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https://github.com/leopard-ai/betty
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
https://github.com/leopard-ai/betty
artificial-intelligence autodiff automatic-differentiation bilevel-optimization deep-learning hyperparameter-optimization implicit-differentiation machine-learning meta-learning multilevel-optimization neural-architecture-search pytorch reinforcement-learning
Last synced: about 2 months ago
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Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
- Host: GitHub
- URL: https://github.com/leopard-ai/betty
- Owner: leopard-ai
- License: apache-2.0
- Created: 2022-06-29T13:30:04.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-26T12:14:39.000Z (3 months ago)
- Last Synced: 2024-08-01T05:52:23.365Z (about 2 months ago)
- Topics: artificial-intelligence, autodiff, automatic-differentiation, bilevel-optimization, deep-learning, hyperparameter-optimization, implicit-differentiation, machine-learning, meta-learning, multilevel-optimization, neural-architecture-search, pytorch, reinforcement-learning
- Language: Python
- Homepage: https://leopard-ai.github.io/betty/
- Size: 2.85 MB
- Stars: 330
- Watchers: 8
- Forks: 27
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
An automatic differentiation library for generalized meta-learning and multilevel optimization
Docs |
Tutorials |
Examples |
Paper |
Citation |
CASL
[](https://github.com/leopard-ai/betty/blob/main/LICENSE)
```bash
pip install betty-ml
```
## Update
**[Sep 22 2023]** "SAMA: Making Scalable Meta Learning Practical" got accepted at [NeurIPS 2023](https://openreview.net/forum?id=LV_MeMS38Q9)!
**[Jan 21 2023]** Betty got accepted as a *notable-top-5% (oral)* paper at [ICLR 2023](https://openreview.net/forum?id=LV_MeMS38Q9)!
**[Jan 12 2023]** We release *Betty v0.2* with new distributed training support for meta-learning! Currently
available features are:
- Distributed Data Parallel [(DDP)](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)
- ZeRO Redundancy Optimizer [(ZeRO)](https://pytorch.org/tutorials/recipes/zero_redundancy_optimizer.html)
- *(experimental)* Fully Sharded Data Parallel [(FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html)
You can now easily scale up meta-learning (or even meta-meta-learning) with one-liner change!
- **Example**: [Meta-Weight-Net with RoBERTa](examples/bert_data_reweighting/)
- **Tutorial**: [link](https://leopard-ai.github.io/betty/tutorial/intermediate/intermediate_distributed.html)
## Introduction
Betty is a [PyTorch](https://pytorch.org) library for generalized meta-learning (GML)
and multilevel optimization (MLO) that allows a **simple** and **modular**
programming interface for a number of **large-scale** applications including
[meta-learning](examples/implicit_maml/),
[hyperparameter optimization](examples/logistic_regression_hpo/),
[neural architecture search](examples/neural_architecture_search/),
[data reweighting](examples/learning_to_reweight/), and many more.
With Betty, users simply need to do two things to implement any GML/MLO programs:
1. Define each level's optimization problem using the [Problem](#problem) class.
2. Define the hierarchical problem structure using the [Engine](#engine) class.
## Quick Start
### Problem
#### Basics
Each level problem can be defined with seven components: (1) module, (2) optimizer, (3)
data loader, (4) loss function, (5) problem configuration, (6) name, and (7) other
optional components (e.g. learning rate scheduler). The loss function (4) can be
defined via the `training_step` method, while all other components can be provided
through the class constructor. For example, an image classification problem can be
defined as follows:
```python
from betty.problems import ImplicitProblem
from betty.configs import Config
# set up module, optimizer, data loader (i.e. (1)-(3))
cls_module, cls_optimizer, cls_data_loader = setup_classification()
class Classifier(ImplicitProblem):
# set up loss function
def training_step(self, batch):
inputs, labels = batch
outputs = self.module(inputs)
loss = F.cross_entropy(outputs, labels)
return loss
# set up problem configuration
cls_config = Config(type='darts', unroll_steps=1, log_step=100)
# Classifier problem class instantiation
cls_prob = Classifier(name='classifier',
module=cls_module,
optimizer=cls_optimizer,
train_data_loader=cls_data_loader,
config=cls_config)
```
#### Interactions between problems
In GML/MLO, each problem will often need to access modules from other problems to
define its loss function. This can be achieved by using the `name` attribute as
follows:
```python
class HPO(ImplicitProblem):
def training_step(self, batch):
# set up hyperparameter optimization loss
...
