From 253576967c990aba78031367067c9619cc2dd8bc Mon Sep 17 00:00:00 2001
From: iomgaa <iomgaaycz@gmail.com>
Date: Thu, 8 May 2025 21:11:05 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86train=5Fembedding?=
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---
train_embedding.py | 306 ++++++++++++++++++++++++++++++---------------
1 file changed, 208 insertions(+), 98 deletions(-)
diff --git a/train_embedding.py b/train_embedding.py
index fbb363a..7a4493d 100644
--- a/train_embedding.py
+++ b/train_embedding.py
@@ -12,31 +12,122 @@ import torch.distributed as dist
from torch import optim, nn
from torch.nn.parallel import DistributedDataParallel
from torch.optim.lr_scheduler import CosineAnnealingLR
-from torch.utils.data import DataLoader, DistributedSampler
+from torch.utils.data import DataLoader, DistributedSampler, Dataset
from contextlib import nullcontext
+import random
+import numpy as np
+import json
from transformers import AutoTokenizer
# Removed: from model.model import MiniMindLM
from model.LMConfig import LMConfig
-from model.dataset import PretrainDataset
+# from model.dataset import PretrainDataset
warnings.filterwarnings('ignore')
-# Define a simple model for pretraining embeddings
-class EmbeddingPretrainer(nn.Module):
+# Define a Word2Vec-style CBOW model
+class CBOWModel(nn.Module):
def __init__(self, config: LMConfig):
super().__init__()
- self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
- self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False)
- # Tie weights (optional but common)
- # self.tok_embeddings.weight = self.lm_head.weight
+ self.vocab_size = config.vocab_size
+ self.embedding_dim = config.dim
+
+ # Input embeddings (context words)
+ self.embeddings = nn.Embedding(config.vocab_size, config.dim)
+
+ # Output weights for target prediction
+ self.output_weights = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+ # Initialize weights
+ self.init_weights()
+
+ def init_weights(self):
+ # Xavier initialization for better convergence
+ nn.init.xavier_uniform_(self.embeddings.weight)
+ nn.init.xavier_uniform_(self.output_weights.weight)
+
+ def forward(self, context_words):
+ # context_words shape: [batch_size, context_size]，context_size可变
+
+ # Get embeddings for all context words
+ embeds = self.embeddings(context_words) # [batch_size, context_size, embedding_dim]
+
+ # Average the context word embeddings along context dimension
+ embeds = torch.mean(embeds, dim=1) # [batch_size, embedding_dim]
+
+ # Predict the target word
+ output = self.output_weights(embeds) # [batch_size, vocab_size]
+
+ return output
- def forward(self, input_ids):
- hidden_states = self.tok_embeddings(input_ids)
- logits = self.lm_head(hidden_states)
- return logits
+
+# Word2Vec CBOW dataset
+class CBOWDataset(Dataset):
+ def __init__(self, data_path, tokenizer, max_length=512, window_size=5):
+ super().__init__()
+ self.tokenizer = tokenizer
+ self.window_size = window_size
+ self.max_length = max_length
+ self.samples = self.load_data(data_path)
+
+ def load_data(self, path):
+ samples = []
+ with open(path, 'r', encoding='utf-8') as f:
+ for line_num, line in enumerate(f, 1):
+ data = json.loads(line.strip())
+ samples.append(data)
+ return samples
+
+ def __len__(self):
+ return len(self.samples)
+
+ def __getitem__(self, index):
+ sample = self.samples[index]
+
+ # 构建输入文本
