/
inference.py
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/
inference.py
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import os
import numpy as np
import torch
from datasets import load_dataset
from transformers import LlamaTokenizer, AutoTokenizer, AutoModelForCausalLM, AutoConfig, AutoModel
from peft import PeftModel
import argparse
from tqdm import tqdm
import json
import jsonlines
from data_generate import DataGenerate
from config import CONFIG
parser = argparse.ArgumentParser()
parser.add_argument('--model_name_or_path', type=str, required=True)
parser.add_argument('--model_type', type=str, required=True)
parser.add_argument('--lora_path', type=str, required=True)
parser.add_argument('--data_file', type=str)
parser.add_argument('--output_path', type=str, required=True)
parser.add_argument('--cache_dir', type=str, default=None)
parser.add_argument('--temperature', type=float, default=0.1)
parser.add_argument('--topk', type=int, default=40)
parser.add_argument('--topp', type=int, default=0.9)
parser.add_argument('--do_sample', type=bool, default=True)
parser.add_argument('--num_beams', type=int, default=1)
parser.add_argument('--repetition_penalty', type=float, default=1.2)
parser.add_argument('--max_new_tokens', type=int, default=400)
args = parser.parse_args()
generation_config = dict(
temperature=args.temperature,
top_k=args.topk,
top_p=args.topp,
do_sample=args.do_sample,
num_beams=args.num_beams,
repetition_penalty=args.repetition_penalty,
max_new_tokens=args.max_new_tokens
)
sample_data = '感冒了怎么办?'
def main():
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
load_type = torch.float16
cfg = CONFIG
# 载入base_model
if args.model_type in cfg.MODEL_MAP.keys():
base_model = cfg.MODEL_MAP[args.model_type].from_pretrained(
args.model_name_or_path,
torch_dtype=load_type,
trust_remote_code=True,
).half()
else:
base_model = AutoModel.from_pretrained(
args.model_name_or_path,
torch_dtype=load_type,
trust_remote_code=True,
).half()
# lora
model = PeftModel.from_pretrained(
base_model,
args.lora_path,
).to(device)
model.eval()
# 分词器
if args.model_type in cfg.TOKENIZER_MAP.keys():
tokenizer = cfg.TOKENIZER_MAP[args.model_type].from_pretrained(
args.model_name_or_path,
trust_remote_code=True
)
else:
tokenizer = AutoTokenizer.from_pretrained(
args.model_name_or_path,
trust_remote_code=True
)
# tokenizer.bos_token_id = cfg.SPECIAL_IDS[args.model_name_or_path.split('/')[-1]]['bos_id']
# tokenizer.eos_token_id = cfg.SPECIAL_IDS[args.model_name_or_path.split('/')[-1]]['eos_id']
# tokenizer.pad_token_id = cfg.SPECIAL_IDS[args.model_name_or_path.split('/')[-1]]['pad_id']
# test data
if args.data_file is None:
examples = sample_data
else:
if args.data_file.endswith('.txt'):
with open(args.data_file, 'r') as f:
examples = [eval(l) for l in f.readlines()]
elif args.data_file.endswith('.jsonl') or args.data_file.endswith('.json'):
examples = []
with open(args.data_file, 'r') as f:
for data in jsonlines.Reader(f):
examples.append(data)
print("first 5 examples:")
for example in examples[:5]:
print(example)
# inference
with torch.no_grad():
print("Start inference.")
results = []
for example in tqdm(examples[:2000]):
if args.data_file.endswith('.txt'):
input_text = example
else:
# input_text = example['input']
input_text = example['instruction'][0] + example['input']
inputs = tokenizer(input_text, return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**generation_config
)
s = generation_output[0]
response = tokenizer.decode(s, skip_special_tokens=True)
response = response.replace(input_text, '')
if args.data_file.endswith('.txt'):
results.append({"Input":input_text, "Output":response})
elif args.data_file.endswith('.json') or args.data_file.endswith('.jsonl'):
example['prediction'] = response
results.append(example)
with jsonlines.open(os.path.join(args.output_path, 'generation_results.jsonl'), 'w') as writer:
writer.write_all(results)
with open(os.path.join(args.output_path, 'generation_config.json'), 'w') as f:
json.dump(generation_config, f, ensure_ascii=False, indent=4)
if __name__ == '__main__':
main()