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import argparse
import hashlib
import json
import math
import random
import time
from collections import Counter
from datetime import datetime, timezone
from pathlib import Path
import torch
from safetensors.torch import load_file, save_file
from train_foundation_head import write
from train_workflow_backbone import forward
from workflow_workspace_supervision import WorkspaceSupervisor
from veyra.constants import QUESTION_TYPES
from veyra.data import read_records
from veyra.option_model import OptionModel
from veyra.proper_learning import distribution_losses, retention_losses
from veyra.workflow_facts import auxiliary_targets
from veyra.workflow_learning import SHARES, annotate, selection_key, summarize_logits
def read(path):
return json.loads(Path(path).read_text(encoding="utf-8"))
def digest(path):
return hashlib.sha256(Path(path).read_bytes()).hexdigest()
def development_risk(logits, tensors, indices, temperatures):
types, valid = tensors["types"][indices], tensors["valid"][indices]
values = logits / torch.tensor(temperatures)[types, None]
confidence, prediction = values.masked_fill(~valid, -1e9).softmax(-1).max(-1)
gold = tensors["gold"][indices]
errors = 1 - tensors["targets"][indices].gather(1, prediction[:, None]).squeeze(1)
errors = torch.where(gold >= 0, (prediction != gold).float(), errors)
result = {}
for type_id, kind in enumerate(QUESTION_TYPES):
selected = torch.where(types == type_id)[0]
order = selected[torch.argsort(confidence[selected], descending=True, stable=True)]
count = len(order)
counts = torch.arange(1, count + 1)
risk = errors[order].cumsum(0) / counts
boundaries = torch.ones(count, dtype=torch.bool)
boundaries[:-1] = confidence[order[:-1]] != confidence[order[1:]]
eligible = torch.where((risk <= 0.15) & (counts >= 20) & boundaries)[0]
result[kind] = {
"questions": count,
"risk_at_60pct_coverage": float(risk[max(0, math.floor(count * 0.6) - 1)]),
"maximum_coverage_at_15pct_error": (int(eligible[-1]) + 1) / count
if len(eligible)
else 0.0,
}
return result
@torch.inference_mode()
def assess(model, supervisor, enabled, records, root, rows, tensors, indices, targets, eligible):
model.eval()
logits, fact_prediction, fact_target = [], [], []
for i in indices.tolist():
with supervisor.observe(enabled and bool(eligible[i])):
values, _ = forward(model, records[rows[i]["id"]], root)
logits.append(values[0].cpu())
if enabled and eligible[i]:
fact_prediction.append(supervisor.logits()[0].sigmoid().cpu())
fact_target.append(targets[i])
logits = torch.stack(logits)
metrics = summarize_logits(logits, tensors, rows, indices, model.calibration.temperatures)
result = {
"metrics": metrics,
"development_risk_diagnostic": development_risk(
logits, tensors, indices, model.calibration.temperatures
),
}
if fact_prediction:
prediction, target = torch.stack(fact_prediction), torch.stack(fact_target)
observed = (target == 0) | (target == 1)
result["auxiliary_diagnostic"] = {
"questions": len(prediction),
"condition_probability_mse": float((prediction - target).square().mean()),
"observed_condition_accuracy": float(
((prediction[observed] >= 0.5) == (target[observed] == 1)).float().mean()
),
"constant_half_mse": float((target - 0.5).square().mean()),
}
return result
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=Path, required=True)
parser.add_argument("--arm", required=True)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
if args.output.exists():
raise FileExistsError("workspace study outputs are immutable")
config = read(args.config)
if config["status"] != "declared_before_training":
raise ValueError("declare the follow-up experiment before optimization")
arm = config["arms"][args.arm]
parent, data, features = map(Path, (config["parent"], config["records"], config["features"]))
if (
digest(parent / "head.safetensors") != config["parent_weights_sha256"]
or digest(parent / "manifest.json") != config["parent_manifest_sha256"]
