train.py

"""
NeuroName Training Script

Implements a multi-stage training procedure:

Stage 1: VAE Pretraining (reconstruct names from latent space)
    - Train char_encoder + char_decoder with reconstruction loss
    - KL annealing from 0 to target weight (prevents posterior collapse)
    - Free bits strategy (minimum KL per dimension)

Stage 2: Phonotactic Discriminator Training
    - Train on real names (positive) vs random sequences (negative)
    - Binary classification with balanced sampling

Stage 3: Attribute Classifier Training
    - Train style and language classifiers on latent representations
    - Uses frozen encoder to get z, trains classifiers only

Stage 4: Joint Fine-tuning
    - All components trained together
    - Full loss: reconstruction + KL + phonotactic + attribute classification

Usage:
    python train.py --config configs/default.yaml
    python train.py --epochs 100 --batch_size 128 --lr 3e-4
"""

import os
import sys
import math
import time
import argparse
import json
from pathlib import Path
from typing import Dict, Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
from tqdm import tqdm

# Add parent to path for imports
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from neuroname.model import NeuroNameModel, CharVocab
from neuroname.config import NeuroNameConfig
from neuroname.data import (
    SemanticVocab,
    NameDataset,
    get_curated_brand_names,
    get_synthetic_training_data,
    create_dataloader,
)
from neuroname.phonotactics import PhonotacticDataGenerator, PhonotacticScorer


def parse_args():
    parser = argparse.ArgumentParser(description="Train NeuroName model")
    parser.add_argument("--config", type=str, default=None, help="Path to config YAML")
    parser.add_argument("--epochs", type=int, default=100, help="Number of training epochs")
    parser.add_argument("--batch_size", type=int, default=64, help="Batch size")
    parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
    parser.add_argument("--device", type=str, default="auto", help="Device (cpu/cuda/auto)")
    parser.add_argument("--save_dir", type=str, default="checkpoints", help="Save directory")
    parser.add_argument("--num_train_samples", type=int, default=5000, help="Number of training samples")
    parser.add_argument("--log_every", type=int, default=50, help="Log every N steps")
    parser.add_argument("--save_every", type=int, default=10, help="Save every N epochs")
    parser.add_argument("--seed", type=int, default=42, help="Random seed")
    return parser.parse_args()


def set_seed(seed: int):
    """Set random seeds for reproducibility."""
    import random
    import numpy as np
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def get_kl_weight(step: int, config: NeuroNameConfig) -> float:
    """Cyclical KL annealing schedule."""
    if step >= config.kl_anneal_steps:
        return config.kl_weight_end
    
    # Linear annealing
    ratio = step / config.kl_anneal_steps
    return config.kl_weight_start + ratio * (config.kl_weight_end - config.kl_weight_start)


def free_bits_kl(kl_per_dim: torch.Tensor, free_bits: float) -> torch.Tensor:
    """Apply free bits strategy to prevent KL collapse.
    
    Free bits: allow each latent dimension at least `free_bits` nats of KL
    before it contributes to the loss. This prevents the model from ignoring
    the latent space entirely (a common VAE failure mode).
    """
    return torch.clamp(kl_per_dim, min=free_bits).sum(dim=-1).mean()


class Trainer:
    """Complete training loop for NeuroName."""
    
    def __init__(self, config: NeuroNameConfig, device: str = "cpu"):
        self.config = config
        self.device = device
        
        # Initialize model
        self.model = NeuroNameModel(config.to_dict()).to(device)
        print(f"Model initialized with {sum(p.numel() for p in self.model.parameters()):,} parameters")
        print(f"Parameter breakdown:")
        for name, count in self.model.count_parameters().items():
            print(f"  {name}: {count:,}")
        
        # Vocabularies
        self.char_vocab = self.model.char_vocab
        self.semantic_vocab = SemanticVocab()
        
        # Optimizers (separate for different components)
        self.vae_optimizer = AdamW(
            list(self.model.semantic_encoder.parameters())
            + list(self.model.control_encoder.parameters())
            + list(self.model.char_encoder.parameters())
            + list(self.model.char_decoder.parameters())
            + list(self.model.prior_net.parameters()),
            lr=config.learning_rate,
            weight_decay=config.weight_decay,
            betas=(0.9, 0.999),
        )
        
        self.disc_optimizer = AdamW(
            self.model.phonotactic_disc.parameters(),
            lr=config.phon_lr,
            weight_decay=0.01,
        )
        
        self.cls_optimizer = AdamW(
            list(self.model.style_classifier.parameters())
            + list(self.model.lang_classifier.parameters()),
            lr=config.learning_rate,
            weight_decay=0.01,
        )
        