+ text = f"{self.tokenizer.bos_token}{str(sample['text'])}{self.tokenizer.eos_token}"
+ encoding = self.tokenizer(
+ text,
+ max_length=self.max_length,
+ padding='max_length',
+ truncation=True,
+ return_tensors='pt'
+ )
+
+ # 获取token ids
+ input_ids = encoding.input_ids.squeeze()
+ # 过滤掉padding
+ attention_mask = encoding.attention_mask.squeeze()
+ valid_indices = torch.where(attention_mask == 1)[0]
+ valid_input_ids = input_ids[valid_indices]
+
+ # 确保有足够的token进行CBOW训练
+ if len(valid_input_ids) <= 2 * self.window_size + 1:
+ # 如果token不足，随机选择一个不同的样本
+ return self.__getitem__(random.randint(0, len(self.samples) - 1))
+
+ # 随机选择一个中心位置（不包括首尾的特殊token）
+ # 确保中心位置两边都有至少window_size个token
+ min_center_pos = self.window_size + 1 # 避开起始token
+ max_center_pos = len(valid_input_ids) - self.window_size - 1 # 避开结束token
+
+ if max_center_pos <= min_center_pos:
+ return self.__getitem__(random.randint(0, len(self.samples) - 1))
+
+ center_pos = random.randint(min_center_pos, max_center_pos)
+
+ # 目标词（中心词）
+ target = valid_input_ids[center_pos].unsqueeze(0)
+
+ # 上下文词（中心词前后的词）
+ context = torch.cat([
+ valid_input_ids[center_pos - self.window_size:center_pos],
+ valid_input_ids[center_pos + 1:center_pos + self.window_size + 1]
+ ])
+
+ return context, target
def Logger(content):
@@ -52,14 +143,16 @@ def get_lr(current_step, total_steps, lr):
def train_epoch(epoch, wandb):
- loss_fct = nn.CrossEntropyLoss(reduction='none', ignore_index=0) # Assuming 0 is pad_token_id
+ loss_fct = nn.CrossEntropyLoss()
start_time = time.time()
- for step, (X, Y, loss_mask) in enumerate(train_loader):
+ total_loss = 0
+ total_samples = 0
+
+ for step, (context, target) in enumerate(train_loader):
try:
# 将数据加载到设备上
- X = X.to(args.device)
- Y = Y.to(args.device)
- loss_mask = loss_mask.to(args.device)
+ context = context.to(args.device)
+ target = target.to(args.device)
# 更新学习率
lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)
@@ -67,28 +160,28 @@ def train_epoch(epoch, wandb):
param_group['lr'] = lr
with ctx:
- logits = model(X) # Model returns logits directly
- loss = loss_fct(
- logits.view(-1, logits.size(-1)),
- Y.view(-1)
- ).view(Y.size())
- loss = (loss * loss_mask).sum() / loss_mask.sum()
- # Removed: loss += res.aux_loss
+ # Forward pass
+ logits = model(context) # [batch_size, vocab_size]
+ # target是[batch_size, 1]，需要squeeze成[batch_size]来匹配CrossEntropyLoss的预期
+ loss = loss_fct(logits, target.squeeze())
loss = loss / args.accumulation_steps
# Print data types for debugging
- if step == 0 and (not ddp or dist.get_rank() == 0): # Print only for the first step of the first epoch on the main process
+ if step == 0 and (not ddp or dist.get_rank() == 0):
Logger("---- Data Type Check ----")
- Logger(f"X.dtype: {X.dtype}")
- if hasattr(model, 'module'): # DDP case
+ Logger(f"context.dtype: {context.dtype}")
+ Logger(f"context.shape: {context.shape}")
+ Logger(f"target.dtype: {target.dtype}")
+ Logger(f"target.shape: {target.shape}")
+ if hasattr(model, 'module'): # DDP case
Logger(f"Model parameter dtype: {next(model.module.parameters()).dtype}")
- else: # Non-DDP case
+ else: # Non-DDP case
Logger(f"Model parameter dtype: {next(model.parameters()).dtype}")
- Logger(f"logits.dtype: {logits.dtype}") # Changed from res.logits.dtype
+ Logger(f"logits.dtype: {logits.dtype}")
+ Logger(f"logits.shape: {logits.shape}")
Logger(f"loss.dtype: {loss.dtype}")
