418 lines
17 KiB
Python
418 lines
17 KiB
Python
import os
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# 设置环境变量
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os.environ["WANDB_MODE"] = "offline" # 或者使用 "dryrun"
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import platform
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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 pandas as pd
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import torch
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import torch.distributed as dist
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from torch import optim, nn
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from torch.nn.parallel import DistributedDataParallel
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from torch.utils.data import DataLoader, DistributedSampler, Dataset
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from contextlib import nullcontext
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import random
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import numpy as np
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import json
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from transformers import AutoTokenizer
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# Removed: from model.model import MiniMindLM
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from model.LMConfig import LMConfig
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# from model.dataset import PretrainDataset
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warnings.filterwarnings('ignore')
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# Define a Word2Vec-style CBOW model
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class CBOWModel(nn.Module):
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def __init__(self, config: LMConfig):
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super().__init__()
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self.vocab_size = config.vocab_size
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self.embedding_dim = config.dim
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# Input embeddings (context words)
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self.embeddings = nn.Embedding(config.vocab_size, config.dim)
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# Output weights for target prediction
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self.output_weights = nn.Linear(config.dim, config.vocab_size, bias=False)
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# Initialize weights
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self.init_weights()
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def init_weights(self):
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# Xavier initialization for better convergence
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nn.init.xavier_uniform_(self.embeddings.weight)
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nn.init.xavier_uniform_(self.output_weights.weight)
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def forward(self, context_words):
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# context_words shape: [batch_size, context_size]，context_size可变
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# Get embeddings for all context words
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embeds = self.embeddings(context_words) # [batch_size, context_size, embedding_dim]
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# Average the context word embeddings along context dimension
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embeds = torch.mean(embeds, dim=1) # [batch_size, embedding_dim]
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# Predict the target word
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output = self.output_weights(embeds) # [batch_size, vocab_size]
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return output
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# Word2Vec CBOW dataset
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class CBOWDataset(Dataset):
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def __init__(self, data_path, tokenizer, max_length=512, window_size=5):
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super().__init__()
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self.tokenizer = tokenizer
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self.window_size = window_size
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self.max_length = max_length
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self.samples = self.load_data(data_path)
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def load_data(self, path):
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samples = []
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with open(path, 'r', encoding='utf-8') as f:
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for line_num, line in enumerate(f, 1):
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data = json.loads(line.strip())
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samples.append(data)
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return samples
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, index):
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sample = self.samples[index]
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# 构建输入文本
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text = f"{self.tokenizer.bos_token}{str(sample['text'])}{self.tokenizer.eos_token}"
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encoding = self.tokenizer(
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text,
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max_length=self.max_length,
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padding='max_length',
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truncation=True,
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return_tensors='pt'
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)
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# 获取token ids
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input_ids = encoding.input_ids.squeeze()
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# 过滤掉padding
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attention_mask = encoding.attention_mask.squeeze()
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valid_indices = torch.where(attention_mask == 1)[0]
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valid_input_ids = input_ids[valid_indices]
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# 确保有足够的token进行CBOW训练
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if len(valid_input_ids) <= 2 * self.window_size + 1:
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# 如果token不足，随机选择一个不同的样本
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return self.__getitem__(random.randint(0, len(self.samples) - 1))
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# 随机选择一个中心位置（不包括首尾的特殊token）
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# 确保中心位置两边都有至少window_size个token
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min_center_pos = self.window_size + 1 # 避开起始token
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max_center_pos = len(valid_input_ids) - self.window_size - 1 # 避开结束token
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if max_center_pos <= min_center_pos:
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return self.__getitem__(random.randint(0, len(self.samples) - 1))
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center_pos = random.randint(min_center_pos, max_center_pos)
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# 目标词（中心词）
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target = valid_input_ids[center_pos].unsqueeze(0)
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# 上下文词（中心词前后的词）
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context = torch.cat([
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valid_input_ids[center_pos - self.window_size:center_pos],
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valid_input_ids[center_pos + 1:center_pos + self.window_size + 1]
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])
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return context, target
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def Logger(content):
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# 如果没有使用ddp或者ddp的主设备，那么就打印
