def integrate(self, score, data, *args):
done = False
count = 0
step_count = self.steps if self.step > 0 else 10 * self.steps
while not done:
make_differentiable(data)
make_differentiable(args)
energy = score(data + self.noise * torch.randn_like(data), *args)
if isinstance(energy, (list, tuple)):
energy, *_ = energy
gradient = ag.grad(energy, data, torch.ones_like(energy))[0]
if self.max_norm:
gradient = clip_grad_by_norm(gradient, self.max_norm)
data = data - self.rate * gradient
if self.clamp is not None:
data = data.clamp(*self.clamp)
data = data.detach()
done = count >= step_count
if self.target is not None:
done = done and bool((energy.mean(dim=0) <= self.target).all())
count += 1
if (count + 1) % 500 == 0:
data.random_()
self.step += 1
return data
def step(self, score, data, *args):
make_differentiable(data)
make_differentiable(args)
# data = ...
if isinstance(data, (list, tuple)):
data = [
item + noise * torch.randn_like(item)
for noise, item in zip(self.noise, data)
]
else:
data = data + self.noise * torch.randn_like(data)
energy = score(data, *args)
if isinstance(energy, (list, tuple)):
energy, *_ = energy
gradient = ag.grad(energy, data, torch.ones_like(energy))
if isinstance(data, (list, tuple)):
data = list(data)
for idx, (rate, clamp, gradval) in enumerate(
zip(self.rate, self.clamp, gradient)):
data[idx] = data[idx] - rate * gradval
if clamp is not None:
data[idx] = data[idx].clamp(*clamp)
else:
gradient = gradient[0]
if self.max_norm:
gradient = clip_grad_by_norm(gradient, self.max_norm)
data = data - self.rate * gradient
if self.clamp is not None:
data = data.clamp(*self.clamp)
return data
def integrate(self, score, data, *args):
data = data.clone()
current_energy, *_ = score(data, *args)
for idx in range(self.steps):
make_differentiable(data)
make_differentiable(args)
energy = score(data, *args)
if isinstance(energy, (list, tuple)):
energy, *_ = energy
gradient = ag.grad(energy, data.tensor, torch.ones_like(energy))[0]
if self.max_norm:
gradient = clip_grad_by_norm(gradient, self.max_norm)
# attempt at gradient based local update of discrete variables:
grad_prob = (-500 * gradient).softmax(dim=1)
new_prob = self.noise + self.rate * grad_prob + (
1 - self.noise - self.rate) * data.tensor
new_val = hard_one_hot(new_prob.log())
data.tensor = new_val
data = data.detach()
return data
def forward(self, inputs, noise, *args):
with torch.enable_grad():
make_differentiable(inputs)
cond = torch.zeros(inputs.size(0),
10,
dtype=inputs.dtype,
device=inputs.device)
offset = (torch.log(noise) / torch.log(torch.tensor(0.60))).long()
cond[torch.arange(inputs.size(0)), offset.view(-1)] = 1
out = self.preprocess(inputs)
count = 0
for bn, proj, block in zip(self.bn, self.project, self.blocks):
out = func.elu(bn(proj(out) + block(out), cond))
count += 1
if count % 5 == 0:
out = func.avg_pool2d(out, 2)
out = self.postprocess(out)
out = func.adaptive_avg_pool2d(out, 1).view(-1, 128)
logits = self.predict(out)
energy = -logits.logsumexp(dim=1)
score = -torch.autograd.grad(energy,
inputs,
torch.ones_like(energy),
create_graph=True,
retain_graph=True)[0]
return score, logits
def mutate(self, score, data, *args):
result = data.clone()
make_differentiable(result)
make_differentiable(args)
energy = score(result, *args)
gradient = ag.grad(energy, result,
torch.ones(*energy.shape, device=result.device))[0]
# position choice
position_gradient = -gradient.sum(dim=1)
position_distribution = torch.distributions.Categorical(
logits=position_gradient)
position_proposal = position_distribution.sample()
# change choice
change_gradient = -gradient[torch.arange(0, gradient.size(0)), :,
position_proposal]
change_distribution = torch.distributions.Categorical(
logits=change_gradient)
change_proposal = change_distribution.sample()
# mutate:
result[torch.arange(0, result.size(0)), :, position_proposal] = 0
result[torch.arange(0, result.size(0)), change_proposal,
position_proposal] = 1
return result.detach()
def run_energy(self, data):
data, labels = data
make_differentiable(data)
input_data, *data_args = self.data_key(data)
real_logits = self.score(input_data, *data_args)
