializer_line_start": 55, + "initializer_source": "self.rt = rt\nself.n = rt.n\nself.device = rt.device\nself.dt = rt.dt\nmeta = rt.table.to_pandas().sort_values(\"neuron_idx\").reset_index(drop=True)\nself.meta = meta\ncls = meta.annotation_class.fillna(\"\").astype(str).to_numpy()\ntyp = meta.annotation_type.fillna(\"\").astype(str).to_numpy()\nself.kc = np.flatnonzero(cls == \"Kenyon_Cell\").astype(np.int32)\nself.mbon = np.flatnonzero(cls == \"MBON\").astype(np.int32)\nself.pn = np.flatnonzero(cls == \"ALPN\").astype(np.int32)\nself.apl = np.flatnonzero(typ == \"APL\").astype(np.int32)\nif len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError(\"frozen APL/KC identity changed\")\nself.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))\nidx = rt.indices_np\nraw = rt.raw_np\napl_out = np.isin(self.sources, self.apl)\nself.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)\ninput_matrix = np.zeros((len(self.apl), self.n), np.float32)\noutput_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)\nkc_lookup = np.full(self.n, -1, np.int32)\nkc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)\nallowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)\napl_lookup = {int(x): i for i, x in enumerate(self.apl)}\nfor eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])\nfor eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):\n output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])\nself.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))\nself.output_scale = float(output_matrix[output_matrix > 0].mean())\nself.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)\nself.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)\nself.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)\nself.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)", + "learning_initializer_suffix_executed": false, + "original_run_loop_executed": false, + "sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "state_update_line": 153, + "state_update_source": "apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))" + }, + "first_ast_difference": null, + "float64_measurement": { + "drive": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 1.5e-323, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "inhibition": { + "bound_violations": 0, + "elements": 406400, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 2.6e-322, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + }, + "state": { + "bound_violations": 0, + "elements": 200, + "max_abs_error": 0.0, + "max_bound_utilization": 0.0, + "max_error_bound": 4.69e-321, + "max_relative_error_nonzero_reference": 0.0, + "mean_abs_error": 0.0, + "zero_reference_nonzero_actual": 0 + } + }, + "gain_f32_bits": "3c23d70a", + "gpu_case_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu/zero-r0", + "independent_cpu_closed_loop_oracle": false, + "input_sha256": { + "apl-drive.float32.npy": "15fe3726f298f2ebcf01f147d8dbd995d41c1e9413138d858f341b8aa0df48bd", + "apl-inhibition.float32.npy": "429a69417d057704b5a9364e5dc9968d7a690c83f43b096c9284be47835d4c9f", + "apl-state.float32.npy": "15fe3726f298f2ebcf01f147d8dbd995d41c1e9413138d858f341b8aa0df48bd", + "state.npz": "acf8be16ad5ac4fab3a0fb96cd1b322b86cb0089f506f57a7d4a6043f4e18dff", + "summary.json": "737fba9a39f9c30a2e4e337df01f1fc30ad482ca0357c8a3c8cd92170f2e35db" + }, + "limitations": [ + "AST shadow and candidate use the same original Torch/CUDA primitives; this validates preserved component semantics, not an independent CPU whole-brain oracle.", + "Float64 is a non-feedback mathematical measurement with explicit gamma error bounds, not replacement APL dynamics.", + "Replay uses the recorded actual spike train; it does not establish equivalence for untested closed-loop inputs.", + "The N-1 positive-sum bound is deliberately conservative; passing it does not prove physiological validity or learning." + ], + "matrix_checks": { + "apl_globals": true, + "apl_out_ids": true, + "input_matrix": true, + "input_raw": true, + "input_scale": true, + "kc_globals": true, + "output_matrix": true, + "output_raw": true, + "output_scale": true, + "pn_globals": true + }, + "pass_all": true, + "reconstructed_array_sha256": { + "apl_globals": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "apl_out_ids": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "input_matrix": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "input_raw": "a7153b9a7c7cdc2700bd97f9ed96c7402c360172ebb739099303d057c9bbea11", + "kc_globals": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "output_raw": "2a3e4f18ac86c6d557f5dc85b6f99060fc1bd2d4c0c53241714acc2aee24dc0e", + "pn_globals": "56c4eb47cb535fcf990017b13a29d026f91a28397e6c206bd24a99598d2588ff" + }, + "scope": "Frozen APL component extraction and recorded-spike replay only", + "steps": 100, + "theoretical_bound": { + "applicable": true, + "assumptions": "nonnegative f32 matrices, spikes exactly 0/1, fixed stored f32 decay/gain, normal finite RN binary32/64 operations; no reduced-precision matmul", + "bound_arithmetic": "Each positive bound operation is rounded toward +infinity via nextafter; denominator toward -infinity", + "drive_n": 166699, + "f64_values_used_to_drive_brain": false, + "gamma_definition": "gamma_n=(n*u)/(1-n*u), upward-rounded; u32=2^-24,u64=2^-53", + "gamma_drive_f32": 0.010035750241849791, + "gamma_drive_f64": 1.8507306798541427e-11, + "max_observed_nonzero_drive_terms": 0, + "no_cpu_gpu_bitwise_expectation_for_f64_measurement": true, + "normal_or_zero_arithmetic": true, + "output_bound": "q*sum(B*E_p) + gamma_(2*A+1),p*q*sum(B*abs(s_p)); compare with E32+E64", + "state_recurrence": "E_p[t] <= d*E_p[t-1] + gamma_2,p*(d*abs(s_p[t-1])+D_upper) + (1+u_p)*gamma_(N-1),p*D_upper", + "tolerance_fitted_to_observed_error": false + }, + "value_units": { + "drive": "legacy normalized-contact spike drive", + "inhibition": "legacy mV", + "state": "legacy normalized-drive accumulator" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/gpu-reference/extension.json b/registry/phase4ag-author-apl-r2/evidence/gpu-reference/extension.json new file mode 100644 index 0000000..2406fe8 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/gpu-reference/extension.json @@ -0,0 +1,17 @@ +{ + "binary": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/gpu-reference/extension-build/male_cns_lif_deterministic_v2_ext.so", + "binary_sha256": "b057d18b0f83cd1e1a72c38796d4c1915b8cd955afe96317b2fa4dcfe20b850a", + "build_seconds": 54.775170615001116, + "flags": { + "cuda": [ + "-O3", + "--fmad=false", + "-std=c++20" + ], + "cxx": [ + "-O3", + "-ffp-contract=off" + ] + }, + "status": "PASS" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r0/apl-component.json b/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r0/apl-component.json new file mode 100644 index 0000000..4a6de0c --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r0/apl-component.json @@ -0,0 +1,45 @@ +{ + "ok": true, + "protocol": "phase4ag-apl-v1", + "n_neurons": 166700, + "n_apl": 2, + "n_kc": 4064, + "dt_ms": 0.1, + "files_sha256": { + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19" + }, + "array_sha256": { + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6" + }, + "decay_f32_bits": "3f7eb923", + "gain_f32_bits": "3c23d70a", + "environment": { + "torch": "2.14.0+cu130", + "numpy": "2.2.6", + "cuda": "13.0", + "cudnn": 92400, + "device_name": "NVIDIA GeForce RTX 4090", + "device_index": 0, + "device_capability": [ + 8, + 9 + ], + "deterministic_algorithms": false, + "cudnn_deterministic": false, + "cudnn_benchmark": false, + "cuda_matmul_allow_tf32": false, + "cudnn_allow_tf32": true, + "float32_matmul_precision": "highest", + "CUBLAS_WORKSPACE_CONFIG": null, + "precision_configuration_changed_by_component": false + }, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "h_units": "legacy mV", + "initial_state": "float32 zeros" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r0/summary.json b/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r0/summary.json new file mode 100644 index 0000000..221fc62 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r0/summary.json @@ -0,0 +1,106 @@ +{ + "case": "apl_only", + "complete": true, + "pass_all": true, + "pass_all_scope": "internal finite/recording/event-count integrity only; independent equivalence not claimed", + "steps": 100, + "completed_ticks": 100, + "nodes": 44, + "edges": 287, + "precision": "float64", + "apl_precision": "float32", + "state_units": "SI volts", + "total_spikes": 2, + "requested": 2, + "accepted_requests": 2, + "rejected_requests": 0, + "arriving_edges": 50, + "accepted_arriving_edges": 47, + "counts_match_sparse_events": true, + "all_finite": true, + "record_history": true, + "apl_fast_edges_zeroed": 50, + "kc_targets_present": 20, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "apl_component": { + "ok": true, + "protocol": "phase4ag-apl-v1", + "n_neurons": 166700, + "n_apl": 2, + "n_kc": 4064, + "dt_ms": 0.1, + "files_sha256": { + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19" + }, + "array_sha256": { + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6" + }, + "decay_f32_bits": "3f7eb923", + "gain_f32_bits": "3c23d70a", + "environment": { + "torch": "2.14.0+cu130", + "numpy": "2.2.6", + "cuda": "13.0", + "cudnn": 92400, + "device_name": "NVIDIA GeForce RTX 4090", + "device_index": 0, + "device_capability": [ + 8, + 9 + ], + "deterministic_algorithms": false, + "cudnn_deterministic": false, + "cudnn_benchmark": false, + "cuda_matmul_allow_tf32": false, + "cudnn_allow_tf32": true, + "float32_matmul_precision": "highest", + "CUBLAS_WORKSPACE_CONFIG": null, + "precision_configuration_changed_by_component": false + }, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "h_units": "legacy mV", + "initial_state": "float32 zeros" + }, + "apl_schedule": "end, after threshold/reset and guarded arrivals, unconditional all KC", + "apl_si_conversion": "h32 -> float64 -> multiply float64 0.001 -> separate float64 