import os import platform import time import math import warnings import pandas as pd import torch import torch.nn.functional as F import torch.distributed as dist from contextlib import nullcontext from torch import optim from torch.nn.parallel import DistributedDataParallel from torch.optim.lr_scheduler import CosineAnnealingLR from torch.utils.data import DataLoader, DistributedSampler from transformers import AutoTokenizer, AutoModel from model.model import Transformer from model.LMConfig import LMConfig from model.dataset import SFTDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(it, all): warmup_iters = 0 lr_decay_iters = all min_lr = learning_rate / epochs if it < warmup_iters: return learning_rate * it / warmup_iters if it > lr_decay_iters: return min_lr decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) assert 0 <= decay_ratio <= 1 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) return min_lr + coeff * (learning_rate - min_lr) # ------------------------------------------------------------------------------ def train_epoch(epoch, wandb): start_time = time.time() for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(device) Y = Y.to(device) loss_mask = loss_mask.to(device) lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: logits = model(X, Y).logits loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none') loss_mask = loss_mask.view(-1) loss = torch.sum(loss * loss_mask) / loss_mask.sum() # Backward pass scaler.scale(loss).backward() # Unscale gradients and clip them scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # Update parameters scaler.step(optimizer) scaler.update() # Zero the gradients optimizer.zero_grad(set_to_none=True) # 打印日志 if step % 100 == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.8f} epoch_Time:{}min:'.format( epoch, epochs, step, iter_per_epoch, loss, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0): model.eval() # torch.save(model.state_dict(), '{}/sft_iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch))) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{save_dir}/full_sft_{lm_config.dim}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() torch.save(state_dict, ckp) model.train() def init_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') model_from = 1 # 1从权重，2用transformers def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) if model_from == 1: model = Transformer(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'./out/pretrain_{lm_config.dim}{moe_path}.pth' state_dict = torch.load(ckp, map_location=device) unwanted_prefix = '_orig_mod.' for k, v in list(state_dict.items()): if k.startswith(unwanted_prefix): state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) model.load_state_dict(state_dict, strict=False) else: model = AutoModel.from_pretrained('./minimind', trust_remote_code=True) Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万') model = model.to(device) return model, tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) # I/O if __name__ == "__main__": # ----------------------------------------------------------------------------- lm_config = LMConfig() max_seq_len = lm_config.max_seq_len out_dir = 'out' epochs = 19 gradient_accumulation_steps = 1 batch_size = 40 learning_rate = 1e-4 device = 'cuda:0' if torch.cuda.is_available() else 'cpu' dtype = 'bfloat16' # dtype = 'float16' save_dir = os.path.join(out_dir) os.makedirs(save_dir, exist_ok=True) tokens_per_iter = gradient_accumulation_steps * batch_size * max_seq_len os.makedirs(out_dir, exist_ok=True) torch.manual_seed(1337) device_type = device if "cuda" in device else "cpu" use_wandb = False # 是否使用wandb wandb_project = "MiniMind-Full-SFT" wandb_run_name = f"MiniMind-Full-SFT-Epoch-{epochs}-BatchSize-{batch_size}-LearningRate-{learning_rate}" if use_wandb: import wandb wandb.init(project=wandb_project, name=wandb_run_name) else: wandb = None ctx = ( nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ) ### ddp config ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" if ddp: init_distributed_mode() device = torch.device(DEVICE) # ----------------------------------------------------------------------------- model, tokenizer = init_model(lm_config) # -----init dataloader------ df = pd.read_csv('./dataset/sft_data_single.csv') df = df.sample(frac=1.0) train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=batch_size, pin_memory=False, drop_last=False, shuffle=False, num_workers=8, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype)) # optimizer optimizer = optim.Adam(model.parameters(), lr=learning_rate) iter_per_epoch = len(train_loader) # compile the model if False and not lm_config.use_moe and platform.system() != 'Windows' and float( torch.__version__.split('.')[0]) >= 2: Logger("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) # requires PyTorch 2.0 if ddp: # Ignore the pos_cis buffer so that DDP does not broadcast it at # construction time since NCCL does not support ComplexFloat model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) # training loop for epoch in range(epochs): train_epoch(epoch, wandb)