train.py

#!/usr/bin/env python3
"""
Training script for Circuit Transformer.

Usage:
    python circuits/train.py --data hf:roneneldan/TinyStories --preset tiny --epochs 1 --gpu 0
    python circuits/train.py --data path/to/corpus.txt --dims 256 --layers 6 --fp16
"""

import gc
import os
import time
import math
import random
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import GradScaler
from torch.amp import autocast

from .config import CircuitConfig, parse_args
from .model import CircuitTransformer, count_parameters
from .mirrored import MirroredConfig, MirroredTransformer, count_mirrored_parameters
from .graft_g2lu import G2LU_GraftedModel, save_g2lu_checkpoint
from .layers import build_word_start_table, compute_word_positions
from .data import get_tokenizer, load_data, create_dataloader

def corrupt_tokens(input_ids, ratio, vocab_size):
    """Replace random tokens with random vocab tokens for denoising autoencoder.

    Returns (corrupted_ids, mask) where mask is True at corrupted positions.
    """
    mask = torch.rand(input_ids.shape, device=input_ids.device) < ratio
    mask[:, 0] = False  # never corrupt first token (BOS/start)
    random_tokens = torch.randint(0, vocab_size, input_ids.shape, device=input_ids.device)
    corrupted = input_ids.clone()
    corrupted[mask] = random_tokens[mask]
    return corrupted, mask


@torch.no_grad()
def evaluate(config, model, dataloader, device, use_amp=False, amp_dtype=torch.float16, mid_run_eval=False,
             word_start_table=None):
    """Run validation and return avg loss + perplexity."""
    model.eval()
    total_loss = 0.0
    n_batches = 0

    for batch in dataloader:
        input_ids = batch["input_ids"].to(device)
        labels = batch["labels"].to(device)
        word_positions = None
        if word_start_table is not None:
            word_positions = compute_word_positions(input_ids, word_start_table)

        if use_amp:
            with autocast('cuda', dtype=amp_dtype):
                output = model(input_ids, labels=labels, word_positions=word_positions)
        else:
            output = model(input_ids, labels=labels, word_positions=word_positions)

        total_loss += output["loss"].item()
        n_batches += 1

        if n_batches % (config.log_every * 10)  == 0:
            avg_loss = total_loss / max(n_batches, 1)
            ppl = math.exp(min(avg_loss, 20))
            print(
                f"batch {n_batches:6d}/{len(dataloader):6d} | "
                f"Loss {total_loss / n_batches:.4f} | "
                f"PPL {ppl:8.2f}"
            )

        if mid_run_eval and n_batches >= 1500 :
            break

    if not mid_run_eval:
        model.train()

    avg_loss = total_loss / max(n_batches, 1)
    ppl = math.exp(min(avg_loss, 20))  # cap to avoid overflow

    return avg_loss, ppl


def get_lr(step: int, warmup_steps: int, max_steps: int, max_lr: float, min_lr: float = 0.0, delay: int = 0) -> float:
    """Cosine learning rate schedule with warmup and optional delay.

    With delay > 0, the schedule is shifted:
      Steps 0..delay:                    LR = 0 (frozen)
      Steps delay..delay+warmup:         linear ramp 0 → max_lr
      Steps delay+warmup..max_steps:     cosine decay max_lr → min_lr
    """
    if step < delay:
        return 0.0
    effective_step = step - delay
    effective_max = max(1, max_steps - delay)
    if effective_step < warmup_steps:
        return max_lr * effective_step / warmup_steps
    if effective_step >= effective_max:
        return min_lr
    progress = (effective_step - warmup_steps) / (effective_max - warmup_steps)
    return min_lr + 0.5 * (max_lr - min_lr) * (1 + math.cos(math.pi * progress))


def save_checkpoint(
    model: nn.Module,
    optimizer: torch.optim.Optimizer,
    step: int,
    epoch: int,
    loss: float,
    config,
    path: str,
    model_type: str = "standard",
    epoch_step: int = 0,
    best_val_loss: float | None = None,
    scaler=None,
    tokenizer_name: str = "gpt2",
):
    """Save training checkpoint.

