def word_p(self, val):
if isinstance(val, float):
val = self.beta.new_tensor(val)
if not isinstance(val, torch.FloatTensor) and not isinstance(val, torch.cuda.FloatTensor):
raise InvalidArgumentError("word_p should be a float or FloatTensor, not {}.".format(type(val)))
if val > self.min_word_p and val <= 1.:
self._word_p = val
elif val > 1.:
self._word_p = self.min_word_p.new_tensor(1.)
elif val <= self.min_word_p:
self._word_p = self.min_word_p.new_tensor(self.min_word_p.item())
def min_rate(self, val):
if isinstance(val, float):
val = torch.tensor(val, device=self.device)
if not isinstance(val, torch.FloatTensor) and not isinstance(
val, torch.cuda.FloatTensor):
raise InvalidArgumentError(
"min_rate should be a float or FloatTensor.")
if val > 0:
self._min_rate = val
else:
self._min_rate = torch.tensor(0., device=self.device)
def _unpack_data(self, data, N):
"""Unpacks the input data to the forward pass and supplies missing data.
Args:
data(list of torch.Tensor): data provided to forward pass. We assume the following ordering
[input, length(optional), mask(optional), reversed(optional), reversed_length(optional)]
N(int): the number of data tensors to return. Can be 1-4.
Returns:
x_in(torch.Tensor): batch of input sequences.
x_len(torch.Tensor): lengths of input sequences or None.
x_mask(torch.Tensor): mask over the padding of input sequences that are not of max length or None.
x_reverse(torch.Tensor): batch of reversed input sequences or None.
x_len_reverse(torch.Tensor): lengths of reversed input sequences or None.
"""
# Checks and padding of data, so we have N tensors or None to process
if not isinstance(data[0], torch.Tensor):
raise InvalidArgumentError("Data should contain a torch Tensor with data at the first position.")
if N < 1 or N > 4:
raise InvalidArgumentError("N should be between 1 and 4.")
data = (data + [None, ] * N)[:N]
for d in data:
if not isinstance(d, torch.Tensor) and d is not None:
raise InvalidArgumentError("Data should contain only torch Tensors or None.")
# If no mask is given, we create an empty mask as placeholder.
if N > 2 and data[2] is None:
x_mask = torch.ones(data[0].shape).to(self.device)
if data[1] is not None:
warn("Data length is given without mask. Assuming all sentences are of the same length. Sentences shorter than {} words will not be masked.".format(self.seq_len))
# When the reversed data is not given, we assume no padding and reverse the sequence ourselves
if N > 3 and data[3] is None:
warn("Reversed data not provided. We assume no padding and reverse the data cheaply.")
indices = torch.arange(data[0].shape[1] - 1, -1, -1)
data[3] = x_in.index_select(1, indices)
return data
def _gaussian_kl_divergence(self, mu_1, var_1, mu_2, var_2, mask, dim):
"""Computes the batch KL-divergence between two Gaussian distributions with diagonal covariance."""
if mu_2 is None and var_2 is None:
return 0.5 * torch.sum((-torch.log(var_1) + var_1 + mu_1**2 - 1) *
mask.unsqueeze(dim),
dim=dim)
elif mu_2 is not None and var_2 is not None:
return 0.5 * torch.sum(
(torch.log(var_2) - torch.log(var_1) + var_1 / var_2 +
(mu_2 - mu_1)**2 / var_2 - 1) * mask.unsqueeze(dim),
dim=dim)
else:
raise InvalidArgumentError(
"Either provide mu_2 and var_2 or neither.")
def constraint(self, vals):
if type(vals) != list:
if isinstance(vals, str):
vals = [vals]
else:
raise InvalidArgumentError(
'constraint should be a list or str')
for val in vals:
if val not in ['mdr', 'mmd', 'elbo']:
raise UnknownArgumentError(
'constraint {} unknown. Please choose [mdr, mmd].'.format(
val))
self._constraint = vals
def _gaussian_sample_z(self, mu, var, shape, det):
"""Sample from a Gaussian distribution with mean mu and variance var."""
