https://github.com/eth-sri/constrained-diffusion
Constrained Decoding of Diffusion LLMs with Context-Free Grammars.
https://github.com/eth-sri/constrained-diffusion
constrained-decoding diffusion diffusion-model fill-in-the-middle llm llms llms-benchmarking multi-region-infilling
Last synced: 8 days ago
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Constrained Decoding of Diffusion LLMs with Context-Free Grammars.
- Host: GitHub
- URL: https://github.com/eth-sri/constrained-diffusion
- Owner: eth-sri
- License: mit
- Created: 2025-08-09T19:56:44.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-08-27T15:41:07.000Z (about 1 month ago)
- Last Synced: 2025-08-27T20:41:55.429Z (about 1 month ago)
- Topics: constrained-decoding, diffusion, diffusion-model, fill-in-the-middle, llm, llms, llms-benchmarking, multi-region-infilling
- Language: Rust
- Homepage: https://constrained-diffusion.ai
- Size: 4.62 MB
- Stars: 18
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-llm-constrained-decoding - eth-sri/constrained-diffusion - Region Infilling (CFG) |  | (Libraries)
- awesome-llm-constrained-decoding - eth-sri/constrained-diffusion - Region Infilling (CFG) |  | (Libraries)
README
Constrained Decoding of Diffusion LLMs
with Context-Free Grammars
[](https://arxiv.org/abs/2508.10111)
[](https://github.com/eth-sri/constrained-diffusion/actions/workflows/python-tests.yml)
[](https://github.com/eth-sri/constrained-diffusion/actions/workflows/rustformlang-tests.yml)
[](https://github.com/eth-sri/constrained-diffusion/actions/workflows/regex-dfa-tests.yml)
[](LICENSE)
This repository contains the implementation of [Constrained Decoding of Diffusion LLMs with Context-Free Grammars](https://arxiv.org/abs/2508.10111), including techniques for multi-region constrained generation. Our method guarantees syntactic correctness while improving functional correctness by up to 7%.
## 🚀 Overview
We present the first generalized method for constrained decoding of multi-region infilling and out-of-order generation models. Our approach:
- **Works with SOTA diffusion LLMs** like LLaDA, Dream-Coder and DiffuCoder for non-autoregressive generation
- **Also works for Fill-in-the-Middle (FIM) and Multi-Region Infilling (MRI) models** like StarCoder, DeepSeek Coder, and CodeGemma
- **Supports multiple constraint languages** through context-free grammars (examples provided are JSON Schema, C++, and SMILES)
- **Guarantees syntactic correctness** wrt. the grammar
- **Improves functional correctness** by up to 7% with minimal computational overhead
## 📦 Installation
### Prerequisites
- [Python](https://www.python.org/) 3.11+
- [Rust](https://www.rust-lang.org/) (for building the formal language library)
- CUDA-compatible GPU (for inference)
### Setup
We recommend using a virtual environment to avoid conflicts with other Python packages.
0. **Clone the repository and set up virtual enviroment:**
```bash
git clone https://github.com/eth-sri/constrained-diffusion.git
cd constrained-diffusion
python3 -m venv venv
source venv/bin/activate
```
1. **Build and install Rust bindings:**
```bash
cd rustformlang_bindings
pip install maturin
maturin build --release
pip install .
cd ..
```
2. **Install the main package:**
```bash
pip install -e .
```
4. **Verify installation:**
```bash
pytest tests
```
## 🔧 Usage & Demo
Check out [`example.py`](example.py) for a complete example of how to use the constrained decoding mechanism.
In general, you want to first load a model and then load a constraint language, such as C++ or JSON Schema. The example below shows abbreviated code on how to use the `GSAI-ML/LLaDA-8B-Instruct` model with a C++ constraint.
Replace the model name with any diffusion LLM of your choice, such as `apple/DiffuCoder-7B-Instruct`.
```bash
python3 example.py
```
This is a visualization of our constrained decoding mechanism on output similar to that created by LLaDA 7b.