    Args:
        epoch: Next epoch to start on resume (completed epoch count).
        epoch_step: Batches already processed in `epoch` (0 if epoch is complete).
        optimizer_mid: Middle optimizer for dual-path training (optional).
    """
    checkpoint = {
        "model": model.state_dict(),
        "optimizer": optimizer.state_dict(),
        "step": step,
        "epoch": epoch,
        "epoch_step": epoch_step,
        "loss": loss,
        "config": config.to_dict(),
        "model_type": model_type,
        "tokenizer_name": tokenizer_name,
    }
    if best_val_loss is not None:
        checkpoint["best_val_loss"] = best_val_loss
    if scaler is not None:
        checkpoint["scaler"] = scaler.state_dict()
    torch.save(checkpoint, path)


def _migrate_state_dict(state_dict: dict, model: nn.Module) -> dict:
    """Migrate checkpoint state_dict to match current model architecture.

    Handles upgrades like SwiGLU → MirroredSwiGLU (dual_gate_middle).
    """
    model_keys = set(model.state_dict().keys())
    ckpt_keys = set(state_dict.keys())

    missing = model_keys - ckpt_keys
    unexpected = ckpt_keys - model_keys

    if not missing and not unexpected:
        return state_dict  # perfect match, no migration needed

    migrated = dict(state_dict)
    migrations = []

    # SwiGLU → MirroredSwiGLU: w3 → gate_expand (dual_gate_middle upgrade)
    for key in list(unexpected):
        if ".ffn.gate_expand.weight" in key:
            new_key = key.replace(".ffn.gate_expand.weight", ".ffn.w3.weight")
            if new_key in missing:
                migrated[new_key] = migrated.pop(key)
                missing.discard(new_key)
                unexpected.discard(key)
                migrations.append(f"  {key} → {new_key}")
        if ".ffn.gate_compress.weight" in key:
            new_key = key.replace(".ffn.gate_compress.weight", ".ffn.w4.weight")
            if new_key in missing:
                migrated[new_key] = migrated.pop(key)
                missing.discard(new_key)
                unexpected.discard(key)
                migrations.append(f"  {key} → {new_key}")

    if migrations:
        print(f"State dict migration ({len(migrations)} keys renamed):")
        for m in migrations:
            print(m)
        # Report remaining missing keys (freshly initialized)
        still_missing = model_keys - set(migrated.keys())
        if still_missing:
            print(f"  New parameters (freshly initialized): {len(still_missing)}")
            for k in sorted(still_missing):
                print(f"    {k}")

    return migrated


def load_checkpoint(path: str, model: nn.Module, optimizer: torch.optim.Optimizer = None,
                    scaler=None, reset:bool = False):
    """Load training checkpoint. Returns dict with resume info."""
    checkpoint = torch.load(path, map_location="cpu", weights_only=False)
    state_dict = _migrate_state_dict(checkpoint["model"], model)
    model.load_state_dict(state_dict, strict=False)
    if not reset:
        if optimizer is not None and "optimizer" in checkpoint:
            optimizer.load_state_dict(checkpoint["optimizer"])
        if scaler is not None and "scaler" in checkpoint:
            scaler.load_state_dict(checkpoint["scaler"])
    return {
        "step": checkpoint.get("step", 0),
        "epoch": checkpoint.get("epoch", 0),
        "epoch_step": checkpoint.get("epoch_step", 0),
        "best_val_loss": checkpoint.get("best_val_loss", float("inf")),
    }


def train():
    config, args = parse_args()

    # Setup device
    device = torch.device(f"cuda:{config.gpu}" if torch.cuda.is_available() else "cpu")
    print(f"Device: {device}")

