add pretrain code

2025-05-19 08:37:39 +08:00
parent 7e55276b43
commit 2db94bfa0f
11 changed files with 909350 additions and 1 deletions
@@ -0,0 +1,99 @@
+import torch
+from src_llm.modeling_dolly import DollyForCausalLM
+from transformers import (
+    Trainer,
+    TrainingArguments,
+    DataCollatorForLanguageModeling,
+    AutoTokenizer,
+    set_seed
+)
+from datasets import load_dataset
+from accelerate import Accelerator
+import os
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+accelerator = Accelerator()
+set_seed(42)
+
+model_name = "/mnt/han.luzhi/weights_llm"  
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = DollyForCausalLM.from_pretrained(model_name)
+model.gradient_checkpointing_enable()
+dataset = load_dataset('text', data_files={'train': '/mnt/han.luzhi/corpus/wikipedia.txt'})
+
+def preprocess_function(examples):
+    return tokenizer(
+        examples['text'],
+        truncation=True,
+        max_length=512,
+        padding='max_length',
+        return_tensors='pt'
+    )
+
+tokenized_datasets = dataset.map(
+    preprocess_function,
+    batched=True,
+    remove_columns=['text'], 
+    num_proc=4  
+)
+
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False 
+)
+
+training_args = TrainingArguments(
+    output_dir="/mnt/han.luzhi/result",
+    do_eval=False,  
+    save_strategy="steps", 
+    save_steps=500,
+    learning_rate=5e-5,
+    per_device_train_batch_size=26,
+    num_train_epochs=5,
+    logging_dir="/mnt/han.luzhi/logs",
+    logging_steps=10,
+    save_total_limit=2,
+    fp16=True, 
+    dataloader_num_workers=4,
+    gradient_accumulation_steps=2, 
+    report_to="tensorboard",  
+    optim="adamw_torch",  
+    lr_scheduler_type="cosine", 
+    warmup_steps=500, 
+    weight_decay=0.01,
+    local_rank=int(os.environ.get("LOCAL_RANK", 0)),  
+    disable_tqdm=False, 
+    logging_first_step=True,  
+    log_level="info",  
+    log_level_replica="info",  
+    ddp_find_unused_parameters=False, 
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets['train'],
+    processing_class=tokenizer, 
+    data_collator=data_collator,
+)
+
+logger.info("***** 开始训练 *****")
+logger.info(f" 进程数 = {accelerator.num_processes}")
+logger.info(f" 设备 = {accelerator.device}")
+logger.info(f" 总批大小 = {training_args.per_device_train_batch_size * accelerator.num_processes * training_args.gradient_accumulation_steps}")
+
+trainer.train()
+
+if accelerator.is_main_process: 
+    trainer.save_model("/mnt/han.luzhi/result/final")
+    tokenizer.save_pretrained("/mnt/han.luzhi/result/final")
+    logger.info("***** 训练完成，模型已保存 *****")
+
+##  python3 -m torch.distributed.launch --nproc_per_node=4 --master_port=12345 pretrain_transformers.py