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finetuning_pt.py
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finetuning_pt.py
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# -*- coding:utf-8 -*-
# @project: ChatGLM-6b-int4-Finetuning
# @filename: finetuning_pt
# @author: SgangX
# @time: 2023/5/15 13:51
"""
文件说明:
"""
from modeling_chatglm import ChatGLMForConditionalGeneration
from tokenization_chatglm import ChatGLMTokenizer
from configuration_chatglm import ChatGLMConfig
import torch
import deepspeed
import argparse
from torch.utils.data import RandomSampler, DataLoader
from data_set import Seq2SeqDataSet, coll_fn
import os
from shutil import copy
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='data/spo_0.json', type=str, help='')
parser.add_argument('--model_dir', default="../chatglm-6b-int4", type=str, help='')
parser.add_argument('--num_train_epochs', default=20, 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='output_dir_pt_20/', type=str, help='')
parser.add_argument('--log_steps', type=int, default=10, help='')
parser.add_argument('--max_len', type=int, default=768, help='')
parser.add_argument('--max_src_len', type=int, default=450, help='')
parser.add_argument('--pre_seq_len', type=int, default=16, help='')
parser.add_argument('--prefix_projection', type=bool, default=True, help='')
parser.add_argument('--local_rank', type=int, default=0, help='')
parser.add_argument('--prompt_text', type=str,
default="你现在是一个信息抽取模型,请你帮我抽取出关系内容为\"性能故障\", \"部件故障\", \"组成\"和 \"检测工具\"的相关三元组,三元组内部用\"_\"连接,三元组之间用\\n分割。文本:",
help='')
return parser.parse_args()
def main():
args = set_args()
config = ChatGLMConfig.from_pretrained(args.model_dir)
config.pre_seq_len = args.pre_seq_len
config.prefix_projection = args.prefix_projection
model = ChatGLMForConditionalGeneration.from_pretrained(args.model_dir, config=config)
tokenizer = ChatGLMTokenizer.from_pretrained(args.model_dir)
model = model.half().cuda()
if args.prefix_projection:
model.gradient_checkpointing_enable()
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
},
"steps_per_print": args.log_steps
}
for name, param in model.named_parameters():
if not any(nd in name for nd in ["prefix_encoder"]):
param.requires_grad = False
print_trainable_parameters(model)
for name, param in model.named_parameters():
if param.requires_grad == True:
print(name)
train_dataset = Seq2SeqDataSet(args.train_path, tokenizer, args.max_len, args.max_src_len, args.prompt_text)
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, use_cache=False)
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.save_pretrained(save_dir)
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=1 deepspeed finetuning_pt.py