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training_code/train_dpo.py

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+"""
+DPO (Direct Preference Optimization) training for the 1B Transformer.
+
+Takes the SFT model and aligns it with human preferences using
+UltraFeedback preference pairs.
+
+DPO Loss:
+  L = -log sigma(beta * (log pi(yw|x)/pi_ref(yw|x) - log pi(yl|x)/pi_ref(yl|x)))
+
+Launch: torchrun --nproc_per_node=8 train_dpo.py
+"""
+
+import os
+import sys
+import math
+import time
+import json
+import datetime
+
+import torch
+import torch.nn.functional as F
+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.dpo_data import DPODataset, dpo_collate_fn
+
+
+# === Config ===
+SFT_CHECKPOINT = "/jfs/deepak-kumar/checkpoints_sft/sft_final.pt"
+DPO_CHECKPOINT_DIR = "/jfs/deepak-kumar/checkpoints_dpo"
+LOG_DIR = "/home/jovyan/training/logs"
+DATA_CACHE = "/jfs/deepak-kumar/data"
+
+NUM_EPOCHS = 1
+BATCH_SIZE_PER_GPU = 2
+GRADIENT_ACCUMULATION = 4       # effective batch = 2 * 8 * 4 = 64
+MAX_SEQ_LEN = 1024
+LEARNING_RATE = 5e-7            # very low LR for DPO
+MIN_LR = 1e-7
+WARMUP_STEPS = 100
+WEIGHT_DECAY = 0.01
+GRAD_CLIP = 1.0
+BETA = 0.1                      # DPO temperature
+LOG_INTERVAL = 10
+SAVE_INTERVAL = 200
+
+
+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 get_per_token_logps(model, input_ids, prompt_lens):
+    """
+    Compute sum of log probabilities for response tokens only.
+    input_ids: [B, S] full sequence (prompt + response)
+    prompt_lens: [B] where response starts
+    Returns: [B] sum of log probs over response tokens
+    """
+    # Clone input to avoid inplace issues with shared RoPE buffers
+    inp = input_ids[:, :-1].contiguous()
+    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+        logits, _ = model(inp)
+
+    labels = input_ids[:, 1:].contiguous()
+    log_probs = F.log_softmax(logits.float(), dim=-1)
+    token_logps = log_probs.gather(2, labels.unsqueeze(2)).squeeze(2)
+
+    B, S = token_logps.shape
+    mask = torch.zeros_like(token_logps)
+    for b in range(B):
+        pl = prompt_lens[b].item()
+        response_start = max(0, pl - 1)
+        seq_len = (labels[b] != 0).sum().item()
+        mask[b, response_start:seq_len] = 1.0
+
+    return (token_logps * mask).sum(dim=1)
+
+
+def dpo_loss(policy_chosen_logps, policy_rejected_logps,
+             ref_chosen_logps, ref_rejected_logps, beta=0.1):
+    """Compute DPO loss and metrics."""
+    chosen_rewards = beta * (policy_chosen_logps - ref_chosen_logps)
+    rejected_rewards = beta * (policy_rejected_logps - ref_rejected_logps)
+
+    logits = chosen_rewards - rejected_rewards
+    loss = -F.logsigmoid(logits).mean()
+
+    with torch.no_grad():
+        chosen_better = (chosen_rewards > rejected_rewards).float().mean()
+        reward_margin = (chosen_rewards - rejected_rewards).mean()
+
+    return loss, chosen_better.item(), reward_margin.item()
+
+
+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(DPO_CHECKPOINT_DIR, exist_ok=True)
+        os.makedirs(LOG_DIR, exist_ok=True)
+        print("=" * 70)
+        print("  DPO: PREFERENCE ALIGNMENT FOR 1B TRANSFORMER")
+        print("=" * 70)
+
+    tokenizer = get_tokenizer()
+    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)
+
+    model_config = ModelConfig()
+    model_config.vocab_size = len(tokenizer)
+
+    if rank == 0:
+        print(f"[Init] Loading SFT model from {SFT_CHECKPOINT}")
+
+    # Policy model (trainable)
+    policy = Transformer(model_config)
+    ckpt = torch.load(SFT_CHECKPOINT, map_location="cpu", weights_only=False)
+    policy.load_state_dict(ckpt["model"])
+    sft_step = ckpt.get("step", 0)
+    if rank == 0:
+        print(f"[Init] SFT model loaded (step {sft_step})")
+
+    # Reference model (frozen copy)
+    ref_model = Transformer(model_config)
+    ref_model.load_state_dict(ckpt["model"])
+    del ckpt
+
+    policy = policy.to(device)
+    ref_model = ref_model.to(device).bfloat16()
+    ref_model.eval()
+    for p in ref_model.parameters():
+        p.requires_grad = False
+
+    policy = DDP(policy, device_ids=[local_rank])
+
+    if rank == 0:
+        n = sum(p.numel() for p in policy.parameters())
+        print(f"[Init] Params: {n:,} | GPUs: {world_size}x H100")
+        print(f"[Init] Beta: {BETA} | LR: {LEARNING_RATE}")
+
+    # Dataset
+    dataset = DPODataset(
+        tokenizer=tokenizer,
+        max_seq_len=MAX_SEQ_LEN,
+        split="train",
+        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: dpo_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):,} preference pairs")
+        print(f"[Init] Effective batch: {eff_batch} | Steps/epoch: {steps_per_epoch}")
+        print(f"[Init] Total steps: {total_steps}")
