"""Training script for LLaMA model. CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --config tmp/fast_benchmark/120M_model_tiny_stories_dp=4.json CUDA_DEVICE_MAX_CONNECTIONS=1 debugpy-run -p 5678 -m torch.distributed.run -- --nproc_per_node=4 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 train.py --config tmp/dummy/llama2_7b_benchmark.json """ import os import inspect import json import time import datetime import argparse import torch.nn.functional as F import torch, torch.distributed as dist from torch.optim import AdamW from transformers import AutoConfig from picotron.context_parallel.context_parallel import apply_context_parallel from picotron.tensor_parallel.tensor_parallel import apply_tensor_parallel import picotron.process_group_manager as pgm from picotron.utils import average_loss_across_dp_cp_ranks, set_all_seed, print, to_readable_format, get_mfu, get_num_params from picotron.checkpoint import CheckpointManager from picotron.checkpoint import init_model_with_dematerialized_weights, init_model_with_materialized_weights from picotron.data import MicroBatchDataLoader from picotron.process_group_manager import setup_process_group_manager from picotron.pipeline_parallel.pipeline_parallel import train_step_pipeline_1f1b, train_step_pipeline_afab, PipelineParallel from picotron.data_parallel.data_parallel import DataParallelBucket from picotron.model import Llama from picotron.utils import download_model import wandb def train_step(model, data_loader, device): acc_loss = 0.0 requires_grad_sync = pgm.process_group_manager.cp_dp_world_size > 1 for i in range(data_loader.grad_acc_steps): # get the next batch batch = next(data_loader) input_ids = batch["input_ids"].to(device) target_ids = batch["target_ids"].to(device) # disable gradient synchronization for all but the last micro-batch if requires_grad_sync: model.require_backward_grad_sync = (i == data_loader.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') / data_loader.grad_acc_steps loss.backward() acc_loss += loss.item() return acc_loss if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="", help="Path to config file") args = parser.parse_args() with open(args.config, "r") as f: config = json.load(f) os.environ["OMP_NUM_THREADS"] = config["environment"]["OMP_NUM_THREADS"] os.environ["TOKENIZERS_PARALLELISM"] = config["environment"]["TOKENIZERS_PARALLELISM"] os.environ["FLASH_ATTEN"] = config["environment"]["FLASH_ATTEN"] os.environ["DEVICE"] = "cpu" if config["distributed"]["use_cpu"] else "cuda" if config["environment"].get("HF_TOKEN") is None: if "HF_TOKEN" not in os.environ: raise ValueError("HF_TOKEN is neither set in the config file nor in the environment") else: if "HF_TOKEN" not in os.environ: os.environ["HF_TOKEN"] = config["environment"]["HF_TOKEN"] else: print("Warning: HF_TOKEN is set in the environment and the config file. Using the environment variable.") dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() and not config["distributed"]["use_cpu"] else torch.float32 assert (dtype == torch.bfloat16 and os.getenv("FLASH_ATTEN") == "1") or os.getenv("FLASH_ATTEN") != "1", "Kernel operations requires dtype=torch.bfloat16" local_rank = int(os.environ["LOCAL_RANK"]) global_rank = int(os.environ["RANK"]) world_size = int(os.environ["WORLD_SIZE"]) backend = "gloo" if config["distributed"]["use_cpu"] else "nccl" assert config["training"]["seq_length"] % config["distributed"]["cp_size"] == 0, "seq_length must be divisible by cp_size for Context Parallelism" assert world_size == config["distributed"]["tp_size"] * config["distributed"]["pp_size"] * config["distributed"]["dp_size"] * config["distributed"]["cp_size"], "world_size must be equal to tp_size * pp_size * dp_size * cp_size" if backend == "nccl": torch.cuda.set_device(local_rank) device = torch.device("cuda", local_rank) else: device = torch.device("cpu") dist.init_process_group(rank=global_rank, world_size=world_size, backend=backend, init_method=f"env://", timeout=datetime.timedelta(minutes=3)) setup_process_group_manager( tp_size=config["distributed"]["tp_size"], cp_size=config["distributed"]["cp_size"], pp_size=config["distributed"]["pp_size"], dp_size=config["distributed"]["dp_size"] ) is_wandb_rank = pgm.process_group_manager.tp_rank == 0 and pgm.process_group_manager.dp_rank == 0 and pgm.process_group_manager.cp_rank == 0 and pgm.process_group_manager.pp_is_last_stage set_all_seed(config["training"]["seed"]) start_time = time.time() data_loader = MicroBatchDataLoader( micro_batch_size=config["training"]["micro_batch_size"], seq_length=config["training"]["seq_length"], dataset_name=config["dataset"]["name"], tokenizer_name=config["model"]["name"], grad_acc_steps=config["training"]["gradient_accumulation_steps"], device=device, num_workers=config["dataset"]["num_workers"], num_proc=config["dataset"]["num_proc"], num_samples=config["training"].get("num_samples", None), subset_name=config["dataset"].get("subset_name", None), split=config["dataset"].get("split", "train") ) # download model on the first rank, assume all ranks have access to the same filesystem if pgm.process_group_manager.global_rank == 0: download_model(config["model"]["name"], os.environ["HF_TOKEN"]) dist.barrier() print(f"init dataloader time: {time.time()-start_time:.2f}s", is_print_rank=is_wandb_rank) tokens_per_step = data_loader.global_batch_size * config["training"]["seq_length"] if pgm.process_group_manager.global_rank == 0: print("Tokens per step:", to_readable_format(tokens_per_step), is_print_rank=is_wandb_rank) if is_wandb_rank and config["logging"]["use_wandb"]: wandb.init( project="picotron", name=f"{config['logging']['run_name']}_{to_readable_format(tokens_per_step)}_{pgm.process_group_manager}", config={ "tensor_parallel_size": pgm.process_group_manager.tp_world_size, "context_parallel_size": pgm.process_group_manager.cp_world_size, "pipeline_parallel_size": pgm.process_group_manager.pp_world_size, "data_parallel_size": pgm.process_group_manager.dp_world_size, "model": config["model"]["name"], "dataset": config["dataset"]["name"], "max_tokens": config["training"]["max_tokens"], "learning_rate": config["training"]["learning_rate"], "seed": config["training"]["seed"], "micro_batch_size": data_loader.micro_batch_size, "global_batch_size": data_loader.global_batch_size, "gradient_accumulation": data_loader.grad_acc_steps, }, ) if pgm.process_group_manager.global_rank == 0: print(f"rank {pgm.process_group_manager.global_rank}: Creating model config") model_config = AutoConfig.from_pretrained(config["model"]["name"]) # twist the model structure if specified in the config file model_config.num_hidden_layers = model_config.num_hidden_layers if "num_hidden_layers" not in config["model"] else config["model"]["num_hidden_layers"] model_config.num_attention_heads = model_config.num_attention_heads if "num_attention_heads" not in config["model"] else config["model"]["num_attention_heads"] model_config.num_key_value_heads = model_config.num_key_value_heads if "num_key_value_heads" not in config["model"] else config["model"]["num_key_value_heads"] model_config.max_position_embeddings = config["training"]["seq_length"] objects = [model_config] else: objects = [None] dist.broadcast_object_list(objects, src=0, device=device) model_config = objects[0] print(f"rank {pgm.process_group_manager.global_rank}: Broadcasting model_config to all ranks", is_print_rank=pgm.process_group_manager.global_rank==0) dist.barrier() print(f"rank {pgm.process_group_manager.global_rank}: Initializing model meta