def forward(self, input, targets, args, n_particles, criterion, test=False):
"""
This version takes the inputs, and does not expose the logits, but instead
computes the losses directly
"""
# run the input and teacher-forcing inputs through the embedding layers here
seq_len, batch_sz = input.size()
emb = self.inp_embedding(input)
hidden = self.init_hidden(batch_sz, self.nhid, 2) # bidirectional
hidden_states, (h, c) = self.encoder(emb, hidden)
# teacher-forcing
out_emb = self.dropout(self.dec_embedding(targets))
# now, we'll replicate it for each particle - it's currently [seq_len x batch_sz x nhid]
hidden_states = hidden_states.repeat(1, n_particles, 1)
out_emb = out_emb.repeat(1, n_particles, 1)
# now [seq_len x (n_particles x batch_sz) x nhid]
# out_emb, hidden_states should be viewed as (n_particles x batch_sz) - this means that's true for h as well
# run the z-decoder at this point, evaluating the NLL at each step
p_h = self.init_hidden(batch_sz * n_particles, self.z_dim, squeeze=True) # initially zero
h = self.init_hidden(batch_sz * n_particles, self.z_dim, squeeze=True)
d_h = self.init_hidden(batch_sz * n_particles, self.nhid, squeeze=True)
nlls = hidden_states.data.new(seq_len, batch_sz * n_particles)
loss = 0
accumulated_weights = -math.log(n_particles) # will contain log w_{t - 1}
resamples = 0
for i in range(seq_len):
h = self.z_decoder(hidden_states[i], h)
logits = self.logits(h)
# build the next z sample
if test:
q = OneHotCategorical(logits=logits)
z = q.sample()
else:
q = RelaxedOneHotCategorical(temperature=self.temp, logits=logits)
z = q.rsample()
h = z
# prior
if test:
p = OneHotCategorical(logits=p_h)
else:
p = RelaxedOneHotCategorical(temperature=self.temp_prior, logits=p_h)
# now, compute the log-likelihood of the data given this mean, and the input out_emb
d_h = self.decoder(torch.cat([z, out_emb[i]], 1), d_h)
decoder_logits = self.out_embedding(d_h)
NLL = criterion(decoder_logits, input[i].repeat(n_particles))
nlls[i] = NLL.data
# compute the weight using `reweight` on page (4)
f_term = p.log_prob(z) # prior
r_term = q.log_prob(z) # proposal
alpha = -NLL + args.anneal * (f_term - r_term)
wa = accumulated_weights + alpha.view(n_particles, batch_sz)
# sample ancestors, and reindex everything
Z = log_sum_exp(wa, dim=0) # line 7
if (Z.data > 0.1).any():
pdb.set_trace()
loss += Z # line 8
accumulated_weights = wa - Z # line 9
probs = accumulated_weights.data.exp()
probs += 0.01
probs = probs / probs.sum(0, keepdim=True)
effective_sample_size = 1./probs.pow(2).sum(0)
# resample / RSAMP if 3 batch elements need resampling
if ((effective_sample_size / n_particles) < 0.3).sum() > 0:
resamples += 1
ancestors = torch.multinomial(probs.transpose(0, 1), n_particles, True)
# now, reindex, which is the most important thing
offsets = n_particles * torch.arange(batch_sz).unsqueeze(1).repeat(1, n_particles).long()
if ancestors.is_cuda:
offsets = offsets.cuda()
unrolled_idx = Variable(ancestors.t().contiguous()+offsets).view(-1)
h = torch.index_select(h, 0, unrolled_idx)
p_h = torch.index_select(p_h, 0, unrolled_idx)
d_h = torch.index_select(d_h, 0, unrolled_idx)
# reset accumulated_weights
accumulated_weights = -math.log(n_particles) # will contain log w_{t - 1}
if i != seq_len - 1:
# build the next mean prediction, feeding in the correct ancestor
p_h = self.ar_prior_logits(torch.cat([h, out_emb[i]], 1), p_h)
# now, we calculate the final log-marginal estimator
nll = nlls.view(seq_len, n_particles, batch_sz).mean(1).sum()
return -loss.sum(), nll, (seq_len * batch_sz), resamples
def sampled_filter(self, input, args, n_particles, emb, hidden_states):
seq_len, batch_sz = input.size()
T = F.log_softmax(self.T, 0) # NOTE: in log-space
pi = F.log_softmax(self.pi, 0) # NOTE: in log-space
emit = self.calc_emit()
hidden_states = hidden_states.repeat(1, n_particles, 1)
nlls = hidden_states.data.new(seq_len, batch_sz * n_particles)
loss = 0
accumulated_weights = -math.log(
n_particles) # will contain log w_{t - 1}
resamples = 0
# in log probability space
prior_logits = pi.unsqueeze(0).expand(batch_sz * n_particles,
self.z_dim)
for i in range(seq_len):
# the approximate posterior comes from the same thing as before
logits = self.logits(hidden_states[i])
if not self.training:
# this is crucial!!
p = OneHotCategorical(logits=prior_logits)
q = OneHotCategorical(logits=logits)
z = q.sample()
else:
p = RelaxedOneHotCategorical(temperature=self.temp_prior,
logits=prior_logits)
q = RelaxedOneHotCategorical(temperature=self.temp,
logits=logits)
z = q.rsample()
# now, compute the log-likelihood of the data given this z-sample
emission = F.embedding(input[i].repeat(n_particles), emit)
NLL = -(emission * z).sum(1)
# NLL = -self.decode(z, input[i].repeat(n_particles), (emit,)) # diff. w.r.t. z
nlls[i] = NLL.data
# compute the weight using `reweight` on page (4)
f_term = p.log_prob(z) # prior
r_term = q.log_prob(z) # proposal
alpha = -NLL + (f_term - r_term)
wa = accumulated_weights + alpha.view(n_particles, batch_sz)
Z = log_sum_exp(wa, dim=0) # line 7
loss += Z # line 8
accumulated_weights = wa - Z # F.log_softmax(wa, dim=0) # line 9
# sample ancestors, and reindex everything
if args.filter:
probs = accumulated_weights.data.exp()
probs += 0.01
probs = probs / probs.sum(0, keepdim=True)
effective_sample_size = 1. / probs.pow(2).sum(0)
# probs is [n_particles, batch_sz]
# ancestors [2 x 15] = [[0, 0, 0, ..., 0], [0, 1, 2, 3, ...]]
# offsets [2 x 15] = [[0, 0, 0, ..., 0], [1, 1, 1, 1, ...]]
# resample / RSAMP
if ((effective_sample_size / n_particles) < 0.3).sum() > 0:
resamples += 1
ancestors = torch.multinomial(probs.transpose(0, 1),
n_particles, True)
# now, reindex, which is the most important thing
offsets = n_particles * torch.arange(batch_sz).unsqueeze(
1).repeat(1, n_particles).long()
if ancestors.is_cuda:
offsets = offsets.cuda()
unrolled_idx = Variable(ancestors + offsets).view(-1)
z = torch.index_select(z, 0, unrolled_idx)
# reset accumulated_weights
accumulated_weights = -math.log(
n_particles) # will contain log w_{t - 1}
if i != seq_len - 1:
# now in log-probability space
prior_logits = log_sum_exp(T.unsqueeze(0) + z.unsqueeze(1), 2)
if self.training:
(-loss.sum() /
(seq_len * batch_sz * n_particles)).backward(retain_graph=True)
return -loss.sum(), nlls.sum(), seq_len * batch_sz * n_particles, 0
def forward(self, input, args, n_particles, test=False):
T = nn.Softmax(dim=0)(self.T) # NOTE: not in log-space
pi = nn.Softmax(dim=0)(self.pi)
emit = self.calc_emit()
# run the input and teacher-forcing inputs through the embedding layers here
seq_len, batch_sz = input.size()
emb = self.inp_embedding(input)
hidden = self.init_hidden(batch_sz, self.nhid, 2) # bidirectional
hidden_states, (_, _) = self.encoder(emb, hidden)
hidden_states = hidden_states.repeat(1, n_particles, 1)
# run the z-decoder at this point, evaluating the NLL at each step
z = self.init_hidden(batch_sz * n_particles, self.z_dim, squeeze=True)
nlls = hidden_states.data.new(seq_len, batch_sz * n_particles)
loss = 0
# now a log-prob
prior_probs = pi.unsqueeze(0).expand(batch_sz * n_particles,
self.z_dim)
logits = self.init_hidden(batch_sz * n_particles,
self.z_dim,
squeeze=True)
for i in range(seq_len):
# logits = self.logits(torch.cat([hidden_states[i], h], 1))
# logits = self.logits(nn.functional.relu(self.z_decoder(hidden_states[i], logits)))
logits = self.logits(
nn.functional.relu(
self.z_decoder(torch.cat([hidden_states[i], z], 1),
logits)))
# build the next z sample
if test:
q = OneHotCategorical(logits=logits)
z = q.sample()
else:
q = RelaxedOneHotCategorical(temperature=self.temp,
logits=logits)
z = q.rsample()
lse = log_sum_exp(logits, dim=1).view(-1, 1)