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if not ddp or dist.get_rank() == 0:
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print(content)
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def get_lr(current_step, total_steps, lr):
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# 更新学习率
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# \text{get\_lr}(c, t, l) = \frac{l}{10} + 0.5 \cdot l \cdot \left(1 + \cos\left(\frac{\pi \cdot c}{t}\right)\right)
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return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
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def train_epoch(epoch, wandb):
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loss_fct = nn.CrossEntropyLoss()
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start_time = time.time()
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total_loss = 0
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total_samples = 0
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for step, (context, target) in enumerate(train_loader):
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try:
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# 将数据加载到设备上
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context = context.to(args.device)
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target = target.to(args.device)
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# 更新学习率
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lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, 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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with ctx:
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# Forward pass
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logits = model(context) # [batch_size, vocab_size]
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# target是[batch_size, 1]，需要squeeze成[batch_size]来匹配CrossEntropyLoss的预期
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loss = loss_fct(logits, target.squeeze())
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loss = loss / args.accumulation_steps
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# Print data types for debugging
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if step == 0 and (not ddp or dist.get_rank() == 0):
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Logger("---- Data Type Check ----")
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Logger(f"context.dtype: {context.dtype}")
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Logger(f"context.shape: {context.shape}")
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Logger(f"target.dtype: {target.dtype}")
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Logger(f"target.shape: {target.shape}")
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if hasattr(model, 'module'): # DDP case
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Logger(f"Model parameter dtype: {next(model.module.parameters()).dtype}")
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else: # Non-DDP case
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Logger(f"Model parameter dtype: {next(model.parameters()).dtype}")
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Logger(f"logits.dtype: {logits.dtype}")
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Logger(f"logits.shape: {logits.shape}")
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Logger(f"loss.dtype: {loss.dtype}")
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Logger("-------------------------")
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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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total_loss += loss.item() * args.accumulation_steps
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total_samples += 1
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# 打印日志
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if step % args.log_interval == 0:
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spend_time = time.time() - start_time
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avg_loss = total_loss / total_samples if total_samples > 0 else 0
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format(
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epoch + 1,
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args.epochs,
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step,
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iter_per_epoch,
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avg_loss,
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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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if (wandb is not None) and (not ddp or dist.get_rank() == 0):
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wandb.log({"loss": avg_loss,
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"lr": optimizer.param_groups[-1]['lr'],
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"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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except Exception as e:
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print(f"Error occurred: {str(e)}")
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import traceback
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traceback.print_exc()
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# Modified checkpoint path for error
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save_path = f'{args.save_dir}/word2vec_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_ERROR.pth'
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if os.path.exists(save_path):
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os.remove(save_path)
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.embeddings.state_dict()
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else:
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state_dict = model.embeddings.state_dict()
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torch.save(state_dict, save_path)
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for name, param in model.named_parameters():
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if param.grad is not None and torch.isnan(param.grad).any():
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print(f"NaN gradient in parameter: {name}")
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for name, param in model.named_parameters():
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if param.grad is not None and torch.isnan(param.grad).any():
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print(f"Parameter {name} values: {param.data}")
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print(f"Parameter {name} gradients: {param.grad}")
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raise ValueError("NaN gradient detected")
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# Save model once at the end of each epoch
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if not ddp or dist.get_rank() == 0:
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model.eval()
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ckp = f'{args.save_dir}/word2vec_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_epoch{epoch+1}.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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embedding_state_dict = model.module.embeddings.state_dict()
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else:
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embedding_state_dict = model.embeddings.state_dict()
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torch.save(embedding_state_dict, ckp)
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Logger(f"Saved word2vec embedding for epoch {epoch+1} to {ckp}")
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model.train()
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def init_model(lm_config_params: LMConfig):
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# 加载tokenizer
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
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# Update vocab_size in lm_config if tokenizer has a different one
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if tokenizer.vocab_size != lm_config_params.vocab_size:
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Logger(f"Updating lm_config.vocab_size from {lm_config_params.vocab_size} to {tokenizer.vocab_size} based on tokenizer.")
