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import os
from datasets import load_dataset
import torch
import json
from transformers import AutoTokenizer, AutoModel, LlamaTokenizer, LlamaForCausalLM, AutoModelForCausalLM
from tqdm import tqdm
import argparse
# DEBUG
# os.chdir(os.path.dirname(os.path.abspath(__file__)))
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="chatglm2-6b")
parser.add_argument("--top_k", type=int, default=3)
parser.add_argument("--data", type=str, default="B500")
return parser.parse_args(args)
# This is the customized building prompt for chat models, here is an example for ChatGLM2
def build_chat(tokenizer, prompt, model_name):
if "chatglm" in model_name:
prompt = tokenizer.build_prompt(prompt)
elif "longchat" in model_name or "vicuna" in model_name:
from fastchat.model import get_conversation_template
conv = get_conversation_template("vicuna")
conv.append_message(conv.roles[0], prompt)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
elif "llama2" in model_name:
prompt = f"[INST]{prompt}[/INST]"
elif "xgen" in model_name:
header = (
"A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
prompt = header + f" ### Human: {prompt}\n###"
elif "internlm" in model_name:
prompt = f"<|User|>:{prompt}<eoh>\n<|Bot|>:"
return prompt
def get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device, model_name, args):
preds = [{}] * len(data)
if os.path.exists(f"{args.model}_pred_{args.data}_{args.top_k}/{dataset}.jsonl"):
with open(f"{args.model}_pred_{args.data}_{args.top_k}/{dataset}.jsonl", "r", encoding="utf-8") as f:
for index, item in enumerate(f):
preds[index] = json.loads(item)
for index, json_obj in enumerate(tqdm(data, desc=f"{dataset}")):
if preds[index] != {}:
continue
if args.top_k != 0:
json_obj['context'] = "".join(json_obj['retrieved'][:args.top_k])
prompt = prompt_format.format(**json_obj)
prompt = build_chat(tokenizer, prompt, model_name)
if "chatgpt" in model_name:
output = openai.ChatCompletion.create(model="gpt-3.5-turbo-16k",
messages=[{"role": "user", "content": prompt}], max_tokens=max_gen,
temperature=1.0)
pred = output['choices'][0]['message']['content']
context_length = output['usage']['prompt_tokens']
else:
# truncate to fit max_length (we suggest truncate in the middle, since the left and right side may contain crucial instructions)
tokenized_prompt = tokenizer(prompt, truncation=False, return_tensors="pt").input_ids[0]
if len(tokenized_prompt) > max_length:
half = int(max_length/2)
prompt = tokenizer.decode(tokenized_prompt[:half], skip_special_tokens=True)+tokenizer.decode(tokenized_prompt[-half:], skip_special_tokens=True)
if dataset not in ["trec", "triviaqa", "samsum", "lsht", "lcc", "repobench-p"]: # chat models are better off without build prompt on these tasks
prompt = build_chat(tokenizer, prompt, model_name)
input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
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if dataset == "samsum": # prevent illegal output on samsum (model endlessly repeat "\nDialogue"), might be a prompting issue
output = model.generate(
**input,
max_new_tokens=max_gen,
num_beams=1,
do_sample=False,
temperature=1.0,
min_length=context_length+1,
eos_token_id=[tokenizer.eos_token_id, tokenizer.encode("\n", add_special_tokens=False)[-1]],
)[0]
else:
output = model.generate(
**input,
max_new_tokens=max_gen,
num_beams=1,
do_sample=False,
temperature=1.0,
)[0]
pred = tokenizer.decode(output[context_length:], skip_special_tokens=True)
pred = post_process(pred, model_name)
preds[index] = {"pred": pred, "answers": json_obj["answers"], "all_classes": json_obj["all_classes"],
"context_length": context_length}
with open(f"{args.model}_pred_{args.data}_{args.top_k}/{dataset}.jsonl", "w", encoding="utf-8") as f:
for pred in preds:
json.dump(pred, f, ensure_ascii=False)
f.write('\n')
return preds
def post_process(response, model_name):
if "xgen" in model_name:
response = response.strip().replace("Assistant:", "")
elif "internlm" in model_name:
response = response.split("<eoa>")[0]
return response
def load_model_and_tokenizer(model2path, model_name, device):
if "chatgpt" in model_name:
return model_name, model_name
else:
if "chatglm" in model_name or "internlm" in model_name or "xgen" in model_name:
tokenizer = AutoTokenizer.from_pretrained(model2path[model_name], trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model2path[model_name], trust_remote_code=True, torch_dtype=torch.bfloat16).to(device)
elif "llama2" in model_name:
tokenizer = LlamaTokenizer.from_pretrained(model2path[model_name])
model = LlamaForCausalLM.from_pretrained(model2path[model_name], torch_dtype=torch.bfloat16).to(device)
elif "longchat" in model_name or "vicuna" in model_name:
from fastchat.model import load_model
model, _ = load_model(
model2path[model_name],
device='cpu',
num_gpus=0,
load_8bit=False,
cpu_offloading=False,
debug=False,
)
model = model.to(device)
model = model.bfloat16()
tokenizer = AutoTokenizer.from_pretrained(model2path[model_name], trust_remote_code=True, use_fast=False)
model = model.eval()
return model, tokenizer
if __name__ == '__main__':
args = parse_args()
model_name = args.model
if "chatgpt" in model_name:
import openai
# openai.api_base=""
openai.api_key = "YOUR_KEY"
# Retrieval is fit for these datasets
datasets = ["multifieldqa_en", "qasper", "2wikimqa", "dureader", \
"hotpotqa", "narrativeqa", "musique", "multifieldqa_zh"]
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# load configs
model2path = json.load(open("../config/model2path.json", "r"))
model2maxlen = json.load(open("../config/model2maxlen.json", "r"))
# we design specific prompt format and max generation length for each task, feel free to modify them to optimize model output
dataset2prompt = json.load(open("../config/dataset2prompt.json", "r"))
dataset2maxlen = json.load(open("../config/dataset2maxlen.json", "r"))
# define your model
model, tokenizer = load_model_and_tokenizer(model2path, model_name, device)
max_length = model2maxlen[model_name]
# predict on each dataset
os.makedirs(f"{args.model}_pred_{args.data}_{args.top_k}", exist_ok=True)
for dataset in datasets:
data = load_dataset(f'../LongBench/{args.data}/LongBench.py', dataset, split='test',
download_mode='force_redownload') # force to load from dir
prompt_format = dataset2prompt[dataset]
max_gen = dataset2maxlen[dataset]
preds = get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device, model_name, args)