or digest(data) != config["records_sha256"]
or digest(config["release_protocol"]) != config["release_protocol_sha256"]
or digest(config["baseline_development"]) != config["baseline_development_sha256"]
):
raise ValueError("declared model, data or acceptance protocol changed")
spec, cache = read(features / "specification.json"), read(features / "manifest.json")
if spec["records_sha256"] != digest(data) or spec["calibration_or_final_encoded"] is not False:
raise ValueError("feature cache is not the declared training/development set")
for name, key in (
("features.safetensors", "features_sha256"),
("records.json", "metadata_sha256"),
("specification.json", "specification_sha256"),
):
if digest(features / name) != cache[key]:
raise ValueError("feature cache bytes changed")
tensors, rows = load_file(features / "features.safetensors"), read(features / "records.json")
records = {r.id: r for r in read_records(data) if r.split in {"train", "dev"}}
annotate(rows, records.values())
targets, eligible = auxiliary_targets(rows, records.values())
train = [i for i, row in enumerate(rows) if row["split"] == "train"]
dev = torch.tensor([i for i, row in enumerate(rows) if row["split"] == "dev"])
if int(eligible[train].sum()) != 3840 or int(eligible[dev].sum()) != 1200:
raise ValueError("unexpected primitive supervision population")
conditional_audit = Path("data/workflow-v12/conditional-target-audit.json")
included_groups = {row["group"] for row in rows}
audited_targets = {
item["group"]: item["posterior"]
for item in read(conditional_audit)
if item["group"] in included_groups
}
for i, row in enumerate(rows):
if row["domain"] == "uncertainty" and not torch.allclose(
targets[i], torch.tensor(audited_targets[row["group"]]), atol=1e-6
):
raise ValueError("primitive target differs from the original evidence model")
counts = Counter(rows[i]["domain"] for i in train)
weights = {domain: SHARES[domain] * len(train) / count for domain, count in counts.items()}
grouped = {}
for i in train:
grouped.setdefault(rows[i]["group"], []).append(i)
orders = []
for epoch in range(config["epochs"]):
groups = list(grouped.values())
random.Random(config["seed"] + epoch).shuffle(groups)
orders.append([i for group in groups for i in group])
baseline = read(config["baseline_development"])["dev"]
protocol = {
"started_at_utc": datetime.now(timezone.utc).isoformat(),
"configuration": config,
"config_sha256": digest(args.config),
"arm": args.arm,
"source_sha256": {
str(path): digest(path)
for path in (
Path(__file__),
Path("scripts/workflow_workspace_supervision.py"),
Path("scripts/train_workflow_backbone.py"),
Path("src/veyra/workflow_facts.py"),
Path("src/veyra/workflow_learning.py"),
Path("src/veyra/proper_learning.py"),
Path("src/veyra/option_model.py"),
)
},
"feature_cache": cache,
"conditional_audit_sha256": digest(conditional_audit),
"conditional_targets_match_audit": True,
"domain_shares": SHARES,
"calibration_or_final_used": False,
"primary_experiment_candidates_changed": False,
"inference_modules_added": 0,
"generated_intermediate_tokens": 0,
}
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)
supervisor = WorkspaceSupervisor(model, config["seed"])
adapters = [p for name, p in model.named_parameters() if ".lora_" in name]
groups = [
{"params": adapters, "lr": config["learning_rate"]},
{
"params": list(model.readout.parameters()) + list(model.binding_head.parameters()),
"lr": config["learning_rate"] * 0.1,
},
]
if arm["auxiliary_weight"] > 0:
groups.append({"params": supervisor.decoder.parameters(), "lr": config["auxiliary_lr"]})
optimizer = torch.optim.AdamW(groups, weight_decay=0.01)
accumulation = config["accumulation"]
total_steps = len(orders) * math.ceil(len(train) / accumulation)
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)
temperatures = model.calibration.temperatures
history, steps, processed, total_loss, auxiliary_count, auxiliary_sum = [], 0, 0, 0.0, 0, 0.0
