Full Code of uber-research/EvoGrad for AI

Repository: uber-research/EvoGrad
Branch: master
Commit: 53afad88074d
Files: 14
Total size: 26.5 KB

Directory structure:
gitextract_9ie9iprt/

├── .gitignore
├── LICENSE
├── README.md
├── demos/
│   ├── cartpole.py
│   ├── max_ent_interference.py
│   ├── max_var_interference.py
│   ├── standard_interference.py
│   └── standard_quadratic.py
├── evograd/
│   ├── __init__.py
│   ├── distributions.py
│   ├── expectation.py
│   └── noise.py
├── requirements.txt
└── setup.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
venv
__pycache__
*.pyo
*.pyc
build
dist
*.egg-info
*.swp


================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# EvoGrad

EvoGrad is a lightweight tool for differentiating through expectation,
built on top of PyTorch.

Tools that enable fast and flexible experimentation democratize and accelerate machine learning research.
However, one field that so far has not been greatly impacted by automatic differentiation tools is evolutionary computation
The reason is that most evolutionary algorithms are gradient-free:
they do not follow any explicit mathematical gradient (i.e., the mathematically optimal local direction of improvement), and instead proceed through a generate-and-test heuristic.
In other words, they create new variants, test them out, and keep the best.

Recent and exciting research in evolutionary algorithms for deep reinforcement learning, however, has highlighted how a specific class of evolutionary algorithms can benefit from auto-differentiation.
Work from OpenAI demonstrated that a form of Natural Evolution Strategies (NES) is massively scalable, and competitive with modern deep reinforcement learning algorithms.

EvoGrad enables fast prototyping of NES-like algorithms.
We believe there are many interesting algorithms yet to be discovered in this vein, and we hope this library will help to catalyze progress in the machine learning community.

## Examples
### Natural Evolution Strategies
As a first example, we’ll implement the simplified NES algorithm of [Salimans et al. (2017)](https://openai.com/blog/evolution-strategies/) in EvoGrad.
EvoGrad provides several probability distributions which may be used in the expectation function.
We will use a normal distribution because it is the most common choice in practice.

Let’s consider the problem of finding a fitness peak in a simple 1-D search space.

We can define our population distribution over this search space to be initially centered at 1.0, with a fixed variance of 0.05, with the following Python code:

```python
mu = torch.tensor([1.0], requires_grad=True)
p = Normal(mu, 0.05)
```

Next, let’s define a simple fitness function that rewards individuals for approaching the location 5.0 in the search space:

```python
def fitness(xs):
	return -(x - 5.0) ** 2
```

Each generation of evolution in NES takes samples from the population distribution and evaluates the fitness of each of those individual samples. Here we sample and evaluate 100 individuals from the distribution:

```python
sample = p.sample(n=100)
fitnesses = fitness(sample)
```

Optionally, we can apply a [whitening transformation](https://en.wikipedia.org/wiki/Whitening_transformation) to the fitnesses (a form of pre-processing that often increases NES performance), like this:

```python
fitnesses = (fitnesses - fitnesses.mean()) / fitnesses.std()
```

Now we can use these calculated fitness values to estimate the mean fitness over our population distribution:

```python
mean = expectation(fitnesses, sample, p=p)
```

Although we could have estimated the mean value directly with the snippet:
`mean = fitnesses.mean()`, what we gain by instead using the EvoGrad `expectation` function is the ability to backpropagate through mean.
We can then use the resulting auto-differentiated gradients to optimize the center of the 1D Gaussian population distribution (mu) through gradient descent (here, to increase the expected fitness value of the population):

```python
mean.backward()
with torch.no_grad():
	mu += alpha * mu.grad
	mu.grad.zero_()
```

### Maximizing Variance
As a more sophisticated example, rather than maximizing the mean fitness, we can maximize the variance of behaviors in the population.
While fitness is a measure of quality for a fixed task, in some situations we want to prepare for the unknown, and instead might want our population to contain a diversity of behaviors that can easily be adapted to solve a wide range of possible future tasks.

