import math import os import time from functools import partial import torch from arguments import parse_args from evaluation import evaluate from loss import LossForPretraining from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider from pretrain_utils import get_lr_scheduler, get_model, get_optimizer, save_ckpt from tqdm import tqdm from transformers import AutoTokenizer from utils.exp_util import get_mem_info, get_tflops, log_args, throughput_calculator from utils.global_vars import get_tensorboard_writer, get_timers, set_global_variables from utils.logger import Logger import colossalai import colossalai.nn as col_nn from colossalai.context import ParallelMode from colossalai.core import global_context as gpc from colossalai.nn.optimizer import HybridAdam from colossalai.nn.parallel import ZeroDDP from colossalai.tensor import ProcessGroup from colossalai.utils import get_current_device from colossalai.zero import ZeroOptimizer from colossalai.zero.gemini import ChunkManager, ColoInitContext, GeminiManager from colossalai.zero.legacy import ShardedModelV2, ShardedOptimizerV2, ZeroInitContext from colossalai.zero.legacy.shard_utils import TensorShardStrategy def main(): args = parse_args() launch_time = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime()) tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) os.environ['CUDA_LAUNCH_BLOCKING'] = '1' logger = Logger(os.path.join(args.log_path, launch_time), cuda=torch.cuda.is_available(), debug=args.vscode_debug) if args.vscode_debug: colossalai.launch(config={}, rank=args.rank, world_size=args.world_size, host=args.host, port=args.port, backend=args.backend) args.local_rank = -1 args.log_interval = 1 else: colossalai.launch_from_torch(args.colossal_config) # args.colossal_config args.local_rank = int(os.environ["LOCAL_RANK"]) logger.info( f'launch_from_torch, world size: {torch.distributed.get_world_size()} | ' + f'ParallelMode.MODEL: {ParallelMode.MODEL} | ParallelMode.DATA: {ParallelMode.DATA} | ParallelMode.TENSOR: {ParallelMode.TENSOR}' ) log_args(logger, args) args.tokenizer = tokenizer args.logger = logger set_global_variables(launch_time, args.tensorboard_path) use_zero = hasattr(gpc.config, 'zero') world_size = torch.distributed.get_world_size() # build model, optimizer and criterion if use_zero: shard_strategy = TensorShardStrategy() with ZeroInitContext(target_device=torch.cuda.current_device(), shard_strategy=shard_strategy, shard_param=True): config, model, numel = get_model(args, logger) # model = ShardedModelV2(model, shard_strategy, tensor_placement_policy='cpu', reuse_fp16_shard=True) else: config, model, numel = get_model(args, logger) logger.info("no_zero") if torch.distributed.get_rank() == 0: os.mkdir(os.path.join(args.ckpt_path, launch_time)) logger.info(f'Model numel: {numel}') get_tflops_func = partial(get_tflops, numel, args.train_micro_batch_size_per_gpu, args.max_seq_length) # len(dataloader) steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size total_steps = steps_per_epoch * args.epoch # build optimizer and lr_scheduler start_epoch = 0 start_shard = 0 global_step = 0 if args.resume_train: assert os.path.exists(args.load_optimizer_lr) o_l_state_dict = torch.load(args.load_optimizer_lr, map_location='cpu') o_l_state_dict['lr_scheduler']['last_epoch'] = o_l_state_dict['lr_scheduler']['last_epoch'] - 1 optimizer = get_optimizer(model, lr=args.lr) optimizer.load_state_dict(o_l_state_dict['optimizer']) # o_l_state_dict['lr_scheduler']['last_epoch'] lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=o_l_state_dict['lr_scheduler']['last_epoch']) for state in optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda(f"cuda:{torch.cuda.current_device()}") # if you want delete the above three code, have to move the model to gpu, because in optimizer.step() lr_scheduler.load_state_dict(o_l_state_dict['lr_scheduler']) start_epoch = o_l_state_dict['epoch'] start_shard = o_l_state_dict['shard'] + 1 # global_step = o_l_state_dict['global_step'] + 1 logger.info( f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}' ) else: optimizer = get_optimizer(model, lr=args.lr) lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) # optimizer = gpc.config.optimizer.pop('type')( # model.parameters(), **gpc.config.optimizer) # optimizer = ShardedOptimizerV2(model, optimizer, initial_scale=2**5) criterion = LossForPretraining(config.vocab_size) # build dataloader pretrain_dataset_provider = NvidiaBertDatasetProvider(args) # initialize with colossalai engine, _, _, lr_scheduelr = colossalai.initialize(model=model, optimizer=optimizer, criterion=criterion, lr_scheduler=lr_scheduler) logger.info(get_mem_info(prefix='After init model, ')) best_loss = None eval_loss = 0 train_loss = 0 timers = get_timers() timers('interval_time').start() timers('epoch_time').start() timers('shard_time').start() for epoch in range(start_epoch, args.epoch): for shard in range(start_shard, len(os.listdir(args.data_path_prefix))): dataset_iterator, total_length = pretrain_dataset_provider.get_shard(shard) # pretrain_dataset_provider.prefetch_shard(shard + 1) # may cause cpu memory overload if torch.distributed.get_rank() == 0: iterator_data = tqdm(enumerate(dataset_iterator), total=(total_length // args.train_micro_batch_size_per_gpu // world_size), colour='cyan', smoothing=1) else: iterator_data = enumerate(dataset_iterator) engine.train() for step, batch_data in iterator_data: # batch_data = pretrain_dataset_provider.get_batch(batch_index) input_ids = batch_data[0].cuda(f"cuda:{torch.cuda.current_device()}") attention_mask = batch_data[1].cuda(f"cuda:{torch.cuda.current_device()}") token_type_ids = batch_data[2].cuda(f"cuda:{torch.cuda.current_device()}") mlm_label = batch_data[3].cuda(f"cuda:{torch.cuda.current_device()}") # nsp_label = batch_data[5].cuda() output = engine(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) loss = engine.criterion(output.logits, mlm_label) pretrain_dataset_provider.prefetch_batch() engine.backward(loss) train_loss += loss.float().item() # if (step + 1) % args.accumulation_step == 0: engine.step() lr_scheduelr.step() engine.zero_grad() global_step += 1 if global_step % args.log_interval == 0 and global_step != 0 \ and torch.distributed.get_rank() == 0: elapsed_time = timers('interval_time').elapsed(reset=False) elapsed_time_per_iteration = elapsed_time / global_step samples_per_sec, tflops, approx_parameters_in_billions = throughput_calculator( numel, args, config, elapsed_time, global_step, world_size) cur_loss = train_loss / args.log_interval current_lr = lr_scheduelr.get_last_lr()[0] log_str = f'| epoch: {epoch} | shard: {shard} | step: {global_step} | lr {current_lr:.7f} | elapsed_time: {elapsed_time / 60 :.3f} minutes ' + \ f'| mins/batch: {elapsed_time_per_iteration :.3f} seconds | loss: {cur_loss:.7f} | ppl: {math.exp(cur_loss):.3f} | TFLOPS: {get_tflops_func(elapsed_time_per_iteration):.3f} or {tflops:.3f}' logger.info(log_str, print_=False) if args.wandb: tensorboard_log = get_tensorboard_writer() tensorboard_log.log_train( { 'lr': current_lr, 'loss': cur_loss, 'ppl': math.exp(cur_loss), 'mins_batch': elapsed_time_per_iteration }, global_step) train_loss = 0 logger.info(f'epoch {epoch} shard {shard} has cost {timers("shard_time").elapsed() / 60 :.3f} mins') logger.info('*' * 100) eval_loss += evaluate(engine, args, logger, global_step) save_ckpt(engine.model, optimizer, lr_scheduelr, os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, shard, global_step) eval_loss /= len(os.listdir(args.data_path_prefix)) logger.info( f'epoch {epoch} | shard_length {len(os.listdir(args.data_path_prefix))} | elapsed_time: {timers("epoch_time").elapsed() / 60 :.3f} mins' + f'eval_loss: {eval_loss} | ppl: {math.exp(eval_loss)}') logger.info('-' * 100) if args.wandb and torch.distributed.get_rank() == 0: tensorboard_log = get_tensorboard_writer() tensorboard_log.log_eval({ 'all_eval_shard_loss': eval_loss, }, epoch) start_shard = 0 eval_loss = 0 pretrain_dataset_provider.release_shard() logger.info('Congratulation, training has finished!!!') if __name__ == '__main__': main()