started = time.perf_counter()
torch.cuda.reset_peak_memory_stats()
try:
for epoch, order in enumerate(orders, 1):
model.train()
model.encoder.model.eval()
for start in range(0, len(order), accumulation):
batch = order[start : start + accumulation]
optimizer.zero_grad(set_to_none=True)
for i in batch:
use_auxiliary = arm["auxiliary_weight"] > 0 and bool(eligible[i])
with supervisor.observe(use_auxiliary):
logits, hidden = forward(model, records[rows[i]["id"]], data.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,
config["method"],
).mean()
if rows[i]["retention"]:
reference = (
tensors["parent_logits"][i : i + 1].to(logits.device) / scale
)
anchor = (
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 * anchor
)
loss = loss * weights[rows[i]["domain"]]
if use_auxiliary:
if not supervisor.captured.requires_grad:
raise ValueError("primitive loss is detached from the backbone")
aux_loss = torch.nn.functional.binary_cross_entropy_with_logits(
supervisor.logits(), targets[i : i + 1].to(logits.device)
)
if auxiliary_count == 0:
gradients = torch.autograd.grad(
aux_loss, adapters, retain_graph=True, allow_unused=True
)
norms = [
float(g.detach().abs().max())
for g in gradients
if g is not None
]
if (
not norms
or not all(math.isfinite(n) for n in norms)
or max(norms) == 0
):
raise ValueError(
"no finite nonzero primitive-to-backbone gradient"
)
write(
args.output / "auxiliary-gradient-path.json",
{
"backbone_parameters_with_gradient": len(norms),
"backbone_parameters_with_nonzero_gradient": sum(
n > 0 for n in norms
),
"maximum_absolute_gradient": max(norms),
"same_decision_forward": True,
"optimizer_steps_before_check": steps,
},
)
del gradients, norms
loss = loss + arm["auxiliary_weight"] * aux_loss
auxiliary_count += 1
auxiliary_sum += float(aux_loss.detach())
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 group in optimizer.param_groups for p in group["params"]],
1.0,
error_if_nonfinite=True,
)
optimizer.step()
scheduler.step()
steps += 1
if steps % 25 == 0:
progress = {
"epoch": epoch,
"processed": processed,
"total": len(train) * len(orders),
"steps": steps,
"mean_loss": total_loss / processed,
"auxiliary_bce": auxiliary_sum / auxiliary_count
if auxiliary_count
else None,
"elapsed_seconds": time.perf_counter() - started,
}
write(args.output / "progress.json", progress)
print(json.dumps(progress), flush=True)
result = assess(
model,
supervisor,
arm["auxiliary_weight"] > 0,
records,
data.parent,
rows,
tensors,
dev,
targets,
eligible,
)
checkpoint = args.output / f"epoch-{epoch}" / "checkpoint"
metadata = {
"completed": True,
"intermediate": True,
"method": "workspace-" + args.arm,
"seed": config["seed"],
"epochs": epoch,
"optimizer_steps": steps,
"backbone_updated": True,
"parent_weights_sha256": config["parent_weights_sha256"],
"dataset_sha256": digest(data),
"protocol_sha256": digest(args.output / "protocol.json"),
"selected_dev": result["metrics"],
"selection": selection_key(result["metrics"], baseline),
"development_risk_diagnostic": result["development_risk_diagnostic"],
"auxiliary_diagnostic": result.get("auxiliary_diagnostic"),
"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(checkpoint, metadata)
if arm["auxiliary_weight"] > 0:
save_file(
{
name: value.detach().cpu().contiguous()
for name, value in supervisor.decoder.state_dict().items()
},
checkpoint.parent / "auxiliary-training-only.safetensors",
)
history.append({"checkpoint": str(checkpoint), **metadata})
write(args.output / "history.json", history)
print(
json.dumps(
{
"checkpoint": str(checkpoint),
"selection": metadata["selection"],
"auxiliary": result.get("auxiliary_diagnostic"),
}
),
flush=True,
)
finally:
supervisor.close()
write(
args.output / "complete.json",
{
"completed": True,
"candidates": [item["checkpoint"] for item in history],
"release_allowed": False,
},
)
if __name__ == "__main__":
main()
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