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pred_long_bench.py
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import os
from datasets import load_dataset
import torch
import json
from tqdm import tqdm
import numpy as np
import random
import argparse
os.environ["WANDB_DISABLED"] = "true"
from utils.process_args import process_args
from transformers import LlamaConfig, MistralConfig, AutoTokenizer
# This is the customized building prompt for chat models
def build_chat(tokenizer, prompt, model_name):
# For results in KIVI paper (Llama, Llama-Chat, Mistral-7B-v0.1), we do not apply any special treatment to the prompt.
# For lmsys/longchat-7b-v1.5-32k and mistralai/Mistral-7B-Instruct-v0.2, we need to rewrite the prompt a little bit.
# Update: we add the template for the new llama-3-instruct model
if "llama-3" in model_name.lower() and "instruct" in model_name.lower():
messages = [
{"role": "user", "content": prompt},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
elif "longchat" in model_name.lower():
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 "mistral-v0.2-instruct" in model_name.lower():
messages = [
{
"role": "user",
"content": prompt
}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device, model_name):
preds = []
for json_obj in tqdm(data):
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]
# if "chatglm3" in model:
# 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)
input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
context_length = input.input_ids.shape[-1]
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.append({"pred": pred, "answers": json_obj["answers"], "all_classes": json_obj["all_classes"], "length": json_obj["length"]})
return preds
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)
if __name__ == '__main__':
seed_everything(42)
# args = parse_args()
model2path = json.load(open("config/model2path.json", "r"))
model2maxlen = json.load(open("config/model2maxlen.json", "r"))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# model_name = args.model
# define your model
model_args, data_args, training_args = process_args()
# print(model_args, data_args, training_args)
model_name = model_args.model_name_or_path.split("/")[-1]
# dtype = torch.bfloat16 if training_args.bf16 else torch.float
dtype = torch.float16
if 'llama' in model_args.model_name_or_path.lower() or 'longchat' in model_args.model_name_or_path.lower():
config = LlamaConfig.from_pretrained(model_args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path,
use_fast=False,
trust_remote_code=True,
tokenizer_type='llama')
# model_max_length=training_args.model_max_length)
elif 'mistral' in model_args.model_name_or_path.lower():
config = MistralConfig.from_pretrained(model_args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path,
use_fast=False,
trust_remote_code=True)
else:
raise NotImplementedError
if 'llama' in model_args.model_name_or_path.lower() or 'longchat' in model_args.model_name_or_path.lower():
if model_args.k_bits < 16 and model_args.v_bits < 16:
from models.llama_kivi import LlamaForCausalLM_KIVI
config.k_bits = model_args.k_bits
config.v_bits = model_args.v_bits
config.group_size = model_args.group_size
config.residual_length = model_args.residual_length
config.use_flash = True # Note: We activate the flashattention to speed up the inference
model = LlamaForCausalLM_KIVI.from_pretrained(
pretrained_model_name_or_path=model_args.model_name_or_path,
config=config,
cache_dir=training_args.cache_dir,
torch_dtype=dtype,
low_cpu_mem_usage=True,
device_map="auto",
)
else:
from transformers import LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_args.model_name_or_path,
config=config,
cache_dir=training_args.cache_dir,
torch_dtype=dtype,
low_cpu_mem_usage=True,
use_flash_attention_2=True,
device_map="auto",
)
elif 'mistral' in model_args.model_name_or_path.lower():
if model_args.k_bits < 16 and model_args.v_bits < 16:
from models.mistral_kivi import MistralForCausalLM_KIVI
config.k_bits = model_args.k_bits
config.v_bits = model_args.v_bits
config.group_size = model_args.group_size
config.residual_length = model_args.residual_length
config.use_flash = True
model = MistralForCausalLM_KIVI.from_pretrained(
pretrained_model_name_or_path=model_args.model_name_or_path,
config=config,
cache_dir=training_args.cache_dir,
torch_dtype=dtype,
low_cpu_mem_usage=True,
device_map="auto",
)
else:
from transformers import MistralForCausalLM
model = MistralForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_args.model_name_or_path,
config=config,
cache_dir=training_args.cache_dir,
torch_dtype=dtype,
low_cpu_mem_usage=True,
use_flash_attention_2=True,
device_map="auto",
)
else:
raise NotImplementedError
#
# Load model directly
# tokenizer = AutoTokenizer.from_pretrained("togethercomputer/LLaMA-2-7B-32K")
# model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K")
model.eval()
max_length = model2maxlen[model_name]
if data_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 = ["triviaqa", "qasper", "trec", "samsum", "lcc", "repobench-p", "qmsum", "multi_news"]
# 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")
for dataset in datasets:
if data_args.e:
data = load_dataset('THUDM/LongBench', f"{dataset}_e", split='test')
if not os.path.exists(f"pred_e/{model_name}_{max_length}_{model_args.k_bits}bits_group{model_args.group_size}_residual{model_args.residual_length}"):
os.makedirs(f"pred_e/{model_name}_{max_length}_{model_args.k_bits}bits_group{model_args.group_size}_residual{model_args.residual_length}")
out_path = f"pred_e/{model_name}_{max_length}_{model_args.k_bits}bits_group{model_args.group_size}_residual{model_args.residual_length}/{dataset}.jsonl"
else:
data = load_dataset('THUDM/LongBench', dataset, split='test')
if not os.path.exists(f"pred/{model_name}_{max_length}_{model_args.k_bits}bits_group{model_args.group_size}_residual{model_args.residual_length}"):
os.makedirs(f"pred/{model_name}_{max_length}_{model_args.k_bits}bits_group{model_args.group_size}_residual{model_args.residual_length}")
out_path = f"pred/{model_name}_{max_length}_{model_args.k_bits}bits_group{model_args.group_size}_residual{model_args.residual_length}/{dataset}.jsonl"
prompt_format = dataset2prompt[dataset]
max_gen = dataset2maxlen[dataset]
preds = get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device, model_name)
with open(out_path, "w", encoding="utf-8") as f:
for pred in preds:
json.dump(pred, f, ensure_ascii=False)
f.write('\n')