    # Load tokenizer and data
    print(f"Loading data from: {args.data}")
    model_type = args.arch
    tokenizer_name = getattr(args, 'tokenizer', 'gpt2')
    if model_type == "graft_g2lu":
        tokenizer_name = args.pretrained
    tokenizer = get_tokenizer(tokenizer_name)
    config.vocab_size = len(tokenizer)
    print(f"Tokenizer: {tokenizer_name} (vocab_size={config.vocab_size})")
    cache_dir = None if args.no_cache else args.cache_dir
    dataset = load_data(
        args.data,
        tokenizer,
        config.max_seq_len,
        text_column=args.text_column,
        num_samples=args.num_samples,
        cache_dir=cache_dir,
        data_format=args.data_format,
    )
    print(f"Loaded {len(dataset):,} chunks")

    # Train/val split
    val_split = args.val_split
    if val_split > 0 and len(dataset) > 20:
        train_dataset, val_dataset = dataset.split(val_split)
        print(f"Split: {len(train_dataset):,} train / {len(val_dataset):,} val ({val_split:.0%})")
    else:
        train_dataset = dataset
        val_dataset = None

    # Create dataloaders
    dataloader = create_dataloader(
        train_dataset,
        config.batch_size,
        shuffle=True,
    )
    val_dataloader = None
    if val_dataset is not None:
        val_dataloader = create_dataloader(
            val_dataset,
            config.batch_size,
            shuffle=False,
        )

    # Create model
    if model_type == "mirrored":
        model_config = MirroredConfig(
            vocab_size=config.vocab_size,
            hidden_size=config.hidden_size,
            num_heads=config.num_heads,
            num_kv_heads=config.num_kv_heads,
            num_layers=config.num_layers,
            n_middle=args.n_middle,
            max_seq_len=config.max_seq_len,
            dropout=config.dropout,
            use_g2lu=not getattr(args, 'no_g2lu', False),
            aux_skip_k=getattr(args, 'aux_skip', 0),
            aux_skip_weight=getattr(args, 'aux_weight', 0.1),
            word_rope_dims=getattr(config, 'word_rope_dims', 0),
            word_rope_base=getattr(config, 'word_rope_base', 10.0),
            embed_dim=getattr(config, 'embed_dim', 0),
            head_dim=getattr(config, 'head_dim', 0),
        )
        model = MirroredTransformer(model_config).to(device)
        param_info = count_mirrored_parameters(model)
        num_params = param_info["unique"]
        print(f"Model: MirroredTransformer")
        print(f"  Virtual layers: {model.total_virtual_layers} ({model_config.n_mirror} mirror pairs + {model_config.n_middle} middle)")
        print(f"  Parameters: {num_params:,} ({num_params/1e6:.1f}M unique)")
        print(f"  Shared FFN base: {param_info['shared_ffn_base']:,}")
        print(f"  Direction gates: {param_info['direction_gates']:,}")
        print(f"  FFN gating: {'G²LU (nested dual gate)' if model_config.use_g2lu else 'SwiGLU (vanilla)'}")
        if model_config.num_kv_heads is not None:
            print(f"  GQA: {model_config.num_heads}Q / {model_config.num_kv_heads}KV ({model_config.num_heads // model_config.num_kv_heads}:1 ratio)")
        if model_config.aux_skip_k > 0:
            print(f"  Aux skip prediction: t+{model_config.aux_skip_k} (weight={model_config.aux_skip_weight})")
        if getattr(model_config, 'embed_dim', 0) > 0:
            std_embed = config.vocab_size * config.hidden_size
            fact_embed = config.vocab_size * model_config.embed_dim + model_config.embed_dim * config.hidden_size
            print(f"  Factorized embedding: {model_config.embed_dim} → {config.hidden_size} (saves {(std_embed - fact_embed):,} params)")
        if getattr(model_config, 'head_dim', 0) > 0:
            std_head = config.hidden_size * config.vocab_size
            mlp_head = config.hidden_size * model_config.head_dim + model_config.head_dim * config.vocab_size
            print(f"  MLP head: {config.hidden_size} → {model_config.head_dim} → vocab (saves {(std_head - mlp_head):,} params)")
    elif model_type == "graft_g2lu":
        assert args.pretrained, "--pretrained is required for graft_g2lu architecture"
        amp_dtype = torch.bfloat16 if config.bf16 else (torch.float16 if config.fp16 else torch.float32)
        model = G2LU_GraftedModel(
            pretrained_name=args.pretrained,
            align_weight=args.align_weight,
            warmup_steps=args.graft_warmup,
            device=device,
            dtype=amp_dtype,
        )
        model_config = None  # No CircuitConfig for HF models
        num_params = sum(p.numel() for p in model.model.parameters() if p.requires_grad)
    else:
        model_config = config
        model = CircuitTransformer(config).to(device)
        num_params = count_parameters(model)
        print(f"Model: CircuitTransformer")
        print(f"  Parameters: {num_params:,} ({num_params/1e6:.1f}M)")
        if getattr(config, 'aux_skip_k', 0) > 0:
            print(f"  Aux skip prediction: t+{config.aux_skip_k} (weight={config.aux_skip_weight})")
        if getattr(config, 'embed_dim', 0) > 0:
            std_embed = config.vocab_size * config.hidden_size
            fact_embed = config.vocab_size * config.embed_dim + config.embed_dim * config.hidden_size
            print(f"  Factorized embedding: {config.embed_dim} → {config.hidden_size} (saves {(std_embed - fact_embed):,} params)")
        if getattr(config, 'head_dim', 0) > 0:
            std_head = config.hidden_size * config.vocab_size
            mlp_head = config.hidden_size * config.head_dim + config.head_dim * config.vocab_size
            print(f"  MLP head: {config.hidden_size} → {config.head_dim} → vocab (saves {(std_head - mlp_head):,} params)")

