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#!/usr/bin/env python3
"""Small author-code EP endpoint for structured-measurement-bias screening."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import random
import subprocess
import sys
import time
import numpy as np
import torch
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from sdil.rain_ep_adapter import ( # noqa: E402
RainGradientCorrector,
attach_to_rain_estimator,
observe_rain_neutral,
)
PINNED_REVISION = "6b253fd8a5d267535f58ab79992256ef10031ceb"
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--author-root", type=Path, required=True)
parser.add_argument("--device", default="cuda")
parser.add_argument(
"--mode", choices=sorted(RainGradientCorrector.MODES), required=True)
parser.add_argument("--bias-ratio", type=float, default=0.5)
parser.add_argument("--predictor-rate", type=float, default=0.1)
parser.add_argument("--neutral-cadence", type=int, default=1)
parser.add_argument("--calibration-batches", type=int, default=0)
parser.add_argument("--epochs", type=int, default=2)
parser.add_argument("--train-limit", type=int, default=2048)
parser.add_argument("--test-limit", type=int, default=1024)
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--training-iterations", type=int, default=12)
parser.add_argument("--inference-iterations", type=int, default=30)
parser.add_argument("--deterministic", action="store_true")
parser.add_argument("--seed", type=int, default=1988)
parser.add_argument("--output", type=Path, required=True)
return parser.parse_args()
def revision(path: Path) -> str:
return subprocess.check_output(
["git", "-C", str(path), "rev-parse", "HEAD"], text=True).strip()
def accuracy(cost, size: int) -> float:
return float((~cost.error_fn()).float().sum()) / size
@torch.no_grad()
def evaluate(network, cost, minimizer, loader) -> tuple[float, float]:
total_correct = 0.0
total_cost = 0.0
total = 0
for x, y in loader:
network.set_input(x, reset=True)
minimizer.compute_equilibrium()
cost.set_target(y)
batch = x.shape[0]
total_correct += accuracy(cost, batch) * batch
total_cost += float(cost.eval().sum())
total += batch
return total_correct / total, total_cost / total
def main() -> None:
args = parse_args()
if args.calibration_batches < 0:
raise ValueError("calibration batches must be nonnegative")
author_root = args.author_root.resolve()
author_revision = revision(author_root)
if author_revision != PINNED_REVISION:
raise ValueError(
f"expected Rain revision {PINNED_REVISION}, got {author_revision}")
sys.path.insert(0, str(author_root))
from datasets import load_fashion_mnist
from model.function.cost import SquaredError
from model.function.network import Network
from model.hopfield.minimizer import FixedPointMinimizer
from model.hopfield.network import ConvHopfieldEnergy28
from training.sgd import AugmentedFunction, EquilibriumProp
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
if args.deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.use_deterministic_algorithms(True)
device = torch.device(args.device)
training_data, test_data = load_fashion_mnist(
normalize=True, augment_32x32=False)
train_generator = torch.Generator().manual_seed(args.seed)
train_indices = torch.randperm(
len(training_data), generator=train_generator)[:args.train_limit]
test_indices = torch.arange(min(args.test_limit, len(test_data)))
training_loader = torch.utils.data.DataLoader(
torch.utils.data.Subset(training_data, train_indices.tolist()),
batch_size=args.batch_size, shuffle=True, generator=train_generator,
num_workers=0)
calibration_generator = torch.Generator().manual_seed(args.seed + 104729)
calibration_loader = torch.utils.data.DataLoader(
torch.utils.data.Subset(training_data, train_indices.tolist()),
batch_size=args.batch_size, shuffle=True,
generator=calibration_generator, num_workers=0)
test_loader = torch.utils.data.DataLoader(
torch.utils.data.Subset(test_data, test_indices.tolist()),
batch_size=args.batch_size, shuffle=False, num_workers=0)
energy = ConvHopfieldEnergy28(
num_inputs=1, num_hiddens_1=32, num_hiddens_2=64,
num_outputs=10, weight_gains=[0.6, 0.6, 1.5])