device", is_print_rank=is_wandb_rank) start_time = time.time() with init_model_with_dematerialized_weights(): model = Llama(config=model_config) if pgm.process_group_manager.tp_world_size > 1: model = apply_tensor_parallel(model) if pgm.process_group_manager.pp_world_size > 1: model = PipelineParallel(model, model_config) model = init_model_with_materialized_weights(model, model_config, save_dir=f"./hf_model_safetensors/") #TODO: load existing checkpoint here to continue pre-training if pgm.process_group_manager.cp_world_size > 1: model = apply_context_parallel(model) model.to(dtype).to(device) if pgm.process_group_manager.dp_world_size > 1: model = DataParallelBucket(model) print(f"init model parallel time: {time.time()-start_time:.2f}s", is_print_rank=is_wandb_rank) model.train() num_params = get_num_params(model) print(f"Number of parameters: {to_readable_format(num_params)}", is_print_rank=is_wandb_rank) tensor_shapes = (data_loader.micro_batch_size, data_loader.seq_length_per_gpu, model_config.hidden_size) extra_args = dict() if config["model"]["use_fused_adam"]: fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters use_fused = fused_available and device == 'cuda' extra_args = dict(fused=True) if use_fused else dict() optimizer = AdamW(model.parameters(), lr=config["training"]["learning_rate"], **extra_args) checkpoint_manager = CheckpointManager() trained_tokens, step = 0, 0 if config["checkpoint"]["load_path"]: step, trained_tokens = checkpoint_manager.load_checkpoint(model, optimizer, config["checkpoint"]["load_path"]) dist.barrier() while config["training"]["max_tokens"] is None or trained_tokens < config["training"]["max_tokens"]: step_start_time = time.time() optimizer.zero_grad() if pgm.process_group_manager.pp_world_size > 1: if config["distributed"]["pp_engine"] == "afab": loss = train_step_pipeline_afab(model, data_loader, tensor_shapes, device, dtype) elif config["distributed"]["pp_engine"] == "1f1b": loss = train_step_pipeline_1f1b(model, data_loader, tensor_shapes, device, dtype) else: raise ValueError(f"Invalid pipeline parallel engine: {config['distributed']['pp_engine']}") else: loss = train_step(model, data_loader, device) loss = average_loss_across_dp_cp_ranks(loss, device) optimizer.step() trained_tokens += tokens_per_step step += 1 if hasattr(model, 'reset'): model.reset() step_duration = time.time() - step_start_time tokens_per_second = tokens_per_step / step_duration tokens_per_second_per_gpu = tokens_per_second / world_size mfu = get_mfu(tokens_per_second_per_gpu, num_params, model_config) if is_wandb_rank: print( f"[rank {pgm.process_group_manager.global_rank}] " f"Step: {step:<5d} | " f"Loss: {loss:6.4f} | " f"Global batch size: {to_readable_format(tokens_per_step):>7s} | " f"Tokens/s: {to_readable_format(tokens_per_second):>7s} | " f"Tokens/s/GPU: {to_readable_format(tokens_per_second_per_gpu):>7s} | " f"Tokens: {to_readable_format(trained_tokens):>7s}{('/' + to_readable_format(config['training']['max_tokens'])) if config['training']['max_tokens'] else ''} | " f"MFU: {mfu:5.2f}% | " f"Memory usage: {torch.cuda.memory_reserved() / 1e9:6.2f}GB", is_print_rank=is_wandb_rank ) if config["logging"]["use_wandb"]: wandb.log({ "loss": loss, "tokens_per_step": tokens_per_step, "tokens_per_second": tokens_per_step / step_duration, "mfu": mfu, "tokens_per_second_per_gpu": tokens_per_second_per_gpu, "memory_usage": torch.cuda.memory_reserved() / 1e9, "trained_tokens": trained_tokens }) if step % config["checkpoint"]["save_frequency"] == 0: checkpoint_manager.save_checkpoint(model, optimizer, step, trained_tokens, config["checkpoint"]["save_dir"]+f"/{step}") if step >= config["training"]["total_train_steps"]: break if is_wandb_rank and config["logging"]["use_wandb"]: wandb.finish() dist.destroy_process_group()