    # Build word-position table if enabled
    word_rope_dims = getattr(config, 'word_rope_dims', 0)
    if word_rope_dims > 0:
        word_start_table = build_word_start_table(tokenizer, len(tokenizer)).to(device)
        print(f"  Word-position RoPE: {word_rope_dims} dims, base={getattr(config, 'word_rope_base', 10.0)}")
        print(f"  Word starters in vocab: {word_start_table.sum().item():,} / {len(tokenizer):,}")
    else:
        word_start_table = None

    # Keep raw reference for set_gate_step (torch.compile wraps the model)
    raw_model = model

    # Optionally compile
    if config.compile and hasattr(torch, "compile"):
        print("Compiling model with torch.compile...")
        model = torch.compile(raw_model)

    # Optimizer — with optional staggered warmup and dual-path training
    grad_accum = getattr(args, 'grad_accum', 1)

    opt_params = list(raw_model.trainable_parameters()) if model_type == "graft_g2lu" else model.parameters()
    optimizer = torch.optim.AdamW(
        opt_params,
        lr=config.learning_rate,
        weight_decay=config.weight_decay,
        betas=(0.9, 0.95),
    )

    # Mixed precision
    use_amp = (config.fp16 or config.bf16) and device.type == "cuda"
    amp_dtype = torch.bfloat16 if config.bf16 else torch.float16
    scaler = GradScaler() if (config.fp16 and use_amp) else None
    if use_amp:
        print(f"  Mixed precision: {'BF16' if config.bf16 else 'FP16'}" +
              (" (no scaler)" if scaler is None else " (with GradScaler)"))

