juliusberner / sde_sampler

Improved sampling via learned diffusions (ICLR2024) and an optimal control perspective on diffusion-based generative modeling (TMLR2024)

Sampling via learned diffusions: sde_sampler

Accompanying code for the paper 'Improved sampling via learned diffusions' [ICLR'24,BibTeX] and 'An optimal control perspective on diffusion-based generative modeling' [TMLR'24,BibTeX].

This repo contains various methods (DIS, Bridge, DDS, PIS) to sample from unnormalized densities by learning to control stochastic differential equations (SDEs). Given an unnormalized target density $\rho=Zp_{\mathrm{target}}$, where $Z = \int \rho(x) \mathrm{d}x$, we optimize a neural network $u$ to control the SDE $$\mathrm{d}X^u_t = (\mu + \sigma u)(X^u_t,t) \mathrm{d}t + \sigma(t) \mathrm{d}W_t, \quad X^u_0 \sim p_{\mathrm{prior}},$$ such that $X^u_T \sim p_{\mathrm{target}}$. Then one can sample from the prior $p_{\mathrm{prior}}$ and simulate the SDE $X^u$ to obtain samples from $p_{\mathrm{target}}$.

Installation

• Repo: First clone the repo:

  git clone [e-mail]:juliusberner/sde_sampler.git
  cd sde_sampler
  

• Environment: We recommend using Conda to set up the codebase:

  conda create -n sde_sampler python==3.9 pip --yes
  conda activate sde_sampler
  

• GPU: If you have a GPU, check your CUDA version using nvidia-smi and install compatible cuda (for pykeops) and torch/torchvision packages using the PyTorch install guide (see here for previous versions). For instance, if your CUDA version is >=11.7, you could run:

  conda install pytorch==2.0.1 torchvision==0.15.2 pytorch-cuda=11.7 cuda-minimal-build=11.7 -c pytorch -c nvidia --yes
  

• CPU: In case you do not have a GPU, you can replace the previous step with:

  conda install pytorch torchvision cpuonly -c pytorch --yes
  

• Packages: Now, you can install the sde_sampler package:

  pip install -e .
  

• Wandb: Finally, login to your wandb account:

  wandb login
  

  You can also omit this step and add the wandb.mode=disabled command line arg to your runs.

• Additional requirements: See targets, for requirements and steps which are only needed for specific targets.

• Test: To test the pykeops and torch installations on a machine with GPU:

  python -c "import torch; print(torch.cuda.is_available())"
  python -c "import pykeops; pykeops.test_torch_bindings()"
  

Quick Start

Sample from a shifted double well using the DIS solver and the log-variance divergence:

python scripts/main.py target=dw_shift solver=basic_dis loss.method=lv

• This outputs a link to the metrics and plots: wandb: 🚀 View run at <link>
• You also find the metrics/plots/checkpoints locally at logs/<Y-m-d>/<H-M-S>.

How-To

Setup

We use:

• hydra for config management and experiment execution.
• wandb for experiment tracking and hyperparameter sweeps.

Configs

All configs can be found in the folder conf. You can just adapt the existing .yaml configs or add new ones. Configs are hierarchical and will be dynamically created by composition, see the Hydra intro or run

python scripts/main.py --help

Most important:

1. Configs are based on conf/base.yaml and the solver-specific config in conf/solver.

2. Config defaults can be overridden on the command line analogously as specified in the defaults list in each config. For instance:

   • conf/base.yaml contains the default solver: dis. To select the PIS solver in conf/solver/basic_pis.yaml, we can add the command line arg solver=basic_pis.
   • conf/solver/basic_pis.yaml contains the default /model@generative_ctrl: score. To use the model in conf/model/clipped.yaml, we can add the command line arg model@generative_ctrl=clipped.
   • To add a new default use +. To add the learning rate scheduler conf/lr_scheduler/multi_step.yaml, use the command line arg +lr_scheduler=multi_step.

3. Each individual entry of the config can be overriden on the command line using the nested config keys separated by dots, e.g., generative_ctrl.base_model.channels=32.

4. You can also change the wandb setting in conf/base.yaml. To change the project to test, add wandb.project=test to the args.

Combining the examples above:

python scripts/main.py solver=basic_pis model@generative_ctrl=clipped generative_ctrl.base_model.channels=32 +lr_scheduler=multi_step wandb.project=test

Multi-run & Slurm

Run the experiment above on the shifted double well as well as a Gaussian mixture model for both the log-variance and the KL divergence (using the hydra multi-run flag -m/--multirun):

python scripts/main.py -m +launcher=<launcher> target=dw_shift,gmm solver=basic_dis loss.method=kl,lv

Set <launcher> to

• joblib if you work on a local machine. This uses the joblib library.
• slurm if you work on a Slurm cluster. You might need to adapt the default configs in conf/launcher/slurm.yaml to your cluster.

