conditional-flow-matching/runner/README.md

In abstracting out the relevant losses from our pytorch-lightning implementation we have moved all of the pytorch-lightning code to runner. Every command that worked before for lightning should now be run from within the runner directory. V0 is now frozen in the V0 branch.

Train model with default configuration

cd runner

# train on CPU
python src/train.py trainer=cpu

# train on GPU
python src/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

python src/train.py experiment=experiment_name

You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 datamodule.batch_size=64

You can also train a large set of models in parallel with SLURM as shown in scripts/two-dim-cfm.sh which trains the models used in the first 3 lines of Table 2.

Code Contributions

This repo is extracted from a larger private codebase which loses the original commit history which contains work from other authors of the papers.

Project Structure

The directory structure of new project looks like this:

│
│── runner                    <- Shell scripts
|   ├── data                   <- Project data
|   ├── logs                   <- Logs generated by hydra and lightning loggers
|   ├── scripts                   <- Shell scripts
│   ├── configs                   <- Hydra configuration files
│   │   ├── callbacks                <- Callbacks configs
│   │   ├── debug                    <- Debugging configs
│   │   ├── datamodule               <- Datamodule configs
│   │   ├── experiment               <- Experiment configs
│   │   ├── extras                   <- Extra utilities configs
│   │   ├── hparams_search           <- Hyperparameter search configs
│   │   ├── hydra                    <- Hydra configs
│   │   ├── launcher                 <- Hydra launcher configs
│   │   ├── local                    <- Local configs
│   │   ├── logger                   <- Logger configs
│   │   ├── model                    <- Model configs
│   │   ├── paths                    <- Project paths configs
│   │   ├── trainer                  <- Trainer configs
│   │   │
│   │   ├── eval.yaml             <- Main config for evaluation
│   │   └── train.yaml            <- Main config for training
│   ├── src                       <- Source code
│   │   ├── datamodules              <- Lightning datamodules
│   │   ├── models                   <- Lightning models
│   │   ├── utils                    <- Utility scripts
│   │   │
│   │   ├── eval.py                  <- Run evaluation
│   │   └── train.py                 <- Run training
│   │
│   ├── tests                     <- Tests of any kind
│   └── README.md

⚡  Your Superpowers

Override any config parameter from command line

python train.py trainer.max_epochs=20 model.optimizer.lr=1e-4

Note: You can also add new parameters with + sign.

python train.py +model.new_param="owo"

Train on CPU, GPU, multi-GPU and TPU

# train on CPU
python train.py trainer=cpu

# train on 1 GPU
python train.py trainer=gpu

# train on TPU
python train.py +trainer.tpu_cores=8

# train with DDP (Distributed Data Parallel) (4 GPUs)
python train.py trainer=ddp trainer.devices=4

# train with DDP (Distributed Data Parallel) (8 GPUs, 2 nodes)
python train.py trainer=ddp trainer.devices=4 trainer.num_nodes=2

# simulate DDP on CPU processes
python train.py trainer=ddp_sim trainer.devices=2

# accelerate training on mac
python train.py trainer=mps

Warning: Currently there are problems with DDP mode, read this issue to learn more.

Train with mixed precision

# train with pytorch native automatic mixed precision (AMP)
python train.py trainer=gpu +trainer.precision=16

Train model with any logger available in PyTorch Lightning, like W&B or Tensorboard

# set project and entity names in `configs/logger/wandb`
wandb:
  project: "your_project_name"
  entity: "your_wandb_team_name"

# train model with Weights&Biases (link to wandb dashboard should appear in the terminal)
python train.py logger=wandb

Note: Lightning provides convenient integrations with most popular logging frameworks. Learn more here.

Note: Using wandb requires you to setup account first. After that just complete the config as below.

Note: Click here to see example wandb dashboard generated with this template.

Train model with chosen experiment config

python train.py experiment=example

Note: Experiment configs are placed in configs/experiment/.

Attach some callbacks to run

python train.py callbacks=default

Note: Callbacks can be used for things such as as model checkpointing, early stopping and many more.

Note: Callbacks configs are placed in configs/callbacks/.