    # Resume from checkpoint
    start_step = 0
    start_epoch = 0
    skip_batches = 0
    best_val_loss = float("inf")
    if args.resume:
        print(f"Resuming from: {args.resume}")
        resume_info = load_checkpoint(args.resume, model, optimizer, scaler, args.reset)
        if not args.reset:
            start_step = resume_info["step"]
            start_epoch = resume_info["epoch"]
            skip_batches = resume_info["epoch_step"]
        best_val_loss = resume_info["best_val_loss"]
        print(f"Resumed at step {start_step}, epoch {start_epoch}" +
              (f", skipping {skip_batches} batches" if skip_batches > 0 else ""))
        if best_val_loss < float("inf"):
            print(f"  Best val loss so far: {best_val_loss:.4f} (PPL {math.exp(min(best_val_loss, 20)):.2f})")

    # Setup checkpoint directory
    checkpoint_dir = Path(config.checkpoint_dir)
    checkpoint_dir.mkdir(parents=True, exist_ok=True)

    # Training loop
    steps_per_epoch = math.ceil(len(dataloader) / grad_accum)
    max_steps = config.epochs * steps_per_epoch
    tokens_per_step = config.batch_size * grad_accum * config.max_seq_len
    total_train_tokens = config.epochs * len(dataloader) * config.batch_size * config.max_seq_len
    step = start_step
    model.train()

    print(f"\nStarting training:")
    print(f"  Epochs: {config.epochs}")
    print(f"  Batch size: {config.batch_size}" +
          (f" x {grad_accum} accum = {config.batch_size * grad_accum} effective" if grad_accum > 1 else ""))
    print(f"  Steps per epoch: {steps_per_epoch}" +
          (f" ({len(dataloader)} micro-batches)" if grad_accum > 1 else ""))
    print(f"  Total steps: {max_steps}")
    print(f"  Total tokens: {total_train_tokens:,} ({total_train_tokens/1e6:.1f}M)")
    if num_params > 0:
        print(f"  Tokens/param ratio: {total_train_tokens/num_params:.1f}x (Chinchilla=20x)")
    print(f"  Learning rate: {config.learning_rate}" +
          (f" → {config.min_lr}" if config.min_lr > 0 else ""))
    print(f"  Mixed precision: {use_amp}")
    print(f"  Validation: {'enabled' if val_dataloader else 'disabled'}")
    print()

    total_loss = 0.0
    log_steps = 0
    total_tokens_seen = step * tokens_per_step
    # best_val_loss already set in resume section above
    h_mid_buffer = None
    last_align_val = float("inf")
    start_time = time.time()

    for epoch in range(start_epoch, config.epochs):
        epoch_start = time.time()
        epoch_loss = 0.0
        epoch_steps = 0

        micro_batches = []
        epoch_micro_batches = skip_batches if epoch == start_epoch else 0

        for batch_idx, batch in enumerate(dataloader):
            # Skip already-processed batches on resume
            if epoch == start_epoch and batch_idx < skip_batches:
                continue

            micro_batches.append(batch)
            epoch_micro_batches += 1

            # Accumulate micro-batches (flush at accum boundary or epoch end)
            if len(micro_batches) < grad_accum and batch_idx < len(dataloader) - 1:
                continue

            n_micro = len(micro_batches)
            actual_tokens = n_micro * config.batch_size * config.max_seq_len

            # Update learning rate (per-group delays for staggered warmup)
            for param_group in optimizer.param_groups:
                delay = param_group.get("delay", 0)
                param_group["lr"] = get_lr(step, config.warmup_steps, max_steps, config.learning_rate, min_lr=config.min_lr, delay=delay)
            lr = optimizer.param_groups[0]["lr"]  # for logging

            loss_ed_val = None
            loss_align_val = None
            grad_norm_mid = None
            absorb_loss_val = None

            # Update blend alpha for G²LU grafting
            if model_type == "graft_g2lu":
                raw_model.set_step(step)

