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finetuning_lora_sft.py
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# -*- coding:utf-8 -*-
"""
文件说明：给予LoRA算法进行SFT
CUDA_VISIBLE_DEVICES=0 deepspeed finetuning_lora_sft.py --num_train_epochs 5 --train_batch_size 2 --lora_r 8
CUDA_VISIBLE_DEVICES=0 deepspeed finetuning_lora_sft.py --num_train_epochs 2 --train_batch_size 2 --lora_r 8 && shutdown now
CUDA_VISIBLE_DEVICES=0,1 deepspeed finetuning_lora_sft.py --num_train_epochs 2 --train_batch_size 2 --lora_r 8 && shutdown now
"""
from modeling_chatglm import ChatGLMForConditionalGeneration
from tokenization_chatglm import ChatGLMTokenizer
import torch
import deepspeed
import argparse
from torch.utils.data import RandomSampler, DataLoader
from data_set_sft import SFTDataSet, coll_fn
import os
import transformers
from shutil import copy
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, \
set_peft_model_state_dict
def print_trainable_parameters(model):
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
num_params = param.numel()
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
trainable_params += num_params
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}")
def set_args():
parser = argparse.ArgumentParser()
# parser.add_argument('--train_path', default='/app/ChatGLM-Deepspeed-LoRA/data/test.json', type=str, help='')
parser.add_argument('--train_path', default='/app/ChatGLM-Deepspeed-LoRA/data/alpaca_gpt4_data_zh_zx.json', type=str, help='')
parser.add_argument('--model_dir', default="/root/autodl-tmp/chatglm-6b", type=str, help='')
parser.add_argument('--num_train_epochs', default=1, type=int, help='')
parser.add_argument('--train_batch_size', default=2, type=int, help='')
parser.add_argument('--gradient_accumulation_steps', default=1, type=int, help='')
parser.add_argument('--output_dir', default='/app/ChatGLM-Deepspeed-LoRA/output_dir_lora/0503-speed', type=str, help='')
parser.add_argument('--log_steps', type=int, default=10, help='')
parser.add_argument('--max_seq_length', type=int, default=512, help='')
parser.add_argument('--local_rank', type=int, default=0, help='')
parser.add_argument('--lora_r', type=int, default=8, help='')
# parser.add_argument('--master_port', type=int, default=6666, help='')
return parser.parse_args()
def main():
args = set_args()
model = ChatGLMForConditionalGeneration.from_pretrained(args.model_dir)
tokenizer = ChatGLMTokenizer.from_pretrained(args.model_dir)
config = transformers.AutoConfig.from_pretrained(args.model_dir,trust_remote_code=True)
# Lora_config = LoraConfig(
# task_type="CAUSAL_LM",
# inference_mode=False,
# r=args.lora_r,
# lora_alpha=32,
# lora_dropout=0.1,
# )
# Lora_config = LoraConfig(
# task_type="CAUSAL_LM",
# inference_mode=False,
# r=32,
# lora_alpha=32,
# lora_dropout=0.1,
# target_modules=["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"],
# )
Lora_config = LoraConfig(r=args.lora_r,
lora_alpha=32,
target_modules=["query_key_value"],
lora_dropout=0.1,
bias="none",
task_type="CAUSAL_LM",
inference_mode=False,
)
model = get_peft_model(model, Lora_config)
model = model.cuda()
conf = {"train_micro_batch_size_per_gpu": args.train_batch_size,
"gradient_accumulation_steps": args.gradient_accumulation_steps,
"optimizer": {
"type": "Adam",
"params": {
"lr": 1e-5,
"betas": [
0.9,
0.95
],
"eps": 1e-8,
"weight_decay": 5e-4
}
},
"fp16": {
"enabled": True
},
"zero_optimization": {
"stage": 1,
"offload_optimizer": {
"device": "cpu",
"pin_memory": True
},
"allgather_partitions": True,
"allgather_bucket_size": 2e8,
"overlap_comm": True,
"reduce_scatter": True,
"reduce_bucket_size": 2e8,
"contiguous_gradients": True
},
# "zero_optimization": {
# "stage": 2,
# "offload_optimizer": {
# "device": "cpu",
# "pin_memory": True
# },
# "allgather_partitions": True,
# "allgather_bucket_size": 2e8,
# "reduce_scatter": True,
# "reduce_bucket_size": 2e8,
# "overlap_comm": True,
# "contiguous_gradients": True
# },
# "zero_optimization": {
# "stage": 3,
# "contiguous_gradients": True,
# "stage3_max_live_parameters": 1e9,
# "stage3_max_reuse_distance": 1e9,
# "stage3_prefetch_bucket_size": 1e7,
# "stage3_param_persistence_threshold": 1e5,
# "reduce_bucket_size": 1e7,
# "sub_group_size": 1e9,
# "offload_optimizer": {
# "device": "cpu"
# },
# "offload_param": {
# "device": "cpu"
# }
# },
"steps_per_print": args.log_steps
}
print_trainable_parameters(model)
for name, param in model.named_parameters():
if param.requires_grad == True:
print(name)
train_dataset = SFTDataSet(args.train_path, tokenizer, config, args.max_seq_length)
train_dataloader = DataLoader(train_dataset,
batch_size=conf["train_micro_batch_size_per_gpu"],
sampler=RandomSampler(train_dataset),
collate_fn=coll_fn,
drop_last=True,
num_workers=0)
model_engine, optimizer, _, _ = deepspeed.initialize(config=conf,
model=model,
model_parameters=model.parameters())
model_engine.train()
global_step = 0
for i_epoch in range(args.num_train_epochs):
train_iter = iter(train_dataloader)
for step, batch in enumerate(train_iter):
input_ids = batch["input_ids"].cuda()
labels = batch["labels"].cuda()
outputs = model_engine.forward(input_ids=input_ids, labels=labels)
loss = outputs[0]
if conf["gradient_accumulation_steps"] > 1:
loss = loss / conf["gradient_accumulation_steps"]
model_engine.backward(loss)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
if (step + 1) % conf["gradient_accumulation_steps"] == 0:
model_engine.step()
global_step += 1
if global_step % args.log_steps == 0:
print("loss:{}, global_step:{}".format(float(loss.item()), global_step))
save_dir = os.path.join(args.output_dir, f"global_step-{global_step}")
model_engine.save_pretrained(save_dir, overwrite_output_dir=True)
copy(os.path.join(args.model_dir, "tokenizer_config.json"), os.path.join(save_dir, "tokenizer_config.json"))
copy(os.path.join(args.model_dir, "ice_text.model"), os.path.join(save_dir, "ice_text.model"))
if __name__ == "__main__":
main()
# CUDA_VISIBLE_DEVICES=0 deepspeed --master_port 5555 finetuning_lora_sft.py