""" torchrun --nproc_per_node 4 train.py --tp_size 4 --micro_batch_size 4 --gradient_accumulation_steps 8 --seq_len 128 --max_tokens 40960 --num_proc 16 --run_name tp_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 def train_step(model, dataloader, device): acc_loss = 0.0 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) 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) model.to(dtype).to(device) 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, ) 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 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()