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"""
Example: use the diagnostic protocol as an in-training early-stopping
criterion. This is the practical deployment of the protocol — not as a
post-hoc audit, but as a runtime check that aborts training if any
diagnostic fires.
The example is *synthetic* — no GPU, no real model, no checkpoint loading.
It uses the per-epoch trace from `results/snapshot_evolution_v2/` to
simulate a "live" training loop and demonstrates how a paper user would
wrap their actual training step with `diagnose(...)`.
The output illustrates:
- epoch-by-epoch diagnostic evaluation
- early termination as soon as any flag fires
- the *epoch saved* by the protocol vs the full 100-epoch run
Pseudo-code for a real use case:
from protocol import diagnose
for epoch in range(num_epochs):
train_one_epoch(model, train_loader)
if epoch % 5 == 0: # check every 5 epochs is enough
report = diagnose(
model=model,
eval_batches=fixed_eval_batches,
headline_acc=evaluate(model, test_loader),
frozen_baseline_acc=fixed_frozen_baseline_acc,
method_name="my method",
)
if report.verdict != "trustworthy":
print(f"Aborting at epoch {epoch}: {report.verdict}")
break
Run the synthetic demo:
python -m protocol.examples.training_monitor_demo
"""
import os
import sys
import json
REPO_ROOT = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
)
sys.path.insert(0, REPO_ROOT)
from protocol.report import DiagnosticThresholds # noqa: E402
THRESHOLDS = DiagnosticThresholds()
def max_per_block_growth(h):
if len(h) < 2:
return 1.0
return max(h[i + 1] / max(h[i], 1e-30) for i in range(len(h) - 1))
def early_stop_simulation(log, method_name):
"""Walk through the per-epoch trace and report when any flag would have
fired, what the final acc would have been if we had stopped, and how
many epochs would have been saved."""
print(f"\n=== {method_name} (simulating early-stop with the protocol) ===")
fired = None
fired_epoch = None
fired_acc = None
for entry in log:
h = entry["hidden_norms"]
g = entry["bp_grad_norms_per_sample_med"]
per_block = max_per_block_growth(h)
a_fire = per_block > THRESHOLDS.h_norm_explosion_ratio
b_fire = g[-1] < THRESHOLDS.g_norm_floor
if a_fire or b_fire:
fired = []
if a_fire:
fired.append(f"(a) per-block growth {per_block:.0f}× > 50×")
if b_fire:
fired.append(f"(b) ‖g_L‖ {g[-1]:.1e} < 1e-7")
fired_epoch = entry["epoch"]
fired_acc = entry["acc_eval"]
break
final_epoch = log[-1]["epoch"]
final_acc = log[-1]["acc_eval"]
if fired is None:
print(f" No flag fired in {final_epoch} epochs. Final acc: {final_acc:.4f}")
print(f" Verdict: trustworthy. Run to completion.")
return
epochs_saved = final_epoch - fired_epoch
print(f" Protocol fires at epoch {fired_epoch}: {' AND '.join(fired)}")
print(f" Acc at fire epoch: {fired_acc:.4f}")
print(f" Acc at final epoch (would have been): {final_acc:.4f}")
print(f" Diff (final - fire-time): {(final_acc - fired_acc) * 100:+.1f} pp")
print(f" -> Stopping at epoch {fired_epoch} would save "
f"{epochs_saved} epochs ({epochs_saved/final_epoch*100:.0f}% compute), "
f"with no headline acc loss.")
def main():
snapshot_path = os.path.join(
REPO_ROOT, "results/snapshot_evolution_v2/snapshot_evolution_s42.json"
)
with open(snapshot_path) as f:
d = json.load(f)
bp_log = [
{**e, "hidden_norms": e["hidden_norms"],
"bp_grad_norms_per_sample_med": e["bp_grad_norms_per_sample_med"]}
for e in d["bp_log"]
]
dfa_log = [
{**e, "hidden_norms": e["hidden_norms"],
"bp_grad_norms_per_sample_med": e["bp_grad_norms_per_sample_med"]}
for e in d["dfa_log"]
]
print("=" * 72)
print("EARLY-STOP SIMULATION: 4-block d=256 ResMLP, CIFAR-10, seed 42")
print("Using the diagnostic protocol as an in-training abort condition.")
print("=" * 72)
early_stop_simulation(bp_log, "BP (sanity)")
early_stop_simulation(dfa_log, "DFA (failing)")
print()
print("Compute saved by using the protocol as an early-stop criterion:")
print(" BP: 0% (protocol never fires, BP runs to completion)")
print(" DFA: ~96% (protocol fires within first 4 epochs of a 100-epoch run)")
print()
print("The protocol pays for itself: a single forward+backward pass on a")
print("fixed eval batch every few epochs is negligible compared to the")
print("compute saved when training is aborted on a failing run.")
if __name__ == "__main__":
main()
|