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lm_config_params.vocab_size = tokenizer.vocab_size
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# 加载word2vec CBOW模型
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model = CBOWModel(lm_config_params).to(args.device)
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# 打印模型参数
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Logger(f'CBOW Model total parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} Million')
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return model, tokenizer
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def init_distributed_mode():
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if not ddp: return #如果没有启用分布式数据并行(DDP)，直接返回，不执行任何操作。
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global ddp_local_rank, DEVICE #声明这两个变量为全局变量，以便在函数外部也能访问它们。
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dist.init_process_group(backend="nccl") #初始化分布式进程组，使用NCCL后端（NVIDIA Collective Communications Library），这是NVIDIA GPU之间通信的优化库。
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ddp_rank = int(os.environ["RANK"]) #从环境变量获取当前进程的全局编号。
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ddp_local_rank = int(os.environ["LOCAL_RANK"]) #从环境变量获取当前进程的本地编号。
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ddp_world_size = int(os.environ["WORLD_SIZE"]) #从环境变量获取当前进程组中的进程总数。
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DEVICE = f"cuda:{ddp_local_rank}" #根据本地编号选择GPU设备。
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torch.cuda.set_device(DEVICE) #设置当前进程的GPU设备。
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# torchrun --nproc_per_node 2 train_embedding.py
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind Word2Vec Embedding Training")
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parser.add_argument("--out_dir", type=str, default="out_word2vec")
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parser.add_argument("--epochs", type=int, default=3)
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parser.add_argument("--batch_size", type=int, default=256)
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parser.add_argument("--learning_rate", type=float, default=5e-4)
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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", default=False, action="store_true")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Word2Vec-Training")
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parser.add_argument("--num_workers", type=int, default=32)
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parser.add_argument("--ddp", action="store_true")
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parser.add_argument("--accumulation_steps", type=int, default=8)
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parser.add_argument("--grad_clip", type=float, default=1.0)
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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('--local_rank', type=int, default=-1)
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parser.add_argument('--dim', default=768, type=int)
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parser.add_argument('--max_seq_len', default=512, type=int)
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parser.add_argument("--data_path", type=str, default="./dataset/pretrain_hq.jsonl")
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parser.add_argument('--vocab_size', default=6400, type=int)
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parser.add_argument('--window_size', default=5, type=int)
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args = parser.parse_args()
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# Create LMConfig with relevant parameters for embedding
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lm_config = LMConfig(
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dim=args.dim,
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vocab_size=args.vocab_size, # Will be updated by tokenizer
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max_seq_len=args.max_seq_len,
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n_layers=1, # Minimal
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n_heads=1, # Minimal
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n_kv_heads=1 #Minimal
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)
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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 * lm_config.max_seq_len
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print(f"tokens_per_iter: {tokens_per_iter}")
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device_type = "cuda" if "cuda" in args.device else "cpu"
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# Determine the torch dtype
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pt_dtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[args.dtype]
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args.wandb_run_name = f"MiniMind-Word2Vec-Dim-{args.dim}-Vocab-{lm_config.vocab_size}-Window-{args.window_size}"
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ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=pt_dtype)
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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" # Default values, will be overwritten in DDP
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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() # This sets DEVICE and ddp_local_rank
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args.device = torch.device(DEVICE) # Ensure args.device is updated
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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_all(base_seed + rank) # Use seed_all for DDP
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if args.use_wandb and (not ddp or dist.get_rank() == 0): # Check rank for DDP wandb init
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import wandb
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wandb.init(project=args.wandb_project, name=args.wandb_run_name, config=args)
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else:
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wandb = None
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model, tokenizer = init_model(lm_config) # Pass the lm_config instance
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# Update lm_config vocab_size again after tokenizer to ensure consistency for save path name
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if lm_config.vocab_size != tokenizer.vocab_size:
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lm_config.vocab_size = tokenizer.vocab_size
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args.wandb_run_name = f"MiniMind-Word2Vec-Dim-{args.dim}-Vocab-{lm_config.vocab_size}-Window-{args.window_size}"
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if wandb is not None and (not ddp or dist.get_rank() == 0):
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wandb.config.update({'vocab_size': lm_config.vocab_size, 'wandb_run_name': args.wandb_run_name}, allow_val_change=True)
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# 添加collate函数处理不同长度的序列
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def collate_cbow_batch(batch):
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# 提取context和target
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contexts, targets = zip(*batch)
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# 获取当前批次中最长的context长度
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max_len = max([ctx.size(0) for ctx in contexts])
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# 创建填充后的tensor
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padded_contexts = torch.zeros(len(contexts), max_len, dtype=torch.long)
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# 填充每个context
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for i, ctx in enumerate(contexts):
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ctx_len = ctx.size(0)
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padded_contexts[i, :ctx_len] = ctx
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# 将targets stack成一个tensor
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stacked_targets = torch.stack(targets)
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return padded_contexts, stacked_targets
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# Create Word2Vec CBOW dataset
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train_ds = CBOWDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len, window_size=args.window_size)
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train_sampler = DistributedSampler(train_ds, shuffle=True, seed=base_seed) 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=True,
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shuffle=(train_sampler is None),
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num_workers=args.num_workers,
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sampler=train_sampler,
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collate_fn=collate_cbow_batch
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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 = DistributedDataParallel(model, device_ids=[ddp_local_rank])
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iter_per_epoch = len(train_loader)
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Logger(f"Starting Word2Vec CBOW training for {args.epochs} epochs with {iter_per_epoch} iterations per epoch.")
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for epoch in range(args.epochs):
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if ddp:
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train_sampler.set_epoch(epoch)
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train_epoch(epoch, wandb)
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if wandb is not None and (not ddp or dist.get_rank() == 0):
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wandb.finish()
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Logger("Word2Vec embedding training finished.") |