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hyper-nn

(★ 107)

Easy Hypernetworks in Pytorch and Jax

파일 탐색기

최종 버전 다운로드 (.zip)

- - main.yml
- - __init__.py
  - dynamic_hypernet.py
  - hypernet.py
  - linear_hypernet.py
  - utils.py
- - __init__.py
  - dynamic_hypernet.py
  - hypernet.py
  - linear_hypernet.py
  - utils.py
- __init__.py
- base.py
- dynamic_hypernetwork.drawio.svg
- DynamicWeights.png
- LunarLanderWeights.png
- torch_lunar_lander.gif
- - JaxDynamicHyperRNN.ipynb
  - TorchDynamicHyperRNN.ipynb
- - JaxHyperMNIST.ipynb
  - TorchHyperMNIST.ipynb
- - HyperJaxLunarLander.mp4
  - HyperTorchLunarLander.mp4
  - JaxHyperReinforce.ipynb
  - torch_lunar_lander.gif
  - TorchHyperReinforce.ipynb
- Intro.ipynb
.flake8
.gitignore
LICENSE
Makefile
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/shyamsn97/hyper-nn

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd hyper-nn

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

$ pip install hyper-nn

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Make

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.

make

생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.

✅ 에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.

// repository documentation

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# hyper-nn -- Easy Hypernetworks in Pytorch and Flax
[![PyPi version](https://badgen.net/pypi/v/hyper-nn/)](https://pypi.org/project/hyper-nn)

**Note: This library is experimental and currently under development - the flax implementations in particular are far from perfect and can be improved. If you have any suggestions on how to improve this library, please open a github issue or feel free to reach out directly!**

`hyper-nn` gives users with the ability to create easily customizable [Hypernetworks](https://arxiv.org/abs/1609.09106) for almost any generic `torch.nn.Module` from [Pytorch](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) and `flax.linen.Module` from [Flax](https://flax.readthedocs.io/en/latest/flax.linen.html). Our Hypernetwork objects are also `torch.nn.Modules` and `flax.linen.Modules`, allowing for easy integration with existing systems. For Pytorch, we make use of the amazing library [`functorch`](https://github.com/pytorch/functorch)

<p align="center">Generating Policy Weights for Lunar Lander</p>

<p float="left">
  <img width="54%" src="https://raw.githubusercontent.com/shyamsn97/hyper-nn/main/images/torch_lunar_lander.gif">
  <img width="45%" src="https://raw.githubusercontent.com/shyamsn97/hyper-nn/main/images/LunarLanderWeights.png">
</p>

<br></br>

<p align="center">Dynamic Weights for each character in a name generator</p>

<p float="center" align="center">
  <img width="100%" src="https://raw.githubusercontent.com/shyamsn97/hyper-nn/main/images/DynamicWeights.png">
</p>

---

## Install
`hyper-nn` tested on python 3.8+

#### Installing with pip
```bash
$ pip install hyper-nn
```

#### Installing from source
```bash
$ git clone git@github.com:shyamsn97/hyper-nn.git
$ cd hyper-nn
$ python setup.py install
```

For gpu functionality with Jax, you will need to follow the instructions [here](https://github.com/google/jax#installation)

---
## What are Hypernetworks?
[Hypernetworks](https://arxiv.org/abs/1609.09106), simply put, are neural networks that generate parameters for another neural network. They can be incredibly powerful, being able to represent large networks while using only a fraction of their parameters.

Hypernetworks generally come in two variants, static or dynamic. Static Hypernetworks have a fixed or learned embedding and weight generator that outputs the target networks’ weights deterministically. Dynamic Hypernetworks instead receive inputs and use them to generate dynamic weights.

<p align="center">
  <img width="75%" src="https://raw.githubusercontent.com/shyamsn97/hyper-nn/main/images/dynamic_hypernetwork.drawio.svg">
</p>
`hyper-nn` allows you to design Hypernetworks with flexibility and ease, all you have to do is implement the `generate_params` method, which outputs a parameter vector. We also include basic versions of this, composed of two linear components:

- `embedding_module` that holds information about layers(s) in the target network, or more generally a chunk of the target networks weights. By default this outputs a matrix of size `num_embeddings x embedding_dim`
- `weight_generator`, which takes in the embedding and outputs a flat parameter vector for the target network. By default this module outputs chunks in the size of `weight_chunk_dim`, which is calculated automatically as `num_target_parameters // num_embeddings`.