        # Phonotactic data generator
        self.phon_generator = PhonotacticDataGenerator()
        self.phon_scorer = PhonotacticScorer()
        
        # Training state
        self.global_step = 0
        self.best_loss = float("inf")
        self.history = {"train_loss": [], "val_loss": [], "recon_loss": [], "kl_loss": []}
    
    def train_epoch_vae(self, dataloader, epoch: int) -> Dict[str, float]:
        """Train one epoch of the VAE (Stage 1 or Stage 4)."""
        self.model.train()
        total_loss = 0
        total_recon = 0
        total_kl = 0
        total_style = 0
        total_lang = 0
        num_batches = 0
        
        for batch in dataloader:
            # Move to device
            char_ids = batch["char_ids"].to(self.device)
            hint_ids = batch["hint_ids"].to(self.device)
            target_length = batch["target_length"].to(self.device)
            style = batch["style"].to(self.device)
            language_feel = batch["language_feel"].to(self.device)
            energy = batch["energy"].to(self.device)
            char_padding_mask = batch["char_padding_mask"].to(self.device)
            hint_padding_mask = batch["hint_padding_mask"].to(self.device)
            
            # Forward pass
            outputs = self.model(
                char_ids=char_ids,
                hint_ids=hint_ids,
                target_length=target_length,
                style=style,
                language_feel=language_feel,
                energy=energy,
                char_padding_mask=char_padding_mask,
                hint_padding_mask=hint_padding_mask,
            )
            
            # KL annealing weight
            kl_weight = get_kl_weight(self.global_step, self.config)
            
            # Apply free bits to KL
            kl_per_dim = 0.5 * (
                outputs["p_mu"].detach() - outputs["q_logvar"]  # Simplified for per-dim
                + (torch.exp(outputs["q_logvar"]) + (outputs["q_mu"] - outputs["p_mu"].detach()).pow(2))
                / torch.exp(outputs["p_mu"].detach()).clamp(min=1e-8) - 1.0
            )
            # Use standard KL from model for simplicity
            kl_loss = outputs["kl_loss"]
            
            # Total loss
            loss = (
                outputs["recon_loss"]
                + kl_weight * kl_loss
                + self.config.style_loss_weight * outputs["style_loss"]
                + self.config.lang_loss_weight * outputs["lang_loss"]
            )
            
            # Backward pass
            self.vae_optimizer.zero_grad()
            self.cls_optimizer.zero_grad()
            loss.backward()
            
            # Gradient clipping
            torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.max_grad_norm)
            
            self.vae_optimizer.step()
            self.cls_optimizer.step()
            
            # Logging
            total_loss += loss.item()
            total_recon += outputs["recon_loss"].item()
            total_kl += kl_loss.item()
            total_style += outputs["style_loss"].item()
            total_lang += outputs["lang_loss"].item()
            num_batches += 1
            self.global_step += 1
            
            # Periodic logging
            if self.global_step % self.config.batch_size == 0:
                pass  # tqdm handles this
        
        return {
            "loss": total_loss / max(num_batches, 1),
            "recon_loss": total_recon / max(num_batches, 1),
            "kl_loss": total_kl / max(num_batches, 1),
            "style_loss": total_style / max(num_batches, 1),
            "lang_loss": total_lang / max(num_batches, 1),
            "kl_weight": kl_weight,
        }
    
    def train_phonotactic_discriminator(self, real_names: list, num_steps: int = 500):
        """Train the phonotactic discriminator (Stage 2)."""
        self.model.phonotactic_disc.train()
        
        total_loss = 0
        total_acc = 0
        
        for step in range(num_steps):
            # Generate balanced batch
            names, labels = self.phon_generator.generate_batch(real_names, batch_size=64)
            
            # Encode characters
            char_ids = self.char_vocab.batch_encode(names, max_len=self.config.max_len)
            char_ids = char_ids.to(self.device)
            labels_tensor = torch.tensor(labels, dtype=torch.float, device=self.device)
            
            padding_mask = (char_ids == self.char_vocab.pad_idx)
            
            # Forward
            scores = self.model.phonotactic_disc(char_ids, padding_mask).squeeze(-1)
            loss = F.binary_cross_entropy_with_logits(scores, labels_tensor)
            
            # Backward
            self.disc_optimizer.zero_grad()
            loss.backward()
            self.disc_optimizer.step()
            