energy.set_device(str(device))
network = Network(energy)
cost = SquaredError(energy.layers()[-1])
augmented = AugmentedFunction(energy, cost)
training_minimizer = FixedPointMinimizer(
augmented, network.free_layers())
training_minimizer.mode = "asynchronous"
training_minimizer.num_iterations = args.training_iterations
estimator = EquilibriumProp(
energy.params(), energy.layers(), augmented, cost,
training_minimizer)
estimator.variant = "positive"
estimator.nudging = 0.25
corrector = RainGradientCorrector(
mode=args.mode,
bias_ratio=args.bias_ratio,
predictor_rate=args.predictor_rate,
neutral_cadence=args.neutral_cadence,
seed=args.seed + 1729)
attach_to_rain_estimator(estimator, corrector)
inference_minimizer = FixedPointMinimizer(
energy, network.free_layers())
inference_minimizer.mode = "asynchronous"
inference_minimizer.num_iterations = args.inference_iterations
learning_rates = [0.02] * len(energy.params())
parameter_groups = [
{"params": parameter.state, "lr": learning_rate}
for parameter, learning_rate in zip(energy.params(), learning_rates)
]
optimizer = torch.optim.SGD(
parameter_groups, lr=0.1, momentum=0.9, weight_decay=3e-4)
metrics = []
start = time.time()
calibration_start = time.time()
calibration_observations = 0
if args.calibration_batches:
if args.mode not in {"constant", "innovation"}:
raise ValueError(
"precalibration is defined only for constant or innovation mode")
while calibration_observations < args.calibration_batches:
for x, _ in calibration_loader:
network.set_input(x, reset=False)
inference_minimizer.compute_equilibrium()
observe_rain_neutral(estimator, corrector)
calibration_observations += 1
if calibration_observations == args.calibration_batches:
break
calibration_seconds = time.time() - calibration_start
for epoch in range(1, args.epochs + 1):
total_cost = 0.0
total_correct = 0.0
total = 0
for x, y in training_loader:
network.set_input(x, reset=False)
inference_minimizer.compute_equilibrium()
cost.set_target(y)
batch = x.shape[0]
total_cost += float(cost.eval().sum())
total_correct += accuracy(cost, batch) * batch
total += batch
gradients = estimator.compute_gradient()
if any(gradient.requires_grad for gradient in gradients):
raise AssertionError("adapter produced a requires-grad tensor")
for parameter, gradient in zip(energy.params(), gradients):
parameter.state.grad = gradient
optimizer.step()
for parameter in energy.params():
parameter.clamp_()
test_accuracy, test_cost = evaluate(
network, cost, inference_minimizer, test_loader)
finite = all(
bool(torch.isfinite(parameter.state).all())
for parameter in energy.params()
)
record = {
"epoch": epoch,
"train_accuracy": total_correct / total,
"train_cost": total_cost / total,
"test_accuracy": test_accuracy,
"test_cost": test_cost,
"finite": finite,
"corrector": dict(corrector.last_diagnostics),
"wall_seconds": time.time() - start,
}
metrics.append(record)
print(json.dumps(record), flush=True)
if not finite:
break
report = {
"schema": "rain_ep_structured_bias_screen_v1",
"author": {
"repository": "https://github.com/rain-neuromorphics/energy-based-learning",
"revision": author_revision,
},
"protocol": {
"dataset": "FashionMNIST",
"network": "author ConvHopfieldEnergy28 32-64-10",
"algorithm": "positive equilibrium propagation",
"mode": args.mode,
"bias_ratio": args.bias_ratio,
"predictor_rate": args.predictor_rate,
"neutral_cadence": args.neutral_cadence,
"calibration_batches": args.calibration_batches,
"calibration_observations": calibration_observations,
"calibration_seconds": calibration_seconds,
"epochs": args.epochs,
"train_limit": args.train_limit,
"test_limit": args.test_limit,
"batch_size": args.batch_size,
"training_iterations": args.training_iterations,
"inference_iterations": args.inference_iterations,
"seed": args.seed,
"device": str(device),
"deterministic": args.deterministic,
"autodiff_used_for_learning": False,
},
"metrics": metrics,
"epochs_completed": len(metrics),
"final": metrics[-1],
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(report, indent=2) + "\n")
print(f"wrote {args.output}")
if __name__ == "__main__":
main()
|