# sample after first pass over real data, to catch
# possible batch-norm shenanigans without blowing up.
fake = self.sample()
if self.step_id % self.report_interval == 0:
detached, *args = self.data_key(to_device(fake, "cpu"))
self.each_generate(detached.detach(), *args)
make_differentiable(fake)
input_fake, *fake_args = self.data_key(fake)
fake_logits = self.score(input_fake, *fake_args)
real_result = self.logit_energy(real_logits, *data_args)
fake_result = self.logit_energy(fake_logits, *fake_args)
# set integrator target, if appropriate.
if self.integrator.target is None:
self.integrator.target = real_result.detach().mean(dim=0)
self.integrator.target = 0.6 * self.integrator.target + 0.4 * real_result.detach(
).mean(dim=0)
return real_result, fake_result, real_logits, labels
def run_sliced_score(score, data, args, noise_level=0.0):
if noise_level:
data = gaussian_noise(data, noise_level)
make_differentiable(data)
score_val = score(data, noise_level, *args)
loss = sliced_score(score_val, data)
return loss, namespace(data=data, score=score_val, noise_level=noise_level)
def run_discriminator(self, data):
with torch.no_grad():
_, fake_batches, _ = self.run_generator(data)
make_differentiable(fake_batches)
make_differentiable(data)
fake_result = self.discriminator(fake_batches[0])
real_result = self.discriminator(data)
return fake_batches[0], data, fake_result, real_result
def run_energy(self, data):
data, *args = self.data_key(data)
noisy, sigma = self.noise(data)
make_differentiable(noisy)
result = self.score(noisy, sigma, *args)
return (
result, #.view(result.size(0), -1),
data, #.view(result.size(0), -1),
noisy, #.view(result.size(0), -1),
sigma, #.view(result.size(0), -1)
)
def run_discriminator(self, data):
with torch.no_grad():
fake = self.run_generator(data)
make_differentiable(fake)
make_differentiable(data)
_, fake_batch, _, _ = fake
inputs, available, requested = data
fake_result = self._run_discriminator_aux(inputs, fake_batch,
available, requested)
real_result = self._run_discriminator_aux(inputs, inputs, available,
requested)
return fake, data, fake_result, real_result
def sample(self):
buffer_iter = iter(self.buffer_loader(self.buffer))
data, *args = to_device(self.data_key(next(buffer_iter)), self.device)
data = self.integrator.integrate(self.score, data, *args).detach()
detached = data.detach().cpu()
update = (to_device((detached[idx], *[arg[idx]
for arg in args]), "cpu")
for idx in range(data.size(0)))
make_differentiable(update, toggle=False)
self.buffer.update(update)
return to_device((detached, *args), self.device)
def sample(self):
buffer_iter = iter(self.buffer_loader(self.buffer))
data, *args = to_device(self.data_key(next(buffer_iter)), self.device)
self.score.eval()
data = detach(self.integrator.integrate(self.score, data, *args))
self.score.train()
detached = to_device(data, "cpu")
make_differentiable(args, toggle=False)
update = self.decompose_batch(detached, *args)
make_differentiable(update, toggle=False)
self.buffer.update(update)
return to_device((detached, *args), self.device)
def sample(self):
buffer_iter = iter(self.buffer_loader(self.buffer))
data, *args = to_device(self.data_key(next(buffer_iter)), self.device)
self.sampler.eval()
with torch.no_grad():
data = self.integrate(data, *args)
self.sampler.train()
detached = to_device(data.detach(), "cpu")
update = self.decompose_batch(detached, *args)
make_differentiable(update, toggle=False)
self.buffer.update(update)
return to_device((detached, *args), self.device)
def integrate(self, score, data, *args):
for idx in range(self.steps):
make_differentiable(data)
make_differentiable(args)
energy, *_ = score(data + self.noise * torch.randn_like(data),
*args)
gradient = ag.grad(energy, data,
torch.ones(*energy.shape,
device=data.device))[0]
if self.max_norm:
gradient = clip_grad_by_norm(gradient, self.max_norm)
