301 lines
12 KiB
Python
301 lines
12 KiB
Python
# 导入必要的库
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import os
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import sys
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__package__ = "trainer" # 设置包名
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# 将项目根目录添加到系统路径
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import time
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import math
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import warnings
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import torch
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import torch.nn.functional as F
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import torch.distributed as dist
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from contextlib import nullcontext
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from torch import optim
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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from dataset.lm_dataset import SFTDataset
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warnings.filterwarnings('ignore')
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# 日志打印函数
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# 在分布式训练时只在主进程(rank=0)上打印日志
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def Logger(content):
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if not ddp or dist.get_rank() == 0:
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print(content)
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# 余弦学习率调度器
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# 在训练过程中逐渐降低学习率，最终降到初始值的1/10
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def get_lr(current_step, total_steps, lr):
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return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
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# 知识蒸馏损失函数
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# 通过KL散度计算学生模型和教师模型输出分布之间的差异
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def distillation_loss_fn(student_logits, teacher_logits, temperature=1.0, reduction='batchmean'):
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# 计算教师模型的概率分布（停止梯度传播）
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with torch.no_grad():
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teacher_probs = F.softmax(teacher_logits / temperature, hidden_size=-1).detach()
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student_log_probs = F.log_softmax(student_logits / temperature, hidden_size=-1)
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kl = F.kl_div(
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student_log_probs,
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teacher_probs,
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reduction=reduction
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)
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return (temperature ** 2) * kl
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# 训练一个epoch
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# alpha: 用于平衡蒸馏损失和交叉熵损失的权重
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# temperature: 软目标的温度系数，用于控制知识蒸馏中概率分布的平滑程度
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def train_epoch(epoch, wandb, alpha=0.0, temperature=1.0):
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start_time = time.time()
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# 确保教师模型处于评估模式且参数冻结
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if teacher_model is not None:
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teacher_model.eval()
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teacher_model.requires_grad_(False)
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for step, (X, Y, loss_mask) in enumerate(train_loader):
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X = X.to(args.device)
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Y = Y.to(args.device)
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loss_mask = loss_mask.to(args.device)
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lr = get_lr(epoch * iter_per_epoch + step,
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args.epochs * iter_per_epoch,
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args.learning_rate)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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# 前向传播（学生模型）
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with ctx:
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res = model(X)
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student_logits = res.logits
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# 教师模型前向传播（只在eval & no_grad）
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if teacher_model is not None:
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with torch.no_grad():
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teacher_logits = teacher_model(X).logits
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vocab_size_student = student_logits.size(-1) # N
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teacher_logits = teacher_logits[..., :vocab_size_student]
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# ========== 计算损失 ==========
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# 1) Ground-Truth CE Loss（可选）
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loss_mask_flat = loss_mask.view(-1)
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ce_loss = F.cross_entropy(
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student_logits.view(-1, student_logits.size(-1)),
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Y.view(-1),
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ignore_index=0,
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reduction='none'
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)
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ce_loss = torch.sum(ce_loss * loss_mask_flat) / loss_mask_flat.sum()
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if lm_config_student.use_moe:
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ce_loss += res.aux_loss
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# 2) Distillation Loss（可选）
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if teacher_model is not None:
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# 只在有效token位置做蒸馏
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distill_loss = distillation_loss_fn(
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student_logits.view(-1, student_logits.size(-1))[loss_mask_flat == 1],
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teacher_logits.view(-1, teacher_logits.size(-1))[loss_mask_flat == 1],
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temperature=temperature
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)
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else:
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distill_loss = torch.tensor(0.0, device=args.device)
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# 3) 总损失 = alpha * CE + (1-alpha) * Distill
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loss = (alpha * ce_loss + (1 - alpha) * distill_loss) / args.accumulation_steps
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scaler.scale(loss).backward()
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if (step + 1) % args.accumulation_steps == 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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if step % args.log_interval == 0:
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spend_time = time.time() - start_time
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.4f} lr:{:.12f} epoch_Time:{}min:'.format(
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epoch,
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args.epochs - 1,
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step,
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iter_per_epoch,
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loss.item(),
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optimizer.param_groups[-1]['lr'],
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spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60
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)
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)
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if (wandb is not None) and (not ddp or dist.get_rank() == 0):
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wandb.log({
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"loss": loss.item(),
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"ce_loss": ce_loss.item(),
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"distill_loss": distill_loss.item() if teacher_model is not None else 0.0,
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"lr": optimizer.param_groups[-1]['lr'],
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"last-time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60
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})
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if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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moe_path = '_moe' if lm_config_student.use_moe else ''
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ckp = f'{args.save_dir}/full_dist_{lm_config_student.hidden_size}{moe_path}.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存
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torch.save(state_dict, ckp)
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model.train()
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# 初始化学生模型
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# 学生模型通常比教师模型更小，但需要保持相同的任务能力
