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import os
import sys
import argparse
from datetime import datetime
from functools import partial
import datasets
import torch
from torch.utils.tensorboard import SummaryWriter
import wandb
from transformers import (
AutoTokenizer,
AutoModel,
AutoModelForCausalLM,
TrainingArguments,
Trainer,
DataCollatorForSeq2Seq
)
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.integrations import TensorBoardCallback
# Importing LoRA specific modules
from peft import (
TaskType,
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict
)
from utils import *
# Replace with your own api_key and project name
os.environ['WANDB_API_KEY'] = 'ecf1e5e4f47441d46822d38a3249d62e8fc94db4'
os.environ['WANDB_PROJECT'] = 'fingpt-benchmark'
def main(args):
"""
Main function to execute the training script.
:param args: Command line arguments
"""
# Parse the model name and determine if it should be fetched from a remote source
model_name = parse_model_name(args.base_model, args.from_remote)
# Load the pre-trained causal language model
model = AutoModelForCausalLM.from_pretrained(
model_name,
# load_in_8bit=True,
# device_map="auto",
trust_remote_code=True
)
# Print model architecture for the first process in distributed training
if args.local_rank == 0:
print(model)
# Load tokenizer associated with the pre-trained model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# Apply model specific tokenization settings
if args.base_model != 'mpt':
tokenizer.padding_side = "left"
if args.base_model == 'qwen':
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids('<|endoftext|>')
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids('<|extra_0|>')
# Ensure padding token is set correctly
if not tokenizer.pad_token or tokenizer.pad_token_id == tokenizer.eos_token_id:
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.resize_token_embeddings(len(tokenizer))
# Load training and testing datasets
dataset_list = load_dataset(args.dataset, args.from_remote)
dataset_train = datasets.concatenate_datasets([d['train'] for d in dataset_list]).shuffle(seed=42)
if args.test_dataset:
dataset_list = load_dataset(args.test_dataset, args.from_remote)
dataset_test = datasets.concatenate_datasets([d['test'] for d in dataset_list])
dataset = datasets.DatasetDict({'train': dataset_train, 'test': dataset_test})
# Display first sample from the training dataset
print(dataset['train'][0])
# Filter out samples that exceed the maximum token length and remove unused columns
dataset = dataset.map(partial(tokenize, args, tokenizer))
print('original dataset length: ', len(dataset['train']))
dataset = dataset.filter(lambda x: not x['exceed_max_length'])
print('filtered dataset length: ', len(dataset['train']))
dataset = dataset.remove_columns(['instruction', 'input', 'output', 'exceed_max_length'])
print(dataset['train'][0])
# Create a timestamp for model saving
current_time = datetime.now()
formatted_time = current_time.strftime('%Y%m%d%H%M')
# Set up training arguments
training_args = TrainingArguments(
output_dir=f'finetuned_models/{args.run_name}_{formatted_time}', # 保存位置
logging_steps=args.log_interval,
num_train_epochs=args.num_epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
gradient_accumulation_steps=args.gradient_steps,
dataloader_num_workers=args.num_workers,
learning_rate=args.learning_rate,
warmup_ratio=args.warmup_ratio,
lr_scheduler_type=args.scheduler,
save_steps=args.eval_steps,
eval_steps=args.eval_steps,
fp16=True,
# fp16_full_eval=True,
deepspeed=args.ds_config,
evaluation_strategy=args.evaluation_strategy,
load_best_model_at_end=args.load_best_model,
remove_unused_columns=False,
report_to='wandb',
run_name=args.run_name
)
if not args.base_model == 'mpt':
model.gradient_checkpointing_enable()
model.enable_input_require_grads()
model.is_parallelizable = True
model.model_parallel = True
model.config.use_cache = (
False
)
# model = prepare_model_for_int8_training(model
# setup peft for lora
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
target_modules=lora_module_dict[args.base_model],
bias='none',
)
model = get_peft_model(model, peft_config)
# Initialize TensorBoard for logging
writer = SummaryWriter()
# Initialize the trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
data_collator=DataCollatorForSeq2Seq(
tokenizer, padding=True,
return_tensors="pt"
),
callbacks=[TensorBoardCallback(writer)],
)
# if torch.__version__ >= "2" and sys.platform != "win32":
# model = torch.compile(model)
# Clear CUDA cache and start training
torch.cuda.empty_cache()
trainer.train()
writer.close()
# Save the fine-tuned model
model.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
# Argument parser for command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", default=0, type=int)
parser.add_argument("--run_name", default='local-test', type=str)
parser.add_argument("--dataset", required=True, type=str)
parser.add_argument("--test_dataset", type=str)
parser.add_argument("--base_model", required=True, type=str, choices=['chatglm2', 'llama2', 'llama2-13b', 'llama2-13b-nr', 'baichuan', 'falcon', 'internlm', 'qwen', 'mpt', 'bloom'])
parser.add_argument("--max_length", default=512, type=int)
parser.add_argument("--batch_size", default=4, type=int, help="The train batch size per device")
parser.add_argument("--learning_rate", default=1e-4, type=float, help="The learning rate")
parser.add_argument("--num_epochs", default=8, type=float, help="The training epochs")
parser.add_argument("--gradient_steps", default=8, type=float, help="The gradient accumulation steps")
parser.add_argument("--num_workers", default=8, type=int, help="dataloader workers")
parser.add_argument("--log_interval", default=20, type=int)
parser.add_argument("--warmup_ratio", default=0.05, type=float)
parser.add_argument("--ds_config", default='./config_new.json', type=str)
parser.add_argument("--scheduler", default='linear', type=str)
parser.add_argument("--instruct_template", default='default')
parser.add_argument("--evaluation_strategy", default='steps', type=str)
parser.add_argument("--load_best_model", default='False', type=bool)
parser.add_argument("--eval_steps", default=0.1, type=float)
parser.add_argument("--from_remote", default=False, type=bool)
args = parser.parse_args()
# Login to Weights and Biases
wandb.login()
# Run the main function
main(args)
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