data = self.update(data - self.rate * gradient)
return data
def run_regularizer(self, data):
make_differentiable(data)
codes = self.encode(data)
discriminator_result = self.discriminator(data, *codes)
gradient = torch.autograd.grad(
discriminator_result,
self.mixing_key(data),
grad_outputs=torch.ones_like(discriminator_result),
create_graph=True,
retain_graph=True)
gradient = gradient.view(gradient.size(0), -1)
gradient = (gradient**2).sum(dim=1)
return gradient
def run_energy(self, real, fake, args):
make_differentiable(real)
real_result = self.score(real, *args)
grad = ag.grad(real_result,
real,
grad_outputs=torch.ones_like(real_result),
create_graph=True,
retain_graph=True)[0]
grad_norm = (grad.view(grad.size(0), -1)**2).sum(dim=1)
fake_result = self.score(fake, *args)
return real_result, fake_result, grad_norm
def energy_score_aux(energy, grad_args, *args, **kwargs):
grad_vars = _select(args, kwargs, grad_args)
make_differentiable(grad_vars)
E = energy(*args, **kwargs)
score = grad(
E,
grad_vars,
# E = -log p + C -> score = -grad E
grad_outputs=-torch.ones_like(E),
create_graph=True)
if len(score) == 1:
score = score[0]
return score
def integrate(self, score, data, *args):
for idx in range(self.steps):
make_differentiable(data)
make_differentiable(args)
energy = score(data + self.noise * torch.randn_like(data), *args)
if isinstance(energy, (list, tuple)):
energy, *_ = energy
gradient = ag.grad(energy, data, torch.ones_like(energy))[0]
if self.max_norm:
gradient = clip_grad_by_norm(gradient, self.max_norm)
data = data - self.rate * gradient
if self.clamp is not None:
data = data.clamp(*self.clamp)
return data
def run_energy(self, data):
fake = self.sample()
if self.step_id % self.report_interval == 0:
detached, *args = self.data_key(to_device(fake, "cpu"))
self.each_generate(detached.detach(), *args)
make_differentiable(fake)
make_differentiable(data)
input_data, *data_args = self.data_key(data)
input_fake, *fake_args = self.data_key(fake)
real_result = self.score(input_data, *data_args)
fake_result = self.score(input_fake, *fake_args)
return real_result, fake_result
def forward(self, inputs, sigma, *args):
with torch.enable_grad():
make_differentiable(inputs)
processed = self.process(inputs.view(inputs.size(0), -1))
condition = self.condition(sigma.view(sigma.size(0), -1))
scale = self.scale(condition)
bias = self.bias(condition)
logits = self.predict(processed * scale + bias)
energy = -logits.logsumexp(dim=1)
score = torch.autograd.grad(energy,
inputs,
grad_outputs=torch.ones_like(energy),
retain_graph=True,
create_graph=True)[0]
return score, logits
def integrate(self, score, data, *args):
for idx in range(self.steps):
make_differentiable(data)
make_differentiable(args)
energy = score(data, *args)
if isinstance(energy, (list, tuple)):
energy, *_ = energy
gradient = self.gradient_factor * ag.grad(
energy, data, torch.ones_like(energy))[0]
noise = self.noise * torch.randn_like(
data) if self.take_noise else 0.0
data = data - self.noise**2 / 2 * gradient + noise
if self.clamp is not None:
data = data.clamp(*self.clamp)
return data
def run_energy(self, data):
make_differentiable(data)
input_data, *data_args = self.data_key(data)
real_result = self.score(input_data, *data_args)
# sample after first pass over real data, to catch
# possible batch-norm shenanigans without blowing up.
fake = self.sample()
if self.step_id % self.report_interval == 0:
detached, *args = self.data_key(to_device(fake, "cpu"))
self.each_generate(detached.detach(), *args)
make_differentiable(fake)
input_fake, *fake_args = self.data_key(fake)
#self.score.eval()
fake_result = self.score(input_fake, *fake_args)
#self.score.train()
return real_result, fake_result
def contrastive_step(self, data):
"""Performs a single step of contrastive training.
Args:
data: data points used for training.