To do so, we need a quantification of behavior, which we can call a behavior characterization. Similarly to how you can evaluate an individual parameter vector drawn from the population distribution to establish its fitness (e.g. how far does this controller cause a robot to walk?), you could evaluate such a draw and return some quantification of its behavior (e.g., what position does a robot controlled by this neural network locomote to?).

For this example, let’s choose a simple but illustrative, 1D behavior characterization, namely, the product of two sine waves (one with much faster frequency than the other):

```python
def behavior(x):
	return 5 * torch.sin(0.2 * x) * torch.sin(20 * x)
```

Now, instead of estimating the mean fitness, we can calculate a statistic that reflects the diversity of sampled behaviors. The variance of a distribution is one metric of diversity, and one variant of evolvability ES measures and optimizes such variance of behaviors sampled from the population distribution:

```python
sample = p.sample(n=100)
behaviors = behavior(sample)
zscore = (behaviors - behaviors.mean()) / behaviors.std()
variance = expectation(zscore ** 2, sample, p=p)
```

### Maximizing Entropy
In the previous example, the gradient would be relatively straightforward to compute by hand.
However, sometimes we may need to maximize objectives whose derivatives would be much more challenging to derive.
In particular, this final example will seek to maximize the entropy of the distribution of behaviors (a variant of evolvability ES).

Note that for this example you'll also have to install `scipy` from pip.

To create a differentiable estimate of entropy, we first compute the pairwise distances between the different behaviors.
Next, we create a smooth probability distribution by fitting a [kernel density estimate](https://en.wikipedia.org/wiki/Kernel_density_estimation):

```python
dists = scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(behaviors, "sqeuclidean"))
kernel = torch.tensor(scipy.exp(-dists / k_sigma ** 2), dtype=torch.float32)
p_x = expectation(kernel, sample, p=p, dim=1)
```

Then, we can use these probabilities to estimate the [entropy of the distribution](https://en.wikipedia.org/wiki/Entropy_(information_theory)), and run gradient descent on it as before:

```python
entropy = expectation(-torch.log(p_x), sample, p=p)
```

Full code for these examples can be found in the `demos` directory of
this repository.

## Installation
Either install EvoGrad from pip:
```
pip install evograd
```
Or from the source code in this repository:
```
git clone github.com/uber-research/EvoGrad
cd EvoGrad
pip install -r requirements.txt
pip install -e .
```


## About

Development of EvoGrad was led by [Alex Gajewski](https://github.com/agajews) as a Summer intern at Uber AI Labs.


================================================
FILE: demos/cartpole.py
================================================
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
#
# See the License for the specific language governing permissions and
# limitations under the License.


import torch
import numpy as np

from evograd import expectation
from evograd.distributions import Normal

import gym


def simulate_single(weights):
    total_reward = 0.0
    num_run = 10
    for t in range(num_run):
        observation = env.reset()
        for i in range(300):
            action = 1 if np.dot(weights, observation) > 0 else 0
            observation, reward, done, info = env.step(action)
            total_reward += reward
            if done:
                break
    return total_reward / num_run


def simulate(batch_weights):
    rewards = []
    for weights in batch_weights:
        rewards.append(simulate_single(weights.numpy()))
    return torch.tensor(rewards)


mu = torch.randn(4, requires_grad=True)  # population mean
npop = 50  # population size
std = 0.5  # noise standard deviation
alpha = 0.03  # learning rate
p = Normal(mu, std)
env = gym.make("CartPole-v0")

for t in range(2000):
    sample = p.sample(npop)
    fitnesses = simulate(sample)
    scaled_fitnesses = (fitnesses - fitnesses.mean()) / fitnesses.std()
    mean = expectation(scaled_fitnesses, sample, p=p)
    mean.backward()

    with torch.no_grad():
        mu += alpha * mu.grad
        mu.grad.zero_()

    print("step: {}, mean fitness: {:0.5}".format(t, float(fitnesses.mean())))


================================================
FILE: demos/max_ent_interference.py
================================================
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
#
# See the License for the specific language governing permissions and
# limitations under the License.