            # === Standard single-path training with accumulation ===
            optimizer.zero_grad()
            accum_loss = 0.0
            accum_aux = 0.0
            accum_align = 0.0

            for mb in micro_batches:
                mb_ids = mb["input_ids"].to(device)
                mb_labels = mb["labels"].to(device)
                word_positions = None
                if word_start_table is not None:
                    word_positions = compute_word_positions(mb_ids, word_start_table)
                if use_amp:
                    with autocast('cuda', dtype=amp_dtype):
                        output = model(mb_ids, labels=mb_labels, word_positions=word_positions)
                else:
                    output = model(mb_ids, labels=mb_labels, word_positions=word_positions)
                if scaler:
                    scaler.scale(output["loss"] / n_micro).backward()
                else:
                    (output["loss"] / n_micro).backward()
                accum_loss += output["loss"].item()
                if "aux_loss" in output:
                    accum_aux += output["aux_loss"].item()
                if "align_loss" in output:
                    accum_align += output["align_loss"].item()

            if scaler:
                scaler.unscale_(optimizer)
            clip_params = list(raw_model.trainable_parameters()) if model_type == "graft_g2lu" else model.parameters()
            grad_norm = nn.utils.clip_grad_norm_(clip_params, config.grad_clip).item()
            if scaler:
                scaler.step(optimizer)
                scaler.update()
            else:
                optimizer.step()
            optimizer.zero_grad()

            loss_val = accum_loss / n_micro
            aux_loss_val = accum_aux / n_micro if accum_aux > 0 else None
            align_loss_val = accum_align / n_micro if accum_align > 0 else None

            total_loss += loss_val
            epoch_loss += loss_val
            epoch_steps += 1
            log_steps += 1
            total_tokens_seen += actual_tokens
            step += 1

            # Logging
            if step % config.log_every == 0:
                avg_loss = total_loss / max(log_steps, 1)
                ppl = math.exp(min(avg_loss, 20))
                elapsed = time.time() - start_time
                tok_s = (log_steps * tokens_per_step) / max(elapsed, 1e-6)

                extra = ""
                if aux_loss_val is not None:
                    extra += f" | Aux {aux_loss_val:.3f}"
                if align_loss_val is not None:
                    extra += f" | Align {align_loss_val:.4f}"
                    
                print(
                    f"Step {step:6d} | "
                    f"Epoch {epoch+1}/{config.epochs} | "
                    f"Loss {avg_loss:.4f} | "
                    f"PPL {ppl:8.2f} | "
                    f"GradN {grad_norm:.3f} | "
                    f"LR {lr:.2e} | "
                    f"Tok/s {tok_s:.0f}"
                    f"{extra}"
                )

                total_loss = 0.0
                log_steps = 0
                start_time = time.time()

            # Checkpointing
            if step % config.save_every == 0:
                ckpt_path = checkpoint_dir / f"step_{step:06d}.pt"
                if model_type == "graft_g2lu":
                    save_g2lu_checkpoint(raw_model, optimizer, step, epoch, loss_val, str(ckpt_path),
                                        epoch_step=epoch_micro_batches, best_val_loss=best_val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
                else:
                    save_checkpoint(model, optimizer, step, epoch, loss_val, model_config, str(ckpt_path), model_type,
                                   epoch_step=epoch_micro_batches, best_val_loss=best_val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
                print(f"  Saved checkpoint: {ckpt_path}")
                gc.collect()
                torch.cuda.empty_cache()

            # Mid-training validation
            val_every = getattr(args, 'val_every', 0)
            if val_every > 0 and step % val_every == 0 and val_dataloader:
                val_loss, val_ppl = evaluate(config, model, val_dataloader, device, use_amp, amp_dtype, mid_run_eval=True, word_start_table=word_start_table)
                avg_train = epoch_loss / max(epoch_steps, 1)
                gap = val_loss - avg_train
                print(f"  [Val @ step {step}] Loss: {val_loss:.4f} | PPL: {val_ppl:.2f} | Gap: {gap:+.4f}")
                if val_loss < best_val_loss:
                    best_val_loss = val_loss
                    best_path = checkpoint_dir / "best.pt"
                    if model_type == "graft_g2lu":
                        save_g2lu_checkpoint(raw_model, optimizer, step, epoch, val_loss, str(best_path),
                                            epoch_step=epoch_micro_batches, best_val_loss=val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
                    else:
                        save_checkpoint(model, optimizer, step, epoch, val_loss, model_config, str(best_path), model_type,
                                       epoch_step=epoch_micro_batches, best_val_loss=val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
                    print(f"  New best! Saved: {best_path}")
                gc.collect()
                torch.cuda.empty_cache()

            micro_batches = []