Both `embedding_module` and `weight_generator` are represented as `torch.nn.Module` and `flax.linen.Module` objects. a `Module` can be passed in as `custom_embedding_module` or `custom_weight_generator`, or it can be defined in the methods `make_embedding_module` or `make_weight_generator`.

The `generate_params` method feeds the output from `embedding_module` into `weight_generator` to output the target parameters.

The `forward` method takes in a list of inputs and uses the generated parameters to calculate the output. This method acts as the main method for both jax and torch hypernetworks

### [Torch Hypernetwork](hypernn/torch/hypernet.py#L81)
```python
...
  def make_embedding_module(self) -> nn.Module:
      return nn.Embedding(self.num_embeddings, self.embedding_dim)

def make_weight_generator(self) -> nn.Module:
      return nn.Linear(self.embedding_dim, self.weight_chunk_dim)

def generate_params(self, *args, **kwargs) -> Tuple[torch.Tensor, Dict[str, Any]]:
      embedding = self.embedding_module(
          torch.arange(self.num_embeddings, device=self.device)
      )
      generated_params = self.weight_generator(embedding).view(-1)
      return generated_params, {"embedding": embedding}

def target_forward(
      self,
      *args,
      generated_params: torch.Tensor,
      assert_parameter_shapes: bool = True,
      **kwargs,
  ) -> torch.Tensor:
      if assert_parameter_shapes:
          self.assert_parameter_shapes(generated_params)

return self.target_network(generated_params, *args, **kwargs)

def forward(
      self,
      *args,
      generated_params: Optional[torch.Tensor] = None,
      has_aux: bool = False,
      assert_parameter_shapes: bool = True,
      generate_params_kwargs: Dict[str, Any] = {},
      **kwargs,
  ):
      """
      Main method for creating / using generated parameters and passing in input into the target network

Args:
          generated_params (Optional[torch.Tensor], optional): Generated parameters of the target network. If not provided, the hypernetwork will generate the parameters. Defaults to None.
          has_aux (bool, optional): If True, return the auxiliary output from generate_params method. Defaults to False.
          assert_parameter_shapes (bool, optional): If True, raise an error if generated_params does not have shape (num_target_parameters,). Defaults to True.
          generate_params_kwargs (Dict[str, Any], optional): kwargs to be passed to generate_params method
          *args, *kwargs, arguments to be passed into the target network (also gets passed into generate_params)
      Returns:
          output (torch.Tensor) | (torch.Tensor, Dict[str, torch.Tensor]): returns output from target network and optionally auxiliary output.
      """
      aux_output = {}
      if generated_params is None:
          generated_params, aux_output = self.generate_params(
              *args, **kwargs, **generate_params_kwargs
          )

if has_aux:
          return (
              self.target_forward(
                  *args,
                  generated_params=generated_params,
                  assert_parameter_shapes=assert_parameter_shapes,
                  **kwargs,
              ),
              generated_params,
              aux_output,
          )
      return self.target_forward(
          *args,
          generated_params=generated_params,
          assert_parameter_shapes=assert_parameter_shapes,
          **kwargs,
      )

...
```
### [Flax Hypernetwork](hypernn/jax/hypernet.py#L76)
```python
...
  def make_embedding_module(self):
      return nn.Embed(
          self.num_embeddings,
          self.embedding_dim,
          embedding_init=jax.nn.initializers.uniform(),
      )

def make_weight_generator(self):
      return nn.Dense(self.weight_chunk_dim)

def generate_params(self, *args, **kwargs) -> Tuple[jnp.array, Dict[str, Any]]:
      embedding = self.embedding_module(jnp.arange(0, self.num_embeddings))
      generated_params = self.weight_generator(embedding).reshape(-1)
      return generated_params, {"embedding": embedding}

def target_forward(
      self,
      *args,
      generated_params: jnp.array,
      assert_parameter_shapes: bool = True,
      **kwargs,
  ) -> jnp.array:

if assert_parameter_shapes:
          self.assert_parameter_shapes(generated_params)

param_tree = create_param_tree(
          generated_params, self.target_weight_shapes, self.target_treedef
      )

return self.target_network.apply(param_tree, *args, **kwargs)

def forward(
      self,
      *args,
      generated_params: Optional[jnp.array] = None,
      has_aux: bool = False,
      assert_parameter_shapes: bool = True,
      generate_params_kwargs: Dict[str, Any] = {},
      **kwargs,
  ) -> Tuple[jnp.array, List[jnp.array]]:
      """
      Main method for creating / using generated parameters and passing in input into the target network

Args:
          generated_params (Optional[jnp.array], optional): Generated parameters of the target network. If not provided, the hypernetwork will generate the parameters. Defaults to None.
          has_aux (bool, optional): If True, return the auxiliary output from generate_params method. Defaults to False.
          assert_parameter_shapes (bool, optional): If True, raise an error if generated_params does not have shape (num_target_parameters,). Defaults to True.
          generate_params_kwargs (Dict[str, Any], optional): kwargs to be passed to generate_params method

Returns:
          output (torch.Tensor) | (jnp.array, Dict[str, jnp.array]): returns output from target network and optionally auxiliary output.
      """
      aux_output = {}
      if generated_params is None:
          generated_params, aux_output = self.generate_params(
              *args, **kwargs, **generate_params_kwargs
          )

if has_aux:
          return (
              self.target_forward(
                  *args,
                  generated_params=generated_params,
                  assert_parameter_shapes=assert_parameter_shapes,
                  **kwargs,
              ),
              generated_params,
              aux_output,
          )
      return self.target_forward(
          *args,
          generated_params=generated_params,
          assert_parameter_shapes=assert_parameter_shapes,
          **kwargs,
      )

...
```

