""" Phase 10A.8B: Alpha Sweep Scaffold. Core question: What is the optimal blend weight alpha for each auxiliary type? 9 branches from the same DFA checkpoint at t0=5: 1. continue_DFA — pure DFA baseline 2. blend_perlayer_vector_alpha025 — PerLayerVector, alpha=0.25 3. blend_perlayer_vector_alpha050 — PerLayerVector, alpha=0.50 4. blend_perlayer_vector_alpha075 — PerLayerVector, alpha=0.75 5. blend_perlayer_vector_alpha090 — PerLayerVector, alpha=0.90 6. blend_random_trainable_alpha025 — VectorCreditNet, alpha=0.25 7. blend_random_trainable_alpha050 — VectorCreditNet, alpha=0.50 8. blend_random_trainable_alpha075 — VectorCreditNet, alpha=0.75 9. blend_random_trainable_alpha090 — VectorCreditNet, alpha=0.90 """ import os import sys import json import argparse import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.utils.data import DataLoader import torchvision import torchvision.transforms as transforms import copy sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from models.residual_mlp import ResidualMLP from models.value_net import SinusoidalTimeEmbed from metrics.credit_metrics import cosine_similarity_batch, perturbation_correlation # --------------------------------------------------------------------------- # Auxiliary network architectures # --------------------------------------------------------------------------- class VectorCreditNet(nn.Module): """Standard Vec: takes (h, t, s) -> d_hidden credit vector.""" def __init__(self, d_hidden, s_dim, time_embed_dim=32, hidden_dim=256, num_layers=3): super().__init__() self.ln = nn.LayerNorm(d_hidden) self.time_embed = SinusoidalTimeEmbed(time_embed_dim) input_dim = d_hidden + time_embed_dim + s_dim layers = [] for i in range(num_layers): in_d = input_dim if i == 0 else hidden_dim layers.append(nn.Linear(in_d, hidden_dim)) layers.append(nn.GELU()) layers.append(nn.Linear(hidden_dim, d_hidden)) self.net = nn.Sequential(*layers) def forward(self, h, t, s): return self.net(torch.cat([self.ln(h), self.time_embed(t), s], dim=-1)) class PerLayerVector(nn.Module): """No network: each block l has a trainable nn.Parameter v_l of shape (d_hidden,). All samples in a batch receive the same v_l (broadcast). forward(h, t, s) ignores h, t, s and returns v_l expanded to (batch, d_hidden). Must call set_block(l) before forward to select the right block vector. """ def __init__(self, d_hidden, num_blocks): super().__init__() # Initialize with small random values (std=0.01) self.vectors = nn.ParameterList( [nn.Parameter(torch.randn(d_hidden) * 0.01) for _ in range(num_blocks)] ) self._block_idx = 0 def set_block(self, l): self._block_idx = l def forward(self, h, t, s): batch = h.size(0) return self.vectors[self._block_idx].unsqueeze(0).expand(batch, -1) # --------------------------------------------------------------------------- # Data # --------------------------------------------------------------------------- def get_cifar10(batch_size=128): transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))]) trainset = torchvision.datasets.CIFAR10( root='./data', train=True, download=True, transform=transform_train) testset = torchvision.datasets.CIFAR10( root='./data', train=False, download=True, transform=transform_test) return (DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True), DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)) # --------------------------------------------------------------------------- # Evaluation helpers # --------------------------------------------------------------------------- def evaluate(model, test_loader, device): model.eval(); c, t = 0, 0 with torch.no_grad(): for x, y in test_loader: x = x.view(x.size(0), -1).to(device); y = y.to(device) c += (model(x).argmax(1) == y).sum().item(); t += x.size(0) return c / t def compute_diagnostics(model, aux_net, Bs, test_loader, device, credit_mode, alpha=0.75): """Compute mean Gamma (BP cosine) and mean rho (perturbation correlation).""" model.eval() if aux_net is not None: aux_net.eval() L = model.num_blocks for x, y in test_loader: x = x.view(x.size(0), -1).to(device); y = y.to(device); break batch = x.size(0) # BP pass for hidden gradients (offline eval only, not used for training) was_frozen = not next(model.parameters()).requires_grad if was_frozen: for p in model.parameters(): p.requires_grad_(True) model.zero_grad() lo, hbp = model(x, return_hidden=True) for l in range(L + 1): hbp[l].retain_grad() F.cross_entropy(lo, y).backward() bp = {l: hbp[l].grad.detach().clone() for l in range(L + 1)} if was_frozen: for p in model.parameters(): p.requires_grad_(False) with torch.no_grad(): lo2, hi = model(x, return_hidden=True) eT = lo2.softmax(-1); eT[torch.arange(batch), y] -= 1; s = eT.detach() gammas, rhos = [], [] for l in range(L): h_l = hi[l].detach() t_l = torch.full((batch,), l / L, device=device) if credit_mode == 'dfa': a_l = (s @ Bs[l].T).detach() elif credit_mode == 'blend' and aux_net is not None: a_dfa = (s @ Bs[l].T).detach() if isinstance(aux_net, PerLayerVector): aux_net.set_block(l) a_aux = aux_net(h_l, t_l, s).detach() rd = (a_dfa ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 rv = (a_aux ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 a_l = alpha * a_aux / rv + (1 - alpha) * a_dfa / rd else: a_l = (s @ Bs[l].T).detach() gammas.append(cosine_similarity_batch(a_l, bp[l])) def make_fwd(sl): def f(h): with torch.no_grad(): c = h for i in range(sl, L): c = c + model.blocks[i](c) return F.cross_entropy( model.out_head(model.out_ln(c)), y, reduction='none') return f rhos.append(perturbation_correlation(h_l, a_l, make_fwd(l), epsilon=1e-3, M=16)) return float(np.mean(gammas)), float(np.mean(rhos)) # --------------------------------------------------------------------------- # DFA training + checkpoint # --------------------------------------------------------------------------- def train_dfa_get_checkpoint(model, train_loader, test_loader, device, total_epochs, t0, lr, wd): d = model.d_hidden; L = model.num_blocks Bs = [torch.randn(d, 10, device=device) / np.sqrt(10) for _ in range(L)] block_opts = [optim.AdamW(b.parameters(), lr=lr, weight_decay=wd) for b in model.blocks] embed_opt = optim.AdamW(model.embed.parameters(), lr=lr, weight_decay=wd) head_opt = optim.AdamW( list(model.out_head.parameters()) + list(model.out_ln.parameters()), lr=lr, weight_decay=wd) scheds = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=total_epochs) for o in block_opts] + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=total_epochs), optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=total_epochs)]) ckpt = None for epoch in range(1, total_epochs + 1): model.train(); tl, c, t = 0, 0, 0 for x, y in train_loader: x = x.view(x.size(0), -1).to(device); y = y.to(device); b = x.size(0) with torch.no_grad(): lo, hi = model(x, return_hidden=True); lv = F.cross_entropy(lo, y) eT = lo.softmax(-1); eT[torch.arange(b), y] -= 1 hL = hi[-1].detach() lo2 = F.cross_entropy(model.out_head(model.out_ln(hL)), y) head_opt.zero_grad(); lo2.backward(); head_opt.step() for l in range(L): a = (eT @ Bs[l].T).detach() rm = (a ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 f = model.blocks[l](hi[l].detach()) ll = (f * (a / rm)).sum(-1).mean() block_opts[l].zero_grad(); ll.backward() torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0) block_opts[l].step() a0 = (eT @ Bs[0].T).detach() r0 = (a0 ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 el = (model.embed(x) * (a0 / r0)).sum(-1).mean() embed_opt.zero_grad(); el.backward(); embed_opt.step() tl += lv.item() * b; c += (lo.argmax(1) == y).sum().item(); t += b for s in scheds: s.step() if epoch == t0: acc = evaluate(model, test_loader, device) ckpt = {'model': copy.deepcopy(model.state_dict()), 'Bs': [B.clone() for B in Bs], 'acc': acc} print(f" [DFA] Checkpoint at epoch {t0}: acc={acc:.4f}") if epoch % 10 == 0: print(f" [DFA] Epoch {epoch}: acc={evaluate(model, test_loader, device):.4f}") return Bs, ckpt # --------------------------------------------------------------------------- # Branch runner # --------------------------------------------------------------------------- def run_branch(model, aux_net, Bs, train_loader, test_loader, device, t0, total_epochs, branch_type, alpha, lr, lr_fb, wd, M, branch_name=''): """ Run a training branch from a loaded checkpoint. branch_type options: 'dfa' — pure DFA baseline 'blend_perlayer' — blend with PerLayerVector trained online (perturbation targets) 'blend_trainable' — blend with VectorCreditNet trained online (perturbation targets) alpha is fixed for the entire run (no warmup/decay). Both aux types are trained online continuously after handoff. """ d = model.d_hidden; L = model.num_blocks; eps_pert = 1e-3 trainable_types = {'blend_perlayer', 'blend_trainable'} aux_trained = (branch_type in trainable_types) and (aux_net is not None) block_opts = [optim.AdamW(b.parameters(), lr=lr, weight_decay=wd) for b in model.blocks] embed_opt = optim.AdamW(model.embed.parameters(), lr=lr, weight_decay=wd) head_opt = optim.AdamW( list(model.out_head.parameters()) + list(model.out_ln.parameters()), lr=lr, weight_decay=wd) if aux_trained: aux_opt = optim.Adam(aux_net.parameters(), lr=lr_fb) else: aux_opt = None scheds = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=total_epochs) for o in block_opts] + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=total_epochs), optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=total_epochs)]) # Advance schedulers to match checkpoint epoch for _ in range(t0): for s in scheds: s.step() log = {'test_acc': [], 'train_loss': [], 'gamma': [], 'rho': [], 'alpha_eff': []} diag_epochs = set( list(range(t0 + 1, min(t0 + 6, total_epochs + 1))) + [t0 + 8, t0 + 10, t0 + 15, t0 + 20] + list(range(t0 + 10, total_epochs + 1, 10)) + [total_epochs]) for epoch in range(t0 + 1, total_epochs + 1): model.train() if aux_net is not None: aux_net.train() if aux_opt is not None else aux_net.eval() tl, c, t = 0, 0, 0 epoch_aux_norms, epoch_dfa_norms = [], [] for x, y in train_loader: x = x.view(x.size(0), -1).to(device); y = y.to(device); batch = x.size(0) with torch.no_grad(): lo, hi = model(x, return_hidden=True); lv = F.cross_entropy(lo, y) eT = lo.softmax(-1); eT[torch.arange(batch), y] -= 1; s = eT.detach() hL = hi[-1].detach() # ---------------------------------------------------------------- # Train auxiliary network (if applicable) # ---------------------------------------------------------------- if aux_opt is not None: if branch_type == 'blend_trainable': # Standard VectorCreditNet: terminal matching + perturbation targets t_L = torch.ones(batch, device=device) a_term = aux_net(hL, t_L, s) hL_req = hL.clone().requires_grad_(True) ce = F.cross_entropy( model.out_head(model.out_ln(hL_req)), y, reduction='sum') dL = torch.autograd.grad(ce, hL_req)[0].detach() loss_term = ((a_term - dL) ** 2).sum(-1).mean() lt = np.random.randint(0, L) h_l = hi[lt].detach() t_l = torch.full((batch,), lt / L, device=device) a_l = aux_net(h_l, t_l, s) lp2 = torch.tensor(0.0, device=device) for _ in range(M): v = torch.randn_like(h_l) v = v / (v.norm(-1, keepdim=True) + 1e-8) with torch.no_grad(): lp = F.cross_entropy( model.forward_from_layer(h_l + eps_pert * v, lt), y, reduction='none') lm = F.cross_entropy( model.forward_from_layer(h_l - eps_pert * v, lt), y, reduction='none') gj = (lp - lm) / (2 * eps_pert) lp2 = lp2 + (((a_l * v).sum(-1) - gj.detach()) ** 2).mean() lp2 /= M vl = loss_term + lp2 elif branch_type == 'blend_perlayer': # PerLayerVector: perturbation-based loss only (no terminal matching). # v_l is the per-layer parameter (shared across all samples in batch). # Also add terminal matching: a_L should match delta_L. # Terminal matching: set_block(L-1) and match grad at last layer. lt = np.random.randint(0, L) h_l = hi[lt].detach() t_l = torch.full((batch,), lt / L, device=device) aux_net.set_block(lt) a_l = aux_net(h_l, t_l, s) # (batch, d) — same v_lt broadcast lp2 = torch.tensor(0.0, device=device) for _ in range(M): v = torch.randn_like(h_l) v = v / (v.norm(-1, keepdim=True) + 1e-8) with torch.no_grad(): lp = F.cross_entropy( model.forward_from_layer(h_l + eps_pert * v, lt), y, reduction='none') lm = F.cross_entropy( model.forward_from_layer(h_l - eps_pert * v, lt), y, reduction='none') gj = (lp - lm) / (2 * eps_pert) # — v_l is shared across batch, v is per-sample lp2 = lp2 + (((a_l * v).sum(-1) - gj.detach()) ** 2).mean() lp2 /= M # Terminal matching: v_{L-1} should approximate delta_L aux_net.set_block(L - 1) a_term = aux_net(hL, torch.ones(batch, device=device), s) hL_req = hL.clone().requires_grad_(True) ce = F.cross_entropy( model.out_head(model.out_ln(hL_req)), y, reduction='sum') dL = torch.autograd.grad(ce, hL_req)[0].detach() loss_term = ((a_term - dL) ** 2).sum(-1).mean() vl = lp2 + loss_term else: vl = None if vl is not None: aux_opt.zero_grad(); vl.backward() torch.nn.utils.clip_grad_norm_(aux_net.parameters(), 1.0) aux_opt.step() # ---------------------------------------------------------------- # Compute credits for each block # ---------------------------------------------------------------- dfa_credits = [(eT @ Bs[l].T).detach() for l in range(L)] credits = [] for l in range(L): a_dfa = dfa_credits[l] rms_d = (a_dfa ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 if branch_type == 'dfa': credits.append(a_dfa / rms_d) else: # All blend branches h_l = hi[l].detach() t_l = torch.full((batch,), l / L, device=device) with torch.no_grad(): if isinstance(aux_net, PerLayerVector): aux_net.set_block(l) a_aux = aux_net(h_l, t_l, s).detach() rms_v = (a_aux ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 a_blend = alpha * a_aux / rms_v + (1 - alpha) * a_dfa / rms_d credits.append(a_blend) # Track norms for alpha_eff a_c = credits[-1] if branch_type == 'dfa': epoch_aux_norms.append(0.0) epoch_dfa_norms.append(a_c.norm().item()) else: a_dfa_n = a_dfa / rms_d rms_v2 = (a_aux ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 epoch_aux_norms.append((alpha * a_aux / rms_v2).norm().item()) epoch_dfa_norms.append(((1 - alpha) * a_dfa_n).norm().item()) # ---------------------------------------------------------------- # Update output head (local exact gradient — allowed) # ---------------------------------------------------------------- lo2 = F.cross_entropy(model.out_head(model.out_ln(hL)), y) head_opt.zero_grad(); lo2.backward(); head_opt.step() # ---------------------------------------------------------------- # Update blocks with local surrogate # ---------------------------------------------------------------- for l in range(L): a = credits[l] rm = (a ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 f = model.blocks[l](hi[l].detach()) ll = (f * (a / rm)).sum(-1).mean() block_opts[l].zero_grad(); ll.backward() torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0) block_opts[l].step() # Update embedding with block-0 credit a0 = credits[0] r0 = (a0 ** 2).mean(-1, keepdim=True).sqrt() + 1e-6 el = (model.embed(x) * (a0 / r0)).sum(-1).mean() embed_opt.zero_grad(); el.backward(); embed_opt.step() tl += lv.item() * batch; c += (lo.argmax(1) == y).sum().item(); t += batch for sch in scheds: sch.step() ta = evaluate(model, test_loader, device) log['test_acc'].append(ta); log['train_loss'].append(tl / t) mean_aux = np.mean(epoch_aux_norms) if epoch_aux_norms else 0.0 mean_dfa = np.mean(epoch_dfa_norms) if epoch_dfa_norms else 1.0 aeff = mean_aux / (mean_aux + mean_dfa + 1e-12) log['alpha_eff'].append((epoch, aeff)) if epoch in diag_epochs: cm = 'blend' if branch_type != 'dfa' else 'dfa' gamma, rho = compute_diagnostics( model, aux_net if branch_type != 'dfa' else None, Bs, test_loader, device, cm, alpha) log['gamma'].append((epoch, gamma)); log['rho'].append((epoch, rho)) if epoch <= t0 + 15 or epoch % 20 == 0 or epoch == total_epochs: print(f" [{branch_name}] Ep {epoch}: acc={ta:.4f}, " f"G={gamma:.4f}, r={rho:.4f}, aeff={aeff:.3f}, alpha={alpha:.2f}") elif epoch % 10 == 0 or epoch == total_epochs: print(f" [{branch_name}] Ep {epoch}: acc={ta:.4f}, alpha={alpha:.2f}") return log # --------------------------------------------------------------------------- # Main experiment # --------------------------------------------------------------------------- def run_experiment(args): device = torch.device(f'cuda:{args.gpu}' if torch.cuda.is_available() else 'cpu') print(f"Using device: {device}") os.makedirs(args.output_dir, exist_ok=True) torch.manual_seed(args.seed); np.random.seed(args.seed) torch.cuda.manual_seed_all(args.seed) train_loader, test_loader = get_cifar10(args.batch_size) input_dim = 32 * 32 * 3; L = args.num_blocks; d = args.d_hidden # ---------------------------------------------------------------- # Step 1: Train DFA and capture checkpoint at t0 # ---------------------------------------------------------------- print(f"\n{'='*60}\nTraining DFA baseline (checkpoint at t0={args.t0})\n{'='*60}") model_dfa = ResidualMLP(input_dim, d, 10, L).to(device) Bs, ckpt = train_dfa_get_checkpoint( model_dfa, train_loader, test_loader, device, args.epochs, args.t0, args.lr, args.wd) print(f" Checkpoint acc at t0={args.t0}: {ckpt['acc']:.4f}") # ---------------------------------------------------------------- # Step 2: Define and run all 9 branches # ---------------------------------------------------------------- VEC_SEED = args.seed + 7777 def make_vec(): torch.manual_seed(VEC_SEED) return VectorCreditNet(d_hidden=d, s_dim=10).to(device) def make_perlayer(): torch.manual_seed(VEC_SEED) return PerLayerVector(d_hidden=d, num_blocks=L).to(device) # (name, branch_type, aux_factory, alpha) ALPHAS = [0.25, 0.50, 0.75, 0.90] branches = [('continue_DFA', 'dfa', lambda: None, 0.0)] for a in ALPHAS: tag = f"{int(a*100):03d}" branches.append((f'blend_perlayer_vector_alpha{tag}', 'blend_perlayer', make_perlayer, a)) for a in ALPHAS: tag = f"{int(a*100):03d}" branches.append((f'blend_random_trainable_alpha{tag}', 'blend_trainable', make_vec, a)) all_results = {} for bname, btype, aux_factory, alpha in branches: print(f"\n{'='*60}\n{bname}\n{'='*60}") model_b = ResidualMLP(input_dim, d, 10, L).to(device) model_b.load_state_dict(ckpt['model']) aux_net_b = aux_factory() log = run_branch( model_b, aux_net_b, ckpt['Bs'], train_loader, test_loader, device, args.t0, args.epochs, btype, alpha, args.lr, args.lr_fb, args.wd, args.M, branch_name=bname) all_results[bname] = log all_results[bname]['alpha'] = alpha print(f" {bname} final acc: {log['test_acc'][-1]:.4f}") # ---------------------------------------------------------------- # Step 3: Summary table # ---------------------------------------------------------------- dfa_final = all_results['continue_DFA']['test_acc'][-1] print(f"\n{'='*95}") print("SUMMARY — Phase 10A.8B: Alpha Sweep") print(f"{'='*95}") print(f"{'Branch':<40} {'alpha':>5} {'@20':>6} {'final':>7} {'diff vs DFA':>11}") print("-" * 73) for bname, log in all_results.items(): accs = log['test_acc'] alpha = log['alpha'] idx20 = max(0, 20 - args.t0 - 1) acc20 = accs[idx20] if len(accs) > idx20 else accs[-1] final = accs[-1] diff = final - dfa_final print(f"{bname:<40} {alpha:>5.2f} {acc20:>6.4f} {final:>7.4f} {diff:>+11.4f}") # ---------------------------------------------------------------- # Step 4: Optimal alpha per method type # ---------------------------------------------------------------- print(f"\n{'='*60}") print("OPTIMAL ALPHA PER METHOD TYPE") print(f"{'='*60}") # PerLayerVector branches perlayer_results = { bname: log for bname, log in all_results.items() if bname.startswith('blend_perlayer_vector_alpha')} if perlayer_results: best_plv = max(perlayer_results.items(), key=lambda kv: kv[1]['test_acc'][-1]) print(f" PerLayerVector best alpha: {best_plv[1]['alpha']:.2f} " f"(branch={best_plv[0]}, final={best_plv[1]['test_acc'][-1]:.4f})") for bname in sorted(perlayer_results.keys()): log = perlayer_results[bname] diff = log['test_acc'][-1] - dfa_final print(f" alpha={log['alpha']:.2f}: final={log['test_acc'][-1]:.4f} " f"({diff:+.4f} vs DFA)") print() # VectorCreditNet (random_trainable) branches trainable_results = { bname: log for bname, log in all_results.items() if bname.startswith('blend_random_trainable_alpha')} if trainable_results: best_rt = max(trainable_results.items(), key=lambda kv: kv[1]['test_acc'][-1]) print(f" VectorCreditNet best alpha: {best_rt[1]['alpha']:.2f} " f"(branch={best_rt[0]}, final={best_rt[1]['test_acc'][-1]:.4f})") for bname in sorted(trainable_results.keys()): log = trainable_results[bname] diff = log['test_acc'][-1] - dfa_final print(f" alpha={log['alpha']:.2f}: final={log['test_acc'][-1]:.4f} " f"({diff:+.4f} vs DFA)") # ---------------------------------------------------------------- # Step 5: Save results # ---------------------------------------------------------------- save_data = { 'args': vars(args), 'dfa_ckpt_acc': float(ckpt['acc']), 'dfa_final_acc': float(dfa_final), } for bname, log in all_results.items(): save_data[bname] = { 'alpha': log['alpha'], 'test_acc': log['test_acc'], 'train_loss': log['train_loss'], 'gamma': log['gamma'], 'rho': log['rho'], 'alpha_eff': log['alpha_eff'], } out_path = os.path.join(args.output_dir, f'alpha_sweep_t{args.t0}_s{args.seed}.json') with open(out_path, 'w') as f: json.dump(save_data, f, indent=2, default=float) print(f"\nSaved to {out_path}") def main(): parser = argparse.ArgumentParser( description='Phase 10A.8B: Alpha Sweep Scaffold') parser.add_argument('--num_blocks', type=int, default=4) parser.add_argument('--d_hidden', type=int, default=256) parser.add_argument('--batch_size', type=int, default=128) parser.add_argument('--epochs', type=int, default=100) parser.add_argument('--t0', type=int, default=5) parser.add_argument('--lr', type=float, default=1e-3) parser.add_argument('--lr_fb', type=float, default=1e-3) parser.add_argument('--wd', type=float, default=0.01) parser.add_argument('--M', type=int, default=4) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--gpu', type=int, default=3) parser.add_argument('--output_dir', type=str, default='results/alpha_sweep_scaffold') args = parser.parse_args() run_experiment(args) if __name__ == '__main__': main()