hululuzhu/solidity-t5

A code generation T5 model for solidity (web3 smart contract)

• See https://github.com/hululuzhu/solidity-t5 for more context

How to use this trained model

• A hello world example to use this model, notice the input text includes
  • Header solidity version like pragma solidity ^0.5.7
  • Ancestor class/library info, e.g. public functions and constants from ParentA
  • Contract/Library/Interface declaration header, e.g. HelloWorld ended with {
• Or simply use the test widget on the right side of the window and test, however the quality is known to be worse without decoding params

# !pip install transformers -q

from transformers import AutoTokenizer, T5ForConditionalGeneration

DEVICE = 'cuda'  # fallback to cpu if you do not have cuda
tokenizer = AutoTokenizer.from_pretrained("hululuzhu/solidity-t5")
model = T5ForConditionalGeneration.from_pretrained("hululuzhu/solidity-t5").to(DEVICE)

text = """pragma solidity ^0.5.7;
// Context: ParentA | Functions: helloA helloB | Constants: constantA 
contract HelloWorld is ParentA {"""
input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids.to(DEVICE)

# Need to tune beam/topk/topp params to get good outcome
generated_ids = model.generate(input_ids, max_length=256, num_beams=5, top_p=0.95, top_k=50)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

# Expect outcome
"""
string public constant name = "Hello World";
...
uint256 public constant override returns (uint256) {
return initialSupply;
}
function initialSupply() public view returns (uint256) {
...
"""

Background

• Base T5 code model: https://huggingface.co/Salesforce/codet5-large
• Source data: https://huggingface.co/datasets/mwritescode/slither-audited-smart-contracts

  • Processing steps: Clean, contract-level segmentation sepration, split in and out

  • After processing input sample

    pragma solidity 0.5.7;
    // Context: PauserRole | Functions: isPauser addPauser renouncePauser | Constants: 
    contract Pausable is PauserRole {
    

  • After processing output sample (notice indentation is bad, this is intentional to reduce token size)

    event Paused(address account);
    event Unpaused(address account);
    bool private _pausableActive;
    bool private _paused;
    constructor () internal {
    _paused = false;
    }
    function paused() public view returns (bool) {
    return _paused;
    }
    modifier whenNotPaused() {
    require(!_paused);
    _;
    }
    modifier whenPaused() {
    require(_paused);
    _;
    }
    function pause() public onlyPauser whenNotPaused whenPausableActive {
    _paused = true;
    emit Paused(msg.sender);
    }
    function unpause() public onlyPauser whenPaused whenPausableActive {
    _paused = false;
    emit Unpaused(msg.sender);
    }
    function _setPausableActive(bool _active) internal {
    _pausableActive = _active;
    }
    modifier whenPausableActive() {
    require(_pausableActive);
    _;
    }
    }
    

• Source training code: See the end to end notebook at code dir here

Future TODO

• The model is significantly under-trained because of lack of GPU budget, need 10x colab resources (~$100 for full train)
• This is quite limited on how the model is used, potentially we could switch to GPT2 decoder-only to compare, but CodeT5 has its strong code optimization
• Need more classifiers (T5 or BERT alike) to detect potential defects.