if mu is None and var is None and shape is not None:
if det:
return torch.zeros(shape, device=self.device)
else:
return self.error.sample(shape)
elif mu is not None and var is not None:
if det:
return mu
else:
return mu + torch.sqrt(var) * self.error.sample(var.shape)
else:
raise InvalidArgumentError("Provide either mu and var or neither with a shape.")
def __init__(self, device, seq_len, word_p, word_p_enc, parameter_p,
encoder_p, drop_type, min_rate, unk_index, css, N, rnn_type,
kl_step, beta, lamb, mmd, ann_mode, rate_mode, posterior,
hinge_weight, ann_word, word_step, v_dim, x_dim, h_dim, s_dim,
z_dim, l_dim, h_dim_enc, l_dim_enc, lagrangian, constraint,
max_mmd, max_elbo, alpha):
super(GenerativeDecoder,
self).__init__(device, seq_len, word_p, parameter_p, drop_type,
unk_index, css, N, rnn_type, v_dim, x_dim, h_dim,
s_dim, l_dim)
# Recurrent dropout was never implemented for the VAE's because it doesn't work well
if self.drop_type == "recurrent":
raise InvalidArgumentError(
"Recurrent dropout not implemented for this model. Please choose ['varied', 'shared']"
)
# LSTM's are not supported because GRU's work equally well (with less parameters)
if self.rnn_type == "LSTM":
raise InvalidArgumentError(
"LSTM not implemented for this model. Please choose ['GRU']")
# Choose between the vMF-autoencoder and Gauss-autoencoder
self.posterior = posterior
# Encoder architecture settings
self.encoder_p = torch.tensor(encoder_p,
device=self.device,
dtype=torch.float)
self.h_dim_enc = h_dim_enc
self.l_dim_enc = l_dim_enc
self.word_p_enc = word_p_enc
# Optimization hyperparameters
self.min_rate = torch.tensor(
min_rate, device=self.device,
dtype=torch.float) # minimum Rate of hinge/FB
self.beta = torch.tensor(beta, device=self.device,
dtype=torch.float) # beta value of beta-VAE
self.alpha = torch.tensor(alpha, device=self.device,
dtype=torch.float) # alpha value of InfoVAE
self.lamb = torch.tensor(lamb, device=self.device,
dtype=torch.float) # lambda value of InfoVAE
self.kl_step = torch.tensor(
kl_step, device=self.device,
dtype=torch.float) # Step size of KL annealing
self.hinge_weight = torch.tensor(
hinge_weight, device=self.device,
dtype=torch.float) # Weight of hinge loss
# Step size of word dropout annealing
self.word_step = torch.tensor(word_step,
device=self.device,
dtype=torch.float)
self.max_mmd = torch.tensor(max_mmd,
device=self.device,
dtype=torch.float) # Maximum MMD
self.max_elbo = torch.tensor(max_elbo,
device=self.device,
dtype=torch.float) # Maximum ELBO
# Optimization modes
self.mmd = mmd # When true, we add the maximum mean discrepancy to the loss, and optimize the InfoVAE
self.ann_mode = ann_mode # The mode of annealing
self.rate_mode = rate_mode # How to force the VAE to encode a minimum rate
self.ann_word = ann_word # Whether to anneal word dropout
# The weight of the constraint in the Lagrangian dual function
# Hardcoded start at 1.01
self.lagrangian = lagrangian
self.constraint = constraint
self.lag_weight = Parameter(torch.tensor([1.01] *
len(self.constraint)))
if self.ann_word:
self.word_p = 1.
self.z_dim = z_dim
# We start the scale factor at zero, to be incremented linearly with kl_step every forward pass
if self.ann_mode == "linear":
self.scale = torch.tensor(self.kl_step.item() * self.beta.item(),
device=self.device)
# Or we start the scale at 10%, to be increased or decreased in 10% increments based on a desired rate
elif self.ann_mode == "sfb":
self.scale = torch.tensor(0.1 * self.beta.item(),
device=self.device)
# This switch should be manually managed from training/testing scripts to select generating from prior/posterior
self.use_prior = False
# N(0, I) error distribution to sample from latent spaces with reparameterized gradient
self.error = Normal(torch.tensor(0., device=device),
torch.tensor(1., device=device))
def seq_len(self, val):
if not isinstance(val, int):
raise InvalidArgumentError("seq_len should be an integer.")
self._seq_len = val
