"""Step 7: Engelken-style interflossing. This is intentionally not a mixed objective. Ordinary task-training steps use only the supervised ACT loss. Flossing episodes use only a Lyapunov-spectrum conditioning loss, then task training resumes. Paper mapping: - preflossing: run a floss-only episode before task training. - interflossing: run short floss-only episodes at selected training steps. - no persistent L_task + alpha * L_floss term is used here. """ from __future__ import annotations import argparse import importlib import json import sys import time from pathlib import Path import numpy as np import torch import torch.nn.functional as F import yaml HRM_DIR = Path("/home/yurenh2/rrm/hrm") TRM_DIR = Path("/home/yurenh2/rrm/trm") def import_stack(model_type: str): repo_dir = HRM_DIR if model_type == "hrm" else TRM_DIR sys.path.insert(0, str(repo_dir)) if model_type == "hrm": model_mod = importlib.import_module("models.hrm.hrm_act_v1") model_cls = model_mod.HierarchicalReasoningModel_ACTV1 else: model_mod = importlib.import_module("models.recursive_reasoning.trm") model_cls = model_mod.TinyRecursiveReasoningModel_ACTV1 losses_mod = importlib.import_module("models.losses") optim_mod = importlib.import_module("adam_atan2") sparse_mod = importlib.import_module("models.sparse_embedding") return model_cls, losses_mod.ACTLossHead, optim_mod.AdamATan2, sparse_mod.CastedSparseEmbeddingSignSGD_Distributed def parse_step_list(text: str) -> set[int]: if not text.strip(): return set() out = set() for part in text.split(","): part = part.strip() if not part: continue out.add(int(part)) return out def build_interfloss_steps(args) -> set[int]: steps = parse_step_list(args.interfloss_at) if args.interfloss_every and args.interfloss_every > 0: start = max(args.interfloss_start, 0) stop = args.interfloss_stop if args.interfloss_stop >= 0 else args.train_steps stop = min(stop, args.train_steps) steps.update(range(start, stop + 1, args.interfloss_every)) return steps def load_model(model_type: str, ckpt_root: Path, ckpt_name: str, device: str, batch_size_override: int | None = None): model_cls, loss_head_cls, adam_cls, sparse_cls = import_stack(model_type) cfg = yaml.safe_load((ckpt_root / "all_config.yaml").read_text()) arch_cfg = dict(cfg["arch"]) data_path = Path(cfg.get("data_path") or cfg["data_paths"][0]) train_meta = json.loads((data_path / "train" / "dataset.json").read_text()) arch_cfg.update( batch_size=batch_size_override or cfg["global_batch_size"], seq_len=train_meta["seq_len"], vocab_size=train_meta["vocab_size"], num_puzzle_identifiers=train_meta["num_puzzle_identifiers"], causal=False, ) cfg["data_path"] = str(data_path) with torch.device(device): base = model_cls(arch_cfg) head = loss_head_cls(base, loss_type=arch_cfg["loss"]["loss_type"]) if ckpt_name != "__random__": sd = torch.load(ckpt_root / ckpt_name, map_location="cpu", weights_only=True) stripped = {k.replace("_orig_mod.", ""): v for k, v in sd.items()} missing, unexpected = head.load_state_dict(stripped, strict=False) print(f"[load {ckpt_name}] missing={len(missing)} unexpected={len(unexpected)}") else: print("[load __random__] random initialization from config") return head, base, cfg, adam_cls, sparse_cls def jvp_train(f, x, v): return torch.autograd.functional.jvp(f, x, v=v, create_graph=True, strict=False) def compute_joint_lyap_spec(model_type, base, batch, k_lyap, lyap_act_steps, device, seed, lyap_start_act=0): inner = base.inner cfg = inner.config bsz = batch["inputs"].shape[0] seq_full = cfg.seq_len + inner.puzzle_emb_len hidden = cfg.hidden_size dim = seq_full * hidden z_h = inner.H_init.unsqueeze(0).expand(bsz, seq_full, hidden).clone().to(inner.forward_dtype) z_l = inner.L_init.unsqueeze(0).expand(bsz, seq_full, hidden).clone().to(inner.forward_dtype) seq_info = {"cos_sin": inner.rotary_emb() if hasattr(inner, "rotary_emb") else None} input_embeddings = inner._input_embeddings(batch["inputs"], batch["puzzle_identifiers"]) # Optional late-window