log_probs = logits - lse
# now, compute the log-likelihood of the data given this z-sample
# so emission is [batch_sz x z_dim], i.e. emission[i, j] is the log-probability of getting this
# data for element i given choice z
emission = F.embedding(input[i].repeat(n_particles), emit)
NLL = -log_sum_exp(emission + log_probs, 1)
nlls[i] = NLL.data
KL = (log_probs.exp() * (log_probs -
(prior_probs + 1e-16).log())).sum(1)
loss += (NLL + KL)
if i != seq_len - 1:
prior_probs = (T.unsqueeze(0) * z.unsqueeze(1)).sum(2)
# now, we calculate the final log-marginal estimator
return loss.sum(), nlls.sum(), (seq_len * batch_sz * n_particles), 0
def forward(self, input, args, n_particles, test=False):
"""
evaluation is the IWAE-10 bound
"""
if test:
n_particles = 10
else:
n_particles = 1
pi = F.log_softmax(self.pi, 0)
# run the input and teacher-forcing inputs through the embedding layers here
seq_len, batch_sz = input.size()
emb = self.inp_embedding(input)
hidden = self.init_hidden(batch_sz, self.nhid, 2) # bidirectional
hidden_states, (_, _) = self.encoder(emb, hidden)
hidden_states = hidden_states.repeat(1, n_particles, 1)
# run the z-decoder at this point, evaluating the NLL at each step
h = (Variable(
hidden_states.data.new(batch_sz * n_particles,
self.hidden_size).zero_()),
Variable(
hidden_states.data.new(batch_sz * n_particles,
self.hidden_size).zero_()))
nlls = hidden_states.data.new(seq_len, batch_sz * n_particles)
loss = 0
# now a log-prob
prior_logits = pi.unsqueeze(0).expand(batch_sz * n_particles,
self.z_dim)
prior_h = (Variable(torch.zeros(batch_sz * n_particles, 50).cuda()),
Variable(torch.zeros(batch_sz * n_particles, 50).cuda()))
logits = self.init_hidden(batch_sz * n_particles,
self.z_dim,
squeeze=True)
feed = None
x_emb = self.lockdrop(emb, self.dropout_x)
for i in range(seq_len):
# build the next z sample - not differentiable! we don't train the inference network
logits = F.log_softmax(self.logits(hidden_states[i]), 1)
if test:
q = OneHotCategorical(logits=logits)
# p = OneHotCategorical(logits=prior_logits)
z = q.sample()
else:
q = RelaxedOneHotCategorical(temperature=self.temp,
logits=logits)
# p = RelaxedOneHotCategorical(temperature=self.temp_prior, logits=prior_logits)
z = q.rsample()
# this should be batch_sz x x_dim
scores = torch.mm(self.project(torch.cat([h[0], z], 1)),
self.emit.t())
NLL = nn.CrossEntropyLoss(reduce=False)(
scores, input[i].repeat(n_particles))
# KL = q.log_prob(z) - p.log_prob(z)
KL = (logits.exp() * (logits - prior_logits)).sum(1)
loss += (NLL + KL)
# else:
# loss += (NLL + args.anneal * KL)
nlls[i] = NLL.data
# set things up for next time
if i != seq_len - 1:
feed = torch.cat(
[emb[i].repeat(n_particles, 1),
self.z_emb(z)], 1)
prior_h = self.z_decoder(feed, prior_h)
prior_logits = F.log_softmax(self.project_z(prior_h[0]), 1)
h = self.hidden_rnn(x_emb[i].repeat(n_particles, 1),
h) # feed the next word into the RNN
if n_particles != 1:
loss = -log_sum_exp(-loss.view(n_particles, batch_sz),
0) + math.log(n_particles)
NLL = -log_sum_exp(
-nlls.view(seq_len, n_particles, batch_sz), 1) + math.log(
n_particles) # not quite accurate, but what can you do
else:
NLL = nlls
# now, we calculate the final log-marginal estimator
return loss.sum(), NLL.sum(), (seq_len * batch_sz), 0
def forward(self, input, args, n_particles, test=False):
T = F.log_softmax(self.T, 0) # NOTE: in log-space
