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b9301f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 | """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}")
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