measurement: first move to a later recursive state # without differentiating through the warmup trajectory. This regularizes # local late-stage stability instead of penalizing useful early expansion. warmup_acts = min(max(lyap_start_act, 0), cfg.halt_max_steps) if warmup_acts > 0: with torch.no_grad(): for _act in range(warmup_acts): for _h in range(cfg.H_cycles): for _l in range(cfg.L_cycles): z_l = inner.L_level(z_l, z_h + input_embeddings, **seq_info) if model_type == "trm": z_h = inner.L_level(z_h, z_l, **seq_info) else: z_h = inner.H_level(z_h, z_l, **seq_info) z_h = z_h.detach() z_l = z_l.detach() gen = torch.Generator(device=device).manual_seed(seed) q0 = torch.randn(bsz, 2 * dim, k_lyap, device=device, dtype=torch.float32, generator=gen) q, _ = torch.linalg.qr(q0) log_r_sum = torch.zeros(bsz, k_lyap, device=device, dtype=torch.float32) n_steps = 0 n_act = min(lyap_act_steps, max(cfg.halt_max_steps - warmup_acts, 1)) for _act in range(n_act): for _h in range(cfg.H_cycles): for _l in range(cfg.L_cycles): v_h = q[:, :dim, :] v_l = q[:, dim:, :] v_comb = v_h + v_l new_v_l_cols = [] f_l = lambda z: inner.L_level(z, z_h + input_embeddings, **seq_info) for i in range(k_lyap): v_i = v_comb[:, :, i].reshape(bsz, seq_full, hidden).to(inner.forward_dtype) z_l_new, d_v = jvp_train(f_l, z_l, v_i) new_v_l_cols.append(d_v.reshape(bsz, dim).to(torch.float32)) q = torch.cat([v_h, torch.stack(new_v_l_cols, dim=-1)], dim=1) z_l = z_l_new q, r = torch.linalg.qr(q) log_r_sum = log_r_sum + r.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() n_steps += 1 v_h = q[:, :dim, :] v_l = q[:, dim:, :] v_comb = v_h + v_l new_v_h_cols = [] if model_type == "trm": f_h = lambda z: inner.L_level(z, z_l, **seq_info) else: f_h = lambda z: inner.H_level(z, z_l, **seq_info) for i in range(k_lyap): v_i = v_comb[:, :, i].reshape(bsz, seq_full, hidden).to(inner.forward_dtype) z_h_new, d_v = jvp_train(f_h, z_h, v_i) new_v_h_cols.append(d_v.reshape(bsz, dim).to(torch.float32)) q = torch.cat([torch.stack(new_v_h_cols, dim=-1), v_l], dim=1) z_h = z_h_new q, r = torch.linalg.qr(q) log_r_sum = log_r_sum + r.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() n_steps += 1 return log_r_sum / max(n_steps, 1) def floss_loss_from_spec(spec, mode: str, lambda_star: float): if mode == "engelken_l2": return (spec ** 2).mean(), spec if mode == "spectrum_cf": excess = (spec - lambda_star).clamp_min(0.0) return (excess ** 2).mean(), excess if mode == "volume_cf": volume = spec.mean(dim=1) excess = (volume - lambda_star).clamp_min(0.0) return (excess ** 2).mean(), excess if mode == "top1_cf": excess = (spec[:, 0] - lambda_star).clamp_min(0.0) return (excess ** 2).mean(), excess raise ValueError(f"unknown floss mode: {mode}") 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 sample_replay_batch(data_path: Path, n_samples: int, seed: int): 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") idx = rng.choice(len(inputs), size=n_samples, replace=False) return { "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 move_batch(batch: dict[str, torch.Tensor], device: str): return {k: v.to(device) for k, v in batch.items()} def rollout_logits(base, batch, device): with torch.device(device): carry = base.initial_carry(batch) for _ in range(base.config.halt_max_steps): carry, outputs = base(carry=carry, batch=batch) return outputs["logits"] def build_kl_replay(args, base, data_path, device, episode_idx): if args.kl_beta <= 0 or args.kl_replay_size <= 0: return None replay = sample_replay_batch( data_path, n_samples=args.kl_replay_size, seed=args.seed + 200000 + episode_idx, ) teacher_chunks = [] base.eval() with torch.no_grad(): for start in range(0, args.kl_replay_size, args.kl_batch_size): end = min(start + args.kl_batch_size, args.kl_replay_size) batch = move_batch({k: v[start:end] for k, v in replay.items()}, device) logits = rollout_logits(base, batch, device) teacher_chunks.append(logits.detach().to(torch.float32).cpu()) replay["teacher_logits"] = torch.cat(teacher_chunks, dim=0) replay["mask"] = replay["labels"] > 