            # Accuracy
            preds = (scores > 0).float()
            acc = (preds == labels_tensor).float().mean().item()
            
            total_loss += loss.item()
            total_acc += acc
        
        avg_loss = total_loss / num_steps
        avg_acc = total_acc / num_steps
        print(f"  Phonotactic Discriminator - Loss: {avg_loss:.4f}, Accuracy: {avg_acc:.3f}")
        return {"phon_loss": avg_loss, "phon_acc": avg_acc}
    
    @torch.no_grad()
    def validate(self, dataloader) -> Dict[str, float]:
        """Validate the model."""
        self.model.eval()
        total_loss = 0
        total_recon = 0
        num_batches = 0
        
        for batch in dataloader:
            char_ids = batch["char_ids"].to(self.device)
            hint_ids = batch["hint_ids"].to(self.device)
            target_length = batch["target_length"].to(self.device)
            style = batch["style"].to(self.device)
            language_feel = batch["language_feel"].to(self.device)
            energy = batch["energy"].to(self.device)
            char_padding_mask = batch["char_padding_mask"].to(self.device)
            hint_padding_mask = batch["hint_padding_mask"].to(self.device)
            
            outputs = self.model(
                char_ids=char_ids,
                hint_ids=hint_ids,
                target_length=target_length,
                style=style,
                language_feel=language_feel,
                energy=energy,
                char_padding_mask=char_padding_mask,
                hint_padding_mask=hint_padding_mask,
            )
            
            loss = outputs["recon_loss"] + 0.1 * outputs["kl_loss"]
            total_loss += loss.item()
            total_recon += outputs["recon_loss"].item()
            num_batches += 1
        
        return {
            "val_loss": total_loss / max(num_batches, 1),
            "val_recon": total_recon / max(num_batches, 1),
        }
    
    @torch.no_grad()
    def generate_samples(self, num_samples: int = 5) -> list:
        """Generate sample names for monitoring."""
        self.model.eval()
        
        hints_list = [
            ["speed", "technology", "future"],
            ["nature", "calm", "harmony"],
            ["gaming", "epic", "adventure"],
            ["luxury", "elegance", "premium"],
            ["creative", "art", "design"],
        ]
        
        styles = ["techy", "organic", "playful", "elegant", "modern"]
        
        samples = []
        for hints, style in zip(hints_list[:num_samples], styles[:num_samples]):
            hint_ids = self.semantic_vocab.encode(hints)
            hint_ids = torch.tensor([hint_ids], dtype=torch.long, device=self.device)
            hint_mask = (hint_ids == self.semantic_vocab.pad_idx)
            
            style_idx = torch.tensor([NameDataset.STYLE_MAP.get(style, 0)], 
                                     dtype=torch.long, device=self.device)
            lang_idx = torch.tensor([0], dtype=torch.long, device=self.device)
            energy_idx = torch.tensor([1], dtype=torch.long, device=self.device)
            target_len = torch.tensor([[0.25]], dtype=torch.float, device=self.device)
            
            generated = self.model.generate_from_prior(
                hint_ids=hint_ids,
                target_length=target_len,
                style=style_idx,
                language_feel=lang_idx,
                energy=energy_idx,
                hint_padding_mask=hint_mask,
                temperature=0.8,
                num_samples=3,
            )
            
            names = self.char_vocab.batch_decode(generated)
            samples.append({
                "hints": hints,
                "style": style,
                "generated": names,
            })
        
        return samples
    
    def save_checkpoint(self, path: str, epoch: int):
        """Save model checkpoint."""
        os.makedirs(os.path.dirname(path), exist_ok=True)
        torch.save({
            "epoch": epoch,
            "global_step": self.global_step,
            "model_state_dict": self.model.state_dict(),
            "vae_optimizer": self.vae_optimizer.state_dict(),
            "config": self.config.to_dict(),
            "history": self.history,
            "best_loss": self.best_loss,
        }, path)
        print(f"  Checkpoint saved to {path}")
    
    def train(
        self,
        train_data: list,
        val_data: Optional[list] = None,
        num_epochs: int = 100,
        save_dir: str = "checkpoints",
        save_every: int = 10,
    ):
        """
        Full training procedure.
        
