def combine_states(self, hyps):
"""Batch all states in different hyptheses.
"""
states = MapDict()
# Combine states
for name in self.model.state_names():
states[name] = torch.cat([h["state"][name] for h in hyps], 1)
# Combine last tokens
last_tokens = torch.tensor([h["tokens"][-1] for h in hyps])
if torch.cuda.is_available():
last_tokens = last_tokens.cuda()
states.feedback_embed = self.model.lookup_feedback(last_tokens)
return states
def combine_states(self, t, hyps):
"""Batch all states in different hyptheses.
Args:
t - time step
hyps - hypotheses
"""
states = MapDict({"t": t})
# Combine states
for name in self.model.state_names():
states[name] = torch.cat([h["state"][name] for h in hyps], 1)
# Combine last tokens
last_tokens = torch.tensor([h["tokens"][-1] for h in hyps])
if torch.cuda.is_available():
last_tokens = last_tokens.cuda()
states.prev_token = last_tokens.unsqueeze(0)
states.feedback_embed = self.model.lookup_feedback(last_tokens)
return states