Minimind/3-full_sft.py

import os
import platform
import argparse
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.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 = args.warmup_iters
    lr_decay_iters = all
    min_lr = args.learning_rate / 10

    if it < warmup_iters:
        return args.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 * (args.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(args.device)
        Y = Y.to(args.device)
        loss_mask = loss_mask.to(args.device)
        lr = get_lr(epoch * iter_per_epoch + step, args.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()

        scaler.scale(loss).backward()

        if (step + 1) % args.accumulation_steps == 0:
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)

            scaler.step(optimizer)
            scaler.update()

            optimizer.zero_grad(set_to_none=True)

        if step % args.log_interval == 0:
            spend_time = time.time() - start_time
            Logger(
                'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
                    epoch,
                    args.epochs,
                    step,
                    iter_per_epoch,
                    loss.item(),
                    optimizer.param_groups[-1]['lr'],
                    spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
            if wandb is not None:
                wandb.log({"loss": loss.item(),
                           "lr": optimizer.param_groups[-1]['lr'],
                           "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})

        if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
            model.eval()
            moe_path = '_moe' if lm_config.use_moe else ''
            ckp = f'{args.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():
    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=args.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(args.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)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="MiniMind Full SFT")
    parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
    parser.add_argument("--epochs", type=int, default=19, help="Number of epochs")
    parser.add_argument("--batch_size", type=int, default=40, help="Batch size")
    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use")
    parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
    parser.add_argument("--use_wandb", action="store_true", help="Use Weights & Biases")
    parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT", help="Weights & Biases project name")
    parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading")
    parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel")
    parser.add_argument("--accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
    parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold")
    parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations")
    parser.add_argument("--log_interval", type=int, default=100, help="Logging interval")
    parser.add_argument("--save_interval", type=int, default=1000, help="Model saving interval")

    args = parser.parse_args()

    lm_config = LMConfig()
    max_seq_len = lm_config.max_seq_len
    args.save_dir = os.path.join(args.out_dir)
    os.makedirs(args.save_dir, exist_ok=True)
    os.makedirs(args.out_dir, exist_ok=True)
    tokens_per_iter = args.batch_size * max_seq_len
    torch.manual_seed(1337)
    device_type = "cuda" if "cuda" in args.device else "cpu"

    args.wandb_run_name = f"MiniMind-Full-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"

    ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
    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()
        args.device = torch.device(DEVICE)

    if args.use_wandb and (not ddp or ddp_local_rank == 0):
        import wandb
        wandb.init(project=args.wandb_project, name=args.wandb_run_name)
    else:
        wandb = None

    model, tokenizer = init_model()

    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=args.batch_size,
        pin_memory=True,
        drop_last=False,
        shuffle=False,
        num_workers=args.num_workers,
        sampler=train_sampler
    )

    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == args.dtype))
    optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)

    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)

    if ddp:
        model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
        model = DistributedDataParallel(model, device_ids=[ddp_local_rank])

    iter_per_epoch = len(train_loader)
    for epoch in range(args.epochs):
        train_epoch(epoch, wandb)