Merge pull request #325 from rsepassi/push

v1.2.4

.travis.yml

@@ -8,9 +8,22 @@ before_install:
 install:
   - pip install tensorflow
   - pip install .[tests]
+env:
+  global:
+    - T2T_PROBLEM=algorithmic_reverse_binary40_test
+    - T2T_DATA_DIR=/tmp/t2t-data
+    - T2T_TRAIN_DIR=/tmp/t2t-train
 script:
-  - pytest --ignore=tensor2tensor/utils/registry_test.py --ignore=tensor2tensor/utils/trainer_utils_test.py --ignore=tensor2tensor/problems_test.py
+  - pytest --ignore=tensor2tensor/utils/registry_test.py --ignore=tensor2tensor/utils/trainer_utils_test.py --ignore=tensor2tensor/problems_test.py --ignore=tensor2tensor/tpu/tpu_trainer_lib_test.py
   - pytest tensor2tensor/utils/registry_test.py
   - pytest tensor2tensor/utils/trainer_utils_test.py
+  - t2t-datagen 2>&1 | grep translate && echo passed
+  - python -c "from tensor2tensor.models import transformer; print(transformer.Transformer.__name__)"
+  - t2t-trainer --registry_help
+  - mkdir $T2T_DATA_DIR
+  - mkdir $T2T_TRAIN_DIR
+  - t2t-datagen --problem=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR
+  - t2t-trainer --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --train_steps=5 --eval_steps=5 --output_dir=$T2T_TRAIN_DIR
+  - t2t-decoder --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --output_dir=$T2T_TRAIN_DIR
 git:
-  depth: 3
+  depth: 3

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.2.3',
+    version='1.2.4',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',

tensor2tensor/bin/t2t-decoder

@@ -75,7 +75,7 @@ def main(_):
 
   hparams = trainer_utils.create_hparams(
       FLAGS.hparams_set, data_dir, passed_hparams=FLAGS.hparams)
-  hparams = trainer_utils.add_problem_hparams(hparams, FLAGS.problems)
+  trainer_utils.add_problem_hparams(hparams, FLAGS.problems)
   estimator, _ = trainer_utils.create_experiment_components(
       data_dir=data_dir,
       model_name=FLAGS.model,
@@ -90,9 +90,12 @@ def main(_):
     decoding.decode_from_file(estimator, FLAGS.decode_from_file, decode_hp,
                               FLAGS.decode_to_file)
   else:
-    decoding.decode_from_dataset(estimator,
-                                 FLAGS.problems.split("-"), decode_hp,
-                                 FLAGS.decode_to_file)
+    decoding.decode_from_dataset(
+        estimator,
+        FLAGS.problems.split("-"),
+        decode_hp,
+        decode_to_file=FLAGS.decode_to_file,
+        dataset_split="test" if FLAGS.eval_use_test_set else None)
 
 
 if __name__ == "__main__":

tensor2tensor/bin/t2t-trainer

@@ -68,6 +68,8 @@ def main(_):
   trainer_utils.validate_flags()
   output_dir = os.path.expanduser(FLAGS.output_dir)
   tmp_dir = os.path.expanduser(FLAGS.tmp_dir)
+  if not FLAGS.data_dir:
+    raise ValueError("You must specify a --data_dir")
   data_dir = os.path.expanduser(FLAGS.data_dir)
   tf.gfile.MakeDirs(output_dir)
 

tensor2tensor/data_generators/algorithmic.py

@@ -62,13 +62,15 @@ def num_shards(self):
     return 10
 
   def generate_data(self, data_dir, _, task_id=-1):
+
     def generator_eos(nbr_symbols, max_length, nbr_cases):
       """Shift by NUM_RESERVED_IDS and append EOS token."""
       for case in self.generator(nbr_symbols, max_length, nbr_cases):
         new_case = {}
         for feature in case:
-          new_case[feature] = [i + text_encoder.NUM_RESERVED_TOKENS
-                               for i in case[feature]] + [text_encoder.EOS_ID]
+          new_case[feature] = [
+              i + text_encoder.NUM_RESERVED_TOKENS for i in case[feature]
+          ] + [text_encoder.EOS_ID]
         yield new_case
 
     utils.generate_dataset_and_shuffle(
@@ -154,10 +156,7 @@ def generator(self, nbr_symbols, max_length, nbr_cases):
     for _ in xrange(nbr_cases):
       l = np.random.randint(max_length) + 1
       inputs = [np.random.randint(nbr_symbols - shift) for _ in xrange(l)]
-      yield {
-          "inputs": inputs,
-          "targets": [i + shift for i in inputs]
-      }
+      yield {"inputs": inputs, "targets": [i + shift for i in inputs]}
 