subtraction", + "build": { + "sources": { + "author_ext.cpp": "208a937d17bbb041b9bf6b25b89264ee566830b60609e58491b44583d8541f15", + "author_ext.cu": "669f166e18ce9a3f63fc723db8b0a8b74f7fa4757194b1bd65b4cf6008a7787e" + }, + "torch": "2.14.0+cu130", + "torch_cuda": "13.0", + "arch_list": "8.9", + "cuda_flags": [ + "-O2", + "--fmad=false", + "--ftz=false", + "--prec-div=true", + "--prec-sqrt=true" + ] + }, + "sealed_ae_backend_sha256": "03758d07572f4b44d272059d9308fac534268ad201b57bdd77d69fee95e67278", + "shared_apl_component_oracle_limitation": true, + "oracle_validation_claim": false, + "learning": false, + "performance_runtime_claim": false, + "cpu_sync_each_tick": true, + "build_or_cache_seconds": 0.16583250899930135, + "diagnostic_simulation_seconds": 0.3160589560002336, + "wall_seconds": 0.7141985609996482, + "peak_allocated_vram_bytes": 10754560, + "peak_reserved_vram_bytes": 23068672, + "input_sha256": { + "spec.json": "ab07aab0cd2a76dbf73c5838ce354aefd7acafd406645f6ccd2be3d2b76abdc8", + "requests.npz": "6b633838a29f5aee68f83e7fe97e95e5cf15063280a639114d404afb4dd5cbe8", + "base_weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865", + "coefficients.json": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r1/apl-component.json b/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r1/apl-component.json new file mode 100644 index 0000000..4a6de0c --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/gpu/apl_only-r1/apl-component.json @@ -0,0 +1,45 @@ +{ + "ok": true, + "protocol": "phase4ag-apl-v1", + "n_neurons": 166700, + "n_apl": 2, + "n_kc": 4064, + "dt_ms": 0.1, + "files_sha256": { + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19" + }, + "array_sha256": { + "input_matrix.npy": 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+ "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/compiled-code.json new file mode 100644 index 0000000..43939c5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n lastspike = _array_default_neurons_lastspike\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_v[_idx] = v\n _array_default_neurons_g[_idx] = g\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/generated-code.json new file mode 100644 index 0000000..e46548e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n lastspike = _array_default_neurons_lastspike\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_v[_idx] = v\n _array_default_neurons_g[_idx] = g\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/input-audit.json new file mode 100644 index 0000000..2d930bc --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8", + "requests.npz": "6b633838a29f5aee68f83e7fe97e95e5cf15063280a639114d404afb4dd5cbe8", + "spec.json": "ab07aab0cd2a76dbf73c5838ce354aefd7acafd406645f6ccd2be3d2b76abdc8" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 10.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "apl_only", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 100, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/output-schema.json new file mode 100644 index 0000000..769b764 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 100, + 2 + ], + "inhibition": [ + 100, + 4064 + ], + "state": [ + 100, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 100, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/queue-audit.json new file mode 100644 index 0000000..aaad63e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 100, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 100, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/summary.json new file mode 100644 index 0000000..6f7c962 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/apl_only/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 100, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 2, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 100, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "f42e10e38960132803d2eb1c3ccf3570e7a8d6618ed2d77d60bf830bf2c4812e", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "apl_only", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1111370366, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "306299f8fb7e6901ce92c3e146566f8ce1f65138090daefbbe367f013d2eebaa", + "ref_rejected_requests": 0, + "requests": 2, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 2, + "state_hashes_sha256": "e2936c94d40319270491846542963ea9439c94891008003edf2436366ee42e8e", + "steps": 100, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/compiled-code.json new file mode 100644 index 0000000..f410449 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/generated-code.json new file mode 100644 index 0000000..557202e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/input-audit.json new file mode 100644 index 0000000..4dc160b --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8", + "requests.npz": "2b5c4d1491dd65ac70a2e23b8d4db9be7f0798fabff114c308a753ccb58a051f", + "spec.json": "a80077e0d792a9aa4789d47ac69c751e5e79014a5b7f3a8f650e6147613b5feb" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 12.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "arrival_collision", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 120, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/output-schema.json new file mode 100644 index 0000000..6ba0376 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 120, + 2 + ], + "inhibition": [ + 120, + 4064 + ], + "state": [ + 120, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 120, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/queue-audit.json new file mode 100644 index 0000000..294441b --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 120, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 120, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/summary.json new file mode 100644 index 0000000..81c5f46 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/arrival_collision/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 120, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 3, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 120, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "39136eaca57a5fc7eee6e4c41af79421a0e5931cdfba474e33949a775d5db53e", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "arrival_collision", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1096166406, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "a14e60c29eee17afa66692052d5aa7661c6269785a5672ab116b9ca096a247e0", + "ref_rejected_requests": 1, + "requests": 4, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 3, + "state_hashes_sha256": "0bb726cf2815518ea4fe632d6eb162fecd1865cef54ec0fa25ba22a20f479772", + "steps": 120, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/apl-mask-audit.json new file mode 100644 index 0000000..28143e5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 4064, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "a8c19cde743bd456bcb2e988d504f805b6943d6ceb97cd241d6e1e472ab5a591", + "count": 5845, + "effective_weights_file_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b", + "effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/compiled-code.json new file mode 100644 index 0000000..6c220b1 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n lastspike = _array_default_neurons_lastspike\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_v[_idx] = v\n _array_default_neurons_g[_idx] = g\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/generated-code.json new file mode 100644 index 0000000..062042a --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n lastspike = _array_default_neurons_lastspike\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_v[_idx] = v\n _array_default_neurons_g[_idx] = g\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/input-audit.json new file mode 100644 index 0000000..5ac5a44 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/input-audit.json @@ -0,0 +1,72 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "requests.npz": "d49fb00b3870c8e923c0e5fc903536c0d8985555e5052c23ebc6f963c8af5314", + "spec.json": "c6bece47f42a0a262f907d16a50ef6e12c37029f84c2184b5a15f79a7388c680" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fb9033398abb7585ae63472ced9d8c8cab787f69965dc9ce5fa409ab105bc1bc", + "connectivity.parquet": "1362c1102b74f5552f4b01fca956574fd6252f749e31493c4a00a1bc9eab00b3", + "dst.npy": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "edge_id.npy": "49260615ff2b6432ce3dea9ce2c7ab8f83d06c2fb7491e58e08355fcedf86a30", + "nodes.npy": "5b511a98e8fbf8d65bbc70b93e75ecae19366f650b7842a45611bfad62ed83dc", + "ptr.npy": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "signed_raw.npy": "f9884ccb8930b32420b27c69271df78034a8350fe5a5c9b76a63c259268efc09", + "src.npy": "bfd625ce690fc21eb0a8b9dede831688ab73b789045ba83ddf4baeefeddf1fc3", + "weights.npy": "462839ffc3feb8f6c278044488082b1c8e410e7f23229d6b5e29d1b7042416a1" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 100.0, + "expected_external_events": 102.24431762695312, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/full", + "kind": "calibration", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "mode": "matched_energy", + "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": "cal-matched_energy-00-s51101", + "odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N", + "odor_index": 0, + "odor_name": "ethyl (R)-(-)-3-hydroxybutanoate", + "output_budget_bytes": 16106127360, + "precision": "float64", + "rate_sum_hz": 1022.4431762695312, + "record_history": false, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "seed": 51101, + "steps": 1000, + "target_count": 48, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/output-schema.json new file mode 100644 index 0000000..f0e7ecd --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 1000, + 2 + ], + "inhibition": [ + 1000, + 4064 + ], + "state": [ + 1000, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": false, + "history_shape": [ + 1000, + 166700 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/queue-audit.json new file mode 100644 index 