Use different tricks available in Pytorch Lightning

# gradient clipping may be enabled to avoid exploding gradients
python train.py +trainer.gradient_clip_val=0.5

# run validation loop 4 times during a training epoch
python train.py +trainer.val_check_interval=0.25

# accumulate gradients
python train.py +trainer.accumulate_grad_batches=10

# terminate training after 12 hours
python train.py +trainer.max_time="00:12:00:00"

Note: PyTorch Lightning provides about 40+ useful trainer flags.

Easily debug

# runs 1 epoch in default debugging mode
# changes logging directory to `logs/debugs/...`
# sets level of all command line loggers to 'DEBUG'
# enforces debug-friendly configuration
python train.py debug=default

# run 1 train, val and test loop, using only 1 batch
python train.py debug=fdr

# print execution time profiling
python train.py debug=profiler

# try overfitting to 1 batch
python train.py debug=overfit

# raise exception if there are any numerical anomalies in tensors, like NaN or +/-inf
python train.py +trainer.detect_anomaly=true

# log second gradient norm of the model
python train.py +trainer.track_grad_norm=2

# use only 20% of the data
python train.py +trainer.limit_train_batches=0.2 \
+trainer.limit_val_batches=0.2 +trainer.limit_test_batches=0.2

Note: Visit configs/debug/ for different debugging configs.

Resume training from checkpoint

python train.py ckpt_path="/path/to/ckpt/name.ckpt"

Note: Checkpoint can be either path or URL.

Note: Currently loading ckpt doesn't resume logger experiment, but it will be supported in future Lightning release.

Evaluate checkpoint on test dataset

python eval.py ckpt_path="/path/to/ckpt/name.ckpt"

Note: Checkpoint can be either path or URL.

Create a sweep over hyperparameters

# this will run 6 experiments one after the other,
# each with different combination of batch_size and learning rate
python train.py -m datamodule.batch_size=32,64,128 model.lr=0.001,0.0005

Note: Hydra composes configs lazily at job launch time. If you change code or configs after launching a job/sweep, the final composed configs might be impacted.

Create a sweep over hyperparameters with Optuna

# this will run hyperparameter search defined in `configs/hparams_search/mnist_optuna.yaml`
# over chosen experiment config
python train.py -m hparams_search=mnist_optuna experiment=example

Note: Using Optuna Sweeper doesn't require you to add any boilerplate to your code, everything is defined in a single config file.

Warning: Optuna sweeps are not failure-resistant (if one job crashes then the whole sweep crashes).

Execute all experiments from folder

python train.py -m 'experiment=glob(*)'

Note: Hydra provides special syntax for controlling behavior of multiruns. Learn more here. The command above executes all experiments from configs/experiment/.

Execute run for multiple different seeds

python train.py -m seed=1,2,3,4,5 trainer.deterministic=True logger=csv tags=["benchmark"]

Note: trainer.deterministic=True makes pytorch more deterministic but impacts the performance.

Execute sweep on a remote AWS cluster

Note: This should be achievable with simple config using Ray AWS launcher for Hydra. Example is not implemented in this template.

Use Hydra tab completion

Note: Hydra allows you to autocomplete config argument overrides in shell as you write them, by pressing tab key. Read the docs.

Apply pre-commit hooks

pre-commit run -a

Note: Apply pre-commit hooks to do things like auto-formatting code and configs, performing code analysis or removing output from jupyter notebooks. See # Best Practices for more.

Run tests

# run all tests
pytest

# run tests from specific file
pytest tests/test_train.py

# run all tests except the ones marked as slow
pytest -k "not slow"

Use tags

Each experiment should be tagged in order to easily filter them across files or in logger UI:

python train.py tags=["mnist","experiment_X"]

If no tags are provided, you will be asked to input them from command line:

>>> python train.py tags=[]
[2022-07-11 15:40:09,358][src.utils.utils][INFO] - Enforcing tags! <cfg.extras.enforce_tags=True>
[2022-07-11 15:40:09,359][src.utils.rich_utils][WARNING] - No tags provided in config. Prompting user to input tags...
Enter a list of comma separated tags (dev):

If no tags are provided for multirun, an error will be raised:

>>> python train.py -m +x=1,2,3 tags=[]
ValueError: Specify tags before launching a multirun!

Note: Appending lists from command line is currently not supported in hydra :(