Logger("-------------------------")
-
scaler.scale(loss).backward()
if (step + 1) % args.accumulation_steps == 0:
@@ -99,52 +192,43 @@ def train_epoch(epoch, wandb):
scaler.update()
optimizer.zero_grad(set_to_none=True)
+
+ total_loss += loss.item() * args.accumulation_steps
+ total_samples += 1
# 打印日志
if step % args.log_interval == 0:
spend_time = time.time() - start_time
+ avg_loss = total_loss / total_samples if total_samples > 0 else 0
Logger(
'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format(
epoch + 1,
args.epochs,
step,
iter_per_epoch,
- loss.item() * args.accumulation_steps,
+ avg_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.item() * args.accumulation_steps,
+ wandb.log({"loss": avg_loss,
"lr": optimizer.param_groups[-1]['lr'],
"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
- # 保存模型
- if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
- model.eval()
- # Modified checkpoint path and content
- ckp = f'{args.save_dir}/pretrained_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}.pth'
-
- if isinstance(model, torch.nn.parallel.DistributedDataParallel):
- embedding_state_dict = model.module.tok_embeddings.state_dict()
- else:
- embedding_state_dict = model.tok_embeddings.state_dict()
-
- torch.save(embedding_state_dict, ckp)
- Logger(f"Saved pretrained embedding to {ckp}")
- model.train()
-
except Exception as e:
print(f"Error occurred: {str(e)}")
+ import traceback
+ traceback.print_exc()
# Modified checkpoint path for error
- save_path = f'{args.save_dir}/pretrained_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_ERROR.pth'
- if os.path.exists(save_path):
+ save_path = f'{args.save_dir}/word2vec_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_ERROR.pth'
+ if os.path.exists(save_path):
os.remove(save_path)
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
- state_dict = model.module.tok_embeddings.state_dict()
+ state_dict = model.module.embeddings.state_dict()
else:
- state_dict = model.tok_embeddings.state_dict()
- torch.save(state_dict, save_path) # Save embedding state dict on error
+ state_dict = model.embeddings.state_dict()
+ torch.save(state_dict, save_path)
for name, param in model.named_parameters():
if param.grad is not None and torch.isnan(param.grad).any():
@@ -156,9 +240,23 @@ def train_epoch(epoch, wandb):
print(f"Parameter {name} gradients: {param.grad}")
raise ValueError("NaN gradient detected")
+
+ # Save model once at the end of each epoch
+ if not ddp or dist.get_rank() == 0:
+ model.eval()
+ ckp = f'{args.save_dir}/word2vec_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_epoch{epoch+1}.pth'
+
+ if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+ embedding_state_dict = model.module.embeddings.state_dict()
+ else:
+ embedding_state_dict = model.embeddings.state_dict()
+
+ torch.save(embedding_state_dict, ckp)
+ Logger(f"Saved word2vec embedding for epoch {epoch+1} to {ckp}")
+ model.train()
-def init_model(lm_config_params: LMConfig): # Renamed for clarity
+def init_model(lm_config_params: LMConfig):
# 加载tokenizer
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
# Update vocab_size in lm_config if tokenizer has a different one
@@ -166,10 +264,10 @@ def init_model(lm_config_params: LMConfig): # Renamed for clarity
Logger(f"Updating lm_config.vocab_size from {lm_config_params.vocab_size} to {tokenizer.vocab_size} based on tokenizer.")
