From 6093fe5eb453947154fe39ddb75740f09eb306ef Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Tue, 21 Jul 2026 07:34:09 -0500 Subject: feat: add paper-faithful local learning baselines --- experiments/baseline_run.py | 241 ++++++++++++++++++++++++++++++++++++++++++ experiments/baseline_smoke.py | 107 +++++++++++++++++++ experiments/baseline_sweep.sh | 37 +++++++ 3 files changed, 385 insertions(+) create mode 100644 experiments/baseline_run.py create mode 100644 experiments/baseline_smoke.py create mode 100755 experiments/baseline_sweep.sh (limited to 'experiments') diff --git a/experiments/baseline_run.py b/experiments/baseline_run.py new file mode 100644 index 0000000..a96e24d --- /dev/null +++ b/experiments/baseline_run.py @@ -0,0 +1,241 @@ +"""Canonical runners for non-backprop baselines outside the SDIL family. + +The methods intentionally do not all share one architecture: Forward-Forward +has goodness layers instead of a classifier readout, while Equilibrium +Propagation is an energy-based recurrent network with symmetric connections. +Forcing either into SDILNet would make the comparison look uniform while +silently changing the algorithm. Every JSON result records the protocol. +""" +import argparse +import json +import os +import subprocess +import sys +import time + +import torch +import torch.nn.functional as F + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from sdil.data import get_dataset, onehot +from sdil.local_baselines import FANet, PEPITANet, FFNet, EPNet + + +METHOD_SOURCES = { + "fa": { + "paper": "https://www.nature.com/articles/ncomms13276", + "protocol": "fixed random sequential feedback matrices", + }, + "pepita": { + "paper": "https://proceedings.mlr.press/v162/dellaferrera22a.html", + "code": "https://github.com/GiorgiaD/PEPITA", + "protocol": "ERIN two-forward-pass rule; He-uniform; ReLU; F scale 0.05", + }, + "ff": { + "paper": "https://www.cs.toronto.edu/~hinton/absps/FFXfinal.pdf", + "reference": "https://github.com/mpezeshki/pytorch_forward_forward", + "protocol": "greedy layerwise goodness; input length normalization; Adam", + }, + "ep": { + "paper": "https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2017.00024/full", + "code": "https://github.com/bscellier/Towards-a-Biologically-Plausible-Backprop", + "protocol": "two-phase energy-gradient dynamics; random beta sign", + }, +} + +_MNIST_STATS = {"mnist": (0.1307, 0.3081), "fmnist": (0.2860, 0.3530)} +_CIFAR_MEAN = torch.tensor([0.4914, 0.4822, 0.4465]) +_CIFAR_STD = torch.tensor([0.2470, 0.2435, 0.2616]) + + +def code_provenance(): + root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + try: + commit = subprocess.run(["git", "rev-parse", "HEAD"], cwd=root, check=True, + capture_output=True, text=True).stdout.strip() + dirty = bool(subprocess.run( + ["git", "status", "--porcelain", "--untracked-files=no"], cwd=root, + check=True, capture_output=True, text=True).stdout.strip()) + return {"git_commit": commit, "git_dirty": dirty} + except (OSError, subprocess.CalledProcessError): + return {"git_commit": None, "git_dirty": None} + + +def canonical_input(x, dataset, method, enabled=True): + """Restore [0,1] pixels where the authors' protocol requires them.""" + if not enabled or method not in ("pepita", "ep"): + return x + if dataset in _MNIST_STATS: + mean, std = _MNIST_STATS[dataset] + return (x * std + mean).clamp(0, 1) + if dataset == "cifar10": + mean = _CIFAR_MEAN.to(x.device, x.dtype).view(1, 3, 1) + std = _CIFAR_STD.to(x.device, x.dtype).view(1, 3, 1) + return (x.view(-1, 3, 1024) * std + mean).clamp(0, 1).view_as(x) + return x + + +@torch.no_grad() +def evaluate_method(net, loader, method, dataset, canonical): + correct = total = 0 + loss_sum = 0.0 + for x, y in loader: + x = canonical_input(x, dataset, method, canonical) + if method == "ff": + pred = net.predict(x) + elif method == "ep": + state = net._settle(x, beta=0.0, T=net.T_free) + logits = state[-1] + pred = logits.argmax(1) + loss_sum += F.mse_loss(logits, onehot(y, 10), reduction="sum").item() + else: + logits = net.logits(x) + pred = logits.argmax(1) + loss_sum += F.cross_entropy(logits, y, reduction="sum").item() + correct += (pred == y).sum().item() + total += y.shape[0] + return correct / total, loss_sum / total if method != "ff" else float("nan") + + +def ep_learning_rates(depth, base_eta=None): + """Layerwise rates from the authors' one/two/three-hidden-layer runs.""" + canonical = { + 1: [0.1, 0.05], + 2: [0.4, 0.1, 0.01], + 3: [0.128, 0.032, 0.008, 0.002], + } + if base_eta is not None: + return [base_eta / (4 ** l) for l in range(depth + 1)] + if depth in canonical: + return canonical[depth] + # The original implementation already required rapidly shrinking rates as + # depth grew. Continue its depth-3 geometric schedule for an explicit, + # logged extrapolation rather than pretending there is a canonical one. + return [0.128 / (4 ** l) for l in range(depth + 1)] + + +def build(args, n_in, device): + sizes = [n_in] + [args.width] * args.depth + [10] + if args.method == "fa": + return FANet(sizes, act=args.act, device=device, seed=args.seed, + residual=bool(args.residual), b_scale=args.feedback_scale) + if args.method == "pepita": + return PEPITANet(sizes, act="relu", device=device, seed=args.seed, + f_scale=args.pepita_f_scale, keep_prob=args.keep_prob) + if args.method == "ff": + # FF has only goodness layers; a 10-unit SDIL-style output is not part + # of the original supervised algorithm. + return FFNet([n_in] + [args.width] * args.depth, device=device, + seed=args.seed, threshold=args.ff_threshold) + if args.method == "ep": + return EPNet(sizes, device=device, seed=args.seed, beta=args.ep_beta, + dt=args.ep_dt, T_free=args.ep_free_steps, + T_nudge=args.ep_nudge_steps, random_beta_sign=True) + raise ValueError(args.method) + + +def train(args): + torch.manual_seed(args.seed) + device = args.device + train_loader, test_loader, n_in, n_out = get_dataset( + args.dataset, args.batch_size, device=device) + net = build(args, n_in, device) + canonical = bool(args.canonical_preprocess) + log = { + "args": vars(args), + "method_source": METHOD_SOURCES[args.method], + "provenance": code_provenance(), + "steps": [], + "final": {}, + } + t0 = time.time() + + if args.method == "ff": + eta = 0.03 if args.eta is None else args.eta + for layer in range(args.depth): + for epoch in range(args.epochs): + batches = 0 + for x, y in train_loader: + loss = net.train_layer(layer, x, y, eta) + batches += 1 + if args.max_batches and batches >= args.max_batches: + break + print(f"[{args.tag}] layer {layer} epoch {epoch} local_loss {loss:.4f}", + flush=True) + acc, test_loss = evaluate_method(net, test_loader, args.method, + args.dataset, canonical) + log["steps"].append({"layer_end": layer, "test_acc": acc, + "local_loss": loss}) + print(f"[{args.tag}] layer {layer} test_acc {acc:.4f}", flush=True) + else: + eta = args.eta + if eta is None: + eta = 0.05 if args.method == "fa" else (0.1 if args.method == "pepita" else None) + ep_etas = ep_learning_rates(args.depth, eta) if args.method == "ep" else None + for epoch in range(args.epochs): + if args.method == "pepita" and epoch in (60, 90): + eta *= 0.1 + batches = 0 + for x, y in train_loader: + x = canonical_input(x, args.dataset, args.method, canonical) + yoh = onehot(y, n_out, device=device) + if args.method == "fa": + loss = net.fa_step(x, y, yoh, eta, args.momentum) + elif args.method == "pepita": + loss = net.pepita_step(x, y, yoh, eta, args.momentum) + else: + loss = net.train_step(x, y, yoh, ep_etas) + batches += 1 + if args.max_batches and batches >= args.max_batches: + break + acc, test_loss = evaluate_method(net, test_loader, args.method, + args.dataset, canonical) + log["steps"].append({"epoch_end": epoch, "test_acc": acc, + "test_loss": test_loss, "train_loss": loss}) + print(f"[{args.tag}] epoch {epoch} loss {loss:.4f} test_acc {acc:.4f}", + flush=True) + + acc, test_loss = evaluate_method(net, test_loader, args.method, + args.dataset, canonical) + log["final"] = {"test_acc": acc, "test_loss": test_loss, + "wall_s": time.time() - t0} + os.makedirs(args.outdir, exist_ok=True) + path = os.path.join(args.outdir, f"{args.tag}.json") + with open(path, "w") as f: + json.dump(log, f) + print(f"[{args.tag}] DONE test_acc={acc:.4f} -> {path}", flush=True) + return log + + +def get_args(): + p = argparse.ArgumentParser() + p.add_argument("--method", required=True, choices=["fa", "pepita", "ff", "ep"]) + p.add_argument("--dataset", default="mnist", choices=["mnist", "fmnist", "cifar10"]) + p.add_argument("--depth", type=int, default=2) + p.add_argument("--width", type=int, default=500) + p.add_argument("--epochs", type=int, default=10, + help="FF: epochs per greedy layer; other methods: global epochs") + p.add_argument("--batch_size", type=int, default=64) + p.add_argument("--max_batches", type=int, default=0) + p.add_argument("--eta", type=float, default=None) + p.add_argument("--momentum", type=float, default=0.9) + p.add_argument("--act", default="tanh", choices=["tanh", "gelu", "silu", "relu"]) + p.add_argument("--residual", type=int, default=0) + p.add_argument("--feedback_scale", type=float, default=1.0) + p.add_argument("--pepita_f_scale", type=float, default=0.05) + p.add_argument("--keep_prob", type=float, default=0.9) + p.add_argument("--ff_threshold", type=float, default=2.0) + p.add_argument("--ep_beta", type=float, default=0.5) + p.add_argument("--ep_dt", type=float, default=0.5) + p.add_argument("--ep_free_steps", type=int, default=20) + p.add_argument("--ep_nudge_steps", type=int, default=4) + p.add_argument("--canonical_preprocess", type=int, default=1) + p.add_argument("--seed", type=int, default=0) + p.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + p.add_argument("--outdir", default="results") + p.add_argument("--tag", default="baseline") + return p.parse_args() + + +if __name__ == "__main__": + train(get_args()) diff --git a/experiments/baseline_smoke.py b/experiments/baseline_smoke.py new file mode 100644 index 0000000..4bc3d85 --- /dev/null +++ b/experiments/baseline_smoke.py @@ -0,0 +1,107 @@ +"""Mechanism checks for the publication baselines (CPU, no dataset needed).""" +import os +import sys + +import torch + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from sdil.data import onehot +from sdil.local_baselines import FANet, PEPITANet, FFNet, EPNet + + +def check_fa_residual_transport(): + torch.manual_seed(1) + x = torch.randn(32, 8) + y = torch.randint(0, 3, (32,)) + yoh = onehot(y, 3) + residual = FANet([8, 6, 6, 6, 3], seed=2, residual=True) + plain = FANet([8, 6, 6, 6, 3], seed=2, residual=False) + for net in (residual, plain): + for l in (1, 2): + net.B[l].zero_() + before_res = [w.clone() for w in residual.W] + before_plain = [w.clone() for w in plain.W] + residual.fa_step(x, y, yoh, eta=0.01) + plain.fa_step(x, y, yoh, eta=0.01) + res_changes = [(a - b).norm().item() for a, b in zip(residual.W, before_res)] + plain_changes = [(a - b).norm().item() for a, b in zip(plain.W, before_plain)] + assert all(v > 0 for v in res_changes) + assert plain_changes[0] == 0 and plain_changes[1] == 0 + assert plain_changes[2] > 0 and plain_changes[3] > 0 + print("FA residual identity transport:", res_changes) + + +def check_pepita_output_rule(): + torch.manual_seed(2) + net = PEPITANet([5, 4, 3], act="relu", seed=3, keep_prob=1.0) + x = torch.rand(16, 5) + y = torch.randint(0, 3, (16,)) + yoh = onehot(y, 3) + with torch.no_grad(): + clean = net._pepita_forward(x, None) + error = torch.softmax(clean[-1], 1) - yoh + mod = net._pepita_forward(x + error @ net.Fproj.t(), None) + mod_error = torch.softmax(mod[-1], 1) - yoh + expected = -(mod_error.t() @ mod[-2]) / x.shape[0] + old = net.W[-1].clone() + net.pepita_step(x, y, yoh, eta=0.1, momentum=0.0) + assert torch.allclose(net.W[-1] - old, 0.1 * expected, atol=1e-7, rtol=1e-5) + assert net.Fproj.abs().max() <= (6.0 / 5) ** 0.5 * 0.05 + 1e-7 + print("PEPITA modulated-output delta: exact") + + +def ff_loss(net, layer, x, y, neg): + hp = net._inputs_to_layer(layer, net._overlay(x, y)) + hn = net._inputs_to_layer(layer, net._overlay(x, neg)) + gp = net._layer_forward(layer, hp).pow(2).mean(1) + gn = net._layer_forward(layer, hn).pow(2).mean(1) + return (torch.nn.functional.softplus(-gp + net.thr) + + torch.nn.functional.softplus(gn - net.thr)).mean().item() + + +def check_ff_local_optimization(): + torch.manual_seed(3) + net = FFNet([20, 32, 32], seed=4) + x = torch.randn(128, 20) + y = torch.randint(0, 10, (128,)) + neg = (y + 1) % 10 + initial = ff_loss(net, 0, x, y, neg) + for _ in range(80): + net.train_layer(0, x, y, eta=0.03, negative_labels=neg) + final = ff_loss(net, 0, x, y, neg) + assert final < initial - 0.1 + assert net._layer_forward(0, x).shape == (128, 32) + print(f"FF layer-local loss: {initial:.4f} -> {final:.4f}") + + +def check_ep_energy_dynamics(): + torch.manual_seed(4) + net = EPNet([4, 3, 2], seed=5, dt=0.2, random_beta_sign=False) + x = torch.rand(7, 4) + y = onehot(torch.randint(0, 2, (7,)), 2) + s = [torch.rand(7, 3) * 0.8 + 0.1, torch.rand(7, 2) * 0.8 + 0.1] + beta = 0.3 + manual = [] + for k in range(net.L): + below = x if k == 0 else s[k - 1] + drive = -s[k] + below @ net.W[k].t() + net.b[k] + if k < net.L - 1: + drive = drive + s[k + 1] @ net.W[k + 1] + else: + drive = drive + 2 * beta * (y - s[k]) + manual.append((s[k] + net.dt * drive).clamp(0, 1)) + actual = net._settle(x, y, beta=beta, s=[v.clone() for v in s], T=1) + assert all(torch.allclose(a, b, atol=1e-7) for a, b in zip(actual, manual)) + old = [w.clone() for w in net.W] + net.train_step(x, y.argmax(1), y, eta=[0.01, 0.005]) + assert all(torch.isfinite(w).all() for w in net.W) + assert any(not torch.equal(a, b) for a, b in zip(net.W, old)) + print("EP one-step -d(E+beta*C)/ds dynamics: exact") + + +if __name__ == "__main__": + check_fa_residual_transport() + check_pepita_output_rule() + check_ff_local_optimization() + check_ep_energy_dynamics() + print("ALL BASELINE SMOKE CHECKS PASSED") diff --git a/experiments/baseline_sweep.sh b/experiments/baseline_sweep.sh new file mode 100755 index 0000000..bd3ff7b --- /dev/null +++ b/experiments/baseline_sweep.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +# Run canonical non-backprop baselines. Method-specific knobs can be appended. +# Usage: baseline_sweep.sh "" "" [extra args...] +set -eu + +cd "$(dirname "$0")/.." +PY=/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3 +GPU="${1:?GPU index required}" +METHODS="${2:-fa pepita ff ep}" +SEEDS="${3:-0}" +DATASET="${4:-mnist}" +DEPTH="${5:-2}" +WIDTH="${6:-500}" +EPOCHS="${7:-10}" +PREFIX="${8:-canonical}" +shift 8 || true + +export CUDA_VISIBLE_DEVICES="$GPU" +export OMP_NUM_THREADS=2 +mkdir -p results logs/baselines + +for seed in $SEEDS; do + for method in $METHODS; do + tag="${PREFIX}_${DATASET}_${method}_w${WIDTH}_d${DEPTH}_s${seed}" + result="results/${tag}.json" + log="logs/baselines/${tag}.log" + if [[ -f "$result" ]]; then + echo "skip $tag (result exists)" + continue + fi + echo ">>> $tag $(date --iso-8601=seconds) gpu=$GPU" + "$PY" experiments/baseline_run.py --method "$method" --dataset "$DATASET" \ + --depth "$DEPTH" --width "$WIDTH" --epochs "$EPOCHS" --seed "$seed" \ + --tag "$tag" --outdir results "$@" > "$log" 2>&1 + grep -h DONE "$log" + done +done -- cgit v1.2.3