root
124 lines
3.6 KiB
Python
Executable File
124 lines
3.6 KiB
Python
Executable File
from modeling_dolly import DollyForCausalLM
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from configuration_dolly import DollyConfig
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from transformers import (
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Trainer,
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TrainingArguments,
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DataCollatorForLanguageModeling,
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AutoTokenizer,
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set_seed
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)
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from datasets import load_dataset
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from accelerate import Accelerator
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import os
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import logging
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import glob
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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accelerator = Accelerator()
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set_seed(42)
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model_name = "../weights/weights_llm"
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tokenizer_path=""
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resume_option = None
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first_time=True
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# resume_option = True
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# resume_option = "../checkpoints_ds/checkpoint-500"
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if first_time:
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model = DollyForCausalLM(DollyConfig())
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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if isinstance(resume_option, str) and os.path.exists(resume_option):
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logger.info(f"从指定检查点 {resume_option} 加载模型...")
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model = DollyForCausalLM.from_pretrained(resume_option)
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else:
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logger.info("未找到检查点，从预训练模型加载...")
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logger.info(f"从预训练模型 {model_name} 加载...")
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model = DollyForCausalLM.from_pretrained(model_name)
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model.gradient_checkpointing_enable()
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data_files = glob.glob("../corpus/*.jsonl")
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logger.info(f"找到训练语料: {data_files}")
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dataset = load_dataset('json', data_files=data_files, split='train')
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# dataset = load_dataset('text', data_files={'train': '../corpus/wikipedia.txt'})
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def preprocess_function(examples):
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return tokenizer(
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examples['text'],
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truncation=True,
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max_length=512,
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padding='max_length',
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return_tensors='pt'
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)
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tokenized_datasets = dataset.map(
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preprocess_function,
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batched=True,
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# remove_columns=['text'],
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remove_columns=dataset.column_names,
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num_proc=16
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)
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data_collator = DataCollatorForLanguageModeling(
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tokenizer=tokenizer,
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mlm=False
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)
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training_args = TrainingArguments(
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output_dir="../checkpoints_ds",
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deepspeed="../deepspeed_config/ds_stage2.json",
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do_eval=False,
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save_strategy="steps",
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save_steps=500,
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learning_rate=1e-4,
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per_device_train_batch_size=36,
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num_train_epochs=2,
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logging_dir="../logs",
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logging_steps=20,
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save_total_limit=2,
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fp16=True,
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dataloader_num_workers=3,
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gradient_accumulation_steps=2,
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report_to="tensorboard",
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optim="adamw_torch",
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lr_scheduler_type="cosine",
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warmup_steps=500,
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weight_decay=0.01,
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local_rank=int(os.environ.get("LOCAL_RANK", 0)),
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disable_tqdm=False,
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logging_first_step=True,
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log_level="info",
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ddp_find_unused_parameters=False,
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overwrite_output_dir=True,
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)
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_datasets,
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processing_class=tokenizer,
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data_collator=data_collator,
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)
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logger.info("***** 开始训练 *****")
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logger.info(f" 进程数 = {accelerator.num_processes}")
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logger.info(f" 设备 = {accelerator.device}")
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logger.info(f" 总批大小 = {training_args.per_device_train_batch_size * accelerator.num_processes * training_args.gradient_accumulation_steps}")
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if resume_option !=None:
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trainer.train(resume_from_checkpoint=resume_option)
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else:
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trainer.train()
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if accelerator.is_main_process:
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trainer.save_model("../result/final")
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tokenizer.save_pretrained("../result/final")
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logger.info("***** 训练完成，模型已保存 *****")
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## deepspeed --num_gpus=4 --master_port=12345 pretrain_transformers.py
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# deepspeed --include="localhost:1,2,3" --master_port=12345 pretrain_transformers.py |