"""Continuous trainer for Toroidal Fractal Intelligence.""" from __future__ import annotations import json import random import time from pathlib import Path from typing import Optional import numpy as np import torch import torch.nn as nn from ..toroidal.model import ToroidalFractalIntelligence from ..evaluation.metrics import ToroidalMetrics from legacy.data import InfiniteDataLoader class ToroidalTrainer: """Train the continuous state one token transition at a time.""" def __init__(self, model: ToroidalFractalIntelligence, dataloader: torch.utils.data.DataLoader | InfiniteDataLoader, optimizer: torch.optim.Optimizer, save_dir: str = "./checkpoints", log_interval: int = 100, save_interval: int = 1000) -> None: self.model = model self.dataloader = dataloader self.optimizer = optimizer self.save_dir = Path(save_dir) self.save_dir.mkdir(parents=True, exist_ok=True) self.log_interval = log_interval self.save_interval = save_interval self.metrics = ToroidalMetrics() self.step = 0 self.epoch = 0 self.best_loss = float("inf") self.training_log: list[dict] = [] @staticmethod def _tokens(batch): if isinstance(batch, dict): tokens = batch["input_ids"] else: tokens = batch if tokens.ndim == 1: tokens = tokens.unsqueeze(0) if tokens.ndim > 2: tokens = tokens.squeeze(-1) return tokens.long() def _train_batch(self, batch) -> tuple[torch.Tensor, int]: """Consume a batch as a continuous token stream and return mean loss.""" tokens = self._tokens(batch) if tokens.shape[1] < 2: return tokens.new_zeros((), dtype=torch.float32), 0 # A continuous architecture must see transitions in order. Do not flatten # a sequence into a fake batch and do not make one prediction serve all # unrelated targets. losses = [] for row in tokens: for t in range(tokens.shape[1] - 1): output = self.model(row[t:t + 1]) logits = output["logits"] target = row[t + 1:t + 2] losses.append(nn.functional.cross_entropy(logits, target)) if not losses: return tokens.new_zeros((), dtype=torch.float32), 0 return torch.stack(losses).mean(), len(losses) def _log_step(self, loss: float) -> dict: metric = { "step": self.step, "loss": loss, "perplexity": float(torch.exp(torch.tensor(loss))), "n_atoms": len(self.model.atoms), "n_aggregates": 0, "n_abstractions": 0, "consolidation_count": self.model.consolidation_count, } self.training_log.append(metric) self.metrics.log_metrics( perplexity=metric["perplexity"], n_atoms=metric["n_atoms"], n_aggregates=metric["n_aggregates"], n_abstractions=metric["n_abstractions"], consolidation_count=metric["consolidation_count"], loss=loss, n_parameters=sum(p.numel() for p in self.model.parameters()), ) return metric def train_epoch(self) -> dict: self.model.train() total_loss = 0.0 total_tokens = 0 for batch in self.dataloader: self.optimizer.zero_grad(set_to_none=True) loss, n = self._train_batch(batch) if n == 0: continue loss.backward() torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) self.optimizer.step() self.step += 1 total_loss += loss.item() * n total_tokens += n if self.step % self.log_interval == 0: m = self._log_step(loss.item()) print(f"Step {self.step}: loss={loss.item():.4f}, perplexity={m['perplexity']:.2f}, atoms={m['n_atoms']}") if self.step % self.save_interval == 0: self.save() self.epoch += 1 mean = total_loss / max(total_tokens, 1) return {"loss": mean, "perplexity": float(torch.exp(torch.tensor(mean))), "step": self.step, "epoch": self.epoch} def train(self, max_steps: int = 10000, validation_interval: int = 1000) -> dict: print(f"Starting training for {max_steps} steps...") start = time.time() last_loss = float("inf") iterator = iter(self.dataloader) while self.step < max_steps: batch = next(iterator) self.optimizer.zero_grad(set_to_none=True) loss, n = self._train_batch(batch) if n == 0: continue loss.backward() torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) self.optimizer.step() self.step += 1 last_loss = loss.item() if self.step % self.log_interval == 0: m = self._log_step(last_loss) print(f"Step {self.step}/{max_steps}: loss={last_loss:.4f}, perplexity={m['perplexity']:.2f}, atoms={m['n_atoms']}") if self.step % self.save_interval == 0: self.save() if self.step % validation_interval == 0: val = self.validate() if val["loss"] < self.best_loss: self.best_loss = val["loss"] self.save("best_model.pt") self.save("final_model.pt") return {"final_loss": last_loss, "best_loss": self.best_loss, "total_steps": self.step, "total_time": time.time() - start} @torch.no_grad() def validate(self) -> dict: # Validation uses the same sequential transition semantics as training. self.model.eval() losses = [] for i, batch in enumerate(self.dataloader): if i >= 10: break tokens = self._tokens(batch) for row in tokens: for t in range(tokens.shape[1] - 1): out = self.model(row[t:t + 1]) losses.append(nn.functional.cross_entropy(out["logits"], row[t + 1:t + 2]).item()) loss = sum(losses) / max(len(losses), 1) return {"loss": loss, "perplexity": float(torch.exp(torch.tensor(loss))), "n_atoms": len(self.model.atoms)} @staticmethod def _get_rng_state() -> dict: """Capture every RNG used by the training and input pipelines.""" return { "python": random.getstate(), "numpy": np.random.get_state(), "torch": torch.get_rng_state(), "cuda": torch.cuda.get_rng_state_all() if torch.cuda.is_available() else None, } @staticmethod def _set_rng_state(state: dict) -> None: """Restore RNG state without advancing any generator.""" random.setstate(state["python"]) np.random.set_state(state["numpy"]) torch.set_rng_state(state["torch"]) if state.get("cuda") is not None and torch.cuda.is_available(): torch.cuda.set_rng_state_all(state["cuda"]) def _training_state(self) -> dict: """Return state needed to continue the next optimizer step exactly.""" state = { "optimizer": self.optimizer.state_dict(), "trainer": { "step": self.step, "epoch": self.epoch, "best_loss": self.best_loss, "training_log": self.training_log, "model_training": self.model.training, }, "rng": self._get_rng_state(), } if hasattr(self.dataloader, "state_dict"): state["dataloader"] = self.dataloader.state_dict() return state def save(self, filename: Optional[str] = None) -> str: filename = filename or f"checkpoint_step_{self.step}.pt" path = self.save_dir / filename self.model.save(str(path), extra_state=self._training_state()) with open(self.save_dir / f"training_log_step_{self.step}.json", "w") as f: json.dump(self.training_log[-100:], f, indent=2) print(f"Saved checkpoint to {path}") return str(path) def load(self, path: str) -> None: training = self.model.load(path) if training is not None: optimizer_state = training.get("optimizer") if optimizer_state is not None: self.optimizer.load_state_dict(optimizer_state) trainer_state = training.get("trainer", {}) self.step = int(trainer_state.get("step", self.step)) self.epoch = int(trainer_state.get("epoch", self.epoch)) self.best_loss = float(trainer_state.get("best_loss", self.best_loss)) self.training_log = trainer_state.get("training_log", self.training_log) if "model_training" in trainer_state: self.model.train(bool(trainer_state["model_training"])) dataloader_state = training.get("dataloader") if dataloader_state is not None and hasattr(self.dataloader, "load_state_dict"): self.dataloader.load_state_dict(dataloader_state) rng_state = training.get("rng") if rng_state is not None: self._set_rng_state(rng_state) print(f"Loaded checkpoint from {path}")