[2025.06.10] adjust directory structure

2025-06-10 14:04:57 +08:00
parent ef2f7a1bc5
commit c6e171e8ed
29 changed files with 0 additions and 0 deletions
@@ -0,0 +1,120 @@
+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
+import glob
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+accelerator = Accelerator()
+set_seed(42)
+
+model_name = "/mnt/han.luzhi/dolly_llm/weights_llm"  
+resume_option = None                     
+# resume_option = True                    
+# resume_option = "/mnt/han.luzhi/dolly_llm/checkpoints_ds/checkpoint-500"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+if isinstance(resume_option, str) and os.path.exists(resume_option):
+    logger.info(f"从指定检查点 {resume_option} 加载模型...")
+    model = DollyForCausalLM.from_pretrained(resume_option)
+else:
+    logger.info("未找到检查点，从预训练模型加载...")
+    logger.info(f"从预训练模型 {model_name} 加载...")
+    model = DollyForCausalLM.from_pretrained(model_name)
+model.gradient_checkpointing_enable()
+
+data_files = glob.glob("/mnt/han.luzhi/dolly_llm/corpus/*.jsonl")
+logger.info(f"找到训练语料: {data_files}")
+
+dataset = load_dataset('json', data_files=data_files, split='train')
+# dataset = load_dataset('text', data_files={'train': '/mnt/han.luzhi/dolly_llm/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'], 
+    remove_columns=dataset.column_names,
+    num_proc=16  
+)
+
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False 
+)
+
+training_args = TrainingArguments(
+    output_dir="/mnt/han.luzhi/dolly_llm/checkpoints_ds",
+    deepspeed="/mnt/han.luzhi/dolly_llm/deepspeed_config/ds_stage2.json",  
+    do_eval=False,
+    save_strategy="steps",
+    save_steps=500,
+    learning_rate=1e-4,
+    per_device_train_batch_size=36,
+    num_train_epochs=2,
+    logging_dir="/mnt/han.luzhi/dolly_llm/logs",
+    logging_steps=20,
+    save_total_limit=2,
+    fp16=True,  
+    dataloader_num_workers=3,
+    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",
+    ddp_find_unused_parameters=False,
+    overwrite_output_dir=True,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets,
+    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}")
+
+if resume_option !=None:
+    trainer.train(resume_from_checkpoint=resume_option)
+else:
+    trainer.train()
+
+
+if accelerator.is_main_process: 
+    trainer.save_model("/mnt/han.luzhi/dolly_llm/result/final")
+    tokenizer.save_pretrained("/mnt/han.luzhi/dolly_llm/result/final")
+    logger.info("***** 训练完成，模型已保存 *****")
+
+##  deepspeed --num_gpus=4 --master_port=12345 pretrain_transformers.py
+#   deepspeed --include="localhost:1,2,3" --master_port=12345 pretrain_transformers.py