def _compute_next_token_score_batch(self, token_idx, condition_id):
# Get prediction of the model - p(T|S)
current_token_id_for_each_candidate = self._cur_candidates[:, token_idx - 1]
self._hidden_states_batch, next_token_score_batch = \
get_next_token_log_prob_one_step(self._nn_model, self._thought_batch, self._hidden_states_batch,
current_token_id_for_each_candidate, condition_id)
# Candidates[:, :token_idx], as usual, means "All tokens preceding token_idx for each candidate"
# We use all preceding tokens to compute counts and penalize current distribution using these counts.
next_token_score_batch = self._penalize_by_repetition(next_token_score_batch,
self._cur_candidates[:, :token_idx])
# next_token_score_batch here is (num_candidates x vocab_size). For each candidate we find all the
# banned tokens and kill their scores to -inf.
next_token_score_batch[:, self._service_tokens_ids.banned_tokens_ids] = -np.inf
return next_token_score_batch
def _compute_next_token_score_batch(self, token_idx, condition_id):
# Get prediction of the model - p(T|S)
current_token_id_for_each_candidate = self._cur_candidates[:, token_idx - 1]
self._hidden_states_batch, next_token_score_batch = \
get_next_token_log_prob_one_step(self._nn_model, self._thought_batch, self._hidden_states_batch,
current_token_id_for_each_candidate, condition_id)
# Candidates[:, :token_idx], as usual, means "All tokens preceding token_idx for each candidate"
# We use all preceding tokens to compute counts and penalize current distribution using these counts.
next_token_score_batch = self._penalize_by_repetition(next_token_score_batch,
self._cur_candidates[:, :token_idx])
# next_token_score_batch here is (num_candidates x vocab_size). For each candidate we find all the
# banned tokens and kill their scores to -inf.
next_token_score_batch[:, self._service_tokens_ids.banned_tokens_ids] = -np.inf
return next_token_score_batch
def _predict_log_probabilities_one_step(nn_model, x_batch, y_batch):
"""
Predict answers for every sequence token by token until EOS_TOKEN occurred in the sequence using sampling with temperature.
All the rest of the sequence is filled with PAD_TOKENs.
"""
thought_vectors_batch = nn_model.get_thought_vectors(x_batch)
hidden_states_batch = np.zeros((x_batch.shape[0], DECODER_DEPTH, HIDDEN_LAYER_DIMENSION), dtype=np.float32)
total_log_probs = np.zeros((y_batch.shape[0], y_batch.shape[1] - 1, nn_model.vocab_size))
for token_idx in xrange(1, y_batch.shape[1]):
hidden_states_batch, next_token_log_probs_batch = \
get_next_token_log_prob_one_step(nn_model, thought_vectors_batch, hidden_states_batch,
y_batch[:, token_idx - 1], condition_ids=None)
# total_log_probs has shape (batch_size x num_tokens x vocab_size)
total_log_probs[:, token_idx - 1, :] = next_token_log_probs_batch
return total_log_probs