0000000..11368a5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 1000, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 1000, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/summary.json new file mode 100644 index 0000000..8148c64 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-matched_energy-00-s51101/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 5845, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 1000, + "apl_fast_edges_zeroed": 5845, + "applicable_requests": 89, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 1000, + "edges": 25582938, + "effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9", + "events_sha256": "f57af8126b151c9af0fef3b065f70ceae72e270b001b8447f91f10bc161cd0bd", + "history_recorded": false, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 4064, + "name": "cal-matched_energy-00-s51101", + "no_poi": true, + "no_training": true, + "nodes": 166700, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1628114768, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "628317164ce25c2b0b90236fe0f68d02cbcec586f9676cc03a4be1ed7e53d622", + "ref_rejected_requests": 28, + "requests": 117, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 29518, + "state_hashes_sha256": "7d07fe889f1f968fc83efb8d661cd6803e430dcf0914e75b2476db2e5c987396", + "steps": 1000, + "weights_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/apl-mask-audit.json new file mode 100644 index 0000000..28143e5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 4064, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "a8c19cde743bd456bcb2e988d504f805b6943d6ceb97cd241d6e1e472ab5a591", + "count": 5845, + "effective_weights_file_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b", + "effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/compiled-code.json new file mode 100644 index 0000000..4383ac8 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n t = _array_defaultclock_t[0]\n lastspike = _array_default_neurons_lastspike\n v = _array_default_neurons_v.copy()\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n rfc = _array_default_neurons_rfc\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/generated-code.json new file mode 100644 index 0000000..7673a41 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n t = _array_defaultclock_t[0]\n lastspike = _array_default_neurons_lastspike\n v = _array_default_neurons_v.copy()\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n rfc = _array_default_neurons_rfc\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/input-audit.json new file mode 100644 index 0000000..de3830c --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/input-audit.json @@ -0,0 +1,72 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "requests.npz": "9f87b434e8da524ce31b4563ad89ab47189dc7035c9f827840f3250aad81083d", + "spec.json": "a664118cd5506ac7e093e43234a586b9c6fe06b9d9e3a2975d264975f6789bb7" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fb9033398abb7585ae63472ced9d8c8cab787f69965dc9ce5fa409ab105bc1bc", + "connectivity.parquet": "1362c1102b74f5552f4b01fca956574fd6252f749e31493c4a00a1bc9eab00b3", + "dst.npy": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150", + "edge_id.npy": "49260615ff2b6432ce3dea9ce2c7ab8f83d06c2fb7491e58e08355fcedf86a30", + "nodes.npy": "5b511a98e8fbf8d65bbc70b93e75ecae19366f650b7842a45611bfad62ed83dc", + "ptr.npy": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4", + "signed_raw.npy": "f9884ccb8930b32420b27c69271df78034a8350fe5a5c9b76a63c259268efc09", + "src.npy": "bfd625ce690fc21eb0a8b9dede831688ab73b789045ba83ddf4baeefeddf1fc3", + "weights.npy": "462839ffc3feb8f6c278044488082b1c8e410e7f23229d6b5e29d1b7042416a1" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 100.0, + "expected_external_events": 105.90211181640625, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/full", + "kind": "calibration", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "cal-physiological-00-s51101", + "odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N", + "odor_index": 0, + "odor_name": "ethyl (R)-(-)-3-hydroxybutanoate", + "output_budget_bytes": 16106127360, + "precision": "float64", + "rate_sum_hz": 1059.0211181640625, + "record_history": false, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "seed": 51101, + "steps": 1000, + "target_count": 48, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/output-schema.json new file mode 100644 index 0000000..f0e7ecd --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 1000, + 2 + ], + "inhibition": [ + 1000, + 4064 + ], + "state": [ + 1000, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": false, + "history_shape": [ + 1000, + 166700 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/queue-audit.json new file mode 100644 index 0000000..11368a5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 1000, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 1000, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/summary.json new file mode 100644 index 0000000..0308c4c --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/cal-physiological-00-s51101/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 5845, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 1000, + "apl_fast_edges_zeroed": 5845, + "applicable_requests": 93, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 1000, + "edges": 25582938, + "effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9", + "events_sha256": "4980889373ddb0681fd3444881a59242681890b9c0fa0aa20154ad9376c3cdbd", + "history_recorded": false, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 4064, + "name": "cal-physiological-00-s51101", + "no_poi": true, + "no_training": true, + "nodes": 166700, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1384814228, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "8ed73b17d00fc6a67b807c3d36f5c9b82a974f17f1ce2d3bfd9f8d6c15de5a01", + "ref_rejected_requests": 28, + "requests": 121, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 29938, + "state_hashes_sha256": "f9134739ed6a2b7558957a2bc47ad375d831b733d4c259a826e4be296bcb0cc9", + "steps": 1000, + "weights_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/compiled-code.json new file mode 100644 index 0000000..dc94f2e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/generated-code.json new file mode 100644 index 0000000..429bd0a --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/input-audit.json new file mode 100644 index 0000000..3d9e824 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "61d667618406364f38d34a9eb2af328a74c662b17b63dc9ac33f90ab06952df7", + "requests.npz": "817f0fb0919c87927068fb4923854e2d2bfd1f362a6401695a26127ac032e019", + "spec.json": "39adb14fd5ca410840d6ed69e76c818bf82545bfca62b623de67dea5fdc00164" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 40.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "high_fanin", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 400, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/output-schema.json new file mode 100644 index 0000000..d0b9e94 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 400, + 2 + ], + "inhibition": [ + 400, + 4064 + ], + "state": [ + 400, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 400, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/queue-audit.json new file mode 100644 index 0000000..35a1402 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 400, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 400, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/summary.json new file mode 100644 index 0000000..b4abbc2 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/high_fanin/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 400, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 114, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 400, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "86cdf5a06b77c8ff57cd82cb8a814fd3ac0130ef55bdc03fe74dacbc4bc01f95", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "high_fanin", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1141452780, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "02252a8546c104e0808ab55ca181e8e32c181ae9ad0ccbe13c33f008474725f6", + "ref_rejected_requests": 0, + "requests": 114, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 157, + "state_hashes_sha256": "badb44d874c9262789e61561fee3a5d2bc83451c7e5d1f402bfaa7b462512101", + "steps": 400, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/compiled-code.json new file mode 100644 index 0000000..5a4c38f --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory.copy()\n lastspike = _array_default_neurons_lastspike\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/generated-code.json new file mode 100644 index 0000000..9b31eed --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory.copy()\n lastspike = _array_default_neurons_lastspike\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/input-audit.json new file mode 100644 index 0000000..0df959c --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8", + "requests.npz": "38d5b1a50b3c05c23f5652847e3598b1e7c9f9ad217442de0617054727b8c595", + "spec.json": "a61805aca6b71a23eb1b172bdfd49dae7a4a3631c031de693ad1e02e2fa2eb86" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 10.