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"""FA-only snapshot evolution for StudentNet (synthetic teacher-student)."""
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, TensorDataset
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from experiments.confirmatory_paper_experiments import (
StudentNet, TeacherNet, generate_synth_dataset, set_seed
)
from experiments.snapshot_synth_residual_explosion import diagnose_synth
def train_fa_synth(model, train_loader, x_eval, y_eval, device, epochs, lr, wd):
"""Canonical FA for StudentNet: mean reduction, grad before step, no clipping."""
d_hidden = model.d_hidden
L = model.num_blocks
Bs = [torch.randn(d_hidden, d_hidden, device=device) / np.sqrt(d_hidden) for _ in range(L)]
block_opts = [optim.AdamW(b.parameters(), lr=lr, weight_decay=wd) for b in model.blocks]
head_opt = optim.AdamW(model.out_head.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(head_opt, T_max=epochs)]
log = []
d0 = diagnose_synth(model, x_eval, y_eval); d0['epoch'] = 0; log.append(d0)
print(f" [FA] Ep 0: acc={d0['acc_eval']:.4f}", flush=True)
for ep in range(1, epochs + 1):
model.train()
for x, y in train_loader:
x = x.to(device); y = y.to(device)
with torch.no_grad():
logits, hiddens = model(x, return_hidden=True)
# Head update — grad BEFORE step (old head)
hL_det = hiddens[-1].detach().requires_grad_(True)
logits_out = model.out_head(hL_det)
loss_out = F.cross_entropy(logits_out, y) # mean reduction
head_opt.zero_grad()
loss_out.backward()
a_credit = hL_det.grad.detach()
head_opt.step()
# Top-down block updates, propagate credit after each
for l in range(L - 1, -1, -1):
h_l = hiddens[l].detach()
rms = (a_credit ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
f_l = model.blocks[l](h_l)
local_loss = (f_l * (a_credit / rms)).sum(dim=-1).mean()
block_opts[l].zero_grad()
local_loss.backward()
block_opts[l].step() # no clipping
a_credit = (a_credit @ Bs[l]).detach()
# No embed for StudentNet (input is already d_hidden)
for s in all_sch: s.step()
d = diagnose_synth(model, x_eval, y_eval); d['epoch'] = ep; log.append(d)
if ep % 5 == 0 or ep in (1, epochs):
print(f" [FA] Ep {ep}: ||h_L||={d['hidden_norms'][-1]:.3e} "
f"||g||={d['bp_grad_per_sample_l2_med'][2]:.3e} "
f"acc={d['acc_eval']:.4f}", flush=True)
return log
def main():
p = argparse.ArgumentParser()
p.add_argument('--output', type=str, required=True)
p.add_argument('--epochs', type=int, default=80)
p.add_argument('--seed', type=int, default=42)
p.add_argument('--alpha', type=float, default=1.0)
p.add_argument('--depth', type=int, default=4)
p.add_argument('--d_hidden', type=int, default=128)
args = p.parse_args()
device = torch.device('cuda:0')
L, d, C = args.depth, args.d_hidden, 10
set_seed(args.seed)
teacher = TeacherNet(d, L, C, args.alpha, seed=0).to(device)
X_tr, Y_tr = generate_synth_dataset(teacher, 50*256, d, device, seed=args.seed)
X_te, Y_te = generate_synth_dataset(teacher, 2000, d, device, seed=args.seed+10000)
train_loader = DataLoader(TensorDataset(X_tr, Y_tr), batch_size=256, shuffle=True)
print(f"StudentNet FA: alpha={args.alpha}, L={L}, d={d}, seed={args.seed}", flush=True)
set_seed(args.seed)
model = StudentNet(d, C, L, args.alpha).to(device)
fa_log = train_fa_synth(model, train_loader, X_te.to(device), Y_te.to(device),
device, args.epochs, 1e-3, 0.01)
with open(args.output, 'w') as f:
json.dump({'fa_log': fa_log, 'seed': args.seed, 'alpha': args.alpha,
'depth': L, 'd_hidden': d}, f, indent=2)
print(f"Saved: {args.output}", flush=True)
if __name__ == '__main__':
main()
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