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Premchandyadav369
feat: deploy interactive Q-TensorFormer demo with trained weights and Gradio 4 UI
67ded87 | """ | |
| src/validator.py | |
| Scientific Claim Validator & Empirical Consistency Auditor for Q-TensorFormer. | |
| Enforces empirical integrity across all research outputs: | |
| 1. Validates classification of every reported metric: | |
| MEASURED: Obtained directly from hardware timing/profiling. | |
| ESTIMATED: Computed using explicit, calibrated models. | |
| SIMULATED: Run on classical quantum statevector simulator. | |
| PROJECTED: Theoretical analytical scale-up calculation. | |
| 2. Flags and rejects: | |
| - Unsupported "quantum advantage" claims without real QPU hardware execution. | |
| - Latency claims derived solely from FLOP counts. | |
| - "Zero runtime overhead" or "zero overhead" claims (slicing overhead ~20-50 us is empirically verified). | |
| - Unproven asymptotic guarantees (e.g. "asymptotic convergence guaranteed"). | |
| - Fabricated benchmark percentages. | |
| 3. Audits output artifact completeness: | |
| Verifies that all 13 core JSON result files and 14 publication figure PNGs exist. | |
| """ | |
| import json | |
| import re | |
| import sys | |
| import os | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple, Any, Set, Optional | |
| class ScientificClaimValidator: | |
| """ | |
| Automated validator for research claims, benchmark tables, and model card disclosures. | |
| """ | |
| ALLOWED_CLASSIFICATIONS = {"MEASURED", "ESTIMATED", "SIMULATED", "PROJECTED"} | |
| FORBIDDEN_PHRASES = [ | |
| "quantum advantage in nlp", | |
| "quantum speedup on cpu", | |
| "first ever adaptive transformer", | |
| "first entropy-based transformer", | |
| "zero latency overhead with quantum simulation", | |
| "zero runtime overhead", | |
| "asymptotic convergence to the exact boundary", | |
| "asymptotic pareto optimality guaranteed", | |
| ] | |
| REQUIRED_JSON_ARTIFACTS = [ | |
| "marginal_value_results.json", | |
| "information_state_ablation.json", | |
| "adaptive_rank_results.json", | |
| "nested_tt_analysis.json", | |
| "gqa_audit.json", | |
| "kv_rate_distortion.json", | |
| "controller_convergence.json", | |
| "hysteresis_results.json", | |
| "quantum_utility.json", | |
| "detailed_latency_profile.json", | |
| "pareto_frontiers.json", | |
| "counter_hypothesis_results.json", | |
| "comprehensive_comparison.json", | |
| "baseline_comparison_master.json", | |
| "counterfactual_learning_results.json", | |
| "hierarchical_kv_results.json", | |
| "phase_profiling_results.json", | |
| "matched_budget_evaluations.json", | |
| "workload_adaptation_results.json", | |
| "quantum_utility_boundary.json", | |
| "scaling_projections.json", | |
| ] | |
| REQUIRED_FIGURE_ARTIFACTS = [ | |
| "figure1_architecture.png", | |
| "figure2_marginal_value_r2.png", | |
| "figure3_8d_lofo_importance.png", | |
| "figure4_nested_tt_suboptimality.png", | |
| "figure5_adaptive_rank_latency_traffic.png", | |
| "figure6_gqa_memory_traffic.png", | |
| "figure7_kv_rate_distortion.png", | |
| "figure8_controller_convergence.png", | |
| "figure9_hysteresis_churn_jitter.png", | |
| "figure10_quantum_utility_tradeoff.png", | |
| "figure11_subsystem_latency_breakdown.png", | |
| "figure12_hardware_roofline.png", | |
| "figure13_multi_pareto_frontiers.png", | |
| "figure14_counter_hypothesis_boundaries.png", | |
| "figure15_baseline_pareto_frontiers.png", | |
| "figure16_baseline_improvement_radar.png", | |
| ] | |
| def __init__(self, root_dir: Optional[Path] = None): | |
| self.root_dir = root_dir or Path(__file__).parent.parent | |
| self.validation_errors: List[str] = [] | |
| self.warnings: List[str] = [] | |
| def validate_metric_record(self, record: Dict[str, Any]) -> bool: | |
| """Validate a single benchmark metric entry.""" | |
| metric_name = record.get("metric", "unknown") | |
| classification = record.get("classification", "").upper() | |
| if classification not in self.ALLOWED_CLASSIFICATIONS: | |
| self.validation_errors.append( | |