pi = F.log_softmax(self.pi, 0) # NOTE: in log-space
emit = self.calc_emit()
# run the input and teacher-forcing inputs through the embedding layers here
seq_len, batch_sz = input.size()
emb = self.inp_embedding(input)
hidden = self.init_hidden(batch_sz, self.nhid, 2) # bidirectional
hidden_states, (_, _) = self.encoder(emb, hidden)
hidden_states = hidden_states.repeat(1, n_particles, 1)
# run the z-decoder at this point, evaluating the NLL at each step
h = self.init_hidden(batch_sz * n_particles, self.z_dim, squeeze=True)
nlls = hidden_states.data.new(seq_len, batch_sz * n_particles)
loss = 0
accumulated_weights = -math.log(
n_particles) # will contain log w_{t - 1}
resamples = 0
# in log probability space
prior_probs = pi.unsqueeze(0).expand(batch_sz * n_particles,
self.z_dim)
logits = self.init_hidden(batch_sz * n_particles,
self.z_dim,
squeeze=True)
for i in range(seq_len):
# logits = self.logits(nn.functional.relu(self.z_decoder(hidden_states[i], logits)))
logits = self.logits(
nn.functional.relu(
self.z_decoder(torch.cat([hidden_states[i], h], 1),
logits)))
# build the next z sample
if any_nans(logits):
pdb.set_trace()
if test:
q = OneHotCategorical(logits=logits)
z = q.sample()
else:
q = RelaxedOneHotCategorical(temperature=self.temp,
logits=logits)
z = q.rsample()
h = z
# prior
if any_nans(prior_probs):
pdb.set_trace()
if test:
p = OneHotCategorical(logits=prior_probs)
else:
p = RelaxedOneHotCategorical(temperature=self.temp_prior,
logits=prior_probs)
if any_nans(prior_probs):
pdb.set_trace()
if any_nans(logits):
pdb.set_trace()
# now, compute the log-likelihood of the data given this z-sample
NLL = -self.decode(z, input[i].repeat(n_particles),
(emit, )) # diff. w.r.t. z
nlls[i] = NLL.data
# compute the weight using `reweight` on page (4)
f_term = p.log_prob(z) # prior
r_term = q.log_prob(z) # proposal
alpha = -NLL + (f_term - r_term)
wa = accumulated_weights + alpha.view(n_particles, batch_sz)
# sample ancestors, and reindex everything
Z = log_sum_exp(wa, dim=0) # line 7
loss += Z # line 8
accumulated_weights = wa - Z # line 9
if args.filter:
probs = accumulated_weights.data.exp()
probs += 0.01
probs = probs / probs.sum(0, keepdim=True)
effective_sample_size = 1. / probs.pow(2).sum(0)
# probs is [n_particles, batch_sz]
# ancestors [2 x 15] = [[0, 0, 0, ..., 0], [0, 1, 2, 3, ...]]
# offsets [2 x 15] = [[0, 0, 0, ..., 0], [1, 1, 1, 1, ...]]
# resample / RSAMP
if ((effective_sample_size / n_particles) < 0.3).sum() > 0:
resamples += 1
ancestors = torch.multinomial(probs.transpose(0, 1),
n_particles, True)
# now, reindex, which is the most important thing
offsets = n_particles * torch.arange(batch_sz).unsqueeze(
1).repeat(1, n_particles).long()
if ancestors.is_cuda:
offsets = offsets.cuda()
unrolled_idx = Variable(ancestors + offsets).view(-1)
h = torch.index_select(h, 0, unrolled_idx)
# reset accumulated_weights
accumulated_weights = -math.log(
n_particles) # will contain log w_{t - 1}
if i != seq_len - 1:
# now in probability space
prior_probs = log_sum_exp(T.unsqueeze(0) + z.unsqueeze(1), 2)
# let's normalize things - slower, but safer
# prior_probs += 0.01
# prior_probs = prior_probs / prior_probs.sum(1, keepdim=True)
# # if ((prior_probs.sum(1) - 1) > 1e-3).any()[0]:
# pdb.set_trace()
if any_nans(loss):
pdb.set_trace()
# now, we calculate the final log-marginal estimator
return -loss.sum(), nlls.sum(), (seq_len * batch_sz *
n_particles), resamples