import scipy
import scipy.spatial
import torch
import numpy as np

from evograd import expectation
from evograd.distributions import Normal


def fun(x):
    return 5 * torch.sin(0.2 * x) * torch.sin(20 * x)


mu = torch.tensor([1.0], requires_grad=True)
npop = 500  # population size
std = 0.5  # noise standard deviation
k_sigma = 1.0  # kernel standard deviation
alpha = 0.10  # learning rate
p = Normal(mu, std)

for t in range(2000):
    sample = p.sample(npop)
    novelties = fun(sample)
    novelties = (novelties - novelties.mean()) / novelties.std()
    dists = scipy.spatial.distance.squareform(
        scipy.spatial.distance.pdist(novelties, "sqeuclidean")
    )
    kernel = torch.tensor(scipy.exp(-dists / k_sigma ** 2), dtype=torch.float32)
    p_x = expectation(kernel, sample, p=p)
    entropy = expectation(-torch.log(p_x), sample, p=p)
    entropy.backward()

    with torch.no_grad():
        mu += alpha * mu.grad
        mu.grad.zero_()

    print("step: {}, estimated entropy: {:0.5}".format(t, float(mu)))


================================================
FILE: demos/max_var_interference.py
================================================
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
#
# See the License for the specific language governing permissions and
# limitations under the License.


import torch
import numpy as np

from evograd import expectation
from evograd.distributions import Normal


def fun(x):
    return 5 * torch.sin(0.2 * x) * torch.sin(20 * x)


mu = torch.tensor([1.0], requires_grad=True)
npop = 500  # population size
std = 0.5  # noise standard deviation
alpha = 0.03  # learning rate
p = Normal(mu, std)

for t in range(2000):
    sample = p.sample(npop)
    behaviors = fun(sample)
    zscores = (behaviors - behaviors.mean()) / behaviors.std()
    variance = expectation(zscores ** 2, sample, p=p)
    variance.backward()

    with torch.no_grad():
        mu += alpha * mu.grad
        mu.grad.zero_()

    print("step: {}, estimated variance: {:0.5}".format(t, float(mu)))


================================================
FILE: demos/standard_interference.py
================================================
#Copyright (c) 2019 Uber Technologies, Inc.
#
#Licensed under the Uber Non-Commercial License (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at the root directory of this project. 
#
#See the License for the specific language governing permissions and
#limitations under the License.


import torch

from evograd import expectation
from evograd.distributions import Normal


def fun(x):
    return 5 * torch.sin(0.2 * x) * torch.sin(20 * x)


mu = torch.tensor([1.0], requires_grad=True)
npop = 500  # population size
std = 0.5  # noise standard deviation
alpha = 0.03  # learning rate
p = Normal(mu, std)

for t in range(2000):
    sample = p.sample(npop)
    fitnesses = fun(sample)
    fitnesses = (fitnesses - fitnesses.mean()) / fitnesses.std()
    mean = expectation(fitnesses, sample, p=p)
    mean.backward()

    with torch.no_grad():
        mu += alpha * mu.grad
        mu.grad.zero_()

    print('step: {}, mean fitness: {:0.5}'.format(t, float(mu)))


================================================
FILE: demos/standard_quadratic.py
================================================
#Copyright (c) 2019 Uber Technologies, Inc.
#
#Licensed under the Uber Non-Commercial License (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at the root directory of this project. 
#
#See the License for the specific language governing permissions and
#limitations under the License.


import torch

from evograd import expectation
from evograd.distributions import Normal


def fun(x):
    return -(x - 5.0) ** 2


mu = torch.tensor([1.0], requires_grad=True)
npop = 500  # population size
std = 0.5  # noise standard deviation
alpha = 0.03  # learning rate
p = Normal(mu, std)

for t in range(2000):
    sample = p.sample(npop)
    fitnesses = fun(sample)
    fitnesses = (fitnesses - fitnesses.mean()) / fitnesses.std()
    mean = expectation(fitnesses, sample, p=p)
    mean.backward()

    with torch.no_grad():
        mu += alpha * mu.grad
        mu.grad.zero_()

    print('step: {}, mean fitness: {:0.5}'.format(t, float(mu)))


