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47709ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | #!/usr/bin/env python3
"""Run one arm of the locked WikiText-train QH032 screen."""
from __future__ import annotations
import argparse
import hashlib
import json
import math
import random
import sys
import time
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
import torch
from transformers import AutoModelForMultimodalLM
PROJECT = Path(__file__).resolve().parents[2]
LOCK = PROJECT / "artifacts/qh032-train-screen-lock.json"
TOKENS = PROJECT / "datasets/processed/wikitext2_qwen38/train-tokens.pt"
OUTPUT_DIR = PROJECT / "artifacts/qh032_train_screen"
sys.path.insert(0, str(PROJECT / "code/src"))
from quantum_qwen38.qh032_pauli_observable_replacement import ( # noqa: E402
ExactPauliObservableCore, QH032Config, install_qh032, unique_trainable_parameters,
)
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 block(tokens, index, length, device):
start = index * length
return tokens[start : start + length].to(device=device, dtype=torch.long).unsqueeze(0)
def set_projections(model, modules) -> None:
for index, module in modules.items():
model.model.language_model.layers[index].self_attn.v_proj = module
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--candidate", choices=["base", "sharedsvd1024", "qh032", "cc032"], required=True)
return parser.parse_args()
@torch.inference_mode()
def evaluate(model, tokens, indices, length):
rows, seconds = [], []
for index in indices:
inputs = block(tokens, index, length, model.device)
started = time.perf_counter()
loss = model(input_ids=inputs, labels=inputs, use_cache=False).loss
rows.append({"block_index": index, "mean_token_nll": float(loss), "seconds": time.perf_counter() - started})
seconds.append(rows[-1]["seconds"])
nll = float(np.mean([row["mean_token_nll"] for row in rows]))
return {
"metrics": {"sequence_count": len(rows), "mean_token_nll": nll, "token_perplexity": math.exp(nll),
"mean_sequence_seconds": float(np.mean(seconds))},
"rows": rows,
}
def train(model, replacements, originals, tokens, lock):
parameters = list(unique_trainable_parameters(replacements))
protocol = lock["training_protocol"]
optimizer = torch.optim.AdamW(parameters, lr=protocol["learning_rate"], weight_decay=protocol["weight_decay"])
backbone = model.model.language_model
config = QH032Config()
core = replacements[config.layer_indices[0]].core
calls_before = int(core.circuit_calls.item())
losses, seconds, theta_grads, alpha_grads = [], [], [], []
for position, index in enumerate(lock["train_indices"], start=1):
inputs = block(tokens, int(index), int(lock["sequence_length"]), model.device)
set_projections(model, originals)
with torch.inference_mode():
teacher = backbone(input_ids=inputs, use_cache=False, return_dict=True).last_hidden_state.detach()
set_projections(model, replacements)
optimizer.zero_grad(set_to_none=True)
started = time.perf_counter()
student = backbone(input_ids=inputs, use_cache=False, return_dict=True).last_hidden_state
denominator = teacher.float().square().mean().clamp_min(1.0e-8)
loss = (student.float() - teacher.float()).square().mean() / denominator
loss.backward()
theta_grads.append(float(core.theta.grad.float().norm()))
alpha_grads.append(float(core.alpha.grad.float().norm()))
torch.nn.utils.clip_grad_norm_(parameters, protocol["gradient_clip_norm"])
optimizer.step()
torch.cuda.synchronize()
losses.append(float(loss.detach()))
seconds.append(time.perf_counter() - started)
if position % 64 == 0:
print(json.dumps({"trained": position, "recent_loss": float(np.mean(losses[-64:])), "recent_seconds": float(np.mean(seconds[-64:]))}), flush=True)
calls = int(core.circuit_calls.item()) - calls_before
expected = len(lock["train_indices"]) * len(config.layer_indices) if isinstance(core, ExactPauliObservableCore) else 0
