auto: daily sync

This commit is contained in:
2026-02-21 08:00:14 +00:00
parent 7acda7f55f
commit 39212aaf81
2 changed files with 400 additions and 0 deletions

194
war-room-codex-analysis.md Normal file
View File

@@ -0,0 +1,194 @@
# War-Room Codex Analysis (Code-Level Reality)
Date: 2026-02-20
Scope: Static + runtime checks starting from `atomizer.py` and `optimization_engine/run_optimization.py`.
## 1) Entry Point Reality
### A. `atomizer.py` is runnable
- Verified: `python3 atomizer.py --help` exits `0`.
- Top-level imports are valid.
- Command handlers branch into two very different worlds:
- **Atomizer UX path**: `intake`, `gate`, `finalize`.
- **Legacy study-script launcher**: `neural-optimize` shells into per-study `studies/<name>/run_optimization.py`.
### B. `optimization_engine/run_optimization.py` is currently broken at import time
- Verified: `python3 optimization_engine/run_optimization.py --help` exits `1`.
- Failure:
- `optimization_engine/run_optimization.py:39`
- imports `optimization_engine.future.llm_optimization_runner`
- which imports `optimization_engine.extractor_orchestrator` at `optimization_engine/future/llm_optimization_runner.py:26`
- but only `optimization_engine/future/extractor_orchestrator.py` exists.
- Result: this entrypoint never reaches argument parsing.
## 2) Import Chains and Dependency Graph
## 2.1 Atomizer chain (actual reachable modules)
```mermaid
graph TD
A[atomizer.py]
A --> TL[optimization_engine.config.template_loader]
A --> AT[optimization_engine.processors.surrogates.auto_trainer]
A --> SV[optimization_engine.validators.study_validator]
A --> IN[optimization_engine.intake]
A --> VG[optimization_engine.validation]
A --> HR[optimization_engine.reporting.html_report]
SV --> CV[validators.config_validator]
SV --> MV[validators.model_validator]
SV --> RV[validators.results_validator]
```
Key behavior:
- `neural-optimize` does **not** call a central engine runner; it executes `studies/<study>/run_optimization.py` via subprocess.
- This makes runtime behavior depend on many heterogeneous study scripts.
## 2.2 Unified runner chain (intended, but broken)
```mermaid
graph TD
R[optimization_engine/run_optimization.py]
R --> WA[future.llm_workflow_analyzer]
R --> LR[future.llm_optimization_runner]
R --> NXU[nx.updater]
R --> NXS[nx.solver]
R --> CR[core.runner]
LR --> HM[plugins.hook_manager]
LR -.broken import.-> EO[optimization_engine.extractor_orchestrator (missing)]
LR -.broken import.-> IC[optimization_engine.inline_code_generator (missing)]
LR -.broken import.-> HG[optimization_engine.hook_generator (missing)]
```
Observations:
- `OptimizationRunner` is imported in `optimization_engine/run_optimization.py:40` but not used.
- Manual mode is scaffolding and exits (`optimization_engine/run_optimization.py:286-320`).
## 3) Tight vs Loose Coupling
### Tight coupling (high-risk refactor areas)
- `atomizer.py` to repository layout (`studies/<study>/run_optimization.py`) and command shelling.
- `core/runner.py` to config schema and exact extractor return shape (`result[metric_name]`).
- Validation gate and intake to specific extractor functions and NX solver assumptions.
- Template loader to incorrect engine-relative paths and missing module names.
### Loose coupling (good seams)
- Hook function interface (`dict -> optional dict`) is flexible.
- Extractor call abstraction (`name -> callable`) in runners can be standardized.
- Lazy imports in `atomizer.py` for intake/gate/finalize reduce startup coupling.
## 4) Circular Dependencies
Detected import cycles are limited and not the main blocker:
- `optimization_engine.extractors -> optimization_engine.extractors.extract_zernike_figure -> optimization_engine.extractors`
- `optimization_engine.model_discovery -> optimization_engine.model_discovery` (package self-cycle artifact)
Main instability is from **missing/incorrect module paths**, not classic circular imports.
