training_code/train_sft.py

"""
SFT (Supervised Fine-Tuning) script for the 1B Transformer.

Takes the pretrained base model and fine-tunes it on instruction-response
conversations from UltraChat 200K.

Launch: torchrun --nproc_per_node=8 train_sft.py
"""

import os
import sys
import math
import time
import json
import datetime

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from model.config import ModelConfig
from model.transformer import Transformer
from model.data import get_tokenizer
from model.sft_data import SFTDataset, sft_collate_fn


# === Config ===
BASE_CHECKPOINT = "/jfs/deepak-kumar/checkpoints/step_19000.pt"
SFT_CHECKPOINT_DIR = "/jfs/deepak-kumar/checkpoints_sft"
LOG_DIR = "/home/jovyan/training/logs"
DATA_CACHE = "/jfs/deepak-kumar/data"

NUM_EPOCHS = 2
BATCH_SIZE_PER_GPU = 4
GRADIENT_ACCUMULATION = 4     # effective batch = 4 * 8 * 4 = 128
MAX_SEQ_LEN = 2048
LEARNING_RATE = 2e-5          # much lower than pretraining — we're fine-tuning
MIN_LR = 2e-6
WARMUP_STEPS = 200
WEIGHT_DECAY = 0.01
GRAD_CLIP = 1.0
LOG_INTERVAL = 10
SAVE_INTERVAL = 500


def get_cosine_lr(step, warmup_steps, total_steps, max_lr, min_lr):
    if step < warmup_steps:
        return max_lr * step / max(warmup_steps, 1)
    progress = (step - warmup_steps) / max(total_steps - warmup_steps, 1)
    return min_lr + 0.5 * (max_lr - min_lr) * (1 + math.cos(math.pi * progress))


def main():
    dist.init_process_group("nccl", timeout=datetime.timedelta(minutes=30))
    rank = int(os.environ.get("RANK", 0))
    local_rank = int(os.environ.get("LOCAL_RANK", 0))
    world_size = int(os.environ.get("WORLD_SIZE", 1))
    torch.cuda.set_device(local_rank)
    device = torch.device(f"cuda:{local_rank}")

    if rank == 0:
        os.makedirs(SFT_CHECKPOINT_DIR, exist_ok=True)
        os.makedirs(LOG_DIR, exist_ok=True)
        print("=" * 70)
        print("  SFT: INSTRUCTION FINE-TUNING 1B TRANSFORMER")
        print("=" * 70)

    # Tokenizer
    tokenizer = get_tokenizer()

    # Load base model
    model_config = ModelConfig()
    torch.manual_seed(42)
    model = Transformer(model_config)

    if rank == 0:
        print(f"[Init] Loading base model from {BASE_CHECKPOINT}")
    ckpt = torch.load(BASE_CHECKPOINT, map_location="cpu", weights_only=False)
    model.load_state_dict(ckpt["model"])
    base_step = ckpt.get("step", 0)
    base_loss = ckpt.get("loss", "?")
    if rank == 0:
        print(f"[Init] Base model: step={base_step}, pretrain_loss={base_loss}")
    del ckpt

    # Add chat tokens to embedding — expand vocab if needed
    special_tokens = ["<|user|>", "<|assistant|>", "<|end|>"]
    vocab = tokenizer.get_vocab()
    new_tokens = [t for t in special_tokens if t not in vocab]
    if new_tokens:
        tokenizer.add_tokens(new_tokens, special_tokens=True)

    new_vocab_size = len(tokenizer)
    if new_vocab_size > model_config.vocab_size:
        if rank == 0:
            print(f"[Init] Expanding vocab: {model_config.vocab_size} -> {new_vocab_size}")

        old_emb_weight = model.tok_embeddings.weight.data
        model.tok_embeddings = torch.nn.Embedding(new_vocab_size, model_config.hidden_dim)
        model.tok_embeddings.weight.data[:model_config.vocab_size] = old_emb_weight
        # Init new token embeddings as mean of existing (better than random)
        mean_emb = old_emb_weight.mean(dim=0)
        for i in range(model_config.vocab_size, new_vocab_size):
            model.tok_embeddings.weight.data[i] = mean_emb

        old_output_weight = model.output.weight.data
        model.output = torch.nn.Linear(model_config.hidden_dim, new_vocab_size, bias=False)
        model.output.weight.data[:model_config.vocab_size] = old_output_weight

        model.config.vocab_size = new_vocab_size

    model = model.to(device)
    model = DDP(model, device_ids=[local_rank])

    if rank == 0:
        n = sum(p.numel() for p in model.parameters())
        print(f"[Init] Params: {n:,} | GPUs: {world_size}x H100")