+        print("-" * 70)
+
+    decay_params = [p for n, p in policy.named_parameters() if p.dim() >= 2 and p.requires_grad]
+    nodecay_params = [p for n, p in policy.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)
+
+    policy.train()
+    global_step = 0
+    running_loss = 0.0
+    running_acc = 0.0
+    running_margin = 0.0
+    t0 = time.time()
+
+    log_file = open(os.path.join(LOG_DIR, "dpo_log.jsonl"), "w") if rank == 0 else None
+
+    for epoch in range(NUM_EPOCHS):
+        sampler.set_epoch(epoch)
+        data_iter = iter(dataloader)
+
+        if rank == 0:
+            print(f"\n[Epoch {epoch + 1}/{NUM_EPOCHS}]")
+
+        while True:
+            optimizer.zero_grad(set_to_none=True)
+            batch_loss = 0.0
+            batch_acc = 0.0
+            batch_margin = 0.0
+            valid_micros = 0
+
+            for _ in range(GRADIENT_ACCUMULATION):
+                try:
+                    batch = next(data_iter)
+                except StopIteration:
+                    break
+
+                chosen_ids = batch["chosen_ids"].to(device, non_blocking=True)
+                rejected_ids = batch["rejected_ids"].to(device, non_blocking=True)
+                prompt_lens = batch["prompt_lens"].to(device, non_blocking=True)
+
+                policy_chosen_logps = get_per_token_logps(policy, chosen_ids, prompt_lens)
+                policy_rejected_logps = get_per_token_logps(policy, rejected_ids, prompt_lens)
+
+                with torch.no_grad():
+                    ref_chosen_logps = get_per_token_logps(ref_model, chosen_ids, prompt_lens)
+                    ref_rejected_logps = get_per_token_logps(ref_model, rejected_ids, prompt_lens)
+
+                loss, acc, margin = dpo_loss(
+                    policy_chosen_logps, policy_rejected_logps,
+                    ref_chosen_logps, ref_rejected_logps,
+                    beta=BETA,
+                )
+                loss = loss / GRADIENT_ACCUMULATION
+                loss.backward()
+
+                batch_loss += loss.item()
+                batch_acc += acc
+                batch_margin += margin
+                valid_micros += 1
+
+            if valid_micros == 0:
+                break
+
+            torch.nn.utils.clip_grad_norm_(policy.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
+            running_acc += batch_acc / valid_micros
+            running_margin += batch_margin / valid_micros
+
+            if global_step % LOG_INTERVAL == 0:
+                avg_loss = running_loss / LOG_INTERVAL
+                avg_acc = running_acc / LOG_INTERVAL
+                avg_margin = running_margin / LOG_INTERVAL
+                elapsed = time.time() - t0
+                pct = 100.0 * global_step / total_steps
+                eta = (elapsed / max(global_step, 1)) * (total_steps - global_step)
+
+                if rank == 0:
+                    gpu_mem = torch.cuda.max_memory_allocated(device) / 1e9
+                    print(
+                        f"  [Step {global_step:>5d}/{total_steps}] "
+                        f"loss={avg_loss:.4f} | acc={avg_acc:.1%} | "
+                        f"margin={avg_margin:.3f} | 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, "loss": round(avg_loss, 4),
+                            "accuracy": round(avg_acc, 4),
+                            "reward_margin": round(avg_margin, 4),
+                            "lr": lr, "elapsed_s": round(elapsed, 1),
+                        }) + "\n")
+                        log_file.flush()
+
+                running_loss = 0.0
+                running_acc = 0.0
+                running_margin = 0.0
+
+            if global_step % SAVE_INTERVAL == 0:
+                dist.barrier()
+                if rank == 0:
+                    path = os.path.join(DPO_CHECKPOINT_DIR, f"dpo_step_{global_step}.pt")
+                    torch.save({
+                        "step": global_step,
+                        "model": policy.module.state_dict(),
+                        "config": model_config.__dict__,
+                        "vocab_size": model_config.vocab_size,
+                    }, path)
+                    print(f"  >> Checkpoint: {path}", flush=True)
+                dist.barrier()
+
+    # Final save
+    dist.barrier()
+    if rank == 0:
+        final_path = os.path.join(DPO_CHECKPOINT_DIR, "dpo_final.pt")
+        torch.save({
+            "step": global_step,
+            "model": policy.module.state_dict(),
+            "config": model_config.__dict__,
+            "vocab_size": model_config.vocab_size,
+        }, final_path)
+        total_time = time.time() - t0
+        print("=" * 70)
+        print(f"  DPO 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()