step5_data_parallel_naive/train.py

"""
torchrun --nproc_per_node 4 train.py --dp_size 4 --micro_batch_size 1 --gradient_accumulation_steps 8 --seq_len 128 --max_tokens 40960 --num_proc 16 --run_name dp_naive --use_wandb
"""
import os
import time
import wandb
import datetime
import torch
import torch.nn.functional as F
import torch.distributed as dist
import argparse
from torch.optim import AdamW
from transformers import AutoConfig

import lovely_tensors as lt; lt.monkey_patch()

from model import Llama
from dataloader import MicroBatchDataLoader
import process_group_manager as pgm
from process_group_manager import setup_process_group_manager
from utils import set_all_seed, print, to_readable_format

from tensor_parallel import apply_tensor_parallel
from data_parallel import DataParallelNaive

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)

    # Need to move the model to the device before wrapping it with DataParallel.
    # Otherwise, the hook will get attached to the CPU model and not the GPU model.
    model.to(dtype).to(device)

    if pgm.process_group_manager.dp_world_size > 1:
        model = DataParallelNaive(model)
    
    model.train()

    dist.barrier()

    optimizer = AdamW(model.parameters(), lr=args.learning_rate)

    dist.barrier()
    
    # Create dataloader
    dataloader = MicroBatchDataLoader(
        seq_len=args.seq_len,
        micro_batch_size=args.micro_batch_size,
        grad_acc_steps=args.gradient_accumulation_steps,
        dataset_name=args.dataset_name,
        tokenizer_name=args.model_name,
        max_tokens=args.max_tokens,
        num_workers=args.num_workers,
        num_proc=args.num_proc,
        seed=args.seed,
    )
   
    tokens_per_step = dataloader.global_batch_size * args.seq_len
    if pgm.process_group_manager.global_rank == 0:
        print("Tokens per step:", to_readable_format(tokens_per_step), is_print_rank=is_wandb_rank)

    trained_token, step = 0, 0

    dist.barrier()

    # Training loop
    while trained_token < args.max_tokens:

        step_start_time = time.time()
        optimizer.zero_grad()

        loss = train_step(model, dataloader, device)

        optimizer.step()

        step_duration = time.time() - step_start_time
        trained_token += tokens_per_step
        step += 1

        # In DDP implementation, we need to reset the gradient buffers
        if hasattr(model, 'reset'):
            model.reset()
    
        print(f"[rank {pgm.process_group_manager.global_rank}] Step: {step}, Loss: {loss:.4f}, "
            f"Global batch size (with seq_len): {to_readable_format(tokens_per_step)}, "
            f"Tokens/s: {to_readable_format(tokens_per_step / step_duration)}, "
            f"Tokens/s/GPU: {to_readable_format(tokens_per_step / step_duration / world_size)}, "
            f"Tokens: {to_readable_format(trained_token)}{('/' + to_readable_format(args.max_tokens))}, "
            f"Memory usage: {torch.cuda.memory_reserved() / 1e9:.2f}GB"
            , is_print_rank=is_wandb_rank
        )
        
        if is_wandb_rank and args.use_wandb:
            wandb.log({"loss": loss, "tokens_per_step": tokens_per_step, "tokens_per_second": tokens_per_step / step_duration,\
                "memory_usage": torch.cuda.memory_reserved() / 1e9, "trained_tokens": tokens_per_step})

    if is_wandb_rank and args.use_wandb:
        wandb.finish()

    dist.destroy_process_group()