Simple and Effective Masked Diffusion Language Models (NeurIPS 2024)

By Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov

[Update April 14, 2025: An improved implementation is available here: DUO Github repo.]

[Update Jun 3, 2025: MDMs with KV caching: Eso-LMs Github repo.]

We introduce MDLM, a Masked discrete Diffusion Language Model that features a novel (SUBS)titution based parameterization which simplifies the absorbing state diffusion loss to a mixture of classical masked language modeling losses. In doing so, we achieve SOTA perplexity numbers on LM1B and OpenWebText among diffusion models while achiving competitive zero-shot perplexity with SOTA AR models on numerous datasets. We provide a demo in this notebook or and a video tutorial here:

In this repo, we release: * The MDLM framework. 1. SUBStitution based parameterization 2. Simplified loss calculation for masked diffusion processes * Baseline implementations [Examples]: 1. Autoregressive model that matches the SOTA AR performance on LM1B. 2. Score Entropy Based Discrete Diffusion SEDD. 3. An efficient implementation of the absorbing state D3PM that beats the previous SOTA text diffusion model SEDD on LM1B. * Samplers 1. Ancestral sampling as proposed in D3PM. 2. Analytic sampler as proposed in SEDD. 3. Our proposed efficient sampler that - makes MDLM ~3-4x faster than the existing diffusion models. [Example] - supports semi-autoregressive (SAR) generation. [Example]

Code Organization

1. main.py: Routines for training and evaluation
2. noise_schedule.py: Noise schedules
3. diffusion.py: Forward/reverse diffusion
4. dataloader.py: Dataloaders
5. utils.py: LR scheduler, logging, fsspec handling
6. models/: Denoising network architectures. Supports DiT, AR transformer, and Mamba
7. configs/: Config files for datasets/denoising networks/noise schedules/LR schedules
8. scripts/: Shell scripts for training/evaluation

Getting started in this repository

To get started, create a conda environment containing the required dependencies.

bash conda env create -f requirements.yaml conda activate mdlm

Create the following directories to store saved models and slurm logs: bash mkdir outputs mkdir watch_folder and run the training as a batch job: bash sbatch scripts/train_owt_mdlm.sh

Checkpoints

We have uploaded MDLM model trained on OpenWebText for 1M training steps to the Huggingface hub 🤗: kuleshov-group/mdlm-owt Furthermore, we have released the checkpoints for the AR and SEDD baselines trained on OpenWebText in this Google Drive folder.

Reproducing Experiments

Below, we describe the steps required for reproducing the experiments in the paper. Throughout, the main entry point for running experiments is the main.py script. We also provide sample slurm scripts for launching pre-training and downstream fine-tuning experiments in the scrips/ directory.

Generate Samples

The argument to sampling.predictor specifies the sampler which takes one of the following values: * ddpm_cache: our proposed sampler that's ~3-4x faster than the samplers propsed in D3PM and SEDD. * ddpm: Ancestral sampling proposed in D3PM. * analytic: Analytic sampler proposed in SEDD.

In the following table we report wall clock time to generate 64 samples on a single A5000 GPU with batch_size=1. $T$ denotes the time discretization of the reverse process. | | $T=5k (\downarrow)$ | $T=10k (\downarrow)$ | |-------------------------|---------------------|----------------------| | SEDD | 127.1 | 229.3 | | MDLM + ddpm | 113.8 | 206.6 | | MDLM +ddpm_cache | 40.1 | 60.4 |

To generate samples from a pre-trained model use one of the following commands:

Huggingface model

bash python main.py \ mode=sample_eval \ eval.checkpoint_path=kuleshov-group/mdlm-owt \ data=openwebtext-split \ model.length=1024 \ sampling.predictor=ddpm_cache \ sampling.steps=1000 \ loader.eval_batch_size=1 \ sampling.num_sample_batches=10 \ backbone=hf_dit