lm_config_params.vocab_size = tokenizer.vocab_size
- # 加载模型
- model = EmbeddingPretrainer(lm_config_params).to(args.device) # Use EmbeddingPretrainer
+ # 加载word2vec CBOW模型
+ model = CBOWModel(lm_config_params).to(args.device)
# 打印模型参数
- Logger(f'EmbeddingPretrainer total parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} Million')
+ Logger(f'CBOW Model total parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} Million')
return model, tokenizer
@@ -187,32 +285,27 @@ def init_distributed_mode():
# torchrun --nproc_per_node 2 train_embedding.py
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="MiniMind Embedding Pretraining") # Changed description
- parser.add_argument("--out_dir", type=str, default="out_embedding") # Changed default out_dir
- # 若要以最快速度实现zero则epochs设置为1轮；否则应当利用有限的数据训练2~6个epochs。
+ parser = argparse.ArgumentParser(description="MiniMind Word2Vec Embedding Training")
+ parser.add_argument("--out_dir", type=str, default="out_word2vec")
parser.add_argument("--epochs", type=int, default=3)
- parser.add_argument("--batch_size", type=int, default=32) # Smaller batch size might be needed if memory is an issue
+ parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--learning_rate", type=float, default=5e-4)
- parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") #如果GPU可用，则使用GPU，否则使用CPU。
+ parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
parser.add_argument("--dtype", type=str, default="bfloat16")
parser.add_argument("--use_wandb", default=False, action="store_true")
- parser.add_argument("--wandb_project", type=str, default="MiniMind-Embedding-Pretrain") # Changed project name
- parser.add_argument("--num_workers", type=int, default=8)
+ parser.add_argument("--wandb_project", type=str, default="MiniMind-Word2Vec-Training")
+ parser.add_argument("--num_workers", type=int, default=32)
parser.add_argument("--ddp", action="store_true")
- parser.add_argument("--accumulation_steps", type=int, default=8) #梯度累积步数，用于控制梯度更新频率。
- parser.add_argument("--grad_clip", type=float, default=1.0) #梯度裁剪阈值，用于防止梯度爆炸。
- # parser.add_argument("--warmup_iters", type=int, default=0) #预热迭代次数，用于控制学习率预热过程。 (Can be kept or removed)
- parser.add_argument("--log_interval", type=int, default=100) #日志打印间隔，用于控制日志打印的频率。
- parser.add_argument("--save_interval", type=int, default=100) #模型保存间隔，用于控制模型保存的频率。
- parser.add_argument('--local_rank', type=int, default=-1) #本地进程编号，用于分布式训练。
- parser.add_argument('--dim', default=768, type=int) #模型维度，用于控制模型的大小。
- # Removed n_layers, use_moe as they are not relevant for EmbeddingPretrainer
- # parser.add_argument('--n_layers', default=8, type=int)
- parser.add_argument('--max_seq_len', default=512, type=int) #最大序列长度，用于控制输入序列的最大长度。
- # parser.add_argument('--use_moe', default=False, type=bool)
- parser.add_argument("--data_path", type=str, default="./dataset/pretrain_hq.jsonl") #数据路径，用于控制数据集的路径。
- # Add vocab_size to args, though it will be overridden by tokenizer if different
+ parser.add_argument("--accumulation_steps", type=int, default=8)
+ parser.add_argument("--grad_clip", type=float, default=1.0)
+ parser.add_argument("--log_interval", type=int, default=100)
+ parser.add_argument("--save_interval", type=int, default=100)
+ parser.add_argument('--local_rank', type=int, default=-1)
+ parser.add_argument('--dim', default=768, type=int)
+ parser.add_argument('--max_seq_len', default=512, type=int)
+ parser.add_argument("--data_path", type=str, default="./dataset/pretrain_hq.jsonl")
parser.add_argument('--vocab_size', default=6400, type=int)
+ parser.add_argument('--window_size', default=5, type=int)
args = parser.parse_args()
@@ -222,24 +315,21 @@ if __name__ == "__main__":
dim=args.dim,
vocab_size=args.vocab_size, # Will be updated by tokenizer
max_seq_len=args.max_seq_len,
- # n_layers, n_heads, etc. are not directly used by EmbeddingPretrainer but LMConfig requires them
- # We can set them to default or minimal values if they cause issues, or modify LMConfig
- # For now, using defaults from LMConfig definition for unneeded params.