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def init_student_model(lm_config):
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# 加载预训练的分词器
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tokenizer = AutoTokenizer.from_pretrained('../model/')
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# 初始化学生模型
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model = MiniMindForCausalLM(lm_config)
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# 根据是否使用MoE设置权重路径
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth'
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# 加载预训练权重
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state_dict = torch.load(ckp, map_location=args.device)
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model.load_state_dict(state_dict, strict=False)
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# 打印模型可训练参数总量
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Logger(f'学生模型(LLM)总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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# 将模型移动到指定设备
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model = model.to(args.device)
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return model, tokenizer
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# 初始化教师模型
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# 教师模型通常是一个更大、性能更好的预训练模型
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def init_teacher_model(lm_config):
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# 初始化教师模型
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model = MiniMindForCausalLM(lm_config)
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# 根据是否使用MoE设置权重路径
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth'
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# 加载预训练权重
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state_dict = torch.load(ckp, map_location=args.device)
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model.load_state_dict(state_dict, strict=False)
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# 打印模型可训练参数总量
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Logger(f'教师模型(LLM)总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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# 将模型移动到指定设备
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model = model.to(args.device)
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return model
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def init_distributed_mode():
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if not ddp: return
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global ddp_local_rank, DEVICE
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dist.init_process_group(backend="nccl")
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ddp_rank = int(os.environ["RANK"])
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ddp_local_rank = int(os.environ["LOCAL_RANK"])
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ddp_world_size = int(os.environ["WORLD_SIZE"])
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DEVICE = f"cuda:{ddp_local_rank}"
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torch.cuda.set_device(DEVICE)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind Full SFT")
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parser.add_argument("--out_dir", type=str, default="../out")
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parser.add_argument("--epochs", type=int, default=6)
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parser.add_argument("--batch_size", type=int, default=32)
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parser.add_argument("--learning_rate", type=float, default=5e-6)
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
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parser.add_argument("--dtype", type=str, default="bfloat16")
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parser.add_argument("--use_wandb", action="store_true")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT")
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parser.add_argument("--num_workers", type=int, default=1)
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parser.add_argument("--ddp", action="store_true")
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parser.add_argument("--accumulation_steps", type=int, default=1)
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parser.add_argument("--grad_clip", type=float, default=1.0)
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parser.add_argument("--warmup_iters", type=int, default=0)
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parser.add_argument("--log_interval", type=int, default=100)
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parser.add_argument("--save_interval", type=int, default=100)
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parser.add_argument("--max_seq_len", type=int, default=512)
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parser.add_argument('--local_rank', type=int, default=-1)
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parser.add_argument("--data_path", type=str, default="../dataset/sft_xxx.jsonl")
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parser.add_argument("--wandb_api_key", type=str, default=None, help="WandB API Key，用于无交互环境自动登录")
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args = parser.parse_args()
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# 定义学生模型和教师模型
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lm_config_student = MiniMindConfig(hidden_size=512, num_hidden_layers=8)
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lm_config_teacher = MiniMindConfig(hidden_size=768, num_hidden_layers=16)
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args.save_dir = os.path.join(args.out_dir)
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os.makedirs(args.save_dir, exist_ok=True)
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os.makedirs(args.out_dir, exist_ok=True)
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tokens_per_iter = args.batch_size * args.max_seq_len
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device_type = "cuda" if "cuda" in args.device else "cpu"
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args.wandb_run_name = f"MiniMind-Dist-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
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ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
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ddp_local_rank, DEVICE = 0, "cuda:0"
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base_seed = 1337
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torch.manual_seed(base_seed)
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torch.cuda.manual_seed(base_seed)
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if ddp:
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init_distributed_mode()
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args.device = torch.device(DEVICE)
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rank = dist.get_rank()
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torch.manual_seed(base_seed + rank)
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# 同时设置 CUDA 的随机种子
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torch.cuda.manual_seed(base_seed + rank)
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if args.use_wandb and (not ddp or ddp_local_rank == 0):
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import wandb
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# 优先使用命令行参数，其次环境变量
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api_key = args.wandb_api_key or os.environ.get("WANDB_API_KEY", None)
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if api_key is not None:
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# 自动登录，适用于无交互环境
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wandb.login(key=api_key)
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# 初始化 wandb 任务
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wandb.init(project=args.wandb_project, name=args.wandb_run_name)
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else:
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wandb = None
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# 初始化学生模型和教师模型
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model, tokenizer = init_student_model(lm_config_student)
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teacher_model = init_teacher_model(lm_config_teacher)
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train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
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train_sampler = DistributedSampler(train_ds) if ddp else None
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train_loader = DataLoader(
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train_ds,
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batch_size=args.batch_size,
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pin_memory=True,
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drop_last=False,
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shuffle=False,
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num_workers=args.num_workers,
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sampler=train_sampler
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)
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
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if ddp:
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model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
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iter_per_epoch = len(train_loader)
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for epoch in range(args.epochs):
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train_epoch(epoch, wandb)
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