        # --- Epoch summary ---
        epoch_elapsed = time.time() - epoch_start
        avg_epoch_loss = epoch_loss / max(epoch_steps, 1)
        epoch_ppl = math.exp(min(avg_epoch_loss, 20))

        print(f"\n{'='*70}")
        print(f"Epoch {epoch+1}/{config.epochs} complete in {epoch_elapsed:.0f}s")
        print(f"  Train loss: {avg_epoch_loss:.4f} | Train PPL: {epoch_ppl:.2f}")
        print(f"  Tokens seen: {total_tokens_seen:,} ({total_tokens_seen/1e6:.1f}M)")

        # Validation
        if val_dataloader:
            val_loss, val_ppl = evaluate(config, model, val_dataloader, device, use_amp, amp_dtype, word_start_table=word_start_table)
            gap = val_loss - avg_epoch_loss
            print(f"  Val loss:   {val_loss:.4f} | Val PPL:   {val_ppl:.2f} | Gap: {gap:+.4f}")

            if val_loss < best_val_loss:
                best_val_loss = val_loss
                best_path = checkpoint_dir / "best.pt"
                if model_type == "graft_g2lu":
                    save_g2lu_checkpoint(raw_model, optimizer, step, epoch + 1, val_loss, str(best_path),
                                        epoch_step=0, best_val_loss=val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
                else:
                    save_checkpoint(model, optimizer, step, epoch + 1, val_loss, model_config, str(best_path), model_type,
                                   epoch_step=0, best_val_loss=val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
                print(f"  New best! Saved: {best_path}")
            # Free validation tensors
            gc.collect()
            torch.cuda.empty_cache()
        print(f"{'='*70}\n")

        # Save epoch checkpoint
        ckpt_path = checkpoint_dir / f"epoch_{epoch+1:02d}.pt"
        if model_type == "graft_g2lu":
            save_g2lu_checkpoint(raw_model, optimizer, step, epoch + 1, avg_epoch_loss, str(ckpt_path),
                                epoch_step=0, best_val_loss=best_val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
        else:
            save_checkpoint(model, optimizer, step, epoch + 1, avg_epoch_loss, model_config, str(ckpt_path), model_type,
                           epoch_step=0, best_val_loss=best_val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
        gc.collect()
        torch.cuda.empty_cache()

    # Save final checkpoint
    if step == start_step:
        print(f"\nNo training performed (already at step {step}/{max_steps}).")
        print(f"  To train more epochs, increase --epochs beyond {config.epochs}.")
    else:
        final_path = checkpoint_dir / "latest.pt"
        if model_type == "graft_g2lu":
            save_g2lu_checkpoint(raw_model, optimizer, step, config.epochs, avg_epoch_loss, str(final_path),
                                epoch_step=0, best_val_loss=best_val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
        else:
            save_checkpoint(model, optimizer, step, config.epochs, avg_epoch_loss, model_config, str(final_path), model_type,
                           epoch_step=0, best_val_loss=best_val_loss, scaler=scaler, tokenizer_name=tokenizer_name)
        print(f"\nTraining complete.")
        print(f"  Final train loss: {avg_epoch_loss:.4f} | PPL: {epoch_ppl:.2f}")
        if val_dataloader:
            print(f"  Best val loss: {best_val_loss:.4f} | PPL: {math.exp(min(best_val_loss, 20)):.2f}")
        print(f"  Total tokens: {total_tokens_seen:,}")
        print(f"  Checkpoints: {final_path}")


if __name__ == "__main__":
    train()