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fix(tests/engine): RL demo contract + harden engine exporters #684
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| # CODEOWNERS — single active maintainer; second maintainer position open (see GOVERNANCE.md) | ||
| * @Lemniscate-world | ||
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| # Docs and governance require lead maintainer | ||
| /docs/ @Lemniscate-world | ||
| *.md @Lemniscate-world | ||
| .github/ @Lemniscate-world | ||
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| # Core engine — lead maintainer primary | ||
| /neuraldbg/ @Lemniscate-world |
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| { | ||
| "syncedAt": "2026-10-02T11:06:50.0556132Z", | ||
| "generator": "sync-rules.ps1", | ||
| "agentsSha256": "7faefac0cbac9534585b2b64e86c7d6cae11ab34b6b066c8f27b315aefb48c0b", | ||
| "ruleCount": 59 | ||
| } |
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@@ -115,7 +115,7 @@ def train_ppo(model, num_steps=20, lr=3e-4, state_dim=8, action_dim=4, | |
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| # Merge RL detector events into NeuralDBG events | ||
| if rl_detector is not None: | ||
| for i, rl_event in enumerate(rl_detector.dump_events()): | ||
| # Create an event-like object compatible with NeuralDBG's Event protocol | ||
| merged = type('RLEvent', (), { | ||
| 'event_type': rl_event['event_type'], | ||
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@@ -129,12 +129,19 @@ def train_ppo(model, num_steps=20, lr=3e-4, state_dim=8, action_dim=4, | |
| 'to_state': rl_event['event_type'], | ||
| 'to_dict': lambda self, d=rl_event: d, | ||
| })() | ||
| # Unique id per instance (cf SemanticEvent.id) for graph exporters. | ||
| merged.id = f"rl_{rl_event['step']}_{i}_{rl_event['event_type']}" | ||
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There was a problem hiding this comment. 1. Variance-collapse graphs stay empty train_ppo now assigns id and confidence to merged RL events, but scenario_reward_variance_collapse uses a duplicate merge block that assigns neither attribute. Its normal 20-step run emits a reward-anomaly event, after which Mermaid export raises on event.id and coupling detection can raise on event.confidence, while analyze_results silently replaces those failures with empty output. Agent Prompt
Contributor
There was a problem hiding this comment. P2: The unique-id fix is only applied in Prompt for AI agents |
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| dbg.events.append(merged) | ||
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| return dbg, rl_detector | ||
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| def analyze_results(dbg, rl_detector=None): | ||
| # Scenarios return (dbg, rl_detector) tuples; accept them directly. | ||
| if isinstance(dbg, tuple): | ||
| dbg, _rl = dbg | ||
| if rl_detector is None: | ||
| rl_detector = _rl | ||
| result = { | ||
| "events": dbg.events, | ||
| } | ||
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@@ -36,11 +36,15 @@ def detect(self, window: int = 5) -> List[Dict[str, Any]]: | |
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| trigger = event1.id | ||
| consequence = event2.id | ||
| # Tolerate foreign event-likes (e.g. merged RLDetector | ||
| # events whose event_type is a plain string). | ||
| t1 = getattr(event1.event_type, "value", event1.event_type) | ||
| t2 = getattr(event2.event_type, "value", event2.event_type) | ||
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There was a problem hiding this comment. 🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win Normalize event types before applying the coupling bonus. When plain strings match 🤖 Prompt for AI Agents |
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| candidate = { | ||
| "trigger": trigger, | ||
| "consequence": consequence, | ||
| "trigger_label": f"{t1} in {event1.layer_name}", | ||
| "consequence_label": f"{t2} in {event2.layer_name}", | ||
| "step_difference": step_diff, | ||
| "confidence": confidence, | ||
| "is_causal_candidate": True, | ||
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@@ -635,9 +635,10 @@ def export_mermaid_causal_graph(self) -> str: | |||||
| lines = ["graph TD"] | ||||||
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| for event in self.dbg.events: | ||||||
| # Tolerate foreign event-likes (e.g. merged RLDetector events | ||||||
| # whose event_type is a plain string, not an EventType enum). | ||||||
| etype = getattr(event.event_type, "value", event.event_type) | ||||||
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Contributor
There was a problem hiding this comment. P2: The new tolerance only covers Prompt for AI agents |
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| label = f"{etype} in {event.layer_name} (Step {event.step})" | ||||||
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Contributor
There was a problem hiding this comment. P3: For plain-string event types this tolerates, the value is interpolated unescaped into Prompt for AI agents
Suggested change
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| lines.append(f' E_{event.id}["{label}"]') | ||||||
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| couplings = self.dbg.detect_coupled_failures() | ||||||
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@@ -78,8 +78,9 @@ filterwarnings = [ | |||||
| "ignore::FutureWarning:torch.nn.modules.module.*", | ||||||
| "ignore::DeprecationWarning:torch.jit.*", | ||||||
| # Expected internal warnings — intentional design decisions | ||||||
| # NB: no colon allowed inside message regex (pytest splits on unescaped ":") | ||||||
| "ignore:Model is wrapped in DataParallel.*:UserWarning", | ||||||
| "ignore:Model is already compiled.*:UserWarning", | ||||||
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Contributor
There was a problem hiding this comment. P2: The Prompt for AI agentsThere was a problem hiding this comment. 🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win Match the warning producer’s prefix. The warning from Suggested fix- "ignore:Model is already compiled.*:UserWarning",
+ "ignore:NeuralDbg.*Model is already compiled.*:UserWarning",📝 Committable suggestion
Suggested change
🤖 Prompt for AI Agents
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| [tool.coverage.run] | ||||||
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@@ -11,21 +11,23 @@ | |
| HAS_LIGHTNING = False | ||
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| if HAS_LIGHTNING: | ||
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| class LinearModel(pl.LightningModule): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.net = nn.Linear(16, 2) | ||
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| def forward(self, x): | ||
| return self.net(x) | ||
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| def training_step(self, batch, batch_idx): | ||
| x, y = batch | ||
| loss = nn.functional.cross_entropy(self(x), y) | ||
| return loss | ||
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| def configure_optimizers(self): | ||
| return torch.optim.SGD(self.parameters(), lr=0.01) | ||
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| @pytest.mark.skipif(not HAS_LIGHTNING, reason="pytorch_lightning not installed") | ||
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P3: Removing the rule_101_tensor_and_pytest_safety index entry leaves rules/rule_101_tensor_and_pytest_safety.md unreferenced. Delete the stale file (and mirror the renamed rule_114_tensor_and_pytest_safety.md in rules/) to keep the index and rules dir in sync.
Prompt for AI agents