0 return replay def kl_preservation_loss(args, base, replay, step, device): if replay is None: return torch.zeros((), device=device) n_replay = replay["inputs"].shape[0] batch_size = min(args.kl_batch_size, n_replay) start = (step * batch_size) % n_replay if start + batch_size <= n_replay: idx = torch.arange(start, start + batch_size) else: idx = torch.cat([torch.arange(start, n_replay), torch.arange(0, start + batch_size - n_replay)]) batch = move_batch( { "inputs": replay["inputs"][idx], "labels": replay["labels"][idx], "puzzle_identifiers": replay["puzzle_identifiers"][idx], }, device, ) teacher_logits = replay["teacher_logits"][idx].to(device) mask = replay["mask"][idx].to(device) was_training = base.training base.eval() student_logits = rollout_logits(base, batch, device).to(torch.float32) if was_training: base.train() set_puzzle_embedding_mode(base, args.train_puzzle_emb) temp = args.kl_temperature student_logp = F.log_softmax(student_logits / temp, dim=-1) teacher_p = F.softmax(teacher_logits / temp, dim=-1) kl_per_token = F.kl_div(student_logp, teacher_p, reduction="none").sum(dim=-1) * (temp ** 2) if mask.any(): return kl_per_token[mask].mean() return kl_per_token.mean() 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 start in range(0, n_samples, batch_size): end = min(start + batch_size, n_samples) idx = idx_all[start:end] 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 write_log(path: str, log: dict): Path(path).write_text(json.dumps(log, indent=2)) def freeze_puzzle_embedding(base): base.inner.puzzle_emb.eval() def set_puzzle_embedding_mode(base, train_puzzle_emb: bool): if train_puzzle_emb: base.inner.puzzle_emb.train() else: freeze_puzzle_embedding(base) def make_optimizers(args, base, head, adam_cls, sparse_cls, lr: float, weight_decay: float, train_puzzle_emb: bool): optimizers = [] if train_puzzle_emb and getattr(base.inner.config, "puzzle_emb_ndim", 0) > 0: optimizers.append( sparse_cls( base.inner.puzzle_emb.buffers(), lr=lr if args.puzzle_emb_lr is None else args.puzzle_emb_lr, weight_decay=args.puzzle_emb_weight_decay, world_size=1, ) ) optimizers.append(adam_cls(head.parameters(), lr=lr, betas=(0.9, 0.95), weight_decay=weight_decay)) return optimizers def optim_zero_grad(optimizers): for optim in optimizers: optim.zero_grad(set_to_none=True) def optim_step(optimizers): for optim in optimizers: optim.step() def run_floss_episode(args, head, base, adam_cls, data_path, device, log, episode_idx, train_step): print( f"\n=== Floss episode {episode_idx} at train_step={train_step}: " f"{args.floss_steps} steps, mode={args.floss_mode}, lr={args.floss_lr} ===", flush=True, ) optimizers = make_optimizers( args, base, head, adam_cls, args.sparse_cls, lr=args.floss_lr, weight_decay=0.0, train_puzzle_emb=False, ) replay = build_kl_replay(args, base, data_path, device, episode_idx) train_iter = load_train_batches( data_path, args.floss_batch_size, args.floss_steps, seed=args.seed + 100000 + episode_idx * 1000, ) episode = {"episode": episode_idx, "train_step": train_step, "steps": []} t0 = time.time() for step, batch in enumerate(train_iter): batch = {k: v.to(device) for k, v in batch.items()} head.train() set_puzzle_embedding_mode(base, False) spec = compute_joint_lyap_spec( args.model, base, batch, k_lyap=args.k_lyap, lyap_act_steps=args.lyap_act_steps, device=device, seed=args.seed + episode_idx * 10000 + step, lyap_start_act=args.lyap_start_act, ) optim_zero_grad(optimizers) floss_loss, excess = floss_loss_from_spec(spec, args.floss_mode, args.lambda_star) floss_loss.backward() kl_loss = kl_preservation_loss(args, base, replay, step, device) if args.kl_beta > 0: (args.kl_beta * kl_loss).backward() torch.nn.utils.clip_grad_norm_([p for p in head.parameters() if p.requires_grad], 1.0) optim_step(optimizers) detached = spec.detach() total_loss = floss_loss.detach() + args.kl_beta * kl_loss.detach() rec = { "step": step, "loss": float(total_loss.item()), "floss_loss": float(floss_loss.item()), "kl_loss": float(kl_loss.item()), "lyap1_mean": float(detached[:, 0].mean().item()), "lyap1_max": float(detached[:, 0].max().item()), "lyap_mean": float(detached.mean().item()), "volume_mean": float(detached.mean(dim=1).mean().item()), "volume_max": float(detached.mean(dim=1).max().item()), "frac_active": float((excess.detach() > 0).float().mean().item()), } episode["steps"].append(rec) if step % args.floss_log_every == 0 or step == args.floss_steps - 1: print( f" F[{step:>4}/{args.floss_steps}] dt={time.time() - t0:.1f}s " f"loss={rec['loss']:.6f} floss={rec['floss_loss']:.6f} " f"kl={rec['kl_loss']:.6f} lyap1={rec['lyap1_mean']:+.4f} " f"vol={rec['volume_mean']:+.4f} active={rec['frac_active']:.2f}", flush=True, ) log["floss_episodes"].append(episode) if args.eval_after_floss: acc, tok_acc = evaluate(head, base, data_path, args.eval_n, args.eval_batch_size, device) print(f" >> FLOSS EVAL train_step={train_step}: exact_acc={acc:.4f}", flush=True) log["evals"].append( {"kind": "after_floss", "train_step": train_step, "episode": episode_idx, "acc": acc, "tok_acc": tok_acc} ) write_log(args.out, log) def run_task_step(args, head, base, batch, optimizers, device): batch = {k: v.to(device) for k, v in batch.items()} head.train() set_puzzle_embedding_mode(base, args.train_puzzle_emb) with torch.device(device): carry = base.initial_carry(batch) loss_sum = 0.0 n_loss = 0 for _ in range(base.config.halt_max_steps): carry, loss, _metrics, _outputs, all_finish = head(return_keys=[], carry=carry, batch=batch) loss_sum = loss_sum + loss n_loss += 1 if all_finish: break sup_loss = loss_sum / max(n_loss, 1) / batch["inputs"].shape[0] optim_zero_grad(optimizers) sup_loss.backward() torch.nn.utils.clip_grad_norm_([p for p in head.parameters() if p.requires_grad], 1.0) optim_step(optimizers) return sup_loss def main(): parser = argparse.ArgumentParser() parser.add_argument("--model", choices=["hrm", "trm"], required=True) parser.add_argument("--ckpt-root", required=True) parser.add_argument("--ckpt-name", required=True, help="Checkpoint file name, or __random__ to initialize from config without loading weights.") parser.add_argument("--train-steps", type=int, default=10000) parser.add_argument("--batch-size", type=int, default=8) parser.add_argument("--task-batch-size", type=int, default=None, help="Supervised task microbatch size. Defaults to --batch-size.") parser.add_argument("--floss-batch-size", type=int, default=None, help="Flossing microbatch size. Defaults to --batch-size.") parser.add_argument("--train-lr", type=float, default=1e-5) parser.add_argument("--floss-lr", type=float, default=1e-4) parser.add_argument("--floss-steps", type=int, default=500) parser.add_argument("--interfloss-at", default="0,500") parser.add_argument("--interfloss-every", type=int, default=0, help="If >0, also run floss episodes periodically every N task optimizer steps.") parser.add_argument("--interfloss-start", type=int, default=0, help="First task optimizer step for periodic interfloss.") parser.add_argument("--interfloss-stop", type=int, default=-1, help="Last task optimizer step for periodic interfloss. -1 means train_steps.") parser.add_argument("--floss-mode", choices=["engelken_l2", "spectrum_cf", "volume_cf", "top1_cf"], default="engelken_l2") parser.add_argument("--lambda-star", type=float, default=0.0) parser.add_argument("--k-lyap", type=int, default=8) parser.add_argument("--lyap-act-steps", type=int, default=4) parser.add_argument("--lyap-start-act", type=int, default=0, help="Warm up this many ACT steps before measuring/flossing the Lyapunov window.") parser.add_argument("--seed", type=int, default=42) parser.add_argument("--eval-every", type=int, default=1000) parser.add_argument("--eval-n", type=int, default=512) parser.add_argument("--eval-batch-size", type=int, default=32) parser.add_argument("--floss-log-every", type=int, default=10) parser.add_argument("--eval-after-floss", action=argparse.BooleanOptionalAction, default=True) parser.add_argument("--kl-beta", type=float, default=0.0, help="Episode-start