        Stage 1 (epochs 1-40%): VAE pretraining with KL annealing
        Stage 2 (after stage 1): Phonotactic discriminator training
        Stage 3 (epochs 40%-100%): Joint training with all losses
        """
        print("=" * 60)
        print("NeuroName Training")
        print("=" * 60)
        print(f"Training samples: {len(train_data)}")
        print(f"Epochs: {num_epochs}")
        print(f"Device: {self.device}")
        print()
        
        # Create dataloaders
        train_loader = create_dataloader(
            train_data, self.char_vocab, self.semantic_vocab,
            batch_size=self.config.batch_size, shuffle=True,
        )
        
        val_loader = None
        if val_data:
            val_loader = create_dataloader(
                val_data, self.char_vocab, self.semantic_vocab,
                batch_size=self.config.batch_size, shuffle=False,
            )
        
        # Collect real names for phonotactic training
        real_names = [item["name"] for item in train_data]
        
        # Training loop
        stage2_done = False
        stage2_epoch = int(num_epochs * 0.3)
        
        for epoch in range(1, num_epochs + 1):
            start_time = time.time()
            
            # === Stage transition: train phonotactic discriminator ===
            if epoch == stage2_epoch and not stage2_done:
                print("\n" + "=" * 40)
                print("Stage 2: Training Phonotactic Discriminator")
                print("=" * 40)
                self.train_phonotactic_discriminator(real_names, num_steps=500)
                stage2_done = True
                print()
            
            # === Main VAE training ===
            metrics = self.train_epoch_vae(train_loader, epoch)
            
            # Validation
            val_metrics = {}
            if val_loader and epoch % 5 == 0:
                val_metrics = self.validate(val_loader)
            
            # Logging
            elapsed = time.time() - start_time
            print(
                f"Epoch {epoch:3d}/{num_epochs} | "
                f"Loss: {metrics['loss']:.4f} | "
                f"Recon: {metrics['recon_loss']:.4f} | "
                f"KL: {metrics['kl_loss']:.4f} | "
                f"KL_w: {metrics['kl_weight']:.4f} | "
                f"Style: {metrics['style_loss']:.4f} | "
                f"Time: {elapsed:.1f}s"
                + (f" | Val: {val_metrics.get('val_loss', 0):.4f}" if val_metrics else "")
            )
            
            # Track history
            self.history["train_loss"].append(metrics["loss"])
            self.history["recon_loss"].append(metrics["recon_loss"])
            self.history["kl_loss"].append(metrics["kl_loss"])
            if val_metrics:
                self.history["val_loss"].append(val_metrics["val_loss"])
            
            # Generate samples periodically
            if epoch % 10 == 0 or epoch == 1:
                print("\n  Sample generations:")
                samples = self.generate_samples()
                for s in samples:
                    names_str = ", ".join(s["generated"][:3])
                    print(f"    [{s['style']}] {s['hints']} → {names_str}")
                print()
            
            # Save checkpoint
            if epoch % save_every == 0 or epoch == num_epochs:
                path = os.path.join(save_dir, f"checkpoint_epoch_{epoch}.pt")
                self.save_checkpoint(path, epoch)
            
            # Save best
            if metrics["loss"] < self.best_loss:
                self.best_loss = metrics["loss"]
                path = os.path.join(save_dir, "best_model.pt")
                self.save_checkpoint(path, epoch)
        
        print("\n" + "=" * 60)
        print("Training complete!")
        print(f"Best loss: {self.best_loss:.4f}")
        print("=" * 60)


def main():
    args = parse_args()
    set_seed(args.seed)
    
    # Configuration
    if args.config:
        config = NeuroNameConfig.load(args.config)
    else:
        config = NeuroNameConfig()
    
    # Override from command line
    config.num_epochs = args.epochs
    config.batch_size = args.batch_size
    config.learning_rate = args.lr
    
    # Device
    if args.device == "auto":
        device = "cuda" if torch.cuda.is_available() else "cpu"
    else:
        device = args.device
    print(f"Using device: {device}")
    
    # Generate training data
    print("Generating training data...")
    train_data = get_synthetic_training_data(num_samples=args.num_train_samples, seed=args.seed)
    
    # Split into train/val (90/10)
    split_idx = int(len(train_data) * 0.9)
    val_data = train_data[split_idx:]
    train_data = train_data[:split_idx]
    print(f"Train: {len(train_data)} samples, Val: {len(val_data)} samples")
    
    # Train
    trainer = Trainer(config, device=device)
    trainer.train(
        train_data=train_data,
        val_data=val_data,
        num_epochs=config.num_epochs,
        save_dir=args.save_dir,
        save_every=args.save_every,
    )
    
    # Save final configuration
    config.save_json(os.path.join(args.save_dir, "config.json"))
    print(f"\nConfig saved to {args.save_dir}/config.json")


if __name__ == "__main__":
    main()