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"""Stiefel factored feedback training for local transformer.
Replaces FA's random B with: δ_l = α_l · (e_L @ C^T) @ U_l^T
where C is fixed row-orthonormal, U_l is per-layer learnable on Stiefel.
Each block uses fused attention backward + GELU STE + center_scale LN.
Head trained via detached CE loss. Embedding frozen.
g_l reconstruction modules provide local proxy signal for U_l updates.
"""
import argparse
import json
import math
import pickle
import time
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from model_local import LocalGPTConfig
from train_recon import ReconTransformer, get_batch, FeedbackModule
from stiefel_feedback import StiefelFeedbackSystem
def main():
p = argparse.ArgumentParser()
p.add_argument("--run_name", type=str, required=True)
p.add_argument("--seed", type=int, default=1337)
p.add_argument("--data_dir", type=str, default="data/shakespeare_char")
p.add_argument("--out_dir", type=str, default="runs_local")
p.add_argument("--block_size", type=int, default=256)
p.add_argument("--batch_size", type=int, default=64)
p.add_argument("--n_layer", type=int, default=6)
p.add_argument("--n_head", type=int, default=6)
p.add_argument("--n_embd", type=int, default=384)
p.add_argument("--dropout", type=float, default=0.2)
p.add_argument("--max_iters", type=int, default=5000)
p.add_argument("--warmup_iters", type=int, default=100)
p.add_argument("--max_lr", type=float, default=1e-3)
p.add_argument("--min_lr", type=float, default=1e-4)
p.add_argument("--rank", type=int, default=128)
p.add_argument("--eta_B", type=float, default=3e-5)
p.add_argument("--freeze_fb_steps", type=int, default=200)
p.add_argument("--sigma_recon", type=float, default=0.1)
p.add_argument("--eta_target", type=float, default=0.1)
p.add_argument("--eval_interval", type=int, default=250)
p.add_argument("--eval_iters", type=int, default=100)
p.add_argument("--log_interval", type=int, default=50)
p.add_argument("--attn_mode", type=str, default="softmax")
args = p.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = "cuda" if torch.cuda.is_available() else "cpu"
data_dir = Path(args.data_dir)
with open(data_dir / "meta.pkl", "rb") as f:
meta = pickle.load(f)
vocab_size = meta["vocab_size"]
run_dir = Path(args.out_dir) / args.run_name
run_dir.mkdir(parents=True, exist_ok=True)
log_path = run_dir / "log.jsonl"
log_path.write_text("")
cfg = LocalGPTConfig(
block_size=args.block_size, vocab_size=vocab_size,
n_layer=args.n_layer, n_head=args.n_head, n_embd=args.n_embd,
dropout=args.dropout, attn_mode=args.attn_mode,
method="bp", # intra-block standard autograd with fused attention
fuse_attn_local=True, ste_gelu=True, ln_mode="center_scale",
)
model = ReconTransformer(cfg).to(device)
# Stiefel feedback system
layer_dims = [args.n_embd] * args.n_layer # each block output is d_model
fb_system = StiefelFeedbackSystem(vocab_size, layer_dims, rank=args.rank).to(device)
n_params = sum(p.numel() for p in model.parameters())
n_fb = sum(p.numel() for p in fb_system.parameters())
# Optimizers: forward (blocks + head), feedback g_l (reconstruction)
forward_params = list(model.head.parameters()) + list(model.ln_f.parameters())
for block in model.blocks:
forward_params.extend(block.parameters())
feedback_g_params = list(model.feedbacks.parameters())
opt_fwd = torch.optim.AdamW(forward_params, lr=args.max_lr, weight_decay=0.1)
opt_fb_g = torch.optim.AdamW(feedback_g_params, lr=args.max_lr, weight_decay=0.01)
# U_l and α_l are updated manually via Stiefel retraction, not via optimizer
t0 = time.time()
def log(rec):
rec["t"] = time.time() - t0
with open(log_path, "a") as f:
f.write(json.dumps(rec) + "\n")
log({"event": "start", "method": "stiefel_factored", "params": n_params,
