File size: 10,061 Bytes
f9d0103 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | """Shared-backbone adaptation with frozen-baseline replay and development-only checkpoints."""
import argparse
import hashlib
import json
import math
import random
import time
from collections import Counter
from pathlib import Path
import torch
from safetensors.torch import load_file
from train_foundation_head import write
from veyra.data import read_records
from veyra.option_model import OptionModel
from veyra.proper_learning import distribution_losses, retention_losses
from veyra.workflow_learning import SHARES, annotate, selection_key, summarize_logits
def digest(path):
return hashlib.sha256(path.read_bytes()).hexdigest()
def forward(model, record, root):
states, mappings, _, conditions = model.encode(record.request, root, capture_condition=True)
if states.shape[0] != 1 or len(mappings) != 1:
raise ValueError("require one independent decision")
logits = model.readout(states) + model.binding_head(states, model.condition_readout(conditions))
return logits, states
@torch.inference_mode()
def assess(model, records, root, rows, tensors, indices, temperatures):
model.eval()
logits = torch.stack(
[forward(model, records[rows[i]["id"]], root)[0][0].cpu() for i in indices.tolist()]
)
return summarize_logits(logits, tensors, rows, indices, temperatures)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--learning-rate", type=float, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--records", type=Path, default=Path("data/workflow-v12/records.jsonl"))
parser.add_argument("--features", type=Path, default=Path("data/features-workflow-v12"))
parser.add_argument("--head-experiment", type=Path, default=Path("runs/workflow-v12-head"))
args = parser.parse_args()
if args.output.exists():
raise FileExistsError("backbone experiment output is immutable")
configuration = Path("configs/workflow-release-v12.json")
config = json.loads(configuration.read_text(encoding="utf-8"))
if (
args.learning_rate not in config["backbone_learning_rates"]
or config["backbone_epochs"] != 1
):
raise ValueError("run outside the frozen experiment plan")
selected = json.loads((args.head_experiment / "selection.json").read_text(encoding="utf-8"))[
"selected"
]
parent = Path(selected["checkpoint"])
parent_manifest = json.loads((parent / "manifest.json").read_text(encoding="utf-8"))
temperatures = parent_manifest["calibration"]["temperatures"]
baseline = json.loads((args.head_experiment / "baseline.json").read_text(encoding="utf-8"))[
"dev"
]
spec = json.loads((args.features / "specification.json").read_text(encoding="utf-8"))
if digest(args.records) != spec["records_sha256"]:
raise ValueError("data changed after feature extraction")
tensors = load_file(args.features / "features.safetensors")
rows = json.loads((args.features / "records.json").read_text(encoding="utf-8"))
records = {r.id: r for r in read_records(args.records) if r.split in {"train", "dev"}}
annotate(rows, records.values())
train = [i for i, r in enumerate(rows) if r["split"] == "train"]
dev = torch.tensor([i for i, r in enumerate(rows) if r["split"] == "dev"])
groups = {}
for i in train:
groups.setdefault(rows[i]["group"], []).append(i)
units = list(groups.values())
random.Random(config["seed"]).shuffle(units)
order = [i for unit in units for i in unit]
segments = [order[: len(order) // 2], order[len(order) // 2 :]]
counts = Counter(rows[i]["domain"] for i in train)
weights = {d: SHARES[d] * len(train) / n for d, n in counts.items()}
protocol = {
"arguments": vars(args),
"release_protocol_sha256": digest(configuration),
"parent": str(parent),
"parent_weights_sha256": digest(parent / "head.safetensors"),
"dataset_sha256": digest(args.records),
"method": selected["method"],
"seed": config["seed"],
"domain_shares": SHARES,
"accumulation": 8,
"head_lr_multiplier": 0.1,
"retention": (
"0.25 supervised + 4*temperature-2 KL + 0.1 cosine feature anchoring on replay"
),
"source_sha256": {
str(p): digest(p)
for p in (
Path(__file__),
