"""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()