================================================
FILE: evograd/__init__.py
================================================
#Copyright (c) 2019 Uber Technologies, Inc.
#
#Licensed under the Uber Non-Commercial License (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at the root directory of this project. 
#
#See the License for the specific language governing permissions and
#limitations under the License.


from .expectation import expectation


================================================
FILE: evograd/distributions.py
================================================
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
#
# See the License for the specific language governing permissions and
# limitations under the License.


import numpy as np
import torch

from .noise import noise


class NormalProbRatio(torch.autograd.Function):
    @staticmethod
    def forward(ctx, mu, sigma, descriptors, decode_fn):
        ctx.save_for_backward(mu)
        ctx.sigma = sigma
        ctx.descriptors = descriptors
        ctx.decode_fn = decode_fn
        res = torch.ones(len(descriptors), dtype=torch.float32)
        res.requires_grad = True
        return res

    @staticmethod
    def backward(ctx, grad_output):
        mu, = ctx.saved_tensors
        theta = ctx.decode_fn(ctx.descriptors)
        grad = (theta - mu) / ctx.sigma ** 2 * grad_output.unsqueeze(1)
        return (grad, None, None, None)


class MixNormalProbRatio(torch.autograd.Function):
    @staticmethod
    def forward(ctx, mus, sigma, descriptors, decode_fn):
        ctx.save_for_backward(mus)
        ctx.sigma = sigma
        ctx.descriptors = descriptors
        ctx.decode_fn = decode_fn
        res = torch.ones(len(descriptors), dtype=torch.float32)
        res.requires_grad = True
        return res

    @staticmethod
    def backward(ctx, grad_output):
        mus, = ctx.saved_tensors
        thetas = ctx.decode_fn(ctx.descriptors)
        epsilons = [thetas - mu for mu in mus]
        grads = torch.stack(
            [
                (epsilon / ctx.sigma ** 2)
                / (
                    1
                    + sum(
                        torch.exp(
                            -0.5
                            * (other.dot(other) - epsilon.dot(epsilon))
                            / ctx.sigma ** 2
                        )
                        for other in epsilons
                        if other is not epsilon
                    )
                )
                * grad_output
                for epsilon in epsilons
            ]
        )
        return (grads, None, None, None)


class Distribution:
    def __init__(self, device, random_state=None):
        if random_state is None:
            random_state = np.random.RandomState()  # pylint: disable=no-member
        self.random_state = random_state
        self.device = device


class Normal(Distribution):
    def __init__(self, mu, sigma, random_state=None):
        """
        mu: torch.tensor
        sigma: torch.tensor or float
        """
        super().__init__(mu.device, random_state)
        self.mu = mu
        self.sigma = sigma

    def ratio(self, descriptors):
        return NormalProbRatio.apply(self.mu, self.sigma, descriptors, self.decode)

    def sample(self, n, encode=False):
        n_epsilons = n
        noise_inds = np.asarray(
            [
                noise.sample_index(self.random_state, len(self.mu))
                for _ in range(n_epsilons)
            ],
            dtype="int",
        )
        descriptors = [(idx, 1) for idx in noise_inds]
        if encode:
            return descriptors
        thetas = torch.stack([self.decode(descriptor) for descriptor in descriptors])
        return thetas

    def decode(self, descriptor):
        if not isinstance(descriptor, tuple):
            # assert isinstance(descriptor, torch.tensor)
            return descriptor
        noise_idx, direction = descriptor
        epsilon = torch.tensor(noise.get(noise_idx, len(self.mu)), device=self.device)
        with torch.no_grad():
            return self.mu + direction * self.sigma * epsilon


class PairedNormal(Normal):
    def sample(self, n, encode=False):
        assert n % 2 == 0
        n_epsilons = n // 2
        noise_inds = np.asarray(
            [
                noise.sample_index(self.random_state, len(self.mu))
                for _ in range(n_epsilons)
            ],
            dtype="int",
        )
        descriptors = [(idx, 1) for idx in noise_inds] + [
            (idx, -1) for idx in noise_inds
        ]
        if encode:
            return descriptors
        thetas = torch.stack([self.decode(descriptor) for descriptor in descriptors])
        return thetas


