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"""
DFA canonical λ=1e-2 training + checkpoint save + fresh-B null calibration.
Runs after the main penalty sweep to produce the null calibration on the canonical checkpoint.
"""
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
from torch.utils.data import DataLoader
import torchvision, torchvision.transforms as transforms
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models.residual_mlp import ResidualMLP
from metrics.credit_metrics import cosine_similarity_batch
def get_data(batch_size=128):
tv_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)),
])
tv = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
])
tr = torchvision.datasets.CIFAR10('./data', True, download=True, transform=tv_train)
te = torchvision.datasets.CIFAR10('./data', False, download=True, transform=tv)
return (DataLoader(tr, batch_size=batch_size, shuffle=True, num_workers=2),
DataLoader(te, batch_size=batch_size, shuffle=False, num_workers=2))
def train_dfa_canonical(model, train_loader, device, epochs, lr, wd, penalty_lam):
"""Canonical DFA from cifar_resmlp.py: no grad clipping, mean reduction."""
d = model.d_hidden
L = model.num_blocks
C = 10
Bs = [torch.randn(d, C, device=device) / np.sqrt(C) for _ in range(L)]
block_opts = [optim.AdamW(block.parameters(), lr=lr, weight_decay=wd) for block 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)
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)])
for epoch 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_det = hiddens[-1].detach()
logits_out = model.out_head(model.out_ln(hL_det))
loss_out = F.cross_entropy(logits_out, y)
head_opt.zero_grad(); loss_out.backward(); head_opt.step()
for l in range(L):
h_l = hiddens[l].detach()
a_dfa = (e_T @ Bs[l].T).detach()
rms = (a_dfa ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
f_l = model.blocks[l](h_l)
local_loss = (f_l * (a_dfa / rms)).sum(dim=-1).mean()
if penalty_lam > 0:
local_loss = local_loss + penalty_lam * (f_l ** 2).sum(dim=-1).mean()
block_opts[l].zero_grad(); local_loss.backward(); block_opts[l].step()
a_0 = (e_T @ Bs[0].T).detach()
rms_0 = (a_0 ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
h0 = model.embed(x)
embed_loss = (h0 * (a_0 / rms_0)).sum(dim=-1).mean()
embed_opt.zero_grad(); embed_loss.backward(); embed_opt.step()
for s in all_sch: s.step()
if epoch % 10 == 0 or epoch == epochs:
print(f" [DFA pen] ep {epoch}", flush=True)
return Bs
def compute_deep_cosine(model, Bs, x_eval, y_eval, device):
"""Compute per-layer DFA cosine on eval buffer."""
model.eval()
L = model.num_blocks
h0 = model.embed(x_eval.detach())
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_per_layer = []
for l in range(L):
a_dfa = (e_T @ Bs[l].T).detach()
cos_per_layer.append(cosine_similarity_batch(a_dfa, grads[l].detach()))
acc = (logits.argmax(-1) == y_eval).float().mean().item()
g_norms = [g.norm(dim=-1).median().item() for g in grads]
h_norms = [h.detach().norm(dim=-1).median().item() for h in hs]
return cos_per_layer, acc, g_norms, h_norms
def main():
p = argparse.ArgumentParser()
p.add_argument('--seed', type=int, default=42)
p.add_argument('--output_dir', type=str, default='results/dfa_canonical_freshB')
p.add_argument('--n_fresh', type=int, default=20)
args = p.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
device = torch.device('cuda:0')
train_loader, test_loader = get_data(128)
# Fixed 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)
L, d, C = 4, 256, 10
# Train DFA with λ=1e-2
print(f"Training DFA canonical λ=0.01, seed={args.seed}", flush=True)
torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
model = ResidualMLP(3072, d, C, L).to(device)
training_Bs = train_dfa_canonical(model, train_loader, device, 30, 1e-3, 0.01, 0.01)
# Save checkpoint
ckpt_path = os.path.join(args.output_dir, f'dfa_canonical_lam0.01_s{args.seed}.pt')
torch.save({'state_dict': model.state_dict(),
'Bs': [B.cpu() for B in training_Bs],
'seed': args.seed}, ckpt_path)
print(f"Saved checkpoint: {ckpt_path}", flush=True)
# Compute cosine with training Bs
cos_training, acc, g_norms, h_norms = compute_deep_cosine(model, training_Bs, x_eval, y_eval, device)
deep_cos_training = float(np.mean(cos_training[1:])) # exclude layer 0
print(f"Training-Bs: acc={acc:.4f}, deep cos={deep_cos_training:+.4f}")
print(f" per-layer cos: {[f'{c:+.4f}' for c in cos_training]}")
print(f" ||g_l||: {[f'{g:.2e}' for g in g_norms]}")
print(f" ||h_l||: {[f'{h:.2e}' for h in h_norms]}")
# Fresh-B null calibration
print(f"\nFresh-B null calibration ({args.n_fresh} draws)...", flush=True)
fresh_deep_cos = []
fresh_per_layer = []
for i in range(args.n_fresh):
fresh_Bs = [torch.randn(d, C, device=device) / np.sqrt(C) for _ in range(L)]
cos_fresh, _, _, _ = compute_deep_cosine(model, fresh_Bs, x_eval, y_eval, device)
deep_fresh = float(np.mean(cos_fresh[1:]))
fresh_deep_cos.append(deep_fresh)
fresh_per_layer.append(cos_fresh)
fresh_mean = np.mean(fresh_deep_cos)
fresh_std_ddof1 = np.std(fresh_deep_cos, ddof=1)
print(f"Fresh-Bs deep cos: {fresh_mean:+.4f} ± {fresh_std_ddof1:.4f} (ddof=1)")
# Save results
out = {
'description': f'Canonical DFA λ=0.01 s={args.seed} + fresh-B null (N={args.n_fresh})',
'training_Bs_deep_cos': deep_cos_training,
'training_Bs_per_layer_cos': cos_training,
'training_Bs_acc': acc,
'training_Bs_g_norms': g_norms,
'training_Bs_h_norms': h_norms,
'fresh_Bs_n_draws': args.n_fresh,
'fresh_Bs_deep_cos_per_draw': fresh_deep_cos,
'fresh_Bs_deep_mean': fresh_mean,
'fresh_Bs_deep_std_ddof1': fresh_std_ddof1,
'fresh_Bs_per_layer_mean': [float(np.mean([fl[l] for fl in fresh_per_layer])) for l in range(L)],
}
out_path = os.path.join(args.output_dir, f'freshB_null_canonical_s{args.seed}.json')
with open(out_path, 'w') as f:
json.dump(out, f, indent=2)
print(f"Saved: {out_path}", flush=True)
if __name__ == '__main__':
main()
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