    # Dataset (only load on each process)
    dataset = SFTDataset(
        tokenizer=tokenizer,
        max_seq_len=MAX_SEQ_LEN,
        split="train_sft",
        cache_dir=DATA_CACHE,
    )

    sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=True)
    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=BATCH_SIZE_PER_GPU,
        sampler=sampler,
        num_workers=4,
        pin_memory=True,
        collate_fn=lambda b: sft_collate_fn(b, pad_id=tokenizer.pad_token_id),
    )

    steps_per_epoch = len(dataloader) // GRADIENT_ACCUMULATION
    total_steps = steps_per_epoch * NUM_EPOCHS

    if rank == 0:
        eff_batch = BATCH_SIZE_PER_GPU * world_size * GRADIENT_ACCUMULATION
        print(f"[Init] Dataset: {len(dataset):,} examples")
        print(f"[Init] Effective batch: {eff_batch} | Steps/epoch: {steps_per_epoch}")
        print(f"[Init] Total steps: {total_steps} | Epochs: {NUM_EPOCHS}")
        print(f"[Init] LR: {LEARNING_RATE} → {MIN_LR} (cosine)")
        print("-" * 70)

    # Optimizer — lower LR for fine-tuning
    decay_params = [p for n, p in model.named_parameters() if p.dim() >= 2 and p.requires_grad]
    nodecay_params = [p for n, p in model.named_parameters() if p.dim() < 2 and p.requires_grad]
    optimizer = torch.optim.AdamW([
        {"params": decay_params, "weight_decay": WEIGHT_DECAY},
        {"params": nodecay_params, "weight_decay": 0.0},
    ], lr=LEARNING_RATE, betas=(0.9, 0.95), fused=True)

    # Training
    model.train()
    global_step = 0
    running_loss = 0.0
    t0 = time.time()
    step_t0 = time.time()

    log_file = open(os.path.join(LOG_DIR, "sft_log.jsonl"), "w") if rank == 0 else None

    for epoch in range(NUM_EPOCHS):
        sampler.set_epoch(epoch)
        data_iter = iter(dataloader)
        micro_step = 0

        if rank == 0:
            print(f"\n[Epoch {epoch + 1}/{NUM_EPOCHS}]")

        while True:
            optimizer.zero_grad(set_to_none=True)
            batch_loss = 0.0

            for _ in range(GRADIENT_ACCUMULATION):
                try:
                    input_ids, labels = next(data_iter)
                except StopIteration:
                    break

                input_ids = input_ids.to(device, non_blocking=True)
                labels = labels.to(device, non_blocking=True)

                with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
                    _, loss = model(input_ids, labels)
                    loss = loss / GRADIENT_ACCUMULATION

                loss.backward()
                batch_loss += loss.item()
                micro_step += 1

            if batch_loss == 0:
                break

            torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)

            lr = get_cosine_lr(global_step, WARMUP_STEPS, total_steps, LEARNING_RATE, MIN_LR)
            for pg in optimizer.param_groups:
                pg["lr"] = lr

            optimizer.step()
            global_step += 1
            running_loss += batch_loss

            if global_step % LOG_INTERVAL == 0:
                dt = time.time() - step_t0
                avg = running_loss / LOG_INTERVAL
                elapsed = time.time() - t0
                pct = 100.0 * global_step / total_steps

                if rank == 0:
                    gpu_mem = torch.cuda.max_memory_allocated(device) / 1e9
                    eta = (elapsed / max(global_step, 1)) * (total_steps - global_step)
                    print(
                        f"  [Step {global_step:>5d}/{total_steps}] "
                        f"loss={avg:.4f} | lr={lr:.2e} | "
                        f"GPU={gpu_mem:.1f}GB | {pct:.1f}% | ETA={eta/60:.0f}m",
                        flush=True,
                    )
                    if log_file:
                        log_file.write(json.dumps({
                            "step": global_step, "epoch": epoch + 1,
                            "loss": round(avg, 4), "lr": lr,
                            "elapsed_s": round(elapsed, 1),
                        }) + "\n")
                        log_file.flush()

                running_loss = 0.0
                step_t0 = time.time()

            if global_step % SAVE_INTERVAL == 0:
                dist.barrier()
                if rank == 0:
                    path = os.path.join(SFT_CHECKPOINT_DIR, f"sft_step_{global_step}.pt")
                    torch.save({
                        "step": global_step,
                        "model": model.module.state_dict(),
                        "config": model_config.__dict__,
                        "vocab_size": new_vocab_size,
                    }, path)
                    print(f"  >> Checkpoint: {path}", flush=True)
                dist.barrier()

    # Final save
    dist.barrier()
    if rank == 0:
        final_path = os.path.join(SFT_CHECKPOINT_DIR, "sft_final.pt")
        torch.save({
            "step": global_step,
            "model": model.module.state_dict(),
            "config": model_config.__dict__,
            "vocab_size": new_vocab_size,
        }, final_path)
        total_time = time.time() - t0
        print("=" * 70)
        print(f"  SFT COMPLETE")
        print(f"  Steps: {global_step:,} | Epochs: {NUM_EPOCHS}")
        print(f"  Time: {total_time/60:.1f} minutes")
        print(f"  Final model: {final_path}")
        print("=" * 70)
        if log_file:
            log_file.close()

    dist.destroy_process_group()


if __name__ == "__main__":
    main()