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "kc_ref_boundary", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 100, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/output-schema.json new file mode 100644 index 0000000..769b764 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 100, + 2 + ], + "inhibition": [ + 100, + 4064 + ], + "state": [ + 100, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 100, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/queue-audit.json new file mode 100644 index 0000000..aaad63e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 100, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 100, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/summary.json new file mode 100644 index 0000000..5e2bb25 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/kc_ref_boundary/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 100, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 3, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 100, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "6e702482c91247c306339d0ad9822550c8e560a8db1b50d91db775c7500701f7", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "kc_ref_boundary", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1064481295, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "72f55a0b9cc9bec5d5e066240b81ad6a44f85b97f0f6681f22ee5d41d459ed55", + "ref_rejected_requests": 3, + "requests": 6, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 4, + "state_hashes_sha256": "6dff6c90260330994a1f5bc5862dafb0258121aaf27a6b4c28ad94b28c0ffb1d", + "steps": 100, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/compiled-code.json new file mode 100644 index 0000000..978d5bb --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_v[_idx] = v\n _array_default_neurons_g[_idx] = g\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/generated-code.json new file mode 100644 index 0000000..335837c --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n _array_default_neurons_g[:] = g\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_v[_idx] = v\n _array_default_neurons_g[_idx] = g\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/input-audit.json new file mode 100644 index 0000000..85cd6f6 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "1a4ba4d3e86b06300c645f6b02cba2d8927b8ef22003f91ccfaea39b5dc24b73", + "requests.npz": "ccaa5da628e1659f6b42a3400927cff598017d104485ca60010b45248a37eba3", + "spec.json": "7a31ef85b1f00c4710a3bbeb63faf035d2497d6d1c653392d1e9c7bc3f1585a6" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 10.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "natural_request", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 100, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/output-schema.json new file mode 100644 index 0000000..769b764 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 100, + 2 + ], + "inhibition": [ + 100, + 4064 + ], + "state": [ + 100, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 100, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/queue-audit.json new file mode 100644 index 0000000..aaad63e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 100, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 100, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/summary.json new file mode 100644 index 0000000..52dfa13 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/natural_request/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 100, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 1, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 100, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "7601dfaea8202baf80d25587d5dfdd4c9e0483f5339c7d794e958000332f50bb", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "natural_request", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1103768903, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "6ed5f7b813bbae5230b8955f970ae6b0b140dd0eb33f5ea06cd2aad41f4dcae3", + "ref_rejected_requests": 0, + "requests": 1, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 2, + "state_hashes_sha256": "66acc728c7f38ccc29f5996c3d71f23682101964c70eb1d1296b81a499fad62b", + "steps": 100, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/compiled-code.json new file mode 100644 index 0000000..14141e1 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n t = _array_defaultclock_t[0]\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/generated-code.json new file mode 100644 index 0000000..e609a98 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n t = _array_defaultclock_t[0]\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/input-audit.json new file mode 100644 index 0000000..65547de --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8", + "requests.npz": "ac00687a2d58d91e72527f8833817588ec2caa1ad274b2694c0ef43e19bdb531", + "spec.json": "ed7596015d5199d496af300b492cca1815c846c04a61c104deb613bec0aecc26" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 30.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "pn_pulse", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 300, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/output-schema.json new file mode 100644 index 0000000..80bd2c4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 300, + 2 + ], + "inhibition": [ + 300, + 4064 + ], + "state": [ + 300, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 300, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/queue-audit.json new file mode 100644 index 0000000..d91d929 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 300, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 300, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/summary.json new file mode 100644 index 0000000..fd9e780 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/pn_pulse/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 300, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 1, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 300, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "cfb623e0cbddf4f5dc58384e4ef080cbfaf6330c8c02607d2a001e146bcf7d52", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "pn_pulse", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1056879425, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "9183c97b03f28017025534018cd82c55d5c050ed6bd9c912e36e06fe80776a98", + "ref_rejected_requests": 0, + "requests": 1, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 1, + "state_hashes_sha256": "09985efa2511d1efb1b4d44002e4e0c82c63a56596453553081285396b0a0a58", + "steps": 300, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/compiled-code.json new file mode 100644 index 0000000..1cd1f8b --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n lastspike = _array_default_neurons_lastspike\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/generated-code.json new file mode 100644 index 0000000..30fa7c7 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n lastspike = _array_default_neurons_lastspike\n dt = _array_defaultclock_dt[0]\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/input-audit.json new file mode 100644 index 0000000..68655a2 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8", + "requests.npz": "8740c2ae2faa339f222a75155f7c824b12f37ed24f2e850fcadbe70db5ebde50", + "spec.json": "0ed5a1175989b2ce7f125a9ee2ff2c70c1817133eb26cbd13a808e7c81b729c9" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 30.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "two_sources", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 300, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/output-schema.json new file mode 100644 index 0000000..80bd2c4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 300, + 2 + ], + "inhibition": [ + 300, + 4064 + ], + "state": [ + 300, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 300, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/queue-audit.json new file mode 100644 index 0000000..d91d929 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 300, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 300, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/summary.json new file mode 100644 index 0000000..5e9fded --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/two_sources/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 300, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 10, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 300, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "05a62080eeeeedcaa00d0c4940485ee517980697546d037d7f8b78a724e4526f", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "two_sources", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1087065282, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "b5af9041389ca83f9054622b9b515a27f74c85848d039fcfa78959f34924821d", + "ref_rejected_requests": 0, + "requests": 10, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 13, + "state_hashes_sha256": "77167ce3a937b862b0876c8cda1588e7bdfa88825f59dda3788c9a4610b29e9e", + "steps": 300, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/apl-mask-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/apl-mask-audit.json new file mode 100644 index 0000000..bdfb8ee --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/apl-mask-audit.json @@ -0,0 +1,12 @@ +{ + "KC_targets_in_case": 20, + "base_graph_files_modified": false, + "base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585", + "count": 50, + "effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406", + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "fullgraph_required_mask_count": 5845, + "graded_targets": "only KC identities present in this LIF case", + "ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825", + "rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/apl-worker-handshake.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/apl-worker-handshake.json new file mode 100644 index 0000000..bbaffe4 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/apl-worker-handshake.json @@ -0,0 +1,56 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "--bundle-dir", + "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "--dt-ms", + "0.1" + ], + "handshake": { + "array_sha256": { + "apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6", + "input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e", + "kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75", + "output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9" + }, + "decay_f32_bits": "3f7eb923", + "dt_ms": 0.1, + "environment": { + "CUBLAS_WORKSPACE_CONFIG": null, + "cuda": "13.0", + "cuda_matmul_allow_tf32": false, + "cudnn": 92400, + "cudnn_allow_tf32": true, + "cudnn_benchmark": false, + "cudnn_deterministic": false, + "deterministic_algorithms": false, + "device_capability": [ + 8, + 9 + ], + "device_index": 0, + "device_name": "NVIDIA GeForce RTX 4090", + "float32_matmul_precision": "highest", + "numpy": "2.2.6", + "precision_configuration_changed_by_component": false, + "torch": "2.14.0+cu130" + }, + "files_sha256": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "gain_f32_bits": "3c23d70a", + "h_units": "legacy mV", + "initial_state": "float32 zeros", + "n_apl": 2, + "n_kc": 4064, + "n_neurons": 166700, + "ok": true, + "operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32", + "protocol": "phase4ag-apl-v1" + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/coefficients.