---
## Quick Usage

for detailed examples see [notebooks](notebooks/)
- [Generating weights for a CNN on MNIST](notebooks/mnist/)
- [Lunar Lander Reinforce (Vanilla Policy Gradient)](notebooks/reinforce/)
- [Dynamic Hypernetworks for name generation](notebooks/dynamic_hypernetworks/)

The main classes to use are `TorchHyperNetwork` and `JaxHyperNetwork` and those that inherit them. Instead of constructing them directly, use the `from_target` method, shown below. After this you can use the hypernetwork exactly like any other `nn.Module`!

`hyper-nn` also makes it easy to create Dynamic Hypernetworks that use inputs to create target weights. Basic implementations (both < 100 lines) are provided with `JaxDynamicHyperNetwork` and `TorchDynamicHyperNetwork`, which use an rnn and current input to generate weights.

To create hypernetworks, its easier to use the `from_target` method instead of instantiating it directly because some parameters are calculated automatically for you.

### Pytorch
```python
import torch
import torch.nn as nn

from hypernn.torch import TorchHyperNetwork, TorchLinearHyperNetwork, TorchDynamicHyperNetwork

# any module
target_network = nn.Sequential(
    nn.Linear(32, 64),
    nn.ReLU(),
    nn.Linear(64, 32)
)

EMBEDDING_DIM = 4
NUM_EMBEDDINGS = 32

hypernetwork = TorchLinearHyperNetwork(
    target_network = target_network,
    embedding_dim = EMBEDDING_DIM,
    num_embeddings = NUM_EMBEDDINGS
)

# now we can use the hypernetwork like any other nn.Module
inp = torch.zeros((1, 32))

# by default we only output what we'd expect from the target network
output = hypernetwork(inp)

# return aux_output
output, generated_params, aux_output = hypernetwork(inp, has_aux=True)

# generate params separately
generated_params, aux_output = hypernetwork.generate_params()
output = hypernetwork(inp, generated_params=generated_params)

### Dynamic Hypernetwork

dynamic_hypernetwork = TorchDynamicHyperNetwork(
    input_dim = 32,
    target_network = target_network,
    embedding_dim = EMBEDDING_DIM,
    num_embeddings = NUM_EMBEDDINGS
)

output = dynamic_hypernetwork(inp, generate_params_kwargs=dict(x=inp))

# by default we only output what we'd expect from the target network
output = dynamic_hypernetwork(inp, generate_params_kwargs=dict(x=inp, hidden_state=torch.zeros((1,32))))