   @property
   def dev_length(self):
@@ -191,10 +190,7 @@ def generator(self, nbr_symbols, max_length, nbr_cases):
     for _ in xrange(nbr_cases):
       l = np.random.randint(max_length) + 1
       inputs = [np.random.randint(nbr_symbols) for _ in xrange(l)]
-      yield {
-          "inputs": inputs,
-          "targets": list(reversed(inputs))
-      }
+      yield {"inputs": inputs, "targets": list(reversed(inputs))}
 
 
 @registry.register_problem
@@ -272,10 +268,7 @@ def reverse_generator_nlplike(nbr_symbols,
   for _ in xrange(nbr_cases):
     l = int(abs(np.random.normal(loc=max_length / 2, scale=std_dev)) + 1)
     inputs = zipf_random_sample(distr_map, l)
-    yield {
-        "inputs": inputs,
-        "targets": list(reversed(inputs))
-    }
+    yield {"inputs": inputs, "targets": list(reversed(inputs))}
 
 
 @registry.register_problem
@@ -287,8 +280,8 @@ def num_symbols(self):
     return 8000
 
   def generator(self, nbr_symbols, max_length, nbr_cases):
-    return reverse_generator_nlplike(
-        nbr_symbols, max_length, nbr_cases, 10, 1.300)
+    return reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, 10,
+                                     1.300)
 
   @property
   def train_length(self):
@@ -308,8 +301,8 @@ def num_symbols(self):
     return 32000
 
   def generator(self, nbr_symbols, max_length, nbr_cases):
-    return reverse_generator_nlplike(
-        nbr_symbols, max_length, nbr_cases, 10, 1.050)
+    return reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, 10,
+                                     1.050)
 
 
 def lower_endian_to_number(l, base):
@@ -431,3 +424,28 @@ class AlgorithmicMultiplicationDecimal40(AlgorithmicMultiplicationBinary40):
   @property
   def num_symbols(self):
     return 10
+
+
+@registry.register_problem
+class AlgorithmicReverseBinary40Test(AlgorithmicReverseBinary40):
+  """Test Problem with tiny dataset."""
+
+  @property
+  def train_length(self):
+    return 10
+
+  @property
+  def dev_length(self):
+    return 10
+
+  @property
+  def train_size(self):
+    return 1000
+
+  @property
+  def dev_size(self):
+    return 100
+
+  @property
+  def num_shards(self):
+    return 1

tensor2tensor/data_generators/all_problems_test.py

@@ -0,0 +1,36 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for Tensor2Tensor's all_problems.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+from tensor2tensor.data_generators import all_problems
+
+import tensorflow as tf
+
+
+class AllProblemsTest(tf.test.TestCase):
+
+  def testImport(self):
+    """Make sure that importing all_problems doesn't break."""
+    self.assertIsNotNone(all_problems)
+
+
+if __name__ == '__main__':
+  tf.test.main()

tensor2tensor/data_generators/image.py

@@ -650,6 +650,7 @@ def generator(self, data_dir, tmp_dir, is_training):
 class ImageCifar10Plain(ImageCifar10):
 
   def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"] = tf.to_int64(example["inputs"])
     return example
 
 

tensor2tensor/data_generators/lm1b.py

@@ -36,7 +36,6 @@
 
 import tensorflow as tf
 
-
 # End-of-sentence marker (should correspond to the position of EOS in the
 # RESERVED_TOKENS list in text_encoder.py)
 EOS = 1
@@ -59,9 +58,10 @@ def _original_vocab(tmp_dir):
   vocab_filepath = os.path.join(tmp_dir, vocab_filename)
   if not os.path.exists(vocab_filepath):
     generator_utils.maybe_download(tmp_dir, vocab_filename, vocab_url)
-  return set(
-      [text_encoder.native_to_unicode(l.strip()) for l in
-       tf.gfile.Open(vocab_filepath)])
+  return set([
+      text_encoder.native_to_unicode(l.strip())
+      for l in tf.gfile.Open(vocab_filepath)
+  ])
 