n_layers=1, # Minimal
n_heads=1, # Minimal
n_kv_heads=1 #Minimal
)
- args.save_dir = os.path.join(args.out_dir) #创建保存目录。
- os.makedirs(args.save_dir, exist_ok=True) #创建保存目录。
- os.makedirs(args.out_dir, exist_ok=True) #创建输出目录。
- tokens_per_iter = args.batch_size * lm_config.max_seq_len #计算每个迭代步骤的token数量。
+ 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 * lm_config.max_seq_len
print(f"tokens_per_iter: {tokens_per_iter}")
- device_type = "cuda" if "cuda" in args.device else "cpu" #确定设备类型。
+ device_type = "cuda" if "cuda" in args.device else "cpu"
# Determine the torch dtype
pt_dtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[args.dtype]
- args.wandb_run_name = f"MiniMind-Embedding-Pretrain-Dim-{args.dim}-Vocab-{lm_config.vocab_size}" # Updated run name
+ args.wandb_run_name = f"MiniMind-Word2Vec-Dim-{args.dim}-Vocab-{lm_config.vocab_size}-Window-{args.window_size}"
ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=pt_dtype)
@@ -270,39 +360,59 @@ if __name__ == "__main__":
# Update lm_config vocab_size again after tokenizer to ensure consistency for save path name
if lm_config.vocab_size != tokenizer.vocab_size:
lm_config.vocab_size = tokenizer.vocab_size
- args.wandb_run_name = f"MiniMind-Embedding-Pretrain-Dim-{args.dim}-Vocab-{lm_config.vocab_size}"
+ args.wandb_run_name = f"MiniMind-Word2Vec-Dim-{args.dim}-Vocab-{lm_config.vocab_size}-Window-{args.window_size}"
if wandb is not None and (not ddp or dist.get_rank() == 0):
wandb.config.update({'vocab_size': lm_config.vocab_size, 'wandb_run_name': args.wandb_run_name}, allow_val_change=True)
+ # 添加collate函数处理不同长度的序列
+ def collate_cbow_batch(batch):
+ # 提取context和target
+ contexts, targets = zip(*batch)
+
+ # 获取当前批次中最长的context长度
+ max_len = max([ctx.size(0) for ctx in contexts])
+
+ # 创建填充后的tensor
+ padded_contexts = torch.zeros(len(contexts), max_len, dtype=torch.long)
+
+ # 填充每个context
+ for i, ctx in enumerate(contexts):
+ ctx_len = ctx.size(0)
+ padded_contexts[i, :ctx_len] = ctx
+
+ # 将targets stack成一个tensor
+ stacked_targets = torch.stack(targets)
+
+ return padded_contexts, stacked_targets
- train_ds = PretrainDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len)
- train_sampler = DistributedSampler(train_ds, shuffle=True, seed=base_seed) if ddp else None # Added shuffle and seed
+ # Create Word2Vec CBOW dataset
+ train_ds = CBOWDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len, window_size=args.window_size)
+ train_sampler = DistributedSampler(train_ds, shuffle=True, seed=base_seed) if ddp else None
train_loader = DataLoader(
train_ds,
batch_size=args.batch_size,
pin_memory=True,
- drop_last=True, # Set to True for more stable training step counts
- shuffle=(train_sampler is None), # Shuffle only if not using DDP sampler
+ drop_last=True,
+ shuffle=(train_sampler is None),
num_workers=args.num_workers,
- sampler=train_sampler
+ sampler=train_sampler,
+ collate_fn=collate_cbow_batch
)
- scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) # bfloat16 also uses scaler
+ 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"} # Not relevant for EmbeddingPretrainer
model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
- # torch.autograd.set_detect_anomaly(True) # Can be enabled for debugging
iter_per_epoch = len(train_loader)
- Logger(f"Starting training for {args.epochs} epochs with {iter_per_epoch} iterations per epoch.")
+ Logger(f"Starting Word2Vec CBOW training for {args.epochs} epochs with {iter_per_epoch} iterations per epoch.")
for epoch in range(args.epochs):
if ddp:
- train_sampler.set_epoch(epoch) # Important for DDP shuffling
+ train_sampler.set_epoch(epoch)
train_epoch(epoch, wandb)
- if wandb is not None and (not ddp or dist.get_rank() == 0) :
+ if wandb is not None and (not ddp or dist.get_rank() == 0):
wandb.finish()
- Logger("Embedding pretraining finished.")
\ No newline at end of file
+ Logger("Word2Vec embedding training finished.")
\ No newline at end of file