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pytorch_toy.py
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pytorch_toy.py
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from __future__ import absolute_import
from __future__ import print_function
from itertools import product
import argparse
import numpy as np
import torch
class QFunc(torch.nn.Module):
'''Control variate for RELAX'''
def __init__(self, num_latents, hidden_size=10):
super(QFunc, self).__init__()
self.h1 = torch.nn.Linear(num_latents, hidden_size)
self.nonlin = torch.nn.Tanh()
self.out = torch.nn.Linear(hidden_size, 1)
def forward(self, z):
# the multiplication by 2 and subtraction is from toy.py...
# it doesn't change the bias of the estimator, I guess
z = self.h1(z * 2. - 1.)
z = self.nonlin(z)
z = self.out(z)
return z
def loss_func(b, t):
return ((b - t) ** 2).mean(1)
def _parse_args(args):
parser = argparse.ArgumentParser(
description='Toy experiment from backpropagation throught the void, '
'written in pytorch')
parser.add_argument(
'--estimator', choices=['reinforce', 'relax', 'rebar'],
default='reinforce')
parser.add_argument('--rand-seed', type=int, default=42)
parser.add_argument('--iters', type=int, default=5000)
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--target', type=float, default=.499)
parser.add_argument('--num-latents', type=int, default=1)
parser.add_argument('--lr', type=float, default=.01)
return parser.parse_args(args)
def reinforce(f_b, b, logits, **kwargs):
log_prob = torch.distributions.Bernoulli(logits=logits).log_prob(b)
d_log_prob = torch.autograd.grad(
[log_prob], [logits], grad_outputs=torch.ones_like(log_prob))[0]
d_logits = f_b.unsqueeze(1) * d_log_prob
return d_logits
def _get_z_tilde(logits, b, v):
theta = torch.sigmoid(logits)
v_prime = v * (b - 1.) * (theta - 1.) + b * (v * theta + 1. - theta)
z_tilde = logits + torch.log(v_prime) - torch.log1p(-v_prime)
return z_tilde
def rebar(
f_b, b, logits, z, v, eta, log_temp, target, loss_func=loss_func,
**kwargs):
z_tilde = _get_z_tilde(logits, b, v)
temp = torch.exp(log_temp).unsqueeze(0)
sig_z = torch.sigmoid(z / temp)
sig_z_tilde = torch.sigmoid(z_tilde / temp)
f_z = loss_func(sig_z, target)
f_z_tilde = loss_func(sig_z_tilde, target)
log_prob = torch.distributions.Bernoulli(logits=logits).log_prob(b)
d_log_prob = torch.autograd.grad(
[log_prob], [logits], grad_outputs=torch.ones_like(log_prob))[0]
d_f_z = torch.autograd.grad(
[f_z], [logits], grad_outputs=torch.ones_like(f_z),
create_graph=True, retain_graph=True)[0]
d_f_z_tilde = torch.autograd.grad(
[f_z_tilde], [logits], grad_outputs=torch.ones_like(f_z_tilde),
create_graph=True, retain_graph=True)[0]
diff = f_b.unsqueeze(1) - eta * f_z_tilde.unsqueeze(1)
d_logits = diff * d_log_prob + eta * (d_f_z - d_f_z_tilde)
var_loss = (d_logits ** 2).mean()
var_loss.backward()
return d_logits.detach()
def relax(f_b, b, logits, z, v, log_temp, q_func, **kwargs):
z_tilde = _get_z_tilde(logits, b, v)
temp = torch.exp(log_temp).unsqueeze(0)
sig_z = torch.sigmoid(z / temp)
sig_z_tilde = torch.sigmoid(z_tilde / temp)
f_z = q_func(sig_z)[:, 0]
f_z_tilde = q_func(sig_z_tilde)[:, 0]
log_prob = torch.distributions.Bernoulli(logits=logits).log_prob(b)
d_log_prob = torch.autograd.grad(
[log_prob], [logits], grad_outputs=torch.ones_like(log_prob))[0]
d_f_z = torch.autograd.grad(
[f_z], [logits], grad_outputs=torch.ones_like(f_z),
create_graph=True, retain_graph=True)[0]
d_f_z_tilde = torch.autograd.grad(
[f_z_tilde], [logits], grad_outputs=torch.ones_like(f_z_tilde),
create_graph=True, retain_graph=True)[0]
diff = f_b.unsqueeze(1) - f_z_tilde.unsqueeze(1)
d_logits = diff * d_log_prob + d_f_z - d_f_z_tilde
var_loss = (d_logits.mean(0) ** 2).mean()
var_loss.backward()
return d_logits.detach()
def run_toy_example(args=None):
args = _parse_args(args)
print('Target is {}'.format(args.target))
target = torch.Tensor(1, args.num_latents)
target.fill_(args.target)
logits = torch.zeros(args.num_latents, requires_grad=True)
eta = torch.ones(args.num_latents, requires_grad=True)
log_temp = torch.from_numpy(
np.array([.5] * args.num_latents, dtype=np.float32))
log_temp.requires_grad_(True)
q_func = QFunc(args.num_latents)
torch.manual_seed(args.rand_seed)
if args.estimator == 'reinforce':
estimator = reinforce
tunable = []
elif args.estimator == 'rebar':
estimator = rebar
tunable = [eta, log_temp]
else:
estimator = relax
tunable = [log_temp] + list(q_func.parameters())
logit_optim = torch.optim.Adam([logits], lr=args.lr)
if tunable:
tune_optim = torch.optim.Adam(tunable, lr=args.lr)
else:
tune_optim = None
for i in range(args.iters):
logit_optim.zero_grad()
if tune_optim:
tune_optim.zero_grad()
u = torch.rand(args.batch_size, args.num_latents)
v = torch.rand(args.batch_size, args.num_latents)
z = logits + torch.log(u) - torch.log1p(-u)
b = z.gt(0.).type_as(z)
f_b = loss_func(b, target)
d_logits = estimator(
f_b=f_b, b=b, u=u, v=v, z=z, target=target, logits=logits,
log_temp=log_temp, eta=eta, q_func=q_func,
)
logits.backward(d_logits.mean(0)) # mean of batch
d_logits = d_logits.numpy()
logit_optim.step()
if tune_optim:
tune_optim.step()
thetas = torch.sigmoid(logits.detach()).numpy()
loss = thetas * (1 - args.target) ** 2
loss += (1 - thetas) * args.target ** 2
loss = loss.mean()
mean = d_logits.mean()
std = d_logits.std()
print(
'Iter: {} Loss: {:.03f} Thetas: {} Mean: {:.03f} Std: {:.03f} '
'Temp: {:.03f}'.format(
i, loss, thetas, mean, std, torch.exp(log_temp).item())
)
if __name__ == '__main__':
run_toy_example()