| |
| """Lock disjoint WikiText-train blocks for QH032 pre-holdout screening.""" |
|
|
| from __future__ import annotations |
|
|
| import hashlib |
| import json |
| from datetime import datetime, timezone |
| from pathlib import Path |
|
|
| import numpy as np |
| import torch |
|
|
|
|
| PROJECT = Path(__file__).resolve().parents[2] |
| ARTIFACTS = PROJECT / "artifacts" |
| TOKENS = PROJECT / "datasets/processed/wikitext2_qwen38/train-tokens.pt" |
| STATIC = ARTIFACTS / "qh032-static-validation.json" |
| SMOKE = ARTIFACTS / "qh032-full-model-smoke.json" |
| CONFIRMATION = ARTIFACTS / "c4_qh031_confirmation/qh031-confirmation-paired-analysis.json" |
| OUTPUT = ARTIFACTS / "qh032-train-screen-lock.json" |
| SEED = 20260851 |
| LENGTH = 256 |
| TRAIN_COUNT = 256 |
| EVAL_COUNT = 64 |
|
|
|
|
| def sha256(path: Path) -> str: |
| digest = hashlib.sha256() |
| with path.open("rb") as handle: |
| for chunk in iter(lambda: handle.read(8 << 20), b""): |
| digest.update(chunk) |
| return digest.hexdigest() |
|
|
|
|
| def prior_train_indices() -> tuple[set[int], int]: |
| used: set[int] = set() |
| parsed = 0 |
| for path in ARTIFACTS.rglob("*.json"): |
| if path == OUTPUT: |
| continue |
| try: |
| payload = json.loads(path.read_text(encoding="utf-8")) |
| except (OSError, UnicodeDecodeError, json.JSONDecodeError): |
| continue |
| parsed += 1 |
| def walk(value: object) -> None: |
| if isinstance(value, dict): |
| for key, child in value.items(): |
| if key in {"train_indices", "train_block_indices"} and isinstance(child, list): |
| used.update(int(item) for item in child) |
| elif key == "train_block_index" and isinstance(child, int): |
| used.add(int(child)) |
| walk(child) |
| elif isinstance(value, list): |
| for child in value: |
| walk(child) |
| walk(payload) |
| return used, parsed |
|
|
|
|
| def main() -> None: |
| for path in (TOKENS, STATIC, SMOKE, CONFIRMATION): |
| if not path.exists(): |
| raise FileNotFoundError(path) |
| if json.loads(STATIC.read_text())["status"] != "pass" or json.loads(SMOKE.read_text())["status"] != "pass": |
| raise RuntimeError("QH032 engineering gates did not pass") |
| confirmation = json.loads(CONFIRMATION.read_text()) |
| if confirmation["verdict"] != "retain_qh031_compression_reject_quantum_specific_confirmation": |
| raise RuntimeError("QH032 requires the recorded QH031 core rejection") |
| tokens = torch.load(TOKENS, map_location="cpu", weights_only=True) |
| complete = len(tokens) // LENGTH |
| used, parsed = prior_train_indices() |
| available = np.asarray(sorted(set(range(complete)) - used), dtype=np.int64) |
| selected = np.random.default_rng(SEED).permutation(available)[: TRAIN_COUNT + EVAL_COUNT].tolist() |
| if len(selected) != TRAIN_COUNT + EVAL_COUNT: |
| raise RuntimeError("insufficient unused WikiText train blocks") |
| train_indices, eval_indices = selected[:TRAIN_COUNT], selected[TRAIN_COUNT:] |
| if set(train_indices) & set(eval_indices) or set(selected) & used: |
| raise AssertionError("QH032 train screen overlap") |
| payload = { |
| "status": "locked_before_qh032_train_only_screen_results", |
| "candidate": "QH-032 Pauli-observable nonlinear replacement", |
| "purpose": "Pre-holdout train-split screen; cannot support a quality or quantum-advantage claim.", |
| "parent_confirmation": {"path": str(CONFIRMATION), "sha256": sha256(CONFIRMATION), "verdict": confirmation["verdict"]}, |
| "engineering_gates": { |
| "static": {"path": str(STATIC), "sha256": sha256(STATIC)}, |
| "full_model_smoke": {"path": str(SMOKE), "sha256": sha256(SMOKE)}, |
| }, |
| "architecture": { |
| "layers": [55, 59], "rank": 1024, "qubits": 10, |
| "circuit_parameters": 20, "pauli_observables": 60, |
| "layer_gains": 2, "total_trainable": 82, |
| "net_model_parameter_reduction": 3145646, |
| }, |
| "dataset": "Salesforce/wikitext wikitext-2-raw-v1 train only", |
| "dataset_revision": "b08601e04326c79dfdd32d625aee71d232d685c3", |
| "tokens_sha256": sha256(TOKENS), |
| "sequence_length": LENGTH, |
| "seed": SEED, |
| "train_indices": train_indices, |
| "eval_indices": eval_indices, |
| "train_count": TRAIN_COUNT, |
| "eval_count": EVAL_COUNT, |
| "excluded_prior_train_indices_count": len(used), |
| "available_before_selection": len(available), |
| "prior_artifact_json_files_parsed": parsed, |
| "overlap": 0, |
| "training_protocol": { |
| "objective": "relative MSE of final normalized hidden states", |
| "optimizer": "AdamW", "learning_rate": 0.003, "weight_decay": 0.0, |
| "gradient_clip_norm": 1.0, "steps": TRAIN_COUNT, |
| "all_original_and_shared_basis_parameters_frozen": True, |
| }, |
| "screen_rule": { |
| "continue_to_new_C4_lock_only_if": [ |
| "QH032 point NLL is lower than shared-SVD own-zero", |
| "QH032 point NLL is lower than equal-parameter CC032", |
| ], |
| "no_CI_or_quantum_claim_from_train_screen": True, |
| }, |
| "validation_rows_inspected": 0, |
| "test_rows_inspected": 0, |
| "created_at": datetime.now(timezone.utc).isoformat(), |
| } |
| OUTPUT.write_text(json.dumps(payload, indent=2), encoding="utf-8") |
| print(json.dumps({ |
| "status": payload["status"], "train_count": TRAIN_COUNT, "eval_count": EVAL_COUNT, |
| "excluded_prior_train": len(used), "available_before_selection": len(available), |
| "record": str(OUTPUT), "sha256": sha256(OUTPUT), |
| }, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
