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"""Train HRM-Orth (orthogonal-patched HRM) from scratch on Sudoku.
Per codex round 2 recommendation (Q6 pivot): patch HRM Block (attn+SwiGLU+rms_norm)
with Lipschitz-bounded versions (cosine attn + OrthLinear+MaxMin + weighted residual).
Keeps HRM's H_level/L_level/ACT framework intact.
"""
from __future__ import annotations
import sys, os, json, math, time, argparse
from pathlib import Path
import numpy as np
import torch
ROOT = Path("/home/yurenh2/rrm/srm")
sys.path.insert(0, str(ROOT))
from models.srm.hrm_orth_v1 import HierarchicalReasoningModel_ACTV1 as HRMOrth
from models.losses import ACTLossHead
from models.sparse_embedding import CastedSparseEmbeddingSignSGD_Distributed
from adam_atan2 import AdamATan2
def build_model(data_path: Path, batch_size: int, device: str,
hidden_size: int = 256, num_heads: int = 4,
H_cycles: int = 2, L_cycles: int = 2, H_layers: int = 4, L_layers: int = 4,
orth_s_min: float = 0.95, cosine_attn_tau: float = 8.0):
train_meta = json.loads((data_path / "train" / "dataset.json").read_text())
arch_cfg = dict(
H_cycles=H_cycles, H_layers=H_layers,
L_cycles=L_cycles, L_layers=L_layers,
expansion=4,
halt_exploration_prob=0.1,
halt_max_steps=16,
hidden_size=hidden_size,
num_heads=num_heads,
pos_encodings="rope",
puzzle_emb_ndim=hidden_size,
batch_size=batch_size,
vocab_size=train_meta["vocab_size"],
seq_len=train_meta["seq_len"],
num_puzzle_identifiers=train_meta["num_puzzle_identifiers"],
forward_dtype="bfloat16",
orth_s_min=orth_s_min,
cosine_attn_tau=cosine_attn_tau,
)
with torch.device(device):
base = HRMOrth(arch_cfg)
head = ACTLossHead(base, loss_type="stablemax_cross_entropy")
return head, base, train_meta
def load_train_batches(data_path: Path, batch_size: int, n_iters: int, seed: int = 0):
rng = np.random.default_rng(seed)
inputs = np.load(data_path / "train" / "all__inputs.npy")
labels = np.load(data_path / "train" / "all__labels.npy")
pid = np.load(data_path / "train" / "all__puzzle_identifiers.npy")
N = len(inputs)
for _ in range(n_iters):
idx = rng.choice(N, size=batch_size, replace=False)
yield {
"inputs": torch.from_numpy(inputs[idx].astype(np.int32)),
"labels": torch.from_numpy(labels[idx].astype(np.int32)),
"puzzle_identifiers": torch.from_numpy(pid[idx].astype(np.int32)),
}
def evaluate(head, base, data_path, n_samples, batch_size, device, seed=42):
rng = np.random.default_rng(seed)
inputs = np.load(data_path / "test" / "all__inputs.npy")
labels = np.load(data_path / "test" / "all__labels.npy")
pid = np.load(data_path / "test" / "all__puzzle_identifiers.npy")
idx_all = rng.choice(len(inputs), size=n_samples, replace=False)
head.eval()
correct = 0; token_correct = 0; token_total = 0
for s in range(0, n_samples, batch_size):
e = min(s + batch_size, n_samples)
idx = idx_all[s:e]
batch = {
"inputs": torch.from_numpy(inputs[idx].astype(np.int32)).to(device),
"labels": torch.from_numpy(labels[idx].astype(np.int32)).to(device),
"puzzle_identifiers": torch.from_numpy(pid[idx].astype(np.int32)).to(device),
}
with torch.no_grad():
with torch.device(device):
carry = base.initial_carry(batch)
for _ in range(base.config.halt_max_steps):
carry, outputs = base(carry=carry, batch=batch)
preds = outputs["logits"].argmax(dim=-1)
mask = batch["labels"] > 0
exact = ((preds == batch["labels"]) | ~mask).all(dim=-1).float()
correct += exact.sum().item()
token_correct += ((preds == batch["labels"]) & mask).sum().item()
token_total += mask.sum().item()
return correct / n_samples, token_correct / max(token_total, 1)
def warmup_constant_lr(step, base_lr, warmup):
return base_lr * step / max(1, warmup) if step < warmup else base_lr
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--data-path", default="/home/yurenh2/rrm/data/sudoku-extreme-1k-aug-1000")
ap.add_argument("--n-steps", type=int, default=3000)
ap.add_argument("--batch-size", type=int, default=8)
ap.add_argument("--lr", type=float, default=1e-4)
ap.add_argument("--puzzle-emb-lr", type=float, default=1e-4)
ap.add_argument("--warmup-steps", type=int, default=200)
ap.add_argument("--weight-decay", type=float, default=1.0)
ap.add_argument("--hidden-size", type=int, default=256)
ap.add_argument("--num-heads", type=int, default=4)