"""
if self.step_id % self.report_interval == 0:
self.visualize(data)
self.optimizer.zero_grad()
data = to_device(data, self.device)
make_differentiable(data)
args = self.run_networks(data)
loss_val = self.loss(*args)
self.log_statistics(loss_val, name="total loss")
loss_val.backward()
self.optimizer.step()
def run_energy(self, data):
fake = self.sample()
if self.oos_penalty:
oos = self.out_of_sample()
if self.step_id % self.report_interval == 0:
detached, *args = self.data_key(fake)
self.each_generate(detached, *args)
make_differentiable(fake)
make_differentiable(data)
if self.oos_penalty:
make_differentiable(oos)
input_data, reference_data, *data_args = self.data_key(data)
input_fake, reference_fake, *fake_args = self.data_key(fake)
if self.oos_penalty:
input_oos, reference_oos, *oos_args = self.data_key(oos)
real_result, real_parameters = self.score(input_data, reference_data,
*data_args)
fake_result, fake_parameters = self.score(input_fake, reference_fake,
*fake_args)
oos_result = None
oos_parameters = None
if self.oos_penalty:
oos_result, oos_parameters = self.score(input_oos, reference_oos,
*oos_args)
return real_result, fake_result, oos_result, real_parameters, fake_parameters, oos_parameters
def run_energy(self, data):
make_differentiable(data)
input_data, *data_args = self.data_key(data)
real_result = self.score(input_data, *data_args)
# sample after first pass over real data, to catch
# possible batch-norm shenanigans without blowing up.
fake = self.sample()
if self.step_id % self.report_interval == 0:
detached, *args = self.data_key(to_device(fake, "cpu"))
self.each_generate(detach(detached), *args)
make_differentiable(fake)
input_fake, *fake_args = self.data_key(fake)
self.score.eval()
fake_update_result = None
if self.sampler_likelihood:
# Sampler log likelihood:
fake_result = self.score(input_fake, *fake_args)
fake_update = self.integrator.step(self.score, input_fake,
*fake_args)
self.update_target()
fake_update_result = self.target_score(fake_update, *fake_args)
comparison = None
if self.maximum_entropy:
# Sampler entropy:
compare_update = fake_update
compare_target = to_device(
self.data_key(self.buffer.sample(self.batch_size))[0],
compare_update.device)
if hasattr(self.score, "embed"):
compare_update = self.target_score.embedding(compare_update)
compare_target = self.target_score.embedding(compare_target)
comparison = self.compare(compare_update, compare_target.detach())
self.score.train()
return real_result, fake_result, fake_update_result, comparison
def energy_loss(self, score, data, noisy, sigma):
vectors = self.noise_vectors(score)
make_differentiable(vectors)
grad_v = (score * vectors).view(score.size(0), -1).sum()
jacobian = torch.autograd.grad(grad_v,
noisy.tensor,
retain_graph=True,
create_graph=True)[0]
# jacograd = torch.autograd.grad(jacobian.mean(), noisy.tensor, retain_graph=True)
# print(jacobian)
# print(jacograd)
norm = (score**2).view(score.size(0), -1).sum(dim=-1) / 2
jacobian = (vectors * jacobian).view(score.size(0), -1).sum(dim=-1)
result = (norm + jacobian) * sigma.view(score.size(0), -1)**2
result = result.mean()
self.current_losses["ebm"] = float(result)
return result
def integrate(self, score, data, *args):
data = data.clone()
result = data.clone()
current_energy = score(data, *args)
for idx in range(self.steps):
make_differentiable(data)
make_differentiable(args)
energy, deltas = score(data, *args, return_deltas=True)
# attempt at gradient based local update of discrete variables:
grad_prob = torch.zeros_like(deltas)
grad_prob[torch.arange(deltas.size(0)), deltas.argmax(dim=1)] = 1
if self.scale is not None:
grad_prob = (self.scale * deltas).softmax(dim=1)
new_prob = self.noise + self.rate * grad_prob + (
1 - self.noise - self.rate) * data.tensor
new_val = hard_one_hot(new_prob.log())
data.tensor = new_val
data = data.detach()
return data
def run_discriminator(self, data):
with torch.no_grad():
_, (fake_fw, fake_rv), _ = self.run_generator(data)
make_differentiable(fake_fw)
make_differentiable(fake_rv)
make_differentiable(data)
real_result_fw = self.fw_discriminator(data[1])
fake_result_fw = self.fw_discriminator(fake_fw)
real_result_rv = self.rv_discriminator(data[0])
fake_result_rv = self.rv_discriminator(fake_rv)
real_result = (real_result_fw, real_result_rv)
fake_result = (fake_result_fw, fake_result_rv)
fake_batch = fake_fw, fake_rv
real_batch = (data[1], data[0])
return fake_batch, real_batch, fake_result, real_result
def run_energy(self, data):
data, *args = self.data_key(data)
make_differentiable(data)
critic = self.critic(data, *args)
score = self.score(data, *args)
return data, score, critic