# HPO problem class instantiation
hpo_prob = HPO(name='hpo', module=...)
class Classifier(ImplicitProblem):
def training_step(self, batch):
inputs, labels = batch
outputs = self.module(inputs)
loss = F.cross_entropy(outputs, labels)
"""
accessing weight decay hyperparameter from another
problem HPO can be achieved by its name 'hpo'
"""
weight_decay = self.hpo()
reg_loss = weight_decay * sum(
[p.norm().pow(2) for p in self.module.parameters()]
)
return loss + reg_loss
cls_prob = Classifier(name='classifier', module=...)
```
### Engine
#### Basics
The `Engine` class handles the hierarchical dependencies between problems. In GML/MLO,
there are two types of dependencies: upper-to-lower (`u2l`) and lower-to-upper (`l2u`).
Both types of dependencies can be defined with a Python dictionary, where the key is
the starting node and the value is the list of destination nodes.
```python
from betty import Engine
from betty.configs import EngineConfig
# set up all involved problems
problems = [cls_prob, hpo_prob]
# set up upper-to-lower and lower-to-upper dependencies
u2l = {hpo_prob: [cls_prob]}
l2u = {cls_prob: [hpo_prob]}
dependencies = {'u2l': u2l, 'l2u': l2u}
# set up Engine configuration
engine_config = EngineConfig(train_iters=10000, valid_step=100)
# instantiate Engine class
engine = Engine(problems=problems,
dependencies=dependencies,
config=engine_config)
# execute multilevel optimization
engine.run()
```
Since `Engine` manages the whole GML/MLO program, you can also perform a global validation
stage within it. All problems that comprise the GML/MLO program can again be accessed with
their names.
```python
class HPOEngine(Engine):
# set up global validation
@torch.no_grad()
def validation(self):
loss = 0
for inputs, labels in test_loader:
outputs = self.classifer(inputs)
loss += F.cross_entropy(outputs, targets)
# Returned dict will be automatically logged after each validation
return {'loss': loss}
...
engine = HPOEngine(problems=problems,
dependencies=dependencies,
config=engine_config)
engine.run()
```
Once we define all optimization problems and the hierarchical dependencies between them
with, respectively, the `Problem` class and the `Engine` class, all complicated internal
mechanisms of GML/MLO such as gradient calculation and optimization execution order will
be handled by Betty. For more details and advanced features, users can check out our
[Documentation](https://leopard-ai.github.io/betty/) and
[Tutorials](https://leopard-ai.github.io/betty/tutorial/basic/basic.html).
Happy multilevel optimization programming!
## Applications
We provide reference implementations of several GML/MLO applications, including:
- [Hyperparameter Optimization](examples/logistic_regression_hpo/)
- [Neural Architecture Search](examples/neural_architecture_search/)
- [Data Reweighting](examples/learning_to_reweight/)
- [Domain Adaptation for Pretraining & Finetuning](examples/learning_by_ignoring/)
- [(Implicit) Model-Agnostic Meta-Learning](examples/implicit_maml)
While each of the above examples traditionally has a distinct implementation style, note
that our implementations share the same code structure thanks to Betty. More examples
are on the way!
## Features
### Gradient Approximation Methods
- Implicit Differentiation
- Finite Difference (or T1-T2) ([DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055))
- Neumann Series ([Optimizing Millions of Hyperparameters by Implicit Differentiation](http://proceedings.mlr.press/v108/lorraine20a/lorraine20a.pdf))
- Conjugate Gradient ([Meta-Learning with Implicit Gradients](https://proceedings.neurips.cc/paper/2019/file/072b030ba126b2f4b2374f342be9ed44-Paper.pdf))
- Iterative Differentiation
- Reverse-mode Automatic Differentiation ([Model-Agnostic Meta-Learning (MAML)](https://arxiv.org/abs/1703.03400))
### Training
- Gradient accumulation
- FP16/BF16 training
- Distributed data-parallel training
- Gradient clipping
### Logging
- [(PyTorch) TensorBoard](https://pytorch.org/docs/stable/tensorboard.html)
- [wandb](https://github.com/wandb/client)
## Contributing
We welcome contributions from the community! Please see our [contributing
guidelines](CONTRIBUTING.md) for details on how to contribute to Betty.
## Citation
If you use Betty in your research, please cite [our
paper](https://arxiv.org/abs/2207.02849) with the following Bibtex entry.
```
@inproceedings{
choe2023betty,
title={Betty: An Automatic Differentiation Library for Multilevel Optimization},
author={Sang Keun Choe and Willie Neiswanger and Pengtao Xie and Eric Xing},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=LV_MeMS38Q9}
}
```
## License
Betty is licensed under the [Apache 2.0 License](LICENSE).