 
 def _replace_oov(original_vocab, line):
@@ -81,19 +81,19 @@ def _replace_oov(original_vocab, line):
 
 
 def _train_data_filenames(tmp_dir):
-  return [os.path.join(
-      tmp_dir,
-      "1-billion-word-language-modeling-benchmark-r13output",
-      "training-monolingual.tokenized.shuffled",
-      "news.en-%05d-of-00100" % i) for i in xrange(1, 100)]
+  return [
+      os.path.join(tmp_dir,
+                   "1-billion-word-language-modeling-benchmark-r13output",
+                   "training-monolingual.tokenized.shuffled",
+                   "news.en-%05d-of-00100" % i) for i in xrange(1, 100)
+  ]
 
 
 def _dev_data_filename(tmp_dir):
-  return os.path.join(
-      tmp_dir,
-      "1-billion-word-language-modeling-benchmark-r13output",
-      "heldout-monolingual.tokenized.shuffled",
-      "news.en.heldout-00000-of-00050")
+  return os.path.join(tmp_dir,
+                      "1-billion-word-language-modeling-benchmark-r13output",
+                      "heldout-monolingual.tokenized.shuffled",
+                      "news.en.heldout-00000-of-00050")
 
 
 def _maybe_download_corpus(tmp_dir):
@@ -112,17 +112,18 @@ def _maybe_download_corpus(tmp_dir):
       corpus_tar.extractall(tmp_dir)
 
 
-def _get_or_build_subword_text_encoder(tmp_dir):
+def _get_or_build_subword_text_encoder(tmp_dir, vocab_filepath):
   """Builds a SubwordTextEncoder based on the corpus.
 
   Args:
     tmp_dir: directory containing dataset.
+    vocab_filepath: path to store (or load) vocab.
+
   Returns:
     a SubwordTextEncoder.
   """
-  filepath = os.path.join(tmp_dir, "lm1b_32k.subword_text_encoder")
-  if tf.gfile.Exists(filepath):
-    return text_encoder.SubwordTextEncoder(filepath)
+  if tf.gfile.Exists(vocab_filepath):
+    return text_encoder.SubwordTextEncoder(vocab_filepath)
   _maybe_download_corpus(tmp_dir)
   original_vocab = _original_vocab(tmp_dir)
   token_counts = defaultdict(int)
@@ -138,7 +139,7 @@ def _get_or_build_subword_text_encoder(tmp_dir):
       break
   ret = text_encoder.SubwordTextEncoder()
   ret.build_from_token_counts(token_counts, min_count=5)
-  ret.store_to_file(filepath)
+  ret.store_to_file(vocab_filepath)
   return ret
 
 
@@ -152,7 +153,7 @@ def is_character_level(self):
 
   @property
   def has_inputs(self):
-    return True
+    return False
 
   @property
   def input_space_id(self):
@@ -184,25 +185,26 @@ def targeted_vocab_size(self):
   def use_train_shards_for_dev(self):
     return True
 
-  def generator(self, tmp_dir, train, characters=False):
+  def generator(self, data_dir, tmp_dir, is_training):
     """Generator for lm1b sentences.
 
     Args:
-      tmp_dir: a string.
-      train: a boolean.
-      characters: a boolean
+      data_dir: data dir.
+      tmp_dir: tmp dir.
+      is_training: a boolean.
 
     Yields:
       A dictionary {"inputs": [0], "targets": [<subword ids>]}
     """
     _maybe_download_corpus(tmp_dir)
     original_vocab = _original_vocab(tmp_dir)
-    files = (_train_data_filenames(tmp_dir) if train
-             else [_dev_data_filename(tmp_dir)])
-    if characters:
+    files = (_train_data_filenames(tmp_dir)
+             if is_training else [_dev_data_filename(tmp_dir)])
+    if self.is_character_level:
       encoder = text_encoder.ByteTextEncoder()
     else:
-      encoder = _get_or_build_subword_text_encoder(tmp_dir)
+      vocab_filepath = os.path.join(data_dir, self.vocab_file)
+      encoder = _get_or_build_subword_text_encoder(tmp_dir, vocab_filepath)
     for filepath in files:
       tf.logging.info("filepath = %s", filepath)
       for line in tf.gfile.Open(filepath):