Path("src/veyra/workflow_learning.py"),
Path("src/veyra/proper_learning.py"),
Path("src/veyra/option_model.py"),
)
},
"condition_readout_frozen": True,
"vision_weights_frozen": True,
"final_or_calibration_used": False,
}
write(args.output / "protocol.json", protocol)
torch.manual_seed(config["seed"])
torch.set_num_threads(4)
model = OptionModel.load(parent, local_files_only=True, merge=False)
model.condition_readout.requires_grad_(False)
adapters = [p for name, p in model.named_parameters() if ".lora_" in name]
optimizer = torch.optim.AdamW(
[
{"params": adapters, "lr": args.learning_rate},
{
"params": list(model.readout.parameters()) + list(model.binding_head.parameters()),
"lr": args.learning_rate * 0.1,
},
],
weight_decay=0.01,
)
total_steps = sum(math.ceil(len(segment) / 8) for segment in segments)
def rate(step):
if step < 30:
return (step + 1) / 30
return 0.1 + 0.45 * (1 + math.cos(math.pi * min(1, (step - 30) / max(1, total_steps - 30))))
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, rate)
started, processed, steps, total_loss, history = time.perf_counter(), 0, 0, 0.0, []
torch.cuda.reset_peak_memory_stats()
for segment_index, segment in enumerate(segments):
model.train()
model.encoder.model.eval()
for start in range(0, len(segment), 8):
batch = segment[start : start + 8]
optimizer.zero_grad(set_to_none=True)
for i in batch:
logits, hidden = forward(model, records[rows[i]["id"]], args.records.parent)
valid = tensors["valid"][i : i + 1].to(logits.device)
types = tensors["types"][i : i + 1].to(logits.device)
scale = torch.tensor(temperatures, device=logits.device)[types, None]
scaled = logits / scale
loss = distribution_losses(
scaled,
tensors["targets"][i : i + 1].to(logits.device),
valid,
types,
selected["method"],
).mean()
if rows[i]["retention"]:
reference = tensors["parent_logits"][i : i + 1].to(logits.device) / scale
anchoring = (
1
- torch.nn.functional.cosine_similarity(
hidden, tensors["states"][i : i + 1].to(hidden.device)
).mean()
)
loss = (
0.25 * loss
+ 4 * retention_losses(scaled, reference, valid).mean()
+ 0.1 * anchoring
)
loss = loss * weights[rows[i]["domain"]]
if not torch.isfinite(loss):
raise ValueError("nonfinite training objective")
(loss / len(batch)).backward()
total_loss += float(loss.detach())
processed += 1
torch.nn.utils.clip_grad_norm_(
[p for p in model.parameters() if p.requires_grad], 1.0, error_if_nonfinite=True
)
optimizer.step()
scheduler.step()
steps += 1
if steps % 25 == 0:
progress = {
"processed": processed,
"total": len(train),
"steps": steps,
"mean_loss": total_loss / processed,
"elapsed_seconds": time.perf_counter() - started,
}
write(args.output / "progress.json", progress)
print(json.dumps(progress), flush=True)
metrics = assess(model, records, args.records.parent, rows, tensors, dev, temperatures)
path = args.output / ("half" if segment_index == 0 else "full")
metadata = {
"completed": True,
"intermediate": True,
"optimizer_steps": steps,
"method": selected["method"],
"seed": config["seed"],
"epochs": 0.5 * (segment_index + 1),
"backbone_updated": True,
"parent_training": parent_manifest["training"],
"dataset_sha256": digest(args.records),
"protocol_sha256": digest(args.output / "protocol.json"),
"selected_dev": metrics,
"selection": selection_key(metrics, baseline),
"elapsed_seconds": time.perf_counter() - started,
"peak_memory_mib": torch.cuda.max_memory_allocated() / 2**20,
"final_evaluated_at_selection": False,
"smoke": False,
}
model.save(path, metadata)
# OptionModel.save preserves this model's frozen parent calibration until final fitting.
history.append({"checkpoint": str(path), **metadata})
write(args.output / "history.json", history)
print(
json.dumps(
{
"checkpoint": str(path),
"selection": metadata["selection"],
"domains": metrics["by_domain"],
}
),
flush=True,
)
write(
args.output / "complete.json",
{
"completed": True,
"candidates": [h["checkpoint"] for h in history],
"requires_merged_dev_selection": True,
},
)
if __name__ == "__main__":
main()
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