"fb_params": n_fb, "rank": args.rank, "config": vars(args)})
print(f"[{args.run_name}] stiefel factored, params={n_params/1e6:.2f}M, fb={n_fb/1e3:.1f}K, rank={args.rank}")
def lr_schedule(it):
if it < args.warmup_iters:
return args.max_lr * (it + 1) / (args.warmup_iters + 1)
decay = 0.5 * (1 + math.cos(math.pi * (it - args.warmup_iters) /
max(1, args.max_iters - args.warmup_iters)))
return args.min_lr + decay * (args.max_lr - args.min_lr)
@torch.no_grad()
def eval_loss():
model.eval()
losses = torch.zeros(args.eval_iters)
for k in range(args.eval_iters):
X, Y = get_batch("val", data_dir, args.block_size, args.batch_size, device)
acts = model.forward_activations(X)
logits = model.logits_from_h(acts[-1])
loss = F.cross_entropy(logits.view(-1, vocab_size), Y.view(-1))
losses[k] = loss.item()
model.train()
return losses.mean().item()
model.train()
for it in range(args.max_iters + 1):
lr = lr_schedule(it)
for g in opt_fwd.param_groups:
g["lr"] = lr
if it % args.eval_interval == 0 or it == args.max_iters:
val = eval_loss()
log({"event": "eval", "iter": it, "val_loss": val, "lr": lr})
print(f"[{args.run_name}] iter {it:5d} val {val:.4f} lr {lr:.4g}")
if it == args.max_iters:
break
X, Y = get_batch("train", data_dir, args.block_size, args.batch_size, device)
# 1. Forward pass
activations = model.forward_activations(X)
logits = model.logits_from_h(activations[-1])
ce_loss = F.cross_entropy(logits.view(-1, vocab_size), Y.view(-1))
# 2. Compute e_L and compress
with torch.no_grad():
probs = F.softmax(logits.detach(), dim=-1)
onehot = F.one_hot(Y, num_classes=vocab_size).float()
e_L = (probs - onehot) / Y.numel()
c = fb_system.compress_error(e_L)
# 3. Compute per-layer δ via Stiefel feedback
deltas = fb_system.compute_deltas(c)
# 4. Train g_l (reconstruction feedback modules)
opt_fb_g.zero_grad()
recon_loss = model.reconstruction_loss(activations, sigma=args.sigma_recon)
recon_loss.backward()
opt_fb_g.step()
# 5. Get local proxy signals g_hat_l from reconstruction modules
g_hats = []
for l in range(cfg.n_layer):
with torch.no_grad():
h_l = activations[l].detach()
h_lp1 = activations[l + 1].detach()
g_hat_l = model.feedbacks[l](h_lp1) - h_l # reconstruction error
g_hats.append(g_hat_l)
# 6. Update Stiefel feedback (U_l, α_l)
frozen = (it < args.freeze_fb_steps)
fb_diags = fb_system.update_all(g_hats, c, frozen=frozen, eta_B=args.eta_B)
# 7. Train forward weights via block-local loss using Stiefel δ as targets
opt_fwd.zero_grad()
# 7a. Head via detached CE
h_L_det = activations[-1].detach()
logits_head = model.logits_from_h(h_L_det)
head_loss = F.cross_entropy(logits_head.view(-1, vocab_size), Y.view(-1))
head_loss.backward()
# 7b. Each block: local target = h_l + δ_l (feedback signal as target displacement)
for l in range(cfg.n_layer):
h_l = activations[l] if l == 0 else activations[l].detach()
h_lp1 = activations[l + 1].detach()
# Target for block l's output: current output + δ_l displacement
target_lp1 = h_lp1 - deltas[l].detach() # push toward lower loss
h_lp1_pred = model.blocks[l](h_l)
block_loss = F.mse_loss(h_lp1_pred, target_lp1)
block_loss.backward()
torch.nn.utils.clip_grad_norm_(forward_params, 1.0)
opt_fwd.step()
if it % args.log_interval == 0:
fb_info = {}
if not frozen and fb_diags:
fb_info = {
"alpha_mean": sum(d.get("alpha", 0) for d in fb_diags) / len(fb_diags),
"rho_mean": sum(d.get("rho", 0) for d in fb_diags) / len(fb_diags),
"Delta_frob_mean": sum(d.get("Delta_frob", 0) for d in fb_diags) / len(fb_diags),
}
log({"event": "step", "iter": it, "ce_loss": ce_loss.item(),
"recon_loss": recon_loss.item(), "head_loss": head_loss.item(),
"frozen": frozen, **fb_info, "lr": lr})
if __name__ == "__main__":
main()
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