Remove TODOs that will never fulfill.

PiperOrigin-RevId: 320124801

official/modeling/tf_utils.py

@@ -88,7 +88,6 @@ def is_special_none_tensor(tensor):
   return tensor.shape.ndims == 0 and tensor.dtype == tf.int32
 
 
-# TODO(hongkuny): consider moving custom string-map lookup to keras api.
 def get_activation(identifier):
   """Maps a identifier to a Python function, e.g., "relu" => `tf.nn.relu`.
 

official/nlp/bert/model_training_utils.py

@@ -559,7 +559,6 @@ def run_customized_training_loop(
     for metric in model.metrics:
       training_summary[metric.name] = _float_metric_value(metric)
     if eval_metrics:
-      # TODO(hongkuny): Cleans up summary reporting in text.
       training_summary['last_train_metrics'] = _float_metric_value(
           train_metrics[0])
       training_summary['eval_metrics'] = _float_metric_value(eval_metrics[0])

official/nlp/transformer/transformer.py

@@ -52,7 +52,6 @@ def create_model(params, is_train):
       logits = tf.keras.layers.Lambda(lambda x: x, name="logits",
                                       dtype=tf.float32)(logits)
       model = tf.keras.Model([inputs, targets], logits)
-      # TODO(reedwm): Can we do this loss in float16 instead of float32?
       loss = metrics.transformer_loss(
           logits, targets, label_smoothing, vocab_size)
       model.add_loss(loss)
@@ -238,7 +237,6 @@ class Transformer(tf.keras.Model):
     decoder_self_attention_bias = model_utils.get_decoder_self_attention_bias(
         max_decode_length, dtype=self.params["dtype"])
 
-    # TODO(b/139770046): Refactor code with better naming of i.
     def symbols_to_logits_fn(ids, i, cache):
       """Generate logits for next potential IDs.
 

official/nlp/transformer/transformer_main.py

@@ -248,7 +248,6 @@ class TransformerTask(object):
       callbacks = [cb for cb in callbacks
                    if isinstance(cb, keras_utils.TimeHistory)]
 
-    # TODO(b/139418525): Refactor the custom training loop logic.
     @tf.function
     def train_steps(iterator, steps):
       """Training steps function for TPU runs.
@@ -422,8 +421,6 @@ class TransformerTask(object):
     """Loads model weights when it is provided."""
     if init_weight_path:
       logging.info("Load weights: {}".format(init_weight_path))
-      # TODO(b/139414977): Having the same variable restoring method for both
-      # TPU and GPU.
       if self.use_tpu:
         checkpoint = tf.train.Checkpoint(
             model=model, optimizer=self._create_optimizer())