Local checkpoint

bash python main.py \ mode=sample_eval \ eval.checkpoint_path=/path/to/checkpoint/mdlm.ckpt \ data=openwebtext-split \ model.length=1024 \ sampling.predictor=ddpm_cache \ sampling.steps=10000 \ loader.eval_batch_size=1 \ sampling.num_sample_batches=1 \ backbone=dit

Semi-AR sample generation

MDLM can also generate samples of arbitrary length in a semi-autoregressive (SAR) manner. We generate 200 sequences of length 2048 tokens on a single 3090 GPU and evaluate generative perplexity under a pre-trained GPT-2 model. In the below table we find that in addition to achieving better generative perplexity, MDLM enables 25-30x faster SAR decoding relative to SSD-LM.

| | Gen. PPL ($\downarrow$) | Sec/Seq ($\downarrow$) | |---------------------|-------------------------|------------------------| | SSD-LM | 35.43 | 2473.9 | | MDLM +ddpm_cache | 27.18 | 89.3 |

Gen. PPL: Generation Perplexity, Sec/Seq: Seconds per Sequence

bash python main.py \ mode=sample_eval \ eval.checkpoint_path=kuleshov-group/mdlm-owt \ data=openwebtext-split \ parameterization=subs \ model.length=1024 \ sampling.predictor=ddpm_cache \ sampling.steps=1000 \ loader.eval_batch_size=1 \ sampling.num_sample_batches=2 \ sampling.semi_ar=True \ sampling.stride_length=512 \ sampling.num_strides=2 \ backbone=hf_dit

Train

To train MDLM from scratch on OpenWebText use the following command: python main.py \ model=small \ data=openwebtext-split \ wandb.name=mdlm-owt \ parameterization=subs \ model.length=1024 \ eval.compute_generative_perplexity=True \ sampling.steps=1000 The arguments loader.batch_size and loader.eval_batch_size allow you to control the global batch size and the batch size per GPU. If loader.batch_size * num_gpus is less than the global batch size, PyTorch Lightning will resort to gradient accumulation. You can also launch a training job on Slurm using the command: sbatch scripts/train_owt_mdlm.sh. The slurm scripts to train the Auto-regressive and SEDD baselines are as follows respectively: scripts/train_lm1b_ar.sh, scripts/train_owt_sedd.sh.

Eval

To compute test perplexity, use mode=ppl_eval. Example scripts provided in scripts/. An example command for perplexity evaluation on OpenWebText is: python main.py \ mode=ppl_eval \ loader.batch_size=16 \ loader.eval_batch_size=16 \ data=openwebtext-split \ model=small \ parameterization=subs \ backbone=dit \ model.length=1024 \ eval.checkpoint_path=/path/to/checkpoint/mdlm.ckpt \ +wandb.offline=true

Baseline evaluation

We release the checkpoints for the baselines: SEDD and AR trained on OpenWebText in this Google Drive folder. Download the checkpoints: ar.ckpt, sedd.ckpt and use the following commands to compute test perplexity:

AR

bash python main.py \ mode=ppl_eval \ loader.batch_size=16 \ loader.eval_batch_size=16 \ data=openwebtext-split \ model=small-ar \ parameterization=ar \ backbone=ar \ model.length=1024 \ eval.checkpoint_path=/path/to/checkpoint/ar.ckpt \ +wandb.offline=true

SEDD

bash python main.py \ mode=ppl_eval \ loader.batch_size=16 \ loader.eval_batch_size=16 \ data=openwebtext-split \ model=small \ parameterization=sedd \ backbone=dit \ model.length=1024 \ eval.checkpoint_path=/path/to/checkpoint/sedd.ckpt \ time_conditioning=True \ sampling.predictor=analytic \ +wandb.offline=true

Acknowledgements

This repository was built off of SEDD.

Citation

@inproceedings{ sahoo2024simple, title={Simple and Effective Masked Diffusion Language Models}, author={Subham Sekhar Sahoo and Marianne Arriola and Aaron Gokaslan and Edgar Mariano Marroquin and Alexander M Rush and Yair Schiff and Justin T Chiu and Volodymyr Kuleshov}, booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}, year={2024}, url={https://openreview.net/forum?id=L4uaAR4ArM} }