Wandb sweeps

You can find an examplary sweep in conf/sweeps.

1. Invoke the sweep:

wandb sweep conf/sweeps/<sweep_name>.yaml

2. Start agents as described in the output of the previous command. For slurm, you can use

SWEEP_ID=<wandb_entity>/<wandb_project>/<sweep_id> sbatch -a 0-<num agents> bin/slurm_sweep.sh

Resuming

You can resume a run by specifying its wandb id wandb.id=<wandb-id>. With the default settings, you can run

python scripts/main.py --config-name=setup wandb.id=<wandb-id>

and the previous configuration and latest ckeckpoint will be automatically downloaded from wandb. When using the same log directory, the wandb id is inferred automatically (which is useful for slurm preemption). You can also add the log directory manually via the command line arg hydra.run.dir=<logs/Y-m-d/H-M-S>. For more flexibility (e.g, adapting configs and command line args), you can also specify the checkpoint ckpt_file=<path-to-ckpt-file> directly.

Experiments

Solvers

Our predefined solvers in conf/solver include the following methods:

1. Time-Reversed Diffusion Sampler (DIS) solver=dis (see our paper)

2. Denoising Diffusion Sampler (DDS) solver=dds (see the DDS repo; note that we also provide solver=dds_euler, which uses the Euler integrator for DDS instead).

3. Path Integral Sampler (PIS) solver=pis (see the PIS repo)

4. Bridge sampler (Bridge) solver=bridge (see our paper; this can be viewed as generalized Schrödinger bridge)

5. Unadjusted Langevin algorithm (ULA) solver=langevin

The configs with prefix basic_ in conf/solver are simplified and can easily be adapted to specific targets or settings.

For all solvers except langevin, you can use either the KL divergence loss.method=kl or the log-variance divergence loss.method=lv (see our paper). For the first three solvers, the log-variance divergence can also be computed over trajectories with the same initial point by using loss.method=lv_traj. In most of our experiments, the log-variance divergence led to improved performance.

Targets

Our predefined targets in conf/target include the following distributions:

• Funnel target=funnel (10d, see our paper and the PIS repo)

• Gaussian Mixture Model target=gmm (2d, see our paper and the PIS repo)

• Multi/Double-Well target=dw_shift, target=mw, and target=mw_50d (1d/5d/50d, see our paper)

• Gaussian target=gauss_shift (1d)

• Image: target=img (2d, see the SNF repo): For better visualization of the image density, we suggest to use eval_batch_size=500000.

• Rings target=rings (2d, see the PIS repo)

• Rosenbrock: target=rosenbrock (15d, to test samplers for global optimization, see arxiv:2111.00402 and wikipedia)

• Alanine Dipeptide target=aladip (60d, see the FAB repo): Install the following additional requirements

  conda install -c conda-forge openmm openmmtools=0.23.1 --yes
  pip install boltzgen@git+https://github.com/VincentStimper/boltzmann-generators.git@v1.0
  

  Then, download the evaluation data using

  bash bin/download_aladip.sh
  

• NICE target=nice (196d, see arxiv:2208.07698 and https://github.com/fmu2/NICE): First, train the NICE model on MNIST using

  python scripts/train_nice.py
  

  or, on a slurm cluster, using

  sbatch bin/slurm_train_nice.sh
  

  This saves a checkpoint in data/nice.pt, which is then automatically used.

• Log Gaussian Cox Process target=cox (1600d, see the PIS repo)

References

If you use parts of this codebase in your research, please use the following BibTeX entries.

@article{berner2024optimal,
  title={An optimal control perspective on diffusion-based generative modeling},
  author={Berner, Julius and Richter, Lorenz and Ullrich, Karen},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2024},
  url={https://openreview.net/forum?id=oYIjw37pTP},
}

@inproceedings{richter2024improved,
  title={Improved sampling via learned diffusions},
  author={Richter, Lorenz and Berner, Julius},
  booktitle={International Conference on Learning Representations},
  year={2024}
}

License

The majority of the project is licensed under MIT. Portions of the project are adapted from other repositories (as mentioned in the code):

• https://github.com/fmu2/NICE is also licensed under MIT,
• https://github.com/yang-song/score_sde is licensed under Apache-2.0,
• https://github.com/noegroup/stochastic_normalizing_flows is licensed under BSD-3-Clause,
• the repositories https://github.com/lollcat/fab-torch, https://github.com/qsh-zh/pis, and https://github.com/fwilliams/scalable-pytorch-sinkhorn do not provide licenses.