301 lines
12 KiB
Python
301 lines
12 KiB
Python
import os
|
||
import sys
|
||
__package__ = "trainer"
|
||
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
|
||
|
||
import argparse
|
||
import time
|
||
import math
|
||
import warnings
|
||
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, AutoModelForCausalLM
|
||
from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
|
||
from dataset.lm_dataset import DPODataset
|
||
|
||
warnings.filterwarnings('ignore')
|
||
|
||
|
||
def Logger(content):
|
||
if not ddp or dist.get_rank() == 0:
|
||
print(content)
|
||
|
||
|
||
def get_lr(current_step, total_steps, lr):
|
||
return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
|
||
|
||
|
||
def logits_to_probs(logits, labels):
|
||
"""将模型输出的logits转换为token级别的概率
|
||
|
||
Args:
|
||
logits: 模型输出的logits，形状为(batch_size, seq_len, vocab_size)
|
||
labels: 真实标签，形状为(batch_size, seq_len)
|
||
|
||
Returns:
|
||
probs: token级别的概率，形状为(batch_size, seq_len)
|
||
"""
|
||
# 对logits进行softmax得到概率分布
|
||
log_probs = F.log_softmax(logits, dim=2)
|
||
# 收集每个位置上目标token的概率
|
||
probs = torch.gather(log_probs, dim=2, index=labels.unsqueeze(2)).squeeze(-1)
|
||
return probs
|
||
|
||
|
||
def dpo_loss(ref_probs, probs, mask, beta):
|
||
"""计算Direct Preference Optimization (DPO)损失
|
||
|
||
DPO通过最大化偏好数据中chosen和rejected样本的对数概率比来优化模型。
|
||
具体来说，它使用一个参考模型(通常是训练前的模型)来防止策略过度偏离。
|
||
|
||
Args:
|
||
ref_probs: 参考模型的token概率，形状为(batch_size, seq_len)
|
||
probs: 当前模型的token概率，形状为(batch_size, seq_len)
|
||
mask: 有效token的掩码，形状为(batch_size, seq_len)
|
||
beta: 温度系数，用于控制策略的保守程度
|
||
|
||
Returns:
|
||
loss: 标量损失值
|
||
"""
|
||
# 计算序列级别的对数概率，通过mask只考虑有效token
|
||
seq_lengths = mask.sum(dim=1, keepdim=True) # (batch_size, 1)
|
||
ref_probs = (ref_probs * mask).sum(dim=1) / seq_lengths.squeeze()
|
||
probs = (probs * mask).sum(dim=1) / seq_lengths.squeeze()
|
||
|
||
# 将batch分成chosen和rejected两部分
|
||
batch_size = ref_probs.shape[0]
|
||
chosen_ref_probs = ref_probs[:batch_size // 2] # 参考模型对chosen样本的概率
|
||
reject_ref_probs = ref_probs[batch_size // 2:] # 参考模型对rejected样本的概率
|
||
chosen_probs = probs[:batch_size // 2] # 当前模型对chosen样本的概率
|
||
reject_probs = probs[batch_size // 2:] # 当前模型对rejected样本的概率
|
||
|
||
# 计算当前模型和参考模型的对数概率比
|
||
pi_logratios = chosen_probs - reject_probs # 当前模型的对数比
|
||
ref_logratios = chosen_ref_probs - reject_ref_probs # 参考模型的对数比
|
||
# 计算最终的logits并应用温度系数
|
||
logits = pi_logratios - ref_logratios
|
||
# 计算损失：最大化chosen相对于rejected的概率
|
||
loss = -F.logsigmoid(beta * logits)
|
||
return loss.mean()
|
||
|
||
|
||
def train_epoch(epoch, wandb):
|
||
"""训练一个epoch的DPO优化
|
||
|
||
使用人类偏好数据进行训练，每个batch包含chosen和rejected两部分样本。
|
||
通过最大化chosen样本相对于rejected样本的概率来优化模型。
|
||
"""
|
||
start_time = time.time()
|
||
for step, batch in enumerate(train_loader):
|
||
x_chosen = batch['x_chosen'].to(args.device)
|
||
x_rejected = batch['x_rejected'].to(args.device)
|
||
y_chosen = batch['y_chosen'].to(args.device)
|
||
y_rejected = batch['y_rejected'].to(args.device)
|
||
mask_chosen = batch['mask_chosen'].to(args.device)
|
||
mask_rejected = batch['mask_rejected'].to(args.device)
|
||
x = torch.cat([x_chosen, x_rejected], dim=0)
|
||
y = torch.cat([y_chosen, y_rejected], dim=0)
|
||
mask = torch.cat([mask_chosen, mask_rejected], dim=0)
|
||
|
||
lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)
|
||
for param_group in optimizer.param_groups:
|
||
param_group['lr'] = lr
|
||
|
||
with ctx:
|
||
with torch.no_grad():
|
||
ref_outputs = ref_model(x)
|
||
ref_logits = ref_outputs.logits
|
||
ref_probs = logits_to_probs(ref_logits, y)
|
||
ref_probs = ref_probs * mask
|
||
outputs = model(x)
|
||
logits = outputs.logits
|
||
probs = logits_to_probs(logits, y)
|
||
probs = probs * mask
|
||
loss = dpo_loss(ref_probs, probs, mask, beta=0.1)
|
||
loss = loss / args.accumulation_steps
|
||
|
||
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:{:.12f} epoch_Time:{}min:'.format(
|
||
epoch + 1,
|
||
args.epochs,
|
||
step,
|
||
iter_per_epoch,
|
||
loss.item() * args.accumulation_steps,
|
||
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 * args.accumulation_steps,
|
||
"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}/rlhf_{lm_config.hidden_size}{moe_path}.pth'
