step5_data_parallel_naive/train.py [26:135]:
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def train_step(model, dataloader, device):
    acc_loss = 0.0

    requires_grad_sync = pgm.process_group_manager.dp_world_size > 1

    for i in range(dataloader.grad_acc_steps):
        # get the next batch
        batch = next(dataloader)
        input_ids = batch["input_ids"].to(device)
        target_ids = batch["target_ids"].to(device)

        # enable gradient synchronization for the last micro-batch only
        if requires_grad_sync:
            model.require_backward_grad_sync = (i == dataloader.grad_acc_steps - 1)

        outputs = model(input_ids=input_ids)

        # compute the loss
        batch_size, seq_len = input_ids.shape
        target_ids = target_ids.reshape(-1)
        outputs = outputs.view(seq_len*batch_size, -1)
        loss = F.cross_entropy(outputs, target_ids, reduction='mean') / dataloader.grad_acc_steps

        loss.backward()

        acc_loss += loss.item()

    return acc_loss

if __name__ == "__main__":    
    parser = argparse.ArgumentParser(description="Training script for LLaMA model")
    
    # Environment arguments
    parser.add_argument("--omp_num_threads", type=str, default="1")
    parser.add_argument("--tokenizers_parallelism", type=str, default="false")

    # Model arguments
    parser.add_argument("--model_name", type=str, default="HuggingFaceTB/SmolLM-360M-Instruct")
    parser.add_argument("--num_hidden_layers", type=int, default=32)
    parser.add_argument("--num_attention_heads", type=int, default=16)
    parser.add_argument("--num_key_value_heads", type=int, default=4)

    # Dataset arguments
    parser.add_argument("--dataset_name", type=str, default="roneneldan/TinyStories")
    parser.add_argument("--num_workers", type=int, default=1)
    parser.add_argument("--num_proc", type=int, default=4)

    # Training arguments
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--learning_rate", type=float, default=3e-4)
    parser.add_argument("--seq_len", type=int, default=32)
    parser.add_argument("--micro_batch_size", type=int, default=1)
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
    parser.add_argument("--max_tokens", type=int, default=1e6)
    
    # Distributed training arguments
    parser.add_argument("--tp_size", type=int, default=1, help="Tensor Parallel size")
    parser.add_argument("--dp_size", type=int, default=1, help="Data Parallel size")
    parser.add_argument("--pp_size", type=int, default=1, help="Pipeline Parallel size")
    parser.add_argument("--pp_engine", type=str, default="afab", choices=["1f1b", "afab"])

    # Logging arguments
    parser.add_argument("--run_name", type=str, default="default_run")
    parser.add_argument("--use_wandb", action="store_true")

    args = parser.parse_args()

    # Set environment variables
    os.environ["OMP_NUM_THREADS"] = args.omp_num_threads
    os.environ["TOKENIZERS_PARALLELISM"] = args.tokenizers_parallelism
    os.environ["DEVICE"] = "cuda"
    
    local_rank = int(os.environ["LOCAL_RANK"])
    global_rank = int(os.environ["RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    backend = "nccl"
    torch.cuda.set_device(local_rank)
    device = torch.device("cuda", local_rank)
    dtype = torch.bfloat16

    dist.init_process_group(rank=global_rank, world_size=world_size, backend=backend, init_method=f"env://", timeout=datetime.timedelta(minutes=2))
    setup_process_group_manager(dp_size=args.dp_size, pp_size=args.pp_size, tp_size=args.tp_size)

    is_wandb_rank = pgm.process_group_manager.tp_rank == 0 and pgm.process_group_manager.dp_rank == 0 and pgm.process_group_manager.pp_is_last_stage
    set_all_seed(args.seed)

    if is_wandb_rank and args.use_wandb:
        wandb.init(
            project="picotron_tutorial",
            name=f"{args.run_name}_{pgm.process_group_manager}",
            config={
                "tensor_parallel_size": pgm.process_group_manager.tp_world_size,
                "pipeline_parallel_size": pgm.process_group_manager.pp_world_size,
                "data_parallel_size": pgm.process_group_manager.dp_world_size,
                "model": args.model_name,
                "learning_rate": args.learning_rate,
                "seed": args.seed,
            },
        )

    model_config = AutoConfig.from_pretrained(args.model_name)
    model_config.num_hidden_layers = args.num_hidden_layers
    model_config.num_attention_heads = args.num_attention_heads
    model_config.num_key_value_heads = args.num_key_value_heads
    model_config.max_position_embeddings = args.seq_len

    model = Llama(config=model_config)

    if pgm.process_group_manager.tp_world_size > 1:
        model = apply_tensor_parallel(model)
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step7_pipeline_parallel_afab/train.py [35:144]:
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def train_step(model, dataloader, device):
    acc_loss = 0.0

    requires_grad_sync = pgm.process_group_manager.dp_world_size > 1

    for i in range(dataloader.grad_acc_steps):
        # get the next batch
        batch = next(dataloader)
        input_ids = batch["input_ids"].to(device)
        target_ids = batch["target_ids"].to(device)