```

### Jax
```python
import flax.linen as nn
import jax.numpy as jnp
from jax import random

from hypernn.jax import JaxHyperNetwork, JaxLinearHyperNetwork, JaxDynamicHyperNetwork

# any module
target_network = nn.Sequential(
    [
        nn.Dense(64),
        nn.relu,
        nn.Dense(32)
    ]
)

EMBEDDING_DIM = 4
NUM_EMBEDDINGS = 32

hypernetwork = JaxLinearHyperNetwork.from_target(
    target_network = target_network,
    embedding_dim = EMBEDDING_DIM,
    num_embeddings = NUM_EMBEDDINGS,
    inputs=jnp.zeros((1, 32)) # jax needs this to initialize target weights
)

# now we can use the hypernetwork like any other nn.Module
inp = jnp.zeros((1, 32))
key = random.PRNGKey(0)
hypernetwork_params = hypernetwork.init(key, inp) # flax needs to initialize hypernetwork parameters first

# by default we only output what we'd expect from the target network
output = hypernetwork.apply(hypernetwork_params, inp)

# return aux_output
output, generated_params, aux_output = hypernetwork.apply(hypernetwork_params, inp, has_aux=True)

# generate params separately
generated_params, aux_output = hypernetwork.apply(hypernetwork_params, method=hypernetwork.generate_params)

output = hypernetwork.apply(hypernetwork_params, inp, generated_params=generated_params)

### Dynamic Hypernetwork

dynamic_hypernetwork = JaxDynamicHyperNetwork.from_target(
    input_dim = 32,
    target_network = target_network,
    embedding_dim = EMBEDDING_DIM,
    num_embeddings = NUM_EMBEDDINGS,
    inputs=jnp.zeros((1, 32)) # jax needs this to initialize target weights
)
dynamic_hypernetwork_params = dynamic_hypernetwork.init(key, inp, generate_params_kwargs=dict(x=inp, hidden_state=jnp.zeros((1,32)))) # flax needs to initialize hypernetwork parameters first

# by default we only output what we'd expect from the target network
output = dynamic_hypernetwork.apply(dynamic_hypernetwork_params, inp, generate_params_kwargs=dict(x=inp, hidden_state=jnp.zeros((1,32))))

# by default we only output what we'd expect from the target network
output = dynamic_hypernetwork.apply(dynamic_hypernetwork_params, inp, generate_params_kwargs=dict(x=inp, hidden_state=jnp.zeros((1,32))))

```

## Customizing Hypernetworks
`hyper-nn` makes it easy to customize and create more complex hypernetworks.

The main components to modify are the methods `generate_params`. This allows for complete control over how the hypernetwork generates parameters

For example, here we extend the linear hypernetwork which uses components `embedding_module` and `weight_generator`. We implement a hypernetwork that could be useful in a multi task setting, where a one hot encoded class embedding is concatenated to every row in the embedding matrix outputted by the `embedding_module`. In addition, we override both our `make_embedding_module` and `make_weight_generator` methods to output customized modules. This whole class implementation is under 50 lines of code!

```python
from typing import Optional, Iterable, Any, Tuple, Dict
import torch
import torch.nn as nn
# static hypernetwork
from hypernn.torch import TorchHyperNetwork
from hypernn.torch.utils import get_weight_chunk_dims

class MultiTaskHypernetwork(TorchHyperNetwork):
    def __init__(
        self,
        num_tasks: int,
        target_network: nn.Module,
        num_target_parameters: Optional[int] = None,
        embedding_dim: int = 100,
        num_embeddings: int = 3,
        weight_chunk_dim: Optional[int] = None,
    ):
        super().__init__(
                    target_network = target_network,
                    num_target_parameters = num_target_parameters,
                )
        self.num_tasks = num_tasks
        self.embedding_dim = embedding_dim
        self.num_embeddings = num_embeddings
        self.weight_chunk_dim = weight_chunk_dim
        if weight_chunk_dim is None:
            self.weight_chunk_dim = get_weight_chunk_dims(
                self.num_target_parameters, num_embeddings
            )
        self.embedding_module = self.make_embedding_module()
        self.weight_generator = self.make_weight_generator()

def make_embedding_module(self) -> nn.Module:
        embedding = nn.Embedding(self.num_embeddings, 8)
        return nn.Sequential(
            embedding,
            nn.Tanh(),
            nn.Linear(8, self.embedding_dim),
            nn.Tanh(),
        )

def make_weight_generator(self) -> nn.Module:
        return nn.Sequential(
            nn.Linear(self.embedding_dim + self.num_tasks, 32),
            nn.Tanh(),
            nn.Linear(32, self.weight_chunk_dim)
        )

def generate_params(
        self, one_hot_task_embedding: torch.Tensor
    ) -> Tuple[torch.Tensor, Dict[str, Any]]:
        embedding = self.embedding_module(
            torch.arange(self.num_embeddings, device=self.device)
        )
        one_hot_task_embedding = one_hot_task_embedding.repeat(self.num_embeddings, 1) # repeat to concat to embedding
        concatenated = torch.cat((embedding, one_hot_task_embedding), dim=-1)
        generated_params = self.weight_generator(concatenated).view(-1)
        return generated_params, {"embedding": embedding}

# usage
target_network = nn.Sequential(
    nn.Linear(32, 64),
    nn.ReLU(),
    nn.Linear(64, 32)
)

NUM_TASKS = 4
EMBEDDING_DIM = 4
NUM_EMBEDDINGS = 32

hypernetwork = MultiTaskHypernetwork(
    num_tasks = NUM_TASKS,
    target_network = target_network,
    embedding_dim = EMBEDDING_DIM,
    num_embeddings = NUM_EMBEDDINGS
)
inp = torch.zeros((1, 32))
one_hot_task_embedding = torch.tensor([0.0,0.0,1.0,0.0]).view((1,4))

out = hypernetwork(inp, generate_params_kwargs=dict(one_hot_task_embedding=one_hot_task_embedding))
```

