arise-sustech/llm4decompile-1.3b
14101
1. Introduction of LLM4Decompile
LLM4Decompile aims to decompile x86 assembly instructions into C. It is finetuned from Deepseek-Coder on 4B tokens of assembly-C pairs compiled from AnghaBench.
- Github Repository: LLM4Decompile
- Paper link: For more details check out the paper.
2. Evaluation Results
3. How to Use
Here give an example of how to use our model. First compile the C code into binary, disassemble the binary into assembly instructions:
import subprocess
import os
import re
digit_pattern = r'\b0x[a-fA-F0-9]+\b'# binary codes in Hexadecimal
zeros_pattern = r'^0+\s'#0s
OPT = ["O0", "O1", "O2", "O3"]
fileName = 'path/to/file'
with open(fileName+'.c','r') as f:#original file
c_func = f.read()
for opt_state in OPT:
output_file = fileName +'_' + opt_state
input_file = fileName+'.c'
compile_command = f'gcc -c -o {output_file}.o {input_file} -{opt_state} -lm'#compile the code with GCC on Linux
subprocess.run(compile_command, shell=True, check=True)
compile_command = f'objdump -d {output_file}.o > {output_file}.s'#disassemble the binary file into assembly instructions
subprocess.run(compile_command, shell=True, check=True)
input_asm = ''
with open(output_file+'.s') as f:#asm file
asm= f.read()
asm = asm.split('Disassembly of section .text:')[-1].strip()
for tmp in asm.split('\n'):
tmp_asm = tmp.split('\t')[-1]#remove the binary code
tmp_asm = tmp_asm.split('#')[0].strip()#remove the comments
input_asm+=tmp_asm+'\n'
input_asm = re.sub(zeros_pattern, '', input_asm)
before = f"# This is the assembly code with {opt_state} optimization:\n"#prompt
after = "\n# What is the source code?\n"#prompt
input_asm_prompt = before+input_asm.strip()+after
with open(fileName +'_' + opt_state +'.asm','w',encoding='utf-8') as f:
f.write(input_asm_prompt)Then use LLM4Decompile to translate the assembly instructions into C:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_path = 'arise-sustech/llm4decompile-1.3b'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16).cuda()
with open(fileName +'_' + opt_state +'.asm','r') as f:#original file
asm_func = f.read()
inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=512)
c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])4. License
This code repository is licensed under the DeepSeek License.
5. Contact
If you have any questions, please raise an issue.
6. Citation
@misc{tan2024llm4decompile,
title={LLM4Decompile: Decompiling Binary Code with Large Language Models},
author={Hanzhuo Tan and Qi Luo and Jing Li and Yuqun Zhang},
year={2024},
eprint={2403.05286},
archivePrefix={arXiv},
primaryClass={cs.PL}
}