Internal change

PiperOrigin-RevId: 285765110

Author: saberkun

official/transformer/model/beam_search.py

@@ -323,13 +323,16 @@ def _search_step(self, state):
       new state dictionary.
     """
     # Grow alive sequences by one token.
-    new_seq, new_log_probs, new_cache = self._grow_alive_seq(state)
+    new_seq, new_log_probs, topk_ids, new_cache = self._grow_alive_seq(state)
+    new_finished_flags = tf.equal(topk_ids, self.eos_id)
     # Collect top beam_size alive sequences
-    alive_state = self._get_new_alive_state(new_seq, new_log_probs, new_cache)
+    alive_state = self._get_new_alive_state(new_seq, new_log_probs,
+                                            new_finished_flags, new_cache)
 
     # Combine newly finished sequences with existing finished sequences, and
     # collect the top k scoring sequences.
-    finished_state = self._get_new_finished_state(state, new_seq, new_log_probs)
+    finished_state = self._get_new_finished_state(state, new_seq, new_log_probs,
+                                                  new_finished_flags)
 
     # Increment loop index and create new state dictionary
     new_state = {_StateKeys.CUR_INDEX: state[_StateKeys.CUR_INDEX] + 1}
@@ -407,18 +410,20 @@ def _grow_alive_seq(self, state):
                                              tf.expand_dims(topk_ids, axis=0))
       topk_seq = tf.transpose(topk_seq, perm=[1, 2, 0])
     else:
-      topk_ids = tf.expand_dims(topk_ids, axis=2)
-      topk_seq = tf.concat([topk_seq, topk_ids], axis=2)
-    return topk_seq, topk_log_probs, new_cache
+      topk_seq = tf.concat([topk_seq, tf.expand_dims(topk_ids, axis=2)], axis=2)
+    return topk_seq, topk_log_probs, topk_ids, new_cache
 
-  def _get_new_alive_state(self, new_seq, new_log_probs, new_cache):
+  def _get_new_alive_state(self, new_seq, new_log_probs, new_finished_flags,
+                           new_cache):
     """Gather the top k sequences that are still alive.
 
     Args:
       new_seq: New sequences generated by growing the current alive sequences
         int32 tensor with shape [batch_size, 2 * beam_size, cur_index + 1]
-      new_log_probs: Log probabilities of new sequences
-        float32 tensor with shape [batch_size, beam_size]
+      new_log_probs: Log probabilities of new sequences float32 tensor with
+        shape [batch_size, beam_size]
+      new_finished_flags: A boolean Tensor indicates which sequences are live
+        inside the beam.
       new_cache: Dict of cached values for each sequence.
 
     Returns:
@@ -428,7 +433,6 @@ def _get_new_alive_state(self, new_seq, new_log_probs, new_cache):
          Dict cache storing decoder states for top alive sequences}
     """
     # To prevent finished sequences from being considered, set log probs to -inf
-    new_finished_flags = tf.equal(new_seq[:, :, -1], self.eos_id)
     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(
@@ -441,15 +445,18 @@ def _get_new_alive_state(self, new_seq, new_log_probs, new_cache):
         _StateKeys.ALIVE_CACHE: top_alive_cache
     }
 
-  def _get_new_finished_state(self, state, new_seq, new_log_probs):
+  def _get_new_finished_state(self, state, new_seq, new_log_probs,
+                              new_finished_flags):
     """Combine new and old finished sequences, and gather the top k sequences.
 
     Args:
       state: A dictionary with the current loop state.
       new_seq: New sequences generated by growing the current alive sequences
         int32 tensor with shape [batch_size, beam_size, i + 1]
-      new_log_probs: Log probabilities of new sequences
-        float32 tensor with shape [batch_size, beam_size]
+      new_log_probs: Log probabilities of new sequences float32 tensor with
+        shape [batch_size, beam_size]
+      new_finished_flags: A boolean Tensor indicates which sequences are live
+        inside the beam.
 
     Returns:
       Dictionary with finished keys from _StateKeys:
@@ -476,7 +483,6 @@ def _get_new_finished_state(self, state, new_seq, new_log_probs):
     new_scores = new_log_probs / length_norm
 
     # Set the scores of the still-alive seq in new_seq to large negative values.
-    new_finished_flags = tf.equal(new_seq[:, :, -1], self.eos_id)
     new_scores += ((1. - tf.cast(new_finished_flags, self.dtype)) *
                    -inf(self.dtype))