Merge of PR #1446

PiperOrigin-RevId: 233438134

Author: lgeiger

tensor2tensor/data_generators/cipher.py

@@ -213,9 +213,9 @@ def encipher_vigenere(plaintext, plain_vocab, key):
   """
   ciphertext = []
   # generate Vigenere table
-  layers = []
-  for i in range(len(plain_vocab)):
-    layers.append(ShiftEncryptionLayer(plain_vocab, i))
+  layers = [
+      ShiftEncryptionLayer(plain_vocab, i) for i in range(len(plain_vocab))
+  ]
 
   for i, sentence in enumerate(plaintext):
     cipher_sentence = []

tensor2tensor/data_generators/wiki_revision_utils.py

@@ -491,10 +491,8 @@ def edit_distance_filter(source_target_input, max_equal_to_diff_ratio=0):
   if not max_equal_to_diff_ratio:
     return source_target_input, thrown_out_count
 
-  for i in range(len(source_target_input)):
-    src = source_target_input[i][0]
-    tgt = source_target_input[i][1]
-    opcodes = fast_match_sequences(src, tgt)
+  for src_tgt in source_target_input:
+    opcodes = fast_match_sequences(*src_tgt)
     diff_char_count = 0
     equal_char_count = 0
     for tag, i1, i2, j1, j2 in opcodes:
@@ -504,7 +502,7 @@ def edit_distance_filter(source_target_input, max_equal_to_diff_ratio=0):
       else:
         equal_char_count += i2 - i1
     if diff_char_count <= max_equal_to_diff_ratio * equal_char_count:
-      source_target_output.append(source_target_input[i])
+      source_target_output.append(src_tgt)
     else:
       thrown_out_count += 1
   return source_target_output, thrown_out_count

tensor2tensor/layers/common_layers.py

@@ -2334,10 +2334,10 @@ def ravanbakhsh_set_layer(layer_size,
 
 def fn_device_dependency_dict():
   """State container for fn_device_dependency."""
-  if not hasattr(tf.get_default_graph(), "dependency_dict"):
-    setattr(tf.get_default_graph(), "dependency_dict",
-            collections.defaultdict(list))
-  return tf.get_default_graph().dependency_dict
+  default_graph = tf.get_default_graph()
+  if not hasattr(default_graph, "dependency_dict"):
+    default_graph.dependency_dict = collections.defaultdict(list)
+  return default_graph.dependency_dict
 
 
 @contextlib.contextmanager
@@ -2791,8 +2791,7 @@ def shape_list(x):
   shape = tf.shape(x)
 
   ret = []
-  for i in range(len(static)):
-    dim = static[i]
+  for i, dim in enumerate(static):
     if dim is None:
       dim = shape[i]
     ret.append(dim)

tensor2tensor/rl/trainer_model_based.py

@@ -126,7 +126,7 @@ def train_supervised(problem, model_name, hparams, data_dir, output_dir,
                      schedule="continuous_train_and_eval"):
   """Train supervised."""
   if local_eval_frequency is None:
-    local_eval_frequency = getattr(FLAGS, "local_eval_frequency")
+    local_eval_frequency = FLAGS.local_eval_frequency
 
   exp_fn = trainer_lib.create_experiment_fn(
       model_name, problem, data_dir, train_steps, eval_steps,

tensor2tensor/serving/serving_utils.py

@@ -115,9 +115,9 @@ def _make_grpc_request(examples):
     scores = tf.make_ndarray(response.outputs["scores"])
     assert len(outputs) == len(scores)
     return [{
-        "outputs": outputs[i],
-        "scores": scores[i]
-    } for i in range(len(outputs))]
+        "outputs": output,
+        "scores": score
+    } for output, score in zip(outputs, scores)]
 
   return _make_grpc_request