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/coefficients.json new file mode 100644 index 0000000..48ffa23 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/coefficients.json @@ -0,0 +1,72 @@ +{ + "c0": -0.00025935108198051754, + "cg": 0.004937935295309027, + "cv": 0.9950124791926823, + "gd": 0.9801986733067553, + "metadata": { + "constant_values_are_double": true, + "float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.", + "g_expression": "gd * old_g", + "hex": { + "c0": "-0x1.0ff306a505980p-12", + "cg": "0x1.439cce9a65d5bp-8", + "cv": "0x1.fd7246927d28bp-1", + "gd": "0x1.f5dc99badec5bp-1", + "reset": "-0x1.a9fbe76c8b43ap-5", + "seed_volt": "-0x1.6872b020c49bap-5", + "threshold": "-0x1.70a3d70a3d70ap-5" + }, + "names": { + "c0": "_lio_8", + "cg": "_lio_9", + "cv": "_lio_10", + "gd": "_lio_2" + }, + "provenance": { + "c0": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_8", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cg": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_9", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "cv": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_10", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "gd": { + "code_object": "default_neurons_stateupdater_codeobject_1", + "name": "_lio_2", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "reset": { + "code_object": "default_neurons_spike_resetter_codeobject_1", + "name": "v_rst", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + }, + "seed_volt": { + "source": "common request interface: unchanged v_th + 1 mV" + }, + "threshold": { + "code_object": "default_neurons_spike_thresholder_codeobject_1", + "name": "v_th", + "namespace_dtype": "float64", + "source": "CodeObject.namespace" + } + }, + "units": "SI volts and dimensionless coefficients", + "voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))" + }, + "reset": -0.052000000000000005, + "seed_volt": -0.044, + "threshold": -0.045 +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/compiled-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/compiled-code.json new file mode 100644 index 0000000..56b3769 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/compiled-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory.copy()\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/environment.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/environment.json new file mode 100644 index 0000000..e7885d3 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/environment.json @@ -0,0 +1,17 @@ +{ + "APL_precision": "float32 in separate science-Python process", + "Torch_imported_into_Brian": false, + "author_precision": "float64", + "codegen": "numpy", + "packages": { + "brian2": "2.5.1", + "cython": "0.29.33", + "numpy": "1.22.3", + "pandas": "1.4.3", + "pyarrow": "11.0.0", + "scipy": "1.8.1", + "sympy": "1.12" + }, + "platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35", + "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/generated-code.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/generated-code.json new file mode 100644 index 0000000..fba3d4d --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/generated-code.json @@ -0,0 +1,124 @@ +[ + { + "active": true, + "code_objects": [], + "name": "default_neurons", + "order": 0, + "type": "NeuronGroup", + "when": "start" + }, + { + "active": true, + "code_objects": [], + "name": "default_synapses", + "order": 0, + "type": "Synapses", + "when": "start" + }, + { + "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory.copy()\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n g = _array_default_neurons_g.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_stateupdater_codeobject_1" + } + ], + "name": "default_neurons_stateupdater", + "order": 0, + "type": "StateUpdater", + "when": "groups" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_common_requests", + "order": 0, + "type": "NetworkOperation", + "when": "before_thresholds" + }, + { + "abstract_code": "_cond = (v > v_th) and not_refractory", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_thresholder_codeobject_1" + } + ], + "name": "default_neurons_spike_thresholder", + "order": 0, + "type": "Thresholder", + "when": "thresholds" + }, + { + "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "spikemonitor_codeobject_1" + } + ], + "name": "spikemonitor", + "order": 1, + "type": "SpikeMonitor", + "when": "thresholds" + }, + { + "abstract_code": "g += w\n", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\n", + "name": "default_synapses_pre_push_spikes" + }, + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_synapses_pre_codeobject_1" + } + ], + "name": "default_synapses_pre", + "order": -1, + "type": "SynapticPathway", + "when": "synapses" + }, + { + "abstract_code": "v = v_rst; w = 0; g = 0 * mV", + "active": true, + "code_objects": [ + { + "generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n", + "name": "default_neurons_spike_resetter_codeobject_1" + } + ], + "name": "default_neurons_spike_resetter", + "order": 0, + "type": "Resetter", + "when": "resets" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_legacy_apl_unconditional", + "order": 0, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "oracle_after_apl_record", + "order": 1, + "type": "NetworkOperation", + "when": "end" + }, + { + "active": true, + "code_objects": [], + "name": "zz_apl_queue_installer", + "order": 1000000, + "type": "QueueInstaller", + "when": "end" + } +] diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/input-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/input-audit.json new file mode 100644 index 0000000..69ee183 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/input-audit.json @@ -0,0 +1,65 @@ +{ + "ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409", + "af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961", + "author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e", + "bundle_hashes": { + "apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19", + "input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6", + "kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd", + "output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73" + }, + "candidate_trace_used_as_input": false, + "case_hashes": { + "arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8", + "requests.npz": "517b500f350b0825f7cc9c639beb62d06764de286b13e0835cb26c5f83ff106a", + "spec.json": "ca4f0859761882b54abfc2390669b7a5ef9a70c4e97b915dc90754443ff8b663" + }, + "coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64", + "graph_hashes": { + "completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f", + "connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b", + "dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6", + "edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765", + "nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd", + "ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81", + "signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471", + "src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f", + "weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865" + }, + "legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291", + "scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence", + "spec": { + "apl": { + "gain": 0.01, + "normalization": 1.0, + "tau_ms": 20.0 + }, + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle", + "apl_precision": "float32", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2", + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "dt_ms": 0.1, + "duration_ms": 10.0, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures", + "kind": "fixture", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py", + "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": "zero", + "output_budget_bytes": 16106127360, + "precision": "float64", + "record_history": true, + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "steps": 100, + "training": false + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/output-schema.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/output-schema.json new file mode 100644 index 0000000..769b764 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/output-schema.json @@ -0,0 +1,45 @@ +{ + "APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction", + "APL_dtype": "float32", + "APL_h_units": "mV; gain already applied in float32", + "APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition", + "APL_shapes": { + "drive": [ + 100, + 2 + ], + "inhibition": [ + 100, + 4064 + ], + "state": [ + 100, + 2 + ] + }, + "APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1", + "complete_marker": "summary.json complete=true only after worker exit and all checks", + "history_recorded": true, + "history_shape": [ + 100, + 44 + ], + "queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals", + "request_columns": [ + "tick", + "neuron_idx", + "attempted", + "accepted", + "rejected" + ], + "state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref", + "state_hash_file": "tick-state-hashes.jsonl", + "state_npz": [ + "step", + "neuron_idx", + "counts", + "v", + "g", + "ref" + ] +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/parameter-checks.