if calls != expected:
raise AssertionError(f"training simulator calls {calls} != {expected}")
frozen_grads = sum(int(p.grad is not None) for p in model.parameters() if not p.requires_grad)
if frozen_grads:
raise AssertionError("frozen parameter received gradient")
edge = 32
return {
"steps": len(losses), "loss_first_window_mean": float(np.mean(losses[:edge])),
"loss_last_window_mean": float(np.mean(losses[-edge:])), "loss_min": float(np.min(losses)),
"theta_gradient_norm_mean": float(np.mean(theta_grads)), "alpha_gradient_norm_mean": float(np.mean(alpha_grads)),
"mean_student_step_seconds": float(np.mean(seconds)), "simulator_calls": calls,
"frozen_gradient_tensor_count": frozen_grads, "final_theta_l2_norm": float(core.theta.detach().norm()),
"final_alpha_l2_norm": float(core.alpha.detach().norm()),
"final_gammas": {str(i): float(m.gamma.detach()) for i, m in replacements.items()},
"losses": losses,
}
def checkpoint(replacements):
first = sorted(replacements)[0]
return {
"core.theta": replacements[first].core.theta.detach().cpu(),
"core.alpha": replacements[first].core.alpha.detach().cpu(),
**{f"gamma.{index}": module.gamma.detach().cpu() for index, module in replacements.items()},
}
def main():
args = parse_args()
if not torch.cuda.is_available():
raise RuntimeError("QH032 screen is GPU-only")
lock = json.loads(LOCK.read_text(encoding="utf-8"))
if lock["status"] != "locked_before_qh032_train_only_screen_results":
raise RuntimeError("invalid QH032 train-screen lock")
random.seed(lock["seed"]); np.random.seed(lock["seed"]); torch.manual_seed(lock["seed"]); torch.cuda.manual_seed_all(lock["seed"])
tokens = torch.load(TOKENS, map_location="cpu", weights_only=True)
model_dir = Path((PROJECT / "records/active_model_path.txt").read_text().strip())
model = AutoModelForMultimodalLM.from_pretrained(
model_dir, local_files_only=True, dtype=torch.bfloat16, device_map="balanced",
low_cpu_mem_usage=True, attn_implementation="sdpa",
)
model.eval(); model.config.use_cache = False
replacements = originals = None
if args.candidate == "base":
model.requires_grad_(False)
audit = {"candidate": "base", "net_model_parameter_reduction": 0, "trainable_parameter_count": 0}
training = {"steps": 0}
else:
branch = "classical" if args.candidate == "cc032" else "quantum"
replacements, originals, audit = install_qh032(model, QH032Config(), branch=branch)
if args.candidate == "sharedsvd1024":
for module in replacements.values():
module.branch_enabled = False; module.requires_grad_(False)
training = {"steps": 0}
else:
training = train(model, replacements, originals, tokens, lock)
evaluation = evaluate(model, tokens, [int(i) for i in lock["eval_indices"]], int(lock["sequence_length"]))
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
if replacements is not None and args.candidate in {"qh032", "cc032"}:
torch.save(checkpoint(replacements), OUTPUT_DIR / f"{args.candidate}-screen-checkpoint.pt")
payload = {
"status": "ok", "candidate": args.candidate, "dataset_lock": str(LOCK), "dataset_lock_sha256": sha256(LOCK),
"dataset": {"name": lock["dataset"], "train_indices": lock["train_indices"] if training["steps"] else [],
"eval_indices": lock["eval_indices"], "validation_rows_inspected": 0, "test_rows_inspected": 0},
"injection": audit, "training": training, "evaluation": evaluation,
"created_at": datetime.now(timezone.utc).isoformat(),
}
output = OUTPUT_DIR / f"screen-{args.candidate}-s{lock['seed']}-n{lock['train_count']}-e{lock['eval_count']}.json"
output.write_text(json.dumps(payload, indent=2), encoding="utf-8")
print(json.dumps({"status": "ok", "candidate": args.candidate, "nll": evaluation["metrics"]["mean_token_nll"],
"output": str(output), "sha256": sha256(output)}, indent=2))
if __name__ == "__main__":
main()
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