ChoiceLayer: prefix decoding

returnn/tf/layers/rec.py

@@ -4504,7 +4504,7 @@ def __init__(self, beam_size, keep_beams=False,
                length_normalization=True,
                length_normalization_exponent=1.0,
                custom_score_combine=None,
-               source_beam_sizes=None, scheduled_sampling=False, cheating=False,
+               source_beam_sizes=None, scheduled_sampling=False, cheating=False, prefix_target=None,
                explicit_search_sources=None,
                **kwargs):
     """
@@ -4546,6 +4546,7 @@ def __init__(self, beam_size, keep_beams=False,
     self.search_scores_base = None
     self.search_scores_combined = None
     # We assume log-softmax here, inside the rec layer.
+    self.prefix_target = prefix_target
 
     if self.search_flag:
       if cheating:
@@ -4699,6 +4700,13 @@ def __init__(self, beam_size, keep_beams=False,
           cheating_exclusive=cheating_exclusive)
         self.search_choices.set_src_beams(src_beams)  # (batch, beam) -> beam_in idx
         labels = tf.reshape(labels, [net_batch_dim * beam_size])  # (batch * beam)
+
+        if self.prefix_target:
+          assert len(self.sources) == 1, "Prefix decoding not yet implemented for multiple sources."
+          labels, scores = self._enforce_prefixes(
+            top_k_labels=labels, all_scores=scores_comb, top_k_scores=scores, batch_dim=net_batch_dim,
+            beam_size=beam_size)
+
         labels = tf.cast(labels, self.output.dtype)
 
         if len(self.sources) > 1:
@@ -5008,6 +5016,59 @@ def _get_cheating_targets_and_src_beam_idxs(self, scores):
     src_beams = src_beams[:, 0]  # (batch,)
     return cheating_gold_targets, src_beams
 
+  def _enforce_prefixes(self, top_k_labels, all_scores, top_k_scores, batch_dim, beam_size):
+      """
+      This function replaces the target labels from beam search by the ones predefined by the target prefixes as long
+      as search is still at a position within the prefix. We also replace the scores such that they correspond to a
+      prediction of the prefixes.
+
+      :param tf.Tensor top_k_labels: target labels from beam seach, shape (batch * beam,)
+      :param tf.Tensor all_scores: scores before beam pruning, used to lookup prefix scores, shape (batch, beam, dim)
+      :param tf.Tensor top_k_scores: scores after beam pruning, shape (batch, beam)
+      :param tf.Tensor|int batch_dim: number of sequences in batch (without beam)
+      :param int beam_size: outgoing beam size of this layer
+      :return: labels (batch * beam,) and scores (batch, beam) of self.prefix_target as long as within prefix, after
+        that top_k_labels and top_k_scores from beam search
+      :rtype: (tf.Tensor, tf.Tensor)
+      """
+      assert self.prefix_target
+
+      # Get the labels of the prefixes which should be enforced. They are padded with zeros, therefore we will
+      # get zeros for those sequences where the current timestep is beyond the length of the prefix.
+      target_prefix_labels = self._get_target_value(
+        target=self.prefix_target).get_placeholder_as_batch_major()  # (batch * beam,), int32
+
+      # Get prefixes that have already ended (i.e. have a smaller length than the current time step).
+      target_prefix_ended = tf.equal(target_prefix_labels, 0)
+
+      # Select between the prefixes and the labels from free decoding, depending on whether the prefix
+      # has still got to be enforced.
+      labels = tf.where(target_prefix_ended, top_k_labels, target_prefix_labels)
+
+      # Get rid of the redundant beam, all entries are the same, only keep first entry.
+      target_prefix_labels = tf.reshape(target_prefix_labels, [batch_dim, beam_size])[:, 0]  # (batch,)
+
+      # Now also get the scores for the prefixes. First, select only the first entry of the incoming beam as all entries
+      # are the same if we are still within the prefix.
+      all_scores = all_scores[:, 0, :]  # (batch, dim)
+
+      # Gather scores for the prefix labels.
+      from TFUtil import nd_indices
+      target_prefix_nd_indices = nd_indices(target_prefix_labels)
+      prefix_scores = tf.expand_dims(tf.gather_nd(all_scores, target_prefix_nd_indices), axis=-1)  # (batch, 1)
+
+      # Create an artificial beam, where all but the first scores are infinite. Tiling the one entry we have would
+      # lead to a beam of all equal hypotheses for the rest of the search.
+      # Conceptually similar to TFUtil.filter_ended_scores().
+      prefix_scores_padding = tf.fill([batch_dim, beam_size - 1], -1.e30)
+      prefix_scores = tf.concat([prefix_scores, prefix_scores_padding], axis=1)
+
+      # Use prefix scores for sequences where the prefix has not ended yet.
+      target_prefix_ended = tf.reshape(target_prefix_ended, [batch_dim, beam_size])
+      scores = tf.where(target_prefix_ended, top_k_scores, prefix_scores)  # (batch, beam)
+
+      return labels, scores
+
   @classmethod
   def transform_config_dict(cls, d, network, get_layer):
     """
@@ -5063,8 +5124,8 @@ def _create_search_beam(cls, name, beam_size, sources, network):
       name="%s%s" % (network.get_absolute_name_prefix(), name))
 
   @classmethod
-  def get_out_data_from_opts(cls, name, sources, target, network,
-                             beam_size, search=NotSpecified, scheduled_sampling=False, cheating=False, **kwargs):
+  def get_out_data_from_opts(cls, name, sources, target, network, beam_size, search=NotSpecified,
+                             scheduled_sampling=False, cheating=False, prefix_target=None, **kwargs):
     """
     :param str name:
     :param list[LayerBase] sources:
@@ -5099,6 +5160,8 @@ def get_out_data_from_opts(cls, name, sources, target, network,
       out_data.batch = out_data.batch.copy_set_beam(out_data.beam)
     if cheating or scheduled_sampling or not search:
       cls._static_get_target_value(target=target, network=network, mark_data_key_as_used=True)  # mark as used
+    if search and prefix_target:
+      cls._static_get_target_value(target=prefix_target, network=network, mark_data_key_as_used=True)  # mark as used
     return out_data
 
   def get_sub_layer(self, layer_name):