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/parameter-checks.json new file mode 100644 index 0000000..6ed5906 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/parameter-checks.json @@ -0,0 +1,34 @@ +{ + "membrane_tau_ms": { + "author": 20.0, + "case": 20.0 + }, + "refractory_ms": { + "author": 2.2, + "case": 2.2 + }, + "reset_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "resting_potential_mv": { + "author": -52.0, + "case": -52.0 + }, + "synaptic_delay_ms": { + "author": 1.8, + "case": 1.8 + }, + "synaptic_tau_ms": { + "author": 5.0, + "case": 5.0 + }, + "threshold_mv": { + "author": -45.0, + "case": -45.0 + }, + "w_syn_mv_per_anatomical_synapse": { + "author": 0.275, + "case": 0.275 + } +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/queue-audit.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/queue-audit.json new file mode 100644 index 0000000..aaad63e --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/queue-audit.json @@ -0,0 +1,15 @@ +{ + "advance_calls": 100, + "forwarding_contract": "sealed AE/AF design", + "installations": [ + "default_synapses_pre_codeobject", + "default_synapses_pre_codeobject_1" + ], + "limit": "Current wrapper was not independently rerun without the queue proxy", + "new_unwrapped_repeat_performed": false, + "original_edge_ids_not_sorted_or_filtered": true, + "peek_calls": 100, + "post_threshold_pre_synaptic_guard_observed": true, + "same_peek_object_returned": true, + "zero_weight_and_refractory_rejected_edges_retained": true +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/schedule.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/schedule.json new file mode 100644 index 0000000..14342f5 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/schedule.json @@ -0,0 +1,11 @@ +{ + "schedule": [ + "start", + "groups", + "thresholds", + "synapses", + "resets", + "end" + ], + "summary": " object | part of | Clock dt | when | order | active\n---------------------------------------------------+-------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes " +} diff --git a/registry/phase4ag-author-apl-r2/evidence/oracle/zero/summary.json b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/summary.json new file mode 100644 index 0000000..bd05378 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/oracle/zero/summary.json @@ -0,0 +1,37 @@ +{ + "APL_fast_edges_zeroed": 50, + "APL_precision": "float32", + "APL_worker_exit_status": 0, + "APL_worker_ticks": 100, + "apl_fast_edges_zeroed": 50, + "applicable_requests": 0, + "backend": "shared-APL-component closed-loop Brian2 oracle", + "candidate_trace_used_as_input": false, + "complete": true, + "completed_ticks": 100, + "edges": 287, + "effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6", + "events_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "history_recorded": true, + "independent_CPU_all_components_claim": false, + "kc_targets_present": 20, + "name": "zero", + "no_poi": true, + "no_training": true, + "nodes": 44, + "output_budget_bytes": 16106127360, + "output_bytes_before_summary": 1034298413, + "pass_all": true, + "pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate", + "precision": "float64", + "queue_sha256": "487cd06e47971aef19030eba81cd910a252354752fe9d275857d832420f28c86", + "ref_rejected_requests": 0, + "requests": 0, + "scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation", + "single_continuous_run": true, + "source_and_inputs_unchanged": true, + "spikes": 0, + "state_hashes_sha256": "351b5dfaad76fd9bfacb230a8d131d9d0b1af41771d49c50c1278d6b398d010d", + "steps": 100, + "weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406" +} diff --git 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a/registry/phase4ag-author-apl-r2/evidence/protocol/encoder-audit.json b/registry/phase4ag-author-apl-r2/evidence/protocol/encoder-audit.json new file mode 100644 index 0000000..053c12a --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/protocol/encoder-audit.json @@ -0,0 +1,19 @@ +{ + "panel_count": 12, + "cal_count": 6, + "median_mapped_response_sum": 2.556107997894287, + "target_rng_column_order": "Original pn_drive, no sorting before torch.rand", + "seeds": [ + 51101, + 51102, + 51103, + 51104, + 51105 + ], + "requested_before_any_dynamics": true, + "formal_AB_run": false, + "heldout_run": false, + "torch": "2.14.0+cu130", + "numpy": "2.2.6", + "generation": "Original rates*.1/1000 float32 GPU Bernoulli, independent per-case original seed" +} diff --git a/registry/phase4ag-author-apl-r2/evidence/protocol/execution-preflight.json b/registry/phase4ag-author-apl-r2/evidence/protocol/execution-preflight.json new file mode 100644 index 0000000..5ebc800 --- /dev/null +++ b/registry/phase4ag-author-apl-r2/evidence/protocol/execution-preflight.json @@ -0,0 +1,18 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "-c", + "import sys,json,importlib.util; print(json.dumps(dict(executable=sys.executable,prefix=sys.prefix,base_prefix=sys.base_prefix,numpy_visible=importlib.util.find_spec(\"numpy\") is not None,torch_visible=importlib.util.find_spec(\"torch\") is not None)))" + ], + "observed": { + "executable": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "prefix": "/data/digital-drosophila/models/male-cns-lif/.venv", + "base_prefix": "/usr", + "numpy_visible": true, + "torch_visible": true + }, + "before_any_trajectory": true, + "torch_imported": false, + "CUDA_initialized": false +} diff --git a/registry/phase4ag-author-apl-r2/evidence/protocol/fixture-identities.csv b/registry/phase4ag-author-apl-r2/evidence/protocol/fixture-identities.csv new file mode 100644 index 0000000..0c81159 --- 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+5693,16207,ALPN,VC5_lvPN,acetylcholine +7784,18540,Kenyon_Cell,KCg-s1,acetylcholine +10679,21778,Kenyon_Cell,KCg-s1,acetylcholine +13533,24959,Kenyon_Cell,KCg-m,acetylcholine +20721,33086,Kenyon_Cell,KCa'b'-ap2,acetylcholine +23644,36376,Kenyon_Cell,KCa'b'-ap2,acetylcholine +23943,36723,Kenyon_Cell,KCg-d,acetylcholine +27845,41131,Kenyon_Cell,KCg-m,acetylcholine +30590,44260,Kenyon_Cell,KCg-m,acetylcholine +33250,47301,Kenyon_Cell,KCa'b'-ap1,acetylcholine +33327,47391,Kenyon_Cell,KCa'b'-ap2,acetylcholine +38891,53863,Kenyon_Cell,KCab-c,acetylcholine +57446,76352,Kenyon_Cell,KCa'b'-ap2,acetylcholine +58405,77539,Kenyon_Cell,KCa'b'-ap2,acetylcholine +82235,109220,Kenyon_Cell,KCa'b'-ap2,acetylcholine +82312,109321,Kenyon_Cell,KC,acetylcholine +88824,118636,Kenyon_Cell,KCa'b'-m,acetylcholine +100135,137021,Kenyon_Cell,KCa'b'-ap2,acetylcholine +107868,153774,Kenyon_Cell,KCa'b'-m,acetylcholine +120835,214044,Kenyon_Cell,KCab-m,acetylcholine +130612,520206,Kenyon_Cell,KCg-d,acetylcholine 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100644 index 0000000..f0d8d02 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/REPORT.md @@ -0,0 +1,14 @@ +# Phase 4AG: author LIF + unchanged legacy graded APL + +Status: `INCOMPLETE-NUMERICAL-EXECUTION`. + +Numerical closed-loop gate: False; original complete fixed six-CAL gate: False. +The independent Brian LIF and candidate use independent state but share the APL arithmetic component. Original frozen APL AST replay and a float64 error audit are separately recorded. +Author LIF uses float64; APL retains float32, tau20ms/gain0.01/normalization1, full original matrices and order. No learning/parameter search. +The explicitly hypothesized unconditional end-APL write includes refractory KC. Original fast APL mask leaves1,212 non-KC edges unreplaced; this scope limitation was not silently fixed. +Old checksums and five frozen reference trials: True. +Successfully compared cases: []. +Detailed CAL, exact comparisons, component errors, event/request traces and execution exceptions are linked from audit/final-status.json. +Wall/VRAM here include per-tick CPU synchronization, IPC and hashes. They are diagnostic costs, not runtime-performance benchmarks. +No heldout dynamic outputs, formal A/B, learning, game, reward, decoder or new candidate. +Next gate: Audit first failure in this explicitly declared hypothesis; no gain search, no heldout tuning, no conditioning diff --git a/registry/phase4ag-author-apl/evidence/apl-bundle/provenance.json b/registry/phase4ag-author-apl/evidence/apl-bundle/provenance.json new file mode 100644 index 0000000..8bb4ef7 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/apl-bundle/provenance.json @@ -0,0 +1,30 @@ +{ + "canonical_root": "/data/digital-drosophila/datasets/male-cns-v1.0/processed/neuron-only", + "source": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "source_sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "parameters": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "lock_file": "/data/digital-drosophila/experiments/phase4j-mb-physiology-repair/calibration/locked-parameters.json", + "lock_sha256": "1894cc4b4082ba9b2d069edf87c5eccb75292aa11947ad9c48acf661037ff3e7", + "full_dimensions": [ + [ + 2, + 166700 + ], + [ + 2, + 4064 + ] + ], + "APL": 2, + "KC": 4064, + "ALPN": 686, + "APL_fast_edges": 5845, + "APL_nonKC_edges": 1212, + "APL_nonKC_contacts": 13712, + "normalization_recomputed_in_original_order": true, + "raw_files_modified": false +} diff --git a/registry/phase4ag-author-apl/evidence/apl-bundle/scales.json b/registry/phase4ag-author-apl/evidence/apl-bundle/scales.json new file mode 100644 index 0000000..ce97422 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/apl-bundle/scales.json @@ -0,0 +1,4 @@ +{ + "input_scale": 49.65891647338867, + "output_scale": 42.34836959838867 +} diff --git a/registry/phase4ag-author-apl/evidence/audit/execution-path-diagnosis.json b/registry/phase4ag-author-apl/evidence/audit/execution-path-diagnosis.json new file mode 100644 index 0000000..f3dd314 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/audit/execution-path-diagnosis.json @@ -0,0 +1,15 @@ +{ + "phase": "4AG-first-attempt", + "classification": "EXECUTION-ERROR-NOT-DYNAMICAL-FAILURE", + "completed_candidate_runs": ["zero-r0", "zero-r1", "zero-r2"], + "completed_reference_ticks": 0, + "root_cause": "oracle_apl.py resolved the declared venv Python symlink before Popen, launching