## 5) Dead Code / Orphan Findings
## 5.1 `optimization_engine/future/` (what is actually wired)
Direct non-test references:
- `llm_workflow_analyzer.py`: referenced by `optimization_engine/run_optimization.py`.
- `llm_optimization_runner.py`: referenced by `optimization_engine/run_optimization.py` and some study scripts.
- `report_generator.py`: referenced by dashboard backend route.
Mostly test/deprecation only:
- `research_agent.py`, `step_classifier.py`, `targeted_research_planner.py`, `workflow_decomposer.py`, `pynastran_research_agent.py`.
Practically dead due broken imports:
- `future/extractor_orchestrator.py`, `future/inline_code_generator.py`, `future/hook_generator.py` are intended runtime pieces, but callers import them using missing top-level paths.
## 5.2 Extractors (used vs orphaned)
Clearly used in runtime paths:
- `extract_displacement.py`, `extract_von_mises_stress.py`, `bdf_mass_extractor.py`
- via validation/intake/wizard/base runner.
Used mainly by studies/tools/dashboard:
- `extract_frequency.py`, `extract_mass_from_expression.py`, `extract_zernike*.py`, `op2_extractor.py`, `stiffness_calculator.py`.
Likely orphaned (no non-test references found):
- `field_data_extractor.py`
- `zernike_helpers.py`
Low-usage / isolated:
- `extract_stress_field_2d.py` (single tools reference)
- `extract_zernike_surface.py` (single study script reference)
## 5.3 Orphaned module references (hard breaks)
- Missing module imports in code:
- `optimization_engine.extractor_orchestrator` (missing)
- `optimization_engine.inline_code_generator` (missing)
- `optimization_engine.hook_generator` (missing)
- `optimization_engine.study_runner` (missing)
Evidence:
- `optimization_engine/future/llm_optimization_runner.py:26-28`
- `optimization_engine/config/setup_wizard.py:26-27`
- generated script template in `optimization_engine/config/template_loader.py:216`
## 6) Hooks/Plugins: Wired vs Scaffolding
There are **two distinct hook concepts**:
1. `optimization_engine/plugins/*`:
- lifecycle hook framework (`pre_solve`, `post_solve`, etc.) used by runners.
2. `optimization_engine/hooks/*`:
- NX Open CAD/CAE API wrappers (not plugin lifecycle hooks).
### What is actually wired
- `core/runner.py` executes hook points across trial lifecycle.
- `future/llm_optimization_runner.py` also executes lifecycle hooks.
### Why most plugins are not actually loaded
- Wrong plugin directory resolution:
- `core/runner.py:76` uses `Path(__file__).parent / 'plugins'` -> `optimization_engine/core/plugins` (does not exist).
- `future/llm_optimization_runner.py:140` uses `optimization_engine/future/plugins` (does not exist).
- `config/setup_wizard.py:426` same issue (`optimization_engine/config/plugins`).
- Real plugin directory is `optimization_engine/plugins/`.
### Additional plugin scaffolding mismatches
- Hook point enum uses `custom_objective` (`plugins/hooks.py:24`) but directory present is `plugins/custom_objectives/` (plural).
- `safety_factor_constraint.py` defines `register_hooks` but returns `[hook]` without calling `hook_manager.register_hook(...)` (`plugins/post_calculation/safety_factor_constraint.py:88-90`), so loader does not register it.
Net: hook execution calls exist, but effective loaded-hook count is often zero.
## 7) Data Flow Through Actual Code
## 7.1 `atomizer.py` main flows
1. `neural-optimize`:
- validate study via `validators.study_validator`
- inspect training state via `AutoTrainer`
- subprocess into `studies/<study>/run_optimization.py`
- optional post-run retraining
2. `intake`:
- `IntakeProcessor` populates context, introspection, baseline
3. `gate`:
- `ValidationGate` validates spec + optional test trials + extractor probes
4. `finalize`:
- `HTMLReportGenerator` builds report
## 7.2 `optimization_engine/run_optimization.py` intended flow
- Parse args -> validate `prt/sim`
- `--llm`: analyze request -> setup updater/solver closures -> `LLMOptimizationRunner` -> Optuna loop
- `--config`: currently stub that exits
Actual current behavior: import-time crash before step 1.