> 
## 📁 Project Structure
```
├── constrained_diffusion/ # Main package
│ ├── constrain_utils.py # Constraint generation utilities
│ ├── cfgs/ # Context-free grammar definitions
│ └── eval/ # Evaluation frameworks
│ ├── dllm/ # Evaluation framework for DLLMs
│ └── mri/ # Evaluation framework for Multi-Region Infilling
├── rustformlang/ # Rust formal language library
├── rustformlang_bindings/ # Python bindings for Rust library
├── eval/ # Evaluation scripts and results
│ ├── dllm/ # DLLM task evaluations
│ ├── mri/ # Multi-Region infilling evaluations
│ └── figures/ # Result visualization
├── benchmark_generation/ # Benchmark generation tools
└── docs/ # Project website
```
## 🧪 Evaluation
### Datasets
We run MRI and diffusion LLMs on the following datasets:
| Dataset | Setting | Description | Download |
|---------|---------|-------------------------------------------------------|----------|
| C++ | MRI | C++ code generation tasks with multi-region infilling | [🤗 HuggingFace](https://huggingface.co/datasets/eth-sri/HumanEval-MRI-Cpp) |
| C++ | DLM | C++ code generation tasks with diffusion LLMs | [🤗 HuggingFace](https://huggingface.co/datasets/zai-org/humaneval-x) |
| JSON | DLM | Data extraction, following a JSON Schema | [🤗 HuggingFace](https://huggingface.co/datasets/eth-sri/json-mode-eval-extended) |
| SMILES | DLM | Chemical compound representation in SMILES | [🤗 HuggingFace](https://huggingface.co/datasets/eth-sri/smiles-eval) |
> You can download the results of our evaluation using the following link: [Download Results](https://files.sri.inf.ethz.ch/constrained-diffusion/results.zip).
> Unzip the file in the `results/` directory to access the evaluation results.
### Running Inference
For the MRI models, we provide an execution harness for the C++ HumanEval multi-region dataset.
To execute task 11 on the 1-region dataset with constraints and traces enabled, use the following command:
```bash
python3 -m constrained_diffusion.eval.mri.generic_inference \
--max-tokens 256 \
--model_name deepseek-ai/deepseek-coder-6.7b-base \
--seed 0 \
--temp 1 \
--dataset-name HumanEval/MRI/cpp/1 \
--constrained True \
--trace True \
--task_id /11_
```
For the diffusion LLMs, use the following command for the JSON dataset.
```bash
python3 -m constrained_diffusion.eval.dllm.generic_inference \
--max-tokens 256 \
--model_name apple/DiffuCoder-7B-Instruct \
--seed 0 \
--temp 0.2 \
--dataset-name jsonschema \
--steps 32 \
--constrained True \
--trace True \
--task_id _37
```
A general orchestration script for all experiments in the main paper is provided in `eval/mri/run_mri.py` and `eval/dllm/run_dllm.py`.
The results are stored in the `results/` directory, with each configuration's results in a separate file.
### Running Evaluation
Evaluation of result correctness is decoupled from the inference step. The following assumes that the inference step above was executed correctly and results lie in `results`.
> Note: For SMILES evaluation, you need to install `rdkit`and `partialsmiles`: `pip install rdkit partialsmiles`
Make sure to have sufficient memory and CPU cores available, as the evaluation scripts can be memory-intensive.
```bash
# Evaluate all files in the results folder
bash eval/check_all_individually.sh results/*
```
### More details
You can find more details on the evaluation scripts, for example on how to reproduce the figures from the paper, in the README in the `eval/` directory: [README](eval/README.md).
## 🤝 Contributing
We welcome contributions! When contributing, please make sure to activate pre-commit hooks to ensure code quality and consistency. You can install pre-commit hooks with:
```bash
pip install pre-commit
pre-commit install
```
### Adding New Constraint Languages
1. Define the grammar in `constrained_outoforder/cfgs/`
2. Implement lexical mapping in `check_lex_map.py`
3. Add tests in `tests/test_cfgs/`
4. Update documentation
### Adding New Evaluation Tasks
1. [Create a new constraint language](#adding-new-constraint-languages)
2. Implement a dataset in `constrained_outoforder/eval/[dllm|mri]/datasets/your_task.py`
4. Register the dataset using `register_dataset()`
3. Add evaluation logic in `eval/[dllm|mri]/your_task/checker.py`
### Adding a New Model
1. Implement the model in `constrained_outoforder/eval/[dllm|mri]/models/your_model.py`
2. Register the model using `register_model()`
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🔗 Links
- **Paper**: [arXiv:2508.10111](https://arxiv.org/abs/2508.10111)
- **Project Website**: [Constrained Decoding Paper Website + Demo](https://constrained-diffusion.ai)
- **Rustformlang README**: [Rustformlang Docs](rustformlang/)
## 📚 Citation
If you use this work in your research, please cite:
```bibtex
@article{mundler2025constraineddiffusion,
title={Constrained Decoding of Diffusion LLMs with Context-Free Grammars},
author={Niels Mündler and Jasper Dekoninck and Martin Vechev},
year={2025},
eprint={2508.10111},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2508.10111}
}
```
This work was done by the [Secure, Reliable and Intelligent Systems Lab](https://sri.inf.ethz.ch/) at [ETH Zurich](https://ethz.ch).