        # enable gradient synchronization for the last micro-batch only
        if requires_grad_sync:
            model.require_backward_grad_sync = (i == dataloader.grad_acc_steps - 1)

        outputs = model(input_ids=input_ids)

        # compute the loss
        batch_size, seq_len = input_ids.shape
        target_ids = target_ids.reshape(-1)
        outputs = outputs.view(seq_len*batch_size, -1)
        loss = F.cross_entropy(outputs, target_ids, reduction='mean') / dataloader.grad_acc_steps

        loss.backward()

        acc_loss += loss.item()

    return acc_loss

if __name__ == "__main__":    
    parser = argparse.ArgumentParser(description="Training script for LLaMA model")
    
    # Environment arguments
    parser.add_argument("--omp_num_threads", type=str, default="1")
    parser.add_argument("--tokenizers_parallelism", type=str, default="false")

    # Model arguments
    parser.add_argument("--model_name", type=str, default="HuggingFaceTB/SmolLM-360M-Instruct")
    parser.add_argument("--num_hidden_layers", type=int, default=32)
    parser.add_argument("--num_attention_heads", type=int, default=16)
    parser.add_argument("--num_key_value_heads", type=int, default=4)

    # Dataset arguments
    parser.add_argument("--dataset_name", type=str, default="roneneldan/TinyStories")
    parser.add_argument("--num_workers", type=int, default=1)
    parser.add_argument("--num_proc", type=int, default=4)

    # Training arguments
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--learning_rate", type=float, default=3e-4)
    parser.add_argument("--seq_len", type=int, default=32)
    parser.add_argument("--micro_batch_size", type=int, default=1)
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
    parser.add_argument("--max_tokens", type=int, default=1e6)
    
    # Distributed training arguments
    parser.add_argument("--tp_size", type=int, default=1, help="Tensor Parallel size")
    parser.add_argument("--dp_size", type=int, default=1, help="Data Parallel size")
    parser.add_argument("--pp_size", type=int, default=1, help="Pipeline Parallel size")
    parser.add_argument("--pp_engine", type=str, default="afab", choices=["1f1b", "afab"])

    # Logging arguments
    parser.add_argument("--run_name", type=str, default="default_run")
    parser.add_argument("--use_wandb", action="store_true")

    args = parser.parse_args()

    # Set environment variables
    os.environ["OMP_NUM_THREADS"] = args.omp_num_threads
    os.environ["TOKENIZERS_PARALLELISM"] = args.tokenizers_parallelism
    os.environ["DEVICE"] = "cuda"
    
    local_rank = int(os.environ["LOCAL_RANK"])
    global_rank = int(os.environ["RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    backend = "nccl"
    torch.cuda.set_device(local_rank)
    device = torch.device("cuda", local_rank)
    dtype = torch.bfloat16

    dist.init_process_group(rank=global_rank, world_size=world_size, backend=backend, init_method=f"env://", timeout=datetime.timedelta(minutes=2))
    setup_process_group_manager(dp_size=args.dp_size, pp_size=args.pp_size, tp_size=args.tp_size)

    is_wandb_rank = pgm.process_group_manager.tp_rank == 0 and pgm.process_group_manager.dp_rank == 0 and pgm.process_group_manager.pp_is_last_stage
    set_all_seed(args.seed)

    if is_wandb_rank and args.use_wandb:
        wandb.init(
            project="picotron_tutorial",
            name=f"{args.run_name}_{pgm.process_group_manager}",
            config={
                "tensor_parallel_size": pgm.process_group_manager.tp_world_size,
                "pipeline_parallel_size": pgm.process_group_manager.pp_world_size,
                "data_parallel_size": pgm.process_group_manager.dp_world_size,
                "model": args.model_name,
                "learning_rate": args.learning_rate,
                "seed": args.seed,
            },
        )

    model_config = AutoConfig.from_pretrained(args.model_name)
    model_config.num_hidden_layers = args.num_hidden_layers
    model_config.num_attention_heads = args.num_attention_heads
    model_config.num_key_value_heads = args.num_key_value_heads
    model_config.max_position_embeddings = args.seq_len

    model = Llama(config=model_config)

    if pgm.process_group_manager.tp_world_size > 1:
        model = apply_tensor_parallel(model)
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