## 8) High-Risk Inconsistencies Blocking Migration
- Broken import namespace split between `future/*` and expected top-level modules.
- Template system points to wrong template/study roots and generates scripts importing missing `study_runner`.
- Hook framework looks complete but plugin discovery paths are wrong in all main call sites.
- `atomizer` delegates execution to many inconsistent study-local scripts, preventing predictable architecture.

206
war-room-migration-plan.md Normal file
View File

@@ -0,0 +1,206 @@
# War-Room Migration Plan (Prioritized, Code-Level)
Date: 2026-02-20
Goal: Stabilize entrypoints and create a coherent, testable architecture from the current code reality.
## Phase 0 - Stop The Bleeding (P0, 1-2 days)
Risk: High if skipped, Low implementation risk
Moves:
- Fix broken module import paths in runtime code:
- `optimization_engine/future/llm_optimization_runner.py`
- `optimization_engine/config/setup_wizard.py`
- Make `optimization_engine/run_optimization.py` importable/executable.
- Fix plugin directory resolution in all loaders:
- `core/runner.py`
- `future/llm_optimization_runner.py`
- `config/setup_wizard.py`
Interface changes:
- None to user CLI shape.
- Internal module paths become explicit (`optimization_engine.future.*` or extracted to a new stable package).
Dependency order:
1. Fix import paths.
2. Fix plugin discovery path.
3. Add smoke tests for `atomizer.py --help` and `optimization_engine/run_optimization.py --help`.
Concrete refactor steps:
1. Replace missing imports with real module paths:
- `optimization_engine.extractor_orchestrator` -> `optimization_engine.future.extractor_orchestrator`
- `optimization_engine.inline_code_generator` -> `optimization_engine.future.inline_code_generator`
- `optimization_engine.hook_generator` -> `optimization_engine.future.hook_generator`
2. In hook loaders, resolve plugin root to `optimization_engine/plugins` (shared constant helper).
3. Add assertion/telemetry when no hooks are loaded to avoid silent no-op behavior.
## Phase 1 - Entrypoint Contract Unification (P0, 2-4 days)
Risk: Medium
Moves:
- Define a single canonical optimization runner API used by both:
- `atomizer.py` workflows
- `optimization_engine/run_optimization.py`
- Remove dead import (`OptimizationRunner`) from unified runner unless actually used.
- Implement manual mode or explicitly remove it behind feature flag.
Interface changes:
- Introduce a stable internal entry function, e.g. `optimization_engine.app.run(spec)`.
- Keep CLI flags backward-compatible in this phase.
Dependency order:
1. Stabilize Phase 0.
2. Create adapter layer from old study scripts to new runner API.
3. Route `run_optimization.py` through this API.
Concrete refactor steps:
1. Create `optimization_engine/app/runner_service.py` with:
- config parsing
- model update/solve/extract loop
- hook execution
2. Convert `run_optimization.py` to thin CLI adapter.
3. Add compatibility wrapper for existing study scripts.
## Phase 2 - Study Script Decoupling (P1, 1-2 weeks)
Risk: High (behavioral)
Moves:
- Stop subprocess dependence on arbitrary `studies/<study>/run_optimization.py` from `atomizer.py`.
- Replace with deterministic “load study spec + run canonical engine”.
Interface changes:
- `atomizer neural-optimize --study X` resolves study metadata and calls canonical engine directly.
- Study-local scripts become optional wrappers, not required runtime dependency.
Dependency order:
1. Canonical runner available (Phase 1).
2. Study resolver module.
3. Migrate one or two representative studies first (UAV + M1 mirror).
Concrete refactor steps:
1. Implement study resolver for `1_setup/optimization_config.json` and/or `atomizer_spec.json`.
2. Add `atomizer` path for direct in-process execution.
3. Keep fallback to legacy script for unmigrated studies with explicit warning.
## Phase 3 - Hooks/Plugins Hardening (P1, 3-5 days)
Risk: Medium
Moves:
- Consolidate lifecycle hook system (`plugins`) and clarify that NX Open wrappers (`hooks`) are separate.