---
## Advanced: Using vmap for batching operations
This is useful when dealing with dynamic hypernetworks that generate different params depending on inputs.

### Pytorch
```python
import torch.nn as nn
from functorch import vmap

# dynamic hypernetwork
from hypernn.torch import TorchDynamicHyperNetwork

# any module
target_network = nn.Sequential(
    nn.Linear(8, 256),
    nn.ReLU(),
    nn.Linear(256, 32)
)

EMBEDDING_DIM = 4
NUM_EMBEDDINGS = 32

# conditioned on input to generate param vector
hypernetwork = TorchDynamicHyperNetwork(
    target_network = target_network,
    embedding_dim = EMBEDDING_DIM,
    num_embeddings = NUM_EMBEDDINGS,
    input_dim = 8
)

# batch of 10 inputs
inp = torch.randn((10, 1, 8))

# use with a for loop
outputs = []
for i in range(10):
    outputs.append(hypernetwork(inp[i], generate_params_kwargs=dict(x=inp[i])))
outputs = torch.stack(outputs)
assert outputs.size() == (10, 1, 32)

# using vmap
from typing import Dict, Any

def forward(
    generated_params,
    *args,
    has_aux: bool = False,
    assert_parameter_shapes: bool = True,
    generate_params_kwargs: Dict[str, Any] = {},
    **kwargs
):
    return hypernetwork.forward(*args,
                                generated_params=generated_params,
                                has_aux=has_aux,
                                assert_parameter_shapes=assert_parameter_shapes,
                                generate_params_kwargs=generate_params_kwargs,
                                **kwargs)

generated_vmap_params, aux_output = vmap(hypernetwork.generate_params)(inp)
outputs = vmap(forward)(generated_vmap_params, inp)

assert outputs.size() == (10, 1, 32)
```
## Future Plans
Here's a list of some stuff that will hopefully be added to the library. If anyone has other suggestions, please reach out / create an issue!

- [x] MNIST example
- [x] Lunar Lander Example
- [x] Dynamic Hypernetwork Example
- [x] Multi-task Hypernetwork Example
- [ ] Dedicated documentation website
- [ ] Efficient batching for DynamicJaxHypernetwork
- [ ] Implementation of [HyperTransformer](https://arxiv.org/abs/2201.04182)
- [ ] Implementation of [Recomposing the Reinforcement Learning Building Blocks with Hypernetworks](https://arxiv.org/abs/2106.06842)
- [ ] Implementation of [Goal-Conditioned Generators of Deep Policies
](https://arxiv.org/abs/2207.01570)

---
## Citing hyper-nn

If you use this software in your publications, please cite it by using the following BibTeX entry.

```bibtex
@misc{sudhakaran2022,
  author = {Sudhakaran, Shyam Sudhakaran},
  title = {hyper-nn},
  howpublished = {\url{https://github.com/shyamsn97/hyper-nn}},
  year = {2022},
}
```