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"""
Penalty intervention sweep: DFA + lambda x {0, 1e-4, 1e-2} with per-epoch trajectory.
Includes fresh-B null calibration on the lambda=1e-2 checkpoint.
Usage:
python reproduce/penalty_sweep.py --seeds 42 123 456 --gpu 0
"""
import os, sys, json, argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from reproduce.train_methods import get_data, evaluate, make_model, _pool_hidden, _get_head_logits
from metrics.credit_metrics import cosine_similarity_batch
def train_dfa_trajectory(seed, train_loader, test_loader, device, epochs, lam, num_classes=10):
"""DFA with per-epoch ||h_L||, ||g_L|| logging."""
torch.manual_seed(seed); np.random.seed(seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)
from models.residual_mlp import ResidualMLP
model = ResidualMLP(3072, 256, num_classes, 4).to(device)
d, L, C = 256, 4, num_classes
Bs = [torch.randn(d, C, device=device) / np.sqrt(C) for _ in range(L)]
block_opts = [optim.AdamW(b.parameters(), lr=1e-3, weight_decay=0.01) for b in model.blocks]
embed_opt = optim.AdamW(model.embed.parameters(), lr=1e-3, weight_decay=0.01)
head_opt = optim.AdamW(list(model.out_head.parameters()) + list(model.out_ln.parameters()),
lr=1e-3, weight_decay=0.01)
all_sch = [optim.lr_scheduler.CosineAnnealingLR(o, T_max=epochs) for o in block_opts] + \
[optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=epochs),
optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)]
# Eval buffer
xs, ys = [], []
for x, y in test_loader:
xs.append(x.view(x.size(0), -1)); ys.append(y)
if sum(xb.size(0) for xb in xs) >= 128: break
x_eval = torch.cat(xs)[:128].to(device)
y_eval = torch.cat(ys)[:128].to(device)
def diagnose():
model.eval()
with torch.no_grad():
_, hi = model(x_eval, return_hidden=True)
h_L = hi[-1].norm(dim=-1).median().item()
h0 = model.embed(x_eval)
hs = [h0.clone().requires_grad_(True)]
for b in model.blocks:
hs.append(hs[-1] + b(hs[-1]))
logits = model.out_head(model.out_ln(hs[-1]))
loss = F.cross_entropy(logits, y_eval)
grads = torch.autograd.grad(loss, hs)
g_L = grads[-1].norm(dim=-1).median().item()
acc = (logits.argmax(-1) == y_eval).float().mean().item()
model.train()
return h_L, g_L, acc
log = []
h, g, a = diagnose()
log.append({'epoch': 0, 'h_L': h, 'g_L': g, 'acc': a})
for ep in range(1, epochs + 1):
model.train()
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():
logits, hiddens = model(x, return_hidden=True)
e_T = logits.softmax(-1); e_T[torch.arange(batch), y] -= 1
hL = hiddens[-1].detach()
head_opt.zero_grad()
F.cross_entropy(model.out_head(model.out_ln(hL)), y).backward()
head_opt.step()
for l in range(L):
a_dfa = (e_T @ Bs[l].T).detach()
rms = (a_dfa ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
f_l = model.blocks[l](hiddens[l].detach())
local_loss = (f_l * (a_dfa / rms)).sum(-1).mean()
if lam > 0:
local_loss = local_loss + lam * (f_l ** 2).sum(-1).mean()
block_opts[l].zero_grad(); local_loss.backward(); block_opts[l].step()
a0 = (e_T @ Bs[0].T).detach()
rms0 = (a0 ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
h0 = model.embed(x)
embed_opt.zero_grad(); (h0 * (a0 / rms0)).sum(-1).mean().backward(); embed_opt.step()
for s in all_sch: s.step()
h, g, a = diagnose()
log.append({'epoch': ep, 'h_L': h, 'g_L': g, 'acc': a})
if ep % 10 == 0 or ep == epochs:
print(f" [lam={lam}] s={seed} ep {ep}: ||h_L||={h:.3e} ||g_L||={g:.3e} acc={a:.4f}", flush=True)
return log, model, Bs
def fresh_b_null(model, x_eval, y_eval, training_Bs, n_draws=20):
"""Fresh-B null calibration on a trained checkpoint."""
model.eval()
d, L, C = 256, 4, len(training_Bs[0][0]) if training_Bs[0].dim() == 2 else 10
device = x_eval.device
def deep_cos_with_Bs(Bs):
h0 = model.embed(x_eval)
hs = [h0.clone().requires_grad_(True)]
for b in model.blocks:
hs.append(hs[-1] + b(hs[-1]))
logits = model.out_head(model.out_ln(hs[-1]))
loss = F.cross_entropy(logits, y_eval)
grads = torch.autograd.grad(loss, hs)
with torch.no_grad():
e_T = logits.softmax(-1)
e_T[torch.arange(x_eval.size(0)), y_eval] -= 1
cos_layers = []
for l in range(L):
a = (e_T @ Bs[l].T).detach()
cos_layers.append(cosine_similarity_batch(a, grads[l].detach()))
return float(np.mean(cos_layers[1:])) # deep = exclude layer 0
train_cos = deep_cos_with_Bs(training_Bs)
fresh_cos = []
for _ in range(n_draws):
fresh_Bs = [torch.randn(d, C, device=device) / np.sqrt(C) for _ in range(L)]
fresh_cos.append(deep_cos_with_Bs(fresh_Bs))
return {
'training_Bs_deep_cos': train_cos,
'fresh_Bs_deep_mean': float(np.mean(fresh_cos)),
'fresh_Bs_deep_std_ddof1': float(np.std(fresh_cos, ddof=1)),
'n_draws': n_draws,
}
def main():
p = argparse.ArgumentParser()
p.add_argument('--seeds', nargs='+', type=int, default=[42, 123, 456])
p.add_argument('--epochs', type=int, default=30)
p.add_argument('--lambdas', nargs='+', type=float, default=[0.0, 1e-4, 1e-2])
p.add_argument('--gpu', type=int, default=0)
p.add_argument('--output_dir', type=str, default='results/penalty_sweep')
args = p.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
device = torch.device(f'cuda:{args.gpu}' if torch.cuda.is_available() else 'cpu')
train_loader, test_loader, _ = get_data('cifar10', 128)
results = {}
for lam in args.lambdas:
lam_key = f'lam_{lam}'
results[lam_key] = {}
for seed in args.seeds:
print(f"\n=== lambda={lam}, seed={seed} ===", flush=True)
log, model, Bs = train_dfa_trajectory(seed, train_loader, test_loader, device, args.epochs, lam)
results[lam_key][str(seed)] = log
# Fresh-B null on lambda=1e-2, seed=42 only
if lam == 1e-2 and seed == 42:
xs, ys = [], []
for x, y in test_loader:
xs.append(x.view(x.size(0), -1)); ys.append(y)
if sum(xb.size(0) for xb in xs) >= 128: break
x_eval = torch.cat(xs)[:128].to(device)
y_eval = torch.cat(ys)[:128].to(device)
null = fresh_b_null(model, x_eval, y_eval, Bs)
results['fresh_b_null'] = null
print(f" Fresh-B: training={null['training_Bs_deep_cos']:+.4f}, "
f"fresh={null['fresh_Bs_deep_mean']:+.4f} +/- {null['fresh_Bs_deep_std_ddof1']:.4f}")
with open(os.path.join(args.output_dir, 'penalty_sweep.json'), 'w') as f:
json.dump(results, f, indent=2)
print(f"\nSaved: {args.output_dir}/penalty_sweep.json")
if __name__ == '__main__':
main()
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