replay-logit KL weight during floss-only steps.") parser.add_argument("--kl-replay-size", type=int, default=64) parser.add_argument("--kl-batch-size", type=int, default=8) parser.add_argument("--kl-temperature", type=float, default=1.0) parser.add_argument("--init-seed", type=int, default=None, help="Torch seed used before model construction. Use this for matched from-scratch runs.") parser.add_argument("--train-puzzle-emb", action=argparse.BooleanOptionalAction, default=False, help="Train sparse puzzle embeddings. Requires --batch-size to match the model local embedding batch.") parser.add_argument("--puzzle-emb-lr", type=float, default=None, help="Sparse puzzle embedding LR. Defaults to current phase LR.") parser.add_argument("--puzzle-emb-weight-decay", type=float, default=1.0) parser.add_argument("--out", default="step7_interfloss_log.json") args = parser.parse_args() if args.task_batch_size is None: args.task_batch_size = args.batch_size if args.floss_batch_size is None: args.floss_batch_size = args.batch_size args.batch_size = args.task_batch_size device = "cuda" if args.init_seed is not None: torch.manual_seed(args.init_seed) np.random.seed(args.init_seed) interfloss_steps = build_interfloss_steps(args) head, base, cfg, adam_cls, sparse_cls = load_model( args.model, Path(args.ckpt_root), args.ckpt_name, device, batch_size_override=args.task_batch_size if args.train_puzzle_emb else None, ) args.sparse_cls = sparse_cls data_path = Path(cfg["data_path"]) print(f"\n=== Initial eval (loaded {args.ckpt_name}) ===") acc0, tok0 = evaluate(head, base, data_path, args.eval_n, args.eval_batch_size, device) print(f" initial: exact_acc={acc0:.4f} token_acc={tok0:.4f}", flush=True) log = { "args": {k: v for k, v in vars(args).items() if k != "sparse_cls"}, "initial_acc": acc0, "initial_tok_acc": tok0, "interfloss_steps": sorted(interfloss_steps), "task_steps": [], "floss_episodes": [], "evals": [{"kind": "initial", "train_step": 0, "acc": acc0, "tok_acc": tok0}], } write_log(args.out, log) task_optimizers = make_optimizers( args, base, head, adam_cls, sparse_cls, lr=args.train_lr, weight_decay=cfg["weight_decay"], train_puzzle_emb=args.train_puzzle_emb, ) train_iter = load_train_batches(data_path, args.task_batch_size, args.train_steps, seed=args.seed) episode_idx = 0 t0 = time.time() for train_step, batch in enumerate(train_iter): if train_step in interfloss_steps: run_floss_episode(args, head, base, adam_cls, data_path, device, log, episode_idx, train_step) episode_idx += 1 sup_loss = run_task_step(args, head, base, batch, task_optimizers, device) rec = {"train_step": train_step + 1, "sup_loss": float(sup_loss.item())} log["task_steps"].append(rec) if train_step % 50 == 0 or train_step == args.train_steps - 1: print( f" T[{train_step + 1:>5}/{args.train_steps}] dt={time.time() - t0:.1f}s " f"sup={rec['sup_loss']:.4f}", flush=True, ) if (train_step + 1) % args.eval_every == 0: acc, tok_acc = evaluate(head, base, data_path, args.eval_n, args.eval_batch_size, device) print(f" >> TASK EVAL @ step {train_step + 1}: exact_acc={acc:.4f} delta={acc - acc0:+.4f}", flush=True) log["evals"].append({"kind": "task", "train_step": train_step + 1, "acc": acc, "tok_acc": tok_acc}) write_log(args.out, log) if args.train_steps in interfloss_steps: run_floss_episode(args, head, base, adam_cls, data_path, device, log, episode_idx, args.train_steps) acc_f, tok_f = evaluate(head, base, data_path, args.eval_n, args.eval_batch_size, device) print("\n=== Final eval ===") print(f" initial={acc0:.4f} final={acc_f:.4f} delta={acc_f - acc0:+.4f}", flush=True) log["final_acc"] = acc_f log["final_tok_acc"] = tok_f log["evals"].append({"kind": "final", "train_step": args.train_steps, "acc": acc_f, "tok_acc": tok_f}) write_log(args.out, log) print(f"log -> {args.out}") if __name__ == "__main__": main()