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from transformers import AutoTokenizer, LlamaTokenizer, LlamaForCausalLM, AutoModelForCausalLM
import numpy as np
import random
import argparse
from llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
import torch.distributed as dist
import torch.multiprocessing as mp
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default=None, choices=["llama2-7b-chat-4k", "longchat-v1.5-7b-32k", "xgen-7b-8k", "internlm-7b-8k", "chatglm2-6b", "chatglm2-6b-32k", "chatglm3-6b-32k", "vicuna-v1.5-7b-16k"])
parser.add_argument('--e', action='store_true', help="Evaluate on LongBench-E")
return parser.parse_args(args)
# This is the customized building prompt for chat models
def build_chat(tokenizer, prompt, 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)
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 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 get_pred(rank, world_size, data, max_length, max_gen, prompt_format, dataset, device, model_name, model2path, out_path):
device = torch.device(f'cuda:{rank}')
model, tokenizer = load_model_and_tokenizer(model2path[model_name], model_name, device)
prompt = prompt_format.format(**json_obj)
# 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]
tokenized_prompt = tokenizer(prompt, truncation=False, return_tensors="pt", add_special_tokens=False).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 prompts on these tasks
prompt = build_chat(tokenizer, prompt, model_name)
if dataset in ["trec", "triviaqa", "samsum", "lsht", "lcc", "repobench-p"]:
input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
else:
input = prompt.to(device)
else:
input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
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)
with open(out_path, "a", encoding="utf-8") as f:
json.dump({"pred": pred, "answers": json_obj["answers"], "all_classes": json_obj["all_classes"], "length": json_obj["length"]}, f, ensure_ascii=False)
f.write('\n')
dist.destroy_process_group()
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def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed_all(seed)
def load_model_and_tokenizer(path, model_name, device):
if "chatglm" in model_name or "internlm" in model_name or "xgen" in model_name:
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(path, trust_remote_code=True, torch_dtype=torch.bfloat16).to(device)
elif "llama2" in model_name:
replace_llama_attn_with_flash_attn()
tokenizer = LlamaTokenizer.from_pretrained(path)
model = LlamaForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16).to(device)
elif "longchat" in model_name or "vicuna" in model_name:
from fastchat.model import load_model
replace_llama_attn_with_flash_attn()
model, _ = load_model(
path,
device='cpu',
num_gpus=0,
load_8bit=False,
cpu_offloading=False,
debug=False,
)
model = model.to(device)
model = model.bfloat16()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
model = model.eval()
return model, tokenizer
world_size = torch.cuda.device_count()
mp.set_start_method('spawn', force=True)
model2path = json.load(open("config/model2path.json", "r"))
model2maxlen = json.load(open("config/model2maxlen.json", "r"))
model_name = args.model
# define your model
max_length = model2maxlen[model_name]
if args.e:
datasets = ["qasper", "multifieldqa_en", "hotpotqa", "2wikimqa", "gov_report", "multi_news", \
"trec", "triviaqa", "samsum", "passage_count", "passage_retrieval_en", "lcc", "repobench-p"]
else:
datasets = ["narrativeqa", "qasper", "multifieldqa_en", "multifieldqa_zh", "hotpotqa", "2wikimqa", "musique", \
"dureader", "gov_report", "qmsum", "multi_news", "vcsum", "trec", "triviaqa", "samsum", "lsht", \
"passage_count", "passage_retrieval_en", "passage_retrieval_zh", "lcc", "repobench-p"]
# 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"))
# predict on each dataset
if not os.path.exists("pred"):
os.makedirs("pred")
if not os.path.exists("pred_e"):
os.makedirs("pred_e")
if args.e:
data = load_dataset('THUDM/LongBench', f"{dataset}_e", split='test')
if not os.path.exists(f"pred_e/{model_name}"):
os.makedirs(f"pred_e/{model_name}")
out_path = f"pred_e/{model_name}/{dataset}.jsonl"
else:
data = load_dataset('THUDM/LongBench', dataset, split='test')
if not os.path.exists(f"pred/{model_name}"):
os.makedirs(f"pred/{model_name}")
out_path = f"pred/{model_name}/{dataset}.jsonl"
prompt_format = dataset2prompt[dataset]
max_gen = dataset2maxlen[dataset]
data_all = [data_sample for data_sample in data]
data_subsets = [data_all[i::world_size] for i in range(world_size)]
processes = []
for rank in range(world_size):
p = mp.Process(target=get_pred, args=(rank, world_size, data_subsets[rank], max_length, \
max_gen, prompt_format, dataset, device, model_name, model2path, out_path))
p.start()
processes.append(p)
for p in processes:
