Move _gather_beams into the SequenceBeamSearch class.

PiperOrigin-RevId: 373191989

official/nlp/modeling/ops/beam_search.py

@@ -218,9 +218,9 @@ class SequenceBeamSearch(tf.Module):
       # Extract the alive sequences that generate the highest log probabilities
       # after being extended.
       topk_beam_indices = topk_indices // self.vocab_size
-      topk_seq, new_cache = _gather_beams([alive_seq, new_cache],
-                                          topk_beam_indices, batch_size,
-                                          beams_to_keep)
+      topk_seq, new_cache = self._gather_beams([alive_seq, new_cache],
+                                               topk_beam_indices, batch_size,
+                                               beams_to_keep)
 
       # Append the most probable IDs to the topk sequences
       topk_ids = topk_indices % self.vocab_size
@@ -259,9 +259,10 @@ class SequenceBeamSearch(tf.Module):
       new_log_probs += tf.cast(new_finished_flags,
                                self.dtype) * -inf(self.dtype)
 
-      top_alive_seq, top_alive_log_probs, top_alive_cache = _gather_topk_beams(
-          [new_seq, new_log_probs, new_cache], new_log_probs, batch_size,
-          self.beam_size)
+      _, topk_indexes = tf.nn.top_k(new_log_probs, k=self.beam_size)
+      top_alive_seq, top_alive_log_probs, top_alive_cache = (
+          self._gather_beams([new_seq, new_log_probs, new_cache],
+                             topk_indexes, batch_size, self.beam_size))
 
       return {
           _StateKeys.ALIVE_SEQ: top_alive_seq,
@@ -316,9 +317,10 @@ class SequenceBeamSearch(tf.Module):
       finished_flags = tf.concat([finished_flags, new_finished_flags], axis=1)
 
       # Return the finished sequences with the best scores.
+      _, topk_indexes = tf.nn.top_k(finished_scores, k=self.beam_size)
       top_finished_seq, top_finished_scores, top_finished_flags = (
-          _gather_topk_beams([finished_seq, finished_scores, finished_flags],
-                             finished_scores, batch_size, self.beam_size))
+          self._gather_beams([finished_seq, finished_scores, finished_flags],
+                             topk_indexes, batch_size, self.beam_size))
 
       return {
           _StateKeys.FINISHED_SEQ: top_finished_seq,
@@ -538,6 +540,43 @@ class SequenceBeamSearch(tf.Module):
         not_at_max_decode_length,
         tf.logical_not(worst_finished_score_better_than_best_alive_score))
 
+  @staticmethod
+  def _gather_beams(nested, beam_indices, batch_size, new_beam_size):
+    """Gather beams from nested structure of tensors.
+
+    Each tensor in nested represents a batch of beams, where beam refers to a
+    single search state (beam search involves searching through multiple states
+    in parallel).
+
+    This function is used to gather the top beams, specified by
+    beam_indices, from the nested tensors.
+
+    Args:
+      nested: Nested structure (tensor, list, tuple or dict) containing tensors
+        with shape [batch_size, beam_size, ...].
+      beam_indices: int32 tensor with shape [batch_size, new_beam_size]. Each
+        value in beam_indices must be between [0, beam_size), and are not
+        necessarily unique.
+      batch_size: int size of batch
+      new_beam_size: int number of beams to be pulled from the nested tensors.
+
+    Returns:
+      Nested structure containing tensors with shape
+        [batch_size, new_beam_size, ...]
+    """
+    # Computes the i'th coodinate that contains the batch index for gather_nd.
+    # Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..].
+    batch_pos = tf.range(batch_size * new_beam_size) // new_beam_size
+    batch_pos = tf.reshape(batch_pos, [batch_size, new_beam_size])
+
+    # Create coordinates to be passed to tf.gather_nd. Stacking creates a tensor
+    # with shape [batch_size, beam_size, 2], where the last dimension contains
+    # the (i, j) gathering coordinates.
+    coordinates = tf.stack([batch_pos, beam_indices], axis=2)
+
+    return tf.nest.map_structure(lambda state: tf.gather_nd(state, coordinates),
+                                 nested)
+
 
 def sequence_beam_search(symbols_to_logits_fn,
                          initial_ids,
@@ -663,46 +702,3 @@ def _unflatten_beam_dim(tensor, batch_size, beam_size):
   shape = _shape_list(tensor)
   new_shape = [batch_size, beam_size] + shape[1:]
   return tf.reshape(tensor, new_shape)
-
-
-def _gather_beams(nested, beam_indices, batch_size, new_beam_size):
-  """Gather beams from nested structure of tensors.
-
-  Each tensor in nested represents a batch of beams, where beam refers to a
-  single search state (beam search involves searching through multiple states
-  in parallel).
-
-  This function is used to gather the top beams, specified by
-  beam_indices, from the nested tensors.
-
-  Args:
-    nested: Nested structure (tensor, list, tuple or dict) containing tensors
-      with shape [batch_size, beam_size, ...].
-    beam_indices: int32 tensor with shape [batch_size, new_beam_size]. Each
-      value in beam_indices must be between [0, beam_size), and are not
-      necessarily unique.
-    batch_size: int size of batch
-    new_beam_size: int number of beams to be pulled from the nested tensors.
-
-  Returns:
-    Nested structure containing tensors with shape
-      [batch_size, new_beam_size, ...]
-  """
-  # Computes the i'th coodinate that contains the batch index for gather_nd.
-  # Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..].
-  batch_pos = tf.range(batch_size * new_beam_size) // new_beam_size
-  batch_pos = tf.reshape(batch_pos, [batch_size, new_beam_size])
-
-  # Create coordinates to be passed to tf.gather_nd. Stacking creates a tensor
-  # with shape [batch_size, beam_size, 2], where the last dimension contains
-  # the (i, j) gathering coordinates.
-  coordinates = tf.stack([batch_pos, beam_indices], axis=2)
-
-  return tf.nest.map_structure(lambda state: tf.gather_nd(state, coordinates),
-                               nested)
-
-
-def _gather_topk_beams(nested, score_or_log_prob, batch_size, beam_size):
-  """Gather top beams from nested structure."""
-  _, topk_indexes = tf.nn.top_k(score_or_log_prob, k=beam_size)
-  return _gather_beams(nested, topk_indexes, batch_size, beam_size)

official/nlp/modeling/ops/beam_search_test.py

@@ -54,16 +54,7 @@ class BeamSearchTests(tf.test.TestCase, parameterized.TestCase):
     #                  [16 17 18 19]
     #                  [20 21 22 23]]]
 
-    y = beam_search._gather_beams(x, [[1, 2], [0, 2]], 2, 2)
-    self.assertAllEqual(
-        [[[4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [20, 21, 22, 23]]],
-        y)
-
-  def test_gather_topk_beams(self):
-    x = tf.reshape(tf.range(24), [2, 3, 4])
-    x_scores = [[0, 1, 1], [1, 0, 1]]
-
-    y = beam_search._gather_topk_beams(x, x_scores, 2, 2)
+    y = beam_search.SequenceBeamSearch._gather_beams(x, [[1, 2], [0, 2]], 2, 2)
     self.assertAllEqual(
         [[[4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [20, 21, 22, 23]]],
         y)