|
||
|
||
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
|
||
state_dict = model.module.state_dict()
|
||
else:
|
||
state_dict = model.state_dict()
|
||
state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存
|
||
torch.save(state_dict, ckp)
|
||
model.train()
|
||
|
||
|
||
def init_model(lm_config):
|
||
"""初始化DPO训练所需的模型
|
||
|
||
包括：
|
||
1. 主模型：用于优化的模型
|
||
2. 参考模型：用于计算KL散度，防止策略过度偏离
|
||
3. 分词器：用于处理输入文本
|
||
"""
|
||
# 加载分词器
|
||
tokenizer = AutoTokenizer.from_pretrained('../model/')
|
||
|
||
# 初始化主模型
|
||
model = MiniMindForCausalLM(lm_config)
|
||
moe_path = '_moe' if lm_config.use_moe else ''
|
||
ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth'
|
||
state_dict = torch.load(ckp, map_location=args.device)
|
||
model.load_state_dict(state_dict, strict=False)
|
||
|
||
# 初始化参考模型（固定参数的SFT模型）
|
||
ref_model = MiniMindForCausalLM(lm_config)
|
||
ref_model.load_state_dict(state_dict, strict=False)
|
||
ref_model.eval() # 设置为评估模式
|
||
ref_model.requires_grad_(False) # 冻结所有参数
|
||
|
||
Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
|
||
# 将模型移动到指定设备
|
||
model = model.to(args.device)
|
||
ref_model = ref_model.to(args.device)
|
||
|
||
return model, ref_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__":
|
||
# 配置命令行参数
|
||
parser = argparse.ArgumentParser(description="MiniMind RLHF")
|
||
|
||
# 基础训练参数
|
||
parser.add_argument("--out_dir", type=str, default="../out", help="输出目录")
|
||
parser.add_argument("--epochs", type=int, default=2, help="训练轮数")
|
||
parser.add_argument("--batch_size", type=int, default=4, help="批次大小")
|
||
# DPO训练使用较小的学习率，防止策略过度偏离
|
||
parser.add_argument("--learning_rate", type=float, default=1e-8, help="学习率，建议<=1e-8，否则容易遗忘训坏")
|
||
parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
|
||
parser.add_argument("--dtype", type=str, default="bfloat16", help="训练精度")
|
||
|
||
# 日志和监控参数
|
||
parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb记录训练过程")
|
||
parser.add_argument("--wandb_project", type=str, default="MiniMind-RLHF-SFT", help="wandb项目名称")
|
||
parser.add_argument("--log_interval", type=int, default=100, help="日志打印间隔")
|
||
parser.add_argument("--save_interval", type=int, default=100, help="模型保存间隔")
|
||
|
||
# 分布式训练参数
|
||
parser.add_argument("--num_workers", type=int, default=1, help="数据加载进程数")
|
||
parser.add_argument("--ddp", action="store_true", help="是否使用分布式训练")
|
||
parser.add_argument('--local_rank', type=int, default=-1, help="分布式训练的本地进程编号")
|
||
|
||
# 优化器参数
|
||
parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
|
||
parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
|
||
parser.add_argument("--warmup_iters", type=int, default=0, help="学习率预热迭代次数")
|
||
|
||
# 模型参数
|
||
parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度")
|
||
parser.add_argument('--num_hidden_layers', default=8, type=int, help="Transformer层数")
|
||
parser.add_argument('--max_seq_len', default=1024, type=int, help="最大序列长度")
|
||
parser.add_argument('--use_moe', default=False, type=bool, help="是否使用MoE")
|
||
|
||
# 数据参数
|
||
parser.add_argument("--data_path", type=str, default="../dataset/dpo.jsonl", help="训练数据路径，包含人类偏好对")
|
||
|
||
args = parser.parse_args()
|
||
|
||
lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe)
|
||
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 * args.max_seq_len
|
||
device_type = "cuda" if "cuda" in args.device else "cpu"
|
||
|
||
args.wandb_run_name = f"MiniMind-Full-DPO-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"
|
||
base_seed = 1337
|
||
torch.manual_seed(base_seed)
|
||
torch.cuda.manual_seed(base_seed)
|
||
|
||
if ddp:
|
||
init_distributed_mode()
|
||
args.device = torch.device(DEVICE)
|
||
rank = dist.get_rank()
|
||
torch.manual_seed(base_seed + rank)
|
||
# 同时设置 CUDA 的随机种子
|
||
torch.cuda.manual_seed(base_seed + rank)
|
||
|
||
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, ref_model, tokenizer = init_model(lm_config)
|
||
|
||
train_ds = DPODataset(args.data_path, tokenizer, max_length=args.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 in ['float16', 'bfloat16']))
|
||
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
|
||
|
||
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)
|