ap.add_argument("--H-cycles", type=int, default=2)
ap.add_argument("--L-cycles", type=int, default=2)
ap.add_argument("--H-layers", type=int, default=4)
ap.add_argument("--L-layers", type=int, default=4)
ap.add_argument("--orth-s-min", type=float, default=0.95, help="min diag scale (weak orthogonality)")
ap.add_argument("--cosine-attn-tau", type=float, default=8.0)
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--eval-every", type=int, default=300)
ap.add_argument("--eval-n", type=int, default=512)
ap.add_argument("--eval-batch-size", type=int, default=32)
ap.add_argument("--out", required=True)
ap.add_argument("--save-ckpt", default="")
args = ap.parse_args()
device = "cuda"
torch.manual_seed(args.seed); np.random.seed(args.seed)
data_path = Path(args.data_path)
head, base, train_meta = build_model(
data_path, args.batch_size, device,
hidden_size=args.hidden_size, num_heads=args.num_heads,
H_cycles=args.H_cycles, L_cycles=args.L_cycles,
H_layers=args.H_layers, L_layers=args.L_layers,
orth_s_min=args.orth_s_min, cosine_attn_tau=args.cosine_attn_tau,
)
n_params = sum(p.numel() for p in head.parameters())
print(f"Built HRM-Orth | params={n_params:,} | hidden={args.hidden_size} "
f"H_layers={args.H_layers} L_layers={args.L_layers} "
f"s_min={args.orth_s_min} τ={args.cosine_attn_tau}")
puzzle_emb_opt = CastedSparseEmbeddingSignSGD_Distributed(
base.inner.puzzle_emb.buffers(), lr=0, weight_decay=args.weight_decay, world_size=1)
main_opt = AdamATan2(head.parameters(), lr=0, betas=(0.9, 0.95), weight_decay=args.weight_decay)
acc0, tacc0 = evaluate(head, base, data_path, args.eval_n, args.eval_batch_size, device)
print(f"=== step 0 (random init): exact_acc = {acc0:.4f} token_acc = {tacc0:.4f} ===")
log = {"args": vars(args), "n_params": n_params, "initial_acc": acc0, "initial_tok_acc": tacc0, "steps": [], "evals": []}
log["evals"].append({"step": 0, "acc": acc0, "tok_acc": tacc0})
t0 = time.time()
train_iter = load_train_batches(data_path, args.batch_size, args.n_steps, seed=args.seed)
for step, batch in enumerate(train_iter):
batch = {k: v.to(device) for k, v in batch.items()}
cur_lr = warmup_constant_lr(step, args.lr, args.warmup_steps)
cur_pe_lr = warmup_constant_lr(step, args.puzzle_emb_lr, args.warmup_steps)
for pg in main_opt.param_groups: pg["lr"] = cur_lr
for pg in puzzle_emb_opt.param_groups: pg["lr"] = cur_pe_lr
head.train()
with torch.device(device):
carry = base.initial_carry(batch)
sup_loss_sum = 0.0; n_loss = 0
for _ in range(base.config.halt_max_steps):
carry, l, metrics, _, all_finish = head(return_keys=[], carry=carry, batch=batch)
sup_loss_sum = sup_loss_sum + l
n_loss += 1
if all_finish: break
sup_loss = sup_loss_sum / max(n_loss, 1) / args.batch_size
puzzle_emb_opt.zero_grad(set_to_none=True)
main_opt.zero_grad(set_to_none=True)
sup_loss.backward()
torch.nn.utils.clip_grad_norm_([p for p in head.parameters() if p.requires_grad], 1.0)
main_opt.step()
puzzle_emb_opt.step()
rec = {"step": step, "lr": cur_lr, "sup_loss": float(sup_loss.item())}
log["steps"].append(rec)
if step % 25 == 0 or step == args.n_steps - 1:
print(f" [{step:>4}/{args.n_steps}] dt={time.time()-t0:.0f}s lr={cur_lr:.1e} "
f"sup={rec['sup_loss']:.4f}", flush=True)
if (step + 1) % args.eval_every == 0 or step == args.n_steps - 1:
acc, tacc = evaluate(head, base, data_path, args.eval_n, args.eval_batch_size, device)
print(f" >> EVAL @ {step+1}: exact_acc={acc:.4f} tok_acc={tacc:.4f} (Δ init: {acc-acc0:+.4f})", flush=True)
log["evals"].append({"step": step + 1, "acc": acc, "tok_acc": tacc})
log["final_acc"] = log["evals"][-1]["acc"]
log["final_tok_acc"] = log["evals"][-1]["tok_acc"]
Path(args.out).parent.mkdir(parents=True, exist_ok=True)
Path(args.out).write_text(json.dumps(log, indent=2))
print(f"\n=== DONE === init {acc0:.4f} → final {log['final_acc']:.4f} log → {args.out}")
if args.save_ckpt:
Path(args.save_ckpt).parent.mkdir(parents=True, exist_ok=True)
torch.save({"state_dict": head.state_dict(), "args": vars(args),
"n_steps_trained": args.n_steps, "final_acc": log["final_acc"], "n_params": n_params},
args.save_ckpt)
print(f"checkpoint → {args.save_ckpt}")
if __name__ == "__main__":
main()
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