system Python without numpy", + "declared_entry": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "verified_resolved_target": "/usr/bin/python3.10", + "verified_declared_entry_numpy_version": "2.2.6", + "evidence": ["logs/oracle-zero-r0.log", "oracle/zero/error.json", "oracle/zero/apl-worker.stderr.log", "audit/stopped-fixtures.json"], + "repair_scope": "New independent execution revision preserves the venv invocation path; all old registered source and outputs remain unchanged", + "dynamics_or_APL_parameters_changed": false, + "calibration_started": false, + "learning_started": false +} diff --git a/registry/phase4ag-author-apl/evidence/audit/final-status.json b/registry/phase4ag-author-apl/evidence/audit/final-status.json new file mode 100644 index 0000000..8024e07 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/audit/final-status.json @@ -0,0 +1,119 @@ +{ + "status": "INCOMPLETE-NUMERICAL-EXECUTION", + "numerical_gate_pass": false, + "fixed_CAL_representation_pass": false, + "gates": { + "fixtures": null, + "probes": null, + "calibration": null + }, + "closed_loop_comparisons": [], + "component_results": {}, + "repeat_results": {}, + "missing_numerical_evidence": { + "comparisons": [ + "apl_only", + "arrival_collision", + "cal-matched_energy-00-s51101", + "cal-physiological-00-s51101", + "high_fanin", + "kc_ref_boundary", + "natural_request", + "pn_pulse", + "two_sources", + "zero" + ], + "components": [ + "apl_only", + "arrival_collision", + "cal-matched_energy-00-s51101", + "cal-physiological-00-s51101", + "high_fanin", + "kc_ref_boundary", + "natural_request", + "pn_pulse", + "two_sources", + "zero" + ], + "repeats": [ + "apl_only-r1", + "apl_only-r2", + "arrival_collision-r1", + "arrival_collision-r2", + "high_fanin-r1", + "high_fanin-r2", + "kc_ref_boundary-r1", + "kc_ref_boundary-r2", + "natural_request-r1", + "natural_request-r2", + "pn_pulse-r1", + "pn_pulse-r2", + "two_sources-r1", + "two_sources-r2", + "zero-r1", + "zero-r2" + ] + }, + "calibration": null, + "stopped": [ + { + "path": "audit/stopped-fixtures.json", + "report": { + "stage": "fixtures", + "type": "RuntimeError", + "message": "oracle zero failed; inspect /data/digital-drosophila/experiments/phase4ag-author-apl/logs/oracle-zero-r0.log", + "traceback": "Traceback (most recent call last):\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 43, in main\n rows=[gate_case(n,CFG['fixture_repeats']) for n in CFG['fixtures']]\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 43, in \n rows=[gate_case(n,CFG['fixture_repeats']) for n in CFG['fixtures']]\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 28, in gate_case\n child('oracle',name)\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 21, in child\n if result.returncode:raise RuntimeError(f'{backend} {name} failed; inspect {log}')\nRuntimeError: oracle zero failed; inspect /data/digital-drosophila/experiments/phase4ag-author-apl/logs/oracle-zero-r0.log\n", + "later_stages_not_permitted": true, + "partial_outputs_preserved": true, + "parameters_changed": false + } + } + ], + "old_frozen_integrity_and_five_reference_pass": true, + "measurements": [ + { + "case": "zero", + "total_spikes": 0, + "accepted_requests": 0, + "nodes": 44, + "edges": 287, + "diagnostic_simulation_seconds": 0.26006703700022626, + "peak_allocated_vram_bytes": 10748416 + }, + { + "case": "zero", + "total_spikes": 0, + "accepted_requests": 0, + "nodes": 44, + "edges": 287, + "diagnostic_simulation_seconds": 0.24460422299944184, + "peak_allocated_vram_bytes": 10748416 + }, + { + "case": "zero", + "total_spikes": 0, + "accepted_requests": 0, + "nodes": 44, + "edges": 287, + "diagnostic_simulation_seconds": 0.21965846300008707, + "peak_allocated_vram_bytes": 10748416 + } + ], + "unchanged_APL_parameters": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "author_LIF_precision": "float64", + "legacy_APL_precision": "float32", + "explicit_hypothesis": "Author LIF plus unchanged legacy graded APL operator at end, after reset, unconditional KC g write. Mixed precision: h32 promoted to float64, then multiplied by float64 .001 SI, then subtracted. Not original author's APL model.", + "APL_nonKC_fast_edges_unreplaced": 1212, + "shared_APL_component_oracle_limitation": true, + "whole_brain_universal_equivalence_claim": false, + "heldout_generalization_tested": false, + "formal_AB_tested": false, + "learning_achieved": false, + "candidate_generated": false, + "payload_bytes_before_manifest": 1035661187, + "next_gate": "Audit first failure in this explicitly declared hypothesis; no gain search, no heldout tuning, no conditioning" +} diff --git a/registry/phase4ag-author-apl/evidence/audit/preflight-path-correction.json b/registry/phase4ag-author-apl/evidence/audit/preflight-path-correction.json new file mode 100644 index 0000000..8c3ef37 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/audit/preflight-path-correction.json @@ -0,0 +1,14 @@ +{ + "phase": "4AG", + "event": "First prepare stopped before protocol registration or any trajectory", + "error": "FileNotFoundError: models/male-cns-lif/phase4p_revalidate.py", + "verified_actual_source": "models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py", + "actual_source_sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346", + "original_declared_content_hash_unchanged": true, + "experiment_files_before_correction": 0, + "preregistration_existed": false, + "scientific_trajectories_executed": 0, + "correction": "Resolve actual frozen source path; allow already-created empty scaffolding only before preregistration", + "model_parameters_or_numerical_operations_changed": false, + "frozen_files_modified": false +} diff --git a/registry/phase4ag-author-apl/evidence/audit/stopped-fixtures.json b/registry/phase4ag-author-apl/evidence/audit/stopped-fixtures.json new file mode 100644 index 0000000..1d7a2f6 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/audit/stopped-fixtures.json @@ -0,0 +1,9 @@ +{ + "stage": "fixtures", + "type": "RuntimeError", + "message": "oracle zero failed; inspect /data/digital-drosophila/experiments/phase4ag-author-apl/logs/oracle-zero-r0.log", + "traceback": "Traceback (most recent call last):\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 43, in main\n rows=[gate_case(n,CFG['fixture_repeats']) for n in CFG['fixtures']]\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 43, in \n rows=[gate_case(n,CFG['fixture_repeats']) for n in CFG['fixtures']]\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 28, in gate_case\n child('oracle',name)\n File \"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/run_suite.py\", line 21, in child\n if result.returncode:raise RuntimeError(f'{backend} {name} failed; inspect {log}')\nRuntimeError: oracle zero failed; inspect /data/digital-drosophila/experiments/phase4ag-author-apl/logs/oracle-zero-r0.log\n", + "later_stages_not_permitted": true, + "partial_outputs_preserved": true, + "parameters_changed": false +} diff --git a/registry/phase4ag-author-apl/evidence/cases/apl_only/spec.json b/registry/phase4ag-author-apl/evidence/cases/apl_only/spec.json new file mode 100644 index 0000000..29eeb4a --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/cases/apl_only/spec.json @@ -0,0 +1,34 @@ +{ + "name": "apl_only", + "kind": "fixture", + "steps": 100, + "dt_ms": 0.1, + "duration_ms": 10.0, + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "synaptic_tau_ms": 5.0, + "refractory_ms": 2.2, + "resting_potential_mv": -52.0, + "reset_potential_mv": -52.0, + "threshold_mv": -45.0, + "synaptic_delay_ms": 1.8, + "w_syn_mv_per_anatomical_synapse": 0.275 + }, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/graphs/fixtures", + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/apl-bundle", + "apl": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "record_history": true, + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl", + "output_budget_bytes": 16106127360, + "precision": "float64", + "apl_precision": "float32", + "training": false +} diff --git a/registry/phase4ag-author-apl/evidence/cases/arrival_collision/spec.json b/registry/phase4ag-author-apl/evidence/cases/arrival_collision/spec.json new file mode 100644 index 0000000..759d5fb --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/cases/arrival_collision/spec.json @@ -0,0 +1,34 @@ +{ + "name": "arrival_collision", + "kind": "fixture", + "steps": 120, + "dt_ms": 0.1, + "duration_ms": 12.0, + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "synaptic_tau_ms": 5.0, + "refractory_ms": 2.2, + "resting_potential_mv": -52.0, + "reset_potential_mv": -52.0, + "threshold_mv": -45.0, + "synaptic_delay_ms": 1.8, + "w_syn_mv_per_anatomical_synapse": 0.275 + }, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/graphs/fixtures", + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/apl-bundle", + "apl": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "record_history": true, + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl", + "output_budget_bytes": 16106127360, + "precision": "float64", + "apl_precision": "float32", + "training": false +} diff --git a/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51101/spec.json b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51101/spec.json new file mode 100644 index 0000000..90304a7 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51101/spec.json @@ -0,0 +1,42 @@ +{ + "name": "cal-matched_energy-00-s51101", + "kind": "calibration", + "steps": 1000, + "dt_ms": 0.1, + "duration_ms": 100.0, + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "synaptic_tau_ms": 5.0, + "refractory_ms": 2.2, + "resting_potential_mv": -52.0, + "reset_potential_mv": -52.0, + "threshold_mv": -45.0, + "synaptic_delay_ms": 1.8, + "w_syn_mv_per_anatomical_synapse": 0.275 + }, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/graphs/full", + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/apl-bundle", + "apl": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "record_history": false, + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl", + "output_budget_bytes": 