- Fix mismatches:
- `custom_objective` hook point vs `custom_objectives` folder naming.
- broken plugin registration patterns (e.g. return list without registering).
Interface changes:
- Standard plugin authoring contract:
- `register_hooks(hook_manager) -> None`
- must call `hook_manager.register_hook(...)`
Dependency order:
1. Shared plugin root helper.
2. Plugin validation lint.
3. Hook contract docs + tests.
Concrete refactor steps:
1. Add plugin linter test to fail on non-registered plugins.
2. Normalize folder naming to enum values.
3. Add runtime summary printout for loaded/active hooks at each hook point.
## Phase 4 - Extractor Surface Rationalization (P1, 1 week)
Risk: Medium
Moves:
- Classify extractors into tiers:
- Tier A (active runtime): displacement/stress/mass/frequency basics
- Tier B (domain-specific): zernike, optical, special studies
- Tier C (candidate deprecate): no references (`field_data_extractor.py`, `zernike_helpers.py`)
- Move tier C to `archive/` or mark deprecated.
Interface changes:
- New extractor registry metadata: status (`active`, `experimental`, `deprecated`).
Dependency order:
1. Build reference report snapshot.
2. Mark deprecated modules.
3. Remove only after one release cycle.
Concrete refactor steps:
1. Add machine-readable registry file for extractors.
2. Enforce registration for runtime-eligible extractors.
3. Update callers to consume registry, not free-form imports.
## Phase 5 - Template/Study Generation Repair (P1, 2-4 days)
Risk: Medium
Moves:
- Fix template loader roots and generated script imports.
- Current issues:
- templates resolve to `optimization_engine/templates` instead of repo `templates/` JSON set.
- studies root points to missing `optimization_engine/studies`.
- generated scripts import missing `optimization_engine.study_runner`.
Interface changes:
- `atomizer create-study` should generate runnable study directly against canonical runner API.
Dependency order:
1. Canonical runner available (Phase 1).
2. Replace generated script template.
3. Add integration test for create+validate flow.
Concrete refactor steps:
1. Correct template/study root resolution to repo-level paths.
2. Replace `study_runner` import with canonical service call.
3. Validate generated study with `atomizer validate` in CI smoke test.
## Phase 6 - Future Folder Re-homing (P2, 1 week)
Risk: Low/Medium
Moves:
- Split `optimization_engine/future/` into:
- `experimental/` (not production-wired)
- `runtime/` (production-wired)
- Remove deprecation aliases from `optimization_engine/__init__.py` that point to non-runtime pieces unless intentionally supported.
Interface changes:
- Explicit import policy: production code cannot import `experimental` without feature flag.
Dependency order:
1. Tag modules by runtime usage.
2. Move files and add shims.
3. Remove shims after transition window.
Concrete refactor steps:
1. Create `optimization_engine/experimental/`.
2. Move low-usage research modules.
3. Keep only proven runtime modules in primary namespace.
## Phase 7 - Verification and Guardrails (P0 ongoing)
Risk: High if omitted
Moves:
- Add CI checks that would have caught current breakage:
- import smoke for both entrypoints
- plugin load count assertions
- create-study runnable check
Concrete refactor steps:
1. Add tests:
- `test_entrypoint_imports.py`
- `test_plugin_discovery_paths.py`
- `test_template_loader_paths.py`
2. Add static scan that fails on unresolved `optimization_engine.<module>` imports.
3. Add architecture decision record (ADR) for canonical runner + plugin model.
## Dependency-Critical Ordering Summary
1. Phase 0 (import/plugin path fixes)
2. Phase 1 (canonical runner contract)
3. Phase 2 and 5 (atomizer/study generation migration)
4. Phase 3 (hooks hardening)
5. Phase 4 and 6 (extractor/future rationalization)
6. Phase 7 continuously
## Quick Wins (do immediately)
1. Fix `run_optimization.py` import crash.
2. Fix all three plugin loader paths.
3. Remove or wire `OptimizationRunner` import in unified runner.
4. Fix `template_loader` roots and `study_runner` reference.