class MixNormal(Distribution):
    def __init__(self, mus, sigma, random_state=None):
        """
        mu: torch.tensor
        sigma: torch.tensor or float
        """
        super().__init__(mus[0].device, random_state)
        self.mus = [mu for mu in mus]
        self.sigma = sigma

    def ratio(self, descriptor):
        return MixNormalProbRatio.apply(self.mus, self.sigma, descriptor, self.decode)

    def sample(self, n, encode=False):
        n_epsilons = n
        noise_ids = np.asarray(
            [
                noise.sample_index(self.random_state, len(self.mus[0]))
                for _ in range(n_epsilons)
            ],
            dtype="int",
        )
        mu_ids = np.random.randint(len(self.mus), size=n)
        descriptors = [
            (noise_id, mu_id, 1) for noise_id, mu_id in zip(noise_ids, mu_ids)
        ]
        if encode:
            return descriptors
        thetas = torch.stack([self.decode(descriptor) for descriptor in descriptors])
        return thetas

    def decode(self, descriptor):
        if not isinstance(descriptor, tuple):
            # assert isinstance(descriptor, torch.tensor)
            return descriptor
        noise_id, mu_id, direction = descriptor
        epsilon = torch.tensor(
            noise.get(noise_id, len(self.mus[0])), device=self.device
        )
        with torch.no_grad():
            return self.mus[mu_id] + direction * self.sigma * epsilon


================================================
FILE: evograd/expectation.py
================================================
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
#
# See the License for the specific language governing permissions and
# limitations under the License.


import torch


def unsqueeze_as(x, y):
    assert len(y.shape) >= len(x.shape)
    for _ in range(len(y.shape) - len(x.shape)):
        x = torch.unsqueeze(x, -1)
    return x


def expectation(vals, sample, p):
    ratio = unsqueeze_as(p.ratio(sample), vals)
    prod = vals * ratio
    return prod.mean(dim=0)


================================================
FILE: evograd/noise.py
================================================
#Copyright (c) 2019 Uber Technologies, Inc.
#
#Licensed under the Uber Non-Commercial License (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at the root directory of this project. 
#
#See the License for the specific language governing permissions and
#limitations under the License.


import logging

import numpy as np

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

debug = True


class SharedNoiseTable:
    def __init__(self):
        import ctypes
        import multiprocessing

        seed = 42
        # 1 gigabyte of 32-bit numbers. Will actually sample 2 gigabytes below.
        count = 250000000 if not debug else 10000000
        logger.info("Sampling {} random numbers with seed {}".format(count, seed))
        self._shared_mem = multiprocessing.Array(ctypes.c_float, count)
        self.noise = np.ctypeslib.as_array(self._shared_mem.get_obj())
        assert self.noise.dtype == np.float32
        self.noise[:] = np.random.RandomState(seed).randn(  # pylint: disable=no-member
            count
        )  # 64-bit to 32-bit conversion here
        logger.info("Sampled {} bytes".format(self.noise.size * 4))

    def get(self, i, dim):
        return self.noise[i : i + dim]

    def sample_index(self, stream, dim):
        return stream.randint(0, len(self.noise) - dim + 1)


noise = SharedNoiseTable()


================================================
FILE: requirements.txt
================================================
numpy==1.16.4
torch==1.1.0.post2


================================================
FILE: setup.py
================================================
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
#
# See the License for the specific language governing permissions and
# limitations under the License.


import setuptools

with open("README.md", "r") as fh:
    long_description = fh.read()

setuptools.setup(
    name="evograd",
    version="0.1.2",
    author="Alex Gajewski",
    author_email="agajews@gmail.com",
    description="A lightweight tool for differentiating through expectations",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/uber-research/EvoGrad",
    packages=setuptools.find_packages(),
    install_requires=["numpy", "torch"],
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
)