16106127360, + "precision": "float64", + "apl_precision": "float32", + "training": false, + "odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N", + "odor_name": "ethyl (R)-(-)-3-hydroxybutanoate", + "mode": "matched_energy", + "seed": 51101, + "odor_index": 0, + "expected_external_events": 102.24431762695312, + "target_count": 48, + "rate_sum_hz": 1022.4431762695312 +} diff --git a/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51102/spec.json b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51102/spec.json new file mode 100644 index 0000000..119f73c --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51102/spec.json @@ -0,0 +1,42 @@ +{ + "name": "cal-matched_energy-00-s51102", + "kind": "calibration", + "steps": 1000, + "dt_ms": 0.1, + "duration_ms": 100.0, + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "synaptic_tau_ms": 5.0, + "refractory_ms": 2.2, + "resting_potential_mv": -52.0, + "reset_potential_mv": -52.0, + "threshold_mv": -45.0, + "synaptic_delay_ms": 1.8, + "w_syn_mv_per_anatomical_synapse": 0.275 + }, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/graphs/full", + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/apl-bundle", + "apl": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "record_history": false, + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl", + "output_budget_bytes": 16106127360, + "precision": "float64", + "apl_precision": "float32", + "training": false, + "odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N", + "odor_name": "ethyl (R)-(-)-3-hydroxybutanoate", + "mode": "matched_energy", + "seed": 51102, + "odor_index": 0, + "expected_external_events": 102.24431762695312, + "target_count": 48, + "rate_sum_hz": 1022.4431762695312 +} diff --git a/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51103/spec.json b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51103/spec.json new file mode 100644 index 0000000..f3ccc4c --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51103/spec.json @@ -0,0 +1,42 @@ +{ + "name": "cal-matched_energy-00-s51103", + "kind": "calibration", + "steps": 1000, + "dt_ms": 0.1, + "duration_ms": 100.0, + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "synaptic_tau_ms": 5.0, + "refractory_ms": 2.2, + "resting_potential_mv": -52.0, + "reset_potential_mv": -52.0, + "threshold_mv": -45.0, + "synaptic_delay_ms": 1.8, + "w_syn_mv_per_anatomical_synapse": 0.275 + }, + "graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/graphs/full", + "apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl/apl-bundle", + "apl": { + "tau_ms": 20.0, + "gain": 0.01, + "normalization": 1.0 + }, + "record_history": false, + "coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json", + "science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v1/legacy_apl_worker.py", + "budget_root": "/data/digital-drosophila/experiments/phase4ag-author-apl", + "output_budget_bytes": 16106127360, + "precision": "float64", + "apl_precision": "float32", + "training": false, + "odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N", + "odor_name": "ethyl (R)-(-)-3-hydroxybutanoate", + "mode": "matched_energy", + "seed": 51103, + "odor_index": 0, + "expected_external_events": 102.24431762695312, + "target_count": 48, + "rate_sum_hz": 1022.4431762695312 +} diff --git a/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51104/spec.json b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51104/spec.json new file mode 100644 index 0000000..22f4973 --- /dev/null +++ b/registry/phase4ag-author-apl/evidence/cases/cal-matched_energy-00-s51104/spec.json @@ -0,0 +1,42 @@ +{ + "name": "cal-matched_energy-00-s51104", + "kind": "calibration", + "steps": 1000, + "dt_ms": 0.1, + "duration_ms": 100.0, + "model": { + "dt_ms": 0.1, + "membrane_tau_ms": 20.0, + "synaptic_tau_ms": 5.0, + 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+def main(): + check();verification={str(p.relative_to(ROOT)):manifest_check(p,digest) for p in (MODEL,EXP)} + assert all(v['pass'] for v in verification.values()) + registry=ROOT/'registry/phase4ag-author-apl';registry.mkdir(exist_ok=False);records=[] + manifest=json.loads((EXP/'manifest.json').read_text()) + for p in [EXP/r['path'] for r in manifest['files']]+[EXP/'manifest.json',EXP/'checksums.sha256']: + if p.stat().st_size>4*1024**2 or p.suffix not in ('.json','.csv','.md','.sha256'):continue + if p.name in ('completeness.csv','requests.csv'):continue + dst=registry/'evidence'/p.relative_to(EXP);dst.parent.mkdir(parents=True,exist_ok=True) + with p.open('rb') as src,dst.open('xb') as out:shutil.copyfileobj(src,out) + assert digest(dst)==digest(p) + records.append(dict(source=str(p.relative_to(ROOT)),copy=str(dst.relative_to(registry)),sha256=digest(p),bytes=p.stat().st_size)) + save(registry/'evidence-index.json',records);save(registry/'seal-verification.json',verification) + target=ROOT/'docs/progress/phase4ag-author-apl.md' + with (EXP/'REPORT.md').open('rb') as src,target.open('xb') as out:shutil.copyfileobj(src,out) + print(json.dumps(dict(copied=len(records),verification=verification),ensure_ascii=False)) + + +if __name__=='__main__':main() diff --git a/tools/publish_phase4ag_r2.py b/tools/publish_phase4ag_r2.py new file mode 100644 index 0000000..ef0a322 --- /dev/null +++ b/tools/publish_phase4ag_r2.py @@ -0,0 +1,34 @@ +"""Publish small sealed AG evidence without claiming bulk remote backup.""" +import json +from pathlib import Path +import shutil +import sys + +ROOT=Path('/data/digital-drosophila') +MODEL=ROOT/'models/male-cns-lif/author-apl-lab-v2' +EXP=ROOT/'experiments/phase4ag-author-apl-r2' +sys.path.insert(0,str(MODEL)) +from context import save,digest,check +sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1')) +from diagnostic import manifest_check + + +def main(): + check();verification={str(p.relative_to(ROOT)):manifest_check(p,digest) for p in (MODEL,EXP)} + assert all(v['pass'] for v in verification.values()) + registry=ROOT/'registry/phase4ag-author-apl-r2';registry.mkdir(exist_ok=False);records=[] + manifest=json.loads((EXP/'manifest.json').read_text()) + for p in [EXP/r['path'] for r in manifest['files']]+[EXP/'manifest.json',EXP/'checksums.sha256']: + if p.stat().st_size>4*1024**2 or p.suffix not in ('.json','.csv','.md','.sha256'):continue + if p.name in ('completeness.csv','requests.csv'):continue + dst=registry/'evidence'/p.relative_to(EXP);dst.parent.mkdir(parents=True,exist_ok=True) + with p.open('rb') as src,dst.open('xb') as out:shutil.copyfileobj(src,out) + assert digest(dst)==digest(p) + records.append(dict(source=str(p.relative_to(ROOT)),copy=str(dst.relative_to(registry)),sha256=digest(p),bytes=p.stat().st_size)) + save(registry/'evidence-index.json',records);save(registry/'seal-verification.json',verification) + target=ROOT/'docs/progress/phase4ag-author-apl-r2.md' + with (EXP/'REPORT.md').open('rb') as src,target.open('xb') as out:shutil.copyfileobj(src,out) + print(json.dumps(dict(copied=len(records),verification=verification),ensure_ascii=False)) + + +if __name__=='__main__':main() diff --git a/tools/record_tests_phase4ag.py b/tools/record_tests_phase4ag.py new file mode 100644 index 0000000..95a94d5 --- /dev/null +++ b/tools/record_tests_phase4ag.py @@ -0,0 +1,25 @@ +"""Capture pure protocol/aggregation unit tests; no brain simulation.""" +import json +from pathlib import Path +import subprocess +import sys + +ROOT=Path('/data/digital-drosophila') +MODEL=ROOT/'models/male-cns-lif/author-apl-lab-v1' +EXP=ROOT/'experiments/phase4ag-author-apl' +sys.path.insert(0,str(MODEL)) +from context import check,save,digest + +check();results=[] +for name,python in ( + ('science',ROOT/'models/male-cns-lif/.venv/bin/python'), + ('pinned-brian',ROOT/'experiments/phase4ad-author-semantics/environment/.venv/bin/python')): + log=EXP/'regression'/('unit-tests-'+name+'.log') + with log.open('x',encoding='utf-8') as out: + result=subprocess.run([str(python),'-B','-m','unittest','discover','-s',str(MODEL),'-p','test_*.py','-v'], + cwd=MODEL,stdout=out,stderr=subprocess.STDOUT,timeout=120,check=False) + results.append(dict(environment=name,pass_all=result.returncode==0,log=str(log.relative_to(EXP)),sha256=digest(log))) +save(EXP/'regression/unit-tests.json',dict(pass_all=all(r['pass_all'] for r in results),results=results, + scope='Pure protocol and synthetic aggregation only, no scientific trajectory',runner_sha256=digest(__file__))) +assert all(r['pass_all'] for r in results) +print(json.dumps(results),flush=True) diff --git a/tools/record_tests_phase4ag_r2.py b/tools/record_tests_phase4ag_r2.py new file mode 100644 index 0000000..3725813 --- /dev/null +++ b/tools/record_tests_phase4ag_r2.py @@ -0,0 +1,25 @@ +"""Capture pure protocol/aggregation unit tests; no brain simulation.""" +import json +from pathlib import Path +import subprocess +import sys + +ROOT=Path('/data/digital-drosophila') +MODEL=ROOT/'models/male-cns-lif/author-apl-lab-v2' +EXP=ROOT/'experiments/phase4ag-author-apl-r2' +sys.path.insert(0,str(MODEL)) +from context import check,save,digest + +check();results=[] +for name,python in ( + ('science',ROOT/'models/male-cns-lif/.venv/bin/python'), + ('pinned-brian',ROOT/'experiments/phase4ad-author-semantics/environment/.venv/bin/python')): + log=EXP/'regression'/('unit-tests-'+name+'.log') + with log.open('x',encoding='utf-8') as out: + result=subprocess.run([str(python),'-B','-m','unittest','discover','-s',str(MODEL),'-p','test_*.py','-v'], + cwd=MODEL,stdout=out,stderr=subprocess.STDOUT,timeout=120,check=False) + results.append(dict(environment=name,pass_all=result.returncode==0,log=str(log.relative_to(EXP)),sha256=digest(log))) +save(EXP/'regression/unit-tests.json',dict(pass_all=all(r['pass_all'] for r in results),results=results, + scope='Pure protocol and synthetic aggregation only, no scientific trajectory',runner_sha256=digest(__file__))) +assert all(r['pass_all'] for r in results) +print(json.dumps(results),flush=True)