| f"Metric '{metric_name}' has invalid classification '{classification}'. " | |
| f"Must be one of: {self.ALLOWED_CLASSIFICATIONS}" | |
| ) | |
| return False | |
| if "latency" in metric_name.lower() and classification == "MEASURED" and "hardware" not in record: | |
| self.warnings.append( | |
| f"Measured latency '{metric_name}' should specify hardware device used for measurement." | |
| ) | |
| return True | |
| def validate_document_text(self, text: str, doc_name: str = "Document") -> bool: | |
| """Scan text for forbidden/unsubstantiated claims.""" | |
| clean_text = text.lower() | |
| passed = True | |
| for phrase in self.FORBIDDEN_PHRASES: | |
| if phrase in clean_text: | |
| self.validation_errors.append( | |
| f"[{doc_name}] Found unsubstantiated claim phrase: '{phrase}'" | |
| ) | |
| passed = False | |
| return passed | |
| def validate_artifacts(self) -> bool: | |
| """Audit that all empirical outputs and publication figures exist and are valid.""" | |
| outputs_dir = self.root_dir / "outputs" | |
| figures_dir = outputs_dir / "figures" | |
| passed = True | |
| if not outputs_dir.exists(): | |
| self.validation_errors.append(f"Outputs directory {outputs_dir} does not exist.") | |
| return False | |
| # 1. Audit JSON files | |
| for j_name in self.REQUIRED_JSON_ARTIFACTS: | |
| j_path = outputs_dir / j_name | |
| if not j_path.exists(): | |
| self.validation_errors.append(f"Missing required empirical artifact: outputs/{j_name}") | |
| passed = False | |
| else: | |
| try: | |
| with open(j_path, "r", encoding="utf-8") as f: | |
| data = json.load(f) | |
| if not data: | |
| self.validation_errors.append(f"Empty artifact: outputs/{j_name}") | |
| passed = False | |
| except Exception as e: | |
| self.validation_errors.append(f"Corrupt JSON in outputs/{j_name}: {e}") | |
| passed = False | |
| # 2. Audit Figures | |
| if not figures_dir.exists(): | |
| self.validation_errors.append(f"Figures directory {figures_dir} does not exist.") | |
| return False | |
| for f_name in self.REQUIRED_FIGURE_ARTIFACTS: | |
| f_path = figures_dir / f_name | |
| if not f_path.exists(): | |
| self.validation_errors.append(f"Missing required figure artifact: outputs/figures/{f_name}") | |
| passed = False | |
| elif f_path.stat().st_size < 1000: | |
| self.validation_errors.append(f"Figure {f_name} is too small (< 1KB), possible blank render.") | |
| passed = False | |
| return passed | |
| def generate_report(self) -> Dict[str, Any]: | |
| return { | |
| "status": "FAILED" if self.validation_errors else "PASSED", | |
| "errors": self.validation_errors, | |
| "warnings": self.warnings, | |
| "total_errors": len(self.validation_errors), | |
| "total_warnings": len(self.warnings), | |
| "artifacts_verified": len(self.REQUIRED_JSON_ARTIFACTS) + len(self.REQUIRED_FIGURE_ARTIFACTS), | |
| } | |
| def main(): | |
| root_dir = Path(__file__).parent.parent | |
| validator = ScientificClaimValidator(root_dir) | |
| # 1. Audit artifacts | |
| validator.validate_artifacts() | |
| # 2. Audit documents | |
| for doc in ["README.md", "MODEL_CARD.md", "docs/CLAIM_TO_CODE_MAP.md", "docs/ARCHITECTURE_AUDIT.md"]: | |
| p = root_dir / doc | |
| if p.exists(): | |
| content = p.read_text(encoding="utf-8") | |
| validator.validate_document_text(content, doc_name=doc) | |
| report = validator.generate_report() | |
| print("=" * 70) | |
| print("SCIENTIFIC CLAIM & ARTIFACT CONSISTENCY VALIDATION REPORT") | |
| print("=" * 70) | |
| print(f"Status: {report['status']}") | |
| print(f"Verified Artifacts: {report['artifacts_verified']} files") | |
| print(f"Errors ({report['total_errors']}):") | |
| for err in report["errors"]: | |
| print(f" [ERROR] {err}") | |
| print(f"Warnings ({report['total_warnings']}):") | |
| for warn in report["warnings"]: | |
| print(f" [WARN] {warn}") | |
| print("=" * 70) | |
| if report["status"] == "FAILED": | |
| sys.exit(1) | |
| else: | |
| print("[PASSED] All scientific claims and empirical artifacts verified successfully!") | |
| if __name__ == "__main__": | |
| from typing import Optional | |
| main() | |