Fix type annotation for length_normalization_fn, which should be Optional.

Both unit tests and real use cases are passing None to it.

PiperOrigin-RevId: 385468558

official/nlp/modeling/ops/sampling_module.py

@@ -15,7 +15,7 @@
 """Sampling module for top_k, top_p and greedy decoding."""
 
 import abc
-from typing import Any, Callable, Dict
+from typing import Any, Callable, Dict, Optional
 
 import numpy as np
 import tensorflow as tf
@@ -98,10 +98,10 @@ def sample_top_p(logits, top_p):
   ], -1)
 
   # Scatter sorted indices to original indexes.
-  indices_to_remove = scatter_values_on_batch_indices(
-      sorted_indices_to_remove, sorted_indices)
-  top_p_logits = set_tensor_by_indices_to_value(
-      logits, indices_to_remove, np.NINF)
+  indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove,
+                                                      sorted_indices)
+  top_p_logits = set_tensor_by_indices_to_value(logits, indices_to_remove,
+                                                np.NINF)
   return top_p_logits
 
 
@@ -121,13 +121,12 @@ def scatter_values_on_batch_indices(values, batch_indices):
   tensor_shape = decoding_module.shape_list(batch_indices)
   broad_casted_batch_dims = tf.reshape(
       tf.broadcast_to(
-          tf.expand_dims(tf.range(tensor_shape[0]), axis=-1),
-          tensor_shape), [1, -1])
+          tf.expand_dims(tf.range(tensor_shape[0]), axis=-1), tensor_shape),
+      [1, -1])
   pair_indices = tf.transpose(
       tf.concat([broad_casted_batch_dims,
                  tf.reshape(batch_indices, [1, -1])], 0))
-  return tf.scatter_nd(pair_indices,
-                       tf.reshape(values, [-1]), tensor_shape)
+  return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), tensor_shape)
 
 
 def set_tensor_by_indices_to_value(input_tensor, indices, value):
@@ -137,6 +136,7 @@ def set_tensor_by_indices_to_value(input_tensor, indices, value):
     input_tensor: float (batch_size, dim)
     indices: bool (batch_size, dim)
     value: float scalar
+
   Returns:
     output_tensor: same shape as input_tensor.
   """
@@ -150,11 +150,12 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
 
   def __init__(self,
                symbols_to_logits_fn,
-               length_normalization_fn: Callable[[int, tf.DType], float],
                vocab_size: int,
                max_decode_length: int,
                eos_id: int,
                padded_decode: bool,
+               length_normalization_fn: Optional[Callable[[int, tf.DType],
+                                                          float]] = None,
                top_k=0,
                top_p=1.0,
                sample_temperature=0.0,
@@ -170,8 +171,8 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
     self.max_decode_length = max_decode_length
     self.top_k = tf.convert_to_tensor(top_k, dtype=tf.int32)
     self.top_p = tf.convert_to_tensor(top_p, dtype=tf.float32)
-    self.sample_temperature = tf.convert_to_tensor(sample_temperature,
-                                                   dtype=tf.float32)
+    self.sample_temperature = tf.convert_to_tensor(
+        sample_temperature, dtype=tf.float32)
     self.enable_greedy = enable_greedy
     super(SamplingModule, self).__init__(
         length_normalization_fn=length_normalization_fn, dtype=dtype)
@@ -330,10 +331,7 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
 
     return state, state_shape_invariants
 
-  def _get_new_alive_state(
-      self,
-      new_seq: tf.Tensor,
-      new_log_probs: tf.Tensor,
+  def _get_new_alive_state(self, new_seq: tf.Tensor, new_log_probs: tf.Tensor,
                            new_finished_flags: tf.Tensor,
                            new_cache: Dict[str, tf.Tensor]) -> Dict[str, Any]:
     """Gather the sequences that are still alive.
@@ -360,9 +358,7 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
         decoding_module.StateKeys.ALIVE_CACHE: new_cache
     }
 
-  def _get_new_finished_state(self,
-                              state: Dict[str, Any],
-                              new_seq: tf.Tensor,
+  def _get_new_finished_state(self, state: Dict[str, Any], new_seq: tf.Tensor,
                               new_log_probs: tf.Tensor,
                               new_finished_flags: tf.Tensor,
                               batch_size: int) -> Dict[str, tf.Tensor]:
@@ -421,10 +417,9 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
       length_norm = self.length_normalization_fn(self.max_decode_length + 1,
                                                  self.dtype)
       alive_log_probs = alive_log_probs / length_norm
-    seq_cond = decoding_module.expand_to_same_rank(
-        finished_cond, finished_seq)
-    score_cond = decoding_module.expand_to_same_rank(
-        finished_cond, finished_scores)
+    seq_cond = decoding_module.expand_to_same_rank(finished_cond, finished_seq)
+    score_cond = decoding_module.expand_to_same_rank(finished_cond,
+                                                     finished_scores)
     finished_seq = tf.where(seq_cond, finished_seq, alive_seq)
     finished_scores = tf.where(score_cond, finished_scores, alive_log_probs)
     return finished_seq, finished_scores